Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Social media can also help to create and maintain social capital, fostering inclusion within web-based communities [61]; for instance, members of the lesbian, gay, bisexual, transgender, queer, and similar minority community report greater levels of social support over the web [62]. This review supports evidence from previous studies that show that the link between social media use and mental well-being is conflicting, with there being some benefits and some disadvantages of social media use related to mental well-being [9,25]. The variation in effectiveness across the studies could be due to individual differences [63]. Different people will have different responses to social media, and self-regulatory capabilities may be affected by factors such as gender, age, and personality traits [64]. Previous research has shown that those with neurotic or introverted tendencies have a higher risk of addiction to internet content [65]. Others may not be affected by social media use owing to elevated levels of digital resilience. Digital resilience is a person’s ability to cope with the negative consequences of being over the web, such as cyberbullying and misleading information [66,67]. Gender has been found to be a moderating factor in previous studies examining the relationship between social media use and mental well-being, with adolescent girls seeming to experience more negative effects from social media use than adolescent boys [4,68,69]. In this review, we found that 1 (4%) of the 23 studies showed that abstinence increased positive affect and life satisfaction for women but not for men, also suggesting that gender may affect the relationship between social media use and mental well-being [41]. Future research is needed to explore who may be most affected by problematic social media use to enable the development of more targeted interventions to improve mental well-being. LimitationsThere was a large degree of heterogeneity in the studies reviewed, with several different intervention types and outcome measures used. Therefore, it was not possible to conduct a meta-analysis to provide integrated results on the outcomes of the social media use interventions [70]. A further limitation to this review is that the search strategy may not have retrieved all relevant papers owing to the inclusion of English-language publications only. Our review also did not include unpublished studies; thus, it was not possible to estimate the degree of publication bias. [SEP] Justify:

Output: The text contrasts the claim by indicating that the relationship between social media use and mental well-being is conflicting, with both benefits and disadvantages highlighted. It mentions that limitations in the effects of social media vary by individual differences such as gender, age, and personality traits. For example, it states that 'gender has been found to be a moderating factor,' suggesting that consequences are not universally positive and may vary negatively for certain groups. These insights collectively argue against the idea that social media algorithms always produce positive outcomes for mental well-being.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: Participants responded significantly faster to the trained words (M = 568. 2, SE = 3. 0)Footnote 4 than to the untrained words (M = 602. 2, SE = 3. 0) [F 1(1, 13 = 36. 9, p <. 001; F 2(1, 103) = 19. 0, p <. 001]. They were also significantly more accurate for the trained words (M = 4. 6 %, SE = 0. 9) than for the untrained words (M = 12. 1, SE = 0. 9) [F 1(1, 13) = 17. 1, p <. 001; F 2(1, 103) = 8. 4, p <. 005]. Responses were significantly faster for the related words (M = 555. 3, SE = 6. 1) than for the unrelated words (M = 585. 9, SE = 6. 1) [F 1(1, 13) = 5. 8, p <. 05; F 2(1, 33) = 8. 8, p <. 01] but were nonsignificantly more accurate for the related words (M = 3. 3, SE = 0. 9) than for the unrelated words (M = 5. 9, SE = 0. 9) [F 1(1, 13) = 1. 8, p =. 2; F 2(1, 33) = 1. 4, p >. 2]. Cued-recall testResponses were coded and analyzed in the same way as in Experiment 1. Overall, performance was better than in the previous experiments (see Table 3), but the proportion of correct responses remained higher for the related than for the unrelated items [F 1(1, 14) = 16. 3, p <. 001; F 2(1, 34) = 31. 6, p <. 001]. However, there was no significant main effect of Relatedness on the numbers of properties that were recalled for these correct responses [F 1(1, 14) < 1; F 2(1, 34) < 1]. A plot of the distributions of these individual responses (Fig. 2) shows similar distributions for the related and unrelated meanings. [SEP] Justify:

Output: The text does not provide information that directly supports or contradicts the claim regarding semantic memory and its involvement in understanding meanings. It focuses on participant responses to trained, untrained, related, and unrelated words, but does not discuss the concept of semantic memory or its role in comprehension.


Input: Task: corroborate the claim using the given text. Claim: Nanomaterials are being developed for more effective cancer treatments. [SEP] Text: Adams JM, Cory S. The Bcl-2-regulated apoptosis switch: mechanism and therapeutic potential. Immunol 2009;19:488–96. Search in 182. . Co-delivery of Doxorubicin and Bcl-2 siRNA by mesoporous silica nanop7. 10. 1002/smll. 200900621Search in 183. -X, Cheng S-X. Co-delivery of genes and drugs with nanostructured calcium carbonate for cancer therapy. RSC Adv 2012;2:1820–6. 10. 1039/c1ra00527hSearch in 184. -S, Park K, Nam H, et al. Tumor-homing multifunctional nanop27. 10. 1016/j. jconrel. 2010. 04. 004Search in 185. Yang J, Lee C-H, Ko H-J, Suh J-S, Yoon H-G, Lee K, et al. Multifunctional magneto-polymeric nanohybrids for targeted detection and synergistic therapeutic effects on breast cancer. Engl 2007;46:8836–9. 10. 1002/anie. 200703554Search in 186. . In vivo imaging of siRNA delivery and silencing in tumors. 2007;13:372–7. 10. 1038/nm1486Search in 187. Poly(ε-caprolactone)-graft-poly(2-(N, N-dimethylamino) ethyl methacrylate) nanop41. 10. 1039/c0jm00506aSearch in 188. Taton TA. Boning up on biology. Nature 2001;412:491–2. 10. 1038/35087687Search in 189. . Preparation and characterization of nano-hydroxyapatite/chitosan composite scaffolds. J 2005;75:275–82. 10. 1002/jbm. a. 30414Search in 190. Biocompatibility evaluation of nano-rod hydroxyapatite/gelatin coated with nano-HAp as a novel scaffold using mesenchymal stem cells. J 2010;92:1244–55. Search in 191. . Hierarchically biomimetic bone scaffold materials: nano-HA/collagen/PLA composite. [SEP] Justify:

Output: The text corroborates the claim by referencing multiple studies involving the use of nanomaterials, including the co-delivery of drugs and genes for cancer therapy, as well as multifunctional nanoparticles aimed specifically at targeting tumors. Phrases such as 'co-delivery of Doxorubicin and Bcl-2 siRNA by mesoporous silica nanoparticles' and 'multifunctional magneto-polymeric nanohybrids for targeted detection and synergistic therapeutic effects on breast cancer' clearly indicate that innovative nanomaterials are being developed with the intention of enhancing cancer treatment efficacy.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: This review also did not explore the impact of the type of social media use, such as passive use or active use, because this was out of the scope of the review, but this could affect mental well-being. Active use denotes direct messaging, posting, or responding to content, whereas passive use corresponds to scrolling and browsing profiles. Previous literature has suggested that passive use is associated with greater declines in subjective well-being [71,72], but a recent review found that this was not supported across 40 survey-based studies [73]. The review suggested that future studies should explore the characteristics of the content of social media as well as its senders and receivers to understand how different uses of social media affect mental well-being [73]. Understanding this relationship could help to develop more targeted problematic social media use interventions that move beyond simply aiming to reduce time spent on social media by targeting the reduction of specific negative activities or interactions. A major limitation of the studies included in this review is that the majority (16/23, 70%) relied largely on convenience samples of those who were likely to be interested in reducing their social media use and improving their mental health. In addition, more than half (16/23, 70%) of the studies recruited university students. Therefore, these results must be interpreted with caution because they are not generalizable to all adults and are likely to be more relevant for young adults. Furthermore, none of the studies received a strong global score for quality using the. The sustainability of these interventions is also difficult to establish because most of the interventions (20/23, 87%) lasted <1 month, and the outcomes were assessed immediately after the interventions. Only 2 (9%) of the 23 studies included a longer-term measure. Brailovskaia et al [52] found consistency with their short-term recorded outcomes, with no significant difference in life satisfaction and depression between groups 3 months after the intervention. By contrast, Chen et al [22] found that the significant improvements in depression and loneliness for the intervention group continued to remain at 2 months. ConclusionsThere is some evidence that social media use interventions are effective in improving mental well-being in adults, especially for depression and when using therapy-based interventions. Current experimental research is of low quality, with issues of selection bias making it difficult to generalize the findings. Further experimental and longitudinal research is needed with representative samples to investigate who may benefit most from social media use interventions. [SEP] Justify:

Output: The text contrasts the claim by highlighting that previous literature suggests passive social media use may lead to declines in subjective well-being, indicating that not all uses of social media have a positive impact. Moreover, it discusses varying outcomes from studies on social media use interventions, emphasizing the need for further research and caution in interpreting existing findings. This demonstrates that the relationship between social media use and mental well-being is complex and not necessarily positive.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: 001 335. doi:10. 1016/0010-0285(76)90010-4 246. doi:10. 1016/j. jml. 2004. 05. 005 794. doi:10. 1037/0278-7393. 21. 3. 785 5. Scale. San Antonio, TX. : Harcourt Assessment. Rodd, J. M. (2001). Semantic representation and lexical competition: Evidence from ambiguity (Unpublished Ph. D. thesis). University of Cambridge, Cambridge, U. K. Rodd, J. M. (2004). The effect of semantic ambiguity on reading aloud: A twist in the tale. & Review, 11, 440–445. doi:10. 3758/BF03196592 266. doi:10. 1006/jmla. 2001. 2810 104. doi:10. 1207/s15516709cog2801_4 2011. Schneider, W. , Eschman, A. , & Zuccolotto, A. (2002). E-Prime user’s guide. Pittsburgh, PA. : Tools, Inc. 1. Electronic supplementary materialBelow is the link to the electronic supplementary material. ESM 1(PDF 123 kb)3. Trained wordsant–path, bandage–fee, bone–silk, bruise–heap, cake–join, carpet–spy, carton–snake, crude–rust, foam–slot, fog–widow, grin–hive, growl–winch, pearl–crew, pouch–feast, sip–dawn, slim–farm, stain–cage, stub–soup. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily consists of references, citations, and an incomplete list of words rather than discussing semantic memory or its role in understanding meanings of words and sentences. There is no information provided that directly supports or refutes the claim.


Input: Task: corroborate the claim using the given text. Claim: Nanotechnology-based sensors are used in diagnostic devices. [SEP] Text: The key part of this technique is the transfer printing of the MEMS structure onto a flexible substrate [Fig. 1(b)]58 with a predefined cavity, and accurate alignment of the structure and cavity is essential. 75,76 Experimental implementation of the FlexMEMS technique has shown that the performance of the flexible device is comparable to that of its counterparts on rigid substrates. For different substrates, both flexible Lamb wave resonators (LWRs) on polyimide (PI) and polyethylene terephthalate (PET) substrates [Fig. 1(c)] have been realized. 74 FlexMEMS-enabled devices have demonstrated excellent mechanical flexibility and electrical performance during and after bending trials. Special release techniques have also been used in lieu of stamps to fabricate flexible devices. For example, the thermal-release transfer-printing technique was used to directly pick up a prepared three-dimensional (3D) PMUT and transfer-print it onto a mica substrate, thereby avoiding the problems encountered when picking up using a thermal-release tape without additional complex treatment. The PMUT device was used for curved surface imaging because of its excellent flexibility and high transparency [Fig. 1(d)]. 77 Transfer printing can be used to fabricate a flexible-device array without additional processing steps. In a batch-fabrication method, the production efficiency can be greatly enhanced, and the cost of fabrication can be accordingly reduced. 78,79 Flexible film bulk acoustic-wave filters consisting of several FBARs were fabricated using this method. 78 In another example, a high-aspect-ratio ultrathin strain sensor was integrated with a flexible printed-circuit substrate [Fig. 1(e)]. 79 Jiang et al. 80,81 successfully demonstrated large-area transfer printing of 3D high-aspect-ratio MEMS structures using the difference in adhesion between the donor and receptor [Fig. 1(f)]. Transfer printing of a device array can also be done in a step-by-step and repetitive manner. Using a transfer machine with high resolution and multidimensional position control, micrometer-level transfer accuracy and a large throughput per hour can also be achieved. B. Direct integration into a flexible substrate Integrating MEMS devices and sensors with flexible substrates82 can provide new capabilities and greatly enhance functionality. [SEP] Justify:

Output: The text corroborates the claim by discussing the integration of MEMS devices and sensors with flexible substrates, which are essential components in nanotechnology-based sensors. The description of using advanced techniques like transfer printing and the mention of flexible devices that can be utilized in applications such as imaging indicate that these technologies are relevant for use in diagnostic devices, supporting the claim.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Health and care professionals should be aware of the growing evidence that reducing social media use alone is unlikely to benefit mental well-being. Taking a more therapy-based approach and reflecting on how and why individuals are interacting with social media and managing these behaviors could help to improve mental well-being. 2020. URL: [accessed 2022-08-26] Mental health of children and young people in England 2021 - wave 2 follow up to the 2017 survey. of Children and Surveys. 2021. URL: https://digital. nhs. uk/data-and-information/publications/statistical/mental- health-of-children-and-young-people-in-england/2021-follow-up-to-the-2017-survey [accessed 2022-09-02] Mental health of children and young people in England 2022 - wave 3 follow up to the 2017 survey. Service Digital. 2022. URL: https://digital. nhs. uk/data-and-information/publications/statistical/mental-health-of-children-and-young -people-in-england/2022-follow-up-to-the-2017-survey [accessed 2022-12-01] Henzel V, Håkansson A. Hooked on virtual social life. Problematic social media use and associations with mental distress and addictive disorders. PLoS One. Apr 8, 2021;16(4):e0248406. [FREE Full text] [CrossRef] [Medline]Hall M. Facebook. Britannica. 2022. URL: [accessed 2022-08-19] Number of social media users worldwide from 2018 to 2027 (in billions). Statista. 2022. URL: [accessed 2022-11-04] Ventola C. Social media and health care professionals: benefits, risks, and best practices. P T. Jul 2014;39(7):491-520. [FREE Full text] [Medline]Brevers D, Turel O. Strategies for self-controlling social media use: classification and role in preventing social media addiction symptoms. J Behav Addict. Sep 01, 2019;8(3):554-563. [FREE Full text] [CrossRef] [Medline]Orben A. [SEP] Justify:

Output: The text contrasts the claim by highlighting that reducing social media use alone is insufficient for benefiting mental well-being. Instead, it suggests that a therapy-based approach and reflection on individuals' interactions with social media may improve mental well-being, implying that social media algorithms do not always have a positive impact.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: Untrained wordsbald, barber, bee, beef, bet, blur, bold, braid, brat, breeze, brink, brown, bulk, cash, cave, cheer, chin, cork, craft, crouch, dart, deaf, exam, fake, frost, fur, fuss, gap, gate, gift, goose, grain, groom, gulp, gut, hash, haste, hood, hum, joke, Lad, lake, lion, lunch, malt, mild, parish, raid, ready, rich, ritual, rude, shack, shield, shoe, shut, skirt, slave, sled, soap, stool, surge, symptom, tall, theme, thrill, toilet, torch, trump, turf, whack, wool. Nonwordsbarr, beaf, berch, berd, blak, bloo, bom, carst, cawl, chare, cloun, cloze, coffey, craine, crie, dert, dide, doar, draip, drunck, durt, earlie, exakt, fale, fatt, ficks, flor, foks, frunt, fynd, gawl, gerl, gitar, goast, gole, graf, grean, groope, gunn, gurth, hanned, hed, hoap, horce, hugg, hye, jirk, jore, jym, kab, kap, kar, keap, kee, klean, kog, kold, krib, leest, legg, lemun, liv, lyne, maik, muffen, muther, naim, neer, nees, nite, noad, noat, nome, nurve, oan, outcide, pance, peny, peppur, poaks, polese, powk, reech, rong, rume, sadd, scid, scin, sheat, skalp, skore, smaul, sord, sto, swerl, tair, tite, tode, toun, towl, traid, trane, trea, treet, trye, wede, werld, wround. [SEP] Justify:

Output: The text lists a collection of both trained words and nonwords without discussing the concept of semantic memory or its role in understanding word meanings and sentences. As such, it does not provide any relevant information to support or refute the claim, rendering it irrelevant.


Input: Task: corroborate the claim using the given text. Claim: Nanotechnology-based sensors are used in diagnostic devices. [SEP] Text: 126,127 Figure 6(c) shows an origami-structured flexible electronic substrate whose faces are parallel to the target of the attachment surface. The parallel faces are important from an engineering point of view in that they make direct contact with the surfaces of the target of attachment and mounted electronic elements such as sensors or light emitters. These characteristics allow the realization of flexible devices that can detect a shear force or flow velocity parallel to the object surfaces. 128 Using a kirigami structure, Yamamoto et al. 129 designed an all-printed planar-type multifunctional health-monitoring patch based on an in-plane PET film integrated with an acceleration sensor for motion detection and with skin temperature and electrocardiogram sensors [Fig. 6(d)]. FIG. 6. View largeDownload slide(a) : (i) device side of flat structure; (ii) handle side of flat structure; (iii) folded structure. The double-sided fabrication process and through-wafer interconnects enable efficient utilization of the IMU internal volume. Reproduced with permission from Efimovskaya et al. , J. Microelectromech. Syst. 26, 1030–1039. Copyright 2017 IEEE. (b) Wafer-level fabrication process for a double-sided IMU with through-wafer interconnects. et al. , in 2015 on and Systems (ISISS), 4. Copyright 2015 IEEE. Reproduced with permission from IEEE. (c) Flexible substrates with (i) origami and (ii) kirigami structures, and (iii) origami-structure substrate with parallel faces in its folded state. et al. , in 2020 IEEE 33rd on (MEMS), Vancouver, Canada, 912. Copyright 2020 IEEE. Reproduced with permission from IEEE. (d) Multifunction in-plane flexible patch. et al. , in 2017 IEEE 30th on Micro-Electro-(MEMS), 242. Copyright 2017 IEEE. Reproduced with permission from IEEE. FIG. 6. View largeDownload slide(a) : (i) device side of flat structure; (ii) handle side of flat structure; (iii) folded structure. The double-sided fabrication process and through-wafer interconnects enable efficient utilization of the IMU internal volume. Reproduced with permission from Efimovskaya et al. , J. [SEP] Justify:

Output: The text corroborates the claim by describing how devices made with nanotechnology, specifically those utilizing origami and kirigami structures, integrate sensors for health monitoring. It mentions the design of a multifunctional health-monitoring patch that incorporates acceleration sensors for motion detection, as well as skin temperature and electrocardiogram sensors, highlighting their use in diagnostic applications.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Teenagers, screens and social media: a narrative review of reviews and key studies. Psychiatr Epidemiol. Apr 2020;55(4):407-414. [CrossRef] [Medline]. Active social network sites use and loneliness: the mediating role of social support and self-esteem. Curr Psychol. Feb 13, 2020;41(3):1279-1286. [CrossRef]. Face-to-face or Facebook: can social connectedness be derived online? Behav. May 2013;29(3):604-609. [FREE Full text] [CrossRef]. A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Adolesc Youth. Mar 21, 2019;25(1):79-93. [FREE Full text] [CrossRef]#StatusOfmedia and young people’s mental health and wellbeing. for Public Health. URL: [accessed 2023-07-02]. Prevalence of social media addiction across 32 nations: meta-analysis with subgroup analysis of classification schemes and cultural values. Addict Behav. Jun 2021;117:106845. [FREE Full text] [CrossRef] [Medline]Swar B, Hameed T. Fear of missing out, social media engagement, smartphone addiction and distraction: moderating role of self-help mobile apps-based interventions in the youth. In: Proceedings of the 10th Conference on Systems and Technologies. Presented at: '17; February 21-23, 2017, 2017;139-146; Porto, Portugal. URL: [CrossRef]Uyaroğlu Ö. A cross-sectional study of social media addiction and social and emotional loneliness in university students in Turkey. Care. Oct 2022;58(4):2263-2271. [CrossRef] [Medline] Global prevalence of digital addiction in general population: a systematic review and meta-analysis. Rev. Mar 2022;92:102128. [CrossRef] [Medline]. Social networks use disorder and associations with depression and anxiety symptoms: a systematic review of recent research in China. Front Psychol. Feb 21, 2020;11:211. [FREE Full text] [CrossRef] [Medline]. The effect of solution-focused group counseling intervention on college students' internet addiction: a pilot study. . [SEP] Justify:

Output: The text presents multiple studies and reviews that discuss the influence of social media on mental health, particularly highlighting negative outcomes such as depression, anxiety, and loneliness among adolescents and university students. By referencing systematic reviews and meta-analyses indicating social media addiction and its associations with depression and anxiety symptoms, it contradicts the claim that social media algorithms always have a positive impact on user mental well-being.


Input: Task: pass the claim using the given text. Claim: Syntactic parsing in the brain is influenced by both grammatical rules and context. [SEP] Text: Javascript is disabled in your browser. Please enable Javascript to view PeerJ. Related research -language identification model based on GCN fusing associated information View 10. 7717/peerj-cs. 984 Published 2022-06-03 Accepted 2022-04-25 Received 2022-03-23 and Speech, language, Graph convolutional neural network, Associated information, Mutual information, Dependency syntactic relation Copyright © 2022 Meng et al. is an open access 2022. Formulaic language identification model based on GCN fusing associated information. PeerJ 8:e984 language is a general term for ready-made structures in a language. It usually has fixed grammatical structure, stable language expression meaning and specific use context. The use of formulaic language can coordinate sentence generation in the process of writing and communication, and can significantly improve the idiomaticity and logic of machine translation, intelligent question answering and so on. New formulaic language is generated almost every day, and how to accurately identify them is a topic worthy of research. To this end, this 1. First, the text’s semantic features and part-of-speech features are extracted and fused through late fusion. The fusion results are used as the basic features of the model. Then the mutual information between words is calculated, the dependency syntactic parsing of sentences is analyzed, and the generated adjacency matrix and basic features are input into GCN for feature representation. Finally, the feature vector is input to the CRF layer for decoding to obtain the label category of each character, so as to obtain the formulaic language. Figure 1: The overall structure of the model. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-1 Basic feature extraction module In the process of natural language processing, the computer cannot directly use text data. The text data needs to be expressed as a feature vector, and then the feature vector is used as the model’s input. This 1. From the table, it can be found that multi-word units with fixed sentence patterns are more likely to be formulaic language. Then we assign a unique code to each result after part-of-speech tagging, so as to convert the text data into vectors. Finally, we input them into the embedding layer for training to generate word embedding vectors as part-of-speech features. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses a model for language identification and feature extraction in natural language processing without addressing how syntactic parsing in the brain is influenced by grammatical rules and context. It primarily focuses on technical aspects of language processing rather than cognitive linguistic theories.


Input: Task: corroborate the claim using the given text. Claim: Nanotechnology-based sensors are used in diagnostic devices. [SEP] Text: Intrinsically stretchable and healable semiconducting polymer for organic transistors. Nature 2016; 539(7629):411–415. Mechanically durable and highly stretchable transistors employing carbon nanotube semiconductor and electrodes. 2016;28(22):4441–4448. A skin-inspired organic digital mechanoreceptor. Science 2015;350(6258):313–316. A wearable chemical–electrophysiological hybrid biosensing system for real-time health and fitness monitoring. 2016;7:11650. Flexible polymer transistors with high pressure sensitivity for application in electronic skin and health monitoring. 2013;4:1859. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 2016;529(7587):509–514. A graphene-based electrochemical device with thermoresponsive microneedles for diabetes monitoring and therapy. 2016;11(6):566–572. A soft, wearable microfluidic device for the capture, storage, and colorimetric sensing of sweat. Med 2016;8(366):366ra165. Macroelectronics: Perspectives on technology and applications. Proc IEEE 2005;93(7):1239–1256. Pentacene-based radio-frequency identification circuitry. Lett 2003;82(22):3964–3966. Low-cost all-polymer integrated circuits. Lett 1998;73(1):108–110. Amorphous silicon detector and thin film transistor technology for large-area imaging of X-rays. 2000;31(11-12):883–891. Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention. 2005;9(3):325–336. A hemispherical electronic eye camera based on compressible silicon optoelectronics. Nature 2008;454(7205):748–753. IC-integrated flexible shear-stress sensor skin. J 2003;12(5):740–747. Optical MEMS for photonic switching-compact and stable optical crossconnect switches for simple, fast, and flexible wavelength applications in recent photonic networks. 2005;11(2):383–394. Flexible shear-stress sensor skin and its application to unmanned aerial vehicles. Sens Actuators, A 2003;105(3):321–329. [SEP] Justify:

Output: The text corroborates the claim by referencing various advancements in technology that include wearable biosensing systems, flexible polymer transistors, and microfluidic devices, which all represent applications of nanotechnology in health monitoring and diagnostics. Phrases like 'wearable chemical electrophysiological hybrid biosensing system for real-time health and fitness monitoring' indicate the integration of nanotechnology-based sensors in devices used for diagnostic purposes.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Apr 07, 2020;17(7):2519. [FREE Full text] [CrossRef] [Medline]. A web-based intervention for social media addiction disorder management in higher education: quantitative survey study. J Res. Oct 02, 2019;21(10):e14834. [FREE Full text] [CrossRef] [Medline]. MyTime: designing and evaluating an intervention for smartphone non-use. In: Proceedings of the 2016 CHI Conference on in Computing Systems. Presented at: CHI '16; May 7-12, 2016, 2016;4746-4757; San Jose, CA. URL: [CrossRef] Problematic social media use and depressive outcomes among college students in China: observational and experimental findings. . Apr 19, 2022;19(9):4937. [FREE Full text] [CrossRef] [Medline]Bandura A. Social cognitive theory of self-regulation. Process. Dec 1991;50(2):248-287. [FREE Full text] [CrossRef]Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. Apr 23, 2011;6:42. [FREE Full text] [CrossRef] [Medline]von Lindern E. Digital detox: an effective solution in the smartphone era? A systematic literature review. Commun. May 2021;10(2):190-215. [FREE Full text] [CrossRef] Comparative effectiveness of mind-body exercise versus cognitive behavioral therapy for college students with problematic smartphone use: a randomized controlled trial. Promot. 2020;22(4):271-282. [FREE Full text] [CrossRef] 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372:n71. [FREE Full text] [CrossRef] [Medline]Plackett R. Protocol interventions for problematic social media use. Framework. 2022. URL: [accessed 2022-12-08]. Toward a new definition of mental health. World Psychiatry. Jun 2015;14(2):231-233. [FREE Full text] [CrossRef] [Medline]Deniz M. Fear of missing out (FoMO) mediate relations between social self-efficacy and life satisfaction. Crit. [SEP] Justify:

Output: The text discusses various negative aspects of social media use, including topics such as problematic social media use, social media addiction, and depressive outcomes among college students. This indicates a contrast to the claim that social media algorithms have a positive impact on user mental well-being, as it implies that social media can contribute to negative mental health outcomes.


Input: Task: pass the claim using the given text. Claim: Syntactic parsing in the brain is influenced by both grammatical rules and context. [SEP] Text: Table 1: The examples of Part-of-speech analysis result. Formulaic language Part-of-speech tagging Sentence structure X is fundamental to -Link verb-X plays a vital role in the metabolism of attempts have been made to this innovative study, Smith showed that guided by “that” There were some negative comments about be. . . DOI: 10. 7717/peerjcs. 984/table-1 In addition, for text processing, characters, words, phrases more reflect the lexical information of the text rather than its semantic information, so they can not accurately express the content of the text. For formulaic language, it is a multi-word unit with high frequency and has complete structure, meaning and function, so semantic feature is also an essential feature of formulaic language. This 2. Figure 2: Structure diagram of Bi-LSTM model. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-2 In Fig. 2, xt represents the input of the network at time t, the LSTM in the box is the standard LSTM model, y t → is the output of the forward LSTM at time t, and y ⃖ t is the output of the reverse LSTM at time t. ⊕ indicates splicing operation. The output representation of Bi-LSTM at time t is defined as y t = y → t : y ⃖ t that is, the output at time t is directly spliced by the forward output and the reverse output. Feature fusion Feature fusion includes early fusion and late fusion. Early fusion is to fuse multi-layer features first and then train the model on the fused features (only after complete fusion, unified training). In this 3. Figure 3: Structure diagram of early fusion model. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-3 Compared with early fusion, late fusion first trains the model with a single feature and then fuses the training results of multiple models. The advantage of late fusion method is that it can flexibly select the model’s results and improve the fault tolerance of the system. The amount of calculation of fusion information is reduced and the real-time performance of the system is improved (Khaleghi, Khamis & Karray, 2013). In this 4. [SEP] Justify:

Output: The text does not address the claim directly or provide information about syntactic parsing in relation to grammatical rules and context. Instead, it focuses on various aspects of part-of-speech tagging and model structures such as Bi-LSTM and feature fusion, which are not pertinent to the claim about syntactic parsing in the brain.


Input: Task: corroborate the claim using the given text. Claim: Nanotechnology-based sensors are used in diagnostic devices. [SEP] Text: Flex-to-Rigid (F2R): A generic platform for the fabrication and assembly of flexible sensors for minimally invasive instruments. 2013;13(10):3873–3882. Flexible piezoelectric micromachined ultrasonic transducer (pMUT) for application in brain stimulation. 2016;23(7):2321–2328. Flexible SOI-based piezoelectric micromachined ultrasound transducer (PMUT) arrays. 2019 20th on Solid-State Sensors, Actuators and Microsystems & (& ). Berlin, Germany. 2019. 253. Bendable piezoelectric micromachined ultrasound transducer (PMUT) arrays based on silicon-on-insulator (SOI) technology. J 2020;29(3):378–386. A flexible piezoelectric micromachined ultrasound transducer. RSC Adv 2013;3(47): 24900–24905. A flexible ultrasound transducer array with micro-machined bulk PZT. Sensors 2015;15(2):2538–2547. Flexible ultrasonic transducer array with bulk PZT for adjuvant treatment of bone injury. Sensors 2019;20(1):86. Flexible piezoelectric micromachined ultrasonic transducers towards new applications. 2018 (IUS). Kobe, Japan. 2018. Curved micromachined ultrasonic transducers. IEEE Symposium on Ultrasonics, 2003. Honolulu, USA. 2003. 576. Low-stress fabrication of 3D polymer free standing structures using lamination of photosensitive films. 2008;14(8):1205–1214. A novel stretchable CMUT array using liquid-metal electrodes on a PDMS substrate. 2012 7th on Nano/ (NEMS). Kyoto, Japan. 2012. 107. Development of a novel transparent flexible capacitive micromachined ultrasonic transducer. Sensors 2017;17(6):1443. BT. Fabrication of flexible transducer arrays with through-wafer electrical interconnects based on trench refilling with PDMS. J 2008;17(2):446–452. [SEP] Justify:

Output: The text suggests the integration of flexible sensors and various types of piezoelectric micromachined ultrasonic transducers (pMUTs), which can be key components in diagnostic devices. The mention of 'flexible sensors for minimally invasive instruments' aligns with the use of nanotechnology in developing sensors to enhance diagnostic capabilities.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Aug 23, 2021;34(1):28. [FREE Full text] [CrossRef] [Medline]Alutaybi A, Al-. of (FoMO) on social media: the FoMO-R method. . Aug 23, 2020;17(17):6128. [FREE Full text] [CrossRef] [Medline]. Motivational, emotional, and behavioral correlates of fear of missing out. Behav. Jul 2013;29(4):1841-1848. [FREE Full text] [CrossRef]McHugh M. Interrater reliability: the kappa statistic. (Zagreb). 2012;22(3):276-282. [FREE Full text] [Medline]. A process for systematically reviewing the literature: providing the research evidence for public health nursing interventions. Based Nurs. 2004;1(3):176-184. [CrossRef] [Medline]O’Connell C. How FOMO (Fear of Missing Out), the smartphone, and social media may be affecting university students in the Middle East. N Psychol. Mar 01, 2020;22(1):83-102. [FREE Full text] [CrossRef]Brown L, Kuss DJ. Fear of missing out, mental wellbeing, and social connectedness: a seven-day social media abstinence trial. . Jun 24, 2020;17(12):4566. [FREE Full text] [CrossRef] [Medline]. Cognitive behavioral therapy-based short-term abstinence intervention for problematic social media use: improved well-being and underlying mechanisms. Psychiatr Q. Jun 2021;92(2):761-779. [CrossRef] [Medline]M, Ahmadi F. A new method to measure and decrease the online social networking addiction. Psychiatry. Dec 2018;10(4):e12330. [CrossRef] [Medline]. Mind over matter: testing the efficacy of an online randomized controlled trial to reduce distraction from smartphone use. . Jul 05, 2020;17(13):4842. [CrossRef] [Medline]. Social media addiction: its impact, mediation, and intervention. Cyberpsychol. Feb 21, 2019;13(1):1-4. [FREE Full text] [CrossRef]. Taking a short break from Instagram: the effects on subjective well-being. Soc Netw. [SEP] Justify:

Output: The text discusses various studies and research findings related to social media, fear of missing out (FoMO), and mental well-being. It is implied that social media usage can have negative effects on mental well-being, as indicated by terms like 'fear of missing out' and references to interventions for problematic social media use. This contrasts with the claim that social media algorithms always have a positive impact on mental well-being.


Input: Task: pass the claim using the given text. Claim: Syntactic parsing in the brain is influenced by both grammatical rules and context. [SEP] Text: Figure 4: Structure diagram of late fusion model. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-4 Feature extraction module fusing associated information The basic feature extraction module uses the word embedding model to train in large-scale text. It can obtain word vectors rich in text semantic features and part-of-speech features, but the syntactic structure information in the text is ignored. As the basis of language understanding, the syntactic structure can effectively represent the grammatical structure of the text and reveal the relationship between the components of the text. For formulaic language, it is a multi-word unit with high frequency of co-occurrence, and several words with high correlations may form formulaic language. Therefore, it is very important to select the features that can represent the relationship between words for identifying formulaic language. Based on this, this 5. Among them, “play an invalid role in” is a formulaic language. From the figure, it can be seen that there are complex dependencies between these five words. Therefore, dependency syntactic parsing between words can express the dependency between two words. The closer the relationship is, the more likely it is to form a formulaic language. Figure 5: Examples of dependency syntactic relation. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-5 Feature representation based on GCN In the formulaic language identification using dependency syntactic relation, the existing studies mostly use the dependency syntax in the text to construct rules, extract features, and then input them into the classifier to recognize formulaic language through classification. Although this method can achieve certain results, the nonlinear semantic relationship between components in sentences has not been learned and utilized. Spatially, the relationship between words can be represented by a graph through mutual information and dependency parsing. The GCN is used to process the correlation information, so that the nonlinear semantic relationship between words in the sentence can be extracted. In the feature representation based on GCN, the mutual information and dependency syntactic relation between words are used to determine the word connection relationship, and the basic features of words are represented as nodes. [SEP] Justify:

Output: The text does not directly address the influence of grammatical rules and context on syntactic parsing in the brain. Instead, it discusses the feature extraction and dependency syntactic relations in formulaic language identification without mentioning any specific aspects of how these processes relate to brain function or influence.


Input: Task: corroborate the claim using the given text. Claim: Nanotechnology-based sensors are used in diagnostic devices. [SEP] Text: Fabrication of a curved row–column addressed capacitive micromachined ultrasonic transducer array. J 2016;25(4):675–682. Development of flexible ZnO thin film surface acoustic wave strain sensors on ultrathin glass substrates. J 2015;25(11):115005. Film bulk acoustic resonators integrated on arbitrary substrates using a polymer support layer. 2015;5:9510. Adapted transfer printing technique for flexible film bulk acoustic resonator fabrication. 20th on for Chemistry and 2016. Dublin, Ireland. 2016. 1050. 160. Air-gap type film bulk acoustic resonator using flexible thin substrate. Sens Actuators, A 2005;117(1):62–70. An investigation on efficient acoustic energy reflection of flexible film bulk acoustic resonators. Eng 2018;1(2):129–132. Novel flexible FBAR on PET substrate. 2014 on and Solid-(EDSSC). Chengdu, China. 2014. Ultrasonic electrostatic actuators on a flexible substrate. 2012 IEEE 25th on 2012. Paris, France. 2012. 1196. Highly flexible piezoelectric MEMS resonators encapsulated in polymer thin films. 2018 (MEMS). Belfast, UK. 2018. 173. Flexible surface acoustic wave resonators built on disposable plastic film for electronics and lab-on-a-chip applications. 2013;3(1):2140. An implantable capacitive pressure sensor for biomedical applications. Sens Actuators, A 2007;134(2):382–388. A novel pressure sensor with a PDMS diaphragm. 2008;85(5-6):1054–1058. Miniaturized implantable pressure and oxygen sensors based on polydimethylsiloxane thin films. 2009;29(3):685–690. Z. MEMS absolute pressure sensor on a flexible substrate. 2012 IEEE 25th on (MEMS). Paris, France. 2012. 578. A flexible pressure sensor based on low-cost electroplated-Ni film induced cracking for wearable devices application. 2018 IEEE 13th Conference on Nano/ (NEMS). Singapore. 2018. 435. Differential piezoresistive wind speed sensor on flexible substrate. 2020;56(4):201–203. MEMS force sensor in a flexible substrate using nichrome piezoresistors. [SEP] Justify:

Output: The text corroborates the claim by mentioning various types of sensors, including 'miniaturized implantable pressure and oxygen sensors based on polydimethylsiloxane thin films' and 'a flexible surface acoustic wave resonators built on disposable plastic film for electronics and lab-on-a-chip applications.' These descriptions imply the use of nanotechnology principles in sensor fabrication for diagnostic applications.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Feb 2020;23(2):107-112. [CrossRef] [Medline]. Experimentally manipulating social media abstinence: results of a four-week diary study. Media Psychol. Nov 05, 2019;24(2):259-275. [FREE Full text] [CrossRef]. Taking a break: the effect of taking a vacation from Facebook and Instagram on subjective well-being. PLoS One. Jun 06, 2019;14(6):e0217743. [FREE Full text] [CrossRef] [Medline]. Taking a one-week break from social media improves well-being, depression, and anxiety: a randomized controlled trial. Soc Netw. May 2022;25(5):287-293. [CrossRef] [Medline]. Social media use only helps, and does not harm, daily interactions and well-being. Behav. Jun 25, 2021;2(1):1-32. [FREE Full text] [CrossRef]. The economic effects of Facebook. Exp Econ. Sep 26, 2019;23(2):575-602. [FREE Full text] [CrossRef]. Does taking a short break from social media have a positive effect on well-being? Evidence from three preregistered field experiments. J Sci. Feb 07, 2021;6(3):507-514. [FREE Full text] [CrossRef]Tromholt M. experiment: quitting Facebook leads to higher levels of well-being. Soc Netw. Nov 2016;19(11):661-666. [CrossRef] [Medline]. Short abstinence from online social networking sites reduces perceived stress, especially in excessive users. Psychiatry Res. Dec 2018;270:947-953. [CrossRef] [Medline]Vally Z, D'Souza CG. Abstinence from social media use, subjective well-being, stress, and loneliness. Care. Oct 2019;55(4):752-759. [CrossRef] [Medline]. The burden of online friends: the effects of giving up Facebook on stress and well-being. J Soc Psychol. 2018;158(4):496-507. [CrossRef] [Medline]Brailovskaia J, Ströse F, Schillack H, Margraf J. use – more well-being and a healthier lifestyle? An experimental intervention study. Behav. [SEP] Justify:

Output: The text contrasts the claim by highlighting various studies that suggest taking breaks from social media can improve well-being, reduce depression, and decrease stress. For instance, one study states that 'taking a one-week break from social media improves well-being, depression, and anxiety,' which implies that social media use is not always beneficial, thus challenging the assertion that social media algorithms have a consistently positive impact on mental well-being.


Input: Task: pass the claim using the given text. Claim: Syntactic parsing in the brain is influenced by both grammatical rules and context. [SEP] Text: For the GCN with mutual information as input, firstly, the corpus is used as the data set to calculate the mutual information value between words, the word is used as the node, the mutual information value between nodes is used as the representation of the edge. The element aij in its adjacency matrix AɛRN∗Nrepresents the mutual information value between the ith node and the jth node in the graph. For the GCN with dependency syntactic relation as input, firstly, the dependency syntax of sentences is analyzed, with words as nodes and the dependency between words as the representation of edges. The element aij in its adjacency matrix AɛRN∗Nrepresents the dependency between the ith node and the jth node in the graph. If there is a dependency between the two nodes, the aij is 1, otherwise it is 0. For example, in the example of dependency syntactic parsing “evaluations play an invaluable role in X. ”, the adjacency matrix A constructed based on dependency syntactic parsing is shown in Fig. 6. Figure 6: Adjacency matrix constructed based on dependency syntactic parsing. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-6 In order to directly conduct deep learning modeling on graph data, the specific method adopts a variant model of convolution neural network-proposed by Kipf & Welling (2016), and the structure is shown in Fig. 7. The channels input by GCN is C, that is, the feature vector dimension of each node Xi is C, the channels output by GCN is F, that is, the feature vector dimension of each node Zi is F, and finally the label Yi of the node is predicted by the feature of the node. Figure 7: The structure diagram of GCN. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-7 Specifically, given a graph G = (V, E), V is a vertex set containing N nodes, E is an edge set including self-loop edges (that is, each vertex is connected to itself). The feature information of graph G = (V, E)can be represented by Laplace matrix, as shown in Eq. (11). (11) L = D − A. [SEP] Justify:

Output: The text discusses methods for calculating mutual information and creating adjacency matrices for syntactic parsing in the context of graph convolutional networks (GCNs). However, it does not address or provide any evidence regarding the influence of grammatical rules and context on syntactic parsing in the brain. Thus, it is irrelevant to the specific claim.


Input: Task: corroborate the claim using the given text. Claim: Research is ongoing to use nanotechnology in regenerative medicine. [SEP] Text: Moodley and colleagues performed a xenograft implantation in which they injected human amnion epithelial cells parenterally into bleomycin-treated severe combined immunodeficiency (SCID) mice as pulmonary fibrosis model and demonstrated reduced inflammation and fibrosis [8]. The injected cells also developed an alveolar epithelial phenotype with lamellar body formation and expression of surfactants A and D. More interesting, Kajstura and coworkers recently identified stem cells from adult human lung tissue using the stem cell antigen marker c-kit [9]. After being administrated into an injured mouse lung, the cells demonstrated pluripotent capability of generating human bronchioles, alveoli, and pulmonary vessels within the damaged organ. Although some disputes exist on tissue-specific adult human lung stem cells in Kajstura study [10, 11], these evidences collectively suggest that isolated adult lung stem cells are potentially of great clinical importance for cell-based therapies in pulmonary diseases. This review will briefly highlight the recent progresses on mouse lung stem cell research and its applications in some clinical trials for specific lung diseases such as IPF and COPD. We will mainly discuss the endogenous lung stem cells named as epithelial stem/progenitor cells (EpiSPCs) in different anatomic locations, mesenchymal stem cells (MSCs), and endothelial progenitor cells (EPCs). In addition, we will briefly discuss novel bioengineering approaches to generate implantable lung tissues or organs ex vivo and in vivo. Stem cells from different origins may be repopulated into the decellularized or bioengineered scaffold to create new functional organs. Lung stem cells: classification, origin, biomarkers, and functionRecent studies mainly in mice have identified several adult stem cell lines in distinct anatomic locations of lung [12–14]. There might be some equivocal terms to define each cell line. In general, these diverse cell lines can be called lung endogenous stem/progenitor cells. According to the ability to differentiate, all stem cells can be categorized into 5 groups: totipotent, pluripotent, multipotent, oligopotent and unipotent [15]. Most lung endogenous stem/progenitor cells belong to multipotent or oligopotent cells. Alveolar epithelial cells (AECs) type II are unipotent since they only differentiate into type I cells [16]. [SEP] Justify:

Output: The text corroborates the claim as it discusses recent advancements in lung stem cell research and the application of bioengineering approaches, which are key aspects of regenerative medicine. It mentions the potential for creating new functional organs through repopulating stem cells into decellularized or bioengineered scaffolds, indicating that research efforts are actively exploring innovative solutions in this field.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Jul 2020;108:106332. [CrossRef]. Taking a break from social media improves wellbeing through sleep quality. Soc Netw. Jun 2021;24(6):421-425. [CrossRef] [Medline]. No more FOMO: limiting social media decreases loneliness and depression. J Psychol. Dec 2018;37(10):751-768. [CrossRef]. Too much of a good thing: who we follow, what we do, and how much time we spend on social media affects well-being. J Psychol. Feb 2021;40(1):46-68. [FREE Full text] [CrossRef]. The effects of reducing social media use on body esteem among transitional-aged youth. J Psychol. Dec 2021;40(6):481-507. [CrossRef]. A short scale for measuring loneliness in large surveys: results from two population-based studies. Res Aging. 2004;26(6):655-672. [FREE Full text] [CrossRef] [Medline]. Therapy. StatIsland. (FL). StatPearls Publishing; 2022. URL: [accessed 2022-08-13]. Concerns about Facebook among users and abstainers: relationships with individual differences and Facebook use. Psychol Sci. 2016;2(3):261-274. [FREE Full text] [CrossRef]Rideout V, Fox S. Digital health practices, social media use, and mental well-being among teens and young adults in the U. S. HopeLab. 2018. URL: [accessed 2022-08-13]. Social media and mental health challenges. J. Jul 2019;28(2):155-159. [FREE Full text] [CrossRef] [Medline]Escobar- For better or for worse? A systematic review of the evidence on social media use and depression among lesbian, gay, and bisexual minorities. . Jul 23, 2018;5(3):e10496. [FREE Full text] [CrossRef] [Medline]Du J, Kerkhof P, van Koningsbruggen GM. The reciprocal relationships between social media self-control failure, mindfulness and wellbeing: a longitudinal study. PLoS One. Aug 04, 2021;16(8):e0255648. [FREE Full text] [CrossRef] [Medline]. [SEP] Justify:

Output: The text contrasts the claim by highlighting studies that indicate negative effects of social media use on mental well-being. For example, it notes that taking a break from social media improves well-being through better sleep quality and that limiting social media decreases loneliness and depression. This evidence suggests that social media algorithms do not consistently have a positive impact, undermining the assertion made in the claim.


Input: Task: pass the claim using the given text. Claim: Syntactic parsing in the brain is influenced by both grammatical rules and context. [SEP] Text: Or use the symmetric normalized Laplasse matrix, as shown in Eq. (12): (12) L s y s = I N − D − 1 2 A D − 1 2. where A is the adjacency matrix of the graph, IN is the n-order identity matrix; D = diag(d) is the degree matrix of the vertex, as shown in Eq. (13). (13) D i i = ∑ j A i j. Based on the Fourier transform of graph, the graph convolution formula can be expressed as Eq. (14): (14) g ∗ x = U U T g ⋅ U T x. In the formula, x is the basic feature vector of the node; g is convolution kernel; U is the eigenvector matrix of Laplace matrix L. In order to reduce the amount of calculation, scholars used Chebyshev polynomials to simplify the graph convolution formula in 2017. Finally, the layered propagation formula of graph convolution can be expressed as Eq. (15): (15) X l + 1 = σ D ̃ − 1 2 A ̃ D ̃ − 1 2 X l W l. In the formula, A ̃ = A + I N D ̃ = ∑ A ̃ σ is the activation function; W is the weight matrix to be trained. Label representation module Field (CRF) is a statistical-based learning model first proposed by Lafferty, McCallum & Pereira (2001). Through the calculation and statistics of known features, the conditional probability of unknown features can be inferred. Based on the maximum entropy model and hidden Markov model, this model is mainly used for sequence tagging. It has achieved good recognition results in named entity recognition, syntactic analysis and part-of-speech tagging in the field of natural language processing. In the model of this 8. Among them, the input observation sequence is represented by X, and X is used as a precondition. P(y|x) is defined as a joint probability distribution, and the maximum value among them is selected as the corresponding sequence label (Ren, Zhang & Zhang, 2019). Figure 8: The structure diagram of linear-chain CRF. Download full-size image DOI: 10. 7717/peerjcs. [SEP] Justify:

Output: The text provides information on graph convolution formulas and models used in natural language processing, such as the conditional random field (CRF) and does not address the influence of grammatical rules or context on syntactic parsing in the brain. Thus, it is irrelevant to the claim being analyzed.


Input: Task: corroborate the claim using the given text. Claim: Research is ongoing to use nanotechnology in regenerative medicine. [SEP] Text: These tissue injury signaling systems would have injury location and potency specificity; helping the repair mechanism recognize the site of the injured organ. They would also share a common pathway for mobilizing the common repair mechanism such as the mobilization of bone marrow cells. The communication between the site of injury and the mobilized repairing materials requires an ensured transportation, vascular and/or lymph system. A damage to any of these signaling, communication, and transportation systems would result in suppression of the self-repair of the injured organ. Rejuvenation of the self-repair mechanism in the elder or under the chronic injury condition would provide an alternative approach to regenerative medicine. This approach, in contrast to the exogenously prepared repair materials, focuses on mobilization of the self-directed and active rather than passive repair of the injured organ. In this context, the rejuvenation would involve identification and renewal of the tissue injury signaling molecules or pathways. The signaling molecules require a well-connected network, which is different from the intracellular signaling transduction system and demands well-maintained vascular and/or lymph transportation systems. The recruitment of the repair mechanism such as bone marrow cells, and the regeneration of the failed organ are fundamental processes responsible for the effective self-repair. The repair of the structural injury only becomes meaningful if the recovery of the physiological function occurs. Therefore, rehabilitation of the renewed organ is required for the eventual regeneration. In this commentary, we will consolidate evidence that shows the possibility of rejuvenation of the self-repair mechanism in the elder or under the chronic injury condition, and discuss the elements that are required for the rejuvenation. If rejuvenation of the self-repair or self-renewal mechanism can be developed, an alternative, if not a better, achievement of the regenerative medicine would be expected. Renewal of the tissue injury signalingThe tissue injury signaling system should exist in the mammalian system, which would be an active and rescue-requiring signaling system generated from the site of the injured organ and transduced to communicate with the remote repair mechanism. This signaling system is different from that operates in the cell, which is a passive response to the trigger outside of the cell and is generated from the cell membrane, transducing to the inside of the cell. The tissue injury signaling system would ensure the rescue to take place when an injury occurs in any organ in the body. A good example of this system would be the immune response to an injury to an organ. [SEP] Justify:

Output: The text corroborates the claim by discussing the mechanisms of self-repair in regenerative medicine, highlighting the importance of signaling systems and communication in ensuring effective repair processes. It specifically mentions the potential for rejuvenation of self-repair mechanisms as an alternative to traditional regenerative methods, which may align with ongoing research in nanotechnology aimed at enhancing these processes in regenerative medicine.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: The influence of personality, parental behaviors, and self-esteem on Internet addiction: a study of Chinese college students. Soc Netw. Feb 2014;17(2):104-110. [FREE Full text] [CrossRef] [Medline]. Social media and mental health: benefits, risks, and opportunities for research and practice. J Sci. Sep 2020;5(3):245-257. [FREE Full text] [CrossRef] [Medline]. Digital resilience mediates healthy use of technology. J. Dec 2022;90(4):195-199. [CrossRef] [Medline]Orben A, Przybylski AK. The association between adolescent well-being and digital technology use. Behav. Feb 2019;3(2):173-182. [CrossRef] [Medline]Çimke S, Cerit E. Social media addiction, cyberbullying and cyber victimization of university students. Nurs. Oct 2021;35(5):499-503. [CrossRef] [Medline]. Reasons or excuses for avoiding meta-analysis in forest plots. BMJ. Jun 21, 2008;336(7658):1413-1415. [FREE Full text] [CrossRef] [Medline]Escobar- Passive and active social media use and depressive symptoms among adults. Soc Netw. Jul 2018;21(7):437-443. [CrossRef] [Medline]. Adolescent social media user types and their mental health and well-being: results from a longitudinal survey of 13-14-year-olds in the United Kingdom. JCPP Adv. Mar 10, 2022;2(2):e12071. [CrossRef] [Medline]Verduyn P, Ybarra O, Résibois M, Jonides J, Kross E. Do social network sites enhance or undermine subjective well-being? A critical review. Policy Rev. Jan 13, 2017;11(1):274-302. [FREE Full text] [CrossRef]Valkenburg PM, van Driel II, Beyens I. The associations of active and passive social media use with well-being: A critical scoping review. Soc. Dec 31, 2021;24(2):530-549. [SEP] Justify:

Output: The text critically reviews various studies and articles that explore the relationship between social media use and mental health. It mentions both the benefits and risks associated with social media, indicating that the impact on mental well-being may not be uniformly positive. For example, the critical review titled 'Do social network sites enhance or undermine subjective well-being?' suggests that there are factors that can lead to negative outcomes, contrasting with the claim that social media algorithms always have a positive impact.


Input: Task: pass the claim using the given text. Claim: Syntactic parsing in the brain is influenced by both grammatical rules and context. [SEP] Text: 984/fig-8 order to recognize formulaic language in the text more accurately, this 64. During model training, Adam optimizer is selected as the optimization algorithm, the learning rate is set to 0. 001, and the decay ratio is set to 0. 9. Ablation experiment In order to better verify the effect of formulaic language identification model based on GCN fusing associated information, seven comparative experiments were conducted to determine which feature is more important for formulaic language identification by setting ablation experiments. The specific methods are as follows: Before_Bi-LSTM: The part-of-speech features generated by PyTorch word embedding and the semantic features generated by GloVe word embedding are fused by early fusion. The fused feature vector is input into Bi-LSTM to extract the context semantic relationship. Finally, the features generated by Bi-LSTM are input into CRF to complete the recognition of formulaic language. Before_CNN: The part-of-speech features generated by PyTorch word embedding and the semantic features generated by GloVe word embedding are fused by early fusion. The fused feature vector is input into CNN. The size of convolution kernel is 3 * 3, and a total of two layers of CNN are set. Finally, the features generated by CNN are input into CRF to complete the recognition of formulaic language. After_Bi-LSTM: The part-of-speech features generated by PyTorch word embedding and the semantic features generated by GloVe word embedding are fused by late fusion. That is, the two feature vectors are input into Bi-LSTM respectively, and then the processed vectors are fused. Finally, the fused features are input into CRF to complete the recognition of formulaic language. After_Bi-LSTM_CNN: Based on After_Bi-LSTM, this method adds a layer of CNN between Bi-LSTM and CRF. The size of convolution kernel is 3 * 3, and a total of two layers of CNN are set. Bi-LSTM_DS_GCN: The part-of-speech features generated by PyTorch word embedding and the semantic features generated by GloVe word embedding are fused by late fusion. [SEP] Justify:

Output: The text does not directly address the claim regarding syntactic parsing in the brain being influenced by grammatical rules and context. Instead, it discusses various methods for recognizing formulaic language through models like Bi-LSTM and CNN, without making any connections to syntactic parsing or the roles of grammatical rules or context.


Input: Task: corroborate the claim using the given text. Claim: Research is ongoing to use nanotechnology in regenerative medicine. [SEP] Text: Some efforts have focused on bone marrow stem cells in an attempt to treat multiple organ failures and to promote regeneration. On average, bone marrow constitutes 4% of the total body mass of humans; the hematopoietic compartment of bone marrow produces approximately 500 billion blood cells per day, which use the bone marrow vasculature as a conduit to the body's systemic circulation [55]. Bone marrow is also a key component of the lymphatic system, producing lymphocytes that support the body's immune system [56]. Besides these routine activities of bone marrow, the recruitment of bone marrow stem cells in response to tissue injury was one of the major topics of current undertaking of regenerative medicine [49, 50]. Progenitor cells have been considered as a major source of tissue repair or regeneration in response to tissue injury [57, 58]. A progenitor cell, like a stem cell, has a tendency to differentiate into a specific type of cell, but is already more specific than a stem cell and is pushed to differentiate into its "target" cell. The most important difference between stem cells and progenitor cells is that stem cells can replicate indefinitely, whereas progenitor cells can divide only a limited number of times. Most progenitor cells are described as oligopotent [59, 60]. Therefore, they may be compared to adult stem cells. But progenitor cells are said to be in a further stage of cell differentiation. They are in the “center” between stem cells and fully differentiated cells. The kind of potency they have depends on the type of their "parent" stem cells and also on their niche. Some progenitor cells were found to move through the body and migrate towards the tissue where they are needed. In this context, many properties of adult stem cells are shared by progenitor cells [61]. However, the adult stem cells are quite different from embryonic stem cells that are true stem cells; they are pluripotent and show unlimited capacity for self-renewal. In contrast, it has not been comprehensively demonstrated for the adult stem cells of their capacities for unlimited self-renewal and plasticity [62]. Progenitor cells are found in adult organisms and they act as a repair system for the body. They replenish specialized cells, but also maintain the blood, skin and intestinal tissues [63–66]. [SEP] Justify:

Output: The text corroborates the claim by discussing the recruitment of bone marrow stem cells and progenitor cells in response to tissue injury as a major topic in regenerative medicine. It mentions the roles of these cells in tissue repair and regeneration, which aligns with the ongoing research in utilizing nanotechnology within this field. Although the text does not explicitly mention nanotechnology, the focus on stem cells and regeneration supports the notion of ongoing research in regenerative medicine.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Introduction: “Good for People, Good for Society”In the wake of the scandal that broke in early 2018 (Cadwalladr & Graham-Harrison, 2018), whereby the data of millions of Facebook users were sold to a private political consultancy firm without explicit informed consent, faced questions during two sessions in front the US Senate and Congress concerning its business practices and social values. During his Senate hearing, Zuckerberg was asked to comment on recent reports linking Facebook usage with mental health issues such as addiction and depression. Such issues are common themes of contemporary Western mass media coverage of Facebook (Busby, 2018; Carey, 2019; O’Neil, 2019), and are explored in psychological studies that associate increased Facebook usage with decreased subjective well-being (Kross et al. , 2013), higher levels of depression (Feinstein et al. , 2013), and increased envy in users (Appel et al. , 2016). Moreover, (2011) argues that individuals who spend extensive periods online can become isolated within their immediate communities, while (2017) links the rising levels of anxiety in teenagers to increased social media usage and reliance on the smartphone. Adding to this, Facebook’s former vice-president of user growth had recently criticized the “dopamine-driven feedback loops” of Facebook’s liking system in a speech at School (Wong, 2017), and early investor Roger McNamee (2017), in an online 2018. Moreover, choosing to adopt either of these two modes of activity is implicitly presented as better or worse user decision, respectively, with this binary being presented in terms of health. However, as the philosopher of well-being (2001) argues:to determine what is goodness and defect of character, disposition, and choice, we must consider what human good is and how human beings live: in other words, what kind of living thing a human being is. (p. 51)Thus, the claims of “healthy” modes of usership expressed by Facebook’s representatives are normative in the sense that they establish behaviors that can be said to benefit the human subject on Facebook, while determining other forms of activity as deleterious. will show below, Facebook constructs this model of health in direct relation to findings from their research into social psychology and HCI. [SEP] Justify:

Output: The text contrasts the claim by discussing various studies and expert opinions that link increased Facebook usage with negative mental health outcomes, such as addiction, depression, and isolation. It explicitly mentions that psychological studies associate Facebook usage with decreased subjective well-being and higher levels of depression, stating, "psychological studies that associate increased Facebook usage with decreased subjective well-being." This indicates that social media algorithms may have detrimental effects on user mental well-being, opposing the claim that they always have a positive impact.


Input: Task: pass the claim using the given text. Claim: Syntactic parsing in the brain is influenced by both grammatical rules and context. [SEP] Text: The fused feature vector is used as the basic feature and the matrix generated by dependency syntactic relation is used as the associated feature, which are input into GCN for feature representation. Finally, the features generated by GCN are input into CRF to complete the recognition of formulaic language. Bi-LSTM_MI_GCN: The difference from Bi-LSTM_DS_GCN is that the matrix generated by dependency syntactic relation is replaced with the matrix generated by the mutual information between words as the associated feature. Bi-LSTM_MI_DS_GCN: This is the model proposed in this 9. 2, train_time represents the time required to train 100 pieces of data, and test_time represents the time required to test 100 pieces of data. Analysis of experimental results: (1) The difference between Experiment 1 and Experiment 2 lies in the comparison between Bi-LSTM and CNN. The experimental results show that Bi-LSTM is much better than CNN in feature extraction. Because the identification of formulaic language is a typical sequence labeling problem, Bi-LSTM can capture the long-distance and long-term dependence of sentence context information and can capture the two-way information of sentences. However, CNN cannot capture the long-distance dependence information well, so it is better to use Bi-LSTM in formulaic language recognition. It is worth noting that the Recall in the result of using CNN to extract features is relatively high, indicating that it can recognize more formulaic languages. However, it can also recognize many non-formulaic languages, so the Precision is not very high. Table 2: Comparison of experimental results of ablation experiment. F1-score Train_time Test_time 1 Before_Bi-LSTM 62. 12 81. 46 70. 49 9. 40775 3. 83115 2 Before_CNN 34. 23 50. 33 40. 75 11. 24228 5. 06429 3 After_Bi-LSTM 77. 08 73. 51 75. 25 11. 96498 5. 16604 4 After_Bi-LSTM_CNN 72. 46 66. 23 69. 20 22. 86475 8. 34037 5 Bi-LSTM_DS_GCN 65. 22 69. 54 67. 31 12. 20419 4. 36681 6 Bi-LSTM_MI_GCN 74. [SEP] Justify:

Output: The text does not address the claim regarding syntactic parsing in the brain being influenced by grammatical rules and context. Instead, it focuses on technical aspects of using Bi-LSTM and CNN in feature extraction for formulaic language recognition without discussing the underlying cognitive processes or the interplay of grammatical rules and context.


Input: Task: corroborate the claim using the given text. Claim: Research is ongoing to use nanotechnology in regenerative medicine. [SEP] Text: This process involves both local responses and the recruitment of remote repair mechanisms. This repair process leads to different results between human adults and early stage of life. In human adults, a non-functioning mass of fibrotic tissue known as a scar is often observed at the completion of the repair process. By contrast, early in gestation, injured fetal tissues can be completely recreated without fibrosis, which is the process of regeneration. Regeneration means the regrowth of a damaged or missing organ part from the remaining tissue. Some organs retain high ability to regenerate throughout adult life, such as the liver. If part of the liver is lost by disease or injury, the liver grows back to its original size, though not necessarily to its original shape. However, many other organs are much less capable of regenerating in the adult life. A goal of regenerative medicine is to find and reactivate the missing elements or the suppressed process of regeneration in adult tissues, which exist early in gestation or remain in some organs in the adult life. Regenerative strategies include the rearrangement of pre-existing tissue, the use of progenitor cells and adult stem cells, and the dedifferentiation and/or transdifferentiation of cells. Dedifferentiation of cells means that they lose their tissue-specific characteristics as tissue remodels during the regeneration process. Transdifferentiation of cells is when they lose their tissue-specific characteristics during the regeneration process, and then re-differentiate to a different kind of cells. Multiple regulatory mechanisms operate in the regeneration process and distinguishingly function in one tissue type or the other. All these strategies result in the re-establishment of appropriate tissue polarity, structure, and form [13, 14, 41, 42, 73]. In a study understanding the process of kidney damage and regeneration, a molecular regenerative pathway was identified [74]. This pathway involves macrophages that respond to tissue injury by producing Wnt7b. The Wnt7b is important to the formation of kidney tissues during embryonic organ development. In the regeneration of injured kidneys, macrophages, by migrating to the injured kidney and producing Wnt7b, re-establish an early molecular program operating in the organ development that becomes beneficial to tissue regeneration. Wnt7b belongs to Wnt family of proteins, which regulate cell growth, proliferation and differentiation [75, 76]. [SEP] Justify:

Output: The text corroborates the claim by discussing ongoing research efforts within regenerative medicine to find and reactivate regenerative processes in adult tissues. Although the text does not explicitly mention nanotechnology, it describes various regenerative strategies and identifies molecular pathways involved, which are areas where nanotechnology could play a significant role.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: It is upon these quasi-psychological foundations (Rose, 1998) that Facebook feels authorized to construct, and recommend, a particular version of human sociality within which its services can be understood as valuable, and indeed, necessary for its users’ well-being. For the philosopher of biological science (1991), writing in the early 20th century, however, the definition of healthy human states in Western discourses of medicine constitute “an attachment to some value,” as opposed to a “disposition which can be revealed and explained as a fact” (p. 57). Canguilhem (1991) suggests that “the concept of health is not one of an existence, but of a norm whose function and value is to be brought into contact with existence in order to stimulate modification” (p. 87). In this way, health is not a stable concept existing in abstraction from the historical milieu of its enunciation, but can actually be viewed as a point around which human individuals can adjust their behavior in relation to certain categories of ideal normality. to be able to establish online activity that is “better” and “healthy” for humans, thus, a pre-existing model of normal human nature is necessary, and particular types of human existence need to be identified as valuable to the individual upon this basis. This process of determining valuable human activity is characteristic of eudaimonic accounts of well-being—the attempt to establish what it means to live well as a human in time (Aristotle, 2004; Raibley, 2013). There are two main threads of the psychological and philosophical study of well-being; hedonistic and eudaimonic accounts (Haybron, 2008; Kahneman et al. , 1999). Whereas hedonistic theories focus on human well-being in terms of pleasure attainment and pain avoidance (Parfit, 1984), the eudaimonic approach links well-being with a notion of human flourishing (Kraut, 2007). Eudaimonic well-being can be understood in terms of procedural self-fulfillment, meaningful existence, and well-functioning, with the psychologists Ryan and Deci (2008) describing eudaimonic psychology as concentrating on “the content of one’s life, and the processes involved in living well” (p. 140). [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of well-being, which include eudaimonic and hedonistic accounts. These theories imply that well-being is not simply guaranteed or positively impacted by social media algorithms. Instead, concepts such as procedural self-fulfillment and meaningful existence suggest that algorithms' influence on human sociality may not necessarily result in positive outcomes, indicating that their impact on mental well-being can be both positive and negative.


Input: Task: pass the claim using the given text. Claim: Syntactic parsing in the brain is influenced by both grammatical rules and context. [SEP] Text: 19 76. 16 75. 16 11. 19024 3. 63964 7 Bi-LSTM_MI_DS_GCN 81. 71 85. 37 83. 50 13. 55199 5. 35704 DOI: 10. 7717/peerjcs. 984/table-2 Figure 9: Comparison of experimental results of ablation experiment. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-9 (2) The difference between Experiment 1 and Experiment 3 is that the way of feature fusion is different. Experiment 1 adopts early fusion and Experiment 3 adopts late fusion. The experimental results show that the late fusion method has higher Precision and the early fusion method has higher Recall. The main reason is that the early fusion method can recognize more results when recognizing formulaic languages, but it can also recognize many non-formulaic languages, so its Precision is low. The features of the late fusion method are more accurate, and the recognition results are not as much as those of the early fusion, but the formulaic languages can be recognized more accurately. According to the F1-scores of the two methods, the effect of late fusion is better than that of early fusion. (3) Experiment 4 adds CNN layer based on Experiment 3, but the results after adding CNN are not as good as the previous results. The main reason is that CNN captures local correlations and each layer of CNN has a fixed span. Naturally, this layer can only model limited distance information. 4, Bi-LSTM has obtained the long-distance dependency of the context, and then CNN is added after Bi-LSTM to deeply abstract the features, some correct formulaic languages are filtered out, resulting in a significant decline in Precision and Recall. (4) Experiment 5 adds feature extraction based on GCN fusing dependency syntactic parsing on the basis of Experiment 3. The experimental results showed that after adding dependency syntactic features, the Recall and Precision are reduced. Because dependency syntactic relation mainly focuses on the dependency between two words in a sentence, it is easy to cause that the extracted word string does not belong to formulaic language, so the recognition effect is not good. Experiment 6 adds feature extraction based on GCN fusing mutual information on the basis of Experiment 3. [SEP] Justify:

Output: The text is irrelevant to the claim, as it discusses various experimental results regarding feature fusion methods and their impact on the precision and recall of language recognition, without addressing how syntactic parsing in the brain is influenced by grammatical rules or context.


Input: Task: corroborate the claim using the given text. Claim: Research is ongoing to use nanotechnology in regenerative medicine. [SEP] Text: of : 2325-9620 Toggle navigation & & 1. doi:/10. 4172/2325-9620. 1000101 Nanotechnology to Epigenomics and The objective of regenerative medicine is to repair tissues and organs that are damaged by trauma, disease or aging. The stem cell based technologies have promised to advance therapeutic applications including organ transplants and tissue repair. Although more studies are now being conducted to reveal mechanisms regulating stem cell fate, epigenetic events that contribute to cell differentiation are still relatively poorly defined. Next generation sequencing combined with chromatin immunoprecipitation (ChIPseq) or bisulfite sequencing (BS-seq) is the most common tool to study histone modifications and DNA methylation. However, it requires an abundance of research material and has limited ability to detect different epigenetic signatures in the same cell type. The recent development of the single chromatin analysis at the nanoscale (SCAN) using the nanofluidic single-molecule sorter allows the simultaneous detection of multiple epigenetic marks in a small number of cells. Keywords: Nanotechnology; Nanofluidics; Epigenome; Chromatin; Chip-seq; Regenerative medicine; Next generation sequencing Keywords Nanotechnology; Nanofluidics; Epigenome; Chromatin; Chip-seq; Regenerative medicine; Next generation sequencing is a highly regulated process throughout the life cycle of an organism for maintaining or restoring the normal function of cells and tissues. Regenerative medicine deals with the processes of replacing or regenerating human tissues or organs to restore normal function [1]. One of the main obstacles in regenerative medicine is to develop technologies that allow optimal recovery of damaged tissues. In this respect bioengineering of artificial organs might solve some of the problems associated with poor tissue regeneration. Stem cell research has emerged as one of the most promising areas in cell transplantation including rapid advancement of personalized tissue regeneration therapy to reprogram adult cells into induced pluripotent stem cells (iPSCs) [2]. These cells carry many properties of embryonic stem cells (ESCs) and capable of differentiating into any type of body cell. Thus, there is little doubt that progress in stem cell therapy will enable an organism to regenerate damaged tissues or organs with new cells derived from a patientâ�? �? s own cells. [SEP] Justify:

Output: The text corroborates the claim by discussing advancements in nanotechnology and its applications in regenerative medicine, specifically mentioning the development of the single chromatin analysis at the nanoscale (SCAN) method that utilizes nanofluidics for detecting multiple epigenetic marks in small numbers of cells. This indicates that research integrating nanotechnology into regenerative medicine is being actively pursued.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: For the philosopher Lorraine Besser-Jones (2016), moreover, living-well is “flourishing qua human being” (p. 187). Eudaimonic well-being is therefore oriented toward the recognition and fulfillment of innate and distinctly human capacities. To identify the distinct traits, needs, and qualities of the human subjects/users that act as the idealized target for Facebook’s eudaimonic construct of well-being, I will now turn to the stated research upon which Facebook erects the division between active (healthy) and passive (unhealthy) usership. Access to this research can be gained via a blog post found upon Facebook’s blog series. Hard Questions, as its very first post in June 2017 states, was intended to make Facebook “more open and accountable” (Schrage, 2017) by offering an insight into how Facebook publicly conceptualizes and presents its action on various socio-political issues involving its services, addressing topics such as terrorist activity on Facebook, false news, data privacy, democracy, and user well-being (Schrage, 2017). Accredited to David Ginsberg, then director of research at Facebook, and Moira Burke, a social psychologist at Facebook, the blog post concerning user well-being is titled “Is spending time on social media bad for us? ” (Ginsberg & Burke, 2017). Here, Ginsberg and Burke present Facebook’s well-being research, which suggests it is how people choose to use social media that determines the extent to which it is a positive or negative force for their health. Social UserOn this blog, foreshadowing the quotes from Facebook’s leaders offered above, Ginsberg and Burke (2017) write,According to the research, it really comes down to how you use the technology. For example, on social media, you can passively scroll through posts, much like watching TV, or actively interact with friends—messaging and commenting on each other’s posts. Just like in person, interacting with people you care about can be beneficial, while simply watching others from the sidelines may make you feel worse. The blog post contains hyperlinks to particular psychological studies to bolster these claims. These include, for instance, papers by et al. [SEP] Justify:

Output: The text contrasts the claim by indicating that the impact of social media on mental well-being is contingent on how users engage with the platform. It notes that Ginsberg and Burke’s research suggests that ‘it really comes down to how you use the technology,’ highlighting that active interaction with friends can be beneficial, while passive usage may negatively affect well-being, which directly refutes the notion that social media algorithms always have a positive impact.


Input: Task: pass the claim using the given text. Claim: Syntactic parsing in the brain is influenced by both grammatical rules and context. [SEP] Text: The experimental results show that after adding the mutual information features, the Recall increases, indicating that the number of correctly recognized formulaic language is more. However, due to the reduction of Precision, F1-socre is not improved. At the same time, compared with experiment 5, it shows that mutual information features can better represent the characteristics of formulaic language than dependency syntactic features. (5) Experiment 7 (the model proposed in this 3. 7% higher than that of experiment 3 (the experiment with the highest F1-score among other experiments). This shows that the model in this 10. As can be seen from the figure, the effect of using 3-layer graph convolution is the best for dependency syntactic parsing. Figure 10: Influence of different network layers on GCN based on dependency parsing. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-10 (2) Experiment 6 is a GCN structure based on mutual information. Experiments are carried out by setting 1, 2, 3, 4 and 5-layer graph convolution respectively. The experimental results are shown in Fig. 11. As can be seen from the figure, the effect of using 2-layer graph convolution is the best for mutual information. Figure 11: Influence of different network layers on GCN based on mutual information. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-11 Analysis of experimental results: It is generally believed that increasing the number of network layers can reduce the error and improve the accuracy, but it will also complicate the network, thus increasing the training time of the network and the tendency of over fitting. Therefore, the number of network layers required by different features is different. In this 12. Figure 12: Comparison of experimental results of three models. Download full-size image DOI: 10. 7717/peerjcs. 984/fig-12 Analysis of experimental results: (1) As the CNN_Bi-LSTM_CRF model is the primary method in named entity recognition, the experimental results show that this model does not perform well in the task of formulaic language recognition. It can be seen that in different tasks, although the two tasks are very similar, we should also start from the essence of the object in the task, mine the characteristics that can represent the research object, and design a unique model. [SEP] Justify:

Output: The text does not address the claim regarding syntactic parsing in the brain and its influence by grammatical rules and context. Instead, it focuses on experimental results related to graph convolutional networks and their performance in recognizing formulaic language, which is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Cryptography is essential for securing data and communications online. [SEP] Text: The misunderstanding responsibilities, issues in confidentiality, lack of standards, interoperability issues and malicious insiders in the cloud computing caused the several issues to preserve the data from the attacks. Security issues have been categorized into data breaches, malicious attack, data loss, and inadequate diligence, sensitive data access, data segregation, etc. [2]. The confidentiality, integrity and availability must be ensured to achieve data security. The data security has become a complex challenge in cloud computing due to the following reasons: ・ The necessity to guard the confidential and sensitive data related to government and business organizations. ・ The sharing of cloud infrastructure among various clients. ・ Legal and regulatory compliances during data mobility. ・ Issues in auditing and reporting. ・ Lack of backup and storage standards. ・ Key management issues and unauthorized access In order to overcome these security challenges, there are several solutions available in secure cloud storage server. The confidentiality can be attained by applying the access controls, authentication mechanisms, cryptography schemes etc. The access to control and authentication mechanisms allows the authorized user access, whereas the cryptography techniques allows only the specific user, who possess the keys to access the data. Hence, the cryptography schemes are the best way to provide data security, in which the user cannot access the data without the knowledge of key. Generally, most of the cryptography techniques include three major steps as follows: ・ Key generation; ・ Encryption; ・ Decryption. Figure 1 shows the data flow and message flow in cloud storage server for secure data transmission. Key generation is the process of producing the keys that are used for encryption and decryption. The encryption is the process of converting the original data into an unreadable form known as cipher text by the keys. The decryption is the process of retrieving back the original message from the cipher text using the appropriate keys. The cryptography schemes are classified into two types such as symmetric or conventional cryptography and asymmetric or public key cryptography. The symmetric cryptography uses same key for both encryption and decryption, whereas the public key cryptography uses different keys. The symmetric cryptography schemes are fast but there is no guarantee for secure key distribution. As they use the same key for encryption and decryption, the third party, who is snooping while key transmission may decrypt the data. In order to overcome this issue, public key cryptography is introduced with a pair of keys, namely, public key and private key. [SEP] Justify:

Output: The text corroborates the claim by highlighting the importance of cryptography in ensuring data security. It states that cryptography techniques allow access only to specific users who possess the keys, emphasizing its role in maintaining confidentiality. The text explains that cryptography schemes are needed to guard sensitive data and facilitate secure data transmission, thus supporting the assertion that cryptography is essential for securing data and communications online.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: (2015, 2017) that survey the links between different types of Facebook activity and user well-being, and Toma and Hancock’s (2013) research into Facebook and self-affirmation. Such research examines the psychological impact of Facebook through the binary of active and passive usage. These studies are referenced by Burke and Kraut as they seem to show that measures of well-being can increase if Facebook users engage in certain active habits—private messaging, liking friends’ photos and commenting for instance, as opposed to passive habits such as scrolling down the without interacting with posts, or failing to message friends and family. The first reference to Facebook’s own research on well-being on the blog is a paper co-authored by herself and Robert E. Kraut (2016). In this paper, Burke and Kraut suggest that Facebook exists as a tool that could benefit users’ well-being if used correctly to actively interact with others on the platform. Their argument, Burke and Kraut (2016) write, works “upon the assumption that people benefit from social interaction with others” (p. 266). This “assumption” is partly justified with a reference to Baumeister and Leary’s (1995) evolutionary concept of human belonging. Baumeister and Leary posit that humans have evolved to be reciprocally caring and social creatures to maintain evolutionary advantages in potentially threatening environments. The positive emotional ties that bind humans with others in familial groups, friendship circles, and romantic relationships are said to be distinct human characteristics that grant access to reproductive mates, mutual protection and resources such as shelter and food. Conversely, Baumeister and Leary (1995) claim that the “lack of [social] attachments is linked to a variety of ill effects on health, adjustment, and well-being” (p. 497), suggesting that human empathy and communication developed as evolutionary and physiological necessities of human survival. In referencing the evolutionary psychology of Baumeister and Leary, Burke and Kraut stake out a particular presentation of the Facebook user as an inherently “social” animal—a being whose healthy internal psyche and physical vitality are linked to communicating and socializing with other like creatures. Solidifying this notion, Burke and Kraut (2016) write thathumans are a social species, with many survival advantages accruing from our connections to others. (p. [SEP] Justify:

Output: The text contrasts the claim by discussing research that highlights the dual nature of Facebook usage on well-being. While it mentions that active engagement with Facebook can enhance well-being, it also implicitly points out that passive usage is detrimental. The studies referenced imply that social interaction is essential for mental health, and a lack of it can have 'ill effects on health, adjustment, and well-being.' This suggests that while social media can have positive aspects, it does not always guarantee a positive impact on mental health, thus opposing the claim that social media algorithms always positively influence user mental well-being.