Input: Task: corroborate the claim using the given text. Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: 1, the risk allele frequency as well as the odds ratios of known T2DM variants are compared among a control group (n = 632, men : women = 287 : 345), T2DM group (n = 761, men : women = 354 : 407), and GDM group (n = 869) in Koreans [22, 24]. For most of the variants, there was an increasing trend of risk allele frequencies from control to T2DM and from T2DM to GDM. Lauenborg et al. [23] also found that variants in TCF7L2, CDKAL1, and TCF2 were significantly associated with the risk of GDM in Europeans, consisting of 283 GDM women and 2,446 glucose-tolerant control women. Variants in KCNQ1, which were first identified in an T2DM GWA study, were also significantly associated with the risk of GDM in Koreans (p < 0. 05) [25]. Recently, it was reported that TPH1 and HTR2B play a crucial role in regulating pancreatic β-cell mass during pregnancy in a mouse model and that their genetic alterations could result in GDM [17]. A total 6 genetic variants in HTR2B and 11 variants in TPH1 were identified and genotyped in Korean GDM women and control subjects [26]. Although there were no significant associations of these variants with the risk of GDM, they were associated with measures of obesity and weight gain during pregnancy [26]. GWA Study of GDM Following these candidate approach studies was a two-staged GWA study that was performed in Korean GDM women [27]. A total of 468 GDM women and 1,242 nondiabetic control women were genotyped using Affymetrix Genome-5. 0. Variants that passed the prespecified p-value threshold in the stage 1 genome scan were further genotyped in 931 GDM women and 783 nondiabetic control women. Two variants, one located in an intron of CDKAL1 (rs7754840) and one near MTNR1B (rs10830962), were associated with a risk of GDM at a genome-wide significance level (p = 6. 65 × 10-16 and p = 2. 49 × 10-13, respectively). [SEP] Justify:

Output: The text corroborates the claim by discussing genetic variants associated with gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM). It describes how genetic studies, including genome-wide association studies (GWAS), have identified specific genetic variants related to disease risk, which aligns with the assertion that AI can help identify genetic risk factors for diseases.


Input: Task: contrast the claim using the given text. Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: (4) In all the existing schemes, public verification is not considered because the identity of the sender or the recipient must be given in the ciphertext in plaintext form, thus there are no requirements for public verification. But in a completely anonymous scheme, public verification is a necessity for recipients so that receiving or operating on unwanted messages is prevented. To summarize, the ciphertext in our scheme no longer contains the real identity information of all participants, thus our scheme meets anonymity of the sender and recipients at the same time, and efficiently protects the privacy of all involved participants. Even more important, this scheme possesses fair decryption and public verification properties. Furthermore, our scheme is easy to implement in exsiting applications. Here, we also take a network conference application as an example. In such a case, a message sender needs only to transform the plaintext message to a ciphertext message using our encryption algorithm and then broadcasts it through the broadcast communication channel, while a message recipient simply needs to decrypt the ciphertext using our decryption algorithm. Our scheme requires only extra encryption or decryption operations for each participant and leaves the original implementation untouched, which in fact should represent an easy implementation of our scheme. While our scheme has the advantages mentioned above, it also has some disadvantages, namely its application, which increases the costs for the implementation. For example, it probably takes a great deal to establish PKG and maintain it, which may affect routine application of our scheme to some extent. to the nature of broadcasting communications, the anonymity of both the sender and the recipient is of upmost importance in multi-recipient signcryption. However, almost none of the existing multi-recipient signcryption schemes take the anonymity of recipients into account. Although there are several schemes that provide a solution to the anonymity of the signer, they have known limitations. Owing to practical application requirements, a completely anonymous multi-recipient signcryption becomes more and more important. Aiming at the participants’ anonymity, a completely anonymous multi-recipient signcryption is proposed in this paper. The new scheme ensures anonymity of all participants, the sender and all recipients. Furthermore, each recipient can easily judge whether the received message is from an authorized source, but he/she cannot determine the true identity of the sender. Each recipient can easily judge whether he/she is an authorized recipient, but he/she cannot determine the identity of any other authorized recipient. [SEP] Justify:

Output: The text contrasts the claim by explaining that while the proposed encryption scheme ensures anonymity for all participants, the real identity information of senders and recipients is still present in the ciphertext in certain transactional contexts. It states that participants cannot determine the true identity of the sender or other recipients, indicating that complete anonymity may not be guaranteed in all cases, particularly with existing schemes that do not prioritize public verification.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: et al. in November 2023[27] emphasized EAV-UNet, a technology for accurately detecting lesion locations. Optimizing feature extraction, using automated segmentation algorithms to find anomalous regions, and strengthening the structure. The approach replaces the U-Net encoder with the VGG-19. To improve feature details, they added a CBAM (Channel and Mechanism) module to the decoder. They added an edge identification part to the encoder to extract important edge characteristics from the stream. This strategy obtained an F1 score of 96. 1%. et al. in April 2023 [28] proposed that Deep learning algorithms were used to analyze brain MRI scans of tumors to detect the methylation status of the MGMT promoter. Researchers used deep learning algorithms and a large public MRI dataset of 585 people to predict the methylation status of the MGMT promoter in glioblastoma tumors. They evaluated these models using Grad-CAM, occlusion sensitivity, feature visualizations, and training loss landscapes. The highest AUC by fold and the largest mean AUC are detected. The mean AUC becomes saturated at 0. 69. Qureshi1[29] et al. in 2023 proposed a novel two-stage (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and tumorous structural size by radiomic characteristics (and LBP). The novice rejection method was shown to be highly effective in picking and isolating negative training cases from the original dataset. The fused feature vectors are then employed by k-NN and SVM classifiers for training and testing. The highest classification performance is (96. 84 0. 09%), (96. 08 0. 10) %, and (97. 44 0. 14) % for detecting MGMT methylation status in patients with glioblastoma. In august 2022, Do et al. [SEP] Justify:

Output: The text discusses various advancements in deep learning algorithms for analyzing brain MRI scans and detecting tumor-related features, but it does not address or provide any information regarding the prediction of emotions based on brain scans. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: This was the first GWA study investigating the genetic risk factors of GDM. Although variants in MTNR1B have been previously reported to be associated with increased fasting glucose levels, the study was the first to report that MTNR1B variants are associated with GDM at a genome-wide significance level. One of the limitations was that it was not sufficiently powered to find truly novel genetic variants that were only specific in GDM. It should be noted that current GWA and GWA meta-analysis performed in T2DM recruits more than 100,000 cases and controls [28]. One of the interesting findings was that genetic variants of T2DM were enriched in GDM subjects. Among the 34 confirmed T2DM genetic variants, 8 were associated with a risk of GDM [27]. In addition, when the β-coefficients (or odds ratio) of the variants derived from the logistic regression were compared between GDM and T2DM, there was a significant positive correlation of β-coefficients between the two [27]. These findings suggest that GDM and T2DM might share similar genetic backgrounds, at least in part. Perspectives in of studies have opened a new era in diabetes research. Our knowledge on the genetic predisposition of GDM as well as T2DM is expected to increase even faster as next-generation sequencing technology is applied to this field. There should be even larger GWA studies on GDM, and GWA meta-analyses should be available. In this way, we could find variants that have smaller effect sizes but are more specific to GDM than T2DM. In order to understand the genetic determinants of glucose and insulin concentration during pregnancy, genetic associations regarding these quantitative traits should be investigated. A fast way is to see whether genetic variants that are known to affect glucose or insulin concentration in the normal population also affect glucose or insulin concentration during pregnancy [29]. In addition, a GWA study on glucose or insulin concentration during pregnancy should also be helpful. In this way, we would be able to better understand the pathophysiology of GDM. By definition, GDM encompasses women with pre-exiting T2DM, maturity onset diabetes of the young (MODY), or even type 1 diabetes patients that were not diagnosed previously. It would be important to know the proportion of MODY patients that constitute GDM. [SEP] Justify:

Output: The text corroborates the claim by discussing a Genome-Wide Association (GWA) study that investigates genetic risk factors for gestational diabetes mellitus (GDM) and highlights that findings suggest GDM may share genetic backgrounds with type 2 diabetes mellitus (T2DM). It mentions that the application of next-generation sequencing technology and the necessity for larger GWA studies will likely increase understanding of genetic predisposition, which aligns with the aim of using AI in genomics to identify genetic risk factors for diseases.


Input: Task: contrast the claim using the given text. Claim: Everyone can earn money through cryptocurrency investments. [SEP] Text: AbstractBitcoin (Nakamoto, 2008) is an electronic cash system designed to work without central management. Despite recent enthusiasm, Bitcoin (BTC) and other so-called cryptocurrencies are not ideal as means for payment, because of instability of their market prices against major currencies. This 6. 1 Bitcoin as a (Nakamoto, 2008) is an electronic cash system designed to work without central management. Despite recent enthusiasm, Bitcoin (BTC) and other so-called cryptocurrencies are not ideal as means for payment, because of instability of their market prices against major currencies. This 17. 31 million BTCs had been issued in the market with ID numbers (about 82% of 21 million BTCs). As of 2018, roughly every ten minutes on average, 12. 5 BTCs were being issued with new IDs. This procedure of new issue is implemented as a reward for the first person/group to validate transactions without double spends that have been collected in a block. This is a competition of validation via computation, with the aim of solving a specific mathematical problem. Footnote 4 This computation is described as mining, and those who conduct mining are miners. The speed of new issue of Bitcoin on the register record is set to be halved every four years. At the beginning of the Bitcoin system in January 2009, the reward was 50 BTCs per ten minutes; it was halved to 25 BTCs per ten minutes on November 28, 2012. It remained the same reward per ten minutes until it was halved to 12. 5 BTCs per ten minutes on July 9, 2016,Footnote 5 and this halving process will continue until 2140, when the new issue of BTC will be terminated. Total circulation of BTC will be fixed at a little less than 21 million BTCs. There are differences between a real estate registry system and the Bitcoin system. In Japan, for instance, the real estate register record is kept exclusively by the Bureau and the public is only allowed to read the record. In contrast, the virtual registry book that contains all information on Bitcoin transactions and ownership is maintained individually among participants. This decentralized nature of virtual registry bookkeeping activity may create some inconsistencies among participants. In the Bitcoin protocol, when an identical Bitcoin segment is used twice for different payments—leading to a Bitcoin segment having two branches (double spends)—the majority decision rule is used to determine which payment is genuine. [SEP] Justify:

Output: The text contrasts the claim by highlighting the instability of cryptocurrencies like Bitcoin as a means of payment. It states that despite recent enthusiasm, cryptocurrencies are not ideal due to market price volatility, suggesting that not everyone who invests might gain money from such investments.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: [30] the radiomics features extracted from multimodal images from magnetic resonance imaging (MRI) had undergone a two stage feature selection method, including an extreme (XGBoost) feature selection model followed by a genetic algorithm (GA)-based wrapper model for extracting the most meaningful radiomics features for predictive purposes. According to the cross-validation results, the GA-based wrapper model performed well in predicting the MGMT methylation status in GBM, with a sensitivity of 0. 894, specificity of 0. 966, and accuracy of 0. 925. Application of the extracted GBM radiomics features on a low-grade glioma (LGG) dataset also achieved a sensitivity 0. 780, specificity 0. 620, and accuracy 0. 750, indicating the potential of the selected radiomics features to be applied more widely on both low- and high-grade gliomas. After image preprocessing and feature extraction, et al [31] in 2022 built and compared the performance of two types of machine-learning (ML) models in June 2022. The first type was set up using all MRI sequences (T1WI, T2WI, contrast enhancement (CE), _b_high, DWI_b_low, and ADC), while the second type was established using single MRI sequences as described above. technique was used to find seven radiomic features for the ML model based on all sequences. The predictive accuracy was 0. 993 and 0. 750 in the training and validation sets, respectively, and the area under curves (AUCs) were 1. 000 and 0. 754 in the two sets, respectively. For the ML model based on single sequences, the numbers of selected features were 8, 10, 10, 13,9, 7, and 6 for T1WI, T2_b_high, DWI_b_low, and ADC, respectively, with predictive accuracies of 0. 797 ~ 1. 000 and 0. 583 ~ 0. 694 in the training and validation sets respectively, and the AUCs of 0. 874 ~ 1. 000 and 0. 538 ~ 0. 697 in the two sets, respectively. In the independent validation set, the T1WI-based model performed best, while the CE-based model performed worst. 2022 et al. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the use of radiomics features extracted from MRI for predictive modeling in brain tumors rather than addressing the application of advanced algorithms for predicting emotions based on brain scans.


Input: Task: corroborate the claim using the given text. Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: In particular, about 15% of GDM patients remain diabetic at early postpartum periods, and a significant portion of these subjects might fall into the category of MODY. The contribution of genetic variants known to cause MODY, such as GCK and HNF1A, in GDM has been reviewed in recent literature [30]. However, whole-exome sequencing will provide us with better bird's eye view on the contribution of MODY genes in GDM. In addition, it might be able to find novel MODY genes in those who have persistent diabetes after GDM pregnancy and also have a strong family history of diabetes. One of the first steps in translating the genetic information into clinical practice would be to predict the future development of T2DM in GDM women. GDM women are at particularly high risk of developing T2DM and require preventive measures and early screening of T2DM. Genetic information might improve our prediction of T2DM in women with a history of GDM. This is an area of active research [31, 32], and we are looking forward to studies that use genotype risk scores in predicting T2DM in GDM women. A similar approach could also be applied in predicting GDM, as more genetic variants associated with GDM are expected to be revealed. The functional consequences of the current common genetic variants identified through GWA studies of GDM are not well understood yet. It is not known whether they are markers in linkage disequilibrium with nearby causal variants or whether they have unknown but relevant functional roles. Next-generation sequencing might give answers to these questions, but a huge number of samples and much effort will be required. GDM women are at increased risk of developing T2DM and have familial clustering of T2DM. A common pathophysiology that is shared by GDM and T2DM is impaired compensatory increase in insulin secretion to overcome the increased insulin resistance. The GWA study of GDM enabled us to investigate the common genetic risk factors of GDM, and it revealed that GDM and T2DM share similar genetic backgrounds, at least in part (Fig. 2). Although this information has significantly improved our insight in the pathogenesis of GDM, there are more unanswered questions remaining that should be explicitly expressed. Using the technology of next-generation sequencing, it is expected that more causal, rare variants will be identified. This genetic information would ultimately improve our prediction of GDM and future T2DM. [SEP] Justify:

Output: The text corroborates the claim by discussing how genetic information can significantly improve predictions of diseases such as type 2 diabetes mellitus (T2DM) in women with a history of gestational diabetes mellitus (GDM). It highlights the role of genetic variants and the potential of next-generation sequencing to identify these variants and their functions, which aligns with the use of AI in genomics to identify genetic risk factors.


Input: Task: contrast the claim using the given text. Claim: Everyone can earn money through cryptocurrency investments. [SEP] Text: In case of standard electronic money, an issuer of electronic money receives participation fees directly from the retail shops; they are paid not by the electronic money they issue, but by central bank notes. Central banks themselves pay maintenance costs and receive service rewards in the money they issue. In the case of Bitcoin, the miner who contributes to the maintenance of the system receives Bitcoins as their reward, so it resembles the central bank system. A difference between the Bitcoin system and the central bank system lies in the fact that the former gives a reward to a miner who happens to win the mining competition while the latter receives a reward constantly. If there is a single miner in the Bitcoin system, r can be any arbitrary 256 bit value (n can be zero). In such a case, the competition mechanism that guarantees a validity of proof of work does not work and we require some alternative. If an alternative works, it could be sufficient to prevent double spends. This situation can be described as the mint model of cryptocurrency. The mint model differs from the Bitcoin model in the sense that the former model uses a finality confirmation structure with legal enforcement, while the latter model uses a finality confirmation structure via mining competition. Note again that the winner of the competition is the only competitor to be rewarded with Bitcoin. The probability of winning a reward must be based on the proportional computational power of an individual miner to the total computational power of all mining participants: all miners may expect to receive rewards proportional to their computational power after a reasonable number of mining competitions. Footnote 15 Then we must ask ourselves, can the proof of work contribute to the stability of Bitcoin value? Nakamoto (2008) states that “once a predetermined number of coins have entered circulation, the incentive can transition entirely to transaction fees and be completely inflation-free”(p. 4). The answer is no. As Fig. 6. 2 amply illustrates, the values of Bitcoin as measured in U. S. dollars fluctuate wildly compared with those of other foreign currencies. The reason for this high volatility is apparent. Demand for Bitcoin, regardless of the motivation for holding (i. e. , payment or speculation), increases as its price decreases and vice versa. As Fig. 6. 3 shows, the demand curve of Bitcoin, therefore, would be downward slopingFootnote 16 while supply the curve of Bitcoin at any point of time would be vertical. [SEP] Justify:

Output: The text contrasts the claim by explaining the mechanics of Bitcoin mining and emphasizing the volatility of Bitcoin values. It highlights that miners receive rewards only when they win mining competitions, implying that not everyone can earn money through cryptocurrency investments. Additionally, the mention of the fluctuating value of Bitcoin further contradicts the claim by suggesting that investment outcomes can be uncertain and not guaranteed for everyone.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: [32] developed a symmetric Deep Learning-based U-Net framework based on FLAIR's 512 _ 512 segmented maps as the ground truth mask. Their findings: The final group included 208 patients with an average _ standard deviation of age (years) of fifty-six _ fifteen and an M/F ratio of 130/78. The DSC of the generated mask was 0. 93. Prediction of IDH-1 and MGMT status achieved AUCs of 0. 88 and 0. 62, respectively. Survival prediction of < 18 months proved an AUC of 0. 75. Their deep learning-based framework can detect and segment gliomas with excellent performance for the prediction of IDH-1 biomarker status and survival.  2022 et al [33], trained a residual network (ResNet) to give a binary prediction of MGMT promoter methylation status. Instead of using images as an input, as in existing research, our research extracted radiomics features from a selected region of interest (ROI) in different modalities of MR images and used them as the input of the model. Based on the ROI of the whole tumor, the predictive capacity of the T1CE and ADC model achieved the highest AUC value of 0. 85. Based on the ROI of the tumor core, the T1CE and ADC model gained the most elevated AUC value of 0. 90. After comparison, the T1CE combined with the ADC model based on the ROI of the tumor core showed the best performance, with the highest average accuracy (0. 91) and AUC (0. 90) among all models. The deep learning method using MRI radiomics has excellent diagnostic performance with a high accuracy in predicting MGMT promoter methylation in diffuse gliomas. 3 Data source and collectionThree cohorts define the dataset: Training, Validation, and Testing with size (7 Gigabyte) ~ 400000 image. The dataset is published on Kaggle website in May 2021. These three cohorts are structured as follows: Each independent case has a dedicated folder shown by a five-digit number. Each of these "case" folders has four sub-folders, one for each of the structural multiparametric MRI (mpMRIs) scans in DICOM format. [SEP] Justify:

Output: The text does not address the claim regarding advanced algorithms predicting emotions based on brain scans. Instead, it discusses the application of deep learning algorithms for predicting glioma biomarker status and survival, utilizing MRI images and radiomics features. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: Implication of genetic variants near TCF7L2, SLC30A8, 1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians. Diabetes 2008;57:2226–2233. PMID: 18469204. 25. . Polymorphisms in KCNQ1 are associated with gestational diabetes in a Korean population. Paediatr 2010;74:333–338. PMID: 20606385. 26. Association of variations in TPH1 and HTR2B with gestational weight gain and measures of obesity. Obesity (Silver Spring) 2012;20:233–238. PMID: 21836641. 27. A genome-wide association study of gestational diabetes mellitus in Korean women. Diabetes 2012;61:531–541. PMID: 22233651. 28. è AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. 2012;44:981–990. PMID: 22885922. 29. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. 2012;44:991–1005. PMID: 22885924. 30. . Genetics in gestational diabetes mellitus: association with incidence, severity, pregnancy outcome and response to treatment. Rev 2010;6:393–399. PMID: 20879971. 31. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Med 2008;359:2208–2219. PMID: 19020323. 32. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Med 2008;359:2220–2232. PMID: 19020324. Fig. 1 Comparison of risk allele frequencies of known type 2 diabetes mellitus (T2DM) genetic variants among a control group (n = 632, men : women = 287 : 345), T2DM group (n = 761, men : women = 354 : 407), and gestational diabetes mellitus (GDM) group (n = 869) in Koreans. [SEP] Justify:

Output: The text provides evidence that genetic variants are implicated in various diseases such as type 2 diabetes and obesity, specifically mentioning association studies on genetic polymorphisms. This supports the claim that AI is used in genomics to identify genetic risk factors, as such studies often utilize AI for analysis and interpretation of genomic data.


Input: Task: contrast the claim using the given text. Claim: Everyone can earn money through cryptocurrency investments. [SEP] Text: To be more precise, it falls to some price lower than the average cost per unit but above the average variable cost. The miners would continue mining because it is rational to keep operations as long as return/revenue exceeds variable cost (i. e. , total cost minus fixed cost); the eventual operational loss would be smaller than that incurred by immediate stoppage. According to some reports on Bitcoin mining, many large-scale miners who entered after the Bitcoin boom in late 2013 continue running their operations even with negative returns. They may not actively anticipate the return of above-1,000 dollar/Bitcoin days, but they might simply assume that eventual operational loss would be minimized by continued operation. (5) Miners may also migrate to another mine in which they can continue mining, should computational powers be convertible to the new mine. Footnote 21 As we mentioned before, if the miners migrate to other mines, the size of M decreases and the expected return per unit would recover. By this mechanism, Bitcoin mining can survive even under a very volatile Bitcoin price. On the other hand, miners’ computing equipment may reach the end of its useful life, and miners might have to stop mining before they recover all their sunk costs. (6) Bitcoin mining might end another way. If the Bitcoin price drops sharply below the average variable cost, all miners would exit from mining. Many miners entered the Bitcoin mining competition after the Bitcoin boom in late 2013. Their computational power would be expected to be broadly similar. Footnote 22 If that is the case, the miners’ exit strategy would not be a gradual one but could be sudden. If the Bitcoin price drops below a threshold, the Bitcoin system as a whole may collapse or the Bitcoin users are limited to a very small number of inner members with which Bitcoin is exchanged at a very small scale. Once all miners leave Bitcoin mining, no one would be engaged in the proof of work. A validation of a block would be delayed or stopped, and in consequence Bitcoin would cease to be a useable currency. This type of risk does not exist in gold mining. Footnote 23 From the above observations, it is clear that the Bitcoin system intrinsically manifests dual instability. The first instability stems from an inflexible supply curve of Bitcoin, which amplifies Bitcoin price volatility; the miners’ revenue/reward fully absorbs any price changes. There is no price stabilization mechanism. The second instability comes from risks to the sustainability of mining. [SEP] Justify:

Output: The text contrasts the claim by discussing the volatility and risks associated with Bitcoin mining and investments. It highlights that miners may continue operations despite negative returns, indicating that not all participants in the cryptocurrency market will profit. Furthermore, it mentions potential collapse of the Bitcoin system if prices drop below a certain threshold, suggesting that not everyone can reliably earn money through cryptocurrency investments.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: The exact MP MRI scans attributes included for each patient are [34]: (FLAIR), T1-weighted pre-contrast (T1w), T1-weighted post-contrast (T1Gd) and T2-weighted (T2). The proposed model focuses on two tasks: segmenting tumor subregions and Radiogenomic classification of MGMT promoter methylation. Figure 1 displays mpMRIs images from the BraTS-2021 dataset. 4 The proposed methodThe proposed method will apply mining techniques like improved k-means algorithm and (CNN) Convolutional neural network for Radiogenomic Classification. predict the genetic subtype of glioblastoma using MRI (magnetic resonance imaging) scans to train and test the proposed model to detect for the presence of MGMT promoter methylation as shown in Fig. 2. In glioblastoma patients, methylation of the O6-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. By achieving the research results, this will help brain cancer patients receive less invasive diagnoses and treatments. The introduction of new and customized treatment strategies before surgery has the potential to improve the management, survival, and prospects of patients with brain cancer. 4. 1 Preprocessing steps: Data preprocessing step is a vital stage in segmentation that can improve the network model's efficiency. The BraTS-2021 dataset uses standardized preprocessing processes for all magnetic resonance imaging (MRI scans). Pre-processing steps included converting the NIfTI file format, co-registering using a template of normal adult human brain anatomy using an MRI-based atlas (SRI2459) and resizing to 1mm3. isotropic resolution followed by skull stripping to isolate the cerebral cortex and cerebellum from the remaining part of the brain. Preprocessing was done u001 00sing freely available tools like (CaPTk) and Segmentation (FeTS). The preprocessing channel began with data normalization the conversion of the DICOM file format to NIfTI files. The BraTS-2021 dataset has two tasks: Radiogenomic segmentation and classification. classification, the files are not co-registered. By using k-means clustering algorithm the MRI scans divided into three clusters (grade ) as shown in Fig. 3. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the technical processes related to MRI scans and tumor classification in glioblastoma patients rather than addressing the prediction of emotions using advanced algorithms. There is no mention of emotion prediction or emotional analysis in the provided text.


Input: Task: corroborate the claim using the given text. Claim: AI technology in healthcare is universally accessible and affordable. [SEP] Text: India takes the top spot amongst 148 countries in terms of affordability of ICT technology17. Hence, the implementation of ICT in HSM should be an easy choice. Since most companies are using cloud-based platforms, '[this helps] to reduce infrastructure cost. Scaling up becomes easier on a cloud environment'18. The HSM is also efficacious in disseminating information for health awareness, immunizations or precautions to be taken during epidemics (such as Ebola). Companies implementing the HSM have helped create and sustain thousands of rural jobs, both direct and indirect. Lessons learnedHSM is an effective mechanism that allows for greater distribution while minimizing costs when paired with ICT. There are many areas in emerging nations in Africa and where healthcare accessibility is limited. This also holds true for remote areas in advanced nations such as Sweden, Finland, Russia, Australia, Canada and the USA where there is limited access due to weather conditions and other factors. HSM paired with ICT will effectively service such regions, reducing unnecessary travel and other related costs. 'You don't need a PhD in economics to figure out this model is a lot cheaper and probably a lot more convenient for patients'10. healthcare companies such as iKure, Vaatsalya Healthcare, (Dental), are demonstrating that using HSM paired with ICT not only allows for treatment of medical conditions but also lowers costs and improves availability, making health care accessible and affordable to the nation. AcknowledgementsThe author would like to express special thanks to the management team at iKure (www. ikure. in and www. ikuretechsoft. com) (- Founder & CEO, and ), who walked him through the implementation of iKure's hub and spoke model and for sharing their company's information. 1. Govindarajan V, Ramamurti R. Delivering world-class health care, affordably. Review (Internet) 2013; 2. Available: (Accessed 26 February 2014). 2. Museum. History - in the Air, 1955. (Internet). Available: (Accessed 7 August 2015). 3. Rivett G. of the System. 1823-2015 (Internet) 2015. Available: (Accessed 2 August 2015). 4. Dawson B. Interim report on the future provision of medical and allied services. Great Britain, Ministry of Health. (Internet) 1920. [SEP] Justify:

Output: The text corroborates the claim by highlighting that the implementation of ICT (Information and Communication Technology) in healthcare, particularly through models like HSM (Hub and Spoke Model), significantly lowers costs and improves accessibility, making healthcare more accessible and affordable. It notes that companies like iKure are demonstrating this approach effectively, and states, 'You don't need a PhD in economics to figure out this model is a lot cheaper and probably a lot more convenient for patients,' which supports the claim about affordability and accessibility of AI technology in healthcare.


Input: Task: contrast the claim using the given text. Claim: Everyone can earn money through cryptocurrency investments. [SEP] Text: Our rule, on the contrary, depends on an economic principle, i. e. , the cost structure of the mining that is real economic activity. 6. 5. 5 of Bitcoin as CurrencyWe have analyzed the Bitcoin system in general and the role of mining as the proof of work. We’ve proposed an alternative to Bitcoin, (IBC), that is supposed to overcome the inherent instability of Bitcoin. But can IBC compete with major currencies issued by major central banks? In this section, we note one of many possible problems with such cryptocurrencies. Cryptocurrencies are more expensive to produce while they cannot be absorbed once produced. The production costs are hard to retrieve. Bank notes issued by the central banks require some printing and material costs. These costs are negligible compared with the face (nominal) value. Also, bank notes are reversible between new issues and absorption because the central bank basically buys and sells securities with bank notes. These points are fundamental shortfalls of cryptocurrency. As currently described, cryptocurrency values are based on associated production costs. This mechanism is similar to commodity money, notably gold and silver coins. Historically gold and silver coins have been replaced by credit (or fiat) money basically because of the above-mentioned points. Shall we choose bank notes or a cryptocurrency? There is no unconditional answer. Bitcoin-type cryptocurrencies, with some amendments, can be reasonably competitive with central bank notes in terms of value/price stability. Currency competition in the sense of θ Friedrich A. Hayek (1976) is desirable. Such competition must be encouraged, not only between central bank notes and a cryptocurrency, but also between central bank notes and among different crypto-currencies. The key differentiation of Bitcoin from central bank notes and existing digital cash-type electronic money is a framework in which all vintage information of each segment of Bitcoin is recorded. Footnote 28 Not many people are aware of this useful feature of Bitcoin. If this feature is introduced in bank note-like electronic money, each atom of bank note-like electronic money with its vintage information can reflect time value, i. e. , each note is priced differently according to the time passed since its issuance. In other words, we can provide interest with each note. This system implies that owners of bank note-like electronic money can receive interest or pay some penalty, depending on economic conditions. In the current central banking system, these benefits are transferred to the government as seigniorage. [SEP] Justify:

Output: The text contrasts the claim by highlighting significant challenges associated with cryptocurrencies, particularly their production costs and market absorption. It mentions that cryptocurrencies are more expensive to produce and that production costs are hard to retrieve, indicating that not everyone may find success or profit in cryptocurrency investments. The text also emphasizes the instability of these currencies compared to traditional currencies issued by central banks, which further undermines the claim that everyone can earn money through such investments.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: The k-means clustering algorithm, which is an unsupervised learning algorithm, clusters each pixel in MRI intensity in which in-homogeneities can be modeled, resulting in a segmented image of an MRI with the same intensity regions and segments all three brain matters, namely grey matter, white matter, and dark matter. 4. 2 The rejection sampling algorithm: The rejection algorithm, shown in Fig. 4, was executed for each class [35]. The goal is to remove instances that do not add discrimination to Radiogenomic classification processes. To load the whole picture set for either MGMT− (Class "0") or MGMT+ (Class "1"), follow the "Datapath" of the binary class. The algorithm stops after fetching a certain number of photos. The rejection sampling removes redundant/irrelevant images from specific classes using a threshold based on the sum of pixel values (Th) [36]. After comprehensive examination, Th was discovered to be equal to zero for best results. After RA, the result includes details for each rejected file, as well as better class details from changed Datapath loading. The difference in a class's first instance count should match the rejected files count plus the final class size (after RA). The RA reduced the number of cases by 26. 75% and 27. 51% respectively. In the first stage, a 3D convolutional neural network (CNN) architecture is constructed to extract brain tumors, and the extracted tumors are fed into a pretrained CNN model for feature extraction which is shown in Fig. 5. The obtained features are sent into a correlation-based selection process, which provides the best features. For the final segmentation, the selected characteristics are validated using a feed-forward neural network. 4. 3 The second stage of the proposed model: For classification, the dataset is registered to a standard template provided by NIfTI. files. The NIfTI format cuts all related metadata, including Information (PHI), from DICOM files. Skull-stripping reduces the amount of facial reconstruction that may be used for patient face recognition. This method is based on representation learning and is independent of MRI input. To classify radio genes, mpMRIs images are preprocessed to create skull-stripped volumes. These images are then translated from NIfTI to DICOM format. [SEP] Justify:

Output: The text discusses various algorithms related to MRI image analysis and segmentation but does not mention anything about predicting emotions based on brain scans. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI technology in healthcare is universally accessible and affordable. [SEP] Text: Quantitative data shows how the program significantly reduced participants’ sense of career anxiety, improved their sense of hope for the future, and built their self-efficacy while moderately impacting their ability to set goals. This study provides insight on how to implement an institution-wide initiative centred on design thinking. Keywords: life design; design your life; coaching; design thinking; learning; empowerment; hopefulness; self-efficacy; goal setting; study. DOI: 10. 1504/IJTCS. 2025. 10066480 The thief within: a case of broken trust and employee theft by Wendy S. Becker Abstract: Small businesses often lack the resources to provide adequate human resource management systems. Management may lack experience in human resources and neglect to implement adequate control mechanisms. The case is based on a real-life small business collective in which a lack of internal policy and procedures led to insider theft. Students analyse the case using research reviewed from five disciplines clinical psychology, industrial/organisational psychology, criminology, workplace security and organisational science. The objective of the case is to improve student awareness of the importance of basic human resource management functions. Students are asked to put themselves in the managers position and respond to how to prevent future employee theft. Keywords: employee theft; human resource management; clinical psychology; industrial/organisational psychology; criminology; workplace security; organisational science; retail employer; pilferage; petty theft; grand theft. DOI: 10. 1504/IJTCS. 2025. 10066513 Larkai healthcare: enabling AI in diagnostics and making healthcare accessible and affordable with disruptive technology by Jordi Diaz, Abstract: This case study delves into the entrepreneurial careers of the founder and co-founder of Larkai Healthcare. The company’s flagship product, WREN, is a smart multiparameter portable device that seamlessly integrates measures of respiratory rate, body temperature, NIBP (Non-invasive blood pressure), and ECG (electrocardiogram) to revolutionize patient monitoring. The benefits of WREN are seen in better patient care, more accessibility to healthcare, and more efficiency. The case explores the origins of Larkai, Pritam’s product developmental history, Abhilash’s business and financial heft, and the difficulties encountered in early business when artificial intelligence was still in the realm of theory. [SEP] Justify:

Output: The text corroborates the claim by discussing the case study of Larkai Healthcare, which aims to make healthcare more accessible and affordable through its AI-driven product WREN. The description highlights that the integration of multiple health monitoring measures in the device has the potential to improve patient care and increase accessibility to healthcare services.


Input: Task: contrast the claim using the given text. Claim: Everyone can earn money through cryptocurrency investments. [SEP] Text: The minimum unit of BTC is not 1 BTC, but it can be divided into 1/108 units of BTC. 3. In fact, settlement is made over (multiple) part of lots that can only be identified as quantities. But we believe that this metaphor by a real estate register record captures the essence of BTC trading. 4. We will discuss this problem in detail in Sect. 6. 2. 5. Four years after January 2009 and November 2012 must be January 2013 and November 2016, respectively. The actual events seem to happen quicker than the original statement. This is due to the program that sets a reward to be halved in every 210 thousand BTC block extensions, i. e. , a mining reward is halved not by calendar date, but by the block extension numbers. In Sect. 6. 2, the meaning of block extension is fully explained. 6. According to Nakamoto (2008), the system is supposed to authenticate the longest blockchain. In practice, however, the chain whose “total difficulty”, which is the sum of difficulties to win the mathematical lottery associated with each block in the chain, is the greatest (therefore usually the longest chain) and prevails. 7. Eyal and Sirer (2013) illustrates that Bitcoin’s mining algorithm is not incentive-compatible, and that the Bitcoin ecosystem is open to manipulation, and potential takeover, by miners seeking to maximize their rewards. It points out that collective miners having more than as little as 33% of the total computational power (instead of widely believed 50%) can cheat the system with selfish mining and earn more than their fair share. 8. For example, in case of ten-trillion-yen deposits by 1,000 million people, although it is possible to keep the ownership records of each yen, it may require very large computational and maintenance costs. Design of such a system is far more complex than a bank account-type of record keeping. 9. A hash value is the value returned by a hash function that maps data of arbitrary size to data of fixed size. A cryptographic hash function is a one-way hash function, so that it is practically impossible to recreate the input data from its hash value. The same hash value will always result from the same data, but modifying the data by even one bit will completely change the hash value. In this 6. 2. 10. [SEP] Justify:

Output: The text contrasts the claim by illustrating potential issues within the Bitcoin ecosystem that could prevent individuals from earning money. It mentions that the Bitcoin mining algorithm may not be incentive-compatible and highlights how miners can collude to manipulate the system, potentially disenfranchising other participants in the market. The assertion that collective miners with more than 33% of computational power can exploit the system indicates that earning money through cryptocurrency is not guaranteed for everyone, thus directly opposing the claim.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Finally, a two-step data deidentification process is performed, which includes the use of the (Processor) anonymizer and the authorization of these result DICOM files. This removes superfluous tags, including PHI entries, from DICOM headers. Radiogenomic images are classified as "0" or "1" based on the presence of MGMT promoter methylation status. T1W, T12W and segmentation result may be combined in either class based on annotation by expert radiologists and pathologists. To speed up training on a large dataset, images are transformed from DICOM (12 bits) to jpg (8 bits) format and scaled from 512×512 to 64×64. This also addresses memory difficulties with an average-price GPU-based portable system. The rejection algorithm has been run for each class. The goal is to remove instances that do not add discrimination to Radiogenomic classification processes. To load the whole picture set for either MGMT− (Class "0") or MGMT+ (Class "1"), follow the "Datapath" of the binary class. The algorithm stops after retrieving a certain number of photos. The rejection algorithm eliminates redundant or irrelevant photos from a specific class using a threshold based on the sum of pixel values (Th). After comprehensive examination, Th was discovered to be equivalent to zero for optimal results. After RA, the result includes details for each rejected file, as well as better class details from changed data path loading. The difference in a class's initial instance count should equal the rejected files count plus the final class size (after RA). Data volume augmentation is employed to increase model resilience and prevent overfitting. tumor segmentation tasks often involve data augmentation methods such as flip, rotations, random noise, scaling, elastic deformations, and the mixed data (mix-up) method (46,47). The SoftMax function adjusts the outputs of a convolutional neural network (CNN) so that they sum to one. This means that the model output values are scaled to represent relative "probabilities" that the network predicted a specific category. The SoftMax activation produces one value for each node in the output layer. The output values will be probabilities that sum to 1. 0. When modeling a multi-class classification problem, the data must first be prepared. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the data processing, classification, and augmentation methods used in radiogenomics without any mention of predicting emotions based on brain scans. There is no connection made between algorithms predicting emotions and the technical details provided in the text.


Input: Task: corroborate the claim using the given text. Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: On the other hand, since Bitcoin is a decentralized technology, it was warmly adopted by anarchist organizations worldwide [1, 2, 3], but for similar reasons, it was attacked by many others [4, 5, 6, 7, 8, 9]. Some attacked the Bitcoin from the fear of shaking the current centers of powers, but most attacked it out of ignorance. Strangely enough, even in the libertarian community, which in general embraced the new currency, there are some that used allegedly Austrian economics arguments to debunk the foundation of the Bitcoin economy [10, 11, 12, 13, 14]. In general, we encounter two strategies to attack Bitcoin: (1) presenting multiple minor or even clearly erroneous arguments. Consequently, the arguments keep reappearing, despite the fact that they are constantly refuted. And (2) using fundamental economic laws to allegedly demonstrate that Bitcoin does not possess the essential properties of money. The object of the 2. a Ponzi scheme? The most common criticism against Bitcoin is that Bitcoin is actually a Ponzi scheme [7, 10]. Clearly, there is no resemblance between the two. A Ponzi scheme is a fraudulent investment operation. The investors are made to believe that they gain from the investment operation, while in fact, the money comes from new investment, that is, a Ponzi scheme is a pyramid fraud. While some cryptocurrencies or tokens do seem to be a Ponzi scheme, Bitcoin is definitely not. Everything about Bitcoin: its algorithm, its network, and its development projects are completely transparent. The network, the mining process, and the entire project are all decentralized. There are no managers, no organizers, and no control. Therefore, there could be no fraud. Bitcoin does not even have a pyramid structure. An investor in Bitcoin can make a profit by selling it at a higher price, just like in any other trade. Unlike pyramid structures, the owner of Bitcoins does not have to convince multiple people to invest in order to make a profit. It is true that in both cases, the first investors gain more than the last ones, and that their profit rises with the number of traders. However, by the law of demand, it is clear that the price rises with the increase in demand, that is, the price increases with the number of buyers. This is valid for any commodity, and Bitcoin is no exception. This is definitely not a pyramid structure’s fingerprint. [SEP] Justify:

Output: The text corroborates the claim by stating that 'Everything about Bitcoin: its algorithm, its network, and its development projects are completely transparent.' It emphasizes the decentralized and transparent characteristics of Bitcoin, which is a cryptocurrency built on blockchain technology, thus supporting the claim about the foundational role of blockchain in ensuring transparency in cryptocurrencies.


Input: Task: contrast the claim using the given text. Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Initially, address the constant value ( ∝) of the model as, (1) Fa0=Argmin∑k=1KL(bk,∝) The number of n iterations belongs to [1−N]. Then computes the gradient direction of residuals as follows, (2) Rnk=−[∂L(bk,F(ak))∂(ak)]Fa=Fan−1 The essential learner h is utilized to fit sample data and get the initial model. According to the least square approach, the parameter βin of the model is obtained as, (3) βin=Argmin∑k=1K(b∗k−∝) The loss function is minimized. According to Eq. (4), a new step size of the model, namely the current model weight, is calculated. (4) ∝in=∑k=1KL(bk,Fakn−1+∝) The updated model is as follows: (5) Fa0=Fakn−1+V∑i=1In∝inl(a∈rin) When the raw data has limited dimension and size as input, the feature branch points information must be computed multiple times. Using an excellent learning rate will make optimization easier (Sprangers, Schelter & de Rijke, 2021). Still, it is usually supposed to be a smaller value, such as 0. 1. This shows how the framework of gradient boost predicts complex targets, such as finding the disease or syndrome of the patient with the help of the sensors with IoT (Velthoen et al. , 2021). Blockchain for healthcare As the digitalization process is disseminated speedaily, the attacks of cyber security and data breaches have increased; thus, information about patients becomes more complicated than before, so the highest standard of privacy for data is more critical in the healthcare system. In case security is breached, the individuals whose health information was inappropriately accessed face several potential harms (Fu et al. , 2023). Thus, to increase the system’s safety, the authentication of data using blockchain technology is incorporated into this system. [SEP] Justify:

Output: The text contrasts the claim by stating that despite the integration of blockchain technology to enhance data security in healthcare, there are still significant challenges, such as increased cyber security attacks and data breaches, which complicate patient information management. It emphasizes that the highest standard of privacy is critical, indicating that blockchain alone does not guarantee data security under all circumstances, especially in the context of cyber threats.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: The target variable containing the class labels is first label encoded, which means that each class label is assigned an integer ranging from zero to N-1, where N represents the number of class labels. The labeled (or integer-encoded) target variables are then one-hot encoded. This is a probabilistic representation of the class label, like the SoftMax output. A vector is generated with a position for each class label and position. All values are denoted as 0 (unmethylated MGMT), with a 1 (methylated MGMT) indicating the position of the class label. 4. 4 : After building the proposed deep learning model, the performance of this model should be measured by different accuracy measures to evaluate it [37]. In this paper, there are two models(clustering model and classification model), so evaluation of their performance was measured by using these measures shown in the following equations (4, 5, 6, 7) and MCC (Matthew’s correlation coefficient)equation [38]. [SEP] Justify:

Output: The text is largely technical and focuses on the process of encoding class labels and evaluating deep learning models. It does not provide any information regarding the predictive capabilities of advanced algorithms in relation to emotions or brain scans, thus rendering it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Bitcoin is a chimera, in that regard, since it is both digital, that is, intangible, and scarce. In fact, the essence of the blockchain technology is the creation of scarce digital assets. That is, the blockchain creates artificial scarcity. Therefore, despite the complex philosophical issue, I believe there is a consensus that Bitcoins can be owned, and property rights can and should be applied to it. There is a claim, however, that Bitcoin advocates ignore traditional property rights, contracts, and traditional legal systems, which were developed and refined for millennia [12]. It is true that currently, it is easier to evade law enforcement using cryptocurrencies than using most bank transactions. The facts that there is nothing tangible in the Bitcoin network and that digital transactions can take place without the regulated banking system complicate law enforcement. However, on the one hand, it can be regarded as a technical difficulty, which may be solved using artificial intelligence technologies, and on the other hand, one may argue that these properties of the Bitcoin network only strengthen property rights, because they prevent government confiscation of private property (private money). Not only aren’t crypto networks against contracts, but also smart contracts are an integral part of these networks. The most well-known is the Ethereum network, but there are many more cryptocurrencies with this property (like Neo, EOS, Cardano, etc. ), and even the Bitcoin network can support smart contracts (Mastercoin, Rootstock, Particl, etc. ). Therefore, the blockchain technology eliminates the need for (external) law enforcement in some cases, because the contracts are enforced within the blockchain itself. In general, cryptocurrencies strengthen private property and property rights. They do not weaken them. 17. Bitcoin and crime Some politicians and economists believe that Bitcoin and other cryptocurrencies have no value to society since most of their users are outlaws and criminals [28]. For one thing, one has to make a distinction between the different cryptocurrencies. Most of the criminal activity was transferred from the Bitcoin network to the more private cryptos (such as Monero, Zcash, Verge, etc. ) since transactions in the Bitcoin network can be traced. Moreover, most of the illegal trade that takes place on the Internet belongs to the category of victimless crimes (like drugs, gambling, and tax evasion), whose prohibition is disputed. [SEP] Justify:

Output: The text corroborates the claim by explaining that blockchain technology creates scarcity in digital assets, and its properties allow the enforcement of contracts within the blockchain itself. It suggests that smart contracts are integral to many cryptocurrencies, reinforcing the idea that blockchain underpins these currencies and enhances transparency in transactions. Furthermore, it indicates that transactions can be traced on the Bitcoin network, which supports the transparency aspect of blockchain technology.


Input: Task: contrast the claim using the given text. Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Some features for authentication using blockchain, namely the decryption keys and signing keys on the device, encryption and verification keys, are stored on a blockchain, and it protects data against cyber-attacks (Le et al. , 2021). For an authorised individual to participate in the review process, the user’s identity is necessary. Usually, the authentication is based on the user id and password. Still, blockchain uses an infrastructure of public keys and smart contracts as authentication processes based on certificates, as illustrated in Fig. 3. This removes human factors from the authentication process. These certificates were formulated using a key size higher than a size of a password, as 2,048 bits. These certificates contain the expiry date to reduce the risk of long-drawn-out exposure. Blockchain technology can leverage smart contracts to validate user certificates and effectively mitigate the risk of a single point of failure (Zou et al. , 2021). Figure 3: Proposed authentication process using blockchain technology. Download full-size image DOI: 10. 7717/peerj-cs. 1308/fig-3 Thus, to restrict access to a particular function, a method of authorization is considered to mitigate the risk of information disclosure for unauthorized users. The restriction of help to users is based on the distributed list of points: action, object, contextual, and subject. The action attribute consists of activities that allow users to perform, like retrieving data and updating records. The object attribute is used to implement which type of object can be accessed by a user or doctor that is medical data related to a particular department (Diao, Wang & Gong, 2022). At last, the subject attributes denote a user’s request for data access. These attribute types are encoded in a smart contract and shared across the blockchain network. For recording each action, a user identity is used on the network of the blockchain method. A transaction is used to add, update, or retrieve data from a database. This is also used for sharing information between authorized users. An administrator or doctor creates the flow of information in the authentication-based blockchain method and system-based health records and revokes digital certificates (Zhou et al. , 2022). A CRL gets generated while revoking a user’s digital certificates. The peers in the blockchain network receive a CRL update command to update the CRL stored on the peers. The users who require access can authenticate with healthcare records by giving certificates to the system. [SEP] Justify:

Output: The text contrasts the claim by discussing various aspects of blockchain security, such as authentication processes and key management, while emphasizing that the system is designed to mitigate risks rather than guarantee complete security. The mention of a 'risk of information disclosure for unauthorized users' illustrates that there are limitations to the security provided by blockchain technology.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: All the experiments have been conducted using open-source libraries, and the standard programming tools used to tune the proposed system using Google Co-lab environment (CPU : Intel(R) Xeon(R) CPU @ 2. 20GHz,GPU : T4 16GB, RAM : 16 GB. In most deep learning projects, the training and validation losses are shown simultaneously on a graph. The goal of this is to diagnose the model's performance and determine which aspects require adjustment. In Fig. 6 below, the training loss and validation loss both drop and stabilize at a certain point. This represents an optimal fit, that is, a model that does not overfit or underfit. A confusion matrix is essential for evaluating the performance of a classification model. It provides a comprehensive examination of true positive, true negative, false positive, and false negative predictions, allowing for a more in-depth understanding of a model's recall, accuracy, precision, and overall effectiveness in class differentiation. When there is an uneven class distribution in a dataset, this matrix is extremely useful for assessing a model's performance beyond simple accuracy measurements. As shown in Fig. 7 the confusion matrix for the experiment. Matrix was built with four distinct quadrants: (Quadrant), (Quadrant), (Quadrant), (Quadrant). True shows that the numbers were accurately anticipated, whereas False indicates that there was a mistake or incorrect forecast. Now that we have created a Confusion Matrix, we may use various methods to assess the model's quality. First, let us look at accuracy. After using the rejection algorithm, the accuracy results of the experiment are improved with different metrics. The following figures show the improvement in the results. First, Fig. 8 shows the improvement in the accuracy result. Another typical statistic is AUC, or area under the receiver operating characteristic (ROC) curve. The receiver operating characteristic curve compares the true positive (TP) and false positive (FP) rates at various classification levels. Because AUC is a metric that uses the probabilities of class predictions, we can be more confident in a model with a higher AUC score than one with a lower score, even if their accuracies are comparable. In Fig. 9, it shows the enhancement in area under the curve result before and after applying the rejection algorithm which increases from 0. 94 to 0. 99. The F1 score is a machine learning evaluation metric that finds a model's accuracy. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses technical aspects related to machine learning models, evaluation metrics, and performance assessments without mentioning advanced algorithms predicting emotions or using brain scans. It provides details about the confusion matrix, accuracy measurements, and metrics like AUC, but does not relate to the claim about predicting emotions.


Input: Task: corroborate the claim using the given text. Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Bitcoin is not the only thing that can be sent via the blockchain, in fact, almost any kind of information can be sent via the transactions, and this is an irreversible process, since any such information will remain in the blockchain for eternity, and everyone in the network will have an access to this information. Bitcoins are the cost of perpetuating the information. In other words, Bitcoins can be regarded as the “real estate” of the blockchain. The more Bitcoins a person has, the more information he can send on the blockchain. There are all kinds of data encapsulated on the blockchain: poems, prays, political statements, commercials, and even photos [36]. In fact, it is amazing how many kinds of information can be found there. It is not difficult to find many applications for this kind of information retention. One of the special applications is the announcement of contracts. Such contracts will be confirmed by the blockchain. Or another important application is a decentralized registrar of real estate (or any other type of property). With the Bitcoin network, one can announce ownership of objects, the information will be available instantaneously, and since it is not centralized, then there is no fear of losing this information. The important thing is to realize that there is no direct connection between this information and the value of Bitcoin. The relation is indirect. The value of these announcements is directly related to the number of nodes in the network. The more people are connected to the Bitcoin network, the more valuable this information is. Therefore, if the network is large enough, then people will like to use it by sending and announcing information. However, it is clear that when the network grows, sending information becomes more valuable, and therefore the value of the network’s coin, which controls it, increases as well. The more coins a person owns, the more he controls the network. Again we see that owning Bitcoin is like owning real estate on the blockchain. Clearly, at its inception, when the Bitcoin network was small, the value of publishing data over it was low, but then the Bitcoin’s value was accordingly small. In fact, in its genesis, the Bitcoin’s value could have been only the subjective value of the first few geeks, who mined it (including Satoshi himself). When the network grows, the Bitcoin value increases exponentially, just like the laser power. [SEP] Justify:

Output: The text corroborates the claim by explaining that Bitcoin operates on a blockchain that permanently records transactions, making information accessible to everyone in the network. It highlights that all types of data can be sent via the blockchain, emphasizing its transparency and the decentralized nature that ensures data availability without fear of loss. Additionally, the mention of contracts being confirmed by the blockchain and the implications for ownership information aligns with the idea of transparency that is foundational to blockchain technology.


Input: Task: contrast the claim using the given text. Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Figure 4 shows the centralized approval architecture with distributed blockchain method for revokes digital certificates from the distributed blockchain method. These certificates are then shared with users and received by peers in the network; they are established by running a smart contract that validates the credentials and returns a response. When the above-stated conditions are valid, the system allows access to the user; otherwise, it denies access to users (Aljuhani et al. , 2022). The privacy preservation of hospital data uses blockchain technology that secures and maintains the hospital data as a ledger. This is referred to as a distributed database; a group of individual people updates that. In this technology, each user or device is named a node. The user is a patient, the wearable devices send the data to the peer network such as the hospital node, and these electronic health records of a patient data are immutable. When updating the record initially, it needs a user for the process. Figure 4: Centralized approval architecture with distributed blockchain method. Download full-size image DOI: 10. 7717/peerj-cs. 1308/fig-4 Data structures created by blockchain technology include built-in security features. It eliminates duplication, streamlines procedures, eases the load of audits, boosts security, and guarantees data integrity. It keeps a secure, decentralised log of cryptocurrency transactions. It has a distinctive and secure digital identification reference that enables the distribution and recording of digital information without its editing. From above figure a new distributed blockchain aspect diagram concerning the healthcare system and respective public keys smart contracts with 2,048 bits for revokes digital certificates. It will maintain the adequate security verification of the hospital data using privacy preservation of hospital data based on blockchain technology. This will be illustrated in the following and algorithms. Pseudocode for the security verification of the hospital data using privacy preservation of hospital data based on blockchain Initially, the sensor collects data from a patient Data. Store→. Store→blockchain () For each user Create digital certificate revokes. Certificate→(CA) certificate authority generate URL end for for each user authentication validate certificate user→peer authenticates if (CA sign present in the certificate) && (user present in CRL) allow user access else refuse user access end if end for Output: allow or refuse the user access DOI: 10. 7717/peerj-cs. 1308/table-5 The permissioned blockchain technology needs authentication access to verify certificates and intelligent contracts. [SEP] Justify:

Output: The text contrasts the claim by discussing the specific conditions under which blockchain technology provides data security, stating that access is dependent on the 'valid conditions' set in a smart contract. It highlights that while blockchain has built-in security features, it still requires authentication and user validation processes, indicating that data security is not guaranteed under all circumstances but rather conditioned on these processes.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: It combines a model's precision and recall scores. The F1 score uses the harmonic mean to combine accuracy and memory, and maximizing the F1 score means maximizing both precision and recall at the same time. As a result, researchers have adopted the F1 score as their preferred method for evaluating their models alongside accuracy. Figure 10 shows us the f1 score result before and after using the rejection algorithm in the proposed model. The result is increased from 0. 95 to 0. 99 after using the rejection algorithm. In machine learning, precision score refers to the fraction of positively predicted labels that are correct. Precision is often referred to as the positive predictive value. Precision is mostly used for predicting the positive class, and false positives have a higher cost than false negatives. in Fig. 11, it shows the change in the precision result for the proposed model before and after applying the rejection algorithm. correlation coefficient (MCC) is a statistic that can be used to evaluate the effectiveness of classification models. This statistic is especially useful when the two classes are unbalanced, meaning that one class appears significantly more than the other. Figure 12 shows the comparison between the result of MCC before and after using the rejection algorithm. Table 1 below shows the comparison results by different accuracy metrics before and after applying the rejection algorithm. Table 1 comparison results before and after applied rejection algorithm Accuracy metric Result before rejection algorithm Result after rejection algorithm Accuracy 0. 95 0. 9913 F1 score 0. 95 0. 99 Precision 0. 95 0. 99 MCC 0. 90 0. 99 AUC 0. 94 0. 99 2 below discuss the results for the experiment (precision, recall, f1 score and the test accuracy) Table 2 The final experiment result: f-1 score 1. 00 0. 99 0. 99 19314 methylated MGMT 0. 99 1. 00 0. 99 20988 Accuracy 0. 9913 40302 Macro avg 0. 99 0. 99 0. 99 40302 Weighted avg 0. 99 0. 99 0. 99 40302 Test accuracy = 99. 13 After ending the experiment on python, Table 3 below discusses the Deep learning parameters used in proposed deep learning module. Table 3 Deep learning parameters used in proposed module. DL parameters Optimal values 0. 0001 Momentum 0. 99 0. [SEP] Justify:

Output: The text does not address the claim about advanced algorithms predicting emotions based on brain scans. Instead, it focuses on the evaluation of machine learning models using metrics like F1 score, precision, recall, and accuracy, without any indication of emotion prediction or brain scans.


Input: Task: corroborate the claim using the given text. Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Dori decides to transfer $100 to Aris. In the physical world, it’s a straightforward exchange: Dori now has no money, and Aris has $100. Simple, right? ! But let us imagine the following situation:In the digital scenario, Dori’s transfer of a photo representing $100 to Aris introduces a unique challenge. Unlike the physical transfer of money, the digital photo is duplicable, leading to questions about its ownership and potential misuse. retain a copy of the photo, share it with additional friends, or even post it publicly on social media where it could be freely downloaded? The inherent replicability of digital assets like these challenges the traditional notion of ownership and transfer, highlighting the need for a secure and trustworthy system to manage digital transactions. Blockchain technology, with its decentralized and tamper-resistant characteristics, aims to address such issues in the digital realm. Digital LedgerTo address the questions raised earlier, we rely on the concept of a digital ledger. The key is to have a record of the quantity of $100 digital photos owned by Dori and how she distributes them among her friends. However, it’s not Dori’s responsibility to maintain these records. In the contemporary landscape of digital banking transactions, this record-keeping role is assumed by the bank. The bank, subject to regulatory oversight, ensures transparency about the funds Dori possesses and how she utilizes them. In this context, the bank functions as a third-party entity, independent and responsible for maintaining accurate records. Nonetheless, it’s crucial to remember that digital banking transactions are underpinned by actual, physical banknotes. The quantity and issuance of these banknotes are regulated by the respective central bank. Here, we return to the initial point: the bank resolved the issue of digitally distributing physical banknotes. However, in the second scenario, Dori possesses a digital photo of a banknote. Questions arise: Who captured that photo? How did Dori obtain it? What are her intentions for spending it? Furthermore, uncertainties linger regarding the total number of such digital photos and whether they hold genuine value. Blockchain digital ledger conceptBlockchain provides a straightforward solution to the scenario involving the digital photo on Dori’s phone and the question of record-keeping. [SEP] Justify:

Output: The text corroborates the claim by explaining how blockchain technology addresses challenges in digital transactions, specifically regarding the ownership and transfer of digital assets. It states that blockchain, due to its decentralized and tamper-resistant characteristics, provides a secure method to manage digital transactions. This aligns with the claim that blockchain technology ensures transparency, as it enables a reliable digital ledger needed for the ownership and distribution of digital currency.


Input: Task: contrast the claim using the given text. Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Systems can identify user access and restrict unauthorized access to system functions (Abyaneh, Zorba & Hamdaoui, 2022). Generally, this research system uses password authentication, but this research system is replacing this authentication with certificate-based authentication. This certificate-based authentication removes human interference during the process of authentication. These certificates are created with a critical size of 2,048-bit, which is larger than the size of an average password and includes the expiration date, reducing the risk of prolonged data exposure (Beniiche, Rostami & Maier, 2022). Blockchain technology has the advantage of smart contracts that validate the user certificates and can efficaciously mitigate the risk of a single point of failure. The administration creates digital certificates to maintain the electronic health record (EHR) system, and the authority revokes issues in the certificates. The certificate revocation list (CRL) is then generated among all network peers (Park et al. , 2023). Before that, the system needs to authenticate by digital certificates (Khan et al. , 2022). The certificate is passed to the client, which is then sent to a peer in the network. The peer authenticates the user by running the smart contract, which contains information about the user/doctors, action attribute, object attributes, and contextual and subject attributes (Xu et al. , 2021). That validates the certificate and returns a response. This response influences the authentication condition of the user’s access as allowing or refusing. Algorithm for improving the data transmission method for wireless transmission Input: Triangles (G), L-link, R-result Output: Predict the path Require: G = (V, E) Load(G) Triangles = (G) return null initialize: N = size (Triangles), k= 1 While (k ≤ N) New_L= Predict (Triangles) [i] For each_L ∈ New_L If (R_contains L) R [L]++ = R + L increment k R = : 10. 7717/peerj-cs. 1308/table-6 Tracking the location of a threatening person Medical records can be misused unless coded to hide patients’ identifying information. If a patient has been treated for a particular disease and their medical records are not held in confidence might be insecure. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of data security. Although it mentions that blockchain technology can mitigate risks like a single point of failure, it also points out that 'medical records can be misused unless coded to hide patients’ identifying information,' implying that blockchain does not guarantee absolute security.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: 0001 1 Channels 64 Channels 128 Channels 256 3 × 3 Stride 1 Padding Conv1 & Conv2: 1, Conv3: 0 (Note: Adjusted in forward pass) 2 × 2 2 After Conv-2 (128 Output Channels) (%) 20 7 × 7 × 256 512 512 64 512 + 64 2 ReLU (Unit) Comparison results of the literature survey research: In this part, we discuss and analyze the last results of the literature survey papers which used the same dataset with the results of the proposed model as shown in Table 4. Table 4 Comparison results of the literature survey research: Name of researcher Date of publishing Accuracy rate Used technique et al. [33] June 2022 accuracy = 0. 91 residual network (ResNet) et al. [32] September 2022 Accuracy = 0. 93 Deep learning-based U-Net framework et al. [31] June 2022 Accuracy = 0. 993 machine-learning (ML) models Do et al. [30] august 2022 Accuracy = 0. 925 an Boosting (XGBoost) Qureshi1 et al. [29] 2023 Accuracy = 97. 44% Extraction (DLRFE) module et al. [28] April 2023 mean AUC = 0. 69 Deep learning algorithms et al. [27] November 2023 F1 score = 96. 1%. using automated segmentation algorithms et al. [26] December 2023 Dice value = 86. 6 the Model (SAM) and a voting network Anton François et al. [25] January 2024 Accuracy = 0. 94 Machine learning approach et al. [24] January 2024 Accuracy = 0. 6174 Deep learning approach (the Exception model) Emad dawood et al. 2024 Accuracy = 99. 13% deep convolutional neural network (DCNN) 6 ConclusionOur research proposes MGMT-PMP, a novel classification approach that combines imaging and genomics to predict Radiogenomic classes based on genetic variation and radiation sensitivity. This paper proposes a fine-tuned CNN architecture, the DLRFE module, for extracting latent features from mpMRIs scans. The module captures quantitative knowledge about the spatial distribution and size of tumors. Finally, a dynamic feature vector is created and used for Radiogenomic classification. The rejection algorithm effectively selects and outlines instances from the main dataset. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses details about a novel classification approach for predicting Radiogenomic classes based on imaging and genomic data, without mentioning emotions or the prediction of emotions based on brain scans.


Input: Task: corroborate the claim using the given text. Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Rather than relying on a third party like a bank, trusted friend, or notary, blockchain proposes that the record should be decentralized and shared with everyone interested in sharing digital photos of banknotes through their mobile phones or computers. In contrast to a centralized model, blockchain introduces a distributed approach. Rather than having a centralized ledger tracking the number of digital photos of $100 on Dori’s phone or computer, this information is dispersed among the phones and computers of all participants involved. This includes not only Dori but also Aris and others engaged in exchanging digital photos for $100 via mobile phones or computers. The blockchain diligently records and disseminates this information to everyone. shares her unique digital photo of $100 with Aris, the blockchain accurately registers an “outflow” from Dori and an “inflow” to Aris, ensuring verification and distribution across the network. Attempting to send the same digital photo to 100 more friends becomes impractical since the blockchain records of all others are not synchronized. In their records, Dori no longer owns a digital photo of $100, leading to the rejection of her attempted transaction. Consequently, Dori no longer possesses the digital photo, while Aris does. 2. 1 Distributed and traditional databaseBlockchain operates as a decentralized database, recording a comprehensive audit trail of all transactions. Unlike traditional databases, it is managed by a network of computers, with no single computer holding sole responsibility for storage or administration. This ensures that any computer can join or leave the network without compromising data integrity or availability. The database can be restored from scratch by any computer, simply by downloading it from the network and processing the audit trail. In contrast, traditional databases are typically controlled by a single organization with complete authority, including the ability to manipulate data. While this may not pose issues in certain contexts, the sensitive nature of financial transactions creates a risk of data manipulation and falsification. To counter this, banks undergo continuous audit and regulatory scrutiny, facing persistent threats from external malicious hackers. Blockchain addresses these challenges by allowing the database to be managed by a distributed network, making it publicly accessible to everyone. This open model enables anyone to create a redundant copy of the database and verify it against other copies. However, this approach is best suited for static data that does not change over time. For dynamic data, a consensus problem arises when changes are needed after the database has been distributed. [SEP] Justify:

Output: The text corroborates the claim by explaining how blockchain technology operates as a decentralized database that ensures transparency and integrity of transactions. It contrasts traditional centralized databases by demonstrating that blockchain does not rely on a single authority and instead allows a network of computers to manage the records, which ensures that all participants can verify transactions, reflecting the principle of transparency in cryptocurrencies.


Input: Task: contrast the claim using the given text. Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Therefore, these records are secured using a piece of code that is difficult to access by unauthorized people. Although the information is confirmed, there might be a chance to steal medical data with personal information by a trusted person who knows a private key or by a hacker who can break the security code and access the data illegally to make money or for some other reasons by threatening patients (Akter et al. , 2022). They protect data whenever information is accessed illegally or when a patient is intimate about any threatening call, message, or another. There is a chance of doctors getting threatened as the details of doctors are also stored in the medical record based on their treatments and so on (Kumar et al. , 2022). When a threat is reported, management tracks a person’s location using hybrid microwave transmission, which combines millimeter-wave and microwave. The millimeter wave is a spectrum band with a wavelength between 10 to 1 mm with a highly high-frequency band. Thus, millimeter waves can hardly be used for long-distance applications. A microwave uses a beam of radio waves in the microwave frequency range to transmit data between two locations which can be located within a few feet or meters or miles or kilometers. It also has higher bandwidth and a limited line of sight mode (Hsiao & Sung, 2022). Both are used in communication, but millimeter wave has a higher frequency range than microwaves (Wan et al. , 2022). Thus by combining these wave transmission methods, the distance covered, line of sight, amount of data carried from sender to receiver, and speed of data transmission can be effectively increased; this is called hybrid microwave transmission. In this system, hybrid microwave transmission tracks a person at the time of the call. If a person is not disturbed through the ring, it might be through a message or any other method. Considering it, a person can be tracked faster to a considerable distance (Althubiti et al. , 2022). To find the location, this research uses a MapQuest open-source mapping service connected to a tracking system to view the exact location of a threatening person. It provides street details and directions for different countries (Deng et al. , 2022). The user is provided access to a map of any place in the world. [SEP] Justify:

Output: The text contrasts the claim by stating that there is a possibility of stealing medical data despite blockchain's code being difficult to access unlawfully. It highlights scenarios in which data can potentially be compromised, such as when a trusted individual has access to a private key or when a hacker breaks security protocols. This directly undermines the assertion that blockchain guarantees data security under all circumstances.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: A hybrid feature collection (HFS) is created by fusing latent and radiomic data. by applying the proposed model on mpMRIs scans yielded 99. 13% test classification. Using the BraTS-2021 dataset, we compared the proposed approach to other brain tumor classifiers and found that rejection algorithm significantly improved Radiogenomic classification performance. Results show that combining dynamic and static features with HFS improves classification performance compared to using separate features. This research study has wide-ranging ramifications. This study connects genetic variation with radiomic-based data characterization techniques, bridging the gap between two distinct areas of research. The second idea is to use it in real-time brain tumor surgery to remove leftover tumor cells by chemotherapy instead of radiotherapy. Third, the proposed technology offers a portable and cost-effective alternative to dedicated machines for brain surgery. This study has significant implications for the clinical management and survival of GB patients. Declarationswork was conducted in partial fulfillment of the requirements for a Ph. D. in information systems and was supported by the University of Zagazig at Egypt. Funding is subject to STDF/EKB’s review of eligibility and approval. Under the agreement between with the Science, Technology and Authority and the Al-Publishing (OA - Access Open) which aims to support the scientific research system through funding (OA) publishing (for researchers in the of Egypt). Zagazig university is one of the shared institutions in this agreement. Author ContributionAll authors reviewed the manuscript, wrote the main manuscript text and prepared all figures and tables10. 15379/ijmst. v10i5. 2506 Lodzinski, Paulina, Marek G. Beby, Jacek Furtak, Janusz Kowalewski, and Marzena A. Lewandowska. 2021. "Prognostic and in Gliomas" of 22, no. 19: 10373. surgery for glioblastoma: Recent advances and modern challenges, Neuro-Oncology Practice, Issue 5, October 2022, Pages 364–379, Hanif, F. , Muzaffar, K. , Perveen, K. , Malhi, S. M. , & Sime, S. hU. (2017). Glioblastoma Multiforme: A Review of its Epidemiology and Pathogenesis through and Treatment. [SEP] Justify:

Output: The text does not address the claim regarding predicting emotions based on brain scans. Instead, it discusses a hybrid feature collection for improving classification performance in brain tumor diagnosis and surgery. There is no mention of emotions or advanced algorithms related to emotional predictions; thus, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Determining the validity, permission, and order of changes becomes crucial. Allowing any entity with a copy of the database to make changes could lead to desynchronization, and a consensus must be reached on which database reflects the true state. Introducing an entity with the privilege to make changes first, followed by others copying the database, attempts to address this issue. However, this raises concerns of potential data manipulation and censorship by the chosen entity for personal gain. A solution could involve a rotational system for managing entities and copying the “correct” state of the database. Yet, determining the order and process of changing controlling entities remains a question [4]. 2. 2 Blockchain: the final solutionBlockchain technology addresses these challenges by establishing a network of computers, known as nodes, each of which possesses a copy of the database and adheres to a set of rules defined as a consensus protocol. The consensus protocol outlines the sequence in which nodes can introduce changes to the database, ensuring unanimous agreement among all network nodes regarding the database’s state. This structure prevents any single entity from having the authority to manipulate or censor transactions. Moreover, the blockchain includes an immutable audit trail documenting all modifications to the database. This feature enables all controlling entities to scrutinize and validate the accuracy of the database. The audit trail comprises individual changes referred to as transactions. A set of transactions added to the database by a single node constitutes a “block. ” Each block contains a reference to the preceding block in the blockchain, establishing a chronological order [4]. The blockchain, essentially a chain of blocks, interconnects each block with the previous one and includes a list of new transactions occurring after the preceding block. When a new node joins the network, it initiates an empty database and retrieves all previous blocks from the network. This process ensures alignment with the databases of all other nodes. Fundamentally, blockchain orchestrates the sequence of transactions within the database, enabling anyone to verify its accuracy by reconstructing it from the beginning. 2. 3 Blockchain in actionBlockchain technology finds widespread application in Bitcoin, a prominent example of its utilization. Bitcoin operates as a digital cryptocurrency, employing blockchain for monitoring transactions and issuing digital currency units. When an individual intends to spend Bitcoins, they generate a transaction specifying the amount and recipient. This transaction is digitally signed and disseminated to nodes within the Bitcoin network. [SEP] Justify:

Output: The text corroborates the claim by explaining how blockchain technology functions, emphasizing its role in ensuring transparency within cryptocurrencies. It describes how each node in a blockchain has a copy of the database and adheres to consensus rules, preventing any single entity from manipulating or censoring transactions. Furthermore, the mention of an immutable audit trail that documents all changes reinforces the claim that blockchain technology ensures transparency, as it allows verification of the database's accuracy.