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# zbMATH — the first resource for mathematics

Geometric quantization of integrable systems with hyperbolic singularities. (English) Zbl 1191.53058
The main result of this very interesting paper is Theorem 6.1: Let $$(M,\omega,F)$$ be a two dimensional, compact, completely integrable system whose moment map has only nondegenerate singularities. Suppose $$M$$ has a prequantum line bundle $$\mathbb L$$ and let $${\mathcal J}$$ be the sheaf of sections of $$\mathbb L$$ flat along the leaves. The cohomology $$H^1(M, {\mathcal J})$$ has two contributions of the form $$\mathbb C^{\mathbb N}$$ for each hyperbolic singularity, each one corresponding to a space of Taylor series in one complex variable. It also has one $$\mathbb C$$ term for each non-singular Bohr-Sommerfeld leaf. The cohomology in other degrees is zero.

##### MSC:
 53D50 Geometric quantization
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# Estimation of Cross Classified Value of Travel Time Using Binary Logit Model on Egyptian Roads

Heba Mahmoud Bakry1, Yusra M.H. Elgohary2, Aya Farag3, *, Ibrahim M.I. Ramadan1
1 Department of Civil Engineering, Faculty of Engineering at Shubra, Benha University, Cairo, Egypt
2 Department of Transport Economics, Egyptian National Institute of Transport, Cairo, Egypt
3 High Institute for Engineering & Technology - Obour. K 21 Cairo/Belbeis Rd. Egypt

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open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address Correspondence to this author at the High Institute for Engineering & Technology - Obour. K 21 Cairo/Belbeis Rd. Egypt; Tel: 00201095375488; E-mail: civieng.ayafarag@gmail.com

## Abstract

### Objective:

In this study, cross-classified categories of Value of Travel Time (VOT) are estimated using the binary logit model for the users of both public and private vehicles who have diverse income levels and go on different trips. The travelers’ income level has a large impact on their VOT and on their willingness to pay regarding other factors that impact the travelers’ choice of the most suitable means of transport for them.

### Methods:

The research uses the combined stated and the revealed preference survey and applies the logistic regression analysis to estimate the categorized VOT on Egyptian roads. The data sets consist of 1123 respondents who were approached to collect information related to the socio-economic factors and the trips’ characteristics. Each respondent was requested to choose an alternative to each of the choice sets’ levels. All data sets were collected in light of the Binary Logit analysis and recorded concerning the socio-economic factors. A total of 33 models were developed to predict the VOT.

### Results:

According to the given analysis, VOT for private car owners (32.5 LE/HR about 2.06 $/ HR) is higher than that for public transport users (18.3 LE/HR about 1.16$/ HR) in Egypt. As for the diverse income level groups, a positive correlation is found between each traveler and his/her income. To clarify, the travelers’ VOT increases when their income levels improve.

Concerning the purpose of the trip, the VOT reaches its peak for travelers who travel for the sake of work as their VOT is approximately 38.82 LE/HR, about 2.47$/HR compared to the other travelers who have different traveling purposes other than doing business. Keywords: Cross classification, Value of travel time, Revealed preference survey, Stated preference survey, Logistic regression analysis, Binary logit model. ## 1. INTRODUCTION Regarding the transportation field, studies show that travelers who value their time have shown a willingness to pay more money to reduce the amount of time spent on their traveling; however, there are indeed several crucial factors that influence their decision of paying more money. For instance, the travelers’ objectives, the duration of the trip or its length, and the character traits that differ from one individual to the other are the most significant factors. As a result of such factors, each individual would have his VOT and it is uncommon for different travelers to have similar VOT. The significance of reducing travel time is embodied in three main concepts. The first concept revolves around replacing the time wasted in traveling with another activity that would give either the travelers or their employers a monetary gain. The second concept suggests replacing the time wasted in traveling by doing some sort of entertaining activity, or in doing an appropriate recreational activity for those who can afford it. The third concept states that the traveler might face discomfort, stress, or exhaustion during the trip. Accordingly, reducing the time spent traveling would be more beneficial to the traveler. The quantification of VOT is based on the data collected from surveys to establish models, followed by the cost coefficients and the time used for the estimated VOT. Three levels of cross-classification matrices have been used to describe the relationship between VOT and travel modes, VOT and different income levels, and VOT and travel purposes. This paper uses logistic regression analysis to build models for the estimation of VOT and shows its application on Egyptian roads. The author [1] first puts forward the idea of VOT using 1965’s discrete choice modeling which was based on random utility theories. In the following decades, after the sixties, the development of computers continued to promote such research. As for the analysis of this research, the Statistical Package for Social Sciences (SPSS) was used and several Excel Macros were developed as well [2]. The structure of the paper is as follows: The first section includes a summary of related literature, while the next section gives an overview of the application methods and the methodology of this study. Both sections are followed by highlighting the specification of the model and the outcomes of estimating the entire data. The last section sheds light on the conclusions of this study, along with giving some recommendations for further research. ## 2. LITERATURE REVIEW Concerning the principle of VOT, delay and travel time can be transferred to cash amounts, but VOT is a very dynamic measure that depends on many parameters and changes that differ from one country to another and from one person to the other. In terms of data collection and the models used, the purpose of this literature review is to present the latest technology utilized for modeling VOT. Many efforts have been made in the past, with the international value of time review studies [3, 4] reporting values utilized in various countries and road jurisdictions. Some of these studies were conducted at the national level in many countries such as the United States, the United Kingdom, Sweden, France, and others as localized studies for specific projects such as expressways, tolls, and many small scales report studies to estimate VOT values and clarify their variation across different variables, to improve and make a cost-benefit analysis and forecasting procedures more pragmatic. Individual studies have also made several efforts to analyze VOT variance over multiple subgroups of variables such as diverse income and distance bands, different travel reasons, gender, and so on. Various approaches for calculating monetary VOT have been established based on average wage rates or willingness-to-pay (seen behavior of people who sell time for money and research to evaluate people's value of time savings). According to the current AAHSTO standards, the standard value for travel by car and bus is 50% of the wage rate, which is increased to 70% for personal automobile travel to represent the positive distance elasticity. Waiting, walking, and transfer time is all worth twice as much, and corporations payout 100% of their total compensation for travel. Considered experimental data gathering approaches such as stated preference, as well as results based on the typical revealed preference method, an example of willingness to pay to estimate VOT. A study [5] employed a combination of revealed preference (RP) and stated preference (SP) data to analyze passenger choice behavior regarding the use of restricted lanes (MLS). The data was taken from the South Florida Expressway, and an average value for VOT and VOR was calculated using mixed logit modeling. Using a poll of stated preferences, the value of travel time in Nanjing is evaluated [6]; respondents are offered six expressed preference questions for each trip purpose. In terms of estimating VOT for different trip purposes, Stated preference (SP) survey data for Beijing were collected to approximate VOT for those who traveled for business and leisure purposes under various circumstances [7]. To approximate a particular VOT for leisure journeys [8], uses real driving options between open access and toll roads to perform a Monte Carlo simulation to define generalizable outcomes for subsequent valuation studies. Furthermore, the authors [9] estimated Using a dataset on the respondents' time-use and spending allocation for a one-week reporting period, the value of leisure (VoL), the value of travel time savings (VTTS), and the consequent value of time assigned to travel (VAT) for workers in the Canton of Zurich, Switzerland. To calculate VOT, various models have been developed [10] to estimate the value of travel time savings for Japanese road users when choosing between an expressway and a non-expressway route using a standard binary logit and a mixed binary logit model, the mode choice approach to assess the significance of time by distributing questionnaires to travelers was used in a study [11]. The method is used to estimate these figures using groupings of cars that are used on weekdays and weekends. The Binary Logit model was developed to approximate the importance of travel time for different income levels as well as varied journey lengths in the form of individuals traveling to work during the morning cycle in the Calicut region [12]. ## 3. MATERIALS AND METHODS Regression analysis is a type of predictive modeling technique for evaluating the relationship between one or more independent variables (the “X” variable) and a dependent variable (often referred to as the “Y” variable). When two or more independent variables are used to predict or explain the outcome of the dependent variable, this is known as multiple regression. Linear regression and logistic regression are the two major methods of regression analysis. For predictive analysis, linear regression is commonly employed in statistics. It essentially determines the degree to which a dependent variable and one or more independent variables have a linear relationship. Linear regression produces a trend line shown among a group of data points as an output. Logistic regression is the second form of regression analysis. It is the chance of something happening that is calculated (or predicted) using logistic regression analysis. Three purposes can be served by regression analysis: 1. Predicting the consequences or consequences of specified changes. 2. Predict future trends and values. 3. Assessing the impact of independent variables on a dependent variable (or, in other words, determining the strength of distinct predictors). Logistic regression is a solution for grouping. The logistic regression model uses the logistic function to constrain the output of a linear equation between 0 and 1, instead of fitting a straight line or a hyperplane. For predictive analytics and simulation, this method of statistical analysis is mostly used and addressed more commonly. Although that, it could be beneficial to consider when each form is the most efficient. ## 4. THEORETICAL FRAMEWORK By fitting data to a logistic curve, logistic regression, also known as the logistic model or logit model, evaluates the relationship between many independent variables and a categorical dependent variable and determines the likelihood of occurrence of an event. The following are the three types of logistic regression: • Binary logistic regression: The statistical technique used to forecast the relationship between the dependent variable (Y) and the independent variable (X) when the dependent variable is binary is known as binary logistic regression. • Multinomial logistic regression: When you have one categorical dependent variable with two or more unordered levels, multinomial logistic regression and its extension mixed multinomial logit are utilized (i.e, two or more discrete outcomes). • Ordinal logistic regression: When the dependent variable (Y) is ordered, ordinal logistic regression is utilized (i.e., ordinal). The dependent variable has a logical order as well as more than two levels or categories. The logistic regression model is based on a logistic function that can be expressed as follows [13]: When e represents the Euler number and x represents the value of the explanatory variable X. Depending on the determined value, it can be written in a variety of ways. If the probability of success is estimated (assuming Y is a dichotomous variable with values of 1) for the occurrence of the event of interest (success) and 0 for the opposite case (failure), the logistic regression model is defined by the equation: where βi i = 0, . . ., k are logistic regression coefficients, x1, x2, . . ., xk – independent variables, which can be both measurable and qualitative. Logistic regression can also be understood in terms of the probability of the event being examined occurring (success): The probabilities are calculated by dividing the likelihood of an event happening P(A) by the probability of an event not happening 1 − P(A): The logistic regression equation looks such as this when there is only one independent variable: Logit form of the logistic model could be obtained by logarithmizing both sides of the equation: The logistic regression model has its own set of criteria. Its application is constrained by the test sample size, which must be more than or equal to n > 10(k + 1), where k is the number of predictors. In this article, the binary logit model is selected as the basic model. The maximum likelihood (ML) technique, which maximizes the probability of classifying the observed data into the right category given the regression coefficients, is used to create the best-fitting function in logistic regression. For the structural portion of the utility function, the general formulation is: where: • β are the coefficients to be estimated. • Travel cost and travel time are the variables associated with travel cost and travel time, respectively. • Ɛ other illustrating parameters in the model. • The utility function is unitless. The cost coefficient and the time coefficient capture the sensitivity of the passengers in adjusting the time of travel and the cost. Thus, their ratio should be used for exchanging time and transport costs; in other words, VOT. Please check the formulation below for more details: The coefficient ratio of the time of travel over the travel cost coefficient will lead to a result in pounds/minute (or in pounds/HR. when multiplied by 60). ## 5. SURVEY DESIGN DATA The data collected may either be emanated data from revealed preferences (RP) or stated preferences (SP). RP data reflect the individual travelers' actions and can be collected through polls and field studies. The SP data is the traveler’s behavior, which can be derived from SP surveys and simulators in hypothetical scenarios. There were three sections of the questionnaire. The first element intends to gather the individual's socio-economic results. The second section gives information about the recent journey on a typical day. As for the third part, the stated preference SP of a given choice is compiled. Responses in the form of “choice” among the presented choice alternatives were utilized to develop utility models and the estimated coefficients from the developed models were used to estimate VOT measures. The quantitative attributes such as travel time and travel cost were considered for the preparation of alternatives. A major consideration in selecting attribute levels is the range and degree of variation. Four levels were considered for each attribute. Attributes and the corresponding levels used in the experiment are given in Table 1. The survey of the interviewees’ samples was altered over eight weeks. The survey sample was chosen in light of the population of the residents in Egypt using Google Forms, where respondents need to be signed into their Google accounts before they can reply. Several questionnaires were also obtained via interviews; 1123 from a total of 1370 analyzable selected respondents. The specification of the questionnaire used in the experiment and the preliminary analysis overview statistics are shown in Table 2. Table 1. Choice set levels.  Attributes Level 1 Level 2 Level 3 Level 4 Travel time in minutes Remain the same Reduced by 15% Reduced by 25% Reduced by 35% Travel cost in pounds Remain the same Increased by 20% Increased by 40% Increased by 80% The data has been coded for several levels to get a comprehensive evaluation of each attribute. Some variables are categorized into two levels, such as gender (i.e., Male = 0 & Female = 1), while other variables were classified into five levels, such as age (years) and the length of the trip. Some levels indicate concepts that cannot be coded like ‘Others’. Therefore, the analysis purposes levels which have the same concepts were combined into one level or blotted out as the software is capable of dealing with the coded data only. The preliminary research clarified that gender seems to be equal since 49.9% are males and 50.1% are females. Modal split showed that 43.5% of surveyed people choose public transportation and 47.6% choose Private cars for their trips. It is observed that 62.2% of trips are Work trips. Table 2. Descriptive statistics for the socio-economic factors and the trip’s characteristics.  Parts* Ranges Percentage% Part (1) Socio-economic Details Male Female 49.9% 50.1% Married Unmarried Others 62.8% 34.7% 2.5% 18-25 25-35 35-45 45-60 >60 20.6% 54% 16.8% 6.3% 2.3% Without Medium Above medium High Above High 0% 8.6% 24.7% 56.7% 10% 0-2000 2000-4000 4000-10000 10000-15000 >15000 29.5% 27.5% 26.5% 7.4% 9.1% Unemployed Government emp. Private emp. Public sector Free business 25.7% 2.1% 38.7% 22.4% 11.1% Part (2) Trip Details Work Study Entertaining Shopping Others 62.6% 11.1% 9.7% 10.9% 2.1% Private car Public transportation Others 47.6% 43.5% 9.1% 0-10 10-20 20-35 35-50 >50 12.2% 14.7% 17.5 % 22.2% 33.4% written by the respondent Max.=720 Min.=4 written by the respondent Max.=700 Min.=2 Part (3) Stated Preference 0.85 TT1, 1.2 TC1 0.80 TT1, 1.4 TC1 0.65 TT1, 1.8 TC1 TT1& TC1 remain the same 68.40% 18.18% 10.22% 3.2% * The questionnaire form can be viewed through the following link: https://forms.gle/PbRyeygWYCzdvEW6 ## 6. MODEL ESTIMATION In a variety of public transport policy and planning applications, Value of Time (VOT) initiatives are valuable. Nevertheless, VOT is a latent variable that cannot be directly calculated. As a consequence, methodologies have been established for the indirect measurement of the VOT. ### 6.1. Binary Logistic Regression The binary logistic regression model is used to analyze binary outcome variables. It also makes use of the relationship between independent variables and dependent (or outcome) variables that is discrete. This model can be used to investigate the impact of a certain exposure on a variety of outcome variables, such as: • Comparing the level of an outcome variable in two exposure groups • Comparing more than two exposure groups through the use of indicator variables to estimate the effect of different levels of a categorical variable compared to a baseline. ### 6.2. Mode of Travel Cross Classification The transportation service is considered one of the most important elements that enhance the development of any country due to the significant role of transportation in many aspects such as the social and the economic ones. Therefore, transportation planners need to control the modal split between available modes of transport and promote particular modes such as public transport, walking, and cycling. The different preferences percentages of each respondent for each available mode of transportation are shown in Fig. (1). In the previous figure, it can be seen that private car owners have more willingness than public transport users to pay money to save time. However, reducing the travel time by 15% remains the highest preference for the users of each mode and that increased the travel cost by 20%. The study was limited to private cars and public transportation only because they are the most widely used vehicles for urban traveling in Egypt; other modes like walking and cycling are not common. Binary logistic regression is used to evaluate travel cost and travel time coefficients, to measure VOT. The models were calibrated separately for private cars and public transportation modes. All the variables presented in Table 3 have significant parameter estimates and logical signs. The logistic regression model is statistically significant when p is < or = 0.05. For the statistical significance of the test asterisk* and ** have been used for significance values. As an example, Travel time* (p = 0.021) (p =0.008), Travel cost* (p = 0.004) (p = 0.032), for private car and public transportation were respectively added significantly to the model. The coefficient estimates for evaluating VOT and several variables were evaluated during the calibration process, the statistical test used is presented in Table 4. The Hosmer-Lemeshow test the null hypothesis that the predictions made by the model fit perfectly with the observed group memberships. A chi-square statistic is computed comparing the observed frequencies with those expected under the linear model. A non-significant chi-square (p = 0.85) for private cars and (p=0.70) for public transportation indicates that the data fit the model well. Cox & Snell R Square and Nagelkerke R Square values are methods of calculating the explained variation. These values are sometimes referred to as (pseudo R2) values. Therefore, the explained variation in the dependent variable, which was based on private cars, ranges from 24.0% to 33.0%, and 30.0% to 38.0% for public transportation.  Fig. (1). Choice set levels concerning the mode of travel. Table 3. Estimations from the binary mode choice model (Private car versus public transportation).  - Variable Private Car Public Transportation Coefficient Coefficient - Travel Time TT(min)* 0.0231 0.0493 Travel Cost TC(pound)* 0.0426 0.1606 Trip Details Trip purpose** Work Study Entertaining 0.451 1.271 0.547 1.558 0.359 1.822 Trip length** (km) 0-10 10-20 20-35 35-50 0.910 0.553 1.648 0.442 0.777 0.345 1.493 0.782 Socio-economic Details Gender** Gender 0.772 0.990 Age** ( years) 18-25 25-35 35-45 45-60 2.567 0.487 1.298 0.434 1.298 0.545 0.123 1.287 Marital status** Married Unmarried 0.755 0.877 17360. 0.372 Education level** Without Medium Above med. High 0.410 1.565 0.607 0.589 0.690 0.597 1.217 0.142 Occupation** Unemployed Government Private emp. Public sector 0.229 0.971 0.461 0.459 0.148 0.279 0.093 0.705 Monthly income* (pound) 0-4000 4000-10000 0.943 0.866 1.0700 0.523 * Statistically significant p ≤ 0.05. ** non-significant p> 0.05. Table 4. Evaluate the value of travel time VOT for Private car and public transportation.  Statistical Test Private Car Public Transportation Chi-square sig. 0.85 0.70 Cox & Snell R Square 0.2454 0.3063 Nagelkerke R Square 0.3375 0.3821 VOT (LE/hr) 32.5 18.3 VOT ($/hr) 2.07 1.17

Variation in VOT between the private car (32.5 LE/HR.) and public transportation (18.3 LE/HR.) is based on the intuition of the vehicle owners’ that they can control time and choose their travel path, which is indeed not available for public transportation users.

#### 6.3. Personal Monthly Income Cross Classification

The average wage rate is recommended as a basis for determining the value of time unless reliable information on the earnings of users of transportation is available. Both theoretical and empirical research indicate that the value of time can be significantly higher or lower than the current wage rate; depending on the activities people are involved in [14]. Therefore, in this research, various classes of income, such as the variance of VOT, could be interpreted. The coefficient estimates for various income levels using binary logit models and VOT values are shown in Table 3, and preferences’ percentages of different personal monthly income levels are shown in Fig. (2).

The previous figure showed that the travelers’ willingness to pay for saving time increases when their income increases. This is logical because travelers aim to save time and arrive at their destination as soon as possible; however, the available budget restricts their decision. As seen in the table below, the first choice set level (A) – reduced the travel time by 15%, and increased the travel cost by 20% - and that is why this choice was the most preferable one for all the monthly income groups. This could also be considered a reflection of a certain socio-economic condition. Furthermore, binary logit analysis is applied in Table 5 to estimate VOT for each personal monthly income group separately.

The traveler’s travel time, travel cost, and occupation are the most significant variables in all the monthly income models. The negative coefficient for travel time and travel cost indicates the decreased utility associated with the increased travel time and travel cost. The traveler’s occupation is logically significant as the expected income and the responsibilities of the higher-level employees are much more than that of the lower-level employees. The calibration process and the statistical test used are shown in Table 6.

As previously mentioned, the travelers’ VOT increases when their income increases. VOT variation ranges between 13.5 LE/HR and 89.4 LE/HR. A non-significant chi-square for all monthly income levels (p> 0.05) indicates that the data fit the model well. The explained variation in the dependent variable, which was based on monthly income level, ranges from 35.4% to 37.8% for monthly income between (0-4000), ranges from 25.6% to 48.1%, and 39.5% to 11.1% for monthly income between (4000 – 10000) and >10000 respectively.

 Fig. (2). Choice set levels concerning personal monthly income groups.

Table 5. Estimations from the binary monthly income model.
 Variable 0 - 4000 4000 – 10000 >10000 Coefficient Coefficient Coefficient * Travel Time TT(min) -0.024 -0.077 -0.088 Travel Cost TC(pound)* -0.106 -0.110 -0.059 Trip Details Trip purpose** Work Study Entertaining 0.119 2.286 0.198 0.235 0.770 1.441 0.535 1. 494 0.666 Trip length** (km) 0-10 10-20 20-35 35-50 1. 830 0.686 1.385 0.713 0.461 0.512 0.837 0.324 0.913 0.690 0.150 2. 225 Mode of travel** Private car Public transportation 0.585 0.087 0.178 0.053 0.905 0.261 Socio-economic Details Gender** gender 0.939 0.175 0.927 Age** ( years) 18-25 25-35 35-45 45-60 1. 060 0.913 0.293 1. 807 0.235 0.933 0.833 1. 315 0.849 1. 675 0.892 0.275 Marital status** Married Unmarried 0.500 0.525 0.235 0.736 0.664 0.315 Occupation* Unemployed Government Private emp. Public sector 1.824 0.983 0.508 2.207 0.805 2.129 0.052 0.910 0.411 1.614 0.831 1. 203 Education level** Without Medium Above Med High 0.195 1. 074 0.897 0.151 0.550 0.369 0.405 1. 686 1. 079 0.267 0.442 0.915
* Statistically significant p ≤ 0.05.
** non-significant p> 0.05.
Table 6. Evaluate the value of travel time VOT for different monthly income levels.
 Statistical Test 0 - 4000 4000 – 10000 >10000 Chi-square sig. 0.139 0.175 0.564 Cox & Snell R Square 0.354 0.256 0.395 Nagelkerke R Square 0.378 0.481 0.111 VOT (LE/hr) 13.5 42.0 89.4 VOT ($/hr) 0.86 2.68 5.69 ### 6.4. Trip Purpose Cross Classification The purpose of a trip greatly influences the traveler’s behavior and mode choice. The figure below is designed to depict four major trip purposes which are: work/business, Study, entertainment, and shopping. All of these activities are relevant to a normal typical day. Work/business trips seem to be the most frequent activity, 62%, compared to the other ones because it is a common regular trip for many people. The preferences’ percentage of different trips’ purposes is shown in Fig. (3). From the figure above, it can be noticed that the traveler’s willingness to pay is higher for work/business trips than the other trips, and this happens because work trips are restricted by certain attendance and departure times and each job has different responsibilities. As for the study-related trips, its travelers show less willingness to pay and this may occur because of the limited income of the students. As for leisure trips, it is found that the travelers’ desire to pay increases as they aim to reduce the time they spent on reaching their destination. In other words, these travelers would certainly pay as much money as needed to save any wasted time on the road trips, especially since most of the trips take a long time because of the long distance (Table 7). As Tables 3, 5, and 7 above show, travel time and travel cost are the most significant variables in all binary trip purpose models. Values of travel time for different trip purposes are given in Table 8.  Fig. (3). Choice set levels concerning the different trip purposes. Table 7. Estimations from the binary trip purpose model.  Variable Work Study Entertaining Shopping Coefficient Coefficient Coefficient Coefficient Trip Details Travel Time TT(min)* 0.011 0.198 0.023 0.035 Travel Cost TC(pound)* 0.017 0.621 0.045 0.087 Trip length** (km) 0-10 10-20 20-35 35-50 0.610 0.919 0.314 0.606 0.610 0.68 0.09 0.919 0.933 0.729 0.719 0.882 0.606 0.704 0.406 0.627 Mode of travel** Private car Public transportation 0.710 0.275 0.021 0.76 0.566 0.848 0.369 0.055 Socio-economic Details Gender** gender 0.604 0.141 0.744 0.335 Age** ( years) 18-25 25-35 35-45 45-60 0.537 0.701 0.239 0.688 0.610 0.919 0.314 0.26 0.933 0.729 0.719 0.882 0.606 0.704 0.406 0.627 Marital status** Married Unmarried 0.934 0.596 0.46 0.36 0.709 0.532 0.413 0.328 Education level** Without Medium Above Med. High 0.338 0.105 0.378 0.777 0.76 0.142 0.910 0.73 0.961 0.427 0.049 0.514 0.641 0.033 0.690 0.640 Occupation** Unemployed Government Private emp. Public sector 0.738 0.850 0.812 0.967 0.00 0.28 0.63 0.32 0.294 0.931 0.559 0.708 0.187 0.198 0.956 0.952 Personal monthly income** (pound) 0-4000 4000-10000 0.471 0.435 0.415 0.20 0.408 0.929 0.127 0.140 * Statistically significant p ≤ 0.05. ** non-significant p> 0.05. Table 8. Evaluate the value of travel time VOT for different trip purposes.  Statistical Test Work Study Entertaining Shopping Chi-square sig. 0.127 0.251 0.156 0.114 Cox & Snell R Square 0.252 0.176 0.342 0.426 Nagelkerke R Square 0.389 0.537 0.474 0.191 VOT (LE/hr) 38.82 19.15 30.66 24.13 VOT ($/hr) 2.47 1.22 1.95 1.54
Table 9. Cross Classing Categories of VOT based on different trip purposes, income levels, and modes of travel.
 Public Transportation Private Car Mode of Travel >10000 4000-10000 0-4000 >10000 4000-10000 0-4000 Income level (pound) 30.62 24.50 15.33 81.30 55.42 31.23 Work trip 12.36 10.64 8.80 20.44 17.13 15.78 study trip 24.24 20.13 13.57 66.65 39.54 25.43 Entertaining trip 21.0 14.43 10.66 42.59 26.87 20.54 Shopping trip
1 Pound = 0.064 Dollar =0.057 Euro

A non-significant chi-square value represents an acceptable model fit. Cox, Snell R Square, and Nagelkerke R Square values of all the models lie between 17% and 53%. The Value of Travel Time varies according to the purpose of the trip; the highest VOT, 38.82 LE/HR, was found on business-oriented trips.

## 7. RESULTS AND DISCUSSIONS

The value of travel time (VOT) is an important input to travel demand modelsand is used to manage and evaluate transportation investment decisions [15, 16]. This paper provides an estimation of the value of travel time using a combined RP-SP method. The analysis is undertaken to achieve the following objectives; I) defining the required methods for estimating the value of travel time depending on the expanded literature review; ii) measuring the value of travel time for private cars and public transportation modes within different income levels and several trip purposes; and iii) finding out the effect Cross classification of socio-economic factors on the travel time value.

Travel time values are critical in the design of transportation infrastructure. In transportation models, the value of time is utilized to commercialize travel time based on the socioeconomic status of travelers. Providing improved travel time is generally among the largest societal benefits of transportation infrastructure projects. Table 9 gives the VOT for different travel experiences in Egypt.

This paper assesses the estimation of the value of time-based on questionnaires distributed to travelers in a preference survey. The findings of this study aim to help researchers and practitioners to explore the applicability of binary logic regression in modeling the value of travel time using combined stated and revealed preference survey data.

Accurate traveler VOT calculations aid in determining the potential worth of such costs/benefits about the required monetary expenditure. Savings in travel time can also result in lower vehicle running expenses. The association between trip duration and reliability is established by [17] establish the relationship between travel time and reliability. This relationship is useful to find the value of reliability savings by improving the network in a case study.

## CONCLUSION

The findings revealed that various socioeconomic demographic variables and trip parameters all played a role in varying degrees of VOT variances. It aids in a better understanding of which characteristics contribute to higher or lower VOT and to what extent.

These findings can be included in the demand forecasting process, resulting in more accurate estimates and analytical capabilities in a variety of applications, including toll feasibility studies, pricing strategy, policy evaluations, and impact analyses.

Finally, the binary logit technique appears to be the most appropriate method for estimating the value of travel time (VOT), as it is an accurate, dependable, straightforward, and broadly applicable logistic regression model.

It is hoped that this study has proved the binary logit model's relevance to the cross-classified Value of Time and that it will be widely used in Egypt soon. It is suggested that more research be done to examine the impact of social and economic factors on the value of travel time for trips of varying lengths and people with occupations other than those listed in this study. Different logistic regression models can also be used to undertake other investigations.

## LIST OF ABBREVIATIONS

 VOT = Value of Travel Time RP = Revealed Preference SP = Stated Preference VTTS = Value of Travel Time Savings VAT = Value of Time Assigned to Travel

Not applicable.

### AVAILABILITY OF DATA AND MATERIALS

The data that support the findings of this study are available within the article.

None.

### CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

Declared none.

## REFERENCES

 [1] G.S. Becker, "A theory of the allocation of time", The Economic Journal, vol. 75, no. 299, pp. 493-517, 1965.CrossRef Link [2] D.G. Kleinbaum, L.L. Kupper, A. Nizam, and E.S. Rosenberg, Applied regression analysis and other multivariable methods, Cengage Learning, 2013. [3] W.G. Waters, J. Evans, C. Leung, B. Stefiuk, and C. Wong, The Value of Time Savings for the Economic Evaluation of Highway Investments in British Columbia: Prepared for the Planning Services Branch, Ministry of Transportation and Highways., Centre for Transportation Studies, University of British Columbia: Victoria, BC, Canada, 1992. [4] I. Waters, and G. William, "Values of travel time savings in road transport project evaluation. Volume 3: Transport policy", Proceedings of the 7th World Conference on Transport ResearchWorld Conference on Transport Research Society, July 16-21, 1996, Sydney, Australia, 1996. [5] M. S. Hossan, H. Asgari, and X. J. T. R. P. A. P. Jin, "Investigating preference heterogeneity in Value of Time (VOT) and Value of Reliability (VOR) estimation for managed lanes", [6] C. Lv, L. Dai, K. Huang, and Z. Liu, "Estimating the value of travel time using mixed logit model: A practical survey in Nanjing", International Conference on Intelligent Interactive Multimedia Systems and Services, Springer., 2018pp. 318-328. [7] C. Xumei, L. Qiaoxian, and D. Guang, "Estimation of travel time values for urban public transport passengers based on SP survey", J. Transpo. Sys. Eng. Inform Technol, vol. 11, no. 4, pp. 77-84, 2011. [8] C. Fezzi, I. J. Bateman, and S. Ferrini, "Using revealed preferences to estimate the value of travel time to recreation sites", J. Environ Econo Manage, vol. 67, no. 1, pp. 58-70, 2014. [9] B. Schmid, "The value of travel time savings and the value of leisure in Zurich: Estimation, decomposition and policy implications", Transportation Research Part A: Policy and Practice, vol. 150, pp. 186-215, 2021. [10] H. Kato, T. Oda, and A. Sakashita, "Valuation of travel time saving with revealed preference data in Japan: Further analysis", Presented at 13th World Conference on Transport Research, Janeiro, July 15-18, 2013, Rio de Janeiro, Brazil, 2013. [11] L. Ambarwati, A.K. Indraistuti, and P.J. Kusumawardhani, "Estimating the value of time and its application", Open Sci. J., vol. 2, no. 2, 2017.CrossRef Link [12] I. Athira, C. Muneera, K. Krishnamurthy, and M.V.L.R. Anjaneyulu, "Estimation of value of travel time for work trips", Transport Res. Proc., vol. 17, pp. 116-123, 2016. [13] H. Wang, R. Zhu, and P.J. Ma, "Optimal subsampling for large sample logistic regression", J. American Stat Assoc, vol. 113, no. 522, pp. 829-844, 2018.CrossRef Link [14] S. R. Jara-Diaz, "The goods/activities framework for discrete travel choices: Indirect utility and value of time", In perpetual motion: Travel behavior research opportunities and application challenges, 2002. [15] D. Metz, "Valuing transport investments based on travel time saving: Inconsistency with United Kingdom policy objectives", Case Studies on Transport Policy, vol. 5, no. 4, pp. 716-721, 2017. [16] P. Mackie, R. Batley, and T.J.C.S.T.P. Worsley, "Valuing transport investments based on travel time savings—A response to David Metz", Case Studies on Transport Policy, vol. 6, no. 4, pp. 638-641, 2018.CrossRef Link [17] S. Mishra, L. Tang, S. Ghader, S. Mahapatra, and L.J.C.S.T.P. Zhang, "Estimation and valuation of travel time reliability for transportation planning applications", Case Studies on Transport Policy, vol. 6, no. 1, pp. 51-62, 2018.CrossRef Link

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text:
# Chapter 3 Current Voltage Sources and Differential Amplifiers¶

## Example 3.1 Pg 53¶

In [1]:
from __future__ import division
# Determine the collector current Ic1 and collector-emitter voltage Vce1 for the difference amplifier circuit

V1 = 0 #    # volt
V2 = -5 #   #volt
Vcm =  5 #   #volt
Vcc = 10#   #volt
Vee = -10 #  #volt
Ie =  1 #  #mA
Rc =  10 #  #kilo ohm

# Transistor parameters
# base current are negligible
Vbe = 0.7 #  # volt

# The collector current of difference amplifier is
Ic1 = Ie/2 #
print 'The  collector  current  of  difference  amplifier  Ic1 = Ic2 = %0.2f'%Ic1,' mA '

# The collector voltages of transistors Q1 and Q2 are expressed as

Vc1  = Vcc-Ic1*Rc #
print 'The collector voltages of transistors Q1 and Q2 are  Vc1 = Vc2 = %0.2f'%Vc1,' volt '

# We know common mode voltage (Vcm) , from this the emitter voltage can be identified as follows
# For the common mode voltage Vcm = 0 V , the emitter voltage is Ve = -0.7 V
# For the common mode voltage Vcm = 5 V , the emitter voltage is Ve =  4.3 V
# For the common mode voltage Vcm = -5 V , the emitter voltage is Ve = -5.7 V

# For the different emitter voltages the collector-emitter voltage can be calculated as

Ve = -0.7 #  # volt
Vce1 = Vc1-Ve#
print 'For Ve = -0.7 Volt  the  collector - emitter  voltage  Vce1  = %0.2f'%Vce1,' Volt'

Ve = 4.3 #  # volt
Vce1 = Vc1-Ve#
print 'For Ve = 4.3 Volt  the  collector - emitter  voltage  Vce1  = %0.2f'%Vce1,' Volt'

Ve = -5.7 #  # volt
Vce1 = Vc1-Ve#
print 'For Ve = -5.7 Volt  the  collector - emitter  voltage  Vce1  = %0.2f'%Vce1,' Volt'

The  collector  current  of  difference  amplifier  Ic1 = Ic2 = 0.50  mA
The collector voltages of transistors Q1 and Q2 are  Vc1 = Vc2 = 5.00  volt
For Ve = -0.7 Volt  the  collector - emitter  voltage  Vce1  = 5.70  Volt
For Ve = 4.3 Volt  the  collector - emitter  voltage  Vce1  = 0.70  Volt
For Ve = -5.7 Volt  the  collector - emitter  voltage  Vce1  = 10.70  Volt

## Example 3.2 Pg 54¶

In [2]:
# To determine the difference-mode and common-mode gain of the difference amplifier

Vcc = 10 # # volt
Vee = -10 #  #volt
Iq  =  0.8 #  #mA
Ie  =  0.8 #  #mA
Rc = 12 #  #kilo-Ohm
Vt = 0.026 #  # volt

# Transistor parameter
beta = 100 #
Rs = 0 #  #Ohm
Ro = 25 #  #kilo-Ohm
# The differential mode gain Ad
gm = (Ie/ 2*Vt) #
# Ad = (gm*r*Rc/r+Rc) #   # where r is r-pi
# For Rb=0 , the differential mode gain is

#But

#The common mode gain Acm
# Acm = - (gm*Rc/1+2*gm*Re+2*Re/r)
print ' The common mode gain Acm = %0.3f'%Acm

 The differential mode gain Ad = 184.6
The common mode gain Acm = -0.237

## Example 3.3 Pg 56¶

In [3]:
# To find the output of a difference amplifier when only common mode signal is applied

# V1 = V2 = Vcm = 200*sin(wt) #   # micro volt (uV)
Acm  = -0.237 #

# When the common mode input signal is applied to the difference amplifier , the difference mode gain is zero
Vcm = 200 #
Vo = Acm*Vcm #
print 'The output of a difference amplifier is  Vo  = %0.2f'%Vo,'sinwt uV '  # multiply by sinwt because it is in Vcm

The output of a difference amplifier is  Vo  = -47.40 sinwt uV

## Example 3.4 Pg 56¶

In [4]:
from math import log10
#Determine the common mode rejection ratio(CMRR) of the difference amplifier

Vcc = 10 # # volt
Vee = -10 #  #volt
Iq  =  0.8 #  #mA
Ie  =  0.8 #  #mA
Rc = 12 #  #kilo-Ohm
Vt = 0.026 #  # volt

# Transistor parameter
beta = 100 #
Rs = 0 #  #Ohm
Ro = 25 #  #kilo-Ohm

# The differential mode gain Ad
gm = (Ie/ 2*Vt) #
# Ad = (gm*r*Rc/r+Rc) #   # where r is r-pi
# For Rb=0 , the differential mode gain is

#But

#The common mode gain Acm
# Acm = - (gm*Rc/1+2*gm*Re+2*Re/r)
print 'The common mode gain Acm = %0.3f'%Acm

# The CMRR of difference amplifier is given as
print 'The CMRR of difference amplifier is = %0.f'%CMRR

# In decibel it can be expressed as
CMRRdb = 20*log10(CMRR)
print 'In decibel CMRR is = %0.2f'%CMRRdb

The differential mode gain Ad = 184.6
The common mode gain Acm = -0.237
The CMRR of difference amplifier is = 389
In decibel CMRR is = 51.80

## Example 3.5 Pg 58¶

In [5]:
# To determine emitter resistance of the difference amplifier

Vcc = 10 # # volt
Vee = -10 #  #volt
Iq  =  0.8 #  #mA
Ie  =  0.8 #  #mA
CMRRdb = 90 #  #dB
Vt = 0.026 #

# Transistor parameter
beta = 100 #

# the CMRR of the difference amplifier is defined as
#CMRR = ((1/2)*(1+((1+beta)*Ie*Re)/beta*Vt))

# CMRRdb = 20*log10(CMRR)
CMRR = 10**(CMRRdb/20)
print ' The CMRR of difference amplifier is = %0.2e'%CMRR

# The resistance RE is calculated as

RE = (((2*CMRR)-1)/((1+beta)*Ie))*(beta*Vt)/1e3
print ' The value of resistance RE is = %0.2f'%RE,' Mohm '

 The CMRR of difference amplifier is = 3.16e+04
The value of resistance RE is = 2.04  Mohm

## Example 3.6 Pg 59¶

In [6]:
from __future__ import division
# determine the differential mode gain when load resistance RL = 100 k ohm

RL = 100*10**3 # # k ohm  # load resistance
IE = 0.20*10**-3 # # mA  # biasing current
VA = 100 # # V # early voltage
VT = 0.026 #  # threshold volt

# the differential gain of differential amplifier with an active load circuit
#Ad = Vo/Vd  = gm(ro2 || ro4 || RL  )
ro2 = (2*VA)/IE#
ro4 = ro2 #
gm = IE/(2*VT) #

 The differential mode gain Ad is = 321


text:
# Semiconductor n-type silicon

• Engineering
Marcis231
Homework Statement:
Calculate the concentration of holes and electrons in n-type silicon (Si) if the resistivity is 20 Ω.cm and the electron mobility of silicon (µn) is 1300 cm2 / volt. second
Relevant Equations:
I still not understand about this, i found so many formulas but I don't know which one i should use for this question
I still not understand about this, i found so many formulas but I don't know which one i should use for this question

Marcis231


text:
# Ising Spin Glass - Optimization

I'm a newbie researcher working on model-based genetic algorithms, mainly linkage learning in both discrete and continuous spaces, using data modeling. I would like to ask you about Ising Spin Glass (ISG) problem in the context of optimization. I've seen a lot of papers running benchmarks on this particular problem. I've googled it, checked wiki but eventually understood nothing :/

1. What is an intuitive definition of this problem, making it well suited for optimization?
2. When referring to ISG problem in papers, which specific model researchers have in mind?
3. How to formulate this problem in the context of optimization? (encoding, fitness evaluation)

I don't ask nor expect a ready solution, some general direction would suffice as I feel kinda lost.

Update 1

I've found some python repo on github. It calculates energy in a following way:

import numpy as np

def totalEnergy(J, configuration):
"""
calculate the energy for a given configuration.
Parameters:
>>> J:
nSpin*nSpin matrix, J[i,j] is the interation between spin i and j.
>>> configuration:
1*nSpin vector, configuration[i] stores the spin at site i.
Returns:
the total energy of this configuration.
"""
# 0.5 for double counting
return 0.5*np.dot(configuration,np.dot(J,configuration))

Update 2

Found some papers:

Once I get through them, I'll code it into my benchmarks package. I'm going to release it on pypi in a few weeks, if anyone's interested.

There's a list of ISG instances at Parameterless Population Pyramid (P3) repository.

-140 1110100000000101010100001011011001010010111011010111000110101010111010101111100010101010100001101010
200
0 1 1
1 2 1
2 3 -1
3 4 -1

It works like this:

-140 - global optimum

111010... - best genome

200 - number of dependencies

From record 3 onwards you got specified dependencies.

Example

Let's take genome 1 0 1 1 1 0 1. Values 0 must be substituted to -1. As problem is binary that is just convention in repo associated with data types underneath.

After transformation we have following genome: 1 -1 1 1 1 -1 1. Let's assume following dependencies:

0 1 1
1 2 1
2 3 -1

We iterate over each row. We take first row 0 1 1. First 2 values are indices. You take gene values at that positions. In genome 1 -1 1 1 1 -1 1 values at these indices (0, 1) are 1 and -1. We multiply those values. We get -1. Third value in this tuple (0 1 1) denotes multiply factor. You take your previous score -1 and multiply it by that factor 1. We get -1. You repeat that process for every dependency. To get overall fitness you just sum up all the subscores.

genome: 1 -1 1 1 1 -1 1

dependencies:

0 1 1 = 1 * -1 * 1 = -1
1 2 1 = -1 * 1 * 1 = -1
2 3 -1 = 1 * 1 * -1 = -1

fitness = sum(-1, -1, -1)

• Can you share a ISIN paper for GA? – Carlos Mougan Jan 16 at 7:59
• Haven't seen one. People only write ising spin glass when referring to the benchmark, without stating particular model. – Piotr Rarus - Reinstate Monica Jan 16 at 17:13
• @CarlosMougan I've found some papers. Definitions are little intuitive, models differ, but maybe you can find something interesting there. – Piotr Rarus - Reinstate Monica Jan 17 at 13:31
• @CarlosMougan I updated post with problem instances and a method. I'll publish python lib with benchmarks in next 1-2 weeks. – Piotr Rarus - Reinstate Monica Jan 21 at 11:07


text:
## Polynomial Remainder Theorem

If the Coefficients of the Polynomial

 (1)

are specified to be Integers, then integral Roots must have a Numerator which is a factor of and a Denominator which is a factor of (with either sign possible). This follows since a Polynomial of Order with integral Roots can be expressed as

 (2)

where the Roots are , , ..., and . Factoring out the s,

 (3)

Now, multiplying through,
 (4)

where we have not bothered with the other terms. Since the first and last Coefficients are and , all the integral roots of (1) are of the form [factors of ]/[factors of ].


text:
# FPM Arithmetic and Geometric Series (Chapter 5)

$\def\frac{\dfrac}$

1. (2011/june/Paper02/q1)
Evaluate $\displaystyle\sum_{n=6}^{20}(2 n-3)$ (3 marks)

2. (2012/june/рарег01/q5)
The first four terms of an arithmetic series, $S$, are

$$\log _{a} 2+\log _{a} 4+\log _{a} 8+\log _{a} 16$$

(a) Write down an expression for the $r$ th term of $S$. (1 mark)

(b) Find an expression for the common difference of $S$. (2 marks)

The sum of the first $n$ terms of $S$ is $S_{n}$

(c) Show that $S_{n}=\displaystyle\frac{1}{2} n(n+1) \log _{a} 2$ (2 marks)

The first four terms of a second arithmetic series, $T$, are

$$\log _{a} 6+\log _{a} 12+\log _{a} 24+\log _{0} 48$$

The sum of the first $n$ terms of $T$ is $T_{n}$

(d) Find $T_{n}-S_{n}$ and simplify your answer. (4 marks)

3. $\left(2013 / \mathrm{jan} / \mathrm{Paper01} / \mathrm{q4}\right)$

(a) Show that $\displaystyle\sum_{r=1}^{n}(3 r-4)=\displaystyle\frac{n}{2}(3 n-5)$ (3 marks)

(b) Hence, or otherwise, evaluate $\displaystyle\sum_{r=1}^{50}(3 r-4)$ (2 marks)

Given that $\displaystyle\sum_{r=1}^{h}(3 r-4)=186$

(c) find the value of $n$ (3 marks)

4. $(2013 / \mathrm{jan} / \mathrm{Paper01} / \mathrm{q} 9)$
The sum $S_{n}$ of the first $n$ terms of an arithmetic series is given by $S_{n}=n(2 n+3) .$ The first term of the series is $a$.

(a) Show that $a=5$ (2 marks)

(b) Find the common difference of the series. (3 marks)

(c) Find the 12 th term of the series. (2 marks)

Given that $1+S_{p+4}=2 S_{p}$

(d) find the value of $p$ (4 marks)

5. (2013/june/paper01/q7)
An arithmetic series has first term $a$ and common difference $d$. The $n$th term of the series is $I_{n}$ and the sum of the first $n$ terms of the series is $S_{n}$

(a) Write down an expression in terms of $a$ and $d$ for

(i) $t_{58}$

(ii) $S_{13}$ (2 marks)

Given that $t_{58}=S_{13}$

(b) show that $d=-\displaystyle\frac{4}{7} a$ (2 marks)

(c) show that $t_{176}=S_{21}$ (4 marks)

(d) find the value of $r$ when $t_{r}=5 t_{9}$ (3 marks)

6. (2014/jan/paper01/q1)
Find $\displaystyle\sum_{y=4}^{40}(7 r-2)$ (4 marks)

7. (2014/jan/paper01/q4)
The sum of the first $n$ terms of an arithmetic series is $2 n(n+3)$

Find

(a) the first term of the series, (1 mark)

(b) the common difference of the series, (3 marks)

(c) the 25 th term of the series. (2 marks)

8. (2014/june/paper01/q4)
The $3 \mathrm{rd}$ term of an arithmetic series is 108 and the 12 th term is 54. Find

(a) the common difference of the series, (3 marks)

(b) the first term of the series. (1 mark)

The sum of the first $n$ terms of the series is $S_{n}$

(c) Show that $S_{n}=3 n(41-n)$ (3 marks)

Given that $S_{n}=1200$

(d) find the two possible values of $n$. (4 marks)

9. (2014/june/paper02/q2)
Evaluate $\displaystyle\sum_{r=5}^{60}(2 r+7)$ (4 marks)

10. (2015/jan/paper02/q7)
The first term of an arithmetic series is $-14$ and the common difference is 4

(a) Find the 15 th term of the series. (2 marks)

(b) Find the sum of the first 25 terms of the series. (3 marks)

The sum of nine consecutive terms of the series is 1422

(c) Find the smallest of these nine terms. (5 marks)

11. ( $2015 /$ june/paper01/q4)
The $\operatorname{sum} S_{n}$ of the first $n$ terms of an arithmetic series is given by $S_{n}=2 n(10-n)$

(a) Write down the first term of the series. (1 mark)

(b) Find the common difference of the series. (2 marks)

Given that $S_{n}>-50$

(c) (i) write down an inequality satisfied by $n$,

(ii) hence find the largest value of $n$ for which $S_{n}>-50$ (4 marks)

12. (2015/june/paper02/q1)
(a) Show that $\displaystyle\sum_{r=1}^{n} r=\displaystyle\frac{n}{2}(1+n)$ (2 marks)

(b) Hence find the sum of all the integers from 1 to 100 inclusive that are not multiples of 7 (3 marks)

13. (2016/jan/paper01/q4)
An arithmetic series has first term $p$ and common difference $p$ where $p \neq 0$ A geometric series also has first term $p$. The common ratio of this geometric series is $r$. The sum of the first three terms of the arithmetic series is equal to the sum of the first three terms of the geometric series.

Given that $r>0$

show that $r=\displaystyle\frac{-1+\sqrt{21}}{2}$ (5 marks)

14. (2016/jan/paper02/q8)
The $n$th term of an arithmetic series is $t_{n}$ where $t_{n}=2 n-3$

The sum of the first $n$ terms of the series is $S_{n}$

(a) Show that $S_{n}=n(n-2)$ (4 marks)

(b) Find the value of $n$ such that $5 t_{n+2}=3 S_{n-3}$ (5 marks)

15. (2016/june/paper01/q4)
The $n$th term of an arithmetic series is $t_{n}$ and the sum of the first $n$ terms of the series is $S_{n}$ Given that $S_{2}=\displaystyle\frac{2}{3} t_{5}$ and that $S_{4}=t_{10}+3$

(a) find

(i) the common difference of the series,

(ii) the first term of the series. (5 marks)

Given also that $S_{p+2}-S_{p}=110$

(b) find the value of $p$ (3 marks)

16. (2017/jan/paper02/q6)
The sum of the first 21 terms of an arithmetic series is 987 and the 8 th term of the series is 35

The first term of the series is $a$ and the common difference is $d$.

(a) Find the value of

(i) $a$,

(ii) $d .$ (5 marks)

The sum, $S_{n}$, of the first $n$ terms of the series is given by $S_{n}=\displaystyle\sum_{r=1}^{n}(A r+B)$, where $A$ and $B$ are integers.

(b) Find the value of

(i) $A$,

(ii) $B$. (3 marks)

(c) Find the least value of $n$ such that $S_{n}>2000$ (5 marks)

17. (2017/june/paper02/q11)
(a) Show that $\log p q^{4}-\log p q^{2}=\log p q^{6}-\log p q^{4}$ (3 marks)

Given that $\log p q^{2}$ and $\log p q^{4}$ are the second and third terms of an arithmetic series, find

(b) the first term of the series, (3 marks)

(c) the sum of the first $n$ terms of the series.

Give your answer in the form $n \log p q^{s}$, expressing $s$ in terms of $n$. (4 marks)

18. (2018/jan/paper02/q2)
(a) Show that $\displaystyle\sum_{r=1}^{n}(3 r+2)=\displaystyle\frac{n}{2}(7+3 n)$ (2 marks)

(b) Hence, or otherwise, evaluate $\displaystyle\sum_{r=10}^{20}(3 r+2)$ (3 marks)

19. (2018/june/paper01/q9)
The 4 th term of an arithmetic series is 108 and the 11 th term is 80

Find

(a) (i) the common difference of the series,

(ii) the first term of the series. (4 marks)

The sum of the first $n$ terms of the series is $S_{n}$

(b) Show that $S_{n}=2 n(61-n)$ (3 marks)

Given that $S_{n}=1100$

(c) find the two possible values of $n$. (4 marks)

20. (2019/juneR $/$ paper01/q6)
(a) Show that $\displaystyle\sum_{r=1}^{n}(4 r-3)=n(2 n-1)$ (3 marks)

(b) Hence, or otherwise, find the least value of $n$ such that $\displaystyle\sum_{r=1}^{n}(4 r-3)>1000$ (3 marks)

Given that $S_{n}=n(2 n-1), t_{n}=(4 n-3)$ and that $18+3 t_{n+7}=S_{n+4}$

(c) find the value of $n$. (4 marks)

21. (2011/june/paper01/q6)
The third term of an arithmetic series is 70 and the sum of the first 10 terms of the series is 450

(a) Calculate the common difference of the series. (4 marks)

The sum of the first $n$ terms of the series is $S_{n}$

Given that $S_{n} \geqslant 350$

(b) find the set of possible values of $n$ (6 marks)

22. (2019/june/paper02/q7)
The sum of the first $n$ terms of an arithmetic series is $A_{n}$ where

$$A_{n}=\displaystyle\sum_{r=1}^{n}(4 r+5)$$

(a) For this arithmetic series, find

(i) the first term,

(ii) the common difference. (2 marks)

The sum of the first $n$ terms of a geometric series is $G_{n}$ where

$$G_{n}=\displaystyle\sum_{r=1}^{n} 4(3)^{r-1}$$

(b) For this geometric series, find

(i) the first term,

(ii) the common ratio. (2 marks)

(c) Find the value of $n$ for which $A_{14}-6=G_{n}$ (5 marks)

1. 345

2.(a) $t_{r}=\log _{a} 2^{r}$ (b) $\log _{a} 2$ (c) Show (d) $n \log _{a} 3$

3.(a) show (b) (c) $n=12$

4.(a) Show (b) $d=4$ (c) $u_{12}=49$ (d) $p=9$

5.(a)(i) $t_{5_{8}}=a+57 d$ (ii) $S_{13}=\displaystyle\frac{13}{2}(2 a+12 d)$ (b) Show (c) Show (d) $r=34$

6. 5624

7.(a) $a=8$ (b) $d=4 \quad$ (c) $u_{25}=104$

8.(a) $\quad d=-6$ (b) Show (c) $n=16,25$

9. 4032

10.(a) $\quad u_{15}=42$ (b) $S_{25}=850$ (c) $a=142$

11.(a) $a=18$ (b) $d=-4$ (c) (i) $2 n(10-n)>-50$ (ii) $n=12$

12.(a) Show (b) 4315

13.Show

14.(a) Show (b) $n=10$

15.(a)(i) $d=4$ (ii) $a=5$ (b) $p=12$

16.$(a)(i) a=7($ ii) $d=4 \quad(b)(i) A=4 \quad$ (ii) $B=3$ (c) $n=31$

17.(a) Show (b) $a=\log p$ (c) $S_{n}=n \log \left(p q^{(n-1)}\right)$

18.(a) Show (b) 517

19.(a) (i) $d=-4$ (ii) $a=120$ (b) Show (c) $n=11,50$

20.(a) Show (b) $n=23$ (c) $n=5$

21.(a) $d=-10$ (b) $n=5,6, \ldots, 14$

22.$(a)(i) \quad a=9$ (ii) $d=4$ (b) $(i) a=4$ (ii) $r=3$ (c) $n=5$


text:
Assignment I: B-Trees¶

In this first assignment, you will be implementing a series of functions for manipulating B-Trees in chidb. This assignment is divided into the following steps:

1. Opening a chidb file
3. Creating and writing a B-Tree node to disk
4. Manipulating B-Tree cells
5. Finding a value in a B-Tree
6. Insertion into a leaf without splitting
7. Insertion with splitting
8. Supporting index B-Trees

All of these steps can be developed independently (the only exception is step 8, which is a cross-cutting feature). Nonetheless, testing a step will usually require that some of the previous steps be implemented (e.g., you can’t find a value in a B-Tree if you can’t open the file itself).

Before you get started¶

Before you start working on this assignment, make sure read The chidb File Format. You are not expected to understand the entire document at first, but you should at least get a general feel for how a chidb file is organized.

Most of your work will be done on the files src/libchidb/btree.[hc]. The header file contains declarations of functions you will have to implement, and which are referred to in the rest of the document. The specification of what the functions must do is included in the source as comments.

Step 1: Opening a chidb file¶

Implement the following functions:

int chidb_Btree_open(const char *filename, chidb *db, BTree **bt)
int chidb_Btree_close(BTree *bt);

Take into account that the chidb_Btree_open function can open an existing file, but can also create an empty database file if given the name of a file that does not exist. The latter functionality will not be possible until Step 3 is completed.

Implement the following functions:

int chidb_Btree_getNodeByPage(BTree *bt, npage_t npage, BTreeNode **node);
int chidb_Btree_freeMemNode(BTree *bt, BTreeNode *btn);

Step 3: Creating and writing a B-Tree node to disk¶

Implement the following function:

int chidb_Btree_newNode(BTree *bt, npage_t *npage, uint8_t type);
int chidb_Btree_initEmptyNode(BTree *bt, npage_t npage, uint8_t type);
int chidb_Btree_writeNode(BTree *bt, BTreeNode *node);

Step 4: Manipulating B-Tree cells¶

Implement the following functions:

int chidb_Btree_getCell(BTreeNode *btn, ncell_t ncell, BTreeCell *cell);
int chidb_Btree_insertCell(BTreeNode *btn, ncell_t ncell, BTreeCell *cell);

Take into account that, once you’ve implemented chidb_Btree_getCell (which is the simpler of the two), you will be able to implement chidb_Btree_find or use the provided chidb_Btree_print. At that point, you will be able to open example database files (available at the chidb website) and verify that you can correctly print out their contents or search for specific values.

Step 5: Finding a value in a B-Tree¶

Implement the following function:

int chidb_Btree_find(BTree *bt, npage_t nroot, key_t key, uint8_t **data, uint16_t *size);

Step 6: Insertion into a leaf without splitting¶

Implement the following functions:

int chidb_Btree_insertInTable(BTree *bt, npage_t nroot,
key_t key, uint8_t *data, uint16_t size);
int chidb_Btree_insert(BTree *bt, npage_t nroot, BTreeCell *btc);
int chidb_Btree_insertNonFull(BTree *bt, npage_t npage, BTreeCell *btc);

Take into account that, at this point, chidb_Btree_insert will be little more than a call to chidb_Btree_insertNonFull. Also, even if at this point you are only inserting in leaf nodes, that doesn’t mean that your implementation shouldn’t work on a database file that does have internal nodes. So, chidb_Btree_insertNonFull should still traverse the tree in search of the leaf node to insert the cell in (but assuming that splitting will not be necessary)

Since the chidb file format is a subset of the SQLite format, once this step is completed you will be able to create a chidb file and open it in SQLite, as long as you include a valid schema table in the file.

Step 7: Insertion with splitting¶

Implement the following function:

int chidb_Btree_split(BTree *bt, npage_t npage_parent, npage_t npage_child,
ncell_t parent_cell, npage_t *npage_child2);

Note that you will also have to modify chidb_Btree_insert and chidb_Btree_insertNonFull to split nodes when necessary.

Step 8: Supporting index B-Trees¶

Supporting index B-Trees affects almost all of the previous functions. As such, this step can either be done from the very beginning (at the cost of complicating the implementation of the previous functions), or added at the end (simplifying the implementation of the previous functions, but adding more work at the end of this assignment).

Besides modifying the previous functions, you will also have to implement the following function:

int chidb_Btree_insertInIndex(BTree *bt, npage_t nroot, key_t keyIdx, key_t keyPk);


text:
# How to report $27 in cap gains affordably I'm preparing my taxes using TurboTax's basic free tier. Last year I had$26.62 in realized long term capital gains. To report this using TurboTax, I need Premier, which is a $54 add on. Even in a fairly low tax bracket, I'm assuming the IRS cares about every cent. Is it possible to file that single form aside from the rest of my return? Do I have to fill out my whole return by hand to avoid this surcharge? How do I keep this trade profitable despite tax season? An annoying conflating factor that applies to me and probably not most people trying to do this: I already sunk$30 or so into the "we retain your filings for you securely" add-on, and I'd like to get some utility out of that money I spent.

• I would argue to not get your taxes prepared by any place that charges you by the form like this... I call this "nickel and diming to death" – Michael Feb 14 '17 at 18:03
• @jamesqf - my tax situation isn't all that complicated (the only unusual thing being that I rent out my former home), yet last year's tax return was over 30 pages including all of the worksheets needed to determine my tax liability. The tax code is way more complicated than it should be. – Johnny Feb 14 '17 at 23:30
• Most folks don't need tax software, just as most folks don't need paid tax preparers (another service offered in the US). But when you figure the value of the time saved, and the reduction in annoyance,. Either option starts to look more attractive. And remember, most of the complexities in US taxes come from opportunities to reduce the amount of tax you pay; if you are willing to give up your deductions and tax breaks --which you can mostly do -- the process becomes much simpler but you pay for that with an increased tax bill. – keshlam Feb 15 '17 at 4:12
• @stanri: It's stayed this way because Intuit has lobbied hard to make sure the tax code remains complicated so that they can keep selling their software. (No, I'm not joking. Look it up.) The government has been trying to fix it but these companies have a different agenda. – user541686 Feb 15 '17 at 4:34
• Maybe just file your normal return. Then, after it's accepted, manually file an amended return that reports that added $26.62. But I have to agree with others saying "Switch service!" I've been happy with TaxAct for a number of years, filing Schedule D with at least dozens of line items, self-employment income and numerous additional schedules, and I don't think it's ever gone up as high as$15. Convenience alone has been worth it. – user2338816 Feb 15 '17 at 11:24

You are not the only one with this problem. When Intuit changed their pricing and services structure in 2015 a lot of people got angry, facing larger fees and having to go through an annoying upgrade just to get the same functionality (such as Schedule D, capital gains).

You have several options:

(1) Forget Turbo Tax and just use paper forms. That is what I do. Paper is reliable.

(2) Use forms mode in Turbo Tax. Of course, that may be even more complicated than simply filing out paper forms.

(3) Use a different service. If your income is below $64,000 the IRS has a free electronic filing service. Other online vendors have full taxes services for less than Turbo Tax. (4) Add the amount to ordinary income. Technically, as long as you report the income, you cannot be penalized, so if you add the capital gain to your ordinary income, then you have paid taxes on the income. Even if they send you a letter, you can send an answer that you added it to ordinary income and that will satisfy them. Of course you pay a higher rate on your$26 if you do that.

(5) If you are in the 15% or below income bracket you are exempt from capital gains, and you can omit it. Don't believe the nervous Nellies who say the IRS will burn your house down if you don't report $26 in capital gains. Penalties are assessed on the percentage of TAXES you did not pay (0.5% penalty per month). Since 0.5% of$0 is $0 your penalty is$0. The IRS knows this. The IRS does not send out assessment letters for $0. (6) Even if you are above the 15% bracket, there is likelihood it is still a no-tax situation (see 5 above). (7) Worst case scenario: you are making a million dollars per year and you omit your$26 capital gains from your return. The IRS will send you an assessment letter for about $10. You can then send them a separate check or money order to pay it. In all honesty I have omitted documented tax items, like taxable interest, that the IRS knows about many times and never gotten an assessment letter. Once I made a serious math error on my return and they sent me an assessment letter, which I just paid, end of story. And that was for a lot more than$26.

The technical verbiage for something like this in IRS lingo is CP-2000, underreported income. As you can see from this official IRS web page, basically what they do is guess how much they think you owe and send you a bill. Then you pay it. If you do so in time, you don't even get a 0.5% interest penalty on your $6.75 owed or whatever it is. (8) Go hog wild. As long as you are risking an assessment on your$26, why not go hog wild and just let the IRS compute all your taxes for you? Make a copy of your income statements, then mail them to the IRS with a letter that says, "Hi, I am Mr. Odinson, my SSN is XXX-XX-XXXX. My address is XYZ. I am unable to compute my taxes due to a confused state of mind. I am hereby requesting a tax assessment for the 2016 tax year." Make sure you sign and date the letter. In all probability they will compute the full assessment and send you a bill (or refund).

• RE: #3 freefilefillableforms for the win! This also applies if your marital income is less than some amount (not sure what the amount is, probably double ~ 128,000) – Peter Turner Feb 14 '17 at 15:57
• Funny you mention it, the price changes actually caused me to start using a CPA now for my taxes – quid Feb 14 '17 at 18:00
• Does #8 open up the taxpayer to adjudication for mental fitness or having a fiduciary appointed? – Freiheit Feb 14 '17 at 21:48
• If it wasn't for regulatory capture and lobbying (way too much money in the tax prep industry for congress to be willing legislate most of it out of existence); #8 done automatically by the IRS using all the data already submitted to them is how it should be done for everyone whose situation isn't complicated by a need to self report addtional income/expenses. – Dan Is Fiddling By Firelight Feb 14 '17 at 22:24
• Don't do #8. CP-2000 is just a courtesy process; the IRS is not obligated to calculate your taxes for you and you could well go to jail instead of getting a CP-2000 notice. The CP-2000 process only exists because it is cheaper for the gov't to calculate your taxes for you than jail you, but from time to time they will make an example of someone. – DepressedDaniel Feb 15 '17 at 3:45

is it possible to file that single form aside from the rest of my return?

Turbotax will generate all the forms necessary to file your return. I recommend you access these forms and file them manually. According to the IRS in order to report capital gains and losses you need to fill out Form 8949 and summarize them on Form 1040 D. Add these two forms to the stack that turbotax generates. Add the total capital gains to line 13 of the Form 1040 which turbotax generated, and adjust the totals on the form accordingly.

• Exactly. TT is a great product, and a victim of its marketing folk trying to differentiate the 3 (main) levels of product variation. – JTP - Apologise to Monica Feb 14 '17 at 13:06
• Does this sort of approach require printing and mailing your entire return, or can you still efile the >90% that turbotax can do and then just mail the two additional forms in? (As an updated/corrected/whatever the IRS calls it return?) – Dan Is Fiddling By Firelight Feb 14 '17 at 14:56
• @DanNeely you can do either one. For the latter you would want to file an amended tax return. – Nosrac Feb 14 '17 at 15:07
• Does this apply to TT online or only desktop version? – stannius Feb 14 '17 at 19:27

Are you sure that TurboTax Deluxe won't be able to handle your capital gains?

When TurboTax removed this functionality from their Deluxe product in 2015 (for tax year 2014), sales plummeted, and they realized that the enormous effective price increase was a colossal mistake. The following year, I understand that they put back all the functionality back into Deluxe, and as far as I know it is still there today.

Deluxe should be able to handle capital gains and Schedule D. Premier offers additional guidance, but if you already know what you want to do, Deluxe should be able to handle everything.

Full disclosure: I was one of the people angered by the TurboTax money grab. I returned my copy of TurboTax that year and purchased H&R Block software instead, which did everything that TurboTax used to do at a lower cost. I still use H&R Block software, and have no desire to go back to TurboTax. It wasn't simply "marketing folk trying to differentiate the 3 (main) levels of product" as @JoeTaxpayer said in a comment, it was the management trying to take advantage of their high market share by steeply raising the prices. They underestimated their customers.

You mention not wanting to lose the value of the $30 you spent on a feature with TurboTax. I'm not sure which feature/add-on you are talking about, but does it make sense to overpay for something annually just to avoid losing$30 in the past?

• Could you pls share your thoughts on why you don't plan to go back to TurboTax (after they restore the pricing scheme)? – xiaomy Feb 14 '17 at 15:36
• @max8126 Price. H&R Block Deluxe is $30, TurboTax Deluxe is$40. I also distrust TurboTax now. I was satisfied with TurboTax before this incident, and had no reason to try something new. TurboTax then gave me a reason, and I found that, for me, the H&R Block software was just as good. Now I have no reason to switch back. – Ben Miller - Remember Monica Feb 14 '17 at 15:44
• FYI these companies routinely discount their products during the late fall/early winter period (several years in a row, at least). Look into buying them early for additional savings. – JBC Feb 14 '17 at 18:53

You could also switch to CreditKarma to file taxes, it's 100% free and just launched. I'm not affiliated with them, just bringing up an alternative.

• I used them for the first time this year, they worked fairly well. Typically I use my dad's H&R block software since he pays for some premium package and ends out with like 4 more free federal files, but didn't have any problems filing with CreditKarma. – Wayne Werner Feb 16 '17 at 13:41

Fill out the form manually, using last year's return as an example of how to report these gains.

Or experiment with one of the low-priced tax programs; I've been told that they are available for as little as \$17, and if your alternative is doing it manually, spending a bit of time checking their results isn't a huge problem.

Or run the basic TTax, and tell it to add the appropriate forms manually. It supports them, it just doesn't have the interview sections to handle them. (@DanielCarson's answer has more details about that.)

Or...


text:
# tkz-fct not creating a visible graph

I've tried copying a pasting code from various examples posted on this site and in the manual, but the graph is never visible.

MWE: (this MWE comes from this posting)

\documentclass[tikz]{standalone}
\usepackage{tkz-fct}
\begin{document}
\begin{tikzpicture}[>=stealth']
\tkzInit[xmin=-15,xmax=25,
ymin=-250,ymax=50,
xstep=5,ystep=50]
\tkzAxeXY
\tkzFct[domain=-10:24,color=blue,very thick,<->]{14*\x-\x**2}
\end{tikzpicture}
\end{document}

Results in:

• A couple notes that may be relevant are: I'm running TeXLive on Mac OSx. I'm compiling the documentn using pdflatex. – A.Ellett Nov 8 '14 at 6:14
• I get the curve with latest miktex. – user11232 Nov 8 '14 at 10:12

Note that tkz-fct doesn't do the calculations itself, they are performed by Gnuplot (I think). Therefore, you need to allow for execution of external programs by compiling with the --shell-escape flag, i.e.

pdflatex -shell-escape filename.tex

There will be a warning in the log related to this, saying something like

Package pgf Warning: Plot data file filename.tkzfct.table' not found. on input line 32.

The .table file is generated by Gnuplot, and read by tkz-fct, without -shell-escape you wont get that file.

• T I'd like to thank you for this posted answer. – David Jan 10 '17 at 2:04
• I just tried the latest version of MikTeX and it still doesn't work, I get the same result as the one shown in the question. I tried to add --enable-write18 as indicated in the manual, but it didn't work. I installed gnuplot on Windows 10 and the environment variable is correct. What's going on? – AndréC Oct 23 '18 at 9:26
• @AndréC Don't know. Did you try with shell-escape? Did you look in the log file for any info? Ask a new question if you can't figure it out. – Torbjørn T. Oct 23 '18 at 9:29
• Yes, I tried your solution, without any effect on me. I have this error: Package pgf Warning: Plot data file test-media.tkzfct.table not found. on input line 9` – AndréC Oct 23 '18 at 9:40
• @AndréC That message indicates Gnuplot wasn't run. But as I said, ask a new question and describe in detail your setup, I don't know what's wrong. – Torbjørn T. Oct 23 '18 at 10:06


text:
# “debconf: DbDriver ”config“: config.dat is locked by another process: Resource temporarily unavailable” while installing packages

I'm trying to install the flashybrid but when I enter the apt-get install flashybrid command, I get this error:

root@user-desktop:/home/user# apt-get install flashybrid
Building dependency tree
0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
1 not fully installed or removed.
After this operation, 0B of additional disk space will be used.
debconf: DbDriver "config": /var/cache/debconf/config.dat is locked by another process: Resource temporarily unavailable
Setting up flashybrid (0.15+nmu2) ...
debconf: DbDriver "config": /var/cache/debconf/config.dat is locked by another process: Resource temporarily unavailable
dpkg: error processing flashybrid (--configure):
subprocess post-installation script returned error exit status 1
Errors were encountered while processing:
flashybrid
E: Sub-process /usr/bin/dpkg returned an error code (1)

I also tried this one to find what is already using that file:

root@user-desktop:/home/user# lsof | grep /var/cache/debconf/config.dat
lsof: WARNING: can't stat() fuse.gvfs-fuse-daemon file system /home/user/.gvfs
Output information may be incomplete.
dpkg-prec 4672 root 4rW REG 8,6 39658 1697827 /var/cache/debconf/config.dat

But I don't know how to fix this.

A solution to this locking problem is detailed in the wiki:

sudo fuser -v /var/cache/debconf/config.dat

Will show you what process is holding the lock:

                     USER        PID ACCESS COMMAND
/var/cache/debconf/config.dat:
root      18210 F.... dpkg-preconfigu

Then you simply need to note down the PID and kill it like so:

sudo kill PID
sudo kill -9 PID  # if the first doesn't work

• This worked fantastic! Thanks a bunch, I didn't know of 'fuser'! – stolsvik Jan 1 '14 at 22:54
• fuser also has the option switch -k which kills the found process that uses the file: sudo fuser -v -k /var/cache/debconf/config.dat will kill the process right away – minni Dec 30 '15 at 9:30
• This should be selected as best answer. – Arda Jan 19 '18 at 21:12
• Worked for me when an upgrade of Debian from Stretch to Buster accidentally got backgrounded. – Adambean Jul 19 '19 at 18:57
• That worked perfectly thanks. However the process holds the file after restart and I have to manually kill it after every restart. Do you have any idea? – Zura Sekhniashvili Aug 24 at 4:30

This is more simple one command

I just did:

sudo rm /var/cache/debconf/*.dat

After I run the apt-get install and (I was installing curl ) and it worked restored the db manual pages.

PS: I removed something with apt-get and I did have this problem. but solved it simply thank also to @rajagenupula

• I used sudo \rm -v /var/cache/debconf/*.dat – Mark Stewart Jan 27 '14 at 5:03
• Don't do this... you can break your system. The correct answer is to use fuser and kill the process, as in Sayed's answer below. – Ryan P Oct 29 '15 at 18:30
• Um, don't delete that. Just kill the process that has the file opened. – jorfus Dec 16 '16 at 18:10
• This could be very dangerous thing to do. Please see answer below – BlackDivine Jan 27 '17 at 9:34
• In my case, there was no process running, so this was the only solution that worked. – MacroMan Aug 17 '18 at 8:13
2. open the location /var/cache/debconf with gksu nautilus
3. move (cut/paste) all of the .dat files to another backup directory.
4. attempt to run again (will likely get a different error)
5. restore those dat files from the aforementioned backup directory.
6. Then try again what you were doing (should work this time).
• I had a similar problem and I just deleted all the files in the debconf folder. – Agmenor Jul 16 '12 at 22:25
• i said there at point 3 . a file with password and with extension . ok now what you're getting . could you post them here by pastebin link . – rɑːdʒɑ Jul 17 '12 at 13:07
• same problem, I needed to remove all files in debconf folder – equivalent8 Nov 7 '12 at 12:11
• i am still getting the error debconf: apt-extracttemplates failed: No such file or directory apt-extracttemplates failed – user106405 Nov 8 '12 at 9:20
• I was getting really quite angry, saw your answer and suddenly there's progress. Thank you. – David Carboni Feb 1 '14 at 11:00
sudo fuser -vik /var/cache/debconf/config.dat

Will display the process and ask if you want to terminate it.

• thanks its help me.. :D – Budi Mulyo Aug 26 '19 at 8:16

There is a process still running blocking debconf. You should wait until it finish. Or find out how it got stuck, since in some cases it will be try to ask you a question but since you don't have a scope to see it. If you wait sometime and things doesn't change you may need to reboot the system, or terminate the process.

sudo kill 4672

or

sudo killall dpkg-prec

This will assure you that your database will not get damaged and will prevent further problems.

• I think it's pointless to use process id 4672 in your first command example. Somebody might use that command as-is, but it won't work as he would very likely have a different process id. – user205301 Nov 6 '13 at 16:56
• @user205301 that's what the second is for ;) – Braiam Nov 6 '13 at 17:09

If you are getting this error in an unattended script, make sure you don't accidentally have two consecutive apt-get upgrade or apt-get dist-upgrade commands running, otherwise the second command can fail to obtain the lock and thus end up in this error.


text:
# BioSKIN&COAT® Formula

Your pet's diet is the foundation of good health. Adding BioSkin&Coat® to a healthy/balanced diet helps support your pet’s healthy physiological processes, sustaining their overall health and well-being.

• For dogs and cats of any age with food intolerances
• Soothes skin-related issues associated with seasonal allergies
• Helps maintain a normal inflammatory response
• Supports skin and coat health
• With defatted seed blend for optimal absorption

## Suggested Use

DIRECTIONS FOR USE: Administer Orally. Half scoop (2.5 g) per 10 lb of body weight to a maximum of 6 scoops daily. Best when taken on a full stomach.

5 g scoop included (equals 1 Tbsp).

## Ingredients

ACTIVE INGREDIENTS PER SCOOP 5 G:
Methylsulphonylmethane (MSM) . . . . . . . . . . . . . . . . . . . 125 mg
Quercetin dihydrate (Styphnolobium japonicum). . . . . . . 100 mg
Grape seed extract 40:1 (Vitis vinifera) . . . . . . . . . . . . . . . . 45 mg
Bromelain (Ananas comosus, stem) . . . . . . . . . . . . . . . . . 72 GDU
L-Cystine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 mg
D-alpha tocopheryl succinate (Vitamin E) . . . . (30 IU) 20 mg AT
Pancrelipase (porcine, pancreas). . . . . . . . . . . . . . . . . . . . . 20 mg

Standardized to contain:
Lipase (8 USP/mg) equivalent to: 160 USP
Amylase (100 USP/mg) equivalent to: 2 000 USP
Protease (100 USP/mg) equivalent to: 2 000 USP
Papain (Carica papaya, fruit) . . . . . . . . . . . . . . . . . . . . 30,000 USP
Protease (Aspergillus oryzæ). . . . . . . . . . . . . . . . . . . . . . . . . 50 HUT
Zinc (Zinc gluconate). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 mcg
Biotin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 mcg

INACTIVE INGREDIENTS: Ascorbic acid, organic seed blend defatted meal (flax, sunflower, sesame), silicon dioxide.


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# How do you use implicit differentiation to find an equation of the tangent line to the curve at the given point ysin16x=xcos2y and (pi/2, pi/4)?

May 4, 2017

$y + 4 x = \frac{9 \pi}{4}$

#### Explanation:

The slope of the tangent to any curve is given by $\frac{\mathrm{dy}}{\mathrm{dx}}$

As $y \sin 16 x = x \cos 2 y$ and differentiating

$\frac{\mathrm{dy}}{\mathrm{dx}} \sin 16 x + 16 y \cos 16 x = 1 \times \cos 2 y - 2 x \sin 2 y \frac{\mathrm{dy}}{\mathrm{dx}}$

or $\frac{\mathrm{dy}}{\mathrm{dx}} \sin 16 x + 2 x \sin 2 y \frac{\mathrm{dy}}{\mathrm{dx}} = \cos 2 y - 16 y \cos 16 x$

or $\frac{\mathrm{dy}}{\mathrm{dx}} = \frac{\cos 2 y - 16 y \cos 16 x}{\sin 16 x + 2 x \sin 2 y}$

Hence at $\left(\frac{\pi}{2} , \frac{\pi}{4}\right)$ slope is

$\frac{\cos 2 \left(\frac{\pi}{4}\right) - 16 \left(\frac{\pi}{4}\right) \cos \left(4 \pi\right)}{\sin \left(8 \pi\right) + 2 \frac{\pi}{2} \sin \left(\frac{\pi}{2}\right)}$

= $\frac{0 - 4 \pi}{0 + \pi} = - 4$

As tangent is at $\left(\frac{\pi}{2} , \frac{\pi}{4}\right)$, its equation is

$\left(y - \frac{\pi}{4}\right) = - 4 \left(x - \frac{\pi}{2}\right)$ or $y + 4 x = \frac{9 \pi}{4}$


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Timezone: »

Poster
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
Jiaxi Gu · Xiaojun Meng · Guansong Lu · Lu Hou · Niu Minzhe · Xiaodan Liang · Lewei Yao · Runhui Huang · Wei Zhang · Xin Jiang · Chunjing XU · Hang Xu

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #1013
Vision-Language Pre-training (VLP) models have shown remarkable performance on various downstream tasks. Their success heavily relies on the scale of pre-trained cross-modal datasets. However, the lack of large-scale datasets and benchmarks in Chinese hinders the development of Chinese VLP models and broader multilingual applications. In this work, we release a large-scale Chinese cross-modal dataset named Wukong, which contains 100 million Chinese image-text pairs collected from the web. Wukong aims to benchmark different multi-modal pre-training methods to facilitate the VLP research and community development. Furthermore, we release a group of models pre-trained with various image encoders (ViT-B/ViT-L/SwinT) and also apply advanced pre-training techniques into VLP such as locked-image text tuning, token-wise similarity in contrastive learning, and reduced-token interaction. Extensive experiments and a benchmarking of different downstream tasks including a new largest human-verified image-text test dataset are also provided. Experiments show that Wukong can serve as a promising Chinese pre-training dataset and benchmark for different cross-modal learning methods. For the zero-shot image classification task on 10 datasets, $Wukong_\text{ViT-L}$ achieves an average accuracy of 73.03%. For the image-text retrieval task, it achieves a mean recall of 71.6% on AIC-ICC which is 12.9% higher than WenLan 2.0. Also, our Wukong models are benchmarked on downstream tasks with other variants on multiple datasets, e.g., Flickr8K-CN, Flickr-30K-CN, COCO-CN, et al. More information can be referred to https://wukong-dataset.github.io/wukong-dataset/.


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## A Holder inequality for the norms of Poissonian Wick products

### Time

May 29 2012 - 2:30pm - 3:25 pm

MA 105

Aurel Stan (OSU)

### Abstract

We define first the Poissonian Wick product using the Charlier polynomials. We find then other representations of the Poisonian Wick product. Using these representations, we prove some Holder type inequalities concerning the $L^p$ norms of Poissonian Wick products. These inequalities are sharp when p equals 1, 2, or infinity.
Last updated by lang.162 on 05/02/12


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# IAS HET Seminar | Aaron Hillman, Princeton University| “Stringy Completions of the Standard Model from the Bottom Up” | Wolfensohn Hall & Zoom

Date
Feb 17, 2023, 2:30 pm4:00 pm
Location
Wolfensohn Hall & Zoom

## Details

Event Description

We study a class of tree-level ansatzes for 2→2 scalar and gauge boson amplitudes inspired by stringy UV completions. These amplitudes manifest Regge boundedness and are exponentially soft for fixed-angle high energy scattering, but unitarity in the form of positive expandability of massive residues is a nontrivial consistency condition.  In particular, unitarity forces these ansatzes to include graviton exchange.  In the context of gauge boson scattering, we consider gauge groups SO(N) and SU(N).  In four dimensions, the bound on the rank of the gauge group is 24 for both groups, and occurs at the maximum allowed value of the gauge coupling:  g_YM^2 = 2 M_s^2/M_P^2. In integer dimensions 5 ≤ D ≤ 10 , we find evidence that the maximum allowed rank r of the gauge group agrees with the swampland conjecture r ≤ 26-D.  The bound is surprisingly identical for both SO(N) and SU(N) in integer spacetime dimensions. We also study the electroweak sector of the standard model via 2→2 Higgs scattering and find interesting constraints relating standard model couplings, the putative string scale, and the Planck scale.

https://theias.zoom.us/j/84232389159?pwd=UAWukoEv3RMKL8ssHZ2nUfIwggISU7.1


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# Difference between revisions of "2012 AMC 10B Problems/Problem 2"

## Problem

A circle of radius 5 is inscribed in a rectangle as shown. The ratio of the length of the rectangle to its width is 2:1. What is the area of the rectangle?

$\textbf{(A)}\ 50\qquad\textbf{(B)}\ 100\qquad\textbf{(C)}\ 125\qquad\textbf{(D)}\ 150\qquad\textbf{(E)}\ 200$

## Solution

Note that the diameter of the circle is equal to the shorter side of the rectangle. Since the radius is $5$, the diameter is $2\cdot 5 = 10$. Since the sides of the rectangle are in a $2:1$ ratio, the longer side has length $2\cdot 10 = 20$. Therefore the area is $20\cdot 10 = 200$ or $\boxed{E}$.


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# What is the frequency of light with a wavelength of 290 nm?

## What is the frequency of light with a wavelength of 290 nm?

THE ELECTROMAGNETIC SPECTRUMRegionwavelengthfrequencyUltraviolet light UV100 nm3*1017 Hz100 nm3*1017 Hz290 nm1.03*1017 Hz

7.5*1014 Hz

14 Hz

## What is the frequency of radiation of wavelength 300 nm?

6. 63xd710u221228J

789 THz

7.5*1014 Hz

63xd710u221219J.

## What is the energy of a photon with a wavelength of 300 nm?

Thus, energy of a photon of radiation having wavelength ${text{300 nm}}$ is $6.63 times {10^{ – 19}}{text{ J}}$.

## What is the energy of the electromagnetic radiation with wavelength 300 nm?

When electromagnetic radiation of wavelength 300 nm falls on the surface of sodium, electrons are emitted with a kinetic energy of 1.68xd710 Jmolu22121. .

## What is the energy of a neutron having wavelength 300 nm?

6. 63xd710u221228J

7.5 xd7 1014 Hz

## How do you calculate the frequency of blue light that has a wavelength of 400 nm?

From the previous problem, the energy of a single 400 nm photon is 3.1 eV

## What is the energy of photon of light whose wavelength is 400 nm?

The wavelength of an electromagnetic radiation is 400 nm. Thus, the wave number will be equal to. (1) 2.5 x 104 cm-1

## What is the energy value of ultraviolet radiation with a wavelength of 300 nm?

When electromagnetic radiation of wavelength 300 nm falls on the surface of sodium, electrons are emitted with a kinetic energy of 1.68xd710 Jmolu22121. .

## What is the frequency of radiation of wavelength 600 nm?

Thus, energy of a photon of radiation having wavelength ${text{300 nm}}$ is $6.63 times {10^{ – 19}}{text{ J}}$.

## What is the energy in J of light that has a wavelength of 450 nm?

u21d2Ehcu03bb6.62xd710u221234xd73xd7108450xd710u221294.4xd710u221219 J

## What is the frequency in Hz of blue light with a wavelength of 450 nm?

Usually, blue light is one kind of visible spectrum which wavelength is about 450nm-500nm frequency is about 610THZ-670THz.

6xd710-4 Hz

## What is the frequency of a 450 nm wavelength?

The frequency of blue light that has a wavelength of 450 nm is 6.7xd71014 6.7 xd7 10 14 Hz

## How do you find the frequency of a wavelength in NM?

– The energy of the electromagnetic radiation with a wavelength of 420 nm is 4.73xd710u221219 J or 2.95 ev (electron volt) .

## What is the energy of a photon with a wavelength of 420 nm?

Usually, blue light is one kind of visible spectrum which wavelength is about 450nm-500nm frequency is about 610THZ-670THz.

## What is the energy of a photon in a beam of infrared radiation of wavelength 1240 nm?

6. 63xd710u221228J

## What is the energy of a 300 nm ultraviolet photon?

When electromagnetic radiation of wavelength 300 nm falls on the surface of sodium, electrons are emitted with a kinetic energy of 1.68xd710 Jmolu22121. .

## What is the energy of a photon with a wavelength of 250 nm?

energy in one 300nm photon 6.63 x 10-19 Joules

## When a light wavelength of 300 nm falls on the photoelectric?

The energy of a photon varies directly with its frequency and inversely with its wavelength. So an Einstein of light of the wavelength 250 nm contains exactly twice the energy of an Einstein of light of 500 nm. Each photon (each quantum) of 250 nm has twice the energy of a 500 nm photon.