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Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. [ABSTRACT FROM AUTHOR]

Objective: Detection of vascular endothelial growth factor (VEGF) levels in ocular tissue may perhaps provide insight into the role of VEGF in the pathogenesis and progression of diabetic retinopathy (DR). The aim of this study was to evaluate the levels of VEGF in tears and serum amongst type 2 diabetes mellitus (DM) patients. Methods: A comparative cross-sectional study was conducted between August 2016 and May 2018 involving type 2 DM patients with no DR, non-proliferative DR (NPDR), and proliferative DR (PDR). Tear samples were collected using no.41 Whatman filter paper (Schirmer strips) and 5 mL blood samples were drawn by venous puncture. VEGF levels in tears and serum were measured by enzyme-linked immunosorbent assay. Results: A total of 88 type 2 DM patients (no DR: 30 patients, NPDR: 28 patients, PDR: 30 patients) were included in the study. Mean tear VEGF levels were significantly higher in the NPDR and PDR groups (114.4 SD 52.5 pg/mL and 150.8 SD 49.7 pg/mL, respectively) compared to the no DR group (40.4 SD 26.5 pg/mL, p < 0.001). There was no significant difference in the mean serum VEGF levels between the three groups. There was a fair correlation between serum and tear VEGF levels (p = 0.015, r = 0.263). Conclusion: VEGF levels in tears were significantly higher amongst diabetic patients with DR compared to those without DR and were significantly associated with the severity of DR. There was a fair correlation between serum and tear VEGF levels. Detection of VEGF in tears is a good non-invasive predictor test for the severity of DR. A large cohort study is needed for further evaluation. [ABSTRACT FROM AUTHOR]

International collaboration is becoming increasingly important for the advancement of science. To gain a more precise understanding of how factors such as international collaboration influence publication success, we divide publication success into two categories: journal placement and citation performance. Analyzing all papers published between 1996 and 2012 in eight disciplines, we find that those with more countries in their affiliations performed better in both categories. Furthermore, specific countries vary in their effects both individually and in combination. Finally, we look at the relationship between national output (in papers published) and input (in citations received) over the 17 years, expanding upon prior depictions by also plotting an expected proportion of citations based on Journal Placement. Discrepancies between this expectation and the realized proportion of citations illuminate trends in performance, such as the decline of the Global North in response to rapidly developing countries, especially China. Yet, most countries' show little to no discrepancy, meaning that, in most cases, citation proportion can be predicted by Journal Placement alone. This reveals an extreme asymmetry between the opinions of a few reviewers and the degree to which paper acceptance and citation rates influence career advancement. [ABSTRACT FROM AUTHOR]

The soil–water characteristic curve (SWCC) is an important parameter of unsaturated soil, and almost all the engineering characteristics of unsaturated soil are more or less related to the SWCC. The SWCC contains important information for geotechnical engineering, water engineering, hydrogeology modelling and climate modelling. It is noted that the experimental measurement of SWCC is costly and time consuming, which limits the implementation of principles of unsaturated soil mechanics in practical engineering. The indirect method, which estimates the SWCC from the index properties of soil, can provide the SWCC with the errors which are within tolerance in practical engineering. In addition, the indirect method can determine SWCC very fast and almost with no cost. In this paper, the domestic sandy soils are selected and the index properties of those sands are used to correlate the SWCC fitting parameters. Consequently, mathematical equations are proposed to estimate SWCC from index properties of domestic sands. The proposed models are trained from 44 sets of experimental data and verified with another independent 8 sets of experimental data from published literature. It is observed that the results from the proposed model agree well with the experimental data from literature. [ABSTRACT FROM AUTHOR]

Based on the energy consumption data of the Changjiang River Administration of Navigation Affairs public building located in Wuhan, China, this paper analyzes the energy consumption characteristics of public buildings. Using the DesignBuilder energy simulation software (v7.0.0.096), various factors influencing building energy consumption are investigated. Furthermore, SPSS (v26.0.0.0) is employed to conduct correlation analyses between measured and simulated energy consumption data. The results indicate that the most significant factors affecting energy consumption are outdoor temperature, office equipment usage time, anticipated room temperature settings, boiler usage time, and per capita daily water consumption. By exploring the factors influencing energy consumption in public buildings, this study contributes novel insights into setting targets, adjusting schemes, and achieving energy-saving effects. [ABSTRACT FROM AUTHOR]

Reliable quantification in biological systems of endogenous low- and high-molecular substances, drugs and their metabolites, is of particular importance in diagnosis and therapy, and in basic and clinical research. The analytical characteristics of analytical approaches have many differences, including in core features such as accuracy, precision, specificity, and limits of detection (LOD) and quantitation (LOQ). Several different mathematic approaches were developed and used for the comparison of two analytical methods applied to the same chemical compound in the same biological sample. Generally, comparisons of results obtained by two analytical methods yields different quantitative results. Yet, which mathematical approach gives the most reliable results? Which mathematical approach is best suited to demonstrate agreement between the methods, or the superiority of an analytical method A over analytical method B? The simplest and most frequently used method of comparison is the linear regression analysis of data observed by method A (y) and the data observed by method B (x): y = α + βx. In 1986, Bland and Altman indicated that linear regression analysis, notably the use of the correlation coefficient, is inappropriate for method-comparison. Instead, Bland and Altman have suggested an alternative approach, which is generally known as the Bland–Altman approach. Originally, this method of comparison was applied in medicine, for instance, to measure blood pressure by two devices. The Bland–Altman approach was rapidly adapted in analytical chemistry and in clinical chemistry. To date, the approach suggested by Bland–Altman approach is one of the most widely used mathematical approaches for method-comparison. With about 37,000 citations, the original paper published in the journal The Lancet in 1986 is among the most frequently cited scientific papers in this area to date. Nevertheless, the Bland–Altman approach has not been really set on a quantitative basis. No criteria have been proposed thus far, in which the Bland–Altman approach can form the basis on which analytical agreement or the better analytical method can be demonstrated. In this article, the Bland–Altman approach is re-valuated from a quantitative bioanalytical perspective, and an attempt is made to propose acceptance criteria. For this purpose, different analytical methods were compared with Gold Standard analytical methods based on mass spectrometry (MS) and tandem mass spectrometry (MS/MS), i.e., GC-MS, GC-MS/MS, LC-MS and LC-MS/MS. Other chromatographic and non-chromatographic methods were also considered. The results for several different endogenous substances, including nitrate, anandamide, homoarginine, creatinine and malondialdehyde in human plasma, serum and urine are discussed. In addition to the Bland–Altman approach, linear regression analysis and the Oldham–Eksborg method-comparison approaches were used and compared. Special emphasis was given to the relation of difference and mean in the Bland–Altman approach. Currently available guidelines for method validation were also considered. Acceptance criteria for method agreement were proposed, including the slope and correlation coefficient in linear regression, and the coefficient of variation for the percentage difference in the Bland–Altman and Oldham–Eksborg approaches. [ABSTRACT FROM AUTHOR]

Despite their recognized limitations, bibliometric assessments of scientific productivity have been widely adopted. We describe here an improved method to quantify the influence of a research article by making novel use of its co-citation network to field-normalize the number of citations it has received. Article citation rates are divided by an expected citation rate that is derived from performance of articles in the same field and benchmarked to a peer comparison group. The resulting Relative Citation Ratio is article level and field independent and provides an alternative to the invalid practice of using journal impact factors to identify influential papers. To illustrate one application of our method, we analyzed 88,835 articles published between 2003 and 2010 and found that the National Institutes of Health awardees who authored those papers occupy relatively stable positions of influence across all disciplines. We demonstrate that the values generated by this method strongly correlate with the opinions of subject matter experts in biomedical research and suggest that the same approach should be generally applicable to articles published in all areas of science. A beta version of iCite, our web tool for calculating Relative Citation Ratios of articles listed in PubMed, is available at . [ABSTRACT FROM AUTHOR]

This paper aims to demonstrate the impact of coal enterprises’ vertical integration on the performance of innovation corporate social responsibility (ICSR) and to elaborate its specific transmission path through financing structure and market power. This paper takes the data of A-share listed coal company from 2008 to 2017 as samples, uses input-output table method to measure the degree of vertical integration, and explores the relationship between the four factors through multiple linear regression and Bootstrap method. We found that: (1) the vertical integration of coal enterprises has positive incentives for the promotion of ICSR. (2) Financing structure and market power play a chain intermediary role in this process of incentive. (3) In areas with high marketization process, vertical integration has more significant incentive effect on ICSR. The paper extends the research on the relationship between vertical integration and innovation, which provides a reference for the improvement of China’s supply-side reform and corporate social responsibility (CSR) fulfillment policy. It is helpful to promote the sustainable development of the coal industry, stimulate the innovation vitality of enterprises, and improve the fulfillment of CSR. [ABSTRACT FROM AUTHOR]

Тhe mаin rеаsоn for the еxchаnge rate sеnsitivity is the dоubts in its crеdibility rеlаtеd to the trаde, wеаknesses in the finаnciаl sеctоr, еxternаl shоcks, pоlitical errоrs, cоuntry risk, еtc. Тhis paper discuss about potential currency crisis in the Republic of N. Macedonia for the period before pandemic and give the answer to what kind of lessons should we take into account regarding past period which is measured in the research. The paper presents the results of a possible crisis scenario in N. Macedonia and give statistical analysis of the relationship of the indicators of a currency crisis, examining two questions - are disorders in Macedonia that can cause a currency crisis and which generation currency crises would be the closest for Macedonia. [ABSTRACT FROM AUTHOR]

Using matching and regression analyses, we measure the difference in citations between articles posted to Academia.edu and other articles from similar journals, controlling for field, impact factor, and other variables. Based on a sample size of 31,216 papers, we find that a paper in a median impact factor journal uploaded to Academia.edu receives 16% more citations after one year than a similar article not available online, 51% more citations after three years, and 69% after five years. We also found that articles also posted to Academia.edu had 58% more citations than articles only posted to other online venues, such as personal and departmental home pages, after five years. [ABSTRACT FROM AUTHOR]

The focus of this paper is to conduct research on MHD Casson-Williamson nanofluids flow over an inclined plate with the influence of thermal radiation and Soret-Dufour mechanisms using spectral collocation and statistical analysis. The simultaneous flow of Casson-Williamson non-Newtonian fluids in the presence of a magnetic field, Joule heating, chemical reaction, and viscous dissipation is modelled by utilizing partial differential equations (PDEs). These equations were transformed into coupled nonlinear ordinary differential equations by using an appropriate similarity variable. A numerical and statistical analysis was performed on the transformed equations to obtain significant effects of flow parameters on velocity, temperature, and concentration distributions. An increase in the value of the thermal radiation is observed to increase the fluid temperature, thermal condition, and thermal boundary layer thickness. An increased value of magnetic parameter was observed to decrease the fluid velocity. Due to a magnetic field and the dynamic plastic viscosity, the hydrodynamic boundary layer was observed to decline due to an increase in Casson parameter. The present study was compared with studies in the literature and found in good agreement. [ABSTRACT FROM AUTHOR]

Soil is one of the most complex systems on Earth, functioning at the interface between the lithosphere, biosphere, hydrosphere, and atmosphere and generating a multitude of functions. Moreover, soil constitutes the belowground environment from which plants capture water and nutrients. Despite their great importance, soil properties are often not sufficiently considered in other disciplines, especially in spatial studies of plant distributions. Most soil properties are available as point data and, to be used in spatial analyses, need to be generalised over entire regions (i.e. digital soil mapping). Three categories of statistical approaches can be used for such purpose: geostatistical approaches (GSA), predictive-statistical approaches (PSA), and hybrid approaches (HA) that combine the two previous ones. How then to choose the best approach in a given soil study context? Does it depend on the soil properties to be spatialized, the study area’s characteristics, and/or the availability of soil data? The main aims of this study was to review the use of these three approaches to derive maps of soil properties in relation to the soil parameters, the study area characteristics, and the number of soil samples. We evidenced that the approaches that tend to show the best performance for spatializing soil properties were not necessarily the ones most used in practice. Although PSA was the most widely used, it tended to be outperformed by HA in many cases, but the latter was far less used. However, as the study settings were not always properly described and not all situations were represented in the set of papers analysed, more comparative studies would be needed across a wider range of regions, soil properties, and spatial scales to provide robust conclusions on the best spatialization methods in a specific context. [ABSTRACT FROM AUTHOR]

In the field of forest pest and disease research, researchers have combined the experience and data accumulated over many years and conducted long-term and systematic observations of the research object; they have used regression analysis to determine the factors that affect the occurrence of pests and diseases. This traditional approach is time-consuming and highly dependent on expert experience. In this paper, we propose a multicombination multivariable linear regression model to quantitatively describe the multiple linear combinations of relationship between multiple independent variables and a single dependent variable. Based on this model and a data flow model combined with statistical principles and visualization techniques, we propose a multicombination multivariable linear regression visual analysis method to assist researchers in quickly assessing the correlations between the disease indexes of forest diseases and pests and the factors that may affect the pest and disease occurrences. Based on this approach, a multicombination multivariable linear regression visual analysis system was designed and implemented, and the cases of a given forest pest and disease data set were analyzed. It is shown that the multicombination multivariable linear regression visual analysis method can effectively assist researchers in quickly understanding pest and disease data, determining impact factors, and finding relevant laws. [ABSTRACT FROM AUTHOR]

Peer-reviewed publications focusing on climate change are growing exponentially with the consequence that the uptake and influence of individual papers varies greatly. Here, we derive metrics of narrativity from psychology and literary theory, and use these metrics to test the hypothesis that more narrative climate change writing is more likely to be influential, using citation frequency as a proxy for influence. From a sample of 732 scientific abstracts drawn from the climate change literature, we find that articles with more narrative abstracts are cited more often. This effect is closely associated with journal identity: higher-impact journals tend to feature more narrative articles, and these articles tend to be cited more often. These results suggest that writing in a more narrative style increases the uptake and influence of articles in climate literature, and perhaps in scientific literature more broadly. [ABSTRACT FROM AUTHOR]

This paper proposes a mixture regression model-based method for drug sensitivity prediction. The proposed method explicitly addresses two fundamental issues in drug sensitivity prediction, namely, population heterogeneity and feature selection pertaining to each of the subpopulations. The mixture regression model is estimated using the imputation-conditional consistency algorithm, and the resulting estimator is consistent. This paper also proposes an average-BIC criterion for determining the number of components for the mixture regression model. The proposed method is applied to the CCLE dataset, and the numerical results indicate that the proposed method can make a drastic improvement over the existing ones, such as random forest, support vector regression, and regularized linear regression, in both drug sensitivity prediction and feature selection. The p-values for the comparisons in drug sensitivity prediction can reach the order O(10−8) or lower for the drugs with heterogeneous populations. [ABSTRACT FROM AUTHOR]

In clinical neuropsychology the cognitive abilities of neurological patients are commonly estimated using well-established paper-based tests. Typically, scores on some tests remain relatively well preserved, whilst others exhibit a significant and disproportionate decline. Scores on those tests that measure preserved cognitive functions (so-called ‘hold’ tests) may be used to estimate premorbid abilities, including scores in non-hold tests that would have been expected prior to the onset of cognitive impairment. Many hold tests entail word reading, with each word being graded as correctly or incorrectly pronounced. Inevitably, such tests are likely to contain words that provide little or no diagnostic power (i.e., can be eliminated without negatively affecting prediction accuracy). In this paper, a genetic algorithm is developed and demonstrated, using n = 92 neurologically healthy participants, to identify optimal word subsets from the National Adult Reading Test that minimize the mean error in predicting the most widely used clinical measure of IQ and cognitive ability, the Wechsler Adult Intelligence Scale Fourth Edition IQ. In addition to requiring only 17–20 of the original 50 words (suggesting that this test could be revised to be up to 66% shorter) and minimizing mean prediction error, the algorithm increases the proportion of the variance in the predicted variable explained in comparison to using all words (from r2 = 0.46 to r2 = 0.61). In a clinical setting this would improve estimates of premorbid cognitive function and, if an abbreviated revision to this test were to be adopted, reduce the arduousness of the test for patients. The proposed method is evaluated with jackknifing and leave one out cross validation. The general approach may be used to optimize the relationship between any two psychological tests by finding the question subset in one test that minimizes the prediction error in a second test by training the genetic algorithm using data collected from participants upon whom both tests have been administered. This approach may also be used to develop new predictive tests, since it provides a method to identify an optimal subset of a set of candidate questions (for which empirical data have been collected) that maximizes prediction accuracy and the proportion of variance in the predicted variable that can be explained. [ABSTRACT FROM AUTHOR]

Penalized selection criteria like AIC or BIC are among the most popular methods for variable selection. Their theoretical properties have been studied intensively and are well understood, but making use of them in case of high-dimensional data is difficult due to the non-convex optimization problem induced by L0 penalties. In this paper we introduce an adaptive ridge procedure (AR), where iteratively weighted ridge problems are solved whose weights are updated in such a way that the procedure converges towards selection with L0 penalties. After introducing AR its specific shrinkage properties are studied in the particular case of orthogonal linear regression. Based on extensive simulations for the non-orthogonal case as well as for Poisson regression the performance of AR is studied and compared with SCAD and adaptive LASSO. Furthermore an efficient implementation of AR in the context of least-squares segmentation is presented. The paper ends with an illustrative example of applying AR to analyze GWAS data. [ABSTRACT FROM AUTHOR]

In general, dirty banknotes that have creases or soiled surfaces should be replaced by new banknotes, whereas clean banknotes should be recirculated. Therefore, the accurate classification of banknote fitness when sorting paper currency is an important and challenging task. Most previous research has focused on sensors that used visible, infrared, and ultraviolet light. Furthermore, there was little previous research on the fitness classification for Indian paper currency. Therefore, we propose a new method for classifying the fitness of Indian banknotes, with a one-dimensional line image sensor that uses only visible light. The fitness of banknotes is usually determined by various factors such as soiling, creases, and tears, etc. although we just consider banknote soiling in our research. This research is novel in the following four ways: first, there has been little research conducted on fitness classification for the Indian Rupee using visible-light images. Second, the classification is conducted based on the features extracted from the regions of interest (ROIs), which contain little texture. Third, 1-level discrete wavelet transformation (DWT) is used to extract the features for discriminating between fit and unfit banknotes. Fourth, the optimal DWT features that represent the fitness and unfitness of banknotes are selected based on linear regression analysis with ground-truth data measured by densitometer. In addition, the selected features are used as the inputs to a support vector machine (SVM) for the final classification of banknote fitness. Experimental results showed that our method outperforms other methods. [ABSTRACT FROM AUTHOR]

This paper calculated the Duration Magnitude (MD) equation using data from the Iraqi Meteorological Organization and seismology (IMOS). It is an empirically determined equation and expressed as: MD = 0.8582 × log(τcoda) - 0.0000262 × R + 2.1203 ± c The epicentral distance and local geological conditions affect the duration of the coda. The data is obtained from 7 seismic stations in the network. The new proposed duration magnitude equation results from applying linear regression analysis to the data of a seismic signal duration with correlation coefficient R²=0.76; and a standard deviation value of 0.049. Station corrections indicated by Sc are also specified for the seismic stations that range from -0.024 to + 0.02. [ABSTRACT FROM AUTHOR]

In order to make the landscape design concept break through the traditional barriers and achieve diversification and nationalized development, this paper proposes a landscape design model that integrates traditional culture to effectively integrate traditional culture and landscape design. First use active adaptation technology to test the algorithm convergence ability, and then sample the basic situation of landscape design, reconstruct the collected information, construct the characteristic information flow, and use the linear regression analysis model and the Internet of Things technology to construct the distribution The structure model is used to analyze its characteristics. Then, by introducing the nonlinear dynamic inertia weight coefficient, according to the traditional particle swarm algorithm, the optimized particle swarm algorithm is proposed, and its robustness is tested, the error is analyzed, and the error values are all less than 0.1.Finally, the model in this paper is experimentally tested in a quantitative way. The results show that the traditional culture and landscape design fusion model proposed in this paper has a reasonable error value and has practical application significance.

Due to the fact that large forgings are critical components in the large machinery equipments, the dimensions measurement of the key parts plays an important role in the forging process. A non-contact real-time dimension measurement system based on green laser scanning for the large thermal forgings is proposed in this paper. First of all, the mathematical measurement model is established. The mapping relationship between the two-dimensional (2-D) image and the three-dimensional (3-D) outside dimensions of forgings can be acquired. Secondly, a light plane translational scanning model is derived, and the optical centre motion trail is calculated by using linear regression analysis in this paper. Finally, the measurement of forging's overall outside dimensions is realized by a data fusion of each section's dimension information through continuous scanning the forging and the real-time capturing images. The experimental results demonstrate that the measurement method proposed in the paper is viable. [ABSTRACT FROM AUTHOR]

Purpose: The 2014 outbreak of Ebola virus disease (EVD) in West Africa was the largest in history. Starting in September 2014, International Medical Corps (IMC) operated five Ebola treatment units (ETUs) in Sierra Leone and Liberia. This paper explores how future infectious disease outbreak facilities in resource-limited settings can be planned, organized, and managed by analyzing data collected on water, sanitation, and hygiene (WASH) and infection prevention control (IPC) protocols. Design/Methodology/Approach: We conducted a retrospective cohort study by analyzing WASH/IPC activity data routinely recorded on paper forms or white boards at ETUs during the outbreak and later merged into a database from two IMC-run ETUs in Sierra Leone between December 2014 and December 2015. Findings: The IMC WASH/IPC database contains data from over 369 days. Our results highlight parameters key to designing and maintaining an ETU. High concentration chlorine solution usage was highly correlated with both daily patient occupancy and high-risk zone staff entries; low concentration chlorine usage was less well explained by these measures. There is high demand for laundering and disinfecting of personal protective equipment (PPE) on a daily basis and approximately 1 (0–4) piece of PPE is damaged each day. Research limitations/Implications: Lack of standardization in the type and format of data collected at ETUs made constructing the WASH/IPC database difficult. However, the data presented here may help inform humanitarian response operations in future epidemics. [ABSTRACT FROM AUTHOR]

Using convenient stratification criteria such as geographical regions or other natural conditions like age, gender, etc., is not beneficial in order to maximize the precision of the estimates of variables of interest. Thus, one has to look for an efficient stratification design to divide the whole population into homogeneous strata that achieves higher precision in the estimation. In this paper, a procedure for determining Optimum Stratum Boundaries (OSB) and Optimum Sample Sizes (OSS) for each stratum of a variable of interest in health surveys is developed. The determination of OSB and OSS based on the study variable is not feasible in practice since the study variable is not available prior to the survey. Since many variables in health surveys are generally skewed, the proposed technique considers the readily-available auxiliary variables to determine the OSB and OSS. This stratification problem is formulated into a Mathematical Programming Problem (MPP) that seeks minimization of the variance of the estimated population parameter under Neyman allocation. It is then solved for the OSB by using a dynamic programming (DP) technique. A numerical example with a real data set of a population, aiming to estimate the Haemoglobin content in women in a national Iron Deficiency Anaemia survey, is presented to illustrate the procedure developed in this paper. Upon comparisons with other methods available in literature, results reveal that the proposed approach yields a substantial gain in efficiency over the other methods. A simulation study also reveals similar results. [ABSTRACT FROM AUTHOR]

Tensile strength and fracture toughness, important parameters of the rock for engineering applications are difficult to measure. Thus this paper selected three kinds of granite samples (grain sizes = 1.01mm, 2.12mm and 3mm), used the combined experiments of physical and numerical simulation (RFPA-DIP version) to conduct three-point-bending (3-p-b) tests with different notches and introduced the acoustic emission monitor system to analyze the fracture mechanism around the notch tips. To study the effects of grain size on the tensile strength and toughness of rock samples, a modified fracture model was established linking fictitious crack to the grain size so that the microstructure of the specimens and fictitious crack growth can be considered together. The fractal method was introduced to represent microstructure of three kinds of granites and used to determine the length of fictitious crack. It is a simple and novel method to calculate the tensile strength and fracture toughness directly. Finally, the theoretical model was verified by the comparison to the numerical experiments by calculating the nominal strength σn and maximum loads Pmax. [ABSTRACT FROM AUTHOR]

A considerable body of research has documented the negative effects of job burnout on employees and their organizations, emphasizing the importance of the identification of early signs of the phenomenon for the purposes of prevention and intervention. However, such timely identification is difficult due to the time and cost of assessing the burnout levels of all employees in an organization using established scales. In this paper, we propose an innovative way to identify employees at risk of job burnout by analyzing their e-mail communication patterns. Building on the Job Demands–Resources model, we theorize about the relationship between e-mail communication patterns and levels of employee exhaustion and disengagement (two dimensions of burnout). We analyzed 52,190 e-mails exchanged between 57 employees of a medium sized R&D company over a five-month period. We then related these employees’ communication patterns to their levels of burnout, collected using an established scale (the OLBI–Oldenburg Burnout Inventory). Our results provide support for the overall proposition of the paper, that e-mail communications can be used to identify individuals at risk of job burnout. Our models explain up to 34% of the variance of burnout and up to 37% and 19% respectively of the variance of exhaustion and disengagement. They also successfully distinguish between employees with a higher risk of burnout and those with lower levels of risk (F1 score of 84% with recall of 100% and 73% precision). We discuss the implications of our results and present suggestions for future research. [ABSTRACT FROM AUTHOR]

This paper discussed the aging behaviour of Kevlar fibre reinforced flexible pipe subjected to long-term monotonic internal pressure. Multi-group of pipe internal pressure tests that last for 10,000 h were conducted. The pressure-time curves of each test samples were collected. Two typical failure modes of the samples were specified. Linear regression analysis of the original pressure-time data was conducted and expression between characteristic pressurePand service timeT, which is capable to provide fast evaluation of pipe qualification, was presented. Based on suitable selection of safety factor, the design pressures under different service time were calibrated. Analysis shows that the LTHS50/LTHS10 ratio meets the requirements claimed by ASTM D2992, thus these calibrated design pressures can be used for engineering practice. [ABSTRACT FROM AUTHOR]

This paper evaluates linear regression analysis, BP neural network, and a random forest prediction model for the prediction of jujube water demand. The results highlight that the R2 of the random forest is 0.941 and the residual distribution is the most stable. Hence, the random forest is more suitable for prediction, and therefore, an intelligent irrigation system is established employing random forest, where the cloud server is the upper computer and a Raspberry Pi is the lower computer, and at the same time, a PC and a mobile interface was built to present various information about the developed irrigation system. [ABSTRACT FROM AUTHOR]

Although studies have increasingly linked air pollution to specific health outcomes, less well understood is how public perceptions of air quality respond to changing pollutant levels. The growing availability of air pollution measurements and the proliferation of social media provide an opportunity to gauge public discussion of air quality conditions. In this paper, we consider particulate matter (PM) measurements from four Chinese megacities (Beijing, Shanghai, Guangzhou, and Chengdu) together with 112 million posts on Weibo (a popular Chinese microblogging system) from corresponding days in 2011–2013 to identify terms whose frequency was most correlated with PM levels. These correlations are used to construct an Air Discussion Index (ADI) for estimating daily PM based on the content of Weibo posts. In Beijing, the Chinese city with the most PM as measured by U.S. Embassy monitor stations, we found a strong correlation (R = 0.88) between the ADI and measured PM. In other Chinese cities with lower pollution levels, the correlation was weaker. Nonetheless, our results show that social media may be a useful proxy measurement for pollution, particularly when traditional measurement stations are unavailable, censored or misreported. [ABSTRACT FROM AUTHOR]

The serum osmolarity mainly results from the inorganic ions and the small molecules and only in small extent from the protein ions. In serum sodium, potassium, chloride, bicarbonate, urea and glucose are the only components present in high enough concentrations to individually affect the osmolality. There are several different formulas for the calculation of human serum osmolarity. It has not been demonstrated which of the formulas is most effective for serum from various animal species. The goal of this study is to estimate the serum osmolarity in pigs from the concentrations of the main electrolytes and the glucose and urea content, and to establish the contribution of each osmotic component. Linear regression analysis was carried out to determine the best predictors of serum osmolarity in chickens. Two equations were also deduced for calculating serum osmolarity using manual regression analysis: y = 1.8117 Na + Urea + Glucose + 26.05 and y = 1.8933 (Na+K) + Urea + Glucose + 4.93.

The main aim of industries in the present scenario is to increase their rate of production in order to meet the increase in demand. The major problems faced by manufacturers are machine idle time, frequent replacement of tool due to wear and breakdown. These problems lead to increase in the idling time of production machines thereby causing considerable decrease in the rate of production. Hence, the need for optimization of tool life has become necessary. This paper involves a linear regression analysis to increase the effectiveness of the tool. For this purpose an experiment was conducted by considering the factors affecting the performance of the tool. In this analysis, five factors such as depth of cut, feed rate, cutting speed, hardness of tool and tool geometry has been taken and their effect on tool life are analyzed. In this paper, all the factors have been analyzed separately by keeping other factors as constant, thereby enabling a brief analysis on factors affecting tool life. By the above procedure the factor which has the most influence on the tool life has been determined. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze the relationship between positive psychological quality and college students' sense of responsibility for innovation and entrepreneurship from the perspective of positive psychology, to explore the cultivation model that can effectively improve college students' sense of responsibility for innovation and entrepreneurship, and to promote their success in entrepreneurship. In this study, a total of 1,500 college students were selected for questionnaire survey. ANOVA was used to analyze the differences of innovation and entrepreneurship responsibilities in demographic variables; factor analysis models were used to explore the factors that influence college students' sense of responsibility for innovation and entrepreneurship; and Spearman correlation and linear regression were used to analyze the relationship between college students' positive quality and innovation and entrepreneurship. The results showed that the average scores of individual responsibility, team responsibility, and social responsibility were 3.290, 3.624, and 3.720, respectively; individual responsibility differed significantly at the grade level; group responsibilities and social responsibilities were significantly different at the grade and gender levels; the linear fitting between benevolence, super-excellence, bravery, restraint, and wisdom with team responsibilities all reached significant levels, among which the wisdom coefficient was the highest; the linear fitting between syngroup, excellence, bravery, modesty, and wisdom with social responsibility reached a significant level, among which the wisdom coefficient was the highest; the linear fitting between syngroup, excellence, bravery, modesty, and wisdom with personal responsibility reached a significant level, among which the coefficient of excellence was the highest. This indicated that positive psychological qualities such as syngroup, excellence, modesty, benevolence, super-excellence, bravery, restraint, and wisdom were the influencing factors of college students' sense of responsibility for innovation and entrepreneurship. Among them, the role of wisdom is the most noteworthy in predicting social and group responsibilities, and super-excellent is the most significant predictor for individual responsibility. [ABSTRACT FROM AUTHOR]

In construction practice, contractually agreed costs are often exceeded, which interferes with the sustainable realization of construction projects. The research described in this paper covers 24 new construction, renovation and reconstruction projects in the Republic of Croatia realized in the years 2006 to 2017, in order to analyse the occurrence of cost overruns more precisely with regard to the source of the overruns. It was found that additional work is the main source of cost overruns: firstly, additional work as a result of the client's change orders and then unforeseen construction work as a result of unforeseen circumstances. As for the additional works, they are carried out at the client's request and are not necessary for the safety and stability of the building. Using linear regression and "soft computing" methods, the possibility of modelling the relationship between contractually agreed and realized construction costs with satisfactory accuracy was tested. The model with the values of the natural logarithms of the variables, modelled according to the time-cost model of Bromilow, proved to be of the highest accuracy. [ABSTRACT FROM AUTHOR]

The problem of handling non-ignorable non-response has been typically addressed under the design-based approach using the well-known sub-sampling technique introduced by Hansen and Hurwitz [1946, Journal of the American Statistical Association, Vol 41(236), Page 517- 529]. Alternatively, the model-based paradigm emphasizes on utilizing the underlying model relationship between the outcome variable and one or more covariate(s) whose population values are known prior to the survey. This article utilizes the model relationship between the study variable and covariate(s) for handling non-ignorable non-response and obtaining an unbiased estimator for the population total under the sub-sampling technique. The main idea is to combine the estimates obtained from the sample on first call and the sub-sample from second call using separate model relationships. The contribution of this paper helps us in providing unbiased estimates with an improved efficiency under model-based paradigm in presence of non-ignorable non-response. The provided method is more economical than the available estimators under callback methods as we are working sub-sampling and also increase response rate as a stronger mode of interview is employed for data collection. A numerical study using Monte Carlo is presented to illustrate the behavior of the proposed and the efficiency comparison. [ABSTRACT FROM AUTHOR]

Slope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommendation through local information embedding. First, we employ clustering algorithms to obtain the user clusters as well as item clusters to represent local information. Second, we predict ratings using the local information of users and items in the same cluster. The local information can detect strong localized associations shared within clusters. Third, we design different fusion approaches based on the local information embedding. In this way, both under-fitting and over-fitting problems are alleviated. Experiment results on the real datasets show that our approaches defeats slope one in terms of both mean absolute error and root mean square error. [ABSTRACT FROM AUTHOR]

There is a continuous need to improve existing project management decision-making support models, particularly those for monitoring and control are needed to increase chances for success. In this paper, potential of early warning in project management was focused, along with its connection to project success via project success factors. First, a systematic literature review was conducted, along with the focus group method, in order to identify project success factors. The selected success factors were also collected on 93 water infrastructure projects through a survey. By the means of linear regression analysis, critical success factors were finally determined. The results were integrated in the early warning system algorithm, composed by three modules - detection, validation and response module. The response module is composed by three dimensions: 1) risk, constraint and change management, 2) incorporation of project management competences and 3) application of project management methods, tools and techniques. The proposed early warning system was tested on three infrastructure projects. The results confirmed that improved early warning system can contribute in increase of project management success. As original database is composed by projects from only one country (Croatia), in the final stage of the research proposed approach was checked in five countries from the Central or South East Europe (Czech Republic, Slovenia, Serbia, North Macedonia, Bosnia and Herzegovina). The results from the final stage confirmed potential of the proposed approach as declared originally, so it is reasonable to expect success in early warning system's implementation due to similarities of critical success factors on projects as well as project management problems in general, that countries in regions share. This research presents new and creative way in linking early warning and project success, as well as interpretation of early response through different dimensions in project management. Also, based on the results of this research, it is possible to create a useful practical tool for managing other types of projects. [ABSTRACT FROM AUTHOR]

Purpose: Self-regulation is the capacity to regulate attention, emotion, and behaviour to pursue long-term goals. The current study examined the associations between role model presence and self-regulation during early adolescence, controlling for hopefulness, using a large population-based data set from the Tokyo Teen Cohort study. Methods: Adolescents, aged 12 years, identified a role model using a single item on a paper questionnaire: ‘Who is the person you most look up to?’ Level of hopefulness was also assessed using a single question: ‘To what extent do you feel hopeful about the future of your life?’ Trained investigators evaluated self-regulation. Results: Of 2550 adolescents, 2279 (89.4%) identified a role model. After adjusting for level of hopefulness, identifying a role model was associated with higher levels of self-regulation in comparison to indications of no role model. Hopeful future expectations were also associated with higher self-regulation; however, the beta coefficient was smaller than role model presence in the multivariate linear regression analysis. Conclusions: Role model presence was significantly associated with higher self-regulation among early adolescents. Educational environments should focus on support for adolescents with no role models. [ABSTRACT FROM AUTHOR]

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price. [ABSTRACT FROM AUTHOR]

A number of circular regression models have been proposed in the literature. In recent years, there is a strong interest shown on the subject of outlier detection in circular regression. An outlier detection procedure can be developed by defining a new statistic in terms of the circular residuals. In this paper, we propose a new measure which transforms the circular residuals into linear measures using a trigonometric function. We then employ the row deletion approach to identify observations that affect the measure the most, a candidate of outlier. The corresponding cut-off points and the performance of the detection procedure when applied on Down and Mardia’s model are studied via simulations. For illustration, we apply the procedure on circadian data. [ABSTRACT FROM AUTHOR]

Virtual try-on services, which significantly reduce return rates and enhance user shopping experiences, pose a crucial question: how can user willingness to use these services be increased? Additionally, "Generation Z" consumers, known for their unique traits and significant consumption potential, have been relatively understudied in this context. To address this theoretical gap, this study employs the Stimulus–Organism–Response (S-O-R) theory as its research framework, using online virtual shoe try-on services as a case study. Focusing on "Generation Z" consumers, this study utilizes literature review, user research, factor analysis, and linear regression to establish a user experience evaluation scale and behavior model. The user experience evaluation scale identifies positive elements such as convenience, price value, visual information acquisition, emotional value, and social interaction. The negative elements include technical limitations, personalized service deficiencies, and uncertainty. In the behavior model, all elements influence user attitudes. Notably, only the negative elements directly affect users' willingness to use the service, except social interaction among the positive elements, which positively impacts usage intention. This study also reveals that "Generation Z" consumers have novel requirements for social interaction. This research effectively addresses existing theoretical gaps and provides a foundational theory for the development of related services and technologies. [ABSTRACT FROM AUTHOR]

Tension members are key members that maintain stability and improve the strength of structures such as cable-stayed bridges, PSC structures, and slopes. Their application has recently been expanded to new fields such as mooring lines in subsea structures and aerospace fields. However, the tensile strength of the tension members can be abnormal owing to various risk factors that may lead to the collapse of the entire structure. Therefore, continuous tension monitoring is necessary to ensure structural safety. In this study, an improved elasto-magnetic (E/M) sensor was used to monitor tension force using a nondestructive method. General E/M sensors have limitations that make it difficult to apply them to operating tension members owing to their solenoid structure, which requires field winding. To overcome this problem, the magnetization part of the E/M sensor was improved to a yoke-type sensor, which was used in this study. For the development of the sensors, the numerical design and magnetization performance verification of the sensor were performed through eddy current solution-type simulations using ANSYS Maxwell. Using the manufactured yoke-type E/M sensor, the induced voltage signals according to the tension force of the specimen increasing from 0 to 10 tons at 1-ton intervals were repeatedly measured using DAQ with wireless communication. The measured signals were indexed using peak-to-peak value of induced voltages and used to analyze the signal change patterns as the tension increased. Finally, the analyzed results were compared with those of a solenoid-type E/M sensor to confirm the same pattern. Therefore, it was confirmed that the tension force of a tension member can be estimated using the proposed yoke-type E/M sensor. This is expected to become an effective tension monitoring technology through performance optimization and usability verification studies for each target tension member in the future. [ABSTRACT FROM AUTHOR]

Reliable quantification in biological systems of endogenous low- and high-molecular substances, drugs and their metabolites, is of particular importance in diagnosis and therapy, and in basic and clinical research. The analytical characteristics of analytical approaches have many differences, including in core features such as accuracy, precision, specificity, and limits of detection (LOD) and quantitation (LOQ). Several different mathematic approaches were developed and used for the comparison of two analytical methods applied to the same chemical compound in the same biological sample. Generally, comparisons of results obtained by two analytical methods yields different quantitative results. Yet, which mathematical approach gives the most reliable results? Which mathematical approach is best suited to demonstrate agreement between the methods, or the superiority of an analytical method A over analytical method B? The simplest and most frequently used method of comparison is the linear regression analysis of data observed by method A (y) and the data observed by method B (x): y = α + βx. In 1986, Bland and Altman indicated that linear regression analysis, notably the use of the correlation coefficient, is inappropriate for method-comparison. Instead, Bland and Altman have suggested an alternative approach, which is generally known as the Bland–Altman approach. Originally, this method of comparison was applied in medicine, for instance, to measure blood pressure by two devices. The Bland–Altman approach was rapidly adapted in analytical chemistry and in clinical chemistry. To date, the approach suggested by Bland–Altman approach is one of the most widely used mathematical approaches for method-comparison. With about 37,000 citations, the original paper published in the journal The Lancet in 1986 is among the most frequently cited scientific papers in this area to date. Nevertheless, the Bland–Altman approach has not been really set on a quantitative basis. No criteria have been proposed thus far, in which the Bland–Altman approach can form the basis on which analytical agreement or the better analytical method can be demonstrated. In this article, the Bland–Altman approach is re-valuated from a quantitative bioanalytical perspective, and an attempt is made to propose acceptance criteria. For this purpose, different analytical methods were compared with Gold Standard analytical methods based on mass spectrometry (MS) and tandem mass spectrometry (MS/MS), i.e., GC-MS, GC-MS/MS, LC-MS and LC-MS/MS. Other chromatographic and non-chromatographic methods were also considered. The results for several different endogenous substances, including nitrate, anandamide, homoarginine, creatinine and malondialdehyde in human plasma, serum and urine are discussed. In addition to the Bland–Altman approach, linear regression analysis and the Oldham–Eksborg method-comparison approaches were used and compared. Special emphasis was given to the relation of difference and mean in the Bland–Altman approach. Currently available guidelines for method validation were also considered. Acceptance criteria for method agreement were proposed, including the slope and correlation coefficient in linear regression, and the coefficient of variation for the percentage difference in the Bland–Altman and Oldham–Eksborg approaches.