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Traffic calming techniques are extensively used as safety tools to reduce the speed of motorised vehicles along any type of paved road. The objective of this study is to evaluate the efficacy of isolated speed humps on urban roads in India and to suggest an optimised geometry of speed humps for the desired level of speed reduction. For this, five different urban road sections were identified in Delhi and video graphic data were collected at each study section. The median speed of vehicles at each grid is estimated and speed profiles were developed for each category of vehicle approaching the speed hump. The speed behaviour of the vehicles approaching a circular or parabolic shape of isolated speed hump are examined and compared. This study developed speed profile models using regression techniques for an individual vehicle and also for the combined data. The observed speed of the vehicle was considered as a response variable and the physical properties of the speed humps were considered as explanatory variables. Further, the combined linear equation was considered for determining the optimum height and entry radius of the speed hump for the desired speed by using the simplex method.

Background In Taekwondo competitions, the rule is that points are scored when the impact of the kick reaches a predetermined threshold of strength. This study aimed to explore the adequacy of the protector and scoring system (PSS) designed to determine taekwondo body scoring based on a reference group model (RGM). Specifically, the kicking impact of the PSS was calculated using data from 188 matches fought during of 2018 Jakarta Asian Games. The RGM was designed based on empirical data by classifying the victory and defeat groups by gender and weight class, and the scoring method was set according to these criteria. Result The result of this study are as follows. First, there was no difference in the average impact of kicks of taekwondo players by weight class. Second, result of setting up the kick scoring impact standards of taekwondo PSS by classifying the winning and non-winning groups, the kick scoring impact set by the WT was found to be high in all weight classes except 58 kg. Lastly, result of comparing the settings of impact to score according to weight classes, the kick scoring impact standard set by the WT was higher in heavyweight (men's: under 80 kg, + 80 kg, women's: under 67 kg, over 67 kg) than in the lightweight (men's: under 58 kg, under 63 kg, under 68 kg, women's: under 49 kg, under 53 kg, under 57 kg). Conclusion The kick scoring impact set by the WT was found to be high in all weight classes except for the under 58 kg class defining kick scoring impact based on the standards of WT-certified PSS by classifying the matches into winning and non-winning groups. Finally, as a result of comparing the scoring impact settings according to weight class, the kick scoring impact standard set by the WT was higher for the heavier weight classes than for the lighter weight classes.

As the financial markets have evolved, the cross-shareholding networks have been formed among Chinese enterprises with equity as the concluded relationship. Exploring the impact of the cross-shareholding networks on financing constraints has important implications for the decisions of manufacturing companies. This paper uses the social network method to characterize the cross-shareholding networks of manufacturing companies from 2007 to 2019 and explores the effects of the cross-shareholding networks on financing constraints. The innovation of this paper is to explore the relationship between the two. It is concluded that the centrality or structural holes richness of manufacturing companies in the cross-shareholding network is inversely related to the financing constraint. The higher the centrality or the richer the number of structural holes, the lower the level of financing constraints.

Based on the 3E Methodology, in this study, the performance of community time bank mutual assistance services are empirically evaluated from three aspects of economy, effectiveness, and efficiency. We investigate and then hypothesize four impacts that affect the performance of community time bank mutual assistance services, including external supervision, organizational credibility, organizational management, and public welfare atmosphere. Therefore, the empirical data from Nansha District, Guangzhou, China, and the Structural Equation Model are used to empirically test the above hypotheses. The results show that: first, the performance of community time bank mutual assistance services is at a medium level; second, external supervision has a positive effect on the efficiency and effectiveness of time bank mutual assistance services, but no significant relationship with economy; third, organizational credibility has a positive effect on the efficiency and effectiveness, but no significant relationship with economy; forth, organizational management has a positive effect on the economy, efficiency and effectiveness; fifth, public welfare atmosphere has a positive effect on the efficiency, but no significant relationship with economy and effectiveness. Based on the above results, four policy recommendations to promote the sustainable development of community time bank mutual assistance services are also provided.

A perennial problem in bibliometrics is the appropriate distribution of authorship credit for coauthored publications. Several credit allocation methods and formulas have been introduced, but there has been little empirical validation as to which method best reflects the typical contributions of coauthors. This paper presents a validation of credit allocation methods using a new data set of author-provided percentage contribution figures obtained from the coauthored publications in cumulative PhD theses by authors from three countries that contain contribution statements. The comparison of allocation schemes shows that harmonic counting performs best and arithmetic and geometric counting also perform well, while fractional counting and first author counting perform relatively poorly.

Mobile money is an electronic system of transferring money from person to person. The mobile money service has expanded its coverage all over the world and there is hardly any country that do not practice any form of mobile money transfer. Somalia is one of the countries that embraced mobile money unconditionally as there is lack of traditional financial institutions providing financial services since the collapse of central government in 1991. Somalians accepted mobile money because it has made money transfer easier for them to pay bill and shopping. However, there are hesitation factors that hinder the full scale functioning of the system and makes people hesitate to use mobile money. Currently mobile money users practice very limited mobile money functions such as sending and receiving, withdrawal, top up and internet recharge. Other mobile money functions such as pay tuition fees, payrolls, payments for purchase t, utility payment and saving money into mobile money account are lagging behind. This empirical study explores the inconvenience factors that lead people to hesitate to use mobile money in a large scale. In this study, 650 survey questionnaire were distributed among mobile money users in Somalia. The questionnaires were distributed through online Google form. A total of 375 respondents submitted their responses and all the answers were recorded into SPSS. IBM-SPSS statistics 22 were used to statistically analyses the data. Factor analysis for data validity and scale analysis for data reliability, frequency and descriptive statistics were conducted to analyze the data. The study found that there are numerous mobile money hesitation factors that make Somalian people to hesitate fully practicing the system. These hesitation factors include perceived risk of financial loss, perceived risk of system error, perceived risk of authentication weaknesses, lack of regulation and policy and interoperability between the mobile money service providers. This study concludes that hesitation factors needs to be addressed that will improve the level of mobile money usage into full scale. Among factors that may reduce hesitation factors of the usage of mobile money services in Somalia are high level accuracy of mobile money authentication system, operative interoperability platform, highly effective compensation system and functioning mobile money regulations and policy.

The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modelling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modelling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and several appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.

The acquisition of translation equivalents is often considered a special component of bilingual children's vocabulary development, as bilinguals have to learn words that share the same meaning across their two languages. This study examined three contrasting accounts for bilingual children's acquisition of translation equivalents relative to singlets (i.e., words that are first labels for a referent): the Avoidance Account whereby translation equivalents are harder to learn, the Preference Account whereby translation equivalents are easier to learn, and the Neutral Account whereby translation equivalents and singlets are learned similarly. To adjudicate between these accounts, Study 1 explored patterns of translation equivalent learning under a novel computational model — the Bilingual Vocabulary Model — which quantifies translation equivalent knowledge as a function of the probability of learning words in each language, and includes a bias parameter that varies the difficulty of learning translation equivalents according to each account. Study 2 tested model-derived predictions against vocabulary data from 200 French–English bilingual children aged 18–33 months. Results showed a close match between the model predictions and bilingual children's patterns of translation equivalent learning. At smaller vocabulary sizes, data matched the Preference Account, while at larger vocabulary sizes they matched the Neutral Account. Our findings show that patterns of translation equivalent learning emerge predictably from the word learning process, and potentially reveal a qualitative shift in translation equivalent learning as bilingual children develop and learn more words.

Introduction:The purpose of this research was to understand the preferences of patients receiving integrative medicine services for return of aggregate study results.Methods:A brief online survey (survey 1) was sent to 341 cancer patients receiving integrative medicine interventions; subsequently, a minimally revised survey (survey 2) was sent to 812 individuals with various medical conditions who had been either research participants in integrative medicine studies (n = 446) or patients (n = 346) of mind–body medicine.Results:Feedback to a model plain language summary was elicited from survey 1 and survey 2 respondents. Seventy-seven survey recipients (23%) responded to survey 1, and 134 survey recipients (17%) responded to survey 2. The majority of respondents to the surveys were female and 51–70 years of age. Ninety percent of responders to survey 1 and 89% of responders to survey 2 indicated that researchers should share overall results of a study with participants. In terms of the means of result distribution, 37%–47% preferred email, while 22%–27% indicated that, as long as the results are shared, it did not matter how this occurred. Of 38 survey 1 respondents who had previously participated in a clinical trial, 37% had received the results of their study. In survey 2, 63 individuals indicated that they previously participated in clinical trials, but only 16% recalled receiving results.Conclusions:These results confirm that the majority (89%–90%) of integrative medicine patients are interested in receiving the results of clinical trials. The majority (82%–94%) of respondents felt the model plain language summary of results provided was helpful.

This paper takes China’s GEM listed companies from 2007 to 2020 as the research object, and empirically tests the impact of equity pledge of major shareholders on enterprise innovation investment. It is found that the equity pledge of major shareholders will restrain the innovation investment of enterprises. Further research finds that enterprises with high internal control level, the inhibiting effect of equity pledge of major shareholders on innovation investment will be weakened, that is, internal control plays a negative regulating role.

This paper takes China’s A-share listed companies from 2015 to 2019 as the research object, and empirically tests the impact of executive compensation stickiness on enterprise innovation investment. It is found that executive compensation stickiness is positively correlated with the innovation investment of corporate and it is more significant in private enterprises. In addition, Institutional investors participate in corporate governance to play a positive governance effect, and strengthen the positive correlation between the stickiness of executive compensation and corporate innovation investment.

Background In Scandinavia, there is a strong tradition for research and quality monitoring based on registry data. In Denmark, Norway and Sweden, 63 clinical registries collect data on disease characteristics, treatment and outcome of various cancer diagnoses and groups based on process-related and outcome-related variables. Aim We describe the cancer-related clinical registries, compare organizational structures and quality indicators and provide examples of how these registries have been used to monitor clinical performance, develop prediction models, assess outcome and provide quality benchmarks. Further, we define unmet needs such as inclusion of patient-reported outcome variables, harmonization of variables and barriers for data sharing. Results and conclusions The clinical registry framework provides an empirical basis for evidence-based development of high-quality and equitable cancer care. The registries can be used to follow implementation of new treatment principles and monitor patterns of care across geographical areas and patient groups. At the same time, the lessons learnt suggest that further developments and coordination are needed to utilize the full potential of the registry initiative in cancer care. publishedVersion

A technique based on empirical data and finite element (FE) analysis to predict the fracture life of Al-1050 beams with the help of its fundamental mode is presented in this study. Experiments were performed on a non-prismatic beam vibrating with a constant value of the amplitude at the fixed end until the complete fracture of the specimen was achieved. The beam was vibrating at its fundamental mode to achieve fracture in less time. A power law model was used to acquire the possible trends between the values of natural frequencies and the number of cycles recorded during these experiments. These trends were further compared with a numerically modelled specimen but with artificial cracks. FE modal analysis was used for this comparison. An error of less than 1% was observed in the estimated number of total cycles obtained through the power law model before fracture, compared to those obtained using the numerical approach. Using this approach, the fracture life was also predicted for specimens of different lengths.

Conservationists can use social network analysis to improve targeting for behaviour-change interventions, selecting individuals to target who will go on to inform or influence others. However, collecting sociometric data is expensive. Using empirical data from a case study in Cambodia and simulations we examine the conditions under which collecting this data is cost-effective. Our results show that targeting interventions using sociometric data can lead to greater dissemination of information and adoption of new behaviours. However, these approaches are not cost-effective for small interventions implemented in only a few communities, and it is an order of magnitude cheaper to achieve the same results by simply targeting more individuals in each community at random. For interventions across multiple communities, network data from one community could inform rules-of-thumb that can be applied to boost the effectiveness of interventions. In rural Cambodia, this approach is worthwhile if it can inform interventions covering at least 21 villages. Our findings provide a framework for understanding how insights from network sciences, such as targeting clusters of individuals for interventions that aim to change behaviour, can make a practical contribution to conservation.

BackgroundCovid-19 disease is pandemic in more than 85% of the countries in the world, with about 10 million cases and 0.5 million deaths as on July 2, 2020. In India reporting of the first case was on January 30, 2020, and to prevent rapid community spread of the disease nationwide lockdown phase was imposed from March 25- June 1, 2020. Our objective was to assess various epidemiological measures during the lockdown phase.MethodsWe used daily reporting of confirmed cases by the Ministry of Health and Family Welfare, Government of India during the period March 19-June 1, 2020. Using statistical packages STATA and R-packages, we fitted three statistical distributions (Gamma, Weibull and Log-normal) to the daily recorded new cases. We estimated daily incidence rate and death rate per million population, generation time and Basic Reproduction numbers.ResultsDuring the lockdown phase, the daily per cent increase in the cumulative number of cases showed negative exponential growth with 0.022 as an instantaneous rate of decrease. The average incidence rate with a 95% confidence interval (CI) was 1.84 (1.43-2.25). Day specific incidence rate per million (revealed the exponential pattern with 0.069 as the instantaneous rate of increase per day, which accounted for the doubling time of the disease (10 days; 95% CI: 9.25-10.93). Case fatality rate (2.92%; 95% CI: 2.82% −3.02%) and overall death rate was 1.14 (95% CI: 0.87-1.41) per million. were abysmally low. Statistical distribution fitting of new cases found to be satisfactory with Gamma distribution. Basic reproduction numbers 1.83 (95% CI: 1.82-1.83) was less.ConclusionIn India, with a population density of about 450 per Km2, the virulent of COVID-19 transmission was interrupted significantly with 70 days lockdown during the early transmission stage. We observed a considerable decline in all the epidemiological indices compared to the corresponding indices recorded during the same period in the severely affected countries.

In qualitative interviews with a diverse group of experts, the vast majority believed unregulated researchers should seek out independent oversight. Reasons included the need for objectivity, protecting app users from research risks, and consistency in standards for the ethical conduct of research. Concerns included burdening minimal risk research and limitations in current systems of oversight. Literature and analysis supports the use of IRBs even when not required by regulations, and the need for evidence-based improvements in IRB processes.

BACKGROUND Fiber tracking with diffusion-weighted MRI has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. PURPOSE To assess the variability for an algorithm in group studies reproducibility is of critical context. However, reproducibility does not assess the validity of the brain connections. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do no capture variabilities because of in vivo physiological factors. The ISMRM 2017 TraCED challenge was created to fulfill the gap. STUDY TYPE A systematic review of algorithms and tract reproducibility studies. SUBJECTS Single healthy volunteers. FIELD STRENGTH/SEQUENCE 3.0T, two different scanners by the same manufacturer. The multishell acquisition included b-values of 1000, 2000, and 3000 s/mm2 with 20, 45, and 64 diffusion gradient directions per shell, respectively. ASSESSMENT Nine international groups submitted 46 tractography algorithm entries each consisting 16 tracts per scan. The algorithms were assessed using intraclass correlation (ICC) and the Dice similarity measure. STATISTICAL TESTS Containment analysis was performed to assess if the submitted algorithms had containment within tracts of larger volume submissions. This also serves the purpose to detect if spurious submissions had been made. RESULTS The top five submissions had high ICC and Dice >0.88. Reproducibility was high within the top five submissions when assessed across sessions or across scanners: 0.87-0.97. Containment analysis shows that the top five submissions are contained within larger volume submissions. From the total of 16 tracts as an outcome relatively the number of tracts with high, moderate, and low reproducibility were 8, 4, and 4. DATA CONCLUSION The different methods clearly result in fundamentally different tract structures at the more conservative specificity choices. Data and challenge infrastructure remain available for continued analysis and provide a platform for comparison. LEVEL OF EVIDENCE 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:234-249.

During a pandemic, leaders and decision-makers are compelled to urgently make decisions due to public health concerns, public and expert opinions, and other factors. It is typical, particularly in the early phases of a pandemic, for decisions to depend on partial or missing information. A current trend in scientific publication is to present repeatable results, accompanied by data sources and artefacts. This results in complete transparency and auditability of the results, as well as a platform for future research. CoronaCaster is a tool based on the probabilistic programming method, built for transparent forecasting of pandemic cases, hospital capacity, and mortality rate. Probabilistic programming, i.e. Bayesian inference, has been shown to perform well with time series prediction challenges with small sample size and great uncertainty. CoronaCaster uses an advanced Bayesian method to obtain model parameters and their confidence intervals for the user-selected shape function (polynomial, exponential or sigmoid). They are obtained by sampling parameter space using the training period data.

Based on 2008-2016 years’ A-Share Listed Companies in China, using the capital market to lift the short selling constraints, this paper investigates its impact on the company’s provision for asset impairment. The study found that the lifting of short selling constraints policy led to a significant decrease in the asset impairment provision ratio. And the significance of the company’s provision for reducing the amount of assets that can be reversed is higher than the provision for impairment of non-reversible assets. The article extends the research framework of economic consequences of lifting short selling constraints, and supplements the literature on the factors that affect the company’s provision for asset impairment.

Since the publication of the Organisation for Economic Co-operation and Development (OECD) avian acute oral guideline, OECD 223, empirical data have become available to compare the performance of OECD 223 with statistical simulations used to validate this guideline and with empirical data for US Environmental Protection Agency Office of Chemical Safety and Pollution Prevention (USEPA OCSPP) guideline OCSPP 850.2100. Empirical studies comprised 244 for Northern bobwhite, of which 73 were dose-response tests and 171 were limit tests. Of the dose-response tests, 26 were conducted to OECD 223 (using 3-4 stages) and 33 to OCSPP 850.2100 (using the single 50-bird design). Data were collected from 5 avian testing laboratories from studies performed between 2006 and 2013. The success with which the LD50 and slope could be determined was 100% and 96% for OECD 223 (mean 26 birds per test) and 100% and 51% for OCSPP 850.2100 (mean 50 birds per test). This was consistent with the statistical simulations. Control mortality across all species and designs amounted to 0.26% (n = 2655) with only single mortalities occurring in any 1 study and

Prior research found that the Chinese patent system is more pro-patentee than once believed. Patent owners performed much better in both infringement lawsuits and post-grant validity reviews in China than in many other countries, such as the United States and Germany. Also, after a finding of infringement, Chinese courts were quite lenient with regard to permanent injunctions. All these pro-patentee tendencies, together with the rapid growth of China's software industry, cast doubt on the prevalence of troll activities in China. This article analyzes 3435 patent infringement lawsuits decided by local people's courts in China in 2015 and 2016 and provides some valuable insights into two important questions: how often do patent trolls litigate in China, and do they adopt different litigation tactics from practicing entities? Based on empirical data, this article finds that, interestingly, both the number and the size of patent trolls in China are relatively small. Although nearly half (1534, or 44.7%) of all patent infringement lawsuits were initiated by non-practicing entities (NPEs), individual inventors, instead of professional businesses, accounted for an overwhelming majority of all NPE cases (1528, or 99.6%). Patent assertion entities (PAEs), by contrast, only initiated four out of 3435 cases (0.1%) in the two-year period under review. If only patents in the computer industry are taken into consideration, then PAEs initiated zero computer-related lawsuits. Nonetheless, this article does observe some trollish litigation tactics worthy of attention and further research. There were 47 repeat litigants – litigants who initiated 10 or more cases; some performed “commercial enforcement,” asserting patents against multiple small retailers instead of against one large manufacturer; and patent owners, on average, waited for a rather long time before enforcing their patents in China. All these behaviors may introduce bias to the incentives to innovate and put pressure on the overall functioning of the Chinese patent system.

International audience; Given the proliferation of cataclysmic predictions about antibiotic resistance, cases of which are estimated to amount to 12 500 per year in France, we herein decided to compare the empirical clinical microbiology data from our institution with estimates and predictions from 10 major international scientific articles and reports. The analysis of 7 years of antibiotic resistance data from 10 bacterial species and genera of clinical interest from our institution identified no deaths that were directly attributable to extremely drug-resistant bacteria. By comparing our observations to the 10 articles and reports studied herein, we concluded that their results lack empirical data. Interventions are urgently needed to significantly reduce both mortality and the healthcare costs associated with bacterial infections, including the implementation of local and national laboratory data-based surveillance systems for the routine surveillance of antibiotic resistance that would be helpful for a better understanding of how to manage antibiotic-resistant bacteria in the future.

In this work, I briefly introduce the theoretical foundations and main aspects of electronic learning, in order to explore and evaluate the instruction schemes implemented as a result of the digital transformation. I base my findings on the analysis of a questionnaire-based survey administered to a sample of students. My research aims at examining the efficiency of the implementation of digital instruction and the accompanying challenges at various levels of the education sphere. I was interested in students’ Information and Communications Technology (ICT) preferences and usage habits and whether or not they possess vital digital competences in their own view. The digitally scheduled education programs introduced in response to the COVID-19 pandemic could not fully reach a level required for effective learning according to research findings. The responses revealed that several schools did not implement uniform systems, the educational materials were not suitable for independent learning, the given materials did not have a modular structure, the respective texts did not reflect a system oriented perspective, and it was proven that neither students nor teachers possessed digital literacy skills considered to be currently vital. The most important result of this statistical analysis entails that those students were more successful in the digital home-based education, who spent more time with Information and Communications Technology (ICT) use, regularly played with or used experience-oriented software and relied on smart devices for most of their studies. © 2021, Budapest Tech Polytechnical Institution. All rights reserved.

The question of business performance assessment has been discussed by many researchers. Some resulting indicators of business performance can be applied in all enterprises (e.g. ROE, ROA), some have limited use (e.g. Tobin´s Q). This paper focuses on finding the aspects (indicators), that could affect business performance itself, rather than to propose new indicators of business performance. The results of this analysis could help the management of enterprise to focus on the right objectives in the future. The research adopted quantitative approach. It is based on analysis of fourteen indicators (i.e. their values) processed in order to prove or disprove their statistical interrelationship with chosen performance indicators (ROE, ROS). The dataset, describing 784 large enterprises in the Czech Republic, was downloaded from Albertina – Gold edition. Data were statistically tested using Spearman´s correlation coefficient. Findings of this research indicate that in the case of few indicators (such as Equity or Total debt in the case of ROS) the relationship can be proven. These indicators are presented, and results are discussed in the paper, together with the possible future research. The question of business performance assessment has been discussed by many researchers. Some resulting indicators of business performance can be applied in all enterprises (e.g. ROE, ROA), some have limited use (e.g. Tobin´s Q). This paper focuses on finding the aspects (indicators), that could affect business performance itself, rather than to propose new indicators of business performance. The results of this analysis could help the management of enterprise to focus on the right objectives in the future. The research adopted quantitative approach. It is based on analysis of fourteen indicators (i.e. their values) processed in order to prove or disprove their statistical interrelationship with chosen performance indicators (ROE, ROS). The dataset, describing 784 large enterprises in the Czech Republic, was downloaded from Albertina – Gold edition. Data were statistically tested using Spearman´s correlation coefficient. Findings of this research indicate that in the case of few indicators (such as Equity or Total debt in the case of ROS) the relationship can be proven. These indicators are presented, and results are discussed in the paper, together with the possible future research.

Gonorrhoea notifications are rapidly rising in men who have sex with men (MSM). We developed a model to assess mouthwash as a novel intervention for gonorrhoea control.We developed a model of Neisseria gonorrhoeae (NG) transmission to explain anatomic site-specific prevalence of gonorrhoea among MSM. The model was calibrated to available epidemiological and behavioral data. We estimated the contribution of various sexual acts to gonorrhoea incidence and evaluate the potential impacts of screening scale-up and utilization of mouthwash on the gonorrhoea epidemic.We calibrated the model to prevalence of oropharyngeal, anal, and urethral gonorrhoea of 8.6% (7.7-9.5%), 8.3% (7.4-9.1%), and 0.20% (0.04-0.35%), respectively, among MSM. Oropharynx to oropharynx transmission through kissing is estimated to account for nearly three quarters of all incident cases (71.6% [64.4-80.5%]) of gonorrhoea in MSM. Substantially increasing annual oropharynx screening for gonorrhoea from the current 40% to 100% may only halve the prevalence of gonorrhoea in MSM. In contrast, the use of mouthwash with moderate efficacy (additional 1% clearance per daily use) would further reduce the corresponding prevalence rates to 3.1% (2.2-4.4%), 3.8% (2.3-4.9%), and 0.10% (0.06-0.11%), and a high-efficacy mouthwash (additional 1.5% clearance per daily use) may further halve the gonorrhoea prevalence. Without oropharynx to oropharynx transmission, we could not replicate current prevalence data.Despite a dearth of empirical data, our model suggests that kissing could potentially play an important role in NG transmission among MSM. Control through sexually transmitted infection screening alone is unlikely to have a substantial impact on the gonorrhoea epidemic in MSM.

Background The size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). This can be achieved by using ontologies and through representing them as semantic graphs. Here, we discuss two different semantic graph approaches of representing empirical data and metadata in a knowledge graph, with phenotype descriptions as an example. Almost all phenotype descriptions are still being published as unstructured natural language texts, with far-reaching consequences for their FAIRness, substantially impeding their overall usability within the life sciences. However, with an increasing amount of anatomy ontologies becoming available and semantic applications emerging, a solution to this problem becomes available. Researchers are starting to document and communicate phenotype descriptions through the Web in the form of highly formalized and structured semantic graphs that use ontology terms and Uniform Resource Identifiers (URIs) to circumvent the problems connected with unstructured texts. Results Using phenotype descriptions as an example, we compare and evaluate two basic representations of empirical data and their accompanying metadata in the form of semantic graphs: the class-based TBox semantic graph approach called Semantic Phenotype and the instance-based ABox semantic graph approach called Phenotype Knowledge Graph. Their main difference is that only the ABox approach allows for identifying every individual part and property mentioned in the description in a knowledge graph. This technical difference results in substantial practical consequences that significantly affect the overall usability of empirical data. The consequences affect findability, accessibility, and explorability of empirical data as well as their comparability, expandability, universal usability and reusability, and overall machine-actionability. Moreover, TBox semantic graphs often require querying under entailment regimes, which is computationally more complex. Conclusions We conclude that, from a conceptual point of view, the advantages of the instance-based ABox semantic graph approach outweigh its shortcomings and outweigh the advantages of the class-based TBox semantic graph approach. Therefore, we recommend the instance-based ABox approach as a FAIR approach for documenting and communicating empirical data and metadata in a knowledge graph.

This article tries to introduce the perspective of the “heterogeneity” of the neighbor enterprise into the research on the problem of the financing efficiency of the small and medium sized enterprises (SMEs). It takes enterprises in the China industrial enterprises database from 2005 to 2007 as the object of the research and defines the neighbor enterprise at a town (village, street) scale. This article selects the financing efficiency, enterprise scale, technical innovation of enterprise as the indexes to measure the “heterogeneity” of the neighbor enterprise, and examines whether they have a significant relationship with the enterprises themselves or not. Through building panel data model and the empirical analysis, this article finds that the financing efficiency and the degree of technological innovation of the neighbor enterprise are positively related to the financing efficiency of enterprises themselves, and the scale of the neighbor enterprise has a negative correlation with the financing efficiency of enterprises themselves. What’s more, this paper distinguishes the neighbor enterprise from the industry unusual neighbor according to whether enterprises are belong to the same industry or not at the same town(village, street), finding the neighbor enterprises which are the same industry have a great more relationship with the financing efficiency of SMEs than the neighbor enterprises which are the non trade.

Rainfall-induced hazards, such as landslides, debris flows, and floods cause significant damage to transportation infrastructure. However, an accurate assessment of rainfall-induced hazard risk to transportation infrastructure is limited by the lack of regional and asset-tailored vulnerability curves. This study aims to use multi-source empirical damage data to generate vulnerability curves and assess the risk of transportation infrastructure to rainfall-induced hazards. The methodology is exemplified through a case study for the Chinese national railway infrastructure. In doing so, regional and national-level vulnerability curves are derived based on historical railway damage records. This is combined with daily precipitation data and the railway infrastructure market value to estimate regional- and national-level risk. The results show large variations in the shape of the vulnerability curves across the different regions. The railway infrastructure in Northeast and Northwest China is more vulnerable to rainfall-induced hazards due to low protection standards. The expected annual damage (EAD) ranges from 1.88 to 5.98 billion RMB for the Chinese railway infrastructure, with a mean value of 3.91 billion RMB. However, the risk of railway infrastructure in China shows high spatial differences due to the spatially uneven precipitation characteristics, exposure distribution, and vulnerability curves. The South, East and Central provinces have a high risk to rainfall-induced hazards, resulting in an average EAD of 184 million RMB, 176 and 156 million RMB, respectively, whereas the risk in the Northeast and Northwest provinces are estimated to be relatively lower. The usage of multi-source empirical data offer opportunities to perform risk assessments that include spatial detail among regions. These risk assessments are highly needed in order to make effective decisions to make our infrastructure resilient.

Studies on evacuation behavior are often based on regular favorable scenarios, but more urban areas are adversely affected by natural disasters, many of them under extreme geographic conditions, and very little is known on how these conditions affect evacuation processes, especially in communities with neither experience nor disaster education.We collected empirical data during announced evacuation practices in a landslide-prone urban area from La Paz, Bolivia. Based on this experiment, we measured time, velocity and participants’ behavior, then process results and input them as parameters to a 3-dimensional (3D) agent-based evacuation simulation model of the evacuation practice location to simulate real scenario evacuations focused on community residents walking on stairs and steep streets.Our objective is to explain procedures for simulating two evacuation cases with different premises and to compare results from the two.Results show that one case is more effective simply by following a simple rule of evacuation path selection. Our ultimate purpose is to create a compelling graphic tool for teaching persons about early short-term evacuation, including the importance of early planned evacuation. It also provides persons with opportunities participate in virtual drills.

Evidence suggests that domestic cold water service sizing methods in many countries around the world tend to overestimate the actual peak water volume flow rate. Oversizing domestic water service systems does waste materials and money, but it also increases the length of time that it takes for water to pass through the system which can increase water temperatures with the associated risks that presents. This paper compares the three commonly used UK domestic cold water service sizing methods and reveals variance in the methods, the design flow rates calculated and the amount of diversity applied by each method. BS EN 806 returns the lowest design flow rates of the three methods and also applies significantly greater diversity. Empirical domestic cold water service volume flow rate data from two case study buildings revealed that all three UK sizing methods significantly overestimated the peak water volume flow rates but that BS EN 806 was the closest. Additional empirical data from seven more buildings have been used to validate the data from this study and add confidence to the findings. This research provides useful evidence to help engineers select the most appropriate UK domestic cold water service sizing method and to anticipate the likely range and fluctuation of domestic cold water service flow rates. Practical application: Engineers tend to be conservative by nature and generally err on the side of caution to ensure that there is never any cause for customer complaint about their designs. This is understandable but there can be negative consequences if the full implications of such decisions are not fully understood. This paper reveals significant oversizing compared to empirical data from each of the three UK domestic water service sizing methods and highlights the reasons for this. The practical application of this paper lies in the presented results data and analysis which will help engineers make this important choice between the available sizing methods.

How many training data are needed to learn a supervised task? It is often observed that the generalization error decreases as $n^{-\beta}$ where $n$ is the number of training examples and $\beta$ an exponent that depends on both data and algorithm. In this work we measure $\beta$ when applying kernel methods to real datasets. For MNIST we find $\beta\approx 0.4$ and for CIFAR10 $\beta\approx 0.1$, for both regression and classification tasks, and for Gaussian or Laplace kernels. To rationalize the existence of non-trivial exponents that can be independent of the specific kernel used, we study the Teacher-Student framework for kernels. In this scheme, a Teacher generates data according to a Gaussian random field, and a Student learns them via kernel regression. With a simplifying assumption -- namely that the data are sampled from a regular lattice -- we derive analytically $\beta$ for translation invariant kernels, using previous results from the kriging literature. Provided that the Student is not too sensitive to high frequencies, $\beta$ depends only on the smoothness and dimension of the training data. We confirm numerically that these predictions hold when the training points are sampled at random on a hypersphere. Overall, the test error is found to be controlled by the magnitude of the projection of the true function on the kernel eigenvectors whose rank is larger than $n$. Using this idea we predict relate the exponent $\beta$ to an exponent $a$ describing how the coefficients of the true function in the eigenbasis of the kernel decay with rank. We extract $a$ from real data by performing kernel PCA, leading to $\beta\approx0.36$ for MNIST and $\beta\approx0.07$ for CIFAR10, in good agreement with observations. We argue that these rather large exponents are possible due to the small effective dimension of the data., Comment: We added (i) the prediction of the exponent $\beta$ for real data using kernel PCA; (ii) the generalization of our results to non-Gaussian data from reference [11] (Bordelon et al., "Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks")

This paper develops a probabilistic approach that could use empirical data to derive values of performance shaping factor (PSF) multipliers for use in quantitative human reliability analysis (HRA). The proposed approach is illustrated with data on sleep deprivation effects on performance. A review of existing HRA methods reveals that sleep deprivation is not explicitly included at present, and expert opinion is frequently used to inform HRA model multipliers. In this paper, quantitative data from empirical studies regarding the effect of continuous hours of wakefulness on performance measures (reaction time, accuracy, and number of lapses) are used to develop a method to derive PSF multiplier values for sleep deprivation, in the context of the SPAR-H model. Data is extracted from the identified studies according to the meta-analysis research synthesis method and used to investigate performance trends and error probabilities. The error probabilities in test and control conditions are compared, and the resulting probability ratios are suggested for use in informing the selection of PSF multipliers in HRA methods. Although illustrated for sleep deprivation, the proposed methodology is general, and can be applied to other performance shaping factors.

We present 'embo', a Python package to analyze empirical data using the Information Bottleneck (IB) method and its variants, such as the Deterministic Information Bottleneck (DIB). Given two random variables 'X' and 'Y', the IB finds the stochastic mapping 'M' of 'X' that encodes the most information about 'Y', subject to a constraint on the information that 'M' is allowed to retain about 'X'. Despite the popularity of the IB, an accessible implementation of the reference algorithm oriented towards ease of use on empirical data was missing. Embo is optimized for the common case of discrete, low-dimensional data. Embo is fast, provides a standard data-processing pipeline, offers a parallel implementation of key computational steps, and includes reasonable defaults for the method parameters. Embo is broadly applicable to different problem domains, as it can be employed with any dataset consisting in joint observations of two discrete variables. It is available from the Python Package Index (PyPI), Zenodo and GitLab.

Advancement in the intelligence field can only be achieved through new observations and the presentation of new empirical data. This is a continuous process and includes how we as employees engage with software and technical solutions. Just as it is impossible to teach or learn anything in marketing today without a deep understanding of digital marketing, in the same way is it impossible to make advancements in intelligence studies without first-hand experience with business intelligence software and new IT-equipment. Management and IT have indeed merged. This understanding has been an integrated part of JISIB since the journal started some eight years ago. And as always, we are less interested in how new technologies are developed (for that there are excellent technical journals) than about the management practice of these developments. This issue follows very much on this track.

The introduction of legal content to Google Scholar made United States case law and law journal articles accessible to an unprecedented extent. With case law freely available and accurate bibliographic information for articles, could Google Scholar be accurate and complete enough for correctional institutions to forgo purchasing either print publications or fee-based services for these materials? This article empirically assesses whether Google Scholar can reliably answer the questions of inmates in a correctional facility, the Baltimore City Detention Center. As a comparison, the same questions are tested in Westlaw Correctional, a subscription database marketed to correctional institutions.

This article makes an empirical analysis on influence of RD for state-controlled enterprises, current and last R&D investment have positive influence on corporate performance but not significant.

Evaluation of forest landscape model (FLM) predictions is indispensable to establish the credibility of predictions. We present a framework that evaluates short- and long-term FLM predictions at site and landscape scales. Site-scale evaluation is conducted through comparing raster cell-level predictions with inventory plot data whereas landscape-scale evaluation is conducted through comparing predictions stratified by extraneous drivers with aggregated values in inventory plots. Long-term predictions are evaluated using empirical data and knowledge. We demonstrate the applicability of the framework using LANDIS PRO FLM. We showed how inventory data were used to initialize the landscape and calibrate model parameters. Evaluation of the short-term LANDIS PRO predictions based on multiple metrics showed good overall performance at site and landscape scales. The predicted long-term stand development patterns were consistent with the established theories of stand dynamics. The predicted long-term forest composition and successional trajectories conformed well to empirical old-growth studies in the region. We present a framework for evaluating the short- and long-term forest landscape model predictions at site and landscape scales.Site-scale evaluation is conducted through comparing cell-level predictions with inventory plot data.Landscape-scale evaluation is conducted through comparing predictions stratified by extraneous drivers with aggregated values in inventory plots.We successfully evaluated the LANDIS PRO forest landscape model predictions using empirical data and knowledge and showed reasonable performances at both scales.