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Understanding the intellectual structure of geosciences is crucial for identifying emerging topics, which can guide future research and funding strategies. This study employs Latent Dirichlet Allocation (LDA) topic modeling and co-word network analysis to explore the intellectual landscape of highly cited geoscientific papers. Through empirical analysis, the study identifies 15 research areas, including climate change, geological processes, environmental impacts, and advancements in data analysis and remote sensing. These findings highlight the prominent role of big data analysis and machine learning methods across various geoscientific domains. Additionally, a notable gap has been identified in the integration of these computational methods with research on organic chemistry and formation processes, suggesting a potential direction for future exploration and innovation. [ABSTRACT FROM AUTHOR]

Lead white (basic lead carbonate), before its effective ban in much of Europe from 1989 onwards, was among the most versatile and widespread but also one of the most harmful pigments available. This paper is a critical discussion of the regulation of lead white paint and its consequences from the point of view of conservation and artistic practice in Europe. It looks at who benefited from exemptions to bans, and whether there was resistance to them. Buildings and artworks are both taken into account. The paper discusses nuances in regulation in four European countries which have notable differences in approach (France, Austria, Germany, and the United Kingdom). The centralised regulatory framework of the United Kingdom allows data analysis on the authorised use of the paint since its restriction up to the present day. This dataset shows a steady decrease in the use of the paint by conservators across disciplines. The paper concludes by considering the professional and ethical implications of lead white paint for artists and conservators, in the context of varied regulation and a decline in use. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) holds the potential to revolutionize the way businesses operate. With the promise of generating creative solutions for complex problems, AI's transformative impact on the business landscape is undeniable. Against this backdrop, business schools become crucial in preparing future professionals with relevant tools and techniques associated with AI. This paper investigates the integration of GitHub Copilot in conjunction with Python libraries such as Pandas and Plotly, within two sections of an introductory information systems course. We examine how business students perceive GitHub Copilot's ease of use, utility, and relevance, particularly in tasks involving code generation for data analysis and visualization. Our findings indicate a generally positive perception of GitHub Copilot among business students in terms of its ease of use, utility, and relevance. However, notable gender differences emerged; male students reported finding GitHub Copilot easier to use and their interactions with the tool clearer and more understandable compared to their female counterparts. The paper also provides an overview of the course assignment and presents the visualizations produced, offering valuable insights into the practical application of GitHub Copilot in an information systems course setting. [ABSTRACT FROM AUTHOR]

The paper builds on the framework proposed by Pattison and Snijders (2012) for specifying exponential random graph models (ERGMs) for social networks. We briefly review the two-dimensional hierarchy of potential dependence structures for network tie variables that they outlined and provide proofs of the relationships among the model forms and of the nature of their sufficient statistics, noting that models in the hierarchy have the potential to reflect the outcome of processes of cohesion, closure, boundary and bridge formation and path creation over short or longer network distances. We then focus on the so-called partial inclusion dependence assumptions among network tie variables and the pendant-triangle , or paw , statistics to which they give rise, and illustrate their application in an empirical setting. We argue that the partial inclusion assumption leads to models that can reflect processes of boundary and bridge formation and that the model hierarchy provides a broad and useful framework for the statistical analysis of network data. We demonstrate in the chosen setting that pendant-triangle (or paw) effects, in particular, lead to a marked improvement in goodness-of-fit and hence add a potentially valuable capacity for modelling social networks. • The paper provides a principled basis for some new effects for exponential random graph models for social networks • We describe how these new terms can be used to understand processes of boundary and bridge formation in networks • We demonstrate that these new terms improve goodness-of-fit in an illustrative application [ABSTRACT FROM AUTHOR]

The ever-changing and temporary nature of Random Access Memory (RAM) presents distinct challenges and possibilities for digital forensic investigations. This research paper offers a comprehensive analysis of RAM data retrieval techniques within the realm of forensic studies. Starting with a comprehensive explanation of the importance of RAM in forensic investigations, this paper explores the different techniques used to extract volatile data from RAM. These methods involve conducting live system analysis, memory imaging, and memory dumping techniques, each with their own set of advantages and limitations. In addition, the paper delves into the complexities of data acquisition, preservation, and analysis, taking into account factors such as system state volatility, encryption, and anti-forensic measures. In addition, this paper explores the practicality and effectiveness of RAM data retrieval methods on various operating systems and hardware setups. It assesses the strengths and weaknesses of commonly used forensic tools and frameworks for RAM analysis, showcasing their performance in practical situations. In addition, the research explores the latest developments and trends in RAM forensics. This includes the use of machine learning algorithms to automate memory analysis and the creation of specialized forensic techniques for cloud environments. Finally, this paper brings together the findings to provide valuable insights into the best practices for RAM data retrieval in forensic investigations. It highlights the significance of meticulous documentation, maintaining a clear chain of custody, and adhering to legal standards. Through the synthesis of established knowledge and the exploration of emerging trends, this research makes a significant contribution to the field of RAM forensics. It offers valuable guidance for digital forensic practitioners, law enforcement agencies, and researchers, aiding in their understanding and advancement of the subject. [ABSTRACT FROM AUTHOR]

Machine learning algorithm is one of the most popular research fields in the 21st century, and it is widely used in various industries. This paper first discusses the development status, characteristics and intelligent classification technology of e-commerce based on various methods. Then, through statistical analysis of multiple samples, this paper establishes a model to predict the quantity of goods and other time-related changes in the future period of time, and proposes an improved variance estimate to achieve the best effect under different data scenarios. Finally, the experiment proves that the response time of the linear regression algorithm fluctuates between 20ms and 25ms, and the overall fluctuation is small. For different samples, the response time of the linear regression algorithm is maintained in a relatively stable range, showing the stability of different input data. The response time of the decision tree algorithm fluctuates between 28ms and 33ms, showing a certain fluctuation. For different samples, the response time of decision tree algorithm fluctuates greatly, which may be affected by the characteristics of input data and show different processing efficiency. The response time of support vector machine algorithm fluctuates between 14ms and 18ms, and the change is relatively stable. For different samples, the response time of SVM algorithm is kept at a relatively stable level, showing the consistency of input data processing ability. [ABSTRACT FROM AUTHOR]

The prosperity of e-commerce has made our lives more convenient, but the logistics behind it has been silently paying. The e-commerce industry is expanding at an unprecedented rate, and with it comes explosive growth in logistics demand, especially in the warehouse distribution link. In order to meet this demand, the warehouse distribution system needs to be improved both in terms of technology and efficiency. This rapid development also brings a series of problems. First of all is the problem of logistics congestion. Due to the surge in orders, especially during peak shopping periods, logistics centers are often under great pressure. This not only affects the speed of delivery, but can also lead to damage or loss of goods. The purpose of this paper is to find an effective method to optimize the logistics warehouse allocation system of e-commerce, and to provide an efficient and reliable logistics optimization scheme for e-commerce enterprises. In this paper, data analysis and simulation methods are used to analyze. The experimental results show that PSO has the largest improvement in the order clerk operation efficiency (13%) and the smallest improvement in the order status (1%). [ABSTRACT FROM AUTHOR]

It is posited that solving the problem of relative poverty (RP) first requires the identification of its cause. We use data from China Family Panel Studies to examine the impact of risk preference on RP of urban and rural families in China and the underlying mechanism. This paper contributes to the existing body of evidence on the determinants of RP. The results show that a high degree of risk preference is conducive to alleviating families' RP. The effect is still significant after dealing with potential endogeneity through instrumental variables. Furthermore, the effect is more obvious among health insurance participation families, endowment insurance participation families, families with low life satisfaction, non-poor families, especially middle-income families, urban families, male-headed families and families in the West in China. Further analysis suggests that risk preference affects RP by promoting entrepreneurship and credit in urban and rural families. Families with a higher risk preference are more likely to engage in entrepreneurship and utilize credit, which in turn helps alleviate their RP. Overall, this paper provides new empirical evidence for understanding the mechanism of risk preference influencing RP. [ABSTRACT FROM AUTHOR]

In law, the term 'smart contract' has been used loosely with no one definition winning out. In an attempt to ameliorate this, the Law Commission of England and Wales has endeavoured to add the word 'legal' to 'smart contract'. No relief is found in the computer coding world, where 'smart contract' is used to indicate not a single but several forms of computer codes that do not involve (necessarily) two parties. Through a sample smart contract use case, this paper identifies more than six such coding concepts and constructs which have been corroborated by the results of trend data analysis. Turning to the public, statistics gathered show a very limited understanding of the terms 'smart contract' and 'smart legal contract' (and their implications). From these findings, this paper recognises the inappropriateness of the use of the single term 'smart contract' for the many diverse iterations as used by computer scientists; and the unsuitability of the word 'contract' as part of the term 'smart contract' by computer scientists because of the term's legal import. The redundancy of the term 'smart legal contract' is established, and in conclusion a definition of 'smart contract' which only considers universal, future-proof characteristics is proposed. In this we disagree with the definitions offered by the Law Commission. The definition we have proffered actively contemplates the legacy use of the term in both law and technology, and is broad enough to be sector- and future-adaptable, and technology-agnostic. [ABSTRACT FROM AUTHOR]

Statistical analysis is an important part of the research process. Researchers are advised to include a statistician from the moment that the study is being planned. The statistical plan informs the research process, including sample size requirements and the most robust data collection. Once the data are collected, descriptive and inferential statistical analyses are performed. The results of this analysis determine whether the findings are significant, which leads to an interpretation of the findings. The importance of the statistical plan and analysis for the researcher is self-evident. However, it is also important for the reader of published papers to have some knowledge of statistical analysis. This allows critical review of all aspects of the published manuscript. The intent of this paper is to review some basic statistical concepts and thus allow the reader to become a better consumer of the literature. [ABSTRACT FROM AUTHOR]

The unlicensed industrial, scientific, and medical (ISM) band at 2.4 GHz has gained increased attention recently due to the high data rate communication systems developed to operate in this band. The paper presents measurement results of fading characteristics, multipath parameters and background interference for these frequencies. Some statistical analysis of the measured data is presented. The paper provides information that may be useful in design and deployment of communication systems operating in the 2.4 GHz ISM band, like those compliant with IEEE 802.11 standard and Bluetooth open wireless standard. [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text. Introduction.: High-quality patient care requires that providers understand and optimize each health care team member's roles and responsibilities. Review of Literature.: The purpose of this paper was to examine the contemporary knowledge and perceptions of physical therapist (PT) practice and education held by advanced practice interprofessional (IP) team members, including physicians, physician assistants (PAs), and advanced practice registered nurses (APRNs). Subjects.: Physicians, PAs, and APRNs licensed in the state of Minnesota. Methods.: Eligible individuals were invited to participate in an online survey assessing understanding of PT practice and education. Descriptive and nonparametric statistical analyses were used to evaluate respondent demographics, practice characteristics, and perceptions and knowledge of PT education and practice. Results.: Survey responses were analyzed from 442 respondents including 210 APRNs, 182 physicians, 48 PAs, and 2 clinicians of unknown profession. Results demonstrated that most referring providers did not learn about physical therapy during their academic or posttraining education. Those who interacted with PTs more frequently perceived greater benefit for their patients, tended to refer to PTs more often, and demonstrated greater knowledge of PT practice. Poorly understood areas of PT practice and education included PT practice settings, specialties, salary, level of education, and the ability to see patients through direct access. Discussion and Conclusion.: Multiple areas of poor provider knowledge regarding PT education and practice were identified; many unchanged from the literature in the 1980s. Three main areas of growth were identified from which actionable recommendations are made: increase IP interactions with PTs, address areas of poor understanding of PT education and scope, and maximize shifting perceptions of PTs through education and advocacy. This paper illustrates that PT education and practice are not well understood by referring providers, posing a threat to IP collaboration. A lack of collaboration may hinder patient and system outcomes because of suboptimal provider utilization. [ABSTRACT FROM AUTHOR]

This paper explores the varied views of South African immigrants who originate from Zimbabwe on the role of traditional and LGBTQIA leaders in marriages. Despite their perspectives, neither South African traditional leaders nor LGBTQIA leaders are authorised to officiate weddings. This inconsistency creates numerous challenges in terms of solemnising and maintaining marriages, particularly for undocumented South African immigrants who live in the country without visas or permits. The paper presents an Afrocentric analysis of various perspectives gathered on the idea that marriage is the foundation of human existence. It uses mixed methods to gather data and thematic analysis to analyse it. The paper highlights different views on the role of traditional and LGBTQIA leaders in marriages, as believed by the South African immigrant community. The research found that South African immigrants of Zimbabwean origin believe that the constitution should authorise traditional and LGBTQIA leaders to solemnize marriages in their communities. [ABSTRACT FROM AUTHOR]

This paper investigates constant-stress accelerated life tests interrupted by a progressive type-I interval censoring regime. We provide a model based on the Chen distribution with a constant shape parameter and a log-linear connection between the scale parameter and stress loading. Inferential methods, whether classical or Bayesian, are employed to address model parameters and reliability attributes. Classical methods involve the estimation of model parameters through maximum likelihood and midpoint techniques. Bayesian approximations are achieved via the utilization of the Metropolis–Hastings algorithm, Tierney-Kadane procedure, and importance sampling methods. Furthermore, we engage in a discourse on the estimation of confidence intervals, making references to both asymptotic confidence intervals and credible intervals. To conclude, we furnish a simulation study, a corresponding discussion, and supplement these with an analysis of real data. [ABSTRACT FROM AUTHOR]

Social media apps are increasingly being used for boosting customer engagement, yet researches that identify the effect of AR face filter apps usage on customer engagement are scarce. Our study aims to analyse identity dimensions of Social Identity Theory (SIT) as motivators for AR face filter apps usage and their relation with Influence Value Customer Engagement (IVCE). Using the snowballing strategy, data was collected online from 406 respondents from the northern region of India. The collected data was used to develop an empirical model for testing the proposed relationships. Findings of analysis on the data indicated that affective identity has the maximum influence on customers' current and future usage of AR face-filter apps which consequently has a positive association with IVCE. The study is novel in its methodological approach depicting IVCE as a consequent rather than antecedent of customer usage behaviour and unearthing the influence of different identity dimensions of SIT on customers' AR face-filter apps usage behaviour. Usage of trendiness as a mediating variable further adds to the originality of the paper. [ABSTRACT FROM AUTHOR]

Introduction: Uncertainty exists regarding the best method for teaching thrust joint manipulation (TJM) to student physical therapists. The purpose of this study was to compare the effectiveness of Peyton's 4-step (P4) approach with the "see one, do one" (S1D1) approach for teaching students to perform a lumbar spine TJM task in an academic setting. Secondary objectives were to compare the effects of each instructional approach on students' attitudes and beliefs toward spinal TJM and on their motivation to learn to perform lumbar spine TJM. Review of literature: The S1D1 approach is used in the health care professions for teaching clinical tasks to students. It is unclear whether the P4 approach may better prepare students to practice TJM. Subjects: Student physical therapists. Methods: Using a factorial quasi-experimental design, an equal number of students were assigned to a P4 or S1D1 instruction group for the TJM task. Students' performance accuracy, time, and outcome performing TJM in an academic setting were measured. Paper surveys were used to collect data about students' attitudes and beliefs toward spinal TJM and their motivation to learn TJM. A generalized estimating equations approach was used for data analysis. Results: Fifty-eight students (29 per group) completed the study. There was an interaction between the instruction group and time on task performance accuracy favoring the P4 approach (P = .03). There was no interaction between the instruction group and task performance time, task performance outcome, attitudes and beliefs toward spinal TJM, or motivation to learn TJM (all P > .19). Discussion and conclusion: The P4 approach more effectively improved student accuracy when performing the TJM task in an academic setting than the S1D1 approach. However, no differences between instruction were found for performance time or outcome. Students reported a favorable perception of learning lumbar spine TJM. These preliminary results suggest that instructors may use the P4 approach to improve students' TJM procedural knowledge before task practice. However, limitations of the study may affect the internal validity and generalizability of results. [ABSTRACT FROM AUTHOR]

At the top of the evidence-based pyramid, systematic reviews stand out as the most powerful, synthesizing findings from numerous primary studies. Specifically, a quantitative systematic review, known as a meta-analysis, combines results from various studies to address a specific research question. This review serves as a guide on how to: (1) design; (2) perform; and (3) publish an orthopedic arthroplasty systematic review. In Part III, we focus on how to design and perform a meta-analysis. We delineate the advantages and disadvantages of meta-analyses compared to systematic reviews, acknowledging their potential challenges due to time constraints and the complexities posed by study heterogeneity and data availability. Despite these obstacles, a well-executed meta-analysis contributes precision and heightened statistical power, standing at the apex of the evidence-based pyramid. The design of a meta-analysis closely mirrors that of a systematic review, but necessitates the inclusion of effect sizes, variability measures, sample sizes, outcome measures, and overall study characteristics. Effective data presentation involves the use of forest plots, along with analyses for heterogeneities and subgroups. Widely-used software tools are common in this domain, and there is a growing trend toward incorporating artificial intelligence software. Ultimately, the intention is for these papers to act as foundational resources for individuals interested in conducting systematic reviews and meta-analyses in the context of orthopaedic arthroplasty, where applicable. [ABSTRACT FROM AUTHOR]

The applications of modern large language models are diverse and new at the same time. It is forecasted that the scientific society and businesses may experience a long period of time exploring all opportunities and challenges for using them which will allow analysis of the impact on how advanced generative artificial intelligence is changing work occupation activities and performance efficiency. Undoubtedly, today’s question that every business must answer is not if but how to implement large language models, due to their ability to transform numerous business processes. This study aims to give a better understanding on how large language models are contributing to the process of exploratory data analysis as they are not here to replace the traditional methods but to add generative artificial intelligence capabilities to the well-established ones. The results of this paper reveal high level of accuracy of the paired output between operation prompts in OpenAI’s large language model and human-mediated entry. However, such output comparison highlighs the need for more informative and specific input prompts to ascertain this accuracy. Further caveats that need to be placed in consideration refer to possible system downtimes, as well as the expenses incurred with every prompt execution. Nevertheless, the comparative speed of operation of large language models remains their most substantial competitive advantage. Overall, the findings in this paper contribute to understanding that large language models streamline with ease the desired extraction of insightful information which may further be used for better decision-making, good data management, and design of winning growth strategy as is the case of the gaming industry. [ABSTRACT FROM AUTHOR]

This paper aims to provide a first attempt towards analysis innovation ecosystems for cardiovascular pathologies in Germany through the use of a stakeholder model. We present essential stakeholders for the development and deployment of innovations in the field of cardiovascular research and medicine, and the primary functions they fulfill in the context of these innovation ecosystems. The adopted approach consists of the implementation of a multilevel system model for analyzing stakeholders in this particular field. Data acquisition transpired through systematic literature review of multiple articles and studies. Data analysis phases were executed until reaching a point at which the considerable amount of data was discovered, ensuring consistency across various sources. We demonstrate that innovation ecosystems in cardiovascular medicine involve interconnected networks of stakeholders across different fields. Moreover, through an investigation of innovation ecosystems of cardiovascular pathologies particularly in Germany, we present the functions undertaken by each stakeholder, which are essential for the participation in the innovation ecosystems. The findings presented in this paper hold the potential to bring better understanding of cardiovascular pathology innovation ecosystems in Germany. This assertion is substantiated through a comprehensive examination of relevant scientific literature. [ABSTRACT FROM AUTHOR]

Introduction. Understanding how educational pathways may influence clinical competence and work responsibilities is important in providing guidance to academic and clinic stakeholders and physical therapists (PTs) on PT career development. The purpose of this paper was to compare perceived clinical competency and job duties between PTs with formal mentored postprofessional clinical education with PTs without formal postprofessional clinical education. Review of Literature. The understanding of self-perceived clinical competence of PTs overall in the United States is limited, especially as related to the impact of postprofessional education. Furthermore, there is limited understanding of the career pathways and development of job duties of PTs in the United States. Subjects. Two thousand three hundred thirty-four PTs in the United States. Methods. An online survey was sent to licensed PTs. This survey included items measuring self-perceived clinical competency and questions related to weekly job responsibilities. Participants were categorized as residency trained, fellowship trained, or non–residency/fellowship trained. Frequency analyses and Kruskal–Wallis tests with pairwise post hoc tests were performed comparing the 3 groups. Results. Residency-trained (P < .007) and fellowship-trained (P < .001) groups demonstrated elements of higher self-perceived clinical competency compared with the non–residency-/fellowship-trained group. Both the residency- and fellowship-trained groups spent less time with patient care and more time with other responsibilities (e.g., teaching, mentoring, and research) (P < .02) during an average workweek. Fellowship-trained PTs (P < .001) spent more time with administrative duties compared with the non–residency-/fellowship-trained group. Discussion and Conclusion. Results from this paper demonstrate that residency- and fellowship-trained PTs have elements of higher self-perceived clinical competency and spend more of their job duties outside of direct patient care compared with PTs who were not residency or fellowship trained. These results may help guide PTs, academic institutions, and employers in planning and achieving specific career paths. [ABSTRACT FROM AUTHOR]

Systematic reviews are the apex of the evidence-based pyramid, representing the strongest form of evidence synthesizing results from multiple primary studies. In particular, a quantitative systematic review, or meta-analysis, pools results from multiple studies to help answer a respective research question. The aim of this review is to serve as a guide on how to: (1) design, (2) execute, and (3) publish an orthopaedic arthroplasty systematic review. In Part II, we focus on methods to assess data quality through the Cochrane Risk of Bias, Methodological Index for Nonrandomized Studies criteria, or Newcastle-Ottawa scale; enumerate various methods for appropriate data interpretation and analysis; and summarize how to convert respective findings to a publishable manuscript (providing a previously published example). Use of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines is recommended and standard in all scientific literature, including that of orthopedic surgery. Pooled analyses with forest plots and associated odds ratios and 95% confidence intervals are common ways to present data. When converting to a manuscript, it is important to consider and discuss the inherent limitations of systematic reviews, including their inclusion and/or exclusion criteria and overall quality, which can be limited based on the quality of individual studies (eg, publication bias, heterogeneity, search/selection bias). We hope our papers will serve as starting points for those interested in performing an orthopaedic arthroplasty systematic review. [ABSTRACT FROM AUTHOR]

Maintaining water quality in aquatic habitats is critical for the health of aquatic species, particularly fish. This study pioneers an innovative method to water quality classification, leveraging IoT-driven data acquisition and meticulous data labelling with the Aqua-Enviro Index (AEI) by considering the fish habitats. Existing mechanisms fail to capture complex temporal dynamics and depend largely on large amounts of labelled data, exposing fundamental limits. In response, we describe the Deep learning based Convolutional Gated Recurrent Unit Tempo Fusion Network (CGTFN) model, which represents a considerable development in the evaluation of water quality. The model addresses these restrictions by seamlessly merging Convolutional Neural Networks (CNNs) for spatial pattern recognition and Gated Recurrent Units (GRUs) for temporal interactions. The Tempo Fusion mechanism combines spatial, temporal, and contextual data harmoniously, allowing for more sophisticated classifications by recognizing subtle interdependencies among environmental elements. The pioneering CGTFN model outperforms previous models, achieving 99.71 and 99.81% accuracy on both public-env and real-time-env datasets, respectively, exceeding established models at 98.2%. These remarkable findings highlight CGTFN’s disruptive potential in water quality evaluation, bridging the gap between technology and environmental management, with ramifications ranging from aquaculture to resource sustainability.Highlights: In this paper we are working with IoT enabled deep learning based CGFTN model for water quality classification considering impact of environmental variables. The data collected through IoT is labelled using an innovative AEI-driven approach specifically designed for fish habitats. CGTFN model integrates CNNs and GRUs, tackling temporal dynamics limitations. Introduce sophisticated Tempo Fusion, harmonizing spatial, temporal, and contextual data. CGTFN demonstrates disruptive potential, achieving superior accuracy (99.71 and 99.81%) on public and real-time datasets. [ABSTRACT FROM AUTHOR]

The rapid development of the metaverse has sparked extensive discussion on how to estimate its development maturity using quantifiable indicators, which can offer an assessment framework for governing the metaverse. Currently, the measurable methods for assessing the maturity of the metaverse are still in the early stages. Data-driven approaches, which depend on the collection, analysis, and interpretation of large volumes of data to guide decisions and actions, are becoming more important. This paper proposes a data-driven approach to assess the maturity of the metaverse based on K-means-AdaBoost. This method automatically updates the indicator weights based on the knowledge acquired from the model, thereby significantly enhancing the accuracy of model predictions. Our approach assesses the maturity of metaverse systems through a thorough analysis of metaverse data and provides strategic guidance for their development. [ABSTRACT FROM AUTHOR]

In automotive aerodynamics, it is not common to focus on a specific vehicle due to restricted access to the Computer Aided Design (CAD) geometries, their short life span, and limited validation data. For this reason, researchers prefer generic bodies that look like automobiles such as Ahmed body in their investigations. However, the absence of moving ground and rotating wheels makes these generic bodies unrealistic for aerodynamic studies. In this context, including wheels in Computational Fluid Dynamic (CFD) simulations, varying ground, and wheel boundary conditions, and comparing their qualitative and quantitative ow parameters with the original Ahmed body experiment is the main objective of this paper. Results have shown that changing stationary ground and wheel boundaries into moving and rotating boundaries do have minor effects on wake characteristics and drag coefficients. However, just the presence of wheels on the model increases force coefficients significantly (increment in drag and lift coefficients by 27.32% and 188.5 counts, respectively.) even though these boundaries are stationary. As a result, the absence of moving ground and rotating wheels can be tolerated to some extent (especially for experimental studies in which inclusion of moving and rotating boundaries may have difficulties). However, a study cannot be evaluated exactly with a model without wheels. [ABSTRACT FROM AUTHOR]

Wrist fractures are common injuries associated with high disability in the early recovery period. The impact of wrist fractures on safe return to drive is not understood. (1) To compare the proportion of adults who were drivers in car crashes before and after wrist fracture; (2) To examine potential factors (demographic and/or clinical) associated with increased odds of being a driver in a car crash following wrist fracture. Retrospective cohort study. Three state-wide government datasets (MainRoads Western Australia [WA], Hospital Morbidity Data Collection and the Emergency Department Data Collection) were used to obtain and link demographic, clinical and car crash information relating to adults with a wrist fracture sustained between 2008 and 2017. McNemar's tests were used to compare the proportion of drivers in a car crash within the 2 years prior to and following the fracture date. Multivariable logistic regressions were used to identify if any variables were associated with increased odds of crashing in the post-fracture period. Data relating to 37,107 adults revealed a 3.3% (95% CI 3.0%–3.6%, p < 0.05) decrease in the proportion of drivers in a car crash following wrist fracture, persisting for the entire 2 years post-fracture, when compared to the proportion who crashed before their fracture. Those with more severe wrist fracture injury patterns had 79%(95% CI 1.07–3.0, p = 0.03) higher odds of having a crash in the first 3 months following their injury, compared to those with isolated wrist fracture injuries. These results inform and update return to drive recommendations. The reduced proportion of drivers involved in crashes following wrist fracture persisted for 2 years; longer than the expected physical recovery timeframe. It is important that hand therapists actively educate the sub-group of adults with more severe wrist fracture injury patterns of the increased likelihood of car crash for the 3 months following their fracture. • This the first paper to report on car crashes following wrist fracture. • We provide much needed information to assist with return to drive decision-making. • Those with severe injury had 79% higher odds of crashing in the initial 3-months. • Education is recommended for the sub-group with 3-months of increased crash risk. [ABSTRACT FROM AUTHOR]

The advent of predictive policing systems demonstrates an increased interest in more novel forms of data processing for the purpose of crime control. This paper draws on interviews with police practitioners in the Netherlands and the UK to deconstruct the rationalities that are embedded within the turn to predictive identification. Debates on predictive policing have predominantly centred data in the analysis of the institutional and societal implication of prediction, linking its use to the premise of efficiency and accuracy and foregrounding issues around bias and discrimination. Yet, little is known about its actual practice. In policing, I find that studying data as practice surfaces new insights into the relationship between risk and the ways in which crime priorities are operationalised and the security mandate of the state is negotiated. Drawing on Harcourt's (2008) observation that the desire to predict crime says more about the police than it does about a potential offender, I argue that predictive identification is not about prediction, nor about efficiency, but rather it is about optimisation. Here, datafication serves to overcome self-defined organisational challenges within the police. [ABSTRACT FROM AUTHOR]

Type-2 fuzzy information systems (TFISs-2), one interesting form of complex data, are extensions of fuzzy information systems. Uncertainty measurement and attribute reduction of TFISs-2 are key issues when describing and analyzing complex fuzzy data. However, existing researches mainly focus on the traditional fuzzy data, and seldom consider type-2 fuzzy data. This paper focuses on finding an effective indicator to measure the uncertainty of TFISs-2, and carries out the research of attribute reduction based on this indicator. Firstly, the weighted membership distance and the weighted membership similarity are defined, which are used to describe the relation between two objects in TIFSs-2, and σ similarity relations are introduced. Secondly, a fuzzy neighborhood structure is defined based on σ similarity relations. The classical rough set model is extended on TFISs-2, and the definitions of upper and lower approximation, accuracy and roughness are given. Then, a novel indicator called σ self-information is used to describe the uncertainty of TFISs-2, and a heuristic greedy selection attribute reduction algorithm is proposed. Finally, the experimental results show that the constructed uncertainty measurement indicator is suitable for TFISs-2, and the proposed attribute reduction method is effective. This study is beneficial to promote the understanding of complex fuzzy data, and contributes to the data analysis and processing of TFISs-2. [ABSTRACT FROM AUTHOR]

Intellectual analysis of small and middle-sized datasets through machine learning tools presents challenges in various application domains. Existing methods fail to provide sufficient accuracy, and their utilization is accompanied by a range of issues during data analysis. This paper proposes the improvement of the input doubling method for middle-sized data analysis. The existing method employs an augmentation procedure where the augmented data sample increases quadratically. This imposes several limitations on the method's usage for middle-sized data analysis. The authors propose enhancing this method by introducing an additional clustering procedure during data augmentation. The training algorithms and application methods are described, and a visualization of the main steps of its operation is provided. Modeling is performed on two medium-sized datasets. Optimal parameters for the improved method are selected, demonstrating its high efficiency. Specifically, significant reductions in the volumes of augmented datasets (8-9 times for both datasets respectively) are achieved, accompanied by substantial reductions in the training procedure duration of the method (more than 100 and 260 times for both datasets respectively), while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

Determining a suitable methodology for any given research is paramount. This article advocates for the adoption of grounded theory by presenting a qualitative research process employed to make sense of data. The rationale behind choosing grounded theory is discussed. It aims to provide an understanding of grounded theory, uncover the intricacies of data collection and analysis within this framework, delve into the coding process, and share insights through a grounded theory organizer to compare coding strategies and a coding cycle. The paper explores a comprehensive understanding of grounded theory as a valuable approach in research. [ABSTRACT FROM AUTHOR]

The applications of clustering in biomedical is pervasive and ubiquitous. A typical example is gene expression data analysis, where clustering is emerging as a powerful solution for uncovering cancer-related insights. Neutrosophic c -means (NCM) clustering has advantages over other conventional clustering methods in characterizing the uncertainty and imprecision caused by cluster overlap and identifying outliers. Nonetheless, NCM and its derivatives equally treat the contribution of each attribute to the cluster. In biomedical applications, genes (i.e. attributes) should take different importance in identifying different clusters. In this paper, we first propose a self-adaptive attribute-weighted neutrosophic c -means (AWNCM) clustering method to overcome the above defects. Moreover, a new objective function is designed to obtain optimal neutrosophic partition, cluster centers and attribute weights. Since AWNCM tends to be more effective against spherical data, we further develop a kernelized version of AWNCM, called KAWNCM, in order to better satisfy the clustering of some complex data (i.e. non-spherical data). We employ the iterative optimization strategy to obtain the optimal solutions for AWNCM and KAWNCM. The advantage of AWNCM and KAWNCM is to improve performance by learning the importance of each attribute to the cluster while maintaining an efficient solution to cluster overlap and outliers. Extensive experimental results using synthetic data and gene expression data demonstrate the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions. [ABSTRACT FROM AUTHOR]

Childhood allergies are a problem that seems to be forgotten by the Artificial Intelligence community, even if they are affecting millions of children. In this paper we are interested in studying the prevalence of childhood allergies, some demographic stats, and to predict the severity of the most encountered allergy, asthma. For this we have used two publicly available datasets, one for Data Engineering and Exploratory Data Analysis, and the other for Bayesian Neural Networks. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) technology has been extensively researched in various fields, including the field of malware detection. AI models must be trustworthy to introduce AI systems into critical decisionmaking and resource protection roles. The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI. Although various adversarial attack and defense methods are actively being studied, there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks. An AI model's robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and recall. Additional evaluation indicators are necessary to evaluate the robustness of AI models against adversarial attacks. In this paper, a Sophisticated Adversarial Robustness Score (SARS) is proposed for AI model robustness evaluation. SARS uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation process. This evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation indicators. Moreover, the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial attacks. This paper proposed using SARS, calculated based on adversarial attacks, to identify data groups with robustness vulnerability and improve robustness through adversarial training. Through SARS, it is possible to evaluate the level of robustness, which can help developers identify areas for improvement. To validate the proposed method, experiments were conducted using a malware dataset. Through adversarial training, it was confirmed that SARS increased by 70.59%, and the recall reduction rate improved by 64.96%. Through SARS, it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data types. In addition, it is expected that improved models can be achieved by improving resistance to adversarial attacks via methods such as adversarial training. [ABSTRACT FROM AUTHOR]

AI-related research is conducted in various ways, but the reliability of AI prediction results is currently insufficient, so expert decisions are indispensable for tasks that require essential decision-making. XAI (eXplainable AI) is studied to improve the reliability of AI. However, each XAI methodology shows different results in the same data set and exact model. This means that XAI results must be given meaning, and a lot of noise value emerges. This paper proposes the HFD (Hybrid Feature Dropout)- based XAI and evaluation methodology. The proposed XAI methodology can mitigate shortcomings, such as incorrect feature weights and impractical feature selection. There are few XAI evaluation methods. This paper proposed four evaluation criteria that can give practical meaning. As a result of verifying with the malware data set (Data Challenge 2019), we confirmed better results than other XAI methodologies in 4 evaluation criteria. Since the efficiency of interpretation is verified with a reasonable XAI evaluation standard, The practicality of the XAI methodology will be improved. In addition, The usefulness of the XAI methodology will be demonstrated to enhance the reliability of AI, and it helps apply AI results to essential tasks that require expert decision-making. [ABSTRACT FROM AUTHOR]

With the rapid development of blockchain technology, the number of distributed applications continues to increase, so ensuring the security of the network has become particularly important.However, due to its decentralized, decentralized nature, blockchain networks are vulnerable to distributed denial-of-service (DDoS) attacks, which can lead to service stops, causing serious economic losses and social impacts. The research questions in this paper mainly include two aspects: first, the classification of DDoS, which refers to detecting whether blockchain nodes are suffering DDoS attacks, that is, detecting the data of nodes in parallel; The second is the problem of DDoS segmentation, that is, multiple pieces of data that appear at the same time are determined which type of DDoS attack they belong to. In order to solve these problems, this paper proposes a modified PointNet (MPointNet) for the classification and type segmentation of DDoS attacks. A dataset containing multiple DDoS attack types was constructed using the CIC-DDoS2019 dataset, and trained, validated, and tested accordingly. The results show that the proposed DDoS attack classification method has high performance and can be used for the actual blockchain security maintenance process. The accuracy rate of classification tasks reached 99.65%, and the accuracy of type segmentation tasks reached 85.47%. Therefore, the method proposed in this paper has high application value in detecting the classification and segmentation of DDoS attacks. [ABSTRACT FROM AUTHOR]

While the ICH E9(R1) Addendum on "Estimands and Sensitivity Analysis in Clinical Trials" was released in late 2019, the widespread implementation of defining and reporting estimands across clinical trials is still in progress and the engagement of non-statistical functions in this process is also in progress. Case studies are sought after, especially those with documented clinical and regulatory feedback. This paper describes an interdisciplinary process for implementing the estimand framework, devised by the Estimands and Missing Data Working Group (a group with clinical, statistical, and regulatory representation) of the International Society for CNS Clinical Trials and Methodology. This process is illustrated by specific examples using various types of hypothetical trials evaluating a treatment for major depressive disorder. Each of the estimand examples follows the same template and features all steps of the proposed process, including identifying the trial stakeholder(s), the decisions they need to make about the investigated treatment in their specific role and the questions that would support their decision making. Each of the five strategies for handling intercurrent events are addressed in at least one example; the featured endpoints are also diverse, including continuous, binary and time to event. Several examples are presented that include specifications for a potential trial design, key trial implementation elements needed to address the estimand, and main and sensitivity estimator specifications. Ultimately this paper highlights the need to incorporate multi-disciplinary collaborations into implementing the ICH E9(R1) framework. [ABSTRACT FROM AUTHOR]

Evidence-based practice relies on using research evidence to guide clinical decision-making. However, staying current with all published research can be challenging. Many clinicians use review articles that apply predefined methods to locate, identify, and summarize all available evidence on a topic to guide clinical decision-making. This paper discusses the role of review articles, including narrative, scoping, and systematic reviews, to synthesize existing evidence and generate new knowledge. It provides a step-by-step guide to conducting a systematic review and meta-analysis, covering key steps such as formulating a research question, selecting studies, evaluating evidence quality, and reporting results. This paper is intended as a resource for clinicians looking to learn how to conduct systematic reviews and advance evidence-based practice in the field. [ABSTRACT FROM AUTHOR]

Intellectual analysis of small and middle-sized datasets through machine learning tools presents challenges in various application domains. Existing methods fail to provide sufficient accuracy, and their utilization is accompanied by a range of issues during data analysis. This paper proposes the improvement of the input doubling method for middle-sized data analysis. The existing method employs an augmentation procedure where the augmented data sample increases quadratically. This imposes several limitations on the method's usage for middle-sized data analysis. The authors propose enhancing this method by introducing an additional clustering procedure during data augmentation. The training algorithms and application methods are described, and a visualization of the main steps of its operation is provided. Modeling is performed on two medium-sized datasets. Optimal parameters for the improved method are selected, demonstrating its high efficiency. Specifically, significant reductions in the volumes of augmented datasets (8-9 times for both datasets respectively) are achieved, accompanied by substantial reductions in the training procedure duration of the method (more than 100 and 260 times for both datasets respectively), while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

Background: Central monitoring aims at improving the quality of clinical research by pro-actively identifying risks and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. This paper, focusing on statistical data monitoring (SDM), is the second of a series that attempts to quantify the impact of central monitoring in clinical trials. Material and Methods: Quality improvement was assessed in studies using SDM from a single large central monitoring platform. The analysis focused on a total of 1111 sites that were identified as at-risk by the SDM tests and for which the study teams conducted a follow-up investigation. These sites were taken from 159 studies conducted by 23 different clinical development organizations (including both sponsor companies and contract research organizations). Two quality improvement metrics were assessed for each selected site, one based on a site data inconsistency score (DIS, overall -log10P-value of the site compared with all other sites) and the other based on the observed metric value associated with each risk signal. Results: The SDM quality metrics showed improvement in 83% (95% CI, 80–85%) of the sites across therapeutic areas and study phases (primarily phases 2 and 3). In contrast, only 56% (95% CI, 41–70%) of sites showed improvement in 2 historical studies that did not use SDM during study conduct. Conclusion: The results of this analysis provide clear quantitative evidence supporting the hypothesis that the use of SDM in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites. [ABSTRACT FROM AUTHOR]

Copyright of Vjesnik Bibliotekara Hrvatske is the property of Croatian Library Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper comprehensively analyzes data mining techniques and performance metrics applied to a logistic regression model. Principal Component Analysis (PCA) was utilized to diminish the complexity of high-dimensional data, enabling clearer visualization and examination of intricate relationships among variables. The logistic regression model demonstrated commendable performance on both test and train sets, as evidenced by high values of accuracy, precision, recall, ROC AUC, and F1 Score were observed. The provided confusion matrices offered detailed insights into the model's accuracy in classifying positive and negative instances. Concerning our hypotheses, we found no significant relationship between gender and academic performance, supported by a highly significant p-value of 0.0 and a weak positive correlation coefficient of 0.0847. However, we noticed a strong positive correlation of 0.99 between gender and exam characteristics, although it has not reached statistical significance for a p-value of 0.281. Our research contributes valuable insights into data analysis, model evaluation, and the interplay between variables. The findings can inform decision-making in realworld applications and warrant further investigation of identified relationships to enhance practical implications. Future studies should consider exploring additional factors, such as the subject of study, semester, and study year, to further understand their impact on student performance. [ABSTRACT FROM AUTHOR]

Radial forging is an efficient and high-precision process for manufacturing rotary parts such as shafts, axles, and gun barrels. While this process has been extensively investigated in the literature, effect of some parameters like the die transverse angle and the part rotational feed angle has not been adequately studied since simulating several steps of this process with a full three-dimensional model is required for this purpose which is labor-intensive and time-consuming. To bridge this gap, in this paper a three-dimensional nonlinear finite element model has been developed to analyze effects of the die transverse angle and part rotational feed angle in this process. To address the lack of reliable experimental data in the literature, an innovative approach has been proposed and used for validation of the developed finite element model. It has been observed that dies with transverse angle of 155ᵒ and 165ᵒ provide the best performance in producing a part geometry close to the desired shape. However, the most uniform residual stress distribution is obtained in forging by a die with a smaller transverse angle. Furthermore, for improving the shape and quality of the final product, the part rotational feed angle should be reduced as much as possible. [ABSTRACT FROM AUTHOR]

In a society based on data-driven, data inclusion and data access play a significant role in societal development. A called democratization of data through open access, Open Data, must be nurtured by countries to empower their citizens, entrepreneurs, companies, industries, academics, and organizations, in general. Open Data Scoring System is an evaluation system that ranks countries in 22 categories of openness in data, divided into the 3 pillars of sustainability. In this paper, we will present the importance of Industry 4.0 and its relation to sustainability and the role of Data Science in Industry 4.0 assuming an Open Design approach. Then, an analysis is made considering the Gross Domestic Product (GDP) of the most relevant countries worldwide, the USA and China, concerning the six (6) higher ranked categories of openness data of these countries, supported by the Open Data Scoring System from 2015 to 2020. Our findings reveal that in the USA and China the main categories are seven (7), five (5), and 2 (two) categories of economic, social, and environmental sustainability, respectively. Through a correlations and co-occurrences analysis of the open data scoring worldwide reveals that the most significant categories are four (4) economic, one (1) social, and two (2) environmental. [ABSTRACT FROM AUTHOR]

This paper aims to demonstrate the findings obtained through the analysis and application of an unsupervised K-means algorithm on the SINAN database from 2001 to 2022 in Brazil, with the objective of understanding which states have the highest number of tuberculosis cases and identifying similarities among them that may contribute to a higher case rate relative to the local population. We will begin with a brief historical introduction, followed by an overview of the characteristics related to tuberculosis transmission. Subsequently, we will discuss the results obtained from the year-to-year analysis of the collected data. [ABSTRACT FROM AUTHOR]

Driver behavior is one of the principal factors associated with road accidents. Much research to date focusing on Machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors. In this paper, the development of an android mobile application (Driver Alert) is described, with the aim of collecting data from mobile phone sensors data to identify certain patterns and understand drivers' behaviors. Additional information was recorded regarding weather and traffic information, using public API's to complement the data directly collected from the vehicle. Four machine learning models (K-Means, Algorithm Agglomerative Hierarchical, Random Forest and Support Vector Machines) were tested and compared to identify different driver profiles. A native mobile application named DriverAlert was developed to support collect data and make it available, through an online dashboard, to drivers and researchers. Due to the available tools and libraries, it possesses, Python language was used, as it is a powerful programming language for workloads in data science, machine learning, and scientific computing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Science & Technology (JCS&T) is the property of Journal of Computer Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines the effect of economic and non-economic factors on international tourist arrival in Nigeria. Annual data on tourist arrival from 53 countries and explanatory variables from 2010-2016 were analyzed using the dynamic panel data regression technique. Results suggest that travel cost, internet use, political stability and number of hotel rooms are the main determinants of tourist flows. The income of tourist-origin countries outside Africa negatively and significantly affects tourist arrival. Travel cost has a robust negative effect on tourists from within and outside Africa. The number of hotel rooms and urbanization rate positively influence tourist arrival, indicating the importance of tourism infrastructure. Government and policymakers must provide an enabling business environment, infrastructure, and policies to improve the country's global competitiveness. [ABSTRACT FROM AUTHOR]

The incidence of stroke in younger adults is rising, particularly among women living with stroke who face multiple physical, psychological, and social challenges that negatively affect their quality of life. Consequently, women's roles in life would be negatively affected at home, work, and in society. This study aimed to explore the lived experience of women suffering from a stroke in Jordan. This paper uses semi-structured interviews to present a descriptive phenomenological study of eight young women suffering from a stroke. The Colaizzi (1978) method was used to analyze interview transcripts. Three main emerging themes that describe the lived experiences of women with a stroke: 1) Experiencing stroke as a woman; 2) Stroke and the intimate relationship with the spouse; 3) Challenges of women's journey while receiving health care. After their stroke, Jordanian women have experienced profound, interrelated, and multifaceted difficulties in all aspects of their life and relationships inside and outside the family. Whilst healthcare providers recognize these stressful symptoms; however, there is a lack of attention and care to meet these needs. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been involved in several domains. Despite the advantages of using artificial intelligence techniques, some crucial limitations prevent them from being implemented in specific domains and locations. The accuracy, poor quality of gathered data, and processing time are considered major concerns in implementing machine learning techniques, certainly in low-end smart devices. This paper aims to introduce a novel pre-treatment technique dedicated to image text detection that uses the images' pixel divergence and similarity to reduce the image size. Mitigating the image size while keeping its features improves the model training time with an acceptable accuracy rate. The mitigation is reached by gathering similar image pixels in one pixel based on calculated values of the standard deviation σ, where we consider that two pixels are similar if they have approximately the same σ values. The work proposes a new pipeline approach that reduces the size of the image in the input and intermediate layers of a deep learning model based on merged pixels using standard deviation values instead of the whole image. The experimental results prove that this technique significantly improves the performance of existing text detection methods, particularly in challenging scenarios such as using low-end IoT devices that offer low contrast or noisy backgrounds. Compared with other techniques, the proposed technique can potentially be exploited for text detection in IoT-gathered multimedia data with reasonable accuracy in a short computation time. Evaluation of the MSRA-TD500 dataset demonstrates the remarkable performance of our approach, Standard Deviation Network (σNet), with precision and recall values of 93.8% and 85.6%, respectively, that outperform recent research results. [ABSTRACT FROM AUTHOR]

This paper, set against the backdrop of expanding urban rail networks and dynamic urban development, focuses on the distribution and evolution of commercial Points of Interests (POIs) within the central urban rail transit areas of Beijing. The study examines data from four different years—2008, 2013, 2017, and 2020—to observe the temporal evolution of commercial entities. It identifies stable explanatory variables affecting the distribution and evolution of commercial POIs, which include rail transit accessibility, characteristics of the working and residential population distribution around stations, and the construction intensity in the vicinity of station areas. Through statistical analysis and model building, relatively stable linear regression equations were established, with R 2 values generally maintained above 0.5 (except for 2017). The study advances our understanding of the influence of rail transit on urban commercial spaces and how this influence shifts with temporal and urban developmental changes. It elucidates the correlation between changes in the number of businesses and spatial configuration, offering insights and information for urban planners and policy makers. This research also serves as a model for exploring the interplay between urban rail transit and commercial spaces in other major cities. [ABSTRACT FROM AUTHOR]