Information For Decision Research Articles

A guideline-informed language model for paediatric cardiology demonstrates high performance in answering complex medical questions

Abstract Background Paediatric cardiology presents unique challenges with its diverse and complex cases, limited evidence base, and the necessity for multi-expert involvement in decision-making processes. In this context, the introduction of generative pre-trained transformer (GPT) based large language models (LLMs) offers a potential avenue for the provision of complex information and clinical decision support. Purpose This study evaluates the quality of three different GPT LLMs in answering complex medical questions, including a state-of-the-art preview model that incorporates the German paediatric cardiology guidelines. Methods Seven paediatric cardiologists and paediatric cardiac surgeons generated 72 questions, including complex questions and medical cases with associated questions. The questions were categorized by difficulty and required knowledge (factual and experience-based or mostly experience-based). We prompted the questions to three LLMs: GPT 3.5, GPT 4 and a GPT 4 turbo preview. The GPT 4 turbo preview was customized by incorporating all guidelines from the German Society for Paediatric Cardiology by a retrieval function. Employing one complex instruction for all questions, we prompted the LLMs to provide precise and detailed expert-level responses. The responses from each model were evaluated by experts based on relevance, factual accuracy, severity of possible harm, completeness, superfluous content, and age-related appropriateness from 0 (very bad) to 7 (very good). Differences were calculated using the Kruskal-Wallis-test in SPSS Version 28. Results Our findings indicated a good performance of all models regarding the dimensions tested. The figures show the average ratings (Figure 1, Figure 2A) and highlight significant differences after Bonferroni correction in bold (Figure 2B). The GPT 4 turbo preview, including the retrieval of guidelines, provided significantly more relevant (average rating [AR] 5.94, meaning mostly relevant), accurate (AR 5.6, meaning between somewhat and mostly accurate) and complete (AR 5, meaning fairly complete) answers compared to GPT 3.5 and GPT 4. In terms of difficulty levels or the type of questions, there was no significant difference in rating. Relevance ratings were slightly better in factual questions (AR 5.7) than in those requiring more experience-based knowledge (AR 5,3). Although GPT4 had higher average scores compared to GPT 3.5 in all dimensions except superfluous content, the differences in rating were not statistically significant. All models had relevant difficulties considering the age-related aspects of the questions (AR 4.06-4.45, p=0.455). Conclusion This study highlights the potential and limitations of AI language models in addressing complex medical questions in fields characterized by highly individualized decision-making scenarios. The findings advocate for the development of more specialized AI tools in medicine, tailored to specific medical fields and patient age groups.Fig 1:Average ratings of LLMsFig 2:Rating differences between LLMs

Enhanced Cooperative Compressive Spectrum Sensing in Cognitive Radio Networks

ABSTRACTThe demand for bandwidth in cognitive radio networks (CRNs) is growing as applications become increasingly data intensive. Techniques such as compressed spectrum sensing (CSS) and cooperative spectrum sensing (C‐SS) are employed to address this challenge. C‐SS enhances overall detection accuracy and reliability by enabling multiple nodes to share and combine their local sensing data. Conversely, CSS effectively reduces the required information for spectrum usage decision making, thereby improving bandwidth utilization. Integrating these two methods allows CRNs to utilize the spectrum reliably and efficiently, leading to increased spectral efficiency. To further improve reconstruction performance, we leverage the sparsity concept to transcend hardware constraints and merge restrictions from both real and synthesized channels. This approach involves the virtual synthesis of channels, which linearly enhances the signal‐to‐noise ratio (SNR) within the network's size range. Simulation results demonstrate that our proposed method offers significant advantages over single‐node recovery, as validated by simulations and software defined radio (SDR) Implementation. The integration of spectral estimations from various local CR detectors enhances spatial diversity gain and sensing quality, particularly in fading channels. Compared to traditional approaches, our method achieves superior performance, evidenced by an increase in (from 93.97% to 96.52%) with almost the same .

Value relevance of accounting information in Morocco: impact of profitability and earnings management strategies

PurposeThis study examines the value relevance of accounting information for publicly listed Moroccan companies and analyzes how profitability affects stock market valuation. Additionally, the study explores earnings management strategies used to avoid losses and earnings decreases.Design/methodology/approachEmpirical tests use the Ohlson (1995) Price model to assess the extent to which accounting information explains variation in stock prices for the period 2012–2021. Moreover, the study applies the Burgstahler and Dichev (1997) approach to analyze earnings management strategies used by listed Moroccan companies.FindingsThe findings suggest a positive and significant relationship between the book value, net earnings and the market value of stocks. Additionally, profitability is identified as a crucial factor significantly influencing the relevance of accounting figures. Moreover, the analysis of the distribution of earnings reveals that listed Moroccan companies engage in earnings management to avoid losses and decreases in earnings.Practical implicationsThe findings hold practical significance for investors, corporate leaders, accountants and stakeholders relying on accounting information for decisions. They also offer insights for Moroccan stock market regulators to improve accounting quality and market efficiency.Originality/valueThe study shows the extent to which stock prices reflect the accounting information from an emerging country perspective, contributing to a predominantly unexplored area of literature regarding economies in transition. Moreover, the study offers crucial insights into the influence of profitability on the market value of listed Moroccan companies and sheds light on the utilized earnings management strategies.

Evaluating the contributions and economic costs for Walleye fry and fingerling stocking in a large midwestern reservoir

Stocking is a common management practice to maintain Walleye populations where natural recruitment is low or to introduce and sustain Walleye populations outside of their native range. Walleye can be stocked at multiple lengths (e.g., fry and fingerling) and different sizes of stocked Walleye may contribute differently to Walleye year class strength, depending on the system into which they are stocked. The cost associated with stocking also varies by product. Therefore, it is important to determine the contributions from different stocked products within a region or for an individual waterbody. The objectives of our research were to 1) compare the relative contribution of and total length and body condition at the time of capture at age-0 between stocked Walleye fry and fingerlings and naturally produced individuals; and 2) estimate and compare the economic costs of Walleye fry and fingerling production relative to their contribution to the age-0 catch in Lake McConaughy during 2022 and 2023.We collected 178 and 263 age-0 Walleyes from Lake McConaughy during the summer and fall of 2022 and 2023, respectively. Fingerling-stocked Walleye contributed 2.12 times in 2022 and 2.92 times more in 2023 than fry-stocked Walleye. Production cost estimates revealed that fry cost 1.08 and 1.35 times greater than fingerlings per stocking unit in 2022 and 2023, thus fingerlings were deemed the most cost-effective product in this study. Walleye stocked as fry were significantly longer at the time of collection in 2022 and were in significantly better condition in both years compared to fingerling stocked Walleye. Our results provide valuable insights for fisheries managers by offering a case study example as well as offering a practical approach to guide effective management decisions on which stocked product is most appropriate for other systems. Overall, this research enhances the existing literature on Walleye stocking in large reservoir systems, offering practical and useful information for effective management decisions.

Improving the efficiency of aerial surveys for monitoring North American beaver population dynamics

Context The North American beaver (Castor canadensis) was extirpated from much of its range in the US in the 1800s due to fur trapping and change in land use. However, the species has recolonised much of its former range, including the US state of Ohio. Since 2013, the Ohio Division of Wildlife (ODOW) has monitored trends in beaver colony density via aerial surveys of 40 km × 40 km plots classified as low, medium, or high suitability based on the amount of wetland. Nonetheless, the current classification system may miss important correlates of beaver colony density. Aims Our study aimed to (1) identify predictors of beaver colony density (number of colonies inferred from aerial counts of lodges) across Ohio, and (2) develop a model-based classification system to improve the efficacy of monitoring efforts. Methods To predict beaver colony density in Ohio we used an aerial survey dataset of 54 plots (40 km × 40 km) collected by ODOW annually between 2013 and 2020, along with a suite of environmental, anthropogenic, and climate variables in a mixed effects modelling framework. Key results Beaver colony density was positively associated with wetland and reclaimed surface mine areas and inversely associated with the proportion of agricultural lands. There was a negative interaction between wetland and surface mines; in general, beaver colony density increased with wetland and surface mine area. However, in plots with wetland area >1000 ha, beaver colony density was weakly negatively associated with surface mine area. Using median and interquartile ranges of model-averaged predicted beaver colony density, we developed a new classification of low, moderate and high suitability for both the survey plots and the entire state of Ohio. We found that eastern Ohio had high suitability, while the central and western parts of the state had lower suitability for C. canadensis. Conclusions Our approach to identifying predictors for beaver colony density at broad spatial scales highlights the importance of reclaimed surface mines and wetlands for beaver populations, while the model-based habitat classification provides ODOW additional information for monitoring and beaver management decisions. Implications Improved C. canadensis monitoring at the landscape scale using habitat classifications that consider local conditions can both improve annual survey cost-effectiveness and facilitate the sustainable management of this recovering species.

Clinical research for saliva-based therapeutic drug monitoring of linezolid.

Linezolid is primarily used to treat of methicillin-resistant Staphylococcus aureus and multidrug-resistant tuberculosis infections. Thrombocytopenia due to linezolid usage is a concern, and therapeutic drug monitoring has been reported to be effective in its prevention. Plasma concentrations provide valuable information for treatment decisions; however, collecting plasma samples can be burdensome for both patients and healthcare providers. Therefore, there is interest in saliva as an alternative for monitoring, considering its potential to replace plasma samples. Patients hospitalized at Hokkaido University Hospital and Hokkaido Spinal Cord Injury Center between April 2022 and July 2024, who received oral or intravenous linezolid treatment, were enrolled. The concentrations of linezolid were simultaneously measured in plasma and saliva samples. We determined the concentration profiles of linezolid in the saliva and examined the correlation between saliva and plasma linezolid concentrations. Eighteen patients receiving linezolid were enrolled. The average of saliva/plasma (S/P) concentration ratios of linezolid were 1.018. A strong correlation was found between the salivary and plasma concentrations of linezolid (R= .833, P< .001). Notably, in patients receiving intravenous administration of linezolid, the correlation was even more pronounced (R= .885, P< .001). Additionally, when focusing on the S/P ratio of the trough concentrations in the morning and at night, the S/P ratios at night were much closer to 1.0. The concentrations of linezolid in plasma and saliva were similar, indicating their potential applicability in clinical settings. The monitoring of linezolid concentrations in saliva has been shown to be particularly suitable for patients receiving intravenous administration.

Race, Market Size, Segregation and Subsequent Opportunities for Former NFL Head Coaches

Abstract This study examines subsequent head coach opportunities for former National Football League (NFL) head coaches. Under a subsequent CEO career framework, survival analysis is used to examine the effects of race and market characteristics on subsequent NFL head coach opportunities for former head coaches. Observations of former head coaches who neither ended their coaching careers nor possess head coach positions in the observed seasons are used (n = 1,132). Black former NFL head coaches are less likely to secure subsequent NFL head coaching opportunities if their most recent coaching position was in a large media market. However, both Black and non-Black former head coaches who most recently coached in highly segregated metropolitan areas experienced higher likelihoods of securing subsequent head coaching opportunities. This segregated market effect is even stronger for Black former head coaches. The NFL can utilize this information in policy formation decisions regarding hiring policies and practices. Teams and their lead executives can also use this information to identify any personal biases that may arise within the head coach labor market. Coaches may use the information to best position themselves for subsequent career opportunities.

Revisiting the y-ome of Escherichia coli.

The model organism Escherichia coli K-12 has one of the most extensively annotated genomes in terms of functional characterization, yet a significant number of genes, ∼35%, are still considered poorly characterized. Initially genes without known functional understanding were given 'y' gene names. However, due to inconsistency in changing 'y' names to non-'y' names over the years, gene name alone does not provide sufficient information as to the characterization level of genes. Attempts to characterize y-ome genes, i.e. those that lack experimental evidence for function, are ongoing, and recent categorization based on the level of experimental evidence has helped clarify those genes that are well characterized versus uncharacterized. EcoCyc, the most comprehensive, curated genome database for E.coli K-12 substr. MG1655, has updated this approach by expanding the categories to include Partially characterized genes using a set of computational rules that includes keywords, experimental evidence codes and Gene Ontology terms. Approximately half of the previously categorized y-ome genes are now categorized as Partially characterized, leaving 15.5% (738) as Uncharacterized genes in EcoCyc. This new categorization scheme is searchable in the EcoCyc database, will be updated as new experimental evidence is curated and provides important information for research decisions.

Selection of Green Recycling Suppliers for Shared Electric Bikes: A Multi-Criteria Group Decision-Making Method Based on the Basic Uncertain Information Generalized Power Weighted Average Operator and Basic Uncertain Information-Based Best–Middle–Worst TOPSIS Model

This study introduces a novel multi-criteria group evaluation approach grounded in the theory of basic uncertain information (BUI) to facilitate the selection of green recycling suppliers for shared electric bikes. Firstly, a comprehensive index system of green recycling suppliers is established, encompassing recycling capacity, environmental sustainability, financial strength, maintenance capabilities, and policy support, to provide a solid foundation for the scientific selection process. Secondly, the basic uncertain information generalized power weighted average (BUIGPWA) operator is proposed to aggregate group evaluation information with BUI pairs, and some related properties are investigated. Furthermore, the basic uncertain information-based best–middle–worst TOPSIS (BUI-BMW-TOPSIS) model incorporating the best, middle, and worst reference points to enhance decision-making accuracy is proposed. Ultimately, by integrating the BUIGPWA operator for group information aggregation with the BUI-BMW-TOPSIS model to handle multi-criteria decision information, a novel multi-criteria group decision-making (MCGDM) method is constructed to evaluate green recycling suppliers for shared electric bikes. Case analyses and comparative analyses demonstrate that compared with the BUIWA operator, the BUIGPWA operator yields more reliable results because of its consideration of the degree of support among decision-makers. Furthermore, in contrast to the traditional TOPSIS method, the BUI-BMW-TOPSIS model incorporates the credibility of information provided by decision-makers, leading to more trustworthy outcomes. Notably, variations in attribute weights significantly impact the decision-making results. In summary, our methods excel in handling uncertain information and complex multi-criteria group decisions, boosting scientific rigor and reliability, and supporting optimization and sustainability of shared electric bike green recycling suppliers.

B-217 Effective Diagnostic Platform and Method to Differentiate the Infectious From Non-Infectious Viruses With Nucleic Acid Detection Workflow

Abstract Background While qPCR has demonstrated high sensitivity, specificity, and ability to deliver reliable quantitative data in clinical samples, such results do not differentiate between infectious and non-infectious viral particles. In view of the principle of the qPCR method itself, the amplification detects the nucleic acid (DNA/RNA) of the virus, not the virus itself. In many cases, it is still possible to detect DNA/RNA fragments from dead viruses in recovered patients, while they are not infectious anymore. This will lead to prolonged quarantine period or treatment for the patients, which could cause great psychological pressure to the patients and increased healthcare burden. Therefore, an effective detection method that can discriminate viable and inviable pathogens is of great significance. Methods SARS-CoV2 viral RNA, SARS-CoV-2 viruses, or clinical samples with SARS-CoV2 infection were diluted in Delta MTM (Molecular Transport Media) to the respective testing concentrations. The samples were incubated with intercalating dye (PMAxx 100nM) with or without Triton X-100 for 10 min. The mixture was then exposed to the photo-activation device for 5-15 min (@ temperature below 37° C). The samples were then subjected to the standard nucleic acid extraction &amp; purification steps with Qiagen viral RNA extraction kit. The extracted RNA was subjected to amplification with SARS-CoV2 Resolute 2.0 &amp; Fortunate 2.1 assays on BioRAD CFX96. Results The viability PCR (vPCR) efficiency was almost 100% for all SARS-CoV2 RNA input tested suggesting that without viral membrane protection, PMAxx dye effectively bound to the naked RNA after photoactivation process, and the bound RNA was not able to be amplified. The vPCR efficiency was further enhanced with addition of surfactant Triton X-100 suggesting the improved permeability of dye through the viral envelope facilitated the PMAxx binding to the nucleic acid. COVID19 positive clinical samples with viral load ≈ Ct 30 treated with or without PMAxx and photo-activation procedures showed distinctive ΔCt value ranging from 9.27±0.18 to 15.01±0.14. The Ct after dye treatment were all non-numeric (non-detectable) if cut-off Ct sets @ 42.0, indicating the complete inhibition after vPCR pretreatment. Since the clinical samples were undergone heat-inactivation pre-treatment, the result shown here indicating that all samples were non-infectious, which was consistent with the samples tested. Conclusions We developed an effective viability PCR detection method and proprietary vPCR device, which was specifically designed for effective dye photo-activation. In addition, the method takes less than 15 min and could be integrated into the existing standard nucleic acid extraction and amplification workflow. Comparing to the standard PCR test result, this method could provide the information not only on the positivity of the tested pathogen, but also its viability, which could be very essential information for public surveillance and diagnostic decisions for healthcare providers.

Investing in people, not in products: how age, gender, ethnicity, and attractiveness of entrepreneurial teams influence the decision-making of angel investors in Germany

AbstractThe high-risk decision environment and information asymmetries associated with investing in early-stage startups in pitch competitions make angel investors prone to biased decision-making. Drawing from social perception theory, this study examines how angel investors’ decisions are influenced by observed personal characteristics of entrepreneurial teams, based on representative stereotypes. Analyzing a dataset of N = 553 startup pitches from the German televised competition Die Höhle der Löwen, this study reveals that the likelihood of securing deals with German angel investors and the resulting business valuations are linked to certain superficial team characteristics. Specifically, the age, diverse ethnicity, and physical attractiveness of the entrepreneurial team have a significant positive effect on deal probabilities. Moreover, angel investors offer lower deal valuations to teams of older and female entrepreneurs, suggesting the presence of a systematic bias. These insights contribute to understanding the role of stereotypes in entrepreneurial finance and address the challenges related to bias in access to capital for early-stage startups in Germany.

Large-scale consensus in incomplete social network with non-cooperative behaviors and dimension reduction

Obtaining a consensus solution is a formidable challenge for large-scale decision-making (LSDM) in social network (SN). The reaching of large-scale consensus in SN is hindered by serious difficulties, including complex decision information, incomplete social relations, a multitude of decision-makers (DMs), and non-cooperative behaviors. This paper introduces a novel three-stage consensus framework that systematically addresses these challenges by data preprocessing, dimension reduction, and optimization modeling. Firstly, the cloud model is applied to convert the probabilistic linguistic information into numerical information, facilitating computational analysis. Meanwhile, an improved t-norm trust propagation method that incorporates the impact of opinion similarity is developed, ensuring the completeness of SN. Secondly, an improved Louvain algorithm is designed to divide large group into cohesive subgroups, enhancing the manageability of LSDM. On this basis, a three-stage consensus optimization that considers non-cooperative behaviors is proposed, which boasts threefold benefits: (i) Assures the synchronous achievement of local and global consensus. (ii) Implements self-adaptive management mechanism of non-cooperative behaviors. (iii) Provides acceptable adjusted opinions for subgroups and DMs. Finally, detailed numerical experiments and comparative analyses are given to demonstrate the effectiveness of the proposed method.

Maximizing Marketing Value: An Empirical Study on the Framework for Assessing AI and ML Integration in Marketing Management

To address this issue, we conduct an empirical investigation into the application of AI and ML in marketing management using a rich framework that aims to optimize marketing value as much as possible. The framework is structured in four pillars: data gathering and processing, customer insights &amp; segmentation, personalized marketing strategies and performance improvement. The utility and practical implementation of the framework was examined through a mixed-methods study design employing quantitative surveys validated by qualitative interviews. The survey was conducted among marketing practitioners across all industries. We also analyze the extent of AI and ML integration in each component empirically, with qualitative insights providing perspectives on opportunity areas, challenges and best practices. Three of these areas in the framework organizational readiness, resource allocations and skill gaps are highlighted as those with most pronounced differences on AI &amp; ML deployment. To learn more about the dataset, demographics of 38 respondents in sample. The sample has a diversified age distribution and it ranges from 27 to 50 years of age. This demonstrates a significant bit of variation (SD = 7.30) and an average age of around 36-37 years Because there's also diversity when it comes to gender representation, with seven people identifying as "Other", besides the six male and fifteen women. According to the statistics, most people are bachelor’s degree holders although also a lot of masters and doctorate degrees which adds depth into information. Overall, the results provide a glimpse into demographic attributes regarding marketing performance and providing useful information for strategic decision making by doing so on behalf of management in charge with implementing those decisions. Finally, this research provides actual data rather than theory on how AI and ML mechanics are being integrated in the marketing management space. The strategic deployment of AI and ML in this research can significantly improve the efficient utilization of resources, maximize marketing efficiency by displaying key parameters impacting shopping behaviour as possesses with more long standing competitive benefits. When they can lean on hard data, researchers, marketers and decision-makers alike are likely to find a more straightforward path to the tasks that matter most as well as how AI/ML factors into driving real marketing value. The research also underscores the importance of ongoing learning, adapting and aligning strategies to leverage new technology in an ever-evolving field like marketing.

How Does Social Media Influence Tourist Behavior? A Conceptual Framework for Exploration

Purpose of the study: In the era of digitalization, the introduction of social media platforms has fundamentally changed how people access and exchange information, significantly impacting various societal spheres, including the travel and tourism industry. This study presents a conceptual framework of existing literature to examine the relationship between social media and its influence on travel behaviors in choosing tourist destinations. Methodology: An evaluation of recently published research papers, articles, and publications on social media and its influence on travel behaviors was conducted. Main Findings: Visitors may find it challenging to sort through possibilities due to information overload and decision fatigue caused by the abundance of data. However, when it comes to information exposure, perception creation, and decision-making during the pre-trip preparation stage, social media is an essential source of travel-related information. Applications of the study: This study highlights the multifaceted ways social media impacts travelers' decision-making processes, leading to a paradigm shift in how tourist destinations are chosen. Novelty/Originality of the study: This study encompasses review of more than 50 sources that inludes, research papers, blogs, websites, newspaper articles, magazines topresent a comprehensive view on the said title.

Knowledge-Assisted Actor Critic Proximal Policy Optimization-Based Service Function Chain Reconfiguration Algorithm for 6G IoT Scenario.

Future 6G networks will inherit and develop Network Function Virtualization (NFV) architecture. With the NFV-enabled network architecture, it becomes possible to establish different virtual networks within the same infrastructure, create different Virtual Network Functions (VNFs) in different virtual networks, and form Service Function Chains (SFCs) that meet different service requirements through the orderly combination of VNFs. These SFCs can be deployed to physical entities as needed to provide network functions that support different services. To meet the highly dynamic service requirements in the future 6G Internet of Things (IoT) scenario, the highly flexible and efficient SFC reconfiguration algorithm is the key research direction. Deep-learning-based algorithms have shown their advantages in solving this type of dynamic optimization problem. Considering that the efficiency of the traditional Actor Critic (AC) algorithm is limited, the policy does not directly participate in the value function update. In this paper, we use the Proximal Policy Optimization (PPO) clip function to restrict the difference between the new policy and the old policy, to ensure the stability of the updating process. We combine PPO with AC, and further bring the historical decision information as the network knowledge to offer better initial policies, to accelerate the training speed. We also propose the Knowledge = Assisted Actor Critic Proximal Policy Optimization (KA-ACPPO)-based SFC reconfiguration algorithm to ensure the Quality of Service (QoS) of end-to-end services. Simulation results show that the proposed KA-ACPPO algorithm can effectively reduce computing cost and power consumption.

Escala de Clima de Innovación: análisis psicométrico en trabajadores peruanos

the changes caused by the COVID-19 pandemic pushed organizations to attempt to remain current and competitive. Thus, the abilityto respond to new demands and adapt to shifting conditions was an unavoidable requirement, which implies innovating in the development oftheir processes. In that sense, the growing importance of innovative work behaviors in the development of organizations has driven researchersto investigate mechanisms that foster such behaviors in workers. The objective of this study was to analyze the psychometric properties of theInnovation Climate Scale (ICS). The instrumental study involved 273 Peruvian workers (50.916 % male) ranging in age between 22 and 76 yearsold (Mean = 45.224), employed for the most part in the private sector (75.092 %). The psychometric analysis explored item clarity and relevanceby means of assessment by expert judges (content validity). Internal structure was assessed via confirmatory factorial analyses, both of obliquemodels and bi-factor. Finally, scoring reliability (alpha coefficient) and construct reliability (omega coefficient) were estimated. This study findsthe ICS items to be clear and relevant, a greater empirical support for the unidimensional model as its internal structure, and an adequate relia-bility. In conclusion there is favorable evidence of validity and reliability for the application of the ICS in Peruvian companies as a useful tool tomeasure the innovative climate perceived by its workers, providing information for decision making and planning of innovation strategies andorganizational creativity

Physical demands of collegiate basketball practice: a preliminary report on novel methods and metrics.

Knowing the specific physical demands of basketball players can provide useful information for clinical decision making when rehabilitating athletes following injury. The purpose of this observational study was to describe the physical demands of basketball play at the Division I collegiate level using video-based time-motion analysis and introduce a time-efficient alternative method of quantifying demands. Eleven NCAA Division I basketball players (6M, 5F; 4 guards, 4 centers, 3 forwards) participated in the study. Video footage was collected from four practices (2 men's, 2 women's) and used to quantify the types and frequencies of player movements based on definitions from seminal work. A second and simpler method was also used to classify movement. A two-way ANOVA was used to assess significant differences in movement by team (men's, women's) and position (guard, forward, center). There were significant differences in counts of stand/walk (p < 0.001), jog (p = 0.012), run (p = 0.001), stride/sprint (p = 0.04), and medium-intensity shuffling (p < 0.001) per minute and proportion of practice time spent in bodyweight (p < 0.001) or above-bodyweight (p < 0.001) loading between teams. There were significant differences for jog (p = 0.001) and transition (p = 0.07) rates across positions. Position and team are important considerations for rehabilitation and return-to-sport clearance. Quantification of these demands can be reliably acquired through video analysis using a simplified method (estimated foot load) or using traditional methods of movement classification and counts, particularly when applying descriptors that better capture the current style of play.

Exploring dynamics of student course feedback in higher education

This article delves into student course feedback using publicly available data from the University of North Texas. It examines factors influencing elevated student course evaluation ratings in the Information Technology and Decision Sciences department. The study reveals a positive relationship between higher response rates and better evaluation scores. The challenge index, individual course engagement, and difficulty are linked to higher scores. Course level impacts scores, with higher-level courses receiving higher ratings. This prompts further investigation into why full professors may not outperform associates in student ratings, advocating for broader research into characteristics influencing course evaluation performance. The study contributes valuable insights into academia and fostering discussions on business faculty assessment.

Design of a high-dimensional information decision model for smart supply chains using IoT data traceability in the post-public health crisis era

Design of a high-dimensional information decision model for smart supply chains using IoT data traceability in the post-public health crisis era

Systematic review of IPSAS standards: The introduction of IPSAS in public establishments

The ultimate objective of this paper is to examine the impact of the adoption of IPSAS in Morocco on the production of accounting and financial information, in order to inform decision-making processes. IPSAS ushered in a global revolution in public accounting reforms, and Morocco is among the nations that have adopted these standards, starting their implementation several years ago. The integration of IPSAS mobilises a theoretical and conceptual framework encompassing the theories and New Public Management (NPM), as well as the theory of public value. The aim is to harmonise accounting practices with global standards, thereby enhancing the transparency, comparability and credibility of financial information. However, the implementation of IPSAS in Moroccan institutions raises a number of issues and challenges, particularly in terms of adapting systems, training staff and managing costs. To deepen our understanding of this topic, we conducted a bibliometric analysis using the VOSviewer tool. This analysis enabled us to identify influential authors, collaborative networks, and the main and emerging themes in IPSAS research. The results show that the implementation of IPSAS helps to ensure good quality and transparency in public sector financial reporting, providing better information for financial management, accountability and decision making. IPSAS, as international accounting standards specific to the public sector, aim to improve the quality of financial reporting by public entities, thereby enabling more reliable resource allocation decisions and contributing to improved financial transparency and accountability.