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We address business schools' need to develop policies and practices informed by researchers' scholarly impact by offering an improved conceptualization and measure that considers contextual and temporal aspects: the Contextualized Scholarly Impact Index (CSII). We also offer software to calculate individual and institutional-level CSII scores. Our measure of scholarly impact (a) considers impact within the context of the management field, (b) relies on quantity and quality dimensions of research impact, (c) incorporates temporal aspects of impact, and (d) yields scholarly impact scores using the latest data from an inclusive set of influential management journals. CSII provides a transparent and multidimensional measure of scholarly impact to informpolicymaking, facilitate benchmarking of research impact, and support researchers in understanding and enhancing their scholarly contributions. Our study contributes to policymaking and practice by improving the theoretical conceptualization of scholarly impact; offering a metric for comparing the impact of individual scholars, research groups (e.g., teams, centers, and institutes), and business schools; aiding funding agencies in making informed decisions; assisting in the training and development of faculty and doctoral students; supporting policies for review, promotion, and tenure; and providing critical information for talent management practices including selection, succession planning, and rewards. [ABSTRACT FROM AUTHOR]

Technology in libraries has played an essential role in serving today's communities. This study provides an overview of the integrated library systems/software (ILSs) used in libraries in South Sulawesi, Indonesia. It aims to highlight the strengths and possibilities of ILSs and briefly explain their advantages and disadvantages along with the cost of implementation. The data was gathered from questionnaires sent via an online survey and from direct interviews with certain academic libraries over the period of 2019 to 2020. Fifty-three of 67 libraries that fulfilled the study have implemented an ILS. To deeply understand the application, a direct interview with some libraries was conducted to learn the advantages and disadvantages. The result of the study showed that the most used ILSs are SLiMS and INLISlite and other programs like Apollo, Athenium Light, Simpus, Spektra, Jibas, KOHA, and Openlibrary. The budget spent is an average of300 USD. While the ILSs have helped these libraries improve services, IT expertise and adequate resources are needed, especially when the systems present problems. An easy-to-use system that costs less will potentially be used in this area of research. This study will be particularly helpful for any library in Indonesia. These findings may also be generalized to libraries in other countries facing economic and technological similarities. [ABSTRACT FROM AUTHOR]

Multimodal sentiment analysis is an important and active research field. Most methods construct fusion modules based on unimodal representations generated by pretrained models, which lack the deep interaction of multimodal information, especially the rich semantic-emotional information embedded in text. In addition, previous studies have focused on capturing modal coherence information and ignored differentiated information. We propose a text-enhanced multi-interactive attention and multitask learning network (TEMM). First, syntactic dependency graphs and sentiment graphs of the text are constructed, and additional graph embedding representations of the text are obtained using graph convolutional networks and graph attention networks. Then, self-attention and cross-modal attention are applied to explore intramodal and intermodal dynamic interactions, using text as the main cue. Finally, a multitask learning framework is constructed to exert control over the information flow by monitoring the mutual information between the unimodal and multimodal representations and exploiting the classification properties of the unimodal modality to achieve a more comprehensive focus on modal information. The experimental results on the CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets show that the proposed model outperforms state-of-the-art models. [ABSTRACT FROM AUTHOR]

The rapid digitization of the modern world has led to an increase in the collection and processing of personal data. The principles of transparency and consent have become crucial for citizens to understand how their data is being used and to be able to give their voluntary consent to its processing. Ukraine is actively adapting its legislation to EU standards in the field of personal data protection. In particular, the General Data Protection Regulation (GDPR) affects approaches to data collection and processing. Examining transparency and consent in the context of these changes can help identify which aspects of the law need special attention. Citizens are becoming increasingly cautious about who they trust with their data. This article analyzes and evaluates the role and impact of the principles of transparency and consent on the effective mechanism of personal data protection in Ukraine, taking into account modern technological and legislative challenges. Studying these principles demonstrates their importance and relevance in today’s digital society. The principle of transparency determines the need for accessible and understandable information for data subjects regarding the processing of their data. Openness is a fundamental requirement for maintaining trust between organizations and citizens. Providing the ability to control and regulate their data allows data subjects to play a more active role in the processing and use of their personal data. The principle of consent, in turn, emphasizes the voluntary and informed nature of consent to data processing. The consent of the data subject determines the basis for the legal processing of personal data, ensuring harmony between the rights of citizens and the interests of organizations. As technology advances and legislation changes, transparency and consent become even more important aspects. They help build trust between organizations and citizens, ensure responsible use of personal data, and empower individuals to control their own information. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to explore strategic investment in information management and its crucial role in driving financial innovation. By examining the integration of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Big Data Analytics, Cloud Computing, Large Language Models (LLMs), Robotic Process Automation (RPA), Internet of Things (IoT), Cybersecurity Technologies, and Quantum Computing, this research aims to highlight how these technologies enhance decision-making, operational effectiveness, risk management, and compliance within the financial sector. Methodology: The study employs a comprehensive literature review of existing research to analyze the impact of strategic investment in information management on financial innovation. Key technologies are identified and their applications in finance are discussed. The methodology includes synthesizing findings from various sources to present a cohesive understanding of the relationship between information management, technology, and financial innovation. Results: The results indicate that strategic investment in information management significantly enhances financial innovation by leveraging advanced technologies. AI and ML improve predictive analytics and customer personalization, Blockchain ensures secure transactions and transparency, Big Data Analytics enables data-driven decision-making, and Cloud Computing provides scalable solutions. LLMs enhance natural language processing capabilities, RPA automates repetitive tasks, IoT facilitates real-time monitoring, Cybersecurity Technologies protect financial data, and Quantum Computing offers potential breakthroughs in financial modeling and encryption. Implication: The implications of this study suggest that financial institutions should prioritize strategic investments in information management and the adoption of advanced technologies to stay competitive and resilient in the evolving financial landscape. Effective information management practices enable better decision-making, improved operational efficiencies, enhanced risk management, and regulatory compliance, ultimately fostering financial innovation. Contribution: This study contributes to the existing body of knowledge by providing a detailed analysis of the role of strategic investment in information management and its impact on financial innovation. It highlights the importance of integrating advanced technologies in financial practices and offers insights into how these technologies can be leveraged to achieve innovative solutions and improvements in the financial sector. The findings serve as a valuable resource for financial institutions, policymakers, and researchers interested in the intersection of technology and finance. Article Highlights: Efficient data management leads to better decision-making and improved operational effectiveness. Technology plays a key role in leveraging data for innovation in finance. Risk management and compliance are enhanced through effective information management practices. Collaboration and partnerships foster financial innovation and the development of innovative solutions. [ABSTRACT FROM AUTHOR]

Background: In 2022, India's national tuberculosis (TB) elimination program (NTEP) commissioned a national level evaluation of active case finding (ACF) for TB to guide evidence-based strategic planning. As part of this evaluation, based on secondary data analysis we observed that the quality of ACF was suboptimal in 2021. Hence, this study aimed to understand the enablers, barriers, and suggested solutions to improve ACF for TB in India from NTEP staff (provider) perspective. Methods: This was a descriptive qualitative study involving key informant interviews from six districts and eight states, conducted between February and August 2023. We purposively selected key state- district- and sub-district-level program managers and implementers who were experienced and vocal. The interviews were audio recorded and transcribed verbatim by research interns and investigators. Two investigators independently did manual descriptive thematic analysis, and a third investigator resolved inconsistencies. The themes and categories emerged by collating together the results of the coding process. Results: A total of 34 key informant interviews were conducted and of these, four were repeat interviews. Adequate budgets for ACF including incentives, performance review mechanism, engagement of all stakeholders, adopting a community friendly approach, use of rapid diagnostic tests and digitalization were the perceived enablers. In some states ACF was implemented in general population (not restricted to high-risk population) following directives at state level. There were limited mechanisms to ensure ACF quality indicators were met before disbursing incentives and cross-verification of the aggregate ACF care cascade numbers that were reported in Ni-kshay (electronic TB information management system under NTEP). In addition to the state and district level implementers having limited understanding of concepts around ACF (quality indicators, number needed to screen and yield), we also inferred the presence of a 'know-do' gap for many activities under ACF. The suggested solutions were around capacity building and quality improvement strategies. Conclusion: The existing national ACF guidance should be revised to emphasize capacity building, need to carry out ACF in high-risk (not general) population, quality control-linked incentives, and regular implementation monitoring of the activities. This should contribute towards better coverage and improved quality translating into better ACF outcomes. [ABSTRACT FROM AUTHOR]

Energy management, especially in communities, is attracting growing interest but is often marred by confusion between the terms "energy information system" (EIS) and "energy management system" (EMS). This ambiguity hinders understanding of their uses and roles in improving energy performance. Our objective is to clarify these terms and their contributions to energy management. We used a mixed qualitative and quantitative approach to evaluate and compare the criteria of definition, objective, functionality, and role, as well as correlated elements, such as organizational structure, responsibilities, planning, and policy, according to the requirements of ISO 50001, in addition to criteria specific to the two systems. The study revealed differences in the roles of the EIS and the EMS, with a complementarity of 40% between the two systems depending on the positioning of the EIS in relation to the elements of the EMS process. The EIS plays a crucial role in facilitating the implementation of the EMS according to ISO 50001. This clarification contributes to a better understanding of the respective roles of these systems in energy management, thus helping communities to improve their energy performance in an effective manner. [ABSTRACT FROM AUTHOR]

The document "Coproducing Opportunities to Advance Heat Resilience in Southern Arizona" discusses the Southern Arizona Heat Planning Summit held in Tucson on February 3, 2024. The summit aimed to address the increasing risks of extreme heat events in the region, particularly focusing on vulnerable populations like unhoused individuals, undocumented migrants, and outdoor workers. The summit brought together representatives from various sectors to identify gaps and opportunities to improve community heat resilience, resulting in recommendations that were incorporated into Arizona's Extreme Heat Preparedness Plan and the City of Tucson's Heat Strategy. The outcomes of the summit led to the adoption of new plans and policies by local agencies and the establishment of initiatives to enhance heat relief efforts and public awareness in the region. [Extracted from the article]

Based on the spring, spring MVC and MyBatis structures of the cloud platform SSM framework, an information management platform for power grid material supply chain is built. The data layer uses a variety of sensors to collect power grid material supply chain information, and the information is fed back to the data storage layer after being integrated by the logical reorganization function of the persistence layer. The data storage layer uses the multi-sensor supply chain information fusion method based on paste progress to fuse the information and store it in the database. The business logic layer calls the information in the database and uses the improved k-means clustering algorithm to detect the abnormal supply chain data information. After calculation and data control by the control layer, the data management results are displayed through the presentation layer. The experimental results show that the absolute error of data fusion is very low. It can effectively cluster data information and distinguish outlier anomaly information at the same time, and the effect of information management is good. [ABSTRACT FROM AUTHOR]

Objectives. To investigate characteristics of data reported in US medical cannabis registries across states. Methods. Data included 2021 medical cannabis registry reports from 34 states, Puerto Rico, and the District of Columbia (hereafter, states) with active medical cannabis programs. The data from the reports were manually coded into domains and subcategories, including information related to patients (e.g., number, demographics), authorizing clinicians, sales (e.g., content, revenue), license tracking, and health and safety outcomes. Results. Among 36 states, 97% reported total patient number and 75% reported number of authorizing clinicians. Least reported subcategories included patient race/ethnicity (8%), adverse events (11%), therapeutic benefits (6%), and product recalls (6%). States that recently legalized medical cannabis (2013–2018) reported a higher number of subcategories overall, with a median of 11 versus 8 for early adopting states (1996–2012). More medical-use states reported data on authorizing clinicians compared with nonmedical adult-use states but were otherwise similar. Conclusions. Medical cannabis state registries generally reported data on consumers, clinicians, and sales rather than health and safety outcomes. More comprehensive and uniform medical cannabis public health surveillance is needed. (Am J Public Health. 2024;114(S8):S685–S693. https://doi.org/10.2105/AJPH.2024.307728) [ABSTRACT FROM AUTHOR]

In the context of rapid globalization and the swift advancement of information technology (IT), traditional corporate governance models are increasingly challenged by complex business environments and the growing demand for diversified collaboration. These models often exhibit rigidity and inefficiency. The introduction of mobile interactive technology offers new possibilities for the innovation and optimization of corporate governance models. Through mobile interactive networks, resource allocation and collaboration between departments can be conducted flexibly and efficiently. Existing research predominantly focuses on traditional management information systems and centralized resource allocation methods, which frequently overlook the diversity and dynamic nature of internal resource demands within enterprises, making them inadequate for addressing the needs of modern corporations. This study proposes a collaborative interaction mechanism for corporate governance based on matching theory, designing a system model that includes collaborative task demanders and executors, and the social utility of mobile interactive networks. The findings demonstrate that this mechanism enables efficient resource allocation and stable collaborative interactions in complex environments, providing both theoretical support and practical guidance for the innovation of corporate governance models. [ABSTRACT FROM AUTHOR]

The taxi-out time of an airport scene can be categorized into the unimpeded taxi-out time and the additional taxi-out time. Usually, additional taxi-out time is used as a key index to monitor taxi-out performance, and its accurate prediction plays an important role in optimizing the allocation of time slots at an airport and improving scene operation efficiency. Taking Shanghai Pudong International Airport as the research object, we first analyze its layout and construct the origin–destination pairs (ODPs) based on the stand groups and runways. Then, we develop a multiple linear regression model based on the arrival and departure flows to calculate the unimpeded taxi-out times for all ODPs. The actual taxi-out time is then subtracted from the unimpeded taxi-out time to obtain the historical additional taxi-out time of each flight. We propose three new flow features related to the structure: the corridor departure flow, the corridor arrival flow, and the departure flow proportion of ODPs, based on which we construct a dataset for training the prediction model. We then propose an additional taxi-out time prediction model based on the nutcracker optimization algorithm (NOA) and XGBoost and run comparison experiments on the operation data of our target airport. The results show that the optimized prediction model we proposed has the best performance compared with the traditional XGBoost model and other commonly used prediction models, and the proposed structure-related features have high correlations with additional taxi-out time. [ABSTRACT FROM AUTHOR]

This paper presents a significant advancement in information security risk assessment by introducing a flexible and comprehensive model. The research integrates established standards, expert knowledge, machine learning, and ontological modeling to create a multifaceted approach for understanding and managing information security risks. The combination of standards and expert insights forms a robust foundation, ensuring a holistic grasp of the intricate risk landscape. The use of cluster analysis, specifically applying k-means on information security standards, expands the data-driven approach, uncovering patterns not discernible through traditional methods. The integration of machine learning algorithms in the creation of information security risk dendrogram demonstrates effective computational techniques for enhanced risk discovery. The introduction of a heat map as a visualization tool adds innovation, facilitating an intuitive understanding of risk interconnections and prioritization for decision makers. Additionally, a thesaurus optimizes risk descriptions, ensuring comprehensiveness and relevance despite evolving terminologies in the dynamic field of information security. The development of an ontological model for structured risk classification is a significant stride forward, offering an effective means of categorizing information security risks based on ontological relationships. These collective innovations enhance understanding and management of information security risks, paving the way for more effective approaches in the ever-evolving technological landscape. [ABSTRACT FROM AUTHOR]

As a high-value agricultural product, the cold chain logistics management of Gannan navel oranges has global significance. Especially in Africa and Europe, food safety and supply chain quality are critical issues. However, the existing Gannan navel orange logistics information management system relies on traditional databases and information systems, with insufficient data sharing and transparency, affecting the overall coordination of the supply chain and customer satisfaction. In addition, the traditional system is vulnerable to hacker attacks and malicious tampering of data by insiders, resulting in serious economic losses and reputation damage. So the blockchain platform constructed in this study improves the transparency and traceability of logistics data through the SMART-PBFT algorithm of the alliance chain, which optimizes the logistics management process, improves the quality of logistics services, and reduces operating costs. It improves the logistics efficiency and data transparency of local agricultural products, and also provides a demonstrative case for the cold chain management of other high-value agricultural products around the world, with broad application potential and reference value. [ABSTRACT FROM AUTHOR]

Background: Trauma is one of the leading causes of mortality worldwide, and online platforms have become essential sources of information for trauma management. YouTube can play a significant role in helping people access medical information. Methods: YouTube was searched using the keywords management of trauma and assessment of trauma to identify relevant videos. Two authors independently evaluated the videos according to the ATLS (10th edition) guidelines, the modified DISCERN (m‐DISCERN) scale, and the Global Quality Scale (GQS) criteria. The videos that met the study criteria were evaluated based on the provider, video length, and view count. Results: Out of 939 videos, 667 were excluded resulting in 272 videos included in the study. According to the ATLS (10th edition) guidelines, the median score for videos was 8 (IQR 7‐8). Videos uploaded by official institutions and healthcare professionals received higher scores than from uncertain sources (p = 0.003). According to the GQS, 86% of the videos were low or moderate quality; uncertain sources uploaded 78% of low‐quality videos. Conclusion: YouTube is an information source about trauma management that contains videos of varying quality and has a broad audience. Official institutions and healthcare professionals should be aware of this evolving technology and publish up‐to‐date, accurate content to increase awareness about trauma management and help patients distinguish helpful information from misleading content. [ABSTRACT FROM AUTHOR]

A gene bank for farm animal genetic resources (FAGR) is an important facility for the diversity conservation of FAGR. The primary purpose of gene banks for FAGR is the reconstruction of those breeds. With the advent of the post-genomic era, gene banks for FAGR have increasingly become an infrastructure for the support of livestock research and animal breeding, and a platform for international research collaboration. China is one of the richest countries in the world in terms of FAGR. Chinese National Gene Banks for FAGR (CNGBFs) have an important legal status and play an important function in the Chinese FAGR protection system. In this paper, we reviewed the current situation of CNGBFs construction, and systematically collected and analyzed legal rules related to CNGBFs. As results, those legal rules were categorized into two types: (a) organization and management, (b) activities. We summarized problems existing in the current legal rules for CNGBFs from three levels: institution, practical operation, and digital development. Improvement directions of legal rules regarding CNGBFs are proposed. They include clarifying the utilization function of CNGBFs and the ownership of FAGR in CNGBFs. Moreover, improving the mechanism of administrative management, rules on domestic and international access and benefit-sharing, and the system of digital sequence information management are also suggested. The improvements in those legal rules will contribute to the appropriate utilization of Chinese FAGR and international collaborations. [ABSTRACT FROM AUTHOR]

Energy management in datacenters is a major challenge today due to the environmental and economic impact of increasing energy consumption. Efficient placement of virtual machines in physical machines within modern datacenters is crucial for their effective management. In this context, five algorithms named CNN-GA, CNN-greedy, CNN-ABC, CNN-ACO and CNN-PSO, have been developed to minimize hosts' power consumption and ensure service quality with relatively low response times. We propose a comparative approach between the developed algorithms and other existing methods for virtual machine placement. The algorithms use optimization algorithms combined with Convolutional Neural Networks to build predictive models of virtual machine placement. The models were evaluated based on their accuracy and complexity to select the optimal solution. The necessary data is collected using the CloudSim Plus simulator, and the prediction results were used to allocate virtual machines according to the predictions of the models. The main objective of this research is to optimize the management of Information Technology resources within datacenters. This is achieved by seeking a virtual machine placement policy that minimizes hosts' power consumption and ensures an appropriate level of service for users' needs. It considers the imperatives of sustainability, performance, and availability by reducing energy consumption and response times. We studied six scenarios under specific constraints to determine the best model for virtual machines' placement. This approach aims to address current challenges in energy management and operational efficiency. [ABSTRACT FROM AUTHOR]

Objective To understand the epidemiological characteristics of non-occupational carbon monoxide poisoning incidents in Beijing from 2018 to 2022, and to explore targeted prevention and control measures. Methods The data were from the National Public Health Emergency Reporting Management Information System. Descriptive epidemiological methods were used to analyze non-occupational carbon monoxide poisoning incidents in Beijing from 2018 to 2022. Relative risk (RR) was used to assess the risk of morbidity and mortality. Results From 2018 to 2022, a total of 1841 non-occupational carbon monoxide poisoning incidents were reported in Beijing, with 2729 cases of poisoning and 31 deaths. The annual morbidity and mortality were 25.14 and 0.29 cases per million, respectively. There were two peaks of poisoning cases in spring and winter, accounting for 49.98% and 24.00% of the total cases, respectively. The highest incidence occurred between 8 and 11 o 'clock, accounting for 22.83%. In the inner suburbs, outer suburbs and urban areas, the incidence of non-occupational carbon monoxide was 0.96 cases/100 000, 1.27 cases/100 000 and 18.79 cases/100 000, and the mortality was 0.02 cases/100 000, 0.02 cases/100 000 and 0.11 cases/100 000, respectively. The poisoning incidents mainly occurred in residential families in 1633 cases, accounting for 88.70%; This was followed by 118 restaurants, accounting for 6.41 percent. The proportion of household poisoning incidents decreased from 92.10% in 2018 to 84.13% in 2022. The proportion of poisoning incidents in restaurants increased year by year from 4.23% in 2018 to 10.05% in 2022. The proportion of poisoning cases occurring at home decreased from 86.39% in 2018 to 71.84% in 2022. The proportion of poisoning cases in restaurants increased from 9.88% in 2018 to 20.57% in 2022. Conclusion The incidence and death risk of non-occupational carbon monoxide poisoning in Beijing is not balanced among regions, and the proportion of poisoning incidents in restaurants is increasing year by year. Targeted measures should be taken to strengthen the prevention and control work in the outer suburbs and key restaurants. [ABSTRACT FROM AUTHOR]

Background: Long non-coding RNAs (lncRNAs) can prevent, diagnose, and treat a variety of complex human diseases, and it is crucial to establish a method to efficiently predict lncRNA-disease associations. Results: In this paper, we propose a prediction method for the lncRNA-disease association relationship, named LDAGM, which is based on the Graph Convolutional Autoencoder and Multilayer Perceptron model. The method first extracts the functional similarity and Gaussian interaction profile kernel similarity of lncRNAs and miRNAs, as well as the semantic similarity and Gaussian interaction profile kernel similarity of diseases. It then constructs six homogeneous networks and deeply fuses them using a deep topology feature extraction method. The fused networks facilitate feature complementation and deep mining of the original association relationships, capturing the deep connections between nodes. Next, by combining the obtained deep topological features with the similarity network of lncRNA, disease, and miRNA interactions, we construct a multi-view heterogeneous network model. The Graph Convolutional Autoencoder is employed for nonlinear feature extraction. Finally, the extracted nonlinear features are combined with the deep topological features of the multi-view heterogeneous network to obtain the final feature representation of the lncRNA-disease pair. Prediction of the lncRNA-disease association relationship is performed using the Multilayer Perceptron model. To enhance the performance and stability of the Multilayer Perceptron model, we introduce a hidden layer called the aggregation layer in the Multilayer Perceptron model. Through a gate mechanism, it controls the flow of information between each hidden layer in the Multilayer Perceptron model, aiming to achieve optimal feature extraction from each hidden layer. Conclusions: Parameter analysis, ablation studies, and comparison experiments verified the effectiveness of this method, and case studies verified the accuracy of this method in predicting lncRNA-disease association relationships. [ABSTRACT FROM AUTHOR]

To solve the problems of chaotic information management and difficult traceability in the manufacturing process of transformer coils, a traceability and management method oriented towards the manufacturing process of transformer coils has been proposed. This method integrates industrial internet identification resolution and extension of Petri net modeling theory. A comprehensive identification and resolution framework for coil manufacturing processes has been constructed. In this manuscript, the authors proposed an industrial data-sharing space based on the producer-consumer model with unified coding identification. This enables information sharing for all resources, including personnel, machinery, materials, methods, environment, and measurements. A method for modeling extensible identification primitives of coil manufacturing process information was proposed, which formalizes the correlation and data structure of process information. A Petri net model for the comprehensive acquisition and integration of elemental information in coil manufacturing processes, as well as a mathematical model for quality traceability, were constructed, thereby forming a complete path for quality traceability information. Finally, based on the method proposed above, a software and hardware environment for identification and traceability for coil manufacturing was established. Taking a certain type of coil as an example, validation was carried out; the results indicate a significant enhancement in the production management and information traceability capabilities of the coil production workshop. This study provides reference and guidance for the process traceability management of power equipment manufacturing. [ABSTRACT FROM AUTHOR]

Background: Despite being preventable and curable, leprosy remains endemic in some undeveloped regions, including China. Wenshan Zhuang and Miao Autonomous Prefecture (Wenshan prefecture) currently bears the highest leprosy burden in China. In this ecological study, we aimed to analyze the epidemiological characteristics as well as identify and visualize the high-risk townships of Wenshan prefecture using the most updated leprosy data from 2010 to 2022. Methods: Geographical information system combined with spatial scan statistics was used for newly detected leprosy cases abstracted from the Leprosy Management Information System in China. Global Moran's I index was used to uncover the spatial pattern of leprosy at the township level. Spatial scan statistics, encompassing purely temporal, purely spatial, spatial variation in temporal trends, and space-time analysis, were implemented for detecting the risk clusters. Results: Between 2010 and 2022, Wenshan prefecture detected 532 new leprosy cases, comprising 352 (66.17%) males and 180 (33.83%) females. The aggregated time primarily occurred between October 2010 and March 2014. The distribution pattern of newly detected leprosy cases was spatially clustered. We identified four high-risk spatial clusters encompassing 54.51% of the new cases. Furthermore, spatial variation in temporal trends highlighted one cluster as a potential high-risk area. Finally, two space-time clusters were detected, and the most likely cluster was predominantly located in the central and northwest regions of Wenshan prefecture, spanning from January 2010 to September 2013. Conclusions: In this ecology study, we characterized the epidemiological features and temporal and spatial patterns of leprosy in Wenshan prefecture using the most recent leprosy data between 2010 and 2022. Our findings offer scientific insights into the epidemiological profiles and spatiotemporal dynamics of leprosy in Wenshan prefecture. Clinicians and policymakers should pay particular attention to the identified clusters for the prevention and control of leprosy. [ABSTRACT FROM AUTHOR]

One of the new approaches to organizing journal articles is using Semantic Web technologies and new "Semantic Publishing" methods based on the ontological models for semantic modeling and representation of these publications. This study aims to design the metadata ontology model for semantic modeling and representation of scholarly journal articles. This research was carried out using the content analysis method. The study population includes articles published in ISC journals (i.e., journals indexed in ISC). A sample of 200 articles from a Persian journal (i.e., "Iranian Journal of Information Processing & Management") and an English journal (i.e., "International Journal of Information Science and Management (IJISM)") were considered for semantic modeling and representation using the metadata ontology model. The data-gathering method was a systematic observation, and the data-gathering tool was a checklist based on the Semantic Publishing and Referencing Ontologies (SPAR Ontologies). This research identified the metadata entities required for semantic modeling and representation of ISC scholarly journal articles. Then, the metadata ontology model was designed using Protege (an ontology editor) and the RDF representation of the model was created in RDF/XML Syntax. After designing the metadata ontology model, the RDF graph of this model was developed to analyze the entities and relationships between them, and its results were analyzed. The results of RDF graph analysis show that the design of the ontology model and the creation of the RDF links between entities significantly impact the semantic description and representation of metadata entities of the ISC scholarly journal articles and the network of relations between them. [ABSTRACT FROM AUTHOR]

Background: Data demand and use culture have a tremendous impact on the proper allocation of scarce resources and evidence-based decision making. However, primary healthcare managers in the majority of Sub-Saharan African countries continue to struggle with using routine health data for decision-making. Purpose/objective: This study aimed to assess routine health data use for decision making among primary healthcare managers in Dodoma region. Methods: Cross-sectional study design involved 188 primary healthcare managers from Dodoma City Council, Kondoa Town Council and Bahi District Council was conducted. A self-administered questionnaire adapted from the Performance of Routine Information System Management (PRISM) tools was used to collect the data. Data was analysed by using the Statistical Package for Social Science (SPSS) program. Principal Component Analysis was used to find the level of routine health data use, binary logistic regression analysis was used to determine factors associated with routine health data use for decision making among primary healthcare managers. The study was conducted from May to June, 2022. Results: The level of adequate routine health data use for decision making among healthcare managers was 63.30%. Factors associated with adequate routine health data use for decision making among healthcare managers were; respondents characteristics: years of working experience (OR = 1.955, 95% CI= [0.892,4.287]), district surveyed (OR = 4.760, 95%CI= [1.412,16.049]), level of health facility (OR = 3.867, 95%CI= [1.354,7.122]) and male gender (OR = 1.901, 95%CI= [1.027,3.521]). Individual factors: comparing data with strategic objectives (OR = 2.986, 95%CI= [1.233–7.229]), decision based on health needs (OR = 7.330, 95%CI= [1.968–27.295]) and decision based on detection of outbreak (OR = 3.769, 95%CI= [1.091–13.019]). Technical factors: ability to check data accuracy (OR = 3.120, 95%CI= [1.682–5.789]), ability to explain findings and its implication (OR = 2.443, 95%CI= [1.278–4.670]) and ability to use information to identity gaps and targets (OR = 2.621, 95%CI= [1.381–4.974]). Organizational factors: organizational support (OR = 3.530, CI= [1.397–8.919]), analyse data regularly (OR = 2.026, 95%CI= [1.075–3.820]) and displays information on key performance indicators (OR = 3.464, 95%CI= [1.525–7.870]). Conclusion and recommendation: The level of routine health data use for decision making among primary healthcare managers was found to be modest. The level of data demand and use culture may increase more quickly if capacity building is strengthened and issues that de-motivate primary health care managers from using data are addressed. [ABSTRACT FROM AUTHOR]

This paper considers what liberal philosopher Michael Sandel coins the 'moral limits of markets' in relation to the idea of paying people for data about their biometrics and emotions. With Sandel arguing that certain aspects of human life (such as our bodies and body parts) should be beyond monetisation and exchange, others argue that emerging technologies such as Personal Information Management Systems can enable a fairer, paid, data exchange between the individual and the organisation, even regarding highly personal data about our bodies and emotions. With the field of data ethics rarely addressing questions of payment, this paper explores normative questions about data dividends. It does so by conducting a UK-wide, demographically representative online survey to quantitatively assess adults' views on being paid for personal data about their biometrics and emotions via a Personal Information Management System, producing a data dividend, a premise which sees personal data through the prism of markets and property. The paper finds diverse attitudes based on socio-demographic characteristics, the type of personal data sold, and the type of organisation sold to. It argues that (a) Sandel's argument regarding the moral limits of markets has value in protecting fundamental freedoms of those in society who are arguably least able to (such as the poor); but (b) that contexts of use, in particular, blur moral limits regarding fundamental freedoms and markets. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) and blockchain applications have introduced significant benefits to the architecture, engineering, construction, and operation (AECO) industry in recent years. Although publications on BIM and blockchain integration have been increasing, no systematic examination of the present status and managerial implications of integrated BIM and blockchain has been conducted. To bridge this gap, this paper conducts a state-of-the-art review of the development of integrated BIM and blockchain in a built environment. A combination of qualitative and quantitative methods was adopted to synthesize and analyze the research evidence. The results revealed five key managerial implications of BIM integration with blockchain at the project level: design and collaboration, financial management, construction management, information management, and integration management (with other cutting-edge technologies). Challenges and opportunities are outlined and articulated from both technological and managerial perspectives, such as stakeholder management, impact assessment, real-time project management, information redundancy, and incompatibility. [ABSTRACT FROM AUTHOR]

The proliferation of fake news on social media platforms necessitates the development of reliable datasets for effective fake news detection and veracity analysis. In this article, we introduce a veracity dataset of Arabic tweets called "VERA-ARAB", a pioneering large-scale dataset designed to enhance fake news detection in Arabic tweets. VERA-ARAB is a balanced, multi-domain, and multi-dialectal dataset, containing both fake and true news, meticulously verified by fact-checking experts from Misbar. Comprising approximately 20,000 tweets from 13,000 distinct users and covering 884 different claims, the dataset includes detailed information such as news text, user details, and spatiotemporal data, spanning diverse domains like sports and politics. We leveraged the X API to retrieve and structure the dataset, providing a comprehensive data dictionary to describe the raw data and conducting a thorough statistical descriptive analysis. This analysis reveals insightful patterns and distributions, visualized according to data type and nature. We also evaluated the dataset using multiple machine learning classification models, exploring various social and textual features. Our findings indicate promising results, particularly with textual features, underscoring the dataset's potential for enhancing fake news detection. Furthermore, we outline future work aimed at expanding VERA-ARAB to establish it as a benchmark for Arabic tweets in fake news detection. We also discuss other potential applications that could leverage the VERA-ARAB dataset, emphasizing its value and versatility for advancing the field of fake news detection in Arabic social media. Potential applications include user veracity assessment, topic modeling, and named entity recognition, demonstrating the dataset's wide-ranging utility for broader research in information quality management on social media. [ABSTRACT FROM AUTHOR]

Background: Data on the association between the plain ropivacaine dose and maternal hypotension during cesarean delivery are limited. Thus, this study aimed to explore this association. Methods: This retrospective study included patients undergoing cesarean sections under spinal or combined spinal-epidural anesthesia with plain ropivacaine at The First Hospital of Fuyang, Hangzhou, China, between 2018 and 2022. Data were obtained from the anesthesia information management system. Liner trend tests were used to distinguish the linear relationship between spinal hypotension and the plain ropivacaine dose, and receiver operating characteristic curves were used to calculate the dose threshold. Logistic regression was used to adjust for confounders. Sensitivity analyses were performed to evaluate the stability of the results. The secondary outcome was vasopressor use (metaraminol and ephedrine). Results: In total, 1,219 women were included. The incidence of hypotension linearly correlated with the plain ropivacaine dose (adjusted P-value for trend, P < 0.001). Thus, we used a dose threshold of 17.5 mg to compare the dose as a binary variable (≥17.5 mg vs. < 17.5 mg). Plain ropivacaine doses of ≥17.5 mg were associated with a higher incidence of spinal hypotension (adjusted odds ratio: 2.71; 95% confidence interval [1.85–3.95]; P < 0.001). The sensitivity analyses yielded similar results. The plain ropivacaine dose also correlated with metaraminol use but not ephedrine use. Conclusions: The incidence of spinal-induced hypotension in women undergoing cesarean section linearly correlated with the plain ropivacaine dose. The dose threshold for hypotension risk was 17.5 mg. [ABSTRACT FROM AUTHOR]

The growing complexity of cybersecurity threats demands a robust framework that integrates various security domains, addressing the issue of disjointed security practices that fail to comply with evolving regulations. This paper introduces a novel information security management and compliance framework that integrates operational, technical, human, and physical security domains. The aim of this framework is to enable organizations to identify the requisite information security controls and legislative compliance needs effectively. Unlike traditional approaches, this framework systematically aligns with both current and emerging security legislation, including GDPR, NIS2 Directive, and the Artificial Intelligence Act, offering a unified approach to comprehensive security management. The experimental methodology involves evaluating the framework against five distinct risk scenarios to test its effectiveness and adaptability. Each scenario assesses the framework's capability to manage and ensure compliance with specific security controls and regulations. The results demonstrate that the proposed framework not only meets compliance requirements across multiple security domains but also provides a scalable solution for adapting to new threats and regulations efficiently. These findings represent a significant step forward in holistic security management, indicating that organizations can enhance their security posture and legislative compliance simultaneously through this integrated framework. [ABSTRACT FROM AUTHOR]

An application programming interface (API) usage specification, which includes the conditions, calling sequences, and semantic relationships of the API, is important for verifying its correct usage, which is in turn critical for ensuring the security and availability of the target program. However, existing techniques either mine the co-occurring relationships of multiple APIs without considering their semantic relationships, or they use data flow and control flow information to extract semantic beliefs on API pairs but difficult to incorporate when mining specifications for multiple APIs. Hence, we propose an API specification mining approach that efficiently extracts a relatively complete list of the API combinations and semantic relationships between APIs. This approach analyzes a target program in two stages. The first stage uses frequent API set mining based on frequent common API identification and filtration to extract the maximal set of frequent context-sensitive API sequences. In the second stage, the API relationship graph is constructed using three semantic relationships extracted from the symbolic path information, and the specifications containing semantic relationships for multiple APIs are mined. The experimental results on six popular open-source code bases of different scales show that the proposed two-stage approach not only yields better results than existing typical approaches, but also can effectively discover the specifications along with the semantic relationships for multiple APIs. Instance analysis shows that the analysis of security-related API call violations can assist in the cause analysis and patch of software vulnerabilities. [ABSTRACT FROM AUTHOR]

The construction sector's pivotal role in the global economy faces challenges due to its dynamic nature. Inaccurate documentation impacts project cost management, underscoring the need for effective document management systems (DMS), including electronic document management systems (EDMS). This study conducts a systematic literature review to comprehensively examine EDMS implementation, utilization, and effectiveness across sectors. Analyzing peer-reviewed articles and scholarly sources reveals key themes, trends, and findings, providing insights into successful EDMS adoption and best practices. The review contributes evidence-based insights for practitioners, researchers, and policymakers, addressing gaps in knowledge and advancing understanding of EDMS in modern information management. Additionally, it presents a detailed breakdown of publication distribution across sectors, highlighting significant research areas like companies and businesses, education, and information technology and software. Furthermore, analysis of factors influencing employee behavior, including technical factors, employee's personal characteristics, organizational factors, and trust, offers valuable insights into workplace dynamics. Overall, the study offers comprehensive insights into EDMS implementation, guiding future research, and organizational strategies. [ABSTRACT FROM AUTHOR]

This article provides a comprehensive overview of all currently available datasets of the Long-term Ecosystem Research (LTER) site Oberes Stubachtal. The site is located in the Hohe Tauern mountain range (Eastern Alps, Austria) and includes both protected areas (Hohe Tauern National Park) and unprotected areas (Stubach valley). While the main research focus of the site is on high mountains, glaciology, glacial hydrology, and biodiversity, the eLTER Whole-System Approach (WAILS) was used for data selection. This approach involves a systematic screening of all available data to assess their suitability as eLTER Standard Observations (SOs). This includes the geosphere, atmosphere, hydrosphere, biosphere, and sociosphere. These SOs are fundamental to the development of a comprehensive long-term ecosystem research framework. In total, more than 40 datasets have been collated for the LTER site Oberes Stubachtal and included in the Dynamic Ecological Information Management System—Site and Data Registry (DEIMS-SDR), the eLTER's data platform. This paper provides a detailed inventory of the datasets and their primary attributes, evaluates them against the WAILS-required observation data, and offers insights into strategies for future initiatives. All datasets are made available through dedicated repositories for FAIR (findable, accessible, interoperable, reusable) use. [ABSTRACT FROM AUTHOR]

Simple Summary: The present study aimed to provide critical analysis in a systematic review of the peer-reviewed literature indexed in the Scopus database to analyze the studies on ostriches' welfare in the last forty years in order to identify knowledge gaps in the literature. A bibliometric analysis of metadata and content analysis based on text mining and topic modeling techniques has been conducted on a sample of approximately n. 987 publications, confirming the increased focus on ostriches' welfare. Ostriches can be utilized as multipurpose animals suitable for producing meat, eggs, feathers, and leather. This growing interest in ostrich farming leads to an increased demand for comprehensive information on their management. But, little attention is paid to the consequences for their welfare. The study aimed to perform a research literature analysis on ostriches' welfare using the text mining (TM) and topic analysis (TA) methods. It identifies prevailing topics, summarizes their temporal trend within the last forty years, and highlights potential research gaps. According to PRISMA guidelines, a literature exploration was achieved using the Scopus® database, retaining keywords about ostriches' welfare. Papers distributed in the English language from 1983 to 2023 were included. Descriptive statistics, TM, and TA were applied to a total of n. 122 documents included. The findings revealed an increasing trend in research records since 1994. TM recognized the terms with the highest weighted frequency and TA identified the main topics of the research area, in the following order: "health and management", "feeding and nutrition", "welfare reproduction", "egg production", and "welfare during transport". The study confirms the increased focus on ostriches' welfare but shows that further studies are required to ensure the welfare of this species. [ABSTRACT FROM AUTHOR]

Purpose: Design Science (DS) is a relatively new paradigm for addressing complex real-world problems through the design and evaluation of artifacts. Its constituent methodologies are currently being discussed and established in numerous related research fields, such as information systems and management (Hevner et al., 2004). However, a DS methodology that describes the "how to" is largely lacking, not only in the field of OM but in general. The Theory of Constraints (TOC) and its underlying thinking processes (TP) have produced several novel artifacts for addressing ill-structured real-world operations problems (Dettmer, 1997; Goldratt, 1994), but they have not been analyzed from a DS research standpoint. The purpose of this research is to demonstrate how TOC's thinking process methodology can be used for conducting exploratory DS research in Operations and Supply Chain Management (OSCM). Design/methodology/approach: A case study of spare parts replenishment illustrates the use of TOC's thinking processes in DS to structure an initially unstructured problem context and to facilitate the design of a novel solution. Findings: TOC's thinking processes are an effective methodology for problem-solving DS research, enabling the development of novel solutions in initially unstructured and wicked problem situations. Combined with structured CIMO design logic TOC's thinking process offers a systematic method for exploring wicked problems, designing novel solutions, and demonstrating theoretical contributions. Research limitations/implications: The implication for research is that TOC's thinking process methodology can provide important elements of the lacking "how to" methodology for DS research, not only for the field of OM but in general for the field of management. Practical implications: The practical outcome of the research is a novel design for dynamic buffer-based replenishment that extends beyond organizational boundaries. Originality/value: This work shows how the thinking processes can be used in DS research to develop rigorous design propositions for ill-structured problems. [ABSTRACT FROM AUTHOR]

Public–private partnerships (PPPs) are contractual schemes that have relative success in Latin America due to their configuration, where the spirit of the contract lies in an appropriate distribution of risks. This is both the most important and fundamental aspect of this contractual scheme but also the most complex, as it is the main cause of contract modifications (addenda) in countries across the region. In this context, different concessionary contractual frameworks for PPPs on the national road network of Peru were evaluated, revealing common failure indicators that lead to contract modifications within the first six years of the concession. This study offers the development of a management model that includes the good practices of contractual management of NEC4 Option F and the application of BIM information management processes, optimizing the management of infrastructure through emerging technologies of information management supported by agile contractual schemes. [ABSTRACT FROM AUTHOR]

Some firms in the technology sector choose to cooperate with competitors ("co-opetition") in standard setting organizations (SSOs). These SSOs create technology standards that facilitate rapid market penetration of new technologies such as Wi-Fi, Bluetooth, and 4G. Active participation in the standard setting process requires the exchange of proprietary information with competitors. Although the goal of such information sharing is to further a technology or a product market, firms potentially receive an additional benefit from access to competitor and industry information. We examine whether contributing to SSO committees enhances a firm's information set and allows managers to better predict future sales. We find that the centrality of a firm's location within the network of SSO collaborators is positively related to the accuracy of the firm's sales forecasts. This relation is stronger when firms exchange more information with direct competitors and with larger firms, when forecasting is more difficult ex ante, and when firms forecast over longer horizons. Our findings show that collaborating with competitors in the product market provides an important additional benefit of improving the manager's information set. This paper was accepted by David Simchi-Levi, accounting. Funding: This work was supported by the Wharton School, Institut Européen d'Administration des Affaires, the Institut Européen d'Administration des Affaires-Wharton Alliance, and the Mack Institute for Innovation Management. Supplemental Material: The data and online appendix are available at https://doi.org/10.1287/mnsc.2021.03152. [ABSTRACT FROM AUTHOR]

Backgrounds: The use of large language models (LLMs) in medicine can help physicians improve the quality and effectiveness of health care by increasing the efficiency of medical information management, patient care, medical research, and clinical decision-making. Methods: We collected 34 frequently asked questions about glucocorticoid-induced osteoporosis (GIOP), covering topics related to the disease's clinical manifestations, pathogenesis, diagnosis, treatment, prevention, and risk factors. We also generated 25 questions based on the 2022 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis (2022 ACR-GIOP Guideline). Each question was posed to the LLM (ChatGPT-3.5, ChatGPT-4, and Google Gemini), and three senior orthopedic surgeons independently rated the responses generated by the LLMs. Three senior orthopedic surgeons independently rated the answers based on responses ranging between 1 and 4 points. A total score (TS) > 9 indicated 'good' responses, 6 ≤ TS ≤ 9 indicated 'moderate' responses, and TS < 6 indicated 'poor' responses. Results: In response to the general questions related to GIOP and the 2022 ACR-GIOP Guidelines, Google Gemini provided more concise answers than the other LLMs. In terms of pathogenesis, ChatGPT-4 had significantly higher total scores (TSs) than ChatGPT-3.5. The TSs for answering questions related to the 2022 ACR-GIOP Guideline by ChatGPT-4 were significantly higher than those for Google Gemini. ChatGPT-3.5 and ChatGPT-4 had significantly higher self-corrected TSs than pre-corrected TSs, while Google Gemini self-corrected for responses that were not significantly different than before. Conclusions: Our study showed that Google Gemini provides more concise and intuitive responses than ChatGPT-3.5 and ChatGPT-4. ChatGPT-4 performed significantly better than ChatGPT3.5 and Google Gemini in terms of answering general questions about GIOP and the 2022 ACR-GIOP Guidelines. ChatGPT3.5 and ChatGPT-4 self-corrected better than Google Gemini. [ABSTRACT FROM AUTHOR]

Battery pack provides the backup power supply for DC system of power substations. In the event of an AC power outage or other accidents, it is an important guarantee for the reliable operation of power substation. To prevent possible failures, batteries usually require careful maintenance. Common methods are online monitoring, condition assessments, and health management. Among these, model-based techniques are widely used for battery monitoring and prognostics optimization. Data-driven methods are a good alternative solution when no mathematical models are available. As substations develop towards intelligent and unmanned modes, this paper proposes an online battery monitoring and management system based on the "cloud-network-edge-end" Internet of Things (IoT) architecture. Firstly, advanced battery monitoring system based on IoT architecture is reviewed in depth. It provides basis for later designing. Secondly, the battery online monitoring and management system is designed considering functional requirements and data link. Designing functions include ledger management, basic battery information display, real-time display of battery monitoring data, and the visualization of battery alarm information. It can implement online monitoring and intelligent maintenance management for battery operating status. Finally, the designed and developed system is applied in a 110 kV offshore substation, mainly providing battery maintenance suggestions and fault alarm prompts. Typical results of ledger information management, key parameter monitoring and alarm prompt are presented. This verifies the effectiveness and convenience of IoT-based system for the monitoring and management of batteries. [ABSTRACT FROM AUTHOR]

Building height is important information in disaster management and damage assessment. It is also a key parameter in studies such as population modeling and urbanization. Relatively few studies have been conducted on extracting building height in rural areas using imagery from China's Gaofen-7 satellite (GF-7). In this study, we developed a method combining photogrammetry and deep learning to extract building height using GF-7 data in the rural area of Pingquan in northern China. The deep learning model DELaMa was proposed for digital surface model (DSM) editing based on the Large Mask Inpainting (LaMa) architecture. It not only preserves topographic details but also reasonably predicts the topography inside the building mask. The percentile value of the normalized digital surface model (nDSM) in the building footprint was taken as the building height. The extracted building heights in the study area are highly consistent with the reference building heights measured from the ICESat-2 LiDAR point cloud, with an R2 of 0.83, an MAE of 1.81 m and an RMSE of 2.13 m for all validation buildings. Overall, the proposed method in this paper helps to promote the use of satellite data in large-scale building height surveys, especially in rural areas. [ABSTRACT FROM AUTHOR]

Objective: There is a demand to make hospital management information beyond basic key performance indicators (KPIs) accessible for clinicians. Methods: We developed an interactive application (IAPP) in R Shiny to visualize such information. We provided the IAPP source code online. As a use case, we recorded basic KPIs (numbers of patients (NPs), reimbursed valuation ratios (RVRs), mean length of stay (LOS)), main diagnoses (MDGNs), main procedures (MPRCs), and catchment area (CA) by district from April 2022 to March 2024 at the index department in central Germany, where a neurotrauma and spinal surgery service was resumed on 1 April 2022. Case mix indexes (CMIs) were calculated. We retrieved information about online-reported patient satisfaction (ORPS) from an online physician rating platform between January 2022 and March 2024. Information on longitudes and latitudes of the index department and neighbouring hospitals was collected. We calculated car travelling isochrones (CTIs) of the hospitals as a proxy variable for accessibility. Chi-square and Fisher's exact served as statistical tests. Results: During the observation period, the monthly NPs increased from 26 to 43, the RVR showed a 3.96-fold increase, the CMI showed a 2.41-fold increase, and the LOS reached a steady state in the 2nd year after service resumption. CA (p = 0.03), MDGNs, and MPRCs diversified. ORPS trended towards better overall evaluation after service resumption (p = 0.09). CTI mapping identified a unique market position of the index department. Conclusions: The IAPP makes extended hospital management data accessible to clinicians, can inform other stakeholders in healthcare, and can be tailored to local conditions. [ABSTRACT FROM AUTHOR]

Background: Rabies is an incessant public health threat in China. The Ministry of Health implemented the Central Payment for Rabies Prevention and Control Project to assist with rabies prevention and control in a few representative provinces in 2006. Methods: Data on human rabies cases reported by the National Infectious Disease Reporting Information Management System and national surveillance sites from 2006 to 2022 were collected, and statistical and multivariate analyses were then used to assess the effectiveness of current prevention and control efforts. Results: During 2006–2022, a total of 2025 human rabies cases were collected by the national surveillance sites, with incidence rates far above the national average, but the incidence rate was consistent with the national trend. Human rabies cases demonstrated a dual peak distribution in terms of exposure and onset dates, with the peak exposure dates falling mostly in the spring and summer and the peak onset dates occurring mostly in the summer and autumn. Three danger categories are shown by the geographical distribution: high, medium and low. Dogs had a high infection rate (86.93%), with own domesticated dogs accounting for the majority of infections. The rates of post-exposure prophylaxis are not constant. The median incubation period was 71 days. Conclusions: Various measures and policies implemented by the government have played a key role in reducing the incidence of rabies. To effectively prevent and control the resurgence of epidemics and halt the spread of the virus among host animals, it is imperative to prioritize and implement a robust dog management system, accelerate research and development of animal vaccines and improve the level of post-exposure prophylaxis. [ABSTRACT FROM AUTHOR]

Health information management is a vital and constructive component of the health system, refers to the process of producing and collecting, organising and storing, analysing, disseminating and using information. The aim of this study was to evaluate the strengths and weaknesses of the information management system in epidemic infectious diseases in Iran, specifically focusing on the registration, reporting, quality, confidentiality, and security of infectious disease data. This assessment was conducted from the perspective of policymakers and experts responsible for data registration and reporting. After examining the processes of registering and reporting infectious disease data and interviewing experts, a researcher-designed questionnaire was prepared to evaluate the infectious disease information management system. To assess the content validity of the Content Validity Index and Content Validity Ratio Index, a questionnaire was utilized. The reliability of the questionnaire was confirmed using Cronbach's alpha. By employing purposeful sampling and adhering to the inclusion criteria, 150 participants were included in the study. Questionnaires were distributed via email, WhatsApp, or Telegram to employees at various levels of Iran's health and treatment systems who were responsible for registering and reporting infectious disease data. The study encompassed 100 participants who successfully concluded the research. The results highlight that the key strength of healthcare data registration lies in its ability to "depict the epidemic curve during outbreaks of infectious diseases." Conversely, a notable weakness was the "insufficient collaboration from non-academic sectors (e.g., clinics, private laboratories) in registering and reporting infectious diseases. The present study's findings suggest that the issue lies not in the framework itself, but rather in the execution and functionality of the strategies. We can cultivate a repository of reliable and beneficial data by incorporating initiatives like training programs, enforcing regulations with consequences for inadequate data documentation, offering both material and motivational rewards, and streamlining all data collection and reporting systems. [ABSTRACT FROM AUTHOR]

Permanent artificial lighting systems in tourist underground environments promote the proliferation of photoautotrophic biofilms, commonly referred to as lampenflora, on damp rock and sediment surfaces. These green-colored biofilms play a key role in the alteration of native community biodiversity and the irreversible deterioration of colonized substrates. Comprehensive chemical or physical treatments to sustainably remove and control lampenflora are still lacking. This study employs an integrated approach to explore the biodiversity, eco-physiology and molecular composition of lampenflora from the Pertosa-Auletta Cave, in Italy. Reflectance analysis showed that photoautotrophic biofilms are able to absorb the totality of the visible spectrum, reflecting only the near-infrared light. This phenomenon results from the production of secondary pigments and the adaptability of these organisms to different metabolic regimes. The biofilm structure mainly comprises filamentous organisms intertwined with the underlying mineral layer, which promote structural alterations of the rock layer due to the biochemical attack of both prokaryotes (mostly represented by Brasilonema angustatum) and eukaryotes (Ephemerum spinulosum and Pseudostichococcus monallantoides), composing the community. Regardless of the corrosion processes, secondary CaCO3 minerals are also found in the biological matrix, which are probably biologically mediated. These findings provide valuable information for the sustainable control of lampenflora. [ABSTRACT FROM AUTHOR]

There is an increasing need for robust wildlife health programs that provide surveillance and management for diseases in wildlife and wild aquatic populations to manage associated risks. This paper illustrates the value of a systematic method to enhancing wildlife health programs. The U.S. Geological Survey and Mahidol University, Faculty of Veterinary Science, Thailand National Wildlife Health Center formally twinned under the auspices of the World Organisation for Animal Health to enhance wildlife health capacity in Thailand and the Southeast Asia Region. We used a system-wide approach to holistically and interdependently enhance capacity. The project commenced with a wildlife health program needs assessment, and capacity enhancement focused on strengthening the general wildlife health surveillance network and improving wildlife health information management. Activities included partner surveys, interactive and didactic workshops, and individual personnel training. Topics included development of wildlife health information management systems, analysis of the current surveillance network, development of a Theory of Change for a strengthened surveillance network, planning workshops to create a wildlife health network, training on wildlife disease outbreak investigation and field sample collection, leading networks, and individual training on bioinformatics and laboratory techniques. Engagement of stakeholders at all levels, continuous communication throughout the project, use of both strategic planning tools and pedagogical methods, and using iterative and adaptive approaches, were key factors to the success of this project. [ABSTRACT FROM AUTHOR]

Brazil is the largest consumer of pesticides in the world, the amount used causes a lot of damage to the environment (BORSOI et al., 2014). Reverse logistics operates from the planning and control of logistical information equivalent to the process of returning after-sales and consumption products to mastering the cycles of logistics and business (LEITE, 2009). In this sense, the general objective is to evaluate the reverse logistics process used for containers of agricultural pesticides in rural communities in the municipality of São João Evangelista. Based on this study, on-site visits were made to ten family farming properties in the rural municipality, carried out in the month of May of the year 2023, and during these visits, data were collected from observations, photos and dialogues with local farmers. Through conversations with the ranchers, the lack of nearby collection points, the lack of transportation and information, are one of the determining factors in the difficulties encountered, and that cause the incorrect disposal and reuse of packaging. It was observed that there are many difficulties encountered by small livestock farmers in relation to the return of postconsumption containers. The municipality does not offer any structure for receiving these packages until the time of the local study and currently, there is no collection by traders in the region. [ABSTRACT FROM AUTHOR]

Graphology is a branch of science that tries to determine individual characteristics and details of a person based on their handwriting. It was started in 1871 with the devoted work of French cleric-archaeologist Jean-Hippolyte Michon. Today, this branch of science is used to evaluate people’s emotional development, give career advice, and conduct psychological analysis. Today, some company managers use the science of graphology to evaluate the personal characteristics of job applicants. The science of criminology, which contributes to the science of graphology, is the examination of the social and personal causes of any event. It focuses on crime, criminals, and ways to fight crime. Criminology studies are carried out to ensure justice and are carried out using various laws. However, there are also criticisms that graphology doesn’t always yield clear results in criminological studies. To put it more clearly, in the science of evidence (criminology), a broad consensus hasn’t been reached regarding the validity of graphology studies. Starting from this point, the benefits and shortcomings of graphology were analyzed in the study, and evaluations were made about the place and importance of graphology in archival science. In addition, the effect of graphology on archiving science was evaluated by focusing on the connection between the science of criminology. The study, which discusses the contribution of graphology to the sciences of archiving and information and records management, has been completed by making various suggestions that will support future academic studies. [ABSTRACT FROM AUTHOR]

If one must prioritize among the vast array of contributing factors to cancer evolution, environmental-stress-mediated chromosome instability (CIN) should easily surpass individual gene mutations. CIN leads to the emergence of genomically unstable life forms, enabling them to grow dominantly within the stable life form of the host. In contrast, stochastic gene mutations play a role in aiding the growth of the cancer population, with their importance depending on the initial emergence of the new system. Furthermore, many specific gene mutations among the many available can perform this function, decreasing the clinical value of any specific gene mutation. Since these unstable life forms can respond to treatment differently than stable ones, cancer often escapes from drug treatment by forming new systems, which leads to problems during the treatment for patients. To understand how diverse factors impact CIN-mediated macroevolution and genome integrity–ensured microevolution, the concept of two-phased cancer evolution is used to reconcile some major characteristics of cancer, such as bioenergetic, unicellular, and multicellular evolution. Specifically, the spiral of life function model is proposed, which integrates major historical evolutionary innovations and conservation with information management. Unlike normal organismal evolution in the microevolutionary phase, where a given species occupies a specific location within the spiral, cancer populations are highly heterogenous at multiple levels, including epigenetic levels. Individual cells occupy different levels and positions within the spiral, leading to supersystems of mixed cellular populations that exhibit both macro and microevolution. This analysis, utilizing karyotype to define the genetic networks of the cellular system and CIN to determine the instability of the system, as well as considering gene mutation and epigenetics as modifiers of the system for information amplification and usage, explores the high evolutionary potential of cancer. It provides a new, unified understanding of cancer as a supersystem, encouraging efforts to leverage the dynamics of CIN to develop improved treatment options. Moreover, it offers a historically contingent model for organismal evolution that reconciles the roles of both evolutionary innovation and conservation through macroevolution and microevolution, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Equine-assisted intervention (EAI) studies deal with clients, whereas very few studies focus on the effects on animals. To explore that, this study compared manager's subjective views of "good" management and selection criteria for a horse in EAI, using an online survey (n = 51) and objective observations in eight additional facilities. Management decisions and welfare consequences were correlated. One major factor influencing welfare and human–equid interactions appeared to be working modalities, with more EAI facilities practicing groundwork and bitless work. Facilities where equids were the most involved in mixed activities (EAI and classic riding school) had the most equids with compromised welfare. Given EAI clients' particularities, conventional working modalities are less adapted and at risk of increased discomfort for equids. Overall, survey and observational approaches converged but some discrepancies (choice of equid type) appeared. Equine-assisted intervention (EAI) studies deal with clients, whereas very few studies focused on the effects on animals. EAI equids are also submitted to management, which influences their welfare. Management and working conditions depend on human decisions and perception. We gathered information through a survey about facilities managers' strategies (n = 51) and obtained direct information on management and working practices and their consequences on equids' welfare through an observational study (n = eight facilities, 174 equids). Differences in managers' perceptions of good management practices were related to the facility's involvement in EAI, e.g., increased awareness of equids' needs (housing and feeding), especially when EAI was the main activity. A detailed observational study on eight additional facilities confirmed that. Facility management profiles were paralleled by equids' welfare profiles. Clear correlates were found between management decisions and welfare consequences. One major factor influencing welfare and human–equid interactions appeared to be working modalities, with more EAI facilities practicing groundwork and bitless work. Facilities where equids were the most involved in mixed activities had the most equids with compromised welfare. Given EAI clients' particularities, conventional working modalities are less adapted and at risk of increased discomfort for equids. Overall, survey and observational approaches converged but some discrepancies (choice of equid type) appeared between the reported and observed prevalence. [ABSTRACT FROM AUTHOR]

Aims: The South Korean government implemented the narcotics information management system (NIMS) on 18 May 2018 to manage benzodiazepine receptor agonists (BzRAs) and narcotics effectively and establish a reporting mechanism for these drugs. This study assessed the effects of NIMS on inappropriate use of BzRAs. Methods: Using national patient sample data from 2016 to 2020, we analysed adult outpatients who were prescribed oral BzRAs. We conducted a time series and segmented regression analysis using selected indicators to analyse the monthly variations related to the inappropriate use of these medications. Results: The study revealed no significant changes in the indicators of inappropriate BzRA use following the NIMS implementation. Contrary to expectations, there was a significant increase in the proportion of patients exceeding defined daily dose (DDD) and in those receiving concurrent prescriptions of multiple BzRAs, following the implementation of NIMS. The immediate impact of the COVID‐19 pandemic was an increase in DDD exceedance; however, overall, this did not significantly affect BzRA use. Conclusions: The introduction of NIMS did not significantly enhance the management of BzRA misuse. Additional measures, including continuous monitoring, system improvements and comprehensive education for prescribers and patients, are recommended to ensure the appropriate use of psychotropic medications. [ABSTRACT FROM AUTHOR]