Search

Objectives: The whole-body vibration (WBV) effects on muscle strength show inconsistent results. Moreover, there is no study about the WBV effect on stiffness, elasticity, and muscle strength. Therefore, the study aimed to examine the effect of WBV exposure with static squat posture on the stiffness, elasticity, and strength of the lower-limb extensor muscles. Material and methods: Forty healthy untrained young adults were divided into WBV and control groups. The experimental group received WBV exposure on 2 nonconsecutive days of the week, for 6 weeks. The MyotonPRO device was used for the assessment of the knee extensor and the ankle dorsiflexors' stiffness and elasticity. Isometric muscle strength was evaluated with a hand-held dynamometer. All measurements were done by the same assessor at baseline, and the following 6 weeks. Results: Significant group-by-time interactions were found for the elasticity scores of the right (d = 0.84, P = .01) and left (d = 0.77, P = .02) ankle dorsiflexors. Similar to the elasticity measurements, significant group-by-time interactions were observed in the muscle strength scores of the right (d = 0.45, P = .046) and left (d = 1.25, P < .001) ankle dorsiflexors. No significant effects were observed in any of the evaluated muscle stiffness measurements (P > .05), and there was no significant group-by-time interaction in knee-extensor muscle strength and elasticity scores (P > .05). Conclusions: The study results indicate that if the ankle dorsiflexor strength and elasticity are desired to be increased, the 6-week WBV exposure in a static squat posture can be used in healthy individuals. [ABSTRACT FROM AUTHOR]

The logistics management information system (LMIS) plays a crucial role in effective record-keeping and reporting, ensuring efficient management of stock status and consumption data. A proficient LMIS improves accountability and supports informed logistic decisions in healthcare. Conversely, a subpar LMIS negatively affects essential medicine availability, compromising overall healthcare service efficiency. This study aimed to evaluate the status of the logistics management information system and the availability of non-program tracer drugs (NPTDs) in public health facilities within Bahir Dar City. This study employed an institutional-based cross-sectional study. Data were collected from February 20 to April 30, 2022 in 12 public health facilities located in Bahir Dar City. Structured questionnaire and data abstraction formats were used to gather pertinent data. After checking for completeness, the data were analyzed using Microsoft Excel and SPSS version 23. The primary analytical outcomes involved descriptive statistics, encompassing frequencies, averages, and percentages, which were subsequently presented in tables and figures. Bin card and Internal Facility Report and Resupply Form were the only blank LMIS tools available in all health facilities, while stock-record card was the least available, present in only 2(16.7%) facilities. Nine health facilities (75%) used self-prepared forms to request NPTDs from the Ethiopian Pharmaceutical Supply Agency (EPSA) at the end of the review period, whereas 7(58.3%) used official letters for emergency orders. Additionally, seven health facilities (58.3%) used the Health Commodities Management Information System On the day of the visit, 78.68% of NPTDs were available. Tetracycline eye ointment 1% had the longest stock-out duration, lasting for a mean 69.64 days. Rather than using the RRF, most of facilities opted for their own forms to request NPTDs from EPSA. While it is advisable for all health facilities to maintain continuous availability of tracer drugs, this study revealed that the current state of non-program tracer drug availability falls short of meeting this expectation. [ABSTRACT FROM AUTHOR]

This article explores the significance of digitized wildlife trade data in achieving biodiversity goals. It acknowledges the challenges faced by wildlife inspectors in balancing legal trade with biosecurity and endangered species protection. The article emphasizes the fragmented nature of current trade data and proposes real-time species-level interrogation of shipment documents to detect illegal trade and mitigate risks. It also discusses the limitations of harmonized system codes and suggests collecting species-level data to improve monitoring and address biodiversity targets. The article emphasizes the need for governments to adopt data systems that provide real-time analysis and curated information to identify discrepancies and illegal trade, while also highlighting the importance of transparency and efficient processing of shipments with illegal or invasive species. [Extracted from the article]

• For the first time the observations of OCO-2 & 3 was combined to access Xco 2 anomalies. • The Amazon biome has more positive XCO 2 anomalies. • The Brazilian biomes more affected by fire was Amazon, Cerrado and Pantanal. • Xco 2 positive anomalies is correlated with Fire Foci observations. Climate change is a challenge to the global community and one of the causes is the increase of greenhouse gases (GHG) in the atmosphere, especially carbon dioxide (CO 2). The major emission source of this gas into the atmosphere comes from the burning of fossil fuels and biomass burning, on the other hand, the main sink comes from biochemical processes such as photosynthesis. Thus, the observation of CO 2 is a key point to understanding sources and sinks. In this context, The Orbiting Carbon Observatory 2 (OCO-2) and 3 (OCO-3), are a NASA dedicated mission to monitor the column-averaged dry-air mole fraction of carbon dioxide (XCO 2) on a global scale. We combined the OCO-2 and OCO-3 observations to study the spatial distribution of XCO 2 anomalies and how some of these anomalies are related to fire occurrence in the Brazilian Biomes during 2020 and 2021 considering two different seasons, Dry and Wet. The fire occurrence was obtained from Fire Information for Resource Management System (FIRMS) that provides the data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) product of active fires and thermal anomalies at Near-Real Time (MCD14DL, collection 6). The OCO-2/3 observations are affected by cloud formations in wet seasons, we observe that the dry period has more observations. The XCO 2 anomaly values range from ∼ 7.0 ppm to −7.0 ppm and mostly positive anomalies occur in Amazon Biome, and this ecosystem has higher average values for all periods (∼0.9 ppm), compared to the other biomes. The fire occurrence was higher in dry periods, especially in 2020 when unprecedented fire outbreaks were registered in Brazil. The most affected biomes were Pantanal, Cerrado, and Amazon. XCO 2 positive anomalies spatially agree with fire foci over some areas, and the correlation values between them ranged from 0.2 to 0.5 depending on the biome and season, and when considering observations with clouds the correlation is slightly higher. We point out for the first time the possibility of using OCO-2 and 3 combined, also, how positive XCO 2 anomalies are related to fire occurrence in different ecosystems and periods, and the role of cloud detection in this relationship. [ABSTRACT FROM AUTHOR]

The study aimed to identify the nature of excellence management and the extent of its application in Arab and Islamic universities. It also aimed to present a proposed vision for Albaydha University to implement excellence management. The qualitative approach was adopted in its analytical style to analyze a number of literature, research and models. The study concluded by preparing the proposed vision, which is represented in a group of procedures include (leadership, working individuals, strategy and policy, partnership and resources, operations, employee results, The targeted people satisfaction, community service, performance indicator results). The researchers also reached the requirements for applying the proposed vision, the most important of which are: 1. Supporting the senior leadership to implement the excellence model, and shareing it. 2. Reshaping the university's organizational culture according to the vision of the European model of excellence management. 3. Continuous education and training for all employees, including senior leadership at the university. 4. Continuous quality improvement. 5. Establishing an accurate information system for quality management for excellence. Albaydha Journal [ABSTRACT FROM AUTHOR]

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to "mental privacy." In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum's "contextual integrity" theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. [ABSTRACT FROM AUTHOR]

The healthcare sector is continuously evolving, and it is anticipated that the next healthcare model would be information-focused. Cloud computing technology can help the sector handle heterogeneous sources of data and the effective convergence of data. Due to security and privacy considerations, it is challenging to transfer a medical record from multiple hospital databases. Consequently, the article proposed a blockchain-based patient information management system by using the Blockchain-based Privacy-Preserving and Robust Healthcare data (BPPRH). This transfers the medical data to the patient through the cloud more securely. The cloud is modelled with the hypervisor and VMs. During the data transmission or storage in the cloud, there are several attacks like ransomware, malicious insiders, Man-in-the-middle attack, malware injection attacks, and Denial-of-Service attacks (DoS) occur in the network. Modified Fuzzy Particle Swarm Optimization (MFPSO) is presented to detect these threats. After detecting the attacks, Edge-Cloud-based Collaborative Systems (ECCS) prevent the data from attackers with the use of Regularized Maximum Likelihood Estimation, Inverse Network, and Shadow Model Reconstruction models. The results showed that the system provides better security with high accuracy of 99.32% respectively. The proposed Blockchain-based architecture is designed to contribute to the healthcare management systems' robustness and to avoid recorded security limitations in commonly used systems for smart healthcare respectively. [ABSTRACT FROM AUTHOR]

Although enterprises can promote dynamic capabilities by managing their innovation processes, the specific innovation processes and ways to encourage such capabilities have been under-researched. This exploratory research illuminates the relationship between dynamic capabilities and innovation processes through a case study of the largest Chinese e-commerce platform, Taobao, a vast and complex digital platform with various actors and interactions with numerous related platforms. Based on previous research, interviews with ten management personnel at Alibaba from April 2019 to August 2021, and secondary data, we found six innovation processes specific to this digital platform. Furthermore, we found that each of these innovation processes relates to specific dynamic capabilities and, therefore, a platform leader can improve its dynamic capabilities by organising its innovation processes in the digital platform ecosystem. The presented findings show important implications for strategic management and information systems by connecting dynamic capabilities and innovation management theories. In particular, our findings enable us to describe how the innovation processes management induces strong dynamic capabilities, such as environmental scanning and sensing capability, innovation capability, and integrative capability. [ABSTRACT FROM AUTHOR]

International wildlife trade data are frequently used by government agencies, conservation organizations, and scientific researchers to study and protect species from overexploitation and prevent the spread of invasive species and introduction of zoonotic pathogens. Inaccurate data can lead to mistaken conclusions by researchers, the development of unsuccessful remedial conservation actions, and provide government officials with incorrect views of detrimental trade. The U.S. Fish and Wildlife Service (USFWS) maintains the world's most comprehensive national dataset of legal and illegal international wildlife trade recorded by individual shipments and species in its Law Enforcement Management Information System (LEMIS). Although the importance of LEMIS data is not to be understated, the errors and inconsistencies contained therein have not previously been adequately recognized or studied. Based on firsthand experiences with the creation and application of LEMIS data, this manuscript describes a variety of errors, biases, omissions, and an overall lack of data quality assurance. An independent audit of the LEMIS wildlife trade database and the service's policies, procedures, and protocols for managing this system is needed. Additional recommendations are also offered to develop better management standards and bring greater resources for managing LEMIS, asking the nongovernmental organization and intergovernmental organization user communities to play a role. [ABSTRACT FROM AUTHOR]

Within the transition from precision agriculture (task-specific approach) to smart farming (system-specific approach) there is a need to build and evaluate robotic systems that are part of an overall integrated system under a continuous two-way connection and interaction. This paper presented an initial step in creating an integrated system for agri-robotics, enabling two-way communication between an unmanned ground vehicle (UGV) and a farm management information system (FMIS) under the general scope of smart farming implementation. In this initial step, the primary task of route-planning for the agricultural vehicles, as a prerequisite for the execution of any field operation, was selected as a use-case for building and evaluating this integration. The system that was developed involves advanced route-planning algorithms within the cloud-based FMIS, a comprehensive algorithmic package compatible with agricultural vehicles utilizing the Robot Operating System (ROS), and a communicational and computational unit (CCU) interconnecting the FMIS algorithms, the corresponding user interface, and the vehicles. Its analytical module provides valuable information about UGVs' performance metrics, specifically performance indicators of working distance, non-working distance, overlapped area, and field-traversing efficiency. The system was demonstrated via the implementation of two robotic vehicles in route-execution tasks in various operational configurations, field features, and cropping systems (open field, row crops, orchards). The case studies showed variability in the operational performance of the field traversal efficiency to be between 79.2% and 93%, while, when implementing the optimal route-planning functionality of the system, there was an improvement of up to 9.5% in the field efficiency. The demonstrated results indicate that the user can obtain better control over field operations by making alterations to ensure optimum field performance, and the user can have complete supervision of the operation. [ABSTRACT FROM AUTHOR]

OBJECTIVE: There is a need to increase patient and clinician awareness on the effectiveness of pneumococcal vaccination in at-risk groups. The aim of the study was to evaluate the effect of reminders for physicians and patients using the vaccination tracking system created in the hospital information management system (HIMS) on the vaccination rate, and the effect of pneumococcal vaccination on pneumonia-related hospitalization and mortality over a 12-month period. MATERIAL AND METHODS: This prospective observational cohort study was undertaken during a 2-year period in 3 tertiary care centers. Patients were followed up for 12 months following vaccination, and hospital admissions and mortality were recorded via HIMS. During the campaign, HIMS transmitted pneumococcal immunization reminder messages in accordance with guideline recommendations to physicians' computers and patients' mobile phones. Educational posters on pneumococcal vaccination were posted in outpatient clinics. Regular seminars on the evidence for pneumococcal vaccination were organized. All patients who were hospitalized during the follow-up period for chronic obstructive pulmonary disease (COPD), asthma, lung cancer, or pneumonia were analyzed in relation to their vaccination history regarding clinical outcomes. RESULTS: A total of 29 530 patients were included in the study. During the study period, the annual vaccination rate increased by 74.4% and reached 4.8% in 3 hospitals (P = .001). The rates were 3.9% in patients older than 65 years without comorbidities and 5.2% in those with COPD and asthma (P = .002). In pneumococcal vaccine recipients, pneumonia-related hospital mortality was lower (relative risk (RR) = 0.19, CI 0.09-0.35, P < .001). CONCLUSION: It is possible to raise the rate of pneumococcal vaccination through awareness campaigns. Individuals with COPD and asthma are more willing to receive pneumococcal vaccination. Among patients hospitalized for pneumonia, prior pneumococcal vaccination is associated with lower mortality. [ABSTRACT FROM AUTHOR]

The maximum specific gravity of asphalt concrete (AC) mix ( G mm ) is an important parameter without which asphalt mix design cannot be realized. But the experimental procedure for measuring the G mm requires time, consumes electric energy, and generates wastewater. Huge amount of experimental data that can enable the virtualization of the AC mix design process exists. But to date, all standardized AC mix-design procedures are mainly experimental. In this study, non-linear regression analysis and multi-layer Artificial Neural Network (ANN) were utilized to develop prediction models for the G mm of AC mixes. The study utilized 4158 superpave mix-design data points from the Long-Term Pavement Performance (LTPP) information management system (IMS) database. The input variables are asphalt specific gravity G b , asphalt binder content P b , and combined bulk specific gravity of aggregates G sb . The ANN-model ( R = 0.9843 , M S E = 0.00016 ) performed better than the regression model ( R = 0.9241 , M S E = 0.00076 ). A standalone user-friendly MATLAB-based app was developed for the trained ANN-model. The ANN-model is capable of predicting G mm within AASHTO and ASTM standard single-operator precision requirements (± 0.011) 85.9% of the time. The model can predict G mm within a margin of ± 0.021 with a 95% success rate. The resulting air voids which were estimated using the predicted G mm met air-void precision tolerance of ± 0.5 and ± 1.0% in 85.6 and 96.3% of the tests, respectively. The proposed model could minimize the time, energy, and material resources needed during the mix-design process of AC. Standards for AC mix-design should be revised to accommodate more use of prediction models so as to make the design process more sustainable. [ABSTRACT FROM AUTHOR]

Pneumococcal Diseases (PDs) remains a serious public health problem around the world and in China. Pneumococcal vaccination is the most cost-effective measure to prevent PDs. In 2021, the government of Weifang City, Shandong Province, China introduced a free dose of domestic 13-valent Pneumococcal Conjugate Vaccine (PCV 13) to vaccinate registered children aged 6 months–2 years. This study aimed to evaluate the vaccination rate of PCV13 in children aged under 5 years before and after the vaccination program to provide evidences for further improving the prevention and control strategy for PDs. We collected data from the children's vaccination information management system in Weifang City and analyzed the PCV13 vaccination coverage and characteristics in all vaccination clinics of Weifang City for children aged under 5 years. We compared the differences in vaccination rates by gender, birth year, manufacturer, and county before and after innovative immunization strategy. Among the included 593,784 children aged under 5 years, the PCV13 vaccination rate in Weifang was generally low before the innovative immunization strategy. Urban children had a higher PCV13 coverage than rural children (P < 0.001), and parents tended to vaccinate their children with imported PCV13.The full vaccination rate for domestic and imported PCV13 was 0.67 % and 1.70 %, respectively. After the vaccination program, the PCV13 coverage of children increased significantly in all counties within Weifang City (P < 0.001), especially for children above 12 months of age. Most parents preferred to vaccinate their children with domestic PCV13, and the full vaccination rate of domestic and imported PCV13 was 6.59 % and 0.16 %, respectively. The vaccination rate of PCV13 in children is still much lower than the global average, posting a severe health challenge that needs to be addressed thoroughly. To improve the prevention and control strategy for PDs, it is recommended to continue to explore other relevant incentives based on the innovative immunization strategy. Furthermore, it is also recommended that China should incorporate PCV13 into the National Immunization Programs (NIP) as soon as possible. [ABSTRACT FROM AUTHOR]

With the development of research on multi-modal data fusion and its combination with online data management, the application of multi-modal big data fusion in information management systems is more and more extensive. How to integrate multi-modal big data effectively is the key technology to building an efficient information management system. In this paper, based on the combination of a multi-support vector machine and convolution neural network, the feature-level data fusion of multi-source heterogeneous big data is implemented, and it is applied to the real data set to test the relevant model. Experimental results show that this method can not only realize heterogeneous integration of big data, but also has high accuracy and reliability. [ABSTRACT FROM AUTHOR]

The United Kingdom Transfusion Laboratory Collaborative (UKTLC) has established minimum standards for staff qualifications, training, competency, and the use of information technology in hospital transfusion laboratories. These standards aim to ensure patient safety and provide a common management approach across the UK. Compliance with these standards is accepted by regulatory agencies as evidence of quality and can be used for assessment and inspection purposes. The standards cover staffing levels, qualifications, knowledge, and skills required for different roles within the laboratory. They also emphasize the importance of a safety culture and the use of technology to reduce errors. The document outlines these standards and guidelines, highlighting the importance of staff qualifications, training, and competency assessment to ensure patient safety. It also emphasizes the need for robust IT systems to prevent errors and improve efficiency. The standards promote a culture of reporting and learning from errors, with a focus on human factors and systems thinking in incident investigations. The impact of these standards will be monitored by regulatory agencies. [Extracted from the article]

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS. [ABSTRACT FROM AUTHOR]

The accuracy of the current buried pipeline management geographic information system (GIS) is not adequate to serve as base information for road digging control and permit in Taiwan. The existing positioning system for buried pipelines does not offer satisfactorily accurate surveying results for all the scenarios because the positioning accurateness decreases with an increase in the pipeline depth. The costly excavation and direct survey of the exposed pipeline method can provide precise measurement at the designed arbitrary interval and then the discrete point is interpolated into the continuous track. The required traffic controls and excavation permits for roads are the most critical drawback of this technology. The method of in-pipe inertial navigation system (INS) can record the complete, continuous pipe tracks, but such measurement drifts with an increase in the survey length. We proposed a pipeline positioning system by radiating an extremely low-frequency (ELF) magnetic field from the electrical coil placed on the ground to constrain the locating measurement of in-pipe INS. The proposed system could easily expand the diameters or increasing turns of a ground-based coil device to enhance the magnetic field intensity for accurately positioning the deeper buried pipes. The field tests were performed using a ductile cast iron pipe (DIP), and the corresponding results revealed that for the overburden depth of 3.4–4.2 m, the average positioning errors were 0.12 and 1.6 m in the horizontal and vertical directions, respectively. This approach is only plausible for an empty pipeline, and access to both ends of the pipe is required. A four-person crew handled the proposed system to locate the track of a 5-km-long buried pipeline in an 8-h-long working day when the pipeline was unfilled. Any pipeline with diameter larger than 10 cm (4 in.) could apply this system to locate the continuous track with the requirement of accessibility at both ends of the empty pipe. With 1.45-m coil, the maximum detectable buried depth is 15 m, and locating error is less than 20 cm at both directions for the cases with buried depth less than 5 m. Two people need to move and operate the magnetic coil at the ground and another two people must hoist the in-pipe unit at each end of the pipe simultaneously. With this arrangement, the continuous track of buried empty pipe could surveyed for 5 km in length within an 8-h working day. [ABSTRACT FROM AUTHOR]

Objective: To analyze nurses' perception of autonomy to exercise their practices in the context of primary health care. Methods: A descriptive study with a qualitative approach that analyzed 108 interviews carried out with nurses from the four capitals of the southeastern region of Brazil. The interviews, guided by a semi-structured script, were recorded and transcribed. The data produced were processed and explored with the help of the NVIVO® software. Results: Most of the participants were women, white, lived in the same city where they work, and graduated from private institutions. Two categories were organized: repercussions of autonomy and its consequences for solving users' health needs; and (lack of) knowledge about the regulation of nursing practices: challenges for autonomy. Conclusion: Nurses understand the importance of autonomy for their practices, but they face interference in their daily work, either by management, the structure of services or the need for protocols that guarantee the exercise of their professional activity. [ABSTRACT FROM AUTHOR]

Information Management Systems are vital tools for businesses and organizations to manage their data effectively, make informed decisions, ensure data security and compliance, and enhance operational efficiency. This paper presents a robust app roach to information management through the utilization of the Optimized Probabilistic Bidirectional Long Short-Term Memory (OP_Bi-LSTM) model. The study developed into the architecture and application of Simulated Annealing (SA) for hyperparameter optimization, emphasizing the impact of fine-tuning on model performance. OP_Bi-LSTM, rooted in Bidirectional Long Short-Term Memory, exhibits superior sequential data processing capabilities, making it well-suited for a variety of information management tasks. The proposed OP_BILSTM hierarchical architecture further enhances its pattern recognition capabilities in the information management system. Results from extensive experimentation demonstrate the model's versatility and adaptability in various applications, such as sentiment analysis, fraud detection, and time-series forecasting. The proposed OP_BI-LSTM model performance analysis is evaluated for the consideration of the different customers and product data. The propsoed OP_Bi-LSTM model achieves the classification accuracy of 0.98. It is stated that OP_Bi-LSTM emerges as a powerful tool for information management, offering exceptional performance and adaptability. As the field of deep learning and information management continues to evolve, this model holds great promise for addressing complex data chal lenges and facilitating data-driven decision-making in a variety of industries. [ABSTRACT FROM AUTHOR]

Contemporary welfare organizations engage in various evaluation practices to assess the quality of their services. In this paper we report a qualitative exploration of how technology-enabled evaluations are understood by organizational members who participate in quality assurance activities in Swedish social services. The study contributes to critical information systems literature, focusing on the tensions professionals experience in relation to the digital systems they use for evaluations. For example, "quantities" take precedence over the qualities of such work, as information systems constrain ambitions to realize knowledge-based social services. The results reveal three tensions in professionals' evaluation-related activities arising from conflicting uses or desires. One is between desires for flexible systems that enable reflection and standardized digital support systems. Another is between uses or desires for indicators that are meaningful at the operational level and for general, comparable measures at the management level. The third is between desires to use evaluation procedures for learning and control. The study contributes to both theory and practice related to technology-enabled evaluation of welfare services, and critical perspectives on information systems. [ABSTRACT FROM AUTHOR]

The investment in a smart manufacturing factory with corresponding management information systems is strategic, complex, and costly. This paper aims to develop a cost estimation framework for a smart factory management information system (SFMIS). This paper proposed a fuzzy activity-based costing (FABC) model for the evaluation of the SFMIS under conditions of uncertainty. The model presented a multi-step procedure to do the identification, estimation, determination, calculation, and computation of the resource costs, activity costs, and systems as cost objects. The fuzzy Delphi method is adopted, and seventeen experts are surveyed in two rounds of a questionnaire assessing the importance of resources, activities, systems, and industry type and company size. The proposed FABC cost estimation model is illustrated by a case study. This model can identify and allocate resources and estimate SFMIS costs accurately and efficiently. By using this FABC cost evaluation framework, policymakers facilitate smart factory implementation for its management information system under uncertainty. [ABSTRACT FROM AUTHOR]

The need for evidence on postdoctoral research fellows, or 'postdocs', has become increasingly evident globally, as many countries, including South Africa, lack information on these researchers. We used existing sources of quantitative data to provide a longitudinal profile of postdocs at South African public universities. These sources include national research-and-development surveys and data from the national Higher Education Management Information System. Our focus was on postdocs' representation and research intensity (i.e. time spent on research) within the national research system, as well as on two key demographic features, namely nationality and gender. We draw comparisons with findings reported for other countries to situate our results within the global science system. Interpretation of our results leads us to comment on global and national developments and policies relevant to postdocs. Significance: * This article provides, for the first time, longitudinal, quantitative evidence at the national level about postdocs in South Africa, a category of early-career researcher which has been growing in size and relevance but has remained largely invisible. * The findings provide the basis for a critical reflection on current discourses and policies related to postdocs in South Africa. * Interpretation of the findings allows us to identify priorities for future research on postdocs in South Africa that would be most useful to inform both national and institutional policy. [ABSTRACT FROM AUTHOR]

Background: Recognizing the seriousness of the misuse and abuse of medical narcotics, the South Korean government introduced the world's first narcotic management system, the Narcotics Information Management System (NIMS). This study aimed to explore the recent one-year opioid prescribing patterns in South Korea using the NIMS database. Methods: This study analyzed opioid prescription records in South Korea for the year 2022, utilizing the dispensing/administration dataset provided by NIMS. Public data from the Korean Statistical Information Service were also utilized to explore prescription trends over the past four years. The examination covered 16 different opioid analgesics, assessed by the total number of units prescribed based on routes of administration, type of institutions, and patients' sex and age group. Additionally, the disposal rate for each ingredient was computed. Results: In total, 206,941 records of 87,792,968 opioid analgesic units were analyzed. Recently, the overall quantity of prescribed opioid analgesic units has remained relatively stable. The most prescribed ingredient was oral oxycodone, followed by tapentadol and sublingual fentanyl. Tertiary hospitals had the highest number of dispensed units (49.4%), followed by community pharmacies (40.2%). The highest number of prescribed units was attributed to male patients in their 60s. The disposal rates of the oral and transdermal formulations were less than 0.1%. Conclusions: Opioid prescription in South Korea features a high proportion of oral formulations, tertiary hospital administration, pharmacy dispensing, and elderly patients. Sustained education and surveillance of patients and healthcare providers is required. [ABSTRACT FROM AUTHOR]

With the continuous promotion of digital transformation in the field of power transformation, the diversification of application scenarios, and the scale of pilot construction, the real-time, concurrency, and security requirements for data fusion and application support of the power monitoring system, management information system, and other business platforms are getting higher and higher, and this paper puts forward a high concurrency and availability "cloud-side-end collaboration" based on edge computing. This paper proposes a high concurrency and availability "cloud, edge and end collaboration" architecture based on edge computing for substation operation support systems. First, this paper summarizes the development status of domestic substation operation support systems and analyzes the advantages and disadvantages of various technical architectures. Then, a "cloud-side-end cooperative" substation operation support system architecture with "high real-time, high concurrency, high security and high stability" is proposed, which focuses on remote inspection, remote operation, and remote safety control of substation businesses from the perspective of engineering applications. It realizes transparent monitoring of equipment operation, unified management of operation data, and integration of production command and decision-making; solves the problems of dispersed coexistence of multiple systems for dispatching, monitoring, analysis, management, and other businesses, switching between multiple systems, and insufficient real-time and stability of the system; and controls the risks of the grid, reduces the potential safety hazards, and solves the contradiction between the continuous growth of the grid equipment and the shortage of production personnel. The results of engineering application examples show that the proposed architecture compared with the existing system architecture has greater advantages and can meet the requirements of large-scale access to the substation, with feasible popularization and application. [ABSTRACT FROM AUTHOR]

Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as privacy concerns, high cost, and accessibility issues. To overcome these issues, this paper proposed a novel method named bidirectional federated learning-based Gradient Optimization (BFL-GO) for disability management in information systems. In this study, bidirectional long short-term memory (Bi-LSTM) was utilized to capture sequential disability data, and federated learning was employed to enable training in the BFL-GO method. Also, gradient-based optimization was used to adjust the proposed BFL-GO method's parameters during the process of hyperparameter tuning. In this work, the experiments were conducted on the Disability Statistics United States 2018 dataset. The performance evaluation of the BFL-GO method involves analyzing its effectiveness based on evaluation metrics, namely, specificity, F1-score, recall, precision, AUC-ROC, computational time, and accuracy and comparing its performance against existing methods to assess its effectiveness. The experimental results illustrate the effectiveness of the BFL-GO method for disability management in information systems. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, physical education information management system has become an important part of modern education. However, the traditional physical education information management system has some problems in optical communication technology, such as bandwidth limitation and slow transmission speed, which affect the performance and effect of the system. This paper aims to improve the application of optical communication technology in physical education teaching information management system by using machine learning algorithm, and improve the performance and effect of the system. This paper collects the relevant data of PE teaching information management system, and carries on the pre-processing and feature extraction. The appropriate machine learning algorithm was then selected to train and optimize the model and applied to optical communication technology. The new system has higher bandwidth and transmission speed, can process and transmit data faster, and improve the teaching effect. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS) are being introduced with high levels of autonomy. This means that not only the role of the human operator is changing but also the way in which operations are performed. MASS are expected to be uncrewed platforms that are operated from a Remote Control Centre (RCC). As such, the concept of human-machine teams is important. The operation of MASS however shares some resemblance to other uncrewed platforms (e.g. Uncrewed Aerial Vehicles; UAVs) and it may be that lessons could be learned in relation to the development of MASS systems. A systematic literature review has been conducted, focusing on decision making, to generate insights into operating uncrewed vehicle platforms and design recommendations for MASS. Seven themes were revealed: decision support systems, trust, transparency, teams, task/role allocation, accountability and situation awareness. A Network Model was developed to show the interconnections between these themes which was then applied to a case study of an UAV accident. The purpose of doing this was to demonstrate the utility of the model to real-world scenarios and showed how each theme applied within the human-machine team. [ABSTRACT FROM AUTHOR]

The frequency of commercial space launch and re-entry operations is increasing worldwide. Current regulations and procedures ensure safe operations by temporarily closing large volumes of airspace where risks to aircraft would exist in case of non-nominal events during launch or re-entry operations. To maintain the safety of air traffic as the number of space operations rises, effectively providing the right information to the right stakeholders at the right time is key. Through a cooperative agreement, the FAA and DLR are sharing their unique capabilities using the Commercial Space Integration Lab and Air Traffic Validation Center, located in the USA and Germany respectively, to improve situational awareness through real-time data exchange. The project seeks to answer whether U.S. and European ANSPs can respond adequately to a non-nominal event during a launch or re-entry operation that presents a hazard to the airspace system. It leverages existing international data standards and infrastructures by using a data exchange approach based on System Wide Information Management (SWIM). Within the project an initial assessment of the processes, roles and responsibilities for implementing launch and re-entry events in the air traffic systems on both sides of the Atlantic, as well as the requirements for basic functions and performance parameters of a SWIM-based integration, have been completed. The project developed a demonstration model across all system levels as far down as to the ANSPs, namely to the air traffic controllers. Through a series of demonstrations, covering launch scenarios from the U.S. with possible effects on European airspace and vice versa, the project evaluated the technical and operational feasibility of the concept. The key data parameters identified during the analyses shall enable information sharing among various users within the U.S. and European global airspace system. It has been shown, that the systems on both sides of the Atlantic could be connected via standardized protocols und used successfully for exercises in different scenarios. Further research on the best integration of the processes in international Air Traffic Management (ATM) networks and domains will be the follow-on. • Cross-border data exchange improves situational awareness for rocket launches. • Importance of providing information to all stakeholders in the air and in space. • Demonstration between U.S. and European stakeholders is transferable other missions. • SWIM based launch/re-entry data exchange proved as feasible technical approach. [ABSTRACT FROM AUTHOR]

It is considered best practice to conduct a functional analysis and visually inspect data collected to determine the function of problem behavior, which then informs the intervention approaches applied. Visual inspection has been described as a "subjective" process that may be affected by factors unrelated to the data. Structured decision-making guidelines have been established to address some of these shortcomings. The current paper is a follow-up to earlier work describing positive outcomes related to the viability of a decision support system based on structured criteria from Roane et al. Here, we demonstrate important improvements in a computer script's interpretation of functional analysis data, including improvement in agreement between the updated computer script version and experienced human raters (89%) compared to our original agreement outcomes (81%). This paper further supports the use of decision support systems for functional analysis interpretation. [ABSTRACT FROM AUTHOR]

The article describes the project of the JSC Research Institute "Submicron" of development an informational analytical platform for operating the technological route. A database containing a list of serial numbers and product names, all operations along the technological route, users who have access to information about production processes has been created. The advantages of implementing this management information system at the enterprise are explained. The possibilities of tracking the history—dates and times of all operations of the technological route with indication of exact department and responsible person at the current time are shown. [ABSTRACT FROM AUTHOR]