Hasil untuk "Management information systems"

Takalani Girly Nemungadi, Tsakani Furumele, Absalom Mwazha et al.

<h4>Background</h4>In the absence of an active schistosomiasis control programme, the affected community is vulnerable to complications such as female genital schistosomiasis. Research has shown that female genital schistosomiasis is a challenge faced by many African women including those from South Africa. Since 2008, the South African National Department of Health has been trying to resuscitate the schistosomiasis control programme; the programme has not been fully established or implemented. However, there are some surveillance best practices that the country can institutionalise to improve control.<h4>Materials and methods</h4>A descriptive analysis of urogenital schistosomiasis data from the National Health Laboratory Services, Notifiable Medical Conditions Surveillance System, and District Health Information System was conducted in 2023. A document review was also carried out in 2023 to determine surveillance best practices to guide the establishment of sentinel sites for improving schistosomiasis and female genital schistosomiasis control.<h4>Results</h4>The Health Laboratory Services, Notifiable Medical Conditions Surveillance System, and District Health Information System are the existing surveillance and reporting systems. According to the Notifiable Medical Conditions Surveillance System (the overall and central notification system for the notifiable medical conditions), a total of 56529 urogenital schistosomiasis cases were reported nationwide between 2017 and 2021 (ranging from annual cases of 4140-15032). Most cases (>90%) were reported from public health facilities. The country's Regulations on the surveillance and control of notifiable medical conditions stipulate that schistosomiasis is one of the priority conditions that should be notified (within 7 days of clinical or laboratory diagnosis) by all public and private health care providers, as well as public and private health laboratories. The Regulations did not specify female genital schistosomiasis as one of the notifiable medical conditions. As a result, there was no reported data on female genital schistosomiasis and true burden was not known.<h4>Conclusion</h4>The data collected through the National Health Laboratory Services, Notifiable Medical Conditions Surveillance System, and District Health Information System demonstrate that there are formalised schistosomiasis reporting systems, but no female genital schistosomiasis reporting. The existence and use of these surveillance systems demonstrate the country's potential to integrate the systems to enhance the prevention, surveillance, reporting, and management of schistosomiasis and introduction of surveillance for female genital schistosomiasis surveillance. Prioritisation of urogenital schistosomiasis and female genital schistosomiasis surveillance is paramount and will generate valuable information that will guide the review and implementation of the current and old policies that were developed by the National Department of Health and stakeholders.

Muhammad Afaq Hussain, Zhanlong Chen, Biswajeet Pradhan et al.

Study region: The National Highways 85 and 50, key routes of the China–Pakistan Economic Corridor (CPEC) in Balochistan, Pakistan. Study focus: Flooding is a natural disaster that is becoming increasingly frequent and severe. The National Highways 85 and 50 are vulnerable, necessitating accurate flood susceptibility mapping (FSM). Current machine learning (ML) models for FSM often suffer from low efficiency and overfitting. This study introduces an innovative hybrid FSM approach using four heterogeneous ensemble learning (HEL) techniques combined with three ML models: Random Forest (RF), Support Vector Machine (SVM), and Light Gradient Boosting Machine (LGBM). The proposed method was tested using satellite data from Sentinel-1, Sentinel-2, and Landsat-8, analyzing 1371 flood locations and 12 contributing variables. RF, variable importance factors (VIF), and information gain ratio (IGR) were applied to assess multicollinearity. The dataset was split (70:30) for model training and testing, with HEL-based models achieving superior performance over single ML models. New hydrological insights for the region: The stacking model yielded the highest AUROC (0.98), Kappa (0.82), accuracy (0.927), precision (0.963), Matthew’s correlation coefficient (0.820), and F1-score (0.950). HEL-based models proved more stable and resistant to overfitting. IGR analysis identified slope and distance from streams as key factors in FSM. The resulting flood-prone maps provide insights for disaster management adaptation strategies, demonstrating the broader applicability of the developed approach to enhance FSM accuracy and reliability.

Davide Griffa, Alessio Natale, Yuri Merli et al.

<b>Introduction:</b> Chronic ulcers significantly burden healthcare systems, requiring precise measurement and assessment for effective treatment. Traditional methods, such as manual segmentation, are time-consuming and error-prone. This review evaluates the potential of artificial intelligence AI-powered mobile apps for automated ulcer segmentation and their application in clinical settings. <b>Methods:</b> A comprehensive literature search was conducted across PubMed, CINAHL, Cochrane, and Google Scholar databases. The review focused on mobile apps that use fully automatic AI algorithms for wound segmentation. Apps requiring additional hardware or needing more technical documentation were excluded. Vital technological features, clinical validation, and usability were analysed. <b>Results:</b> Ten mobile apps were identified, showing varying levels of segmentation accuracy and clinical validation. However, many apps did not publish sufficient information on the segmentation methods or algorithms used, and most lacked details on the databases employed for training their AI models. Additionally, several apps were unavailable in public repositories, limiting their accessibility and independent evaluation. These factors challenge their integration into clinical practice despite promising preliminary results. <b>Discussion:</b> AI-powered mobile apps offer significant potential for improving wound care by enhancing diagnostic accuracy and reducing the burden on healthcare professionals. Nonetheless, the lack of transparency regarding segmentation techniques, unpublished databases, and the limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption. <b>Conclusions:</b> AI-driven mobile apps for ulcer segmentation could revolutionise chronic wound management. However, overcoming limitations related to transparency, data availability, and accessibility is essential for their successful integration into healthcare systems.

Stephen Wu, Yu Otake, Yosuke Higo et al.

This paper elucidates the challenges and opportunities inherent in integrating data-driven methodologies into geotechnics, drawing inspiration from the success of materials informatics. Highlighting the intricacies of soil complexity, heterogeneity, and the lack of comprehensive data, the discussion underscores the pressing need for community-driven database initiatives and open science movements. By leveraging the transformative power of deep learning, particularly in feature extraction from high-dimensional data and the potential of transfer learning, we envision a paradigm shift towards a more collaborative and innovative geotechnics field. The paper concludes with a forward-looking stance, emphasizing the revolutionary potential brought about by advanced computational tools like large language models in reshaping geotechnics informatics.

Hendra Mayatopani, Nurdiana Handayani, Ri Sabti Septarini et al.

Wild plants or weeds often become enemies or disturb the main cultivated plants. In its development, wild plants or weeds actually have ingredients that are beneficial to the body and can be used as medicine. However, many people still need knowledge about the types of weed plants that have medicinal properties, especially the leaves. The purpose of this research is to classify the image of weed leaves with medicinal properties based on color and texture characteristics with an artificial neural network using a Self-Organizing Map (SOM). To improve information in feature extraction, RGB and HSV color features are used as well as texture features with Gray Level Co-occurrence Matrix (GLCM). Furthermore, the results of feature extraction will be identified as groups or classes with the Self-Organizing Map (SOM) algorithm which divides the input pattern into several groups so that the network output is in the form of a group that is most similar to the input provided. The test produces a precision value of 91.11%, a recall value of 88.17% and an accuracy value of 89.44%. The results of the accuracy of the SOM model for image classification on medicinal weed leaves are in the good category.

Weidong WANG, Hui GAO, Xin SU et al.

For a multi-user integrated sensing and communication system in the network of vehicles, a robust sensing-assisted communication massive multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) transmission scheme was proposed.Due to the limited sensing accuracy of the radar, errors existed in the channel state information (CSI) reconstructed based on sensing parameters.The transmission performance decreased as a result.To address this issue, the CSI in the delay doppler domain was reconstructed based on the sensing parameters by the transmitter firstly.And a robust beam forming scheme was designed considering the CSI error.Secondly, the channel estimation error and inter user interference were perceived by receivers based on sensing parameters.Then the robust receiver was designed by incorporating the perceived interference errors into the signal detector in an analytical way.Finally, numerical simulation results show that the proposed method effectively reduces the system bit error rate and increases the data reception rate of users.The proposed method improves the overall system performance in this situation.

William R. King

Takeshi Yoshida, Yuki Onishi, Takuya Kawahara et al.

Abstract In this study, we propose a method to automate fruit harvesting with a fruit harvesting robot equipped with robotic arms. Given the future growth of the world population, food shortages are expected to accelerate. Since much of Japan’s agriculture is dependent on imports, it is expected to be greatly affected by this upcoming food shortage. In recent years, the number of agricultural workers in Japan has been decreasing and the population is aging. As a result, there is a need to automate and reduce labor in agricultural work using agricultural machinery. In particular, fruit cultivation requires a lot of manual labor due to the variety of orchard conditions and tree shapes, causing mechanization and automation to lag behind. In this study, a dual-armed fruit harvesting robot was designed and fabricated to reach most of the fruits on joint V-shaped trellis that was cultivated and adjusted for the robot. To harvest the fruit, the fruit harvesting robot uses sensors and computer vision to detect and estimate the position of the fruit and then inserts end-effectors into the lower part of the fruit. During this process, there is a possibility of collision within the robot itself or with other fruits depending on the position of the fruit to be harvested. In this study, inverse kinematics and a fast path planning method using random sampling is used to harvest fruits with robot arms. This method makes it possible to control the robot arms without interfering with the fruit or the other robot arm by considering them as obstacles. Through experiments, this study showed that these methods can be used to detect pears and apples outdoors and automatically harvest them using the robot arms.

Jan Kleiner, Jakub Drmola, Miroslav Mares

Endpoint users are usually viewed as the highest-risk element in the field of cybersecurity. At the same time, they need to be protected not just from the individual-level prism but also, from the states perspective, to counter threats like botnets that harvest weakly secured endpoints and forge an army of so-called zombies that are often used to attack critical infrastructure or other systems vital to the state. Measures aimed at citizens like the Israeli hotline for cybersecurity incidents or Estonian educational efforts have already started to be implemented. However, little effort is made to understand the recipients of such measures. Our study uses the survey method to partly fill this gap and investigate how endpoint users (citizens) are willing to protect themselves against cyber threats. To make results more valid, a unique comparison was made between cyber threats and physical threats according to the impact which they had. The results show statistically significant differences between comparable cyber-physical pairs indicating that a large portion of the sample was not able to assess the threat environment appropriately and that state intervention with fitting countermeasures is required. The resultant matrix containing frequencies of answers denotes what portion of respondents are willing to invest a certain amount of time and money into countering given threats, this enables the possible identification of weak points where state investment is needed most.

Amirreza Asnafi, Mohsen Haji Zeinolabedini, Faezeh Ahmadipour

Access to the required information in all available scientific disciplines is one of the most important factors in the survival of that field. In the architecture field, the type of information format differs from other disciplines. The purpose of this study was to identify the behavior of images in the architecture of Shahid Beheshti University. The present study is an applied target and uses a descriptive survey method. The statistical population of the study consists of two groups of students and professors in the architecture major of Shahid Beheshti University. To determine the sample size, the Cochran formula was used and the sample size in this formula was 296 people. The results showed that the architects mainly used images for identifying creative ideas and taking advantage of the details of architectural structures. The type of image content they used was mostly photos, maps, and charts, which could be found in engines and image databases by limiting the size of the image and following related links as long as the image was taken. One of the major obstacles in finding images for architects was the lack of familiarity with the way they were searched. Creativity, proximity to the subject, credibility, and quality of the images were the criteria for selecting content. Considering the library's share in retrieving research-based images, it is suggested that library and library librarians conduct awareness-raising activities at the university's research groups such as brochures, conferences, library visits, and workshops.

Hyder Akmal S., Sundström Agneta, Chowdhury Ehsanul H.

Purpose: This study explores the knowledge development of network-based market orientation (MO) for the internationalization of disruptive innovation (DI) by small and medium-sized enterprises (SMEs).

Dabeeruddin Syed, Haitham Abu-Rub, Ali Ghrayeb et al.

Forecasting of energy consumption in Smart Buildings (SB) and using the extracted information to plan and operate power generation are crucial elements of the Smart Grid (SG) energy management. Prediction of electrical loads and scheduling the generation resources to match the demand enable the utility to mitigate the energy generation cost. Different methodologies have been employed to predict energy consumption at different levels of distribution and transmission systems. In this paper, a novel hybrid deep learning model is proposed to predict energy consumption in smart buildings. The proposed framework consists of two stages, namely, data cleaning, and model building. The data cleaning phase applies pre-processing techniques to the raw data and adds additional features of lag values. In the model-building phase, the hybrid model is trained on the processed data. The hybrid deep learning (DL) model is based on the stacking of fully connected layers, and unidirectional Long Short Term Memory (LSTMs) on bi-directional LSTMs. The proposed model is designed to capture the temporal dependencies of energy consumption on dependent features and to be effective in terms of computational complexity, training time, and forecasting accuracy. The proposed model is evaluated on two benchmark energy consumption datasets yielding superior performance in terms of accuracy when compared with widely used hybrid models such as Convolutional (Conv) Neural Network-LSTM, ConvLSTM, LSTM encoder-decoder model, stacking models, etc. A mean absolute percentage error (MAPE) of 2.00% for case study 1 and a MAPE of 3.71% for case study 2 is obtained for the proposed forecasting DL model in comparison with LSTM-based models that yielded 7.80% MAPE and 5.099% MAPE for two datasets respectively. The proposed model has also been applied for multi-step week-ahead daily forecasting with an improvement of 8.368% and 20.99% in MAPE against the LSTM-based model for the utilized energy consumption datasets respectively.

Shah Nazir, Sulaiman Khan, Habib Ullah Khan et al.

Healthcare systems are transformed digitally with the help of medical technology, information systems, electronic medical records, wearable and smart devices, and handheld devices. The advancement in the medical big data, along with the availability of new computational models in the field of healthcare, has enabled the caretakers and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. The role of medical big data becomes a challenging task in the form of storage, required information retrieval within a limited time, cost efficient solutions in terms care, and many others. Early decision making based healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Scientific programming play a significant role to overcome the existing issues and future problems involved in the management of large scale data in healthcare, such as by assisting in the processing of huge data volumes, complex system modelling, and sourcing derivations from healthcare data and simulations. Therefore, to address this problem efficiently a detailed study and analysis of the available literature work is required to facilitate the doctors and practitioners for making the decisions in identifying the disease and suggest treatment accordingly. The peer reviewed reputed journals are selected for the accumulated of published research work during the period ranges from 2015 - 2019 (a portion of 2020 is also included). A total of 127 relevant articles (conference papers, journal papers, book section, and survey papers) are selected for the assessment and analysis purposes. The proposed research work organizes and summarizes the existing published research work based on the research questions defined and keywords identified for the search process. This analysis on the existence research work will help the doctors and practitioners to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients and suggest medicines accordingly.

R. O. Omorov

Issues, arising in the field of intellectual property rights in connection with the development of artificial intelligence systems and their impact on the development of legal relations in the economy and culture of modern society, have been considered. Aspects of mutual policies in the field of intellectual property rights and the development of artificial intelligence systems for the development of innovation and creativity have been examined. Questions of copyright and ownership in the interaction of man, collective and artificial intelligence or artificial intelligence systems have been raised and proposed. Issues related to artificial intelligence as an object of intellectual property have been considered. The position of the author on the legal personality of artificial intelligence to intellectual property objects created by autonomous artificial intelligence systems has been presented, which is expressed in the answers to the questions of the project of the World Intellectual Property Organization to the wide discussion of interested parties, planned for 2020 at the headquarters of the World Intellectual Property Organization in Geneva. The main conceptual principle of the author on the issues of the planned discussion is to grant the right of copyright and ownership of intellectual property objects created by autonomous artificial intelligence to a dressed subject – a person or collective, a developer of artificial intelligence with fixation of the latter as a sub-subject or instrument of the subject. Traditional categories of intellectual property rights also have been considered, such as patentability and the inventive level of property in connection with the possible generation of these objects by artificial intelligence. Issues related to data, its generation, fabrications and legal relations regarding data have been considered. Harmonization of international intellectual property rights policies to alleviate the technological gap between countries in the context of artificial intelligence development has been examined.

Vorobyeva Julia, Zhutaev Igor, Trukhin Yuri et al.

The solution of specific tasks of management and rational maintenance of energy systems of cities can be ensured by a constantly updated unified urban geographic information system. The article discusses the main possibilities of using geographic information systems to determine the reliability indicators of heat supply networks, increase their efficiency and manageability by the processes of their operation. The analysis of the technical condition of the heat network of the city of Voronezh, the statistics of accidents and repair work on sections of the pipeline. The main shortcomings of the existing reliability accounting system, which does not imply the process of managing repair and restoration work, are identified. The expediency of making operational decisions on the basis of up-to-date information on the condition of sections of the heating network in different areas of the city and different periods of installation and repair is proved. The necessity of creating a constantly updated, updated and updated database of all systems of the city of Voronezh using geographic information technologies is justified. The analysis of factors leading to the need for reconstruction of heat supply systems is carried out. It has been established that the most common reasons leading to the need for reconstruction of heat supply systems are: change in the number of connected consumers; change in flow, temperature or pressure in the heating network; mismatch of energy indicators of the system with modern requirements.

Izza Indah Afkarina Kunny, Sarwanto Moersidik Setyo, Warno Utomo Suyud

The Citarum River is one of the most polluted rivers in the world because of the inadequate waste management system and community ignorance. Plastic is one of the contaminants in the Citarum watershed. In general, plastics less than 5 mm in size are defined as microplastics. Microplastics are persistent and harm the environment. This article aims to determine the potential for pollution and distribution of microplastics in freshwater systems, especially in the Citarum watershed area. Using a combination of literature study methods with Geographical Information Systems (GIS) analysis, this article explains that microplastic contamination has occurred along the Citarum watershed from upstream to downstream, found in water and sediment and fish samples. Facilitated by their small size and high stability in the environment, microplastics can move from the aquatic environment into the food chain and cause longterm damage. This case causes a severe threat to the quality of freshwater in the Citarum watershed. Therefore, this article can be used as a reference for managing pollution in the Citarum watershed area.

P. Weill, Margrethe H. Olson

L. Raymond, F. Bergeron

Çev.: Nazlı Alkan

This study focus on how an individual's particular work context, job characteristics and knowledge-related job requirements affect the relationship between task-technology fit (TTF) and the use of information systems (IS) in knowledge management activities. The literature on Knowledge Management (KM) and Knowledge Management Systems (KMS) is reviewed to identify relevant constructs and their dimensions. Based on this analysis, a model is proposed and tested. Our findings suggest that providing appropriate IT tools that fit tasks alone is no guarantee that they will be employed to leverage the acquisition, transfer and reuse of knowledge. Certain characteristics of jobs, driven by particular work contexts, generate greater need and opportunity for knowledge use. These latter factors moderate the relationship between TTF and actual use of IS for KM purposes: the greater the need and opportunity to conduct knowledgerelated activities, the stronger the relationship between TTF and actual IS use.

Tina Lien Barken, Elin Thygesen, Ulrika Söderhamn

Abstract Background Telemedicine is changing traditional nursing care, and entails nurses performing advanced and complex care within a new clinical environment, and monitoring patients at a distance. Telemedicine practice requires complex disease management, advocating that the nurses’ reasoning and decision-making processes are supported. Computerised decision support systems are being used increasingly to assist reasoning and decision-making in different situations. However, little research has focused on the clinical reasoning of nurses using a computerised decision support system in a telemedicine setting. Therefore, the objective of the study is to explore the process of telemedicine nurses’ clinical reasoning when using a computerised decision support system for the management of patients with chronic obstructive pulmonary disease. The factors influencing the reasoning and decision-making processes were investigated. Methods In this ethnographic study, a combination of data collection methods, including participatory observations, the think-aloud technique, and a focus group interview was employed. Collected data were analysed using qualitative content analysis. Results When telemedicine nurses used a computerised decision support system for the management of patients with complex, unstable chronic obstructive pulmonary disease, two categories emerged: “the process of telemedicine nurses’ reasoning to assess health change” and “the influence of the telemedicine setting on nurses’ reasoning and decision-making processes”. An overall theme, termed “advancing beyond the system”, represented the connection between the reasoning processes and the telemedicine work and setting, where being familiar with the patient functioned as a foundation for the nurses’ clinical reasoning process. Conclusion In the telemedicine setting, when supported by a computerised decision support system, nurses’ reasoning was enabled by the continuous flow of digital clinical data, regular video-mediated contact and shared decision-making with the patient. These factors fostered an in-depth knowledge of the patients and acted as a foundation for the nurses’ reasoning process. Nurses’ reasoning frequently advanced beyond the computerised decision support system recommendations. Future studies are warranted to develop more accurate algorithms, increase system maturity, and improve the integration of the digital clinical information with clinical experiences, to support telemedicine nurses’ reasoning process.