Hasil untuk "Management information systems"

Weerapat Pookkaman, Theeraya Mayakul

Cannabis is well recognized in the medical field despite numerous controversies surrounding it. Its cultivation certainly has economic importance, but regulation is complex. Standardized practices are crucial across the value chain, with traceability vital for monitoring its use. This study introduces a digital medical cannabis framework aimed at balancing the demand and supply. The systematic review incorporates articles, policies and law documentations to identify the factors confirmed by the expert panel. A total of 12 experts from medical, agricultural and technological fields, each holding executive management positions, participated in the semi-structured interviews. The agreement and consensus were determined using the content validity index. The findings show that the medical cannabis supply chain visibility is needed to share data for quality assurance, monitoring and governance. Raw materials, resources, finished goods, expertise, logistics and eligible patient profiles should be transferred in real time from supplier to patient. Transparency, safety and good decision-making need to be promoted. The digital management solution delivers rigorous monitoring and tracing, improves pharmacovigilance, and adapts to national health policies and reimbursement standards. Sharing accurate information with stakeholders encourages centralized management as a farm-to-research-and-clinical service. Demand forecasting is essential for setting production quotas to avoid overproduction and abuse. This paper presents a comprehensive perspective and insights into the medical cannabis supply chain. It contributes to the interdisciplinary knowledge of logistics, health systems and technology management. The proposed framework encourages regulatory trust and fosters engagement among diverse stakeholders, with digital technology serving as the primary facilitator. It emphasizes the crucial role of central administration in enabling demand-supply balancing.

Sangeetha M, Vijayaraj K

This paper synthesizes and maps ten years of research on employee attrition prediction using machine learning (ML) (2014–2025). 161 peer-reviewed English-language articles and reviews were selected from a starting set of 610 records using a structured Scopus search (title, abstract, and keywords) and a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided screening procedure. Both network mapping and bibliometric tools were used: VOSviewer was used for co-occurrence and co-citation mapping, while Biblioshiny was used for descriptive and thematic analysis. As a result of the interdisciplinary nature of the topic, the results show a significant increase in publications after 2021, with contributions mostly appearing in management journals and technical publications like IEEE Access and Expert Systems with Applications. Four groupings emerged from keyword and topic analyses: (1) predictive methods and machine learning approaches; (2) human resource notions, including employee retention and turnover intention; (3) explainable AI and fairness (e.g. SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME); and (4) educational, information technology, and healthcare sector-specific applications. Co-citation networks demonstrate a dual intellectual foundation that combines fundamental ML literature with traditional human resource management turnover ideas, which are connected by new interdisciplinary applications. The results emphasize the necessity of cross-sectoral cooperation, broader implementation of interpretable ML in human resource practice, and more robust theory-driven model building. The paper offers a thorough road map for developing ethical, theoretically grounded attrition prediction research.

Özge Okur, Morris Huang, Lorenzo Angeli et al.

Abstract The digital transformation of education has rapidly evolved in recent years, driven by advancements in technology and further accelerated by the COVID-19 pandemic. Digital education Technologies (DETs) have become integral to higher education, reshaping how institutions deliver learning and manage resources. However, despite the widespread adoption of DETs, there has been limited focus on the sustainability of these technologies. This paper explores how sustainability considerations are integrated into DET selection processes at European Higher Education Institutions (HEIs) through semi-structured interviews with key decision-makers. The research focuses on three sustainability dimensions-environmental, social, and technological-and their impact on decision-making. The results indicate that while HEIs are making efforts toward sustainability, economic considerations still dominate the decision-making process. Moreover, the emphasis across sustainability dimensions remains unbalanced: social dimensions, such as privacy, are prioritized over environmental dimensions due to the former being treated as knockout criteria and due to a lack of reliable data on the environmental impacts of DETs. This study also identifies several challenges, including long procurement processes, limited financial resources, and heavy dependence on external service providers for digital infrastructure. The findings offer insights into how HEIs can better align their digital strategies with broader sustainability goals.

Oleksiy Hetmanenko, Bohdan Tyvodar

Modern society is increasingly faced with the need to improve the transparency and accountability of public authorities in order to combat corruption. In many countries, including Ukraine, corruption remains one of the biggest challenges in public administration, negatively affecting economic development, social justice and citizens' trust in the state. Traditional forms of interaction between citizens and public administration, which involve paper-based procedures and direct contact with officials, often create fertile ground for abuse and bribery. In the context of digitalisation and the growing role of information and communication technologies in all spheres of public life, electronic services (e-services) offer an alternative way for citizens, businesses and public authorities to interact. The use of e-services simplifies administrative procedures, reduces the subjective influence of the human factor and speeds up access to necessary information. All this, in turn, contributes to reducing corruption risks and increasing the effectiveness of management decisions. In Ukraine, the fight against corruption is one of the priorities of state policy, enshrined in strategic documents such as the Sustainable Development Strategy "Ukraine 2021-2025". The implementation of digital initiatives, such as the National Agency for Corruption Prevention's electronic declaration system or the ProZorro public procurement platform, demonstrates the potential of technology to reduce corruption risks. However, the digitisation process is accompanied by challenges, including limited technical infrastructure, low levels of digital literacy, and the need to adapt legislation to new realities. Research on this topic allows us not only to assess the current state of digitisation, but also to offer recommendations for its further development in the law enforcement sphere. The aim of the work is to summarise the main scientifically based approaches to defining digitalisation processes and their characteristics as an anti-corruption tool for public administration in modern conditions. The methodology of the article is structured in such a way as to allow for the study of the development of digitalisation as an anti-corruption tool in public administration, the identification of the essential features of digitalisation in the field of public administration, the limitations of digitalisation processes in public administration, digital tools in the fight against corruption, and the identification of directions for anti-corruption strategy in the promising conditions of European integration. The study was conducted based on the principles of dialectical logic. The multifaceted and multifactorial nature of the development of digitalisation as an anti-corruption tool and public administration necessitates the use of a systematic and situational approach to its study. The work also applied methods of systemic, logical, institutional analysis, and forecasting. The development of provisions and conclusions used the possibilities of comparative studies, rational choice theory, and neo-institutionalism. The study demonstrates that digital technologies, most notably electronic registries, big data analytics systems, blockchain solutions, video surveillance, and whistleblowing platforms, possess considerable potential in the prevention and combatting of corruption in law enforcement. The effectiveness of these reforms is clearly demonstrated by Ukrainian examples, such as the NACP electronic declaration system and the ProZorro public procurement platform. These tools help ensure transparency, automate management processes and minimise the human factor, which is particularly important in an area where significant discretionary powers create opportunities for abuse of authority. At the same time, the process of implementing digital solutions is accompanied by a number of challenges, including technical limitations, insufficient digital literacy among employees, gaps in legislation, and growing cyber threats. In the context of martial law and limited resources, these challenges are becoming even more relevant in Ukraine, necessitating a gradual, adaptive approach to the digitalisation of law enforcement. Further development prospects are linked to the following key areas: integration of interdepartmental digital platforms, strengthening cybersecurity, systematic training of specialists, and deepening international co-operation. The adaptation of best international practices, in particular the recommendations of the OECD and the European Commission, as well as the updating of national legislation to take into account innovative technologies, in particular artificial intelligence, play a special role in this process. Digitalisation, provided it is accompanied by appropriate regulatory, organisational and technical support, can become the basis for a systemic transformation of anti-corruption policy, strengthening trust in law enforcement agencies and establishing the principle of the rule of law. Achieving these goals requires coordinated efforts by the state, civil society and international partners, which will enable the full potential of digital tools in the field of law enforcement to be realised.

somayeh Shirzadi Laskookalayeh

Extended Abstract Background: The inadequacy of the supply of agricultural inputs with the demand for various products of this sector reveals the need for the optimal use of resources and increasing productivity. In this regard, addressing the issue of productivity in rice production is very important due to its essential role in feeding different sections of society, providing food security, reducing dependence on imports and foreign exchange, strengthening trade interactions with other countries, generating income, creating employment, creating balance in the business and capital market, and many other issues. In 2022, Mazandaran Province produced 1.6 million tons of paddy as a strategic product, responsible for 44.47% of Iran's paddy production, and in this sense, it has been ranked first in the country. This province has long been known as the hub of rice production, and this user product, having about 76% of Mazandaran's irrigated crop area, has always made an important contribution to the province's employment. For this purpose, the present study aimed to identify factors affecting the prosperity of the rice production business in Mazandaran Province, focusing on measuring the inefficiency of various production inputs, especially the labor force. Methods: Three institutional, managerial, and policy-market criteria effective in the prosperity of rice production business were extracted in this study. The input criterion includes all production factors affecting the productivity of this product, which includes eight subcriteria as water, labor, land, fertilizer, poison, machinery, capital, and seed. The management criterion is all management actions by relevant organizations and bodies (Jahad Keshavarzi, Regional Water, Room of Commerce), which includes six regulatory, executive, organizational, service, and innovation options. The political-market criterion also covered the macro-government policies that can affect the productivity of rice, and there are six financial, economic, structural, commercial, marketing, and strategic development options. Thus, 19 effective options in the productivity of rice production were considered in this study. In this study, factors affecting the productivity of this product were exracted and prioritized using the Analytical Hierarchy Process (AHP) method, measuring the production efficiency of important cultivars of this product (high-quality rice and high-yielding rice) using the data envelopment analysis method (DEA), and then examining productivity changes over time using the Malmquist Index (MI). The data needed for identifying and prioritizing factors in this research were collected by designing a questionnaire, which was completed based on the opinions of 18 experts, including those from the Agricultural Jihad Organization of Mazandaran Province and Sari City, as well as the academic community. The statistics and information of the Agricultural Jahad Organization of the province were used to complete the data in measuring the productivity of production and efficiency of inputs. Results: The results indicate that among the eight production factors, water, mechanization, and land are the most important input factors in rice production with weights of 0.36, 0.2, and 0.14, respectively. Among the five management factors, benefiting from the opinions of agricultural experts, implementing the optimal cultivation pattern of crops according to the climatic conditions and the status of water resources in the province, and using new technologies in agricultural operations with weights of 0.40, 0.25, and 0.14, respectively, were known as three important and superior factors for the management of rice production business. In addition, the financial, economic options, and improvement of the structure of the rice product marketing system were determined with the weights of 0.30, 0.22, and 0.19, respectively, as three policy-market subcriteria affecting the rice productivity of this province. Based on the findings in the agricultural year of 2017-2018 in the east of this province, Qaemshahr City, the land, machinary, poison, and fertilizer inputs were inefficient at 52.68%, 48.26%, 34.37%, and 33.16%, respectively. In 2018, the inefficiency rates in the use of land, labor, and poison inputs were 71.36%, 15.09%, and 4.46%, respectively. In the production of high-yielding rice in the east of the province, there has been inefficiency in the use of land, machinary, seed, water, and fertilizer inputs. Accordingly, Behshahr City acted inefficiently in consuming the mentioned inputs by 68.29, 52.60, 16.65, 12.63, and 7.55%, respectively. In 1998, the cities of Behshahr and Neka acted inefficiently in the consumption of all the investigated inputs, except for machinery. The percentages of inefficiency in the labor input are 16.14 and 42.07%, respectively. In addition, the productivity growth index values of Malmquist in the production of high-quality rice and high-yielding rice are 1.155 and 1.094, respectively. Hence, it can be concluded that the production productivity of this product has increased in this province. Conclusion: The results indicate that the productivity of different rice varieties has increased during the studied period. In the case of high-yielding rice, however, the technical efficiency of producers in newer technology is lower than in older technology. Therefore, it is necessary for trustee organizations and knowledge-based companies to invest in the research, innovation, and promotion of new technology in training to use this technology. In this study, "water" has been determined as the most important input affecting the productivity of this product; therefore, it is recommended to take necessary measures to promote water storage and reduce its consumption. It is also suggested to provide financial support to rice farmers and the development of knowledge-based companies to provide new irrigation systems. Referring to the results of this study, the use of "machinery" is considered the second most effective factor in increasing productivity. In addition to reducing the cost of manpower and saving time, the uniformity and accuracy of the work are increased with mechanized cultivation, and seedlings are exposed to less damage. However, this issue does not mean to ignore the role and importance of the workforce in the production and elimination of job opportunities. Rather, it is recommended to train skilled and specialized human resources to benefit from mechanization for the long-term stability of the rice production business and stable rural employment.

Inesisa Ncube, Josiline Chigwada, Patrick Ngulube et al.

Background: Government support for the use of indigenous knowledge (IK) for sustainable livelihoods assists in the achievement of sustainable development goals (SDGs). The integration of IK into formal development frameworks has been hindered by institutional barriers or a lack of policy recognition. Objectives: The study sought to assess how the governments of South Africa and Zimbabwe support the use of IK to sustain livelihoods. Method: A qualitative case study approach was used. Data were gathered through interviews and document analysis. Interviews were conducted with government officials and two were chosen from each department or ministry using purposive sampling, and a total of 10 participants were part of the study. The national development plans (NDPs) and indigenous knowledge systems (IKS) policies were analysed to gather information on government support for the use of IK for sustainable livelihoods. Thematic analysis and content analysis were the approaches to data analysis. Results: The findings show that the South African government had implemented various mechanisms to support the use of IK for sustainable livelihoods while little efforts were made in Zimbabwe. Conclusion: The South African government formulated policies that support the use of IK to sustain livelihoods, while IK policy formulation and implementation in Zimbabwe was not evident. Contribution: The research adds to the conversation about the importance of acknowledging IK and promoting its incorporation into development interventions and policy frameworks that can help governments leverage the potential of indigenous communities as keepers of priceless knowledge and guardians of sustainable livelihoods.

Massimiliano Varani, Giovanni Molari, Gianvito Annesi et al.

The increasing complexity of modern agriculture and the growing demand for sustainability have accelerated the adoption of advanced decision-support systems in farm management. Accurate cost estimation of machinery operations is particularly critical in large-scale farming, where machinery represents a substantial portion of operational expenses. Traditional costing methods often rely on assumptions, leading to inefficiencies. Activity-Based Costing (ABC) offers a more accurate alternative by linking costs to specific operations, but its data-intensive nature has limited practical application. This study presents an improved methodology that combines ABC with automated data collection from CANBUS systems integrated into agricultural tractors. CANBUS technology enables the continuous acquisition of detailed operational data, such as engine load, speed, fuel consumption, and positioning, without manual input. These data are processed to classify tasks and allocate resource use precisely. The methodology was applied to a fleet of nine tractors - ranging from utility to high-power models - on a cooperative farm in Italy over a one-year period. The CANBUS data were integrated with economic parameters like depreciation, maintenance, fuel, and labour costs to estimate job-specific and per-hectare costs. While results showed variations in cost structure and usage patterns across tractor types, the primary contribution of this work lies in demonstrating a scalable, automated ABC framework. This approach reduces reliance on manual reporting, enhances cost transparency, and supports more informed machinery management decisions. It offers a strong foundation for future digital tools aimed at improving the efficiency and sustainability of farm operations, especially in data-rich and mechanized agricultural systems.

Salma Omar Bleed, Rasha Abd-Elwahab Attwa, Rabeea Farag Meftah Ali et al.

Recently, the need to develop statistical distributions has become the most important spot. In this context, we employ the α-power transformation (APT) method to convert the generalized Pareto distribution (GPD) into a new distribution. Some statistical properties of the proposed distribution are being studied, such as moments, arithmetic mean, moment-generating function, random variables, entropy, reliability, and hazard function (HF). In addition, the proposed distribution is compared with the Pareto distribution and some other forms of alpha power distributions, such as the alpha power Pareto (APP) distribution, alpha power Rayleigh (APR) distribution, and the alpha power Lomax (APL) distribution. Finally, we demonstrate the benefits of the proposed distribution through a simulation study and two real data sets. It was found that the results showed the MLE method is reliable, the APTGP distribution is a competitive distribution for the aforementioned data set, and it is a mirror image of the Pareto distribution.

Nannan Yang, Liangzhi Li, Ling Han et al.

Efficient prediction and precise depiction of heavy metal concentrations in urban soil are essential for mitigating non-point source pollution and safeguarding public health. Therefore, this research investigated the estimation of soil heavy metal concentrations derived from Gaofen-5 (GF-5) hyperspectral images calibrated by the direct standardization (DS) algorithm. The inversion strategy for soil heavy metal concentrations in response to the two-dimensional soil spectral index (2D-SSI) was proposed by coupling Pearson correlation coefficient (r) and competitive adaptive reweighting algorithm (CARS) for feature selection. The results indicated that the optimal models based on 2D-SSI outperform the models based on calibrated, filtered original spectral bands. For Pb, Cu, Cd, and Hg, the optimal model determination coefficients for the validation data set (RV2) were 0.871 (SVM), 0.883 (BPNN), 0.834 (PLSR), and 0.907 (PLSR), respectively. The spectral features were highlighted in the two-dimensional feature space, and the predicted distribution of heavy metal concentrations was aligned with the observed ground measurements. This study revealed that the prediction strategy based on DS-corrected GF-5 AHSI images with constructed 2D-SSI features can serve as a reliable technical approach for soil heavy metal prediction and pollution prevention.

Gumisirizah Nicholus, Joseph Nzabahimana, Charles M. Muwonge

AbstractIn recent years, there has been a growing emphasis on innovative teaching methods in secondary education as educators seek to enhance students’ critical thinking abilities and motivation to lean. This research focuses on evaluating the impact of a video-based Problem-Based Learning (PBL) approach on the performance and critical thinking ability of Ugandan Form-2 secondary school students. We assessed students’ performance and motivation for critical thinking using a test and their motivation using a motivational scale. The quasi-experimental design involved 144 students selected from four schools in the Sheema District. All students were pretested before the experimental group underwent a video-based PBL intervention and posttested after the intervention. Data entry and analysis were conducted using MS Excel 2016 and SPSS v.25. Inferential statistics revealed a statistically significant difference in critical thinking ability before and after learning the unit on simple machines in physics. Additionally, a positive motivation to learn physics was statistically proven after the intervention. Notably, students from government schools exhibited diverse performance, while private school students showed greater improvement and motivation after the instructional intervention. In conclusion, the results demonstrate that video-based PBL is an effective method for teaching physics and fostering motivation to learn the subject. Consequently, we recommend that teachers incorporate this approach into their daily teaching practices and call upon stakeholders to support the implementation of these resources in the classroom.

Md Arif Hassan, Cong T. Nguyen, Chi-Hieu Nguyen et al.

Web 3.0 is the third generation of the World Wide Web (WWW), concentrating on the critical concepts of decentralization, availability, and increasing client usability. Although Web 3.0 is undoubtedly an essential component of the future Internet, it currently faces critical challenges, including decentralized data collection and management. To overcome these challenges, blockchain has emerged as one of the core technologies for the future development of Web 3.0. In this paper, we propose a novel blockchain-based information management framework, namely Smart Blockchain-based Web, to manage information in Web 3.0 effectively, enhance the security and privacy of users data, bring additional profits, and incentivize users to contribute information to the websites. Particularly, SBW utilizes blockchain technology and smart contracts to manage the decentralized data collection process for Web 3.0 effectively. Moreover, in this framework, we develop an effective consensus mechanism based on Proof-of-Stake to reward the user's information contribution and conduct game theoretical analysis to analyze the users behavior in the considered system. Additionally, we conduct simulations to assess the performance of SBW and investigate the impact of critical parameters on information contribution. The findings confirm our theoretical analysis and demonstrate that our proposed consensus mechanism can incentivize the nodes and users to contribute more information to our systems.

Geoffroy Caillat-Grenier, Andrei Romashchenko, Rustam Zyavgarov

We study the connection between mixing properties for bipartite graphs and materialization of the mutual information in one-shot settings. We show that mixing properties of a graph imply impossibility to extract the mutual information shared by the ends of an edge randomly sampled in the graph. We apply these impossibility results to some questions motivated by information-theoretic cryptography. In particular, we show that communication complexity of a secret key agreement in one-shot setting is inherently uneven: for some inputs, almost all communication complexity inevitably falls on only one party.

Ryan M. Dreifuerst, Robert W. Heath

Obtaining accurate and timely channel state information (CSI) is a fundamental challenge for large antenna systems. Mobile systems like 5G use a beam management framework that joins the initial access, beamforming, CSI acquisition, and data transmission. The design of codebooks for these stages, however, is challenging due to their interrelationships, varying array sizes, and site-specific channel and user distributions. Furthermore, beam management is often focused on single-sector operations while ignoring the overarching network- and system-level optimization. In this paper, we proposed an end-to-end learned codebook design algorithm, network beamspace learning (NBL), that captures and optimizes codebooks to mitigate interference while maximizing the achievable performance with extremely large hybrid arrays. The proposed algorithm requires limited shared information yet designs codebooks that outperform traditional codebooks by over 10dB in beam alignment and achieve more than 25% improvements in network spectral efficiency.

Vika Putri Ariyanti, Tristyanti Yusnitasari

Crude oil price fluctuations affect the business cycle due to affecting the ups and downs of the growth of the economy, which one of the indicators of the economic business cycle phenomenon. The importance of oil price prediction requires a model that can predict future oil prices quickly, easily, and accurately so that it can be used as a reference in determining future policies. Machine learning is an accurate method that can be used in predicting and makes it easier to predict because there is no need to program computers manually. ARIMA is a machine learning algorithm while ARIMA that uses a seasonal component is called SARIMA. Based on background, research purpose is modeling crude oil price forecasting by ARIMA and SARIMA. Forecasting is done on daily crude oil price data taken from Yahoo Finance from January 27, 2020 to January 25, 2023. The evaluation results show the RMSE value of ARIMA and SARIMA is 1.905. The forecast result of 7 days ahead with ARIMA is 86.230003 while SARIMA is 86.260002. The research results are expected to be helpful for policy makers to adopt policies and make the right decisions in the use of crude oil.

Fabricio Ziviani, Elaine Drumond Pires e Silva

Na era digital, as informações são facilmente disseminadas nas redes de comunicação. Novas formas de pensar, agir e vivenciar vão surgindo a todo instante no mundo da comunicação e tecnologia. Todos os setores industriais estão sendo atingidos pela transformação digital. Surgem novos modelos de negócios baseados em uma nova plataforma global, a Internet. Com as Instituições Financeiras (IF) a situação não é diferente, elas precisam se reinventar, para competirem entre si, e enfrentar as ameaças dos novos concorrentes que já nasceram digitais. Nas IF, a forma de oferecer os produtos e serviços ao cliente é de suma importância para o seu sucesso, e a partir dessa perspectiva, o surgimento das startups financeiras altamente tecnológicas (fintechs) desafiam as IF. As fintechs facilitam o acesso aos serviços financeiros, e trazem conveniências ao cliente por meio da inovação tecnológica. Diante desse cenário, o objetivo deste estudo é analisar a relação entre a tecnologia da informação (TI), gestão do conhecimento e inovação na criação de valor nas IF. A pesquisa aborda temas como Web 2.0, TI, modelo de negócio, gestão do conhecimento e inovação, apresentando a TI como catalisadora da inovação. Para analisar o impacto da TI, Web 2.0, gestão do conhecimento e modelo de negócio com relação à inovação, será realizada uma pesquisa de abordagens qualitativa e quantitativa descritiva. Os dados da pesquisa foram coletados por meio de um questionário (e-survey), criado a partir do marco teórico.

Duc Minh Tran, C Louise Thwaites, Jennifer Ilo Van Nuil et al.

BackgroundThere are a host of emergent technologies with the potential to improve hospital care in low- and middle-income countries such as Vietnam. Wearable monitors and artificial intelligence–based decision support systems could be integrated with hospital-based digital health systems such as electronic health records (EHRs) to provide higher level care at a relatively low cost. However, the appropriate and sustainable application of these innovations in low- and middle-income countries requires an understanding of the local government’s requirements and regulations such as technology specifications, cybersecurity, data-sharing protocols, and interoperability. ObjectiveThis scoping review aims to explore the current state of digital health research and the policies that govern the adoption of digital health systems in Vietnamese hospitals. MethodsWe conducted a scoping review using a modification of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed and Web of Science were searched for academic publications, and Thư Viện Pháp Luật, a proprietary database of Vietnamese government documents, and the Vietnam Electronic Health Administration website were searched for government documents. Google Scholar and Google Search were used for snowballing searches. The sources were assessed against predefined eligibility criteria through title, abstract, and full-text screening. Relevant information from the included sources was charted and summarized. The review process was primarily undertaken by one researcher and reviewed by another researcher during each step. ResultsIn total, 11 academic publications and 20 government documents were included in this review. Among the academic studies, 5 reported engineering solutions for information systems in hospitals, 2 assessed readiness for EHR implementation, 1 tested physicians’ performance before and after using clinical decision support software, 1 reported a national laboratory information management system, and 2 reviewed the health system’s capability to implement eHealth and artificial intelligence. Of the 20 government documents, 19 were promulgated from 2013 to 2020. These regulations and guidance cover a wide range of digital health domains, including hospital information management systems, general and interoperability standards, cybersecurity in health organizations, conditions for the provision of health information technology (HIT), electronic health insurance claims, laboratory information systems, HIT maturity, digital health strategies, electronic medical records, EHRs, and eHealth architectural frameworks. ConclusionsResearch about hospital-based digital health systems in Vietnam is very limited, particularly implementation studies. Government regulations and guidance for HIT in health care organizations have been released with increasing frequency since 2013, targeting a variety of information systems such as electronic medical records, EHRs, and laboratory information systems. In general, these policies were focused on the basic specifications and standards that digital health systems need to meet. More research is needed in the future to guide the implementation of digital health care systems in the Vietnam hospital setting.

Lei Yu, Vincent Y. F. Tan

Common information (CI) is ubiquitous in information theory and related areas such as theoretical computer science and discrete probability. However, because there are multiple notions of CI, a unified understanding of the deep interconnections between them is lacking. This monograph seeks to fill this gap by leveraging a small set of mathematical techniques that are applicable across seemingly disparate problems. In Part I, we review the operational tasks and properties associated with Wyner's and Gács-Körner-Witsenhausen's (GKW's) CI. In PartII, we discuss extensions of the former from the perspective of distributed source simulation. This includes the Rényi CI which forms a bridge between Wyner's CI and the exact CI. Via a surprising equivalence between the Rényi CI of order~$\infty$ and the exact CI, we demonstrate the existence of a joint source in which the exact CI strictly exceeds Wyner's CI. Other closely related topics discussed in Part II include the channel synthesis problem and the connection of Wyner's and exact CI to the nonnegative rank of matrices. In Part III, we examine GKW's CI with a more refined lens via the noise stability or NICD problem in which we quantify the agreement probability of extracted bits from a bivariate source. We then extend this to the $k$-user NICD and $q$-stability problems, and discuss various conjectures in information theory and discrete probability, such as the Courtade-Kumar, Li-Médard and Mossell-O'Donnell conjectures. Finally, we consider hypercontractivity and Brascamp-Lieb inequalities, which further generalize noise stability via replacing the Boolean functions therein by nonnnegative functions. The key ideas behind the proofs in Part III can be presented in a pedagogically coherent manner and unified via information-theoretic and Fourier-analytic methods.

Michael X. Cao

[...] In this thesis, we are interested in generalizing factor graphs and the relevant methods toward describing quantum systems. Two generalizations of classical graphical models are investigated, namely double-edge factor graphs (DeFGs) and quantum factor graphs (QFGs). Conventionally, a factor in a factor graph represents a nonnegative real-valued local functions. Two different approaches to generalize factors in classical factor graphs yield DeFGs and QFGs, respectively. We proposed/re-proposed and analyzed generalized versions of belief-propagation algorithms for DeFGs/QFGs. As a particular application of the DeFGs, we investigate the information rate and their upper/lower bounds of classical communications over quantum channels with memory. In this study, we also propose a data-driven method for optimizing the upper/lower bounds on information rate.

Michał Walkowski, Jacek Oko, Sławomir Sujecki

Vulnerability prioritization is an essential element of the vulnerability management process in data communication networks. Accurate prioritization allows the attention to be focused on the most critical vulnerabilities and their timely elimination; otherwise, organizations may face severe financial consequences or damage to their reputations. In addition, the large amounts of data generated by various components of security systems further impede the process of prioritizing the detected vulnerabilities. Therefore, the detection and elimination of critical vulnerabilities are challenging tasks. The solutions proposed for this problem in the scientific literature so far—e.g., PatchRank, SecureRank, Vulcon, CMS, VDNF, or VEST—are not sufficient because they do not consider the context of the organization. On the other hand, commercial solutions, such as Nessus, F-Secure, or Qualys, do not provide detailed information regarding the prioritization procedure, except for the scale. Therefore, in this paper, the authors present an open-source solution called the Vulnerability Management Center (VMC) in order to assist organizations with the vulnerability prioritization process. The VMC presents all calculated results in a standardized way by using a Common Vulnerability Scoring System (CVSS), which allows security analysts to fully understand environmental components’ influences on the criticality of detected vulnerabilities. In order to demonstrate the benefits of using the the open-source VMC software developed here, selected models of a vulnerability management process using CVSS are studied and compared by using three different, real testing environments. The open-source VMC suite developed here, which integrates information collected from an asset database, is shown to accelerate the process of removal for the critical vulnerabilities that are detected. The results show the practicability and efficacy of the selected models and the open-source VMC software, which can thus reduce organizations’ exposure to potential threats.

Edit Gorliczay, Imre Boczonádi, Nikolett Éva Kiss et al.

Due to the intensification of the poultry sector, poultry manure is being produced in increasing quantities, and its on-site management is becoming a critical problem. Animal health problems can be solved by stricter the veterinary and environmental standards. The off-site coupled industrial chicken manure recycling technology (Hosoya compost tea) fundamentally affects the agricultural value of new organic-based products. Due to the limited information available on manure recycling technology-related microbiological changes, this was examined in this study. A pot experiment with a pepper test plant was set up, using two different soils (Arenosol, slightly humous Arenosol) and two different doses (irrigation once a week with 40 mL of compost tea: dose 1, D1; irrigation twice a week with 40 mL of compost tea: dose 2, D2) of compost tea. Compost tea raw materials, compost tea, and compost tea treated soils were tested. The products (granulated manure, compost tea) and their effects were characterized by the following parameters: aerobic bacterial count (log CFU/g), fluorescein diacetate activity (3′,6′-diacetylfluorescein, FDA, µg Fl/g soil), glucosidase enzyme activity (GlA; PNP/µmol/g), and identification of microorganisms in compost tea with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Furthermore, we aimed to investigate how the microbiological indicators tested, and the effect of compost tea on the tested plant, could be interpreted. Based on our results, the microbiological characteristics of the treated soils showed an increase in enzyme activity, in the case of FDA an increase +0.26 μg Fl/g soil at D1, while the GlA increased +1.28 PNP/µmol/g with slightly humous Arenosol soil and increased +2.44 PNP/µmol/g at D1; and the aerobic bacterial count increased +0.15 log CFU/g at D2, +0.35 log CFU/g with slightly humous Arenosol and +0.85 log CFU/g at W8. MALDI-TOF MS results showed that the dominant bacterial genera analyzed were <i>Bacillus</i> sp., <i>Lysinibacillus</i> sp., and <i>Pseudomonas</i> sp. Overall, the microbial inducers we investigated could be a good alternative for evaluating the effects of compost solutions in soil–plant systems. In both soil types, the total chlorophyll content of compost tea-treated pepper (<i>Capsicum annuum</i> L.) had increased as a result of compost tea. D1 is recommended for Arenosol and, D2 for slightly humous Arenosol soil.