Development Of Support System Research Articles

Clinical prediction model for MODY type diabetes mellitus in children

BACKGROUND: MODY (maturity-onset diabetes of the young) is a rare monogenic form of diabetes mellitus, the gold standard of diagnosis is mutations detection in the genes responsible for the development of this form diabetes. Genetic test is expensive and takes a lot of time. The diagnostic criteria for MODY are well known. The development of clinical decision support system (CDSS) which allows physicians based on clinical data to determine who should have molecular genetic testing is relevant.AIM: Provided a retrospective analysis of clinical data of the patients with T1DM and MODY, from 0 to 18 years old, regardless of the duration of the disease to develop the model. Based on clinical data, a feedforward neural network (NN) was implemented - a multilayer perceptron.MATERIALS AND METHODS: Development of the most effective algorithm for predicting MODY in children based on available clinical indicators of 1710 patients with diabetes under the age of 18 years using a multilayer feedforward neural network.RESULTS: The sample consisted of 1710 children under the age of 18 years with T1DM (78%) and MODY (22%) diabetes. For the final configuration of NS the following predictors were selected: gender, age at passport age, age at the diagnosis with DM, HbA1c, BMI SDS, family history of DM, treatment. The performance (quality) assessment of the NN was carried out on a test sample (the area under the ROC (receiver operating characteristics) curve reached 0.97). The positive predictive value of PCPR was achieved at a cut-off value of 0.40 (predicted probability of MODY diabetes 40%). At which the sensitivity was 98%, specificity 93%, PCR with prevalence correction was 78%, and PCR with prevalence correction was 99%, the overall accuracy of the model was 94%.Based on the NN model, a CDSS was developed to determine whether a patient has MODY diabetes, implemented as an application.CONCLUSION: The clinical prediction model MODY developed in this work based on the NN, uses the clinical characteristic available for each patient to determine the probability of the patient having MODY. The use of the developed model in clinical practice will assist in the selection of patients for diagnostic genetic testing for MODY, which will allow for the efficient allocation of healthcare resources, the selection of personalized treatment and patient monitoring.

Can artificial intelligence be integrated into pest monitoring schemes to help achieve sustainable agriculture? An entomological, management and computational perspective

Abstract Recent years have seen significant advances in artificial intelligence (AI) technology. This advancement has enabled the development of decision support systems that support farmers with herbivorous pest identification and pest monitoring. In these systems, the AI supports farmers through the detection, classification and quantification of herbivorous pests. However, many of the systems under development fall short of meeting the demands of the end user, with these shortfalls acting as obstacles that impede the integration of these systems into integrated pest management (IPM) practices. There are four common obstacles that restrict the uptake of these AI‐driven decision support systems. Namely: AI technology effectiveness, functionality under field conditions, the level of computational expertise and power required to use and run the system and system mobility. We propose four criteria that AI‐driven systems need to meet in order to overcome these challenges: (i) The system should be based on effective and efficient AI; (ii) The system should be adaptable and capable of handling ‘real‐world’ image data collected from the field; (iii) Systems should be user‐friendly, device‐driven and low‐cost; (iv) Systems should be mobile and deployable under multiple weather and climate conditions. Systems that meet these criteria are likely to represent innovative and transformative systems that successfully integrate AI technology with IPM principles into tools that can support farmers.

Lung cancer detection based on computed tomography image using convolutional neural networks.

Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages. Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs). The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control. The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model's highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984. The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images.

Unlocking Knowledge in the Digital Savanna: Innovative Approaches to Crafting E-Learning Experiences at the University of Botswana

The digital revolution has reshaped the landscape of education, offering unprecedented opportunities for knowledge dissemination and acquisition. This study presents a comprehensive exploration of innovative e-learning approaches implemented at the University of Botswana, which represents a remarkable case study in the evolving global educational context. The research leverages a rigorous mixed-methods approach, including case studies, surveys, and qualitative interviews with educators and students, to investigate the multifaceted strategies adopted by the university to transform traditional pedagogy. The study explores how the University of Botswana has embraced diverse e-learning methodologies, such as blended learning, gamification, and virtual labs, to enhance its educational delivery. Blended learning, combining in-person and online instruction, has not only expanded access but also yielded positive impacts on student engagement and performance. Gamification strategies have reinvigorated learning by incorporating game elements, fostering motivation, and increasing knowledge retention. Virtual labs have enriched the student experience by enabling hands-on, remote experimentation, overcoming geographical constraints. The findings underscore the University of Botswana's dedication to utilizing digital technology to democratize and elevate education in an African context. Moreover, this research delves into the institutional challenges and successes in integrating e-learning, highlighting the significance of faculty development, technological infrastructure, and student support systems in achieving transformative educational outcomes. The experiences and strategies documented in this study offer valuable insights and best practices that can inform higher education institutions globally as they navigate the ever-evolving digital realm. This research contributes to the broader discourse on the global transformation of education in the digital age, with implications for enhancing access, equity, and educational quality. In an era marked by dynamic technological advancements and changing educational paradigms, the University of Botswana's innovative e-learning initiatives serve as a beacon of inspiration and a source of valuable lessons for academic institutions committed to unlocking knowledge in the digital savanna.

A unified digital twin approach incorporating virtual, physical, and prescriptive analytical components to support adaptive real-time decision-making

Based on an overview of the historical and rapidly expanding literature on digital twins, this paper identifies fundamental capabilities that outline a general and adaptable twin that supports system development, real-time interactions, prescribing courses of action, and actualizing them. We relate these capabilities to business analytics concepts and decision-making processes geared toward rapid adaptation to changing situations. This leads to a general digital twin architecture supporting a system throughout its lifecycle implemented with components including Internet of Things (IoT) devices, a virtual reality environment, network communications, and an analytic simulation. The success of this architecture revolves around an authoritative data source, the Highly Integrated Virtual Environment (HIVE). We demonstrate the architecture and capabilities through a transporter system example. This demonstration highlights important timing and synchronization questions critical to fulfilling the twin’s fundamental role of reacting to evolving real-world conditions. It identifies the importance of lags in decisions, relates it to prescriptive response time and the rate of evolution of the underlying system, and quantifies this impact with new metrics of effectiveness lag and relevancy decay.

Development of Promotional Criteria Decision Support System to Increase Sales of Scarlett Whitening Products through Digital Marketing Strategy

This research delves into the multifaceted landscape of promoting skin whitening products in Indonesia, focusing on consumer behavior, digital marketing strategies, and the development of a Promotion Criteria Decision Support System (PCDSS). Through qualitative analysis of participant interviews and simulated interviews, key insights were gleaned. Consumer preferences, heavily influenced by cultural ideals, prioritize fair skin and product safety and efficacy. Digital marketing emerged as a potent tool, with social media platforms and influencer partnerships driving engagement. The necessity of a tailored PCDSS integrating cultural nuances, regulatory compliance, and consumer insights was underscored. This study provides actionable insights for skincare brands aiming to navigate the Indonesian market effectively.

Performance Analysis of Inclusive Learning Support System and Adaptive Curriculum Development on Academic Success and Independence of Students with Disabilities in Central Java

This study investigates the performance of inclusive learning support systems and adaptive curriculum development on the academic success and independence of students with disabilities in Central Java, Indonesia. A quantitative research approach was employed, with data collected from 125 participants representing students, teachers, parents, and educational professionals involved in inclusive education programs. Structural Equation Modeling (SEM) with Partial Least Squares (PLS) path modeling was utilized to analyze the data. The measurement model demonstrated strong reliability and validity, while the structural model revealed significant positive relationships between inclusive education strategies, academic success, and independence of students. Discriminant validity was established, and the estimated model demonstrated acceptable fit and predictive relevance. Regression results further supported the positive impact of inclusive learning support systems and adaptive curriculum development on the academic success and independence of students. Overall, the findings highlight the importance of inclusive education practices in promoting positive outcomes for students with disabilities and underscore the need for continued efforts to enhance the inclusivity and effectiveness of educational programs in Central Java.

The design and development of a dashboard for improving sustainable healthy food choices

Over the last decade, several digital tools have been designed to provide consumers with nutritional and environmental impact information about their food choices post-consumption. Many of these tools lack behavioral change modules, have low user engagement, and ignore inherent environmental nutrition trade-offs to stimulate dietary change. This study presents the design and development of a decision support system to enhance consumer health while meeting sustainability goals from a pre-consumption perspective. The proposed decision support system, Dashboard for Improving Sustainable Healthy (DISH) food choices, employs behavioral features, traffic light labels, and nudges to inform end-users about the nutritional health performance and environmental impact of meals. DISH uses a simple metric that allows end-users to explore the potential minutes of healthy and productive life gained or lost from consuming 100 kcal of a meal. The metric combines the positive or negative nutritional health effects (μ-DALYs) of consuming a meal and environmental damage (endpoint impact expressed in DALYs) on human health. In the DISH application, end-users are rewarded or deducted EnCoins, which represent the number of silver or gold coins lost or gained based on the cost ($) of environmental damage (midpoint impacts) of a meal compared to reference sustainable healthy and unsustainable and unhealthy meal. DISH's gamification module enables end-users to track the potential minutes of healthy and productive life gained/lost and gold or silver rewards or deductions from consuming 100 kcal of a selected meal through cumulative minutes gained or lost and EnCoins. In promoting a sustainable diet culture, the gamification module enables users to create groups and communities where friends and families can track their sustainability performance through meal decisions. The DISH application is currently available online and can be accessed by an end-user through any device. Further pilot studies will focus on testing the technology in partner campus cafeterias.

Surviving crisis after crisis: strengths and gaps in support for Syrian refugee families living in Lebanon

PurposeAs host to over one million Syrian refugees, Lebanon continues to experience challenges addressing the needs of refugee families. This research examined the experiences of Syrian families with the refugee support system in Lebanon. The purpose of this study was to better understand the strengths and gaps in existing mechanisms of support for these Syrian families, including informal support from family, neighbors and community and more formalized support provided through entities such as nongovernmental organizations and United Nations agencies.Design/methodology/approachData were collected from 46 families displaced by the war and living in Lebanon (N = 351 individuals within 46 families). Collaborative family interviews were conducted with parents, children and often extended family.FindingsThe data identified both strengths and gaps in the refugee support system in Lebanon. Gaps in the refugee support system included inadequate housing, a lack of financial and economic support, challenges with a lack of psychosocial support for pregnant women and support for disabled youth. Despite these challenges, families and community workers reported informal community support as a strong mediator of the challenges in Lebanon. Furthermore, the data find that organizations working with Syrian families are utilizing informal community support through capacity building, to create more effective and sustainable support services.Originality/valueThis study provides an overview of strengths and gaps in supports identified by refugees themselves. The research will inform the development and improvement of better support systems in Lebanon and in other refugee–hosting contexts.

Research and Simulation on the Development of a Hydraulic Prop Support System of Powered Roof Support to Increase Work Safety.

The underground mining environment is currently based on technology that uses mainly analogue sensors in machine and equipment control systems. The primary machine performing the most important functions in a mining system is the powered roof support. In order for it to work properly, it is important that it achieves the required power. To ensure this, it is necessary to continuously and precisely monitor the pressure in the under-piston space of the prop. Due to the extreme environmental conditions, pressure sensors should have high sensitivity, large transmission capacity, small size and light weight. To achieve these requirements, the authors of the article propose to implement a monitoring system based on photonics technology. To achieve this goal, several studies were carried out. The range of these studies included simulations, bench tests and tests under real conditions. The obtained test results showed the possibility of developing the control system for the powered roof support, the additional function to supercharge power. Based on the analysis of the obtained test results, assumptions were developed for the development of a power charging system with monitoring sensors. Based on the guidelines obtained from the research results, thedevelopment of the above prototype based on photonics technology is proposed.

Development and validation of a clinical decision support system to prevent anticoagulant duplications

Background and ObjectiveUnintended duplicate prescriptions of anticoagulants increase the risk of serious adverse events. Clinical Decision Support Systems (CDSSs) can help prevent such medication errors; however, sophisticated algorithms are needed to avoid alert fatigue. This article describes the steps taken in our hospital to develop a CDSS to prevent anticoagulant duplication (AD). MethodsThe project was composed of three phases. In phase I, the status quo was established. In phase II, a clinical pharmacist developed an algorithm to detect ADs using daily data exports. In phase III, the algorithm was integrated into the hospital's electronic health record system. Alerts were reviewed by clinical pharmacists before being sent to the prescribing physician. We conducted a retrospective analysis of all three phases to assess the impact of the interventions on the occurrence and duration of ADs. Phase III was analyzed in more detail regarding the acceptance rate, sensitivity, and specificity of the alerts. ResultsWe identified 91 ADs in 1581 patients receiving two or more anticoagulants during phase I, 70 ADs in 1692 patients in phase II, and 57 ADs in 1575 patients in phase III. Mean durations of ADs were 1.8, 1.4, and 1.1 calendar days during phases I, II, and III, respectively. In comparison to the baseline in phase I, the relative risk reduction of AD in patients treated with at least two different anticoagulants during phase III was 42% (RR: 0.58, CI: 0.42–0.81). A total of 429 alerts were generated during phase III, many of which were self-limiting, and 186 alerts were sent to the respective prescribing physician. The acceptance rate was high at 97%. We calculated a sensitivity of 87.4% and a specificity of 87.9%. ConclusionThe stepwise development of a CDSS for the detection of AD markedly reduced the frequency and duration of medication errors in our hospital, thereby improving patient safety.

Harnessing potential of avian eggshell membrane derived collagen hydrolysate for bone tissue regeneration.

Natural bone grafts are the highly preferred materials for restoring the lost bone, while being constrained of donor availability and risk of disease transmission. As a result, tissue engineering is emerging as an efficacious and competitive technique for bone repair. Bone tissue engineering (TE) scaffolds to support bone regeneration and devoid of aforesaid limitations are being vastly explored and among these the avian eggshell membrane has drawn attention for TE owing to its low immunogenicity, similarity with the extracellular matrix, and easy availability. In this study, the development of bone ingrowth support system from avian eggshell membrane derived collagen hydrolysates (Col-h) is reported. The hydrolysate, cross-linked with glutaraldehyde, was developed into hydrogels with poly-(vinyl alcohol) (PVA) by freeze-thawing and further characterized with ATR-FTIR, XRD, FESEM. The biodegradability, swelling, mechanical, anti-microbial, and biocompatibility evaluation were performed further for the suitability in bone regeneration. The presence of amide I, amide III, and -OH functional groups at 1639cm- 1,1264cm- 1, and 3308cm- 1 respectively and broad peak between 16°-21° (2θ) in XRD data reinstated the composition and form. The maximum ratio of Col-h/PVA that produced well defined hydrogels was 50:50. Though all the hydrogel matrices alluded towards their competitive attributes and applicability towards restorative bone repair, the hydrogel with 40:60 ratios showed better mechanical strength and cell proliferation than its counterparts. The prominent E. coli growth inhibition by the hydrogel matrices was also observed, along with excellent biocompatibility with MG-63 osteoblasts. The findings indicate strongly the promising application of avian eggshell-derived Col-h in supporting bone regeneration.

URBAN PLANNING FACTORS OF LAND USE MONITORING OF UNITED TERRITORIAL COMMUNITIES

There is proof that in the conditions of ensuring transformational processes, issues of formation and use of land and increasing the efficiency of their use at the local level arise. In addition, the presented processes deepen because of the reform of local self-government, which ensures the effectiveness and interaction of stakeholders. The core element in developing these relationships is the united territorial communities and land use directions and features, with consideration of the influence of factors. In the system of land relations of united territorial communities, urban planning factors that influence the development and implementation of modern monitoring tools are particularly important. The study aims to determine the urban planning factors of land use monitoring of united territorial communities. To achieve the goal, we solved the following tasks: characterisation of urban planning factors of land use monitoring and highlighting the peculiarities of the formation of urban planning factors of land use of united territorial communities. Among the peculiarities of the development of land use monitoring of united territorial communities is also the identification of a system of urban planning factors, namely the levels: development and implementation of land management projects regarding the arrangement of territories for urban planning needs; formation and application of the urban development support system; development and implementation of monitoring procedures of urban planning activities; formation and use of urban cadastre; provision of architectural and construction control; development and implementation of comprehensive plans; change in the level of construction activity in united territorial communities. We have formed local factors of urban planning support for land use monitoring of united territorial communities, with most of them determined by qualitative methods. Estimating the indicator of the change in the level of construction activity in the united territorial communities uses quantitative methods. It creates a basis for implementing monitoring procedures through the application of urban planning provision of land use of united territorial communities. Keywords: urban planning factors, land use, united territorial communities, monitoring.

Data-Driven Decision Support Systems for Drug Discovery and Development: A Case Study in Pharmaceutical Management

This paper covers the use and influence of data-driven decision support systems (DSS) on drug management, particularly in the areas of drug exploration and production. The study, which employed a mixed-methods approach, involving literature review, qualitative interviews, quantitative assessments, and case study analysis, reveals the AI, machine learning and big data analytics ability to drive drug discovery and development processes revolution in the pharmaceutical industry. The study shows that handling of DSS allows sound decision making, consequently resulting in improved efficiency, decreased expenses and better innovation throughout the drug development process. Areas like data integration, algorithm robustness, and regulation are identified as the major issues, which offers an insight into the importance of these areas of concern in the effective application of a data-driven approach in pharmaceutical management. Such contrast with the like works underlines the role of our findings as an essential link in the AI-driven healthcare innovations chain. The research provides a foundation for further improving the state of knowledge and understanding of DSS in drug management, guiding future research aiming at expanding conceptual frameworks and designing practical implementations.

To NIRS or not: understanding clinical decision-making of respiratory support management related to acute respiratory failure using Critical Decision Method

Clinical decisions related to noninvasive respiratory support (NIRS) for acute respiratory failure patients are complex. NIRS strategies have favorable benefits for patients when successful but NIRS failure comes with longer ICU stays, increased mortality, and more intubation-related and mechanical ventilator-associated complications. Our goal is to understand how physicians make decisions regarding NIRS and explore the differences between emergency physicians and critical care physicians—who commonly make these decisions. We used the critical decision method to conduct ten semi-structured interviews with five emergency physicians and five critical care physicians from ten different healthcare systems across the United States. We found that physicians similarly use objective and subjective clinical cues but differentially use temporal and contextual cues in their decision-making. Several insights identified in our study can be helpful in the design and development of clinical decision support systems for respiratory support decision-making. Specifically, we recommend two customized distinct clinical decision support systems for respiratory support decision-making, one for each designated clinical setting (i.e., ED and ICU) with tailored designs that support the different decision-making processes.

Appropriate Inclusion: A Novel Framework for the Development of China's Special Education

Purpose This study examines the Action Plan for the Development and Improvement of Special Education (hereinafter, the Action Plan) of the 14th Five-Year Plan, a national blueprint for special education forwarded by the General Office of the State Council of the People's Republic of China in December 2021. Design/Approach/Methods This is an analytical policy review. Findings In analyzing the policy highlights and characteristics of the Action Plan, this policy review identifies four factors motivating the policy: the United Nations’ Convention on the Rights of Persons with Disabilities, the Outline of the People's Republic of China 14th Five-Year Plan for National Economic and Social Development and Long-Range Objectives for 2035, educational equity, and the high-quality development of special education. In addition to proposing three developmental requirements in line with China's national conditions, the Action Plan establishes eight implementation strategies and three support systems for the initial development of high-quality special education. Originality/Value The findings of this article will enhance leaders’ and relevant stakeholders’ understanding of new directions in and future paths of the high-quality development of special education in China.

Innovative approaches in higher education in Ukraine: Trends and prospects

Innovative approaches in higher education in Ukraine are crucial to address changing global education dynamics, technological advancements, and evolving workforce needs. The primary aim is to assess the significance of innovation in shaping the educational experience and teaching methodologies. The research employs a mixed-methods approach, combining surveys and in-depth interviews with educators across various disciplines. The collected data from 125 participants provide insights into their perspectives on the importance of innovation. The results indicate a growing interest in innovative methods among educators and students, highlighting a desire for continuous improvement in the learning process. Сhallenges related to the implementation of innovative approaches are identified, including technological barriers, limited resources, and resistance from within the academic community. In conclusion, while innovation remains a crucial component for advancing higher education in Ukraine, strategic planning and targeted support are essential for successful implementation. The study underscores the need for enhanced international collaboration, a focus on overcoming identified challenges, and the development of a comprehensive support system for educators. These findings contribute to the ongoing discourse on the evolution of higher education in Ukraine and offer insights for policymakers, administrators, and educators seeking to foster a dynamic and effective learning environment.

Insights into Homeschooling in Vietnam: Vietnamese Parents’ Perspectives and Approaches to Cultivating Mother Language Skills and Literary Appreciation for their Children

In the wake of the COVID-19 pandemic, homeschooling has emerged as a viable alternative in Vietnam, raising questions about its effectiveness compared to traditional schooling. While offering personalized learning and cultural immersion, concerns remain regarding social development, academic rigor, and systemic recognition. This qualitative study delves into the experiences of Vietnamese homeschooling families, utilizing semi-structured interviews to uncover thematic patterns in parental narratives. We explore how parents navigate curriculum design, resource utilization, and social interaction to foster their children’s Vietnamese language fluency and literary appreciation. By analyzing their strategies and challenges, our findings inform educational policymakers in developing support systems for homeschooling families and guide parents in creating enriching learning environments. This study opens a crucial dialogue on the future of homeschooling in Vietnam, encouraging stakeholders to explore its potential and address its limitations to ensure every child receives a high-quality education.

Classification of global information internal audit support systems

The creation and functioning of global information systems in economic management fully applies to such an area of economic activity as internal audit of business entities. However, the development of information systems is so rapid that a conventional IS audit is not enough to ensure effective functioning, therefore, the issue of computerization of internal audit is quite relevant and requires the development of an intelligent system to support the internal audit of business When analyzing research and publications, it was found that the disclosure of the main aspects of the use of computer technologies in conducting audit activities and automation of audit processes was best reflected by such scientists. Although the problem of the need for computerization of the audit undoubtedly meets the requirements of the time and has been studied by a certain circle of scientists, for a significant number of domestic business entities this problem still remains unresolved. An intelligent system can help with audit p rocesses and ensure the accuracy of the results of automation and the application of analytical tools. The development of an intelligent internal audit support system is an important and necessary task, as internal audit plays an important role in ensuring effective management and control of a business entity.

Artificial intelligence in intensive care medicine

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.