Development Of Model Research Articles

Выбор аналитической модели и количественных показателей для геоинформационного анализа устойчивого развития территориальных образований

Currently, there are no generally accepted algorithms for assessing, analyzing and forecasting the sustainable development of administrative-territorial units. The authors introduce the definition of this term, which implies the successful functioning of a human community over time carrying out economic activities without threats to its environment. Also, an analytical model for representation of its regional integrated indicator in the form of a vector with differential components, which characterize economic growth, environmental and social metrics, in a three-dimensional affine space, is proposed. Furthermore, we present a multiplicative criterion for assessing partial criteria of sustainable development efficiency and the basic expression for its parameters computation based on the set of economic, environmental and social ones, including such as the availability and quality of natural resources, regional labour resources, eco-factors, etc. The proposed vector model is based on the geoinformation approach and can be deployed within the framework of GIS of sustainable development for geo-analysis, modeling and mapping of natural resource potential and ecological state of intensively advancing territories, considering the dynamics of this process. Important properties of the geoinformation approach to assessing territorial entities’ forward growth, spatial and semantic scalability, are noted. In addition, the functional requirements and preliminary architecture of the core, modules, clients and layers of the related thematic geographic information system for the management, planning and forecasting the said indicator within the framework of digital economy are described

Deep models for multi-view 3D object recognition: a review

This review paper focuses on the progress of deep learning-based methods for multi-view 3D object recognition. It covers the state-of-the-art techniques in this field, specifically those that utilize 3D multi-view data as input representation. The paper provides a comprehensive analysis of the pipeline for deep learning-based multi-view 3D object recognition, including the various techniques employed at each stage. It also presents the latest developments in CNN-based and transformer-based models for multi-view 3D object recognition. The review discusses existing models in detail, including the datasets, camera configurations, view selection strategies, pre-trained CNN architectures, fusion strategies, and recognition performance. Additionally, it examines various computer vision applications that use multi-view classification. Finally, it highlights future directions, factors impacting recognition performance, and trends for the development of multi-view 3D object recognition method.

A Development of English for Tourism Book Based on Tourism Places in Dompu to Improve Students’ Speaking Skills

The aim of this research is to develop a valid, practical, and effective book of English for Tourism based on tourism places in Dompu. This study is a R&D (Research and Development) approach using the 4D model (Define, Design, Develop, and Disseminate). The book that had been developed was implemented in the English learning process at the class XI of Tourism Vocational High School Dompu, Department of Travel and Tourism Business with a total of 27 students, in academic year 2024. The validity test results of the developed book obtained from media expert is 88% (very valid) and 92% (very valid) from material expert. Moreover, the practicality test result from students and teacher was at the level “practical” with average score 3,8 or 76%. Lastly, based on students’ speaking test result, the average total score obtained by 27 students is 77. On other word, 100% students or all 27 students obtained average scores more than or equal to 70. It means all students have successfully done the task with good scores. Based on that, the use of English for tourism book based on tourism places in Dompu is effective to improve students’ speaking skill. Another remarkable finding in this reseach is that both teacher and students strongly agree that the material in book is able to stimulate students to be active in the learning process.

Retraction Note: A systematic review of developments in the 2-tuple linguistic model and its applications in decision analysis

Retraction Note: A systematic review of developments in the 2-tuple linguistic model and its applications in decision analysis

Dual syntax aware graph attention networks with prompt for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a challenging task due to the presence of multiple aspect words with different sentiment polarities in a sentence. Recently, pre-trained language models like BERT have been widely used as context encoders in ABSA. Graph neural networks have also been employed to extract syntactic and semantic information from sentence parsing trees, resulting in superior results. However, dependency trees may establish irrelevant dependencies for sentences with irregular syntax and complex structures. Additionally, previous methods have not fully utilized recent developments in pre-trained language models. Therefore, we propose a Dual Syntax aware Graph attention networks with Prompt (DSGP) model to address these issues. Our model utilizes prompt templates to maximize the potential of pre-trained models and masked vector outputs of templates as supplementary aspect feature representations. We also leverage both dependency trees and constituent trees with graph attention networks to extract different types of syntactic information. The dependency tree captures syntactic correlation between words, while the constituent tree provides a high-level formation of the sentence. Finally, the output from the prompt and parsing trees is fused and fed into a standard classifier. Experimental results on four public datasets demonstrate the competitive performance of our model.

Development of scale and model for evaluating the individual performance appraisals—Process in public management

The need for strategic alignment within HR management increased managers’ concern about individual behavior and how this behavior was related to the achievement of goals. In public management, effectively managing employees’ performance has been necessary since Weber’s bureaucratic administration. The individual performance appraisal is the right tool to assess employees’ competencies. Thus, we proposed the following research question: Which factors, as pointed out by theory, have the most significant influence on the individual performance appraisal process? The quantitative method was applied to answer this question, developing and testing a scale via EFA and a hypothetical model via SEM-CB. The results indicated a scale with 25 items able to access the main points of the IPA process and a hypothetical model with 7 constructs that indicate the influence on employee engagement. The main finding is the significant influence of feedback on the whole process. The main theoretical contribution was the construction of the MIPAS scale, and the practical contribution was to identify the points where managers should focus on improving the IPA process with their subordinates.

The Effectiveness of the Dilemma-STEAM Learning Model Based on Motion Graphic Videos in the Subject of Parabolic Motion

Abstract This study aims to develop and evaluate the feasibility and effectiveness of the Dilemma-STEAM learning model using motion graphic videos in teaching Parabolic Motion. The developed motion graphic videos incorporate five Dilemma-STEAM learning syntaxes: Reflection, Exploration, Elaboration, Integration, and Transformation, which address the topic of Parabolic Motion. The development model utilized in this Research and Development (R&D) method follows the 4D model (Define, Design, Develop, and Disseminate) with the following stages: 1. Needs analysis, 2. Product design, 3. Product development, 4. Feasibility testing and product trials. Based on needs analysis conducted through field observations, it was found that students require learning media in the form of motion graphics for the subject of Parabolic Motion using the Dilemma-STEAM learning model. Feasibility tests by subject matter experts, media experts, and learning experts were conducted using feasibility questionnaires, yielding an average feasibility percentage of 92% for subject matter experts, 89% for media experts, and 78% for learning experts. Additionally, readability tests and trials of the Dilemma-STEAM learning model were conducted with eleventh-grade students, with average effectiveness percentages of 78% and 75%, respectively. Based on the obtained average percentages, it is concluded that the Dilemma-STEAM learning model based on motion graphic videos is feasible and effective as a learning medium for eleventh-grade Physics.

Flipbook Platform E-Module Development: Geographic Information Systems Subject

This research aims to determine the feasibility and effectiveness of e-modules with the Flipbook platform in the Geographic Information Systems (GIS) Class XI Geospatial Engineering subject at SMK Negeri 1 Percut Sei Tuan. This research applies the Research and Development (R&D) method with the ADDIE model (Analysis, Design, Develop, Implement, and Evaluate). The research subjects were 30 students of Class XI Geospatial Engineering at SMK Negeri 1 Percut Sei Tuan. This research involved validation tests by material experts, media experts, and learning design experts, as well as trial stages, namely individual trials on 3 students, small group trials on 6 students, and limited field trials on 30 students. The research results show that the E-Module with the Flipbook platform is suitable for use based on media expert validation of 89.17% (very feasible), material expert validation of 92.13% (very feasible), and learning design expert validation of 88.71% (very feasible). In the individual trial, the score was 91.47% (very decent), the small group trial got a score of 92.33% (very decent), and the field trial got a score of 96.14% (very decent). The use of E-Modules with the Flipbook platform in GIS subjects is more effective than conventional whiteboard media, as can be seen from the results of tcount = 1.91 and tTable = 1.672, where 1.91 > 1.672 at the significance level α = 0.05. This shows that H0 is is rejected, and the alternative hypothesis (Ha) is accepted. This means that there is a noticeable difference in student learning results between the experimental and control classes with a significance level of 5%. The effectiveness value of the E-Module with the Flipbook platform is higher, namely 84.7%, compared to student learning outcomes using whiteboard media of 80.8%.

Model development for bespoke large language models for digital triage assistance in mental health care

Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) — a particularly important use-case for mental health where a majority of routinely-collected patient data lacks structured, machine-readable content.A significant problem for the United Kingdom’s National Health Service (NHS) are the long waiting lists for specialist mental healthcare. According to NHS data (NHS Digital, 2024), in each month of 2023, there were between 370,000 and 470,000 individual new referrals into secondary mental healthcare services. Referrals must be triaged by clinicians, using clinical information contained in the patient’s EHR to arrive at a decision about the most appropriate mental healthcare team to assess and potentially treat these patients.The ability to efficiently recommend a relevant team by ingesting potentially voluminous clinical notes could help services both reduce referral waiting times and with the right technology, improve the evidence available to justify triage decisions.We present and evaluate three different approaches for LLM-based, end-to-end ingestion of variable-length clinical EHR data to assist clinicians when triaging referrals. Our model is able to deliver triage recommendations consistent with existing clinical practices and its architecture was implemented on a single GPU, making it practical for implementation in resource-limited NHS environments where private implementations of LLM technology will be necessary to ensure confidential clinical data are appropriately controlled and governed. Code available at: https://github.com/NtaylorOX/BespokeLLM_Triage.

What can we learn from one-to-one trials in Instructional design? A case from module development

The one-to-one trial phase within the instructional design development model has frequently been neglected in prior research. This stage should provide much in-depth insight considering its nature, which is in-depth face-to-face data collection between designers and participants. In this study, we aim to analyze a particular instance of module development during the one-to-one trial phase. This study is part of the media development stage with the ADDIE model (Analyze, Design, Develop, Implement, and Evaluate) at the implementation stage. We used quantitative data collection through scoring and qualitative data through interviews with six participants involved in the one-to-one trial. The findings revealed that students provided critical feedback on two aspects of the module, particularly its design appeal and suitability for independent learning, aligning with an average participant score of 3 on a 1-5 scale. These results provide an overview that one-to-one as a foundation and initiation for a broader trial plays an essential role in providing a starting point for an implementation where quantitative and qualitative data provide holistic insight.

Proceedings 13th International Workshop on Developments in Computational Models

Proceedings 13th International Workshop on Developments in Computational Models

Developments in Sheaf-Theoretic Models of Natural Language Ambiguities

Developments in Sheaf-Theoretic Models of Natural Language Ambiguities

Adapting Large Language Models for Automated Summarisation of Electronic Medical Records in Clinical Coding.

Encapsulating a patient's clinical narrative into a condensed, informative summary is indispensable to clinical coding. The intricate nature of the clinical text makes the summarisation process challenging for clinical coders. Recent developments in large language models (LLMs) have shown promising performance in clinical text summarisation, particularly in radiology and echocardiographic reports, after adaptation to the clinical domain. To explore the summarisation potential of clinical domain adaptation of LLMs, a clinical text dataset, consisting of electronic medical records paired with "Brief Hospital Course" from the MIMIC-III database, was curated. Two open-source LLMs were then fine-tuned, one pre-trained on biomedical datasets and another on a general-content domain on the curated clinical dataset. The performance of the fine-tuned models against their base models were evaluated. The model pre-trained on biomedical data demonstrated superior performance after clinical domain adaptation. This finding highlights the potential benefits of adapting LLMs pre-trained on a related domain over a more generalised domain and suggests the possible role of clinically adapted LLMs as an assistive tool for clinical coders. Future work should explore adapting more advanced models to enhance model performance in higher-quality clinical datasets.

Diagnosis of ophthalmologic diseases in canines based on images using neural networks for image segmentation

The primary challenge in diagnosing ocular diseases in canines based on images lies in developing an accurate and reliable machine learning method capable of effectively segmenting and diagnosing these conditions through image analysis. Addressing this challenge, the study focuses on developing and rigorously evaluating a machine learning model for diagnosing ocular diseases in canines, employing the U-Net neural network architecture as a foundational element of this investigation.Through this extensive evaluation, the authors identified a model that exhibited good reliability, achieving prediction scores with an Intersection over Union (IoU) exceeding 80 %, as measured by the Jaccard index. The research methodology encompassed a systematic exploration of various neural network backbones (VGG, ResNet, Inception, EfficientNet) and the U-Net model, combined with an extensive model selection process and an in-depth analysis of a custom training dataset consisting of historical images of different medical symptoms and diseases in dog eyes.The results indicate a fairly high degree of accuracy in the segmentation and diagnosis of ocular diseases in canines, demonstrating the model's effectiveness in real-world applications. In conclusion, this potentially makes a significant contribution to the field by utilizing advanced machine-learning techniques to develop image-based diagnostic routines in veterinary ophthalmology. This model's successful development and validation offer a promising new tool for veterinarians and pet owners, enhancing early disease detection and improving health outcomes for canine patients.

Impact of sustainability education on senior student attitudes and behaviors: evidence from India

Purpose This study aims to ascertain the impact education for sustainable development (ESD) on knowledge, attitude and behavior among senior (last year undergraduate) Indian students. Design/methodology/approach This study is a cross-sectional causal investigation with descriptive design using survey method. It uses the knowledge, attitude and behaviors toward the sustainable development scales of the International Institute of Sustainable Development (IISD) for data collection and partial least-square structural equation modelling for empirically testing the proposed research model. Findings The study results establish the importance of sustainability education on knowledge, attitude and behavior. The impact of knowledge on behavior is direct and partially mediated through the attitudinal pathway – indicating central and peripheral routes sustainability related information processing and attitude formation. The results also indicate poor knowledge of the impact of socio-cultural issues on sustainability. Research limitations/implications The study results will help assess the impact current ESD initiatives in India and redesign the ESD curriculum in line with ESD for 2030 initiatives to facilitate the achievement of UN’s sustainable development goals by 2030. This study is limited to senior students and offers scope for extension to other groups. Social implications The study results can help redesign the sustainability curriculum at the school and undergraduate levels in the country in line with ESD for 2023 objectives. The results indicate an urgent need to include the impact of sociocultural issues on sustainability to sensitize Indian students towards the same. Originality/value This study contributes to the sustainability education literature by assessing the impact of sustainability curricula on sustainability knowledge, attitude and behavior among Indian senior students – hitherto uninvestigated. It also will help in the informed redesign/ improvement of ESD curriculum under the ESD for 2030 initiatives.

Can Asian Communicators Think ? Asiacentricity as a Paradigm for Decolonizing the Asian Mind

Asiacentricity is the thought and practice of centering Asians as subjects and agents and Asian cultures as reflective resources in seeing and shaping the Asian world. This article contends that decolonizing the Asian mind is a key for the Asian future, and that the Asiacentric idea can play an important role in this decolonization process. The article divides the definition of Asiacentricity into three parts and then relates each part to the possible future developments of the sadharanikaran model of communication. The article especially sheds light on Asiacentricity as a decoloinal paradigm for intercultural equality and mutuality, which demands that we know our own culture, have the mindset of embracing the best of our culture, and learn how to talk about our culture in relation to another. The present article concludes that to theorize communication is to theorize humanity, while to theorize humanity is to theorize culture.

The Development of Contextual Approach E-Modules to Improve Students’ Mathematical Problem Solving Ability on Junior High School

This study aims to develop an E-Module that can be used by teachers to support the learning process so that students more easily understand material in interactive mathematics at SMPN 4 Cipeundeuy. In order to do so, this research using research and development (Reaserch and Development) with the 4D development model (Define, Design, Develop, and Disseminate). Research conducted using this model is limited to step 3 (Define, Design, Develop) due to time and cost constraints during the research. The subjects of this study were class IX students for limited trials and class VIII students for wide trials. The data that has been collected is then analyzed using Microsoft Excel. The results of the study on the feasibility of teaching materials were seen from the results of the assessment of lesson plans, worksheets, and media with a percentage of 93.80% and the results of assessments by two mathematics teachers with a percentage of 89%. Teaching materials were tested for practicality using a student response questionnaire with a percentage of 90%. Thus this teaching material can be implemented and can be used. The results of this study can be categorized as valid, practical, and very feasible because they have received a good due diligence.

Применение теории множеств для разработки ключевых параметров эффективности трудового сектора пенитенциарной системы

The article considers the development of information management systems, decision support and modelling for production processes in penitentiary institutions. The work outlines the problem of insufficient study of production and labour sector data both from a scientific viewpoint and from the standpoint of developing new software and practical application. It is found that the first steps to solve the above-mentioned problems will be proper data structuring and identifying key performance parameters. Presenting data as sets and studying the interaction between them can be a good solution to the problems posed. Thus, the work formulates the task of determining the key performance parameters of the labour sector of the penitentiary system, and considers the principles of set theory as tools for solving this problem. In addition, the authors use previously created developments such as an indexing system and a software package for collecting statistical data to settle this issue. As a result of the study, rules are determined, as well as relationships that can be applied to detect the key performance parameters and develop an information system, as well as to create databases with improved structuring that are more practically oriented.

Managing opioids and mitigating opioid risks in patients with cancer: An environmental scan of the attitudes, confidence, and practices of ambulatory, community and hospital pharmacists practicing in Canada.

Canada is in the midst of an overdose crisis. The use of prescription opioids in Canada has increased steadily over the past two decades, with stark increases in opioid-induced respiratory depression and related deaths. Opioids are the mainstay of treatment for cancer-related pain. Patients with cancer are not immune to the risks associated with opioid use but are underrepresented in available literature outlining risk mitigation strategies. Pharmacists are ideally placed to employ opioid risk mitigation practices to support safe and effective opioid use for patients with cancer-related pain. However, the current attitudes, confidence, and safety practices of pharmacists around how to best support these patients are not known. This study was a descriptive environmental scan of pharmacists who provide direct patient care in Canada. An electronic questionnaire was built using the web based Opinio software. It was distributed via email by several provincial and national pharmacy organizations and online platforms. The questionnaire consisted of Likert-scale and open-ended questions and was open to participants for a 6-week period from February 12th to March 23rd, 2020. Analysis was conducted using descriptive statistics and qualitative content analysis. Eighty-one responses from pharmacists in nine provinces were included in the analysis. Respondents endorsed limited and varied practices when caring for patients receiving opioids for cancer-related pain. Further, they demonstrated wide ranging confidence and attitudes regarding opioid risk mitigation practices and beliefs. Less than 50% of pharmacists were aware of resources available for their patients with non-medical opioid use, and/or patients at high risk of opioid-induced respiratory depression. Education, resources, and communication were the most commonly reported perceived facilitators and barriers to resource use. Pharmacists in Canada report employing opioid risk mitigation practices with low and varied frequency when caring for patients receiving opioids for cancer-related pain. They endorsed varied confidence and limited awareness of available provider and patient resources. These findings may help inform the development of new education models and evidence-based guidelines. New education models and evidence-based guidelines will support pharmacists in their pharmaceutical care of this vulnerable patient population, ultimately aiming to improve patient outcomes.

Kidney organoids: steps towards better organization and function.

Kidney organoids - 3D representations of kidneys made either from pluripotent or tissue stem cells - have been available for well over a decade. Their application could confer notable benefits over longstanding in vivo approaches with the potential for clinically aligned human cells and reduced ethical burdens. They been used, at a proof-of-concept level, in development in disease modeling (including with patient-derived stem cells), and in screening drugs for efficacy/toxicity. They differ from real kidneys: they represent only foetal-stage tissue, in their simplest forms they lack organ-scale anatomical organization, they lack a properly arranged vascular system, and include non-renal cells. Cell specificity may be improved by better techniques for differentiation and/or sorting. Sequential assembly techniques that mimic the sequence of natural development, and localized sources of differentiation-inducing signals, improve organ-scale anatomy. Organotypic vascularization remains a challenge: capillaries are easy, but the large vessels that should serve them are absent from organoids and, even in cultured real kidneys, these large vessels do not survive without blood flow. Transplantation of organoids into hosts results in their being vascularized (though probably not organotypically) and in some renal function. It will be important to transplant more advanced organoids, with a urine exit, in the near future to assess function more stringently. Transplantation of human foetal kidneys, followed by nephrectomy of host kidneys, keeps rats alive for many weeks, raising hope that, if organoids can be produced even to the limited size and complexity of foetal kidneys, they may one day be useful in renal replacement.