bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of Enterprise Reputation Evaluation Framework Based on Encrypted Decision Tree Algorithm
Authors: Zhou Quanxi , Shen Ruiyu
Published: November 2024

In the era of digital economy, industrial Internet enterprises play an important role in social development. Enterprise reputation is not only crucial for the development of enterprises themselves, but also affects the development and prosperity of the industrial Internet. In the traditional reputation evaluation model, enterprise information is transmitted as data plaintext, and data leakage crisis occurs during this process. On the other hand, due to the confidentiality of evaluation algorithms and parameters by evaluation institutions, the reputation evaluation process is not transparent, and there is a trust crisis between the tested enterprises and the evaluation institutions. This paper aims at the drawbacks of the traditional enter-prise reputation evaluation model. An industrial Internet enterprise reputation evaluation framework that supports data encryption computing has been built. A series of secure communication protocols for the data management and transmission of this framework has also been designed to improve the security of the evaluation model. In order to implement the above framework and protocols, an improved decision tree cryptography algorithm that supports public key instrument and full ciphertext computation based on CKKS methods has been designed. The experimental verification shows that the improved decision tree algorithm with better time performance has little difference in accuracy compared to existing works, making it more suitable for serving practical applications.
Virtual Reality Display Design for CNC Machine Tools
Authors: Wang Xue-ru , Liu Min-yang
Published: November 2024

With the rapid development of digital technology, virtual reality technology has gradually entered the industrial field, and the traditional machine tool display mode is no longer adapted to the needs of the times. This paper applies the 7Ps theory to deeply analyze the problems of CNC machine tools in the traditional brand marketing process, combined with the theory of embodied cognition and customer relationship management theory to put forward a model of factors affecting the design of VR display for CNC machine tools, which is designed to explore the impact of the introduction of VR technology on the enterprise’s brand orientation (brand perception, experience, satisfaction, and loyalty) and sales volume. The experimental results show that VR display design oriented to CNC machine tools significantly improves brand perception, experience, satisfaction, and sales volume, and has little effect on brand loyalty. This study provides new perspectives for researching VR display design and industrial branding of CNC machine tools and helps the intelligent digital transformation of China’s CNC machine tool brands.
Research on Visual Asset Design Strategies for CNC Device AR Assembly
Authors: Wang Zi-yue , Liu Min-yang
Published: November 2024

goes here. In recent years, AR technology has been developing rapidly in manufacturing assembly, which provides innovative technology support for the assembly process of complex products. AR assembly instructions are a method to help users perform manual assembly by superimposing virtual information in the real environment. There have been several studies on visual assets adapted for AR instructions, and the visual assets have also been categorized into clearer categories. However, current studies on AR instructions focus more on the comparison between traditional assembly instructions and AR assembly instructions, failing to consider the information cognitive differences that occur when users are faced with different assembly instruction visual assets. Therefore, this study takes the assembly of computer numerical control (CNC) devices as an example, analyzes the assembly process information involved in the assembly work process in detail, breaks down the action steps, and creates the corresponding visual assets. The designed visual assets are tested for assembly efficiency and usability, and the design strategy priorities of different AR visual assets under different assembly process information are deduced to help standardize the design of AR assembly instructions, providing a theoretical and practical research foundation for the design of related AR assembly instructions.
A Study on the Dilemma and Countermeasures of Practical Teaching of Higher Vocational Electrical Automation Technology Majors Based on Smith’s Policy Implementation Model
Authors: Zeng Fanling
Published: November 2024

Through a questionnaire survey, this paper analyzes the effect and influencing factors of the industry education integration policy in a vocational college in a city. The results show that the industry education integration major of the school under the support of the government has achieved initial success, especially the outstanding performance of the modern apprenticeship class apprenticeship class, and the significant improvement of students’ skills and employment competitiveness. The study also emphasized the importance of integrating teachers’ professional teaching level with practical teaching content. In conclusion, this study provides useful experience and suggestions for the optimization of the electrical automation practice teaching system.
The Construction of Vocational Education Talent Literacy Evaluation System Based on Hierarchical Analysis Model
Authors: Qin Qichao , Lei Yan
Published: November 2024

Based on the analytic hierarchy process (AHP) model, this paper proposes a new evaluation system for vocational college students’ professional quality. By constructing the core literacy framework and specific evaluation index system, TOPSIS evaluation model is used to calculate the evaluation value of students’ core literacy, and analytic hierarchy process model is used to divide students into different types. The experimental results show that the evaluation of core literacy of all kinds of students is generally low, especially in social competence. The prediction accuracy of the model reached 98.9061%. The research shows that the evaluation model is feasible and effective in the quantitative evaluation of vocational students’ professional quality.
An Artificial Intelligence-Based Innovation of Student Behavior Prediction and Management Model
Authors: Li Haitao
Published: November 2024

In this study, we propose an innovative model based on artificial intelligence for predicting and managing student behaviors by addressing the complexity and sparsity of student behaviors in campus big data. First, the problem of insufficient behavioral characterization and timeliness of labels is solved by establishing a cross-modal student behavioral portrait model based on deep learning of multimodal features. Secondly, an effective early warning method for abnormal student behavior is proposed by combining long and short-term memory neural networks and multi-indicator data. Experiments confirm that the method is better than traditional algorithms in terms of accuracy and timeliness, and can effectively improve the effect of student behavior prediction and management, and promote the application of personalized education and behavior prediction.
A Study of Artificial Intelligence-Assisted English Writing Teaching Paths
Authors: Xi Hong
Published: November 2024

The purpose of this study is to explore an AI assisted approach to English writing teaching in order to improve teaching methods and enhance students’ writing ability. Through the application of the intelligent evaluation system, students can obtain the correction information in time and complete the English writing task. Data analysis shows that under the guidance of appropriate writing strategies, students’ writing quality has been significantly improved, especially in terms of vocabulary richness, sentence length, and readability. The conclusion points out that by clarifying learning objectives, guiding writing strategies and providing multiple feedback, students’ writing behavior can be effectively improved and their writing ability can be improved.
A New Approach to Product Innovation: Leveraging DFMA for Design Efficiency
Authors: Li Wei
Published: November 2024

Design for Manufacture and Assembly (DFMA) is a widely adopted methodology. While it offers numerous advantages, it also presents certain challenges. This paper discusses strategies to enhance the efficiency of DFMA including key paths, design thinking, and agile project management by analyzing the product R&D process of a Chinese company. It also demonstrates meticulous methods for selecting manufacturers and achieving collaborative innovation in a rapidly evolving and fiercely competitive market. The findings also shed light on methods of driving transformation and upgrading within the manufacturing sector.
Analyzing and Predicting Tourism Consumption Behavior Based on Big Data and Artificial Intelligence
Authors: Wen Jing
Published: November 2024

In order to make the formulation and implementation of marketing strategies more targeted for consumption by different 1 groups, analysis and prediction of tourism consumption behavior based on big data and artificial intelligence are proposed. Using a large amount of tourism consumption data obtained from the tourism microdata platform, a tourist consumption type prediction model is established, which can predict and judge tourists’ consumption types in the presence of tourism consumption data. Through the K-means cluster analysis method, the data is clustered to achieve the effect of predicting and judging tourist consumption types and levels. The results show that: in the user’s travel consumption data, the average basic travel consumption is 67% of the total consumption, shopping consumption accounts for 27.69%, post and telecommunications accounts for 0.57%, and urban transportation accounts for 4.47%. By comparing the clustering centers of each level with the average data, the consumption characteristics of each level can be obtained. Conclusion: This method achieves the effect of predicting and judging tourists’ consumption types when part of the tourism data is known.
A study on Smart Sensor-Based Co-Optimization Method for Power Communication Networks and Power Grids
Authors: Zhang Yan , Zhang Pu , Yu Qiusheng , Wei Yongjing , Meng Xianglu
Published: November 2024

With the rapid development of new power systems and digital construction of power grids, a large number of power Internet of Things terminals need to be deployed to collect equipment status information in real time. In order to meet the information interaction, online decision-making and resource scheduling needs of the power Internet of Things, existing cloud-edge-end collaborative resource scheduling technology usually adopts a vertical series method, which has the problem of low adaptability. This paper proposes a collaborative optimization method for power communication networks and power grids based on smart sensors, constructs a multi-dimensional vertical and horizontal collaborative resource scheduling framework, and introduces a semi-distributed algorithm based on federated generative adversarial networks. The simulation results show that the algorithm has significant performance advantages while queuing delay is evenly distributed and the average queuing delay is low, verifying its effectiveness and superiority in delay, energy consumption, convergence and other aspects.
Construction of an Integrated Student Management Platform in a Smart Campus Environment
Authors: Li Haitao
Published: November 2024

In order to comprehensively improve the efficiency of student management in colleges and universities, a comprehensive student management platform in a smart campus environment is proposed. From the technical level, we deeply explore the architecture design of the student management platform based on big data technology, and elaborate on the related technologies and algorithms of the student management platform architecture. The platform test results show that: after adding 500 virtual users at one time in the testing tool LoadRunner, setting the functional operation calling node to all functional test cases, using Random mode to execute online the automated scripts of functional test cases imported in advance, the maximum response time is 2.85s, which is lower than the 3-second response time indicator with clear performance requirements and test indicators. Conclusion: Provide reference suggestions for the application of big data analysis technology in college student management under the background of the big data era.
A Study on New Energy Wind Power Prediction Model in Smart Grid Environment
Authors: Liu Guangyu , Qu Dongzhe , Qu Xinying
Published: November 2024

The new energy wind power prediction model in the smart grid environment is studied, and a model based on the attention mechanism of time convolution network and two-way gated loop unit is proposed. The model utilizes time convolution network to mine the association between multidimensional time series data, introduces the residual module to accelerate the convergence speed, and combines the two-way gated recurrent unit and the attention mechanism to construct the time-sequence attention module, in order to improve the accuracy of wind power prediction. The experimental results show that the model performs well in terms of mean absolute value error, mean absolute value percentage error and coefficient of determination, which verifies its efficiency and accuracy in short-term wind power prediction.
Research on the Practice Path of Civic and Political Innovation Course Based on Mobile Cloud Teaching Under the Background of “Internet+”
Authors: Yang Qi
Published: November 2024

Teachers of ideological and political theory courses are updating their teaching concepts to embrace new methods, focusing on enhancing media literacy through mobile cloud teaching. This study proposes an innovative approach to integrating mobile cloud platforms deeply into teaching methods under the “Internet+” framework. It emphasizes optimizing teaching designs, creating collaborative network environments, and using big data to enhance teaching effectiveness. Results show significant improvement in teaching effectiveness, with the “China Series” course receiving high praise from students. Overall, educators can use technologies like the “Blue Ink Cloud Class” to refine teaching practices and continuously improve their approach.
Construction and Empirical Analysis of a Deep Learning-Based Crime Warning Model for Multi-Source Spatio-Temporal Data
Authors: Zheng Xinhao , Li Xuechen
Published: November 2024

Proposed is a deep learning-based crime early warning model using multi-source spatio-temporal data, aiming to predict criminal behavior among key groups in advance for enhanced crime prevention and control. Utilizing an AI analysis platform, a recidivism prediction model for ex-convicts is developed, improving prediction accuracy and offering a scientific basis for proactive crime prevention. Data from ex-convicts, including spatial-temporal, basic attributes, adverse histories, and activity trajectories, are integrated to construct a robust early warning analysis model. The Random Forest algorithm achieves a precision rate of 0.85 and a recall rate of 0.86 for positive samples, highlighting the model’s effectiveness.
A Study of Machine Learning-Based Methods for Analyzing Financial Statements
Authors: Li Tianbao , Zhou Jingbang
Published: November 2024

Research proposes using machine learning for financial statement analysis to aid auditors facing large-scale financial data. Starting with suspicions around “high deposits and loans,” Kangmei Pharmaceutical’s audit concerns were scrutinized through financial statement analysis, A-share market analysis, and quantitative return rate analysis. The study established a hypothesis of significant audit risks and introduced two new audit approaches using big data: machine learning for income tax analysis in financial statements and time series analysis of Kangmei’s stock market data. Findings indicate persistent instability in time series data, notably around a major misstatement in 2018. The return rate declined from 2014 to 2017 and spiked abnormally in 2018. These anomalies suggest abnormal fluctuations in Kangmei’s A-share market data. In conclusion, leveraging big data technology can enhance audit efficiency and provide novel insights for auditors guided by audit standards ensuring thoroughness in critical assessments.
An Empirical Study of Digital Economy Development for Industrial Structure Transformation and Upgrading
Authors: Yuan Zhengzhuo , Wang Fengyi , Liu Huichuan
Published: November 2024

To promote industrial structure transformation and upgrading, empirical research on the digital economy’s impact uses data from 247 cities in China (2016–2019). Using a fixed effect model, findings show significant promotion of industrial structure transformation across dimensions. Sub-sample and alternative variable analyses confirm these results, with controlled endogeneity. Variable impacts vary: negative coefficient for government scale (p<0.01), positive for higher education, openness, urbanization, and infrastructure (p<0.01). Conclusion: Digital economy development primarily drives transformation via consumption upgrade and technology spillover effects.
An Empirical Analysis of Intelligent Risk Identification Models in Corporate Auditing
Authors: Li Tianbao , Zhou Jingbang
Published: November 2024

In order to accurately identify audit risks, an empirical analysis of intelligent risk identification models in corporate audits is proposed. 16 indicators were selected to form the input indicator system of the material misstatement risk assessment model, the SAE algorithm was used to extract features, and the machine learning model BP neural network classifier was used to identify them. The SAE-BP neural network was constructed, and 135 A-share listed companies were selected as the sample was analyzed empirically. Empirical results show that the model has fast operation speed, high average recognition accuracy of the model, which can reach 88.5%, and can carry out high-quality identification of audit risks. Conclusion: This model improves the efficiency of auditing.
Use of Data Analysis in Fitness Monitoring of College Soccer Players
Authors: Wang Peng
Published: November 2024

In order to solve the shortcomings of traditional systems in athletes’ physical fitness monitoring, this article proposes the application of data analysis in physical fitness monitoring of college football players. The method includes: using video surveillance technology in the Internet of Things environment to extract three-dimensional contour features and fusion of edge information, combined with image filtering technology to identify physical status information; applying corner detection to calibrate key points and reconstruct regions to achieve real-time monitoring; using VIX bus processing technology monitors and processes physical status signals. Experimental results show that this method has good real-time performance, accurate and reliable monitoring, and the processing time is 0.87 seconds. The conclusion is that the system can effectively realize real-time monitoring of athletes’ physical condition and has broad application prospects.
Deep Learning-Based Risk Assessment of High-Voltage Transmission Line Interference with Buried Pipelines
Authors: Dong Zhao , Zhang Liguang , Wang Qiang
Published: November 2024

In order to solve the influence of high-voltage transmission lines on the service life of buried pipelines, this paper proposes a interference risk assessment method based on deep learning. The IEEE-RTS79 system is used to generate risk data samples, and an indicator system is established from the four dimensions of voltage out of limit, power flow overload, load loss rate and power flow transfer degree to quantify the grid risk, and the data set is labeled. In order to deal with the problem of sample imbalance, a variety of balance methods are used, and dimensions are reduced through feature selection and principal component analysis. Finally, the improved MLP model is used for training. The experimental results show that the computation time of SVM is only 18.3% of that of traditional methods, and the MLP is 1.4%. The computation time is greatly reduced, meeting the real-time requirements. The conclusion is that this method can quickly and accurately assess the power grid risk and effectively improve the service life of buried pipelines.
A Study on the Diagnosis of the Working Conditions of a Traveling Beam Pumping Unit Based on Artificial Intelligence
Authors: Dong Yu , Niu Li , Bai Yanfeng , Wang Luyang , Liu Yan
Published: November 2024

In order to solve the problem of low accuracy of traditional indicator diagram recognition methods, the research on condition diagnosis of beam pumping units based on artificial intelligence is proposed. Through the application of deep learning convolutional neural network in the field of image recognition, this paper proposes a convolutional neural network model based on LeNet, and realizes the automatic recognition of indicator diagrams. The model built by the research institute takes 15 common downhole working conditions of the pumping unit into consideration while simplifying the model structure, and introduces the Dropout layer and local response normalization layer to prevent the model from over fitting and improve the generalization ability of the model. The experimental results show that the model not only has a fast convergence rate, but also has an average diagnostic accuracy of 94.68% It meets the diagnostic accuracy requirements of pumping unit condition detection. Conclusion: This study provides a basis for the construction of pumping unit well condition intelligent monitoring and early warning system, which is of great significance to the construction of intelligent oilfield and the efficient production of oilfield.
Correlations between Alzheimer’s Disease Cerebrospinal Fluid	Biomarkers and Cerebral Glucose Metabolism after 12 Months of Phenserine	Treatment
Authors: Nordberg Agneta , Kadir Ahmadul , Andreasen Niels , Almkvist Ove , Wall Anders , Blennow Kaj , Långström Bengt , Zetterberg Henrik
Published: November 2024

New therapeutic strategies in Alzheimer’s disease (AD) are focused on targeting amyloid-β (Aβ) to modify the underlying cause of the disease rather than just the symptoms. The aim of this study was to investigate the long-term effects of treatment with the anti-Aβ compound phenserine on (i) cerebrospinal fluid (CSF) biomarkers for Aβ and tau pathology and (ii) brain metabolism as assessed by the regional cerebral metabolic rate for glucose (rCMRglc), using positron emission tomography. Twenty patients with mild AD were included in the study and after 12 months treatment with phenserine, CSF Aβ 40 and α- and β-secretase-cleaved soluble amyloid-β protein precursor (sAβPP) levels had significantly increased and rCMRglc had stabilized. Levels of CSF Aβ 40 and sAβPP correlated positively with rCMRglc and cognition while CSF Aβ 42 levels, the Aβ 42/40 ratio, P-tau, and T-tau correlated negatively with rCMRglc and cognition. In summary, long-term phenserine treatment resulted in increased levels of CSF Aβ 40, sAβPPα, and sAβPPβ, which positively correlated with improvements in rCMRglc and cognition. The study illustrates the value of using biomarkers in the CSF and brain for evaluation of drug effects.
FUS Mutations in Frontotemporal Lobar Degeneration with Amyotrophic Lateral Sclerosis
Authors: Broustal Oriane , Camuzat Agnès , Guillot-Noël Lena , Guy Nathalie , Millecamps Stéphanie , Deffond Didier , Lacomblez Lucette , Golfier Véronique , Hannequin Didier , Salachas François , Camu William , Didic Mira , Dubois Bruno , Meininger Vincent , Ber Isabelle Le , Brice Alexis , the French clinical and genetic research network on FTD/FTD-
Published: November 2024

Rapid advances were made in the knowledge of amyotrophic lateral sclerosis (ALS) with the recent identification of TARDBP and FUS mutations in familial ALS. More recently, FUS-positive inclusions were found in a subset of TDP-43-negative frontotemporal lobar degeneration (FTLD) prompting us to analyze FUS in FTLD and FTLD-ALS patients. The p.Arg521His mutation was identified in a patient who initially had behavioral disorders and rapidly developed ALS. Although the frequency of mutations is low, our study enlarges the phenotypes associated with FUS mutations and shows that FUS could also play a direct pathogenic role in FTLD spectrum of diseases.
VIEWING ENTERPRISES AS SYSTEMS-OF-SYSTEMS (SOS): IMPLICATIONS FOR	SOS RESEARCH
Authors: Madni Azad M. , Moini Assad
Published: November 2024

With modern enterprises continuing to grow in size and complexity, there is a pressing need to make their transformation, management and evolution more understandable and rigorous. In this paper, we argue that viewing enterprises as system-of-systems (SoS) can potentially provide a fresh perspective and unique insights into understanding organizational and operational issues surrounding modern enterprises. We claim that, both as a modeling construct and as a system abstraction, the notion of SoS is well-suited to studying, analyzing and characterizing the structural and behavioral aspects of modern enterprises. Motivated by the potential synergy between these two independent disciplines while recognizing the nascent state of the SoS body of knowledge, we propose a new research direction in applying systems thinking to understanding enterprises and, at the same time, improving the state-of-the-art in enterprise transformation, management, and evolution.
QUALITY ASSESSMENT OF SYSTEM ARCHITECTURES AND THEIR REQUIREMENTS	(QUASAR)
Authors: Firesmith Donald G. , Capell Peter S.
Published: November 2024

The quality of a software-intensive system is largely determined by the quality of its architecture and the quality of the architecturally significant requirements that drive its development. Unfortunately, although quality requirements typically have critical architectural ramifications, requirements engineers using such popular techniques as use case modelling tend to emphasize functional requirements over non-functional quality requirements, with the result that the quality requirements are often poorly specified or not specified at all. Similarly, system architects tend to emphasize a system's logical decomposition structure into major functions and subfunctions and the corresponding static physical decomposition structure into a hierarchy of subsystems. The system architects often do not adequately document how (or even if) these subsystems and subsubsystems collaborate to sufficiently support the achievement of their derived and allocated quality requirements. To address these two problem areas, the SEI QUality Assessment of software-intensive System Architectures and their Requirements (QUASAR) method was created to provide acquisition organizations with a proven and efficient means to determine if quality requirements have been properly engineered and if system architectures sufficiently meet these requirements.
METRICS FOR SERVICE-ORIENTED ARCHITECTURE (SOA) SYSTEMS: WHAT	DEVELOPERS SHOULD KNOW
Authors: Salasin John , Madni Azad M.
Published: November 2024

Service-Oriented Architectures (SOA) provide the first widely applicable approach to tying together diverse "stovepipe" (or "silo") systems. They are, therefore, a "pure" example of how to build Systems of Systems (SOS). With the growing interest in and increasing number of implementations of SOAs, there is a pressing need for measures and metrics that can help SOA developers (Chief Architects or Program Managers) in predicting potential problems in the development/evolution of their SOA system. For SOA-based systems, such assistance is vital since their success depends on melding appropriate technologies and design with organizational characteristics, customer profiles, and functions provided, a Systems of Systems (SOS) concern. This paper discusses an ongoing effort in this area. In addition to providing a useful tool, this effort is technically significant for three key reasons. First, SOA specification and assessment requires consideration of technology factors and their relationships to the organization, and to individual information consumers. Essentially, these are related to metrics for systems engineering. Second, requirements (e.g., ease of use, flexibility) are defined in terms of system characteristics that can be modeled and measured. Thus, the system is defined by a set of evolving models and metrics that allow continual evaluation of progress towards eliminating problems. Third, since requirements are initially expressed with coarse-grained conceptual models, measurement of the characteristics of these models can indicate areas where the system might not meet some requirement, thereby allowing early corrective action. Thereafter, more refined models, can be developed to track the success of corrective actions. This paper provides a background on SOA, challenges for SOA metrics, and proposes a model-based metrics framework for evaluating SOAbased systems on an ongoing basis. It provides exemplar metrics and measures for SOA, and discusses several approaches to metrics quantification and presentation.
ADVANTAGES OF EVOLUTIONARY COMPUTATION USED FOR EXPLORATION IN THE	CREATIVE DESIGN PROCESS
Authors: von Buelow Peter
Published: November 2024

In early phases of design a wide exploration of the design space is crucial to the development of creative solutions. In this regard, Evolutionary Computation (EC), and in particular Genetic Algorithms, contain several qualities that can enhance exploration by opening the search process beyond the focus of finding a single "best" solution. Over the years many researchers in the area of creative thinking including Gordon, de Bono, Parnes, Osborn and others, have suggested design strategies that have interesting parallels in EC processes. For instance, a well known inhibitor of creative thinking is design fixation, where the suggestion of a particular solution makes it difficult to imagine other good solutions. Unlike many other computational search algorithms, EC methods work with populations of "fairly good" solutions. Therefore, there is less danger that creativity will be harmed by design fixation on one "best" solution. This paper shows through a specific example of a truss bridge how an EC based design exploration program can aid the designer by providing a selection of "pretty good" solutions rather than a single optimal solution. Other aspects of the EC program are also discussed including drawbacks to the method such as computational intensity as well as directions of future development.
BRINGING CONSCIOUSNESS TO COGNITIVE NEUROSCIENCE: A COMPUTATIONAL	PERSPECTIVE
Authors: Samsonovich Alexei V.
Published: November 2024

This paper focuses on three connected to each other fundamental beliefs that appear to be unconsciously taken for granted at the basis of modern cognitive neuroscience: (i) the attractor hypothesis, (ii) the neural code hypothesis, and (iii) the supervenience hypothesis about human subjective experience. Their precise understanding and experimental verification is an important challenge that calls for a scientific paradigm change and needs to be addressed for future progress in cognitive neuroscience. The work presents a theoretical analysis and preliminary experimental and modeling results that suggest a roadmap to solving the challenge.
DESIGN AND IMPLEMENTATION OF A GENE NETWORK REVERSE ENGINEERING	METHOD BASED ON MUTUAL INFORMATION
Authors: Madni Azad M. , Andrecut Mircea
Published: November 2024

In this paper, the authors describe a gene network reverse engineering method, which employs mutual information to infer the connections between the genes. Given the expression profile of the genes, determined for different conditions, the method calculates the similarity matrix corresponding to the mutual information between each pair of genes. The approximated matrix can be subsequently refined by using a data processing inequality and an analytically estimated threshold for the statistical significance of mutual information. The authors have used the proposed method to reconstruct a network of 2041 gene transcription factors, measured over 79 human tissues. The numerical results show that the connectivity of the transcription factors network is characterized by a scale free distribution, with an exponent of the power law between 1.5 and 2. The power law for the connectivity distribution implies that the network is extremely heterogeneous; i.e., its topology is dominated by a few highly connected genes, which link the rest of the loosely-connected genes to the system.
Mapping of Health System Performance Indicators to the WHO HSPA Framework
Authors: Adam Lucien , Leung Anthéa Helene , Sariyar Murat
Published: November 2024

Healthcare systems worldwide face escalating costs and demographic changes, necessitating effective evaluation tools to understand their underlying challenges. Switzerland’s high-quality yet costly healthcare system underscores the need for robust assessment methods. Existing international rankings often lack transparency and comparability, highlighting the value of structured frameworks like the Health System Performance Assessment (HSPA) by the World Health Organization (WHO). This framework evaluates healthcare systems across multiple dimensions including governance, resource generation, financing, and service delivery. This paper aims to integrate Swiss healthcare indicators from the Swiss Health Observatory (Obsan) into the HSPA framework, addressing the central research question: How can these indicators be mapped to the HSPA framework, and what insights does this integration provide? Our methodology includes selecting and categorizing Obsan indicators, manually mapping them to HSPA sub-functions, and validating these mappings using word embeddings and cosine similarity. An R Shiny application was developed for interactive visualization. Results demonstrate accurate indicator assignment, enabling intuitive visualization and enhancing data structuring.
Frameworks and Tasks Used in Usability Testing Scripts: A Scoping Review
Authors: Seoparson Sunil , Borycki Elizabeth M. , Kushniruk Andre W. , Kannry Joseph
Published: November 2024

Usability is understood as a critical component to the success of electronic health records and other related healthcare technologies. Usability testing methods routinely employ scripts that help researchers understand how a particular tool works under real world conditions. This scoping review sought to better understand the guiding frameworks, principles, and methodologies employed when generating usability testing scripts to better understand how script generation occurs. Three main themes emerged through qualitative analysis: researchers sought to observe the baseline functionality being tested, the most representative tasks, or the most complex tasks. This scoping review highlights a lack of consistent processes in usability test script generation. There is a need to create standardized usability testing scripts for usability testing.
Scaling up Environmental Governance in Precision Forestry
Authors: Tramontano Adriano , Perillo Giulio , Magliulo Mario , Tamburis Oscar
Published: November 2024

Precision Forestry is an emerging approach that uses digital technologies for data-driven decision-making in environmental management. Traditional methods for assessing tree risk are often subjective and focus on individual trees using mechanical approaches. The #SecureTree model offers an innovative alternative by deploying sensors to measure biophysical parameters like temperature, humidity, and acceleration. Data from these sensors is processed to create a risk assessment map based on the progression of trees’ behaviors. This model is non-invasive and objective, addressing risk more effectively than current methods. Field tests validated the model’s accuracy and highlighted its potential to identify long-term risk trends, enabling better planning for disruptive events and the development of digital strategies for emergency management.
Pill Dispenser with Telecare Extension
Authors: Zigrea Sebastian-Bogdan , Stoicu-Tivadar Vasile
Published: November 2024

The existing pill dispenser systems help elderly people to improve their quality of life, and medication adherence. But these systems lack interactive capabilities with caregivers, a crucial element in comprehensive home care management. The suggested CareConnect aims to bridge this gap by introducing a Telecare extension that not only manages medication adherence but also facilitates interaction between the patient and their caregivers. The system is described in terms of this new approach, the functions, hardware and software. The operation of the system is briefly described. A discussion about the advantages of CareConnect system and the future development directions is finally done as a conclusion.
Measurement of Cumulative Drug Exposure from Clinical Data Warehouse
Authors: Bories Mathilde , Bannay Aurélie , Pierre-Jean Morgane , Bouzille Guillaume , Le Corre Pascal
Published: November 2024

Polypharmacy (PP) and hyperpolypharmacy (HPP), are prevalent among cancer patients and are associated with an increased risk of drug-drug interactions (DDI) and potentially inappropriate medications (PIM). This study aimed to characterize PP, HPP, DDI, and PIM in patients with hematological malignancies hospitalized for hematopoietic stem cell transplantation (HSCT) by introducing a novel metric: cumulative drug exposure. Clinical data warehouse (CDW) records were employed to develop algorithms that quantified patients’ cumulative exposure to these prescribing determinants during hospitalization. This entailed determining the number of days during the hospital stay when the patient was exposed to PP, HPP, PIM and/or DDI. For PIM and DDI, the number of PIMs or DDIs administered per day was taken into account in this calculation. Among 339 HSCT patients, PP and HPP were highly prevalent (over 67% of HSCT patients), almost all patients experienced DDI (over 98% of HSCT patients) and almost all elderly patients were exposed to PIM (over 98% of HSCT patients). Cumulative drug exposure differed between allogeneic and autologous HSCT patients, with allogeneic patients being more exposed to HPP (28.5 days vs 4.7 days for autologous HSCT patients) and DDI (255.6 days vs 58.4 for autologous HSCT patients). This study proposes a novel approach to assessing the impact of prescribing determinants on patient outcomes and provides insights for future research into the association between drug exposure and adverse events. Indeed, the use of cumulative drug exposure as a metric provides a comprehensive view of patient exposure throughout hospitalization, thereby enhancing understanding of the impact of prescribing practices on clinical outcomes.
Securing a Generative AI-Powered Healthcare Chatbot
Authors: Feretzakis Georgios , Anastasiou Athanasios , Pitoglou Stavros , Paxinou Evgenia , Gkoulalas-Divanis Aris , Kalodanis Konstantinos , Tsapelas Ioannis , Kalles Dimitris , Verykios Vassilios S.
Published: November 2024

In Generative Artificial Intelligence (AI), Large Language Models (LLMs) like GPT-4, Gemini, Claude, and Llama, significantly impact healthcare by aiding in patient care, medical research, and administrative tasks. AI-powered chatbots offer real-time responses and manage chronic diseases, improving patient outcomes and operational efficiency. However, these models pose security and ethical challenges, necessitating robust data privacy, adversarial training, and ethical guidelines. This paper proposes a secure, ethical pipeline for deploying AI healthcare chatbots, integrating advanced privacy-preserving techniques and continuous security assessments to enhance data privacy, resilience, and user trust.
“Medical Work for Women by Women”: Rosetta Sherwood Hall’s Perspective on Women’s Hospitals and Women's Education
Authors:   The Korean Association for the Social History of Medicine, Jeong Dahye
Published: November 2024

Rosetta Sherwood Hall (1865-1951) was a leading advocate for advancing the medical education of Korean women. Her advocacy was grounded in the belief that women doctors should be the primary caregivers of women, and that women should manage women's hospitals. She viewed women's hospitals not only as healthcare facilities but also as crucial venues for training women doctors. Hall argued that the location of a women’s hospital should be determined by the need to create an efficient infrastructure that would expand medical and nursing education for Korean women. Despite the trend of merging mission hospitals in the 1920s, Hall was cautious about integrating women’s hospitals into institutions managed by men. She believed such mergers would compromise the value and status of women’s hospitals.
Hall supported both the establishment of a women’s medical school and the inclusion of women in existing medical schools to enhance their medical education. Her focus was not on whether these institutions should be missionary or state-run but on maximizing the effectiveness of medical education for Korean women. At a time when both missionaries and colonial authorities were reducing opportunities for women’s education inside and outside Korea, Hall chose to rely on the will of the Korean people rather than on the missionaries or colonial government.
While many women missionaries shared the goal of “women's work for women,” not all of them were committed to the specific aims of “medical work for women by women” and “hospitals for women by women.” Hall was exceptional in her dedication to these goals, as she maintained her commitment despite fluctuating missionary circumstances and changing health and education policies under the Choson Dynasty and the colonial government from the late 19th century to the early 1930s. She effectively leveraged her diverse networks to advance medical education for Korean women. Her establishment of the Korean Women’s Medical Training Institute led by Koreans, represented her enduring vision for women’s education and proved to be a continuation of her broader educational efforts within and beyond the mission field.
SELETIVIDADE DO HERBICIDA ATRAZINE EM ESPÉCIES DE CROTALÁRIA
Authors: Timossi Paulo César , De Sousa Santos Fenelon Lourenço , Fernandes Maciel Ana Laura , De Carlo Murilo Henrique , Rosa Teixeira Itamar
Published: November 2024

Uma das limitações na adoção crotalárias na rotação de culturas é a ausência de informações quanto à seletividade de herbicidas para o controle de soja guacho. Visando buscar alternativas para o manejo da soja, foi pesquisada a seletividade de atrazine aplicada em pré-emergência de espécies de crotalária na dose de 2,5 kg ha-1 de ingrediente ativo de atrazine. O experimento foi instalado no campo experimental da Universidade Federal de Jataí, adotando-se o delineamento experimental de blocos casualizados no esquema fatorial 3x3+1, composto por três espécies de crotalárias (Crotalaria juncea, C. spectabilis, e C. breviflora), três épocas de aplicação do herbicida atrazine (20, 10 e 0 Dias Anteriores à Semeadura) e um tratamento controle (sem aplicação de atrazine) com quatro repetições. Foi avaliada a densidade de plantas de crotalária emergidas aos 10 dias após a semeadura. Aos 30, 60 e 90 dias avaliou-se a altura, a biomassa fresca e seca das plantas. Conclui-se que a aplicação do atrazine aos 0 DAS interfere no desenvolvimento inicial da Crotalaria juncea e as espécies Crotalaria spectabilis e Crotalaria breviflora não apresentam quaisquer tipos de interferência no estabelecimento e desenvolvimento das plantas quando cultivadas em solo argiloso.
RELATO DE INTERAÇÃO COMENSAL ENTRE Lepas (Lepas) anatifera LINNAEUS, 1758 (CIRRIPEDIA: LEPADIDAE) E Stenocionops furcatus (OLIVIER, 1791) (DECAPODA: EPIALTIDAE), COLETADOS EM UMA ÁREA MARINHA PROTEGIDA NO NORDESTE DO BRASIL
Authors: Arruda Jonata , Pessoa Alessandra Rodrigues , Santos Érika , Costa Anne Karolline , Messias Leonardo Tortoriello , Sommer Iara Braga
Published: November 2024

Relatamos a primeira ocorrência documentada mundialmente da craca de ganso Lepas (Lepas) anatifera Linnaeus, 1758 como epibionte no caranguejo-aranha Stenocionops furcatus. Um caranguejo-aranha macho foi coletado a uma profundidade de 100 metros na costa de Pernambuco, Brasil. Observamos cinco indivíduos de L. (Lepas) anatifera presos ao caranguejo, representando uma nova associação comensal. Esta descoberta melhora o conhecimento sobre a biodiversidade marinha e destaca o potencial de dispersão de L. (Lepas) anatifera. Também enfatiza a importância do monitoramento das interações epibióticas para avaliar seus efeitos nos organismos hospedeiros. Mais pesquisas são recomendadas para explorar as implicações ecológicas e estratégias adaptativas desta relação.
Dampak Aktifitas Samping Jalan Terhadap Kinerja Ruas Jalan Surodinawan Kota Mojokerto
Authors: Cahyono Iwan , Arfaah Saiful , Hifdhia Ummu , Hasib Al Isbilly Mohammad , Hidayat Ruslan
Published: November 2024

Jalan Surodinawan terdapat berbagai aktifitas dari fasilitas-fasilitas yang dibutuhkan masyarakat. Dampak yang ditimbulkan adalah bertambahnya volume kendaraan pada hari dan jam tertentu terutama pada jam-jam sibuk. Tujuan kajian ini adalah untuk mengetahui kinerja ruas jalan serta pengaruh hambatan samping terhadap ruas jalan Surodinawan Kota Mojokerto. Penelitian ini dilakukan dengan membagi Jalan Surodinawan menjadi 3 segmen, metode yang digunakan yaitu PKJI 2014. Hasil analisa dari penelitian ini yaitu volume lalu lintas pada segmen 1 sebesar 1396,53 skr/jam, segmen 2 sebesar 1135,44 skr/jam, dan segmen 3 sebesar 1199,07 skr/jam. Hambatan samping yang terjadi pada segmen 1 sebesar 561,36 kejadian dengan kelas hambatan samping Tinggi, segmen 2 sebesar 187,14 kejadian dengan kelas hambatan samping Rendah, segmen 3 sebesar 169,87 kejadian dengan kelas hambatan samping Rendah. Kinerja ruas jalan Surodinawan Kota Mojokerto masih layak dengan nilai derajat kejenuhan pada segmen 1 sebesar 0,58, segmen 2 sebesar 0,49, segmen 3 sebesar 0,50. Dari analisa kinerja pada ruas jalan Surodinawan Kota Mojokerto dengan tanpa memperhitungkan hambatan samping, didapatkan nilai kapasitas maupun kinerja ruas jalan mengalami peningkatan. Fenomena ini menyatakan bahwa adanya aktifitas samping jalan berdampak signifikan terhadap penurunan kinerja segmen jalan di sepanjang ruas Jl. Surodinawan Kota Mojokerto. Besarnya penurunan kinerja pada segmen 1 sebesar 12%, sedangkan pada segmen 2 dan 3 penurunan kinerja sebesar 4%.
Sistem Informasi Pengawasan Aset di Dinas Pendidikan Kota Surakarta untuk Meningkatkan Efisiensi dan Transparansi
Authors: Arif Nur Utomo Dwi , Rianto Agus , Kusanti Jani
Published: November 2024

Asset monitoring at the Surakarta City Department of Education is still carried out manually by visiting all related institutions that receive financial aid. This activity is conducted daily every year and consumes a significant amount of labor, time, and costs, potentially leading to inefficiencies, data recording errors, and a lack of transparency in the asset reporting and monitoring processes. These issues can hinder decision-making processes related to asset management and reduce the accountability of relevant parties. This research aims to develop a web-based Asset Monitoring Information System using PHP and MySQL to enhance efficiency and transparency in asset management. The system development method used is the System Development Life Cycle (SDLC), which includes the stages of planning, needs analysis, design, implementation, and system testing. The results of this research show that the developed system can accelerate the asset management process, minimize data errors, and improve the accessibility and transparency of asset reporting. The system also facilitates real-time asset monitoring and simplifies the validation process by authorized personnel. The designed system is expected to improve operational efficiency and accountability in asset monitoring at the Surakarta City Department of Education.