bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Legal Issues and Strategic Recommendations for National Security in the Era of Big Data and Artificial Intelligence
Authors: Gao Yihe
Published: January 2025

The extensive application of big data and artificial intelligence technologies has brought both opportunities and challenges to national security. Building and improving the legal system for national security is a necessary condition for a sound national security system. This article analyzes the potential data security and information security issues that may arise from big data and artificial intelligence technologies, and clarifies the deficiencies in relevant laws, systems, and policies. By comparing the legislative situations of big data and artificial intelligence technologies in China and the West, it proposes specific strategic suggestions from three perspectives: strengthening the construction of laws and regulations, establishing and improving regulatory mechanisms, and enhancing the publicity of cyber security.
Research on Realization of Coal Mine Impact Ground Pressure Signal Early Warning Based on BP Neural Network and LSTM
Authors: Wang Wenxu
Published: January 2025

With the reduction of coal resources makes deep coal seam mining important, but also increases the ground stress and geological complexity, especially impact ground pressure.AE signal monitoring technology plays an important role in preventing impact ground pressure by real-time monitoring of cracks and stress changes in coal rock. In this paper, wavelet analysis and RFE techniques are used to extract key features from signals, and BP neural network is chosen as the classification model after comparing multiple machine learning models. Random Forest is accurate but has low recall, and LSTM suffers from slow classification and poor generalization, neither of which is applicable. Compared with prophet and ARIMA models, LSTM performs better in high-frequency data prediction and dealing with fluctuating data, so it constructs a coal mine impact ground pressure signal early warning system based on BP neural network and LSTM to improve the accuracy of coal mine safety monitoring. The innovation of this paper is to use different network frameworks for classifiers and predictors, and by using feature extraction techniques, data transformation is performed to maintain the accuracy of the results while reducing the model complexity, improving the response speed, and enhancing the model generalization ability.
Inquiry of forms: “( )is big” and “the concept of ‘( ) is big’”
Authors: Li Zewei
Published: January 2025

Plato’s Parmenides explores complex discussions about forms, which have puzzled scholars for many years. The topic of how items might possess similar qualities, such "bigness," is one of the main concerns. If something assumes the shape of "bigness," something else will adopt the form of smallness. But what if a lot of things share "bigness"? Nothing can have two conflicting features from an ontological standpoint, therefore why do things seem "smaller" in comparison? Using Frege's philosophical theory, this essay will examine the form of "bigness" in Parmenides and elucidate its essence. This paper will specifically differentiate between the concepts of "( )is big" and "( )'is big."It is possible for anything to be large without completely expressing "the concept of '( ) is big.'"(For example, the shape of largeness). In conclusion, this paper will demonstrate that the question of embodying “bigness” can be resolved by differentiating concepts from proper names.
Simulation Study of Motion Processes Based on Isometric Solenoidal Equation and Differential Evolutionary Algorithm
Authors: Zeng Yaolin , Chen Zhehao , Zhu Xiyue
Published: January 2025

This paper mainly focuses on the position and speed changes during the traveling process of the bench dragon, collision detection, optimization of the turning path and the maximum traveling speed of the dragon head. Using the equidistant solenoidal equation and differential evolution algorithm, the key position coordinate model of each part of the bench dragon team is established and collision detection is carried out. Firstly, this paper deduces the movement path and speed of the dragon head through the solenoidal equation; secondly, based on the collision detection model, it analyzes the collision moments and conditions of the bench dragons in the marching; lastly, through the optimization of the turnaround path and the calculation of the speed, it determines the optimal turnaround path. The study shows that the model can effectively solve the optimization problem in the marching process of the complex dancing dragon team.
Research on Optimization of Travel Plans for Foreign Tourists in China Based on Greedy Algorithm and Simulated Annealing Algorithm
Authors: Ye Lingyu
Published: January 2025

This article leverages the Pandas library in Python to process and analyze extensive datasets, focusing on national tourist attractions. It reveals that 2563 attractions have achieved the highest rating of 5.0. Cities such as Sansha, Wujiaqu, Yuxi, Yiyang, Tianmen, Alar, Weifang, Yantai, Daxing'anling, and Xingtai top the list for the most fully rated scenic spots. A comprehensive evaluation system using the Analytic Hierarchy Process (AHP) is constructed. NLP technology builds a keyword library to match scenic area descriptions, evaluating environment, culture, transportation, climate, and cuisine. City line levels and intercity distances are integrated into an urban evaluation model. The study selects the top 50 cities attracting foreign tourists. A multi-objective 0-1 planning model for tourism planning is formulated to maximize tourist cities and scenic spot ratings. The greedy algorithm generates an initial solution, refined by the simulated annealing algorithm to seek the global optimum.
Optimization of Thermal Management Systems for Electric Vehicles
Authors: Yuan Suyi
Published: January 2025

With the increasing popularity of electric vehicles, efficient thermal management systems are critical to maintaining battery pack performance, safety, and longevity. The rapid cycle of the battery leads to a rapid increase in the heat content, and the heat problem has become the main factor restricting its development. The main benefits of optimizing a battery thermal management system (BTMS) system are improved operational efficiency, reduced costs, enhanced safety, and extended battery life. Due to the rapid development of electric vehicle technology and the urgent need for the thermal challenge of high-density battery systems, this topic has aroused people's interest. The theme of this paper is optimizing thermal management strategies, including materials and innovative cooling designs, that can significantly improve the efficiency and reliability of electric vehicle batteries. One inherent limitation of lithium-ion batteries is that they are used within a specific thermal operating range. The equivalent circuit model (ECM) calculates lithium-ion batteries' thermal output. Different phase change materials (PCM), porous materials, and different system types are studied to determine the most appropriate material to control the heat of the battery pack. This study has significant value for efficient thermal management systems.
Research of Conservation Factors of Wildlife Trade Based on Regression Model
Authors: Han Jiashu , Chang Sihao , Wang Hao
Published: January 2025

The problem of illegal wildlife trade has become an international problem of common concern around the world, and the existing research lacks inquiries on the underlying factors of this problem. In this paper, the ridge regression model, the ARIMA model and the multiple linear regression prediction model are constructed, and the latter two are weighted and optimized. The study finds that the increase of resource input and the increase of national interest play a positive role in combating illegal trade, while the volatility of power factors also has a significant impact on trading activities. The results of the optimized forecasts show that projects to combat wildlife trade have not only short-term effects, but also long-term positive impacts. After sensitivity analysis, the models established in this paper has a high robustness and can provide a reference for the world's anti-wildlife trade activities.
Study on the dynamic adjustment of UAV formation based on purely azimuthal passive positioning
Authors: Liu Wenpeng
Published: January 2025

In recent years, with the continuous development of unmanned aerial vehicle (UAV) technology, the application of UAV formation flight has also more and more extensive, but the UAV formation is easy to be interfered with by the outside world, which affects the formation. In this paper, we use pure orientation passive positioning, the least squares method to fit the UAV orientation, according to the superior and inferior arc model, cross-positioning, to reduce the UAV formation to emit electromagnetic wave signals to the outside. Firstly, the dynamic adjustment model of UAV formation is established to simulate the dynamic adjustment of two kinds of UAV formation from circular UAV formation to conical UAV formation and from rectangular UAV formation to conical UAV formation, and the simulation results show that the dynamic adjustment scheme of UAV formation under the model has a closer change distance and a shorter change time.
A Study on Tennis Momentum Prediction Based on Random Forest Algorithm
Authors: Li Ying , Tan Junye , Yang Xiaokang
Published: January 2025

As a strategic sport, tennis plays a key role in the study of its momentum from the training of players and the decision support of coaches. Firstly, this article obtains open source data of the 2023 Wimbledon 1701 competition, quantifies momentum through whether it is a serving party in the competition and personal skills, and determines 16 indicators. Secondly, a random forest model is built to predict the momentum changes of two players in this game from multiple aspects such as accuracy and AUC. The results show that although Player 1 has shown great momentum many times on the field, Player 2 has a greater momentum, especially within the first 30 minutes. Then, the fluctuations in performance in the competition and the randomness of success were evaluated. The KW test was introduced to test the "momentum" in the game, and the result showed that p-value=0.000<a=0.05, Ho was rejected, which means that the player's performance is positively correlated with the score. Finally, the assessment of the advantages and disadvantages of the model is made and some suggestions are given to provide feedback for coaches and players.
Using Biodegradability of Sewage in Ordinary Pit Latrines to Assess their Agricultural Potentials
Authors:   Department of Civil Engineering, University of Nigeria, Nsukka, Nigeria., F.I. Ugwu  , J.C. Agunwamba  ,   Department of Civil Engineering, University of Nigeria, Nsukka, Nigeria.
Published: January 2025

This research investigated the use of biodegradability of sewage in ordinary pit latrines to assess the performance of such sewage for agricultural purposes. The objective of this paper therefore was to study the biodegradability of faeces in selected pit latrines that would lead to the assessment of the performance of such pits. Both aerobic and anaerobic biodegradability of pit sludges were thus measured. Samples of fresh faecal sludges collected from pit latrines in selected households in Aku community were subjected to laboratory analyses for their physico-chemical and biological characteristics. The results showed that in terms of biochemical oxygen demand (BOD), twelve (12) pits had biodegradability of 80 Parsant and above. In the case of chemical oxygen demand (COD) and suspended solids (SS), nine (9) pits had biodegradability of 80 Parsant and above. For the volatile solids (VS), eleven (11) pits had biodegradability of 80 Parsant and above. Ten (10) pits had ratio of volatile solids to total solids (VS: TS) ranging from 0.90 – 2.93. Using COD: BOD ratio, eleven (11) pits had values ranging from 0.90–1.34 indicating that the pits sludges were readily biodegradable and good for agricultural purposes. Lower values showed lower organic contents and lower biodegradability and therefore unsuitable for agricultural purposes. High COD: BOD, VS:TS ratios should therefore be used to assess the performance of pit latrine sludges for higher agricultural productivity considering good user-behaviour, favourable soil and environmental conditions. Excreta collected from such pits should be used to enhance soil fertility.
Theoretical Investigation of Gravitational Effect on Effective Mass of Electromagnetic Wave
Authors:   Department of Physics, Tri-Chandra Multiple Campus, Tribhuvan University, Kathmandu-44600, Nepal., Khadka Chandra Bahadur
Published: January 2025

This paper proposes that the effective mass of an electromagnetic wave is not zero and further points out that an electromagnetic wave undergoes a frequency shift in a gravitational field only if its effective mass varies with gravity. Based on proposed model, mass of an electromagnetic wave of frequency 𝑓 is given by formula 𝑚 = 7.36x10−51𝑓. This new understanding can be helpful for the analysis of mass, linear momentum, force, frequency, wavelength and energy of electromagnetic waves inside the gravitational field namely at Schwarzschild radius, black hole and so on. A key point of this work is that the gravitational redshift effect can be explained more directly based on the variation of the proposed mass of the electromagnetic wave inside the gravitational field.
Diagnosis of Abdominal Diseases Affecting Major Organs Using CT Image and YOLOV8
Authors:   Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India., Mrs. K. Ramanandhini  , Mr. Pandiarajan S  ,   Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.
Published: January 2025

This study investigates the YOLOv8 method, a popular object detection model, to detect abnormalities in abdominal CT scans. Our study leverages the sophisticated architecture and point-of- care detection capabilities of YOLOv8 to show that the model improves diagnostic accuracy and helps radiologists quickly identify potential panic cases.
Skin Cancer Detection Using Machine Learning
Authors:   Faculty of Science and Technology, Islamic Science University of Malaysia, Nilai, Malaysia., Musa Usman Ibrahim , Roy Apash ,   Professor, School of Computer Applications, Lovely Professional University, Punjab, India., Musa Musa Ibrahim ,   Faculty of Computer Science and IT, Bayero University Kano, Kano, Nigeria., Babani Umar Muhammad ,   Department of Computer Science & Engineering, Integral University, Lucknow (U.P), India., Musa Aminu Ibrahim ,   Head of the Department of Information Technology, ESGT Benin University, Cotonou, Benin Republic.
Published: January 2025

Skin-related issues often serve as indicators of underlying health problems in other parts of the human body, adversely affecting an individual's overall fitness and well-being. However, such issues are frequently ignored, as they are perceived to be either painless or have minimal to no impact on daily life. To address this challenge, this system was developed with the aim of early detection of skin cancer. The system enables users to upload images or videos of the affected area in real-time, utilizing a skin cancer detector to identify existing conditions, determine the cancer type (if present), and provide instant feedback. The system is precisely configured to deliver highperformance results, offering real-time recommendations for medications or treatments based on its findings. By reducing the stress and inconvenience associated with hospital visits for dermatological consultations, this system empowers individuals to identify skin-related issues accurately and make informed decisions about seeking further medical attention from a qualified dermatologist. Powered by image processing using OpenCV, a convolutional neural network (CNN), and machine learning techniques, the system excels in recognizing various skin conditions with high accuracy. It can detect conditions such as nevus, vascular lesions, seborrheic keratosis, basal cell carcinoma, melanoma, pigmented benign keratosis, squamous cell carcinoma, dermatofibroma, and actinic keratosis, while also recommending appropriate remedial health solutions.
On the Mathematical Analysis of a Campylobacteriosis Model for Human and Animal Population
Authors: Timothy Terfa Ashezua  , Kenneth Ifeanyi Isife  , Felix Yakubu Eguda
Published: January 2025

Campylobacteriosis, a major cause of foodborne illnesses has continued to claim millions of lives globally. Thus, concerted needs to be put in place in order to curtail further loss of lives due to the disease. In this study, an analysis is conducted on the reproduction number, sensitivity analysis is also carried out on the parameters of the model connected to the reproduction number and the possibility of backward bifurcation of the campylobacteriosis mathematical model is explored. Results from the sensitivity analysis show that the most sensitive parameters are the infection rates, progression rate and the treatment rate for humans. It is further observed that as the human recruitment rate and treatment rate for symptomatic human population increases, the reproduction number also increases. This implies that it will not be possible to eliminate campylobacteriosis in the community with at least 70% treatment rate administered to the symptomatic human population. The analysis of the campylobacteriosis model reveals that the model exhibits the phenomenon of backward bifurcation under certain conditions, where a stable disease-free equilibrium (DFE) coexists with a stable endemic equilibrium (EE) when the associated reproduction number is less than unity. It is further shown for a special case that, a unique endemic equilibrium exists whenever the associated reproduction number is greater than unity.
Analisis Tingkat Kecerdasan Sosial Siswa Kelas V dalam Pembelajaran IPS
Authors: Rizzaludin Rizzaludin , Afrianti Indah , Prataman Reski Ade Tian
Published: January 2025

Penelitian ini merupakan studi kualitatif yang bertujuan mendeskripsikan kecerdasan sosial siswa dalam pembelajaran IPS di kelas V SDN 15 Woja, Kecamatan Woja, Kabupaten Dompu. Berdasarkan Undang-Undang No. 20 Tahun 2003 Pasal 5 Ayat 4, warga negara dengan potensi kecerdasan istimewa berhak memperoleh pendidikan khusus, termasuk kecerdasan sosial, emosional, intelektual, dan spiritual. Menurut Daniel Goleman (2016), hanya 20% kesuksesan hidup seseorang ditentukan oleh kecerdasan intelektual, sementara 80% sisanya dipengaruhi oleh kesadaran emosional, kecerdasan sosial, dan spiritual. Suyono (2017) menjelaskan bahwa kecerdasan sosial melibatkan kemampuan memahami dinamika sosial, berinteraksi, dan menilai secara objektif hubungan dengan orang lain. Pengumpulan data dilakukan melalui wawancara dan observasi dengan teknik analisis berupa reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa siswa kelas V SDN 15 Woja mampu menjadi pendengar yang baik, memiliki empati, percaya diri, serta berani menyampaikan gagasan di depan teman-temannya dalam proses pembelajaran IPS.
Pengaruh Media Smart box terhadap Hasil Belajar Pendidikan Pancasila di Sekolah Dasar
Authors: Sumiyati Sumiyati , Fauqi Amal , Jumiati Jumiati
Published: January 2025

Penelitian ini dilatarbelakangi oleh kurang efektifnya proses pembelajaran pada mata pelajaran Pancasila di kelas IV SDN 29 Woja, terutama dalam penerapan media pembelajaran yang kurang optimal, sehingga siswa kesulitan memahami materi. Untuk mengatasi masalah tersebut, peneliti mengusulkan penggunaan media smart box yang sesuai dengan tahap operasional konkret untuk memberikan dampak langsung pada pemahaman siswa. Tujuan penelitian ini adalah untuk mengetahui pengaruh penggunaan media smart box terhadap hasil belajar Pancasila siswa kelas IV SDN 29 Woja. Penelitian ini menggunakan pendekatan kuantitatif dengan metode eksperimen. Data dikumpulkan melalui observasi, tes, dan dokumentasi. Teknik analisis data meliputi uji normalitas, uji homogenitas, dan uji hipotesis. Hasil penelitian menunjukkan bahwa penggunaan media smart box memiliki pengaruh signifikan terhadap hasil belajar siswa, yang dibuktikan dengan hasil uji t yang menunjukkan nilai signifikansi sebesar 0,000 (<0,05). Hal ini mengindikasikan bahwa penggunaan media smart box secara efektif meningkatkan hasil belajar siswa pada mata pelajaran Pancasila di kelas IV SDN 29 Woja.
Myocardial perfusion stress test in women
Authors: Milojevic Isidora Grozdic , Petrovic Katja , Radivojevic Olga , Grujicic Lenka , Kotur Milica , Saranovic Dragana Sobic
Published: January 2025

Aim: The utility of the 99mTc-sestamibi myocardial perfusion stress imaging (MPI) over stress echo test (SEHO) in women with intermediate pre-test probability scores. Methodology: Our study included 98 women with suspected ischemic heart disease and intermediate pre-test probability scores who underwent MPI and SEHO. They were followed for 26 ± 6 months. The data about MACE and possible coronary angiography were collected. Results: The SEHO test was pathological in 31% of the study population. Almost half of those patients had atypical chest pain. MPI was positive in 32% of the examined group. In the follow-up period, MACE was present in 36 patients: PCI (20), CABG (8), acute coronary syndrome (8). The results of the MPI had a high statistically significant correlation with the coronary angiography findings in the follow-up period (p < 0.001, μ = 0.878). Additionally, the SEHO test and coronary angiography results correlated well (p < 0.05, μ = 0.582). However, MPI test result and SEHO result were not significantly associated with MACE based on Cox proportional hazard analyses (p > 0.05). Conclusion: MPI may be the complementary procedure for the evaluation of heart conditions in women with intermediatepre-test probability scores, since it correlates well with coronary angiography findings.
The Influence of Product Quality, Service Quality, and Store Atmosphere on Purchasing Decisions McDonald’s Indonesia
Authors: Alifia Shafanisa , Hidayat Agus Maolana
Published: January 2025

The development of globalization has led to the development of fast-food restaurants in the world, one of which is McDonald’s, which is currently growing rapidly in Indonesia. As a large fast-food restaurant, McDonald’s offers various aspects to maintain customer satisfaction. Products, services, and outlets are aspects offered by McDonald’s Indonesia. Therefore, this research focuses on the impact of product quality, service quality, and store atmosphere on purchasing decisions at McDonald’s Indonesia. The population of this study is people who have made purchases at McDonald’s Indonesia with a total sample obtained through distributing questionnaires, namely 413 respondents. Sampling was carried out using the convenience sampling method with respondents who met the criteria, and the data was processed using SMARTPLS version 4 and SPSS software. The results showed that product quality, service quality, and store atmosphere had a positive and significant effect on McDonald’s Indonesia’s purchasing decisions partially and simultaneously.
The Role of Environmental Management Systems (EMS) in Driving Organizational Development and Environmental Sustainability
Authors: Mazraani Ghassan , Tucci Mario
Published: January 2025

An Environmental Management System (EMS) is a framework that helps in assessing the risks a business has on its environment while providing organizations with mitigation strategies to minimize the risks. This study aims to analyze the importance and impact of EMS on organizations and global sustainability goals. This review study uses a descriptive and analytical approach by including 67 studies for the review that were published in the last 5 years. The findings from this review suggest that the integration of EMS along with tools and techniques like ISO 14031, Cross-Functional Team (CFT), Matrix model, and Life Cycle Inventory (LCI) can be very useful in attaining global sustainability goals and maximizing the environmental efficiency of organizations. The study concludes that the integration of EMS in industries not only helps in minimizing environmental pollution but it also helps in managing the overall growth of the organizations.
Theoretical Framework for Dynamic Mechanical Analysis in Material Selection for High-Performance Engineering Applications
Authors: Afolabi Samuel Olabode , Akinsooto Oluwadayomi
Published: January 2025

Dynamic Mechanical Analysis (DMA) is a powerful technique for assessing the viscoelastic properties of materials, providing critical insights into their performance under various conditions. This study develops a theoretical framework for DMA in material selection for high-performance engineering applications. The framework integrates fundamental principles of DMA with practical considerations for selecting materials that meet the demanding requirements of advanced engineering fields, including aerospace, automotive, and electronics. Theoretical aspects of DMA, such as storage modulus, loss modulus, and damping ratio, are explored in detail, illustrating how these parameters correlate with material performance, durability, and stability under dynamic loading conditions. The framework emphasizes the importance of understanding the temperature, frequency, and strain rate dependence of material behavior in predicting their suitability for specific applications. The study also examines the role of DMA in identifying phase transitions, such as glass transition and crystallization, which are crucial for assessing material performance in environments with fluctuating thermal and mechanical stresses. Furthermore, the framework incorporates data from experimental DMA studies, applying these results to real-world engineering scenarios to demonstrate the utility of DMA in material selection. The theoretical framework proposes a multi-criteria decision-making (MCDM) approach for integrating DMA results with other material properties, such as strength, toughness, and fatigue resistance, to provide a holistic material selection process. Case studies are included to showcase the practical application of the framework in the selection of polymers, composites, and metals for high-performance engineering applications. By bridging the gap between DMA theory and material selection, this study contributes to the development of more efficient, reliable, and durable materials for critical engineering applications.
Conceptual Analysis of Strategic Historical Perspectives: Informing Better Decision Making and Planning for SMEs
Authors: Kokogho Eseoghene , Odio Princess Eloho , Ogunsola Olakojo Yusuff , Nwaozomudoh Mark Osemedua
Published: January 2025

This review presents a conceptual analysis of strategic historical perspectives, emphasizing their role in informing better decision-making and planning for Small and Medium-sized Enterprises (SMEs). SMEs face unique challenges, including limited resources and heightened market volatility, necessitating robust strategic planning frameworks. Historical perspectives offer valuable insights into the evolution of market trends, competitive dynamics, and organizational strategies, which can significantly enhance the strategic capabilities of SMEs. By analyzing historical data and patterns, SMEs can identify recurring themes and long-term trends within their industry. This retrospective analysis helps businesses understand the factors that have historically driven success or failure, enabling them to anticipate future challenges and opportunities more effectively. Moreover, historical perspectives provide a contextual foundation for evaluating current strategic options, ensuring that decisions are informed by a comprehensive understanding of past experiences. Strategic historical analysis also facilitates learning from past mistakes and successes, fostering a culture of continuous improvement within SMEs. By systematically reviewing historical case studies and industry developments, SMEs can adopt best practices and avoid common pitfalls that have hindered others in the past. This approach not only enhances strategic foresight but also supports the development of more resilient and adaptable business models. Additionally, integrating historical analysis into strategic planning processes encourages a long-term outlook among SME leaders. This broader perspective helps align short-term actions with long-term objectives, ensuring sustainable growth and competitiveness. Historical insights can also aid in scenario planning, allowing SMEs to prepare for various future contingencies based on historical analogies and trend projections. Furthermore, strategic historical perspectives contribute to more informed and nuanced decision-making by highlighting the interdependencies between different strategic variables. SMEs can better understand the ripple effects of their decisions and develop more coherent and integrated strategic plans. This holistic approach enhances the overall strategic agility and responsiveness of SMEs, enabling them to navigate complex and rapidly changing business environments effectively. In conclusion, leveraging strategic historical perspectives provides SMEs with a powerful tool for enhancing decision-making and planning. By incorporating historical insights into their strategic frameworks, SMEs can achieve more informed, resilient, and forward-looking business strategies, ultimately driving sustained success and competitive advantage.
AI-Powered Economic Forecasting: Challenges and Opportunities in a Data-Driven World
Authors: Kokogho Eseoghene , Odio Princess Eloho , Ogunsola Olakojo Yusuff , Nwaozomudoh Mark Osemedua
Published: January 2025

AI-powered economic forecasting represents a transformative approach to understanding and predicting economic trends in a rapidly evolving data-driven world. By leveraging vast amounts of data, machine learning algorithms, and advanced analytics, AI enhances the accuracy and timeliness of economic forecasts, offering new opportunities for decision-makers across various sectors. This technology allows for real-time analysis of complex datasets, identifying patterns and correlations that traditional models might overlook. The integration of AI into economic forecasting promises significant advancements in policy formulation, investment strategies, and risk management. However, the adoption of AI in economic forecasting is not without challenges. One of the primary concerns is the quality and availability of data. Inconsistent, biased, or incomplete data can lead to inaccurate predictions, undermining the reliability of AI models. Additionally, the black-box nature of some AI algorithms poses transparency issues, making it difficult for users to understand how predictions are generated. Ethical considerations, such as the potential for AI to perpetuate existing biases or the risks associated with automated decision-making, also require careful attention. Despite these challenges, the opportunities presented by AI-powered economic forecasting are substantial. By continuously refining algorithms and improving data governance, the accuracy and reliability of forecasts can be enhanced. Furthermore, the ability of AI to process and analyze vast amounts of data quickly offers a significant competitive advantage in an increasingly complex global economy. The future of economic forecasting will likely see greater collaboration between AI and human expertise, combining the strengths of both to create more nuanced and effective predictive models. In conclusion, while AI-powered economic forecasting faces several challenges, the potential benefits make it a crucial tool for navigating the uncertainties of a data-driven world. As technology and methodologies continue to evolve, AI will play an increasingly vital role in shaping economic strategies and policies, offering both opportunities and challenges that must be thoughtfully managed.
ANÁLISE OPERACIONAL DE PRENSAS DE ROLOS NA COMINUIÇÃO DE MINÉRIO DE COBRE: ESTUDO DE CASO NA MINA DO SALOBO
Authors: Bezerra Cleirine de Sá , Ferreira Kelly Cristina
Published: January 2025

A cominuição é uma etapa crucial na indústria mineral, essencial para a redução do tamanho das partículas e a liberação dos minerais de interesse. Nesse contexto, faz-se necessário buscar equipamentos com uma maior eficiência de cominuição de forma a viabilizar e sustentar empreendimentos da mineração. A prensa de rolos (HPGR) se destaca pela capacidade de otimização dos processos industriais, especialmente pela redução significativa no consumo de energia e menor geração de finos. Este estudo analisa as variáveis operacionais da prensa de rolos, como pressão aplicada e velocidade periférica, são analisadas quanto aos impactos na produtividade, na qualidade do produto final e também no controle de custos e sustentabilidade do processo. Além disso, visa apresentar dados de parâmetros operacionais e principais controles de inspeções na operação da prensa de rolos, como alternativa para otimizar e garantir a performance do equipamento no processo de cominuição do minério de cobre, contribuindo para um melhor entendimento do equipamento e colaborar com avanços e melhorias em projetos similares.
Self-Adapting, Integrated Apparel Robot for Responsive Wearable Comfort Utility
Authors: Lee Minji , T. Matson Eric
Published: January 2025

This research focuses on developing a prototype apparel system that integrates intelligent autonomous agents, human-based sensors, wireless networks, a mobile app, and a small-scale zipper robot. The goal is to create a practical assistive device that dynamically adjusts to user needs, particularly for the elderly and those with self-care challenges. Unlike existing wearable technologies that are overly technical, this system prioritizes usability and adaptability. It enables autonomous control of zipper speed and direction based on user profiles, enhancing comfort and functionality. Initial testing demonstrated effective communication between the zipper robot and mobile app, with adaptive adjustments and manual override features. The prototype serves as a proof-of-concept for future intelligent wearable devices, aiming to improve independence and quality of life.
Digital Networking for Economic Growth: Interactions Between Natural and Artificial Intelligence
Authors: Moro-visconti Roberto
Published: January 2025

This paper investigates how human knowledge, i.e. (Natural Intelligence: NI), mixes with "Artificial Intelligence" (AI), which relies on operating operational data-driven computation methods traditional firms' infrastructures ranch as humanly-based applications formalizing tools. An integrated multilayer network framework is proposed for the efficient and effective creation of economic value. The essential question is: How can copula-driven co-integration through multilayer networks enable NI-AI integration to generate economic value at digitalized firms? This study empirically tests all the cost-benefit improvements likely to be derived from AI technologies across a wide range of industries, from manufacturing and retail to finance. The results provide evidence that huge efficiency improvements, such as optimizing operational protocols and practices to be more effective in decision-making, tend to occur alongside significant reductions in total costs. Copula nodes amplify interactions between NI and AI to achieve a cohesive joint effect capable of delivering maximum economic benefits for participating firms. These findings provide a unique strategy for firms to increase profitability and compete in the digital age.
Real-Time Cognitive Tools for Space Systems
Authors: Cripe Curtis
Published: January 2025

This paper presents an innovative integration of real-time cognitive monitoring technologies with cognitive flexibility and awareness tools, framed within the Skill, Rule, and Knowledge (SRK) model, to enhance human performance and resilience in extreme environments. Conducted at NASA's Lunar/Martian habitat simulator at the University of North Dakota (UND), the study investigates how these advanced technologies improve cognitive readiness, decision-making, and overall mental fitness during long-duration spaceflights (LDSF).The SRK model, developed by Jens Rasmussen, categorizes human cognitive behavior into three levels—skill-based, rule-based, and knowledge-based actions—providing a structured approach to understanding and enhancing cognitive flexibility in high-stakes environments. By leveraging EEG-based monitoring and Brain-Computer Interface (BCI) technologies, this research enables dynamic, real-time assessments and interventions that are critical for maintaining cognitive control and adaptability during space missions.Traditional cognitive training and monitoring methods, which rely on periodic assessments and static protocols, fall short in providing the continuous feedback required for high-pressure scenarios. In contrast, the integrated system described in this study offers ongoing, adaptive training aligned with the SRK model, allowing for more precise and effective cognitive interventions. This approach facilitates seamless transitions between different levels of cognitive control, optimizing decision-making and preventing cognitive overload.The findings from this research have significant implications beyond space missions. The application of these cognitive tools within the SRK framework demonstrates potential for enhancing performance in other high-stakes domains, including aerospace, healthcare, and military operations, where cognitive readiness is paramount.In summary, this paper introduces a novel framework that integrates cognitive monitoring and flexibility tools within the SRK model to sustain cognitive readiness and performance in extreme environments. The results suggest that this approach not only enhances individual cognitive capabilities but also improves overall mission outcomes by reducing cognitive overload and optimizing decision-making processes, making it a valuable contribution to the field of intelligent human-system integration.
The role of Artificial Cognitive Systems in the Implementation of the Aviation Fatigue Risk Management Systems
Authors: Henneberry Debra , Ziakkas Dimitrios
Published: January 2025

Aviation Fatigue Risk Management Systems (FRMS) are crucial for ensuring operational safety by systematically monitoring and mitigating the risks associated with human fatigue in complex and high-demand aviation environments. This paper explores the integration of Artificial Cognitive Systems (ACS) into FRMS, focusing on how these intelligent systems can enhance human decision-making and fatigue management, contributing to improved safety and efficiency in aviation operations. ACS possess the capability to process vast amounts of real-time data and make context-aware decisions, enabling more accurate identification of fatigue risks through predictive analytics, pattern recognition, and human-machine interaction. ACS can complement traditional fatigue management methods in the aviation sector by continuously assessing physiological data, work schedules, environmental conditions, and operational demands to dynamically adapt fatigue risk mitigation strategies. These systems can proactively alert pilots, air traffic controllers, ground staff, and flight crews when fatigue thresholds are reached, enhancing the overall effectiveness of FRMS. This paper analyzes key methodologies and frameworks—including the International Civil Aviation Organization’s Fatigue Risk Management guidelines and regulations by the European Union Aviation Safety Agency (EASA) and the Federal Aviation Administration (FAA)—to illustrate how ACS can be integrated into current fatigue risk systems while adhering to international safety standards. Additionally, we will examine worldwide case studies where ACS has been applied in fatigue monitoring and management within the aviation industry, highlighting the impact of AI-powered decision support systems in reducing fatigue-related incidents and accidents. The analysis also addresses the human factors implications of implementing ACS within FRMS, emphasizing the balance between human oversight and machine-driven recommendations. Understanding the relationship between human cognitive limitations and the capabilities of ACS is critical in ensuring that these systems enhance, rather than hinder, human performance. Through a human-centric approach, ACS can help reduce workload, improve situational awareness, and ultimately provide more reliable fatigue risk management without leading to over-reliance on automated systems. In conclusion, this paper will propose a framework for integrating ACS into FRMS, demonstrating how artificial intelligence-driven solutions can complement human expertise to reduce fatigue-related risks, improve safety, and create a more resilient aviation system. By focusing on both technological advancements and challenges related to human factors, this paper provides a comprehensive roadmap for the future of fatigue risk management in aviation.
Machine Learning-Based Prediction of Emergency Department Prolonged Length of Stay: A Case Study from Italy
Authors: Perliti Scorzoni Paolo , Giovanetti Anita , Bolelli Federico , Grana Costantino
Published: January 2025

Overcrowding in Emergency Departments (EDs) is a pressing concern driven by high patient demand and limited resources. Prolonged Length of Stay (pLOS), a major contributor to this congestion, may lead to adverse outcomes, including patients leaving without being seen, suboptimal clinical care, increased mortality rates, provider burnout, and escalating healthcare costs. This study investigates the application of various Machine Learning (ML) algorithms to predict both LOS and pLOS. A retrospective analysis examined 32,967 accesses at a northern Italian hospital’s ED between 2022 and 2024. Twelve classification algorithms were evaluated in forecasting pLOS, using clinically relevant thresholds. Two data variants were employed for model comparison: one containing only structured data (e.g., demographics and clinical information), while a second one also including features extracted from free-text nursing notes. To enhance the accuracy of LOS prediction, novel queue-based variables capturing the real-time state of the ED were incorporated as additional dynamic predictors.Compared to single-algorithm models, ensemble models demonstrated superior robustness in forecasting both ED-LOS and ED-pLOS. These findings highlight the potential for integrating ML into EDs practices as auxiliary tools to provide valuable insights into patient flow. By identifying patients at high risk of pLOS, healthcare professionals can proactively implement strategies to expedite care, optimize resource allocation, and ultimately improve patient outcomes and ED efficiency, promoting a more effective and sustainable public healthcare delivery.
Explainability in AI-based shift scheduling
Authors: Haid Charlotte , Tran Gia-phong , Fottner Johannes
Published: January 2025

As digitalization becomes more widely used in factories, preference-based shift planning is evolving into an important tool for human-centered work in various workplaces. Human-centered shift planning not only increases the efficiency of work, but above all considers the individual's preferences for certain shifts or activities and thereby empowers human workers. However, the planning algorithm used in previous work is based on AI and the algorithm is not able to explain why certain decisions in scheduling were made. The aim of this publication is to use AI-based shift scheduling as an example to make AI systems comprehensible for everyone and thus increase the transparency of the system as well as the user’s trust. Starting from psychological research, we developed a user-friendly explanatory model, that consists of four parts: Starting section, what-if-scenarios, educational classroom and FAQs. A user survey was then conducted to test the effectiveness of the model. The results show that most respondents find the model intuitive and understandable, although they have different preferences for explanations. This study provides insights into the design of explanations for shift planning systems and examines the effects of different explanatory approaches. It thus is the foundation for further research and development in this area.
Grasping the complexity of UX in the Long-term in Interaction with Embodied Conversational Agents
Authors: Ciliani Joy , Nicoletta Marino Alessia , Teverini Giulia , Marti Patrizia
Published: January 2025

To comprehensively evaluate the experience of interaction between humans and ECAs (Embodied Conversational Agents), it is essential to consider a complex entanglement of dimensions that dynamically develop over time. This paper provides a review of the available methods to evaluate the human-ECAs interaction, and discusses the relation between this experience of use over time. The research was carried out through a systematic literature review, on 5 databases: Scopus, ACM, Web of Science, PubMed and IEEE Xplore. Findings reveal that over 147 publications on human-ECAS interaction, only 13 address the experience evaluation. Of these, only 3 face evaluate the user experience with ECAs over time. The majority of these evaluation methods concentrate on momentary experiences, neglecting the long-term perspective. The paper provides a map of methods to evaluate several user experience dimensions of interactions with ECAs, highlighting the importance to apply these methods not only on the momentary interaction but repeatedly over time.
Investigating Interface Layout Optimization for Enhanced Visual Search Efficiency in Industrial Manufacturing Systems
Authors: Yuan Yuchen , Liu Xiaojun
Published: January 2025

This study examines interface layout optimization to enhance visual search efficiency and cognitive performance in industrial manufacturing systems, addressing the high cognitive demands of human-machine interfaces. By improving information visualization, interface design can significantly enhance operators' comprehension and response time, ultimately boosting production efficiency. Two experiments were conducted: Experiment A assessed six layout configurations, identifying LT and MT as the most efficient for visual search. Experiment B used eye-tracking analysis to compare MT and LT layouts, with Scheme 1 (MT layout) demonstrating superior cognitive efficiency, logical structure, and user satisfaction. These results confirm the effectiveness of the proposed design standards and highlight the need for data-driven, intelligent interface layouts in modern manufacturing. The findings suggest that advanced information visualization within HMIs fosters coordination and efficiency in industrial ecosystems, supporting innovative future developments in interface design.