Fast advances in science and technology have broadened the horizons of innovation and knowledge whilst simultaneously creating a multitude of challenging problems in every aspect of modern life. The HighTech and Innovation Journal is intended as an outlet for individuals conducting research on new ideas and findings in innovation and technology at both the micro and macro levels of analysis. High technology journal is a journal that emphasizes the interconnections among engineering, mathematics, science, technology, and the humanities. HighTech and Innovation Journal is a peer-reviewed, open-access journal devoted to publishing research papers in the fields of integrated engineering and technology. We invite researchers interested in HighTech and Innovation to contribute their original research articles to this journal. Current areas of interest, along with related associate editors, are listed below: Smart Technologies and Applications Dr. Angelica Lo Duca Institute for Informatics and Telematics Pisa, Italy Associate Editor Mechanical Engineering & Adv. Manufacturing Prof. Tong Xin Fujian University of Technology Fujian, China Associate Editor Artificial Intelligence, Big Data, Cognitive & Soft Computing Dr. AleŠ¡ Zamuda University of Maribor Maribor, Slovenia Associate Editor Sustainability, Energy & Clean Technologies Prof. Tetyana Pimonenko Sumy State University Sumy, Ukraine Associate Editor Chemical Engineering and Biotechnology Dr. Anna Ilnicka Nicolaus Copernicus University Torun, Poland Associate Editor Innovation in Materials and Nanotechnology Dr. Ermia Aghaie University of Alberta Alberta, Canada Associate Editor Innovation in Electrical Engineering & Electronic Science Dr. Saadane Rachid Hassania School of Public Works Casablanca, Morocco Associate Editor Infrastructure, Buildings & Construction Tech. Prof. Jamshid Mohammadi University of Illinois at Urbana-Champaign Chicago, United States Associate Editor Medical Technologies Prof. Stéphane Bordas University of Luxembourg Esch-sur-Alzette, Luxembourg Associate Editor Human Services Technology Prof. Xingwei Li Sichuan Agricultural University China Associate Editor Open Special Issues Special Issue "Grand Challenges Initiative: Sustainability and Development" - 2022-2023 Aim and Scope Over the next decade, the global scientific community must take on the challenge of delivering the knowledge required to support efforts to achieve sustainable development in the context of global environmental change. Solution-focused, strategic, interdisciplinary, long-term research is needed to improve our knowledge of the social-environmental risks facing humanity and to provide science-based support for actions to achieve sustainable development. We rapidly need to deepen our understanding of how the Earth system operates in response to human pressures, improve our ability to predict future risk patterns, and explore social transformations in the world that can overcome barriers to sustainability. Guest Editor: Prof. M. R. Kavianpour Affiliations: Professor, Civil Engineering Faculty, K. N. Toosi University of Technology, Iran. Interests: Climate Change; Remote Sensing; Drought; Water Treatment; Flood (Hazard, Estimation and Modeling, ...); Urban Drainage System (Design and Modeling). 0000-0003-3546-6663 Guest Editor: Dr. Grigorios L. Kyriakopoulos Affiliations: School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Greece. Interests: Renewable Energy Sources; Urban and Regional Development; Energy; Development Economics; Human Resource Management; Sustainability. 0000-0003-4875-8534 Guest Editor: Prof. Eugen Rusu Affiliations: Department of Applied Mechanics, Head of Laboratory of Computations and Modeling in Applied Mechanics, "Dunarea de Jos" University of Galati, Galați, Romania. Interests: Renewable Energy in Marine Environment; Energy Conversion; Offshore Technologies; Climate Change in Marine Environment; Resource Evaluation and Modeling. 0000-0001-6899-8442 Manuscripts should be submitted online at www.hightechjournal.org by registering and logging in to this website. For more information Click Here. Published Papers - This Issue is now open for submissions. Most Cited & Viewed Articles The most cited articles from HighTech and Innovation Journal published since 2020, extracted from <a class="sc-kvjqii-0 fYuYRT" title="Scopus" href="https://www.scopus.com/results/results.uri?cc=10&sort=cp-f&src=dm&nlo=&nlr=&nls=&sid=897df5e8b737fec30750e5bda23839d8&sot=a&sdt=a&sl=223&s=REFSRCTITLE+%28+%22HighTech+and+Innovation+Journal%22+%29+OR+REFSRCTITLE+%28+%22HighTech+and+Innovation+J.%22+%29+OR+REFSRCTITLE+%28+%22HighTech.+Innov.+J.%22+%29+OR+REFSRCTITLE+%28+%22HighTech+and+Innov.+J.%22+%29+OR+REFSRCTITLE+%28+%22High+Tech+Innov.+J.%22+%29&ss=cp-f&ps=r-f&editSaveSearch=&origin=resultslist&zone=resultslist" target="_blank">Scopus. Citation Article Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures–The Database of Micro Networks by: B. Fazelabdolabadi, and M.H. Golestan New Technologies and Innovative Solutions in the Development Strategies of Energy Enterprises by: Piotr F. Borowski Sustainability Assessment in Housing Building Organizations for the Design of Strategies against Climate Change by: Ulises Mercado Burciaga Comparative Analysis of Implementation of Solar PV Systems Using the Advanced SPECA Modelling Tool and HOMER Software: Kenyan Scenario by: Samuel Kariuki Kibaara, D. K. Murage, P. Musau, and M. J. Saulo Relationship of Smart Cities and Smart Tourism: An Overview by: Nada Jasim Habeeb, and Shireen Talib Weli The Study of Dynamics Heterogeneity in SiO2 Liquid by: Giap Thi Thuy Trang, N. H. Linh, N. T. T. Linh, and P. H. Kien
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Experimental and Numerical Modeling of a Cross-Flow Turbine Runner Made of HDPE: Experimental and Numerical Approach
This study investigates the viability of high-density polyethylene (HDPE) as a sustainable, low-cost alternative to conventional metallic materials for Cross-flow turbine runners in micro-hydropower systems. The primary goal is to design, manufacture, and validate the hydrodynamic and structural performance of an HDPE runner. A three-stage methodology was applied: CAD-based design, thermoforming fabrication, and performance evaluation through computational fluid dynamics (CFD) and finite element analysis (FEA) using ANSYS. Numerical predictions were validated against experimental data obtained from a hydraulic test bench. Mesh refinement and turbulence modeling were included to ensure numerical reliability. Results show that the HDPE runner achieved efficiencies of 80-83% compared to a geometrically identical steel runner under similar operating conditions. Structural analysis confirmed von Mises stresses (8.5 MPa) and deformations (0.12 mm) remained well below HDPE’s yield strength (22 MPa), validating its mechanical integrity. Statistical comparison revealed a deviation of less than 4% between numerical and experimental results. This research provides a validated framework for using recyclable HDPE in turbine manufacturing. It demonstrates that HDPE can deliver comparable power output to steel while reducing manufacturing costs and environmental impact, offering a sustainable pathway for rural electrification.
Copyright (c) 2026 Francis M. Kifumbi, Guyh Dituba Ngoma, Fouad Erchiqui, Theophile M. Tshibangu
A Data-Driven Adaptive Scheduling Framework for Vehicle Maintenance Using Deep Reinforcement Learning
This paper proposes a data-driven adaptive scheduling method based on the Deep Deterministic Policy Gradient (DDPG) algorithm to address the challenges that traditional vehicle dynamic maintenance scheduling methods struggle to cope with real-time, complex and resource optimization issues. A mathematical model of vehicle dynamic maintenance scheduling is constructed, defining the state space, action space and reward function. Then, the DDPG reinforcement learning framework is used to optimize strategies through the Actor-Critic structure. Contrastive experiments are also carried out in a simulation environment to evaluate the algorithm's performance. The results indicate that the DDPG algorithm achieves an average maintenance response time of 23.4 minutes, approximately 34% shorter than the genetic algorithm. Its resource utilization reaches 88.7%, over 13% higher than traditional methods. Moreover, the maintenance satisfaction score is 4.6 out of 5. The findings show that the algorithm has remarkable advantages in multi-objective scheduling optimization and provides feasible paths and technical support for the intelligence of vehicle dynamic maintenance systems.
Design of a Cost-Effective Educational Unmanned Ground Vehicle Platform with an Auxiliary Computer
This paper presents the development of a cost-effective, modular, and easy-to-assemble educational unmanned ground vehicle (UGV) system designed for hands-on robotics instruction for high school students. Its methodology incorporates frame redesign using CAD and 3D printing, software integration with DroneKit and Ardupilot, as well as the design of activity-based learning modules. Various performance evaluations, including incline testing, Aruco marker performance tests, and focus testing with students, highlighted successful system operation, system engagement, and learning improvements. The UGV could handle slopes of up to 25 degrees, and vision-guided marker tracking worked with precision. Student feedback was positive, with average Likert scale results of 4.63 for excitement and 4.42 for ease of use. Comparative surveys showed increased user satisfaction with the improved design, though wiring organization, GPS accuracy, and occasional snap-fit difficulties were noted for refinement. A two-tailed t-test showed no change in student interest after testing, but many indicated increased confidence if robotics were further offered in senior high school. The novelty and contribution of this study lie in the integration of a snap-fit 3D-printed modular frame, accessible hardware, autonomous capabilities, and curriculum-oriented learning modules, making robotics education more affordable, engaging, and practical for schools with limited resources.
Copyright (c) 2026 Regillkent Esquivias, Isaac Anthony Ferrolino, Wrenn Cyruss Castro, Joshua Erick Lao, Nickolai Lim, Alvin Chua
Improved Skyline-BP Network for Multi-Track MIDI Music Melody Extraction and Style Classification
With the rapid development of the digital music industry, core challenges have emerged concerning the insufficient accuracy of main melody extraction and the poor style classification effect of multi-track MIDI files. To address these issues, this study proposes a novel model based on an improved Skyline algorithm and an optimized BP neural network. The method first standardizes MIDI data into a Time-Pitch-Intensity feature matrix. An improved Skyline algorithm is then used to integrate pitch saliency calculation with temporal continuity screening, enhancing the anti-interference ability for multi-track melodies. For music style classification, an optimized BP network with Adaptive Moment Estimation (Adam) gradient optimization and Residual Connection (ResConnect) is designed to improve learning efficiency and accuracy. Experimental results demonstrated that the proposed model surpassed comparative models in overall performance, with a classical-style main melody extraction accuracy of 94.6% and a 2-track separation accuracy of 95.2%. The experiments were benchmarked on the Lakh MIDI Dataset and MuseScore MIDI Library. The model also exhibits superior robustness against noise interference and faster convergence speed. This study provides reliable technical support for applications like music creation assistance and copyright retrieval.
Advancing Network Security: Integrating Salp Swarm Optimization with LSTM for Intrusion Detection
Over time, intrusion detection systems have grown essential in ensuring network security by identifying malicious activities within network traffic and alerting security teams. Machine learning techniques have been employed to develop these systems. However, these approaches often face challenges related to low accuracy and high false alarm rates. Deep learning models like Long Short-Term Memory (LSTM) are utilized to address these limitations. Despite their potential, LSTM models require numerous iterations to achieve optimal performance. This study introduces an enhanced version of the LSTM algorithm, termed ILSTM, which integrates the Salp Swarm Optimizer (SSO) to boost accuracy. The ILSTM framework was applied to construct an advanced intrusion detection system capable of binary and multi-class classifications. The approach comprises two phases: The first involves training a standard LSTM model to initialize its weights. In contrast, the second employs the SSO hybrid optimization algorithm to fine-tune these weights, enhancing overall performance. The effectiveness of the ILSTM algorithm and the intrusion detection system was assessed using two publicly available datasets, NSL-KDD and LITNET-2020, across nine performance metrics. Results demonstrated that the ILSTM significantly outperformed the conventional LSTM and other comparable deep learning models in accuracy and precision. Specifically, the ILSTM achieved an accuracy of 93.09% and a precision of 96.86%, compared to 82.74% accuracy and 76.49% precision for the standard LSTM. Moreover, the ILSTM exhibited superior performance on both datasets and was statistically validated to be more robust than LSTM. Furthermore, the ILSTM excelled in multiclass intrusion classification tasks, effectively identifying intrusion types.
Copyright (c) 2026 Ahmed Abdelaziz, Mohamed Elhoseny, Vitor Santos
Public Opinion Guidance Model in Major Public Crisis Events Based on Accelerated Genetic Algorithm
The research aims to enhance the effectiveness of the public opinion evolution guidance model, with a particular focus on the influence of opinion leaders, and address the shortcomings of traditional models, such as neglecting opinion leaders and insufficient network topology. Therefore, the relevant scale-free network is used to improve the traditional public opinion evolution model. An improved model integrating the two is proposed by introducing a real coded accelerated genetic algorithm. The experimental results show that the proposed model converges to four opinion clusters, with the average values of negative and positive opinions being 0.399 and 0.370, respectively, demonstrating the trend closest to the actual data. When the parameters are fixed, the ultimate development of public opinion shows obvious changing trends in different situations, and the validity of the model has been proved by practice. The research innovatively introduces the scale-free network based on Barabasi and Albert, and improves the Hegselmann Krause model. Meanwhile, by comprehensively considering the influence of opinion leaders and network topology factors, the model overcomes the shortcomings of traditional models in public opinion guidance and also demonstrates good practicability in practical applications.
Innovative Compositions of Shotcrete Mixtures for Reinforcement of Underground Mine Excavations
The objective of this article is to develop a polymer-modified shotcrete composition to improve underground mine support. The authors propose a new formulation by integrating an aqueous emulsion of SKS-65 GP grade B latex into cementitious matrices. Methods include X-ray diffraction, particle-size analysis, rheological testing, mechanical strength tests, numerical modeling, in-situ trials at the Zholbarysty mine, and statistical evaluation. Findings show a 45% increase in compressive strength and a 30% reduction in rebound loss compared to standard mixtures. Field core samples confirmed reproducibility, with strength values within 1% of those from laboratory-tested cubes. The improved mix allows a 50% reduction in lining thickness, expanding the tunnel cross-section by 5% and lowering operational costs by 39%. Cost-benefit analysis and cross-sectional evaluation validate the approach's efficiency. The novelty of this work lies in combining microstructural insights with field-scale application, clarifying polymer-film formation mechanisms, and presenting an optimized, scalable shotcrete mix design. This integrated method provides a practical and cost-effective reinforcement solution, advancing current shotcrete technologies for underground operations.
Copyright (c) 2026 Talgat Almenov, Raissa Zhanakova, Madiyar Sarybayev, Bulbul Seitkazynova, Din-Mukhammed Shabaz, Bakyt Nurperzent
Financial Cycle Dependence of Monetary and Exchange Rate Policies in an Open Economy
The deepening of globalization has posed challenges to open economies, such as fluctuations in international capital flows and intensified cross-border risk contagion. To explore the impact mechanism of FC on MP and ERP, this paper adopts TVP-VAR, MS-VAR, and MS-DSGE models, and introduces the SVR model as an auxiliary prediction tool to analyze policy dependency characteristics through standardization and periodic decomposition. The results showed that during the 2008 financial crisis, the growth rate of the broad money supply reached 17.0%-20.0%, the Shanghai Interbank Offered Rate rose to 3.6%-5.2%, and the asset price volatility exceeded 20%. During the COVID-19 pandemic in 2020, the volatility of real estate prices reached 7.2%-9.5%. In terms of policy transmission, the impact of asset price shocks on the consumer price index significantly increased after 3 months and reached its peak after 6 months. The regulatory coefficient of interest rate policy on the financial condition index under the high volatility regime was 1.1862, and the response coefficient of the growth rate of the broad money supply to the output gap under the low volatility regime was 0.2156. The SVR model had a prediction accuracy (R2 of 0.85) for the impact of MP on ERVs, especially in capturing nonlinear relationships during financial expansion periods. This achievement demonstrates the significant effect of FC stages on the effectiveness of MP, providing an FC sensitive policy framework for open economies, helping to enhance macroeconomic resilience and maintain internal and external balance.
Breast Cancer Classification Using Deep Feature Extraction and Machine Learning
Early and accurate breast cancer diagnosis remains critical yet challenging in routine practice. This study proposes a simple, reproducible pipeline that combines deep feature extraction from pre-trained CNNs (ResNet50, VGG16, EfficientNet-B0, DenseNet121, MobileNetV2) with classical machine-learning classifiers (logistic regression, SVM, k-NN, decision tree, random forest, gradient boosting, XGBoost, LightGBM, Naïve Bayes, and MLP). Features are computed after standardized preprocessing; class imbalance is addressed with SMOTE when present. We evaluate three image datasets (binary and multiclass) using accuracy, precision, recall/sensitivity, F1, and confusion matrices, and apply paired statistical tests across cross-validation splits. Findings: EfficientNet-B0+MLP and ResNet50+MLP achieve peak accuracies up to 99.6% on high-quality, balanced data, while DenseNet121+MLP with SMOTE attains 97.8% on imbalanced multiclass data. SMOTE yields substantial gains on imbalanced data and negligible effect on balanced sets; decision trees underperform consistently. Novelty/Improvement: Rather than a monolithic end-to-end network, we provide a modular, resource-aware blueprint that (i) disentangles feature extraction from classification, (ii) quantifies when imbalance correction matters, and (iii) reports clinically relevant error types. We further outline explainability with Grad-CAM/SHAP and discuss inference-time trade-offs and real-world workflow integration, offering an interpretable and deployment-friendly alternative to heavier end-to-end models.
Copyright (c) 2026 Raed Alazaidah, Ghassan Samara, Hamza Abu Asi, Suhaila Abuowaida, Hamza A. Mashagba, Azlan Abd Aziz, Samia Larguech, Samir S. Al-Bawri
Interpretable and Uncertainty-Aware Multi-Modal Spatio-Temporal Deep Learning Framework for Regional Economic Forecasting
The objective of this study is to improve the accuracy, interpretability, and reliability of regional economic forecasting, a task essential for effective policy-making, infrastructure planning, and crisis management. Existing econometric and machine learning models often suffer from linear assumptions, limited use of heterogeneous data, and a lack of transparent uncertainty quantification. To address these limitations, we propose a unified multi-modal spatio-temporal deep learning framework that integrates satellite imagery, structured economic indicators, and policy documents through an adaptive cross-modal attention mechanism. The methodology incorporates a spatio-temporal cross-attention module to capture dynamic inter-regional dependencies and temporal patterns, along with a Bayesian neural prediction head to quantify uncertainty. Applied to a 13-year dataset from 75 Chinese cities, the model demonstrates substantial improvements, reducing mean absolute error by 37% compared to XGBoost and achieving 92% PICP (Prediction Interval Coverage Probability) under a 90% confidence threshold. Case studies further validate its ability to trace pandemic-induced economic shocks and reveal latent propagation pathways. The novelty of this work lies in its integrative architecture that jointly advances multi-modal fusion, interpretability, and uncertainty quantification, offering both methodological innovation and practical utility. This framework provides policymakers with transparent, risk-aware predictions and establishes a scalable foundation for next-generation economic forecasting.
The Impact of Performance Expectations and Perceived Behavioral Control on Employees’ AI Adoption
As AI technologies rapidly permeate industries, the key challenge for enterprises is no longer whether to adopt AI, but how to ensure employees can strategically and efficiently leverage AI tools to improve work performance meaningfully. This issue spans multiple dimensions, from employees’ performance expectancy regarding AI’s tangible value to their mastery of operations and application contexts, and their perceived behavioral control. It also involves whether organizations provide sufficient resources, training, and institutional support, and whether team culture and social influence foster learning and knowledge sharing. This study integrates Social Cognitive Theory and Expectation-Confirmation Theory to elucidate the critical roles of performance expectancy and perceived behavioral control in the AI adoption process and to examine how organizational support and social influence affect AI usage performance through these psychological mechanisms. In addition, we assess the moderating effect of creative self-efficacy on AI adoption. Using survey data from 392 technology-sector employees, we conduct an empirical analysis using structural equation modeling. The results indicate that social influence has a greater impact than organizational support. Performance expectancy is the key mediating variable through which AI use enhances work performance. Moreover, creative self-efficacy amplifies the positive effects of managerial support and social influence on performance expectancy and perceived behavioral control. These findings deepen the theoretical foundation of AI adoption and provide practical guidance for enterprises seeking to improve organizational performance and employee productivity through AI technology.
Copyright (c) 2026 Chang-Hong Wu, Ruey-Der Twu, Kuo-Chung Huang
Assessing Concession Period Risks of Public-Private Partnership Infrastructure Projects Using FACULTY
The concession period is critical to Public–Private Partnership (PPP) infrastructure project success because it defines how long private investors operate to recoup costs and earn returns. This study investigates risk factors affecting the concession period in Vietnam’s PPP infrastructure projects and introduces a novel evaluation method called FACULTY (Fuzzy AHP integrating Consequences, Uncertainty, and Likelihood Technology). The research objective is to identify which risks most significantly influence concession duration and to demonstrate an improved risk assessment approach. FACULTY combines fuzzy AHP with a traditional consequence-likelihood analysis to capture uncertainty in expert judgments. By surveying 90 PPP experts and analyzing 27 risk factors across five risk categories: construction, revenue, macroeconomic, political, and legal risks, this study identified the ten most critical concession period risks for PPP infrastructure projects. These include land acquisition, access, and compensation issues; construction cost overruns; schedule delays; design deficiencies; geological and site conditions risks; force majeure; traffic demand risk; environmental risks; population growth; and concession price risk. These findings indicate that land acquisition and construction-related risks dominate, reflecting persistent challenges in Vietnam’s infrastructure delivery. This study provides a comprehensive framework for understanding and addressing risks that influence the concession period, offering valuable insights for policymakers and practitioners aiming to optimize PPP project outcomes.
Correlation Between Agricultural Product Purchases and Live-Streaming Economy in the Digital Economy
Objectives: This paper aims to explore how agricultural product sales via live streaming affects the purchasing behavior in the context of the digital economy and evaluate the correlation between them. Methods: A questionnaire was designed to collect respondent’ personal information and information on their purchases of agricultural products. The correlation between agricultural product purchases and the live-streaming economy was measured. Findings: Most respondents purchased agricultural products on platforms such as Douyin and Taobao, preferred watching live streaming of internet celebrities and farmers, primarily bought fruits, vegetables, whole grains, and coarse cereals, and expressed high satisfaction with their agricultural product purchases. Correlation analysis indicated that the correlation coefficient between agricultural products and purchases in the live-streaming economy was highest at 0.742. Regression analysis found a significant positive correlation between agricultural products, anchors, live streaming, platforms, and agricultural product purchases. Novelty: The research quantifies the relevant information on agricultural product live streaming and purchases through questionnaire analysis. It also reveals the positive influence of the digital economy on agricultural product purchases, providing some references for the further development of agricultural product live streaming.
Copyright (c) 2026 Ziling Wu, Hao Zhang, Xiaoli Wang
Predicting Adolescent Suicide Risk in Smart Cities: An AI-Driven, Privacy-Preserving Architecture
This study aims to improve the accuracy, speed, and safety of suicide risk assessment among adolescents in the digital ecosystems of smart cities. To achieve this goal, an integrated system architecture was developed that combines natural language processing methods, transformer models, and privacy-preserving computation. The methodological part includes large-scale textual data analysis, distributed processing in Apache Spark and Hadoop environments, and the use of federated learning, which allows models to be trained without transferring sensitive source information. The evaluation was conducted on open mental health datasets and supplemented by a series of experiments simulating the system's operation in real time, as well as surveys of specialists – psychologists, educators, and IT experts. The analysis showed that transformer models, particularly BERT, significantly outperform classical algorithms, achieving an AUC-ROC of 0.96 and an F1 score of 0.92 with an average response time of 2.4 seconds. Survey participants noted the importance of transparency and data protection, and the proposed architecture received high marks for reducing the risk of information leaks and providing robust audit mechanisms. The novelty of the work lies in the combination of predictive analytics, federated learning, differential privacy, and blockchain traceability in a single application-oriented system. The results show that ethically sound and rapid suicide risk detection can be implemented in schools, medical institutions, and municipal services, providing both practical benefits and contributing to methodological advancements.
Copyright (c) 2026 Serik Aliaskarov , Orazmukhamed Bekmurat, Vassily Serbin, Kanibek Sansyzbay, Yelena Bakhtiyarova, Emil Andekin
Investigating the Correlation Between Bitcoin Trading Volume and Technical Indicators Using Data Mining Techniques
This study aims to examine the relationship between Bitcoin trading volume and key technical indicators using data-mining techniques to better understand how trading activity influences momentum and volatility in blockchain markets. The methodology involves analyzing a historical dataset of Bitcoin’s daily trading records from 2018 to 2023, which includes the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Simple and Exponential Moving Averages (SMA, EMA), and the Average True Range (ATR). Pearson correlation analysis was applied to identify linear associations between trading volume and these technical indicators. The results show significant positive correlations between trading volume and momentum or trend measures such as the 7-day RSI (r = 0.45, p < 0.05), SMA (r = 0.38, p < 0.05), EMA (r = 0.41, p < 0.05), and ATR (r = 0.48, p < 0.05), indicating that higher participation accompanies stronger market momentum and greater price variability. Conversely, the weak and non-significant correlation with MACD (r = –0.12, p = 0.15) suggests that volume has limited influence on lagging trend-reversal signals. The novelty of this study lies in integrating volume-based behavior into technical indicator analysis, extending the traditional volume–price–volatility framework to cryptocurrency markets and providing practical insights for momentum-driven trading strategies and volatility-aware risk management.
Copyright (c) 2026 Athapol Ruangkanjanases, Taqwa Hariguna
Multi-Dimensional Analysis of Subjective and Objective Empowerment Methods in Online Civic Education
To enhance the scientificity and effectiveness of online ideological and political education (Cyber Civics), this article aims to construct a multi-dimensional and quantifiable evaluation model. Methodologically, the article starts from the four dimensions of education subject, object, content, and medium, combines subjective empowerment (hierarchical analysis method AHP) and objective empowerment (entropy power method), and introduces an intelligent optimization algorithm - the long-nosed Cuckoo Optimization Algorithm (COA) to optimize the combination of weights, and constructs the COA-Mixed Cyber Ideology and Political Education Evaluation model. The results show that the model is better than the traditional model in terms of weight distribution, with the four-dimensional index weights of 0.358 for the educational subject, 0.245 for the educational object, 0.207 for the educational content, 0.189 for the educational medium, and the maximum composite score of the sample is 0.875, and the optimization coefficient of the model prediction error is α=0.35, which is significantly better than that of GWO-Mixed (α=0.33) and KOA-Mixed (α=0.33). Mixed (α=0.36). It is concluded that multi-dimensional analysis combined with subjective and objective empowerment and intelligent algorithm optimization can more objectively and accurately assess the effectiveness of online ideological and political education, which provides a feasible path and theoretical support for improving the quality of ideological and political education in colleges and universities.
Copyright (c) 2026 Bin Zhang, Botao Gong, Lijiao Luo
Optimizing Green Business Information Management Systems Through Carbon-Neutral Digital Transformation Pathway Design
This research develops a comprehensive framework for optimizing green business information management systems to achieve carbon neutrality goals through digital transformation. The study conducted cross-sector carbon footprint assessments of information systems across six industries, analyzing emission patterns based on operational scales, industry characteristics, and technological architectures. A multi-tiered optimization model was developed targeting infrastructure, data management, and application layers, validated through empirical data from enterprises undergoing digital transformation. Results reveal a strong negative correlation (r = -0.73) between digital maturity indices and emission intensity, with organizations implementing comprehensive digital transformation achieving average carbon reductions of 31% over five years. The proposed multi-tiered optimization approach enabled 42.6% emission reductions, with technology companies achieving 68% reductions. Economic analysis demonstrates return on investment ranging from 132-278% over five-year periods, with payback periods of 14-36 months. This study advances information management theory by integrating technological architecture with environmental performance governance, providing quantifiable carbon assessment methodologies across system layers and practical implementation matrices for industry-specific applications. The framework enables organizations to balance carbon reduction objectives with operational efficiency, addressing the critical gap between theoretical potential and practical implementation in carbon-neutral transformations.
The Influence of Social Media Influencer Attributes on Brand Equity and Purchase Intention
This study examines how the attributes of social media influencers (SMIs) collectively shape brand equity and purchase intention in the digital marketplace. Building on classical endorsement theories—namely the Source Attractiveness Model, Source Credibility Model, Product Match-Up Hypothesis, and Meaning Transfer Theory—this research develops an integrated framework to explain how influencer traits translate into brand-related outcomes. Data were collected from 200 active social media users in Thailand and analyzed using structural equation modeling (SEM) to test the proposed causal relationships. The results reveal that source attractiveness, product match-up, and meaning transfer significantly enhance brand equity, while source credibility demonstrates a marginal yet positive effect. Furthermore, brand equity strongly predicts purchase intention and mediates the effects of influencer attributes. Theoretically, this study extends endorsement research by integrating four fragmented models into a unified influencer-based framework, advancing understanding of how digital influencers shape consumer-based brand equity. Managerially, the findings guide marketers in selecting influencers whose image, credibility, and symbolic meanings align strategically with brand identity to maximize consumer engagement and behavioral intent.
Copyright (c) 2026 Koravit Narongkananukul, Wilert Puriwat, Danupol Hoonsopon
Impact of Green Building Strategies on Environmental, Economic, and Social Outcomes in Construction
This research investigates the impacts of applying Green Building (GB) principles within Jordanian construction firms, focusing on their effects on environmental, economic, and social dimensions. A descriptive analytical method was employed, suitable for social and humanitarian research contexts. The study targeted a sample of 15 large construction companies listed on the Amman Stock Exchange, utilizing a random sample of 150 individuals, including heads of departments, engineers, designers, architects, supply chain managers, and directors. Data collection was conducted through questionnaires, with 150 distributed and 110 valid responses received, resulting in an 80% response rate. Data analysis was performed using SPSS software version 22 to calculate means, frequencies, and standard deviations. The findings revealed significant impacts of applying GB principles, with a correlation coefficient of 0.754 for environmental quality, indicating that GB practices account for 56.8% of the variance in environmental outcomes. For residents' health, the correlation was 0.643, explaining 41.3% of the variance, while resource preservation showed a strong correlation of 0.749, indicating substantial contributions. Economically, the principles demonstrated a correlation of 0.705, accounting for 57.3% of the variance in economic performance. These findings underscore the necessity of integrating GB practices into construction projects to enhance sustainability, with recommendations including early integration of green design in project development, establishment of comprehensive green education programs, provision of incentives for existing building owners, and securing funding for renewable energy initiatives. Implementing these strategies is crucial for maximizing the effectiveness of GB practices and advancing sustainable development in Jordan.
Copyright (c) 2026 Mansour Safran, Ilias Said, Nasser Abuanzeh, Abdullah O. Baarimah, Samar Abu Saleh, Tharaa M. Al-Zghoul, Ahmed W. Mushtaha, Aawag Mohsen Alawag, Shadia Alqudah
Noise Separation Techniques for Accurate Substation Anomaly Detection: An Intelligent Methodology
To better monitor and characterize sounds produced by substations, this study aims to separate sounds produced by the equipment from environmental ambient noise as a means of improving the relevancy (and ultimately reliability) of the power grid. To do so, we propose a deep learning-based noise monitoring system in an end-network-cloud architecture that enables remote data collection, analysis, and management. This is achieved by developing a deep learning-based noise monitoring system, enabling remote data collection, processing, and management. The proposed method consists of two basic components: a self-designed Panel Response Acquisition device that can collect sufficient acoustic information, and a refined Deep Belief Network (DBN) that is trained with a Dynamic version of the Dwarf Mongoose Optimizer (DDMO) to improve the accuracy of the noise separation process. The performance of the DBN/DDMO model is 13.1 dB for SI-SDRi and 15.7 dB for SDRi, which are large improvements for SI-SNRi and SDRi over AlexNet and CNN-VGG19. This approach minimizes SPL deviations, as shown by a thorough computation regarding several data sets; therefore, it guarantees precise noise quantification under disturbing sounds. By allowing for proactive identification of unusual noise levels, this research supports predictive maintenance methods that can avoid sudden failures and improve the overall reliability of substations.
Copyright (c) 2026 Xiaomeng Zhai, Jingyi Wang, Xi Cheng, Licai Yan, Dianmao Zhang