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.

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.

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.

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.

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.

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.

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.

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.

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.

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.