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Technologies, Volume 13, Issue 11 (November 2025) – 6 articles

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24 pages, 8530 KB

Open AccessArticle

Morphology-Embedded Synergistic Optimization of Thermal and Mechanical Performance in Free-Form Single-Layer Grid Structures

by Bowen Hou, Baoshi Jiang and Bangjian Wang

Technologies 2025, 13(11), 485; https://doi.org/10.3390/technologies13110485 (registering DOI) - 27 Oct 2025

Abstract

Free-form grid structures offer both aesthetic appeal and structural efficiency in long-span roof design and application, yet the potential of morphological design to optimize thermal performance has been long overlooked. This study proposes a multi-objective synergistic optimization framework which can improve the thermal environment and mechanical performance simultaneously for the roof. Focusing on public buildings in hot–humid climates, the research investigates the impact of roof geometry on indoor temperature under extreme thermal loading conditions and long-term thermal loading conditions. Furthermore, the evolution of thermal performance during mechanical performance-driven surface optimization is systematically analyzed. Subsequently, a dynamic proportional adjustment factor is introduced to explore the performance of the optimized results under different performance weights, with thermal and mechanical performance serving as the optimization objectives. Results demonstrate that thermal performance-driven optimization generates saddle-shaped free-form surfaces with alternating peak–valley configurations to achieve self-shadowing effects, reducing indoor temperature by approximately 2 °C but significantly compromising structural stiffness. Conversely, strain energy minimization yields moderate indoor temperature reductions, revealing a positive correlation between strain energy decrease and thermal performance improvement. In the multi-objective optimization considering thermal and mechanical properties, when the strain energy ratio is 0.5–0.7 (optimization balance zone), the indoor temperature decreases, while the structural stiffness and stability bearing capacity increase. This study provides a morphological–structural–environmental synergistic design reference for low-carbon long-span building roofs. Full article

(This article belongs to the Section Construction Technologies)

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24 pages, 2879 KB

Open AccessArticle

Skeleton-Based Real-Time Hand Gesture Recognition Using Data Fusion and Ensemble Multi-Stream CNN Architecture

by Maki K. Habib, Oluwaleke Yusuf and Mohamed Moustafa

Technologies 2025, 13(11), 484; https://doi.org/10.3390/technologies13110484 (registering DOI) - 26 Oct 2025

Abstract

Hand Gesture Recognition (HGR) is a vital technology that enables intuitive human–computer interaction in various domains, including augmented reality, smart environments, and assistive systems. Achieving both high accuracy and real-time performance remains challenging due to the complexity of hand dynamics, individual morphological variations, and computational limitations. This paper presents a lightweight and efficient skeleton-based HGR framework that addresses these challenges through an optimized multi-stream Convolutional Neural Network (CNN) architecture and a trainable ensemble tuner. Dynamic 3D gestures are transformed into structured, noise-minimized 2D spatiotemporal representations via enhanced data-level fusion, supporting robust classification across diverse spatial perspectives. The ensemble tuner strengthens semantic relationships between streams and improves recognition accuracy. Unlike existing solutions that rely on high-end hardware, the proposed framework achieves real-time inference on consumer-grade devices without compromising accuracy. Experimental validation across five benchmark datasets (SHREC2017, DHG1428, FPHA, LMDHG, and CNR) confirms consistent or superior performance with reduced computational overhead. Additional validation on the SBU Kinect Interaction Dataset highlights generalization potential for broader Human Action Recognition (HAR) tasks. This advancement bridges the gap between efficiency and accuracy, supporting scalable deployment in AR/VR, mobile computing, interactive gaming, and resource-constrained environments. Full article

(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)

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18 pages, 2765 KB

Open AccessArticle

Studying the Safety of Femtosecond Laser Applications in Assisted Hatching Technology

by Dmitry S. Sitnikov, Marina V. Kubekina, Anna V. Tvorogova, Victoria S. Agentova, Darya E. Mukhdina, Leonid A. Ilchuk, Yulia Yu. Silaeva and Maxim A. Filatov

Technologies 2025, 13(11), 483; https://doi.org/10.3390/technologies13110483 (registering DOI) - 25 Oct 2025

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Abstract

Laser-assisted hatching (LAH) is used during in vitro fertilization (IVF) to improve the chances of embryo implantation into the uterine wall by creating a small, precise opening in its outer shell (zona pellucida). The primary objective of this study was to evaluate the safety profile of LAH performed using an infrared femtosecond laser system (λ = 1028 nm, E = 155 nJ, and I = 6.5 TW/cm²). We aimed to identify and quantify the potential biological effects of the laser and compare them with results from previous studies that used visible wavelength laser pulses (λ = 514 nm, E = 49 nJ, and I = 2.5 TW/cm²). To achieve this, we designed a controlled experiment using a mouse model. A critical component of our safety assessment involved quantifying the levels of reactive oxygen species (ROS) and analyzing the expression of heat-shock proteins (HSPs). Robust analyses revealed no statistically significant differences in either ROS production or HSP expression—assessed at both the protein and mRNA levels—between embryos in the negative control group and those subjected to the femtosecond LAH procedure. This key finding indicates that neither infrared nor visible femtosecond laser microsurgery of the zona pellucida induced a detectable oxidative or thermal stress response within the tested parameters. Full article

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25 pages, 1163 KB

Open AccessArticle

Advanced Analytical Modeling of Polytropic Gas Flow in Pipelines: Unifying Flow Regimes for Efficient Energy Transport

by Laszlo Garbai, Robert Santa and Mladen Bošnjaković

Technologies 2025, 13(11), 482; https://doi.org/10.3390/technologies13110482 (registering DOI) - 25 Oct 2025

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Abstract

In the present work, a new analytical model of polytropic flow in constant-diameter pipelines is developed to accurately describe the flow of compressible gases, including natural gas and hydrogen, explicitly accounting for heat exchange between the fluid and the environment. In contrast to conventional models that assume isothermal or adiabatic conditions, the proposed model simultaneously accounts for variations in pressure, temperature, density, and entropy, i.e., it is based on a realistic polytropic gas flow formulation. A system of differential equations is established, incorporating the momentum, continuity, energy, and state equations of the gas. An implicit closed-form solution for the specific volume along the pipeline axis is then derived. The model is universal and allows the derivation of special cases such as adiabatic, isothermal, and isentropic flows. Numerical simulations demonstrate the influence of heat flow on the variation in specific volume, highlighting the critical role of heat exchange under real conditions for the optimization and design of energy systems. It is shown that achieving isentropic flow would require the continuous removal of frictional heat, which is not practically feasible. The proposed model therefore provides a clear, reproducible, and easily visualized framework for analyzing gas flows in pipelines, offering valuable support for engineering design and education. In addition, a unified sensitivity analysis of the analytical solutions has been developed, enabling systematic evaluation of parameter influence across the subsonic, near-critical, and heated flow regimes. Full article

(This article belongs to the Topic Advances in Green Energy and Energy Derivatives)

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32 pages, 1860 KB

Open AccessReview

Integrating Artificial Intelligence into Smart Infrastructure Management for Sustainable Urban Planning

by Abdulaziz I. Almulhim

Technologies 2025, 13(11), 481; https://doi.org/10.3390/technologies13110481 (registering DOI) - 23 Oct 2025

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Abstract

This paper systematically reviewed studies on the integration of Artificial Intelligence (AI) into infrastructure management to support sustainable urban planning across three primary domains: predictive maintenance and energy optimization, traffic and mobility systems, and public participation with ethical considerations. Findings from thirty peer-reviewed studies underscore how AI-driven models enhance operational efficiency, sustainability, and governance in smart cities. Effective management of AI-driven smart infrastructure can transform urban planning by optimizing resources efficiency and predictive maintenance, including 15% energy savings, 25–30% cost reductions, 25% congestion reduction, and 18% decrease in travel times. Similarly, participatory digital twins and citizen-centric approaches are found to enhance public participation and help address ethical issues. The findings further reveal that AI-based predictive maintenance frameworks improve system reliability, while deep learning and hybrid models achieve up to 92% accuracy in traffic forecasting. Nonetheless, obstacles to equitable implementation, including the digital divide, privacy infringements, and algorithmic bias, persist. Establishing ethical and participatory frameworks, anchored in responsible AI governance, is therefore vital to promote transparency, accountability, and inclusivity. This study demonstrates that AI-enabled smart infrastructure management strengthens urban planning by enhancing efficiency, sustainability, and social responsiveness. It concludes that achieving sustainable and socially accepted smart cities depends on striking a balance between technological innovation, ethical responsibility, and inclusive governance. Full article

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

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21 pages, 11021 KB

Open AccessArticle

Evaluating and Forecasting Undergraduate Dropouts Using Machine Learning for Domestic and International Students

by Songbo Wang and Jiayi He

Technologies 2025, 13(11), 480; https://doi.org/10.3390/technologies13110480 - 23 Oct 2025

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Abstract

Undergraduate dropout is a multidimensional phenomenon with implications for higher education, economic development, and social and cultural transformation, posing complex challenges for society as a whole. To address this, universities require effective dropout risk assessments for both domestic and international students, enabling the implementation of tailored strategies and support. This study sourced a dataset from multiple faculties, comprising 3544 records for domestic students (Portuguese) and 86 for international students, considering 23 features. To balance the data, Conditional Tabular Generative Adversarial Networks were utilized to generate 487 synthetic samples with comparable statistical characteristics for training (85%) while retaining the original 86 real samples for testing (15%), thus maintaining an identical train–test split for evaluating domestic students. An Automated Machine Learning framework, employing ensemble learning algorithms, achieved outstanding performance, with the Light Gradient Boosting Machine proving the most effective for domestic students and Categorical Boosting for international students, both achieving test accuracies exceeding 0.90. The analysis revealed that improving academic performance during the first and second semesters was key to reducing dropout risks. Once a satisfactory level was reached, further improvements had minimal impact. Therefore, the focus should be on achieving satisfactory grades. Other objective identity factors, such as age and gender, were less influential than academic performance. A web-based application incorporating the developed models was created, offering an open-access tool for forecasting dropout risks, with all code made publicly available for further research into undergraduate performance, which could be extended to other nations. Full article

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