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Keywords = adaptive designs

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32 pages, 2441 KiB  
Review
Tailoring Therapy: Hydrogels as Tunable Platforms for Regenerative Medicine and Cancer Intervention
by Camelia Munteanu, Eftimia Prifti, Adrian Surd and Sorin Marian Mârza
Gels 2025, 11(9), 679; https://doi.org/10.3390/gels11090679 - 24 Aug 2025
Abstract
Hydrogels are water-rich polymeric networks mimicking the body’s extracellular matrix, making them highly biocompatible and ideal for precision medicine. Their “tunable” and “smart” properties enable the precise adjustment of mechanical, chemical, and physical characteristics, allowing responses to specific stimuli such as pH or [...] Read more.
Hydrogels are water-rich polymeric networks mimicking the body’s extracellular matrix, making them highly biocompatible and ideal for precision medicine. Their “tunable” and “smart” properties enable the precise adjustment of mechanical, chemical, and physical characteristics, allowing responses to specific stimuli such as pH or temperature. These versatile materials offer significant advantages over traditional drug delivery by facilitating targeted, localized, and on-demand therapies. Applications range from diagnostics and wound healing to tissue engineering and, notably, cancer therapy, where they deliver anti-cancer agents directly to tumors, minimizing systemic toxicity. Hydrogels’ design involves careful material selection and crosslinking techniques, which dictate properties like swelling, degradation, and porosity—all crucial for their effectiveness. The development of self-healing, tough, and bio-functional hydrogels represents a significant step forward, promising advanced biomaterials that can actively sense, react to, and engage in complex biological processes for a tailored therapeutic approach. Beyond their mechanical resilience and adaptability, these hydrogels open avenues for next-generation therapies, such as dynamic wound dressings that adapt to healing stages, injectable scaffolds that remodel with growing tissue, or smart drug delivery systems that respond to real-time biochemical cues. Full article
(This article belongs to the Special Issue Advances in Hydrogels for Regenerative Medicine)
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38 pages, 5163 KiB  
Article
A Coordinated Adaptive Signal Control Method Based on Queue Evolution and Delay Modeling Approach
by Ruochen Hao, Yongjia Wang, Ziyu Wang, Lide Yang and Tuo Sun
Appl. Sci. 2025, 15(17), 9294; https://doi.org/10.3390/app15179294 - 24 Aug 2025
Abstract
Coordinated adaptive signal control is a proven strategy for improving traffic efficiency and minimizing vehicular delays. First, we develop a Queue Evolution and Delay Model (QEDM) that establishes the relationship between detector-measured queue lengths and model parameters. QEDM accurately characterizes residual queue dynamics [...] Read more.
Coordinated adaptive signal control is a proven strategy for improving traffic efficiency and minimizing vehicular delays. First, we develop a Queue Evolution and Delay Model (QEDM) that establishes the relationship between detector-measured queue lengths and model parameters. QEDM accurately characterizes residual queue dynamics (accumulation and dissipation), significantly enhancing delay estimation accuracy under oversaturated conditions. Secondly, we propose a novel intersection-level signal optimization method that addresses key practical challenges: (1) pedestrian stages, overlap phases; (2) coupling effects between signal cycle and queue length; and (3) stochastic vehicle arrivals in undersaturated conditions. Unlike conventional approaches, this method proactively shortens signal cycles to reduce queues while avoiding suboptimal solutions that artificially “dilute” delays by extending cycles. Thirdly, we introduce an adaptive coordination control framework that maintains arterial-level green-band progression while maximizing intersection-level adaptive optimization flexibility. To bridge theory and practice, we design a cloud–edge–terminal collaborative deployment architecture for scalable signal control implementation and validate the framework through a hardware-in-the-loop simulation platform. Case studies in real-world scenarios demonstrate that the proposed method outperforms existing benchmarks in delay estimation accuracy, average vehicle delay, and travel time in coordinated directions. Additionally, we analyze the influence of coordination constraint update intervals on system performance, providing actionable insights for adaptive control systems. Full article
23 pages, 13017 KiB  
Article
Telerehabilitation Strategy for University Students with Back Pain Based on 3D Animations: Case Study
by Carolina Ponce-Ibarra, Diana-Margarita Córdova-Esparza, Teresa García-Ramírez, Julio-Alejandro Romero-González, Juan Terven, Mauricio Arturo Ibarra-Corona and Rolando Pérez Palacios-Bonilla
Multimodal Technol. Interact. 2025, 9(9), 86; https://doi.org/10.3390/mti9090086 - 24 Aug 2025
Abstract
Nowadays, the use of technology has become increasingly indispensable, leading to prolonged exposure to computers and other screen devices. This situation is common in work areas related to Information and Communication Technologies (ICTs), where people spend long hours in front of a computer. [...] Read more.
Nowadays, the use of technology has become increasingly indispensable, leading to prolonged exposure to computers and other screen devices. This situation is common in work areas related to Information and Communication Technologies (ICTs), where people spend long hours in front of a computer. This exposure has been associated with the development of musculoskeletal disorders, among which nonspecific back pain is particularly prevalent. This observational study presents the design of a telerehabilitation strategy based on 3D animations, which is aimed at enhancing the musculoskeletal health of individuals working or studying in ICT-related fields. The intervention was developed through the Moodle platform and designed using the ADDIE instructional model, incorporating educational content and therapeutic exercises adapted to digital ergonomics. The sample included university students in the field of computer science who were experiencing symptoms associated with prolonged computer use. After a four-week intervention period, the results show favorable changes in pain perception and knowledge of postural hygiene. These findings suggest that a distance-based educational and therapeutic strategy may be a useful approach for the prevention and treatment of back pain in academic settings. Full article
18 pages, 968 KiB  
Article
A New Vehicle–Multi-Drone Collaborative Delivery Path Optimization Approach
by Jinhui Li and Meng Wang
Symmetry 2025, 17(9), 1382; https://doi.org/10.3390/sym17091382 - 24 Aug 2025
Abstract
To address the logistical challenges of traffic congestion and environmental concerns associated with carbon emissions in last-mile delivery, this paper explores the potential of vehicle–drone cooperative delivery. The existing studies are predominantly confined to single-drone scenarios, failing to simultaneously consider the constraints of [...] Read more.
To address the logistical challenges of traffic congestion and environmental concerns associated with carbon emissions in last-mile delivery, this paper explores the potential of vehicle–drone cooperative delivery. The existing studies are predominantly confined to single-drone scenarios, failing to simultaneously consider the constraints of drone payload capacity and endurance. This limitation leads to task allocation imbalance in large-scale customer deliveries and low distribution efficiency. Firstly, a mathematical model for vehicle–multi-drone collaborative delivery with payload and endurance constraint (VMDCD-PEC) is proposed. Secondly, an improved genetic algorithm (IGA) is developed, as follows: 1. designing a hybrid selection strategy to achieve symmetrical equilibrium between exploration and exploitation by adjusting the weights of dynamic fitness–distance balance, greedy selection, and random selection; and 2. introducing the local search operator composed of gene sequence reversal, single-gene slide-down, and random half-swap to improve the neighborhood quality solution mining efficiency. Finally, the experimental results show that compared with a traditional genetic algorithm (GA) and adaptive large neighborhood search (ALNS), the IGA requires less time to find solutions in various test cases and reduces the average cost of the optimal solution by up to 30%. In addition, an analysis of drone payload sensitivity showed that drone payload capacity is negatively correlated with delivery time, and that larger customer sizes corresponded to higher sensitivity. Full article
(This article belongs to the Section Engineering and Materials)
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16 pages, 1381 KiB  
Article
Balancing Energy Consumption and Detection Accuracy in Cardiovascular Disease Diagnosis: A Spiking Neural Network-Based Approach with ECG and PCG Signals
by Guihao Ran, Yijing Wang, Han Zhang, Jiahui Cheng and Dakun Lai
Sensors 2025, 25(17), 5263; https://doi.org/10.3390/s25175263 - 24 Aug 2025
Abstract
Electrocardiogram (ECG) and phonocardiogram (PCG) signals are widely used in the early prevention and diagnosis of cardiovascular diseases (CVDs) due to their ability to accurately reflect cardiac conditions from different physiological perspectives and their ease of acquisition. Currently, some studies have explored the [...] Read more.
Electrocardiogram (ECG) and phonocardiogram (PCG) signals are widely used in the early prevention and diagnosis of cardiovascular diseases (CVDs) due to their ability to accurately reflect cardiac conditions from different physiological perspectives and their ease of acquisition. Currently, some studies have explored the joint use of ECG and PCG signals for disease screening, but few studies have considered the trade-off between classification performance and energy consumption in model design. In this study, we propose a multimodal CVDs detection framework based on Spiking Neural Networks (SNNs), which integrates ECG and PCG signals. A differential fusion strategy at the signal level is employed to generate a fused EPCG signal, from which time–frequency features are extracted using the Adaptive Superlets Transform (ASLT). Two separate Spiking Convolutional Neural Network (SCNN) models are then trained on the ECG and EPCG signals, respectively. A confidence-based dynamic decision-level (CDD) fusion strategy is subsequently employed to perform the final classification. The proposed method is validated on the PhysioNet/CinC Challenge 2016 dataset, achieving an accuracy of 89.74%, an AUC of 89.08%, and an energy consumption of 209.6 μJ. This method not only achieves better balancing performance compared to unimodal signals but also realizes an effective balance between model energy consumption and classification effect, which provides an effective idea for the development of low-power, multimodal medical diagnostic systems. Full article
(This article belongs to the Special Issue Sensors for Heart Rate Monitoring and Cardiovascular Disease)
19 pages, 1479 KiB  
Article
Ada-DF++: A Dual-Branch Adaptive Facial Expression Recognition Method Integrating Global-Aware Spatial Attention and Squeeze-and-Excitation Attention
by Zhi-Rui Li, Zheng-Jie Deng, Xi-Yan Li, Wei-Dong Ke, Si-Jian Yan, Jun-Du Zhang and Chang Liu
Sensors 2025, 25(17), 5258; https://doi.org/10.3390/s25175258 - 24 Aug 2025
Abstract
Facial Expression Recognition (FER) is a research topic of great practical significance. However, existing FER methods still face numerous challenges, particularly in the interaction between spatial and global information, the distinction of subtle expression features, and the attention to key facial regions. This [...] Read more.
Facial Expression Recognition (FER) is a research topic of great practical significance. However, existing FER methods still face numerous challenges, particularly in the interaction between spatial and global information, the distinction of subtle expression features, and the attention to key facial regions. This paper proposes a lightweight Global-Aware Spatial (GAS) Attention module, designed to improve the accuracy and robustness of FER. This module extracts global semantic information from the image via global average pooling and fuses it with local spatial features extracted by convolution, guiding the model to focus on regions highly relevant to facial expressions (such as the mouth and eyes). This effectively suppresses background noise and enhances the model’s ability to perceive subtle expression variations. In addition, we further introduce a Squeeze-and-Excitation (SE) Attention module into the dual-branch architecture to adaptively adjust the channel-wise weights of features, emphasizing critical region information and enhancing the model’s discriminative capacity. Based on these improvements, we develop the Ada-DF++ network model. Experimental results show that the improved model achieves test accuracies of 89.21%, 66.14%, and 63.75% on the RAF-DB, AffectNet (7cls), and AffectNet (8cls) datasets, respectively, outperforming current state-of-the-art methods across multiple benchmarks and demonstrating the effectiveness of the proposed approach for FER tasks. Full article
(This article belongs to the Section Intelligent Sensors)
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18 pages, 1570 KiB  
Article
Design and Validation of a Multidimensional Instrument for Measuring Eco-Social Competences in Education for Sustainability in Early Childhood Education
by M. Teresa Fuertes-Camacho, Frederic Marimon and Sílvia Albareda-Tiana
Sustainability 2025, 17(17), 7629; https://doi.org/10.3390/su17177629 - 24 Aug 2025
Abstract
Education for sustainability requires the integration of eco-social competences that encompass cognitive, affective, and behavioural dimensions to face today’s global challenges. This paper presents the development and initial validation of a multidimensional and adaptive assessment tool designed to assess these competences in early [...] Read more.
Education for sustainability requires the integration of eco-social competences that encompass cognitive, affective, and behavioural dimensions to face today’s global challenges. This paper presents the development and initial validation of a multidimensional and adaptive assessment tool designed to assess these competences in early childhood education. Based on robust international frameworks and pedagogical models such as “CARE-KNOW-DO”, the instrument includes nine items that measure children’s environmental awareness, social responsibility, and ethical sense across three levels: knowledge, emotional engagement, and behaviour. The study involved a sample of 150 children aged 5–6 and showed that, while their knowledge was considerable, emotional engagement played a key mediating role in transforming awareness into action. These findings confirm the theoretical assumption that emotional resonance is essential to bridge the gap between knowledge and behaviour. The tool proposed provides educators with a reliable age-appropriate method to assess eco-social competences and promotes transformative learning practices from an early age onwards. This study addresses the urgent need for using empirical tools in the field and supports the implementation of the Sustainable Development Goals through critical, participatory, and values-based education. Full article
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12 pages, 5061 KiB  
Article
A Programmable Soft Electrothermal Actuator Based on a Functionally Graded Structure for Multiple Deformations
by Fan Bu, Feng Zhu, Zhengyan Zhang and Hanbin Xiao
Polymers 2025, 17(17), 2288; https://doi.org/10.3390/polym17172288 - 24 Aug 2025
Abstract
Soft electrothermal actuators have attracted increasing attention in soft robotics and wearable systems due to their simple structure, low driving voltage, and ease of integration. However, traditional designs based on homogeneous or layered composites often suffer from interfacial failure and limited deformation modes, [...] Read more.
Soft electrothermal actuators have attracted increasing attention in soft robotics and wearable systems due to their simple structure, low driving voltage, and ease of integration. However, traditional designs based on homogeneous or layered composites often suffer from interfacial failure and limited deformation modes, restricting their long-term stability and actuation versatility. In this study, we present a programmable soft electrothermal actuator based on a functionally graded structure composed of polydimethylsiloxane (PDMS)/multiwalled carbon nanotube (MWCNTs) composite material and an embedded EGaIn conductive circuit. Rheological and mechanical characterization confirms the enhancement of viscosity, modulus, and tensile strength with increasing MWCNTs content, confirming that the gradient structure improves mechanical performance. The device shows excellent actuation performance (bending angle up to 117°), fast response (8 s), and durability (100 cycles). The actuator achieves L-shaped, U-shaped, and V-shaped bending deformations through circuit pattern design, demonstrating precise programmability and reconfigurability. This work provides a new strategy for realizing programmable, multimodal deformation in soft systems and offers promising applications in adaptive robotics, smart devices, and human–machine interfaces. Full article
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18 pages, 917 KiB  
Article
ATA-MSTF-Net: An Audio Texture-Aware MultiSpectro-Temporal Attention Fusion Network
by Yubo Su, Haolin Wang, Zhihao Xu, Chengxi Yin, Fucheng Chen and Zhaoguo Wang
Mathematics 2025, 13(17), 2719; https://doi.org/10.3390/math13172719 - 24 Aug 2025
Abstract
Unsupervised anomalous sound detection (ASD) models the normal sounds of machinery through classification operations, thereby identifying anomalies by quantifying deviations. Most recent approaches adopt depthwise separable modules from MobileNetV2. Extensive studies demonstrate that squeeze-and-excitation (SE) modules can enhance model fitting by dynamically weighting [...] Read more.
Unsupervised anomalous sound detection (ASD) models the normal sounds of machinery through classification operations, thereby identifying anomalies by quantifying deviations. Most recent approaches adopt depthwise separable modules from MobileNetV2. Extensive studies demonstrate that squeeze-and-excitation (SE) modules can enhance model fitting by dynamically weighting input features to adjust output distributions. However, we observe that conventional SE modules fail to adapt to the complex spectral textures of audio data. To address this, we propose an Audio Texture Attention (ATA) specifically designed for machine noise data, improving model robustness. Additionally, we integrate an LSTM layer and refine the temporal feature extraction architecture to strengthen the model’s sensitivity to sequential noise patterns. Experimental results on the DCASE 2020 Challenge Task 2 dataset show that our method achieves state-of-the-art performance, with AUC, pAUC, and mAUC scores of 96.15%, 90.58%, and 90.63%, respectively. Full article
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29 pages, 2806 KiB  
Review
Bridging Design and Climate Realities: A Meta-Synthesis of Coastal Landscape Interventions and Climate Integration
by Bo Pang and Brian Deal
Land 2025, 14(9), 1709; https://doi.org/10.3390/land14091709 - 23 Aug 2025
Abstract
This paper is aimed at landscape managers and designers. It looks at 123 real-world coastal landscape projects and organizes them into clear design categories, i.e., wetland restoration, hybrid infrastructure, or urban green spaces. We looked at how these projects were framed (whether they [...] Read more.
This paper is aimed at landscape managers and designers. It looks at 123 real-world coastal landscape projects and organizes them into clear design categories, i.e., wetland restoration, hybrid infrastructure, or urban green spaces. We looked at how these projects were framed (whether they focused on climate adaptation, flood protection, or other goals) and how they tracked performance. We are hoping to bring some clarity to a very scattered field, helping us to see patterns in what is actually being carried out in terms of landscape interventions and increasing sea levels. We are hoping to provide a practical reference for making better, more climate-responsive design decisions. Coastal cities face escalating climate-driven threats from increasing sea levels and storm surges to urban heat islands. These threats are driving increased interest in nature-based solutions (NbSs) as green adaptive alternatives to traditional gray infrastructure. Despite an abundance of individual case studies, there have been few systematic syntheses aimed at landscape designers and managers linking design typologies, project framing, and performance outcomes. This study addresses this gap through a meta-synthesis of 123 implemented coastal landscape interventions aimed directly at landscape-oriented research and professions. Flood risk reduction was the dominant framing strategy (30.9%), followed by climate resilience (24.4%). Critical evidence gaps emerged—only 1.6% employed integrated monitoring approaches, 30.1% provided ambiguous performance documentation, and mean monitoring quality scored 0.89 out of 5.0. While 95.9% of the projects acknowledged SLR as a driver, only 4.1% explicitly integrated climate projections into design parameters. Community monitoring approaches demonstrated significantly higher ecosystem service integration, particularly cultural services (36.4% vs. 6.9%, p<0.001), and enhanced monitoring quality (mean score 1.64 vs. 0.76, p<0.001). Implementation barriers spanned technical constraints, institutional fragmentation, and data limitations, each affecting 20.3% of projects. Geographic analysis revealed evidence generation inequities, with systematic underrepresentation of high-risk regions (Africa: 4.1%; Latin America: 2.4%) versus concentration in well-resourced areas (North America: 27.6%; Europe: 17.1%). Full article
19 pages, 6860 KiB  
Article
Online Anomaly Detection for Nuclear Power Plants via Hybrid Concept Drift
by Jitao Li, Jize Guo, Chao Guo, Tianhao Zhang and Xiaojin Huang
Energies 2025, 18(17), 4491; https://doi.org/10.3390/en18174491 - 23 Aug 2025
Abstract
Timely detection of anomalies in nuclear power plants (NPPs) is essential for operational safety, especially under conditions where process signals deviate gradually or abruptly from nominal patterns. Traditional detection methods often struggle to adapt under transient conditions or in the absence of well-labeled [...] Read more.
Timely detection of anomalies in nuclear power plants (NPPs) is essential for operational safety, especially under conditions where process signals deviate gradually or abruptly from nominal patterns. Traditional detection methods often struggle to adapt under transient conditions or in the absence of well-labeled fault data. To address this challenge, we propose KD-ADWIN, an adaptive concept drift-detection framework designed for unsupervised anomaly detection in dynamic industrial environments. The method integrates three core components: a Kalman-based prediction module to extract smoothed signal trends, a multi-channel detection strategy combining statistical and derivative-based drift indicators, and an adaptive thresholding mechanism that tunes detection sensitivity based on local signal variability. Evaluations on a synthetic dataset show that KD-ADWIN accurately detects both abrupt and gradual drifts, outperforming classical baselines. Further validation using full-scope simulation data from a modular high-temperature gas-cooled reactor (MHTGR) demonstrates its effectiveness in identifying concept drifts under realistic actuator and sensor fault conditions. Full article
(This article belongs to the Special Issue New Challenges in Safety Analysis of Nuclear Reactors)
17 pages, 2271 KiB  
Article
A Novel Protocol for Integrated Assessment of Upper Limbs Using the Optoelectronic Motion Analysis System: Validation and Usability in Healthy People
by Luca Emanuele Molteni, Luigi Piccinini, Daniele Panzeri, Ettore Micheletti and Giuseppe Andreoni
Bioengineering 2025, 12(9), 905; https://doi.org/10.3390/bioengineering12090905 - 23 Aug 2025
Abstract
(1) Background: Upper limb (UL) function plays a central role in daily life, enabling essential tasks such as reaching, grasping, and eating. While numerous tools exist to evaluate UL kinematics, their application in pediatric populations is often limited by a lack of age-specific [...] Read more.
(1) Background: Upper limb (UL) function plays a central role in daily life, enabling essential tasks such as reaching, grasping, and eating. While numerous tools exist to evaluate UL kinematics, their application in pediatric populations is often limited by a lack of age-specific validation. This study presents a novel motion analysis protocol featuring a customized marker set, aimed at assessing UL movements in the three anatomical planes across different age groups, with a focus on pediatric applicability. (2) Materials and Methods: A SmartDX motion capture system was used, with 30 markers positioned on the upper body, referencing the trunk as the root of the kinematic chain. Ten healthy participants (mean age: 18.69 ± 12.45 years; range: 8.0–41.4) without UL impairments were recruited. The broad age range was intentionally selected to assess the protocol’s transversal applicability. (3) Results: Results showed excellent intra-operator reliability for shoulder and wrist kinematics (ICC > 0.906) and good reliability for elbow movements (ICC > 0.755). Inter-operator reliability was good to excellent (shoulder ICC > 0.958; elbow ICC > 0.762; wrist ICC > 0.826) Usability, measured via the System Usability Scale, was rated as good (83.25). (4) Conclusions: The proposed protocol demonstrated strong reliability and practical usability, supporting its adoption in clinical and research settings. Its design allows for adaptability across motion capture platforms, promoting wider implementation in pediatric UL functional assessment. Full article
(This article belongs to the Special Issue Biomechanics and Motion Analysis)
20 pages, 2345 KiB  
Article
Hierarchical Reinforcement Learning-Based Energy Management for Hybrid Electric Vehicles with Gear-Shifting Strategy
by Cong Lan, Hailong Zhang, Yongjuan Zhao, Huipeng Du, Jinglei Ren and Jiangyu Luo
Machines 2025, 13(9), 754; https://doi.org/10.3390/machines13090754 - 23 Aug 2025
Abstract
The energy management strategy (EMS) is a core technology for improving the fuel economy of hybrid electric vehicles (HEVs). However, the coexistence of both discrete and continuous control variables, along with complex physical constraints in HEV powertrains, presents significant challenges for the design [...] Read more.
The energy management strategy (EMS) is a core technology for improving the fuel economy of hybrid electric vehicles (HEVs). However, the coexistence of both discrete and continuous control variables, along with complex physical constraints in HEV powertrains, presents significant challenges for the design of efficient EMSs based on deep reinforcement learning (DRL). To further enhance fuel efficiency and coordinated powertrain control under complex driving conditions, this study proposes a hierarchical DRL-based EMS. The proposed strategy adopts a layered control architecture: the upper layer utilizes the soft actor–critic (SAC) algorithm for continuous control of engine torque, while the lower layer employs a deep Q-network (DQN) for discrete gear selection optimization. Through offline training and online simulation, experimental results demonstrate that the proposed strategy achieves fuel economy performance comparable to dynamic programming (DP), with only a 3.06% difference in fuel consumption. Moreover, it significantly improves computational efficiency, thereby enhancing the feasibility of real-time deployment. This study validates the optimization potential and real-time applicability of hierarchical reinforcement learning for hybrid control in HEV energy management. Furthermore, its adaptability is demonstrated through sustained and stable performance under long-duration, complex urban bus driving conditions. Full article
(This article belongs to the Section Vehicle Engineering)
13 pages, 603 KiB  
Article
Evaluation of Impacts and Sustainability Indicators of Construction in Prefabricated Concrete Houses in Ecuador
by Marcel Paredes and Javier Perez
Sustainability 2025, 17(17), 7616; https://doi.org/10.3390/su17177616 - 23 Aug 2025
Abstract
The construction of prefabricated concrete houses in Ecuador poses significant challenges in terms of environmental and social sustainability, amid growing housing demand and the urgent need to mitigate adverse impacts associated with the construction processes and materials. In particular, the lack of a [...] Read more.
The construction of prefabricated concrete houses in Ecuador poses significant challenges in terms of environmental and social sustainability, amid growing housing demand and the urgent need to mitigate adverse impacts associated with the construction processes and materials. In particular, the lack of a comprehensive assessment of these impacts limits the development of effective strategies to improve the sustainability of the sector. In addition, in rural areas, the design of flexible and adapted solutions is required, as evidenced by recent studies in the Andean area. This study conducts a comprehensive assessment of the impacts and sustainability indicators for prefabricated concrete houses, employing international certification systems such as LEED, BREEAM, and VERDE, to validate various relevant environmental and social indicators. The methodology used is the Hierarchical Analytical Process (AHP), which facilitates the prioritization of impacts through paired comparisons, establishing priorities for decision-making. Hydrological, soil, faunal, floral, and socioeconomic aspects are evaluated in a regional context. The results reveal that the most critical environmental impacts in Ecuador are climate change (28.77%), water depletion (13.73%) and loss of human health (19.17%), generation of non-hazardous waste 8.40%, changes in biodiversity 5%, extraction of mineral resources 12.07%, financial risks 5.33%, loss of aquatic life 4.67%, and loss of fertility 3%, as derived from hierarchical and standardization matrices. Despite being grounded in a literature review and being constrained due to the scarcity of previous projects in the country, this research provides a useful framework for the environmental evaluation and planning of prefabricated housing. To conclude, this study enhances existing methodologies of environmental assessment techniques and practices in the construction of precast concrete and promotes the development of sustainable and socially responsible housing in Ecuador. Full article
(This article belongs to the Special Issue Sustainable Approaches for Developing Concrete and Mortar)
30 pages, 1456 KiB  
Article
Adaptive Stochastic GERT Modeling of UAV Video Transmission for Urban Monitoring Systems
by Serhii Semenov, Magdalena Krupska-Klimczak, Michał Frontczak, Jian Yu, Jiang He and Olena Chernykh
Appl. Sci. 2025, 15(17), 9277; https://doi.org/10.3390/app15179277 - 23 Aug 2025
Abstract
The growing use of unmanned aerial vehicles (UAVs) for real-time video surveillance in smart city and smart region infrastructures requires reliable and delay-aware data transmission models. In urban environments, UAV communication links are subject to stochastic variability, leading to jitter, packet loss, and [...] Read more.
The growing use of unmanned aerial vehicles (UAVs) for real-time video surveillance in smart city and smart region infrastructures requires reliable and delay-aware data transmission models. In urban environments, UAV communication links are subject to stochastic variability, leading to jitter, packet loss, and unstable video delivery. This paper presents a novel approach based on the Graphical Evaluation and Review Technique (GERT) for modeling the transmission of video frames from UAVs over uncertain network paths with probabilistic feedback loops and lognormally distributed delays. The proposed model enables both analytical and numerical evaluation of key Quality-of-Service (QoS) metrics, including mean transmission time and jitter, under varying levels of channel variability. Additionally, the structure of the GERT-based framework allows integration with artificial intelligence mechanisms, particularly for adaptive routing and delay prediction in urban conditions. Spectral analysis of the system’s characteristic function is also performed to identify instability zones and guide buffer design. The results demonstrate that the approach supports flexible, parameterized modeling of UAV video transmission and can be extended to intelligent, learning-based control strategies in complex smart city environments. This makes it suitable for a wide range of applications, including traffic monitoring, infrastructure inspection, and emergency response. Beyond QoS optimization, the framework explicitly accommodates security and privacy preserving operations (e.g., encryption, authentication, on-board redaction), enabling secure UAV video transmission in urban networks. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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