Search Results (16,676)

School classrooms represent complex, interconnected systems where indoor environmental quality critically influences student health, cognitive performance, and educational equity. Yet traditional approaches operate in disciplinary silos, creating systemic failures in design, operation, and maintenance. This narrative review adopts a systems engineering framework to demonstrate how integrated interventions—spanning policy, design, technology, and operations—create resilient, sustainable, and healthy classroom climates. Amid escalating climate change impacts (rising temperatures, heatwaves, wildfires) and emerging threats (airborne pathogens, urban pollution), reactive measures like school closures prove pedagogically counterproductive. This review synthesizes evidence on natural, mechanical, and mixed-mode ventilation systems optimized through advanced control strategies, smart technologies, and health-centred policies. Key findings reveal that synergistic integration of Policy, Management, Construction, Operation, and Smart Technologies, in a systems engineering framework, outperforms singular strategies. Critical interventions include hybrid ventilation coupled with layered defences (HEPA filtration, UVGI), AI-driven adaptive controls using IoT sensors and Model Predictive Control to optimize energy while managing pollutant concentrations, and mandatory IAQ standards rooted in stakeholder education. By framing classrooms as interconnected engineering systems, this work provides actionable insights for architects, engineers, policymakers, and administrators, positioning future school design toward resilience, sustainability, and human-centred health outcomes. Full article

Detecting ships using Synthetic Aperture Radar (SAR) in coastal areas is still difficult due to the impact of coherent speckle noise from the ocean surface, complex land clutter and having multi-scale target representations in the radar imagery. Most of the existing ship detection algorithms lose important target features during downsampling and have difficulty recovering those features through upsampling, resulting in a high number of false detections and missed detections. In this work, we present a new ship detection algorithm called Synergistic Star-Attentive Network with Dynamic Feature Refinement (S²D-Net). First, we create a new backbone called Multi-scale PCCA-StarNet to generate robust feature representations. Within the backbone we implement a Progressive Channel-Coordinate Attention (PCCA) mechanism to create a synergy between global channel filtering and adaptive coordinate locking to decouple ship textures from granular speckle noise. Second, we create a Dynamic Feature Refinement Neck. We develop a content-aware dynamic upsampler called DySample to replace conventional interpolation to improve fidelity of the upsampled feature of small targets. Further, we design a Star-PCCA Feature Aggregation module which fuses features together. Using star-operations and the PCCA mechanism, this module refines semantic features and removes background clutter while aggregating features across multiple scales. Third, we develop a Lightweight Shared Convolutional Detection Head with Quality Estimation (LSCD-LQE). The LSCD-LQE decreases parameter redundancy by using shared convolutional layers and adds a localization quality estimation branch. Therefore, the LSCD-LQE effectively reduces false positive detections through alignment of classification scores with localization quality based on Intersection over Union (IoU) in difficult coastal environments. Our experimental results, using the SSDD and HRSID datasets, show that S²D-Net produces results comparable to representative ship detection algorithms. In particular, on the challenging HRSID inshore subset, our proposed method achieved a mean average precision (mAP) of 82.7%, which is 6.9% greater than the YOLOv11n baseline ship detection algorithm. These results demonstrate that S²D-Net is superior at detecting small coastal vessels and mitigating the detrimental effects of the nearshore complex environment on the performance ship detection using SAR. Full article

The paper presents a method for generating a pseudo-panchromatic (PPAN) orthophotomosaic that is spectrally compatible with the multispectral (MS) orthophotomosaic, and it targets the fusion of unmanned aircraft system (UAS) RGB–MS orthophotomosaics when no true panchromatic band is available. In typical UAS imaging systems, RGB and multispectral sensors operate independently and exhibit different spectral responses and spatial resolutions, making the construction of a spectrally compatible substitution intensity a critical challenge for component substitution fusion. The conventional RGB-derived PPAN preserves spatial detail but is constrained by RGB–MS spectral incompatibility, expressed as reduced corresponding-band similarity. The proposed hybrid intensity (PPAN_E) increases the mean corresponding-band correlation from 0.842 (PPAN_A) to 0.928 (PPAN_E) and reduces the across-site mean SAM from 5.782° to 4.264°, while maintaining spatial sharpness comparable to the RGB-derived intensity. It is proposed that the PPAN_E orthophotomosaic be produced as a hybrid intensity (single band) image. Specifically, a multispectral-visible-derived intensity is resampled onto the RGB grid and statistically integrated with RGB spatial detail, followed by mild high-frequency enhancement to produce the final PPAN_E orthophotomosaic. Principal Component Analysis (PCA) fusion is applied to seven archaeological sites in Northern Greece. Spectral quality is evaluated on the MS grid using band-wise (corresponding-band) correlation and the Spectral Angle Mapper (SAM), while the spatial sharpness of the fused NIR orthophotomosaic is assessed using Tenengrad and Laplacian variance. The PPAN_E orthophotomosaic consistently increases correlations relative to PPAN_A (especially in Red Edge/NIR) and reduces the mean site-mean SAM. PPAN_C yields the lowest SAM but also the lowest spatial sharpness/clarity, whereas PPAN_E maintains spatial sharpness/clarity comparable to PPAN_A, supporting a balance between spectral consistency and spatial detail, as also confirmed through comparative evaluation against established component substitution fusion methods. The approach is reproducible and avoids full histogram matching; instead, it relies on explicitly defined linear standardization steps (mean–std normalization) and controlled spatial sharpening, and performs consistently across different scenes. Full article

Sandwich materials are increasingly used due to the possibility of improving their combined properties. However, some manufacturing operations become more complex, such as drilling, where it is more difficult to determine the optimal cutting conditions that provide the appropriate hole quality. In this context, the quality of the drilled holes of the carbon fiber plates with a foamed polyvinyl chloride core, a material used in marine environments at very low temperatures, among others, is analyzed. Due to the importance of surface quality in operations prior to the assembly of plates and the influence of delamination on the in-service behavior of materials, the objective is to determine the diameter deviation (∆D), circularity (CIR), and delamination (FD) at the entrance and exit of the hole after drilling plates of this material. This sandwich material has been drilled at low temperatures (−15, 0, and 15 °C) using compressed air as cooler. Different cutting conditions have been used regarding rotation speed and feed. An experimental and statistical study, including a response surface optimization for FD, and multiple response surface optimization for ∆D and CIR were used. Several ranges of suitable cutting conditions can be identified for each temperature. Full article

Public transport systems in rapidly urbanizing Gulf cities confront the simultaneous challenge of decreasing emissions while guaranteeing equal access for riders, many of whom rely on transit for economic reasons. Sustainable smart city development necessitates bus services that are both efficient and sensitive to rider needs in adverse weather conditions. This study develops and evaluates a wellbeing-focused planning framework for Dubai’s bus network, filling gaps in prior research that primarily focuses on temperate, choice-based transport environments. The study uses Generalized Structured Component Analysis (GSCA) to analyze how Service Efficiency and Accessibility (SEA), Physical Environment and Passenger Comfort (PEPC), and Service Operations and Assurance (SOA) impact overall journey wellbeing, based on a cross-sectional survey of 491 riders collected from July–August 2024. Data were collected during peak summer conditions, and the analysis followed a structured workflow that operationalized the proposed constructs into measurable indicators and estimated both the measurement and structural components of the GSCA model to find planning relevant wellbeing drivers. The model shows a strong fit (FIT = 0.684; GFI = 0.991; SRMR = 0.056), with SEA (β = 0.504) having the greatest influence on wellbeing, followed by SOA (β = 0.344) and PEPC (β = 0.070). Affordability and information quality are key SEA metrics, highlighting the necessity of economic access and multilingual, real-time communication. Overall, the findings indicate that wellbeing is most strongly shaped by accessibility-oriented service experience attributes particularly affordability and information quality followed by operational assurance, while comfort-related conditions remain significant under high heat exposure during waiting and transfers. On the other hand, the research indicates that operational reliability helps mitigate environmental discomfort in hyper-arid areas. The report suggests focusing on equal prices, digital information accessibility, dependable operations, and climate-adaptive infrastructure to promote sustainable mobility and long-term public transport use. Full article

To address the unobservability of distribution networks caused by insufficient coverage of measurement terminals as well as communication failures and missing data, and to cope with operating-state fluctuations induced by distributed generation integration and external environmental disturbances, this paper proposes an integrated state-estimation and voltage-regulation strategy that combines distribution-network-partitioning-based optimal PMU placement with pseudo-measurement construction using power transfer distribution factors (PTDFs). First, nodal reactive-power sensitivity information is derived from the power-flow Jacobian matrix, and an improved modularity function is employed to obtain the optimal partitioning of the distribution network, based on which PMUs are deployed at partition boundary buses. Second, PTDF-based power pseudo-measurements are constructed for unobservable buses and incorporated into the measurement model via a measurement transformation; a weighted least-squares method is then adopted to achieve system-wide state estimation. Finally, the estimated voltage states are fed into flexible voltage-regulation devices to enable fast and continuous voltage adjustment across buses. Case studies on the IEEE 33-bus system demonstrate that the proposed method effectively improves voltage quality. Full article

Artificial intelligence technology offers an intelligent and efficient new pathway for bearing fault diagnosis, holding significant importance for ensuring the stable operation of industrial systems. However, bearing fault samples are scarce in industrial practice, and traditional data-driven methods exhibit a marked decline in diagnostic performance under conditions of small sample sizes. To address this, this paper proposes a few-shot bearing fault diagnosis method that integrates an Improved Generative Adversarial Network with a CNN-BiLSTM-Attention hybrid network. The method comprises three core stages: in the data augmentation stage, a class-center-constrained Least Squares Generative Adversarial Network (CCC-LSGAN) model featuring class center constraint and joint loss optimization is proposed to generate high-quality fault samples through frequency-domain feature constraints, effectively expanding the training data; in the feature learning stage, a one-dimensional Convolutional Neural Network, Bidirectional Long Short-Term Memory, and Attention hybrid network (1D-CNN-BiLSTM-Attention) hybrid base classifier is constructed, which combines multi-scale convolution, bidirectional temporal modeling, and attention mechanisms to fully extract the spatiotemporal features of vibration signals; in the inference stage, test-time noise augmentation and a multi-model weighted voting ensemble mechanism are introduced to enhance the robustness and generalization capability of the diagnosis. Experimental results based on the PU and CWRU public bearing datasets demonstrate that the proposed method significantly outperforms existing mainstream diagnostic approaches in core metrics, including accuracy, precision, recall, and F1 score. It achieves a diagnostic accuracy of 96.60% on the PU dataset and 98.58% on the CWRU dataset. This method verifies the feasibility of highly reliable diagnosis under few-shot conditions and provides an effective solution for the intelligent operation and maintenance of industrial equipment. Full article

Background and Objectives: Visual impairment and reduced stereovision significantly impact the quality of life and increase fall risk in older adults. While standard clinical assessment of visual functions is essential in this population, its use is often limited by the need for specialized equipment and trained personnel. Tablet-based screening tools offer a practical alternative but require clinical validation. This study aimed to assess the agreement, reliability, and diagnostic performance of a tablet-based screening application (index methods) compared to established clinical reference methods for assessing visual acuity (VA) and stereovision (SV) in adults over 60 years. Materials and Methods: This prospective, non-blinded, cross-sectional, feasibility study included two cohorts: a test–retest group of 24 older adults assessed twice within 7 days, and a clinical cross-sectional group of 135 participants recruited from primary care practices. VA was measured using tablet-based Landolt C test and compared with an ETDRS-style chart, while stereovision was assessed using tablet-based static and dynamic random dot stereograms and compared with the TNO stereotest. Agreement and reliability were evaluated using Bland–Altman analysis, intraclass correlation coefficients (ICC), and receiver operating characteristic (ROC) curves. Results: The index VA method demonstrated good test–retest reliability (ICC = 0.79) with no significant difference between repeated measurements. In the clinical cross-sectional group, visual acuity measurements showed a small mean bias (0.022 logMAR) between the index and reference methods, which remained within clinically acceptable limits, particularly in the intermediate acuity range. For stereovision, the index SV tests showed high test–retest agreement. Using a TNO cutoff of 480 arcsec, the index SV method demonstrated good diagnostic accuracy (AUC 0.87 for static and 0.85 for dynamic stimuli) with high sensitivity for detecting impaired stereovision. Conclusions: The tablet-based index method provided reliable and clinically comparable results for VA and SV assessments in older adults, supporting its potential use as a screening tool in primary care and community-based settings. Full article

Platform screen door (PSD) systems can reduce particulate matter (PM) levels at subway platforms, but transient particle migration between tunnels and platforms still occurs during door operation. Existing control measures, such as tunnel cleaning, ventilation optimization, onboard dust removal devices, and air curtain systems, mainly target background PM concentrations and generally function as passive mitigation strategies. However, the transient dynamics of tunnel-to-platform PM migration during PSD operation remain insufficiently understood. In this study, field measurements and numerical simulations were used to investigate PM migration under realistic subway operating conditions. Field observations were conducted to characterize the spatial distribution of PM and its relationship with tunnel piston wind. A numerical model based on the Discrete Phase Model (DPM) was then developed to simulate particle transport under different PSD operating sequences. The effects of PSD opening delay and opening duration on particle migration were examined to evaluate their influence on migration rates. The results show that adjusting the timing of PSD operation can significantly reduce tunnel-to-platform PM migration, whereas conventional air curtain configurations may enhance interzonal particle exchange under certain conditions. These findings improve the understanding of PSD-related PM transport and provide potential operational strategies for improving air quality in underground rail transit systems. Full article

The intermittent nature of photovoltaic (PV) energy, especially under nonlinear and unbalanced loading situations, has made it more difficult to ensure steady operation as it is increasingly integrated into modern power systems. The Power Quality (PQ) issues cause severe degradation of both system performance and device lifetime. A novel Neural Power Quality Network (NeuPQ-Net) controlled Unified Power Quality Conditioner (UPQC) combined with a Quasi Z-Source Lift (QZSL) converter for PV applications is presented in this research as a novel solution for addressing these issues. The QZSL converter, which is controlled by a Maximum Power Point Tracking (MPPT) algorithm based on Perturb and Observe (P&O), increases the PV source voltage to the necessary DC-link level. A Zebra Optimisation Algorithm tuned PI (ZOA-PI) controller continually adjusts PI gains for quick and accurate regulation, ensuring steady DC-link voltage. Unlike conventional Synchronous Reference Frame (SRF) or Decoupled Double Synchronous Reference Frame (DDSRF)-based reference generation, the proposed NeuPQ-Net operates directly in the abc domain, eliminating Phase-Locked Loop (PLL) dependency and reducing computational complexity. Simulation and hardware prototype validations demonstrate that the proposed system achieves significant improvements in PQ indices, including reduced Total Harmonic Distortion (THD), faster response to transients, and enhanced voltage regulation, while complying with IEEE-519 standards. Full article

The current energy transition has shifted the power system paradigm, including distributed resources (mostly renewables) and energy storage systems, the proper incorporation of which is beneficial for the power system but can also cause issues such as network instability, grid congestion or issues with power quality. Moreover, the exponential electrification of loads, especially ones with dynamic behavior, due to most sectors switching to electric mode, with prominent examples including mobility, heating, hydrogen production and marine applications, can pose challenges for the system operators. The purpose of this paper is to highlight the effects of this transition from the perspective of the distribution and transmission systems in Europe generally, but also in Greece specifically, by presenting key performance indicators (technical, economic, environmental, and social) related to expected EU targets, as well as selected real-life applications, future trends and challenges. Full article

Operational (short-term) planning in open-pit mining is a critical phase for ensuring grade control and production stability, particularly in complex geological environments. While long-term plans define the strategic goals, they often overlook shift-level variability and operational constraints of a shovel-truck system. This paper presents an optimization model based on a genetic algorithm (GA) for shift-by-shift operational planning. The model integrates real-world technological constraints of the equipment used, including fixed shift capacity (2000 t) and various constraints characteristic of active mining locations. The fitness function is designed to minimize the deviations from the targeted quality range for iron (Fe: 47–50%) and silica (SiO₂: ≤11%), while ensuring rational use of mineral reserves. The model was tested on a case study involving eight limonite ore open pits over a period of one production year (1000 shifts). The results show that the GA-generated plan reaches quality requirements in 98.1% of all shifts. This GA approach provides more balanced mining operations and confirms and ensures the achievement of goals from long-term plans, reducing the reliance on large-scale homogenization stockpiles. The developed tool is implemented in Excel/VBA and offers a practical framework for mining engineers to work with. Full article

Controlled-environment agriculture (CEA) and circular production systems require coordinated monitoring of biological and physicochemical processes across trophic levels. This project report presents the implementation of a multi-trophic controlled-environment agriculture demonstrator that integrates computer-vision-based monitoring with established sensor infrastructure for aquaculture, poultry, plants, microalgae, duckweed, and insect modules. Stereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth, while continuous water-quality and environmental measurements (e.g., pH, dissolved oxygen, nitrate, ammonium, temperature, CO₂) provide complementary process data. These data streams are synchronized within a shared database architecture to enable cross-module evaluation of nutrient dynamics, growth progression, and operational stability under real facility conditions. The implemented framework demonstrates how computer vision can extend conventional sensor-based monitoring by directly capturing biological performance indicators across aquatic, terrestrial, and microbial domains. While advanced predictive modeling and full digital twin simulation remain future development steps, the realized data-integration architecture establishes a structural foundation for the systematic evaluation of circular indoor food-production systems. The demonstrator illustrates how multimodal monitoring can support nutrient recirculation, transparency of biological variability, and data-driven assessment within controlled multi-trophic environments. Full article

The integration of renewable energy and power electronics has intensified the occurrence of complex power quality disturbances (PQDs), which increasingly threaten grid stability. To address the challenges of multi-class PQD identification under noisy conditions, this paper proposes a novel framework that combines an enhanced time–frequency analysis method with an optimized AdaBoost decision tree. The main contributions are three-fold: (1) We develop an instantaneous frequency adaptive Fourier synchrosqueezing transform (IFAFSST) equipped with a custom adaptive operator that aligns closely with the frequency modulation patterns in PQD signals, thereby improving time–frequency energy localization. (2) The IFAFSST outputs are decomposed into low-frequency and high-frequency components, from each of which a set of 16 discriminative features is extracted. (3) An improved AdaBoost classifier is introduced, incorporating forward feature selection and Hyperband-based hyperparameter optimization to enhance classification performance. Hyperband accelerates the optimization process by dynamically allocating computing resources and iteratively eliminating suboptimal configurations, thereby enabling efficient determination of the optimal hyperparameters. The method proposed in this paper achieved an accuracy rate of 99.50% on simulated data containing 30 dB white noise and 98.30% on hardware platform data. This framework can effectively handle 23 types of interference, including seven types of single interference, 12 types of double compound interference, three types of triple compound interference, and one type of quadruple compound interference. It performs particularly well in identifying composite interference scenarios. This research has made a significant contribution to power quality analysis, providing a powerful solution with high accuracy and practical applicability, and offering great potential for the implementation of smart grid monitoring systems and the integration of renewable energy. Full article

In this study, we investigated hyaluronic acid (HA) as a functional biopolymer for improving the processing performance of myofibrillar protein (MP) gels. Our focus was on the order of incorporation and concentration of HA as controllable process parameters, and their effects on water-holding capacity, rheological behaviour, texture, colour and microstructure of MP gels. The experimental results demonstrated that HA promoted the formation of a denser and more homogeneous protein network, as confirmed by microstructural analysis and significantly enhanced water retention. From a mechanical perspective, HA incorporation decreased hardness and chewiness while increasing adhesiveness, thereby improving overall gel functionality. Importantly, the simultaneous dissolution of HA with meat and water produced superior outcomes compared to post-addition, highlighting the role of ingredient addition sequence as a relevant process design factor. The slight colour variations remained within acceptable quality limits. Our findings provide new insights into protein hydrocolloid interactions in gel systems and indicate how HA can be strategically integrated into processing operations to improve product yield, quality and consumer acceptance in the meat industry. Full article