Search Results (66)

Low Earth orbit (LEO) satellites are increasingly required to process and securely stream video data for remote sensing, surveillance, and onboard perception applications. However, the strict constraints of onboard computing capability, power budget, and thermal dissipation make conventional encryption schemes difficult to apply to real-time video streaming tasks. To address this challenge, this paper proposes a low-complexity real-time video encryption algorithm for resource-constrained LEO satellites. The proposed method integrates selective encryption with a lightweight permutation–diffusion mechanism to reduce computational overhead while maintaining effective protection of continuous video streams. To enhance security, a chaotic pseudo-random sequence generator is employed to improve encryption randomness, and a dynamic key scheduling strategy is introduced to increase temporal key variability and strengthen resistance to statistical and differential attacks across successive frames. The algorithm is further designed for efficient deployment on embedded onboard platforms with limited hardware resources. Experimental results show that the proposed method achieves favorable performance in encryption speed, computational complexity, information entropy, adjacent pixel correlation, and differential attack resistance. Compared with conventional full-encryption methods, the proposed algorithm offers a more balanced trade-off between security and real-time efficiency, demonstrating its potential for secure video streaming in resource-constrained LEO satellite systems. Full article

Thermal comfort in mixed-mode buildings is challenging due to individual differences in perception, particularly in humid subtropical climates. In Florianópolis, Brazil, dynamic indoor conditions influence occupants’ thermal perception and adaptation. This study investigates how thermal disposition shapes comfort perception. A total of 1032 responses from heat-sensitive users and 733 from cold-sensitive users were collected through electronic questionnaires. The data were analysed using Predicted Mean Vote (PMV), Actual Mean Vote (AMV), and a linear mixed-effects model. Although both groups exhibited average PMV values within the ASHRAE 55 comfort range, their subjective evaluations differed significantly: heat-sensitive users reported warmer sensations, whereas cold-sensitive users reported cooler sensations under similar conditions. Among heat-sensitive users, the PMV–AMV correlation was moderate and strongest under air-conditioning, whereas it was weak and non-significant for cold-sensitive users. Dissatisfaction levels frequently exceeded 20% among heat-sensitive users. Adaptive comfort analysis indicated that most observations fell within acceptability limits for mixed-mode buildings. The mixed-effects model confirmed that thermal disposition significantly moderates the relationship between operative temperature and thermal sensation. These findings highlight the importance of incorporating individual thermal sensitivity into occupant-centred comfort assessments. Full article

Interior built environments influence user behavior through more than deliberate rational evaluation. They shape attention, movement, affective comfort, perceived safety, wayfinding, and well-being through bounded cognition, affective appraisal, heuristics, embodied perception, and automatic approach–avoidance processes. The research gap addressed in this review concerns the fact that prior work on interior environments, wayfinding, indoor environmental quality, neuroarchitecture, atmospherics, and behavioral decision-making remains fragmented across separate studies, and existing reviews rarely explain how these mechanisms can be organized into a design-usable framework for interior built environments. This narrative review synthesizes foundational and recent literature across building design, environmental psychology, neuroarchitecture, virtual reality, indoor environmental quality, wayfinding, and behavioral decision-making to clarify how decision mechanisms translate into interior design variables such as lighting, color, spatial organization, materiality, form, sensory atmosphere, environmental legibility, thermal comfort, and controllability. The review distinguishes bounded rationality, heuristics and biases, dual-process accounts, affective and atmospheric processing, prospect–refuge dynamics, mere exposure, and room-effect research rather than treating them as a single “non-rational” category. It proposes an integrative framework in which interior cues are processed through perceptual and affective appraisal; moderated by individual, cultural, contextual, temporal, and ethical factors; and expressed through behavioral outcomes such as navigation, approach or withdrawal, dwell time, perceived quality, usability, stress regulation, and well-being. The paper contributes to human-centered building design by formalizing a mechanism-based account of how interior environments can support behavior without reducing users to passive recipients of environmental manipulation. It concludes with practical implications for design briefing, post-occupancy evaluation, VR-based testing, healthcare and workplace audits, safety-critical settings, and future longitudinal validation. Full article

Thermal tactile perception plays a crucial role in enhancing realism and immersion in human–machine interaction, virtual/augmented reality, and wearable systems. By exploiting the thermoelectric effect to achieve precisely controllable heating and cooling, wearable thermoelectric devices (WTEDs) offer an effective approach for generating localized and programmable thermal sensations, which calls for a clear understanding of skin temperature regulation mechanisms. In this work, a dynamic thermal conduction model is developed for a skin–WTED integrated system incorporating a nickel foam-reinforced hydrogel heat sink, based on the dual-phase lag (DPL) bioheat conduction theory. The model accounts for blood perfusion and metabolic heat generation in skin tissue, as well as the Thomson effect within the thermoelectric legs and convective heat losses from their side surfaces. The theoretical predictions are validated through human skin temperature regulation experiments using a fabricated WTED, showing close agreement between experiments and simulations and confirming the model’s accuracy and reliability. Based on the validated model, the cooling current, filling factor, and thermoelectric leg height are optimized by minimizing the skin surface temperature. Furthermore, the model is applied to thermal tactile feedback studies, enabling the controlled reproduction of skin thermal sensations associated with common objects, including an iron block, a PMMA plate, and carbonated beverages packaged in aluminum cans and plastic bottles. Overall, this study provides a practical and predictive framework for understanding, optimizing, and applying WTEDs in thermal tactile feedback. Full article

The alcoholic fermentation of wine is a complex, multivariable chemical process. This study proposes a cognitive system for the dynamic control of the industrial wine fermentation process based on acoustic emission signals. The core of the system uses machine learning algorithms to perform perception tasks and predict density as a relevant chemical parameter for control and decision-making during the process. A hydrophone submerged in the fermentation tank is used to monitor the process. At the TRL4 stage we are currently at, measurements were taken at a winery in the Rioja Designation of Origin and were acquired and stored during the alcoholic fermentation process to be used as input data. Manual measurements collected by the winemaker throughout the fermentation process were used to train and validate the results. The performance of the machine learning model was measured using statistical metrics. The results of the experiments show a high correlation between the density calculated using the model and the densities measured by the winemaker. The proposed system is a valid and innovative tool for controlling a process as multivariable as alcoholic fermentation. The anticipatory nature of the acoustic signal with regard to the evolution of temperature in the process is used as the starting point for the new proposal. Its application helps to ensure stable fermentation by reducing the thermal stress on yeasts caused by the thermal shocks of current temperature control systems, improving process control in wineries and serving as a cognitive system for control and decision-making. Full article

Lithium-ion battery thermal runaway has become a key safety hazard restricting the development of electric vehicles. Early precursor signals of thermal runaway are characterized by multi-scale features, weak signal strength and spatial coupling, posing significant challenges for traditional methods in achieving accurate early warning. To solve this problem, a multi-scale spatiotemporal attention network (MSTA-Net) is proposed for battery thermal runaway early warning. First, a systematic feature engineering process is designed, including signal denoising, normalization processing and multi-level feature construction, to fully extract discriminative information from voltage and temperature signals. Then, the MSTA-Net architecture is constructed, which includes three parallel feature extraction branches: local fine perception branch based on 1D depthwise separable convolution to capture transient anomalies, a temporal evolution modeling branch based on bidirectional gated recurrent units to learn long-term trends, and a global spatial dependence branch based on a graph attention network to model the spatial propagation of thermal runaway. Finally, an adaptive fusion gate is designed to dynamically fuse the features of each branch according to the input context. The experimental results on the self-built battery thermal runaway dataset show that the proposed MSTA-Net achieves a recall rate of 98.7%, an average early warning time of 115 s and a false alarm rate of 0 times/h. Compared with traditional machine learning and deep learning models such as Random Forest, LSTM and Transformer, the model has significant advantages in early warning accuracy, timeliness and robustness. Ablation experiments verify the effectiveness of each component of the MSTA-Net. The proposed method can provide reliable early warning of thermal runaway only by using the existing voltage and temperature sensors of the battery management system, which has important engineering application value. Full article

There are differences in imaging characteristics between infrared and visible light images: visible light images can provide rich texture and color information, but imaging is limited in harsh weather conditions. Infrared images are based on the target’s thermal radiation characteristics and have the ability to resist environmental interference but lack details and background information. Effectively integrating the two can significantly enhance scene understanding ability and improve environmental perception and target recognition performance in applications such as intelligent driving. However, existing fusion methods still face challenges, especially in complex scenes where it is difficult to balance the full preservation of target information with the complete presentation of background details, often resulting in difficulties in extracting differentiated features from different modalities. This article proposes a target detection method based on the visible light infrared fusion diffusion model. This method introduces the Stable Diffusion architecture and designs a target perception spatial fusion weight module that can adaptively generate a spatial fusion weight map based on modal differences. By implementing a multi-stage dynamic fusion strategy, the fusion ratio is automatically adjusted at different diffusion stages. A full-step multi-step prediction mechanism is adopted to improve fusion quality and stability. Compared with existing methods, the method proposed in this article has significant advantages. Experiments on multiple publicly available datasets have shown that this method outperforms existing mainstream methods in key metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and ean Absolute Error (MAE) and also demonstrates good detection performance in downstream tasks for object detection. Full article

Urban growth and climate change intensify urban heat islands (UHIs), altering atmospheric stability and promoting the accumulation of particulate matter ≤ 10 µm (PM₁₀) and particulate matter ≤ 2.5 µm (PM_2.5), particularly in high-altitude megacities. However, there remains a scarcity of integrated dynamic models capable of representing these interactions at the intra-urban scale. This study develops a socio-environmental dynamic model to evaluate the influence of UHIs on PM₁₀ and PM_2.5 concentrations across localities of a high-altitude Latin American megacity (Bogotá, Colombia). A dynamic simulation model was developed in Vensim^®, integrating temperature, PM₁₀, PM_2.5, and citizen perception data. Statistical and spatial analyses were conducted to represent intra-urban thermo-atmospheric interactions. The results show that the model captures the influence of UHIs on PM₁₀ and PM_2.5 concentrations. Higher PM concentrations are simulated in localities with high imperviousness (PM₁₀: 33.4–50.4 µg/m³; PM_2.5: 21.5–25.1 µg/m³) and lower PM concentrations in areas with greater vegetation cover. Sensitivity analysis of the dynamic model reveals nonlinear amplifications of up to 15–20 µg/m³ in PM₁₀ and 8–10 µg/m³ in PM_2.5 associated with small thermal variations (1–2 °C). Under scenarios with significant UHI intensity, increases reach 4–6 µg/m³ in PM₁₀ and 3–4 µg/m³ in PM_2.5. These findings confirm that UHIs act as amplifiers of pollution and that urban thermal interventions could reduce PM concentrations by up to 10–20%. Full article

Infrared–visible image fusion aims to integrate structural details, natural appearance, and thermal target information from two source modalities, thereby improving visual perception in complex scenes. However, under challenging conditions such as low illumination, noise, low contrast, and overexposure, existing methods often struggle to stably preserve cross-modal shared features (CMSF) while effectively highlighting single-modal specific features (SMSF). In addition, the absence of real fusion labels limits effective supervised learning. To address these issues, this paper proposes a unified semantic-guided fusion network, termed USF-Net, which jointly models the shared and specific features of infrared and visible images under a unified semantic representation and dynamically adjusts the fusion strategy according to imaging contexts. Specifically, the Shared Feature Alignment and Enhancement (SFAE) module is designed to strengthen consistent modeling of common features across modalities, while the Specific Feature Reweighting Fusion (SFRF) module selectively enhances modality-specific features to achieve stable and controllable fusion. Moreover, the constructed real fusion labels are incorporated into the loss function for collaborative training. Experimental results on multiple public datasets demonstrate that USF-Net achieves superior fusion performance under diverse complex imaging conditions. Full article

Heating, ventilation, and air conditioning (HVAC) systems struggle to maintain optimal thermal comfort because perception is subjective and varies significantly across individuals. Traditional uniform cooling strategies often overlook demographic diversity, leading to inequitable comfort outcomes and inefficient building operations. To address this limitation, this study analyzed a web-based survey of 366 university occupants using a partial proportional odds model with multiple imputation and inverse-frequency weighting. Interaction terms, specifically Age–Activity, Gender–Clothing, and Age–Clothing, were included to assess combined effects that reflect demographic disparities in adaptive capacity. The results show that clothing insulation, activity, age, gender, race/ethnicity, and space type significantly influence thermal responses. Notably, male occupants were more than three times as likely to report feeling too warm (odds ratio [OR] = 3.24), whereas older adults exhibited significantly lower odds of reporting feeling too warm (OR = 0.42). Substantial variation was observed across racial and ethnic groups (ORs ranging from 2.4 to 6.5). These findings highlight the limitations of traditional population-average comfort approaches and provide valuable scientific insights for demand-response-ready HVAC strategies that adjust temperature setpoints dynamically without sacrificing comfort. By offering accurate, real-time estimates across diverse thermal ranges, these occupant-centric models reduce HVAC energy use and associated emissions at the building scale while supporting ancillary services for flexible load shifting and smarter coordination within low-carbon electric grids. Ultimately, incorporating demographic and contextual diversity into building controls reduces unnecessary cooling waste while promoting thermal equity, establishing a human-centric foundation for sustainable built environments. Full article

Rapid urbanization in South Asian coastal cities is systematically dismantling natural cooling infrastructure, driving unprecedented urban heat island (UHI) intensification with severe consequences for human health, energy systems, and urban livability. Despite growing research attention, comprehensive frameworks that simultaneously capture temporal UHI dynamics, machine learning-based thermal projections, and community-grounded validation remain scarce, particularly for secondary coastal cities in tropical developing regions. This study addresses these gaps by investigating UHI dynamics in Chattogram City Corporation (CCC), Bangladesh, through three integrated methodological pillars: (1) multi-temporal remote sensing analysis using Landsat 5 and 8 imagery spanning 2005–2025; (2) comparative evaluation of five machine learning algorithms (LightGBM, Random Forest, XGBoost, SVM, and MLP) for land use/land cover (LULC) classification and land surface temperature (LST) regression, with iterative scenario projections for 2029, 2033, and 2037; and (3) a structured public perception survey of 384 residents validated through participatory mapping and focus group discussions. Landsat analysis revealed dramatic LULC transformations: built-up areas expanded 88% (12,649 to 23,719 acres), while waterbodies declined 53.1% and vegetation decreased 21.9%. Mean LST increased by 9.09 °C (from 30.94 °C to 40.03 °C), with mean UHI intensity rising from 19.59 to 33.88 standardized units over two decades. LightGBM achieved optimal LULC classification (F1-weighted: 0.765) while Random Forest best predicted LST (RMSE: 1.51, R²: 0.809). Projections indicate continued thermal escalation, with mean LST reaching 43.64 °C and UHI intensity exceeding 37.41 standardized units by 2037. Persistent thermal hotspots were identified in the southwestern coastal corridor, western industrial belt, and central business district. Community survey data corroborated satellite-derived patterns, with 73.44% of respondents observing environmental degradation, yet only 22% aware of formal heat mitigation policies, and 87% supporting vegetation-based cooling interventions. This integrated framework advances urban thermal monitoring in tropical coastal cities and provides spatially targeted, community-endorsed evidence for climate-responsive urban planning. Full article

Thermoplastic polyurethane properties are governed by the interplay between soft-segment mobility, hard-segment interactions, and segmented morphology, yet the extent to which atomistic predictions of their thermal and mechanical behavior depend on force-field choice remains insufficiently benchmarked. Here, we combine FTIR, DSC, TGA, and tensile testing with all-atom molecular dynamics simulations to investigate HDI–PEG polyurethane systems across a controlled soft-segment series. Experimentally, films with PEG molecular weights of 400, 1000, and 1500 g/mol were characterized, while simulations were extended to 400–2000 g/mol to compare two complementary force-field frameworks under a consistent protocol: OPLS-AA, a conventional atom-type-based force field, and OpenFF/Sage, a direct-chemical-perception framework augmented here with bespoke torsional refinements. Both force fields reproduce the composition-driven decrease in Tg and density with increasing PEG length, but differ systematically in absolute values, with OPLS-AA predicting higher densities and Tg values than OpenFF. Tensile experiments show the highest elastic modulus for PEG400, a marked decrease at PEG1000, and a partial recovery at PEG1500. Although nanosecond-scale deformation simulations overestimate absolute moduli because they probe high-rate elastic response, they recover composition-dependent stiffness differences, with OpenFF yielding a more pronounced non-monotonic trend than OPLS-AA. Overall, this work provides an experimentally anchored benchmark for assessing which composition-driven trends in HDI–PEG polyurethanes are robust across force-field families, and which observables remain sensitive to model assumptions and simulation scale. Full article

Green façades are acknowledged as passive strategies that reduce heat accumulation, enhance biodiversity, improve particulate matter absorption and provide psycho-physiological benefits for users. However, evaluations of their thermal performance—accounting for seasonality, vegetation density, and leaf characteristics—remain incomplete. This study addresses this gap by assessing two green façade typologies on a sample building located in Northern Italy (Cfa climate). ENVI-met microclimate simulations compared a bare wall with vegetated façades featuring Hedera helix (evergreen) and Parthenocissus tricuspidata (deciduous) across four orientations and seasonal conditions, considering the sample building and the immediate surrounding outdoor space. Both species reduced wall-surface temperatures (T₀) and improved outdoor thermal comfort perception (PET), influenced by LAI dynamics, foliage persistence, and façade orientation. Results indicate that Parthenocissus tricuspidata achieved the greatest cooling effect during hot periods due to higher LAI, with absolute T₀ reductions of up to 22.1 °C on southern façades and 30.0 °C on western façades. In these orientations, PET improvements reached up to 3.0 °C (south) and 8.0 °C (west). Conversely, Hedera helix ensured stable year-round performance and performed better on northern façades during colder periods. The results stress the need for integrated design that aligns plant choice with orientation and seasonal growth to optimize thermal performance, cut cooling demands, and improve outdoor comfort. Full article

Red–Green–Blue–Thermal (RGBT) object detection is critical for robust all-weather perception. However, deploying dual-stream networks on resource-constrained edge devices is severely hindered by insufficiently adaptive multimodal fusion, the loss of small-object features during downsampling, and substantial computational overhead. To address these challenges, we propose GEM-YOLO, a real-time and lightweight RGBT detector. Specifically, an Adaptive Multimodal Gated Fusion Mechanism (GFM) is designed to dynamically calibrate modality weights and suppress noise. Furthermore, Space-to-Depth (SPD) convolutions are integrated into the backbone to achieve lossless downsampling, preventing the feature collapse of small targets. Finally, a lightweight Ghost-Neck is constructed using Ghost modules and GSConv to eliminate computational redundancy. Extensive experiments on the Forward-Looking Infrared (FLIR) and Multi-Modal Multispectral Fusion Dataset (M3FD) datasets demonstrate the effectiveness of the proposed method. With only 7.58 Giga Floating-Point Operations (GFLOPs) and 3.44 million parameters (M), GEM-YOLO reduces the computational cost by 18.6% relative to the dual-stream YOLOv11n baseline. Concurrently, it achieves competitive mean Average Precision at IoU = 0.5 (mAP@50) scores of 82.8% and 69.0% on FLIR and M3FD, respectively, with more evident gains on small-target localization. In practice, GEM-YOLO maintains competitive detection performance while keeping computational overhead low, making it promising for real-time multispectral perception on resource-constrained edge platforms. Full article

In intermittent convective cooling environments created by split air conditioners, the dynamic nature of the environment poses challenges to traditional steady-state thermal comfort models in predicting human thermal comfort. Therefore, this study proposes an alliesthesia-informed machine learning framework that encodes alliesthesia theory into explicit mathematical features for predicting dynamic overall thermal comfort. Data were obtained through controlled experiments under intermittent cooling conditions, and a theory-driven feature set incorporating dynamic set points and physio-psycho gap was constructed. The results demonstrate that the gradient boosting model achieved optimal performance under rigorous subject-level cross-validation (test set R² = 0.71). Interpretability analysis confirmed that model decisions are highly dependent on exposure time and alliesthesia features, whose importance far exceeds that of conventional environmental parameters, revealing that the core of thermal comfort perception lies in the dynamic interplay between physiological states and psychological expectations. Furthermore, the proposed few-shot personalized calibration strategy can effectively accommodate individual differences with minimal user data. This study demonstrates that the framework not only enhances prediction accuracy but also improves model interpretability and generalizability by incorporating alliesthesia-inspired feature representations, offering a new perspective for developing next-generation human-centric intelligent environmental control systems. Full article