Search Results (168)

The accelerated expansion of telework, driven by the COVID-19 pandemic, has transformed global work dynamics. Despite this, limited research exists on the implications of Indoor Environmental Quality (IEQ) on home workspaces. Factors like thermal comfort, lighting, air quality, and noise significantly influence the well-being, productivity, and health of teleworkers. Home spaces are often not designed to meet the environmental quality standards of traditional offices, altering indoor conditions. This scoping review investigates the IEQ–telework relationship, analyzing 41 studies from 18 countries. Findings show that elevated noise levels and insufficient lighting increase stress and fatigue, while inadequate air quality reduces cognitive performance and creativity. Conversely, access to natural light, pleasant views, and thermal comfort improves overall satisfaction and productivity. The study identifies a fragmented and poorly connected research network, with few active global groups studying IEQ in home workspaces. These results underscore the need for interdisciplinary research to address the societal and environmental challenges of teleworking and develop equitable, healthy remote environments. Future studies must consider cultural diversity and underrepresented regions to bridge existing knowledge gaps. Full article

Prolonged exposure to acoustic environments in office settings may impair employees’ cognitive performance, yet the underlying mechanisms remain contested. This study investigated the dual effects of acoustic intensity and sound type on employees’ performance by integrating behavioral measures, subjective assessments, and neurophysiological evidence. Results demonstrated significant interaction effects: while increasing levels of office noise and music generally impaired accuracy and efficiency, dialog at moderate-to-high intensities (65 dBA and 85 dBA) significantly shortened reaction times compared to the low-intensity condition (45 dBA). Mediation analyses reconciled these patterns by revealing distinct psychological pathways: Tension-Anxiety fully mediated the performance decrement under noise. In contrast, Tension-Anxiety and Anger-Hostility served as partial mediators (29.71%) and suppressors (40.01%) in the relationship between dialog intensity and performance index. Mental workload fully mediated the performance benefits of moderate intensity music. Electroencephalography (EEG) analyses further corroborated the behavioral findings, identifying neurophysiological pathways through which acoustic exposure influenced performance. This study integrated behavioral and neural approaches to provide empirical evidence for optimizing indoor acoustic environments that promote health, comfort, and productivity. Full article

The indoor low-voltage power line network is characterized by highly irregular interferences, where background noise coexists with bursty impulsive noise originating from household appliances and switching events. Traditional noise models, which are considered monofractal models, often fail to reproduce the clustering, intermittency, and long-range dependence seen in measurement data. In this paper, a Multifractal Random Walk (MRW) framework tailored for Power Line Communication (PLC) noise modelling is developed. MRW is a continuous time limit process based on discrete-time random walks with stochastic log-normal variance. As such, the formulated MRW framework introduces a stochastic volatility component that modulates Gaussian increments, thus generating heavy-tailed statistics and multifractal scaling laws which are consistent with the measured PLC noise data. Empirical validation is carried out through structure function analysis and covariance of log-amplitudes, both of which reveal scaling characteristics that align well with MRW-simulated predictions. This proposed model captures both the bursty nature and correlation structure of impulsive PLC noise more effectively as compared to the conventional monofractal approaches, thereby providing a mathematically grounded framework for accurate noise generation and the robust system-level performance evaluation of PLC networks. Full article

In recent years, the scientific community has increasingly focused on state-of-the-art techniques, such as MIMO and mmWave transmission, aimed at enhancing the performance of telecommunication channels both quantitatively and qualitatively through various approaches. These efforts often rely on channel models designed to more accurately represent real-world conditions, thereby ensuring that the results are objective and practically applicable. In the present study, we employ one of the most scientifically reliable system- level simulators, Vienna SLS Simulator, to evaluate the performance of a wireless channel that we configure based on the latest standards (3GPP TR 36.873). We take into account the well-known non-symmetrical behavior of mMIMOs, where m stands for microwave MIMOs, in wireless communication systems and analyze the resulting changes in key performance metrics including average cell throughput, average user spectral efficiency and signal-to-interference-plus-noise ratio (SINR). We vary specific parameters such as transmission power, antenna polarization, ratio of indoor to outdoor users, and others with the aim of validating or challenging existing scientific assumptions. Particular attention is given to studying how variations in the aforementioned factors affect channel geometry and spatial uniformity, emphasizing the role of antenna geometry, polarization and user distribution in shaping channel asymmetries in mmWave MU-MIMO systems. Overall, this study provides insights into designing more balanced and efficient wireless systems in realistic urban environments. Full article

This study investigates the acoustic conditions of a design studio (Studio 130) in the Department of Interior Architecture and Environmental Design at Afyon Kocatepe University by integrating 14 weeks of continuous noise measurements with perception data collected from 192 students. Noise measurements were conducted in accordance with ISO 3382-3:2022 guidelines at three locations—window front, door side, and studio midpoint—during morning, noon, and evening periods, with 10 min recordings at each session. The results indicate that when students were present, the equivalent continuous noise level (Leq) reached an average of 65.5 dB(A), with peak levels rising to 72.3 dB(A) during jury sessions. These values substantially exceed the recommended 35 dB(A) classroom threshold by the World Health Organization and the 35–45 dB(A) limits specified in national regulations for indoor educational spaces. Survey findings reveal that 88% of students experienced loss of concentration, 72% reported decreased productivity, 60% had difficulty communicating, and 52% reported fatigue due to noise exposure. Pearson correlation analysis demonstrated a strong relationship between measured noise levels and reported negative effects (r = 0.966). Moreover, independent samples t-test results confirmed that student presence significantly increased studio noise levels (t = 4.98, p < 0.001). The novelty of this research lies in its combined use of longitudinal objective measurements and subjective perception data, addressing the unique open-plan, collaborative, and critique-based pedagogical structure of design studios. The findings highlight that acoustic comfort is a critical component of learning quality in studio-based education. Based on the results, the study proposes several design and material interventions—including spatial dividers, acoustic ceiling panels, fabric-wrapped absorbers, and impact-reducing flooring—to enhance auditory comfort. Overall, the study emphasizes the necessity of integrating acoustic design strategies into studio pedagogy to support concentration, communication, and learning performance. Full article

Accurate extrinsic calibration between radar and camera sensors is essential for reliable multi-modal perception in robotics and autonomous navigation. Traditional calibration methods often rely on artificial targets such as checkerboards or corner reflectors, which can be impractical in dynamic or large-scale environments. This study presents a fully targetless calibration framework that estimates the rigid spatial transformation between radar and camera coordinate frames by aligning their observed trajectories of a moving object. The proposed method integrates You Only Look Once version 5 (YOLOv5)-based 3D object localization for the camera stream with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Sample Consensus (RANSAC) filtering for sparse and noisy radar measurements. A passive temporal synchronization technique, based on Root Mean Square Error (RMSE) minimization, corrects timestamp offsets without requiring hardware triggers. Rigid transformation parameters are computed using Kabsch and Umeyama algorithms, ensuring robust alignment even under millimeter-wave (mmWave) radar sparsity and measurement bias. The framework is experimentally validated in an indoor OptiTrack-equipped laboratory using a Skydio 2 drone as the dynamic target. Results demonstrate sub-degree rotational accuracy and decimeter-level translational error (approximately 0.12–0.27 m depending on the metric), with successful generalization to unseen motion trajectories. The findings highlight the method’s applicability for real-world autonomous systems requiring practical, markerless multi-sensor calibration. Full article

Indoor environmental quality (IEQ), influenced by ventilation and acoustic conditions, directly affects human health and comfort. Existing studies often concern either ventilation or sound insulation alone, neglecting the impact of the trickle ventilator's internal structure and its combination with windows on overall performance. This study introduced a double-chamber model to quantify the ventilation performance of three trickle ventilators using tracer-gas-decay and pressure-difference methods. We calculated the flow coefficient ($C_d$) and flow exponent ($n$) to reveal differences in pressure sensitivity, with trickle ventilator TV2 showing the highest-pressure sensitivity ($C_d$ = 1.34, $n$ = 0.89). The weighted sound reduction index ($R_W$) and weighted sound insulation index for traffic-noise correction ($R_W +{ C}_{tr}$) were measured, showing trickle ventilators TV1-1 and TV1-2, and TV2 were 29 dB, 30 dB, and 34 dB, respectively. And the sound insulation and ventilation performance of window-trickle ventilator combinations were analyzed. Trickle ventilators could enhance acoustic performance for low-insulation windows but reduce it for high-insulation windows. The study also quantitatively balanced ventilation and acoustics. This research provides data support and theoretical guidance for the synergistic optimization of ventilation and sound insulation in building environments and provides guidance on ventilation and noise control strategies suited to different floor levels and outdoor noise environments. Full article

The rapid global ageing population highlights the pressing need for age-friendly housing that supports independent and healthy ageing in place. Indoor environmental quality (IEQ), encompassing thermal comfort, air quality, acoustic environment, lighting, and humidity, is increasingly recognized as a critical determinant of the health and well-being of older adults. Despite this, existing standards and research methodologies often inadequately address the physiological sensitivities and subjective perceptions specific to older populations. This systematic review synthesizes empirical studies published between 2016 and 2025 on IEQ in age-friendly housing. Following PRISMA guidelines, 31 studies were rigorously screened and analyzed using thematic synthesis. Key findings indicate that older adults’ thermal comfort ranges diverge from standard models, indoor air quality and noise levels often fall short of their needs, and their subjective satisfaction remains low. Effective interventions include improved ventilation, enhanced insulation, noise reduction strategies, and the adoption of smart home technologies. Taken together, these findings reveal a significant gap between existing IEQ standards and the needs of older adults and highlight the necessity of both longitudinal, integrated assessments of objective and subjective factors and participatory design strategies to optimize IEQ in age-friendly housing. Full article

Enhancing RAW images acquired under low-light conditions remains a fundamental yet challenging problem in computational photography and image signal processing. Recent deep learning-based approaches have shifted from real paired datasets toward synthetic data generation, where sensor noise is typically simulated through physical modeling. However, most existing methods primarily account for additive noise, neglect multiplicative noise components, and rely on global calibration procedures that fail to capture pixel-level manufacturing variability. Consequently, these methods struggle to faithfully reproduce the complex statistics of real sensor noise. To overcome these limitations, this paper introduces a physically grounded composite noise model that jointly incorporates additive and multiplicative noise components. We further propose a per-pixel noise simulation and calibration strategy, which estimates and synthesizes noise individually for each pixel. This physics-based calibration not only circumvents the constraints of global noise modeling but also captures spatial noise variations arising from microscopic CMOS sensor fabrication differences. Inspired by the recent success of rectified-flow methods in image generation, we integrate our physics-based noise synthesis into a rectified-flow generative framework and present PGRF (Physics-Guided Rectified Flow): a physics-guided rectified-flow framework for low-light RAW image enhancement. PGRF leverages the expressive capacity of rectified flows to model complex data distributions, while physical guidance constrains the generation process toward the desired clean image manifold. To evaluate our method, we constructed the LLID, a dedicated indoor low-light RAW benchmark captured using the Sony A7S II camera. Extensive experiments demonstrate that the proposed framework achieves substantial improvements over state-of-the-art methods in low-light RAW image enhancement. Full article

Indoor air quality (IAQ) and thermal comfort influence the health and cognitive performance of school occupants. This study investigated carbon dioxide (CO₂), nitrogen dioxide (NO₂), thermal comfort, and ventilation rates (VRs) in eight naturally ventilated (NV) primary school classrooms in Ireland during October 2024, combining environmental monitoring with teacher surveys. Mean CO₂ concentrations ranged from 796 ppm to 2469 ppm, exceeding national guidelines in seven of the eight classrooms. NO₂ levels ranged from 3.4 µg/m³ to 7.2 µg/m³, with indoor/outdoor ratios increasing with VRs and influenced by window orientation and road proximity. Indoor temperatures remained within recommended limits, while relative humidity ranged from 53% to 78% mirroring CO₂ trends and exceeding guideline levels in classrooms with lower VRs and temperatures. Occupied VRs ranged from 1.2 L/p/s to 4.1 L/p/s with window opening behaviours, reliant on teachers’ perceptions of thermal comfort, accounting for 84% to 96% of VRs. Ventilation in NV classrooms is often insufficient, yet increasing VRs can compromise thermal comfort and increase ingress of outdoor pollutants and noise. The findings highlight the ineffectiveness of current school ventilation standards, which rely heavily on user operation. Integrating occupant-led strategies, including scheduled purging, awareness campaigns, and pre-emptive air quality alerting, into policy offers practical, immediate pathways to improving IAQ, fostering healthy, sustainable learning environments. Full article

The ventilation system is one of the most important elements of a building for the appropriate insurance of indoor climate parameters. Nowadays, textile ventilation systems are increasingly being used as a solution for low-energy buildings. Greater air movement and distribution in ventilation systems often leads to one of the most noticeable issues for people—increased noise in the indoor environment. One of the solutions is to use noise reducing diffusers. The aim of this research was to design and test a diffuser that fulfills noise regulations, would be light (weight less than 3 kg), be able to flexibly change geometry and have a design that harmonizes with the interior design, could be easily installed into a suspended ceiling, have a simple connection to the ventilation duct and be able to be effortlessly removed for maintenance, and be sustainable (usage of recycled materials). Three types of diffusers were created according to set characteristics and tested. The test results showed that the aim of the research was achieved—the emitted noise levels are below the regulation’s required level of less than 45 dBA. Also, it is light—the weight is 1.7 kg and 2.8 kg, respectively, for square and rectangular diffusers; has a flexible construction and design; is made from recycled materials. Full article

The integration of Mechanical Ventilation with Heat Recovery (MVHR) systems into heritage buildings poses a series of challenges, largely attributable to architectural constraints and conservation requirements. The present study offers an operational campaign of an MVHR system installed during the energy retrofit of a protected residential heritage dwelling in Vitoria-Gasteiz, Spain. Although environmental monitoring was carried out throughout the year, representative spring, autumn and winter days of continuous operation were analysed, as the occupants frequently avoided using the system due to noise perception. This limitation highlights the importance of considering acoustic comfort and user acceptance as critical factors in the long-term viability of MVHR in heritage contexts. The system was assessed under real-life conditions using continuous environmental monitoring, with a focus on indoor air quality (IAQ), thermal efficiency, airflow balance, and pressure losses. Despite the acceptable mean apparent thermal effectiveness (0.74) and total useful efficiency (0.96), the system’s performance was found to be constrained by significant flow imbalance (up to 106%) and elevated pressure drops, which were attributed to the legacy of the duct geometry. The results obtained demonstrate IAQ improved overall, with mean CO₂ concentrations below ~650 ppm across the analysed dataset; however, daily means occasionally exceeded 900–1000 ppm during high-occupancy periods and in the absence of spatially distributed demand control. These exceedances are consistent with the measured outdoor baseline (~400–450 ppm) and reflect the need for post-commissioning balancing and room-level sensing to sustain Category II performance in heritage dwellings. This study provides empirical evidence on the limitations and opportunities of MVHR deployment in historic retrofits, thus informing future guidelines for sustainable interventions in heritage contexts. Full article

The extraction of architectural structural line features can simplify the 3D spatial representation of built environments, reduce the storage and processing burden of large-scale point clouds, and provide essential geometric primitives for downstream modeling tasks. However, existing 3D line extraction methods suffer from incomplete and fragmented contours, with missing or misaligned intersections. To overcome these limitations, this study proposes a patch-level framework for 3D line extraction and structural optimization from building point clouds. The proposed method first partitions point clouds into planar patches and establishes local image planes for each patch, enabling a structured 2D representation of unstructured 3D data. Then, graph-cut segmentation is proposed to extract compact boundary contours, which are vectorized into closed lines and back-projected into 3D space to form the initial line segments. To improve geometric consistency, regularized geometric constraints, including adjacency, collinearity, and orthogonality constraints, are further designed to merge homogeneous segments, refine topology, and strengthen structural outlines. Finally, we evaluated the approach on three indoor building environments and four outdoor scenes, and experimental results show that it reduces noise and redundancy while significantly improving the completeness, closure, and alignment of 3D line features in various complex architectural structures. Full article

Severe sensitivity to various environmental chemicals affects an increasing number of people—a condition referred to as Multiple Chemical Sensitivity (MCS). The responses are both physical and psychological, where avoidance of chemical triggers can lead to social isolation, thereby increasing the level of disability. There is a need for user supportive environments where people with MCS can thrive, both indoors and outdoors. The study resulted in three principles for designing outdoor housing areas: (1) using spatial analysis to create a site layout that minimizes exposure to external and confounding triggers (e.g., noise, visual disturbances); (2) using zoning to clearly delineate private from semi-private areas; and (3) selecting vegetation and materials carefully to avoid triggers. The principles were developed in an iterative process based on existing research combined with a survey involving 58 MCS respondents. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article