Search Results (1,368)

The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article

Mathematical modeling plays a key role in understanding and optimizing transport system operations under uncertain and dynamic conditions. This study proposes a data-driven predictive framework for estimating passenger-accepted vehicle occupancy, addressing a critical gap in transport system planning under public health-related constraints. Using data from a structured survey conducted across seven Southeast European countries (N = 476), the study integrates statistical analysis and machine learning approaches to model acceptable occupancy levels across multiple transport modes, including passenger cars, taxis, tourist buses, and public buses. The problem is formulated as a predictive mapping between multidimensional input variables and occupancy acceptance levels, modeled using both probabilistic and nonlinear function approximation methods. The results highlight that age, gender, and area of residence are the most significant determinants of occupancy acceptance, while education level has limited predictive relevance. Furthermore, a multi-layer feedforward artificial neural network is developed to capture nonlinear relationships between variables, achieving strong predictive performance (minimum MSE = 0.0089). The main contribution of this research lies in linking behavioral data with predictive modeling to quantify acceptable occupancy thresholds and support realistic simulation of passenger responses in crisis conditions. The proposed modeling framework contributes to transport system planning, enabling data-driven capacity management, enhanced safety strategies, and improved resilience of passenger transport operations. Full article

This study examines the gap concerning occupants’ perceived thermal comfort and objectively measured indoor conditions in a green university building in Riyadh. The purpose is to assess occupant satisfaction with thermal conditions, compare subjective responses with physical measurements, and derive design and operational implications for educational buildings in hot-arid climates. The primary aim was to assess occupant satisfaction with indoor thermal conditions and to measure key environmental parameters to provide a thorough assessment of thermal comfort. A cross-sectional approach was used, combining subjective data from the Center for the Built Environment (CBE) Occupant Indoor Environmental Quality (IEQ) survey with objective measurements of air temperature, relative humidity, mean radiant temperature, and air velocity, which were documented over five consecutive working days during the mid-winter period in Riyadh. These parameters were explored using the CBE Thermal Comfort Tool to calculate Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) indices. Statistical analyses examined the relationship between occupant-reported comfort and measured environmental conditions. Results showed that only 36% of occupants reported satisfaction with thermal comfort, while 48% expressed dissatisfaction. In contrast, objective measurements indicated stable indoor conditions within recommended comfort ranges (average temperature 23 °C, humidity 30–34%, MRT 24 °C, air velocity 0.5–1.0 m/s), with PMV values near neutral (−0.2 to 0.0) and PPD below 6%. The observed discrepancy highlights the influence of regional climate, individual adaptability, and perceived control. These findings emphasize the need to integrate both subjective feedback and objective measurements to develop occupant-centered strategies that enhance comfort and well-being in sustainable educational buildings in hot-arid climates. Full article

Indoor environmental quality (IEQ) significantly impacts people’s comfort, health, and productivity in buildings, and modern green rating systems are primarily focused on energy efficiency rather than the direct user experience. This paper analyses the relationship between IEQ and the perceived quality of life (QoL) of certified and conventional office buildings in Malaysia using a mixed-methods design. The questionnaires were completed by 162 employees working in four open-plan offices: two were certified under the Green Building Index (GBI) established in Malaysia, and two were traditional. This was supplemented by 14 semi-structured interviews and 2 focus groups. The factors of IEQ were divided into ambient, designed, and behavioral environments. It was statistically determined that behavioral factors, such as visual privacy, personalization, ergonomics, and control, exhibited the strongest correlations with overall QoL, compared to ambient factors such as air quality or thermal comfort. Green buildings performed better in terms of daylighting and esthetics than conventional buildings, though they did not always deliver higher occupant satisfaction. The results indicate that current green certification frameworks pay insufficient attention to occupant-centered aspects. The proposed research adds a validated IEQ-QoL framework that predicts the incorporation of subjective user experience into building performance indicators, which can be important for certification reform, post-occupancy evaluation (POE), and human-centered sustainable design approaches. Full article

Conventional Heating, Ventilation, and Air Conditioning (HVAC) control systems primarily rely on environmental and physiological parameters, largely ignoring the critical influence of psychological states on thermal comfort. Overlooking this factor often leads to suboptimal occupant satisfaction, energy inefficiency and thus carbon dioxide (CO₂) emissions. To this aim, this study introduces a novel mood-adaptive HVAC control system integrating psychological feedback to decrease CO₂ emissions in office buildings by reducing energy consumption and optimizing comfort. A total of 7000 thermal facial measurement records and high-resolution camera images were collected across seven mood state conditions using video stimuli and the Profile of Mood States (POMS) questionnaire to evaluate mood variations. A dual artificial intelligence system was developed: a Convolutional Neural Network (CNN) for analyzing facial expressions and an Artificial Neural Network (ANN) for processing facial temperatures via thermal imaging. These models collectively predict occupant mood in real-time, and a custom-designed wearable necklace interface transmits this data to dynamically adjust HVAC setpoints. To evaluate system performance, energy consumption was directly measured in real-life operations using an energy analyzer, without relying on simulations. Results indicate that this prototype personalized mood-driven system has the potential to enhance perceived thermal comfort while achieving up to a 20% reduction in carbon emissions compared to conventional systems. This human-centered approach significantly advances intelligent building management and climate change mitigation. Full article

Background/Objectives: Cancer places emotional and psychosocial demands on healthcare professionals; therefore, this study aimed to examine sociodemographic and psychosocial determinants, including emotional competence, empathy, and stigma, and to assess their interrelationships with mental health, attitudes towards cancer, and cancer-related stigma among healthcare professionals involved in cancer care. Methods: This cross-sectional study was conducted from July 2025 to January 2026 via a self-administered questionnaire among 264 hospital and primary care healthcare professionals in Osijek, Croatia (69 men and 195 women; median age 37 years, IQR 31–47, age range 20–64 years), all directly involved in providing healthcare to cancer patients in Croatia. Results: Significant differences were observed by gender, age, occupation, and workplace. Men were more frequently physicians and had higher education levels and socioeconomic status, whereas women achieved higher scores in emotional competence and empathy. Physicians more often had shorter overall work experience and reported greater perceived controllability of cancer. Age-related differences were found in perceived discrimination, stigma, and controllability of cancer. Primary healthcare professionals showed a higher level of empathy and proactivity and a lower perception of cancer as an incurable disease. Higher empathy was associated with lower stigma, while negative emotions and greater proactivity were associated with higher stigma, and emotional competence was a strong predictor of empathy. Conclusions: The study identified notable sociodemographic and psychosocial differences among healthcare professionals. Emotional competence strongly predicted empathy, which was inversely associated with cancer-related stigma, suggesting potential targets for interventions to improve attitudes towards cancer care. Furthermore, women exhibited significantly higher emotional competence and empathy than men, highlighting the importance of incorporating gender-specific perspectives into developing educational and support strategies for cancer healthcare professionals. Full article

Background: A major technical challenge in single-cell transcriptomics is the absence of an integrative analytic pipeline that can simultaneously leverage gene regulatory network (GRN) architecture, AI-assisted gene panel discovery, and functional relevance analyses to generate coherent biological insights. Existing approaches often treat these components independently, focusing on clusters, marker genes, or predictive features without integrating them into a mechanistically grounded framework. Consequently, comprehensive screening that links regulatory association, gene signature screening, and functional interpretation within single-cell datasets remains limited, underscoring the need for an integrated strategy. Methods: We developed an integrative bioinformatics pipeline based on Gene regulatory network–AI–Functional Analysis (GAFA), combining latent-space integration, unsupervised clustering, diffusion pseudotime analysis, lineage-resolved generalized additive modeling, GRN inference, and machine learning-based gene panel discovery. This framework enables systematic mapping of cell-state structure, reconstruction of differentiation and effector trajectories, and identification of transcriptional and regulatory features strongly associated with clinical outcomes. As a case study, we applied the pipeline to NK cell transcriptomes from six CML patients (two early relapse, two late relapse, two durable treatment-free remission—TFR; 15 samples) collected at TKI discontinuation and 6–12 months after therapy cessation. Results: We reanalyzed publicly available scRNA-seq data from a previously published CML cohort to evaluate NK-cell transcriptional programs associated with treatment-free remission and relapse. We resolved six transcriptionally distinct NK cell states spanning CD56^bright-like cytokine-responsive, early activated, terminally mature, cytotoxic, lymphoid trafficking, and HLA-DR⁺ immunoregulatory populations, each exhibiting outcome-specific compositional differences. Pseudotime analysis revealed two major NK cell lineages—a maturation trajectory and a cytotoxic effector trajectory. TFR samples displayed balanced occupancy of both lineages, whereas early relapse samples showed marked depletion of the maturation branch and preferential accumulation in cytotoxic end states. AI-guided feature selection and random forest modeling identified an 18-gene panel that distinguished NK cells from TFR and relapse samples in an exploratory manner. Among them, CST7, FCER1G, GNLY, GZMA, and HLA-C were conventional NK-associated genes, whereas ACTB, CYBA, IFITM2, IFITM3, LYZ, MALAT1, MT2A, MYOM2, NFKBIA, PIM1, S100A8, S100B, and TSC22D3 were novel. The GRN inference further uncovered outcome-specific regulatory modules, with RUNX3, EOMES, ELK4, and REL regulons enriched in TFR, whereas FOSL2 and MAF regulons were enriched in relapse, and their downstream targets linked to IFN-γ signaling, metabolic reprogramming, and immunoregulatory feedback circuits. Conclusions: This AI-enabled single-cell analysis demonstrates how NK cell state composition, differentiation trajectories, and regulatory network rewiring collectively shape TFR versus relapse following TKI discontinuation in CML. The integrative pipeline provides a modular framework that could be extended to additional datasets for data-driven biomarker discovery and mechanistic stratification, and highlights candidate transcriptional regulators and NK cell programs that may be leveraged to improve remission durability, pending validation in larger patient cohorts. Full article

Molecular subtype–guided therapy for breast cancer (BC) remains limited in a subset of patients by suboptimal efficacy, acquired resistance, and the presence of “undruggable” targets. Proteolysis-targeting chimeras (PROTACs) represent a targeted protein degradation (TPD) strategy that differs fundamentally from conventional occupancy-driven inhibition. By inducing ubiquitination of a protein of interest and subsequent proteasomal degradation, PROTACs can directly reduce pathogenic protein abundance and potentially abrogate non-catalytic or scaffolding functions, thereby enabling more durable pathway suppression in selected resistance contexts. This review comprehensively summarizes the mechanisms of action, key molecular design elements, and the developmental landscape of PROTACs, and maps target selection and research progress across BC molecular subtypes. In hormone receptor–positive/HER2-negative BC, clinical translation is most advanced for estrogen receptor alpha-directed PROTACs; Phase III evidence indicates biomarker-dependent efficacy, with clearer benefit signals in resistant subgroups such as estrogen receptor 1 mutations, suggesting that the net clinical benefit of TPD is more likely to be realized through precision stratification. In contrast, in solid-tumor settings, including human epidermal growth factor receptor 2 (HER2)-positive BC and triple-negative breast cancer, PROTAC translation is more frequently constrained by an “exposure–selectivity–therapeutic window” trade-off driven by physicochemical liabilities, insufficient tumor penetration, and broad target expression. Accordingly, engineering strategies—such as antibody/aptamer-mediated targeted delivery, stimulus-responsive prodrugs, nanocarriers, and local administration—are emerging as decisive approaches to enable safe and effective clinical implementation. Looking forward, further progress of PROTACs in BC will depend on expanding the spectrum of E3 ubiquitin ligases and recruitment modalities, establishing predictable and dynamically monitorable biomarker systems, optimizing rational combination/sequencing regimens with exposure- and schedule-guided dosing, and advancing scalable manufacturing and quality control capabilities, thereby translating mechanistic advantages of TPD into verifiable precision-therapy applications. Full article

This paper presents a multimodal transformer-based framework for the joint prediction of indoor thermal comfort and energy efficiency using real-world building management system (BMS) datasets. Unlike traditional comfort models that rely on fixed physical assumptions and subjective surveys, the proposed approach adopts physics-guided, data-driven learning to capture nonlinear and time-dependent interactions among environmental conditions, HVAC operation, and occupancy-related variables. Thermal comfort labels are computed using the PMV–PPD formulation defined by ASHRAE Standard 55, assuming standard metabolic rate and clothing insulation due to the lack of direct measurements in routine BMS data. A temperature-driven baseline HVAC energy proxy is derived using change-point regression. The proposed transformer architecture fuses multivariate temporal sequences to jointly predict both comfort and energy baseline targets through a dual-head regression formulation. The model is validated on two complementary datasets representing steady-state and dynamically perturbed thermal conditions. The proposed approach consistently outperforms linear regression, random forest, and LSTM baselines, achieving mean absolute errors below 0.03 and $R^2$ values exceeding 0.98 with corresponding RMSE values below 0.035 for both targets. Residual and calibration analyses confirm stable, unbiased prediction behavior across wide temperature ranges. The results highlight the strong potential of attention-based multimodal learning for future comfort-aware building energy optimization and digital twin integration. Full article

Thermal comfort is among the primary determinants of habitability in the built environment. In tropical developing countries, however, its treatment in public housing policy has often been limited, fragmented, and, in many cases, subordinated to energy-saving criteria that do not adequately reflect occupant needs or local climatic diversity. This study analyses the integration of thermal comfort within housing policy using a mixed-methods approach combining regulatory analysis with post-occupancy environmental monitoring. Empirical monitoring shows average indoor temperatures between 16.3 °C and 18.5 °C, with more than 80% of recorded hours falling below adaptive comfort thresholds and a predicted dissatisfaction rate (PPD) of approximately 47%. These findings demonstrate that compliance with efficiency-centred sustainability regulation does not necessarily ensure thermally adequate indoor conditions in occupied social housing, highlighting a structural gap in current regulatory frameworks between efficiency-based compliance and thermally adequate indoor conditions in occupied social housing. The analytical framework integrates three dimensions: policy analysis, environmental performance verification, and interpretation of occupant adaptive behaviour. Rather than claiming that Bogotá is statistically representative of all tropical conditions, the paper treats it as an analytically revealing case in which tensions among efficiency-centred regulation, imported comfort standards, and constrained occupant adaptation become visible. The paper also demonstrates that the current Colombian sustainability regulation (Resolution 0194 of 2025) operationalises sustainability primarily through energy and water saving targets and climatic zoning, while lacking explicit, verifiable indicators for thermal comfort, occupant well-being, or health outcomes. Finally, the paper discusses the relevance of locally calibrated standards, standardised field methodologies, and passive design strategies within a broader agenda of energy governance, environmental equity, and housing adequacy. Full article

Occupational Health and Safety systems, as well as physical Ergonomics, serve a common goal, which is to eliminate safety-related injuries within production systems. The analysis of potential hazards that could compromise the safety of operations’ employees assists in preventing a high rate of safety-related injuries. Safer processes result in a high output rate and, hence, a profitable business. Focusing on the accuracy of problem solving and failure prediction analysis on new processes could potentially result in zero safety-related injuries, good-quality products, cost reduction, and the elimination of delays within the processes. This research seeks to add more knowledge to the fields of Occupational Health and Safety systems and Total Productive Maintenance by combining lean manufacturing troubleshooting models with Ergonomic analysis, as well as Hazard Identification Risk Analysis, to predict future kaizen projects for businesses. The proposed upgrade to the problem-solving model was developed by evaluating and reviewing the impact of Ergonomic analysis on different production systems. It was found that Ergonomic analysis provides solutions for a more comfortable working environment; hence, the existing troubleshooting model was combined with an Ergonomic exercise. The proposed model is more beneficial to production systems. It could potentially result in zero safety-related injuries, high-quality products, more accurate problem analysis, and more innovation by enabling kaizen projects. The proposed model was applied in the electronics industry, where it resulted in drastic improvements. The old method, which was causing fatigue, was eliminated, and a new machine was designed and prototyped. The new machine assisted the company in this case study in reducing delays, eliminating defects, and reducing costs. Furthermore, the proposed troubleshooting model evaluated an impactful kaizen project, which was the introduction of new technologies that will eliminate the power-up stage. Full article

The digital and green transitions in the AEC sector require rapid, data-driven workflows to redefine sustainability through real-time performance evaluation. However, the high computational cost of traditional energy simulations often lacks evidence-based feedback during early-stage design. This study introduces a surrogate machine learning framework (S-TCML) designed to bypass traditional energy simulation by providing an instantaneous assessment of thermal comfort. Using a parametric Grasshopper–Honeybee environment, a dataset of 3072 configurations was generated for an office room in Cairo, Egypt. Six machine learning algorithms were benchmarked, with Gradient Boosting and Random Forest demonstrating superior performance in capturing non-linear thermal physics. Validation against the EnergyPlus engine confirmed that S-TCML models deliver predictions in milliseconds—a 99.9% reduction in computational time. The Gradient Boosting model achieved exceptional accuracy with an R² of 0.999 and RMSE of 0.013 for PMV and an R² of 0.995 and RMSE of 0.46% for PPD prediction. Feature importance analysis proved that a tree-based ML model can capture the underlying physical relationship between variables. To bridge the feedback gap, a web-based graphical user interface (GUI) was developed to facilitate proactive design exploration. This framework supports sustainable decision-making and design efficiency, offering scalable, user-friendly tools that protect occupant health and ensure thermal resilience in hot–arid environments. Full article

Traditional Post-Occupancy Evaluation (POE) is static and incompatible with dynamic systems like Digital Twins, creating a digital gap in managing health-oriented urban environments, especially in Urban Underground Spaces (UUS). This paper bridges this gap with a deep learning framework that automates the continuous prediction of human physiological arousal. We created a novel multimodal dataset from in situ experiments, synchronizing first-person video, environmental data, and Galvanic Skin Response (GSR) as a real-time physiological arousal proxy. Our dual-branch spatial–temporal model fuses these data streams to predict GSR with high accuracy (Pearson’s r = 0.72), effectively mapping objective environmental inputs to continuous human physiological dynamics. This framework provides an automated, human-centric analysis engine for urban planning, design validation, and real-time building management. It establishes a foundational ‘human dynamics layer’ for urban Digital Twins, evolving them into predictive tools for simulating human-environment interactions and embedding physiological perception into intelligent urban systems. Full article

The rapid expansion of cardiac imaging has substantially increased patient and occupational exposure to low-dose ionizing radiation. Evidence suggests that cumulative exposures below 100 mSv may contribute to long-term risks of cancer and non-cancer diseases, including cardiovascular disease. However, establishing causality at these dose levels is challenging, as epidemiological studies are limited by heterogeneous endpoints, uncertainties in dose reconstruction, and incomplete control of confounding factors. Molecular biomarkers offer a promising strategy to bridge the gap between radiation exposure and clinically manifest disease, enabling more precise individualized risk assessment and targeted preventive strategies. This review summarizes current evidence on genetic and epigenetic biomarkers for evaluating the biological effects of radiation in cardiac imaging and interventional cardiology and examines their potential role in risk stratification and occupational surveillance. Genetic markers—including γ-H2AX foci, micronucleus assays, and telomere length alterations—alongside epigenetic modifications such as DNA methylation changes and microRNA expression profiles provide sensitive indicators of radiation-induced cellular damage. Integrating biomarker profiling with individualized dosimetry and longitudinal follow-up may improve risk prediction, enhance occupational protection, and support safer, more sustainable imaging practices in contemporary cardiovascular care. Full article

Background: Excessive abdominal fat deposition is a major challenge in the chicken farming industry, making it essential to elucidate the molecular mechanisms underlying chicken adipogenesis. Nuclear Respiratory Factor 1 (NRF1) has been reported to suppress chicken adipogenesis by downregulating peroxisome proliferator-activated receptor gamma (PPARγ) expression. Protein Phosphatase 1 Catalytic Subunit Gamma (PPP1CC) is a multifunctional phosphatase involved in various biological processes; however, its role in chicken adipogenesis remains unclear. Objective: This study aimed to investigate the functional role and underlying mechanism of PPP1CC in chicken preadipocyte differentiation. Methods: Co-immunoprecipitation (Co-IP) and immunofluorescence assays were performed to determine the interaction between PPP1CC and NRF1 in DF1 cells. Bioinformatic analysis predicted potential NRF1 dephosphorylation sites targeted by PPP1CC, based on which NRF1 mutants mimicking dephosphorylation were constructed. Phos-tag SDS-PAGE combined with Western blot analysis were used to verify PPP1CC-mediated dephosphorylation of wild-type NRF1. Dual-luciferase reporter assays were used to evaluate the effect of PPP1CC-mediated dephosphorylation on NRF1-regulated PPARγ P1 promoter transcriptional activity. ChIP-qPCR was employed to assess the occupancy of NRF1 to the PPARγ P1 promoter upon PPP1CC overexpression. The effect of PPP1CC overexpression was assessed on preadipocyte differentiation using Oil Red O staining and marker gene expression analysis. Results: PPP1CC interacted with NRF1 in both the cytoplasm and nucleus of DF1 cells. Overexpression of PPP1CC significantly promoted NRF1 dephosphorylation during oleic acid-induced preadipocyte differentiation and increased endogenous NRF1 expression. Moreover, dual-luciferase assays showed that while PPP1CC strengthened the inhibitory effect of wild-type NRF1 on PPARγ P1 promoter transcriptional activity, it exerted no additional suppression on the already low activity mediated by the dephosphorylation-mimicking NRF1 mutants. Consistently, ChIP-qPCR results demonstrated that PPP1CC overexpression enhanced the occupancy of NRF1 to the PPARγ P1 promoter. Functional assays revealed that PPP1CC overexpression significantly inhibited chicken preadipocyte differentiation. Conclusions: PPP1CC interacts with NRF1 and promotes its dephosphorylation, enhancing NRF1-mediated suppression of PPARγ transcription and ultimately inhibiting chicken preadipocyte differentiation. These results identify the PPP1CC–NRF1–PPARγ regulatory axis and provide a potential molecular target for controlling fat deposition in broiler chickens. Full article