Search Results (2,445)

Intelligent ventilation is positioned as a key axis for reconciling energy efficiency and indoor air quality (IAQ) in residential and non-residential buildings. This review synthesizes 51 recent publications covering control strategies (DCV, MPC, reinforcement learning), IoT architectures and sensor validation, energy recovery (HRV/ERV, anti-frost strategies, low-loss exchangers, PCM-air), active envelope solutions (thermochromic windows) and passive solutions (EAHE), as well as evaluation methodologies (uncertainty, LCA, LCC, digital twin) and smart readiness indicator (SRI) frameworks. Evidence shows ventilation energy savings of up to 60% without degrading IAQ when control is well-designed, but also possible overconsumption when poorly parameterized or contextualized. Performance uncertainty is strongly influenced by occupant emissions and pollutant sources (bioeffluents, formaldehyde, PM2.5). The integration of predictive control, scalable IoT networks, and robust energy recovery, together with life-cycle evaluation and uncertainty analysis, enables more reliable IAQ-energy balances. Gaps are identified in VOC exposure under DCV, robustness to sensor failures, generalization of ML/RL models, and standardization of ventilation effectiveness metrics in natural/mixed modes. Full article

This work presents a data-driven framework for early detection of polymer melt instability in industrial injection moulding using Long Short-Term Memory (LSTM) time-series models. The study uses six months of continuous production data comprising approximately 280,000 injection cycles collected from a fully operational thermoplastic injection line. Because melt behaviour evolves gradually and conventional threshold-based monitoring often fails to capture these transitions, the proposed approach models temporal patterns in torque, pressure, temperature, and rheology to identify drift conditions that precede quality degradation. A physically informed labelling strategy enables supervised learning even with sparse defect annotations by defining volatile zones as short time windows preceding operator-identified non-conforming parts, allowing the model to recognise instability windows minutes before defects emerge. The framework is designed for deployment on standard machine signals without requiring additional sensors, supporting proactive process adjustments, improved stability, and reduced scrap in injection moulding environments. These findings demonstrate the potential of temporal deep-learning models to enhance real-time monitoring and contribute to more robust and adaptive manufacturing operations. Full article

Choline is a central metabolite that connects membrane turnover, neurotransmission, and one-carbon metabolism, and its reliable measurement across diverse biological matrices remains a significant analytical challenge. This review brings together biological context, electrochemical mechanisms, and device engineering to define realistic performance targets for choline sensors in blood, cerebrospinal fluid, extracellular space, and milk. We examine enzymatic sensor architectures ranging from peroxide-based detection to mediated electron transfer via ferrocene derivatives, quinones, and osmium redox polymers and assess how applied potential, oxygen availability, and film structure shape electron-transfer pathways. Evidence for direct electron transfer with choline oxidase is critically evaluated, with emphasis on the essential controls needed to distinguish true flavin-based communication from peroxide-related artefacts. We also examine bienzymatic formats that allow operation at low or negative bias and discuss strategies for matrix-matched validation, selectivity, drift control, and resistance to fouling. To support reliable translation, we outline reporting standards that include matrix-specific concentration ranges, reference electrode notation, mediator characteristics, selectivity panels, and access to raw electrochemical traces. By connecting biological requirements to mechanistic pathways and practical design considerations, this review provides a coherent framework for developing choline sensors that deliver stable, reproducible performance in real samples. Full article

Microplastic pollution is a defining environmental crisis of the Anthropocene, threatening ecosystems and human health due to its persistence and global dispersion. This review synthesizes current knowledge through a chemical engineering framework, analyzing the contaminant’s lifecycle from formation and environmental fate to detection and removal. We systematically evaluate conventional and advanced mitigation technologies, highlighting the potential of engineered adsorbents (e.g., functionalized sponges, biochar) for targeted capture while underscoring the limitations of current wastewater treatment for nano-plastics. The analysis extends beyond end-of-pipe solutions to underscore the imperative for sustainable polymer design and circular economy systems, where biodegradable polymers and chemical recycling must be integrated. Crucially, we identify techno-economic analysis (TEA) and life-cycle assessment (LCA) as essential, yet underdeveloped, tools for quantifying the true cost and sustainability of management strategies. The synthesis concludes that addressing microplastic pollution requires the integrated application of chemical engineering principles across molecular, process, and system scales, and it identifies key research priorities in advanced material design, standardized analytics, hybrid treatment processes, and comprehensive impact modeling. Full article

Transmission tower guy wires are critical flexible tension members ensuring the stability and safe operation of overhead power transmission networks. However, these components are vulnerable to external impacts from falling rocks, ice masses, and other natural hazards, which can cause excessive deformation, anchorage loosening, and catastrophic failure. Current design standards primarily consider static loads, lacking comprehensive models for predicting dynamic impact responses. This study presents a theoretical model for predicting the peak impact response of guy wires by modeling the impact process as a point mass impacting a nonlinear spring system. Using an energy-based elastic potential method combined with cable theory, analytical solutions for axial force, displacement, and peak impact force are derived. Newton–Cotes numerical integration solves the implicit function to obtain closed-form solutions for efficient prediction. Validated through finite element simulations, deviations of peak displacement, peak impact force, and peak axial force between theoretical and numerical results are within ±4%, ±18%, and ±4%, respectively. Using the validated model, parametric studies show that increasing the inclination angle from 15° to 55° slightly reduces peak displacement by 2–4%, impact force by 1–13%, and axial force by 1–10%. Higher prestress (100–300 MPa) decreases displacement and impact force but increases axial force. Longer lengths (15–55 m) cause linear displacement growth and nonlinear force reduction. Impacts near anchorage points help control displacement risks, and impact velocity generally has a more significant influence on response characteristics than impactor mass. This model provides a scientific basis for impact-resistant design of power grid infrastructure and guidance for optimizing de-icing strategies, enhancing transmission system safety and reliability. Full article

Local Interpretable Model-agnostic Explanations (LIME) is a widely used technique for interpreting individual predictions of complex “black-box” models by fitting a simple surrogate model to synthetic perturbations of the input. However, its standard perturbation strategy of sampling features independently from a Gaussian distribution often generates unrealistic samples and neglects inter-feature dependencies. This can lead to low local fidelity (poor approximation of the model’s behavior) and unstable explanations across different runs. This paper presents CoLIME, which is a copula-based perturbation generation framework for LIME, designed to capture the underlying data distribution and inter-feature dependencies more accurately. The framework employs bivariate (2D) copula models to jointly sample correlated features while fitting suitable marginal distributions for individual features. Furthermore, perturbation localization strategies were implemented, restricting perturbations to a defined local radius and maintaining specific property values to ensure that the synthesized samples remain representative of the actual local environment. The proposed approach was evaluated on a network intrusion detection dataset, comparing the fidelity and stability of LIME under Gaussian versus copula-based perturbations, using Ridge regression as the surrogate explainer. Empirically, for the most dependent feature pairs, CoLIME increases mean surrogate fidelity by 21.84–50.31% on the merged CIC-IDS2017/2018 dataset and by 29.28–60.24% on the UNSW-NB15 dataset. Stability is similarly improved, with mean Jaccard similarity gains of 3.78–5.45% and 1.95–2.12%, respectively. These improvements demonstrate that dependency-preserving perturbations provide a significantly more reliable foundation for explaining complex network intrusion detection models. Full article

Customizing diffusion models via Low-Rank Adaptation (LoRA) has become a standard approach for customized concept injection. However, synthesizing multiple customized concepts within a single image remains challenging due to the parameter pollution problem, where naive fusion leads to gradient conflicts and severe quality degradation. In this paper, we introduce ConWave-LoRA, a novel framework designed to achieve hierarchical disentanglement of object and style concepts in LoRAs. Supported by our empirical validation regarding frequency distribution in the latent space, we identify that object identities are predominantly encoded in high-frequency structural perturbations, while artistic styles manifest through low-frequency global layouts. Leveraging this insight, we propose a Discrete Wavelet Transform (DWT) based filtering strategy that projects these concepts into orthogonal optimization subspaces during contrastive learning, thereby isolating structural details from stylistic attributes. Extensive experiments, including expanded ablation studies on LoRA rank sensitivity and style consistency, demonstrate that ConWave-LoRA consistently outperforms strong baselines, producing high-fidelity images that successfully integrate multiple distinct concepts without interference. Full article

To enhance the purification of exhaust gas, a g-C₃N₄/CeO₂/Bi₂O₃ dual type-II heterojunction photocatalysis was designed and prepared to suppress the recombination of electron–hole pairs and improve light energy utilization. The dual type-II heterojunction structure effectively reduced the bandgap (Eg) from 2.5 eV to 2.04 eV, thereby extending the light absorption of photocatalysis into the visible region. Following the design of the heterojunction, a self-cleaning process was developed and applied to asphalt pavement rut plates to evaluate its efficiency in degrading vehicle exhaust under real-road conditions. The coating was systematically characterized in terms of exhaust degradation efficiency, hardness, adhesion, water resistance, freeze–thaw durability, and skid resistance. Under 60 min of natural light irradiation, the purification efficiencies for HC, CO, CO₂, and NO_x reached 22.60%, 19.27%, 14.83%, and 50.01%, respectively. After three-repetition tests, the efficiencies remained high at 21.75%, 19.04%, 14.66%, and 49.83%, demonstrating excellent photocatalytic stability. All other road-performance indicators met the relevant China national standards. The application of this self-cleaning coating in road infrastructure presents a viable strategy for environmental remediation in transportation systems. Full article

The booming number of electric vehicles (EVs) and autonomous vehicles is driving the demand for the development of 5G and connected vehicle technologies. However, the design of compact, multi-band vehicular antennas with multiple communication standard support is complex. Traditional experience-based and parameter-sweeping approaches to antenna optimization are often inefficient and limited in scalability, while machine learning-based methods require extensive datasets, which are computationally intensive. This study proposes a microstrip planar Yagi antenna optimized for Sub-6 GHz 5G and cellular vehicle-to-everything (C-V2X) communication. As a way to approach antenna optimization with lower computing cost and less data, a hybrid optimization strategy is presented that combines parametric analysis and curve fitting based data visualization approaches. The proposed antenna exhibits a reflection coefficient of −31.68 dB and −29.36 dB with 700 MHz and 900 MHz bandwidths for frequencies of 3.5 GHz and 5.9 GHz, respectively. Moreover, the proposed antenna exhibits a peak gain of 7.55 dB with a size of 0.44 × 0.64 ${\lambda }^2$, while achieving a peak efficiency of 90.1%. The antenna has been integrated and simulated in a model Mini Cooper to test the effectiveness of vehicular communication. Full article

Fast-track extubation has emerged as a vital component of Enhanced Recovery After Surgery pathways, designed to optimize recovery and resource utilization after cardiac surgery, contrasting with traditional prolonged ventilation. This review explores the evidence supporting fast-track extubation, detailing patient selection criteria based on preoperative risk factors and functional status and outlining perioperative management strategies. It synthesizes findings from various studies, including randomized controlled trials, retrospective studies, and meta-analyses, focusing on intraoperative techniques such as low-dose opioids, neuromuscular blockade reversal, controlled cardiopulmonary bypass duration, judicious inotrope use, and minimal transfusion, alongside structured postoperative protocols emphasizing early sedative weaning and spontaneous breathing trials. Results demonstrate that fast-track extubation decreases intensive care unit stay, reduces costs and ventilator-associated complications, with a safety comparable to conventional care. Prolonged cardiopulmonary bypass time, dependency on inotropes, and intraoperative blood transfusions are identified as critical predictors of fast-track extubation failure. In conclusion, the successful implementation of fast-track extubation protocols requires a collaborative, multidisciplinary approach, proving essential for improving patient outcomes, minimizing complications such as postoperative delirium, and enhancing hospital efficiency in cardiac surgery. Further research should aim to refine patient selection and standardize protocols across healthcare systems. Full article

Background/Objectives: Oropharyngeal squamous cell carcinoma (OPSCC) management has shifted following recognition of HPV-driven disease. Neoadjuvant chemotherapy (NAC) has historically failed to improve overall survival (OS) in mixed head and neck cohorts, although contemporary HPV-stratified series suggest NAC may enable treatment de-escalation. We aimed to narratively synthesize OPSCC-specific evidence on NAC focusing on primary and nodal response, pathologic complete response (pCR), survival, and functional outcomes. Methods: We conducted a narrative review of PubMed, selecting primary studies in which OPSCC outcomes were reported separately (surgery- or chemoradiotherapy [CRT]-based strategies; HPV status when available). We extracted study design, treatment regimens, response outcomes, survival, and toxicity data. Results: Pre-HPV studies showed variable responses and no consistent OS advantage over locoregional therapy. In the HPV era, non-comparative cohorts of NAC followed by transoral surgery reported substantial downstaging and high pCR rates at both the primary site and regional nodes, with 3–5-year OS frequently ≥80%. NAC+CRT paradigms demonstrated high clinical CR rates and OS exceeding 80–90%, and lower feeding-tube dependence and reduced swallowing morbidity in de-escalated regimens. Comparative retrospective series suggest NAC + surgery may be associated with lower rates of distant metastases and feeding-tube use compared with CRT or upfront surgery, although interpretation is limited by selection bias, regimen heterogeneity, and small sample sizes. Conclusions: While randomized trials have not established an OS advantage for NAC over standard CRT in head and neck cancer overall, HPV-positive OPSCC shows emerging evidence that systemic intensification with NAC may enable surgical and/or radiation de-escalation with promising oncologic and functional outcomes. Full article

Objective: Intraoperative hypothermia is a common perioperative complication. This large-scale, multicenter, prospective clinical study aimed to delineate the occurrence patterns of intraoperative hypothermia in adults and to identify its major independent risk factors, thereby providing evidence-based support for early clinical risk assessment and intervention. Methods: This study adopted a multicenter, prospective, observational design. Eligible participants were screened based on predefined inclusion and exclusion criteria, and a total of 4516 surgical patients (≥18 years) from 12 tertiary general hospitals across China were ultimately enrolled. Core body temperature was continuously monitored intraoperatively using standardized methods. Data on demographic characteristics, surgical and anesthesia-related parameters, and perioperative temperature management interventions were collected. Patients were stratified into groups according to the presence or absence of hypothermia (core temperature <36.0 °C). Univariate analyses were first conducted to identify associated factors, followed by multivariable logistic regression to determine factors independently associated with intraoperative hypothermia. Results: The overall incidence of intraoperative hypothermia among surgical patients was 23.82%. Hypothermia occurred most frequently in patients with a preoperative baseline core temperature ≤ 35.9 °C (85.93%). Among surgical specialties, hand surgery had the highest incidence of hypothermia (51.35%), and among surgical sites, procedures involving the upper extremities showed the highest rate (35.00%). Multivariable logistic regression analysis identified the following as independent risk factors for intraoperative hypothermia: Type of anesthesia (OR = 1.743, 95% CI: 0.834–3.644), ASA classification (OR = 1.408, 95% CI: 1.197–1.657), Surgical approach (OR = 0.735, 95% CI: 0.577–0.936), Skin disinfection site (OR = 2.024, 95% CI: 1.534–2.670), Volume of cold intravenous fluids infused (mL) (OR = 1.365, 95% CI: 1.140–1.633), Volume of transfused blood (U) (OR = 1.116, 95% CI: 0.807–1.542), Intraoperative blood loss (mL) (OR = 1.252, 95% CI: 0.892–1.756), and Duration of surgery (hours) (OR = 2.014, 95% CI: 1.683–2.411). Conclusions: The incidence of intraoperative hypothermia in adults was relatively high at 23.82% and was observed to be associated with multiple modifiable perioperative factors. These findings support the need to strengthen risk assessment and implement individualized temperature management strategies in clinical practice, with the goal of reducing the risk of intraoperative hypothermia and improving perioperative safety and outcomes. Full article

Belimumab, a human monoclonal antibody that works against B-cell activating factor (BAFF), has significantly advanced the management of systemic lupus erythematosus (SLE). Beyond the initial Phase III randomised controlled trials (RCTs) that demonstrated efficacy for belimumab as an add-on to non-biological standard therapy (ST) along with a favourable safety profile, more than 50 post hoc analyses of RCT data have provided additional insights into its clinical utility. These analyses have shown uniformly that belimumab increases the likelihood of achieving meaningful reductions in disease activity, sustained low disease activity, and improved health-related quality of life (HRQoL) outcomes, with more pronounced benefits in serologically active SLE. Studies focusing on organ-specific manifestations revealed that belimumab confers benefits across multiple SLE facets, with prominent effects on musculoskeletal and mucocutaneous symptoms. Along the same lines, post hoc analyses of the BLISS-LN trial demonstrated benefit from belimumab regarding multiple renal outcomes, including reduced renal flare rates, improved glomerular filtration rate, and improved histological findings in repeat kidney biopsies. Long-term extension studies and real-world evidence confirm its durable efficacy and safety, with continued reductions in overall disease activity, glucocorticoid use, and healthcare resource utilisation over several years. By exploring different efficacy endpoints, person-centred outcomes, disease trajectories, and characteristics across organ manifestations, this body of post-marketing evidence has not only enhanced our understanding of belimumab use in SLE but also constitutes a comprehensive framework for future clinical trial design and development of novel therapeutic strategies. The present review summarises key findings of post hoc analyses of RCTs and observational studies of belimumab. Full article

This study evaluates and compares the sustainability performance of selected historic, commercial, and institutional buildings in Istanbul to identify effective climate-responsive and energy-efficient design strategies. The objectives are to assess performance using LEED-based criteria, examine variations across building typologies, and outline implications for future sustainable design. Using an evaluation matrix, responses from 175 experts were analysed across key LEED categories for seven case study buildings. The comparative assessment reveals notable variations in sustainability performance across the seven evaluated buildings. ERKE Green Academy consistently achieved the highest mean scores (≈4.40–4.60), particularly in Sustainable Sites, Water Efficiency, Energy and Atmosphere, and Indoor Environmental Quality. This strong performance reflects its integration of advanced green technologies, optimised daylighting strategies, biophilic elements, and smart system controls. Modern commercial towers, such as the Allianz Tower and Sapphire Tower, recorded strong mean scores (≈4.20–4.50) across categories related to Integrative Design, Energy Efficiency, and Materials and Resources. Their performance is largely driven by intelligent façade systems, double-skin envelopes, automated shading, and high-performance mechanical systems that enhance operational efficiency. In contrast, heritage buildings including Hagia Sophia and Sultan Ahmed Mosque demonstrated moderate yet stable performance levels (≈4.00–4.40). Their strengths were most evident in Indoor Environmental Quality, where passive systems such as thermal mass, natural ventilation, and inherent spatial configurations contribute significantly to occupant comfort. Overall, the findings underscore the complementary value of combining traditional passive strategies with modern smart technologies to achieve resilient, low-energy, and user-responsive architecture. This study is novel as it uniquely demonstrates how traditional passive design strategies and modern smart technologies can be integrated to enhance climate-responsive and energy-efficient performance across diverse building typologies. The study recommends enhanced indoor air quality strategies, occupant education on system use, and stronger policy alignment with LEED standards. Full article

With the in-depth development of digital twin technology in modern agriculture, smart pig farm construction is evolving from basic environmental modeling toward refined, bio-behavior-driven approaches. This study addresses the non-standard body configurations and complex behavioral patterns of pig models by proposing a binding method that combines lightweight skeletal design with automated weight allocation strategies. The method optimizes skeletal layout schemes based on pig physiological structures and behavioral patterns, replacing manual painting processes through geometry-driven weight calculation strategies to achieve a balance between efficiency and animation naturalness. The research constructs a motion template library containing common behaviors such as walking and foraging, conducting quantitative testing and comprehensive evaluation in simulation systems. Experimental results demonstrate that the proposed method achieves significant improvements: it demonstrated superior computational efficiency with 95.2% reduction in computation time, memory storage space reduced by 91.7% through weight matrix sparsification (density controlled at 8.3%), and weight smoothness was maintained at 0.955 while cross-region weight leakage reduced from 15.3% to 2.1%. The method effectively supports animation expression of eight typical pig behavioral patterns with key joint angle errors controlled within 2.3 degrees, providing a technically viable and economically feasible pathway for virtual modeling and intelligent interaction in smart agriculture. Full article