Search Results (776)

To solve the problems of high kernel breakage and low threshing efficiency in the threshing operation of high-moisture maize, this study designs an adaptive threshing device based on the coupled working principle of vortex airflow driving and flexible collision. The adaptive threshing mode enables maize ears to move spirally upward under vortex airflow and make compliant contact with flexible components. By adopting repeated mild collisions instead of the rigid violent impact used in traditional devices, low-damage maize threshing is achieved. Preliminary experiments verify that the layout density of flexible threshing units, tangential airflow velocity, and feeding speed are the key factors affecting threshing performance. A regression orthogonal rotational combination test is conducted to systematically explore the single-factor and interactive effects on threshing efficiency, and the optimal parameter configuration is obtained. The test results show that, under the conditions of circumferential angular spacing of 21.5°, tangential velocity of 45.9 m/s and feed rate of 0.65 kg/s, the maize threshing rate reaches 96.1% while the grain breakage rate is controlled below 0.1%, which is significantly superior to conventional rigid threshing methods. This research provides a new technical scheme and experimental data reference for the low-damage threshing study of high-moisture maize. Full article

Brake wear particles are an increasingly relevant source of traffic-related particulate emissions and are addressed by the recently introduced Euro 7 emission regulation. Airborne fractions of brake wear emissions, in particular, have been associated with adverse effects on human health and other organisms. Although several brake particle mitigation strategies have demonstrated promising results under controlled laboratory conditions, their effectiveness under variable open-road driving conditions remains insufficiently understood. This study therefore investigates the transfer of two test-bench-validated mitigation strategies to a fully instrumented passenger vehicle capable of measuring brake particle number (PN) and particulate mass (PM) emissions. The first strategy is a passive approach based on a modified brake pad–disc material pairing, while the second is an active filtration system that extracts particle-laden air directly from the brake friction zone. Both approaches were evaluated during two open-road driving cycles: a real driving emissions (RDE)-compliant cycle and a more dynamic cycle characterized by higher brake stress. Airborne particle emissions were measured over a size range from 300 nm to 10 µm. During the RDE-compliant cycle, the passive approach reduced PN and PM emissions by 44% and 94%, respectively, compared with the reference brake system. Under the higher thermal and mechanical loads of the dynamic cycle, the reductions decreased to 10% for PN and 64% for PM. The active filtration system achieved an increase in PN of 4% in RDE conditions and 11% under high-severity driving. Nevertheless, PM emissions were reduced by 23–97%, depending on its operating mode of the filtration system and the associated airflow and energy demand. For high-severity driving, the PM emissions have been reduced by 40% compared to the reference braking system. These results show that both mitigation approaches hold the potential to reduce brake particle emissions under open-road conditions, although their effectiveness depends strongly on brake load and system operation. The study extends previous laboratory-based investigations by directly comparing passive and active mitigation strategies on the same vehicle under real-world driving conditions. Full article

The present study addresses the problems of predicting the distribution of CO₂ in a classroom where limited ventilation may compromise air quality. Through Computational Fluid Dynamics (CFD), a detailed analysis of airflow, temperature, and CO₂ dispersion was carried out; however, due to its high computational cost, real-time applications are limited. Therefore, this work proposes a simplified CFD approach to model human breathing based on a constant airflow velocity and an average CO₂ concentration, preserving mass balance while reducing computational demand. Three mathematical breathing models (constant, complete, and sinusoidal) were formulated and compared through CFD simulation with the classroom model. Subsequently, a CO₂ detection module based on SCD40 sensors was developed for the experimental validation of the simplified model, recreating the simulation conditions in a real environment. The results show strong agreement between the simplified model and the complex models, as well as with experimental measurements, with relative errors between 0.05% and 10% at different monitoring points. The proposed model reduced calculation time by more than 98% compared to the sinusoidal model, without compromising accuracy. These results show that the simplified model is an efficient alternative for predicting CO₂ concentration, allowing its integration into real-time control systems. Full article

Accurate thermal management is crucial for ensuring the safety, longevity, and performance of lithium-ion batteries, especially in compact embedded systems like USB chargers, power banks, and IoT nodes. Despite extensive research on predictive thermal models and intelligent control frameworks, their implementation in resource-constrained microcontroller-class devices has been limited. Existing strategies in the literature, such as threshold-based or PID logic, cloud-enabled analytics, machine learning models, and observer-based estimators, are often reactive, computationally intensive, or dependent on external infrastructure, making them unsuitable for low-power, standalone applications. This study introduces a novel Scalable Embedded Thermal Intelligence architecture designed for real-time battery thermal regulation in locally executable, without cloud dependency, low-cost platforms. Unlike conventional methods, the proposed system operates entirely on-device using closed-form models implemented on an ESP32 microcontroller. It combines two synergistic algorithms: a static preemptive model that calculates a safe C-rate at startup based solely on ambient and initial battery temperature, and a dynamic disturbance-aware model that monitors temperature rise per SOC step and adjusts airflow or current adaptively without requiring high memory, floating-point units, or supervisory control. The architecture achieves sub-second response times, <7% RAM, and <25% Flash usage, and does not need cloud connectivity, simulation backend, or complex thermal-management infrastructures such as liquid cooling circuits, phase-change systems, or cloud-supervised architectures. The significant contribution of this work is not the introduction of a new electrochemical–thermal formulation, but the effective integration and application of previously validated closed-form thermal predictors on low-cost microcontroller-class hardware, designed for anticipatory battery thermal regulation while adhering to strict computational limitations. Compared to traditional battery thermal management systems using PCM, liquid-cooling circuits, or cloud-based predictive estimators, the proposed approach eliminates the need for complex thermal hardware, fluidic systems, external computing infrastructure and resource-efficient edge operation. This makes the system suitable for deployment in real-world embedded applications like USB-C smart charging cables, compact IoT power banks, and portable medical devices, where form factors, energy efficiency, and cost are critical. The proposed SETI framework offers a firmware-integrated architecture and a firmware-integrated solution that provides a lightweight embedded alternative for predictive thermal regulation for distributed energy systems and miniaturized electronics. Full article

Energy efficiency is a critical challenge in heating, ventilation, and air conditioning (HVAC) systems in battery electric vehicles (BEVs), as they are among the main auxiliary systems directly affecting driving range. This review examines control-oriented strategies for EV cabin thermal management, focusing on how advanced control can improve energy utilization while maintaining thermal comfort. Specifically, the review examines predictive control methods, optimization-based strategies, and data-driven learning approaches applied to HVAC systems, with particular emphasis on model predictive control, dynamic programming, and reinforcement learning frameworks. The literature shows that advanced controllers can reduce HVAC energy consumption while maintaining thermal comfort; however, most existing studies still focus on whole-cabin air regulation. In contrast, localized actuators, including seat heaters, radiant panels, infrared heaters, and targeted airflow systems, are rarely optimized or incorporated as explicit manipulated variables in control frameworks. This review identifies the lack of coordinated local–global actuator optimization and control as a major research gap. Future EV cabin thermal management should therefore prioritize human-centric, prediction-aware, and safety-constrained control frameworks that jointly optimize global HVAC operation and localized comfort actuation. Full article

Low-cost bipolar-junction-transistor (BJT) thermal pixel arrays provide robust, force-free contact sensing for tactile skins, but their slow frame rate confines contact-timing resolution to the inter-frame interval—252 ms at the 4 Hz rate of the 16 × 16 array studied here—well below the needs of contact-aware control. We propose a four-frame complementary-error-function (erfc) template, derived from one-dimensional semi-infinite heat conduction, that jointly estimates the contact amplitude, the thermal-diffusion parameter, and the sub-frame contact-onset offset (τ₁), solved by a grid-initialized semi-analytic Levenberg–Marquardt scheme (Path A) at deterministic single-pass cost. On 42 contacts from five subjects, the per-contact Cramér–Rao lower bound for τ₁ is 16.2 ms, and the empirical cross-contact dispersion is 83.5 ms; both are internal, model-derived quantities, since no synchronised external timing reference was available. A two-layer rejection pipeline separates 19/19 valid contacts from 2/2 hardware faults; transfers to four held-out subjects (23/23) without retuning; attains an overall AUC of 0.878 on a five-class synthetic disturbance library—ramp and saturating-exponential remain acknowledged failure modes; and rejects 5/6 disturbance trials in a real-airflow stress session. Larger independent cohorts and externally synchronised timing validation remain parameters for future work. Full article

A dual-platform mushroom cultivation system integrating artificial intelligence (AI)-assisted environmental monitoring and controlled-environment agriculture (CEA) was developed to support experiential STEM education across K–12 and undergraduate settings. Hands-on instruction with multicellular fungi is often limited by reliance on microbial models and by constraints associated with field-based activities. To address this gap, we implemented an indoor instructional platform that combines a commercial AI-assisted automated cultivation unit with a tent-based chamber for hands-on environmental control. Representative cultivated species included oyster mushrooms (Pleurotus spp.) and lion’s mane (Hericium erinaceus). The AI-assisted system provided sensor/camera-based monitoring, app-based feedback, and software-assisted regulation of humidity, light, and airflow, whereas the tent-based system enabled direct student manipulation of cultivation conditions. Together, the systems allowed students to observe fungal development, manage environmental parameters, and collect quantitative and qualitative data within a single academic term. Post-harvest activities, including mushroom-based food preparation and tasting, further connected fungal biology with food and sustainability. A matched pre- and post-course survey (n = 30) showed increases in students’ self-reported perceived understanding, cultivation confidence, and engagement, with mean scores increasing from approximately 2–4 to 6–8. Because the survey instrument was not formally validated and no control group was included, these results are interpreted as preliminary self-reported perceptions rather than objective evidence of learning gains. The platform provides a practical model for integrating fungal biology, AI-assisted environmental monitoring, and CEA into STEM education. Full article

This study investigates the aerodynamic performance of a two-building ensemble as an integrated architectural–aerodynamic system, with a focus on airflow conditions relevant to building-integrated wind turbines. The research addresses the question of whether newly designed development can actively improve, rather than deteriorate, airflow conditions above existing buildings. A parametric CFD analysis based on steady-state RANS (SST k–ω) simulations was conducted for multiple geometric configurations of a reference building (A) and a neighboring building (B), varying roof pitch (22–40°) and height. Airflow was evaluated using mean longitudinal velocity (Vy), coefficient of variation (CV), and vector components across three architectural scenarios corresponding to different turbine-integration strategies. The results demonstrate that properly designed geometries can significantly enhance flow quality. In the near-roof scenario (Arch1), the optimal configuration achieved a 24.28% increase in Vy and a 94.53% reduction in CV, indicating strong flow stabilization. In the façade-integration scenario (Arch2), improvements reached +10.40% in Vy and −23.16% in CV, reflecting vertical homogenization of the flow field. In the point-based scenario (Arch3), a local velocity increase of 4.29% was obtained while maintaining directional stability. The findings indicate that building geometry acts as an active design parameter that controls flow intensity, homogeneity, and direction. The study proposes a CFD-based decision framework and demonstrates that architectural form can be deliberately shaped to enhance wind conditions, supporting the integration of wind turbines into coherent building design. Full article

Background: Although single-inhaler triple therapy (SITT) improves COPD control, the specific structural and behavioral predictors of short-term clinical response following therapeutic simplification remain incompletely characterized. Methods: This prospective, multicenter observational study (N = 684) evaluated patients switching from triple therapy regimens involving multiple inhalers to SITT. A clinically meaningful response was defined as an intra-individual reduction of ≥2 points in the validated RADAR score at three months. Results: Therapeutic simplification reduced regimens requiring ≥4 inhalations/day from 46.1% to 14.3%, and poor behavioral adherence from 45.2% to 16.6%. Multivariable models identified an observed responder profile: higher baseline RADAR burden was the strongest predictor of improvement (aOR 2.00), whereas severe airflow limitation (FEV₁ < 50%) attenuated the response. Exploratory mediation analysis indicated that 88.6% of the observed clinical stabilization was not explained by measured adherence changes, and may therefore also encompass unmeasured behavioral, educational or device-related factors. Patients burdened with both high complexity and poor adherence showed the highest rate of combined structural–behavioral improvement (25.0% vs. 4.7% overall). Conclusions: Switching from MITT to SITT was associated with reduced treatment complexity, improved adherence profiles, and short-term improvement in RADAR-defined clinical control. Patients with greater baseline RADAR burden and regimen complexity showed larger observed improvements. Full article

Sleep-related respiratory disturbances are difficult to monitor continuously outside specialized laboratories because conventional polysomnography is resource-intensive and intrusive. This study presents a contactless edge-AI engineering prototype for detecting controlled voluntary respiratory-motion suppression and motion artifacts using a 60 GHz frequency-modulated continuous-wave radar. The system integrates a 60 GHz radar front end, lightweight local preprocessing, an INT8 one-dimensional convolutional neural network deployed on the Analog Devices MAX78000 CNN accelerator (Analog Devices Thailand, Chon Buri, Thailand), and an event-driven Raspberry Pi Zero 2W gateway for alert transmission. Evaluation was performed using a controlled healthy-volunteer dataset consisting of normal breathing, voluntary breath-holding-induced respiratory suppression, and deliberate motion artifact. The final valid test set contained 270 technically valid 30 s windows balanced across the three classes. The INT8 model achieved an overall accuracy of 92.6% (95% confidence interval: 88.8–95.2%), with a macro-averaged precision, recall, and F1-score of 92.6%, 92.6%, and 92.5%, respectively. Active CNN inference on the MAX78000 consumed 0.152 ± 0.011 mJ and was completed in 5.20 ± 0.11 ms, corresponding to approximately 280-fold lower active inference energy than Python 3.14.6/TensorFlow Lite 2.21.0-based execution on the Raspberry Pi Zero 2W. These results demonstrate the feasibility of privacy-aware, low-power respiratory-pattern classification at the edge. However, the study should be interpreted strictly as an engineering proof-of-concept based on controlled voluntary breathing and movement tasks in healthy volunteers. It is not a clinically validated apnea or obstructive sleep apnea detection system and did not include polysomnography, oxygen saturation measurement, airflow sensing, sleep staging, or diagnosed patient cohorts. Full article

Non-exhaust emissions (NEEs), particularly brake wear particles (BWPs), have become a dominant source of traffic-related particulate matter (PM), accounting for approximately 77% of PM10 and 60% of PM2.5 emissions. Accurate quantification of these emissions is essential under increasingly stringent regulations such as Euro 7. However, measurement reliability is strongly influenced by particle transport and sampling losses. This review provides a state-of-the-art analysis of laboratory-scale methodologies for investigating BWP emissions, focusing on pin-on-disc (PoD) tribometers and inertia dynamometer systems. Particular attention is given to chamber design, airflow management, sampling configurations, and the mechanisms governing particle transport efficiency. The literature indicates that PoD systems are often affected by complex and non-uniform flow fields, leading to incomplete particle capture and reduced representativeness, whereas inertia dynamometers, especially when coupled with constant volume sampling (CVS), provide more controlled and reproducible conditions. Key loss mechanisms, including inertial deposition, diffusion, gravitational settling, and non-isokinetic sampling effects, are major contributors to uncertainty. The reviewed studies highlight that aerodynamic limitations in PoD systems, particularly box-shaped chambers, promote flow recirculation and particle losses. Advanced optimization approaches that combine artificial neural networks (ANNs) with computational fluid dynamics (CFD) simulations show strong potential to improve system design and measurement reliability. Full article

The transition toward circular economy practices and CO₂ reduction goals is driving the development of new sound absorption technologies. Traditional absorbers made from mineral wool or foams provide broadband absorption; however, their production is associated with intensive energy consumption and non-renewable resources. This is why the focus has been shifting from the mere substitution of materials to integrated solutions that combine sustainability with structure. This paper reviews recent innovations in sustainable absorbers based on bio-based and recycled materials. The acoustic performance of porous materials depends on such factors such as pore structure, airflow resistivity and geometric parameters such as thickness, multi-layer structure and resonances. At the same time, additive manufacturing (AM) allows creating geometry-controlled absorbers providing advanced acoustic properties. Despite many sustainable absorbers demonstrating sufficient sound absorption properties at medium and high frequencies, their use at low frequencies remains challenging. Additionally, concerns regarding durability, flame retardance, and environmental consistency continue to limit their broader application. Yet, hybrid, multi-material strategies, particularly those combining geopolymer matrices with bio-based or recycled fillers, are identified as a promising route to address these limitations. This review outlines current trends and highlights key challenges and future directions in the design of sustainable sound-absorbing systems. Full article

This paper investigates the potential of localized air-curtain microclimate control to reduce HVAC energy consumption in electric vehicles while maintaining occupant thermal comfort. The study compares conventional full-cabin cooling with driver-focused and passenger-focused air-curtain configurations under controlled ambient conditions of 32 °C. The experimental framework combines analytical airflow and heat-transfer modeling with comparative HVAC performance evaluation using power consumption, time to reach thermal comfort, and Predicted Mean Vote (PMV) analysis. The results show that the air-curtain configurations reduce HVAC power consumption from 3.2 kW for conventional cooling to 2.3 kW and 2.5 kW for the driver- and passenger-focused configurations, corresponding to energy savings of approximately 22–28%. In addition, localized airflow significantly accelerates thermal comfort attainment, reducing stabilization time from 8 min to 4–5 min while maintaining PMV values within acceptable comfort limits. The findings demonstrate that occupant-centered air-curtain microclimate strategies can improve HVAC energy efficiency, reduce auxiliary energy demand, and support more sustainable and range-efficient operation of next-generation electric vehicles. Full article

Residential buildings must now be designed and retrofitted as adaptive climate–health–work systems rather than as static housing units. This structured literature review synthesises peer-reviewed journal and conference evidence on residential taxonomy, ventilation, indoor environmental quality, overheating, airborne infection resilience, post-pandemic occupancy changes and future performance benchmarks. The review shows that single-family and multifamily buildings remain the most practical first-order categories because they differ in envelope exposure, ventilation pathways, system ownership, governance, retrofit feasibility and occupant control. Single-family dwellings generally provide greater household autonomy, roof-based renewable potential and room-level intervention flexibility, but can also carry higher envelope losses, lower density and stronger dependence on occupant operation. Multifamily buildings benefit from compactness and shared infrastructure, yet face additional risks from common services, vertical shafts, stack effects, corridor pressurisation, inter-zonal airflow and collective maintenance. Ventilation evidence indicates that natural, exhaust-only, supply, balanced heat-recovery, hybrid, demand-controlled and filtration-based strategies cannot be ranked universally; their effectiveness depends on climate, airtightness, pollutant source, occupancy, maintenance and governance. This review further shows that overheating, cooling-demand growth, airborne infection preparedness and remote work are shifting residential performance from winter-centric energy efficiency toward year-round thermal resilience, clean-air delivery and prolonged-occupancy functionality. A future taxonomy is therefore proposed around adaptive performance attributes, including thermal resilience, clean-air capacity, ventilation controllability, energy flexibility, remote-work readiness, vulnerability and retrofit potential. The core contribution is a hypothesis-generating, decision-support and benchmark-development framework for aligning residential design, retrofit and policy with health, indoor environmental quality, energy efficiency and carbon performance. Full article

Elevated particulate matter (PM) concentrations pose severe health risks, necessitating green infrastructure mitigation. While deposition is well documented, wind-induced remobilization remains insufficiently quantified. This study establishes a size-fractionated (PM_1–2.5 and PM_2.5–10) wind-induced resuspension and net removal values for six Central European shrub and climbing species (Parthenocissus quinquefolia, Hedera helix, Viburnum opulus, Viburnum lantana, Ligustrum ovalifolium, and Cornus mas) under controlled laboratory conditions. Following standardized aerosol chamber loading, leaves were subjected to constant, laminar airflow velocity of 3 m/s. Numerical quantification of particle counts per unit area (cm²) was performed via scanning electron microscopy with backscattered electron signal processing. Results demonstrate significant interspecific variations. Parthenocissus quinquefolia was most efficient, retaining the highest particle counts (121.6 × 10³ particles/cm² for PM_2.5–10) and achieving net removal rates of 46.3% and 60.5% for PM_1–2.5 and PM_2.5–10, respectively, relative to initial deposition. Cornus mas exhibited the lowest net removal efficiency for coarse particles (21.2% for PM_2.5–10), while Hedera helix showed the highest fractional resuspension rates (k = 1.93 × 10⁻⁴ ∙ s⁻¹ and 2.01 × 10⁻⁴ ∙ s⁻¹, respectively). These species-specific traits are vital for optimizing urban green infrastructure. Ultimately, these findings provide actionable recommendations for targeted plant selection to maximize urban air purification. Full article