Search Results (350)

Conventional grain monitoring systems often rely on isolated data points (e.g., point-based temperature measurements), limiting holistic condition assessment. This study proposes a novel Mechanism and Data Driven (MDD) framework that integrates physical mechanisms with real-time sensor data. The framework quantitatively analyzes solar radiation and external air temperature effects on silo boundaries and introduces a novel interpolation-optimized model parameter initialization technique to enable comprehensive grain condition perception. Rigorous multidimensional validation confirms the method’s accuracy: The novel initialization technique achieved high precision, demonstrating only 1.89% error in Day-2 low-temperature zone predictions (27.02 m² measured vs. 26.52 m² simulated). Temperature fields were accurately reconstructed (≤0.5 °C deviation in YOZ planes), capturing spatiotemporal dynamics with ≤0.45 m² maximum low-temperature zone deviation. Cloud map comparisons showed superior simulation fidelity (SSIM > 0.97). Further analysis revealed a 22.97% reduction in total low-temperature zone area (XOZ plane), with Zone 1 (near south exterior wall) declining 27.64%, Zone 2 (center) 25.30%, and Zone 3 20.35%. For dynamic evolution patterns, high-temperature zones exhibit low moisture (<14%), while low-temperature zones retain elevated moisture (>14%). A strong positive correlation between temperature and relative humidity fields; temperature homogenization drives humidity uniformity. The framework enables holistic monitoring, providing actionable insights for smart ventilation control, condensation risk warnings, and mold prevention. It establishes a robust foundation for intelligent grain storage management, ultimately reducing post-harvest losses. Full article

As the environmental regulations of the International Maritime Organization (IMO) become more stringent, the accurate prediction of ship propulsion performance has become essential. Under ballast conditions where the draft is shallow, the propeller approaches the free surface, causing complex phenomena such as ventilation and surface piercing, which reduce propulsion efficiency. The conventional virtual disk (VD) method cannot adequately capture these free-surface effects, leading to deviations from model propeller results. To resolve this, a correction formula that accounts for the advance ratio (J) and submergence ratio (h/D) has been proposed in previous studies. In this study, the correction formula was simplified and implemented in a CFD environment using a field function, enabling dynamic adjustment of body force based on time-varying submergence depth. A comparative analysis was conducted between the conventional VD, modified VD, and model propeller using POW and self-propulsion simulations for an MR tanker and SP598M propeller. The improved method was validated in calm and regular wave conditions. The results showed that the modified VD method closely matched the performance trends of the model propeller, especially in free surface-interference conditions (e.g., h/D < 0.5). Furthermore, additional validations in wave-induced self-propulsion confirmed that the modified VD method accurately reproduced the reductions in wake fraction and thrust deduction coefficient, unlike the overestimations observed with the conventional VD. These results demonstrate that the modified VD method can reliably predict propulsion performance under real sea states and serve as a practical tool in the early design stage. Full article

Traditionally, duct sizing in ventilation systems is based on balancing pressure losses across all branches, with fan selection performed subsequently. However, this sequential approach is inadequate for systems with distributed fans in the central duct network, where pressure losses can vary significantly. Consequently, when designing the system topology, fan placement and duct sizing must be considered together. Recent research has demonstrated that discrete optimisation methods can account for multiple load cases and produce ventilation layouts that are both cost- and energy-efficient. However, existing approaches usually concentrate on component placement and assume that duct sizing has already been finalised. While this is sufficient for later design stages, it is unsuitable for the early stages of planning, when numerous system configurations must be evaluated quickly. In this work, we present a novel methodology that simultaneously optimises duct sizing, fan placement, and volume flow controller configuration to minimise life-cycle costs. To achieve this, we exploit the structure of the problem and formulate a mixed-integer linear program (MILP), which, unlike existing non-linear models, significantly reduces computation time while introducing only minor approximation errors. The resulting model enables fast and robust early-stage planning, providing optimal solutions in a matter of seconds to minutes, as demonstrated by a case study. The methodology is demonstrated on a case study, yielding an optimal configuration with distributed fans in the central fan station and achieving a 5 reduction in life-cycle costs compared to conventional central designs. The MILP formulation achieves these results within seconds, with linearisation errors in electrical power consumption below 1.4%, confirming the approach’s accuracy and suitability for early-stage planning. Full article

Filtration of submicron particles and nanoparticles is an important problem in nano-industry and in air conditioning and ventilation systems. The presence of submicron particles comprising fungal spores, bacteria, viruses, microplastic, and tobacco-smoke tar in ambient air is a severe problem in air conditioning systems. Many nanotechnology material processes used for catalyst, solar cells, gas sensors, energy storage devices, anti-corrosion and hydrophobic surface coating, optical glasses, ceramics, nanocomposite membranes, textiles, and cosmetics production also generate various types of nanoparticles, which can retain in a conveying gas released into the atmosphere. Particles in this size range are particularly difficult to remove from the air by conventional methods, e.g., electrostatic precipitators, conventional filters, or cyclones. For these reasons, nanofibrous filters produced by electrospinning were developed to remove fine particles from the post-processing gases. The physical basis of electrospinning used for nanofilters production is an employment of electrical forces to create a tangential stress on the surface of a viscous liquid jet, usually a polymer solution, flowing out from a capillary nozzle. The paper presents results for investigation of the filtration process of metal oxide nanoparticles: TiO₂, MgO, and Al₂O₃ by electrospun nanofibrous filter. The filter was produced from polyvinylidene fluoride (PVDF). The concentration of polymer dissolved in dimethylacetamide (DMAC) and acetone mixture was 15 wt.%. The flow rate of polymer solution was 1 mL/h. The nanoparticle aerosol was produced by the atomization of a suspension of these nanoparticles in a solvent (methanol) using an aerosol generator. The experimental results presented in this paper show that nanofilters made of PVDF with surface density of 13 g/m² have a high filtration efficiency for nano- and microparticles, larger than 90%. The gas flow rate through the channel was set to 960 and 670 l/min. The novelty of this paper was the investigation of air filtration from various types of nanoparticles produced by different nanotechnology processes by nanofibrous filters and studies of the morphology of nanoparticle deposited onto the nanofibers. Full article

In recent years, pneumatic proportional valves have become increasingly prevalent in ventilators, particularly proportional solenoid valves. However, these traditional valves face challenges, including a slow response, being prone to overheating from long-term work, and high power consumption. This study presents the development of a fast response, high flow rate, and low power consumption pneumatic proportional valve specifically designed for medical ventilators. Utilizing a piezoelectric bimorph as the actuator, we innovatively eliminate movable components such as springs while ensuring effective sealing of the valve. A support structure was designed to enhance the mechanical performance of the piezoelectric bimorph. A testing platform was established to rigorously assess the valve’s performance. The results indicate that the valve can achieve a maximum output flow rate of approximately 130 L/min at an input pressure of 4 bar, with a hysteresis rate of 25.3%, a response time of under 10 ms, and a power consumption of just 0.07 W. Furthermore, a comparative analysis with existing commercial proportional solenoid valves demonstrated that it has superior performance in terms of response speed, flow rate, and power efficiency. The piezoelectric proportional valve developed in this study holds the potential to replace conventional proportional solenoid valves, significantly enhancing the response speed of ventilators, reducing their overall power consumption, and facilitating the development of portable ventilators. Full article

Background: Flexible bronchoscopy (FB) is a widely used diagnostic and therapeutic procedure in patients with pulmonary disease, many of whom are at risk of gas exchange impairment. FB may exacerbate hypoxaemia due to increased airway resistance, alveolar derecruitment, and haemodynamic fluctuations. Objectives: To assess the effectiveness of non-invasive respiratory support strategies in preventing oxygen desaturation and respiratory complications during FB. Methods: A systematic review and meta-analysis were conducted using PubMed and Cochrane databases, covering studies from 2000 to 2024. Inclusion criteria focused on adult patients undergoing FB with any form of non-invasive oxygen support. Twelve high-quality studies were selected, including randomised trials and prospective cohorts. Results: High-flow therapy (HFT) was more effective than conventional oxygen therapy (COT) in maintaining oxygenation and reducing procedure interruptions, especially in patients with moderate hypoxaemia or risk factors such as obesity and obstructive sleep apnoea. Continuous positive airway pressure (CPAP) and non-invasive ventilation (NIV) offered superior oxygenation and ventilatory support in patients with more severe respiratory or cardiac compromise. Conclusions: Non-invasive respiratory support should be individualised based on patient risk and procedural complexity. HFT benefits mild-to-moderate cases, while CPAP or NIV is preferable in more severe conditions. Further multicentre randomised trials are needed to establish formal guidelines. Full article

This review covers the current state, challenges, and future directions of catalytic combustion technologies for mitigating fugitive methane emissions from the fossil fuel industry. Methane, a potent greenhouse gas, is released from diverse sources, including natural gas production, oil operations, coal mining, and natural gas engines. The paper details the primary emission sources, and addresses the technical difficulties associated with dilute and variable methane streams such as ventilation air methane (VAM) from underground coal mines and low-concentration leaks from oil and gas infrastructure. Catalytic combustion is a useful abatement solution due to its ability to destruct methane in lean and challenging conditions at lower temperatures than conventional combustion, thereby minimizing secondary pollutant formation such as NO_X. The review surveys the key catalyst classes, including precious metals, transition metal oxides, hexa-aluminates, and perovskites, and underscores the crucial role of reactor internals, comparing packed beds, monoliths, and open-cell foams in terms of activity, mass transfer, and pressure drop. The paper discusses advanced reactor designs, including flow-reversal and other recuperative systems, modelling approaches, and the promise of advanced manufacturing for next-generation catalytic devices. The review highlights the research needs for catalyst durability, reactor integration, and real-world deployment to enable reliable methane abatement. Full article

Summer overheating has emerged as the primary comfort challenge in rural housing under a warming climate. Conventional retrofit measures are often infeasible due to high costs and limited technical capacity. This study investigates how spatial configuration influences summer thermal conditions while keeping envelope materials constant, focusing on rural dwellings in the Guanzhong region of China. Three representative prototypes are analyzed: the traditional courtyard type, the deep continuation type, and the progressive combined type. Thermal performance is evaluated using the Predicted Mean Vote (PMV) index through Ladybug and Honeybee simulations based on long-term meteorological data, and validated with multi-room field measurements. Two parametric analyses further test the effects of window opening rates (0.2–0.5) and room width-to-depth ratios (1:1–1:2.5). Results indicate that courtyards and galleries function as transitional zones, creating discrete yet connected thermal units and reducing PMV near edges. Second-floor rooms show a ventilation advantage with an average PMV reduction of 0.08. Enlarging window openings improves PMV only when cross-ventilation paths exist, while ratios wider than 1:2 raise PMV and slightly influence adjacent rooms. Field measurements confirm these simulated patterns. Cross-regional comparisons with Argentina, Brazil, and Japan further demonstrate that once the envelope is adequate, the spatial organization becomes the key driver of summer comfort. The study highlights practical, low-cost strategies such as reallocating high-use rooms to favorable zones, adding targeted shading, and ventilation, and introducing lightweight spatial interventions. These measures enhance summer comfort without invasive construction. Full article

Lower respiratory tract infections (LRTIs) remain a major cause of global morbidity and mortality, yet accurate pathogen identification and risk stratification continue to pose clinical challenges. Lipidomics—the comprehensive analysis of lipid species within biological systems—has emerged as a promising tool to unravel host–pathogen interactions and reveal novel diagnostic and prognostic biomarkers. This systematic review synthesizes evidence from nine original studies applying mass spectrometry-based lipidomic profiling in human LRTIs, including community-acquired pneumonia (CAP), ventilator-associated pneumonia (VAP), and coronavirus disease 2019 (COVID-19). Across diverse study designs, sample types, and analytical platforms, consistent alterations in lipid metabolism were observed. Perturbations in phospholipid classes, particularly phosphatidylcholines (PCs) and lysophosphatidylcholines (LPCs), were frequently associated with disease severity and immune activation. The ratios of PC to LPC and phosphatidylethanolamine (PE) to lysophosphatidylethanolamine (LPE) emerged as markers of inflammatory remodeling. Sphingolipids—including sphingomyelins (SMs) and sphingosine-1-phosphate (S1P)—were identified as key modulators of monocyte and neutrophil activation. Fatty acid–derived lipid mediators such as oxylipins (e.g., 12,13-epoxyoctadecenoic acid and 15-hydroxyeicosatetraenoic acid) and acylcarnitines reflected pathogen-specific immune responses and mitochondrial dysfunction. Several lipid-based classifiers demonstrated superior diagnostic and prognostic performance compared to conventional clinical scores, including the CURB-65 and pneumonia severity index. However, significant heterogeneity in experimental design, lipid identification workflows, and reporting standards limits inter-study comparability. While preliminary findings support the integration of lipidomics into infectious disease research, larger multi-omic and longitudinal studies are required. This review provides the first comprehensive synthesis of lipidomic alterations in human LRTIs and highlights their emerging translational relevance. Full article

Laboratory buildings represent some of the highest energy-consuming infrastructure due to stringent environmental requirements and the continuous operation of specialized equipment. Ensuring both energy efficiency and indoor air quality (IAQ) in such spaces remains a central challenge for sustainable building design and operation. Recent advances in Internet of Things (IoT) systems allow for real-time monitoring of multivariate environmental parameters, including CO₂, total volatile organic compounds (TVOC), PM_2.5, temperature, humidity, and noise. However, these datasets are often noisy or incomplete, complicating conventional monitoring approaches. Supervised anomaly detection methods are ill-suited to such contexts due to the lack of labeled data. In contrast, unsupervised machine learning (ML) techniques can autonomously detect patterns and deviations without annotations, offering a scalable alternative. The challenge of identifying anomalous environmental conditions and latent operational states in laboratory environments is addressed through the application of unsupervised models to 1808 hourly observations collected over four months. Anomaly detection was conducted using Isolation Forest (300 trees, contamination = 0.05) and One-Class Support Vector Machine (One-Class SVM) (RBF kernel, ν = 0.05, γ auto-scaled). Standardized six-dimensional feature vectors captured key environmental and energy-related variables. K-means clustering (k = 3) revealed three persistent operational states: Empty/Cool (42.6%), Experiment (37.6%), and Crowded (19.8%). Detected anomalies included CO₂ surges above 1800 ppm, TVOC concentrations exceeding 4000 ppb, and compound deviations in noise and temperature. The models demonstrated sensitivity to both abrupt and structural anomalies. Latent states were shown to correspond with occupancy patterns, experimental activities, and inactive system operation, offering interpretable environmental profiles. The methodology supports integration into adaptive heating, ventilation, and air conditioning (HVAC) frameworks, enabling real-time, label-free environmental management. Findings contribute to intelligent infrastructure development, particularly in resource-constrained laboratories, and advance progress toward sustainability targets in energy, health, and automation. Full article

Aware of the nefarious effects of excessive exploitation of natural resources and the greenhouse gases emissions linked to building sector, the concept of smart buildings emerged, referring to a building that uses clean energy efficiently. This requires intelligent control systems to manage the use of residential energy consuming devices, namely the HVAC (Heating, Ventilation, Air-conditioning) system. This system consumes up to 50% of the total energy used by a building. In this paper, we introduce a RL (Reinforcement Learning) and MPC-LSTM (Model Predictive Control-Long-Short Term Memory) hybrid control system that combines DNNs (Deep Neural Networks), through RL, with LSTM’s long-short memory technique and MPC’s control characteristics. The goal of our model is to maintain thermal comfort of residents while optimizing energy consumption. Consequently, to train and test our model, we generate our own dataset using a building model of a corporate building in Casablanca, Morocco, combined with weather data of the same city. Simulations confirm the robustness of our model as it outperforms basic control methods in terms of thermal comfort and energy consumption especially during summer. Compared to conventional methods, our approach resulted in a 45.4% and 70.9% reduction in energy consumption, in winter and summer, respectively. Our approach also resulted in 26 less comfort violations during winter. On the other hand, during summer, our approach found a compromise between energy consumption and comfort with no more than 2.5 °C above ideal temperature limit. Full article

Greenhouse vegetable production faces significant challenges due to the non-stationary and nonlinear dynamics of the cultivation environment, which demand adaptive and intelligent control strategies. This study presents an intelligent control system for greenhouse complexes based on artificial neural networks and fuzzy logic, optimized using genetic algorithms. The proposed system dynamically adjusts PI controller parameters to maintain optimal microclimatic conditions, including temperature and humidity, enhancing resource efficiency. Comparative analyses demonstrate that the genetic algorithm-based tuning outperforms traditional and fuzzy adaptation methods, achieving superior transient response with reduced overshoot and settling time. Implementation of the intelligent control system results in energy savings of 10–12% compared to conventional stabilization algorithms, while improving decision-making efficiency for electrotechnical subsystems such as heating and ventilation. These findings support the development of resource-efficient cultivation regimes that reduce energy consumption, stabilize agrotechnical parameters, and increase profitability in greenhouse vegetable production. The approach offers a scalable and adaptable solution for modern greenhouse automation under varying environmental conditions. Full article

With the development of the mining industry, safety issues in underground operations are becoming increasingly relevant. Complex gas conditions in mines, including the presence of explosive and toxic gases, pose a serious threat to the lives of miners and the stability of production processes. This paper describes the development and modeling of an integrated fire monitoring and automatic extinguishing system that combines gas collection, concentration analysis, and rapid response to emergencies. The main components of the system include the following: a gas collection module that uses an array of highly sensitive sensors to continuously measure the concentrations of methane (CH₄), carbon monoxide (CO), and hydrogen sulfide (H₂S) with an accuracy of up to 95%; a gas analysis module that uses data processing algorithms to identify gas concentration threshold exceedances (e.g., CH₄ > 5% vol. or CO > 20 ppm); and an automatic fire extinguishing module that activates nitrogen supply, ventilation, and aerosol/powder fire extinguishers when a threat is detected. Simulation results in MATLAB/Simulink showed that the system reduces the concentration of hazardous gases by 30% within the first 2 s after activation, which significantly increases safety. Additionally, scenarios with various types of fires were analyzed, confirming the effectiveness of the extinguishing modules in mines up to 500 m deep. The integrated system achieves 95% gas detection accuracy, 90 ms response latency, and 40% hazard reduction within 3 s of activation, verified in 500 m deep mine simulations. Quantitative comparison shows a 75% faster response time and 10% higher detection accuracy than conventional systems. The proposed system demonstrates high reliability in difficult conditions, reducing the risk of fires by 75% compared to traditional methods. This work opens up prospects for practical application in the coal industry, especially in regions with a high risk of spontaneous coal combustion, such as India and Germany. Full article

The construction of buildings using shipping containers (SCs) is a way to extend their useful life. They are constructed by modifying the structure, thermal, and acoustic conditioning by improving the envelope and creating openings for lighting and ventilation purposes. This study explores the architectural adaptation of SCs to sustainable residential housing, focusing on structural, thermal, and acoustic performance. The project centers on a case study in Madrid, Spain, transforming four containers into a semi-detached, multilevel dwelling. The design emphasizes modular coordination, spatial flexibility, and structural reinforcement. The retrofit process includes the integration of thermal insulation systems in the ventilated façades and sandwich roof panels to counteract steel’s high thermal conductivity, enhancing energy efficiency. The acoustic performance of the container-based dwelling was assessed through in situ measurements of façade airborne sound insulation and floor impact noisedemonstrating compliance with building code requirements by means of laminated glazing, sealed joints, and floating floors. This represents a novel contribution, given the scarcity of experimental acoustic data for residential buildings made from shipping containers. Results confirm that despite the structure’s low surface mass, appropriate design strategies can achieve the required sound insulation levels, supporting the viability of this lightweight modular construction system. Structural calculations verify the building’s load-bearing capacity post-modification. Overall, the findings support container architecture as a viable and eco-efficient alternative to conventional construction, while highlighting critical design considerations such as thermal performance, sound attenuation, and load redistribution. The results offer valuable data for designers working with container-based systems and contribute to a strategic methodology for the sustainable refurbishment of modular housing. Full article

In tropical regions like sub-Saharan Africa, conventional vapor compression HVAC systems contribute disproportionately to energy use, operating costs, and carbon emissions—particularly in coastal urban areas where humidity-driven cooling demand is extreme. Despite these challenges, viable low-energy alternatives remain largely underexplored for this region. This study presents the first simulation-based assessment of a desiccant indirect evaporative cooling (DIEC) system optimized for the hot–humid coastal climate of Dar es Salaam, Tanzania, addressing a critical gap in sustainable cooling research for coastal Africa. Using OpenStudio (version 3.9.0) and a custom EnergyPlus(version 9.3.0) latent heat removal algorithm, this study models a DIEC-equipped medium office building with 100% outdoor air ventilation and exhaust-air-based desiccant regeneration. The model reflects local construction practices, occupancy profiles, and weather data and is validated with >90% accuracy against experimental benchmarks. Results demonstrate that the DIEC system (1) maintains indoor thermal comfort (23.8–24.0 °C) during peak humidity periods, and (2) reduces annual cooling energy consumption by 10.2% relative to single-speed DX systems. These savings are particularly impactful in a context where electricity prices are rising, and HVAC loads consume 25–40% of building operational budgets. Furthermore, the system’s superior humidity control and stable power demand make it well-suited for integration with decentralized renewable energy sources. By establishing a context-specific benchmark for DIEC performance, this study delivers a novel, regionally tailored strategy for decarbonizing urban cooling in coastal tropical Africa. Full article