Search Results (215)

This article presents a new method of parametric shaping of buildings with buffer spaces characterized by complex forms and effective thermal operation in the moderate climate of the Central Europe Plane. The parameterization of an elaborated thermal qualitative model of buildings with buffer spaces and its configuration based on computer simulations of thermal operation of many discrete models are the specific features of the method. The model uses various original building shapes and a new parametric artificial neural network (a) to automate the calculations and recording of results and (b) to predict a number of new buildings with buffer spaces characterized by effective thermal operation. The configuration of the parametric quantitative model was carried out based on the simulation results of 343 discrete models defined by means of ten independent variables grouping the properties of the building and buffer space related to their forms, materials and air circulation. The analysis performed for the adopted parameter variability ranges indicates a varied impact of these independent variables on the thermal operation of buildings located in a moderate climate. The infiltration and ventilation and physical properties of the windows and walls are the independent variables that most influence the energy savings utilized by the examined buildings with buffer spaces. The optimal values of these variables allow up to 50–60% of the energy supplied by the HVAC system to be saved. The accuracy and universality of the method will continuously be increased in future research by increasing the types and ranges of independent variables. Full article

Background/Objectives: Guillain–Barré syndrome (GBS) is a rare autoimmune peripheral neuropathy that affects both the myelin sheaths and axons of the peripheral nervous system. It is the leading cause of acute neuromuscular paralysis worldwide, with an annual incidence of less than two cases per 100,000 people. Although most patients recover, a small proportion do not regain mobility and even remain dependent on mechanical ventilation. In this study, we refer to the analysis of samples collected from GBS patients at different defined time points during hospital recovery and performed by a medical or research group. Methods: The conditions for whole blood collection, peripheral blood mononuclear cell isolation, and serum collection from GBS patients and volunteer donors are explained. Aliquots of these human samples have been used for red blood cell phenotyping, transcriptomic and proteomic analyses, and serum biochemical parameter studies. Results: The initial sporadic preservation of human samples from GBS patients and control volunteers enabled the creation of a biobank collection for current and future studies related to the diagnosis and treatment of GBS. Conclusions: In this article, we describe the laboratory procedures and the integration of a GBS biobank collection, local medical services, and academic institutions collaborating in its respective field. The report establishes the intra-disciplinary and inter-institutional network to conduct long-term longitudinal studies on GBS. Full article

Mine ventilation systems are critical for ensuring operational safety, yet air leakage remains a pervasive challenge, leading to energy inefficiency and heightened safety risks. Traditional tracer gas methods, while effective in simple networks, exhibit significant errors in complex multi-entry systems due to static empirical parameters and environmental interference. This study proposes an integrated methodology that combines multi-path airflow analysis with dynamic longitudinal dispersion coefficient correction to enhance the accuracy of air leakage detection. Utilizing sulfur hexafluoride (SF6) as the tracer gas, a phased release protocol with temporal isolation was implemented across five strategic points in a coal mine ventilation network. High-precision detectors (Bruel & Kiaer 1302) and the MIVENA system enabled synchronized data acquisition and 3D network modeling. Theoretical models were dynamically calibrated using field-measured airflow velocities and dispersion coefficients. The results revealed three deviation patterns between simulated and measured tracer peaks: Class A deviation showed 98.5% alignment in single-path scenarios, Class B deviation highlighted localized velocity anomalies from Venturi effects, and Class C deviation identified recirculation vortices due to abrupt cross-sectional changes. Simulation accuracy improved from 70% to over 95% after introducing wind speed and dispersion adjustment coefficients, resolving concealed leakage pathways between critical nodes and key nodes. The study demonstrates that the dynamic correction of dispersion coefficients and multi-path decomposition effectively mitigates errors caused by turbulence and geometric irregularities. This approach provides a robust framework for optimizing ventilation systems, reducing invalid airflow losses, and advancing intelligent ventilation management through real-time monitoring integration. Full article

With the increasing complexity of mine ventilation networks, the difficulty of regulating ventilation systems has significantly increased. Lagging regulatory responses are prone to causing problems such as airflow turbulence and insufficient air supply in air-required areas, which seriously threaten the safety of underground operations. To address this challenge, this paper introduces the MOBWO algorithm into the field of ventilation system air volume optimization and proposes a mine air volume optimization and regulation method based on MOBWO. This paper constructs a multi-objective air volume optimization model with the total power of ventilators and the complexity of air pressure regulation as the optimization objectives. Using indicators such as GD and IGD, it compares the performance of the MOBWO algorithm with mainstream optimization algorithms such as NSGA-II and MOPSO and verifies the practicality of the optimization method with the case of the Jinhua Palace Mine. The results show that the MOBWO algorithm has significant advantages over other algorithms in terms of convergence and distribution performance. When applied to the Jinhua Palace Mine, the air volume optimization and regulation using MOBWO can reduce the power of ventilators by 10.3–21.1% compared with that before optimization while reducing the complexity of air volume regulation and the time loss during air volume regulation. This method not only reduces the energy consumption of ventilators but also shortens the regulation timeliness of the ventilation system, which is of great significance for reducing the probability of accidents and ensuring the safety of personnel’s lives and property. Full article

The control of ventilation systems is often performed by automatic algorithms which often do not consider the future evolution of the system in its control politics. Digital twins allow system forecasting for a more sophisticated control. This paper explores a novel methodology to create a Machine Learning (ML) model for the predictive control of a ventilation system combining Computational Fluid Dynamics (CFD) with Artificial Intelligence (AI). This predictive model was created to forecast the temperature and humidity evolution of a ventilated room to be implemented in a digital twin for better unsupervised control strategies. To replicate the full range of annual conditions, a series of CFD simulations were configured and executed based on seasonal data collected by sensors positioned inside and outside the room. These simulations generated a dataset used to develop the predictive model, which was based on a Deep Neural Network (DNN) with fully connected layers. The model’s performance was evaluated, yielding final average absolute errors of 0.34 degrees Kelvin for temperature and 2.2 percentage points for relative humidity. The presented results highlight the potential of this methodology to create AI-driven digital twins for the control of room ventilation. Full article

Enhancing the energy management capabilities of modern smart buildings is essential for energy conservation, which is valuable for modern power networks maintaining a tight power balance under high renewable penetration. This study introduces a data-driven control strategy based on the model predictive control (MPC) for HVAC (heating, ventilation, and air conditioning) systems considering the time-of-use (ToU) electricity rates in Iraq. A multi-layer neural network is first constructed using time-delayed embedding for the modeling of building thermal dynamics, where the rectified linear unit (ReLU) is used as the activation function for the hidden layers. Based on such piecewise affine approximation, an optimization model is developed within the receding horizon control framework, which incorporates the data-driven model and is transformed into a mixed-integer linear programming facilitating efficient problem solving. To validate the efficiency of the proposed approach, a simulation model of the building’s thermal network is constructed using Simscape considering several thermal effects among the building components. Simulation results demonstrate that the proposed approach improves the economic performance of the building while maintaining thermal comfort levels within acceptable range. Full article

Heating, Ventilation and Air Conditioning (HVAC) systems are significant carbon emitters in buildings, and precise regulation is crucial for achieving carbon neutrality. Computer vision-based occupant behavior prediction provides vital data for demand-driven control strategies. Real-time multi-person pose estimation faces challenges in balancing speed and accuracy, especially in complex environments. Traditional top-down methods become computationally expensive as the number of people increases, while bottom-up methods struggle with key point mismatches in dense crowds. This paper introduces the Efficient-RTMO model, which leverages the Parameter Inverted Image Pyramid (PIIP) with hierarchical multi-scale symmetry for lightweight processing of high-resolution images and a deeper network for low-resolution images. This approach reduces computational complexity, particularly in dense crowd scenarios, and incorporates a dynamic sparse connectivity mechanism via the star-shaped dynamic feed-forward network (StarFFN). By optimizing the symmetry structure, it improves inference efficiency and ensures effective feature fusion. Experimental results on the COCO dataset show that Efficient-RTMO outperforms the baseline RTMO model, achieving more than 2× speed improvement and a 0.3 AP increase. Ablation studies confirm that PIIP and StarFFN enhance robustness against occlusions and scale variations, demonstrating their synergistic effectiveness. Full article

Heating, ventilation, and air conditioning (HVAC) systems account for 60% of the energy consumption in commercial buildings. Each year, millions of dollars are spent on electricity bills by commercial building operators. To address this energy consumption challenge, a predictive model named Bayesian optimisation Convolution Neural Network Multivariate Long Short-term Memory (BO CNN-M-LSTM) is introduced in this research. The proposed model is designed to perform load forecasting, optimizing energy usage in commercial buildings. The CNN block extracts local features, whereas the M-LSTM captures temporal dependencies. The hyperparameter fine tuning framework applied Bayesian optimization to enhance output prediction by modifying model properties with data characteristics. Moreover, to improve occupant well-being in commercial buildings, the thermal comfort adaptive model developed by de Dear and Brager was applied to ambient temperature in the preprocessing stage. As a result, across all four datasets, the BO CNN-M-LSTM consistently outperformed other models, achieving an 8% improvement in mean percentage absolute error (MAPE), 2% in normalized root mean square error (NRMSE), and 2% in R² score.This indicates the consistent performance of BO CNN-M-LSTM under varying environmental factors, highlight the model robustness and adaptability. Hence, the BO CNN-M-LSTM model is a highly effective predictive load forecasting tool for commercial building HVAC systems. Full article

Saturation diving is the only viable method that enables divers to withstand prolonged exposure to high-pressure environments, and it is increasingly used in underwater rescue and marine resource development. This study presents the control system design for a specialized saturation diving decontamination decompression chamber. As a multi-compartment structure, the system requires precise inter-cabin pressure differentials to ensure safe decontamination and ventilation control under dynamic conditions, particularly in the presence of potential faults, such as valve offset, actuator malfunction, and chamber leakage. To overcome these challenges, we propose a novel model-based planning and fault-tolerant control framework that enables adaptive responses and maintains resilient system performance. Specifically, we introduce a trajectory-planning algorithm guided by policy networks to improve planning efficiency and robustness under system uncertainty. Additionally, a meta-learning-based fault-tolerant control strategy is proposed to address system disturbances and faults. The experimental results demonstrate that the proposed approach achieves higher cumulative rewards, faster convergence, and improved robustness compared to conventional methods. This work provides an effective and adaptive control solution for human-occupied hyperbaric systems operating in safety-critical environments requiring fail-operational performance. Full article

Indoor environmental quality (IEQ), encompassing thermal comfort and indoor air quality (IAQ), plays a crucial role in occupant well-being and operational performance. Although widely studied individually, integrating thermal comfort and IAQ assessments remains limited, particularly in large-scale tropical commercial settings. Hypermarkets, characterised by spatial heterogeneity and fluctuating occupancy, present challenges that conventional HVAC systems often fail to manage effectively. This study investigates thermal comfort and IAQ variability in a hypermarket located in Gombak, Malaysia, under tropical rainforest conditions based on the Köppen–Geiger climate classification, a widely used system for classifying the world’s climates. Environmental parameters were monitored using a network of IoT-enabled sensors across five functional zones during actual operations. Thermal indices (PMV, PPD) and IAQ metrics (CO₂, TVOC, PM2.5, PM10) were analysed and benchmarked against ASHRAE 55 standards to assess spatial variations and occupant exposure. Results revealed substantial heterogeneity, with the cafeteria zone recording critical discomfort (PPD 93%, CO₂ 900 ppm, TVOC 1500 ppb) due to localised heat and insufficient ventilation. Meanwhile, the intermediate retail zone maintained near-optimal conditions (PPD 12%). Although findings are specific to this hypermarket, the integrated zone-based monitoring provides empirical insights that support the enhancement of IEQ assessment approaches in tropical commercial spaces. By characterising zone-specific thermal comfort and IAQ profiles, this study contributes valuable knowledge toward developing adaptive, occupant-centred HVAC strategies for complex retail environments in hot-humid climates. Full article

An efficient solution strategy based on fluid network modeling, computational fluid dynamics (CFD) and discrete particle modeling (DPM) is presented in order to predict and improve air quality, specifically regarding breathing aerosol concentration, in a person-occupied mechanically ventilated room. The efficiency of the proposed workflow is evaluated for the specific case of a lecture hall. It is found that the actual vent system is imbalanced and inefficient in managing the aerosol concentration within the room. Despite a high volumetric exchange rate, aerosol residence times and local aerosol concentrations remain high over an extended period of time, without additional efforts to alter air flow circulation throughout the room. The proposed strategy illustrates how such changes can be efficiently implemented in the basic 1D/3D co-simulation workflow. Analysis of the lecture hall and vent system shows that the execution time for the overall process workflow can be optimized by the following: (1) CAD geometry generation of the room via 3D laser scanning, (2) mesh generation based on the anticipated air discharge behavior from the vent system and (3) by employing HPC resources. Additional simplifications such as the decoupling of vent air flow and room aerodynamics, as observed for the investigated test case, one-way coupling between air flow and aerosol dispersion at low aerosol concentrations and the successive solution of flow field equations can further reduce the problem’s complexity and processing times. Full article

The air-handling unit (AHU) is an essential component of heating, ventilation, and air-conditioning (HVAC) systems. Hence, detecting the faults in AHUs is essential for maintaining continuous HVAC operation and preventing system breakdowns. The advent of artificial intelligence has transformed the AHU fault diagnosis techniques. Specifically, deep learning has obviated the necessity for manual feature extraction and selection, thereby streamlining the fault diagnosis process. While conventional convolutional neural networks (CNNs) effectively detect defects, incorporating more spatial variables could enhance their performance further. This paper presents a hybrid architecture combining a CNN model with a long short-term memory (LSTM) model to diagnose the faults in AHUs. The advantages of the LSTM model and convolutional layers are combined to identify significant patterns in the input data, which considerably facilitates the detection of AHU defects. The hybrid design enhances the network’s capability to capture both local and global characteristics, thus improving its ability to differentiate between normal and abnormal circumstances. The proposed approach achieves strong diagnostic accuracy, exhibiting high sensitivity to nuanced fault patterns. Furthermore, its efficacy is corroborated through comparisons with state-of-the-art AHU fault identification techniques. Full article

To face the global energy crisis, the requirement of energy transition and sustainable development has emphasized the importance of controlling building energy management systems. Reinforcement learning (RL) has shown notable energy-saving potential in the optimal control of heating, ventilation, and air-conditioning (HVAC) systems. However, the coupling of the algorithms and environments limits the cross-scenario application. This paper develops chiller plant models in OpenAI Gym environments to evaluate different RL algorithms for optimizing condenser water loop control. A shopping mall in Changsha, China, was selected as the case study building. First, an energy simulation model in EnergyPlus was generated using AutoBPS. Then, the OpenAI Gym chiller plant system model was developed and validated by comparing it with the EnergyPlus simulation results. Moreover, two RL algorithms, Deep-Q-Network (DQN) and Double Deep-Q-Network (DDQN), were deployed to control the condenser water flow rate and approach temperature of cooling towers in the RL environment. Finally, the optimization performance of DQN across three climate zones was evaluated using the AutoBPS-Gym toolkit. The findings indicated that during the cooling season in a shopping mall in Changsha, the DQN control method resulted in energy savings of 14.16% for the cooling water system, whereas the DDQN method achieved savings of 14.01%. Using the average control values from DQN, the EnergyPlus simulation recorded an energy-saving rate of 10.42% compared to the baseline. Furthermore, implementing the DQN algorithm across three different climatic zones led to an average energy savings of 4.0%, highlighting the toolkit’s ability to effectively utilize RL for optimal control in various environmental contexts. Full article

The objective of this study was to understand the characteristics of gas migration in a mine system network domain during a period of reversal ventilation. Combining field experiments with the TF1M3D simulation program, we analyzed gas migration and distribution during reversal ventilation in the JIU LI coal mine. The results showed that, after implementation of the airflow reversal process for the entire mine, the gas in the return roadways flowed back to the working face and accumulated with the gas emitted from the working face to form a gas concentration peak, after which the gas concentration gradually decreased in a stepwise manner and finally reached a stable state that was maintained until the end of the reversal ventilation. The peak gas concentration and the peak areas of the gas concentration curve during the airflow reversal were positively correlated with the time of airflow stoppage operation. The gas concentration peak affected the safety of the mine airflow reversal process; therefore, countermeasures and technical plans should be made in advance. The TF1M3D simulation results were consistent with the field experiment results. Full article

Risk assessment in tunnel construction using the drilling and blasting method presents a complex multi-criteria decision-making challenge due to numerous interacting factors. This study develops an advanced risk assessment model integrating game theory-based combination weighting with nonlinear fuzzy analytic hierarchy process (FAHP). The methodology establishes a comprehensive risk evaluation system through the systematic coupling of a work breakdown structure (WBS) and a risk breakdown structure (RBS), effectively combining subjective weights from an analytic hierarchy process (AHP) with objective weights derived through principal component analysis (PCA). A specialized nonlinear operator addresses the inherent fuzziness in the risk evaluation processes. The model is applied to the Daliangshan No. 1 Tunnel flat guide entrance drilling and blasting construction section, with the risk level determined to be high. Detailed analysis further revealed that the detonation network reliability and ventilation system performance constituted the most significant secondary risk elements. Comparative validation demonstrates the model’s superior accuracy over conventional methods in both weight determination and risk classification. The results demonstrate the effectiveness of the proposed model in improving risk assessment accuracy and supporting decision-making in complex tunnel construction environments. Full article