Search Results (7,554)

This study presents an integrated approach combining the Hardy Cross method and a gradient boosting (GB) optimization model to enhance ventilation systems in underground mines, with a specific application at the Jabal Sayid mine in Saudi Arabia. The Hardy Cross method addresses variations in airflow resistance caused by obstacles within ventilation pathways, enabling accurate predictions of the flow distribution across the network. The GB model complements this by optimizing fan placement, pressure control, and airflow intensity to achieve reduced energy consumption and improved efficiency. The results demonstrate significant improvements in fan efficiency, optimized energy usage, and enhanced ventilation effectiveness, achieving a 31.24% reduction in electricity consumption. This study bridges deterministic and machine learning methodologies, offering a novel framework for the real-time optimization of underground mine ventilation systems. By combining the Hardy Cross method with GB, the proposed approach outperforms traditional techniques in predicting and optimizing airflow distribution under dynamic conditions. Full article

Hydraulic fracturing can significantly enhance coalbed methane production, with in-situ stress playing a crucial role in this process. Our study focuses on calculating in-situ stress in the deep 8+9# coal seam in the north-central Zijinshan block. Leveraging data from acoustic logging and hydraulic fracturing tests, we developed a stress prediction model tailored to the area’s geology. We analyzed stress’s impact on fracturing behavior and the origins of mechanical anisotropy in deep coal reservoirs using μ-CT imaging. Our results show that the Anderson-modified model, accounting for transverse isotropy, offers greater accuracy and applicability than traditional models. The study area exhibits a normal faulting stress regime with significant stress contrasts and maximum horizontal principal stress aligned with the east-west geological stress direction. After hydraulic fracturing, fractures form a complex fracture system resembling elongated ellipses in the coal reservoir, primarily extending in the vertical direction. To control fracture height and prevent penetration through the roof and floor, regulatory measures are essential. μ-CT analysis revealed the distribution of primary fractures, pores, and minerals in the coal, contributing to mechanical anisotropy. This research advances CBM development in the Zijinshan block and similar regions by refining stress prediction and fracturing propagation methods. Full article

This article examines the impact of fluid flow dynamics on microbial growth, distribution, and control within food processing systems. Fluid flows, specifically laminar and turbulent flows, significantly influence microbial behaviors, such as biofilm development and microbial adhesion. Laminar flow is highly conducive to biofilm formation and microbial attachment because the flow is smooth and steady. This smooth flow makes it much more difficult to sterilize the surface. Turbulent flow, however, due to its chaotic motion and the shear forces that are present, inhibits microbial growth because it disrupts attachment; however, it also has the potential to contaminate surfaces by dispersing microorganisms. Computational fluid dynamics (CFD) is highlighted as an essential component for food processors to predict fluid movement and enhance numerous fluid-dependent operations, including mixing, cooling, spray drying, and heat transfer. This analysis underscores the significance of fluid dynamics in controlling microbial hazards in food settings, and it discusses some interventions, such as antimicrobial surface treatments and properly designed equipment. Each process step from mixing to cooling, which influences heat transfer and microbial control by ensuring uniform heat distribution and optimizing heat removal, presents unique fluid flow requirements affecting microbial distribution, biofilm formation, and contamination control. Food processors can improve microbial management and enhance product safety by adjusting flow rates, types, and equipment configurations. This article helps provide an understanding of fluid–microbe interactions and offers actionable insights to advance food processing practices, ensuring higher standards of food safety and quality control. Full article

Road hazardous material transportation plays a critical role in road traffic management. Due to the dangerous nature of the cargo, hazardous material transportation trucks (HMTTs) have different route selection and driving characteristics compared to traditional freight trucks. These differences lead to unique travel and emission patterns, which in turn affect traffic management strategies and emission control measures. However, existing research predominantly focuses on safety aspects related to individual vehicle behavior, with limited exploration of the broader travel and emission characteristics of HMTTs. To bridge this gap, this study develops a comprehensive framework for analyzing the travel patterns and emissions of HMTTs. The methodology begins by applying a Gaussian mixture distribution model to identify vehicle stop points, eliminating biases associated with subjective settings. Origin–destination (OD) pairs are then determined through stop time clustering, followed by the extraction of travel characteristics using non-negative matrix factorization. Emissions are subsequently calculated based on the identified trip data. The relationship between emissions and land use characteristics is further analyzed using geographically weighted regression (GWR). Crucially, this study leverages data from the BeiDou Satellite Navigation System, focusing on HMTTs operating within Shanghai. The processed data reveal three distinct travel modes of HMTTs, categorized by spatiotemporal patterns: Daytime—Surrounding cities, Early morning—In-city, and Midnight—Scattered. Moreover, unlike other road vehicles, HMTT emissions are heavily influenced by industrial and company-related points of interest (POIs). These findings highlight the significant role of BeiDou Satellite Navigation System data in optimizing HMTT management strategies to reduce emissions and improve overall safety. Full article

AKNA is identified as a gene that regulates inflammation, immune response, and Epithelial–Mesenchymal Transition (EMT), which plays an important role in the progression of epithelial ovarian cancer. In this study, we analyzed the genotype and allele distribution as well as 3D modeling of one of the AKNA rs10817595 (−1372 C>A). The distribution of genotypes and alleles was analyzed using the T-ARMS PCR method on 63 ovarian cancer samples and 65 controls. AKNA mRNA expression was analyzed using qRT-PCR on 35 low-grade and 28 high-grade samples. Fifteen low-grade and 12 high-grade samples were analyzed for AKNA protein levels using immunohistochemistry. A 3D model of protein structure was constructed using AlphaFold. Significant differences in AKNA protein levels were found. However, no significant correlation was found for relative AKNA mRNA expression with protein levels. This result is thought to be related to decreased immune system response, increased inflammation, and increased EMT in epithelial ovarian cancer. AKNA gene variant (−1372 C>A) can cause a decrease in mRNA and protein levels in the low-grade and high-grade groups, so it has the potential as a genetic susceptibility factor in epithelial ovarian cancer. Full article

Water vapor is an active trace component in the troposphere and has a significant impact on meteorology and the atmospheric environment. In order to meet demands for high-precision water vapor and aerosol observations for numerical weather prediction (NWP), the China Meteorological Administration (CMA) deployed 49 Raman aerosol lidar systems and established the first Raman–Mie scattering lidar network in China (CARLNET) for routine measurements. In this paper, we focus on the water vapor measurement capabilities of the CARLNET. The uncertainty of the water vapor Raman channel calibration coefficient (Cw) is determined using an error propagation formula. The theoretical relationship between the uncertainty of the calibration coefficient and the water vapor mixing ratio (WVMR) is constructed based on least squares fitting. Based on the distribution of lidar in regions with different humidity conditions, the method of real-time calibration and quality control based on radiosonde data is established for the first time. Based on the uncertainty requirements of the World Meteorological Organization for water vapor in data assimilation, the calibration and quality control thresholds of the WVMR in regions with different humidity conditions are determined by fitting real-time lidar and radiosonde data. Lastly, based on the radiosonde results, the calibration algorithm established in this study is used to calibrate CARLNET data from October to December 2023. Compared with traditional calibration results, the results show that the stability and detection accuracy of the CARLNET significantly improved after calibration in regions with different humidity conditions. The deviation of the Cw decreased from 12.84~18.47% to 5.41~11.54%. The inversion error of the WVMR compared to radiosonde decreased from 1.05~0.46 g/kg to 0.82~0.34 g/kg. The reliability of the improved calibration algorithm and the CARLNET’s performance have been verified, enabling them to provide high-precision water vapor products for NWP. Full article

This study addresses the completely distributed consensus control problem for the heterogeneous nonlinear multi-agent system (MAS) with disturbances under switching topology. First, a global sliding mode manifold (GSMM) is designed for the overall MAS dynamic, which maintains stability without oscillations during topology switching after achieving the sliding mode. Subsequently, a consensus sliding mode control protocol (SMCP) is proposed, adopting the common sliding mode control (SMC) format and ensuring the finite-time reachability of the GSMM under topology switching. Finally, the proposed GSMM and SMCP are applied to the formation control of multiple-wheeled mobile robots (WMRs), and simulation results confirm their feasibility and effectiveness. The proposed SMCP design demonstrates key advantages, including a simple control structure, complete robustness to matched disturbance, and reduced-order dynamics under the sliding mode. Full article

With increasing power system complexity and distributed energy penetration, traditional voltage control methods struggle with dynamic changes and complex conditions. While existing deep reinforcement learning (DRL) methods have advanced grid control, challenges persist in leveraging topological features and ensuring computational efficiency. To address these issues, this paper proposes a DRL method combining Graph Convolutional Networks (GCNs) and soft actor-critic (SAC) for voltage control through load shedding. The method uses GCNs to extract higher-order topological features of the power grid, enhancing the state representation capability, while the SAC optimizes the load shedding strategy in continuous action space, dynamically adjusting the control scheme to balance load shedding costs and voltage stability. Results from the simulation of the IEEE 39-bus system indicate that the proposed method significantly reduces the amount of load shedding, improves voltage recovery levels, and demonstrates strong control performance and robustness when dealing with complex disturbances and topological changes. This study provides an innovative solution to voltage control problems in smart grids. Full article

Wildfires pose a significant threat to the entire ecosystem. The impacts of these wildfires can potentially disrupt biodiversity and ecological stability on a large scale. Wildfires may alter the physical and chemical properties of burned soil, such as particle size, loss of organic matter and infiltration capacity. These alterations can lead to increased vulnerability to geohazards such as landslides, mudflows and debris flows, where soil erosion and sediment transport play a crucial role. The present study investigates the impact of wildfire on soil erosion by conducting a series of laboratory experiments. The soil samples were burned using two different methods: using firewood for different burning durations and using a muffle furnace at an accurately controlled temperature within $400{}^{\circ }$C∼$1000{}^{\circ }$C. The burned soils were subsequently subjected to surface erosion by utilizing the constant head method to create a steady water flow to induce the erosion. In addition, empirically based theoretical models are explored to further assess the experimental results. The experimental results reveal a loss of organic matter in the burned soils that ranged from approximately 2% to 10% as the burning temperature rose from $400{}^{\circ }$C to $1000{}^{\circ }$C. The pattern of the grain size distribution shifted to a finer texture in the burned soil. There was also a considerable increase in soil erosion in burned soils, especially at a higher burn severity, where the erosion rate increased by more than five times. The empirical predictions are overall consistent with the experimental results and offer reasonable calibration of relevant soil erosion parameters. These findings demonstrate the importance of post-fire erosion in understanding and mitigating the long-term effects of wildfires on geo-environmental systems. Full article

Grid-connected photovoltaic (PV) systems inject nonsinusoidal currents into the grid at the point of their connection. The technology of the inverter utilized for the conversion of DC power into AC is directly associated with distortion characteristics. Even though pulse-width-modulated (PWM) converters generate considerably lower harmonic distortion than their predecessors, they are responsible for the emergence of a new power quality issue in distribution grids known as supraharmonics, which can cause problems such as overheating and malfunctions of equipment. PV systems are known sources of supraharmonics, but their impact has not yet been thoroughly researched. Due to the multitude of parameters affecting their performance, a more rigorous treatment is required compared to more common nonlinear devices. In this paper, emissions from a three-phase grid-connected PV system are examined by means of a dedicated simulation tool taking into account the specifics of inverter switching action without overly increasing computational cost. The impact of environmental parameters as well as factors affecting the switch control of the converter is investigated. The dependence of the supraharmonic emission of the PV system on the converter characteristics rather than environmental conditions is demonstrated. Furthermore, simulation studies on a network comprising the PV system and an additional supraharmonic-emitting system in simultaneous operation are conducted. Their combined effect on the distortion at the connection point of the network to the grid is assessed by means of a power flow-based approach, capable of quantifying interactions within this network. From the viewpoint of the grid, an increase of supraharmonic-related disturbance at low irradiance conditions is revealed. Full article

How to prevent the influence of precipitation’s localized and sudden characteristics is the most formidable challenge in the quality control (QC) of precipitation observations. However, with sufficiently high spatiotemporal resolution in observational data, nuanced information can aid us in accurately distinguishing between intense, localized precipitation events, and anomalies in precipitation data. China has deployed over 70,000 automatic weather stations (AWSs) that provide high spatiotemporal resolution surface meteorological observations. This study developed a new method for performing QC of precipitation data based on the high spatiotemporal resolution characteristics of observations from surface AWSs in China. The proposed QC algorithm uses the cumulative average method to standardize the probability distribution characteristics of precipitation data and further uses the empirical orthogonal function (EOF) decomposition method to effectively identify the small-scale spatial structure of precipitation data. Leveraging the spatial correlation characteristics of precipitation, partitioned EOF detection with a 0.5° spatial coverage effectively minimizes the influence of local precipitation on quality control. Analysis of precipitation probability distribution reveals that reconstruction based on the first three EOF modes can accurately capture the organized structural features of precipitation within the detection area. Thereby, based on the randomness characteristics of the residuals, when the residual of a certain observation is greater than 2.5 times the standard deviation calculated from all residuals in the region, it can be determined that the data are erroneous. Although the quality control is primarily aimed at accumulated precipitation, the randomness of erroneous data indicates that 84 continuous instances of error data in accumulated precipitation can effectively trace back to erroneous hourly precipitation observations. This ultimately enables the QC of hourly precipitation data from surface AWSs. Analysis of the QC of precipitation data from 2530 AWSs in Jiangxi Province (China) revealed that the new method can effectively identify incorrect precipitation data under the conditions of extreme weather and complex terrain, with an average rejection rate of about 5%. The EOF-based QC method can accurately detect strong precipitation events resulting from small-scale weather disturbances, thereby preventing local heavy rainfall from being incorrectly classified as erroneous data. Comparison with the quality control results in the Tianqing System, an operational QC system of the China Meteorological Administration, revealed that the proposed method has advantages in handling extreme and scattered outliers, and that the precipitation observation data, following quality control procedures, exhibits enhanced similarity with the CMAPS merged precipitation data. The novel quality control approach not only elevates the average spatial correlation coefficient between the two datasets by 0.01 but also diminishes the root mean square error by 1 mm. Full article

The use of low-cost sensors has dramatically increased in recent years in all engineering sectors. In the buildings and automotive field, low-cost sensors open very interesting perspectives, because they allow one to monitor temperature and humidity distributions together with air quality in a widespread and punctual way and allow for the control of all energy parameters. The main issue remains the validation of the measurements. In this work, we propose an innovative approach to verify the measurements given by some low-cost systems built ad hoc for automotive applications. Two independent low-cost measurement systems were set to measure Particulate Air Matter (PM) concentration, TVOC concentration, CO₂ concentration, formaldehyde concentration, air temperature, relative humidity, pressure, air flow velocity, and GPS position. These systems were calibrated for PM concentration measurement by comparison with standard and certified sensors used by the regional authority of the Emilia-Romagna region (ARPAE, Italy) for characterizing air quality. The duration of the analysis, three days, is not representative of the diverse environmental conditions that occur across different seasons. However, the innovation of this approach lies in both the in-field comparison of low-cost and high-quality sensors and the use of proper conversion approaches for mass concentration measurements. A quantitative analysis of the sensors’ performance is given, with a focus on the effects of time granularity, relative humidity, mass conversion from particle counts, and size detection response. The results show that the low-cost sensors’ measurements of air temperature, relative humidity, and particle number concentration are in good agreement with high-quality sensors’ measurements, with a strong impact of relative humidity on performance indicators. Overall, good quality and consistency of the data among the sensors were achieved. Full article

Background: Brazil has witnessed the co-circulation of dengue virus (DENV), Zika virus (ZIKV), and chikungunya virus (CHIKV), with outbreaks exacerbated by environmental factors, social determinants, and poor sanitation. The recent re-emergence of Oropouche virus (OROV) has added complexity to vector control strategies, emphasizing the need for integrated approaches to curb arboviruses spread. We aimed to analyze temporal trends and spatial distributions with national scope of these emerging arboviruses. Methods: An ecological study using data from the Brazilian Notifiable Diseases Information System the period from 2023 to 2024 was undertaken. Temporal trends were evaluated using Joinpoint regression, while spatial analysis was conducted using Moran’s I, and local indicators of spatial association. Results: Dengue fever cases increased by 322%, while Oropouche fever (OF) increased by 300%. The states of Amazonas and Espírito Santo reported increases in OF cases. Moran’s I test revealed spatial clustering of DENV and CHIKV. Two municipalities in the state of Mato Grosso do Sul showed cocirculation of DENV, CHIKV, and ZIKV. Conclusions: This study identified a surge in arbovirus cases between 2023 and 2024, with peak incidences from January to March and October to December, linked to favorable climatic conditions. Clustering patterns and co-circulation of arboviruses highlight the need for tailored control and prevention strategies and targeted interventions to mitigate their impact. Full article

In the context of Industry 5.0, human–robot collaboration (HRC) is increasingly crucial for enabling safe and efficient operations in shared industrial workspaces. This study aims to implement a hybrid robotic architecture based on the Speed and Separation Monitoring (SSM) collaborative scenario defined in ISO/TS 15066. The system calculates the minimum protective separation distance between the robot and the operators and slows down or stops the robot according to the risk assessment computed in real time. Compared to existing solutions, the approach prevents collisions and maximizes workcell production by reducing the robot speed only when the calculated safety index indicates an imminent risk of collision. The proposed distributed software architecture utilizes the ROS2 framework, integrating three modules: (1) a fast and reliable human tracking module based on the OptiTrack system that considerably reduces latency times or false positives, (2) an intention estimation (IE) module, employing a linear Kalman filter (LKF) to predict the operator’s next position and velocity, thus considering the current scenario and not the worst case, and (3) a robot control module that computes the protective separation distance and assesses the safety index by measuring the Euclidean distance between operators and the robot. This module dynamically adjusts robot speed to maintain safety while minimizing unnecessary slowdowns, ensuring the efficiency of collaborative tasks. Experimental results demonstrate that the proposed system effectively balances safety and speed, optimizing overall performance in human–robot collaborative industrial environments, with significant improvements in productivity and reduced risk of accidents. Full article

Safety and stability are critical in manned space missions, requiring an environmental control and life support system (ECLSS) of spacecraft to operate reliably. This study analyzed the time-series characteristics of telemetry data, including total pressure, temperature, and humidity, to predict the ECLSS’s operational state. Existing algorithms for time-series forecasting, including ARIMA, LSTM, TCN, and NBEATS, often struggle with long-sequence forecasting and discrepancies in data distribution, which hinder their ability to deliver accurate predictions. To address these challenges, this study introduces a two-stage normalization method, mean instance normalization (MeanIN), designed to adjust input data distributions and restore output data distributions, thereby significantly enhancing predictive performance. Experimental evaluations on ECLSS telemetry data demonstrate that MeanIN consistently improves model accuracy, with the informer model achieving superior results in long-sequence forecasting tasks. These results underscore the efficacy of MeanIN and its potential to support critical applications in anomaly detection and predictive analysis for spacecraft telemetry data. Full article