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17 pages, 1459 KiB  
Article
Assessing Controlled Traffic Farming as a Precision Agriculture Strategy for Minimising N2O Losses
by Bawatharani Raveendrakumaran, Miles Grafton, Paramsothy Jeyakumar, Peter Bishop and Clive Davies
Nitrogen 2025, 6(3), 63; https://doi.org/10.3390/nitrogen6030063 (registering DOI) - 4 Aug 2025
Abstract
Intensive vegetable farming emits high nitrous oxide (N2O) due to traffic-induced compaction, highlighting the need for preventing nitrogen (N) losses through better traffic management. This study examined the effects of Controlled Traffic Farming (CTF) and Random Traffic Farming (RTF) on N [...] Read more.
Intensive vegetable farming emits high nitrous oxide (N2O) due to traffic-induced compaction, highlighting the need for preventing nitrogen (N) losses through better traffic management. This study examined the effects of Controlled Traffic Farming (CTF) and Random Traffic Farming (RTF) on N2O emissions using intact soil cores (diameter: 18.7 cm; depth: 25 cm) collected from a vegetable production system in Pukekohe, New Zealand. Soil cores from CTF beds, CTF tramlines, and RTF plots were analysed under fertilised (140 kg N/ha) and unfertilised conditions. N2O fluxes were monitored over 58 days using gas chambers. The fertilised RTF system significantly (p < 0.05) increased N2O emissions (5.4 kg N2O–N/ha) compared to the unfertilised RTF system (1.53 kg N2O–N/ha). The emission from fertilised RTF was 46% higher than the maximum N2O emissions (3.7 kg N2O–N/ha) reported under New Zealand pasture conditions. The fertilised CTF system showed a 31.6% reduction in N2O emissions compared to fertilised RTF and did not differ significantly from unfertilised CTF. In general, CTF has demonstrated some resilience against fertiliser-induced N2O emissions, indicating the need for further investigation into its role as a greenhouse gas mitigation strategy. Full article
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17 pages, 3816 KiB  
Article
Charging Station Siting and Capacity Determination Based on a Generalized Least-Cost Model of Traffic Distribution
by Mingzhao Ma, Feng Wang, Lirong Xiong, Yuhonghao Wang and Wenxin Li
Algorithms 2025, 18(8), 479; https://doi.org/10.3390/a18080479 (registering DOI) - 4 Aug 2025
Abstract
With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due [...] Read more.
With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due to low traffic flow, resulting in a waste of resources. Areas with high traffic flow may have fewer charging stations, resulting in long queues and road congestion. The purpose of this study is to optimize the location of charging stations and the number of charging piles in the stations based on the distribution of traffic flow, and to construct a bi-level programming model by analyzing the distribution of traffic flow. The upper-level planning model is the user-balanced flow allocation model, which is solved to obtain the optimal traffic flow allocation of the road network, and the output of the upper-level planning model is used as the input of the lower-layer model. The lower-level planning model is a generalized minimum cost model with driving time, charging waiting time, charging time, and the cost of electricity consumed to reach the destination of the trip as objective functions. In this study, an empirical simulation is conducted on the road network of Hefei City, Anhui Province, utilizing three algorithms—GA, GWO, and PSO—for optimization and sensitivity analysis. The optimized results are compared with the existing charging station deployment scheme in the road network to demonstrate the effectiveness of the proposed methodology. Full article
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26 pages, 1085 KiB  
Article
Evaluating Sustainable Battery Recycling Technologies Using a Fuzzy Multi-Criteria Decision-Making Approach
by Chia-Nan Wang, Nhat-Luong Nhieu and Yen-Hui Wang
Batteries 2025, 11(8), 294; https://doi.org/10.3390/batteries11080294 - 4 Aug 2025
Abstract
The exponential growth of lithium-ion battery consumption has amplified the urgency of identifying sustainable and economically viable recycling solutions. This study proposes an integrated decision-making framework based on the T-Spherical Fuzzy Einstein Interaction Aggregator DEMATEL-CoCoSo approach to comprehensively evaluate and rank battery recycling [...] Read more.
The exponential growth of lithium-ion battery consumption has amplified the urgency of identifying sustainable and economically viable recycling solutions. This study proposes an integrated decision-making framework based on the T-Spherical Fuzzy Einstein Interaction Aggregator DEMATEL-CoCoSo approach to comprehensively evaluate and rank battery recycling technologies under uncertainty. Ten key evaluation criteria—encompassing environmental, economic, and technological dimensions—were identified through expert consultation and literature synthesis. The T-Spherical Fuzzy DEMATEL method was first applied to analyze the causal interdependencies among criteria and determine their relative weights, revealing that environmental drivers such as energy consumption, greenhouse gas emissions, and waste generation exert the most systemic influence. Subsequently, six recycling alternatives were assessed and ranked using the CoCoSo method enhanced by Einstein-based aggregation, which captured the complex interactions present in the experts’ evaluations and assessments. Results indicate that Direct Recycling is the most favorable option, followed by the Hydrometallurgical and Bioleaching methods, while Pyrometallurgical Recycling ranked lowest due to its high energy demands and environmental burden. The proposed hybrid model effectively handles linguistic uncertainty, expert variability, and interdependent evaluation structures, offering a robust decision-support tool for sustainable technology selection in the circular battery economy. The framework is adaptable to other domains requiring structured expert-based evaluations under fuzzy environments. Full article
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22 pages, 2179 KiB  
Article
Conversion of Oil Palm Kernel Shell Wastes into Active Biocarbons by N2 Pyrolysis and CO2 Activation
by Aik Chong Lua
Clean Technol. 2025, 7(3), 66; https://doi.org/10.3390/cleantechnol7030066 (registering DOI) - 4 Aug 2025
Abstract
Oil palm kernel shell is an abundant agricultural waste generated by the palm oil industry. To achieve sustainable use of this waste, oil palm kernel shells were converted into valuable resources as active biocarbons. A two-stage preparation method involving N2 pyrolysis, followed [...] Read more.
Oil palm kernel shell is an abundant agricultural waste generated by the palm oil industry. To achieve sustainable use of this waste, oil palm kernel shells were converted into valuable resources as active biocarbons. A two-stage preparation method involving N2 pyrolysis, followed by CO2 activation, was used to produce the active biocarbon. The optimum pyrolysis conditions that produced the largest BET surface area of 519.1 m2/g were a temperature of 600 °C, a hold time of 2 h, a nitrogen flow rate of 150 cm3/min, and a heating rate of 10 °C/min. The optimum activation conditions to prepare the active biocarbon with the largest micropore surface area or the best micropore/BET surface area combination were a temperature of 950 °C, a CO2 flow rate of 300 cm3/min, a heating rate of 10 °C/min, and a hold time of 3 h, yielding BET and micropore surface areas of 1232.3 and 941.0 m2/g, respectively, and consisting of 76.36% of micropores for the experimental optimisation technique adopted here. This study underscores the importance of optimising both the pyrolysis and activation conditions to produce an active biocarbon with a maximum micropore surface area for gaseous adsorption applications, especially to capture CO2 greenhouse gas, to mitigate global warming and climate change. Such a comprehensive and detailed study on the conversion of oil palm kernel shell into active biocarbon is lacking in the open literature. The research results provide a practical blueprint on the process parameters and technical know-how for the industrial production of highly microporous active biocarbons prepared from oil palm kernel shells. Full article
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21 pages, 4289 KiB  
Article
H2 Transport in Sedimentary Basin
by Luisa Nicoletti, Juan Carlos Hidalgo, Dariusz Strąpoć and Isabelle Moretti
Geosciences 2025, 15(8), 298; https://doi.org/10.3390/geosciences15080298 - 3 Aug 2025
Abstract
Natural hydrogen is generated by fairly deep processes and/or in low-permeability rocks. In such contexts, fluids circulate mainly through the network of faults and fractures. However, hydrogen flows from these hydrogen-generating layers can reach sedimentary rocks with more typical permeability and porosity, allowing [...] Read more.
Natural hydrogen is generated by fairly deep processes and/or in low-permeability rocks. In such contexts, fluids circulate mainly through the network of faults and fractures. However, hydrogen flows from these hydrogen-generating layers can reach sedimentary rocks with more typical permeability and porosity, allowing H2 flows to spread out rather than be concentrated in fractures. In that case, three different H2 transport modes exist: advection (displacement of water carrying dissolved gas), diffusion, and free gas Darcy flow. Numerical models have been run to compare the efficiency of these different modes and the pathway they imply for the H2 in a sedimentary basin with active aquifers. The results show the key roles of these aquifers but also the competition between free gas flow and the dissolved gas displacement which can go in opposite directions. Even with a conservative hypothesis on the H2 charge, a gaseous phase exists at few kilometers deep as well as free gas accumulation. Gaseous phase displacement could be the faster and diffusion is neglectable. The modeling also allows us to predict where H2 is expected in the soil: in fault zones, eventually above accumulations, and, more likely, due to exsolution, above shallow aquifers. Full article
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27 pages, 5026 KiB  
Review
China’s Carbon Emissions Trading Market: Current Situation, Impact Assessment, Challenges, and Suggestions
by Qidi Wang, Jinyan Zhan, Hailin Zhang, Yuhan Cao, Zheng Yang, Quanlong Wu and Ali Raza Otho
Land 2025, 14(8), 1582; https://doi.org/10.3390/land14081582 - 3 Aug 2025
Abstract
As the world’s largest developing and carbon-emitting country, China is accelerating its greenhouse gas (GHG) emission reduction process, and it is of vital importance in achieving the goals set out in the Paris Agreement. This paper examines the historical development and current operation [...] Read more.
As the world’s largest developing and carbon-emitting country, China is accelerating its greenhouse gas (GHG) emission reduction process, and it is of vital importance in achieving the goals set out in the Paris Agreement. This paper examines the historical development and current operation of China’s carbon emissions trading market (CETM). The current progress of research on the implementation of carbon emissions trading policy (CETP) is described in four dimensions: environment, economy, innovation, and society. The results show that CETP generates clear environmental and social benefits but exhibits mixed economic and innovation effects. Furthermore, this paper analyses the challenges of China’s carbon market, including the green paradox, the low carbon price, the imperfections in cap setting and allocation of allowances, the small scope of coverage, and the weakness of the legal supervision system. Ultimately, this paper proposes recommendations for fostering China’s CETM with the anticipation of offering a comprehensive outlook for future research. Full article
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15 pages, 997 KiB  
Article
Reactive Power Optimization Control Method for Distribution Network with Hydropower Based on Improved Discrete Particle Swarm Optimization Algorithm
by Tao Liu, Bin Jia, Shuangxiang Luo, Xiangcong Kong, Yong Zhou and Hongbo Zou
Processes 2025, 13(8), 2455; https://doi.org/10.3390/pr13082455 - 3 Aug 2025
Abstract
With the rapid development of renewable energy, the proportion of small hydropower as a clean energy in the distribution network (DN) is increasing. However, the randomness and intermittence of small hydropower has brought new challenges to the operation of DN; especially, the problems [...] Read more.
With the rapid development of renewable energy, the proportion of small hydropower as a clean energy in the distribution network (DN) is increasing. However, the randomness and intermittence of small hydropower has brought new challenges to the operation of DN; especially, the problems of increasing network loss and reactive voltage exceeding the limit have become increasingly prominent. Aiming at the above problems, this paper proposes a reactive power optimization control method for DN with hydropower based on an improved discrete particle swarm optimization (PSO) algorithm. Firstly, this paper analyzes the specific characteristics of small hydropower and establishes its mathematical model. Secondly, considering the constraints of bus voltage and generator RP output, an extended minimum objective function for system power loss is established, with bus voltage violation serving as the penalty function. Then, in order to solve the following problems: that the traditional discrete PSO algorithm is easy to fall into local optimization and slow convergence, this paper proposes an improved discrete PSO algorithm, which improves the global search ability and convergence speed by introducing adaptive inertia weight. Finally, based on the IEEE-33 buses distribution system as an example, the simulation analysis shows that compared with GA optimization, the line loss can be reduced by 3.4% in the wet season and 13.6% in the dry season. Therefore, the proposed method can effectively reduce the network loss and improve the voltage quality, which verifies the effectiveness and superiority of the proposed method. Full article
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14 pages, 590 KiB  
Article
General Practitioner’s Practice in Romanian Children with Streptococcal Pharyngitis
by Reka Borka Balas, Lorena Elena Meliț, Ancuța Lupu, Boglarka Sandor, Anna Borka Balas and Cristina Oana Mărginean
Medicina 2025, 61(8), 1408; https://doi.org/10.3390/medicina61081408 - 2 Aug 2025
Viewed by 39
Abstract
Background and Objectives: A correct diagnosis of beta-hemolytic group A streptococcus (GAS)-pharyngitis allows the prevention of complications and unnecessary use of antibiotics. The aim of this study was to assess the management of pediatric GAS-pharyngitis in Romanian general practitioners (GPs)’ practice. Material [...] Read more.
Background and Objectives: A correct diagnosis of beta-hemolytic group A streptococcus (GAS)-pharyngitis allows the prevention of complications and unnecessary use of antibiotics. The aim of this study was to assess the management of pediatric GAS-pharyngitis in Romanian general practitioners (GPs)’ practice. Material and Methods: a cross-sectional study was conducted using a questionnaire distributed to Romanian GPs. Results: In total, 56 GPs completed the questionnaire, mostly females (83.9%, n = 47) from an urban area (60.7%, n = 34). They treated 5–10 (35.7%) or more than 10 (32.1%) cases of GAS monthly and considered white exudate on tonsils (92.9%, n = 52) to be the most suggestive clinical sign. Of the GPs, 25% (n = 14) used the Centor Criteria, 10.7% (n = 6) performed a rapid antigen detection test, and 42.9% (n = 24) requested throat culture for diagnosis. The younger GPs used the Centor Criteria significantly more often (p = 0.027) than the older ones. Most GPs (69.6%, n = 39) preferred targeted antibiotic therapy. Amoxicillin-clavulanate was the most commonly used antibiotic (55.4%, n = 31). Most GPs preferred oral antibiotics (89%, n = 50) for 10 days (55.4%, n = 31). Conclusions: Antibiotic treatment was initiated mostly based on clinical symptoms and in a short-course therapy. GPs stated that they prefer targeted antibiotic therapy, but they did not use proper diagnostic tools. Full article
(This article belongs to the Section Pediatrics)
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26 pages, 8736 KiB  
Article
Uncertainty-Aware Fault Diagnosis of Rotating Compressors Using Dual-Graph Attention Networks
by Seungjoo Lee, YoungSeok Kim, Hyun-Jun Choi and Bongjun Ji
Machines 2025, 13(8), 673; https://doi.org/10.3390/machines13080673 (registering DOI) - 1 Aug 2025
Viewed by 186
Abstract
Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity. While Graph Attention Network (GAT) frameworks are widely available, this study advances the state of the art by introducing a [...] Read more.
Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity. While Graph Attention Network (GAT) frameworks are widely available, this study advances the state of the art by introducing a Bayesian GAT method specifically tailored for vibration-based compressor fault diagnosis. The approach integrates domain-specific digital-twin simulations built with Rotordynamic software (1.3.0), and constructs dual adjacency matrices to encode both physically informed and data-driven sensor relationships. Additionally, a hybrid forecasting-and-reconstruction objective enables the model to capture short-term deviations as well as long-term waveform fidelity. Monte Carlo dropout further decomposes prediction uncertainty into aleatoric and epistemic components, providing a more robust and interpretable model. Comparative evaluations against conventional Long Short-Term Memory (LSTM)-based autoencoder and forecasting methods demonstrate that the proposed framework achieves superior fault-detection performance across multiple fault types, including misalignment, bearing failure, and unbalance. Moreover, uncertainty analyses confirm that fault severity correlates with increasing levels of both aleatoric and epistemic uncertainty, reflecting heightened noise and reduced model confidence under more severe conditions. By enhancing GAT fundamentals with a domain-tailored dual-graph strategy, specialized Bayesian inference, and digital-twin data generation, this research delivers a comprehensive and interpretable solution for compressor fault diagnosis, paving the way for more reliable and risk-aware predictive maintenance in complex rotating machinery. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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21 pages, 7362 KiB  
Article
Multi-Layer Path Planning for Complete Structural Inspection Using UAV
by Ho Wang Tong, Boyang Li, Hailong Huang and Chih-Yung Wen
Drones 2025, 9(8), 541; https://doi.org/10.3390/drones9080541 (registering DOI) - 31 Jul 2025
Viewed by 152
Abstract
This article addresses the path planning problem for complete structural inspection using an unmanned aerial vehicle (UAV). The proposed method emphasizes the scalability of the viewpoints and aims to provide practical solutions to different inspection distance requirements, eliminating the need for extra view-planning [...] Read more.
This article addresses the path planning problem for complete structural inspection using an unmanned aerial vehicle (UAV). The proposed method emphasizes the scalability of the viewpoints and aims to provide practical solutions to different inspection distance requirements, eliminating the need for extra view-planning procedures. First, the mixed-viewpoint generation is proposed. Then, the Multi-Layered Angle-Distance Traveling Salesman Problem (ML-ADTSP) is solved, which aims to reduce overall energy consumption and inspection path complexity. A two-step Genetic Algorithm (GA) is used to solve the combinatorial optimization problem. The performance of different crossover functions is also discussed. By solving the ML-ADTSP, the simulation results demonstrate that the mean accelerations of the UAV throughout the inspection path are flattened significantly, improving the overall path smoothness and reducing traversal difficulty. With minor low-level optimization, the proposed framework can be applied to inspect different structures. Full article
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24 pages, 4618 KiB  
Article
A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm
by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan and Zhixin Qin
Sensors 2025, 25(15), 4717; https://doi.org/10.3390/s25154717 (registering DOI) - 31 Jul 2025
Viewed by 168
Abstract
In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in [...] Read more.
In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article
(This article belongs to the Section Industrial Sensors)
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21 pages, 2718 KiB  
Article
Enhancing the Analysis of Rheological Behavior in Clinker-Aided Cementitious Systems Through Large Language Model-Based Synthetic Data Generation
by Murat Eser, Yahya Kaya, Ali Mardani, Metin Bilgin and Mehmet Bozdemir
Materials 2025, 18(15), 3579; https://doi.org/10.3390/ma18153579 - 30 Jul 2025
Viewed by 176
Abstract
This study investigates the parameters influencing the compatibility between cement and polycarboxylate ether (PCE) admixtures in cements produced with various types and dosages of grinding aids (GAs). A total of 29 cement types (including a control) were prepared using seven different GAs at [...] Read more.
This study investigates the parameters influencing the compatibility between cement and polycarboxylate ether (PCE) admixtures in cements produced with various types and dosages of grinding aids (GAs). A total of 29 cement types (including a control) were prepared using seven different GAs at four dosage levels, and 87 paste mixtures were produced with three PCE dosages. Rheological behavior was evaluated via the Herschel–Bulkley model, focusing on dynamic yield stress (DYS) and viscosity. The data were modeled using CNN, Random Forest (RF), and Neural Classification and Regression Tree (NCART), and each model was enhanced with synthetic data generated by Large Language Models (LLMs), resulting in CNN-LLM, RF-LLM, and NCART-LLM variants. All six variants were evaluated using R-squared, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Logcosh. This study is among the first to use LLMs for synthetic data augmentation. It augmented the experimental dataset synthetically and analyzed the effects on the study results. Among the baseline methods, NCART achieved the best performance for both viscosity (MAE = 1.04, RMSE = 1.33, R2 = 0.84, Logcosh = 0.57) and DYS (MAE = 8.73, RMSE = 11.50, R2 = 0.77, Logcosh = 8.09). Among baseline models, NCART performed best, while LLM augmentation significantly improved all models’ predictive accuracy. It was also observed that cements produced with GA exhibited higher DYS and viscosity than the control, likely due to finer particle size distribution. Overall, the study highlights the potential of LLM-based synthetic augmentation in modeling cement admixture compatibility. Full article
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14 pages, 875 KiB  
Article
A Comparative Study of Brain Injury Biomarker S100β During General and Spinal Anesthesia for Caesarean Delivery: A Prospective Study
by Mungun Banzar, Nasantogtokh Erdenebileg, Tulgaa Surjavkhlan, Enkhtsetseg Jamsranjav, Munkhtsetseg Janlav and Ganbold Lundeg
Medicina 2025, 61(8), 1382; https://doi.org/10.3390/medicina61081382 - 30 Jul 2025
Viewed by 700
Abstract
Background and Objectives: Anesthetic agents may influence brain function, and emerging evidence suggests possible neurotoxicity under certain conditions. S100β is a well-established biomarker of brain injury and blood–brain barrier disruption, and its prolonged elevation beyond 6–12 h, despite a short half-life, may [...] Read more.
Background and Objectives: Anesthetic agents may influence brain function, and emerging evidence suggests possible neurotoxicity under certain conditions. S100β is a well-established biomarker of brain injury and blood–brain barrier disruption, and its prolonged elevation beyond 6–12 h, despite a short half-life, may indicate ongoing neuronal injury. Its use in cesarean section (C-section) remains limited, despite the potential neurological implications of both surgical stress and anesthetic technique. This study evaluates potential brain injury during caesarean section by comparing maternal and neonatal S100β levels under general and spinal anesthesia. Materials and Methods: This observational prospective study compared changes in the S100β brain damage biomarker in maternal (pre- and post-surgery) and umbilical artery blood during elective c-sections under general or spinal anesthesia. The 60 parturient women who underwent a C-section from 1 July 2021 to 30 December 2023 were evenly distributed into 2 groups: General anesthesia (GA) (n = 30) and Spinal anesthesia (SA) group (n = 30). It included healthy term pregnant women aged 18–40, ASA I–II and excluded those with major comorbidities or emergency conditions. Results: S100β concentrations slightly increased once the C-section was over in both the SA and GA groups, but without notable differences. In the SA and GA groups, preoperative S100β concentration in maternal blood was 195.1 ± 36.2 ng/L, 193.0 ± 54.3 ng/L, then increased to 200.9 ± 42.9 ng/L, 197.0 ± 42.7 at the end of operation. There was no statistically significant difference in S100β concentrations between the spinal and general anesthesia groups (p = 0.86). Conclusions: S100β concentrations slightly increased after C-section in both groups. The form of anesthesia seems to be irrelevant for the S100β level. However, further research is needed to confirm these findings and fully evaluate any potential long-term effects. Full article
(This article belongs to the Special Issue Advanced Research on Anesthesiology and Pain Management)
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19 pages, 1761 KiB  
Article
Prediction of China’s Silicon Wafer Price: A GA-PSO-BP Model
by Jining Wang, Hui Chen and Lei Wang
Mathematics 2025, 13(15), 2453; https://doi.org/10.3390/math13152453 - 30 Jul 2025
Viewed by 156
Abstract
The BP (Back-Propagation) neural network model (hereafter referred to as the BP model) often gets stuck in local optima when predicting China’s silicon wafer price, which hurts the accuracy of the forecasts. This study addresses the issue by enhancing the BP model. It [...] Read more.
The BP (Back-Propagation) neural network model (hereafter referred to as the BP model) often gets stuck in local optima when predicting China’s silicon wafer price, which hurts the accuracy of the forecasts. This study addresses the issue by enhancing the BP model. It integrates the principles of genetic algorithm (GA) with particle swarm optimization (PSO) to develop a new model called the GA-PSO-BP. This study also considers the material price from both the supply and demand sides of the photovoltaic industry. These prices are important factors in China’s silicon wafer price prediction. This research indicates that improving the BP model by integrating GA allows for a broader exploration of potential solution spaces. This approach helps to prevent local minima and identify the optimal solution. The BP model converges more quickly by using PSO for weight initialization. Additionally, the method by which particles share information decreases the probability of being confined to local optima. The upgraded GA-PSO-BP model demonstrates improved generalization capabilities and makes more accurate predictions. The MAE (Mean Absolute Error) value of the GA-PSO-BP model is 31.01% lower than those of the standalone BP model and also falls by 19.36% and 16.28% relative to the GA-BP and PSO-BP models, respectively. The smaller the value, the closer the prediction result of the model is to the actual value. This model has proven effective and superior in China’s silicon wafer price prediction. This capability makes it an essential resource for market analysis and decision-making within the silicon wafer industry. Full article
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23 pages, 1249 KiB  
Review
Guiding Microbial Crossroads: Syngas-Driven Valorisation of Anaerobic-Digestion Intermediates into Bio-Hydrogen and Volatile Fatty Acids
by Alvaro dos Santos Neto and Mohammad J. Taherzadeh
Bioengineering 2025, 12(8), 816; https://doi.org/10.3390/bioengineering12080816 - 29 Jul 2025
Viewed by 307
Abstract
Anaerobic digestion (AD) has long been valued for producing a biogas–digestate pair, yet its profitability is tightening. Next-generation AD biorefineries now position syngas both as a supplementary feedstock and as a springboard to capture high-value intermediates, hydrogen (H2) and volatile fatty [...] Read more.
Anaerobic digestion (AD) has long been valued for producing a biogas–digestate pair, yet its profitability is tightening. Next-generation AD biorefineries now position syngas both as a supplementary feedstock and as a springboard to capture high-value intermediates, hydrogen (H2) and volatile fatty acids (VFA). This review dissects how complex natural consortia “decide” between hydrogenogenesis and acetogenesis when CO, H2, and CO2 co-exist in the feedstocks, bridging molecular mechanisms with process-scale levers. The map of the bioenergetic contest between the biological water–gas shift reaction and Wood–Ljungdahl pathways is discussed, revealing how electron flow, thermodynamic thresholds, and enzyme inhibition dictate microbial “decision”. Kinetic evidence from pure and mixed cultures is integrated with practical operating factors (gas composition and pressure, pH–temperature spectrum, culture media composition, hydraulic retention time, and cell density), which can bias consortia toward the desired product. Full article
(This article belongs to the Special Issue Anaerobic Digestion Advances in Biomass and Waste Treatment)
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