Search Results (33,827)

The thermal coal supply chain serves as core infrastructure for ensuring the safe and stable supply of electricity in China. Effective risk management and control of this supply chain are therefore critical to national energy security and socio-economic development. However, the thermal coal supply chain involves multiple complex risk dimensions, including cross-regional multi-entity coordination, a complex network structure, and a dynamic policy environment. Traditional risk analysis methods often fall short in depicting the concurrent events and dynamic propagation characteristics inherent to such a system. This necessitates systematically investigating the thermal coal supply chain within the Coal–Electricity Joint Venture (CEJV) operational framework, which primarily involves equity-based consolidation and long-term contractual coordination between coal producers and power generators, to comprehensively analyze its critical risk factors and transmission mechanisms. Initially, based on the integration of coal-fired power joint operation policy evolution and industry characteristics, 28 risk factors were identified across three dimensions: internal enterprise, external environment, and overall structure. These encompassed production fluctuation risks, thermal coal transport process risks, and insufficient supply chain flexibility. A dynamic behavior model for the thermal coal supply chain was constructed by analyzing the causal relationships among these risk factors, based on the operational processes of each link. Utilizing Petri net simulation technology enables a quantitative analysis of supply chain risks, facilitating the identification of bottleneck links and potential risk points. Through model simulation, 18 key risk factors were determined, providing a theoretical basis for optimizing supply chain resilience within CEJV enterprises. The limitations of traditional methods in dynamic process modeling and industrial applicability were addressed through a Petri net-based methodology, thereby establishing a novel analytical paradigm for risk management in complex energy supply chains. Full article

Accurate nearshore bathymetry is an essential dataset for coastal modelling and coastal hazard management, but traditional surveys are expensive and dangerous to conduct in energetic surf zones. Remotely piloted aircraft (RPA) offer a flexible way to collect high spatial and temporal resolution bathymetric data. This study applies deliberately simple workflows with accessible instrumentation to compare video-based and spectral inversion techniques at two contrasting coastal settings: an exposed open beach with relative higher wave energy and turbidity, and a sheltered embayed beach with lower energy conditions. The video-based (UBathy) approach achieved lower errors (0.22–0.41 m RMSE) than the spectral approach (Stumpf) (0.30–0.71 m RMSE), confirming its strength in semi-turbid, low- to moderate-energy settings. Stumpf’s accuracy matched prior findings (~0.5 m errors in clear water) but declined with depth. Areas with sun glint areas and breaking waves are challenging but UBathy performed better in mixed wave conditions. While these errors are higher than traditional hydrographic surveys, they fall within expected RPA-derived ranges presenting opportunities for use in specific coastal management applications. Future improvements may come from reducing reliance on ground control and advancing deep learning-based hybrid methods to filter outliers and improve prediction accuracy on sub-optimal imagery caused by environmental conditions. Full article

Liquefied petroleum gas (LPG), composed mainly of propane and butane, is widely used as an energy source in residential, commercial, and industrial sectors; however, its high flammability poses a critical risk in the event of accidental leaks. In Peru, where LPG constitutes the main domestic energy source, leakage emergencies affect thousands of households each year. This pattern is replicated in developing countries with limited energy infrastructure. Early quantitative detection of propane, the predominant component of Peruvian LPG (~60%), is essential to prevent explosions, poisoning, and greenhouse gas emissions that hinder climate change mitigation strategies. This study presents PropNet-R, a convolutional neural network (CNN) designed to estimate propane concentrations (ppm) from thermal images. A dataset of 3574 thermal images synchronized with concentration measurements was collected under controlled conditions. PropNet-R, composed of four progressive convolutional blocks, was compared with SqueezeNet, VGG19, and ResNet50, all fine-tuned for regression tasks. On the test set, PropNet-R achieved MSE = 0.240, R² = 0.614, MAE = 0.333, and Pearson’s r = 0.786, outperforming SqueezeNet (MSE = 0.374, R² = 0.397), VGG19 (MSE = 0.447, R² = 0.280), and ResNet50 (MSE = 0.474, R² = 0.236). These findings provide empirical evidence that task-specific CNN architectures outperform generic transfer learning models in thermal image-based regression. By enabling continuous and quantitative monitoring of gas leaks, PropNet-R enhances safety in industrial and urban environments, complementing conventional chemical sensors. The proposed model contributes to the development of sustainable infrastructure by reducing gas-related risks, promoting energy security, and strengthening resilient, safe, and environmentally responsible urban systems. Full article

The kernel of intelligent manufacturing is data utilization, where data can be trained to build AI models to generate optimized control parameter values for production processes. The data includes dynamic (or state) data from sensors, process (control) data from devices/equipment, and quality data from the Manufacturing Execution System (MES). Since the data sampling frequency is high, which causes a large amount of collected data, and the production devices are diverse, which leads to varying communication protocols, it is a challenge to store and display this data. This paper attempts to develop a metamodel-based topology representation of a production scene to store and display the data. The idea is to design metamodels for Ends (for devices), Edges (for production lines), and the Cloud (the central control platform), respectively. Then the model of each specific manufacturing scene is an instantiation of the metamodel. Our work has the following advantages: (1) It can store data at cloud, edge and end, such that it provides fast data querying and supports local computing such as local optimization, and automatically displays their physical topology; (2) it provides a template to describe the topological representation of all kinds of production scenes, such that the description of a specific production scene is an instantiation of the metamodel configured by the collected data; and (3) it provides a way to automatically generate code to connect devices and a database by configuring the meta-models without training or tuning a modern large model, which is almost impossible for most companies. This work serves as the bottom layer of a generated operating system for intelligent manufacturing that provides data service for the upper control layer. Full article

This study addresses developing systematic guidelines for the design of concentration control in the oxidation of benzene to maleic anhydride within a tubular reactor. The influence of step size selection and temperature sensor location on the tuning and performance of a PI/P cascade control system applied to the oxidation process was evaluated. The reactor’s dynamic behavior was analyzed using numerical simulations based on the solution of the Fortran mathematical model. Sensor positions and multiple step sizes (from +10% to −10%) were analyzed to characterize reactor dynamics and optimize control parameters. The results show that a controller design corresponding to a −9% step in the jacket temperature offered the best performance, ensuring process stability and selectivity. In contrast, step changes between +10% and −8% caused temperature deviations beyond safe limits. Since maleic anhydride is an essential precursor in the production of resins, plastics, lubricants, and pharmaceutical intermediates, optimizing the efficiency and safety of its production represents a significant benefit to the global chemical industry. Full article

The shallow-water delta-front reservoir in Member II of the Oligocene Dongying Formation (Ed2), located in an oilfield within the Bohai Bay Basin, is a large-scale composite sedimentary system dominated by subaqueous distributary channels and mouth bars. Within this system, reservoir sand bodies exhibit significant thickness, complex internal architecture, poor injection–production correspondence during development, and an ambiguous understanding of remaining oil distribution. To enhance late-stage development efficiency, it is imperative to deepen the understanding of the genesis and evolution of the subaqueous distributary channel–mouth bar system, analyze the internal reservoir architecture, and clarify sand body connectivity relationships. Based on sedimentary physical modeling experiments, integrated with core, well logging, and seismic data, this study systematically reveals the architectural characteristics and spatial stacking patterns of the mouth bar reservoirs using Miall’s architectural element analysis method. The results indicate that the study area is dominated by sand-rich, shallow-water delta front deposits, which display a predominantly coarsening-upward character. The main reservoir units are mouth bar sand bodies (accounting for 30%), with a vertical stacking thickness ranging from 3 to 20 m, and they exhibit lobate distribution patterns in plan view. Sedimentary physical modeling reveals the formation mechanism and stacking patterns of these sand-rich, thick sand bodies. Upon entering the lake, the main distributary channel unloads its sediment, forming accretionary bodies. The main channel then bifurcates, and a new main channel forms in the subsequent unit, which transports sediment away and initiates a new phase of deposition. Multi-phase deposition ultimately builds large-scale lobate complexes composed of channel–mouth bar assemblages. These complexes exhibit internal architectural styles, including channel–channel splicing, channel–bar splicing, and bar–bar splicing. Reservoir architecture analysis demonstrates that an individual distributary channel governs the formation of an individual lobe, whereas multiple distributary channels control the development of composite lobes. These lobes are laterally spliced and vertically superimposed, exhibiting a multi-phase progradational stacking pattern. Dynamic production data analysis validates the reliability of this reservoir architecture classification. This research elucidates the genetic mechanisms of thick sand bodies in delta fronts and establishes a region-specific reservoir architecture model. This study clarifies the spatial distribution of mudstone interlayers and preferential flow pathways within the composite sand bodies. It provides a geological basis for optimizing injection–production strategies and targeting residual oil during the ultra-high water-cut stage. The findings offer critical guidance for the efficient development of shallow-water delta front reservoirs. Full article

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations. Full article

c-Jun N-terminal kinase (JNK) activation has been shown to play a crucial role in the development of various types of cancer. IQ-1S is a JNK inhibitor based on the 11H-indeno[1,2-b]quinoxalin-11-one scaffold. The aim of this study was to investigate the antiproliferative effect of IQ-1S on MCF7 breast cancer cells in both two-dimensional (2D) monolayer and 3D multicellular spheroid test-systems. Non-adherent, non-tethered 3D objects were generated from single MCF7 breast cancer cells in a hydrogel array. IQ-1S was added directly to the cells seeded in the hydrogel array. MCF7 spheroids were grown for 7 days. Spheroid size, growth rate, and morphology were assessed at single-object resolution. The study revealed significant differences in the size, morphology and some vital characteristics of breast cancer 3D objects when treated with the JNK inhibitor compared to vehicle (dimethyl sulfoxide)-treated controls. Spheroids treated with IQ-1S (20 μM) after 7 days are significantly smaller than the control objects. This difference was not attributable to variations in the initial number of cells seeding for the spheroid formation. Morphological examinations showed that 3D multicellular objects grown from IQ-1S-treated cells lose their regular, round morphology, in contrast to control spheroids. Furthermore, cell proliferation measured using a label-free impedance monitoring platform was reduced in monolayer (2D) culture of MCF7 cells in the presence of 10 and 20 μM IQ-1S. MCF7 cells in 2D culture treated with IQ-1S (20 μM) for 72 and 153 h showed a significant increase in apoptosis as assessed by flow cytometry with annexin V/propidium iodide staining. An in silico evaluation showed that compound IQ-1S has generally satisfactory ADME (absorption, distribution, metabolism, and excretion) properties and high bioavailability. We conclude that IQ-1S effectively inhibits the growth of 3D spheroids and MCF7 cells in 2D culture and has a high potential for use in preclinical tumor growth models. Full article

Tropospheric ozone (O₃) is a harmful air pollutant and a short-lived greenhouse gas. To find effective O₃ reduction strategies, it is essential to understand the sensitivity of O₃ concentrations to its precursors, nitrogen oxides (NO_x), and volatile organic compounds (VOC). This study applied the Community Multi-Scale Air Quality model (CMAQ) to assess the effects of domestic anthropogenic emissions in 2015 and 2050. The emission scenarios were based on Japan’s CO₂ reduction targets, assuming an 80% decrease by 2050. Sensitivity analysis was performed by adjusting NO_x and VOC emissions by ±10% and ±20%, respectively, and examining seasonal and regional variations in the O₃ response. The results show that O₃ levels will decrease notably in spring and summer by 2050, although concentrations will still exceed the standards in some areas. NO_x reductions lead to significant O₃ decreases, while VOC reductions show limited benefits, except in urban regions such as Kanto and Kansai. In winter, NO_x reductions may even increase O₃ levels due to weakened titration. Overall, the findings highlight the importance of prioritizing NO_x control measures for effective O₃ mitigation in Japan’s future energy transition. Full article

Water resources are fundamental to human survival, as well as critical to the sustainable progress of the economy and society. This study selects representative indicators and employs the TOPSIS model to evaluate the water resources carrying capacity (WRCC) in the Chang–Zhu–Tan region (2006–2022). Based on this, kernel density estimation and Moran’s I are applied to analyze the spatiotemporal distribution and evolution trends of WRCC. Additionally, the Lorenz curve, Gini coefficient, and imbalance index are utilized to examine the alignment between WRCC and socio-economic growth. Finally, a system dynamics model is used to simulate WRCC and matching dynamics under different scenarios. The findings reveal the following: (1) The overall WRCC is favorable but exhibits a declining temporal trend, with widening inter-district disparities and strong spatial agglomeration. (2) The match between WRCC and economic development is unbalanced, though alignment has gradually improved over time. (3) The WRCC varies across different scenarios. In current development scenario, WRCC declines significantly. In economic priority development and industrial restructuring scenarios, this reduction is slowed. Specifically, in water resource policy control scenario, WRCC can be enhanced. Aside from the industrial restructuring scenario, all other scenarios contribute to improving the coordination between WRCC and economic development. Full article

Precise control of flue gas oxygen content is essential for stable and efficient operation in municipal solid waste incineration (MSWI) systems. However, the strong nonlinearity and time-varying characteristics of combustion processes often lead to poor performance of conventional proportional–integral–derivative (PID) and open-loop model-based control schemes. To overcome these limitations, this study proposes a hybrid intelligent closed-loop control framework that integrates the firefly algorithm (FA) and whale optimization algorithm (WOA) for adaptive tuning of control parameters under dynamic operating conditions. The proposed system comprises four coordinated modules—preset, oxygen content prediction, predictive compensation, and feedback compensation—forming an adaptive multi-layer control loop. Experimental validation was performed using real operational data from 2 × 600 t/d MSWI plant. When the operating conditions changed from stable to variable, the proposed method maintained the flue gas oxygen content at 7.78%, with an overshoot of 1.53%, a relative error of –0.094%, and a settling time of 90 s. In comparison, the MPC-based control achieved 7.75%, with an overshoot of 2.10%, relative error of –0.529%, and settling time of 100 s, while the existing plant control method achieved 7.85%, with an overshoot of 2.35%, relative error of 0.835%, and settling time of 180 s. These results indicate that the proposed FA–WOA hybrid control framework effectively improves response speed by 50%, reduces overshoot by 34.9%, and enhances control accuracy by over 80% compared with the conventional method. Moreover, the system eliminates manual adjustment and achieves stable combustion performance under fluctuating conditions, demonstrating its potential for intelligent oxygen control and automation in large-scale MSWI plants. Full article

A networked system consists of a collection of interconnected autonomous agents that communicate and interact through a shared communication infrastructure. These agents collaborate to pursue common objectives or exhibit coordinated behaviors that would be difficult or impossible for a single agent to achieve alone. With widespread applications in domains such as robotics, smart grids, and communication networks, the coordination and control of networked systems have become a vital research focus—driven by the complexity of distributed interactions and decision-making processes. Graph-based reinforcement learning (GRL) has emerged as a powerful paradigm that combines reinforcement learning with graph signal processing and graph neural networks (GNNs) to develop policies that are relationally aware, scalable, and adaptable to diverse network topologies. This survey aims to advance research in this evolving area by providing a comprehensive overview of GRL in the context of networked coordination and control. It covers the fundamental principles of reinforcement learning and graph neural networks, examines state-of-the-art GRL models and algorithms, reviews training methodologies, discusses key challenges, and highlights real-world applications. By synthesizing theoretical foundations, empirical insights, and open research questions, this survey serves as a cohesive and structured resource for the study and advancement of GRL-enabled networked systems. Full article

Background: Shoulder soft tissue disorders, such as rotator cuff tears and subacromial impingement, are among the most common causes of musculoskeletal disability. Both physical examination tests and imaging techniques are routinely used in clinical settings; however, their respective contributions to patient outcomes and their potential complementarity remain underexplored. Methods: A systematic review and meta-analysis were conducted following PRISMA 2020 guidelines. Controlled clinical studies comparing pre- and post-intervention outcomes in adults with suspected or confirmed shoulder soft tissue pathology were included. Two groups were analyzed: studies using musculoskeletal imaging (ultrasound or MRI) and studies applying orthopedic physical examination tests (e.g., Neer, Hawkins, and Jobe). Functional outcomes were converted into standardized mean differences (SMDs) and synthesized using a random-effects model. Heterogeneity was quantified using the I² statistic. Results: In total, 11 studies met the inclusion criteria (n = 6 imaging, n = 5 orthopedic tests). Imaging-based studies showed a pooled SMD of 4.85 (95% CI: 2.77–6.93), indicating substantial clinical improvement. Orthopedic test-based studies yielded a pooled SMD of 2.34 (95% CI: 1.27–3.41). Heterogeneity was high across both groups (I² > 90%). Conclusions: Imaging was associated with a larger overall clinical effect, while orthopedic tests provided functional insight valuable for screening and monitoring. These findings support the complementary use of both strategies to enhance diagnostic accuracy and treatment planning in shoulder care. Full article

This case describes an outbreak of salmonellosis caused by Salmonella enterica subspecies enterica serotype Agona in a Rottweiler breeding kennel, associated with raw meat-based diet (RMBD) of unlicensed origin. The report presents the clinical, epidemiological, and microbiological characteristics of the outbreak, as well as the control and preventive measures undertaken. Methods: Samples of faeces, vomit, raw food, and environmental surfaces were collected and examined. The isolated pathogen was identified using bacteriological culture, biochemical testing, MALDI-TOF mass spectrometry, and serotyping according to the White–Kauffmann–Le Minor scheme. Antimicrobial susceptibility was determined by the broth microdilution method in accordance with standards of Clinical and Laboratory Standards Institute (CLSI). Results: Clinical signs included vomiting, diarrhoea, lethargy, and dehydration without fever, with disease exacerbation observed in post-partum animals. Extensive carriage and faecal shedding of S. Agona were detected in affected dogs, along with widespread contamination of food and the kennel environment. The isolate was susceptible to some antimicrobial agents but resistant to cephalexin, aminoglycosides, lincosamides, macrolides, and fusidic acid, and showed intermediate susceptibility to polymyxin B. Following discontinuation of raw meat feeding, targeted antimicrobial therapy, and environmental disinfection, all dogs recovered, and subsequent tests for Salmonella spp., were negative. All human contacts also tested negative. Conclusions: This represents the first documented outbreak of S. Agona infection in dogs in Bulgaria linked to a RMBD. The findings emphasise the importance of feed safety, biosecurity, and traceability of feed sources in kennels, as well as the potential zoonotic risk associated with raw feeding practices. The diagnostic and therapeutic measures implemented in this case provide an effective model for managing similar epidemiological events within the One Health framework. Full article

Achieving Artificial General Intelligence (AGI) requires a unified framework capable of modeling the full spectrum of intelligent behavior—from logical reasoning and sensory perception to emotional regulation and collective decision-making. This paper proposes Constrained Object Hierarchies (COH), a neuroscience-inspired theoretical model that represents intelligent systems as hierarchical compositions of objects governed by symbolic structure, neural adaptation, and constraint-based control. Each object is formally defined by a 9-tuple structure: $O=(C,A,M,N,E,I,T,G,D)$, encapsulating its Components, Attributes, Methods, Neural components, Embedding, and governing Identity constraints, Trigger constraints, Goal constraints, and Constraint Daemons. To demonstrate the scope and versatility of COH, we formalize nine distinct intelligence types—including computational, perceptual, motor, affective, and embodied intelligence—each with detailed COH parameters and implementation blueprints. To operationalize the framework, we introduce GISMOL, a Python-based toolkit for instantiating COH objects and executing their constraint systems and neural components. GISMOL supports modular development and integration of intelligent agents, enabling a structured methodology for AGI system design. By unifying symbolic and connectionist paradigms within a constraint-governed architecture, COH provides a scalable and explainable foundation for building general purpose intelligent systems. A comprehensive summary of the research contributions is presented right after the introduction. Full article