Search Results (12,698)

This article proposes a gas-only continuation framework for steady-state loadability analysis in natural gas transmission networks based on a direction-free reformulation of the General Flow Equation (GFE). The proposed formulation introduces signed pipe flows directly as state variables, thereby representing bidirectionality intrinsically. As a result, flow reversals are handled without switching logic, while the branch geometry and criticality mechanism of the underlying gas-network equilibrium map are preserved. On this basis, a Gas Continuation Method (GCM) is developed to trace equilibrium branches directly in native gas-load space under specified gas-load stress. The method distinguishes the last admissible operating point from the mathematical critical point and incorporates a formal diagnosis to determine whether the detected limiting condition is consistent with a Saddle-Node Bifurcation (SNB). The proposed framework is validated on a three-node benchmark, a realistic Belgian gas transmission network, and a 40-node test system. The results show accurate agreement with Newton–Raphson (NR) solutions in the regular operating regime, robust branch tracing near limiting conditions where standalone NR loses convergence, and consistent handling of signed pipe flows under load-induced flow reversal and under algebraic orientations assigned a priori opposite to the solved physical flow. The Belgian and 40-node cases further show that the operational admissibility limit may precede the mathematical critical point, so pressure-based feasibility and branch-level criticality emerge as related but distinct notions. These features make the proposed methodology a rigorous and practical tool for identifying admissibility limits, interpreting critical behavior, and assessing loadability margins in gas transmission networks. Full article

This article aims to examine how the natural gas market evolved following the price shocks observed between 2020 and 2024, paying particular attention to market integration and the persistence of these shocks. The proposed analysis uses daily price data for the Title Transfer Facility (TTF), the main European benchmark traded on the Intercontinental Exchange and quoted in EUR/MWh, as well as Henry Hub (HH), the United States benchmark. These series are combined with a country panel on natural gas production, consumption, and gross domestic product for 59 economies, subject to data availability. The cointegration results show that TTF and HH prices moved together in 2019, but this relationship broke down in 2020 and did not return to its previous pattern in the following years. Granger causality tests point to a one-directional transmission from Henry Hub to Europe. Moreover, GARCH estimates indicate that TTF reacts almost twice as strongly to daily shocks as HH, while volatility remains persistent in both markets. Fixed-effects estimates place the TTF price elasticity of import spending close to 0.5, providing evidence consistent with a causal link between higher natural gas prices and higher domestic energy expenditure. Finally, the clustering analysis complements the econometric modeling by identifying four groups of countries defined by gas import dependency and gas intensity. This classification also offers implications for the global natural gas market since it points to the need for cluster-specific policy approaches rather than a single solution applied to every country. Full article

Rapid identification of fish disease lesions is essential for disease monitoring and early intervention in aquaculture, yet existing detection methods rely heavily on extensive lesion annotations and fixed category definitions. To address the challenges of limited annotated data and heterogeneous supervision, this study proposes PaliGemma2-FishGrounding, a generative vision-language grounding framework for few-shot fish disease lesion localization. The framework reformulates lesion detection as an open-vocabulary grounding task and unifies lesion localization, disease recognition, health-status classification, and symptom understanding within a single instruction-learning paradigm. By integrating heterogeneous supervision from multiple fish disease datasets, the proposed method enables lesion localization through structured vision-language generation rather than conventional closed-set detection. Experimental results on an Epizootic Ulcerative Syndrome (EUS) lesion dataset demonstrate that the proposed framework outperforms YOLOv8-based baselines, achieving an AP50 of 0.3659 and an AR@10 of 0.5378 while maintaining a zero invalid-box rate. Ablation studies further confirm the effectiveness of the instruction-learning strategy and grounding-based design. The results indicate that generative vision-language models can effectively leverage limited lesion annotations and auxiliary disease knowledge for fish disease analysis. This framework provides a practical solution for low-annotation disease monitoring and offers a promising direction for intelligent aquaculture applications under data-scarce conditions. Full article

We report a low-temperature plasma–liquid synthesis of crystalline β-Ga₂O₃ nanoparticles directly from aqueous solution. Pulsed discharge plasma bubbles generate reactive species that drive in situ dehydration and crystallization, bypassing the high-temperature calcination required by conventional methods. By varying the carrier gas, we tune morphology from uniform nanorice structures (He, Ar, and N₂) to amorphous microspheres (O₂ and air), revealing how plasma composition governs interfacial hydroxyl radical chemistry and growth kinetics. This approach demonstrates that localized plasma heating and reactive-species flux can achieve phase-selective oxide crystallization under ambient conditions, establishing plasma bubble reactors as a broadly applicable, low-temperature route for direct aqueous synthesis of crystalline wide-bandgap oxides that bridge solution chemistry and plasma nanomaterials design. Full article

The rising prevalence of neurodevelopmental, emotional, and behavioral disorders in children has prompted interest in dietary strategies that target neuroinflammation, oxidative stress, and gut dysbiosis. Blueberries (Vaccinium spp.) contain substantial amounts of anthocyanins and other neuroactive polyphenols that may confer neuroprotective effects. We summarize the literature published between 2016 and 2025 to examine how the bioactives in blueberries affect symptoms relevant to children with diagnosed neurodevelopmental or emotional–behavioral disorders, including ADHD, mood problems, and cognitive difficulties. Mechanistically, anthocyanins appear to modulate gut microbial composition, modulate neuroinflammation and alleviate oxidative stress via the Nrf2 pathway, and support synaptic plasticity and neurogenesis. Clinical trials, although limited in number and sample size, have reported modest improvements in mood and verbal memory in typically developing children and adolescents, with some gains in attention and executive function. However, direct trials in children with diagnosed neurodevelopmental or emotional–behavioral conditions remain scarce. There are substantial hurdles to translating these findings. Anthocyanins have poor physicochemical stability and low bioavailability, and routine food processing degrades their activity. Emerging solutions such as green extraction from agricultural by-products, colon-targeted microencapsulation, and zero-waste engineering could address these limitations. Rigorous randomized controlled trials in children with diagnosed neurodevelopmental or emotional–behavioral disorders are essential, as are advances in food engineering. Both are needed to move blueberry-based interventions from the laboratory to application. Full article

Buildings account for 40% of global energy consumption and one-third of greenhouse gas emissions. Renewable energy systems (RESs), such as solar photovoltaic (PV) and geothermal heat pumps, are critical technological solutions for decarbonization. Despite the growing literature, existing reviews lack a comprehensive synthesis integrating machine learning (ML), Internet of Things (IoT), and Building Information Modeling (BIM). Following the PRISMA protocol, this paper presents a systematic review of 41 studies published between 2012 and 2025. The review evaluates four primary domains: RES performance, building energy prediction, HVAC optimization, and occupancy-aware management. Quantitative findings reveal that solar PV-integrated buildings achieve electricity cost reductions of 35–64%, while ML-enhanced energy prediction models attain accuracies up to R² = 0.989. Critical research gaps are identified, including the scarcity of real-time sensor integration and geographically inclusive multi-climate datasets. Ultimately, this review contributes a structured synthesis of effective technologies, a comparative analysis of methodological approaches (ML, simulation, hybrid), and actionable future directions. It provides practical guidance for researchers and policymakers toward achieving net-zero energy buildings. This study serves as a definitive reference for the development of sustainable, low-energy built environments. Full article

Two-dimensional black phosphorus (2D BP) has emerged as one of the most promising two-dimensional semiconductors for next-generation micro and nanoelectronics beyond Moore’s Law. It is distinguished by its unique combination of a layer dependent direct bandgap, broadband photoresponse, and pronounced in-plane anisotropy, addressing key intrinsic limitations that have hindered the widespread application of graphene and conventional transition metal dichalcogenides (TMDCs). This review provides a systematic and comprehensive overview of recent advances in the controllable fabrication of 2D BP and its applications in transistors and photodetectors. We first elucidate its crystal lattice structure and fundamental physical properties, then categorize and summarize synthesis strategies based on production scale ranging from small scale methods (e.g., mechanical exfoliation and solution based exfoliation) to large scale methods (e.g., Chemical Vapor Deposition (CVD) and Pulsed Laser Deposition (PLD)), with a particular focus on recent advances in high-speed field-effect transistors and broadband photodetectors. In summary, the key to achieving large-scale controllable synthesis lies in addressing the challenges of high-temperature oxidation of black phosphorus and the uncontrollable diffusion of phosphorus sources. In the future, industrial applications are expected to be realized through CVD based regulation of phosphorus sources, low-temperature growth by PLD, and deep integration with silicon-based processes. Full article

Network latency remains a fundamental bottleneck in vehicle teleoperation, inducing instability and performance degradation in conventional control methods, while predictive techniques like the Smith Predictor offer a theoretical solution, their efficacy is often compromised by unmodelled dynamics and real-world disturbances. This paper presents the first experimental validation of the Successive Reference-Pose Tracking (SRPT) architecture. By streaming future reference poses rather than direct steering commands, SRPT leverages an onboard Nonlinear Model Predictive Controller to compute optimal vehicle actions while inherently accounting for dynamic constraints and network delays. Real-world human-in-the-loop experiments were conducted with four drivers on a test track featuring cornering, double lane-change, and slalom manoeuvres. Quantitative comparisons at 10 km/h across four modes—manual driving, direct teleoperation, a Smith Predictor, and SRPT—demonstrate that SRPT significantly outperforms other teleoperation methods, reducing cross-track error by up to 66% and yielding smoother, more stable control inputs. Furthermore, SRPT uniquely maintained stability during a proof-of-concept trial at 13 km/h, where it proactively moderated vehicle speed to respect actuator limits—a critical safety behavior absent in other modes. This work provides the first tangible evidence that SRPT is a robust and superior framework for delay-resilient vehicle teleoperation in real-world conditions. Full article

Optoelectronic devices are emerging as a cornerstone of advanced renewable energy technologies, offering innovative routes for energy harvesting, conversion, and management with high efficiency and versatility. This review summarizes recent advances in the semiconductor materials engineering field, device configurations, and light–matter interaction mechanisms that underpin advanced optoelectronic systems for solar energy harvesting, solar-driven chemical conversion, and smart grid integration, among others. Emphasis is placed on the breakthroughs achieved in the perovskite and hybrid photovoltaics, photoelectrochemical energy conversion, and nanostructured optoelectronic platforms that enable much-increased light absorption, reduced recombination losses, and scalable large-scale fabrications. Moreover, the challenges closely linked with long-term stability, environmental durability and benevolence, and worldwide deployment are critically addressed, together with the emerging opportunities in AI design, tandem device technological solutions, integrated energy systems, and machine learning approaches for optimizing device performance, thermal management, and energy storage capabilities. Finally, the present review concludes by outlining the future research directions that could accelerate the transition toward high-performance, cost-effective, and sustainable optoelectronic solutions responsive to global renewable energy requirements. Full article

This study develops exact analytical solutions for transient elliptical groundwater flow toward an extended well in an anisotropic fractured confined aquifer and then discusses how the resulting hydraulic response can support groundwater-head interpretation in shale-gas development areas. The environmental connection is made at the aquifer-protection scale: the model is not a shale-gas reservoir production model, and it does not solve contaminant transport directly. Instead, it provides a hydraulic interpretation framework for estimating anisotropy, equivalent fracture length, wellbore-storage effects, and the preferential direction of head propagation around possible leakage points, old wells, fractures, or monitoring wells. Based on Mathieu-function theory and the separation-of-variables method, constant-rate and constant-head solutions are derived in Laplace space and inverted to the time domain with the Stehfest algorithm. The analytical results are validated against COMSOL5.2 finite-element simulations, and the effects of anisotropy coefficient and wellbore storage are analyzed through drawdown and flow-rate type curves. A synthetic but field-style water-head example is included to demonstrate how monitoring records can be converted to drawdown, fitted to the elliptical-flow solution, and used to delineate a preliminary hydraulic response zone. The results show that anisotropy mainly controls early-to-middle time response, whereas wellbore storage may obscure early head changes and delay the recognition of fracture connectivity. Therefore, the solution is best regarded as a rapid hydraulic-screening and monitoring-design tool that can precede, but not replace, site-specific contaminant-transport modeling in shale-gas groundwater-protection studies. The relevant technical issues are possible head disturbances and preferential groundwater pathways associated with surface spills, flowback-water handling, old wells, faults, and fracture-connected water-bearing zones. Because verified local field-monitoring records were not available for us, the application example is explicitly described as a synthetic field-style demonstration; it is used to show the workflow and its limitations, not to claim site-specific prediction of contaminant concentration. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

Background: Some widely used wildland fire behavior models, like the Fire Area Simulator (FARSITE), propagate fire fronts by computing the front-normal velocity (spread rate) as a function of local inputs and the front-normal direction. Such models are sometimes observed to cause the collapse of crown fires into sharp wedge shapes that eliminate heading fire behavior. Aims: We set out to document this phenomenon and, more generally, understand the relationships between fire shapes and spread rate functions. Methods: The phenomenon is studied both mathematically and through simulation experiments. Non-smooth fire fronts are theorized mathematically by an Eikonal partial differential equation ($H(x,\tau ,D\tau )=1$), where the unknown $\tau \left(x\right)$ is the time-of-arrival function and the Hamiltonian $H(x,t,p)$ is positively homogeneous and possibly non-convex in p; convex analysis is used to study viscosity solutions in constant conditions. Results: We show that a fire spread model preserves the smoothness of fire fronts if and only if it is equivalent to using the Huygens principle. Nontrivially, this is equivalent to a convexity criterion on the inverse spread rate profile, which is then the polar dual of the Huygens wavelet; this corresponds to Hamiltonian–Lagrangian duality. The relevance of smoothness-destroying models to crown fire is debated. Exact analytical formulas are derived for fire growth in constant conditions. Conclusions: Our understanding of fire spread models is improved by solving the spread equations in more general ways than previously known. In particular, the collapse of heading crown fires into sharp shapes is now explained. Smoothness-destroying spread models cannot be simulated by algorithms based on travel time like cellular automata; their general well-definedness remains an open question. Fire modelers can use these findings to guide their search for improved crown fire models, and more generally to verify the accuracy of numerical implementations. Full article

In cold, humid environments, even a small amount of ice accumulation on the blade surface can degrade aerodynamic performance, increase drag, induce premature stall and vibration, and raise the risks of shutdown, fatigue, and ice throw. Existing blade anti-icing and de-icing strategies (such as passive coatings, electrothermal heating, hot-air systems, and hybrid designs) struggle to simultaneously meet the requirements of lightweight construction, low-voltage rapid heating, conformability to curved surfaces, erosion resistance, long-term durability, and scalable manufacturing. MXenes, particularly Ti₃C₂T_x, have attracted attention due to their high electrical conductivity, broadband optical absorption, solution processability, tunable interfacial chemistry, and good compatibility with polymer matrices. However, their oxidation issue and blade-scale deployment challenges (coating chemistry, scalable fabrication, real-world testing) remain obstacles. Based on this, this review discusses Ti₃C₂T_x-based anti-icing, de-icing, and defogging strategies for wind turbine blades, with emphasis on material properties, functional mechanisms, coating architectures, fabrication routes, durability, and scalability, and highlights their potential for lightweight and energy-efficient all-weather blade protection. Finally, future research directions for Ti₃C₂T_x-based blade anti-icing and de-icing are prospected. This review not only aims to identify key knowledge gaps in current research but also strives to provide a theoretical reference for the application of Ti₃C₂T_x in the complex service environment of real wind turbine blades, thereby moving beyond idealized laboratory conditions. Full article

In the context of the new power system, modular multilevel converter high-voltage direct current (MMC-HVDC) has become a key technical solution for the large-scale grid integration of wind power. However, when a fault occurs in the AC grid at the system receiving end, the high-voltage direct current (HVDC) system faces challenges such as wind power redundancy, DC overvoltage, and equipment overcurrent. To address this, this paper proposes an energy storage-coordinated fault ride-through (FRT) control strategy suitable for different fault scenarios. The strategy optimizes the allocation of energy storage capacity according to the state of charge (SOC) of the energy storage units (ESUs), preventing individual ESUs from prematurely shutting down and reducing energy dissipation. Finally, a comparison with a conventional DC dissipation resistor scheme on the PSCAD/EMTDC platform demonstrates that the proposed strategy provides smoother power regulation characteristics and smaller DC voltage fluctuations, thereby enhancing the economic efficiency and reliability of system operation. Full article

This paper addresses the bidirectional multi-chamber lock scheduling problem by formulating a multi-objective mixed-integer linear programming (MILP) model that simultaneously minimizes average ship waiting time and maximizes chamber utilization. A tailored adaptive large neighborhood search (ALNS) algorithm is developed specifically based on the principle of the destruction and reconstruction of solutions. The algorithm efficacy is validated using the real-word data from Huai’an Lock of the Subei canal. The scheduling rules and parameters are defined from practical operation records. Simulation results demonstrate that the ALNS-based optimization significantly improves lock performance with average chamber utilization increasing by 12.98% and waiting time decreasing by 44.40%. Sensitivity analyses on objective weights further confirm the robustness of the proposed method. Benchmark comparisons with a greedy heuristic, genetic algorithm (GA), and particle swarm optimization (PSO) highlight the effectiveness and computational efficiency of ALNS. This study further explores a threshold-based directional control strategy, showing that relaxing strict alternating-direction rules under asymmetric traffic demand can improve efficiency. The findings provide practical insights for lock scheduling, offering decision support for lock authorities in designing adaptive scheduling and directional control policies. Full article