Search Results (664)

Active control of injection quantity during start-up idle optimizes automotive diesel engine starting performance, aligning with low-carbon goals. Conventional methods rely on a calibrated demand torque map adjusted by speed, temperature, and pressure variations, requiring extensive labor for calibration and limiting energy-saving and emission improvements. To address this problem, this paper proposes a transient injection quantity active control method for the start-up process based on the variation characteristics of target speed. Firstly, the target speed variation characteristics of the start-up process are optimized by setting different accelerations. Secondly, a transient injection quantity control strategy for the start-up process is proposed based on the target speed variation characteristics. Finally, the control strategy proposed in this paper was compared with the conventional starting injection quantity control method to verify its effectiveness. The results show that the start-up idle control strategy proposed in this paper reduces the cumulative fuel consumption of the start-up process by 25.9% compared to the conventional control method while maintaining an essentially unchanged start-up time. The emissions of hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxides (NOx) exhibit peak reductions of 12.4%, 32.5%, and 62.9%, respectively, along with average concentration drops of 27.2%, 35.1%, and 41.0%. Speed overshoot decreases by 25%, and fluctuation time shortens by 23.6%. The results indicate that the proposed control method not only avoids complicated calibration work and saves labor and material resources but also effectively improves the starting performance, which is of great significance for the diversified development of automotive power sources. Full article

This research presents Geographic Information System (GIS) mapping and development of biomass for combined heat and power (CHP) generation in Nigeria. It includes crop and forest classification, thermodynamic, and exergo-economic analyses using ArcGIS, Engineering Equation Solver, and Microsoft Excel. Syngas generated from biomass residues powered an integrated CHP system combining a gas turbine (GT), dual steam turbine (DST), and a cascade organic Rankine cycle (CORC) plant. The net power output of the integrated system stood at 2911 MW, with a major contribution from the gas turbine cycle (GTC) unit. The system had a total exergy destruction of 6480 MW, mainly in the combustion chamber (2143 MW) and HP-HRSG (1660 MW), and produced 3370.41 MW of heat, with a flue gas exit temperature of 74 °C. The plant’s energy and exergy efficiencies were 87.16% and 50.30%, respectively. The BCHP system showed good economic and environmental performance, with an annualized life cycle cost of USD 93.4 million, unit cost of energy of 0.0076 USD/kWh, and a 7.5-year break-even. The emissions and impact factors align with those of similar existing plants. It demonstrates that biomass residue can significantly support Nigeria’s energy needs and contribute to clean energy goals under the Paris Agreement and UN-SDGs. This work suggests a pathway to tackle energy insecurity, inform policymakers on biomass-to-energy, and serve as a foundation for future techno-economic–environmental assessment of biomass residues across suitable locations in Nigeria. Full article

The assessment of the economic vulnerability of natural disasters is a necessary step in the evaluation of any risks. This study proposes the approach implemented under the H2020 FirEurisk project to value the economic damage of wildfires on a European scale. Economic damage is assessed as the net value change in natural (agricultural and forestry resources and their ecosystem services) and manufactured assets under simulated fire intensity, taking into consideration the time necessary for natural capital to recover to the pre-damaged conditions. We show minimum, maximum, and average damage for European countries and map the critical areas. Damages to provisioning-ecosystem services are more pronounced in Central Europe because of the lower resilience of ecosystems compared to the Mediterranean, suggesting that mitigation measures (such as managing vegetation to reduce fuel; improving access to fire services; and engaging communities through education, agriculture, and forest management participation) must be enforced. We are confident that the approach proposed may stimulate further research to test the goodness of the estimates proposed and suggest where it is more appropriate to invest in fire prevention. Full article

Conventional wildfire assessment products emphasize regional-scale ignition likelihood and potential spread derived from fuels and weather. While useful for broad planning, they do not directly support boundary-aware, scenario-specific decision-making for localized threats to communities in the Wildland–Urban Interface (WUI). This limitation constrains the ability of fire managers to effectively prioritize mitigation efforts and response strategies for ignition events that may lead to severe local impacts. This paper introduces WUI-BTI—a scenario-based, simulation-driven boundary-threat index for the Wildland–Urban Interface that quantifies consequences conditional on an ignition under standardized meteorology, rather than estimating risk. WUI-BTI evaluates ignition locations—referred to as Fire Amplification Sites (FAS)—based on their potential to compromise the defined boundary of a community. For each ignition location, a high-resolution fire spread simulation is conducted. The resulting fire perimeter dynamics are analyzed to extract three key metrics: (1) the minimum distance of fire approach to the community boundary ($D_{min}$) for non-breaching fires; and for breaching fires, (2) the time required for the fire to reach the boundary ($T_p$), and (3) the total length of the community boundary affected by the fire ($L_c$). These raw outputs are mapped through monotone, sigmoid-based transformations to yield a single, interpretable score: breaching fires are scored by the product of an inverse-time urgency term and an extent term, whereas non-breaching fires are scored by proximity alone. The result is a continuous boundary-threat surface that ranks ignition sites by their potential to rapidly and substantially compromise a community boundary. By converting complex simulation outputs into scenario-specific, boundary-aware intelligence, WUI-BTI provides a transparent, quantitative basis for prioritizing fuel treatments, pre-positioning suppression resources, and guiding protective strategies in the WUI for fire managers, land use planners, and emergency response agencies. The framework complements regional hazard layers (e.g., severity classifications) by resolving fine-scale, consequence-focused priorities for specific communities. Full article

Climate change has exacerbated the need for transitional shifts within high-impact sectors, notably maritime transport, which facilitates nearly 90% of global trade. In response, the International Maritime Organization (IMO) has implemented stricter environmental regulations under MARPOL Annex VI, which includes, among other things, the designation of Emission Control Areas (ECAs) and Particularly Sensitive Sea Areas (PSSAs). These regulatory instruments have prompted the uptake of new technologies, such as scrubbers, LNG propulsion, and low-sulfur fuels to mitigate emissions in these zones. However, emerging evidence has raised environmental concerns about these solutions which may offset their intended climate benefits. This study investigates the hypothesis that ECAs and PSSAs act as catalysts for maritime environmental advancements through a systematic mapping of 76 peer-reviewed articles. Drawing on data from Scopus and Web of Science, the study analyzes trends in technological advances, publication timelines, geographic research distribution, and the increasing role of decision-support tools for regulatory compliance. Findings show increased academic outputs particularly in China, North America, and Europe, and suggest that achieving effective emissions reduction requires globally harmonized policies, bridging research practice gaps, and targeted financial support to ensure sustainable outcomes throughout the sector. The study suggests that for ECAs and PSSAs to deliver truly sustainable outcomes, global regulation must be supported by empirical performance assessments, environmental safeguards for compliance technologies, and targeted support for developing maritime regions. Full article

Resonance self-shielding in multi-resonant nuclide media is a dominant physical process in reactor neutronics analysis. This study proposes an improved subgroup method (ISM) based on Padé rational approximation, constructing a high-order rational function mapping between effective and background cross-sections to overcome the precision bottleneck of traditional DSMs and BIMs in nonlinear resonance interference scenarios. The method first generates cross-section relation data via ultra-fine group calculations, then solves subgroup parameters using a positive definite system, with a Spatial Homogenization (SPH) factor introduced for reaction rate conservation. Validation results show that ISM + SPH reduces k-infinity errors from −708 pcm (DSM) to +5 pcm for UO₂ fuel, and from −269 pcm to +45 pcm for MOX fuel with ²³⁹Pu, significantly enhancing neutron transport accuracy in complex fuel systems. This work provides a theoretically rigorous and practically applicable approach for efficient resonance modeling in advanced reactor fuel design. Full article

Rehabilitation plans are based on pavement condition assessments, which are crucial to modern pavement management systems. However, some of the disadvantages of conventional approaches for road maintenance and repair include the time consumption, high costs, visual errors, seasonal limitations, and low accuracy. Continuous and efficient pavement monitoring is essential, necessitating reliable equipment that can function in a variety of weather and traffic conditions. UAVs offer a practical and eco-friendly alternative for tasks including road inspections, dam monitoring, and the production of 3D ground models and orthophotos. They are more affordable, accessible, and safe than traditional field surveys, and they reduce the environmental effects of pavement management by using less fuel and producing less greenhouse gas emissions. This study uses UAV technology in conjunction with ground control points (GCPs) to assess the kind and amount of damage in flexible pavements. Vertical photogrammetric mapping was utilized to produce 3D road models, which were then processed and analyzed using Agisoft Photoscan (Metashape Professional (64 bit)) software. The sorts of fractures, patch areas, and rut depths on pavement surfaces may be accurately identified and measured thanks to this technique. When compared to field exams, the findings demonstrated an outstanding accuracy with errors of around 3.54 mm in the rut depth, 4.44 cm² for patch and pothole areas, and a 96% accuracy rate in identifying cracked locations and crack varieties. This study demonstrates how adding GCPs may enhance the UAV image accuracy, particularly in challenging weather and traffic conditions, and promote sustainable pavement management strategies by lowering carbon emissions and resource consumption. Full article

Wildfire behavior and post-fire effects are strongly modulated by terrain, yet the relative influence of individual topographic factors on burn severity remains incompletely quantified at landscape scales. The Composite Burn Index (CBI) provides a field-calibrated measure of severity, but large-area analyses have been hampered by limited plot density and cumbersome data extraction workflows. In this study, we paired 6150 CBI plots from 234 U.S. wildfire events (1994–2017) with 30 m SRTM DEM, extracting mean elevation, slope, and compass aspect within a 90 m buffer around each plot to minimize geolocation noise. Topographic variables were grouped into ecologically meaningful classes—six elevation belts (≤500 m to >2500 m), six slope bins (≤5° to >25°), and eight aspect octants—and their relationships with CBI were evaluated using Tukey HSD post hoc comparisons. Our findings show that all three factors exerted highly significant influences on severity (p < 0.001): mean CBI peaked in the 1500–2000 m belt (0.42 higher than lowlands), rose almost monotonically with steepness to slopes > 20° (0.37 higher than <5°), and was greatest on east- and northwest-facing slopes (0.19 higher than south-facing aspects). Further analysis revealed that burn severity emerges from strongly context-dependent synergies among elevation, slope, and aspect, rather than from simple additive effects. By demonstrating a rapid, reproducible workflow for terrain-aware severity assessment entirely within GEE, the study provides both methodological guidance and actionable insights for fuel-management planning, risk mapping, and post-fire restoration prioritization. Full article

To address the issues of low efficiency, insufficient coverage, and high energy consumption in wheat sowing path planning for large-scale irregular farmland, this study proposes an improved hybrid particle swarm optimization algorithm (TLG-PSO) for autonomous operational path planning. Building upon the standard PSO, the proposed method introduces a Tent chaotic mapping initialization mechanism, a Logistic-based dynamic inertia weight adjustment strategy, and adaptive Gaussian perturbation optimization to achieve precise control of the agricultural machinery’s driving orientation angle. A comprehensive path planning model is constructed with the objectives of minimizing the effective operation path length, reducing turning frequency, and maximizing coverage rate. Furthermore, cubic Bézier curves are employed for path smoothing, effectively controlling path curvature and ensuring the safety and stability of agricultural operations. The simulation experiment results demonstrate that the TLG-PSO algorithm achieved exceptional full-coverage operation performance across four categories of typical test fields. Compared to conventional fixed-direction path planning strategies, the algorithm reduced average total path length by 6228 m, improved coverage rate by 1.31%, achieved average labor savings of 96.32%, and decreased energy consumption by 6.45%. In large-scale comprehensive testing encompassing 1–27 field plots, the proposed algorithm reduced average total path length by 8472 m (a 5.45% decrease) and achieved average energy savings of 44.21 kW (a 5.48% reduction rate). Comparative experiments with mainstream intelligent optimization algorithms, including GA, ACO, PSO, BreedPSO, and SecPSO, revealed that TLG-PSO reduced path length by 0.16%–0.74% and decreased energy consumption by 0.53%–2.47%. It is worth noting that for large-scale field operations spanning hundreds of acres, even an approximately 1% path reduction translates to substantial fuel and operational time savings, which holds significant practical implications for large-scale agricultural production. Furthermore, TLG-PSO demonstrated exceptional performance in terms of algorithm convergence speed and computational efficiency. The improved TLG-PSO algorithm provides a feasible and efficient solution for autonomous operation of large-scale agricultural machinery. Full article

In recent years, generative AI and autonomous driving have been highly popular topics. Additionally, with the increasing global emphasis on carbon emissions and carbon trading, integrating autonomous driving technologies that can instantly perceive environ-mental changes with vehicle-based generative AI would enable vehicles to better under-stand their surroundings and provide drivers with recommendations for more energy-efficient and comfortable driving. This study employed You Only Look Once version11 (YOLOv11) for visual detection of the driving environment, integrating it with vehicle speed data received from the OBD-II system. All information is integrated and processed using the embedded Nvidia Jetson AGX Orin platform. For visual detection validation, part of the test set includes standard Taiwanese road signs. Experimental results show that incorporating Squeeze-and-Excitation Attention (SEAttention), into YOLOv11 improves the mAP50–95 accuracy by 10.1 percentage points. Generative AI processed this information in real time and provided the driver with appropriate driving recommendations, such as gently braking, detecting a pedestrian ahead, or warning of excessive speed. These recommendations are delivered through voice output to prevent driver distraction caused by looking at an interface. When a red light or pedestrian is detected, early deceleration is suggested, effectively reducing fuel consumption while also enhancing driving comfort, ultimately achieving the goal of energy-efficient driving. Full article

While geostationary satellites can provide continuous near-real-time observations, their low spatial resolution makes it difficult to detect small wildfires. Conversely, polar-orbiting satellites are capable of observing small wildfires at high spatial resolution, but can operate only within restricted observation periods. To improve wildfire spread prediction accuracy using polar-orbiting satellite observations, this paper proposes a novel methodology to accurately reproduce wildfire perimeters observed at specific time points by these satellites. The approach employs a wildfire spread simulator combined with a rate of spread (ROS) adjustment factor. The proposed algorithm derives ROS adjustment factors for each fuel model based on differential evolution, achieving up to a 0.4 increase in the Sørensen index when reproducing wildfire perimeter data at given observation times. Incorporating these factors into simulator-based predictions allows comprehensive consideration of external factors affecting wildfire propagation, which have not been sufficiently accounted for in previous methods. Moreover, considering the frequent occurrence of small wildfires in Korea, this study establishes a mapping between major species of trees in Korea and corresponding Fire Behavior Fuel Models (FBFMs). This serves as an example of appropriately matching major species of trees to FBFMs for wildfire spread prediction in countries where FBFMs have not been previously applied. The methodology’s effectiveness is demonstrated using wildfire perimeter data from polar-orbiting satellite observations and ignition points of recent wildfires in Korea. The proposed algorithm is expected to significantly enhance wildfire response by swiftly providing critical information for accurate wildfire spread prediction. This will facilitate prompt and precise countermeasures for small wildfires independent of external conditions such as weather. Full article

Sustainable aviation fuel (SAF) has demonstrated significant potential to reduce carbon emissions in the aviation industry. Multiple national and international initiatives have been launched to accelerate SAF adoption, yet large-scale commercialization continues to face technological, operational, and regulatory barriers. Industry 4.0 provides a suite of advanced technologies that can address these challenges and improve SAF operations across the supply chain. This study conducts an integrative literature review to identify and synthesize research on the application of Industry 4.0 technologies in the production and distribution of SAF. The findings highlight that technologies such as artificial intelligence (AI), Internet of Things (IoT), blockchain, digital twins, and 3D printing can enhance feedstock logistics, optimize conversion pathways, improve certification and compliance processes, and strengthen overall supply chain transparency and resilience. By mapping these applications to the six key workstreams of the SAF Grand Challenge, this study presents a practical framework linking technological innovation to both strategic and operational aspects of SAF commercialization. Integrating Industry 4.0 solutions into SAF production and supply chains contributes to reducing life cycle greenhouse gas (GHG) emissions, strengthens low-carbon energy systems, and supports the United Nations Sustainable Development Goal 13 (SDG 13). The findings from this research offer practical guidance to policymakers, industry practitioners, investors, and technology developers seeking to accelerate the global shift toward carbon neutrality in aviation. Full article

Tuberculosis (TB) remains a leading cause of death in Bangladesh, disproportionately affecting low socio-economic status (SES) populations. This review, guided by the WHO Social Determinants of Health framework and Rockefeller-Lancet Planetary Health Report, examined how social, economic, and ecological factors link SES to TB burden. The first literature search identified 28 articles focused on SES-TB relationships in Bangladesh. A second search through snowballing and conceptual mapping yielded 55 more papers of diverse source types and disciplines. Low-SES groups face elevated TB risk due to smoking, biomass fuel use, malnutrition, limited education, stigma, financial barriers, and hazardous housing or workplaces. These factors delay care-seeking, worsen outcomes, and fuel transmission, especially among women. High-SES groups more often face comorbidities like diabetes, which increase TB risk. Broader contextual drivers include urbanisation, weak labour protections, cultural norms, and poor governance. Recommendations include housing and labour reform, gender parity in education, and integrating private providers into TB programmes. These align with the WHO End TB Strategy, UN SDGs and Planetary Health Quadruple Aims, which expand the traditional Triple Aim for health system design by integrating environmental sustainability alongside improved patient outcomes, population health, and cost efficiency. Future research should explore trust in frontline workers, reasons for consulting informal carers, links between makeshift housing and TB, and integrating ecological determinants into existing frameworks. Full article

This study proposes an intelligent patrol planning framework that leverages reinforcement learning, spatiotemporal crime forecasting, and simulated sensor telemetry to optimize autonomous vehicle (AV) navigation in urban environments. Crime incidents from Washington DC (2024–2025) and Seattle (2008–2024) are modeled as a dynamic spatiotemporal graph, capturing the evolving intensity and distribution of criminal activity across neighborhoods and time windows. The agent’s state space incorporates synthetic AV sensor inputs—including fuel level, visual anomaly detection, and threat signals—to reflect real-world operational constraints. We evaluate and compare three learning strategies: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). Experimental results show that DDQN outperforms DQN in convergence speed and reward accumulation, while PPO demonstrates greater adaptability in sensor-rich, high-noise conditions. Real-map simulations and hourly risk heatmaps validate the effectiveness of our approach, highlighting its potential to inform scalable, data-driven patrol strategies in next-generation smart cities. Full article

Over the past two decades, rising concerns about teacher stress and professional sustainability have fueled the development of instruments assessing teacher resilience. This review aims to map the existing resilience assessment tools specifically designed for educators, evaluating their theoretical frameworks, psychometric soundness, and contextual relevance. Twelve instruments were analyzed through an extensive literature review of peer-reviewed studies published over the past twenty years, including general, preservice, EFL, and teacher-specific scales for special education. Findings reveal a progression from early instruments emphasizing intrapersonal traits to current tools incorporating ecological and contextual dimensions. While several scales demonstrate satisfactory reliability and cross-cultural applicability, many still suffer from conceptual limitations, insufficient cultural adaptation, or marginal psychometric robustness. This review concludes that despite significant advances, future research must prioritize culturally grounded frameworks, broader subgroup validation, and advanced psychometric methodologies to ensure accurate, inclusive, and practical assessments of teacher resilience across diverse educational settings. Full article