Institutional incentives are widely used to promote cooperation among autonomous, self-regarding agents, from human societies to multi-agent and AI systems. Existing work typically treats incentive design as a bi-objective problem: minimise institutional cost while achieving a high long-run frequency of cooperation. Whether such schemes also maximise social welfare–total population payoff net of institutional expenditure–has remained largely unexplored. We develop a welfare-centric framework for institutional incentives in finite, well-mixed populations playing a social dilemma (Donation Game and Public Goods Game), considering both rewards for cooperators and punishments for defectors. For each mechanism, we derive explicit expressions for expected social welfare and characterise how it depends on incentive efficiency and selection intensity. Analytically, we identify parameter regimes where social welfare has a single optimal incentive level and regimes with qualitative phase transitions, in which welfare becomes non-monotonic with multiple local optima. We prove that any welfare-maximising incentive is either zero or concentrated around a simple closed-form target, and we provide an efficient algorithm to compute these optima. Comparing rewards and punishment, we further derive closed-form conditions under which rewards outperform punishment in terms of social welfare for any given budget. Overall, our results reveal a systematic gap between incentives optimised for cost or cooperation frequency and those that maximise welfare.
This work examines how varying hydrogen addition levels-namely 0% (D100), 10% (D90H10), 20% (D80H20), and 30% (D70H30)-to a premixed diesel-air charge influence combustion behavior and emission formation when tested in a rapid compression machine (RCM). All tests were carried out under conditions of a 423.5 K chamber temperature and a unity equivalence ratio, with the fuel blend being prepared in a premixing chamber prior to its delivery into the combustion chamber. The results indicate that increasing the hydrogen fraction leads to higher in-cylinder pressure and temperature, accompanied by a pronounced shortening of ignition delay. Compared with D100, the D70H30 case shows a 94.85% reduction in ignition delay (1145.88 ms), while the in-cylinder pressure and temperature rise markedly by 108.51% (31.4 bar) and 69.33% (964.93 K), respectively. Regarding emissions, CO levels increased markedly, rising from 0.84 vol% for D100 to 4.35 vol% for D70H30, which corresponds to more than a fivefold increase. In contrast, CO2 shows a pronounced decline as hydrogen content increases, dropping by nearly three times from 9.46 vol% to 3.25 vol%. Meanwhile, unburned hydrocarbons (UHC) emissions also intensify with hydrogen enrichment, with D70H30 exhibiting an increase of 826 ppm, equivalent to a 74.41% rise compared with D100. With increasing hydrogen content, the combustion process accelerates markedly, as evidenced by the reduction in combustion duration from 190 ms for D100 to 77.5 ms for D70H30, representing a shortening of 112.5 ms or 59.21%. Based on the above results, it can be concluded that hydrogen addition has a significant influence on both combustion characteristics and exhaust emissions. These findings deepen the understanding of hydrogen-assisted combustion and establish a useful reference dataset for combustion kinetics and emission analysis in diesel-hydrogen systems, while also supporting the validation of existing kinetic models under RCM conditions. Moreover, the results highlight hydrogen as a practical near-term decarbonization option without requiring major engine design modifications and simultaneously improving ignition behavior. Collectively, this work lays a basis for dual-fuel engine development and for determining appropriate hydrogen blending levels in future applications.
The increasing demand and production of electrical components have introduced significant challenges, particularly regarding raw material scarcity and the environmental impact of discarded semiconductors. This review critically examines the novel movements in electrical production through the lens of the 12 principles of green chemistry, with a strong focus on contributing to the 17 Sustainable Development Goals. Each stage of the production process—mining, extraction, pre-processing, and manufacturing—was analyzed for potential improvements that could lead to greener and more economical practices. Environmentally friendly alternatives were proposed for raw material acquisition, while the production phase emphasized using cleaner materials, such as organic and biomass-based substances. Additionally, fabrication methodologies were optimized to incorporate sustainable chemical treatments. A comprehensive analysis of global recycling trends, with particular attention to temporal and sectoral developments, was conducted. The review also introduces innovative recycling techniques to replace outdated, harmful methods, thereby addressing one of the most critical aspects of sustainable electrical production. This paper aims to provide a roadmap for industry stakeholders and policymakers to enhance the sustainability of electrical production processes and mitigate their environmental impact.
This study evaluates the effectiveness of Cement Deep Mixing (CDM) columns installed outside the excavation zone in reducing diaphragm wall displacement. The analysis considers a deep excavation in very soft clay, characterized by a highly compressible layer approximately 32 m thick. A back-analysis based on the Finite Element Method (FEM) was first conducted to calibrate soil constitutive parameters using field monitoring data, thereby establishing a reliable numerical modeling framework. Subsequently, Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost) models were used to evaluate the influence of key design parameters of the external CDM system on diaphragm wall displacement. The results show that the CDM block depth (L) is the dominant factor, followed by the horizontal distance to the diaphragm wall (a), the block width (B), and the elastic modulus (E). Based on the ANN model, an explicit predictive equation for estimating the maximum lateral displacement of diaphragm walls was developed, with a high coefficient of determination (R2 = 99.5
This study proposes a framework that integrates a probabilistic physically based model with machine learning (ML) techniques for improved landslide susceptibility assessment. A physically based model and Monte Carlo simulation was employed to estimate probability of slope failure and identify stable areas for non-landslide sample extraction. Subsequently, several ML models, including random forest, extreme gradient boosting, and categorical boosting, were trained and combined using a stacking ensemble optimized by the grey wolf optimizer. Model interpretability was enhanced using shapley additive explanations (SHAP). The proposed framework was applied to Saka Town, Japan, where a severe rainfall-induced landslide event occurred in 2018. Results show that the physics-informed sampling strategy significantly improves model performance, achieving higher AUC values (0.859–0.868) compared to random sampling (0.837–0.848). Spatial analysis demonstrates that susceptibility maps generated using the proposed approach are more coherent and better aligned with observed landslide distributions. SHAP analysis further confirms that slope, elevation, and curvature are the most influential factors, and that the physics-informed approach yields more consistent and physically meaningful feature contributions at both global and local scales. Overall, the proposed hybrid framework provides a robust and reliable tool for landslide susceptibility mapping and hazard assessment.
