Search Results (69)

Achieving the construction sector’s dual carbon objectives necessitates scaling green energy adoption in new residential buildings. The current literature critically overlooks four unresolved problems: oversimplified penalty mechanisms, ignoring escalating regulatory costs; static subsidies misaligned with market maturity evolution; systematic exclusion of innovation feedback from energy suppliers; and underexplored behavioral evolution of building owners. This study establishes a government–suppliers–owners evolutionary game framework with dynamically calibrated policies, simulated using MATLAB multi-scenario analysis. Novel findings demonstrate: (1) A dual-threshold penalty effect where excessive fines diminish policy returns due to regulatory costs, requiring dynamic calibration distinct from fixed-penalty approaches; (2) Market-maturity-phased subsidies increasing owner adoption probability by 30% through staged progression; (3) Energy suppliers’ cost-reducing innovations as pivotal feedback drivers resolving coordination failures, overlooked in prior tripartite models; (4) Owners’ adoption motivation shifts from short-term economic incentives to environmentally driven decisions under policy guidance. The framework resolves these gaps through integrated dynamic mechanisms, providing policymakers with evidence-based regulatory thresholds, energy suppliers with cost-reduction targets, and academia with replicable modeling tools. Full article

The rising demand for sustainable protein is driving interest in insects as a raw material for advanced food ingredients. This review collates and critically analyses over 300 studies on the conversion of crickets, mealworms, black soldier flies, and other farmed species into powders, protein isolates, oils, and chitosan-rich fibers with targeted techno-functional roles. This survey maps how thermal pre-treatments, blanch–dry–mill routes, enzymatic hydrolysis, and isoelectric solubilization–precipitation preserve or enhance the water- and oil-holding capacity, emulsification, foaming, and gelation, while also mitigating off-flavors, allergenicity, and microbial risks. A meta-analysis shows insect flours can absorb up to 3.2 g of water g⁻¹, stabilize oil-in-water emulsions for 14 days at 4 °C, and form gels with 180 kPa strength, outperforming or matching eggs, soy, or whey in specific applications. Case studies demonstrate a successful incorporation at 5–15% into bakery, meat analogs and dairy alternatives without sensory penalties, and chitin-derived chitosan films extend the bread shelf life by three days. Comparative life-cycle data indicate 45–80% lower greenhouse gas emissions and land use than equivalent animal-derived ingredients. Collectively, the evidence positions insect-based ingredients as versatile, safe, and climate-smart tools to enhance food quality and sustainability, while outlining research gaps in allergen mitigation, consumer acceptance, and regulatory harmonization. Full article

Urban integrated energy systems (UIESs) play a critical role in facilitating low-carbon and high-efficiency energy transitions. However, existing scheduling strategies predominantly focus on energy quantity and cost, often neglecting the heterogeneity of energy quality across electricity, heat, gas, and hydrogen. This paper presents an exergy-enhanced stochastic optimization framework for the optimal scheduling of electricity–hydrogen urban integrated energy systems (EHUIESs) under multiple uncertainties. By incorporating exergy efficiency evaluation into a Stochastic Optimization–Improved Information Gap Decision Theory (SOI-IGDT) framework, the model dynamically balances economic cost with thermodynamic performance. A penalty-based iterative mechanism is introduced to track exergy deviations and guide the system toward higher energy quality. The proposed approach accounts for uncertainties in renewable output, load variation, and Hydrogen-enriched compressed natural gas (HCNG) combustion. Case studies based on a 186-bus UIES coupled with a 20-node HCNG network show that the method improves exergy efficiency by up to 2.18% while maintaining cost robustness across varying confidence levels. These results underscore the significance of integrating exergy into real-time robust optimization for resilient and high-quality energy scheduling. Full article

Classical epidemic models treat vaccine uptake as an exogenous parameter, yet real-world coverage emerges from strategic choices made by individuals facing uncertain risks. During the last two decades, vaccination games, which combine epidemic dynamics with game theory, behavioural economics, and network science, have become a very important tool for analysing this problem. Here, we synthesise more than 80 theoretical, computational, and empirical studies to clarify how population structure, psychological perception, pathogen complexity, and policy incentives interact to determine vaccination equilibria and epidemic outcomes. Papers are organised along five methodological axes: (i) population topology (well-mixed, static and evolving networks, multilayer systems); (ii) decision heuristics (risk assessment, imitation, prospect theory, memory); (iii) additional processes (information diffusion, non-pharmacological interventions, treatment, quarantine); (iv) policy levers (subsidies, penalties, mandates, communication); and (v) pathogen complexity (multi-strain, zoonotic reservoirs). Common findings across these studies are that voluntary vaccination is almost always sub-optimal; feedback between incidence and behaviour can generate oscillatory outbreaks; local network correlations amplify free-riding but enable cost-effective targeted mandates; psychological distortions such as probability weighting and omission bias materially shift equilibria; and mixed interventions (e.g., quarantine + vaccination) create dual dilemmas that may offset one another. Moreover, empirical work surveys, laboratory games, and field data confirm peer influence and prosocial motives, yet comprehensive model validation remains rare. Bridging the gap between stylised theory and operational policy will require data-driven calibration, scalable multilayer solvers, and explicit modelling of economic and psychological heterogeneity. This review offers a structured roadmap for future research on adaptive vaccination strategies in an increasingly connected and information-rich world. Full article

This paper examines the impact of carbon capture and storage (CCS) deployment on national carbon intensity (CI) across 43 countries from 2010 to 2020. Using a dynamic common correlated effects (DCCE) log–log panel, we estimate the elasticity of CI with respect to sectoral CCS facility counts within four income-group panels and the full sample. In the high-income panel, CCS in direct air capture, cement, iron and steel, power and heat, and natural gas processing sectors produces statistically significant CI declines of 0.15%, 0.13%, 0.095%, 0.092%, and 0.087% per 1% increase in facilities, respectively (all p < 0.05). Upper-middle-income countries exhibit strong CI reductions in direct air capture (–0.22%) and cement (–0.21%) but mixed results in other sectors. Lower-middle- and low-income panels show attenuated or positive elasticities—reflecting early-stage CCS adoption and infrastructure barriers. Robustness checks confirm these patterns both before and after the 2015 Paris Agreement and between emerging and developed economy panels. Spatial analysis reveals that the United States and United Kingdom achieved 30–40% CI reductions over the decade, whereas China, India, and Indonesia realized only 10–20% declines (relative to a 2010 baseline), highlighting regional deployment gaps. Drawing on these detailed income-group insights, we propose tailored policy pathways: in high-income settings, expand tax credits and public–private infrastructure partnerships; in upper-middle-income regions, utilize blended finance and technology-transfer programs; and in lower-income contexts, establish pilot CCS hubs with international support and shared storage networks. We further recommend measures to manage CCS’s energy and water penalties, implement rigorous monitoring to mitigate leakage risks, and design risk-sharing contracts to address economic uncertainties. Full article

Aiming to solve optimization scheduling difficulties caused by the double uncertainty of source-side photovoltaic (PV) output and load-side demand response in active distribution networks, this paper proposes a two-stage distribution robust optimization method. First, the first-stage model with the objective of minimizing power purchase cost and the second-stage model with the co-optimization of active loss, distributed power generation cost, PV abandonment penalty, and load compensation cost under the worst probability distribution are constructed, and multiple constraints such as distribution network currents, node voltages, equipment outputs, and demand responses are comprehensively considered. Secondly, the second-order cone relaxation and linearization technique is adopted to deal with the nonlinear constraints, and the inexact column and constraint generation (iCCG) algorithm is designed to accelerate the solution process. The solution efficiency and accuracy are balanced by dynamically adjusting the convergence gap of the main problem. The simulation results based on the improved IEEE33 bus system show that the proposed method reduces the operation cost by 5.7% compared with the traditional robust optimization, and the cut-load capacity is significantly reduced at a confidence level of 0.95. The iCCG algorithm improves the computational efficiency by 35.2% compared with the traditional CCG algorithm, which verifies the effectiveness of the model in coping with the uncertainties and improving the economy and robustness. Full article

Electrochemical separations use an ionic current to drive the flow of ions across an ion exchange membrane to produce dilute and concentrated streams. The economics of these systems is challenging because passing an ionic current through a dilute solution often requires a small cell gap to lower the ionic resistance and the use of a low current density to minimize the voltage drop across the dilute product stream. Lower salt concentration in the product stream improves the fraction of the salt recovered but increases the electricity cost due to high ohmic losses. The electricity cost is managed by lowering the current density which greatly increases the balance of the plant. The cell configuration demonstrated in this study eliminates the need to pass an ionic current through the diluted product stream. Ionic current passes only through the concentrated product stream, which allows use of high current density and smaller balance of the plant. The cell has three chambers with an anion and cation membrane separating the cathode and anode, respectively, from the concentrated product solution. The device uses zero-gap membrane electrode assemblies to improve the cell voltage and system performance. As ions concentrate in the center compartment, the solution resistance decreases, and the product is recovered with a lower voltage penalty compared to traditional electrodialysis. This lower voltage drop allows for faster feed flow rates and higher current density. Additionally, the larger cell gap for the product provides opportunities for systems with solids suspended in solution. It was found that the ion collection efficiency increased with current due to enhanced convective mass transfer in the feed streams. Full article

To enable autonomous driving in real-world environments that involve a diverse range of geographic variations and complex traffic regulations, it is essential to investigate Deep Reinforcement Learning (DRL) algorithms capable of policy learning in high-dimensional environments characterized by intricate state–action interactions. In particular, closed-loop experiments, which involve continuous interaction between an agent and their driving environment, serve as a critical framework for improving the practical applicability of DRL algorithms in autonomous driving systems. This study empirically analyzes the capabilities of several representative DRL algorithms—namely DDPG, SAC, TD3, PPO, TQC, and CrossQ—in handling various urban driving scenarios using the CARLA simulator within a closed-loop framework. To evaluate the adaptability of each algorithm to geographical variability and complex traffic laws, scenario-specific reward and penalty functions were carefully designed and incorporated. For a comprehensive performance assessment of the DRL algorithms, we defined several driving performance metrics, including Route Completion, Centerline Deviation Mean, Episode Reward Mean, and Success Rate, which collectively reflect the quality of the driving in terms of its completeness, stability, efficiency, and comfort. Experimental results demonstrate that TQC and SAC, both of which adopt off-policy learning and stochastic policies, achieve superior sample efficiency and learning performances. Notably, the presence of geographically variant features—such as traffic lights, intersections, and roundabouts—and their associated traffic rules within a given town pose significant challenges to driving performance, particularly in terms of Route Completion, Success Rate, and lane-keeping stability. In these challenging scenarios, the TQC algorithm achieved a Route Completion rate of 0.91, substantially outperforming the 0.23 rate observed with DDPG. This performance gap highlights the advantage of approaches like TQC and SAC, which address Q-value overestimation through statistical methods, in improving the robustness and effectiveness of autonomous driving in diverse urban environments. Full article

State preemption of local laws may impede tobacco control, yet little research has examined local policy activity before, during, and after preemption. This study addresses this gap. We summarized state laws preempting local smoke-free workplace, youth access, and licensure laws (CDC’s STATE) and local legislative activity before, during, and after preemption (Americans for Nonsmokers’ Rights Foundation) across 1999–2021. Preemption existed for smoke-free workplaces in 18 states, youth access in 21, and licensure in 13. Regarding smoke-free workplace laws, local laws were passed in 5 of 11 states with preemption throughout; among seven states with partial-period preemption, local laws were enacted before preemption or after repeal in three states but during preemption in two. Regarding youth access, localities adopted laws (e.g., addressing purchase/use/possession or e-cigarettes) in 11 of 18 states with preemption throughout; among the three states with partial-period preemption, localities passed laws before preemption in one state and during preemption in two. For licensure, localities passed laws (e.g., licensing requirements/penalties) in eight of nine states with preemption throughout and three of four states with partial-period preemption. Although state preemption reduced local activity, some localities advanced tobacco control during preemption, underscoring the need for localities to exercise autonomy permitted under preemption. Full article

Morro do Banco, a slum in Rio de Janeiro, Brazil, is home to a community composed mainly of Brazilian and Venezuelan nationals, where littering is a persistent issue. This study investigates the causes of littering and examines the differences in perceptions and littering behaviours between residents, addressing a research gap in multicultural slums. A face-to-face survey was conducted with 150 residents, complemented by interviews with community members and professionals from urban cleaning and waste management services. Visual observations were also made. The results indicate that littering is primarily linked to a lack of containers for waste disposal and collection, as well as residents’ failure to dispose of waste at the designated times. There is a notable absence of awareness campaigns aimed at addressing the problem. While both communities recognise the littering issue, Venezuelan residents are less aware of public services and report observing lower levels of littering than Brazilian residents. Furthermore, Brazilians tend to place more responsibility on local authorities, while Venezuelans attribute responsibility to the central government. Venezuelans also express less support for oversight actions involving penalties compared to Brazilians. These findings highlight the need for targeted awareness campaigns and inclusive policies to effectively tackle littering in multicultural slums. Full article

Under the “dual-carbon” strategic framework, the installed capacity of renewable energy sources has continuously increased, while that of conventional generation units has progressively decreased. This structural shift significantly diminishes the operational flexibility of power generation systems and intensifies grid imbalances caused by renewable energy volatility. To address these challenges, this study proposes a carbon-aware multi-timescale virtual power plant (VPP) scheduling framework with coordinated multi-energy integration, which operates through two sequential phases: day-ahead scheduling and intraday rolling optimization. In the day-ahead phase, demand response mechanisms are implemented to activate load-side regulation capabilities, coupled with information gap decision theory (IGDT) to quantify renewable energy uncertainties, thereby establishing optimal baseline schedules. During the intraday phase, rolling horizon optimization is executed based on updated short-term forecasts of renewable energy generation and load demand to determine final dispatch decisions. Numerical simulations demonstrate that the proposed framework achieves a 3.76% reduction in photovoltaic output fluctuations and 3.91% mitigation of wind power variability while maintaining economically viable scheduling costs. Specifically, the intraday optimization phase yields a 1.70% carbon emission reduction and a 7.72% decrease in power exchange costs, albeit with a 3.09% increase in operational costs attributable to power deviation penalties. Full article

Quality inspection is critical for ensuring efficiency and compliance in assembly lines. The increasing adoption of AI-driven technologies, such as machine vision systems, offers significant potential to enhance detection performance and reduce inspection costs. However, these technologies are often integrated with human operators in hybrid inspection systems, posing complex design challenges. Motivated by gaps in the existing research, this paper proposes a novel MILP model that introduces several previously unaddressed capabilities in inspection planning. Specifically, it simultaneously optimizes the inspection method selection, sampling rate, and detection rate across multi-product systems featuring multiple inspection technologies with varying costs and accuracies. This unified formulation represents a substantive advancement over existing models, which typically address only isolated aspects of the problem. The model minimizes the total quality-related costs—comprising investment, inspection, penalty, and rework costs—while considering operational constraints such as workforce availability, inspection and rework time limits, and equipment capacity. Key modeling assumptions include heterogeneous inspection accuracies, product-specific defect probabilities, and the feasibility of partial inspections. The approach is validated on both synthetic datasets and a real-world automotive case study, demonstrating its ability to significantly reduce costs and to highlight the benefits of effectively combining human and machine-based inspections. Full article

The rapid development of the new energy vehicle industry has resulted in a significant number of waste electric vehicle batteries (WEVBs) reaching the end of their useful life. The recycling of these batteries holds both economic and environmental value. As policy is a critical factor influencing the recycling of waste electric vehicle batteries, its role in the network warrants deeper investigation. Based on this, this study integrates both subsidy and penalty policy into the design of the waste electric vehicle battery reverse logistics network (RLN), aiming to examine the effects of single policy and policy combinations, thereby filling the research gap in the existing literature that predominantly focuses on single-policy perspectives. Considering multiple battery types, different recycling technologies, and uncertain recycling quantities and qualities, this study develops a fuzzy mixed-integer programming model to optimize cost and carbon emission. The fuzzy model is transformed into a deterministic equivalent form using expected intervals, expected values, and fuzzy chance-constrained programming. By normalizing and weighting the upper and lower bounds of the multi-objective functions, the model is transformed into a single-objective optimization problem. The effectiveness of the proposed model and solution method was validated through an empirical study on the construction of a waste electric vehicle battery reverse logistics network in Zhengzhou City. The experimental results demonstrate that combined policy outperforms single policy in balancing economic benefits and environmental protection. The results provide decision-making support for policymakers and industry stakeholders in optimizing reverse logistics networks for waste electric vehicle batteries. Full article

Noise, vibration and harshness analyses are of great interest for the latest developments of the gearboxes of electric vehicles. Gearboxes are now the main source of vibrations, since electric powertrains are much quieter than internal combustion engines. Traditionally, the simulation of the non-linear gear dynamics is studied by first performing a series of preliminary static analyses to compute the static transmission error (STE). The STE (i.e., in the form of varying mesh stiffness) is then accepted as the system’s excitation source to compute the dynamic transmission error (DTE). This paper presents a novel approach to analyze the non-linear dynamics of gears which does not require any preliminary static analyses. The method consists of a frequency–domain approach based on the Harmonic Balance Method (HBM) and the Alternating Frequency–Time (AFT) scheme, allowing for much faster simulations when compared to the widely used direct–time integration (DTI). The contact between the teeth is modeled as intermittent and penalty based with a varying gap. The time–varying gap between the teeth is initially approximated to a step function that guarantees the design contact ratio. The methodology introduced is tested on a lumped parameter model of a spur–gear pair already proposed and simulated in the literature. The results obtained with the novel approach are compared with the DTI simulation of the model as a reference. The excellent match between the different approaches validates the reliability of developed methodology. Full article

This study aimed to assess public perceptions and knowledge of, and attitudes towards, zoophilia in Hungary. Conducted between October and December 2021 with 1753 respondents, the survey revealed significant interest and concern regarding zoophilia. The majority (98.3%) of respondents deemed zoophilia to be unacceptable from both health and animal welfare perspectives. Of those surveyed, 98.9% believed that animals possess dignity, and 84.7% felt that zoophilia negatively impacts this dignity. However, awareness of Hungarian legislation on zoophilia was limited, with 38.9% of respondents unaware that such acts are legally prohibited. The survey also highlighted a strong consensus (98.2%) favoring strict penalties for zoophilia. Additionally, the survey uncovered that 14% of respondents had encountered or heard of zoophilic incidents, involving various animals, predominantly dogs. Gender and settlement type significantly influence attitudes toward zoophilic acts, with women and people in capital and major cities being more aware of the legal prohibition and more supportive of strict sanctions. The results underscore a crucial need for enhanced public education on legal standards and ethical considerations regarding zoophilia and suggest a significant gap in understanding and managing the issue, necessitating further research and legislative reforms. Full article