Search Results (177)

Globally, technologically integrated housing solutions are increasingly relevant in addressing the challenges of aging populations and sustainable urban development. Drawing on the Technology Acceptance Model (TAM), this research investigates how perceptions of usefulness, ease of use, and attitudes influence relocation intention to smart retirement villages (SRVs), while also examining any significant differences between the socio-demographic variables and such intention. A total of 305 individuals aged 55 and above participated in an online survey, with data analyzed using IBM SPSS Statistics version 27 and AMOS-SEM version 25. The findings reveal that elderly individuals of Chinese ethnicity, those who are married, and those aged between 66 and 70 are more inclined to relocate to SRVs. Attitude and perceived usefulness significantly predict relocation intention, while perceived ease of use exerts an indirect effect through usefulness. These results highlight the importance of integrating user-centered technological design with socio-cultural and demographic considerations in the development of age-friendly built environments. The study offers insights for urban planners, policymakers, and developers seeking to create inclusive and sustainable smart housing solutions for aging populations. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

Background: Transcranial direct current stimulation (tDCS) has emerged as a promising neuromodulatory tool for language rehabilitation in chronic aphasia. However, the effects of bi-hemispheric, multisite stimulation remain largely unexplored, especially in people with chronic and treatment-resistant language impairments. The goal of this study is to look at the effects on behavior and brain activity of an individualized language training program that combines bi-hemispheric multisite anodal tDCS with personalized language training for Albert, a patient with long-standing, treatment-resistant non-fluent aphasia. Methods: Albert, a right-handed retired physician, had transcortical motor aphasia (TCMA) subsequent to a left-hemispheric ischemic stroke occurring more than six years before the operation. Even after years of traditional treatment, his expressive and receptive language deficits remained severe and persistent despite multiple rounds of traditional therapy. He had 15 sessions of bi-hemispheric multisite anodal tDCS aimed at bilateral dorsal language streams, administered simultaneously with language training customized to address his particular phonological and syntactic deficiencies. Psycholinguistic evaluations were performed at baseline, immediately following the intervention, and at 1, 2, 3, and 6 months post-intervention. Resting-state fMRI was conducted at baseline and following the intervention to evaluate alterations in functional connectivity (FC). Results: We noted statistically significant enhancements in auditory sentence comprehension and oral reading, particularly at the 1- and 3-month follow-ups. Neuroimaging showed decreased functional connectivity (FC) in the left inferior frontal and precentral regions (dorsal stream) and in maladaptive right superior temporal regions, alongside increased FC in left superior temporal areas (ventral stream). This pattern suggests that language networks may be reorganizing in a more efficient way. There was no significant improvement in phonological processing, which may indicate reduced connectivity in the left inferior frontal areas. Conclusions: This case underscores the potential of combining individualized, network-targeted language training with bi-hemispheric multisite tDCS to enhance recovery in chronic, treatment-resistant aphasia. The convergence of behavioral gains and neuroplasticity highlights the importance of precision neuromodulation approaches. However, findings are preliminary and warrant further validation through controlled studies to establish broader efficacy and sustainability of outcomes. Full article

The rapid growth of the wind energy sector has led to a rising number of retired wind turbine blades (RWTBs) globally, posing significant environmental and logistical challenges for sustainable waste management. Handling enormous RWTBs at their end of life (EoL) has a significant negative impact on resource conservation and the environment. Conventional disposal methods, such as landfilling and incineration, raise environmental concerns due to the non-recyclable composite material used in blade manufacturing. This study explores the upcycling potential of RWTBs as innovative construction materials, addressing both waste reduction and resource efficiency in the construction industry. By exploring recent advancements in recycling techniques, this research highlights applications such as structural components, lightweight aggregates for concrete, and reinforcement elements in asphalt pavements. The key findings demonstrate that repurposing blade-derived materials not only reduces landfill dependency but also lowers carbon emissions associated with conventional construction practices. However, challenges including material compatibility, economic feasibility, and standardization require further investigation. This study concludes that upcycling wind turbine blades into construction materials offers a promising pathway toward circular economy goals. To improve technical methods and policy support for large-scale implementation, it recommends collaboration among different fields, such as those related to cementitious and asphalt materials. Full article

Accurately predicting recycling prices at battery recycling sites helps reduce transportation and dismantling costs, ensures economies of scale in the recycling, and supports the sustainable development of the new energy vehicle industry. However, this prediction typically relies on easily accessible surface data, such as battery characteristics and market prices. These data have complex correlations with recycling price, general price prediction methods have low prediction accuracy. To this end, an improved prediction method is proposed to enhance the accuracy of predicting recycling prices through surface data. Firstly, factors influencing recycling prices are selected based on self-factor and market fluctuations, a bidirectional denoising autoencoder and support vector regression model (BDAE-SVR) is established. BDAE is used to adjust the weights of influencing factors to remove noise, extract features related to recycling price. The extracted features are introduced into the SVR model to establish a correspondence between the features and recycling price. Secondly, to have better applications for different batteries, the Grey Wolf algorithm (GWO) is used to adjust the SVR parameters to improve the generalization ability of the prediction model. Finally, taking retired power batteries as an example, the effectiveness of the method is verified. Compared with methods such as random forest (RF), the RMSE predicted by BDAE is decreased from 1.058 to 0.371, indicating better prediction accuracy. Full article

In the wake of global energy transition and the “dual-carbon” goal, the rapid growth of electric vehicles has posed challenges for large-scale lithium-ion battery decommissioning. Retired batteries exhibit dual attributes of strategic resources (cobalt/lithium concentrations several times higher than natural ores) and environmental risks (heavy metal pollution, electrolyte toxicity). This paper systematically reviews pyrometallurgical and hydrometallurgical recovery technologies, identifying bottlenecks: high energy/lithium loss in pyrometallurgy, and corrosion/cost/solvent regeneration issues in hydrometallurgy. To address these, an integrated recycling process is proposed: low-temperature physical separation (liquid nitrogen embrittlement grinding + froth flotation) for cathode–anode separation, mild roasting to convert lithium into water-soluble compounds for efficient metal oxide separation, stepwise alkaline precipitation for high-purity lithium salts, and co-precipitation synthesis of spherical hydroxide precursors followed by segmented sintering to regenerate LiNi_1/3Co_1/3Mn_1/3O₂ cathodes with morphology/electrochemical performance comparable to virgin materials. This low-temperature, precision-controlled methodology effectively addresses the energy-intensive, pollutive, and inefficient limitations inherent in conventional recycling processes. By offering an engineered solution for sustainable large-scale recycling and high-value regeneration of spent ternary lithium ion batteries (LIBs), this approach proves pivotal in advancing circular economy development within the renewable energy sector. Full article

Background: As global ageing accelerates, countries worldwide are increasingly facing social and economic challenges posed by rising older populations. Many nations are responding by optimizing healthcare systems, strengthening community-based ageing models, and promoting healthy ageing policies. The Naturally Occurring Retirement Community (NORC) is gaining recognition as a promising approach due to its cost efficiency and ability to meet diverse ageing-related needs. However, systematic research on the service models of NORCs remains scarce. Objective: This study aims to systematically examine the service offerings of Naturally Occurring Retirement Community Supportive Service Programs (NORC-SSPs) and analyze how these programs contribute to supporting ageing in place. Methods: A qualitative content analysis was conducted on official website information from 60 NORC-SSPs in New York State. Service categories were identified, coded, and compared across different geographic and structural contexts. Results: The analysis shows that education, healthcare management, and recreational activities are the most frequently provided services, with health-related services playing a central role in supporting older adults to age in place. Differences in service priorities were also observed between rural and non-rural settings, as well as between vertical and horizontal built environments, reflecting the adaptability of NORC-SSPs to varying community conditions. Conclusions: By identifying key service characteristics, this study provides insights for policymakers and practitioners in Australia and other countries seeking to implement sustainable, community-based models of ageing support. Grounded in the concept of “ageing in place”, the findings contribute to the development of inclusive and flexible service systems for older adults. Full article

Accelerating the low-carbon transition of China’s coal-fired power sector is essential for advancing national sustainability goals and fulfilling global climate commitments. This study introduces an integrated, data-driven analytical framework to facilitate the sustainable transformation of the coal power sector through coordinated unit-level retirements, new capacity planning, and targeted retrofits. By combining a comprehensive unit-level database with a multi-criteria evaluation framework, the analysis incorporates environmental, technical, and economic factors into decision-making for retirement scheduling. Scenario analyses based on the China Energy Transformation Outlook (CETO 2024) delineate both baseline and ideal carbon neutrality pathways. Optimization algorithms are employed to identify cost-effective retrofit strategies or portfolios, minimizing levelized carbon reduction costs. The findings reveal that cumulative emissions can be reduced by 10–14.9 GtCO₂ by 2060, with advanced technologies like CCUS and co-firing contributing over half of retrofit-driven mitigation. The estimated transition cost of 6.2–6.7 trillion CNY underscores the scale of sustainable investment required. Sensitivity analyses further highlight the critical role of reducing green hydrogen costs to enable deep decarbonization. Overall, this study provides a robust and replicable planning tool to support policymakers in formulating strategies that align coal power sector transformation with long-term sustainability and China’s carbon neutrality commitments. Full article

Retreading is a cornerstone in the remanufacturing process of tires, facilitating the extraction of maximum kilometers (Km) from a tire carcass. Tire remanufacturing plays a crucial role in conserving raw materials, reducing environmental impacts, and lowering the overall operating costs. This study employs predictive modeling techniques to forecast tire performance and optimize resource allocation, departing from traditional approaches, for a bus transport system in India. Machine learning models, including linear regression, ensemble boosted trees, and neural network models, were used. Two scenarios were devised: Scenario I addressed premature failures and optimizing performance to reduce tire procurement and Scenario II used targeted interventions, such as eliminating new tire condemnations and optimizing retread (RT) strategies, and could potentially salvage 169 tires from premature retirement. The results achieved R² values of 0.44, 0.51, and 0.45 and improved values for the test datasets of 0.46, 0.52 and 0.44. By leveraging these models, decision-makers can substantially improve tire mileage, reduce premature condemnations, increase tire production, and drive cost savings in fleet operations. Notably, this approach contributes to enhanced operational efficiency and promotes sustainability by cutting costs by 15–25%, improving tire mileage by 20–30%, and reducing environmental impacts by up to 25%. These results demonstrate the broader implications of predictive modelling as a decision-support tool, highlighting its capacity to drive economic and environmental benefits across industrial logistics and sustainable development. Full article

The widespread scrapping of retired mechanical parts has led to severe waste of resources and environmental burdens, posing a significant challenge to sustainable industrial development. To enable efficient recycling of retired mechanical parts and enhance the sustainability of their remanufacturing processes, the concept of biological genes is adopted to characterize the changes in the information of retired mechanical parts during the remanufacturing process as gene mutations of parts, aiming to maximize remanufacturing potential and devise an optimal generalized remanufacturing strategy for extending part life cycles. However, gene mutation of retired mechanical parts is not an isolated event. The modification of local genes may disrupt the original equilibrium of the part’s state, leading to conflicts such as material–performance, structure–function/performance, and function–performance. These conflicts constitute a major challenge and bottleneck in designing generalized remanufacturing schemes. Therefore, we propose a conflict identification and resolution method for gene mutation of retired mechanical parts. First, gene mutation graph of retired mechanical parts is established to express its all-potential remanufacturing pathways. Using discrimination rules and the element representation method from extenics, mutation conflicts are identified, and a conflict problem model is constructed. Then, the theory of inventive problem solving (TRIZ) engineering parameters are reconstructed and mapped to the mutation conflict parameters. The semantic mapping between the inventive principles and the transforming bridges is established by the Word2Vec algorithm, thereby improving the transforming bridge method to generate conflict resolution solutions. A coexistence degree function of transforming bridges is proposed to verify the feasibility of the resolution solutions. Finally, taking the generalized remanufacturing of a retired gear shaft as an example, we analyze and discuss the process and outcome of resolving gene mutation conflicts, thereby verifying the feasibility and effectiveness of the proposed concepts and methodology. Full article

With the intensification of global climate change, the issue of snow and ice accumulation on roads during winter has become increasingly severe, prompting the widespread application of salt-storage asphalt mixtures in highway construction of alpine regions due to their ability to sustainably release salts for snowmelt. The incorporation of salt-storage fillers significantly compromises the road performance of asphalt mixtures, particularly exacerbating deterioration in low-temperature crack resistance and moisture stability while accelerating pavement distress. Although fiber reinforcement technology has been validated for enhancing asphalt mixture performance, conventional fibers suffer from high production costs and inadequate environmental sustainability. The rapid expansion of the wind energy sector in recent years has generated substantial quantities of retired wind turbine blades (rWTB), posing a global challenge for recycling. This study proposes utilizing rWTB in salt-storage asphalt mixtures and investigates their road performance and underlying mechanisms through experimental analysis. The results demonstrate that rWTB fiber addition markedly improves the mechanical properties of salt-storage asphalt mixtures, yet excessive fiber dosages (>0.3%) induce localized fiber agglomeration, thereby slowing or reversing optimization trends. Given the multi-objective optimization challenge of rWTB fiber incorporation, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm was employed as an optimization tool. In-depth analysis yielded four distinct optimal fiber dosage schemes with performance-oriented priorities: 0.2848%, 0.2903%, 0.2881%, and 0.2882%. These findings provide novel insights for rWTB resource recycling and scientific evidence for enhancing the performance of salt-storage asphalt mixtures. Full article

Many decommissioned wind turbines (WTs) present significant recycling management challenges. Improper disposal wastes resources and generates additional carbon emissions, which contradicts the Sustainable Development Goals (SDGs). This study constructs a sine cosine algorithm (SCA)–ITransformer–BiLSTM deep learning prediction model, integrated with dynamic material flow analysis (DMFA) and a multi-dimensional Energy–Economy–Environment–Society (3E1S) sustainability assessment framework. This hybrid approach systematically reveals the spatiotemporal evolution patterns and circular economy value of WTs in China by synthesizing multi-source heterogeneous data encompassing policy dynamics, technological advancements, and regional resource endowments. Results demonstrate that China will enter a sustained wave of WT retirements post-2030, with an annual decommissioned capacity exceeding 15 GW. By 2050, new installations and retirements will reach a dynamic equilibrium. North and Northwest China are emerging as core retirement zones, accounting for approximately 50% of the national total. Inner Mongolia and Xinjiang face maximum recycling pressures. The recycling of decommissioned WTs could yield approximately CNY 198.5 billion in direct economic benefits and reduce CO₂ equivalent emissions by 4.78 to 8.14 billion tons. The 3E1S framework fills critical gaps in quantifying the comprehensive benefits of equipment retirement, offering a theoretically grounded and practically actionable paradigm for the global wind industry’s circular transition. Full article

This article investigates decision-making strategies for power battery recycling and cascading utilization within the context of rapidly advancing blockchain technology, aiming to enhance the sustainability and efficiency of energy storage systems. A closed-loop recycling supply chain model is proposed, integrating key stakeholders such as power battery manufacturers, OEM (original equipment manufacturer) vehicle manufacturers, third-party recyclers, tiered users, and consumers. The study focuses on critical factors including competition among recycling channels, the level of blockchain-enabled traceability, and the cascading utilization rate of retired batteries. By analyzing four hybrid recycling modes, the research identifies optimal recycling strategies and evaluates their economic and environmental impacts. The findings provide a theoretical foundation and practical insights for improving the sustainability of power battery recycling, contributing to the development of cleaner and more efficient energy systems. Full article

The built environment has been widely recognized as a critical determinant of physical activity among older adults. However, urban–rural disparities and the non-linear effects of environmental features remain underexplored. Using interpretable machine learning (random forest model) on nationwide representative data from 2526 older adults in the China Health and Retirement Longitudinal Study (CHARLS) database, this study identified both common and distinct risk factors for insufficient moderate-to-vigorous physical activity (MVPA) across diverse urban and rural contexts. The results revealed a location-based gradient in physical activity insufficiency: rural areas < suburban areas < central urban areas. Rural older adults faced greater constraints from safety concerns and transportation accessibility limitations. In comparison, urban older adults would benefit from targeted improvements in built environment quality, particularly elevator accessibility and diverse public activity spaces. Furthermore, non-linear relationships were observed between built environment features and physical activity, elucidating the “density paradox”: while moderate urban compactness promoted active behaviors, excessive density (>24,000 persons/km²), perceived overcrowding, and over-proximity to specific facilities (<1 km) were linked to reduced MVPA. These findings underscore the necessity for differentiated policy interventions in urban and rural settings to address the distinct environmental needs of older adults. Meanwhile, in urban planning, it is crucial that we balance spatial compactness and functional diversity within optimal thresholds for creating sustainable and inclusive built environments. Although a compact design may enhance mobility, equal attention must be paid to preventing spatial disorder from over-densification. Full article

Accurate estimation of battery state of health (SOH) is critical to the efficient operation of energy storage battery systems. Furthermore, precise SOH estimation methods can significantly reduce resource waste by extending the battery service life and optimizing retirement strategies, which is compatible with the sustainable development of energy systems under carbon neutrality goals. Conventional methods struggle to comprehensively characterize the health degradation properties of batteries. To address that limitation, this study proposes a data-driven model based on multi-feature analysis using a hybrid convolutional neural network and long short-term memory (CNN-LSTM) architecture, which synergistically extracts multi-dimensional degradation features to enhance SOH estimation accuracy. The framework begins by systematically collecting the voltage, current, and other parameters during charge–discharge cycles to construct a temporally resolved multi-dimensional feature matrix. A correlation analysis employing Pearson correlation coefficients subsequently identifies key health indicators strongly correlated with SOH degradation. At the same time, the K-means clustering method was adopted to identify and process the outliers of CALCE data, which ensures the high quality of data and the stability of the model. Then, CNN-LSTM hybrid neural network architecture was constructed. The experimental results demonstrated that the absolute value of MBE for the dataset provided by CALCE was less than 0.2%. The MAE was less than 0.3%, and the RMSE was less than 0.4%. Furthermore, the proposed method demonstrated a strong performance on the dataset provided by NASA PCoE. The experimental results indicated that the proposed method significantly reduced the estimation error of SOH across the entire battery lifecycle, and they fully verified the superiority and engineering applicability of the algorithm in battery SOH estimation. Full article