Search Results (43)

The global plastic pollution crisis demands a fundamental re-evaluation of materials systems beyond incremental improvements to fossil fuel-based polymers. Bioplastics, polymers derived from renewable biological feedstocks, biodegradable under defined conditions, or both, offer a chemically diverse and rapidly evolving platform for transitioning toward circular materials economies in which the value of carbon, energy, and material is retained across multiple use cycles. This review provides a comprehensive and critically organized account of the bioplastics field, spanning three generations of feedstock development from food crops through lignocellulosic residues to algae and waste streams; primary production pathways including microbial fermentation, ring-opening polymerization, and biosynthesis; forming processes from extrusion and injection molding to additive manufacturing; and the mechanical, thermal, and barrier properties that determine application fitness. Particular emphasis is placed on life cycle assessment, which reveals that bioplastics’ climate benefits are conditional on feedstock choice, land-use management, energy source at manufacturing, and end-of-life pathway, and that burden-shifting from greenhouse gas emissions to land use, water consumption, and eutrophication is a systematic risk requiring integrated LCA evaluation rather than single-metric optimization. The review further examines end-of-life recycling, composting, and biodegradation pathways; market applications across packaging, agriculture, automotive, biomedical, and electronics sectors; and the growing role of artificial intelligence and machine learning in accelerating materials design, process optimization, and lifecycle data management. Critical barriers to scale, such as cost premiums of 20–75% over conventional plastics, inadequate composting infrastructure, recycling stream contamination, regulatory fragmentation, and consumer labeling confusion, are systematically analyzed alongside mitigation strategies. The review concludes with a forward-looking discussion of emerging feedstocks, smart and functional bioplastics, and the policy and infrastructure investments required to translate the environmental promise of bio-based polymers into realized circular economy impact. Full article

Anthropogenic reactive nitrogen (Nr) production now greatly exceeds natural creation, generating cascading environmental impacts on the environment and human health. Effective monitoring of Nr in ecosystems is essential for early warning and policy response, yet the landscape of available monitoring methods is wide and heterogeneous, varying substantially in accuracy, purpose, cost, and ecological scope. This study evaluates nineteen Nr-monitoring methods, grouped into chemistry-based (CM), biodiversity-based (BM), and transplant-based (TM) methods, against 36 fitness-for-purpose (FFP) indicator facets. Facets are organized into intrinsic (direct measurement of nitrogen cycle components), projected (ecological effects the method can indicate), and extrinsic (metaproperties such as cost or precision) types. Scores are derived from 89 core papers, ranked using evidence-normalized weighted-sums (WS) and non-metric multidimensional scaling (NMDS) approaches. CMs generally outperform BMs and TMs, with critical loads, total tissue nitrogen, and nitrogen isotopes leading overall. Lichen diversity, ectomycorrhizal fungi, and Ellenberg’s N lead among BMs, particularly for projected facets. The NMDS reveals a structural divide and monitoring complementarity between CMs and BMs, with CMs serving as early warning indicators, whereas BMs integrate cumulative, persistent ecosystem impacts. While no single method seems adequate for comprehensive monitoring, the FFP matrix facilitates selecting optimal monitoring combinations. Full article

Surface vegetation dynamics in mining areas are characterized by complex non-linear processes associated with anthropogenic disturbance and ecological restoration. Existing remote sensing approaches often face limitations in balancing temporal interpretability and the characterization of long-term vegetation trajectories at regional scales. To address this issue, this study proposes a trajectory-guided weakly supervised framework that integrates parameterized curve fitting with deep temporal learning for mining vegetation monitoring. Based on the characteristic “extraction–reclamation” cycle, six representative vegetation trajectory patterns were pre-defined to describe different stages of degradation and restoration. Long-term NDVI trajectories (1990–2023) derived from Landsat time-series data were modeled using linear and parameterized Sigmoid functions to automatically generate high-quality supervision samples and temporal transition labels. These trajectory-constrained samples were subsequently incorporated into a multi-task BiLSTM-Attention network to simultaneously perform pixel-level change classification and turning-point regression. Applied to the mining clusters of the Dongting Lake Basin, China, the proposed framework achieved an overall classification accuracy of 86.64% (Kappa = 0.83), while the temporal prediction error remained within two years. Results revealed that 28.66% of the 61.20 km² of significantly degraded mining land has undergone effective ecological restoration, with restoration activities increasing sharply between 2012 and 2014 in response to regional environmental policies. By coupling ecological trajectory modeling with weakly supervised temporal learning, this study offers a promising approach for large-scale mining restoration monitoring and ecological assessment. Full article

Microarchitectural side-channels leak data through caches, branch predictors, store buffers, and speculative execution. Evaluating defenses at cycle-model fidelity has forced a choice between functional tools (Spike, QEMU) and gem5’s hours-long Linux boots. ARCHER is a cycle-model-relative RV64IMAFDC RISC-V emulator with pluggable superscalar out-of-order execution, coherent caches, simultaneous multithreading, 1 to 16 harts, and thirteen speculation policies: an unrestricted baseline plus five classical defenses and seven new defenses spanning issue-gate, predictive-redirect, and selective-cleanup mechanism families. It boots Linux 6.6 in under three minutes, runs $3.7\times$ faster (geometric mean) than gem5’s DerivO3CPU on head-to-head bare-metal microbench, matches gem5 within $\pm 20$% on 4 of 10 workloads, matches Chipyard’s fab-ready RTL to a 9.4% mean cycle-count deviation under two fitted match-configurations, and produces bit-identical architectural output to QEMU at a median $88\times$ host-throughput advantage over Chipyard’s Verilator flow. Every published Spectre-family defense drives leakage to zero at sub-0.3% instructions-per-cycle (IPC) loss on a full Linux boot, and each new policy is validated on the attack surface its mechanism defends. The Adaptive Memory Encryption Scheme (AMES) adds a bus-layer engine with four authenticated ciphers (each validated bit-exact against its published specification) and per-leaf mask-XOR for Differential Power Analysis (DPA) hardening; a first-order Correlation Power Analysis consistency check confirms the direction of the theoretical bound under the shipped leakage model. Together, ARCHER and AMES provide a single INI-configurable environment for cycle-model side-channel evaluation in minutes rather than hours. Full article

Although employed women represent one of the fastest-growing segments of the investor population in emerging economies, their investment decision-making is still largely modelled through fragmented, single-determinant frameworks that treat women as a homogeneous group. This study develops and tests an integrated structural model of financial-goal-directed investment orientation among employed women, drawing together Behavioural Finance Theory, the Theory of Planned Behaviour, and the Life-Cycle Hypothesis. Primary data were collected through a structured questionnaire from 951 employed women across the four administrative zones of Bengaluru, India, using stratified random sampling. The measurement model was validated through exploratory and confirmatory factor analysis, and four competing structural specifications were estimated by maximum likelihood; the best-fitting model was selected on the basis of the corrected Akaike Information Criterion and approximate fit indices. The results indicate that risk tolerance is the strongest direct correlate of financial-goal-directed investment orientation, that confidence and self-efficacy operates as the pivotal psychological mediator linking macroeconomic perception to risk-taking, and that market sentiments are the strongest external correlate of investor confidence. Peer influence shows a theoretically important dual association, positively related to risk tolerance while negatively related to confidence. A serial mediation pathway running from market sentiments through confidence and risk tolerance to financial goals is supported. Because the design is cross-sectional, the associations are interpreted as structural relationships consistent with the proposed theoretical framework rather than as established causal effects. This study is exploratory and hypothesis-generating in character. The findings reframe financial-inclusion interventions for employed women around confidence-building rather than information provision, with implications for product design, advisory practice, and policy. Full article

Container sea–rail intermodal transport is pivotal to the low-carbon transformation of global supply chains. However, traditional performance evaluation systems are prone to circular reasoning fallacies due to the nesting of input and output indicators and frequently suffer from spurious regression when analyzing high-dimensional macro time series under small-sample constraints. To address these endogeneity and attribution challenges, this study proposes a four-step progressive causal inference framework. Taking Tianjin Port—a pioneering hub of China’s “road-to-rail” freight restructuring policy—as the empirical subject, we use quarterly operational data covering a complete cycle from 2017Q1 to 2024Q4. First, we construct a strictly exogenous high-quality development index based on turnover efficiency, logistics cost reduction, and carbon emission mitigation, which completely isolates scale input factors. Second, from an initial pool of 35 operational and macroeconomic indicators, 17 candidate variables are rigorously pre-screened according to statistical consistency and logistics system theory. Third, an adaptive Double Machine Learning (DML) model integrated with leave-one-out cross-fitting is applied to disentangle complex collinearity among variables. The results show that DML effectively eliminates confounding noise, accurately identifies 15 true causal drivers, and excludes spurious correlations such as redundant macro-infrastructure investment. Furthermore, a causally weighted composite index reveals that the intermodal system exhibits strong resilience to global supply chain fluctuations and has undergone a four-stage evolution. Its development momentum has fundamentally shifted from extensive scale expansion to a refined mode driven by the synergy of efficiency and service quality. This study provides a robust methodological paradigm for port performance evaluation and targeted decision support for resource allocation. Full article

Whether learned cognition can affect evolutionary outcomes remains a long-standing question. This study addresses a narrower mechanism: whether a model-based planner benefits from learned rules that explicitly condition on the action just taken. The testbed is a spatial artificial ecology with plants, shelters, a predator, reproduction, and a day/night cycle. Five rule-use arms are evaluated on matched simulation seeds. At age 200, agents switch to a weaker learned-lite planner that relies more strongly on learned rule predictions. The pre-specified hypothesis is that access to filtered action-conditioned rules improves outcomes relative to an otherwise identical no-rule-policy baseline, in which rules are still induced and stored but are not used for action selection. In thirty paired replicates under the default reproductive gates, the action-conditioned arm outperforms the no-rule baseline on all four pre-specified primary endpoints. The strongest effect is behavioural: the action arm produces 91.4 additional successful post-switch eating events per run ($d_z=1.56$, 93.3% paired win rate, $p<{10}^{-4}$). It also produces 10 additional crystallized clean-causal rules per replicate ($d_z=0.58$, $p_t=0.0034$). All four primary paired-t p-values remain significant after Bonferroni correction across the four-endpoint family. A diagnostic check shows that omitting reproductive cooldown from the planner’s rollout reverses the arm ordering on the same paired seeds; reinstating cooldown recovers the reported result. Two exploratory checks delimit the claim: broad unfiltered rule access can impair foraging, and a means–ends extension shifts behaviour toward reproduction without producing a robust whole-life fitness gain. Within this simulation, access to action-conditioned rules has a measurable effect on post-switch behaviour that is distinct from passive environmental prediction and from clean-crystallized rules alone. Full article

This study analyzes 1441 industry–university–research (I-U-R) collaborative invention patents (2004–2023) in China’s new energy vehicle (NEV) industry using social network analysis. We propose the “Proximity–Industry Life Cycle” Fit Theory to systematically investigate the influence mechanisms of industrial proximity, geographical proximity, and technological proximity on the evolution of the industry–university–research collaborative innovation network of the new energy vehicle industry across three industry life cycle stages. Key findings include: (1) the network scale expanded significantly while density declined; (2) State Grid Corporation emerged as the core node after 2010; (3) all three proximity dimensions positively influence network evolution, with varying effects across stages—industrial proximity dominates in the emergent stage, while technological proximity becomes the primary driver in later stages. Policy implications: Governments should formulate stage-differentiated policies—encouraging industrial chain collaboration in early stages while promoting technology alliances in mature stages. Core enterprises should be supported to strengthen I-U-R collaboration, and cross-regional innovation platforms should be established to optimize proximity-driven knowledge transfer. Full article

Battery energy storage systems (BESSs) are increasingly deployed by European Balance Responsible Parties (BRPs) to mitigate system-imbalance exposure; yet, techno-economic assessments often represent degradation using fixed-lifetime or equivalent-full-cycle assumptions that obscure the dependence of wear on operating policy and sizing. This study develops a data-driven, parameterised degradation framework for LiFePO₄ (LFP) BESS operating under imbalance duty. Using historical imbalance datasets from five European countries spanning eight transmission system operators (TSOs), annual cycle-induced capacity loss, calendar-induced capacity loss, and total annual capacity loss at 25 °C are mapped as explicit functions of energy-to-power ratio (duration), maximum power rating, depth of discharge, state-of-charge operating bounds, and daily cycling intensity. A degree-2 Ridge specification yields compact, auditable coefficients that transfer across entities (including an out-of-time full-year hold-out for Belgium, 2025). The fitted response surfaces reveal consistent EU-wide operating regimes: cycling-dominant ageing for durations $\le 3$ h, a mixed regime for durations $\approx 3$–6 h, and calendar-dominant ageing for durations $\ge 6$ h, indicating a practical compromise around ≈4–5.5 h. The resulting coefficientised outputs are Techno-Economic Assessment (TEA)-ready and enable risk-aware sizing and state-of-charge policy design for imbalance-focused BESS portfolios. Full article

The project cycle of complex construction projects covers the whole process from project decision-making, design, bidding, construction, completion acceptance, and the initial stage of operation. Among them, the investment risk assessment of complex construction projects focuses on the early decision-making stage of the project, aiming to provide a basis for investment feasibility analysis. The investment risk of complex construction projects is highly nonlinear and uncertain, and the traditional risk assessment methods have limitations in model generalization ability and prediction accuracy. To improve the accuracy and reliability of quantitative risk assessment, this study proposed a novel investment risk assessment model based on the perspective of investors. Firstly, through literature research, a multi-dimensional comprehensive risk assessment index system covering policies and regulations, economic environment, technical management, construction safety, and financial cost was systematically identified and constructed. Subsequently, the Least Squares Support Vector Machine (LSSVM) was used to establish a nonlinear mapping relationship between risk indicators and final risk levels. Aiming at the problem that the parameter selection of the standard LSSVM model has a significant impact on the performance, this paper proposed an improved Firefly Algorithm (IFA) to automatically optimize the penalty factor and kernel function parameters of LSSVM, so as to overcome the blindness of artificial parameter selection and improve the convergence speed and generalization ability of the model. Compared with the classical Firefly Algorithm, IFA strengthens learning and adaptive strategies by adding depth. The conclusions are as follows. (1) Compared with the Backpropagation Neural Network (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), this model showed higher prediction accuracy on the test set, and its accuracy was reduced by about 3%. (2) Compared with FA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), IFA had a stronger global retrieval ability. (3) The model could effectively fit the complex risk nonlinear relationship, and the risk assessment results were highly consistent with the actual situation. Therefore, the risk assessment model based on the improved LSSVM constructed in this study not only provides a more scientific and accurate quantitative tool for investment decision-making of construction projects, but also has important theoretical and practical significance for preventing and resolving significant investment risks. Full article

Background: As South Korea transitions into a super-aged society, promoting sport participation among older adults is increasingly vital for physical health, emotional well-being, and social inclusion. Objective: This study examines how the interplay between gender and social stratification influence sport participation and health among South Koreans aged 60 and above. Methods: Using data from the 2024 Korea National Sports Participation Survey (n = 1779), this study employed Multiple Correspondence Analysis (MCA), cross-tabulation, and one-way ANOVA with Scheffé’s post hoc tests to examine differences in sport participation and health by gender and social stratification such as income, education, and occupation. Results: The analysis revealed significant differences in sport participation and subjective health outcomes by gender and social stratification. Among older men, sport participation varied strongly by socioeconomic status: higher-status men participated in golf, cycling, and bodybuilding, whereas those from lower strata mainly engaged in walking and gateball. In contrast, older women’s participation types were less stratified and more influenced by gender norms, with consistent involvement in walking, aerobics, yoga, and stretching. One-way ANOVA showed statistically significant differences (p < 0.001) in subjective health status and physical fitness by all socioeconomic variables for both genders. Conclusions: Older adults’ sport participation and health in South Korea are constrained by both socioeconomic inequality and entrenched gender norms. Promoting equitable active aging requires policies that both reduce socioeconomic barriers and challenge restrictive gender norms. Full article

Remotely sensed cropland abandonment monitoring is crucial for providing spatially explicit references for maintaining sustainable agricultural practices and ensuring food security. However, abandoned cropland is commonly detected based on multi-date classification or the dynamics of a single vegetation index, with the interactions between vegetation and soil time series often being neglected, leading to a failure to understand its full-life-cycle succession processes. To fill this gap, we propose a new full-life-cycle modeling framework based on the interactive trajectories of vegetation–soil-related endmembers to identify abandoned and reclaimed cropland in Jinan from 2000 to 2022. In this framework, highly accurate annual fractional vegetation- and soil-related endmember time series are generated for Jinan City for the 2000–2022 period using spectral mixture models. These are then used to integrally reconstruct temporal trajectories for complex scenarios (e.g., abandonment, weed invasion, reclamation, and fallow) using logistic and double-logistic models. The parameters of the optimization model (fitting type, change magnitude, start timing, and change duration) are subsequently integrated to develop a rule-based hierarchical identification scheme for cropland abandonment based on these complex scenarios. After applying this scheme, we observed a significant decline in green vegetation (a slope of −0.40% per year) and an increase in the soil fraction (a rate of 0.53% per year). These pathways are mostly linked to a duration between 8 and 15 years, with the beginning of the change trend around 2010. Finally, the results show that our framework can effectively separate abandoned cropland from reclamation dynamics and other classes with satisfactory precision, as indicated by an overall accuracy of 86.02%. Compared to the traditional yearly land cover-based approach (with an overall accuracy of 77.39%), this algorithm can overcome the propagation of classification errors (with product accuracy from 74.47% to 85.11%), especially in terms of improving the ability to capture changes at finer spatial scales. Furthermore, it also provides a better understanding of the whole abandonment process under the influence of multi-factor interactions in the context of specific climatic backgrounds and human disturbances, thus helping to inform adaptive abandonment management and sustainable agricultural policies. Full article

The ecological value of cultivated land carries clear intergenerational implications, requiring a fair allocation mechanism across the full life cycle to address market failures caused by intergenerational externalities. This study constructs an intergenerational allocation model of cultivated land ecological value based on the Diamond framework. The intra-generational consumption elasticity coefficient is derived using the Tapio decoupling theory, with the ratio of cultivated land quantity change to per capita net income change as the core indicator. Statistical data from Lezhi County (1984–2022) are used for empirical analysis. The fitted elasticity coefficient shows a steady decline and eventually converges to zero, indicating a weakening willingness of the current generation to consume ecological value, thus supporting the logic of preserving value for future generations. A simplified Pearl growth curve is then used to construct the development stage coefficient, representing the evolving realization of ecological value among future generations. Engel coefficient forecasts based on the GM(1,1) model enable year-by-year estimation of ecological value allocation to future generations. The results show that (1) the ecological value of cultivated land in Lezhi County is estimated at CNY 65,498,230/ha, (2) the proposed model effectively simulates intergenerational value allocation, (3) decoupling theory supports the hypothesis of equal intergenerational sharing, and (4) the development stage coefficient curve provides a dynamic perspective on long-term ecological value realization. This research offers a data-driven modeling approach that enhances both the fairness and the efficiency of land governance, contributing methodological support for sustainable cultivated land protection and ecological compensation policies. Full article

Second-hand housing transactions are an important part of the housing market. Due to the dual influence of location and price, the sales cycle of second-hand housing has shown significant diversity. As a result, when residents sell or buy second-hand houses, they often cannot accurately and quickly evaluate the cycle of the second-hand house; thus, the transaction fails. For this reason, this paper develops a prediction model of the second-hand housing sales cycle based on the hybrid kernel extreme learning machine (HKELM) optimized using the Improved Crested Porcupine Optimizer (CPO), which has achieved rapid and accurate prediction. Firstly, this paper uses a Stimulus–Organism–Response model to identify 33 factors that affect the second-hand housing sales cycle from three aspects: policy factors, economic factors, and market supply and demand. Then, in order to solve the problems of slow convergence, easy-to-fall-into local optimum, and insufficient optimization performance of the traditional CPO, this paper proposes an improved optimization algorithm for crowned porcupines (Cubic Chaos Mapping Crested Porcupine Optimizer, CMTCPO). Subsequently, this paper puts forward a prediction model of the second-hand housing sales cycle based on an improved CPO-HKELM. The model has the advantages of a simple structure, easy implementation, and fast calculation speed. Finally, this paper selects 400 second-hand houses in eight cities in China as case studies. The case study shows that the maximum relative error based on the model proposed in this paper is only 0.0001784. A ten-fold cross-test proves that the model does not have an over-fitting phenomenon and has high reliability. In addition, this paper discusses the performances of different chaotic maps to improve the CPO and proves that the algorithm including chaotic maps, mixed mutation, and tangent flight has the best performance. Compared with the classical meta-heuristic optimization algorithm, the improved CPO proposed in this paper has the smallest calculation error and the fastest convergence speed. Compared with a BPNN, LSSVM, RF, XGBoost, and LightGBM, the HKELM has advantages in prediction performance, being able to handle high-dimensional complex data sets more effectively and significantly reduce the consumption of computing resources. The relevant research results of this paper are helpful to predict the second-hand housing sales cycle more quickly and accurately. Full article

To address the limitations of existing studies on the built environment and commute mode choice, which primarily focus on the objective and residential built environment, this study investigates how commuters’ perceptions of the built environment at their residences and workplaces influence their choice of commuting mode. First, six latent variables are proposed to characterize the perceived built environment. Then, commuters’ socio-economic and commuting characteristics are treated as exogenous variables. Subsequently, the influence of the perceived built environment on commute mode choice is analyzed using both a Multinomial Logit (MNL) model without latent variables and a Hybrid Choice Model (HCM) incorporating variables related to the perceived built environment. Finally, a case study conducted in Shanghai reveals that the goodness-of-fit value of the HCM improves by approximately 27.4% compared to that of the MNL, indicating that the perceived built environment plays a significant role in explaining commute mode choice. Furthermore, commuters’ socio-economic profiles, commuting characteristics, and perceptions of the built environment all significantly influence their commute mode choices. The perceived built environment at residences has a stronger impact on commute mode choice than that at workplaces. Among the various commute modes of driving, cycling, walking, and public transit, the perceived built environment most significantly influences public transit usage. Based on these findings, several policy implications are offered, providing decision-making support for urban planning and traffic management authorities. Full article