Search Results (4,651)

Ecological restoration—whether active or passive—includes forest development, forest rehabilitation, and a range of other activities that contribute to ecosystem services. To provide a formal framework, we hypothesized how does reforestation (through different forestry practices) affect the conservation of soil functionality? That is, how does reforestation/afforestation/forest restoration improve soil quality? And, specifically, how do they improve physical properties (such as structural stability, infiltration) and chemical properties (such as acidity, electrical conductivity)? For this purpose, we conducted a bibliometric analysis review of the peer-reviewed scientific literature and research reports of numerous articles in order to compile a large database of forest restoration studies, with an emphasis on the Mediterranean region. The final focus was to obtain conclusions about how it affects soil quality. Overall, our examination confirms that deforestation drives a decline in soil carbon and nitrogen, subsequently impairing microbial activity. Consequently, forest removal frequently leads to accelerated erosion, nutrient depletion, and compaction. In contrast, reforestation acts as a critical intervention, stabilizing soil structure, reestablishing fertility, and enhancing soil quality overall. Additionally, three case studies are synthetically presented concerning the short-, medium-, and long-term results of forest restoration projects carried out mainly in central and northern Spain. These cases corroborate the significant role of forest restoration in the control and enhancement of ecosystem services, particularly in relation to soil improvement, the enhancement of hydrological regulation processes within watersheds (runoff, infiltration, erosion), landscape amelioration, and the socio-economic aspects of rural environments. Ultimately, forest restoration is established as a necessary and essential practice in ecological restoration efforts to counteract the impacts of anthropogenic activities. Full article

Motor neuron diseases (MNDs) encompass a clinically heterogeneous group of neurodegenerative conditions with varying impact on dexterity, mobility, decision making, respiratory and bulbar dysfunction. While consensus best-practice recommendations exist for genetic screening, diagnostic work-up, pharmacological and respiratory management, disease-specific facets of driving safety, assessment approaches and intervention strategies to support patients for safe driving have not been comprehensively reviewed. MNDs have unique, phenotype-specific clinical features, which are distinct form other neuromuscular conditions which necessitate a careful and systematic approach to evaluate driving safety. While MNDs are primarily associated with progressive motor impairment, extrapyramidal, cerebellar, cognitive, behavioural, and respiratory manifestations of the disease also affect driving safety and necessitate comprehensive driving assessments and individualised strategies to enable patients to continue to drive. The majority of existing papers focus on amyotrophic lateral sclerosis, and low-incidence MND phenotypes, such as PLS, SBMA, PPS, are glaringly understudied from a driving safety perspective despite the relatively slower progression of these conditions. Beyond the review of specific aspects of driving in MNDs, the main objective of this review paper is to raise awareness of non-motor aspects of MNDs with regard to driving safety and to explore viable strategies to support patients to maintain their independence. Despite the considerable differences in driving regulations around the globe, there are core, disease-specific aspects of MND which are universal. The careful consideration of these clinical factors, comprehensive domain-by-domain assessments, and the implementation of practical, individualised adaptations may enable patients to continue driving safely, maintain their independence and enhance their quality of life. Full article

The mean annual change in aboveground biomass (ΔAGB) is a pivotal indicator for assessing forest carbon cycle dynamics. This study analyzed 791 independent Abies Mill. forest patches across China to elucidate their driving mechanisms by integrating abiotic, anthropogenic, and biotic factors. We employed a spatially explicit framework, including spatial error regression and structural equation modeling (SEM), to account for significant spatial autocorrelation (Moran’s I = 0.375, p < 0.001). Our results show that abiotic factors predominantly dictate ΔAGB, with soil fertility (pH and Total Nitrogen), elevation (DEM), and soil physical properties (Coarse Fragments and Thickness) explaining the majority of deterministic variance. This relatively low explanatory variance (marginal R² = 0.09) likely reflects the high environmental stochasticity inherent in alpine ecosystems. Specifically, soil fertility exerted the strongest positive influence (Std. Estimate = 0.33), while elevation and soil physical constraints were the primary limiting factors. Biotic factors (Stand Age, Height, and Tree Cover) played a subordinate role, contributing only a marginal 2% gain in explained variance (increasing marginal R² from 0.07 to 0.09). Path analysis revealed an “environmental filtering” hierarchy where abiotic factors shape stand structure, which in turn has limited impact on growth dynamics. These findings underscore that carbon management in alpine forests should prioritize habitat quality conservation over simple biotic structural manipulation. Full article

As a core area for soil and water conservation on the Loess Plateau and a national primary shale oil production zone, Qingyang City faces an increasingly acute contradiction between its inherently fragile ecological base and energy development activities. From the dual perspectives of ecological regulating services and production-supporting services, this study selected six key ecosystem services—habitat quality (HQ), soil retention (SR), carbon storage (CS), water yield (WY), food supply (FS), and grassland forage supply (GS)—to comprehensively assess their spatiotemporal evolution, trade-off/synergy relationships, and driving mechanisms from 2000 to 2020. The results indicate: (1) Significant changes occurred in the total amounts and spatial patterns of all ecosystem services during 2000–2020. HQ showed a fluctuating upward trend, while SR, FS, and GS increased overall; by contrast, CS and WY generally declined. (2) Ecosystem services exhibited a differentiated pattern characterized by “intra-category synergy and inter-category trade-off.” Regulating and supporting services were generally dominated by synergistic relationships, although clear differences remained among specific service pairs; provisioning services generally showed trade-offs with regulating services, among which the trade-offs between FS–HQ and between FS–GS were the most pronounced, whereas FS–CS showed a certain degree of synergy. (3) Driving force analysis revealed a continuous decline in the influence of natural factors and a sharp intensification of human activity factors. Groundwater level and land-use intensity became core drivers of pattern shifts, with their explanatory power increasing significantly. The study reveals that ecosystem services in Qingyang have rapidly transitioned from being dominated by natural hydrothermal conditions to being profoundly reshaped by energy development activities, exposing the region to the ecological risk of a “resource curse.” These findings provide a scientific basis and management insights for achieving coordinated development between resource exploitation and ecological conservation in ecologically fragile areas of the Loess Plateau. Full article

New quality productive forces are increasingly recognized as important drivers of coordinated regional development, with urban agglomerations acting as key vehicles for their spatial implementation. Based on the theory of new quality productive forces, this study takes the 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration as its research subjects, spanning the period from 2005 to 2023, and constructs a four-dimensional evaluation index system for new quality productive forces covering economic, social, ecological, and technological dimensions. It employs the entropy method to determine indicator weights and calculate development indices for each dimension and utilizes a coupling coordination model to measure the overall and subsystem-level coordination by analyzing their spatiotemporal evolution characteristics. The results indicate a steady upward trend in the overall coordination level, progressing from a low level to an intermediate level, with the state of coordination continuously improving; spatial differentiation is significant, forming a gradient development pattern centered on Beijing, with marked disparities in coordination levels among cities. Subsystem analysis reveals an imbalanced synergy structure: while economic and ecological synergy levels are relatively high, the coupling and synergy between science and technology and the economy and society remain prominent weaknesses. Most cities in Hebei Province lack sufficient scientific and technological innovation capabilities, resulting in a weak supportive role for economic and social development. Based on these findings, this study proposes policy recommendations such as establishing a regional innovation community, promoting the integration of factor markets, and strengthening collaborative governance of the ecological environment, with the aim of leveraging new quality productive forces to drive a qualitative leap in the coordinated development of the BTH urban agglomeration. Full article

Using 29,278 firm-year observations of Chinese A-share listed firms from 2012 to 2023, this study examines whether data assetisation improves corporate investment efficiency and whether bank fintech conditions shape this relationship. Data assetisation refers to the process through which firms transform data resources into economically valuable, governable, and deployable assets. We construct a text-based proxy from annual reports using a Word2Vec-expanded lexicon and further distinguish between own-use and transactional data assets. The study finds: (1) Data assetisation significantly enhances corporate investment efficiency, with self-use data assets demonstrating a stronger driving effect. (2) Mechanism analysis reveals that data assetisation alleviates underinvestment by easing financing constraints and leveraging the “talent effect”. Concurrently, it mitigates overinvestment by reducing agency problems and accelerating digital transformation, thereby enhancing investment efficiency. (3) Heterogeneity tests indicate that the positive impact of data assetisation on investment efficiency is more pronounced among growth-stage enterprises, technology-intensive firms, and companies operating in regions with high bank liquidity. (4) Banking fintech positively moderates the enhancement of corporate investment efficiency through data assetisation, with a more pronounced effect on alleviating underinvestment. However, it may also exacerbate overinvestment. This study contributes to sustainable economic development by improving resource allocation efficiency, reducing capital misallocation, and supporting high-quality, low-waste, and sustainable growth of the real economy. Consequently, enterprises should vigorously develop data assetisation, applying different types of data assets to specific use cases to unlock data dividends. This approach supports the scientific development of corporate investment decisions and enhances investment efficiency, laying a micro-level foundation for sustainable socio-economic development. Full article

Against the backdrop of the accelerated development of the low-altitude economy and the structural transformation of modern logistics systems, systematically elucidating the key driving factors and their interaction structure is paramount for optimizing operational efficiency, promoting sustainable industry growth, and enhancing policy effectiveness. Integrating an extensive literature review with expert consultations, this study constructs a comprehensive indicator system of influencing factors for the coordinated development of the low-altitude economy and sustainable modern logistics. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to characterize the causal relationships and influence directions among the factors. Empowered by these findings, an Analytic Network Process (ANP) model is established to calculate refined weights, forming a hybrid DEMATEL–ANP analytical framework. The results indicate that technological factors and institutional factors constitute the primary driving layer of the system. Specifically, System Integration and Operational Technology, Flight Control and Scheduling Capability, as well as the Standardisation of Airspace Management and the Completeness of the Regulatory and Standards Framework, exert pivotal influences on the systemic evolution. Social factors and infrastructure factors primarily function as the outcome and feedback layers, with their effectiveness contingent upon the maturity of the core driving elements. Further hybrid weight analysis demonstrates that the ranking of key influencing factors exhibits high stability and robustness. The coordinated development process presents a progressive transmission characteristic from “technology–institution” to “market–application” providing targeted practical guidance for promoting the sustainable and high-quality synergy between the low-altitude economy and modern logistics. Full article

This study constructs an integrated analytical framework combining a Tripartite Coupling Coordination Degree Model (TCCDM), the Tapio decoupling model, and the Generalized Divisia Index Method (GDIM) to investigate the dynamic interactions and driving mechanisms among urban transportation, economic growth, and carbon emissions in Shanghai from 2000 to 2023. The results indicate that the coordination degree among the three systems evolves through three distinct phases: initial imbalance, critical transition, and high-level coordination. A three-dimensional phase diagram further reveals a marked shift from a “low-development, high-emission” pattern toward a balanced, high-quality development trajectory. Decoupling analysis demonstrates that economic growth and carbon emissions, as well as transport development and carbon emissions, have achieved significant decoupling in recent years. However, an expansive negative decoupling between economic growth and transportation highlights emerging challenges in sustaining synergistic development. Decomposition via GDIM shows that the interaction between economic development and transportation has consistently contributed to emission reduction, often exceeding the independent effects of either system. These findings underscore the role of systemic synergy in driving the nonlinear low-carbon transition of megacities. Consequently, policy interventions should adopt an integrated approach that fosters deep collaboration between green transportation transformation and high-quality economic development to effectively achieve carbon neutrality goals. Full article

Microbial communities are the fundamental determinants of Nongxiang Daqu quality. In this study, we systematically investigated the assembly and succession mechanisms of microbial communities during Nongxiang Daqu fermentation. Our findings reveal that this ecological succession is primarily driven by deterministic processes, encompassing dynamic environmental variables and interspecific microbial interactions. Significant stage-specific temporal variations in the community structure were observed, and biomarkers identified via a random forest model further corroborated these dynamic successional patterns. Both the neutral community model and Modified Stochasticity Ratio (MST) tests demonstrated that community assembly is dominated by deterministic processes, the influence of which intensifies as fermentation progresses. Notably, the fungal community exhibited a more pronounced response to these deterministic environmental selections than the bacterial community. Furthermore, co-occurrence network analysis, Mantel tests, and redundancy analysis (RDA) collectively illustrated that microbial interactions and environmental factors—specifically temperature, humidity, oxygen, carbon dioxide, and acidity—synergistically regulate this succession. Crucially, the rates of change in these environmental parameters directly dictated the pace of microbial turnover. Among these, oxygen and acidity had the greatest influence: oxygen accounted for 17.32% and 29.05% of the effects on fungi and bacteria, respectively, while acidity accounted for 16.77% and 25.23%, respectively. Time-series forecasting indicated that community structural assembly and stabilization predominantly conclude within the initial 30 days of fermentation. Ultimately, this study uncovers the ecological driving forces shaping the Nongxiang Daqu microbiome, providing a vital theoretical foundation for the targeted regulation of Daqu microecology and the enhancement of product quality. Full article

This study aims to explore the effects of micro-polluted river water on nitrogen and microbial communities of paddy field soil under different irrigation modes. The experiment was conducted in a water-saving park in Nanjing. By establishing three water quality conditions—clean water, micro-polluted river water, and alternating irrigation—and two moisture conditions—flood irrigation and controlled irrigation—this study investigates the effects of different irrigation patterns on soil nitrogen and microbial communities. The results indicate that, under flood irrigation, the input of micro-polluted river water can effectively alleviate NH₄⁺-N loss during the heading stages of rice growth by 49.3%. Moisture conditions are the primary factor influencing microbial community structure. Although the input of micro-polluted river water reduces community stability, rotation irrigation can increase microbial abundance and enhance network complexity, thereby enhancing the system’s resilience. Redundancy analysis shows that soil moisture, pH, and ion content are the key environmental factors driving microbial distribution. The clean and polluted water rotation irrigation model performs best in maintaining soil nitrogen and microbial health. Rotation irrigation promotes the enrichment of key functional groups, such as Actinobacteria, effectively increasing rice yield. This study provides a theoretical basis for promoting sustainable agricultural production through water resource management. Full article

To address the issues of corn harvester field operations relying on driver visual guidance for row alignment, high labor intensity, and unstable operation accuracy, this study innovatively proposes a “vision-dominant, gateway-enhanced” dual-mode collaborative row-alignment assistance architecture, and independently develops the R²DC-Mask R-CNN instance segmentation network and MCC-KF robust filtering algorithm to form a deeply coupled hardware–software-assisted driving system. The R²DC-Mask R-CNN network is autonomously designed for corn row-detection scenarios, achieving accurate perception in complex field environments; the MCC-KF algorithm innovatively solves the state estimation divergence problem during transient vision failures through a multi-criteria constraint mechanism, ensuring continuous navigation capability; the intelligent gateway and vision system form a confidence-driven master–slave switching mechanism that adaptively enhances system robustness when vision is restricted. Field experiments demonstrate that within the speed range of 0.5–5.0 km/h, the average lateral deviation in the row alignment assisted by the system is 3.82–5.30 cm, the proportion of deviations less than 10 cm exceeds 96%, and all sample deviations remain within 20 cm; at a speed of 3.5 km/h, the system reduces the average grain loss rate from 3.76% under manual operation to 2.65%, a decrease of 29.5%. This system effectively improves row alignment accuracy and harvest quality, providing a practical human–machine collaborative solution for intelligent harvester operations. Full article

Extra-virgin olive oil (EVOO) is a chemically complex lipid matrix whose minor constituents—especially phenolic secoiridoids—drive sensory quality, oxidative stability, and health benefits. However, these bioactives are vulnerable to heat, light, oxygen, and pro-oxidant metals during processing and distribution, while the high cost of EVOO often makes it a target for adulteration and mislabeling. This review critically assesses nano-enabled, food-grade strategies that (i) preserve phenolics and aroma compounds through nanoencapsulation, inclusion complexes, Pickering stabilization, and structured lipid systems; (ii) control their release and bioaccessibility during digestion; and (iii) enhance authenticity verification via sensor-ready packaging, spectroscopy/chemometrics, and digital traceability systems (IoT, machine learning, blockchain). We align these innovations with the “product identity constraints” of the EVOO category and with official quality standards used in routine control (IOC/EU). Finally, we explore circular valorization of olive-mill by-products within food-centered biorefineries, outlining pathways to convert biomass into ingredients, materials, and energy, thus reducing environmental impacts. Research priorities are proposed to develop scalable, regulation-compliant nanotechnologies that extend shelf life and increase consumer trust without compromising EVOO category standards. Full article

As the world’s largest carbon emitter and one of the countries facing severe air pollution challenges, China is under growing pressure to promote coordinated carbon reduction and air pollution control in support of sustainable development. From the perspective of interprovincial trade-embedded emissions, this study examines the spatiotemporal evolution, regional heterogeneity, and driving mechanisms of the synergy between embodied carbon emissions and air pollution emissions across 30 provincial-level regions in China in the 2012–2017 period. The multi-regional input–output (MRIO) model and coupling coordination degree (CCD) model are used to measure embodied emissions and the synergy effect, while the stochastic impacts by regression on population, affluence, and technology (STIRPAT) and geographically and temporally weighted regression (GTWR) models are employed to identify the main driving factors and their spatiotemporal heterogeneity. The results show that the overall synergy index of embodied carbon and air pollution emissions in China showed an increasing trend, and provinces with high-quality coordination shifted southward. Low-carbon policy and technology development mainly acted as positive drivers, whereas air pollution reduction policy and energy intensity tended to exert inhibitory effects; the role of energy consumption was more conditional and stage-specific. These findings provide useful evidence for differentiated governance, coordinated air pollution and carbon reduction, and the green and low-carbon transition. Full article

Innovation is the core driving force behind high-quality development. This study uses a sample of Chinese A-share non-financial listed companies from 2011 to 2024. It empirically examines the impact of digital–green synergy on corporate innovation resilience. We find that digital–green synergy (DG) significantly enhances firm innovation resilience. The baseline regression coefficient is 0.031 (p < 0.01). This conclusion remains robust after addressing endogeneity and conducting various robustness checks. Mechanism tests show that digital–green synergy enhances innovation resilience by improving firms’ absorptive capacity, attracting capital market attention, and cultivating both resource and organizational synergy. Heterogeneity analyses reveal that the impact of this dual transformation depends on firms’ specific characteristics and their internal and external environments. This research provides micro-level evidence on the value-creation mechanisms of dual transformation synergy. The findings offer significant insights for supporting corporate innovation systems in navigating uncertainty and achieving high-quality, sustainable development. Full article

Autonomous driving in dense traffic demands policies that ensure safety, accurate path tracking, and ride comfort, yet reinforcement learning (RL) alone suffers from low sample efficiency and weak safety guarantees, while classical artificial potential field (APF) methods lack adaptability to dynamic scenarios. This paper proposes PIPF-TD3, which integrates APF theory with the Twin Delayed Deep Deterministic Policy Gradient (TD3) by embedding composite potential values and Doppler-weighted gradients as physics-informed features into the state vector. A Hybrid A* planner generates a reference path encoded as an attractive field; repulsive fields model nearby obstacles using real-time perception data; and a multi-objective reward function jointly optimizes path tracking, collision avoidance, and ride comfort. Experiments in CARLA 0.9.14 across two scenarios—a highway segment with mixed obstacles and a signalized intersection with conflicting turning movements—show that PIPF-TD3 achieves 100% task completion with zero collisions, whereas TD3 without potential field guidance suffers a 90% collision rate. PIPF-TD3 reduces mean cross-track error to 0.12 m (72.1% reduction over the rule-based FSM baseline), maintains 67.0% larger safety clearance, and yields RMS longitudinal and lateral accelerations of 1.12 and 0.75 m/s², outperforming the FSM by 37.1% and 42.7%. These results confirm that Doppler-weighted physical priors substantially enhance RL-based driving safety and quality in complex traffic conditions. Full article