Search Results (93)

Soil erosion on sloping cropland is a major threat to agricultural sustainability and ecological security in mountainous regions, yet its spatial distribution and landscape-level structural characteristics remain insufficiently quantified. Taking Zhaotong in northeastern Yunnan, China, as a typical mountainous agricultural region in the upper Yangtze River Basin, this study integrated Sentinel-2 imagery, high-resolution reference data, field survey information, the Google Earth Engine platform, a random forest classifier, the Chinese Soil Loss Equation, and landscape pattern metrics to assess soil erosion on sloping cropland. The land use classification achieved an overall accuracy of 90.70% and a Kappa coefficient of 0.88, providing a reliable basis for sloping cropland extraction. Sloping cropland covered 4218.77 km², accounting for 84.64% of total cropland area, but contributed 1.84 × 10⁷ t·yr⁻¹ of annual soil loss, equivalent to 97.51% of total cropland erosion. The mean erosion rate of sloping cropland reached 4260.50 t·km⁻²·yr⁻¹, and 95.84% of sloping cropland exceeded the soil loss tolerance threshold. County-level analysis revealed strong spatial heterogeneity, with high erosion risks concentrated in northern and eastern mountainous counties. Intensive, Severe, and Extreme erosion occupied only 26.14% of the sloping cropland area but contributed 62.21% of total soil loss. Landscape metrics further showed that Moderate erosion had the highest patch density and edge density, indicating a critical fragmentation stage in erosion development. These findings support a tiered conservation strategy in which high-intensity patches are prioritized for immediate sediment reduction, while fragmented Moderate-erosion (2500–5000 t·km⁻²·yr⁻¹) areas receive preventive management. The proposed framework provides a useful approach for identifying erosion hotspots and supporting targeted soil and water conservation in mountainous agricultural landscapes. Full article

Oncology care increasingly depends on heterogeneous sensing streams generated by computed tomography (CT), radiotherapy planning systems, wearable devices, home respiratory sensors, patient-reported outcomes, and clinical records. These data streams are often processed separately, limiting their value for longitudinal, context-aware review. This study proposes OncoSense-Agent, a reliability-aware agentic multimodal sensing architecture for CT-guided respiratory monitoring in oncology care. The architecture links CT-derived anatomical evidence with wearable physiology, respiratory symptoms, functional assessment, treatment context, and explainable human-in-the-loop review-priority generation. To move beyond a purely conceptual design, we implemented a lung-focused proof-of-concept with six bounded software agents: Imaging Reliability, Wearable Monitoring, Respiratory Review, Treatment Context, Multimodal Fusion, and Explainability. The prototype used real nnU-Net v2 3D lung segmentation metrics from 139 patients with complete bilateral lung CT data as the imaging anchor, while wearable, respiratory, symptom, and treatment-context channels were introduced as deterministic overlays for controlled validation. OncoSense-Agent changed review-priority assignment relative to CT-only assessment in 78/139 cases (56.1%), assigned 111/139 cases (79.9%) to high-priority or high-uncertainty tiers, and showed increasing Safety Gate activation as CT quality declined. Three illustrative cases demonstrate hidden respiratory deterioration, wearable data-quality uncertainty, and treatment-context risk not captured by CT-only assessment. The prototype does not establish clinical diagnostic accuracy, but demonstrates operational, auditable, reliability-aware multimodal review-priority generation for clinician-supervised oncology monitoring. Full article

Public-sector sustainability and climate reporting increasingly address environmental exposure, governance, and financial effects, yet existing frameworks do not adequately disclose preparedness for low-probability, high-impact systemic risks whose probabilities, timing, thresholds, and transmission channels remain deeply uncertain. This article develops a Public-Sector Resilience Reporting Standard (PSRRS) as a pre-standard architecture for government preparedness disclosure. The design has three bounded objectives: diagnose cross-framework disclosure gaps, translate these gaps into a theoretically grounded capability-to-disclosure architecture, and demonstrate its analytical use through an illustrative Florida application and two hazard-neutral stress tests. The documentary corpus includes international sustainability and public-sector reporting standards, ISO and UNDRR resilience and continuity instruments, three Florida resilience documents, and peer-reviewed literature on resilience governance, decision-making under deep uncertainty, critical infrastructure interdependency, catastrophic uncertainty, climate-risk disclosure, public finance, climate-risk pricing, local-government credit risk, investor attention, and ransomware service disruption. A structured interpretive coding protocol classifies each framework as explicit, partial, or not explicit across nine disclosure dimensions; a codebook appendix identifies the assessment criteria, the a priori and inductively refined dimensions, and the validation boundaries. Florida is not treated as a basis for statistical or jurisdictional generalization. Instead, it illustrates how a comparatively developed resilience architecture may disclose statutory continuity, critical-asset data, project ranking, and output metrics while leaving systemic dependencies, adaptive triggers, long-horizon fiscal exposure, residual service risk, distributional effects, and assurance mechanisms insufficiently visible in the reviewed reporting corpus. AMOC and case-grounded cyber-fiscal stress tests show how the PSRRS shifts reporting from hazard inventories and funded projects toward auditable evidence of institutional capacity, adaptive readiness, and public-value protection. The article specifies mandatory, recommended, and optional clauses, evidence requirements, indicator examples, a disclosure index, a sample report structure, and a three-tier pilot conformity model. The contribution is conceptual and operational, but not yet a validated formal standard; cross-jurisdictional piloting, inter-rater coding, cost testing, assurance testing, and stakeholder consultation are identified as the next stage of standardization. Full article

Inorganic scale deposition in crude oil–water transport trunklines is a formidable flow assurance challenge, uniquely exacerbated in extensive gathering networks where multiple production flowlines commingle multiphase fluids. As some fields experience progressively higher water cuts, the mixing of incompatible waters, characterized by diverse thermodynamic profiles and varying concentrations of scaling ions (Ca²⁺, Ba²⁺, Sr²⁺, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{CO}}_3^{2-}$, etc.) triggers severe precipitation. This comprehensive literature review synthesizes seminal and contemporary studies to critically evaluate the state-of-the-art methodologies for assessing scaling risks in these intricate systems. Progressing chronologically and thematically, the analysis details the transition from static, bulk-fluid thermodynamic equilibrium calculations to dynamic, high-fidelity deterministic and probabilistic approaches. These advanced frameworks include Reactive Transport Modeling (RTM), Computational Fluid Dynamics (CFD), and Machine Learning (ML) architectures. Special emphasis is placed on the mathematical governing equations that dictate trunkline-specific phenomena: multi-stream commingling, non-isothermal gradients, probabilistic kinetic induction, and the profound impact of turbulent transport (turbophoresis) on crystal attachment and wall shear detachment. Finally, an integrated, multi-tier flow assurance workflow is proposed to guide future field-scale risk management and digital twin deployment. Full article

Operational risk assessment in complex industrial systems like mineral processing plants is hindered by inherent uncertainty and incomplete information. This study presents an integrated grey system theory-based framework to address this challenge. Combining Grey Multi-Criteria Decision-Making (GST-MCDM) and Grey Relational Analysis (GRA), the methodology enables a systemic analysis that prioritizes risks, quantifies interdependencies, and measures cumulative burden across four key objectives: time, cost, quality, and safety. Applied to a case study at the Gohar Zamin iron ore processing plant (Iran), the model analyzed 26 operational risks, classifying them into Critical (8 risks), Significant (9), and Controllable (9) tiers. Electrical power shortage (RPS: 0.19) and raw material supply delay (RPS: 0.21) were identified as the most critical risks. The analysis quantified that the safety objective bears the highest cumulative risk burden at 32%, primarily due to human factor vulnerabilities, while cyber-physical threats ranked among the top 8 critical risks. Strong interdependencies were revealed, notably a quality cascade (relational grade: 0.84) between poor consumable materials and final product failure. Sensitivity analysis confirmed high model robustness (Spearman’s p = 0.91). The framework provides managers with an actionable tool for strategic, cluster-based mitigation and evidence-based resource allocation, emphasizing investment in human capital as a core risk reduction strategy. This research contributes a replicable, quantitative methodology for enhancing operational resilience under uncertainty in capital-intensive industries. Full article

Background/Objectives: Maternal cell contamination (MCC) is a pervasive challenge in prenatal single-gene testing, as it can cause both false-positive and false-negative results. Despite its clinical significance, quantitative tolerance thresholds for MCC in whole exome sequencing (WES) and Sanger sequencing remain limited. Methods: We established a gradient contamination model (5–95%) using genomic DNA from 20 mother–child pairs and assessed the detection fidelity for single-nucleotide variants (SNVs) and insertions/deletions (InDels) in two clinically relevant scenarios using both WES and Sanger sequencing—Scenario 1 (Fetus Heterozygous/Mother Wild-type) and Scenario 2 (Fetus Wild-type/Mother Heterozygous). Results: In Scenario 1, analysis of the 20 loci evaluated by both WES and Sanger sequencing revealed false-negative rates of 0% when MCC ≤ 30%, which then increased sharply across the 30–70% MCC range and reached 100% at 95% MCC. In Scenario 2, analysis of 12,627 WES loci showed a false-positive rate of ≤1.0% when MCC ≤ 10%, which then increased from 1.0% to 76.6% within the 10–30% MCC range and climbed to 89.8% at 50% MCC. A similar pattern was observed for the 20 loci analyzed by both WES and Sanger sequencing in this scenario. These findings potentially support a three-tier stratification: low-risk (MCC ≤ 30% in Scenario 1 and ≤10% in Scenario 2), moderate-risk (30% < MCC < 70% in Scenario 1, 10% < MCC < 30% in Scenario 2), and high-risk (MCC ≥ 70% in Scenario 1 and ≥30% in Scenario 2). Conclusions: Our findings reveal a clear directional asymmetry of MCC tolerance between the two scenarios, indicating that MCC tolerance depends not only on contamination level but also on the genotype of the contaminating DNA. We further propose a clinical reference framework based on a three-tier risk stratification for prenatal single-gene testing, which can guide result interpretation and laboratory decision-making, including decisions about reliable reporting, orthogonal validation, or re-sampling. Full article

Purpose: Severe spermatogenic impairment, including azoospermia, oligozoospermia, asthenozoospermia, and teratozoospermia, represents a major reproductive health concern, yet no validated screening tool exists for early risk stratification in prepubertal boys. We aimed to develop a five-item evidence-based questionnaire and conduct preliminary clinical evaluation to identify prepubertal boys at risk of developing severe spermatogenic impairment. Methods: This retrospective observational study analyzed medical records of 200 male patients aged 18 years who underwent their first semen analysis between 2014 and 2024 at the University of Catania, Italy. Prepubertal risk factor profiles were retrospectively reconstructed by two independent andrologists blinded to semen analysis results across five evidence-based domains: genetic anomalies (0–5 points), cryptorchidism (0–6 points), gonadotoxic cancer therapy (0–7 points), varicocele (0–4 points), and hormonal/endocrine disorders (0–5 points), yielding a total score of 0–27 points. Scoring weights were based on literature evidence and expert consensus. Reliability was assessed using Cronbach’s α (n = 200), test–retest intraclass correlation coefficient (ICC, n = 30, 2-week interval), and inter-rater Cohen’s κ (n = 50). Receiver operating characteristic (ROC) curve analysis identified optimal cut-offs. Semen analysis outcomes were classified according to WHO 2021 criteria. Results: The newly developed questionnaire demonstrated acceptable internal consistency (Cronbach’s α = 0.81), good test–retest reliability (ICC = 0.89, 95% CI 0.84–0.93), and excellent inter-rater reliability (Cohen’s κ = 0.87). Mean questionnaire scores showed a direct progressive correlation with severity of spermatogenic impairment. The highest scores were observed in azoospermia (mean 14), complete asthenozoospermia (13), and complete teratozoospermia (13). ROC curve analysis identified four clinically meaningful cut-offs: ≥6 (AUC = 0.85, 95% CI 0.79–0.91, Youden index = 0.81), ≥7 (AUC = 0.88, 95% CI 0.83–0.93, Youden = 0.80), ≥11 (AUC = 0.91, 95% CI 0.87–0.95, Youden = 0.79), and ≥13 (AUC = 0.94, 95% CI 0.90–0.97, Youden = 0.87). Four risk strata were defined: LOW (0–6), MEDIUM (7–10), HIGH (11–12), and VERY HIGH (≥13 points). Conclusions: The five-item prepubertal risk questionnaire undergoing preliminary clinical evaluation demonstrates strong correlation with subsequent spermatogenic outcomes, enabling early identification of at-risk boys. The four-tier stratification system provides actionable guidance for clinical management and fertility preservation counseling. However, this cohort was enriched for pathological outcomes due to exclusion of subjects with no known risk factors; cut-offs may not apply to general population screening. Multicenter validation studies in unselected populations are warranted. Full article

Epimedium sagittatum is an important understory shade-tolerant medicinal plant native to China. However, assessments of its responses to climate change under multiple scenarios and time periods remain insufficient, particularly regarding the mitigation of model overfitting and the quantitative disentanglement of hydrothermal constraints. In this study, based on 269 valid occurrence records and 24 initial environmental variables (reduced to 13 after collinearity screening), we employed a parameter-optimized MaxEnt 3.4.4 model (RM = 3.5, FC = QHP) coupled with the BCC-CSM2-MR climate model to project the dynamics of potential suitable habitats under current (1970–2000) and future (2050s, 2070s, 2090s) scenarios (SSP126, SSP370, SSP585). The results showed that the optimized model substantially reduced the risk of overfitting compared with the default parameters (ΔAICc dropped from 82.16 to 0) and achieved an AUC of 0.934. Precipitation of the Driest Quarter (Bio14, 53.4% contribution) and Minimum Temperature of the Coldest Month (Bio6, 20.5% contribution) were identified as the dominant factors governing the species’ distribution, with optimum conditions of ≥1.58 mm and −8.34 °C to 13.61 °C, respectively. Under future climate scenarios, the centroid of suitable habitats shifted progressively southwestward, with a cumulative displacement of approximately 127.47 km under SSP585, and water availability, rather than temperature, dominated the direction of this shift. Under the high-emission scenario, suitable areas exhibited a spatial reorganization characterized by westward expansion and eastward contraction. Based on these findings, this study proposes a three-tier planning framework comprising in situ conservation, climate-smart introduction, and assisted migration, providing a scientific basis for the conservation of germplasm resources and the spatial planning of artificial cultivation of E. sagittatum. Full article

Clinical prediction rules often suffer from a translation gap, balancing high-dimensional statistical accuracy against practical bedside interpretability. This study presents the Surgical Complication Risk Index (S-CRI), an explainable, data-decoupled risk-stratification framework designed to predict post-operative complications using multi-center electronic health registry records (N = 19,965). To ensure strict validation integrity, data partitioning (70% development, n = 13,975; 30% independent holdout testing, n = 5990) was executed before any engineering or risk-tier group isolation. A parsimonious multivariate logistic regression model was fitted within the development cohort, utilizing five predictors: length of stay (LOS) accrued up to the morning of assessment, two institutional categorical groupings, and two historical entry-diagnosis empirical risk tiers. To bridge the translational gap, all fractional regression coefficients were scaled by the baseline anchor and rounded to the nearest whole integer, yielding a simple bedside scorecard where 1 point = 1 inpatient day. On the completely blinded independent holdout cohort, the whole-integer S-CRI demonstrated robust discriminative performance with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.8741 (95% CI: 0.864–0.884) and a Precision–Recall AUC of 0.5785. Setting a baseline operational threshold ≥ 0 yielded an accuracy of 88.18%, a specificity of 96.43%, and a sensitivity of 35.43%, while an optimized integer screening cutoff score of ≥−4 maximized screening capacity (sensitivity: 63.95%; specificity: 91.68%). By enforcing strict temporal landmark constraints to eliminate reverse causality and removing all out-of-sample data leakage, the S-CRI provides an objective, transparent, and interpretable clinical decision support mechanism for early inpatient risk stratification, designed as a supplementary clinical decision-support aid, rather than as a definitive diagnostic replacement for independent clinical judgment. Full article

Fine particulate matter with a diameter ≤2.5 μm (PM_2.5) pollution poses a global public health crisis, demonstrating significant threats to human health. This study focused on the strategically important Chengdu-Chongqing Economic Circle in western China, systematically comparing the toxic effects of urban and rural PM_2.5 across five levels. PMF and regression analysis were used to identify source contributions, dual-omics to pinpoint key molecules, and epidemiological data with a GAM model to assess health risks. Findings demonstrate that rural PM_2.5 possesses greater biotoxicity than its urban counterpart. Cytotoxicity in urban and rural PM_2.5 originated from road dust/vehicle emissions and biomass burning, respectively. Subsequently, integrated omics and molecular biology analyses identify kinesin family member 20A (KIF20A) as a shared key target, which mediates toxicity induced by both urban and rural PM_2.5. Finally, epidemiological analysis reveals that females and ≥65 years old exhibit relatively high sensitivity to urban PM_2.5 exposure trends, with rhinitis showing a comparatively higher impact among various related diseases. The novelty of this work lies in its pioneering application of a multi-tiered investigative approach. This approach spans “environmental samples-cellular mechanisms-population health” within the Chengdu-Chongqing economic circle context, systematically elucidating common and distinct respiratory health risk of urban and rural PM_2.5. This work offers a vital scientific foundation for advancing region-specific, precise air pollution prevention and control measures. Full article

In summary, near-surface icing risk in complex alpine terrain is jointly controlled by freezing conditions, moisture supply, freeze–thaw transitions, and topographic energy processes. Traditional approaches relying on sparse station data or single temperature thresholds fail to capture spatial heterogeneity, and frequent cloud cover together with topographic errors severely limit the application of thermal infrared remote sensing. Taking the area along the Duku Highway in the Tianshan Mountains as the study region, a daily icing risk assessment framework at 250 m resolution was constructed using multi-source remote sensing, ERA5-Land reanalysis data, topographic correction, and an energy–moisture dual-constrained model. A diurnal temperature cycle model, the CAP index, and physics-constrained machine learning were integrated to reconstruct the daily minimum land surface temperature ($T_{s,\mathrm{m}\mathrm{i}\mathrm{n}}$) at 250 m resolution under all weather conditions. A probabilistic two-tier risk assessment model was then established by incorporating moisture, topography, and freeze–thaw transitions. The results show that high-risk zones occur primarily in valleys and topographically constrained corridors rather than the coldest elevations. Validation against Landsat LST (r = 0.886) and the Bayanbulak station (bias −0.76 °C, RMSE 5.62 °C, r = 0.91) confirms spatial and seasonal accuracy. Sensitivity and Monte Carlo analyses indicate the RiskScore is mainly controlled by the low-temperature weight, while upstream parameters are less influential. The framework is best applied as a screening and early-warning product to identify sub-kilometer potential icing corridors, complementing point measurements and short-range forecasts. Full article

Background/Objectives: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication in osteoporosis patients undergoing tooth extraction. This study aimed to develop and evaluate an interpretable, machine learning–derived triage score for rapid risk stratification at the initial dental visit. Methods: This retrospective study included 850 osteoporosis patients (443 MRONJ, 407 controls) in the derivation cohort and 559 independent multicenter MRONJ cases for external evaluation. A reference random forest model identified a hierarchical feature structure, which was translated into an additive integer-weighted scoring system through systematic hyperparameter optimization. Structural tipping points were identified using isotonic regression and first discrete derivative analysis. Internal performance was further characterized by sensitivity, specificity, PPV, NPV, calibration slope and intercept, the Hosmer–Lemeshow test, decision curve analysis, bootstrap optimism correction, and subgroup analyses. External evaluation assessed three-tier distribution concordance and case capture rates with non-inferiority testing. Results: The reference random forest achieved an AUC of 0.792. The final MRONJ triage score (range 0–17) incorporated six binary predictors with mutually exclusive drug route categories. The triage score preserved discriminative performance (AUC 0.772; ΔAUC = 0.020; p = 0.149). Two tipping points at scores 7 and 14 defined three risk tiers: low (0–6; 20.9%), moderate (7–13; 55.3%), and high (≥14; 83.5%). At the moderate-risk threshold (≥7), the score achieved sensitivity 90.3% (95% CI 87.2–92.7%) and specificity 45.0% (40.2–49.8%); at the high-risk threshold (≥14), specificity rose to 91.4% and PPV to 83.1%. Calibration was adequate (slope 0.994; intercept 0.0006; Hosmer–Lemeshow p = 0.381), and decision curve analysis demonstrated higher net benefit than reference strategies across all clinically relevant threshold probabilities. The bootstrap optimism-corrected AUC was 0.778, and discriminative performance remained stable across age, route, duration, and site subgroups (AUC range 0.70–0.79). In the external cohort, the case capture rate at the ≥7 threshold was non-inferior (83.4% vs. 88.0%; Δ = −4.6%; margin −10%). Conclusions: The MRONJ triage score demonstrated stable discrimination and reproducible case capture in an independent multicenter cohort. By relying on six variables obtainable at the initial dental visit, this framework may have the potential to reduce unnecessary tertiary referrals and support safer clinical decision-making, although this benefit was not directly demonstrated and requires confirmation in prospective implementation studies. Full article

Background/Objectives: Despite well-documented chemical and physical hazards in healthcare settings, existing reviews of occupational reproductive risks have largely focused on single-agent risk estimation and have rarely translated occupational hygiene evidence into clinical decision-making frameworks for reproductive counseling and surveillance. This narrative review synthesizes evidence across multiple occupational exposure categories—antineoplastic agents, high-level disinfectants (HLDs), sterilants, and work-organization factors—and proposes an integrated, clinically operational framework for preconception counseling, pregnancy-sensitive risk stratification, exposure-control verification, and reproductive health surveillance among women healthcare workers. Methods: A structured narrative literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, and Embase from database inception through January 2025 and updated in March 2026. The review was guided by a Population–Exposure–Comparison–Outcome (PECO) framework and structured using Search–Appraisal–Synthesis–Analysis (SALSA) principles and the Scale for the Assessment of Narrative Review Articles (SANRA). Evidence quality was summarized using a modified hierarchy-of-evidence classification provided as a reader aid. This narrative review employed structured transparency tools but does not claim the methodological status of a systematic review. Quantitative meta-analytic pooling was not performed owing to substantial heterogeneity across study designs, exposure assessment methods, and outcome definitions; findings were synthesized narratively by exposure category. Results: The strongest and most consistent evidence was identified for occupational exposure to antineoplastic agents, which has been associated with spontaneous abortion, stillbirth, congenital abnormalities, impaired fecundability, and selected cancer-related concerns. HLDs and sterilants represent exposure categories warranting precautionary attention, with some evidence suggesting possible adverse effects on fecundability and early pregnancy maintenance; however, findings are considerably more heterogeneous, context-dependent, and reliant on self-reported exposure assessment than those for antineoplastic agents. Broader workplace factors, including shift work, prolonged working hours, physical workload, and mixed exposures, may further contribute to reproductive risk. The synthesis supports task-specific occupational history taking, exposure-control verification, and pregnancy-sensitive risk stratification. Conclusions: This review provides a multi-exposure, clinically operational framework that bridges occupational hygiene evidence with reproductive healthcare delivery, offering practical decision-support tools for clinicians managing women healthcare workers during preconception, pregnancy, and lactation. The framework includes structured occupational history-taking questions, a clinical decision pathway with evidence-tier classification, and a prevention matrix linking exposure sources to workplace controls and clinical actions. Integrating task-specific occupational history taking into routine reproductive care may improve detection of preventable workplace risks and support timely accommodation, while clinicians should calibrate recommendation strength to the underlying evidence quality for each exposure category. Full article

To address the issues of high energy consumption, high carbon emissions, and the waste of associated energy (AE) in coal mine production, which severely hinder global sustainable development goals, this paper proposes a novel low-carbon economic collaborative optimal scheduling model for a coal mine integrated energy system (CMIES) oriented towards sustainable energy transitions. First, a refined utilization model for AE encompassing coal mine gas, ventilation air methane (VAM), and mine groundwater (GW) is constructed, and a tiered carbon emission trading mechanism (TCET) is introduced to constrain carbon emissions and promote ecological sustainability. Second, a concentrated solar power (CSP) plant is integrated to break the rigid “power determined by heat” constraint of a traditional combined heat and power (CHP) unit, thereby enhancing the system’s scheduling flexibility and renewable energy integration. Meanwhile, abandoned mines are retrofitted into solvent storage tanks to construct an integrated flexible carbon capture system (IFCCS), achieving sustainable reuse of mining wastelands. Finally, to tackle the multi-source, heterogeneous uncertainties on both the source and load sides, a hybrid risk assessment method combining information gap decision theory (IGDT) and conditional value at risk (CVaR) is proposed. Case study results demonstrate that, compared to traditional energy supply modes, the proposed model reduces carbon emissions and total costs in the mining area by 66.04% and 15.97%, respectively. This significantly improves resource utilization efficiency and ecological benefits, providing a highly viable pathway for the sustainable development and clean transition of coal mine operations. Furthermore, the proposed hybrid assessment method can effectively assist decision-makers in achieving a refined trade-off between operating costs and system robustness under varying risk preferences. Full article

As China’s urban renewal shifts comprehensively toward stock optimisation, everyday micro spaces in high-density old districts have emerged as critical yet underexplored carriers for rebuilding grassroots social capital. However, existing research remains largely confined to static assessments of physical form, lacking systematic insight into the process-based evolution of micro spaces and their governance implications. The aim of this study is to develop a process-based analytical framework that explains how everyday micro spaces emerge, evolve, and stabilise in high-density old urban districts, and to translate that explanation into stage- and type-differentiated governance pathways. Drawing on purposive sampling observation of over 170 micro spaces and snowball-sampled in-depth interviews with 45 residents in Xi’an’s walled historic district, this study employs thematic analysis to examine micro space formation, activation, and governance dynamics. A three-dimensional analytical framework of “Spatial Type–Perceived Need–Life Cycle” is constructed, classifying micro spaces into three categories, identifying a three-tier, nine-level perceived needs spectrum, and tracing a five-stage evolutionary process of Discovery–Activity–Renovation–Management–Identity. The findings reveal that residents’ spontaneous practices and psychological ownership formation are the core endogenous drivers of micro space evolution. The primary structural constraints are ambiguous property rights, institutional vacuums, and a structural rupture at the Renovation-to-Management transition, which we conceptualise as the “high-risk window period”. This study proposes a full life-cycle adaptive governance paradigm. Through phased, type-differentiated interventions, it matches governance supply to the evolving demands of each stage. The paradigm offers both theoretical and practical guidance for stimulating the endogenous vitality of everyday micro spaces in old urban districts. Full article