Search Results (26,739)

Accurate cross-dataset state-of-health prediction for lithium-ion batteries remains challenging due to distribution shifts arising from diverse cathode chemistries, operating temperatures, and charge–discharge protocols across heterogeneous battery fleets. Drawing upon established machine learning paradigms, this study tailors a Domain-Adaptive Mixture-of-Experts (DA-MoE) framework to the battery prognostic context, automatically selecting the optimal domain adaptation strategy for each target domain through a physics-aware, lightweight linear gating network comprising merely 32 learnable parameters. The framework integrates a shared Transformer-based backbone with four adaptation strategies spanning the full spectrum of target-domain information utilization, namely zero-shot transfer, Test-Time Adaptation, Fine-Tuning, and Model-Agnostic Meta-Learning. A comprehensive evaluation on 564 battery cells from seven publicly available datasets under Leave-One-Domain-Out Cross-Validation protocol demonstrates that the proposed framework achieves an average coefficient of determination of 0.864 with perfect oracle strategy alignment under full domain training and maintains competitive generalization at an average R² of 0.795 when each target domain is held out during gating network training. Hard argmax selection consistently outperforms weighted fusion across all seven domains with an average margin of +0.027 in R², confirming that the four adaptation strategies compete rather than cooperate in this application context. A feature ablation analysis identifies sample count as the dominant determinant of strategy selection with performance degradation of ΔR² = −0.182 upon removal, followed by the early-cycle degradation slope and early-cycle nonlinearity index as secondary signals, all of which are computable at deployment time without future ground-truth SOH information. The proposed framework provides a practically deployable solution for battery management systems operating across heterogeneous fleets with minimal computational overhead and strong cross-dataset generalization capability. Full article

Androgenetic alopecia (AGA) involves dihydrotestosterone-driven follicular miniaturization compounded by oxidative stress and perifollicular inflammation, while current pharmacotherapies remain limited by adverse effects. Neem (Azadirachta indica A. Juss., Meliaceae) leaves, a phenolic- and limonoid-rich plant widely used in Southeast Asian traditional medicine, were fermented for seven days with a tri-culture consortium of Saccharomyces cerevisiae, Lactobacillus plantarum, and Aspergillus niger (TRI-NE) to evaluate whether expanded microbial diversity enhances bioactivity relative to the unfermented extract (UN-NE). TRI-NE significantly increased total phenolic content, rising from 315.30 to 564.70 mg GAE/g by day 7, alongside an overall enhancement of antioxidant capacity across all assays. Moreover, untargeted metabolomics revealed compositional remodeling, with 67.47% of significantly altered metabolite features upregulated after fermentation, including selective enrichment of quercetin despite reductions in other polyphenols. In human hair follicle dermal papilla cells (HFDPCs) and complementary models, TRI-NE consistently outperformed UN-NE, enhancing paracrine-mediated fibroblast proliferation, preserving cell viability under potassium-channel blockade, suppressing lipopolysaccharide-induced inflammatory nitric oxide production, and attenuating oxidative membrane damage. At the transcriptional level, TRI-NE downregulated androgen metabolism (SRD5A1 and SRD5A2) and pro-regression genes (TGFB1) while upregulating Wnt/β-catenin (CTNNB1), Sonic Hedgehog (SHH, SMO, and GLI1), and angiogenic (VEGF) pathway genes, with effects matching or exceeding standard hair-loss therapeutics. These findings indicate that TRI-NE confers superior bioactivity over the unfermented extract, supporting its potential as a multi-target cosmeceutical candidate for AGA. Full article

Computing capacity has become a metered factor of production for digitally intensive enterprises, yet its consumption exhibits strong temporal heterogeneity—tidal intraday cycles, weekly contrasts, seasonal surges, and project-driven regime shifts—so that individually provisioned capacity is structurally underutilized. This paper proposes a park-level computing power sharing center (CPSC) as an institutional mechanism that converts the temporal complementarity of co-located enterprises into measurable cost savings. We develop a general mode-design framework that separates CPU core-hours from GPU card-hours, characterizes demand via deterministic tides and stochastic modulations, and derives optimal pooled capacity commitments through a newsvendor-type quantile condition. A parametric calibration protocol maps observable temporal features—peak-to-trough ratios, inter-tenant phase spreads, and residual volatility—into closed-form diversity-factor expressions with Monte Carlo confidence intervals. The procurement model covers a multi-option contract menu (on-demand, one–three-year reserved instances, savings plans, and spot), region-specific pricing, hardware class tariffs, and ancillary costs, including network egress, migration, and data sovereignty compliance; benefits are measured relative to each tenant’s individually optimal reserved portfolio, not naive retail procurement. A mechanism design analysis incorporating Shapley value allocation, Bayesian incentive compatibility, and penalty structures ensures individual rationality and robustness to misreporting and strategic load shifting. We further develop an energy model—with utilization-dependent power draw, facility PUE, embodied carbon, and marginal grid emission factors—showing that financial savings translate into genuine emission reductions only when pooling enables physical capacity retirement rather than mere billing reallocation. The framework is applied to twenty representative Chinese parks spanning seven functional categories; all park-level data are reconstructed from public sources using the calibration methodology, and the reported figures are model-derived projections, not empirical measurements. The model yields procurement saving estimates of 4.6–20.2% relative to individually optimal reserved-procurement portfolios, with high-diversity parks at the upper end. Sensitivity analyses across regional tariffs, hardware mixes, and cross-country utilization benchmarks (Uptime Institute, US DOE, EU Commission) confirm robustness and delineate boundary conditions. This paper concludes with a data provenance taxonomy and a phased implementation roadmap. Full article

This paper presents a novel fault-tolerant model predictive control (FTMPC) framework for fuel cell hybrid electric vehicles (FCHEVs) used for postal delivery applications. The main contribution of the proposed FTMPC is the adaptive adjustment of the model predictive control cost function weights based on fault severity. The proposed reformulation incorporates fault characterization across the diverse degradation mechanisms while maintaining reliable vehicle operation. The FTMPC approach dynamically adapts cost function weights and system constraints based on the fault severity index. The resulting control strategy provides fault-aware power allocation between the fuel cell and battery while accounting for the specified operating and safety constraints. To isolate the contribution of the proposed health-dependent adaptation mechanism, a controlled ablation study was performed against a structurally identical fixed-MPC controller under the same vehicle model, driving cycle, initial conditions, prediction and control horizons, solver configuration, and fault scenarios. The adaptive FTMPC achieved a 10.6956% reduction in direct hydrogen consumption relative to the fixed-MPC baseline. Because differences in terminal battery state of charge (SoC) can influence comparisons based solely on hydrogen consumption, a charge-corrected hydrogen-equivalent metric was also evaluated; using this more conservative metric, the adaptive FTMPC retained a 2.7276% improvement. The final quadratic programming implementation achieved a 100% successful optimization rate in the validation run with no fallback-controller activation, while the maximum soft-constraint slack remained on the order of 10−9. Additional sensitivity analyses were conducted to evaluate the influence of relevant vehicle and operating conditions on energy consumption and battery utilization. These results provide direct quantitative evidence of the contribution of the proposed fault-adaptive mechanism and demonstrate its numerical feasibility for FCHEV energy management, while the limitations of the present simulation-based validation are explicitly acknowledged. Full article

In the post-industrial era, art installation interventions have emerged as an important approach to the revitalization of industrial heritage spaces worldwide. However, existing studies on the mechanisms through which art installations influence spatial revitalization and their compatibility with surrounding environments have largely remained focused on the micro-scale design of individual installations, lacking both quantitative empirical evidence and a systematic evaluation framework. From the perspective of the Human–Environment Field System (HEFS), this study develops a systematic installation–space–vitality evaluation framework. Using 52 art installations within the Zhengzhou Oil and Fat Chemical Factory as empirical samples, the study employs a four-quadrant analytical model and multiple regression analysis to systematically investigate the interactive effects of installation attributes and spatial environmental factors on spatial vitality. The objective is to elucidate the mechanisms through which art installations contribute to the revitalization of industrial heritage spaces and to propose differentiated strategies for improving the spatial compatibility of installation interventions. The results indicate that the capacity of art installations to stimulate spatial vitality depends on the synergistic coordination between installations and spatial, with spatial environmental factors exerting a more pronounced influence. Interface richness is the key variable driving the intensity of spatial vitality (β = 0.357, p < 0.01), while the effects of installation typology, volumetric proportion, and facility diversity on spatial vitality are indirectly mediated through their synergistic interactions. Based on the four-quadrant classification, the study further proposes differentiated spatial adaptation guidelines for thematic landmark installations, narrative structural installations, and artistic decorative installations. This study establishes a quantitative explanatory framework, grounded in the HEFS perspective, for understanding how the coupling between art installations and spatial environmental conditions shapes spatial vitality. The findings provide both a theoretical foundation and practical guidance for evidence-based decision-making regarding art installation interventions and for enhancing spatial quality in the revitalization of industrial heritage spaces. Full article

The rapid growth of multi-source, multi-perspective data in healthcare, finance, and social media has increased the need for unsupervised learning methods that integrate diverse views while preserving data privacy and locality. Federated learning (FL) enables collaborative model training without sharing raw data, yet its application to multi-view clustering (MVC) remains underdeveloped. This survey presents the first comprehensive, PRISMA-guided systematic review of federated multi-view clustering (FedMVC). A multidimensional taxonomy classifies existing studies according to FL paradigms (horizontal, vertical, and federated transfer learning); clustering approaches (hard/soft, spectral, density-based, deep, tensor, and non-negative matrix factorization [NMF]-based); aggregation strategies (FedAvg, FedProx, FedOpt, and adaptive optimizers); privacy mechanisms (differential privacy, secure multi-party computation, homomorphic encryption, and trusted execution environments); and non-IID data-handling techniques. Centralized and federated objective functions are examined, with comparisons of computational complexity, communication overhead, privacy guarantees, and scalability. Practical implementation guidance and applications in healthcare, finance, and cross-platform social media are also discussed. Key research challenges include formal privacy–utility trade-offs, convergence under extreme data heterogeneity, and the integration of large language models for improved interpretability. Overall, FedMVC represents a promising framework for privacy-preserving unsupervised learning in distributed environments. Full article

This study proposes an explainable artificial intelligence (XAI) engineering workflow for provider-level healthcare claim audit prioritization using SHAP-guided hybrid retrieval-augmented generation (RAG). The framework integrates provider-level claim aggregation, tree-based risk screening, SHAP explanation, exploratory group-level SHAP clustering, policy concept retrieval, and constrained large language model audit narrative generation. Experiments on a public Medicare provider fraud dataset use the dataset-provided PotentialFraud label as a weak audit prioritization label rather than a legal determination of fraud. The results show that reimbursement exposure, utilization duration, claim repetition, deductible patterns, and beneficiary case mix contribute to provider-level risk scores. Additional cross-validation, calibration, threshold, and scale-confounding analyses indicate that provider size and financial exposure are important confounders, while non-scale and contextual features also retain predictive information. Beyond prediction, the framework organizes local SHAP drivers into exploratory provider archetypes and maps explanation patterns to audit-relevant policy concepts. Compared with pure embedding retrieval, the SHAP-guided hybrid retriever increases policy concept diversity and explanation alignment, although these retrieval metrics do not replace independent expert audit validation. Because the public dataset does not include referral pathways, inter-facility transfers, provider–network relationships, or care-coordination records, the framework cannot determine whether utilization patterns are explained by clinically appropriate referrals, regional access constraints, or multi-level care pathways. Its current applicability is therefore limited to provider-level audit prioritization using the available claims and beneficiary variables. The proposed system is positioned as a reproducible engineering prototype for cautious, human-reviewed audit support rather than a comprehensive or automated fraud determination system. Full article

Nitrate contamination threatens surface water and groundwater quality in karst regions, posing risks to drinking water safety and aquatic ecosystems. Strong surface water–groundwater connectivity in karst recharge areas can accelerate contaminant transport through fractures and conduits. In this study, 166 samples, comprising 100 groundwater samples and 66 surface-water samples, were collected under wet-season, normal-flow, and dry-season conditions from a typical karst recharge area in Fengshan Township, Dafang County, Guizhou Province, China. Hydrochemical analyses, dual nitrate isotope analysis, and isotope-based mixing models were integrated to evaluate potential nitrate source contributions and examine the hydrochemical factors associated with nitrate variability. Groundwater was dominated by Ca–HCO₃ and mixed hydrochemical facies and exhibited relatively stable ionic compositions, whereas surface water showed more diverse facies and greater variability in total dissolved solids, SO₄²⁻, Na⁺, K⁺, and Cl⁻, reflecting a stronger response to external inputs and short-term hydrological processes. NO₃⁻ concentrations ranged from 0.02 to 16.24 mg/L in groundwater and from 0.00 to 41.20 mg/L in surface water, with mean concentrations of 3.07 and 4.38 mg/L, respectively. Mixing-model estimates identified manure and sewage (47%) and soil nitrogen (30%) as the leading potential contributors to groundwater nitrate, whereas manure and sewage had the largest estimated contribution to surface-water nitrate (68%). Given the overlap among the isotopic signatures of potential sources, these percentages represent probable source combinations rather than exact apportionments. The absence of consistent covariation between NO₃⁻ and Cl⁻ indicated that nitrate transport was not controlled solely by conservative mixing but was jointly regulated by source-input intensity, rapid surface-runoff responses, conduit transport, subsurface mixing, dilution, and water–rock interactions. Statistical modeling further showed that groundwater NO₃⁻ variability was associated with the major-ion composition, whereas surface-water NO₃⁻ variability was partly explained by a multiple regression model incorporating SO₄²⁻ and Cl⁻. Together, these findings support a conceptual source-to-transport framework involving external inputs, rapid surface-water responses, karst conduit transport, subsurface mixing, and water–rock interaction. This study provides insight into contrasting potential nitrate sources and transport processes in connected karst surface water-groundwater systems and supports pollution-source tracing, recharge-area management, and drinking-water source protection. Full article

Graminoid encroachment in alpine tundra is often reduced to a simple rise in dominant-species cover, leaving open whether this aboveground shift is coupled to belowground soil conditioning. Here, we tracked the encroachment front of Deyeuxia angustifolia (Kom.) Y.L.Chang in the Changbaishan alpine tundra, integrating plant community surveys, growth trait monitoring, litter decomposition, phenology-matched soil physicochemical properties, enzyme activities, and bacterial 16S rRNA sequencing across slightly and largely encroached plots at 2000 and 2200 m. Abundance-weighted plant community composition differed significantly across habitats (p = 0.0003), with D. angustifolia relative cover surging from 21.8–29.9% in S to 89.9–93.0% in L, substantially displacing Rhododendron aureum and reducing community Shannon diversity and Pielou evenness. Repeated-measures mixed models revealed that D. angustifolia exhibited elevation-dependent growth trait variation (elevation × stage × date interactions for height, leaf width, stem diameter, leaf count, and tillers; all p < 0.05): plants at 2000 m produced more tillers with a higher seasonal maximum tiller count (active and seasonal maximum tillers), whereas plants at 2200 m prioritized culm reinforcement and foliar expansion (increased leaf width, length, and stem diameter) alongside reduced leaf and branch numbers. Litter decomposition linked these patterns to belowground change: rates were similar between stages at 2000 m, but at 2200 m largely encroached plots decomposed more slowly (lower k, p < 0.001; higher September mass remaining, p = 0.002) while inorganic nitrogen and enzyme activities rose, indicating active local nutrient transformation despite slower turnover. Bacterial community composition varied mainly with elevation (p = 0.001), with further contributions from encroachment stage and month, reflecting compositional restructuring rather than simple diversity change. Together, these results depict D. angustifolia encroachment as a continuous process, from establishment through local space occupancy to litter decomposition dynamics and associated soil habitat differentiation, that reorganizes both aboveground structure and belowground habitats and offers ecological evidence for forecasting tundra meadowization under continued warming. Full article

The Soil Quality Index (SQI) is a vital tool for evaluating soil quality; however, traditional approaches seldom integrate microbial data from high-throughput sequencing—commonly used to characterize soil microbial communities—into the SQI framework. This study enhances the SQI by incorporating microbial indicators derived from high-throughput sequencing, establishing a more comprehensive evaluation system. We collected soil samples from mining areas and analyzed their fundamental physicochemical properties and microbial indicators. Four SQI models were constructed using different indicator sets: (1) only physicochemical properties (T-SQI); (2) physicochemical properties and bacterial α-diversity (α-SQI); (3) physicochemical properties, α-diversity, and relative abundances of the top five abundant bacteria (αMA-SQI); and (4) physicochemical properties, α-diversity, and relative abundances of the top five bacteria based on LDA scores (αBM-SQI). Results demonstrated that integrating multi-level microbial indicators improved the rationality of soil quality rankings and significantly strengthened correlations with α-diversity. Gemmatimonadota was consistently selected in the Minimum Data Set (MDS) for both αMA-SQI and αBM-SQI, highlighting its ecological importance. Statistically, microbial indicators at the order and family levels were most suitable for inclusion in the MDS, as their results deviated least from the total dataset. In conclusion, incorporating microbial diversity across taxonomic levels refines the SQI, enabling a more accurate and holistic assessment of soil health. Full article

Groundwater quality degradation in industrial zones presents a critical environmental challenge, as diverse and compositionally complex pollution sources severely constrain precise source identification and the formulation of effective remediation strategies. This study systematically investigates groundwater quality evolution and quantitatively apportions pollution sources in a representative industrial zone in southwestern China, employing an integrated framework of hydrochemical graphical analysis, dual-dimensional hierarchical cluster analysis, and Positive Matrix Factorization (PMF) receptor modeling, based on 189 groundwater samples collected across three hydroperiods (2022–2025) from 94 monitoring wells. (1) The study reveals that overall groundwater quality was unsatisfactory, with Class IV and V waters collectively accounting for 75%, 95%, and 91% across the three campaigns; primary exceedance parameters included ammonia nitrogen, total hardness, Mn, and sulfate. (2) Hydrogeochemical analysis revealed stable HCO₃-Ca type water at background monitoring points, slight contamination influence at diffusion points (occasional HCO₃·SO₄-Ca and Cl·SO₄-Ca types), and pronounced hydrochemical diversification at internal points, evolving from Ca-Cl dominance to the coexistence of Ca-Cl·SO₄, Ca-HCO₃, and other mixed types. (3) The PMF model consistently resolved five pollution sources across all campaigns: agricultural non-point source pollution, geological background, domestic wastewater, industrial emissions, and natural hydrogeochemical evolution. Anthropogenic sources (agricultural, domestic, and industrial) collectively contributed approximately 63.8% of the total contamination load, with individual average contributions of 18.6%, 26.2%, and 19.0%, respectively, indicating that groundwater contamination in the zone is influenced not only by industrial inputs but also substantially by agricultural non-point sources and domestic wastewater. This study reveals a coupled natural–anthropogenic driving mechanism and establishes a replicable integrated framework for pollution source identification and zoned precision management in comparable industrial zone settings. Full article

High-throughput DNA sequencing has revealed that the extensive diversity of Vitis vinifera L. cultivars is structured into several major genetic lineages. Identifying phenotypic traits that reflect these genetic relationships remains an important challenge for grapevine taxonomy, germplasm characterization, and archaeobotany. Here, we present an automated computational workflow implemented in R that reconstructs seed outlines from Elliptic Fourier Descriptors (EFDs) and calculates the J index, a measure of similarity to predefined reference morphotypes, together with curvature profiles along the seed outline. The workflow is reproducible and suitable for high-throughput morphometric analysis. A dataset comprising 2895 seeds from 143 populations representing 85 V. vinifera cultivars was initially organized into three groups according to available pedigree information and previously characterized seed morphotypes. Geometric reference models representing the Hebén, Savagnin Blanc, and Muscat morphotypes were then used to calculate J index values for each population. Based on these values, 74 populations were assigned unambiguously to one of the three reference morphotypes, whereas 69 showed similar affinities to two or three models and were therefore classified as intermediate or undifferentiated. The three primary morphometric groups comprised 41 populations assigned to the Hebén morphotype, 12 to the Savagnin Blanc morphotype, and 21 to the Muscat morphotype. Curvature analysis of the reconstructed seed outlines was subsequently used to characterize local geometric variation and to identify representative populations within each group. The resulting morphotypes showed substantial correspondence with major grapevine haplotypic lineages while also revealing intermediate phenotypes associated with the complex genetic and developmental history of cultivated grapevine. Overall, the workflow provides an objective and reproducible approach for high-throughput seed phenotyping and represents a complementary phenotypic tool for investigating grapevine genetic diversity, germplasm characterization, and the relationships between seed morphology and genetic lineage. Full article

Establishing plantations in areas characterized by fragile natural vegetation emerges as a crucial approach to ecological restoration in karst regions. However, strong environmental filtering in karst regions constrains the ecological functions of understory plant communities. As the key stand structure and soil environmental factors driving community assembly remain elusive, variations in functional diversity are highly unpredictable, hindering the full realization of community functional potential. Based on a two-year investigation across 3 plantation types (36 plots) in the Liujiang River basin, we recorded stand structure and understory species abundances, quantified soil physicochemical properties, measured 8 leaf traits of understory plants encompassing leaf morphological, physiological, and chemical traits, and calculated community-weighted mean trait values to represent community-level leaf traits. Mixed-effects models were employed to elucidate the key drivers of understory leaf functional diversity. Results showed that Eucalyptus grandis × urophylla plantations supported the highest understory species diversity (10 species), temporally increased community-level leaf traits, and increased function diversity over other stands by 20.53%. In Pinus massoniana plantations, community-level leaf traits remained stable, but functional dispersion significantly increased by 24.47% temporally. In Bauhinia purpurea plantations, understory plants exhibited thicker, smaller leaves and significantly higher C:N ratios, accompanied by a 37.77% decline in functional evenness and an 11.42% decline in dispersion. Canopy gap and soil total nitrogen were the critical drivers for understory leaf functional diversity. Under light-nitrogen co-limitation, intensified environmental filtering suppressed relative abundance of subordinate species. Conversely, leveraging high trait plasticity, Bidens pilosa and Miscanthus floridulus dominated community-level trait trade-offs, determining functional variation approaches. Therefore, a synergistic management strategy, optimizing aboveground light and supplementing belowground nitrogen, should be considered to break single-species dominance and maintain functional diversification. Full article

Workplace incivility and workplace polarization represent two increasingly significant challenges in contemporary organizations. Although both phenomena have been studied extensively, their interrelationship remains underdeveloped in organizational behavior research. This conceptual study develops an integrative multilevel framework proposing how workplace incivility and workplace polarization may become reciprocally connected over time within the organizational context. Drawing on the Incivility Spiral Model, Organizational Justice Theory, and Social Identity Theory, the study proposes that repeated interpersonal incivility may contribute to identity-relevant interpretations and identity threat, which may, in turn, strengthen in-group/out-group differentiation. Trust erosion and emotional contagion may complement this process through relational and affective-transmission mechanisms, creating conditions that may support the emergence of workplace polarization. In turn, workplace polarization may reshape civility norms across perceived group boundaries and make expectations of respectful treatment increasingly group-contingent, thereby increasing the likelihood that subsequent workplace incivility will be directed toward perceived out-group members. The central contribution of this study is to explain how ambiguous interpersonal mistreatment may become interpreted as group-based disrespect and theorize how workplace polarization may make expectations of respectful treatment increasingly contingent on perceived group membership. The proposed model further identifies ethical leadership, organizational justice, psychological safety, and diversity climate as distinct contextual conditions that may influence different stages of the incivility-to-polarization pathway, rather than uniformly moderating the entire reciprocal process. By conceptualizing workplace incivility and workplace polarization as potentially reciprocally linked cross-level processes, this study contributes to theory development on workplace mistreatment, intergroup dynamics, and organizational division. It also outlines directions for longitudinal, experimental, multilevel, and network-based research to empirically examine the proposed relationships and highlights practical implications for fostering more respectful, cohesive, and inclusive work environments. Full article

Increasingly, residents prioritize diverse needs for residential buildings, hoping they can provide safe, comfortable, and healthy living environments. User satisfaction-based evaluation can significantly contribute to improving construction quality and optimizing construction management. Considering the strong subjectivity of existing assessment methods and the unreasonable allocation of indicator weights, this study establishes a feasible user satisfaction-based evaluation index system to provide a more objective evaluation of residential construction quality. By integrating the Analytic Hierarchy Process with the Fuzzy Comprehensive Evaluation method, a user satisfaction evaluation model is developed. A case study of a typical residential community in Zhongshan, Guangdong Province, was conducted to verify the scientific validity and applicability of the proposed model, demonstrating that it can effectively reduce the influence of subjective expert judgment. The results show that residents pay particular attention to leakage prevention and residential comfort, which is consistent with the current development trend of residential construction toward enhanced waterproofing performance and green development. The study provides real estate developers with theoretical support for improving construction quality management and optimizing residential living experience; contractors with practical references in optimizing the construction process; and urban–rural development departments with a scientific basis for strengthening supervision, creating acceptance standards, and implementing differentiated management policies. Full article