Search Results (6,124)

Many organizations increasingly adopt personalization techniques to enhance user satisfaction. However, current systems generally cannot automatically infer and interpret individual personality traits (PTs), although these traits are key drivers of user behavior. While Large Language Models (LLMs) are widely used, they remain poorly suited to reliable and explainable Personality Assessment (PA). To address this gap, we propose ExPAM, a novel Explainable Personality Assessment Method that combines hybrid feature fusion with in-context learning in off-the-shelf LLMs to predict Big Five PTs from text. ExPAM explicitly grounds its predictions in interpretable linguistic patterns without requiring LLM fine-tuning. Its hybrid fusion is designed to improve both predictive performance and interpretability in PA. Transformer-based embeddings encode local contextual information, whereas features extracted using the Linguistic Inquiry and Word Count (LIWC) dictionary provide complementary global and local linguistic indicators of PTs. These interpretable feature patterns are included in prompts that guide the LLM to produce both PT predictions and human-understandable explanations. ExPAM shows competitive performance compared with multi-task models on the ChaLearn First Impressions v2 (FIv2) corpus and single-task models on the PANDORA corpus that rely on a single feature set. On FIv2, it achieves a mean accuracy (mAC) of 0.891 and a Concordance Correlation Coefficient (CCC) of 0.333. On PANDORA, it achieves a mean Pearson Correlation Coefficient (PCC) of 0.240 and a CCC of 0.101. Prompting the LLM with hybrid global–local patterns further improves CCC by 9.9% on FIv2 and 15.8% on PANDORA, while changes in mAC and mean PCC remain marginal. Qualitative interpretability analysis reveals trait-specific linguistic patterns, highlighting the potential of ExPAM for psychological research, computational linguistics, and paralinguistic studies. Full article

Having reliable tools for characterizing rainfall-induced soil erosion is fundamental to the effective management of agroforestry systems in order to increase resilience against climate change. Physics-based models provide a robust, comprehensive and widely applicable framework to quantify runoff generation and soil erosion during intense rainfall events in agroforestry catchments. In this work, we propose a novel hydro-erosive model to simulate hydrodynamical flow and bed mobilization, movement and deposition. This hydro-erosive model solves the two-dimensional shallow water equations (SWE-2D) with hydrological source terms for runoff generation, coupled with the 2D depth-averaged solid transport and the soil surface evolution equations. The partial differential system is solved using a finite volume method. Alternative Integral/Differential Bed Slope and explicit upwind/implicit pointwise friction term discretization options can be used to improve performance in terms of numerical stability and conservation. The behavior of different discretization options in this hydro-erosive model is evaluated through an analytical hillslope verification, a benchmark V-catchment rainfall–runoff test and a laboratory dam-break experiment over an erodible bed. The results show that the Differential Bed Slope formulation combined with the upwind friction discretization provides the most accurate and conservative predictions. Also, an Upwind Bed Updating method for integrating soil surface elevation change is compared with the cell-centered integration of the bed change term by suppressing non-physical oscillations without compromising computational efficiency. Overall, the proposed open-source hydro-erosive model provides a reliable and computationally efficient framework for high-resolution simulations of rainfall-induced soil erosion and represents a valuable tool for environmental and agroforestry applications, but appropriate calibration and mesh resolution are required to ensure reliable predictions. Full article

Nowadays, most cancer research still depends on traditional cell culture in Petri dishes or cell culture flasks which do not have the ability to mimic physiological-like conditions in vitro. However, the behavior of cancer cells strongly relies on the interaction with their extracellular microenvironment. Consequently, current advanced approaches focus on three-dimensional (3D) cell culture to overcome such limitations and to enable a better understanding of fundamental processes including cancer development, progression, apoptosis and invasion. However, transcriptional adaptation to and temporal stability within an in vitro 3D microenvironment still appear to be remarkably understudied. In our study, we compared the cellular behavior and whole transcriptome gene expression of three frequently used non-invasive cancer cell lines (HCT-116, A549 and T47D), embedded within a collagen I (Coll I)-based 3D microenvironment to its counterparts grown as simple monolayers in a time-dependent manner. Thereby, changes in morphology and doubling time became apparent between both cultivation systems, and RNA sequencing-based transcriptome-wide analysis revealed a remarkable increase in transcriptional complexity under 3D conditions. In line with the 3D-dependent phenotype, unidirectional shifts for genes involved in cell cycle regulation (e.g., CCNB1, CCNB2), cell–matrix interaction (e.g., ADAM8, ITGA2) and metabolic signaling (e.g., HK2, ENO2) were identified over time, being either activated or repressed. Interestingly, all three cell lines cultured in Coll I matrices displayed a highly distinct RNA content and composition, along with a significantly increased number of expressed protein-coding genes (increase of 3–6%) as well as long non-coding RNAs (increase of 26–48%), suggesting a more multifaceted transcription profile under 3D conditions. Our work clearly highlights that an in vitro 3D Coll I-based cell culture system has an incisive cell-specific impact on the whole transcriptome on a qualitative and quantitative level. This tremendous transcriptional reprogramming implies essential changes in gene regulatory networks and affects phenotypic cancer cell behavior, which should be considered when focusing on downstream applications. Full article

Lead and zinc mineralization have been documented in low-temperature Mississippi Valley type (MVT), Volcanogenic Massive Sulfide (VMS), and high-temperature porphyry and skarn ore deposits. These deposits are characterized by the precipitation of galena (PbS) and sphalerite (ZnS) from a saline hydrothermal fluid. The direct relationship between metal concentration and the total chloride of the fluid has been documented previously; however, the role of acidity has not been studied extensively. Experiments were conducted in René 41 cold-seal pressure vessels at temperatures of 200, 300, and 500 °C and a pressure of 100 MPa to provide better constraints on the formation of galena and sphalerite in hydrothermal systems spanning a range of fluid acidities (pH). The concentrations of Pb and Zn in the synthetic hydrothermal fluids were determined at galena and sphalerite saturation as a function of HCl and at a total chloride of 15 wt.% NaCl_eq.. Zn concentrations ranged from 1.7 (±0.3) × 10² µg/g at 200 °C and an HCl concentration of 2.28 × 10³ µg/g to 2.55 (±0.5) × 10³ µg/g at 500 °C and an HCl concentration of 3.40 × 10⁴ µg/g. Pb concentrations were 1.8 (±0.4) µg/g at 200 °C and a HCl of 2.28 × 10³ µg/g and increased to 7.93 (±1.5) × 10³ µg/g at 500 °C and a HCl concentration of 3.40 × 10⁴ µg/g. Zn/Pb mass ratios in the fluids at sphalerite and galena saturation decreased with increasing temperature. The experimental data demonstrate that the concentration of Pb and Zn in the fluid increase with both temperature and HCl concentration and, consequently, decrease with increasing pH. These results demonstrate that acidic fluids can transport substantially greater concentrations of Pb and Zn than neutral or basic fluids. Experimentally determined slopes of Pb and Zn concentrations as a function of HCl in the fluid provide empirical measurements of the apparent dependence of metal solubility on HCl at a constant total salinity. These results are consistent with Pb and Zn being transported predominantly as chloride-complexes under acidic, although the experiments do not directly determine aqueous metal speciation. Consequently, the experimentally determined HCl dependencies should be interpreted as empirical measures of apparent metal solubility rather than direct measurements of speciation or ligand coordination. The observed dependence of dissolved metal concentrations on HCl likely reflects the combined effects of chloride complexation, increasing HCl association with increasing temperature, non-ideal solution behavior, and changes in the distribution of dissolved chloride- and possibly sulfur-based complexes. Therefore, the neutralization of an acidic Pb- and Zn-bearing, chloride-rich hydrothermal fluid could produce substantial galena and sphalerite mineralization if sufficient reduced sulfur is available. In hydrothermal fluids depleted in reduced sulfur, H₂S must be supplied through sulfate reduction or by mixing with an H₂S-rich fluid to promote galena and sphalerite precipitation. Full article

Background: Depression and anxiety disorders remain among the leading contributors to global disability and represent a major public health challenge. Although evidence-based psychotherapies are available, access to treatment remains limited due to structural, economic, geographical, and workforce-related barriers. Digital mental health interventions have emerged as scalable approaches to reducing this treatment gap, with artificial intelligence (AI)-guided cognitive behavioral therapy (CBT) representing a rapidly developing and clinically relevant extension of digital psychotherapy. Objective: This review aims to synthesize current evidence on digital and AI-guided CBT interventions for depression and anxiety, with a focus on clinical utility, scalability, mechanisms of change, safety considerations, and public health relevance. In addition, the review proposes a clinically oriented conceptual framework for understanding the role of AI-guided CBT within contemporary digital psychiatry. Methods: A focused narrative review was conducted using PubMed, Scopus, and Google Scholar databases, covering publications from 2010 to 2025. Relevant peer-reviewed studies, systematic reviews, meta-analyses, and conceptual papers addressing digital CBT, AI-assisted CBT, conversational agents, symptom monitoring, and digital mental health implementation were identified and analyzed qualitatively. Results: Existing evidence suggests that internet-delivered CBT, mobile applications, and AI-based conversational agents may reduce depressive and anxiety symptoms, particularly in individuals with mild to moderate conditions. However, the evidence base remains heterogeneous, with limitations including short follow-up periods, variability in intervention quality, reliance on self-reported outcomes, and insufficient data on long-term effectiveness, safety, and real-world implementation. Emerging concepts such as digital therapeutic alliance, continuous symptom monitoring, adaptive intervention delivery, and AI-driven personalization may represent key factors influencing engagement and clinical outcomes. Conclusions: AI-guided CBT represents a promising but still evolving component of modern mental health care. These technologies have the potential to improve accessibility, optimize resource allocation, and support stepped-care and hybrid models of treatment. Future research should prioritize rigorous clinical validation, long-term outcome evaluation, transparent safety protocols, ethical governance, and integration into real-world health systems. AI-guided CBT should not be understood as a replacement for clinicians, but as a complementary and scalable extension of evidence-based psychotherapy. Full article

Background: Individuals with intellectual and/or developmental disabilities (IDDs) experience persistent health inequities, exacerbated by the systemic lack of education of health care professionals about their care. In response to a 2020 call from the Administration for Community Living in the United States, five institutions formed the IDD Health Equity Consortium to develop a suite of educational materials and practice experiences to improve the education of health care professionals in the health and health care of individuals with IDDs. Methods: The Collective Impact Model, designed to align organizations and stakeholders around a shared agenda for system change, was used to organize IDD Health Equity Consortium programs. Backbone infrastructure included a cross-sector steering committee, an Advocate Advisory Committee, and three Consortium Action Networks focused on communication, measurement, and education, practice, and policy. A scoping review of the literature was conducted, and a Participatory Planning and Decision-Making process engaged individuals with IDDs, family members, students, faculty, and professionals in identifying important themes for developed materials. Results: Learning modules, case studies, service-learning experiences, and simulation experiences were developed across Consortium institutions and were disseminated across multiple other institutions, with beginning alignment with interprofessional and disability competencies. Mixed-methods evaluation strategies assessed learner outcomes, including knowledge checks and pre and post evaluations using established measures of skills, comfort levels, and approach; and interprofessional socialization, valuing, and collaborative practice behaviors. Conclusions: By involving individuals with IDDs in curriculum development and building multi-institution and cross-sector infrastructure, the Consortium developed scalable materials that address longstanding gaps in health care professional education. The developed suite of educational materials and practice experiences and early evaluation findings provided a foundation for further program refinement and future empirical studies examining long-term effects on practice and clinical outcomes. Full article

The fruit agro-export value chain in northern Chile faces governance and water-related challenges that affect its resilience and long-term sustainability. In the context of sustainable agriculture, these challenges highlight the need for integrated approaches to understand how socio-economic and environmental factors interact. This paper proposes a set of measures to assess resilience using a social simulation model based on the Sociology of Organized Action, implemented through the Social Laboratory (SocLab) platform. The model represents actors, their resources, and their interactions as a system driven by strategic behavior. The proposed measures are applied to analyze both simulation outcomes and the structure of the model. The findings indicate that resilience is constrained by power imbalances among actors and by external pressures related to water scarcity. In particular, simulation results show that the influence of Producers reaches only approximately half that of the Public Sector, revealing a significant power imbalance within the value chain. These imbalances create vulnerabilities that limit the system’s capacity to absorb disturbances and adapt to environmental stressors, which are expected to intensify due to climate change and increasing demand. In addition, the resource associated with coordinating actors around the efficient use of water exhibited the highest relevance among the resources considered, highlighting the central role of water governance in shaping resilience. The study contributes by integrating a simulation-based approach with economic and institutional perspectives, linking the distribution of influence among actors to value allocation along the chain, as conceptualized in marketing margin analysis, and interpreting governance structures through Transaction Cost Economics. This integration provides a more comprehensive understanding of how power distribution, coordination mechanisms, and transaction costs jointly shape resilience and sustainability in agri-food value chains. Full article

Ceramic fiber insulation tile (CFIT) is a brittle porous ceramic fiber material. Its three-dimensional random network structure yields low density, high porosity, low thermal conductivity, and excellent high-temperature stability, making it a widely used material in aerospace thermal protection systems. This renders it particularly crucial to explore fracture dimensions, improve the fracture toughness of materials, and circumvent internal defects. In this study, the numerical simulation and experimental approaches are employed to investigate the fracture behavior and toughening mechanism of CFIT. First, a three-dimensional network model and macroscopic finite element (FE) model of CFIT are established, and the validity of the macroscopic FE model is confirmed by comparison with fracture experimental results. Meanwhile, the effects of CFIT porosity, fiber length, fiber diameter, and fiber orientation angle on fracture toughness are systematically investigated. Furthermore, based on practical requirements, the dimensions and structure of the ceramic fibers are determined, thereby elucidating the mechanism through which changes in crack size influence fracture behavior. Finally, fracture behavior under the macroscopic model is analyzed by introducing different crack sizes and prefabricated defects, and the influence laws of crack size and prefabricated defects on CFIT are determined. In summary, this study provides theoretical guidance for research on the fracture behavior of porous ceramic fiber materials. Full article

Microelectromechanical systems (MEMS) gyroscopes are now standard rate sensors in inertial navigation, automotive electronics, industrial automation, and medical instrumentation because they are inexpensive, compact, and readily integrated. Their accuracy, however, degrades with temperature: damping and quadrature coupling change, and readout-electronics behavior shifts, producing temperature-dependent zero-rate-output drift, elevated random noise, and poorer long-term stability. Hardware- and structure-based temperature compensation address part of the problem but carry cost and generality penalties, which has moved recent work toward data-driven software-based temperature-drift compensation. This review focuses on the fusion algorithms that have come to dominate that literature, organized as a four-stage pipeline: signal decomposition, learning-based drift modeling, adaptive filtering, and signal reconstruction. We examine how optimizer-tuned variational mode decomposition and improved empirical-mode-decomposition variants separate temperature-related components from noise; how deep temporal networks and optimizer-coupled learners model the nonlinear, time-lagged drift; and how adaptive Kalman variants and time-frequency filtering reconstruct a stable output. We close by identifying four open problems that recur across the recent gyroscope-specific work—cross-device generalization, temperature hysteresis, embedded real-time deployment, and physics-informed lightweight modeling. Full article

Production decline in hydraulically fractured horizontal wells in continental shale oil reservoirs is jointly controlled by reservoir heterogeneity, fracture-system evolution, and changes in development practices. It commonly exhibits pronounced stage-dependent characteristics, making it difficult for a single decline model to accurately characterize the full-life-cycle production dynamics. To address this issue, this study focuses on hydraulically fractured horizontal wells in the Qingcheng Oilfield, Ordos Basin. Based on production performance data and staged decline analysis, a full-life-cycle decline-analysis workflow based on production-stage identification was established. First, the production process of individual wells was divided into an early quasi-stable stage, an early decline stage, and a middle-to-late decline stage according to the evolution of the daily oil-rate decline and the relative growth rate of cumulative oil production. Subsequently, the Hu–Chen–Zhang (HCZ), Duong, and Arps models were used to fit the three stages, respectively, thereby constructing a full-life-cycle segmented decline curve. On this basis, the decline behavior of different well types was comparatively analyzed in combination with the production responses of representative wells. The results indicate that the production dynamics of hydraulically fractured horizontal wells in the study area exhibit clear stagewise evolution throughout their life cycles. The HCZ, Duong, and Arps models show good applicability to the early quasi-stable stage, early decline stage, and middle-to-late decline stage, respectively. The combined decline model constructed from these three models can effectively characterize the complete production process of an individual well, including initial high-rate production release, rapid decline, and gradual late-time depletion. For a representative well, the full-life-cycle fitting results yielded a coefficient of determination (R²) of 0.95, a root mean square error (RMSE) of 0.69, and a mean absolute percentage error (MAPE) of 7.34%. According to their decline-dynamic characteristics, the hydraulically fractured horizontal wells in the study area can be classified into two types: medium-to-high initial-rate decline wells and continuously low-rate wells. The former exhibit strong initial production-release capacity but rapid production loss during the early decline stage, whereas the latter are characterized by relatively low initial production rates and persistently low-rate, gradual-decline behavior throughout the production process. The results provide a reference for understanding decline behavior and forecasting production performance of hydraulically fractured horizontal wells in the Qingcheng Oilfield. Full article

Financial systems are highly nonlinear and often influenced by investment cycles, market fluctuations, and external financing activities, which can generate complex dynamic behaviors. This paper proposes a new three-dimensional chaotic financial firm model by extending the classical Bouali financial system through the inclusion of a cyclic reinvestment perturbation represented by a sinusoidal nonlinear term. The proposed modification aims to capture state-dependent nonlinear reinvestment feedback in reinvestment decisions caused by changing economic conditions and business cycles. The dynamical characteristics of the model are investigated using equilibrium analysis, local stability theory, Lyapunov exponents, the Kaplan–Yorke dimension, bifurcation analysis, and multistability analysis. Numerical results show that the system possesses three equilibrium points, all of which are unstable under the selected parameter setting. The system exhibits chaotic dynamics confirmed by a positive maximum Lyapunov exponent and a fractal attractor characterized by a Kaplan–Yorke dimension greater than two. Comparative results indicate that the new model demonstrates richer nonlinear dynamical behavior than existing financial firm systems reported in the literature. Furthermore, bifurcation and Lyapunov spectrum analyses disclose multiple transitions between periodic and chaotic states as system parameters vary, while multistability analysis reveals the coexistence of symmetric periodic and chaotic attractors under identical parameter values but different initial conditions. Finally, total amplitude control and offset boosting techniques are implemented to regulate attractor size and position while preserving the underlying chaotic behavior. These findings demonstrate that cyclic reinvestment perturbations significantly enrich the nonlinear dynamics and symmetry properties of financial firm systems, providing a useful framework for studying complex financial behaviors and chaos control. Full article

Objectives: This systematic review examined studies combining transcranial direct current stimulation (tDCS) with virtual reality (VR) in healthy or non-clinical populations. The aim was to evaluate whether active tDCS provides additional benefits when delivered during VR-based tasks or interventions aimed at improving behavioral abilities. Methods: Following PRISMA guidelines, a comprehensive search of electronic databases (2000–September 2025) identified randomized and non-randomized studies employing simultaneous tDCS and VR in healthy individuals. Studies reporting psychological or cognitive quantitative outcomes were included. Risk of bias was assessed using RoB-2 and ROBINS-I tools. Results: Twelve studies met inclusion criteria. The included studies were highly heterogeneous in terms of sample size, VR systems, stimulation parameters, targeted cortical regions, outcome measures, and control conditions. Most designs compared active tDCS during VR with sham tDCS during the same VR exposure. Therefore, the available evidence mainly addresses whether tDCS adds value to VR-based procedures, rather than whether tDCS and VR exert independent or synergistic effects. Some studies reported favorable between-group effects on emotional control or skill acquisition. However, in other cognitive domains both active and sham/control VR groups improved, suggesting that VR training or exposure itself may have contributed substantially to the observed changes. Moreover, small sample sizes, multiple outcome testing, and limited correction for multiple comparisons reduce confidence in borderline findings. Conclusions: Current evidence suggests that active tDCS may incrementally enhance selected VR-based outcomes in healthy populations, particularly in emotional control and skill-learning domains. However, the literature does not yet support strong claims of synergistic interaction between tDCS and VR. Future studies should use adequately powered factorial designs including VR-only, tDCS-only, combined, and sham/no-intervention conditions to disentangle independent, additive, and interaction effects. Full article

The Richards equation is the renowned equation for studying the characteristics of infiltration in unsaturated soil areas, such as porous media. This paper’s primary goal is to demonstrate how the water transport problem behaves in unsaturated soil. In this work, we investigated approximate solutions of the non-linear time-fractional Richards equation (TFRE) with the help of the Yang transform iterative method (YTIM) and the homotopy perturbation transform method (HPTM). The methods used are novel and attractive, successfully combining the Yang transform method, the new iterative method, and the homotopy perturbation method. Both techniques use an iterative process with fewer computations to efficiently provide rapidly convergent series-type solutions. Two cases of the TFRE are examined through the Caputo derivative in order to illustrate the effectiveness of the applied methods. Graphs for various fractional orders are drawn to illustrate the physical behavior of the obtained solutions. Numerical comparisons among the derived approximate solutions and the precise solution are provided in order to demonstrate the efficacy of the YTIM and HPTM. The absolute error obtained using the suggested approaches was contrasted with that obtained using the q-homotopy analysis transform method (q-HATM). The impact of changing non-integer, spatial, and temporal parameters on the behavior of the resulting solution is also illustrated graphically. Mathematica software is used to obtain the approximate series solution and to create graphical displays of several fractional orders. The findings show that the proposed techniques are easy to implement and may be used to investigate sophisticated physical systems controlled by time-fractional non-linear partial differential equations. Full article

Background/Objectives: The NLRP3 inflammasome–Caspase-1–IL-1β pyroptotic axis participates in peripheral inflammatory responses, yet its function in peripheral–central neuroimmune crosstalk underlying endometriosis (EM)-associated pain remains unclear. This study aimed to clarify whether NLRP3-mediated pyroptosis establishes a brain–lesion neuroimmune axis connecting ectopic lesion inflammation with central neuroimmune remodeling and to explore the therapeutic mechanism of transcranial direct current stimulation (tDCS). Methods: An EM rat model was established to detect NLRP3 pathway expression in ectopic lesions and anterior cingulate cortex (ACC), together with central nervous system pathological alterations. Animals received tDCS intervention to evaluate inflammatory, neuropathological and pain behavioral changes. The closed-loop brain–lesion regulatory circuit was further interpreted. In a clinical cohort including 40 EM patients, pain and quality-of-life scores were compared between active and sham tDCS groups. Results: NLRP3, Caspase-1 and IL-1β were upregulated in ectopic lesions and ACC of EM rats, accompanied by ACC mitochondrial injury, microglial activation and thalamic demyelination. tDCS inhibited pyroptosis-related molecules, decreased systemic proinflammatory cytokines, improved central pathological lesions and relieved pain hypersensitivity. Mechanically, top-down descending pain inhibitory pathways, vagal cholinergic anti-inflammatory pathway and the HPA axis jointly mediate therapeutic effects, whereas circulating cytokines and visceral afferents transmit peripheral inflammatory signals to the brain. Clinical data demonstrated that active tDCS effectively alleviated EM-related pain and improved patients’ quality of life. Conclusions: NLRP3-mediated pyroptosis acts as a key mediator linking peripheral and central neuroimmune communication. Targeting this pathway via tDCS interrupts the inflammation–pain vicious cycle through multiple neuroregulatory pathways and remodels the central neuroimmune microenvironment in endometriosis. Full article

Post-disaster recovery is increasingly understood not as a simple return to pre-event conditions but as a dynamic reorganization of land systems, in which land cover and land use change (LCLUC) provides an operational signature of recovery trajectories. However, most existing assessments reduce recovery to a single dimension—typically vegetation greenness—which can conflate systems that differ fundamentally in their response behavior. This study develops a multi-index Recovery Resilience Index for Land Systems (RRI-LS) within Google Earth Engine and applies it to the catastrophic liquefaction zone of the 2018 Mw 7.5 Palu earthquake (Central Sulawesi, Indonesia). Combining Sentinel-2 spectral indices (NDVI, NDBI, BSI), Dynamic World land-cover labels, and a hybrid Top-of-Atmosphere/Surface-Reflectance baseline to overcome the sparse pre-event archive, we quantify three resilience dimensions—resistance, recovery, and stability—and classify recovery into qualitatively distinct pathways. The hybrid baseline is quantitatively validated: after removing a small systematic offset, the residual discrepancy between TOA- and SR-derived indices is 2.4–4.9 times smaller than the measured disturbance signal. Site-level analysis of the three principal liquefaction hotspots (Balaroa, Petobo, Jono-Oge) and a 1 km grid expansion (n = 962 cells) reveal that disturbance-affected areas did not converge on a single outcome but diverged into bounce-back, transformational, reconstructed (non-vegetated), and degraded pathways; a basin-wide re-run at 250 m (n = 14,157 cells) reproduced the same pathway hierarchy, confirming robustness to grid resolution. Initial disturbance intensity was a poor predictor of long-term recovery (R² = 0.07, p < 0.001), underscoring that recovery is multidimensional and not reducible to a single shock variable. The emergence of a reconstructed, non-vegetated pathway—where bare-soil disturbance is resolved through built surfaces rather than re-greening—demonstrates that vegetation metrics alone are insufficient in human-dominated landscapes. The framework supports a land-system perspective in which recovery is conceptualized as the establishment of new functional equilibria. Full article