Abstract: Automated hate speech moderation systems are increasingly deployed to support large-scale platform governance, yet their reliability and transparency remain critical concerns. While recent advances in explainable AI (XAI) provide tools to interpret model decisions, these methods are often applied post hoc and rarely evaluated in terms of their impact on moderation behavior under real-world conditions.In this work, we present Human-Aligned Rationale Learning (HARL), an explanation-aware training framework that integrates human-annotated rationales into the optimization of hate speech detection models. HARL combines standard classification loss with an attribution alignment objective, encouraging models to ground predictions in human-identified indicators of harmful content. Rather than proposing a new rationale-learning paradigm, the framework is designed to systematically study how explanation-guided supervision influences moderation performance, explanation quality, and deployment-relevant behavior.We evaluate HARL across multiple hate speech detection benchmarks, examining classification performance, explanation plausibility, and partial faithfulness using token-level agreement metrics and prediction-drop analysis. Beyond standard evaluation, we introduce a behavioral analysis framework that assesses moderation outcomes across identity-linked content and varying degrees of code-switching, providing insight into how models behave under socially and linguistically variable conditions.Our results show that explanation-guided supervision improves explanation grounding while maintaining competitive classification performance across both high-resource and low-resource settings. Furthermore, different attribution methods exhibit distinct trade-offs between plausibility, faithfulness, and computational efficiency, highlighting their suitability for different deployment scenarios such as real-time moderation and offline auditing.We position HARL as an applied moderation framework for explanation-aware training and behavioral evaluation, rather than as a new fairness intervention. The findings provide practical insights into how explanation-guided learning can support more transparent, reliable, and inspectable moderation systems in real-world deployment contexts.

Abstract: Humans routinely offload cognitive tasks to their environments. Here we show, employing just basic physics and the Free Energy Principle, that all time-persistent information-processing systems offload information-processing tasks to their environments. Hence all cognitive systems engage in cognitive offloading. We show how ecological niche construction, kinematic replication, bioelectric signaling, the development of communication systems based on shared semantics, and the ability of LLMs to demonstrate fluent language use in the absence of extra-linguistic input all exemplify this offloading process. We conclude that both theoretical understanding of problem-solving abilities and the engineering of such abilities into artifacts will be improved by considering active computation by the environment as a ubiquitous adjunct to cognition in both living and artificial systems.

Abstract: This study develops a 3D-dimensional computational fluid dynamic model of a polymer electrolyte fuel cell cathode gas channel with seven discrete liquid breakthrough inlets, one gas inlet, and a two-phase outlet. Two-phase flow and droplet evolution on the GDL are simulated using the volume-of-fluid method in OpenFOAM. The model agrees well with reported experimental and numerical data in terms of droplet size, morphology, and detachment behavior. Results show that breakthrough geometry governs droplet dynamics: circular openings promote stronger aerodynamic loading and earlier detachment, while sharp-cornered geometries (e.g., triangular and polygonal) stabilize droplets and prolong residence time. Among all investigated geometries, the circular breakthrough exhibits the highest drainage efficiency, in agreement with recent experimental studies demonstrating that laser-drilled circular pores facilitate water removal and reduce oxygen mass-transfer resistance in polymer electrolyte fuel cells. Complex interactions with the GDL surface, gas channel walls, and corners lead to coalescence, sliding, and rivulet formation. Force decomposition reveals the competition among aerodynamic, capillary, adhesion, and shear forces. The study provides a mechanistic basis for geometry-controlled water transport and guidance for GDL design and water management.

Abstract: This article examines Byzantine-period churches and nineteenth-century Greek Orthodox churches in Bursa and its surroundings as an interrelated corpus of Christian sacred architecture in Ottoman Bithynia. Rather than treating the two groups as separate historical categories, it asks how Byzantine spatial and liturgical memory was selectively retained, transformed and made visible under late Ottoman conditions. Drawing on architectural documentation, conservation records, field observations and comparative regional literature, the study analyses plan typology, apse and bema organization, narthex and gallery arrangements, structural systems, material use, façade articulation, ornamentation and public visibility. The comparison shows that nineteenth-century churches did not preserve Byzantine forms unchanged. While domed masonry systems, pastophoria and complex centralized plans were reduced or reconfigured, key liturgical principles, including east-west orientation, apsidal focus and the separation of the sacred core, remained persistent. At the same time, timber roofs, composite masonry, galleries, legible façades and occasional bell towers reflected local building practice, legal reform and communal representation. Bursa thus emerges as a regional case in which post-Byzantine architectural memory was neither passively inherited nor simply lost, but actively reworked within the material, social and spatial conditions of the Ottoman province and within wider debates on Anatolian Christian heritage studies.

Abstract: Radiological emergencies necessitate biomarkers that not only estimate absorbed ionizing radiation (IR) dose but also guide timely interventions to prevent or delay multi-organ injury. Conventional LC–MS-based metabolomics of bulk plasma is constrained by matrix effects that mask low-abundance species. Extracellular vesicles (EVs) constitute a metabolically enriched, underexplored compartment that can provide complementary insight into systemic metabolic and redox responses to IR. Female WAG/RijCmcr rats were exposed to 13.0 Gy leg-out partial-body X-rays and treated with one of three activated protein C (APC) variants-rat wild-type (WT), rat 3K3A-APC, or human WT APC-administered 24- and 48-hours post-irradiation. Longitudinal plasma collections (days 1, 14, 30, and 90) were subjected to metabolomic and lipidomic profiling of whole plasma and matched EV-enriched fractions to define signatures of acute radiation syndrome (ARS) and delayed effects of acute radiation exposure (DEARE), and their modulation by APC. ARS was marked by early dyslipidemia and widespread metabolic disruption, evolving into DEARE with persistent alterations in energy metabolism, protein homeostasis, and nucleotide biosynthesis, consistent with sustained oxidative and inflammatory stress. EV profiles showed matrix-specific, time-dependent trajectories distinct from plasma, with prominent lipid dysregulation and enrichment of fatty acid β-oxidation, sphingolipid, and cholesterol pathway metabolites at day 90. Rat 3K3A-APC promoted early EV metabolic normalization, whereas rat WT APC more effectively mitigated late DEARE-associated changes. Elevated sphingomyelins in plasma EVs at day 90 suggested a compensatory or anti-inflammatory lipid response. These data establish plasma-derived EVs as a sensitive matrix for radiation biomarker discovery and for elucidating APC-mediated modulation of IR-induced metabolic and redox disturbances.

Abstract: Craniocerebral firearm injuries are associated with high mortality rates which increase in proportion to the damage to the brain and skull produced by the projectile, as a result of the kinetic energy dissipated during the projectile-tissue interaction. Ballistic factors that contribute to the brain injury are related to the ballistic behavior of the bullet (whether it yaws, tumbles, mushrooms or disintegrates following skull penetration) and its effects. While these injuries are complicated by the creation of bone fragments causing further damage to the brain tissue, the pressure waves generated intracranially as a result of the temporary cavitation phenomenon are the landmark of bullet penetration of the head. Because within the skull there is no mechanism of pressure relief as in other parts of the body during cavitation, the largely incompressible brain tissue sustains the pressure built up, transmitting the pressure wave and causing indirect bone fractures. Although cavitation occurs with low-velocity projectiles too, high-velocity projectiles are capable of high energy transfer secondary to bullet tumbling, mushrooming and often fragmentation, resulting in marked cavitation and more widespread tissue damage. The sudden increase in the intracranial pressure and the transient deformation of the brain tissue contributes to the development of diffuse brain edema and the cardiac and respiratory centers of the brainstem when not involved in the path of the bullet can still be affected indirectly by the pressure transmission with catastrophic results. Shotgun injuries to the head at close range cause extensive destruction of the brain involving a different mechanism, as the pellets enter the cranial cavity bunched together, thus acting as a single projectile of large diameter.

Abstract: As the global aquaculture industry moves towards high density and integrated efficiency, the precision and immediacy of water quality management have become a key to increasing productivity and reducing risks. Traditional aquaculture relies on manual experience or simple threshold controls which often suffer from response delays and energy waste. This study proposes an IoT environmental prediction model based on Edge Computing, designed specifically to address complex and variable outdoor aquaculture environments. The system integrates multimodal sensor data such as water level, temperature, and turbidity, and employs a 1D-CNN-LSTM (1-Dimensional Convolutional Neural Network - Long Short-Term Memory) model deployed on ESP32 edge computing nodes to achieve low-latency environmental change prediction. Based on five core control rules (bidirectional regulation of water level and temperature, and turbidity control), this study simulates 360 days of operational data in a real-world environment, covering seasonal climate changes and extreme weather events (such as typhoons). Experimental results show that compared with traditional hysteresis control, the predictive control strategy proposed in this study can provide early warnings of environmental anomalies 15 to 60 minutes in advance, effectively increasing the proportion of time water quality parameters are maintained within safe thresholds to 99.8%. This paper details the system architecture, prediction model design, and empirical benefits of long-term simulation data analysis, providing a solution with both academic depth and practical value for smart aquaculture.

Abstract: The sustained growth of urban areas has increased the complexity of managing services, infrastructure, and mobility, creating a need for advanced technological solutions capable of responding dynamically to rapidly changing environments. In this context, adaptive smart urban systems have emerged as an innovative alternative that integrates artificial intelligence (AI) to optimize real-time decision-making. This study presents a systematic literature review focused on the frameworks underpinning these systems, examining the main adaptive AI techniques, current technological trends, and the structural challenges that limit their implementation. The methodology applied is based on the selection and critical analysis of indexed scientific publications, enabling the identification of predominant approaches such as machine learning, deep learning, multi-agent systems, and reinforcement learning. The findings reveal a strong convergence between AI, the Internet of Things (IoT), and Big Data, as well as significant limitations in terms of interoperability, data governance, and scalability. It is concluded that, while the advances are promising, the consolidation of these systems requires a comprehensive approach that combines technological innovation, appropriate regulation, and social sustainability.

Abstract: Environmental concern is widely recognized as a driver of electric vehicle (EV) adoption, yet less is known about whether this relationship differs between current car owners and non-car owners. This distinction matters because car owners evaluate EVs as potential replacement vehicles, while non-car owners may express a more aspira-tional future preference. Using a cross-sectional online survey of 328 adults in Canada, this study examines whether environmental concern predicts EV adoption intentions across ownership groups. Car owners were asked about the likelihood of choosing a plug-in electric vehicle if purchasing another vehicle, whereas non-car owners were asked whether they would prefer to drive an EV instead of a conventional car. Ordered logistic regression results show that environmental concern significantly predicts EV purchase likelihood among car owners (beta = 0.649, p < 0.001; OR approximately 1.91) and EV preference among non-car owners (beta = 0.440, p = 0.018; OR approximately 1.55). Personal environmental responsibility is positive but not statistically significant in the main car-owner model. The findings support Value-Belief-Norm theory while indicating that environmental concern alone may not overcome practical ownership barriers. EV adoption strategies should therefore connect environmental benefits with affordability, charging infrastructure, and everyday usability.

Abstract: Patients with pre-existing chronic obstructive pulmonary disease (COPD) and/or chronic heart failure (CHF) are a particularly high-risk surgical population in whom postoperative pulmonary complications (PPCs) drive a disproportionate share of morbidity, prolonged stay and mortality. Prehabilitation—the structured optimization of functional reserve during the preoperative interval—has emerged as a proactive, largely non-pharmacological strategy to raise that reserve before the surgical insult. Yet the evidence base is organized predominantly by surgical procedure and enrolled in mixed populations, so the patients with the least physiological reserve—those with established COPD or CHF—remain comparatively understudied. This narrative review takes the comorbidity, rather than the incision, as its organizing axis. We summarize the pathophysiology that links chronic cardio-respiratory disease to perioperative respiratory failure; appraise the principal non-pharmacological interventions, with the respiratory components (inspiratory muscle training, pulmonary rehabilitation, breathing techniques) as the core and exercise, nutritional, psychological and smoking-cessation elements as the multimodal context; and re-read the surgical evidence through the lens of the underlying disease. A cross-surgical meta-analysis indicates that preoperative exercise training reduces PPCs (relative risk ≈ 0.52) with no significant difference of effect across surgery type or training modality, supporting a comorbidity-centred rather than procedure-centred framework. We address practical determinants of implementation—timing, patient selection, adherence, home-based and telemonitored delivery, and low-resource applicability—and highlight two cross-cutting problems: heterogeneity of intervention prescription and the lack of standardized PPC definitions. The scarcity of disease-specific evidence is itself the central finding, revealing a mismatch between clinical risk and research investment.

Abstract: Background/Objectives: Walking-induced fatigue disrupts postural balance, but the differentiated effects of walking uphill and downhill remain unclear. The aim was to compare the impact of two walking sequences, either uphill (+10%) or downhill (-20%), with an identical number of steps (7000 steps) on a treadmill at 5.5 km.h-1 on postural balance. Methods: Nineteen healthy young participants performed the two walking sequences sessions (56 and 57 min) eight days apart. Maximal voluntary contraction, central activation ratio, and eyes closed bipedal postural balance (in 3 randomized conditions: an unmanipulated condition, a tendon vibration manipulation condition - TV - and a galvanic vestibular stimulation manipulation condition - GVS), were assessed before (PRE), immediately after (POST), and 20 minutes after (POST20) each walking sequence. Results: Walking uphill and walking downhill sequences generated similar muscular and central fatigue in the POST and POST 20 conditions. In the unmanipulated postural condition, postural balance was disrupted after both walking sequences in the POST condition, with no difference between walking downhill and walking uphill. In the manipulated postural conditions, postural balance was modified by the walking sequences. It was disrupted in the presence of GVS in the POST condition with no difference between walking downhill and uphill, whereas it was not disrupted in the presence of TV and was even improved after walking uphill. Conclusions: Although the postural alteration was broadly similar between the two walking sequences, the disruptive factors would differ between them at the muscular, metabolic and sensory levels.

Abstract: Accurate monitoring of forest carbon stocks represents a critical prerequisite for achieving carbon neutrality. However, conventional remote sensing‑based estimation methods frequently overlook forest heterogeneity, causing systematic overestimation or underestimation. To address this gap, we propose a novel forest carbon stock esti-mation framework that integrates two complementary strategies: (1) forest type‑specific modeling to account for forest heterogeneity, and (2) hyperparameter op-timization to enhance Random Forest model performance. Using ground‑measured carbon stocks and a CCDC‑derived forest vegetation classification map for Hangzhou City, China, we built forest‑type‑specific Random Forest models based on ICESat‑2 canopy height metrics and optimized each model via hyperparameter tuning. The re-sults show that the 70th-90th percentiles and the mean canopy height are relatively highly correlated with carbon stock. Forest‑type‑specific modeling improves estima-tion accuracy, yielding R² gains of 0.10–0.17 and reduced RMSE by 2.28–7.43 Mg C/ha over the non‑stratified model. Integrating forest classification and hyperparameter op-timization strategies improved model R² by 0.16–0.23 and lowered RMSE by 3.05–8.20 Mg C/ha. Overall, this study demonstrates that accounting for forest heterogeneity and applying hyperparameter optimization can significantly enhance the accuracy of for-est carbon stock estimation.

Abstract: Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD), a leading cause of chronic liver disease, encompasses a continuum from steatosis to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis, cirrhosis, and hepatocellular carcinoma. This review aimed to synthesize current evidence on how metabolomic, lipidomic, and spatial multi-omic approaches illuminate MASLD pathogenesis and support precision hepatology. Methods: A structured narrative review was conducted through searches of PubMed, Scopus, and Web of Science, complemented by manual screening of key references. Studies were prioritized when they addressed MASLD biology, metabolic rewiring, lipid remodeling, mitochondrial dysfunction, inflammatory and fibrogenic pathways, gut–liver–adipose crosstalk, biomarker development, or therapeutic monitoring. Results: The reviewed evidence identifies MASLD as a systemic metabolic disorder shaped by excess lipid flux, enhanced de novo lipogenesis, impaired mitochondrial adaptation, oxidative and endoplasmic reticulum stress, sterile inflammation, and hepatic stellate-cell activation. Recurrent metabolomic signatures include altered amino acid, fatty acids, bile acid, and microbial co-metabolite pathways. Lipidomic studies consistently implicate depletion of protective polyunsaturated fatty acids, lysophosphatidylcholines, and phosphatidylcholines, in association with accumulation of diacylglycerols and ceramides, in the transition from steatosis to MASH and fibrosis. Emerging spatial and multi-omic analyses further resolve cell-specific metabolic niches involving hepatocytes, macrophages, endothelial cells, and stellate cells. Conclusions: Metabolomics provides a mechanistic and translational bridge between molecular injury, histological progression, and non-invasive risk stratification in MASLD. Future progress requires standardized analytical workflows, longitudinal validation, causal pathway interrogation, and integration with imaging, genetics, microbiome profiling, and treatment-response phenotyping.

Abstract: Oral targeted therapies now constitute a substantial and growing share of the anticancer armamentarium, shifting drug administration from the controlled intravenous setting to the patient’s home, where systemic exposure becomes contingent on factors that parenteral therapy largely bypasses. Absorption variability, food effects, gastric pH, first-pass metabolism, transporter activity, organ function, concomitant medications, and adherence each contribute to interpatient and intrapatient variability in exposure, and many oral anticancer agents possess narrow therapeutic indices in which modest exposure changes carry clinical consequence. This review synthesizes the pharmacokinetic determinants of oral anticancer drug exposure across twenty-seven exemplar agents spanning the major mechanistic classes—tyrosine kinase, cyclin-dependent kinase 4/6, poly(ADP-ribose) polymerase, Bruton tyrosine kinase, B-cell lymphoma 2, BRAF/MEK, ALK, mTOR, and androgen-axis inhibitors—and translates them into actionable pharmacy practice. We examine the physicochemical and physiological basis of food effects, distinguishing agents that require fasting administration from those that should be taken with food and those with flexible dosing. We address the often underappreciated interaction between acid-suppressive therapy and pH-dependent agents, the dominant role of cytochrome P450 3A4 and the efflux transporters P-glycoprotein and breast cancer resistance protein in mediating drug–drug interactions, and the exposure–response and exposure–toxicity relationships that motivate emerging therapeutic drug monitoring. We highlight several agents whose pharmacokinetic profile diverges sharply from class expectations—including the contrasting acid-suppression sensitivity of acalabrutinib versus ibrutinib, and the perpetrator role of enzalutamide and apalutamide as strong CYP3A4 inducers—and we consider special populations in whom altered pharmacokinetics necessitate individualized management. Synthesizing this evidence, we propose a structured framework through which oncology pharmacists can operationalize pharmacokinetic principles—encompassing interaction screening, meal-timing counseling, acid-suppression review, organ-function assessment, and toxicity monitoring—to optimize the safety and effectiveness of oral anticancer therapy within a precision medicine paradigm.

Abstract: Precision medicine can already produce extraordinary individualized acts of care, often in high-stakes cases where standard pathways have failed. Yet when many patients need comparable work, clinical care cannot replicate it reliably across conditions, payers, and settings. What exists is a precision medicine of exceptions: acts generated under rare conditions rather than through system functions able to make them reliably available to all who could benefit. We name the alternative massive individualization: clinical acts tailored to each case, at scale. The obstacle is whether everyday practice can turn what is known about a person into an individualized course of care for each patient who would benefit. The usual incremental response adds pieces around that work, including funding, referral, decision support, and data sharing, but does not make the care itself repeatable. Drawing on a human-led rapid scoping review with AI-assisted retrieval and synthesis, we identify seven walls: no agreed way to pay for shaping and revising such care; capability that cannot yet be composed around each case; no systematic way to identify patients as candidates for individualization; authority tied to fixed approvals even as the patient’s situation changes; learning trapped inside the boundaries that produced it; integration that delivers outputs without sustaining synthesis; and no one accountable for the care as a whole. These are the operating functions massive individualization requires and current care lacks. Increasingly capable AI, including autonomous agents, makes these functions newly attainable, and their absence more dangerous, because such systems strengthen the arrangements already in place. The decisive test lies in building the functions that make the individualized act the rule rather than the exception, answerable at the level where each patient bears the consequence.

Abstract: This paper proposes a robust and scalable Federated Learning (FL) framework integrated with the IOTA Tangle to address challenges of resource heterogeneity and participation instability in distributed environments. Traditional FL approaches often suffer from "stragglers" and lack a decentralized audit trail, leading to training inefficiencies. To mitigate these issues, we introduce an adaptive client selection mechanism driven by Reinforcement Learning (RL). Our approach employs a multifaceted Quality of Learning (QoL) metric that quantifies client contributions by evaluating update magnitudes, accuracy improvements, and hardware resource utilization. The RL agent integrates these QoL metrics into the reward function to dynamically optimize client subsets, ensuring faster con-vergence and efficient resource allocation. Furthermore, critical training metadata is encapsulated into IOTA Tagged Data blocks and anchored onto the Tangle, providing a tamper-resistant, traceable ledger. Experimental evaluations on the Flower platform demonstrate that our framework significantly improves training efficiency and provides a verifiable execution environment for edge intelligence.

Abstract: Doxorubicin (DOX) is a potent chemotherapeutic agent widely used in the clinical treatment of various malignancies. However, its therapeutic application is significantly constrained by dose-dependent cardiotoxicity, with accelerated cardiac aging emerging as a particularly challenging issue. This presents a critical limitation to its clinical use, emphasizing the urgent need for targeted cardioprotective strategies to mitigate DOX-induced cardiotoxicity. Cellular senescence, marked by permanent cell cycle arrest, plays a key role in the development of DOX-induced cardiac aging. DOX has been shown to induce cellular stress and senescence in multiple cardiac cell types, leading to impaired cardiac function. Understanding the molecular pathways and mechanisms through which DOX triggers cardiac aging is essential for the development of effective interventions to address its cardiotoxic side effects. In this review, we discuss the molecular mechanisms underlying DOX-induced senescence in different cardiac cell types and summarize the current progress in anti-aging therapeutic strategies aimed at alleviating DOX-related cardiac complications.

Abstract:

Non-geniculate coralline red algae of the order Sporolithales are ecologically important calcium carbonate producers in tropical and subtropical seas globally; however, molecular surveys of this group in the Bay of Bengal (BoB) are entirely lacking, and no species of Sporolithon Heydrich has been documented from this embayment. Here we report the first occurrence of Sporolithon from Saint Martin Island (ca. 20°37′N, 92°19′E), the sole coral island of Bangladesh, at the northernmost margin of the BoB. Eight specimens encompassing free-living rhodoliths to encrusting growth forms were collected from intertidal and shallow subtidal habitats in March 2024. Species identification was assessed by combining scanning electron microscopy (SEM) images with a six-locus molecular dataset comprising three plastid-encoded markers (rbcL, psbA, and UPA), the mitochondrial cytochrome c oxidase subunit I 5′ region (COI-5P), and two nuclear-encoded ribosomal markers (LSU and SSU). Both maximum likelihood and Bayesian phylogenetic analyses resolved all BoB specimens within a well-supported clade of Sporolithon indopacificum Maneveldt, Gabrielson & Kangwe, with 0.00–0.59% rbcL and 0.00% psbA sequence divergence from type specimens from Tanzania and previously published sequences from Fiji, China, and Guam (USA). SEM confirmed morpho-anatomical features concordant with the original species description, including a free-living rhodolith habit, monomerous construction, and tetrasporangial compartments with apical pore plugs. This record extends the confirmed range of S. indopacificum into the northeastern Indian Ocean, represents the first documented Sporolithon occurrence in Bangladeshi waters, and highlights the value of combining SEM with multi-locus barcoding for coralline algal surveys in undersampled tropical regions.

Abstract: Introduction: Temporomandibular joint ankylosis (TMJA) management have evolved from gap arthroplasty to total joint replacement (TJR). TMJ Re-ankylosis presents significant challenge in achieving long-term treatment success. This study evaluates the effectiveness of virtual surgical planning (VSP) guided stock TJR in TMJ re-ankylosis. Materials and method: 10 patients with TMJ re-ankylosis were screened according to inclusion and exclusion criteria. Preoperative virtual surgical planning and mock surgery was performed followed by TJR using Stock TMJ Replacement System. Result: This study showed statistically significant improvement in maximum interincisal opening (MIO) from 1.83mm to 37.83mm (p< 0.001), bite force from 0kg to 10.31kg (p< 0.001), masticatory efficiency, diet consistency post stock-TJR with a mean follow-up of 60months. Mandibular range of movements and quality of life improved markedly with minimal postoperative complications. VSP and mock surgery enhanced precision, reduced the intra-operative duration and complications. Conclusion: This study highlights the clinical success of dual validation protocol by integrating VSP guided stock-TJR in TMJ Re-ankylosis. Mock surgery verified digital precision with intraoperative flexibility, ensures optimal prosthesis fit, restored mandibular function, improvement in quality of life (QoL) and functional rehabilitation. With ongoing advancements, VSP guided stock-TJR protocol can be considered effective in TMJ re ankylosis.

Abstract: The topology and spatial extent of regional surface-water bodies fluctuate seasonally, with the most pronounced changes during high-intensity rainfall and flooding. To capture these dynamics, we introduce a novel geomorphological metric, the catchment-to-destination area ratio (C/D ratio). The catchment area represents the upstream contributing surface area, and the destination area represents the spatial extent of the receiving sink, such as a depression or existing water body. Theoretically, the C/D ratio scales proportionally with the rate of water-level (stage) rise in destination basins during wet seasons. As water levels increase, the lateral expansion of receiving basins drive fundamental topological shifts in watershed-network connectivity. Using the C/D ratio and upstream catchment area, we develop a computational simulation algorithm for modeling the seasonal and climate-driven evolution of surface-water configurations from digital elevation models (DEMs). The algorithm distinguishes natural topographic depressions from spurious digital artifacts, supports automated channel routing across low-relief terrain, preserves two-dimensional channel widths, and estimates depression storage capacity for flood-buffer assessment. Evaluation across multiple DEM datasets demonstrates the algorithm’s ability to simulate seasonal variation of the watershed network and identify flood-prone terrain configurations. This framework allows predictions for regional flood-hazard mapping, water-resource planning, and environmental and ecosystem management. Available at https://cs.stanford.edu/people/zjl/flow.