Search Results (8,546)

Accurate estimation of subsurface acoustic impedance is fundamental to quantitative reservoir characterization in seismic exploration. Nevertheless, a single network architecture cannot adequately represent both the local details and the global trends of seismic records within a unified framework, while the severe scarcity of annotated well-log data substantially constrains the generalization capability and predictive accuracy of deep-learning-based inversion approaches. To overcome these limitations, a semi-supervised acoustic impedance inversion framework based on a hybrid deep learning architecture is proposed. The framework employs a cascaded architecture consisting of a multi-scale depthwise separable convolution with channel attention (MSDSE) module and a convolution-augmented Transformer encoder. Seismic data are first processed by the MSDSE module to extract local multi-scale temporal features, and are subsequently passed to the convolution-augmented Transformer encoder, which captures global long-range sequence dependencies while retaining complementary local temporal information. The two modules progress hierarchically and jointly achieve a feature representation that spans from local details to global trends, and the initial low-frequency model is fused with the network output via channel-wise concatenation. Meanwhile, an initial-model constraint together with a physical-consistency constraint are simultaneously imposed within the loss function, thereby improving training stability while fully leveraging the physical information embedded in unlabeled traces. Experiments on both synthetic and field data confirm the effectiveness of the proposed method. The results show that, even with a small number of labels, the method produces stable impedance estimates and outperforms conventional deep learning methods in both generalization and prediction accuracy. Full article

The urban industrial spatial structure is widely recognized as a critical factor shaping public transit travel patterns. However, the relationship between variations in this spatial structure and bus origin-destination (OD) passenger flows remains insufficiently explored. To address this gap, this study proposes two quantitative indicators, namely, Regional Industrial Proximity (RIP) and Regional Industrial Scarcity (RIS), to characterize the differentiation of urban industrial spatial structures. The empirical study is conducted in the core built-up area within Beijing’s 5th Ring Road. The study area is divided into 8.3 km × 8.3 km grids, and a Proximity–Scarcity Relational Graph Convolutional Network (PS-RGCN) model is employed to predict urban bus passenger flows. The dataset primarily comprises approximately 4.21 million smart card transaction records collected over one continuous week, alongside 276,000 Points of Interest (POI) records covering 20 industrial categories. Specifically, the proposed PS-RGCN model achieves the best prediction performance among all benchmark models. The best overall performance is attained when incorporating the RIP of Scenic Spots and Historical Sites as edge relationships, yielding a coefficient of determination R² of 0.888. In comparison, the gravity model yields an R² of 0.858, and the baseline GCN model yields an R² of 0.378, indicating the superior predictive capability of the proposed model. This study verifies that industrial proximity and industrial scarcity exert complementary effects in bus OD flow prediction. Incorporating both factors synergistically into the multi-relational graph neural network framework enables more effective identification of the predictive relationship between urban functions and bus travel demand. Full article

Biochar (BC) and hydrochar (HC) have attracted increasing attention as sustainable soil amendments for improving soil water retention and mitigating agricultural water stress. This review synthesizes and compares the current state of knowledge on the production, physicochemical properties, and hydraulic performance of slow-pyrolysis BC, hydrothermal carbonization hydrochar (HTC HC), and hydrothermal liquefaction hydrochar (HTL HC). The mechanisms governing soil water retention are first examined, followed by a comprehensive review of the effects of amendment properties, feedstock type, thermochemical conversion conditions, particle size, application rate, and soil characteristics on field capacity, permanent wilting point, plant-available water, and water-holding capacity. The available evidence demonstrates that BC generally provides the most consistent improvement in soil hydraulic properties, particularly in coarse-textured soils, whereas the performance of HTC HC is considerably more variable and strongly dependent on hydrothermal conversion conditions and soil characteristics. HTL HC remains largely unexplored but shows promising hydraulic performance and exceptional resistance to biodegradation. Apparently contradictory findings among published studies are shown to arise largely from interactions among feedstock and conversion conditions, resulting amendment properties, soil characteristics, application conditions, and differences in hydraulic evaluation, highlighting the need for integrated mechanistic frameworks rather than interpretation based on individual factors. A comparative assessment of the three materials further considers ecotoxicity, biodegradation, life-cycle assessment, and techno-economic analysis. Overall, BC is currently the most mature soil amendment technology, HTC HC offers important advantages for wet biomass utilization, and HTL HC represents a promising but underdeveloped alternative. Future research should emphasize standardized evaluation methods, long-term field validation, and integrated mechanistic approaches linking production conditions, amendment properties, soil characteristics, and application conditions to enable predictive, application-specific design of carbonaceous soil amendments for sustainable soil water management. Full article

Federated learning (FL) enables collaborative model training across decentralized clients while preserving data privacy, yet real-world deployments still face communication bottlenecks, performance degradation under heterogeneous client data, and limited personalization. In this study, we introduce FedMCP++, a modular and communication-efficient personalized FL framework in which every client owns a complete private model—a lightweight convolutional backbone with a private expert head—and collaboration is carried out entirely through knowledge exchange rather than parameter exchange. In each round, clients share only temperature-softened class predictions and class-wise feature prototypes computed on a small public proxy set; the server fuses them into an accuracy-weighted teacher and broadcasts the result, and clients realign their models through knowledge distillation, an instance-level contrastive objective, and prototype alignment. We evaluate FedMCP++, its ablations, and two knowledge-based baselines on six benchmark vision datasets with 10, 20, and 30 clients. The results indicate dataset-dependent trade-offs rather than uniform superiority: collaborative distillation improves average client-level accuracy over independent local training in twelve of eighteen configurations—most clearly under severe per-client data scarcity (e.g., up to +2.7 percentage points on KMNIST and +2.4 on STL-10 with 20–30 clients)—whereas independent training ensembles remain strongest on SVHN and CIFAR-10 at the studied budgets. Because no parameters are transmitted, the per-round uplink payload is a fixed-size 42.6 KB message, 9.9–12.8× smaller than full-model synchronization, and is invariant to model capacity. These properties make FedMCP++ a flexible framework for personalized FL in wireless edge and Internet of Things environments where bandwidth and privacy constraints are paramount. Full article

Quantifying groundwater storage in karst aquifers remains challenging because of their heterogeneous structure and the scarcity of direct observations. This study proposes a pragmatic approach for characterizing storage and water availability in karst systems at the regional scale using long-term hydrographs from 16 Swiss karst springs and rivers. We distinguish between seasonal storage, associated with recharge events, and low-water storage, which sustains discharge during periods without significant recharge. Seasonal storage was estimated at approximately 30–70 mm for most investigated systems, while total storage reached up to about 140 to 180 mm at some sites, based on recharge–discharge modelling. Low-water storage was estimated to be on the order of 200 mm across most investigated systems, despite differences in hydrogeological settings. Analysis of low-water recession curves showed that most natural springs analyzed in this study followed a similar master recession curve, with low-water conditions defined as beginning at a specific transition discharge of 11.25 L s⁻¹ km⁻². This similarity provides the basis for a regional drought index that estimates the volume of remaining groundwater storage and forecasts discharge several weeks in advance. The results also support a conceptual model in which epikarstic, epiphreatic, and deeper storage compartments attenuate short-term discharge variability while sustaining low-water discharge over prolonged periods. Comparison with non-karst catchments shows that karst hydrogeological systems dampen peak flows but sustain baseflow through distinct storage reservoirs. This approach delivers quantitative indicators to assess drought, manage groundwater, and foresee water shortages in karst regions. Future work will test its validity outside the Swiss dataset. Despite discussed uncertainties in discharge measurements and catchment delineation, results demonstrate that dedicated hydrograph-based analyses can yield robust and transferable insights into karst groundwater storage at regional scales. Full article

Integrated watershed sustainability assessments are still incipient in Ecuador, where scarce and heterogeneous data are limiting factors. This study introduces the Watershed Sustainability Index (WSI) to the Chibunga Basin in Chimborazo, Ecuador, marking its first application in this region. A key aspect of this study is the development of a clear and reproducible method for applying the index, particularly in inter-Andean basins where data are limited. The four dimensions—Hydrology, Environment, Life, and Policy—were assessed through the Pressure–State–Response (PSR) framework by combining secondary statistics, field sampling, GIS land-use analysis, and a two-round Delphi consultation with ten experts to operationalize the institutional (Policy) component. The basin scored 0.38 (low sustainability), with a critical Hydrology dimension (0.08), poor Policy dimension (0.33), moderate–low Life dimension (0.42), and moderate Environment dimension (0.67). The central finding goes beyond these scores: the operationalized PSR framework pinpointed where the management cycle breaks down in practice. Systematically null response scores reveal that unsustainability stems from the failure to translate an existing regulatory framework into formal institutional action, rather than from regulatory absence or physical water scarcity alone, further constrained by unfavorable socioeconomic conditions. The procedure turns the WSI from a scoring tool into a diagnostic one, providing a replicable reference for data-scarce Andean basins. Full article

Background: High-altitude hypobaric hypoxia (HH) is a major environmental stressor that impairs cognitive function, yet effective and widely available therapeutics remain limited. Although Rhodiola rosea has shown neuroprotective effects, its resource scarcity restricts large-scale application. Cistanche tubulosa (Schrenk) Wight, a traditional Chinese functional food, has been increasingly used in health supplements due to its anti-fatigue, anti-dementia, and memory-enhancing properties, suggesting potential benefits against hypoxia-induced cognitive impairment. Objective: This study aimed to investigate the effects of C. tubulosa ethanol extract (CTE) on hippocampal tissue and gut microbiota upon chronic HH exposure using SPF male Sprague–Dawley (SD) rats. Methods: A total of 60 male SD rats were randomly assigned to six experimental groups (n = 10 per group): normoxic control, untreated HH model, positive control (R. rosea), and low-, medium-, high-dose CTE treatment groups. Behavioral tests (Morris water maze), hippocampal histopathology, serum and hippocampal oxidative stress markers (SOD, GSH-Px, MDA), expression of PI3K/Akt/mTOR-HIF-1α signaling pathway proteins (by immunohistochemistry), and gut microbiota composition (by 16S rRNA sequencing) were evaluated. Results: The results demonstrated that CTE significantly improved cognitive function, enhanced SOD and GSH activities, and reduced MDA levels in both hippocampus and serum. CTE also modulated the expression of PI3K/Akt/mTOR-HIF-1α pathway proteins in the hippocampus. Furthermore, CTE altered gut microbial diversity and abundance, increasing the proportion of beneficial bacteria, which may further influence hippocampal function via the gut–brain axis. Conclusion: These findings provide scientific evidence for the application of C. tubulosa as a potential health supplement for high-altitude adaptation and lay a foundation for subsequent research. Full article

The application of Machine Learning (ML) to bridge Structural Health Monitoring (SHM) is constrained by a lack of diverse and labelled datasets. Obtaining high-quality training data from operational infrastructure is inherently difficult because critical assets are typically repaired immediately upon the detection of defects, preventing the collection of data describing diverse failure modes. To address this scarcity and enable the prototyping of robust algorithms, this study presents a framework for generating synthetic modal frequencies using a calibrated Finite Element (FE) model of the S101 bridge. Aleatory uncertainties and environmental variability are incorporated through the stochastic variation of material properties and thermal loads derived from a 20-year climate record. Analysis of the generated dataset revealed that simulated thermal loads induced frequency shifts that often exceeded those caused by minor structural damage, confirming the necessity of training on environmentally representative data. The primary contribution of this work is an open-access, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) synthetic dataset, intended to serve as a standardised benchmark for the SHM research community under conditions of combined structural and environmental uncertainty. To demonstrate the utility of the generated data, the performance of a supervised multi-layer perceptron and an unsupervised k-means clustering algorithm are evaluated, with the supervised approach achieving a maximum classification accuracy of 1.00. However, the framework also reveals a fundamental modelling limitation: the linear FE approach failed to replicate the physical response under pier settlement, producing frequency shifts an order of magnitude below those observed experimentally. Full article

Agricultural by-products and Chinese medicine crop residues, as non-traditional animal feed, have great potential to alleviate the scarcity of traditional feed and reduce the environmental burden. In this study, the effects of adding fermented ginseng by-products to the diet on growth performance, meat quality, flavor characteristics and metabolism of Hongyu roosters were evaluated. A total of 100 Hongyu roosters were randomly assigned to either a control (Con) group fed a basal diet or a fermented ginseng by-products (Fe) group fed a basal diet supplemented with 3% fermented ginseng by-products, with five replicates per group, for 100 days. The results demonstrated that the fermented diet significantly enhanced final body weight and average daily gain (ADG) while reducing the feed conversion ratio (FCR) (p < 0.05). The percentages of breast and thigh muscles and breast muscle fiber diameter were significantly increased. The results also exhibited enhanced water-holding capacity, coupled with significantly decreased shear force and drip loss (p < 0.05). Electronic nose and tongue analyses confirmed enriched meat aroma and optimized taste profiles, which aligned with elevated levels of flavor-precursor amino acids and essential polyunsaturated fatty acids. Serum metabolomics found purine metabolism and the biosynthesis of unsaturated fatty acids to be pivotal pathways. Metabolites related to nucleotide and lipid metabolism were regulated. In conclusion, fermented ginseng by-products serve as an effective functional feed to improve poultry growth performance and meat quality. Full article

Background: Pain is a multidimensional experience shaped not only by nociceptive input but also by cognitive, emotional, and social processes. In musculoskeletal (MSK) rehabilitation, outcome variability among patients with similar clinical presentations suggests that determinants beyond tissue pathology may influence pain perception and recovery. Contextual factors (CFs)—communication, therapeutic relationship, patient expectations, and environmental cues—are increasingly recognized yet remain inconsistently integrated into routine practice. Objective: To map how CFs are represented in the physiotherapy literature indexed under that terminology, and to identify where empirical evidence in MSK rehabilitation is present and where it is absent. Methods: A narrative review with a systematic search strategy, reported following SANRA, was conducted in PubMed and Scopus, searched on 31 March 2026. Records were screened against predefined eligibility criteria, including publication from 2006 onwards and English language. Two reviewers independently screened records and resolved disagreements by consensus. Methodological quality was appraised using the Cochrane RoB2 tool for randomized controlled trials and an adapted Newcastle–Ottawa Scale for observational and cross-sectional studies. Conceptual publications were not formally appraised. Each publication was classified as direct clinical evidence (Tier A), indirect mechanistic or implementation-level evidence (Tier B), or conceptual contribution (Tier C). Results: Eight publications were included: no source provided direct clinical evidence in a musculoskeletal rehabilitation population (Tier A), five provided indirect mechanistic or implementation-level evidence (Tier B), and three were conceptual contributions (Tier C). The single randomised controlled trial retrieved was conducted in asymptomatic volunteers and was at high risk of bias for the between-group comparisons of all reported outcomes. Findings are organised as contextual domains, mediating mechanisms, and outcomes. No direct clinical evidence was retrieved; claims regarding expectations, therapeutic alliance, non-verbal communication, and the clinical environment rest on indirect or conceptual sources. Conclusions: CFs may contribute to pain and rehabilitation variability through cognitive, emotional, and relational pathways, complementing rather than replacing evidence-based care. The scarcity of direct clinical evidence within the literature indexed under this terminology is itself a finding. Construct-level searches and pragmatic trials are required to clarify the contribution of individual contextual domains. Full article

Anyone who has watched a distant scene shimmer above hot pavement has seen atmospheric turbulence destroy image detail. In long-range imaging, turbulence produces spatially varying blur, geometric warping, scintillation, and temporal instability. Recovering the underlying scene is therefore an ill-posed inverse problem, and learned priors must compensate for distortions that simplified optical models capture only partially. We use turbulence mitigation as the umbrella term for all countermeasures and turbulence restoration for its computational core, the estimation of a clean image from degraded observations. From a cognitive-computing perspective, mitigation is not merely image enhancement. It is an uncertainty-constrained visual inference problem in which an intelligent system must reconstruct, interpret, and act on observations relayed through a stochastic physical channel. This critical review examines how the deep learning era has reshaped turbulence mitigation, with physics as the foundation for understanding degradation and designing inductive biases. It traces the architectural progression from convolutional and adversarial networks to Transformers, denoising diffusion models, Mamba and other state-space architectures, and physics-informed frameworks. For each family, we ask a common question: How does it treat the aleatoric uncertainty intrinsic to a random optical channel and the epistemic uncertainty introduced by scarce and simulator-dominated training data? The review also analyzes datasets, simulation strategies, loss functions, and evaluation metrics, and it separates the small body of shared-protocol benchmark evidence from the far larger body of self-reported results that cannot be compared across studies. Persistent obstacles include the synthetic-to-real domain gap, the scarcity of paired real turbulence data, the mismatch between fidelity metrics and downstream task performance, and the computational cost that limits operational deployment. We close with an AI-centered agenda in which uncertainty quantification stands alongside domain adaptation as a first-order priority. Full article

Background/Objectives: Pregnancy is associated with profound hormonal, hemodynamic, and metabolic changes that may affect the auditory and vestibular systems. However, evidence regarding pregnancy-related audiological disturbances remains limited due to the low incidence of these conditions and the scarcity of dedicated studies. The study aimed to identify the most commonly reported audiological changes occurring during pregnancy and the postpartum period with an emphasis on sudden sensorineural hearing loss (SSNHL), its risk factors, clinical presentation, and treatment methods. Methods: A literature search was conducted in PubMed, Embase, Web of Science, and Google Scholar from database inception to November 2025 using combinations of the keywords “pregnancy”, “hearing loss”, and “audiological changes”. The review is based on twenty-four original articles that met the preselected criteria. Results: Current evidence does not support pregnancy as an independent risk factor for SSNHL; the prevalence in obstetric patients is lower than in the control groups. Most cases occur during the third trimester or the postpartum period. Pre-pregnancy obesity and elevated blood pressure were identified as potential risk factors. Higher income levels and rural residency are associated with an increased incidence of SSNHL, whereas gestational diabetes, pre-eclampsia, and pregnancy-related weight gain showed no consistent association with SSNHL. Audiometric studies demonstrated mild, predominantly low-frequency, hearing threshold elevations that were generally transient and resolved after delivery. Tinnitus, nausea, aural fullness, dizziness, and vertigo were the most common ear-related complaints. Most of these disorders resolve spontaneously, but some studies suggest steroid treatment should be introduced. Intratympanic corticosteroid therapy was the most frequently investigated treatment for SSNHL and was associated with favorable hearing outcomes in most reported cases. Limited evidence suggests that Dextran 40 may improve hearing outcomes when used as an adjunctive therapy. Conclusions: Potentially transient cochleovestibular impairment during pregnancy and the postpartum period should not be underestimated and requires further evaluation. Pregnancy-related physiological adaptations may contribute to transient auditory and vestibular disturbances, particularly during late pregnancy and the postpartum period. Although current evidence does not indicate an increased risk of SSNHL during pregnancy, prompt recognition and management remain important to prevent long-term hearing impairment. Further high-quality studies are required to clarify the underlying mechanisms and establish evidence-based treatment recommendations for pregnant patients with SSNHL. Full article

Freshwater scarcity can weaken water governance when hydrological pressure interacts with intensive agricultural demand, regulatory weakness, and political conflict. This research evaluates whether Irrigation District 005 (IR 005) in Chihuahua, northern Mexico, demonstrates local water-governance fragility across three domains: public security, the rule of law, and the ability to sustain water access and food production. A mixed-methods approach integrates legal and human rights documentation, institutional records, published studies, and a structured media review with hydrological, agricultural, climatic, and reservoir data. Water balances were analysed for 1998–2023, precipitation trends for 1980–2020, and crop water requirements were estimated using the Food and Agriculture Organization’s Irrigation and Drainage Paper No. 56 (FAO-56) Penman–Monteith framework, the crop coefficient (Kc), the water-stress coefficient (Ks), the United States Soil Conservation Service (SCS) Curve Number method, and application-efficiency assumptions. The 2020 water conflict resulted in fatalities, injuries, arrests, and documented human rights violations. Rule-of-law capacity was further diminished by unauthorised withdrawals, cultivation beyond authorised irrigation plans, and limited enforcement. The annual water balance shifted to persistent deficits after 2016, reaching an estimated deficit of 2268 cubic hectometres (hm³) in 2020. Annual precipitation did not exhibit a statistically significant monotonic decline during 1980–2020 (Mann–Kendall Z = −0.79, τ = −0.0878, p = 0.4251; Sen’s slope = −1.1628 mm yr⁻¹; Mann–Whitney p = 0.5313), indicating that recent stress is more closely linked to production scale, crop mix, governance conditions, and irrigation efficiency than to a long-term reduction in rainfall. Sensitivity analysis revealed that ±15% changes in Kc and Ks altered gross water requirements by approximately ±16–17%, while equivalent changes in effective precipitation produced changes of only 1–3%. These results demonstrate heightened water-governance fragility resulting from mutually reinforcing hydrological, institutional, and conflict-related pressures. Future research should refine locally calibrated water-demand parameters and develop reproducible monitoring systems that combine hydrological, institutional, satellite, and participatory data to support anticipatory, transparent, and rights-based water governance. Full article

This study examines the complex dynamics of water scarcity and infrastructure challenges in eThekwini Metropolitan Municipality, KwaZulu-Natal, South Africa. Despite its coastal location, the municipality faces persistent water shortages driven by rapid population growth, urbanization, climate change risks, aging and deteriorating infrastructure, and service delivery constraints. Communities including KwaXimba, Phoenix, Inanda, and Tongaat experience inconsistent access to clean water. The study aimed to identify root causes of scarcity, assess the effectiveness of existing water infrastructure, and propose sustainable, community-informed strategies to improve water resilience and equitable access. A qualitative research design was adopted to capture lived experiences and perceptions of water access across seven wards (1, 3, 52, 54, 56, 57, and 61), representing approximately 357,492 residents. Focus group discussions involved five randomly selected participants per ward, identified through councilor databases. Structured discussions were recorded for accuracy. Data were transcribed and analyzed using NVivo through systematic coding, thematic analysis, and organization of data into structured project folders aligned with research objectives. The analysis revealed recurring themes related to infrastructure decay, climate vulnerability, rapid urban expansion, and service delivery inefficiencies, highlighting significant barriers to reliable water access and community well-being. Findings provide evidence-based insights to guide policymakers, planners, and water authorities in strengthening infrastructure planning, climate resilience, and sustainable water governance. Full article

Background: Global antibiotic consumption continues to rise across pediatric and adult populations, while the genotoxic consequences of host eukaryotic exposure remain less systematically characterized than the parallel problem of antimicrobial resistance. Several lines of evidence, from molecular cytogenetics, redox biology, and systems toxicology, now permit a more mechanistically resolved synthesis of antibiotic-induced genome stress than was previously possible, although a substantial fraction of this evidence is preclinical and warrants cautious clinical extrapolation. Scope: This narrative review evaluates the molecular mechanisms, cytogenetic biomarkers, and translational implications of antibiotic-induced genotoxicity, with a primary focus on six clinically prominent classes (fluoroquinolones, nitroimidazoles, aminoglycosides, macrolides, β-lactams, and tetracyclines) and a brief extension to glycopeptides and glycylcyclines. We organize the evidence around three convergent mechanistic axes rather than around individual drugs. Key findings: Accumulating evidence supports three intersecting off-target axes: (i) eukaryotic topoisomerase II interference, principally documented for fluoroquinolones; (ii) mitochondrial dysfunction, reflecting the evolutionary kinship between the mitoribosome and bacterial ribosomes; and (iii) inflammation-coupled redox stress, often amplified by microbiome perturbation. These pathways converge on a common spectrum of DNA lesions—including double-strand breaks, oxidatively modified bases, replication-fork stalling, and chromosomal mis-segregation) detected by complementary assays (CBMN-Cyt, comet, γH2AX, and oxidative and mitochondrial biomarkers). Pediatric, pregnant, geriatric, and oncology populations may represent biologically distinct susceptibility strata, although direct human evidence for several of these inferences remains limited. Limitations: Causal inference is constrained by infection as a confounder, frequent use of supratherapeutic in vitro concentrations, reliance on immortalized cell lines that may not recapitulate primary-cell repair capacity, inter-laboratory variability across cytogenetic assays, and a marked scarcity of pediatric and pregnancy biomonitoring data. Most existing positive signals derive from preclinical models; clinically validated long-term outcomes, particularly carcinogenic endpoints, remain inconsistently demonstrated for most antibiotic classes outside metronidazole. Conclusions: Antibiotic-induced genotoxicity appears to be a measurable and mechanistically tractable dimension of drug safety, though its clinical magnitude in real-world exposure scenarios requires further investigation. Integrating multi-omics, microphysiological systems, single-cell genotoxicology, and AI-assisted prediction may improve risk resolution, particularly in vulnerable populations. We argue that antimicrobial stewardship discussions should consider host genome integrity alongside resistance, while remaining mindful that the mechanistic case currently outpaces clinical-endpoint validation. Full article