Search Results (16,872)

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. Full article

Aquatic macrophytes constitute essential bioindicators of the ecological status of Mediterranean wetlands. We evaluated 136 Andalusian wetlands across four biogeographical regions (Sierra Morena, Betic Ranges, Guadalquivir Valley, and Coastal Zone) by contrasting two methodological approaches. We compared a standard biological valuation index, based on hydrophyte valuation and total species richness, with a biogeographical assessment focused strictly on the originality, singularity, and integrity of hydrophyte assemblages. Results revealed a critical nonlinear decoupling between both metrics. Traditional valuation prioritized the Coastal and Guadalquivir zones, inflating the value of communities saturated by widespread taxa and masking their lower structural integrity. Conversely, the biogeographical analysis identified Sierra Morena as the reservoir of highest structural stability despite its natural species poverty. Furthermore, residual analysis exposed highly original hidden jewels systematically undervalued by standard protocols. Since richness-dependent metrics risk neglecting unique hydrophyte components, we propose a dual conservation strategy integrating irreplaceability and structural integrity. Ultimately, this framework provides actionable insights for the sustainable management of Mediterranean aquatic biodiversity, aligning conservation practices with global ecological sustainability goals. We caution that management decisions based solely on richness thresholds may inadvertently prioritize common habitats over functionally unique but species-poor refugia. Full article

Members of the genus Rhizobium are best known for nitrogen-fixing symbioses with legumes, yet their diversity and evolutionary roles in non-legume hosts remain poorly explored, particularly in arid ecosystems. We report the isolation and characterization of strain AGC32, an endophytic bacterium obtained from surface-sterilized roots of the desert medicinal plant Peganum harmala collected in Moroccan drylands. Phylogenomic analyses placed AGC32 within the genus Rhizobium but clearly distinct from described species, with average nucleotide identity values below 96% and digital DNA–DNA hybridization values below 70%, supporting its designation as a novel species for which the name Rhizobium moroccans sp. nov. is proposed. Comparative genomics revealed extensive structural genome rearrangements relative to its closest sequenced relative, Rhizobium deserti, indicating a divergent evolutionary trajectory. The high-quality draft genome encodes metabolic pathways associated with adaptation to nutrient limitation and environmental stress, including complete allantoin utilization, polyphosphate metabolism, organic acid assimilation, and multiple systems involved in oxidative and osmotic stress tolerance. Phenotypic assays corroborated these genomic predictions, demonstrating the ability to metabolize diverse organic acids and carbohydrates and to express multiple plant growth–promoting traits, including nitrogen fixation and the solubilization of phosphorus, potassium, and silicon. Collectively, these findings expand the ecological and evolutionary diversity of Rhizobium, demonstrate its capacity to associate with non-legume medicinal plants in extreme environments, and highlight desert ecosystems as reservoirs of previously unrecognized microbial diversity with potential applications in sustainable agriculture in arid regions. Full article

Remote sensing enables the analysis of landscape dynamics; however, catastrophic disturbances create new surface conditions that are not adequately captured by retrospectively defined land-cover classes. This study addresses the challenge of temporally matching unsupervised classifications to monitor post-catastrophic floodplain dynamics on Khortytsia Island following the destruction of the Kakhovka Reservoir. Multi-temporal Sentinel-2 Level-2A data from 2022 to 2025 were processed using spectral indices, standardised within a common predictor space, and classified through unsupervised clustering. Cluster solutions from individual dates were then matched based on spectral similarity and spatial continuity, with their temporal interpretation guided by concepts of landscape memory and landscape perception. Higher-order spatiotemporal units were subsequently derived through contextual superclustering. The analysis identified 16 clusters across the study period, with 4 to 12 clusters represented on individual dates. Their temporal coordination enabled the distinction of higher-order units exhibiting contrasting dynamics, including directional trend, seasonal, and mixed types. The proposed framework facilitates the identification of newly formed surface states, their temporal coordination, and their integration into a hierarchical spatiotemporal model of post-catastrophic landscape change. Full article

In response to the significant challenges posed by ice accumulation and contamination from various fluids in complex operating conditions for metallic materials, this study utilises picosecond laser precision machining to develop a ‘slippery surface’ featuring a micro-channel network structure. The core innovation of this study lies in the use of laser-machined micrometre-scale array textures to overcome the limitations of traditional isolated pores. These globally interconnected micro-channels serve as highly efficient reservoirs and dynamic transport channels for lubricants, significantly enhancing the interfacial capillary locking force of the lubricant. Experimental results demonstrate that this unique network geometry endows the surface with exceptional fluid replenishment and self-healing properties, enabling it to exhibit outstanding broad-spectrum hydrophobicity towards various fluids—including water, crude oil and ethanol (surface tension range: 17.9–72.0 mN m⁻¹)—with sliding angles consistently below 12°, whilst effectively slowing the dehydration and solidification processes of biological fluids. At a low temperature of −15 °C, the surface achieved an ice formation delay of up to 286 s, with an ice adhesion strength of only 33.9 kPa, ensuring that accumulated ice could be spontaneously detached under minimal external force. Furthermore, the micro-channel network structure serves as a key protective mechanism against mechanical wear, maintaining robust slippery properties even after three hours of high-pressure water jet scouring (Weber number of 300). This reliable interface, achieved through structural management, provides an efficient and scalable platform for addressing the all-weather anti-icing and antifouling requirements of outdoor infrastructure. Full article

This study investigated the microbiological composition and antimicrobial resistance (AMR) profiles of artisanal goat and sheep milk cheeses produced in Poland. Ten raw milk cheeses (five each from goat and sheep milk) were analyzed using a combined approach involving culture-dependent enumeration, 16S rRNA gene sequencing, and antibiotic susceptibility testing. Microbial counts revealed substantial variability among the samples, with lactic acid bacteria (LAB) dominating the microbiota. Taxonomic analysis confirmed the predominance of Lactococcus, Streptococcus, and lactobacilli, although marked intra-group heterogeneity was observed. Multivariate analyses indicated that sample-specific factors had a greater influence on microbiome composition than milk origin. Among 170 isolates, 28.7% were classified as multidrug-resistant (MDR), being most prevalent in Enterobacterales (100%) and Enterococcus spp. (73%), whereas LAB exhibited low resistance levels (16.2%). Resistance was most frequently associated with aminoglycosides and β-lactams. The resistance results were interpreted according to CLSI guidelines. These findings demonstrate that artisanal cheeses harbor complex, dynamic microbial ecosystems that may serve as reservoirs of antimicrobial resistance. The results highlight that environmental and technological factors, rather than milk source alone, are key drivers of both microbiome structure and resistance distribution, underscoring the need for targeted AMR monitoring in traditional dairy products. Full article

Long COVID (LC), also referred to as post-acute sequelae of SARS-CoV-2 infection, is characterized by persistent symptoms originating 3 months following acute COVID-19, lasting for at least two months and frequently affecting individuals who initially experienced mild to moderate disease. The clinical spectrum is heterogeneous, involving respiratory, cardiovascular, neurological, renal, gastrointestinal, and endocrine systems, thereby posing substantial diagnostic and therapeutic challenges. Despite extensive investigation, the precise immunopathogenic mechanisms underlying LC remain incompletely defined. Accumulating evidence suggests that LC is driven by a multifactorial interplay of persistent viral antigen reservoirs, chronic immune activation, dysregulated innate and adaptive immune responses, autoimmunity, endothelial dysfunction, microvascular injury, and aberrant tissue repair. These systemic immune perturbations manifest variably across different organs, contributing to the diverse clinical phenotypes observed. However, mechanistic clarity is hindered by heterogeneity in study designs, limited longitudinal data, and the absence of standardized immunological profiling. This narrative review provides integrative insights into the immunopathogenesis of LC, synthesizing current evidence on systemic immune dysregulation and organ-specific immunological mechanisms. A conceptual framework is proposed to facilitate a structured understanding of this complex syndrome and to guide future research toward targeted immunomodulatory strategies. Full article

Heavy oil and extra-heavy oil represent mobility-limited petroleum resources because supramolecular associations of asphaltenes and resins, together with strong interfacial resistance, generate extremely high apparent viscosity. In recent years, nanotechnology has emerged as a promising approach for viscosity management and enhanced oil recovery (EOR). This review critically examines recent advances in nano-assisted viscosity reduction from a reservoir-operational perspective and organizes the literature into two field-relevant categories: metal-based and non-metal nano-systems. Metal-based nanoparticles (NPs) mainly promote catalytic aquathermolysis and related bond-cleavage and hydrogen-transfer reactions under hydrothermal conditions, enabling partial upgrading and persistent viscosity reduction during thermal recovery. In contrast, non-metal nano-systems—particularly silica- and graphene-oxide-derived materials—primarily operate through interfacial and structural regulation mechanisms at low or moderate temperatures. These effects include wettability alteration, interfacial-film stabilization, modification of asphaltene aggregation behavior, and the formation of dispersed-flow regimes such as Pickering-type emulsions that reduce apparent flow resistance in multiphase systems. Beyond summarizing nanomaterial types, this review emphasizes reservoir-scale considerations governing field applicability, including brine stability, NPs transport and retention in porous media, and formulation compatibility. Comparative analysis highlights the distinct operational windows of thermal catalytic nano-systems and cold-production nano-systems, providing a reservoir-oriented framework for designing nano-assisted viscosity-reduction technologies. Full article

The middle and lower reaches of the Yangtze River Basin occupy a strategically pivotal position in regional development; yet extreme drought-runoff events pose severe threats to water supply and ecological security. Despite this, systematic research gaps persist, including the lack of a unified definition, standardized identification criteria, and clear understanding of formation mechanisms for extreme drought-runoff. To address these limitations, this study focused on extreme drought-runoff in the basin, utilizing 1956–2024 discharge data from four mainstream hydrological stations and meteorological data from 171 stations. Quantitative discrimination criteria were established via Pearson-III frequency analysis; meteorological characteristics were analyzed using the Meteorological Drought Comprehensive Index; and formation mechanisms were explored through partial correlation analysis and multiple linear regression. This study innovatively proposed a basin-wide three-level quantitative discrimination criterion for drought-runoff based on the June–November flow frequency of key mainstream stations, which is distinguished from single-indicator drought identification methods (SPI/SPEI/SSI) by integrating basin-scale hydrological coherence and seasonal drought characteristics. The results revealed basin-wide extreme drought-runoff in 2006 and 2022, severe drought-runoff in 1972 and 2011, and relatively severe drought-runoff in 1959, 1992, and 2024. Typical extreme drought-runoff events were characterized by sustained low precipitation and high temperatures. Meteorological factors emerged as the primary driver during June–September, while reservoir operation and riverine water intake played secondary roles. Notably, the large-scale reservoir group in the Yangtze River Basin (53 key control reservoirs) helped alleviate drought-runoff impacts from December to May (non-flood season) via water supplementation. These findings provide a robust scientific basis for precise drought-runoff prediction and the development of targeted adaptation strategies in the Yangtze River Basin. Full article

Despite the success of artificial neural networks in solving numerous tasks, they face significant challenges, including difficulties in online adaptation and rapidly increasing energy consumption. As a biologically plausible alternative, spiking neural networks offer promising capabilities for efficient cognitive computing. Recently, a three-element spiking neuron model consisting of a threshold selector, a tunnel diode, and a capacitor was proposed. In this work, we experimentally validate this model using a threshold selector hardware emulator and demonstrate its dynamical equivalence to the biologically plausible Izhikevich neuron model. To evaluate the novel neuron’s applicability for cognitive computing, we implement a liquid state machine (LSM) reservoir architecture with spatially dependent random topology for synaptic weight distribution. Our simulations on the MNIST and Fashion-MNIST benchmarks demonstrate competitive classification accuracy (97.9% and 89.5%, respectively) while offering estimated energy efficiency and processing speed enhancements compared to existing FPGA-based and memristor-based spiking reservoir implementations. The developed reservoir is feasible for processing neuromorphic sensors output, including visual perception tasks. Full article

Background/Objectives: Staphylococcus aureus (S. aureus) intramammary infection remains a major global dairy problem due to its contagious nature, its ability to persist and colonize teat/skin and mucosal niches, and the often-limited bacteriological cure achieved with antimicrobial therapy. Beyond udder health, it is relevant to public health because it can enter raw milk chains and serve as a reservoir for antimicrobial resistance determinants that may circulate between dairy animals and humans. Methods: We assessed S. aureus’ ecology in raw ewe milk from 75 sheep farms in Epirus (Greece) by sampling clinically healthy controls (group A) and clinical mastitis cases pre-treatment (group B), followed by resampling at the first post-withdrawal milking after penicillin/streptomycin treatment (group C1—therapeutic protocol 1), oxytetracycline treatment (group C2—therapeutic protocol 2), or enrofloxacin treatment (group C3—therapeutic protocol 3). Results: S. aureus detection was high and comparable across groups (A 23.0%, B 22.0–30.0%, C 20.0–22.0%), and paired analyses showed no significant pre–post shifts in detection/burden within therapeutic protocols (all p > 0.05). Nevertheless, persistence remained evident. The chromosomal gene mecA was detected in S. aureus strains in all groups, ranging from 13.6% in controls to 54.5% post-withdrawal in group C1, and was also present in the pre-treatment group. In paired sampling animals, mecA was mostly stable, with rare emergence or loss. Across antibiotic classes, within-animal resistance transitions were generally uncommon and non-significant (p > 0.05); β-lactam resistance was fully stable (p = 1.00). Descriptively, resistance to protein synthesis inhibitors tended to decline after therapy in protocol 1 and protocol 3, while protocol 3 showed post-treatment gains in fluoroquinolone resistance. By contrast, virulence-associated phenotype traits shifted after therapy: enterotoxigenicity increased post-withdrawal (especially in the C3 group), Staphylococcal Enterotoxin A (SEA) and Staphylococcal Enterotoxin B (SEB) appeared only post-therapy, Staphylococcal Enterotoxin D (SED) increased significantly in paired isolates (p = 0.002), and strong biofilm adherence increased (in C3, p = 1.5 × 10⁻⁵). Conclusions: The detection of S. aureus after therapy suggests that one possibility is that antimicrobial exposure may select for, or otherwise reshape, the residual intramammary population, rather than reliably eliminating it—an outcome that remains clinically relevant for udder health. Moreover, the persistence of mecA/methicillin-resistant Staphylococcus aureus (MRSA)-compatible profiles indicates that milk released to the food chain after withdrawal compliance may still harbor S. aureus with enhanced preservation capacity and significant food safety relevance. Full article

Internal phosphorus loading is a key process sustaining eutrophication in stratified Baltic Sea coastal systems, yet its long-term dynamics in the Archipelago Sea remain poorly quantified due to limited deep-water monitoring and the absence of sediment time series. This study provides a multidecadal assessment of internal loading from the early 1980s to 2025 using two complementary indicators: (i) seasonal accumulation of total phosphorus in the surface layer (ΔTP) and (ii) the covariation between near-bottom oxygen depletion and dissolved inorganic phosphorus (DIP) release. Temporal associations with external phosphorus inputs from marine fish farming—highly variable during the study period—were analyzed to evaluate whether cumulative loading trajectories coincided with phases of intensified ΔTP. New measurements of drifting filamentous macroalgae from 2025 were additionally used to assess their seasonal contribution to the internal phosphorus pool and their relevance for mitigation. Results show a pronounced multidecadal strengthening of internal loading signals in the mid and inner Archipelago Sea. At the Seili station, ΔTP increased by approximately 6.8 µg L⁻¹ (≈3.4-fold) since the early 1980s. This trend coincided with long-term deterioration of near-bottom oxygen conditions and increasing DIP concentrations, consistent with enhanced sediment phosphorus release. Although cumulative aquaculture loading exhibited simple correlations with ΔTP, detrended analyses indicate that these relationships largely reflect shared long-term trends rather than direct causal linkages. Drifting filamentous macroalgae formed a substantial seasonal phosphorus reservoir (≈146 t P). Overall, internal phosphorus input to the Archipelago Sea has intensified markedly—by an estimated ~70% since the 1980s—highlighting the growing importance of sediment–water feedbacks and legacy phosphorus. Effective mitigation therefore requires strategies that address both internal recycling processes and external nutrient inputs. Targeted removal of drifting filamentous macroalgae may provide a complementary nutrient-export pathway in coastal management. Full article

Reservoir release induced flooding is a major downstream hazard worldwide, yet most warning systems rely on hydraulic modeling and underuse real time reservoir operation data. This study presents a data driven framework to detect flood discharge events, assess downstream operational severity, and forecast daily discharges using deep learning. The approach was validated at the Ba Ha hydropower reservoir (Vietnam) with inflow, discharge, water level, and CHIRPS rainfall data to represent basin-scale precipitation forcing. More than 160 discharge events were identified using a composite Operational Severity Index (OSI) based on peak discharge, duration, and rise rate; although only ~2% were extreme, they posed the greatest risks. Among three Transformer-based models, Informer achieved the best short-term forecasting performance (RMSE ≈ 78 m³/s, R² ≈ 0.80), while Autoformer showed greater stability at longer horizons (3–7 days). In contrast, all models exhibited reduced skill under abrupt and extreme discharge conditions. These results demonstrate that combining trend and anomaly-aware modeling enables reliable discharge prediction and severity assessment without complex hydraulic simulations. The proposed framework provides a practical foundation for reservoir early warning systems by transforming routine operational data into actionable flood-risk information. Full article

(1) Background: University food service establishments are complex environments, where high turnover and handling practices create conditions for microbial persistence. Food-contact surfaces (FCSs) and handlers’ hands (FHs) function as dynamic reservoirs, facilitating the circulation of contaminants within these institutional settings. This study aimed to characterise the microbiological contamination of FCSs and FHs in university food service establishments in Northern Portugal and to evaluate their role as interconnected environmental reservoirs within the indoor built environment. (2) Methods: A total of 590 samples were analysed from two universities in Northern Portugal (L1, L2), comprising 380 FCS and 210 FH samples. Aerobic colony counts (ACCs), Enterobacteriaceae, and Moulds and yeasts (MYs) were analysed according to ISO methods. FH samples were additionally screened for Escherichia coli and Staphylococcus spp. (3) Results: Overall, 35.5% of FCSs were classified as non-compliant, according to microbial criteria based on guideline values from the National Health Institute Dr. Ricardo-Jorge (INSA), with non-compliance primarily driven by elevated ACCs and MYs. Based on a Generalised Linear Model (GLM), establishment types (canteens vs. cafes) were associated with Enterobacteriaceae levels (p = 0.016), whereas ACCs and MYs were not significantly associated with district, establishment type, or functional surface category (p > 0.05). Differences between left and right hands showed small effect sizes, and location was a highly significant determinant of hand hygiene acceptability. (4) Conclusions: FCSs and FHs act as relevant contamination reservoirs in these settings. The results indicate that Enterobacteriaceae levels on FCSs differed between establishment types, while ACCs and MYs showed no significant variation across the evaluated environmental factors. Marked differences in hand hygiene acceptability between campuses support the implementation of targeted interventions, including the optimisation of cleaning and disinfection protocols, the structured training of food handlers, and the routine microbiological monitoring of surfaces and hands to improve institutional food safety. Full article

With the exponential growth of natural gas reserves and utilization scale in China, underground gas storage (UGS) facilities—critical infrastructure within the natural gas production-supply-storage-sales system—have entered a phase of rapid expansion. As the core component connecting subsurface reservoirs with surface systems, wellbore integrity directly influences operational safety and service lifespan of UGS facilities. However, current leakage detection and integrity analysis methodologies for gas storage wellbores remain deficient in effective real-time monitoring capabilities. Traditional methods, however, are constrained by limited spatial coverage and insufficient precision, rendering them inadequate for comprehensive, continuous safety monitoring requirements. To address this industry challenge, this study proposes a real-time wellbore integrity monitoring framework based on distributed fiber optic sensing technology, integrating distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) devices into a synergistic monitoring system. The DTS component enables preliminary localization of potential leakage points through detection of minute temperature anomalies along the wellbore, while the DAS unit accurately identifies acoustic signatures caused by gas leakage within casings via monitoring of acoustic vibration signals propagating along the optical fiber. Through joint analysis of DTS and DAS data streams, real-time diagnosis of wellbore leakage events and integrity status can be achieved. Field trials demonstrated that this hybrid monitoring system achieved leakage localization accuracy within 1.0 m, effectively distinguishing normal operational signals from abnormal leakage characteristics. During actual monitoring operations, no indications of wellbore integrity compromise were detected; only minor noise and interference signals originating from surface construction activities were observed. Full article