Search Results (2,786)

Petroleum contamination significantly impacts soil microbial communities and vegetation; however, the long-term effectiveness of phytoremediation remains poorly understood. This study evaluated soil microbiological activity, polycyclic aromatic hydrocarbon (PAH) concentrations, and physiological responses five years after the remediation of a petroleum spill site in central Poland. Following a pipeline failure in June 2020 that released diesel fuel and gasoline into the riparian habitat, the contaminated area underwent remediation using Urtica dioica L. as the primary phytoremediator. Soil samples from five plots along a contamination gradient were analyzed for microbial abundance (total bacteria, fungi, fluorescent Pseudomonas sp.), PAH fractions (C6–C12, C13–C16, C17–C35), and physicochemical properties. Chlorophyll fluorescence (JIP test) on two species was used to assess plant photosynthetic efficiency. Results revealed that successful PAH degradation required high fungal abundance rather than optimal soil fertility. Plots with 8–9-fold higher fungal populations achieved 69–81% reduction in heavy PAHs (C17–C35), while the Control plot, despite superior physicochemical properties, maintained high contamination due to low fungal colonization. Urtica dioica exhibited exceptional tolerance (stable maximum quantum yield of PSII (Fv/Fm) and elevated photosynthetic performance index (PIabs)) across all contamination levels, whereas Poa trivialis L. showed significant stress responses. The principal component analysis confirmed that soil texture influences fungal establishment, with sandy soils favoring aerobic degradation despite lower nutrient retention. These findings demonstrate that phytoremediation success depends critically on fungal-mediated biodegradation rather than baseline soil quality alone. Full article

Against the backdrop of global climate change, climate warming and increasing nitrogen deposition are profoundly altering carbon (C) and nitrogen (N) cycling in terrestrial ecosystems. Short-term observations are critical for capturing the initial response trajectories of soil C-N dynamics to environmental stress, providing timely insights into early-stage adaptation mechanisms that underpin long-term ecosystem stability. This study investigated the interactive effects of these drivers on soil C and N transformation rates, component dynamics, and their coupling relationships in a warm steppe and a warm shrub grassland within the forest–steppe ecotone of northwestern Liaoning Province. We employed field-controlled experiments using open-top chambers for warming in combination with four nitrogen addition gradients. Results showed warming plus high N addition increased soil total N but reduced net N mineralization, supporting the “N saturation hypothesis”. Though N addition generally suppressed the C conversion rate, low-level N (5 g N m⁻² a⁻¹) mitigated C loss and enhanced it under warming. Soil organic C and microbial biomass C drove C transformation. Warm shrub grassland’s stable mineral-associated organic C pool rose 640.5% (stronger resilience), while warm steppe’s C/N turnover depended on seasons (greater vulnerability); C/N transformations were synchronized in the steppe but independent in shrubland. Full article

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article

Salivary glands produce saliva through precisely coordinated epithelial ion transport processes. Ion channels are essential components of the molecular machinery that convert neural and hormonal signals into targeted ion and water flux. This review focuses on the integrated molecular and cellular mechanisms by which ion channels cooperate to generate salivary fluid under physiological conditions. Saliva formation proceeds through two sequential stages: isotonic primary fluid secretion by acinar cells, followed by ionic modification within the ductal epithelium. Parasympathetic stimulation activates muscarinic M1/3 receptors, initiating intracellular calcium signaling through inositol 1,4,5-trisphosphate-dependent release from the endoplasmic reticulum and sustained calcium entry via Orai1/TRPC channels. Elevated cytosolic calcium activates apical ANO1/TMEM16A chloride channels, the rate-limiting step in acinar fluid secretion, together with basolateral calcium-activated potassium channels that preserve the electrochemical driving force for chloride efflux. Chloride accumulation is maintained by Na⁺/K⁺-ATPase and the Na⁺-K⁺-2Cl⁻ cotransporter, while osmotic gradients drive water movement through apical aquaporin-5 and basolateral aquaporin-1/3. As primary saliva traverses the ductal system, epithelial sodium channels, CFTR, and additional ion transport pathways reabsorb sodium and chloride and secrete potassium and bicarbonate, producing hypotonic final saliva. By synthesizing calcium signaling, chloride and potassium conductance, sodium handling, and epithelial polarity into a unified framework, this review establishes ion channel integration as the fundamental basis of salivary gland fluid secretion. Full article

Infrared small target detection under complex backgrounds remains challenging due to the extremely small target size and low contrast with the surrounding background. These factors make contour information difficult to extract and often cause target features to attenuate or disappear during deep feature learning. To address these issues, this paper proposes a Gradient-Compensation-based Feature Learning Network (GCFLNet). GCFLNet adopts a multi-module collaborative design to enhance feature representation and fusion. First, an Edge Enhancement Module (EEM) is introduced to accurately capture fine-grained edge information of infrared small targets while suppressing background noise through smoothing operations. This provides reliable structural cues for subsequent feature extraction. Second, the extracted edge features are embedded into a Global–Local Feature Interaction (GLFI) module, which is inspired by self-attention mechanisms with dilated convolutions to strengthen global semantic dependencies and local detail representation, enabling effective enhancement of target features. In addition, a Multi-Scale Information Compensation (MSIC) module is designed to exploit the complementary characteristics of multi-scale features across spatial and channel dimensions, guiding efficient fusion of high-level and low-level information. Experimental results on the NUDT and IRSTD-1K datasets demonstrate that GCFLNet outperforms existing state-of-the-art methods, achieving higher detection accuracy and robustness for infrared small targets in complex backgrounds. Full article

The spatial–temporal impact of the El Niño–Southern Oscillation (ENSO) phenomenon in Peru is characterised by marked regional variability, affecting the economy and general well-being. This study focuses on the Piura region, which is highly sensitive to ENSO events, with the aim of determining the implications for land management and climate adaptation in the Peruvian coastal region, particularly in the context of ENSO events. The objective of the study is to ascertain the correlation between sea surface temperature (SST) anomalies and the Normalised Difference Vegetation Index (NDVI) in the region. The researchers employed a machine learning approach to model and predict monthly NDVI behaviour, incorporating spatial and seasonal variables from the Moderate Resolution Imaging Spectroradiometer (MODIS) during two periods of ENSO occurrence on the Peruvian coast (2017; 2023) and the one-year post-occurrence periods (2018; 2024). The results demonstrated a correlation between NDVI and SST anomalies in coastal provinces such as Sechura and Morropón, indicating sensitivity to oceanic conditions. In contrast, high Andean provinces such as Ayabaca and Huancabamba exhibited more moderate values, indicating a weaker dependence on SST variability. The study also found that the NDVI exhibited a marked monthly variation associated with altitudinal gradients and climatic conditions. This research demonstrates the potential of remote sensing and GIS technologies in capturing climate-sensitive land-use dynamics and provides a framework for operational monitoring and decision support. Full article

Urban emergency medical services (EMSs) depend on time-critical accessibility, spatial demand distribution, and resilient transport networks. This study examines how built-environment characteristics shape spatiotemporal population intensity (as a proxy for latent EMS demand) within Shenzhen’s 10 min ambulance-accessible Emergency Medical Circle (EMC), using high-resolution Baidu Huiyan mobile-device data. Human activity intensity was quantified in 200 × 200 m grids and modeled against 20 built-environment indicators using a Gradient Boosting Decision Tree (LightGBM), with SHAP employed for interpretable attribution. By analyzing the distribution density and variance of SHAP dependence patterns, pronounced diurnal shifts in dominant drivers were identified. Medical facility density anchors nocturnal demand, road network permeability dominates pre-dawn mobility, land-use entropy and functional diversity peak during the midday period, while transit hubs and mixed-use amenities consolidate evening activity. The results further reveal critical non-linear thresholds—such as medical facility density (~1.5–2.5 km⁻²) and building density (~45,000–60,000 m² km⁻²)—beyond which marginal contributions diminish or become negative, indicating that proximity alone does not guarantee effective emergency coverage. These findings provide quantitative, time-sensitive guidance for EMC planning, highlighting the need for balanced facility dispersion, network prioritization, and demand-aware spatial design. By integrating high-resolution population dynamics with visually interpretable machine learning, this study advances a human-centered and operationally grounded framework for resilient emergency medical systems. Full article

In this study, we investigated how porewater salinity, temperature, ionic strength, and nutrient behavior vary with depth in the intertidal flats of Lake Komuke, a coastal lagoon in northern Japan. A central feature of this work is the use of nutrient activity and activity coefficients—thermodynamic parameters that more directly represent ion mobility—rather than concentrations alone. Statistical analyses showed that salinity exhibited clear depth-dependent variation and was the primary factor associated with changes in nutrient behavior, whereas temperature showed minimal variation and no detectable effect. Physicochemical modeling using the Pitzer approach demonstrated that increases in salinity and ionic strength with depth led to reductions in the activity coefficients of NO₃⁻, NH₄⁺, and PO₄³⁻, with PO₄³⁻ showing the greatest sensitivity due to its trivalent charge. Nutrient activities displayed contrasting vertical patterns: NO₃⁻ and NH₄⁺ tended to increase with depth, whereas PO₄³⁻ exhibited a peak at −20 cm followed by lower values at deeper, more saline layers. These results indicate that subsurface nutrient mobility in coastal tidal flats is shaped primarily by ionic strength-driven non-ideal behavior and associated geochemical gradients. The findings provide baseline information for understanding nutrient dynamics in brackish sediments and support the improved assessment of subsurface biogeochemical processes in intertidal ecosystems. Full article

Electric motors are widely used in industrial production systems, and various fault modes may occur during long-term operation under complex and noisy conditions. Accurate fault diagnosis remains challenging, especially when signal characteristics vary depending on the operating state. To address this issue, this paper presents a fault diagnosis framework based on a convolutional neural network (CNN), which features adaptive parameter optimization and enhanced feature representation. This method integrates the bee colony algorithm (BCA) into CNN training, adaptively adjusts the model parameters based on signal conditions, and shortens the convergence time compared to traditional gradient-based optimization. In order to improve the extraction of high-frequency and transient fault features, a spatiotemporal fusion architecture is designed, which combines large-kernel convolution, a bottleneck layer, and an improved self-attention (ISA) mechanism. In addition, an engineering-oriented data augmentation strategy based on multi-scale window offset and noise superposition has been applied to one-dimensional vibration signals to improve the robustness of the model. The proposed CNN-BCA-ISA framework is evaluated using a mixed dataset consisting of on-site data collected from a steel plant and a public dataset from Case Western Reserve University (CWRU). The experimental results show that the diagnostic accuracy is 96.4%, and the performance is stable under different noise levels, indicating good generalization abilities under various operating conditions. In addition, a real-time fault diagnosis system based on the proposed framework has been implemented and validated in industrial environments, confirming its feasibility in practical state monitoring applications. Full article

Background: The aim of this study was to develop an artificial intelligence (AI)-based decision support model for evaluating the extraction indication of permanent first molars in pediatric patients using panoramic radiographs, and to investigate the potential contribution of this model to the clinical decision-making process. Methods: This retrospective observational study analyzed 1000 panoramic radiographs obtained from children aged 8–10 years who attended the Clinics of Batman University Faculty of Dentistry for routine dental examination. Among the radiographs meeting the inclusion criteria, a total of 176 panoramic images were selected based on dental maturation assessment using the Demirjian tooth development staging system. Cases in which the permanent second molar was classified as Demirjian stages E–F were labeled as “extraction indication present”, while the remaining stages were labeled as “extraction indication absent”. A balanced dataset was created, consisting of 88 cases in each group. Image features were extracted using Gabor filters and Histogram of Oriented Gradients (HOG). The selected features were analyzed using a Support Vector Machine (SVM) classifier with a radial basis function (RBF) kernel. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (ROC–AUC). Results: The proposed Gabor–HOG–SVM-based AI model achieved an overall classification accuracy of 77.78% with an AUC value of 0.77 in distinguishing between “extraction indication present” and “extraction indication absent” cases. For the extraction-indicated group, the sensitivity was 0.81 and the F1-score was 0.79, whereas for the non-indicated group, the sensitivity and F1-score were 0.74 and 0.77, respectively. No statistically significant differences were observed between the groups in terms of age or sex distribution (p > 0.05). Conclusions: This study demonstrates that artificial intelligence-based analysis of panoramic radiographic images can provide an objective and reproducible decision support approach for evaluating extraction indications of permanent first molars in pediatric patients. The proposed model should be considered as an adjunctive tool to reduce observer-dependent variability rather than a replacement for clinical judgment, and its clinical applicability should be further validated through multicenter and multi-parametric studies. Full article

Temperate oak forests are biodiversity-rich ecosystems, and Mexico is a major center of diversification for Quercus, with high levels of endemism. In Michoacán (central Mexico), the rapid expansion of avocado cultivation has reduced oak forest cover and increased landscape fragmentation. Foliar endophytic fungi can contribute to host performance under biotic and abiotic stress, yet their diversity in endemic Mexican oaks and their response to land-use change remain poorly characterized. Here, we characterized the cultivable foliar endophytic fungal communities associated with Quercus castanea and Quercus obtusata along a forest–avocado orchard cover gradient. We isolated 112 endophytic fungal strains from leaves of Q. castanea (n = 56) and Q. obtusata (n = 56). All isolates belonged to Ascomycota and were assigned to four classes, 10 orders, and 32 genera based on nrITS sequences and genus-level phylogenetic analyses. The most abundant genera were Nigrospora (8%), Xylaria (7%), Nodulisporium (6%), and Daldinia (6%). Patterns of genus exclusivity and richness indices consistently showed higher diversity in forest-dominated landscapes than in orchard-dominated sites. Overall, our results indicate that forest-to-orchard conversion is associated with shifts in the structure of the cultivable foliar endophytic fungal communities of oak species and with a tendency toward reduced diversity in more disturbed landscapes. Further studies integrating culture-dependent and culture-independent approaches are needed to evaluate the functional implications of these patterns for host health and ecosystem resilience. Full article

As a critical climate regulator on the Qinghai–Xizang Plateau, Qinghai Lake exerts important influences on surrounding surface environmental conditions. Using MODIS remote sensing data and topographic information from 2000 to 2024, this study analyzed the spatiotemporal variations in land surface temperature (LST), normalized difference vegetation index (NDVI), and temperature vegetation dryness index (TVDI) in the 10-km riparian zone. The buffer was subdivided into five 2-km distance gradients to quantify the attenuation of lake effects and their interaction with topographic factors. The results indicate pronounced seasonal contrasts and distance-dependent differentiation of surface variables. LST exhibited clear seasonal variability, with peak values in the second and third quarters (Q2 and Q3). During Q2, the near-shore zone (0–2 km) remained notably cooler by approximately 2–3 °C (23.8 °C) than intermediate and distal zones (25.4–26.8 °C), indicating a moderate lake-related cooling effect during the early warm season. NDVI showed consistent seasonal phenology across all buffers, reaching maximum values in Q3, while mean NDVI values increased gradually with distance from the lake, ranging approximately from 0.48 in the near-shore zone to 0.51 in the distal zone. TVDI displayed distinct seasonal and spatial patterns, with relatively low and stable values in the near-shore zone throughout the year and a pronounced seasonal minimum in the distal zone during Q3 (0.57). These findings highlight strong seasonal and spatial heterogeneity of surface environmental conditions in the Qinghai Lake riparian zone. The observed patterns suggest that lake proximity and topographic gradients jointly influence hydrothermal conditions and vegetation dynamics at the landscape scale, providing quantitative evidence for understanding surface–environmental gradients in alpine lake systems. Full article

Efficient and accurate characterization of lithium-ion battery packs is critical for both first-life applications and second-life reuse. Electrochemical Impedance Spectroscopy (EIS) provides detailed insight into internal electrochemical processes, but full-spectrum measurements are time-consuming, especially at low frequencies. This work presents a methodology combining cell-level equivalent circuit modeling, integrated gradients sensitivity analysis, and D-optimal frequency selection to reduce the number of measurement points while preserving parameter identifiability. Individual 16s5p LFP cells were characterized using full-spectrum EIS at 10 °C, and the resulting equivalent circuit models were scaled to the pack level. Integrated gradients were used to quantify the frequency-dependent influence of each parameter on the real and imaginary parts of the impedance, identifying the regions containing the most information. Using the per-frequency Jacobian and the Fisher Information Matrix, a D-optimal frequency selection was performed to demonstrate that a reduced set of measurements is sufficient to estimate key parameters reliably. The results show that variations in parameters due to aging are accurately captured using the reduced frequency set, validating the approach for fast, accurate, and traceable characterization at the pack level. The proposed methodology highlights a systematic strategy for frequency selection, enabling faster EIS measurements, maintaining sensitivity to aging and degradation mechanisms, and supporting standardized and sustainable evaluation of lithium-ion batteries. Full article

Ionizing radiation (IR) exerts complex, dose-dependent biphasic effects on the central nervous system (CNS). This review systematically elucidates the mechanisms underlying the impact of high- and low-dose radiation on neurocognitive function. High-dose radiation (HDR) triggers severe DNA damage, oxidative stress, and neuroinflammatory cascades, leading to neuronal dysfunction, suppression of neurogenesis, and failure of neural circuit reorganization, ultimately resulting in persistent cognitive decline. In contrast, low-dose radiation (LDR) exhibits a unique dual nature: within certain thresholds, it can activate endogenous protective pathways—including DNA repair and antioxidant defenses—thereby promoting neural plasticity and network homeostasis and demonstrating adaptive responses and neuroprotective potential. The research paradigm is shifting from the traditional linear no-threshold (LNT) model towards a dynamic homeostasis model. Future research should prioritize the development of neuroprotective strategies during radiotherapy for high-dose exposure, optimize irradiation modalities, and develop novel radioprotective agents to improve patient outcomes. For LDR, it is crucial to delineate its biological effects and explore its potential for intervening in neurodegenerative diseases. This review aims to provide an integrated theoretical framework for understanding the dose-dependent biphasic regulation of radiation on neurocognition and to outline future directions for developing related protective and therapeutic strategies. Full article

Cyclonic extreme events have recently undergone an important boost over the Mediterranean Sea, a trend closely linked to ongoing strong climate variations. Several studies are explaining the combination of many different effects that increase the frequency of mesoscale vortices’ intensification, namely Mediterranean tropical-like cyclones (TLCs), until the stage of Medicanes. Among these effects, processes like sea–atmosphere energy exchanges, baroclinic instability, and the release of latent heat lead to the intensification of these systems into fully tropical-like structures. This study investigates the formation and development of Ianos, the most intense Mediterranean tropical-like cyclone recorded in recent years, which affected the Ionian Sea and surrounding regions in September 2020. Using satellite observations and remote sensing data, the study applies a dual approach to characterise the system evolution across the spatial and temporal scales. Firstly, proper orthogonal decomposition (POD) is exploited to assess temperature and pressure fluctuations derived from the geostationary database of Meteosat Second Generation (MSG-11)/SEVIRI. POD allows for the identification of dominant modes of variability and the quantification of energy distribution across different spatial structures during the cyclone’s lifecycle. The decomposition reveals that a small number of orthogonal modes capture a significant proportion of the total variance, highlighting the emergence and persistence of coherent structures associated with the cyclone’s core and peripheral convection. To support scale-dependent energy organisation and dissipation within Ianos, total-period and three-period analyses were carried out, in addition to early-stage intensification patterns and implications for meteorological scale assessments. From the study on the temperatures’ spatio-temporal evolution, a comparison in the POD spectra and of the structures during the peak of intensity was carried out between the Ianos TLC and the Faraji and Freddy tropical cyclones. Additional multi-sensor data from Suomi NPP and Sentinel-3 satellites were integrated to analyse the evolution of the same parameters, also taking into account an evaluation of the vertical temperature gradient, over a 4-day period encompassing the full life cycle of Ianos. The study of the daily evolution helps investigate the spatial trends around the warm core regions, identifying the pressure minima for a comparison with the BOLAM and ERA5 databases of the mean sea level pressure. Overall, this study demonstrates the value of combining dynamic decomposition methods with high-resolution satellite datasets to gain insight into the multiscale structure and convective energetics of Mediterranean tropical-like cyclones. Some significant patterns come out from the spatial organisation of deep convection that seem to be linked to the permanent structures of atmospheric fluctuations near the warm core centre. Full article