Search Results (148)

Seasonal hydrology reshapes large-river food webs by altering habitat connectivity and basal resource availability. Trophic baselines from before the 2021 Yangtze ten-year fishing ban are now valuable because monitoring has shifted from fish abundance alone toward food-web function and ecological recovery. We analysed carbon (δ¹³C) and nitrogen (δ¹⁵N) stable isotopes of fish and the baseline bivalve Corbicula fluminea collected in March (dry season) and August (wet season) 2016 from the Jingjiang section of the lower Yangtze River. In the dry season, 100 individuals of 27 species were analysed; species mean δ¹³C ranged from −30.52‰ (Micropercops swinhonis) to −21.19‰ (Aristichthys nobilis) and δ¹⁵N from 6.30‰ (Hypophthalmichthys molitrix) to 14.90‰ (Lophiogobius ocellicauda). In the wet season, 187 individuals of 47 species were analysed; species mean δ¹³C ranged from −32.07‰ (Pseudobrama simoni) to −20.84‰ (Salanx ariakensis) and δ¹⁵N from 6.27‰ (Misgurnus anguillicaudatus) to 14.87‰ (Saurogobio gymnocheilus). Among 24 shared species, δ¹³C differed significantly between seasons (paired t = 4.30, p < 0.001), but δ¹⁵N did not (t = 1.52, p = 0.143). Mean trophic level fell from 3.07 to 2.74 (t = 3.85, p < 0.001). This decline remained significant in a trophic-enrichment-factor sensitivity analysis using 2.5–4.0‰. Community-wide carbon range (CR), nitrogen range (NR), total convex-hull area (TA), mean nearest-neighbour distance (NND), and the standard deviation of nearest-neighbour distance (SDNND) showed larger wet-season CR (9.08 vs. 7.51), slightly larger NR, TA and NND, and lower SDNND. Seasonal hydrology thus mainly altered basal carbon pathways and relative trophic positions rather than reorganising feeding guilds. The dataset provides a pre-ban isotopic baseline for assessing whether post-ban recovery in the lower Yangtze includes restoration of trophic structure and energy-flow pathways. Full article

Three-dimensional (3D) ear morphology is critical for the design of in-the-ear hearing aids, earphones, transcutaneous auricular vagus nerve stimulation (taVNS) electrodes, and auricular reconstruction, yet most existing ear shape models still rely on manually placed landmarks. Here, a fully annotation-free pipeline is presented for constructing a 3D ear atlas and statistical shape model (SSM) of the auricular bowl from 50 surface meshes. Individual ears are iteratively registered to a current atlas using rigid the iterative closest point (ICP) algorithm followed by a bidirectional thin-plate spline (BiTPS) deformation, and dense surface correspondences are established by nearest-neighbour mapping. Registration quality is quantified using mean and maximum nearest-neighbour distance, symmetric Chamfer-$L_2$ distance and coverage. Furthermore, SSM-derived bowl height and width are validated against manual 3D mesh measurements in Geomagic Design X. Across five atlas iterations, the BiTPS pipeline substantially reduces registration errors and increases coverage, and principal component analysis (PCA) derived dimensions show excellent agreement with manual measurements (Pearson $r\ge 0.98$, ICC $\ge 0.98$). The proposed framework yields a stable, anatomically plausible ear atlas and an interpretable low-dimensional SSM without manual landmarks, providing a computational basis for the geometric optimization of ear-related medical and wearable devices. Full article

Rift Valley fever (RVF) is a viral zoonosis endemic in Madagascar, threatening human and animal health as well as the economy. Trade-related livestock movements are a major factor in the spread of RVF virus. While previous RVF research in Madagascar has focused on farmers or general ecology, this study is the first to specifically target livestock traders, the primary drivers for long-distance viral spread, in the Alaotra Mangoro endemic hotspot. This study aimed to assess the level of knowledge, prevailing attitudes and current practices regarding RVF among people engaged in livestock trade in the Alaotra Mangoro region, as well as the factors associated with these KAPs. A descriptive and analytical cross-sectional survey was conducted among 406 livestock traders in five districts of the Alaotra Mangoro region, using a structured questionnaire. A multi-stage sampling approach was employed, utilising purposive selection of markets followed by snowball sampling to reach informal traders often missed by traditional surveys. Generalised linear mixed models were used to analyse factors associated with KAPs regarding RVF. Awareness of RVF was very low (only 18.5% respondents had heard of it), with significant regional disparities (0% in Anosibe An’Ala versus 51.6% in Moramanga). Veterinarians (15.5%), family (12.8%), radio (9.6%) and neighbours (9.6%) were the main sources of information. Understanding of symptoms and modes of transmission (particularly mosquito bites) was limited. Higher levels of education (OR = 181.6; 95% CI: 29.9–1123.7; p < 0.001) and older age (50–60 years) were associated with better knowledge. Proactive attitudes were scarce (21.4%), although more than half (53.4%) believed that RVF is a real disease. Perception of personal risk and the contribution of livestock trade to the spread of the disease was low. However, confidence in animal vaccination was relatively high (60.3%). Preventive practices were highly inadequate. The majority did not wear protective equipment when handling sick animals (94.6%) and rarely avoided touching aborted foetuses (12.6%). Less than half (48.3%) expressed a willingness to report sick or dead animals, and nearly half admitted to having sold or purchased sick livestock (49.5%). Cooking meat (95.1%) and using mosquito nets (74.1%) were the only well-established practices. More than half of respondents (57.9%) lived more than 5 km from veterinary services, and cost was the most frequently cited barrier to consultation. Participation in awareness campaigns was virtually non-existent (5.4%). Results revealed critical gaps in KAP that may contribute to the persistence of RVF. A “One Health” approach is imperative, integrating human, animal and environmental health. Full article

Background/Objectives: Inferior alveolar nerve injury is a common complication of Bilateral Sagittal Split Osteotomy. Preoperative three-dimensional tracing of the mandibular nerve canal using Cone-beam CT may help reduce this risk. We reconstructed the nerve course in 428 consecutively planned BSSO cases and developed a statistical model that quantifies population-level canal position to guide safe, evidence-based osteotomy planning. Methods: Traceable mandibular nerve canal CBCTs from 440 BSSO candidates (2023–2025) were retained. The mandibles and 2.5 mm diameter canals were segmented in Mimics and fused with intraoral scans. Next, the meshes were aligned non-rigidly to a template. A k-nearest-neighbour analysis mapped the outer mandibular surface to canal distances of all the patients on the template mandible model. Results: After excluding 12 scans because the nerve could not be traced, 428 mandibles were studied. The canal’s position varied, with regions near the vertical ramus and premolar regions frequently showing fewer than 3 mm of buccal bone covering the mandibular nerve. In contrast, a preferred safe zone was identified in the second molar region, where more than 97.8% of patients had greater than 5 mm of buccal bone clearance. A population-based colour map was generated to visualise the risk areas and confidence intervals for outer cortex-to-canal distances. Conclusions: This study provides the first high-resolution, population-based 3D map of nerve clearance in BSSO patients. Routine use of CBCT with patient-specific nerve tracing is recommended to reduce the risk of nerve injury. Full article

This study presents the results of monitoring the endangered gastropod Patella ferruginea within a Marine Protected Area in Sardinia. A detailed map of the species distribution was created, and individual density and population structure were analysed by comparing data collected during monitoring campaigns in 2014, 2018, and 2023. A total of 206, 203, and 109 individuals were recorded in 2014, 2018, and 2023, respectively. In 2014 and 2018, 24 and 26 specimens with a maximum diameter of ≥6 cm were observed, while in 2023, only 11 individuals reached this size, with a single specimen measuring 6.5 cm. Linear density showed an average reduction of approximately 37% in the latest campaign compared to the previous one. The coastline under analysis was divided into five sectors based on the degree of protection and exposure to prevailing winds. The overall decline of approximately 50% in the population indicates a decrease affecting all size classes, although it is more pronounced in the larger size classes. Furthermore, analyses of spatial structure using the Minimum Spanning Tree (MST), Clark and Evans’ R index, and Nearest Neighbour Distance (NND) indicate a dispersed distribution already in 2018, which became more pronounced in 2023. These results indicate that current protection measures within the MPA may not be sufficient to ensure long-term population stability, suggesting that conservation strategies for this species should be assessed on a site-specific basis. Full article

Energy-efficient routing is critical for extending the operational lifespan of wireless sensor networks (WSNs). While the Power-Efficient Gathering in Sensor Information Systems (PEGASIS) protocol achieves high efficiency through chain-based data aggregation, its standard round-robin leader selection fails to account for dynamic node factors, such as residual energy and historical reliability. This often leads to premature energy depletion and network instability. To address these limitations, this paper proposes K-NN-PEGASIS, a data-driven machine learning framework that utilises a weighted k-nearest neighbours (K-NN) algorithm for intelligent leader selection. By processing a normalised feature vector comprising residual energy, distance to the base station (BS), node degree, and historical performance, the framework adaptively identifies optimal leaders in each round. Simulations conducted in MATLAB for networks ranging from 100 to 1000 nodes demonstrate that K-NN-PEGASIS improves network lifetime by up to 47.3% and reduces total energy dissipation by 52.8% compared to baseline algorithms. Furthermore, the framework provides passive resilience against routing attacks, reducing the selection of malicious leaders by 96% and maintaining a 32.3% higher packet delivery ratio under attack scenarios. Full article

This work presents a simulation-based approach to support the planning of Terrestrial Laser Scanning (TLS) surveys for landslide monitoring. Starting from an approximate digital model of the slope, the method estimates the spatial distribution of positional error induced by scanner characteristics, laser beam divergence and, critically, by the incidence angle between the laser beam and the local surface normal. Because complex morphologies cause rapid local variations in incidence angle, neighbouring points may exhibit markedly different error magnitudes, making a direct classification of raw error values insufficient to delineate homogeneous areas. To address this, a multidimensional variable is defined for each simulated point, combining position, estimated error, distance from the scanner and incidence angle. After dimensionality reduction through PCA, the dataset is clustered using K-means with a sufficiently large number of clusters to preserve spatial resolution. Each cluster is associated with a representative error level, and clusters are then merged into broader error classes that delineate zones of comparable expected precision. The procedure is repeated for alternative scanner positions, enabling a comparative evaluation of achievable accuracy across the slope and the identification of areas requiring multiple scans. The method provides a quantitative, reproducible framework to guide TLS station selection and optimize survey design in complex morphological settings. Full article

This work assesses the performance of different spatial interpolation methods (inverse distance weighting/IDW family, nearest neighbour, linear interpolation, natural neighbour, and spherical kriging) for the preliminary wind energy assessment and the estimation of wind speed and direction from numerical models that constitute key factors in early-stage feasibility studies for offshore wind farm (OWF) development. Using the CERRA reanalysis dataset over the Mediterranean Sea, long-term measurements from 31 buoys have been used as ground truth data, and the methods’ performance was evaluated through multiple statistical metrics and a weighted aggregated performance metric (WAPM). To ensure statistically robust comparisons, the non-parametric Friedman and Nemenyi tests were applied, along with the Aligned Rank Transform ANOVA to examine interactions between performance and distance from shore. The numerical results suggest that for wind power density and energy production, inverse distance weighted regression (IDW-R) and natural neighbour perform better than the rest of the interpolation methods and should be considered for assessing wind energy characteristics of candidate areas for OWF development. The same methods perform best for wind speed interpolation, while IDW-R and IDW0 (mean of four) perform best for wind direction. One of the most important advantages of the IDW-R is that it reduces local bias and improves accuracy due to the embedded linear regression framework, while its interpolation quality is superior when the available data points are limited. Overall, the numerical results clearly suggest that the selection of an appropriate interpolation method can significantly reduce errors in the preliminary estimation of the available wind power and projected offshore energy production. Full article

Copper(II) compounds exhibit interesting magnetic properties due to halide–halide, copper–halide, and intermolecular hydrogen bond interactions. In this study, seven new copper(II) bromide complexes were synthesised, six of which contain Dabco (1,4-diazabicyclo[2.2.2]octane) as a ligand. Single-crystal X-ray diffraction data were refined using both conventional spherical-atom models and a non-spherical-atom approach implemented in NoSpherA2. Magnetic properties were investigated by temperature-dependent magnetic susceptibility and field-dependent magnetisation measurements, analysed using a molecular field approximation. Crystallographic analysis shows that NoSpherA2 significantly improves the description of hydrogen atom positions, yielding C–H and N–H bond lengths closer to neutron diffraction values than conventional refinement. Magnetic measurements indicate that interactions between mononuclear copper(II) centres are determined primarily by the nature of intermolecular exchange pathways rather than copper–copper separations alone. Despite comparable Cu···Cu distances, complexes lacking N–H···Br hydrogen bonds exhibit only weak antiferromagnetic interactions, whereas stronger coupling, effective up to 150 K, is observed when such hydrogen bonds connect neighbouring complexes. These results highlight the importance of hydrogen-bond topology and three-dimensional connectivity in governing magnetic behaviour in mononuclear copper(II) systems. Full article

Hydrological modelling in lowland catchments is often constrained by flat terrain and sparce meteorological station networks, which limits the accuracy of spatial interpolation of precipitation and temperature. In these conditions, the nearest available station may be located tens of kilometres away, making interpolated meteorological inputs highly uncertain and prone to systematic bias. This study aims to improve interpolated meteorological data for hydrological applications by developing and evaluating a practical bias correction approach suitable for low-relief regions with insufficient station density. Long-term temperatures and precipitation records from 18 meteorological stations in Lithuania (1961–2020) were used as reference data. Meteorological fields were reconstructed using Ordinary Kriging and Spline interpolation and evaluated against observations at monthly and daily time scales using correlation (r), Root Mean Square Error (RMSE), Percent Bias (PBIAS), Nash–Sutcliffe Efficiency (NSE), and Probability of Detection (POD) for precipitation. Bias correction was applied to interpolated datasets using inverse distance weighting (IDW) based on one to four neighbouring stations, reflecting typical distances of 50–70 km between observation sites. The results show that while the interpolation method strongly influences precipitation accuracy, bias correction substantially reduces systematic errors without altering temporal structure. The most robust improvements were obtained using two to three neighbouring stations and an IDW power parameter of one, particularly under flat terrain conditions. When applied as input to the HBV rainfall–runoff model for three representative lowland catchments, bias-corrected interpolated meteorological data consistently improved runoff simulations, bringing model performance closer to that achieved using historical station observations. The findings demonstrate that targeted bias correction is an effective and computationally simple strategy for improving interpolated meteorological data in data-sparse lowland regions. The proposed approach provides practical guidance for hydrological modelling where dense observation networks are unavailable and reliance on interpolation is unavoidable. Full article

The paper presents measurements of frequency dependence of conductivity and real components of complex permittivity of a nanocomposite consisting of electrical pressboard, bio-insulating oil and water nanodroplets with moisture content ranging from 0.6 wt.% to 5 wt.%. Bio-oil meets high environmental requirements—it is fully biodegradable, and its combustion products are significantly less harmful than those of mineral oil. In addition, the use of bio-oil reduces the carbon footprint of power transformer production. The quantum mechanical phenomenon of electron tunnelling between potential wells created by water nanodroplets was used to analyze the experimental results obtained. The study determined the effect of moisture content on the relative relaxation time values. On this basis, the number of water molecules in nanodroplets, their diameters and the concentration of nanodroplets depending on moisture content were determined. The distances over which electrons tunnel in moist pressboard impregnated with bio-oil were determined. These values are the expected values of the probability distribution of the distance between neighbouring nanodroplets. The values of the number of water molecules in nanodroplets are also the expected values of the probability distribution of the number of molecules in nanodroplets. It has been established that during many years of transformer life, several parallel processes occur as the moisture content in bio-oil-impregnated pressboard increases. One of them involves the accumulation of water molecules collected in the pressboard in nanodroplets. The second is an increase in the concentration of nanodroplets. The third is an increase in the average number of water molecules in nanodroplets. Full article

Background/Objectives: Dry Eye Disease (DED) significantly impacts quality of life due to the instability of the tear film and reduced tear production. The limited availability of eye care professionals, combined with traditional diagnostic methods that are invasive, non-portable, and time-consuming, results in delayed detection and hindered treatment. This proof-of-concept study aims to explore the feasibility of using smartphone-based infrared thermography (IRT) as a non-invasive, portable screening method for DED. Methods: This study included infrared thermography (IRT) images of 40 subjects (22 normal and 58 DED). Ocular surface temperature changes at three regions of interest (ROIs): nasal cornea, center cornea, and temporal cornea, were compared with Tear Film Break-up Time (TBUT) and Ocular Surface Disease Index (OSDI) scores. Statistical correlations and independent t-tests were performed, while machine learning (ML) models classified normal vs. DED eyes. Results: In these preliminary results, DED eyes exhibited a significantly faster cooling rate (p < 0.001). TBUT showed a negative correlation with OSDI (r = −0.802, p < 0.001) and positive correlations with cooling rates in the nasal cornea (r = 0.717, p < 0.001), center cornea (r = 0.764, p < 0.001), and temporal cornea (r = 0.669, p < 0.001) regions. Independent t-tests confirmed significant differences between normal and DED eyes across all parameters (p < 0.001). The Quadratic Support Vector Machine (SVM) achieved the highest accuracy among SVM models (90.54%), while the k-Nearest Neighbours (k-NN) model using Euclidean distance (k = 3) outperformed overall with 91.89% accuracy, demonstrating strong potential for DED classification. Conclusions: This study provides initial evidence supporting the use of smartphone-based infrared thermography (IRT) as a screening tool for DED. The promising classification performance highlights the potential of this approach, though further validation on larger and more diverse datasets is necessary to advance toward clinical application. Full article

Governments worldwide have set ambitious targets for decarbonising energy grids, driving the need for increased renewable energy generation and improved energy efficiency. One key strategy for achieving this involves enhanced energy management in buildings, often using machine learning-based forecasting methods. However, such methods typically rely on extensive historical data collected via costly sensor installations—resources that many buildings lack. This study introduces a novel forecasting approach that eliminates the need for large-scale historical datasets or expensive sensors. By integrating custom-built models with existing energy data, the method applies calculated weighting through a distance matrix and accuracy coefficients to generate reliable forecasts. It uses readily available building attributes—such as floor area and functional type to position a new building within the matrix of existing data. A Euclidian distance matrix, akin to a K-nearest neighbour algorithm, determines the appropriate neural network(s) to utilise. These findings are benchmarked against a consolidated, more sophisticated neural network and a long short-term memory neural network. The dataset has hourly granularity over a 24 h horizon. The model consists of five bespoke neural networks, demonstrating the superiority of other models with a 610 s training duration, uses 500 kB of storage, achieves an R² of 0.9, and attains an average forecasting accuracy of 85.12% in predicting the energy consumption of the five buildings studied. This approach not only contributes to the specific goal of a fully decarbonized energy grid by 2050 but also establishes a robust and efficient methodology for maintaining standards with existing benchmarks while providing more control over the method. Full article

In this study, we model a solution surface, with each point having nine components using artificial intelligence (AI), to optimise the effects of ground vibration during blasting operations in an open-pit diamond mine. This model has eight input parameters that can be adjusted by blasting engineers to arrive at a desired output value of ground vibration. It is built using the best performing artificial neural network architecture that best fits the blasting data from 100 blasting events provided by the Debswana diamond mine. Other AI algorithms used to compare the model’s performance were the k-nearest neighbour, support vector machine, and random forest—together with more traditional statistical approaches, i.e., multivariate and regression analyses. The input parameters were burden, spacing, stemming length, hole depth, hole diameter, distance from the blast face to the monitoring point, maximum charge per delay, and powder factor. The optimised model allows for variations in the input values, given the constraints, such that the output ground vibration will be within the minimum acceptable value. Through unconstrained optimisation, the minimum value of ground vibration is around 0.1 mm/s, which is within the vibration range caused by a passing vehicle. Full article

As early detection of voice disorders can significantly improve patients’ situation, the automated detection using Artificial Intelligence techniques can be crucial in various applications in this scope. This paper introduces a multi-objective bio-inspired, AI-based optimization approach for the automated detection of voice disorders. Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). Three publicly available voice disorder datasets have been utilized, and results have been compared based on Inverted-Generational Distance, Hypervolume, spacing, and spread. The results reveal that NSGA-II with the MLP algorithm attained the best convergence and performance. Further, the conformal prediction is leveraged to quantify uncertainty in the feature-selected models, ensuring statistically valid confidence intervals for predictions. Full article