Search Results (321)

Background: The development of osteoarthritis (OA), a whole-joint disorder that is increasingly recognized, depends on subchondral trabecular bone. Variations in the viscoelastic characteristics of trabecular bone have been proposed to affect load distribution and potentially lead to joint degradation. The viscoelastic response of human trabecular bone in both healthy and osteoarthritic situations was investigated using constitutive modeling and stress-relaxation testing. Fifteen tibial trabecular bone specimens were evaluated using Standard Linear Solid (SLS) models and two-branch generalized Maxwell models following uniaxial stress-relaxation testing. Mechanical, energy, and relaxation-related traits were retrieved and compared between groups. Results: Healthy bone tended to relax stress more slowly and to bear mechanical loads over time to a slightly greater extent than osteoarthritic bone, which tended to relax stress more quickly and had poorer mechanical endurance; these differences were not statistically significant. The Generalized Maxwell model suited the experimental data better than the SLS model (R² > 0.98), capturing both short- and long-term relaxation mechanisms. Sensitivity analysis revealed higher parameter variability in OA specimens, suggesting possible differences in mechanical heterogeneity and load-dissipation behavior that require further investigation. Conclusions: Although the observed differences were not statistically significant in this exploratory study, the results suggest potential trends toward altered viscoelastic behavior between healthy and osteoarthritic trabecular bone. Future studies with larger cohorts are needed to further investigate osteoarthritis-related biomechanical alterations. Multi-branch viscoelastic modeling may provide sensitive mechanical descriptors for characterizing the relaxation behavior of subchondral bone. Full article

The growing demand for sustainable protein sources is driving the development of marine by-product utilization. Mussel byssus, which is currently discarded during mussel processing, could be used to produce collagen-derived biomaterials. This study tested the hypothesis that ultrasound-assisted extraction could improve collagen recovery by comparing its efficiency with that of conventional pepsin-assisted extraction. The latter is time-consuming and inefficient. While conventional pepsin-assisted extraction achieved a higher collagen-derived material recovery yield (10.7%) than ultrasound-assisted extraction (5.1%), ultrasound reduced the extraction time from 72 h to just two hours. Fourier transform infrared spectroscopy (FTIR) confirmed that the characteristic amide bands were preserved in the collagen-derived material extracted using both methods. Differential scanning calorimetry (DSC) revealed thermal transitions at 59 °C for pepsin-assisted extracts and 57 °C for ultrasound-assisted extracts, indicating comparable thermal stability. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) revealed predominantly low-molecular-weight protein fragments (<20 kDa), suggesting the presence of collagen-derived peptides rather than intact native collagen. Overall, ultrasound-assisted extraction preserved the structural and thermal characteristics of the extracted material while drastically reducing processing time. This highlights its potential as a rapid, environmentally friendly and sustainable technology for the valorization of mussel byssus within a circular bioeconomy. Full article

Soil pollution constitutes a critical global environmental challenge driven by industrialization, intensive agriculture, urban expansion, mining, and the application of synthetic agrochemicals. This article presents seven annotated Python-based Machine Learning (ML) workflows for soil pollution assessment, structured around five contaminant groups: heavy metals, pesticides, microplastics, per- and polyfluoroalkyl substances (PFAS), and excess macronutrients. The contribution has three distinct components. First, a literature synthesis drawing on more than 100 peer-reviewed studies contextualizes each contaminant group within current spectroscopic, geochemical, and ML-based detection frameworks. Second, a conceptual six-step workflow links field sampling, ML-based analysis, and scenario-based risk modelling to soil ecosystem service (SES) assessment. Third, seven executable Python scripts—implementing Random Forest regression, XGBoost with SHAP explainability, 1-D Convolutional Neural Networks, LSTM time-series forecasting, PCA-based dimensionality reduction, Monte Carlo uncertainty propagation, and GeoPandas geospatial mapping—serve as illustrative demonstrations using a benchmark dataset. All reported performance metrics are derived from synthetic data and represent workflow demonstrations, not validated field results. Radionuclides are acknowledged as an important contaminant class but fall outside the defined scope of this study. The scripts are provided as reproducible templates for adaptation to real contaminated-site datasets. Full article

French coastal systems are characterized by strong environmental gradients and increasing anthropogenic pressures, resulting in rapid land cover transformations across coastal landscapes. This study investigates land cover dynamics along the northern, western, and southern French coasts using Sentinel-2 summer image time series acquired between 2015 and 2025. The research aims to identify the most dynamic coastal regions and determine where land cover transitions are most pronounced. A harmonized workflow was developed in GRASS GIS for preprocessing Sentinel imagery, generating seasonal composites, classifying land cover using a Random Forest (RF) supervised algorithm, and detecting changes through time. All imagery was processed using CORINE Land Cover (Level 1) classification nomenclature and projected to Lambert-93 (EPSG:2154). Comparative analyses were performed among the three coastal regions using statistical indicators of change intensity, persistence, and transition rates. The results reveal substantial regional differences in coastal dynamics, with the southern Mediterranean coast exhibiting the highest transformation rate (22.9% of total area changed, at 2.29% ${\mathrm{yr}}^{-1}$), followed by the northern English Channel coast (18.6%; 1.86% ${\mathrm{yr}}^{-1}$) and the western Atlantic coast (14.2%; 1.42% ${\mathrm{yr}}^{-1}$). Urbanization and natural vegetation loss were identified as dominant transition types across all regions. The study demonstrates the effectiveness of Sentinel-2 time series and open-source GRASS GIS methods for long-term coastal monitoring and provides a reproducible framework for large-scale assessments of coastal land cover dynamics in Europe. Full article

Background/Objectives: The common quail (Coturnix coturnix) is a game species facing conservation challenges, particularly hybridization with the Japanese quail (Coturnix japonica). To address this issue, one proposed measure is the urgent prohibition of releasing farmed quails into the wild. If authorized, mechanisms should be established to guarantee their genetic origin and prevent contamination of native populations. This work focuses on the development of a genetic tool based on Single-Nucleotide Polymorphism (SNP) markers that can differentiate between the two species and their hybrids. Our goal was to incorporate the selected markers into an OpenArray^® platform, to allow efficient, rapid, and cost-effective analysis. Methods: We tested two mitochondrial DNA SNPs (previously described in the literature) as diagnostic markers for species differentiation. We also assessed 24 nuclear DNA SNPs for compatibility with the OpenArray^® platform. Results: Of the 26 total SNPs, eight were excluded due to their limited utility. The remaining 18 SNPs achieved an overall genotyping success rate of 96.21%. Using the OpenArray^® platform with these 18 SNPs in a trial with samples from diverse Spanish field populations, we found 1.00% of C. japonica alleles (affecting 15.63% of individuals), suggesting introgression in the field. Population genetic analyses revealed strong differentiation between species and confirmed the presence of admixed individuals in field populations. Conclusions: This paper presents a new tool to differentiate between quail species and to identify foreign alleles in stocks and populations, by using an open platform system that optimizes the practical application of the diagnostic procedure based on the to-date most-reliable SNP markers for this goal. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

Prickly pear (Opuntia ficus-indica (L.) Mill.) generates substantial agro-industrial by-products, such as press cake, seed, and oil, that remain underexploited despite their recognized phytochemical richness. This study reports the systematic optimization, characterization, and bioactivity profiling of flavonoid-rich extracts recovered from these three matrices. A Box–Behnken design (BBD) coupled with response surface methodology (RSM) was applied to optimize the ultrasound-assisted extraction (UAE) of total flavonoid content (TFC) from press cake using a natural deep eutectic solvent (NADES: fructose–glycerol–sorbitol–water and FGSH), selected through an initial screening of fifteen solvent systems. The quadratic polynomial model showed excellent fit (R² = 0.9852; R²adj = 0.9687; MAPE = 1.31%; Durbin–Watson = 1.857), and optimal extraction conditions were established at 37.6 min extraction time, 35.6% ultrasonic power, and 29.4 °C, yielding a maximum predicted TFC of 54.78 ± 0.49 mg quercetin equivalents (QE)/mL. HPLC-DAD analysis of the press cake extract revealed five isorhamnetin derivatives as the dominant flavonoids, with isorhamnetin-3-O-glucoside (23.18 ± 0.12 mg/g DW) and isorhamnetin-3-O-rutinoside (13.80 ± 0.28 mg/g DW) as the most abundant. Comprehensive bioactivity assessment demonstrated significant antioxidant capacities (CUPRAC: 191.35 ± 3.22 µM AAE; ORAC: 184.44 ± 3.44 µM TE; DPPH: 103.47 ± 9.98 µM TE for press cake extract), potent in cellulo ROS/RNS suppression in a yeast UV-stress model (85.9 ± 1.0% inhibition for press cake), and differential tyrosinase inhibition across fractions (press cake: 32.8%; seed: 57.5%; oil: 83.8%), highlighting the oil as a potent anti-melanogenic ingredient. In silico safety prediction (ProTox-II/pkCSM) confirmed the favorable toxicity profiles of all identified isorhamnetin derivatives (LD₅₀ > 5000 mg/kg; Toxicity Class V). These results collectively position Opuntia ficus-indica by-products as high-value natural sources of bioactive flavonoids with applications in cosmetic, nutraceutical, and dermatological formulations. Full article

Diadromous fish provide key ecological and socio-economic services in European Atlantic catchments, yet their marine phase remains poorly understood and weakly integrated into management. Involving nine partners from Portugal, Spain, France and Ireland, the DiadSea Interreg Atlantic Area initiative aims to fill these critical knowledge gaps on the marine and estuarine phases and to translate this information into coordinated conservation and fisheries management tools. To do so, the project combines historical and newly collected fishery-dependent and -independent data (landings, by-catch, cooperative surveys with commercial and recreational fishers) with advanced microchemical, genetic and environmental DNA (eDNA) analyses to characterize marine distributions, mixing areas and connectivity for shads, Atlantic salmon, sea trout, European eel, sea lamprey and other diadromous species. It also includes innovative case studies on lamprey tagging and intestinal metabarcoding, coastal habitat suitability mapping for shads using river plumes and environmental variables, and joint otolith microchemistry–genomics approaches to reassess European eel panmixia and maternal origin at the Atlantic scale. Species distribution models under present and future climate scenarios, specifically RCP4.5 and RCP8.5, are used to identify priority marine areas for conservation, zones of high temporal turnover and key interfaces ensuring longitudinal (river–sea) and latitudinal connectivity, which will feed into an updated interactive web Atlas of diadromous species. In parallel, DiadSea establishes a transnational observatory of stakeholders to harmonize legislation, co-develop adaptive fisheries management plans and produce climate-aware policy guidelines, while capacity-building actions include an origin-labeling scheme for sustainably harvested diadromous fish, educational games and a comic book to raise awareness among younger generations and the wider public. Together, these work packages will deliver the first integrated, marine-focused, evidence-based and decision-support framework for diadromous fishes in the North-Eastern Atlantic, strengthening conservation, sustainable fisheries and stakeholder engagement under ongoing climate change. Full article

The dataset presented in this manuscript consists of physicochemical, nutritional, and functional characteristics of mamey purees from three West-Indian accessions selected for their processability, i.e., Galion, Ti Jacques, and Sonson. Physicochemical analysis comprised measurements of soluble solid contents, titratable acidity, pH, and color. Proximate analysis corresponded to measurements of humidity, protein, ashes, lipid, and dietary fiber contents, and the calculation of sugar contents and energy value. Biochemical analysis consisted of measurements of polyphenols, flavonoids, carotenoids, and ascorbic acid contents. Antioxidant capacity is also reported by DPPH and ORAC assays, respectively. Raw data and mean values with standard deviations are provided for each characteristic. The data were generated to describe the quality of these mamey purees as an intermediate agrifood industry product. Full article

Modern web application development using frameworks such as React often requires writing a significant amount of initial code before reaching the stage where development becomes engaging. To address this, we developed Lunor, a domain-specific language that can be used in the early phases of front-end development by allowing developers to describe web interfaces in a clear, human-readable syntax that incorporates Markdown for defining the content of a web application. The proposed solution integrates three key components: the Lunor language definition, a template-based code generation, and a Visual Studio Code (VS Code) extension built on the Language Server Protocol, forming a comprehensive environment for efficient web development. Lunor enables rapid prototyping and the creation of simple, yet fully functional web applications, while the generated code remains compatible with standard web technologies for further expansion. Lunor demonstrates that domain-specific languages can simplify front-end web development effectively and integrate seamlessly into the modern web development process. Full article

Background: Today, it is important to measure livestock water consumption to devise sustainable solutions that consider environmental issues, livestock health requirements and animal welfare. Methods: This longitudinal study measured the water consumption of 66 calves subjected to two feeding diets: a recommended diet as control (CON) and an optimised diet (OPT). Individual measurements were collected daily and summarised on a weekly basis over a 20-week period. The analysis considered the impact of environmental conditions depending on the season of the calf’s birth. Results: Before weaning, calves spontaneously drank significant amounts of water in addition to the water brought by the calf milk replacer (CMR), but there was variability between animals. Water consumption among calves in the OPT group was higher than that among calves in the CON group from week 4 onwards (p = 0.005). At weaning, there was a significant increase in water consumption with a total water intake higher in calves in the OPT group compared to calves in the CON group (118.4 L and 78.9 L; p < 0.001). After weaning, water consumption was correlated with the solid feed intake in our model, which did not include direct fodder other than straw. There were no seasonal effects on water consumption before weaning at 9 weeks, but effects were observed after 13 weeks on the feeding plan (p = 0.008), with higher water consumption among calves born in winter and exposed to warmer temperatures in spring. Over a 20-week period, when calves had reached a weight of 180 kg in the OPT group and 150 kg in the CON group, water consumption had reached 1602 L and 1400 L respectively (p < 0.001). Conclusions: Free access to water should be maintained in calf rearing facilities, as water contributes to concentrated CMR and dry solid feed assimilation and the welfare of calves when the feeding plan remains at a modest level, enabling them to tolerate fluctuating environmental conditions. Full article

This work contributes to advancing sustainable energy and waste management strategies by investigating the thermochemical conversion of food-based biomass through pyrolysis, highlighting the role of artificial intelligence (AI) in enhancing process modelling accuracy and optimization efficiency. The main objective is to explore the potential of underutilized biomass resources like spent coffee grounds (SCGs) and DSs (date seeds) for sustainable hydrogen production. Specifically, it aims to optimize the pyrolysis process while evaluating the performance of these resources both individually and as blends. Proximate, ultimate, fibre, TGA/DTG, kinetic, thermodynamic, and Py-Micro-GC analyses were conducted for pure DS, SCG, and blends (75% DS-25% SCG, 50%DS-50%SCG, 25%DS–75%SCG). Blend 3 offered superior hydrogen yield potential but had the highest activation energy (Ea: 313.24 kJ/mol), while Blend 1 exhibited the best activation energy value (Ea: 161.75 kJ/mol). The kinetic modelling based on isoconversional methods (KAS, FWO, and Friedman) identified KAS as the most accurate. These approaches work together to provide a detailed understanding of the pyrolysis process with a particular emphasis on the integration of artificial intelligence (AI). An LSTM model trained with lignocellulosic data predicted TGA curves with exceptional accuracy (R²: 0.9996–0.9998). Full article

Background: Targeted muscle reinnervation (TMR) is increasingly used to enhance prosthetic control and to reduce post-amputation pain. Its implementation across new centers raises questions about the reproducibility of outcomes and the impact of surgical experience. Methods: We compared the first three TMR patients treated in a newly established center in Nantes, France, with three patients treated in a high-volume center in Göttingen, Germany. Functional outcomes were measured using the Box and Block test (BBT), and operative time was recorded. Two French cases were performed with the assistance of a Göttingen-based surgeon. Conclusions: The functional outcomes showed a similar trend in both groups. The mean BBT scores were equivalent, suggesting reliable reinnervation and prosthetic integration even in early cases. Operative times were longer in Nantes, but did not impact outcomes. TMR appears not to have a pronounced learning curve, particularly regarding functional success in early cases under guided protocols. Factors such as assistance from experienced surgeons and favorable donor-to-recipient nerve ratios likely contribute to consistent outcomes. These findings support the reproducibility of TMR across institutions. Results: Within the first two years of rehabilitation we observed improvements in both functional performance and patient-reported quality of life. All six patients across both centers in-creased in BBT scores. All the patients reported an increase in social relationships and psychological health, and two of three patients reported an increase in physical health. Importantly, all six patients discontinued the use of pain medication at 2 years fol-lowing TMR. Furthermore, the French patients reported a decrease from 65–82 mm to 0–31 mm across the patients’ Visual Analog Scale (VAS) pain scores. Full article

This study presents a detailed numerical analysis of impinging propane flames within confined enclosures using the Fire Dynamics Simulator (FDS, v6.5.3). Two archetypal configurations were examined: (i) free buoyant plumes in unconfined environments, and (ii) ceiling-impinging flames under both open and confined conditions. The investigation encompassed a range of heat release rates (0.5–18.6 kW) and five degrees of ventilation confinement. The simulation results confirm that FDS reliably reproduces flame height evolution under free plume conditions, exhibiting strong consistency with Heskestad’s empirical correlation and available experimental benchmarks. Under ceiling impingement, confinement markedly influences the thermal field, the distribution of major gas species (O₂, CO₂, C₃H₈), and the accumulation of unburnt gas. Distinct from previous works primarily centered on unconfined plume dynamics, the present study systematically characterizes the onset of auto-ignition through combined lower flammability limit (LFL) and auto-ignition temperature (AIT) criteria for confined propane combustion. The highest auto-ignition risk was identified in partially confined configurations (Conf. 2 and Conf. 3) at an HRR of 18.6 kW, where unburnt propane concentrations locally exceeded the LFL (≈0.2%) and ceiling temperatures surpassed the AIT of propane (455 °C). The findings elucidate critical trade-offs between ventilation and safety. They also contribute to a validated FDS-based methodology for evaluating fire-induced flow structures, combustion behavior, and ignition hazards in confined spaces. Full article

Land Surface Temperature (LST) plays a key role in climate monitoring, urban heat assessment, and land–atmosphere interactions. However, current thermal infrared satellite sensors cannot simultaneously achieve high spatial and temporal resolution. Spatio-temporal fusion (STF) techniques address this limitation by combining complementary satellite data, one with high spatial but low temporal resolution, and another with high temporal but low spatial resolution. Existing STF techniques, from classical models to modern deep learning (DL) architectures, were primarily developed for surface reflectance (SR). Their application to thermal data remains limited and often overlooks LST-specific spatial and temporal variability. This study provides a focused review of DL-based STF methods for LST. We present a formal mathematical definition of the thermal fusion task, propose a refined taxonomy of relevant DL methods, and analyze the modifications required when adapting SR-oriented models to LST. To support reproducibility and benchmarking, we introduce a new dataset comprising 51 Terra MODIS-Landsat LST pairs from 2013 to 2024, and evaluate representative models to explore their behavior on thermal data. Full article