Search Results (25,929)

Livestock plays a crucial role in the global food system, not only as an important source of nutrients but also as a means of economic and social well-being. It constitutes a critical parameter of agricultural production in Mediterranean countries, with the majority of farms still having a relatively small herd size and depending largely on family labor. The purpose of this study is to record and evaluate the perceptions of livestock farmers in the Region of Western Macedonia, Greece (which represents a typical paradigm of an agricultural region), regarding the future prospects and the actions taken to ensure the sustainability of their farms. The research is based on a survey carried out from May to October, 2024, on ruminant farmers. Selective breeding and crossbreeding with higher-productivity breeds are some of the genetic improvements that are generally applied to increase productivity and were, therefore, investigated in this study. Through gradual crossbreeding, farmers attempt to improve the composition of their initial herds by incorporating high-productivity traits—although without officially participating in any recognized improvement program. This increases the risk of extinction for indigenous breeds, which are abandoned for use by the farmers. Our results also showed that most livestock farms derive from inheritances, with many livestock farmers practicing grazing mainly in mountainous areas and still rearing indigenous breeds. From the farmers’ point of view, more information and education regarding market conditions are needed. Furthermore, the sustainability of farms largely depends on subsidies, which are crucial due to difficulties in economic viability, particularly in mountainous areas. Encouraging the support of market differentiation and public awareness for the nutritional value of products derived from local breeds may serve as a promising agrobiodiversity conservation strategy. Full article

Compared with conventional gas reservoirs, deep shale gas reservoirs are characterized by developed faults and fractures, strong heterogeneity, high stress sensitivity, and complex in situ stress distribution. To address traditional 3D static models’ inability to predict in situ stress changes in strongly heterogeneous reservoirs during fracturing, this study takes the deep shale gas in the Zigong block of the Sichuan Basin as an example. By comprehensively considering the heterogeneity and anisotropy of geomechanical parameters and natural fractures in shale gas reservoirs, a 4D in situ stress multi-physics coupling model for shale gas reservoirs based on geology–engineering integration is established. Through coupling geomechanical parameters with fracturing operation data, the dynamic evolution laws of multi-scale stress fields from single-stage to platform-scale during large-scale fracturing of horizontal wells in deep shale gas reservoirs are systematically studied. The research results show the following: (1) The fracturing process has a significant impact on the magnitude and direction of the stress field. With the injection of fracturing fluid, both the minimum and maximum horizontal principal stresses increase, with the minimum horizontal principal stress rising by 1.8–6.4 MPa and the maximum horizontal principal stress by 1.1–3.2 MPa; near the wellbore, there is an obvious deflection in the direction of in situ stress. (2) As the number of fracturing stages increases, the minimum horizontal principal stress shows an obvious cumulative growth trend, with a more significant increase in the later stages, and there is a phenomenon of stress accumulation along the wellbore, with the stress difference decreasing from 15 MPa to 11 MPa. (3) The on-site adoption of the fracturing operation method featuring overall flush advancement and inter-well staggered fracture placement has achieved good stress balance; comparative analysis shows that the stress communication degree of the 400 m well spacing is weaker than that of the 300 m well spacing. This study provides a more reasonable simulation method for large-scale fracturing development of deep shale gas, which can more accurately predict and evaluate the dynamic stress field changes during fracturing, thereby guiding fracturing operations in actual production. Full article

Numerical simulation has played a crucial role in modeling the behavior of natural gas hydrate (NGH). However, the existing numerical simulators worldwide have exhibited limitations in functionality, convergence, and computational efficiency. In this study, we present a novel numerical simulator, GPSFlow/Hydrate, for modeling the behavior of hydrate-bearing geologic systems and for addressing the limitations in the existing simulators. It is capable of simulating multiphase and multicomponent flow in hydrate-bearing subsurface reservoirs under ambient conditions. The simulator incorporates multiple mass components, various phases, as well as heat transfer, and sand is treated as an independent non-Newtonian flow and modeled as a Bingham fluid. The CH₄ or binary/ternary gas hydrate dissociation or formation, phase changes, and corresponding thermal effects are fully accounted for, as well as various hydrate formation and dissociation mechanisms, such as depressurization, thermal stimulation, and sand flow behavior. In terms of computation, the simulator utilizes a domain decomposition technology to achieve hybrid parallel computing through the use of distributed memory and shared memory. The verification of the GPSFlow/Hydrate simulator are evaluated through two 1D simulation cases, a sand flow simulation case, and five 3D gas production cases. A comparison of the 1D cases with various numerical simulators demonstrated the reliability of GPSFlow/Hydrate, while its application in modeling the sand flow further highlighted its capability to address the challenges of gas hydrate exploitation and its potential for broader practical use. Several successful 3D gas hydrate reservoir simulation cases, based on parameters from the Shenhu region of the South China Sea, revealed the correlation of initial hydrate saturation and reservoir condition with hydrate decomposition and gas production performance. Furthermore, multithread parallel computing achieved a 2–4-fold increase in efficiency over single-thread approaches, ensuring accurate solutions for complex physical processes and large-scale grids. Overall, the development of GPSFlow/Hydrate constitutes a significant scientific contribution to understanding gas hydrate formation and decomposition mechanisms, as well as to advancing multicomponent flow migration modeling and gas hydrate resource development. Full article

The role of science and technology in enhancing beer quality is crucial amid growing market demands. This pilot study assessed the clarity and physicochemical stability of laboratory beers treated post fermentation with three clarifying (fining) agents: two chitosan-based and one collagen-based (fish bladder/isinglass). The beers were brewed with Polish barley malt and hops (alpha acids 7.5% and 14.5%). The measured parameters included pH, colour, turbidity, viscosity, surface tension, and foam volume. Within this small-scale, low-power dataset, both the collagen- and chitosan-based agents improved clarity, with the collagen agent showing the lowest turbidity in this sample. The clarifying agents also influenced the colour and surface tension, while the pH was largely unchanged. The foam volume increased with fining. Shelf-life checks suggested improved stability in clarified beers, with no clear differences between agents under these conditions. These findings are preliminary. The results should be interpreted cautiously due to the limited number of replicates. Larger scale studies with adequate replication are required before translating these observations into brewing practice. Chitosan's effectiveness as a clarifying agent aligns with its high charge density and ability to coagulate suspended particles. This study underscores the importance of selecting appropriate clarifying agents to optimize beer clarity and stability while maintaining essential physicochemical properties. These findings contribute to the brewing industry's efforts to meet consumer expectations for high-quality, stable beer products. Full article

Melt electrowriting (MEW) is an advanced additive manufacturing technology that can produce micro- or nano-scale fibres, achieving accurate fibre deposition, and is suitable for manufacturing high-precision, miniature products. This review introduces the key principles and parameters that influence the performance of melt electrowriting and explores the current mathematical modelling under four stages: (1) heating and extrusion system, (2) formation of the Taylor cone, (3) formation and injection of the melt jet, and (4) deposition of the melt jet. In addition, current applications of melt electrowriting in emerging areas, such as tissue engineering, energy, filtration, and bioengineering, are introduced while discussing its combination with other additive manufacturing technologies. Finally, recent challenges, including production time, cost, and precision are covered, while the future research directions are to improve technology and introduce new materials. Full article

Choosing the most sustainable and ecologically stable soil tillage techniques requires dependence on long-term field trials, which are essential for successful interventions and evidence-based decision-making. This research evaluated several factors, including soil biological activity (CO₂ emission), soil chemical properties (pH (KCl), soil organic matter (SOM)), plant growth physiological indicators (Leaf Area Index (LAI), Soil and Plant Analysis Development (SPAD)), crop yield, and grain quality (Zeleny index, protein %, oil %, and gluten % content), under six soil cultivation methods that represent varying degrees of soil disturbance in a long-term (23 years) tillage experiment. Conventional tillage (ploughing (P)) and conservational tillage techniques (loosening (L), deep cultivation (DC), shallow cultivation (SC), disking (D), and no-till (NT)) were examined for three years (2022, 2023, and 2024) in a winter barley–soybean–winter wheat cropping system. Results indicate that tillage intensity has a differential influence on soil biological parameters, with minor variations in SPAD values across treatments. The findings show significant variations in CO₂ emissions, LAI values, and grain quality in certain years, likely due to the influence of P and L tillage treatments. The novelty of this study lies in determining that, although the short-term effects of soil tillage on crop physiological parameters and grain yield may be minimal under fluctuating climatic conditions, long-term tillage practices significantly influence existing disparities, underscoring the necessity for site-specific and climate-resilient tillage strategies in sustainable crop production. Full article

To address the critical challenges in pyrometallurgical recycling processes—such as poor feedstock adaptability, high energy consumption during roasting conversion, and the low added value of rare earth products—this study systematically investigated the mechanism and process optimization of ammonium bifluoride (NH₄HF₂) roasting for the recovery of neodymium–iron–boron (NdFeB) waste. Thermodynamic analysis confirmed the feasibility of the conversion reaction between NH₄HF₂ and the rare earth components in NdFeB waste. Single-factor experiments were conducted to examine the effects of roasting temperature, reaction time, and NH₄HF₂ dosage on rare earth recovery. The optimal conditions were a roasting temperature of 600 °C, a reaction time of 120 min, and a NH₄HF₂ dosage of 75 wt%, achieving a rare earth recovery rate of 98.81%. Furthermore, the response surface methodology (RSM) was employed to establish a quantitative model correlating process parameters with recovery efficiency. Variance analysis demonstrated that the model was highly significant (F = 136.94, p < 0.0001), with excellent agreement between actual and predicted values (R² = 0.9944). Factor contribution analysis revealed that NH₄HF₂ dosage had the most pronounced impact on rare earth fluorination, followed by roasting temperature and reaction time. Under optimized conditions, the purified rare earth fluoride obtained after acid leaching reached a purity of 99.43%, providing high-quality raw material for producing high-value-added rare earth products. Full article

Understanding the intricate relationship between sensory perception and physicochemical properties of cacao-based products is crucial for advancing quality control and driving product innovation. However, effectively integrating these heterogeneous data sources poses a significant challenge, particularly when sensory evaluations are derived from low-quality, subjective, and often inconsistent annotations provided by multiple experts. We propose a comprehensive framework that leverages a correlated chained Gaussian processes model for learning from crowds, termed MAR-CCGP, specifically designed for a customized Casa Luker database that integrates sensory and physicochemical data on cacao-based products. By formulating sensory evaluations as regression tasks, our approach enables the estimation of continuous perceptual scores from physicochemical inputs, while concurrently inferring the latent, input-dependent reliability of each annotator. To address the inherent noise, subjectivity, and non-stationarity in expert-generated sensory data, we introduce a three-stage methodology: (i) construction of an integrated database that unifies physicochemical parameters with corresponding sensory descriptors; (ii) application of a MAR-CCGP model to infer the underlying ground truth from noisy, crowd-sourced, and non-stationary sensory annotations; and (iii) development of a novel localized expert trustworthiness approach, also based on MAR-CCGP, which dynamically adjusts for variations in annotator consistency across the input space. Our approach provides a robust, interpretable, and scalable solution for learning from heterogeneous and noisy sensory data, establishing a principled foundation for advancing data-driven sensory analysis and product optimization in the food science domain. We validate the effectiveness of our method through a series of experiments on both semi-synthetic data and a novel real-world dataset developed in collaboration with Casa Luker, which integrates sensory evaluations with detailed physicochemical profiles of cacao-based products. Compared to state-of-the-art learning-from-crowds baselines, our framework consistently achieves superior predictive performance and more precise annotator reliability estimation, demonstrating its efficacy in multi-annotator regression settings. Of note, our unique combination of a novel database, robust noisy-data regression, and input-dependent trust scoring sets MAR-CCGP apart from existing approaches. Full article

This article presents a technology for the production of magnesium hydroxide from serpentinite via sulfuric acid leaching of magnesium and purification of the resulting sulfate solution from impurity metals using thermally activated serpentinite (TA-SP) at 750 °C for one hour. Purifying the leach solution is one of the key challenges in obtaining high-purity magnesium compounds from serpentinite. It has been established that the use of thermally activated serpentinite to neutralize the acidic suspension of serpentinite to pH 8.3, prior to treatment with an alkaline agent (sodium hydroxide), has a positive effect on the purity of the precipitated magnesium hydroxide. The influence of the thermal treatment on the acid–base properties of serpentinite, its phase composition, and adsorbent structure parameters, such as specific surface area and micropore distribution, was studied, revealing improvements in the adsorption properties. Flowcharts for the acid leaching and magnesium hydroxide precipitation processes are provided. The flow-sheet that we propose is shown to reduce the number of steps in the process and amount of equipment required for the purification of sulfate solution while ensuring that the magnesium hydroxide product has a purity of at least 99.5%. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

Feed is an important source of antibiotic resistance genes (ARGs) in animals and products, posing significant potential risks to human health and the environment. Ensiling may present a feasible method for reducing ARGs in animal feed. This study involved the addition of four types of lactic acid bacteria (LAB) inoculants, Lactiplantibacillus plantarum (LP), Pediococcus acidilactici (P), Enterococcus faecium (E), and Ligilactobacillus salivarius (LS), to whole-crop corn silage to investigate changes in ARGs, mobile genetic elements (MGEs), and their transmission risks during ensiling. The results indicated that the addition of LAB significantly reduced the ammonia nitrogen content and pH value of whole-crop corn silage, inhibited the growth of harmful microorganisms, and increased the lactic acid content (p < 0.05). The improvement effect was particularly pronounced in the P treatment group. Natural fermentation plays a significant role in reducing ARG abundance, and the addition of different types of lactic acid bacteria helps reduce the abundance of both ARGs and MGEs. Specifically, the LS treatment group exhibited a significant decrease in MGE abundance, potentially reducing the horizontal transmission risk of ARGs. Furthermore, variations in ARG abundance within different LAB strains were detected, showing a consistent trend with that in silage. ARGs and MGEs were correlated with the fermentation parameters and microbial communities (p < 0.05). This suggests that adding LAB with low levels of ARGs to silage can effectively reduce ARG contamination. Bacterial community structure, MGEs, and fermentation quality may act as driving forces for the transfer and dissemination of ARGs in the silage ecosystem. Full article

Excessive use of antibiotics can lead to antibiotic resistance, posing a significant threat to human health and the environment. Chloramphenicol (CAP), once widely used, has been banned in many regions for over 20 years due to its toxicity. Detecting CAP residues in food products is crucial for regulating safe use and preventing unnecessary antibiotic exposure. Electrochemical sensors are low-cost, sensitive, and easily detect CAP. This paper reviews recent research on electrochemical sensors for CAP detection, with a focus on the materials and fabrication techniques employed. The sensors are evaluated based on key performance parameters, including limit of detection, sensitivity, linear range, selectivity, and the ability to perform simultaneous detection. Specifically, we highlight the use of metal and carbon-based electrode modifications, including gold nanoparticles (AuNPs), nickel–cobalt (Ni-Co) hollow nano boxes, platinum–palladium (Pt-Pd), graphene (Gr), and covalent organic frameworks (COFs), as well as molecularly imprinted polymers (MIPs) such as polyaniline (PANI) and poly(o-phenylenediamine) (P(o-PD)). The mechanisms by which these modifications enhance CAP detection are discussed, including improved conductivity, increased surface-to-volume ratio, and enhanced binding site availability. The reviewed sensors demonstrated promising results, with some exhibiting high selectivity and sensitivity, and the effective detection of CAP in complex sample matrices. This review aims to support the development of next-generation sensors for antibiotic monitoring and contribute to global efforts to combat antibiotic resistance. Full article

Background/Objectives: Pompia is an ancient, endemic citrus ecotype native to Sardinia (Italy), characterized by distinctive morphology and high content of bioactive compounds. Despite increasing interest, several aspects of this fruit, including its histological characteristics, remain poorly understood. This study aims to address this gap by investigating the anatomical features and spatial distribution of secretory cavities involved in essential oil (EO) production and accumulation, while also evaluating the EO’s chemical profile and associated biological activity. Methods: Pompia peel (flavedo and albedo) was subjected to histological analysis through fixation, dehydration, resin inclusion and sectioning. Sections were stained with 0.05% toluidine blue and observed under a light microscope to measure different parameters of secretory cavities. Essential oil (EO) was obtained from Pompia peel by hydrodistillation and characterized by gas chromatography–mass spectrometry (GC–MS) analysis. The biological activity of Pompia EO was assessed in vitro using NIH/3T3 fibroblasts, where wound-healing was evaluated by scratch assay and anti-senescence effects by β-galactosidase and γH2AX activity. Results: Microscopic analysis of the peel revealed pronounced variability in depth and size of the secretory cavities, along with the presence of lenticel-like structures in the epidermis. GC–MS analysis showed that Pompia EO is dominated by limonene (89%), with minor compounds including myrcene, geranial and neral. In vitro biological assays demonstrated that the EO promotes cell migration in a wound-healing model at concentrations ≥ 12.5 µg/mL and reduces markers of cellular senescence, including β-galactosidase activity and γH2AX foci, in etoposide-induced senescent fibroblasts. Conclusions: Overall, this study provides the first histological characterization of Pompia peel and confirms the bioactive potential of its EO. These findings support future applications in skin regeneration and anti-aging strategies and contribute to the valorization of this underexplored Citrus ecotype. Full article

In the area of structured cabling systems, optimization, i.e., reducing design errors, mini-mizing the need for rework, and increasing overall design productivity, is a critical factor in both design and maintenance. Traditional CAD methods exhibit 12% cable length miscalculations, which our script methodology mitigates. This paper presents a novel approach to the use of scripts in low voltage cabling systems, with a particular focus on the automatic routing of cables based on modeled cable paths. The proposed approach enables the automated construction and calculation of individual cable routes, as well as the comprehensive storage of associated parameter data. The methodology is discussed at conceptual level, with ideas presented at code and user levels. The effectiveness of this methodology is demonstrated through a case study conducted in the context of a real-world project. Full article

Background/Objectives: Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide, driven largely by underlying coronary artery disease (CAD). Reactive oxygen species (ROS) and reactive nitrogen species (RNS) play pivotal mechanistic roles in endothelial dysfunction, atherosclerotic plaque progression, and subsequent cardiac injury. Excessive production of these reactive species disrupts cellular redox balance, promotes mitochondrial dysfunction, and accelerates vascular inflammation, ultimately contributing to plaque rupture and MI. This study aimed to investigate the mechanistic associations and clinical correlations of individual ROS and RNS markers in patients with MI. Methods: We conducted a case–control study including 86 patients with MI and 60 age- and sex-matched controls without cardiovascular disease, recruited from the Medina Cardiac Center in Saudi Arabia. The MI cohort was subdivided into ST-elevation MI (STEMI, n = 62) and non-ST-elevation MI (NSTEMI, n = 24) to explore potential differences in oxidative and nitrosative stress profiles. Serum levels of multiple ROS (including hydrogen peroxide, hydroxyl radical, and superoxide anion) and RNS (including nitric oxide and peroxynitrite) were quantified using validated fluorescence-based assays. Clinical and biochemical parameters, including lipid profiles, troponin, and left ventricular ejection fraction, were also assessed. Results: Most ROS and RNS markers were significantly elevated in MI patients compared to controls (p < 0.05), except for nitrogen dioxide. Moderate to strong positive correlations were observed between ROS/RNS levels and serum total cholesterol and LDL-cholesterol (p < 0.001). In contrast, weak or non-significant correlations were found between ROS/RNS markers and serum troponin or left ventricular ejection fraction. Both STEMI and NSTEMI subgroups demonstrated significantly higher oxidative and nitrosative stress levels compared to controls, with distinct patterns between the subtypes. Conclusions: This study underscores a mechanistic link between elevated ROS/RNS levels and myocardial infarction, supporting the importance of targeting oxidative and nitrosative pathways as potential therapeutic strategies. Full article