Search Results (24,322)

This study investigates European citizens’ perspectives on farmers’ roles, highlighting gender, age, education, political orientation, community size, social class, and attitudes towards the EU. This study was developed using 21,002 interviews with European Citizens from all 27 EU countries. A quantitative data analysis methodology was used from the European Eurobarometer 97.1 survey. Seven models were formulated and tested. It is shown that men prioritise economic growth and food stability, while women emphasise environmental protection and animal welfare. Younger individuals focus on rural job creation, whereas older citizens value food security. Higher education levels correlate with environmental and animal welfare concerns. Right-leaning citizens favour economic development, whereas left-leaning individuals prioritise ecological issues. Larger communities emphasise economic growth, while smaller ones focus on environmental preservation. Social class influences priorities, with higher classes concerned about sustainability and lower classes about job creation. Pessimistic views about the EU correlate with food safety concerns, while optimistic views align with environmental and animal welfare priorities. These findings suggest that aligning agricultural and food policies with citizens’ diverse needs can foster a more sustainable and resilient European food system. Full article

Environmental pH plays a critical role in microbial fermentation processes. Weizmannia coagulans attracts particular attention for exceptional acid tolerance and lactic acid productivity. Yet acidic stress impacts on its cell division regulation remain unclear. Here, a critical pH value (pH 4.20) for growth inhibition of the Gram-positive bacterium Weizmannia coagulans strain BC99 was first established. Transcriptomic analysis of metabolic pathways was then performed. The multi-layered regulatory network underlying acid stress-induced cell division was elucidated. Integrated transcriptomic and physiological analyses reveal that acid stress triggers multigene expression reprogramming. This drives core metabolic network reorganization, coordinately regulating division processes. RNA-seq analysis demonstrated acid stress triggered differential expression of division genes (FtsZ/Q downregulation), ATP synthase suppression, and peptidoglycan transport reduction, while enhancing membrane rigidification (Cfa) and magnesium homeostasis (CorA). The PhoPR dual-component system emerged as a central regulator, inhibiting septal assembly via RipA hydrolase and RpsU ribosomal suppression while rerouting carbon flux to glycolysis, elucidating bacterial acid adaptation mechanisms. Collectively, these adaptive changes prioritize cell survival over active proliferation under acidic conditions. This study provides molecular insights into how W. coagulans preserves viability under acid stress, offering a theoretical basis for optimizing its performance in probiotic applications. Full article

Industrial food processing generates substantial byproducts, resulting in environmental challenges and economic losses. This study explores the biovalorization of sugar-rich barbecue sauce waste streams through fermentation to create value-added ingredients for sauce production and promote circular economy practices. The barbecue stream was diluted with water at 25 and 50% incorporation levels and fermented at room temperature for 12 days using a microbial consortium comprising three lactic acid bacteria (Lactiplantibacillus plantarum, Lacticaseibacillus rhamnosus, and Weisella confusa) and one yeast (Saccharomyces boulardii). Laboratory-scale fermentation was monitored by measuring pH, total soluble solids, titratable acidity, sugar consumption, and metabolite production. The consortium demonstrated effective performance, reducing pH and TSS and increasing titratable acidity for both incorporation levels over 12 days. The fermented samples were characterized by their antioxidant capacity, color, protein content, humidity, and viscosity. The total phenolic content and antioxidant activity (DPPH) increased significantly (p < 0.05), and the viscosity increased by 254.3% and 48.3% for the fermented streams with 25% and 50% incorporation, respectively. Antimicrobial assays revealed that the fermented samples inhibited typical spoilage bacteria and yeast. This work highlights the potential of fermentation to upcycle barbecue waste, with antimicrobial characteristics contributing to extended shelf life, sustainable food production, and circular economic practices. Full article

Coastal freak waves (CFWs) are sudden and hazardous wave events that occur near shorelines and can pose serious threats to coastal visitors and infrastructure. Due to the complex interactions among coastal bathymetry, wave dynamics, and environmental conditions, the mechanisms governing CFW formation remain poorly understood, making reliable prediction difficult. This study investigates the feasibility of applying machine learning techniques to predict CFW occurrences using observational environmental data. Three machine learning algorithms, the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to generate probability-based predictions of CFW events. Environmental variables derived from buoy observations, including wave characteristics, wind conditions, swell parameters, wave grouping indicators, and nonlinear wave interaction indices, were used as model inputs. Hyperparameters were optimized using grid search combined with k-fold cross-validation. The results show that all three models achieved comparable predictive performance, with AUC values close to 0.80 and overall prediction accuracy around 74%. The ANN model achieved the highest recall, indicating strong capability in detecting CFW events, while the RF and SVM models showed more balanced precision and recall. Analysis of high-probability prediction events suggests that CFW occurrences are associated with swell-dominated conditions, strong wave grouping behavior, and enhanced nonlinear wave interactions. These results demonstrate that machine learning provides a promising framework for probabilistic prediction of coastal freak waves and has potential applications in coastal hazard assessment and early warning systems. Full article

The large-scale use of compost in arable cropping systems is often limited by the large quantities required to meet the crop’s nutritional needs. Palletization can increase the nutrient density of organic fertilizers and improve their logistical feasibility by reducing storage, transport and application costs. This study evaluated the agronomic and environmental performance of compost pellets derived from pig slurry solids and olive pomace, using them as an alternative nitrogen source for wheat (Triticum aestivum L.) cultivated under Mediterranean conditions. A field experiment was conducted during the 2022–2023 growing season, with four treatments arranged in 24 m² replicated plots: an unfertilized control (C); pelletized compost (PSCOP); fresh pig slurry (PS); and mineral fertilization based on monoammonium phosphate and urea (IN). Excluding the control treatment, all fertilized plots received a uniform nitrogen rate of 150 kg N ha⁻¹. Soil chemical properties and nutrient availability (Pext, NH₄⁺-N and NO₃⁻-N) were evaluated at the beginning and end of the experiment, while wheat yield and grain quality were assessed at harvest. Greenhouse gas (GHG) emissions were monitored throughout the cropping season to evaluate environmental impacts. The results showed that the wheat yields achieved with PSCOP were comparable to those obtained with PS, although they remained lower than those achieved with mineral fertilization. Grain quality was not adversely affected by the application of PSCOP. Furthermore, PSCOP resulted in lower GHG emissions than mineral fertilization, with values closer to those observed in the unfertilized control. These findings suggest that pelletized organic fertilizers such as PSCOP may be a promising way to enhance nutrient circularity and reduce reliance on synthetic fertilizers and maintain crop productivity and limit environmental impact in Mediterranean agricultural systems. Full article

Heavy metal contamination in drinking water remains a pervasive global challenge with significant consequences for environmental quality and human health. This review synthesizes findings from recent studies examining heavy metal concentrations in different sources of drinking water, including municipal tap water, groundwater, surface water, and bottled/sachet water across various geographical regions. The study used a systematic review of studies published from 2015 to 2024. The result showed a variation in the concentrations of heavy metals from all the sources, with tap water generally exhibiting lower heavy metal levels. Pb, Fe, Mn, and other metals persist in different sources and from many regions with levels above the permissible limits recommended by the World Health Organization (WHO) in some instances, which were sometimes linked to aging distribution systems and other pollution sources. Bottled and sachet water, commonly regarded as safer alternatives, also showed some levels of heavy metals such as Pb, Cd, and Cr, reflecting inconsistent packaging or production oversight. Surface waters display variability with heavy metals pollution, driven by industrial discharge, mining activities, agricultural runoff, and urban wastewater inputs. Groundwater sources, although naturally shielded, frequently contained elevated concentrations of As, Hg, and Ni due to both geological and anthropogenic factors. Pb concentrations were below detection limit in some of the published papers; however, the values reported in this study ranged from ND to 260.0 µg/L (tap water), ND to 0.259 mg/L (surface water), ND to 0.791 mg/L (groundwater), and ND to 123.15 µg/L (bottled water). Arsenic (As) concentrations ranged from ND to 692 µg/L from different sources, with the highest concentration from groundwater. Collectively, these patterns underscore the need for strengthened monitoring frameworks, improved water treatment technologies, and integrated pollution-prevention strategies. Addressing heavy metal contamination in drinking water requires coordinated policy approach and continuous monitoring to reduce human exposure and safeguard global public health. Full article

This paper estimates how corporate innovation investment responds to climate policy uncertainty using panel data with 3197 listed firms from 2010 to 2022 in China. The findings show that climate policy uncertainty positively contributes to corporate innovation investment, and this result continues to hold after controlling for endogeneity and conducting a series of robustness tests. Furthermore, we find that stringent government environmental regulation serves as a potential mechanism, compelling firms to adopt cleaner production and increase their investment in innovation. Additionally, this positive relationship is stronger for firms with higher government subsidies and disappears for firms with a higher allocation of fixed assets. We also find that firms with fewer connections to the government are more sensitive to climate policy uncertainty and they tend to increase their investment in innovation to mitigate the uncertainty. Furthermore, when firms invest more in innovation during periods of high policy uncertainty, their long-term performance and firm value are likely to improve. This study sheds light on the importance and influence of climate policy uncertainty on corporate innovation investment in China. Full article

Precision agriculture has become a major direction of agricultural technological development in recent decades, addressing efficiency, environmental, and economic challenges simultaneously. Input optimization based on site-specific data collection—particularly variable-rate nutrient application, precision irrigation systems, and targeted crop protection—has been shown to generate measurable cost and resource savings. The aim of the study is to explore and systematically evaluate the economic impacts influencing precision technology in crop production. Although the technical and environmental benefits of precision technologies are widely documented, their economic performance and farm-level profitability remain inconsistently interpreted. The study is based on a systematic literature review of peer-reviewed English-language journal articles retrieved from the Web of Science, Scopus, ScienceDirect, and JSTOR databases. Study selection and evaluation were conducted in accordance with the PRISMA 2020 methodological framework. The literature indicates that precision technologies achieve average input savings of 8–20% and yield increases of 2–6%, while reported return on investment (ROI) values typically range between 5% and 15%. Economic viability is strongly dependent on farm size, with most studies identifying profitability above 100–200 ha. Additional benefits include improved management of soil heterogeneity, enhanced nutrient-use efficiency, and reduced excess input application, although adoption remains constrained by high investment costs and technological complexity. Full article

This study examines the impact of agribusiness corporations (large-scale agricultural enterprises) and family farms on the sustainable development of agriculture and rural areas in Ukraine, and considers implications for SDG-aligned agri-food value chains that rely on stable access to sustainably produced raw materials. The research applies a multi-criteria decision analysis framework integrating economic, environmental and social indicators at the regional level. Using min–max normalisation, scoring and ranking methods, composite indices of economic sustainability, environmental sustainability, and sustainable rural development were constructed for 20 selected Ukrainian regions, and an integral sustainability index was calculated. Spearman’s rank correlation was applied to identify relationships between sustainability indicators and the structural characteristics of agricultural production. The results reveal pronounced interregional differentiation and an overall predominance of economic over environmental sustainability. Regions with a higher share of family farming demonstrate stronger environmental sustainability and more balanced development patterns, whereas dominance of large-scale enterprises is associated with adverse environmental effects. At the same time, relationships between farm structure and sustainable rural development are weak and not statistically significant, suggesting that social sustainability outcomes depend on more complex and context-dependent mechanisms beyond production scale alone. The findings highlight structural trade-offs between economic efficiency and environmental sustainability and underline the importance of regionally differentiated policy instruments. Strengthening support for family farms is identified as a promising mechanism for improving environmental performance and enhancing upstream conditions for sustainability-oriented sourcing and agri-food value chains. Full article

Deep learning has emerged as an effective tool for automatic waste classification, supporting cleaner cities and more sustainable recycling systems. Because environmental protection is central to the United Nations Sustainable Development Goals (SDGs), improving the sorting and processing of everyday waste is a practical step toward this broader objective. In many real-world settings, however, waste is still sorted manually, which is slow, labor-intensive, and prone to human error. Although convolutional neural networks (CNNs) can automate this task with high accuracy, many state-of-the-art models remain too large and computationally demanding for low-cost edge devices intended for deployment in homes, schools, and small recycling facilities. In this work, we investigate lightweight waste-classification models suitable for TinyML deployment while preserving competitive accuracy. We first benchmark multiple CNN architectures to establish a strong baseline, then apply complementary compression strategies including quantization, pruning, singular value decomposition (SVD) low-rank approximation, and knowledge distillation. In addition, we evaluate an RL-guided multi-teacher selection benchmark that adaptively chooses one teacher per minibatch during distillation to improve student training stability, achieving up to 85% accuracy with only 0.496 M parameters (FP32 ≈ 1.89 MB; INT8 ≈ 0.47 MB). Across all experiments, the best accuracy–size trade-off is obtained by combining knowledge distillation with post-training quantization, reducing the model footprint from approximately 16 MB to 281 KB while maintaining 82% accuracy. The resulting model is feasible for deployment on mobile applications and resource-constrained embedded devices based on model size and TensorFlow Lite Micro compatibility. Full article

The expansion of the global agri-food industry has led to the generation of large volumes of processing by-products that, although traditionally treated as waste, represent valuable sources of bioactive phytochemicals with potential for sustainable valorisation. This review critically examines the integration of fruit, vegetable, cereal, and dairy processing side streams into functional dairy products. Particular attention is given to recent advances in green and emerging extraction technologies, including ultrasound-assisted extraction, microwave-assisted extraction, and supercritical fluid extraction, with emphasis on their efficiency, environmental performance, and effects on the stability and recovery of phytochemicals. The review also discusses the health-related properties of these bioactive compounds, including antioxidant, anti-inflammatory, and metabolic regulatory effects, in relation to their incorporation into milk, yogurt, cheese, and ice cream matrices. In addition, key barriers to industrial implementation are assessed, including compound stability, sensory constraints, bioavailability, and current regulatory limitations. Beyond direct fortification, the review also considers broader valorisation pathways, such as the biotechnological production of microbial enzymes from agro-industrial biomass, as relevant strategies for supporting circularity. Overall, this review highlights how sustainable extraction approaches and functional dairy innovation can contribute to improving the nutritional value, resource efficiency, and circularity of the dairy sector. Full article

The decarbonization of the residential sector is a critical component of the European Green Deal, particularly in transition economies like Poland. This study proposes a comprehensive techno-economic optimization of a deeply retrofitted single-family house aiming for net-zero energy building (NZEB) status. The research specifically focuses on the Polish coastal climate zone, characterized by distinct humidity, wind, and temperature profiles compared to inland regions, which significantly influence the efficiency of air-to-water heat pumps (ASHP). Based on a real-world energy audit, the study simulates the synergy between a deep thermal envelope upgrade and a hybrid system comprising an ASHP, photovoltaics (PV), and battery energy storage (BES). This paper presents a detailed economic analysis of such hybrid systems under the new Polish ‘net-billing’ prosumer mechanism. The study evaluates the impact of electricity tariff structures (flat-rate G11 vs. time-of-use G12w) on the investment’s profitability. By calculating key performance indicators—including the levelized cost of energy (LCOE), net present value (NPV), and self-sufficiency ratio (SSR)—the research assesses various system configurations. The initial evaluation indicates that while deep retrofitting significantly reduces heating demand, integrating battery storage plays a critical role in enhancing economic returns under the net-billing framework. The analysis demonstrates that the optimized hybrid system (9.0 kWp PV + 10 kWh BESS) achieves an average annual self-sufficiency ratio (SSR) of 49.8% and reduces the non-renewable primary energy (EP) indicator to 0.0 kWh/(m²·year). Economically, the investment yields a positive NPV of €3194, an IRR of 5.25%, and a LCOE of €0.184/kWh, which is 34% lower than projected grid prices. Furthermore, switching to a time-of-use tariff (G12w) generates an additional 11% (€139) in annual savings. These quantitative findings provide actionable guidelines for policymakers and investors, confirming the financial viability and environmental benefit (annual reduction of 6.12 MgCO₂) of NZEB standards in coastal areas. Full article

Background: The ability of synthetic plastics to persist in the environment and the accumulation of microplastics has intensified the need to explore biological mechanisms capable of interacting with, and possibly degrading, polymeric materials. Microbial enzymes that have extensive catalytic flexibility represent promising candidates in this context. Aim: This study set out to examine the molecular interaction patterns and dynamical stability of Pseudomonas aeruginosa elastase (LasB) with representative structural fragments of typical synthetic plastics to assess the suitability of the enzyme to polymer-derived substrates. Methods: The crystallographic structure of LasB (PDB ID: 1EZM) was retrieved from the Protein Data Bank and pre-prepared with the help of AutoDock4.2.6 Tools. Those polymer-derived ligands that were associated with the major industrial plastics such as polyamide (PA), polyvinyl chloride (PVC), polycarbonate (PC), poly-ethylene terephthalate (PET), polymethyl methacrylate (PMMA), and polyurethane (PUR) were retrieved in the PubChem database and geometrically optimized with the help of the MMFF94 force field. AutoDock Vina, with a specific grid box around the catalytic pocket, including Zn²⁺ ion, was used to perform molecular docking simulations. PyMOL and BIOVIA Discovery Studio software were used to analyze binding conformations, interaction residues and types of intermolecular contacts. Phosphoramidon, a known metalloprotease inhibitor, served as a positive control to confirm the docking protocol. Additional assessment of the structural stability and conformational behavior of the enzyme–ligand complexes was conducted by molecular dynamics (MD) simulations with the Desmond engine and explicit solvent model in a 50 ns trajectory using the OPLS4 force field. RMSD, RMSF, radius of gyration, hydrogen bonding analysis and solvent accessibility parameters were used to measure structural stability. Results: The docking experiment showed varying binding affinities with the test polymers. Polycarbonate (−5.774 kcal/mol) and polyurethane (−5.707 kcal/mol) had the highest in-teractions with the LasB catalytic pocket, polyamide (−5.277 kcal/mol) and PET (−4.483 kcal/mol) followed PMMA and PVC, which had weaker affinities. The following were the important residues involved in interaction networks: Glu141, His140, Val137, Arg198, Tyr114, and Trp115 that were implicated in interaction networks with hydrophobic interactions, π-cation interactions and van der Waals forces that were the major stabilization forces. MD simulations had stabilized complexes, and RMSD values were found to be within acceptable ranges of stability, and ligand-specific changes (around 1.0-3.2 A), which is also in line with stable protein-ligand systems. Phosphoramidon used as a positive control had an RMSD of 1.205 A which is within this stability range. PCA determined various ligand-bound conformational states of LasB with PA in com-pact state, PC and PVC in intermediate states and PUR, PMMA and PET in ex-panded conformations, indicating structur-al stability and adaptability of the binding pocket. Conclusion: These findings show that LasB has a structurally flexible catalytic pocket that can accommodate a wide range of polymer-derived ligands. These results offer an insight into the recognition of enzymes with polymers at the molecular level and also indicate that LasB might help in the interaction of microorganisms with synthetic plastics in environmental systems. Full article

Environmental factors are suspected of triggering rheumatoid arthritis (RA). One such factor is oxidative stress (OS), which is a step in ecotoxin detoxification and a damaging factor. The linkage of ecotoxin-triggered OS with clinical and laboratory RA indices in patients and individuals at the pre-RA stage was studied in patients with early (e) (n = 35) and advanced (a) stages of RA (n = 25) and individuals at pre-RA stages (FDR-First-Degree Relative(s), pre-RA, n = 72) in comparison with 52 healthy individuals without autoimmune and immunoinflammatory diseases in their family history (Controls). Ecotoxins within permissible concentration limits were associated with serum levels of OS markers in all cohorts, including Controls. Serum oxidized low-density lipoprotein (oxLDL) levels in pre-RA and eRA cohorts exceeded Control values. Significant differences were found in anti-oxLDL antibody correlations and OS markers. In pre-RA and both RA cohorts, a relationship was found with regards to serum antibodies to cyclic citrullinated peptide (ACCP) levels. Thus, ecotoxin-induced OS likely triggers pathogenic mechanisms at the pre-RA stage and RA onset. Full article

The growing demand for Critical Raw Materials (CRMs) requires mining practices that align with sustainability and environmental, social, and governance (ESG) principles, while mining training increasingly benefits from advanced digital tools. Virtual Reality (VR) can provide high-resolution site representations that support both interactive learning and data-oriented analysis without operational risk. This study presents a VR-based framework for the quantitative assessment of pit lake rehabilitation using Virtual Excursions (VEs) developed from panoramic imagery and supported by machine-learning correction. High-resolution 360° panoramic images were used to extract geometric characteristics of a rehabilitated pit lake at the LARCO GMMSA Euboea mine site, Greece, including surface area, shoreline length, mean diameter, and maximum diameter. These image-derived estimates were validated against ground-truth data from field surveys and mine-closure documentation. To reduce systematic deviations associated with panoramic image measurements, a supervised multiple linear regression model was applied as a correction step. Validation based on Root Mean Square Error (RMSE) and the coefficient of determination ($R^2$) showed substantial improvement of the corrected estimates relative to the uncorrected image-based measurements. The results demonstrate that panoramic VR imagery can support site-specific quantitative environmental assessment in addition to its educational value. Although the present findings are limited to a single pit lake case study, the proposed workflow provides a structured basis for integrating immersive visualization, image-based measurement, and regression-based correction in post-mining rehabilitation assessment. Full article