Search Results (1,856)

Sustainability education increasingly calls for innovative learning environments that help learners recognize ecological interdependencies and challenge anthropocentric worldviews. Everyday multispecies relationships, such as with companion animals, often underexplored, offer opportunities for cultivating ecological literacy and care. This paper introduces Augmented Ecological Relating (AER), an approach that combines Augmented Reality (AR) with embodied inquiry to explore multispecies perspectives. Going beyond embodied inquiry, AER specifies how digital augmentation can systematically support learners’ iterative noticing, ethical reasoning, and action within everyday multispecies ecosystems. We draw on a virtual summer workshop for adolescents in which participants used AR filters simulating dog and cat vision to investigate their pets’ sensory worlds. We used qualitative case study methods to examine how AR tools mediated human youths’ noticing, inquiry, and reflection. We found that the AR filters used in the study’s context enabled participants to critically reconsider pet behaviors within home ecologies. Participants recognized companion animals as ecological beings with distinct sensory experiences, explored interconnections among humans, animals, and environments, and reflected on ethical responsibilities in multispecies relationships. Through iterative inquiry, youth moved beyond companionship to sustainability-oriented perspectives grounded in relational care, systems thinking, and practical action. By embedding digital augmentation into everyday contexts, AER enabled learners to engage with more-than-human perspectives, fostering ecological awareness, ethical reflection, and sustainability literacy in accessible, meaningful ways. Full article

Air pollution remains a major public health and environmental challenge in China, driven by complex non-linear interactions among anthropogenic activities, meteorological conditions, and topographic features that go beyond simple linear relationships. This study presents a comprehensive spatio-temporal assessment of key air pollutants (CO, NO₂, SO₂, and PM_2.5) and their relationships with Total Column Ozone (TCO) across China’s provinces from 2019 to 2023. Multi-source high-resolution satellite data from Sentinel-5P/TROPOMI, the China High PM_2.5 dataset, MODIS, and ERA5-Land reanalysis were integrated. A tiered analytical framework was applied, combining linear Pearson correlations, non-linear Spearman rank correlations, and interpretable XGBoost machine learning with SHAP values. Results reveal a distinct seasonal “seesaw” pattern, with primary pollutants peaking during winter stagnation and TCO reaching maximum levels in late winter and spring. Non-linear analyses uncover critical threshold effects, including exponential increases in PM_2.5 and SO₂ when surface temperatures drop below 0 °C, very strong SO₂-TCO coupling (ρ = 0.93), and significant pollutant trapping in low-elevation regions (CO-elevation ρ = −0.82). These findings support the development of precision environmental policies with dynamic, temperature-threshold-based emission controls and topography-specific strategies to effectively mitigate air pollution in China. Full article

Supply Chain Management (SCM) has received considerable attention from the industrial community in recent decades. SCM continues to be an interesting and relevant research topic in many business areas such as revealing supply chain integration benefits, uncertainty and risk mitigation methods, decision-making and optimization methodologies, etc. In current supply chain management, huge volumes of data are being developed each second, and emerging technologies such as Radio Frequency Identification (RFID) have amplified the availability of online data. Using Artificial Intelligence (AI) methods that go beyond simply using the huge volume of online data enables Supply Chain (SC) managers to monitor everything in a timely fashion. There are several aspects of an SC that AI—and specifically Artificial Neural Networks (ANNs)—can be applied to better help them manage and optimize. This study aims to review state-of-the-art ANNs and Deep Neural Networks (DNNs) in the field of supply chain management. One hundred high-quality research studies that applied ANNs in supply chain management are reviewed and categorized into four classes: performance optimization, supplier selection, forecasting, and inventory management studies. Our study shows that there is a significant possibility that we could use ANNs and DNNs to better manage supply chains. Across the reviewed studies, neural networks are frequently reported to improve predictive performance and support monitoring/control in complex, nonlinear supply chain settings, often complementing traditional operations research approaches. Finally, the limitations of ANN models and the possibilities for future studies are presented at the end of this study. Full article

Conventional centralized drainage systems exacerbate urban flooding, pollution, and water stress. Low-impact development (LID) is a decentralized alternative; however, its multifunctional benefits, which go beyond the control of stormwater, are often undervalued in planning. This study fills this gap by developing an integrated benefit valuation framework to systematically quantify and estimate the economic value of the co-benefits of five widely implemented LID facilities (vegetated swale, green roof, in-filtration ditch, infiltration trench, and permeable pavement) in Seoul, South Korea. The framework combines annual benefits in four key sectors: water management (runoff reduction), energy savings (building cooling/heating demands), air quality (pollutant deposition and avoided emissions) and climate change (carbon sequestration and mitigation). Applying a transparent, localized spreadsheet model, the results indicate significant multifunctional value for LID systems. While water management provides the primary benefit, there is substantial added value in energy, air quality, and climate co-benefits. In the case of green roofs, such ancillary benefits can exceed hydrological values. The analysis further reveals a consistent scale-benefit relationship and a clear trade-off between the magnitude of benefits and the cost of implementation. This provides evidence of the need for context-sensitive, portfolio-based LID planning. The proposed framework is a practical decision support tool for urban planners and policymakers to consider LID not only as a stormwater solution but also as multifunctional green infrastructure that simultaneously promotes urban water security, energy efficiency, environmental quality, and climate resilience. Full article

Motorcycle touring represents a significant leisure and social activity across Europe, involving a vast number of individual and group participants. The transition to electric mobility, which is currently affecting passenger cars, is going to involve motorcycles in the next few years. This study aims to assess the feasibility of touring with electric-powered motorcycles and understand European motorcyclists’ attitudes toward electric motorcycles. The study involved test rides with commercially available ePTWs in typical touring conditions, followed by an online survey distributed in different languages to motorcycle clubs and social media. Field evaluations yielded empirical insights into the performance of electric motorcycle technologies, while the survey provided detailed data on riding habits, usage patterns, and user concerns regarding future adoption. These findings serve as a strategic resource for stakeholders to advance vehicle and infrastructure development, ultimately supporting a sustainable evolution of the motorcycle sector. Full article

The ovaries are crucial reproductive organs that regulate the menstrual cycle and support pregnancy through the production of steroid hormones. They are highly susceptible to various environmental pollutants, which can lead to ovarian disorders. Luteal phase defect (LPD) and premature ovarian failure (POF) are common ovarian disorders in women. In this study, we integrate network toxicology with molecular docking and molecular dynamics simulations to elucidate the toxicological mechanisms of Benzo(a)pyrene (BaP), a widespread endocrine disruptor, in LPD and POF. Through systematic data mining of the GeneCards and OMIM databases, we identified 1336 targets associated with LPD and 2066 targets related to POF, as well as 220 BaP targets. Venn diagram analysis revealed 36 potential targets for BaP-induced LPD and 43 for BaP-induced POF. GO and KEGG enrichment analyses suggest that BaP-induced LPD and POF may share toxicological mechanisms. PPI network visualization indicated that EGFR, ESR1, and STAT3 are critical common targets for BaP-induced LPD and POF. Molecular docking and molecular dynamics simulations revealed that BaP exhibits strong binding affinity with all three core genes. In KGN cells modeling LPD and POF phenotypes, cellular experiments confirmed that BaP downregulated EGFR and ESR1 expression while upregulating STAT3 expression, thereby supporting the reliability of these targets in BaP-induced ovarian dysfunction. These findings provide insights into BaP-induced reproductive toxicity and offer a foundation for targeted clinical interventions to mitigate the effects of environmental pollutants on women’s reproductive health. Full article

Concrete, as a widely used construction material, suffers from performance degradation due to chloride penetration and sulfate attack in harsh environments. Conventional performance-enhancing methods are costly and emit high levels of carbon dioxide. This study modified graphene oxide (GO) with polycarboxylate superplasticizer (PCE) alone or PCE synergized with a rubber viscosity reducer, optimized dispersion (50 °C water bath for 1 h), and prepared C50 modified concrete (500 kg/m³ cementitious materials, w/b = 0.33). GO contents were 0%, 0.001%, 0.003%, 0.005%; a group with 8% reduced cementitious materials (460 kg/m³) was also tested. Results showed PCE-viscosity reducer synergy better dispersed GO, improving concrete workability. GO accelerated cement hydration via nucleation, refining C-S-H gel and reducing porosity. At 0.005% GO, 56 d drying shrinkage dropped by 29.3% vs. the blank, and 56 d chloride penetration electric flux was 586 C, meeting 100-year service life. Sulfate resistance also improved with higher GO content. Even with 8% less cementitious materials, modified concrete outperformed the blank. This provides support for GO’s application in cement-based materials. Full article

Background: Emerging evidence links the gut microbiome to chronic pain processing. Inulin, a prebiotic fibre, modulates the gut microbiome, while physiotherapy-supported exercise (PSE) improves pain and function. We evaluated the effects of inulin supplementation with and without PSE on knee osteoarthritis (OA) pain. Methods: In a 2 × 2 factorial RCT, 117 community-dwelling adults with knee OA received 6 weeks of: (A) 20 g/day inulin, (B) digital PSE (Joint Academy™), (C) inulin +PSE, or (D) 10 g/day maltodextrin. Primary outcome: pain (Numerical Rating Scale). Secondary: 30 s sit-to-stand (30-CST), timed up and go (TUG), grip strength, and quantitative sensory testing. Serum short-chain fatty acids (SCFAs) and glucagon-like peptide-1 (GLP-1) were measured. The study was not powered to detect synergistic interaction. Results: A total of 117 participants (58.1% female; mean ± SD age = 67.5 ± 9.4 years; BMI = 29.5 ± 5.3 kg/m²; NRS = 3.96 ± 2.67) completed the trial. Pain improved with inulin (baseline-adjusted between-group mean difference (Δ) = −1.11 [95%CI −2.18, −0.04], p = 0.045) and PSE (Δ = −1.55 [95%CI −2.52, −0.58], p = 0.002) compared to placebo, with no synergistic effect. PSE improved TUG (p = 0.02) and 30-CST (p = 0.0004), while inulin improved grip strength (p = 0.002), pressure pain thresholds (p = 0.009) and temporal summation (p = 0.025) compared to placebo and had significantly lower dropout rates (3.6%) compared with PSE (21% p < 0.01). Only inulin increased SCFA butyrate (p = 0.0248) and GLP-1 (p = 0.0109), and higher GLP-1 was associated with improved grip strength, suggesting a gut–muscle link. Conclusions: Inulin and PSE each produced meaningful pain reductions. Only inulin improved pain sensitivity and grip strength, the latter paralleled by increased GLP-1, and had much higher rates of retention compared to PSE. Full article

Background/Objectives: Walking impairment and fatigue are common in multiple sclerosis (MS) and contribute to reduced physical function and quality of life (QoL). This study evaluated the feasibility, safety, and pre–post changes associated with a 12-week treadmill walking training (TWT) programme on walking performance, physical function, fatigue, and QoL in people with MS. Methods: Single-arm pilot study with pre–post assessments (T1–T2). Eleven adults with MS (Expanded Disability Status Scale [EDSS] ≤ 6) completed supervised TWT for 12 weeks (two 25 min sessions/week) at the Complejo Asistencial Universitario de Soria (Spain). Outcomes included SF-36, Timed Up and Go (TUG), 4 m gait speed, Short Physical Performance Battery (SPPB), and Modified Fatigue Impact Scale (MFIS). Within-participant changes were analysed using paired t-tests or Wilcoxon signed-rank tests as appropriate; effect sizes were reported as appropriate for the statistical test. Results: SF-36 total score did not change significantly (p = 0.160), while general health (p = 0.039) and vitality (p = 0.043) improved. Walking performance improved (TUG, p = 0.007; 4 m gait speed, p < 0.001), and physical function increased (SPPB, p = 0.003). Fatigue impact decreased (MFIS total, p = 0.015; physical, p = 0.007; psychosocial, p = 0.026), whereas the cognitive subscale did not change significantly (p = 0.094). Adherence was 91.7%, and no adverse events were reported. Conclusions: In this pilot sample, a 12-week TWT programme was feasible and safe and was associated with improvements in walking performance, physical function, and fatigue, with QoL changes limited to specific SF-36 domains. These findings support proceeding to a randomised controlled trial to establish efficacy. These findings should be interpreted as preliminary and exploratory, given the single-arm pre–post study design. Full article

Fumonisin B1 (FB1) is a secondary metabolite produced by Fusarium species, exhibiting strong toxicity and classified as a Group 2B carcinogen by the International Agency for Research on Cancer. It poses a significant threat to both human and animal health. Therefore, developing a simple and reliable method for FB1 detection and analysis is imperative. In this study, a biosensor based on nucleic acid aptamers was developed, utilizing plasma-modified graphene oxide (mGO) as a fluorescence quencher for FB1 detection. This system leverages the interaction between mGO and FAM-APT (a nucleic acid aptamer labeled with 5-carboxyfluorescein, FAM), achieving fluorescence quenching through fluorescence resonance energy transfer (FRET) under excitation at 490 nm and emission at 520 nm. In the presence of FB1, FAM-APT specifically binds to FB1 and dissociates from the mGO surface, resulting in fluorescence recovery. Quantitative detection of FB1 was achieved by measuring the differential fluorescence intensity. The biosensor demonstrated excellent linearity over a concentration range of 10 to 5 × 10⁶ ng/L, with a detection limit (LOD) as low as 0.16 μg/L. Additionally, the sensor exhibited high specificity for FB1 among six common mycotoxins. In practical sample analysis, recovery rates ranged from 95.8% to 104.7% in corn samples and from 89.3% to 94.5% in rice samples. This aptamer-based biosensor features a simple structure, high sensitivity, and a wide detection range, providing important technical support for advancing mycotoxin research. Full article

Accurately assessing urban green perception is crucial for sustainable urban development and human well-being, yet conventional approaches often depend on simplistic objective metrics and non-immersive, screen-based subjective surveys, undermining ecological validity. This study develops and validates a multidimensional assessment framework that integrates Large Vision Models (LVMs) and Mixed Reality (MR) to couple objective environmental features with immersive human perception. The framework comprises 30 objective and 6 subjective indicators; state-of-the-art LVMs including DINOv2 and Depth Anything were applied to accurately extract objective features from Street View Imagery (SVI); and the MR device, Meta Quest 3, was utilized for the immersive collection of high-quality subjective data. In an empirical study with 74 volunteers in Shenzhen, China, machine learning models trained on MR-based data achieved 20–50% higher R² for subjective perception than models trained on traditional screen-based data. The validated framework was then applied to 61,131 SVIs citywide to map the spatial distribution of multidimensional green perception and to quantify relationships between objective and subjective indicators. Going beyond technical validation, this study demonstrates how the framework serves as a critical tool for urban planning and landscape upgrading. By diagnosing perceptual deficits where greening quantity does not translate into quality experiences, the framework supports a paradigm shift from quantity-oriented greening to perception-oriented spatial optimization. These findings offer actionable insights for policymakers to prioritize interventions that effectively enhance public health and environmental equity in high-density cities. Full article

Polo is a high-speed equestrian sport that imposes mechanical demands on horses and turf, yet limited research has examined the functional behavior of polo playing surfaces. This study characterizes the spatial variability of mechanical surface properties across turf polo pitches representing high-, medium-, and low-handicap categories. Three fields were assessed using lightweight field-based instruments, including the Impact Test Device (ITD), Rotational Peak Shear (RPS) tester, Going Stick© for penetration (GSP) and shear (GSS), and a TDR probe for volumetric moisture content (VMC%). A total of 210–223 grid-based sampling points per pitch were analyzed to evaluate mechanical responses under vertical and horizontal loading conditions. Significant differences among pitches were observed, with ITD and VMC emerging as the indicators of surface behaviour. Spatial analysis revealed heterogeneous within-pitch patterns, expressed as directional gradients and localized variability. Linear discriminant analysis demonstrated that the combined measurements could differentiate pitches associated with different handicap levels with high classification accuracy (0.88). Although the applied instruments do not replicate full equine biomechanical loading, they proved effective in detecting spatial variability in surface uniformity, a functional property relevant to performance and equine welfare. These findings support integration of spatially explicit surface assessments into routine turf management practices. Full article

Irritable bowel syndrome (IBS) is a common gastrointestinal disorder characterized by recurrent abdominal pain and altered bowel habits that significantly impair patients’ quality of life. Dietary triggers of IBS symptoms are common, and consequently, diet-based treatments are often prescribed. We conducted a review of current evidence on dietary interventions for IBS, focusing specifically on the evaluation of the scientific rationale and effectiveness of the most commonly adopted diets. Clinical trials and guideline recommendations were analyzed to assess each diet’s efficacy in symptom relief and patient adherence. Traditional dietary advice, although not a structured diet, but rather a set of lifestyle and dietary recommendations, is commonly recommended as first-line therapy and provides a solid base for symptom improvement in almost half of patients with IBS. Conversely, the low-FODMAP diet is a strict dietary pattern characterized first by the exclusion and then by the gradual and personalized reintroduction of several foods. Several clinical trials have demonstrated the efficacy of a low-FODMAP diet in reducing global IBS symptoms, and due to the established evidence, it is now incorporated into many clinical guidelines as a second- or even first-line approach for patients with IBS. Limited data supports the starch- and sucrose-reduced diet as an option for symptom relief, with evidence stemming from the relatively recent finding of hypomorphic variants of the sucrose-isomaltase gene in a subset of patients with IBS. Nonetheless, its application in clinical practice is still very limited. Data on gluten-free diet is more controversial as although it may benefit a subset of patients with IBS, strong evidence is still lacking for identifying the best candidates for a restrictive diet with a high burden in terms of economical, psychological and social costs. Beyond exclusion diets, a few studies on the Mediterranean diet suggest it may be a potential option with benefits that go beyond IBS symptom relief. Overall, dietary modification can significantly alleviate IBS symptoms. Tailoring recommendations to individual patient triggers may further enhance outcomes. Full article

This study introduces an electrochemical sensing platform based on bimetallic CoAg/rGO and CuAg/rGO nanocomposites for the detection of 1,3-dinitrobenzene (DNB), a highly toxic nitroaromatic compound commonly encountered in industrial effluents and contaminated water systems. The prepared nanocomposites were characterized using SEM, TEM, AFM, XPS, and electrochemical techniques, providing detailed insight into their structural, morphological, and surface properties relevant to electrochemical sensing. The electrochemical behavior of DNB was investigated in phosphate buffer solutions using cyclic voltammetry under optimized experimental conditions. Both CoAg/rGO and CuAg/rGO electrodes exhibited pronounced electrocatalytic activity towards the reduction in DNB, characterized by well-defined reduction peaks. The developed sensors exhibited good analytical performance, with limits of detection of 2.21 µM and 2.47 µM for the CuAg/rGO and CoAg/rGO electrodes, respectively, both showing linear responses in the concentration range of 5–50 µM. Moreover, a clear response to DNB was obtained in the presence of phenols as interferents as well as in spiked real water samples. The integration of characterization results with electrochemical measurements and validation in real water samples supports process-oriented research in environmental monitoring and electrochemical process control. These results confirm that bimetallic rGO-based nanocomposites represent efficient and cost-effective electrode materials for the electrochemical detection of 1,3-dinitrobenzene. Full article

High-throughput sequencing generally results in a list of genes. Which functional groups of genes among the DEGs are meaningful underlying factors to the differential biological/biomedical conditions under investigation? The process to find answers to this question can be called biological functional class enrichment analysis (FunCEA). R is a robust platform for FunCEA due to its accessibility by general users and availability of well-developed R packages for enrichment analysis and visualization, as well as for knowledge databases. Bench scientists in biomedical sciences need accessible and easy-to-understand protocols for FunCEA. This R tutorial provides detailed R scripts or command lines for FunCEA, as well as for data processing and visualization of the enrichment results. It keeps bench scientists in mind and provides supportive and apprehensible descriptions of the R scripts for each task (enrichment analysis, enrichment data processing, and visualization). It describes detailed procedures for the two popular FunCEA methods, the so-called over-representation analysis (ORA) and functional class scoring (FCS). The introduced FunCEA here uses three basic knowledge databases: gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and reactome. R codes for various visualizations (dot plot, term-gene network plot, enrichment map plot, ridge plot, and GSEA plot) are presented and annotated. Since all analyses are conducted in R, no commercial software is needed, yet clusterProfiler can directly access the latest KEGG knowledge database. Full article