Search Results (977)

Climate change has a significant negative impact on agriculture and food production. This trend requires technological development and the adaptation of new technologies in both the grapevine production and winemaking sectors. High temperatures and heat accumulation during the growing season result in faster ripening and a higher sugar content, leading to a higher alcohol content during fermentation. The negative consequences are an imbalanced wine character and consumer reluctance, as lower alcoholic beverages are now in high demand. Over the last decade, several methods have been developed to handle this impact and reduce the alcohol content of wines. In this study, we used the MASTERMIND^® REMOVE membrane-based dealcoholization system to reduce the alcohol concentration in of Pinot gris wines from 12.02% v/v to 10.69% v/v and to investigate the effect on analytical parameters in three steps (0.5%, 1%, and 1.5% reductions) along the treatment. To evaluate the impact of the partial alcohol reduction and identify correlations between the wine chemical parameters, data were analyzed with ANOVA, PCA, multivariate linear regression and cluster analysis. The results showed that except for the extract, sugar content and proline content, the treatment had a significant effect on the chemical parameters. Both free and total SO₂ levels were significantly reduced as well as volatile acid, glycerol and succinic acid levels. It must be highlighted that some parameters were not differing significantly between the untreated and the final wine, while the change was statistically verified in the intermediate steps of the partial alcohol reduction. This was the case for example for n-Propanol, i-Amylalcohol, Acetaldehyde, and Ethyl acetate. The multivariate linear regression model explained 18.84% of the total variance, indicating a modest but meaningful relationship between the alcohol content and the investigated analytical parameters. Our results showed that even if the applied instrument significantly modified some of the wine chemical parameters, those changes would not influence significantly the wine sensory attributes. Full article

Residential green space is an important component of urban green space and one of the major indicators for evaluating the quality of a residential community. Traditional indicators such as the green space ratio only consider the relationship between green space area and total area of the residential community while ignoring the difference in the amount of green space enjoyed by household residents in high-rise and low-rise buildings. Therefore, it is meaningful to estimate household green space and its spatial distribution in residential communities. However, there are frequent difficulties in obtaining specific green space area and household number through ground surveys or consulting with property management units. In this study, taking a composite residential community in Chongqing, China, as the study site, we first employed a five-lens drone to capture its oblique RGB images and generated the DOM (Digital Orthophoto Map). Subsequently, the green space area and distribution in the entire residential community were extracted from the DOM using VDVI (Visible Difference Vegetation Index). The YOLACT (You Only Look At Coefficients) instance segmentation model was used to recognize balconies from the facade images of high-rise buildings to determine their household numbers. Finally, the average green space per household in the entire residential community was calculated to be 67.82 m², and those in the high-rise and low-rise building zones were 51.28 m² and 300 m², respectively. Compared with the green space ratios of 65.5% and 50%, household green space more truly reflected the actual green space occupation in high- and low-rise building zones. Full article

The sustainable integration of technology into educational practices is pivotal for modern teaching and learning. Grounded in the Technology Acceptance Model (TAM), this study explores the psychological and contextual factors that influence technology acceptance among pre-service and in-service teachers. Employing a nonexperimental, cross-sectional design, data were collected from 347 participants to examine the relationships between perceived usefulness, perceived ease of use, attitude toward use, behavioural intention, and actual system use. Results indicate that pre-service teachers demonstrate stronger openness to technology adoption, driven primarily by attitudinal factors, whereas in-service teachers’ acceptance is more closely linked to perceived utility and usability. This study advances the TAM by integrating a dual serial mediation model and testing the moderating role of professional status, thereby offering a nuanced understanding of sustainable digital engagement across career stages. Our findings underscore the importance of fostering positive perceptions and providing differentiated support throughout teachers’ professional trajectories to achieve long-term, meaningful technology adoption in education. Full article

Though previous studies have demonstrated the protective benefits of sports participation against illicit drug use for a general population, how these findings apply to LGBTQ youth remains unknown. This study specifically looks at the relationship between sports participation and prescription drug misuse among sexual and gender minority youth. Using secondary data from the 2019 YRBS, we analyze associations among sports participation, sexual orientation, gender identity, and prescription drug misuse among a representative sample of U.S. high school students in Florida. Our results show that sexual and gender minority youth are at increased risk for prescription drug misuse compared to their heterosexual and cisgender peers. Moreover, sports participation is associated with higher rates of prescription drug misuse among all students, and the nuances of these trends are discussed with particular attention paid to sexual and gender minority youth. These results challenge conventional wisdom about sports participation. Without the addition of new demographic survey questions and LGBTQ youth participation in the YRBS, common myths about sports might have persisted. Our findings point to the meaningful presence of LGBTQ youth in sports, call for research and programming on LGBTQ athletes’ unique needs regarding substance misuse risk, and encourage LGBTQ-inclusive policies and practices within schools and sports programs in particular. Full article

Accurate estimation of biomass higher heating value (HHV) is crucial for designing efficient bioenergy systems. In this study, we developed a Backpropagation artificial neural network (ANN) that predicts HHV from routine proximate/ultimate composition data. The network (9-6-6-1 architecture, trained for 15,000 epochs with learning rate 0.3 and momentum 0.4) was calibrated on 99 diverse Spanish biomass samples (inputs: moisture, ash, volatile matter, fixed carbon, C, H, O, N, S). The optimized ANN achieved strong predictive accuracy (validation R² ≈ 0.81; mean squared error ≈ 1.33 MJ/kg; MAE ≈ 0.77 MJ/kg), representing a substantial improvement over 54 analytical models despite the known complexity and variability of biomass composition. Importantly, in direct comparisons it significantly outperformed 54 published analytical HHV correlations—the ANN achieved substantially higher R² and lower prediction error than any fixed-form formula in the literature. A sensitivity analysis confirmed chemically intuitive trends (higher C/H/FC increase HHV; higher moisture/ash/O reduce it), indicating the model learned meaningful fuel-property relationships. The ANN thus provided a computationally efficient and robust tool for rapid, accurate HHV estimation from compositional data. Future work will expand the dataset, incorporate thermal pretreatment effects, and integrate the model into a user-friendly decision-support platform for bioenergy applications. Full article

Prostate cancer remains one of the most prevalent malignancies among men, and emerging evidence proposed a potential role for periprostatic adipose tissue (PPAT) in tumor progression. However, its relationship with imaging-based risk stratification systems such as PI-RADS remains uncertain. This retrospective observational study aimed to evaluate whether periprostatic and subcutaneous fat thickness are associated with PI-RADS scores or PSA levels in biopsy-naïve patients. We retrospectively reviewed 104 prostate MRI scans performed between January 2020 and January 2024. Fat thickness was measured on axial T2-weighted images, and statistical analyses were conducted using Spearman’s correlation and multiple linear regression. In addition to linear measurements, we also assessed periprostatic fat volume and posterior fat thickness derived from imaging data. No significant correlations were observed between fat thickness (either periprostatic or subcutaneous) and PI-RADS score or PSA values. Similarly, periprostatic fat volume showed only a weak, non-significant correlation with PI-RADS, while posterior fat thickness demonstrated a weak but statistically significant positive association. Additionally, subgroup comparisons between low-risk (PI-RADS < 4) and high-risk (PI-RADS ≥ 4) patients showed no meaningful differences in fat measurements. These findings suggest that simple linear fat thickness measurements may not enhance imaging-based risk assessment in prostate cancer, though regional and volumetric assessments could offer modest added value. Full article

As the fundamental physical carrier for human production and socio-economic endeavors, enhancing urban land green use efficiency (ULGUE) is crucial for realizing sustainable development. To effectively enhance urban land green use efficiency, this study systematically examines the intrinsic relationship between industrial policies and ULGUE based on panel data from 286 Chinese cities (2010–2022), employing an integrated methodology that combines the Difference-in-Differences (DID) model, Super-Efficiency Slacks-Based Measure Data Envelopment Analysis model, and ArcGIS spatial analysis techniques. The findings clearly demonstrate that the establishment of the “Made in China 2025” pilot policy significantly improves urban land green use efficiency in pilot cities, a conclusion that endures following a succession of stringent evaluations. Moreover, studying its mechanisms suggests that the pilot policy primarily enhances urban land green use efficiency by promoting industrial upgrading, accelerating technological innovation, and strengthening environmental regulations. Heterogeneity analysis further indicates that the policy effects are more significant in urban areas characterized by high manufacturing agglomeration, non-provincial capital/non-municipal status, high industrial intelligence levels, and less sophisticated industrial structure. This research not only provides valuable policy insights for China to enhance urban land green use efficiency and promote high-quality regional sustainable development but also offers meaningful references for global efforts toward advancing urban sustainability. Full article

This study explores how to support Turkish–American secondary school students to co-develop covariational reasoning (CR) and representational fluency (RF) in solving contextually based quadratic function tasks in an after-school community center learning setting. We conducted a teaching experiment (n = 8) at a community center. Ongoing and retrospective analyses of classroom interaction and video transcripts revealed a culturally inclusive mathematics learning environment framework with several intertwined components: co-developing CR and RF and community-based practices. This study provides evidence that students coordinate symbolic, tabular, and graphical representations, which not only deepen their understanding of how quantities change in relation to one another but also enable them to interpret and construct representations in increasingly flexible ways. This reciprocal process of co-developing CR and RF allowed students to recognize and express quantitative relationships as meaningful functional relationships, demonstrating a dynamic interplay between reasoning about change and fluency across representations. This study situates learning within culturally inclusive learning environments and acknowledges the reflexive positionality of the teacher–researcher in relation to students. We highlight how shared community-based practices can enhance mathematics teaching and learning. Full article

Genomic instability markers are important hallmarks of aging, as previously evidenced within the European study of biomarkers of human aging, MARK-AGE; however, establishing the specific metabolic determinants of vascular aging is challenging. The objective of the present study was to evaluate the impact of the susceptibility to oxidation of serum LDL particles (LDLox) and the plasma metabolization products of nitric oxide (NOx) on relevant genomic instability markers. The analysis was performed on a MARK-AGE cohort of 1326 subjects (635 men and 691 women, 35–75 years old) randomly recruited from the general population. The Inverse Probability of Treatment Weighting causal inference algorithm was implemented in order to assess the potential causal relationship between the LDLox and NOx octile-based thresholds and three genomic instability markers measured in mononuclear leukocytes: the percentage of telomeres shorter than 3 kb, the initial DNA integrity, and the DNA damage after irradiation with 3.8 Gy. The results showed statistically significant telomere shortening for LDLox, while NOx yielded a significant impact on DNA integrity. Overall, the effect on the genomic instability markers was higher than for the confirmed vascular aging determinants, such as low HDL cholesterol levels, indicating a meaningful impact even for small changes in LDLox and NOx values. Full article

The purpose of this paper is to use an informatics-based analysis to develop a rational design approach to the accelerated screening of nano-composite materials. Using existing nano-composite data, we develop a quantitative structure–activity relationship (QSAR) as a function of polymer matrix chemistry and nano-additive volume, with the property predicted being electrical conductivity. The development of a QSAR for the electrical conductivity of nano-composites presents challenges in representing the polymer matrix chemistry and backbone structure, the additive content, and the interactions between the components while capturing the non-linearity of electrical conductivity with changing nano-additive volume. An important aspect of this work is designing chemistries with small training data sizes, as the uncertainty in modeling is high, and potentially the representated physics may be minimal. In this work, we explore two important components of this aspect. First, an assessment via Uniform Manifold Approximation and Projection (UMAP) is used to assess the variability provided by new data points and how much information is contributed by data, which is significantly more important than the actual data size (i.e., how much new information is provided by each data point?). The second component involves assessing multiple training/testing splits to ensure that any results are not due to a specific case but rather that the results are statistically meaningful. This work will accelerate the rational design of polymer nano-composites by fully considering the large array of possible variables while providing a high-speed screening of polymer chemistries. Full article

Digital technology has become an essential engine of green development and economic progress due to the meteoric rise of new technologies. Our paper seeks to explore the impact of digitalization on environmental, social and governance (ESG) performance in different business contexts. Data were collected from listed firms across 19 Asian countries from 2015 to 2024, covering 1839 firms, yielding 18,390 firm-year observations and establishing a balanced panel data set. We used the dynamic panel data model to test the proposed hypotheses. The findings revealed that digitalization has a significant and positive impact on ESG performance. It also revealed that environmental uncertainty moderates this relationship. Moreover, our analysis indicated that the impact of digitalization on ESG performance is stronger for product (vs. service) firms, stronger for B2B (vs. B2C) firms and stronger for firms in IT-intensive industries. In addition, the analysis indicated that the impact of digitalization on ESG performance is stronger in more dynamic, complex and munificent environments. Our examination offers meaningful implications for theory and practice by expanding our knowledge of the complex mechanism underpinning the positive correlation between digitalization and ESG performance. Full article

In bioinformatics, pathway analyses are used to interpret biological data by mapping measured molecules with known pathways to discover their functional processes and relationships. Pathway analysis has become an essential tool for interpreting large-scale omics data, translating complex gene sets into actionable experimental insights. However, issues inherent to pathway databases and misinterpretations of pathway relevance often result in “pathway fails,” where findings, though statistically significant, lack biological applicability. For example, the Tumor Necrosis Factor (TNF) pathway was originally annotated based on its association with observed tumor necrosis, while it is multifunctional across diverse physiological processes in the body. This review broadly evaluates pathway analysis interpretation, including embedding-based, semantic similarity-based, and network-based approaches to clarify their ideal use-case scenarios. Each method for interpretation is assessed for its strengths, such as high-quality visualizations and ease of use, as well as its limitations, including data redundancy and database compatibility challenges. Despite advancements in the field, the principle of “garbage in, garbage out” (GIGO) shows that input quality and method choice are critical for reliable and biologically meaningful results. Methodological standardization, scalability improvements, and integration with diverse data sources remain areas for further development. By providing critical guidance with contextual examples such as TNF, we aim to help researchers align their objectives with the appropriate method. Advancing pathway analysis interpretation will further enhance the utility of pathway analysis, ultimately propelling progress in systems biology and personalized medicine. Full article

The efficiency decline of Francis turbines, a key component of hydroelectric power generation, presents a multifaceted challenge influenced by interconnected factors such as water quality, incidence angle, erosion, and runner wear. This paper is structured into two main sections to address these issues. The first section applies the Buckingham π theorem to establish a dimensional analysis (DA) framework, providing insights into the relationships among the operational variables and their impact on turbine wear and efficiency loss. Dimensional analysis offers a theoretical basis for understanding the relationships among operational variables and efficiency within the scope of this study. This understanding, in turn, informs the selection and interpretation of features for machine learning (ML) models aimed at the predictive maintenance of the target variable and important features for the next stage. The second section analyzes an extensive dataset collected from a Francis turbine in Colombia, a country that is heavily reliant on hydroelectric power. The dataset consisted of 60,501 samples recorded over 15 days, offering a robust basis for assessing turbine behavior under real-world operating conditions. An exploratory data analysis (EDA) was conducted by integrating linear regression and a time-series analysis to investigate efficiency dynamics. Key variables, including power output, water flow rate, and operational time, were extracted and analyzed to identify patterns and correlations affecting turbine performance. This study seeks to develop a comprehensive understanding of the factors driving Francis turbine efficiency loss and to propose strategies for mitigating wear-induced performance degradation. The synergy lies in DA’s ability to reduce dimensionality and identify meaningful features, which enhances the ML models’ interpretability, while ML leverages these features to model non-linear and time-dependent patterns that DA alone cannot address. This integrated approach results in a linear regression model with a performance (R²-Test = 0.994) and a time series using ARIMA with a performance (R²-Test = 0.999) that allows for the identification of better generalization, demonstrating the power of combining physical principles with advanced data analysis. The preliminary findings provide valuable insights into the dynamic interplay of operational parameters, contributing to the optimization of turbine operation, efficiency enhancement, and lifespan extension. Ultimately, this study supports the sustainability and economic viability of hydroelectric power generation by advancing tools for predictive maintenance and performance optimization. Full article

In this paper, we argue that what is sometimes at fault for the poor attendance and lack of engagement in schools observed from historically marginalized families is a missed opportunity to increase understanding or cultural relevance on the part of schools. In this paper, we use the construct of “counter stories” which has the potential to change the script on the instrumentalist demands of quantity versus quality in parent engagement. By providing examples of what we consider “quality” engagement techniques via the staff’s interpretation of their roles within one demographically rich early learning center, the strategies used to engage parents are documented. Counter-stories of practice show family-centrism as interpreted by school leaders. By describing one community context and its practices of building relationships with newcomer families, relationally driven parent engagement techniques are revealed. The authors highlight how inquiry-based methods surpass the generic approaches described in policy. When parent engagement “arises” from within parents’ motivations and informs authentic knowing (by teachers and school leaders), community systems are elevated. Professionals’ decisions about children and community groups that are informed by families’ knowledge are consequently meaningful and authentic. Full article

Background/Objectives: In an increasingly pervasive digital environment, trait boredom has been identified as a key psychological factor in the onset and maintenance of problematic digital technology use. This systematic review aims to investigate the role of trait boredom in digital behavioral addictions, including problematic smartphone use, Internet and social media overuse, and gaming addiction, through theoretical models such as the I-PACE model and the Compensatory Internet Use Theory (CIUT). Methods: A systematic literature search was conducted across multiple scientific databases (PsycINFO, Web of Science, PubMed, and Scopus), yielding a total of 4603 records. Following the PRISMA guidelines after duplicate removal and screening based on title and abstract, 152 articles were assessed for full-text eligibility, and 28 studies met the predefined inclusion and exclusion criteria and were included in the final review. Results: Findings reveal that trait boredom functions as both a direct and indirect factor in problematic technology use. It serves as a mediator and moderator in the relationship between psychological vulnerabilities (e.g., depression, alexithymia, vulnerable narcissism) and dysfunctional digital behaviors. Furthermore, as an independent variable, it has an influence on technological variables through Fear of Missing Out (FoMO), loneliness, low self-regulation, and dysfunctional metacognitions, while protective factors such as mindfulness and attentional control mitigate its impact. Conclusions: Boredom represents a central psychological lever for understanding behavioral addictions in the digital age and should be considered a key target in preventive and therapeutic interventions focused on enhancing self-regulation and meaningful engagement with free time. Full article