Search Results (26,329)

Academic conferences have been pivotal in scholarly communications, facilitating the exchange of ideas and fostering collaborations among attendees by using advanced sensing, networking, and control technologies. Traditionally held in physical venues, the landscape of academic conferences has been revolutionised by the advent of virtual and hybrid formats as supported by the Internet of Things, Artificial Intelligence, and virtual reality tools. Despite the burgeoning literature on smart conferences, there exists a gap in comprehensive reviews that consolidate the various advancements and methodologies in this domain. This article aims to fill this gap by providing a thorough review of the latest developments in smart conference technologies and practices. It offers a multidimensional analysis, including predictive analytics, smart content delivery, networking improvements, and data-driven assessments. Fundamentally, we frame conference activities as a complex process involving multi-stage planning, real-time dynamic execution, and post-event analysis and refinement. This review specifically highlights how smart technologies are transforming this end-to-end process. Additionally, the concept of parallel intelligence is introduced, exploring its potential to transform future conferences. The significance of this article lies in its holistic perspective, offering valuable insights for enhancing conference planning, attendee engagement, and overall conference experiences. Full article

In arid regions, the ecological restoration of limestone tailings requires sustainable strategies, yet the synergistic effects of substrate optimization and native plant selection remain poorly understood. In this study, we systematically evaluated substrate amendments and native species for rehabilitating limestone tailings in Northern China’s arid zone using a controlled pot experiment. An orthogonal L9(3⁴) experimental design was employed to test three factors: the soil-to-tailings ratio (1:2, 1:1, and 2:1), moisture level (30%, 45%, and 60% of field capacity), and nitrogen addition (0, 5, and 10 g N m⁻²). Five native grass species (Pennisetum centrasiaticum, Setaria viridis, Leymus chinensis, Achnatherum splendens, and Eleusine indica) were grown under these treatment conditions, and plant biomass and key soil nutrient variables were measured. Stepwise regression, structural equation modeling, and principal component analysis were applied to assess plant growth responses and soil nutrient dynamics. The results indicated that a 2:1 soil-to-tailings substrate maintained at 60% moisture content maximized biomass production across all species. Soil total potassium consistently correlated positively with biomass (Standardized β: 0.397–0.603), whereas available potassium showed a negative relationship (Standardized β: −0.825–−0.391). Nutrient dynamics ultimately governed biomass accumulation, accounting for 57.8–84.2% of the biomass variation. P. centrasiaticum ranked as the most effective species, followed by S. viridis, L. chinensis, A. splendens, and E. indica. We concluded that successful restoration under these experimental conditions hinged on key factors: using a 2:1 soil-to-tailings substrate, maintaining 60% soil moisture, and strategically combining deep-rooted P. centrasiaticum with shallow-rooted S. viridis to exploit complementary resource use. This work provides fundamental data and a conceptual framework for rehabilitating arid limestone tailings in similar ecological settings, based on controlled experimental evidence. Full article

The number of students living with disabilities entering post-secondary education, including health professional programs (HPPs), is increasing. However, disabled students continue to experience challenges in both the on-campus and fieldwork components of these programs. The purpose of this study is to understand the experiences and perspectives of fieldwork educators to determine how they can be supported by HPPs when supervising students living with disabilities. An online survey was developed and circulated to fieldwork educators who supervise students in nine HPPs at McMaster University in Canada. Descriptive statistics and content analysis were used to analyze survey responses. The survey was completed by 42 respondents, 10 of whom reported living with a disability and demonstrated a nuanced understanding of accommodation issues. More than half of the total respondents reported they had supervised disabled students in fieldwork; however, only 12 received accommodation plans and evaluation occurred informally, if at all. Respondents requested training on how to accommodate specific student needs. Efficient processes are needed for educator training, and the involvement of educators is needed in accommodation development. Accommodations must be evaluated to ensure students have equitable opportunities for learning and assessment. Educators living with disabilities provide valuable insights on dismantling systemic barriers to inclusion. Full article

Background: Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are prevalent neurodevelopmental conditions in childhood. Beyond biological factors, social and environmental conditions influence developmental experiences and pathways to diagnosis. Nationally representative studies examining multiple social determinants in relation to ADHD, ASD, and comorbidity across recent years remain limited. Methods: We analyzed pooled cross-sectional data from six cycles (2018–2023) of the U.S. National Survey of Children’s Health, including 205,480 children aged 3–17 years. Parent-reported, clinician-diagnosed current ADHD and ASD were the primary outcomes; comorbid ADHD and ASD were examined secondarily. Social determinants included household income relative to the federal poverty level, parental education, health insurance type, food insecurity, and caregiver-reported neighborhood safety. Survey-weighted prevalence estimates and logistic regression models accounted for the complex sampling design and adjusted for demographic, family, regional, and temporal factors. Results: The weighted prevalence of ADHD was 9.7% and ASD was 2.9%; 1.1% of children had comorbid ADHD and ASD. Lower household income, food insecurity, unsafe neighborhood conditions, and lower parental education were associated with higher adjusted odds of both conditions. Boys had substantially higher odds of ADHD and ASD. After adjustment, non-Hispanic Black and Hispanic children had lower odds of ASD than non-Hispanic White children, consistent with differential identification rather than lower underlying prevalence. Comorbidity was concentrated among socially disadvantaged children. Conclusions: ADHD and ASD are socially patterned across U.S. children. Integrating developmental screening with assessment of social risks may support more equitable identification and intervention. Full article

The titanium alloy Ti-6Al-4V is widely used in the aerospace, medical, and automotive industries; however, its machining remains challenging due to its low thermal conductivity and high chemical reactivity. This study investigates the influence of the tool clearance angle on tool wear during the turning of Ti-6Al-4V under wet cutting conditions. A Design of Experiments (DoE) approach was employed, varying the clearance angle, cutting speed, and feed rate to determine their effects on tool wear. Tool wear was analysed using 3D topography measurements. Regression analysis was used to evaluate the experimental data with the main objective of quantifying the impact of the individual factors and their interactions, resulting in the development of a predictive statistical model. The model’s accuracy was assessed using the coefficient of determination (R²) and the adjusted coefficient of determination (R²adj). The results demonstrate that the clearance angle has a significant impact on crater wear formation and overall tool life. An optimised moderate clearance angle reduces tool degradation, enhances tool life, and improves the surface integrity of the machined component. Full article

Background: Schools play a central role in child development and socialization and can function as protective environments that mitigate the effects of adversity. Building on the Social Ecological Model and Community School Transformation, we propose a “Schools-as-Neighborhoods” framework that conceptualizes schools as intentionally designed microenvironments capable of generating social capital, promoting positive childhood experiences, and buffering harmful neighborhood exposures through trauma-informed programming. Methods: We conducted a convergent mixed-methods study across four public and charter schools in Milwaukee, Wisconsin, serving grades five through nine. STRYV365’s peak team and Brain Agents gamified intervention were implemented between 2022–2024. Quantitative surveys and qualitative data assessed students’ lived experiences, exposure to adversity, emotional awareness, coping skills, and school connectedness/climate across multiple waves. Results: Across the four schools (n = 1626 students), baseline academic proficiency was low, and exposure to adversity was high among surveyed participants (n = 321), including bereavement (74%) and family incarceration (56%). Despite these challenges, qualitative findings revealed strengthened emotional regulation, empathy, motivation, and goal setting among students engaged in trauma-informed programming. Teachers reported improved peer interaction and community building during sustained implementation. Conclusion: The Schools-as-Neighborhoods framework highlights the value of trauma-informed, relationship-centered school environments in promoting student well-being. By positioning schools as cohesive ecosystems that foster belonging and cultivate social capital, this approach offers educators and policymakers a pathway for mitigating the effects of hostile lived environments and supporting students’ mental health, social development, and engagement in learning. Full article

The global population growth has driven the widespread adoption of genetically modified crops, with Bt maize, due to its insect resistance, becoming the second most widely planted GM crop. However, studies on the effects of continuous Bt maize cultivation on soil ecosystems are limited, and there is an urgent need to assess its ecological safety at the regional scale. To evaluate the potential effects of continuous cultivation of transgenic Bt maize on the soil ecosystem, a five-season continuous planting experiment was conducted using two Bt maize varieties (5422Bt1 and 5422CBCL) and their near-isogenic conventional maize (5422). After five consecutive planting seasons, bulk soil and rhizosphere soil were collected. The main nutrient contents of the bulk soil were measured, and high-throughput sequencing was employed to analyze microbial diversity and community composition in both soil types. The results showed that, compared with the near-isogenic conventional maize 5422, continuous planting of Bt maize varieties 5422Bt1 and 5422CBCL did not affect the contents of organic matter, total nitrogen, total phosphorus, total potassium, alkaline hydrolyzable nitrogen, available phosphorus, or available potassium in bulk soil. Regarding the microbial communities in bulk soil, there were no significant differences in the α-diversity indices of bacteria and fungi after five consecutive seasons of Bt maize cultivation, compared with soils planted with the near-isogenic conventional maize 5422. Proteobacteria and Ascomycota were the dominant phyla of bacteria and fungi, respectively. Principal coordinate analysis (PCoA) and redundancy analysis (RDA) revealed that the structure of microbial communities in bulk soil was primarily influenced by factors such as OM, TP, TN and AN, whereas the Bt maize varieties had no significant effect on the overall community structure. Regarding the rhizosphere soil microbial communities, compared with the near-isogenic conventional maize 5422, the evenness of the bacterial community in the rhizosphere soil of Bt maize decreased, leading to a reduction in overall diversity, whereas species richness showed no significant change. This change in diversity patterns further contributed to the restructuring of the rhizosphere soil microbial community. In contrast, the fungal community showed no significant differences among treatments, and its community structure remained relatively stable. Proteobacteria and Ascomycota were the dominant phyla of bacteria and fungi, respectively. Principal coordinate analysis (PCoA) indicated that continuous cultivation of Bt maize for five seasons had no significant effect on the structure of either bacterial or fungal communities in the rhizosphere soil. In summary, continuous cultivation of Bt maize did not lead to significant changes in soil nutrient contents or microbial community structures, providing a data foundation and theoretical basis for the scientific evaluation of the environmental safety of transgenic maize in agricultural ecosystems. Full article

Background: An increasing number of university students report feeling lonely, a negative experience arising from a mismatch between perceived and actual social relationships. Loneliness has been linked to poorer mental health. However, the relationship between qualitative (sources of loneliness) and quantitative (high or low) differences in loneliness and mental health is under researched. The aims of this research were to (a) identify profiles of loneliness among university students across three indicators of loneliness, namely, social, family, and romantic indicators, using latent profile analysis (LPA); (b) examine the differences among identified profiles based on dimensions of mental health indicators (depression, anxiety, and stress), social support, and life satisfaction; and (c) assess profile membership based on demographic variables (gender, social isolation, relationship status, and education characteristics) and the Big Five personality traits (extraversion, agreeableness, openness, conscientiousness, and neuroticism). Method: A cross-sectional survey was conducted on 912 university students from five cities in Uttar Pradesh, India. Participants completed questionnaires covering demographic details and validated measures assessing loneliness, depression, stress, anxiety, social support, life satisfaction, and the Big Five personality traits. Data were analyzed using the latent profile module in Jamovi and fit indices, namely, BIC, AIC, and BLRT, and entropy was used to select the best profile. Results: The latent profile analysis identified four profiles for university student loneliness, including Social and emotional lonely (31.4%), Moderate romantic lonely (23.8%), Moderate social lonely (8.2%), and Severe romantic lonely (36.6%). Moreover, the Social and emotional lonely profile scored the highest on depression, anxiety, and stress. The Moderate romantic lonely profile scored the highest on life satisfaction and social support. Being in a relationship decreased the likelihood of being categorized as Severe romantic lonely. In terms of personality, neuroticism was the strongest predictor of profile membership. This study is a step towards identifying at-risk lonely individuals with varying sources of loneliness. Identifying different profiles of lonely individuals will have direct implications for designing interventions that cater to a particular group rather than a one-size-fits-all approach. Full article

The digitalization of business activities is already a reality in most developed countries. India, driven by strong information technology, is rapidly digitalizing across business industries. In the retail industry, this shift is visible in the adoption of omnichannel strategies to enhance value for customers, loyalty and trust, retailer’s image, overall shopping experience, and operational productivity. The present study assesses the extent of omnichannel transformation in the five leading Indian retail sectors viz. Consumer Electronics, Fashion and Apparel, Furniture and Home Decor, Grocery and Supermarkets, and Personal Care and Hygiene. The research design uses mystery shopping approach to collect data from 166 nationally present retailers to analyze their level of omnichannel implementation and digital adoption. The authors performed all statistical analyses and visualizations in R using the ggstatsplot package. The results highlight a less-than-ideal picture of channel integration, suggesting that while top retailers in each sector dominate channel integration, most others have made limited progress. Among the sectors, the Furniture and Home Decor sector leads in channel integration. Other contributions of this study include the enhancement of the existing measuring tool by introducing new indicators. The study reveals gaps in omnichannel implementation to help managers plan strategic improvements. Full article

The DAIC-WOZ dataset is a widely used benchmark for the task of depression severity estimation from multimodal behavioral data. Yet the reliability, reproducibility, and methodological rigor of published machine learning models remain uncertain. In this systematic review, we examined all works published through September 2025 that mention the DAIC-WOZ dataset and report mean absolute error as an evaluation metric. Our search identified 536 papers, of which 414 remained after deduplication. Following title and abstract screening, 132 records were selected for full-text review. After applying eligibility criteria, 66 papers were included in the quality assessment stage. Of these, only five met minimal reproducibility standards (such as clear data partitioning, model description, and training protocol documentation) and were included in this review. We found that published models suffer from poor documentation and methodology, and, inter alia, identified subject leakage as a critical methodological flaw. To illustrate its impact, we conducted experiments on the DAIC-WOZ dataset, comparing the performance of the model trained with and without subject leakage. Our results indicate that leakage produces significant overestimation of the validation performance; however, our evidence is limited to the audio, text, and combined modalities of the DAIC-WOZ dataset. Without leakage, the model consistently performed worse than a simple mean predictor. Aside from poor methodological rigor, we found that the predictive accuracy of the included models is poor: reported MAEs on DAIC-WOZ are of the same magnitude as the dataset’s own PHQ-8 variability, and are comparable to or larger than the variability typically observed in general population samples. We conclude with specific recommendations aimed at improving the methodology, reproducibility, and documentation of manuscripts. Code for our experiments is publicly available. Full article

Peanut, as an important economic crop, is widely cultivated and rich in nutrients. Classifying peanuts based on the number of seeds helps assess yield and economic value, providing a basis for selection and breeding. However, traditional peanut grading relies on manual labor, which is inefficient and time-consuming. To improve detection efficiency and accuracy, this study proposes an improved BTM-YOLOv8 model and tests it on an independently designed pod detection device. In the backbone network, the BiFormer module is introduced, employing a dual-route attention mechanism with dynamic, content-aware, and query-adaptive sparse attention to extract features from densely packed peanuts. In addition, the Triple Attention mechanism is incorporated to strengthen the model’s multidimensional interaction and feature responsiveness. Finally, the original CIoU loss function is replaced with MPDIoU loss, simplifying distance metric computation and enabling more scale-focused optimization in bounding box regression. The results show that BTM-YOLOv8 has stronger detection performance for ‘Quan Hua 557’ peanut pods, with precision, recall, mAP50, and F1 score reaching 98.40%, 96.20%, 99.00%, and 97.29%, respectively. Compared to the original YOLOv8, these values improved by 3.9%, 2.4%, 1.2%, and 3.14%, respectively. Ablation experiments further validate the effectiveness of the introduced modules, showing reduced attention to irrelevant information, enhanced target feature capture, and lower false detection rates. Through comparisons with various mainstream deep learning models, it was further demonstrated that BTM-YOLOv8 performs well in detecting ‘Quan Hua 557’ peanut pods. When comparing the device’s detection results with manual counts, the R2 value was 0.999, and the RMSE value was 12.69, indicating high accuracy. This study improves the efficiency of ‘Quan Hua 557’ peanut pod detection, reduces labor costs, and provides quantifiable data support for breeding, offering a new technical reference for the detection of other crops. Full article

Climate warming is one of the most pressing global changes, with profound consequences for biodiversity, ecosystem functioning, and the provision of ecosystem services. Although warming is expected to alter soil nutrient cycling and plant community structure, the mechanisms through which it reshapes ecosystem multifunctionality (EMF) remain insufficiently understood. Here, we conducted a 3-year field warming experiment in an alpine grassland to assess how warming influences plant diversity, soil nutrients, and their joint effects on EMF. We found that plant α-diversity declined in both control and warming groups in 2021 and partially recovered by 2023, though recovery was weaker under warming. In contrast, β-diversity (turnover) showed a continuous increasing trend under warming across years, although differences from the control were not statistically significant. EMF, evaluated with single- and multi-threshold approaches, exhibited a consistent decline, with warming accelerating this reduction and producing more complex bimodal fluctuations within intermediate threshold ranges (55–75% and 80–90%). Warming also restructured the functional drivers of EMF: soil organic carbon (SOC) and available nitrogen (AN) emerged as dominant regulators, whereas the contributions of total nitrogen and turnover weakened. Collectively, these findings demonstrate that warming not only alters biodiversity patterns and ecosystem functions but also reshapes the soil–plant–function feedbacks that sustain EMF. By identifying SOC and AN as critical mediators, this study highlights a mechanistic pathway through which climate warming may undermine ecosystem resilience and long-term sustainability, providing insights essential for predicting terrestrial ecosystem responses under future climate scenarios. Full article

Biofouling on aquaculture netting increases hydrodynamic drag and restricts water exchange across net cages. The solidity ratio is introduced as a quantitative parameter to characterize fouling severity. Towing tank experiments and computational fluid dynamics (CFD) simulations were used to assess the hydrodynamic behavior of netting under different fouling conditions. Experimental results indicated a nonlinear increase in drag force with increasing solidity. At a flow velocity of 0.90 m/s, the drag force increased by 112.2%, 195.1%, and 295.7% for netting with solidity ratios of 0.445, 0.733, and 0.787, respectively, compared to clean netting (S_n = 0.211). The drag coefficient remained stable within 1.445–1.573 across Re of 995–2189. Numerical simulations demonstrated the evolution of flow fields around netting, including jet flow formation in mesh openings and reverse flow regions and vortex structures behind knots. Under high solidity (S_{n =} 0.733–0.787), complex wake patterns such as dual-peak vortex streets appeared. Therefore, this study confirmed that the solidity ratio is an effective comprehensive parameter for evaluating biofouling effects, providing a theoretical basis for antifouling design and cleaning strategy development for aquaculture cages. Full article

Due to the complex anatomical structure of the distal humerus, elbow joint, and the soft tissue mantle (the triceps brachii muscle, large nerves, and vessels), fractures of the distal humerus and humeral condyle are difficult to treat. In most cases, strong instrumentation is needed to stabilize the fractures. To improve exposure of the distal humerus and humeral condyle, we proposed a caudolateral approach that involves elevating the anconeus muscle and splitting the triceps brachii. This study presents the results of using this approach in 16 canine cadavers. After exposing the distal humerus and maximally flexing the elbow joint, photographs were taken of the condyle from the same distance before and after olecranon osteotomy. The visible surface area of the articular cartilage was then calculated in square pixels after calibrating the photographs. It was possible to reach the distal and middle humerus in all cases. The only vital structure that could be easily identified and protected in all cadavers was the radial nerve. The visible area of the articular surface of the humeral condyle increased after olecranon osteotomy. The A₀ (visible area before osteotomy) was significantly smaller than the A₁ (visible area after osteotomy) in all dogs (p < 0.001). The ratio of A₀ to A₁ ranged from 57% to 67% in 15 dogs (median: 64%, interquartile range (IQR): 61–66%), with a very high value of 85% observed in one dog. This experiment used cadavers with intact elbows. This could limit the study’s findings because the effectiveness of the proposed access in reducing T-Y fractures was not assessed. The caudolateral approach is a valuable alternative to other methods for treating T-Y humeral fractures in dogs. Olecranon osteotomy widens access to the condyle. Further studies are needed to evaluate the necessity of olecranon osteotomy in clinical cases. Full article

This study investigates the influence of electromagnetic field polarization in the non-destructive testing of reinforced concrete structures through both theoretical analysis and experimental validation. Theoretical models predict that the orientation of reinforcement bars relative to the incident electric field significantly affects the scattered signal, influencing their detectability. Laboratory experiments on realistic reinforced concrete specimens presenting both vertical bars and horizontal brackets confirm these predictions, demonstrating that polarization can be exploited to enhance measurement accuracy. These findings provide useful insights into the development of microwave-based diagnostic techniques for structural assessment. Full article