Search Results (7,031)

An intelligent prediction model for rice yield in small-scale cultivation areas can provide precise forecasting results for farmers, rice planting enterprises, and researchers, holding significant importance for agricultural industries and crop science research within small regions. Although machine learning can handle complex nonlinear problems to enhance prediction accuracy, further improvements in models are still needed to accurately predict rice yields in small areas facing complex planting environments, thereby enhancing model performance. This study employs four rice phenotypic traits, namely, panicle angle, panicle length, total branch length, and grain number, along with seven machine learning methods—multiple linear regression, support vector machine, MLP, random forest, GBR, XGBoost, and LightGBM—to construct a yield prediction model group. Subsequently, the top three models with the best performance in individual model predictions are integrated using voting and stacking ensemble methods to obtain the optimal integrated model. Finally, the impact of different rice phenotypic traits on the performance of the stacked ensemble model is explored. Experimental results indicate that the random forest model performs best after individual machine learning modeling, with RMSE, R², and MAPE values of 0.2777, 0.9062, and 17.04%, respectively. After model integration, Stacking–3m demonstrates the best performance, with RMSE, R², and MAPE values of 0.2483, 0.9250, and 6.90%, respectively. Compared to the performance after random forest modeling, the RMSE decreased by 10.58%, R² increased by 1.88%, and MAPE decreased by 0.76%, indicating improved model performance after stacking ensemble. The Stacking–3m model, which demonstrated the best comprehensive evaluation metrics, was selected for model validation, and the validation results were satisfactory, with MAE, R², and MAPE values of 8.3384, 0.9285, and 0.2689, respectively. The above research findings demonstrate that this integrated model possesses high practical value and fills a gap in precise yield prediction for small-scale rice cultivation in the Yunnan Plateau region. Full article

This study fills a crucial gap by offering fresh insights into healthcare entrepreneurs’ characteristics and decision-making, enriching the understanding of entrepreneurial behavior. Employing a mixed-methods approach, by combining qualitative and quantitative data, the researchers examined how factors such as age, gender, education, number of children, marital status, and house ownership influence business orientation. Utilizing both quantitative data, collected through surveys, and statistical analyses, conducted in EViews 12, data drawn from 113 Romanian medical start-ups revealed that marital status was a significant determinant, with single individuals exhibiting a profit-centric approach, while couples or married entrepreneurs prioritize patient well-being. Also, the lower the sense of ownership, the lower the number of co-founders there was in the start-up. The motivation of entrepreneurs involved in start-ups with more than two co-founders, is based on the motivation behind a problem that directly affected the entrepreneurs themselves. An unexpected result was found regarding the healthcare entrepreneurs that perceive failure more due to lack of funding than competition, especially in profit-focused ventures, shaping their decision-making. To shape managerial implications, the authors depicted the swim lane decision-making process diagram based on these insights. Full article

Background: This study investigates digital transformation as a moderating variable in determining the effect of digital technologies, automation, and data integration of upstream and downstream providers on supply chain performance. By filling the existing research gap, the study reveals that more research regarding how digital transformation interventions impact the effectiveness of these technologies for industrial supply chains must be understood. Methods: A structured survey was applied to 181 supply chain managers in manufacturing firms scattered across Jordan. Results: The findings using SmartPLS for statistical analysis indicated that automation has the strongest positive effect on supply chain performance, followed by data integration. But digital technology did not have a significant direct effect, unless it was accompanied by broader digital transformation initiatives. Conclusions: Theoretically, this study reinforces digital transformation theory as a vital framework, whereas in practice, it invokes the strategic deployment of automation and integrated data application designs to underpin supply chain efficiency and competitiveness. Finally, this study offers practical guidance for practitioners who seek to employ the use of digital transformation in the current dynamic business environment. Full article

This study addresses the emerging need for efficient and cost-effective solutions in low-volume production by exploring the mechanical performance and industrial feasibility of cutting tools that are fabricated using stereolithography apparatus (SLA) technology. SLA’s high-resolution capabilities make it suitable for creating precise cutting dies, which were tested on aluminum sheets (Al99.5, 0.3 mm, and AlMg3, 1.0 mm) under a 60-ton hydraulic press. Measurements using digital image correlation (DIC) revealed minimal wear and deformation, with tolerances consistently within IT 0.1 mm. The results demonstrated that SLA-printed tools perform comparably to conventional metal tools in cutting and bending operations, achieving similar surface quality and edge precision while significantly reducing the production time and cost. Despite some limitations in wear resistance, the findings highlight SLA technology’s potential for rapid prototyping and short-run manufacturing in the automotive and electronics sectors. This research fills a critical gap in understanding SLA-based tooling applications, offering insights into process optimization to enhance tool durability and broaden material compatibility. These advancements position SLA technology as a transformative tool-making technology for flexible manufacturing. Full article

The extreme environmental conditions of the Qinhai–Tibetan Plateau (QTP) challenge livestock survival and productivity, yet little is known about how high-altitude environments impact the gut microbiota of dairy cows. To fill this gap, we systematically investigated the differences in the hindgut microbiome between 87 plateau Holstein cows and 72 plain Holstein cows using 16S rRNA gene sequencing. Our analysis revealed that the hindgut microbiota of the plateau group exhibited lower species richness but higher evenness than that in the plain group. Additionally, significant separation in hindgut microbiota composition between the two groups was observed based on altitude, while parity, days in milk, and age did not show a comparable impact. Moreover, altitude had a lasting impact on bacterial communities and their co-occurrence networks, resulting in reduced microbial interactions and lower modularity in the plateau group. Furthermore, we identified four key microbial taxa, the Bacteroidaceae and Rikenellaceae families, as well as the Prevotella and Treponema genera, which were associated with the regulation of carbohydrate digestion and energy metabolism and might help the Holstein cows adapt to the plateau environment. Our findings provide insights into strategies for enhancing the adaptability of dairy cows to high-altitude environments through microbiota modulation, which could ultimately contribute to improving livestock management and sustainability in these extreme environments. Full article

This work examines the indicators of master’s students’ persistence from 2014 to 2021 at a Hispanic-Serving Institution (HSI) in the southern United States. Demographic and academic variables were used in a logistic regression model to predict students’ successful completion across sixteen master’s programs. In this two-fold study, first, we examined the impact of COVID-19 on students enrolled in twelve face-to-face (F2F) programs and evaluated their performance against a pre-pandemic baseline period. Second, we compared student performance in four accelerated online programs against a pre-accelerated baseline. Most demographic variables were insignificant, while all academic variables were significant across program types. However, GPA became an insignificant variable when the F2F programs were forced to move online during the COVID-19 pandemic. During this period, GPA also increased for students who had discontinued their studies. The accelerated online programs recorded a significant decrease in terms enrolled (Term Count) compared to the pre-accelerated baseline. These results add to the limited literature on student success at the master’s level in HSIs, thus filling a vital knowledge gap. This study provides two case studies focusing on how the pandemic and the accelerated online learning model impacted academic persistence at the master’s level at an HSI. Full article

Phytoplasmas are a group of plant-pathogenic, cell-wall-less bacteria vectored primarily by leafhoppers (Hemiptera Cicadellidae), one of the most diverse families of insects. Despite the importance of documenting associations between phytoplasmas, their insect vectors, and plant hosts to prevent disease outbreaks, such knowledge is currently highly incomplete and largely neglects the diversity of the system in natural areas. Here, we used anchored hybrid enrichment (AHE) to recover the DNA of five plant genes (rbcL, matK, ITS1, ITS2, and trnH-psbA) in 58 phloem-feeding leafhoppers from around the world that had previously tested positive for phytoplasma infection. Using BLASTn and a strict filtering approach, we assigned taxonomic classifications to the plant sequences and tested for cophylogenetic signals between potential Deltocephalinae leafhopper vectors and their associated plants. We observed incongruence between plant and insect phylogenies. Many leafhopper species, including presumed grass specialists, fed on distantly related plant lineages; 66% of sampled leafhoppers fed on plants from at least two different orders. By disentangling phytoplasma–leafhopper–plant interactions, we identify locations at risk of phytoplasma disease outbreaks. Furthermore, the observed wide diet breadth raises questions about how phytoplasma infection may manipulate the feeding preference of their insect host and helps fill the gaps in understanding the ecology and diversification of the tripartite association. Full article

This paper presents a systematic literature review (SLR) to provide a comprehensive understanding of higher education literature focusing on the implementation of sustainable campus operations. The implementation of on-campus sustainable operations represents a pivotal path for promoting sustainability in the higher education context. Nonetheless, only a scarce number of studies provide a comprehensive assessment of the existing literature on the topic of sustainable campus operations in higher education institutions (HEIs). Accordingly, our research intends to fill this gap. Our review identified 130 focal articles that were predominantly published in high-impact journals. Key themes identified in the literature include carbon neutrality, energy use, waste management, environmental management, and circular economy, with a notable emphasis on the interconnectedness of these topics. The review also highlights the critical role of institutional leadership, governance structures, stakeholder engagement, and technological innovation in driving sustainability on campuses. Despite the growing body of literature, research gaps were identified, including the need for more comparative studies, exploration of funding mechanisms, and improvements in data collection and reporting practices. This study underscores the importance of adopting a structured, holistic approach to campus sustainability in HEIs, which can inform policy development, enhance decision-making, and pave the way for more effective sustainability strategies. Full article

Due to the all-time and all-weather characteristics of synthetic aperture radar (SAR) data, they have become an important input for optical image restoration, and various cloud removal datasets based on SAR-optical have been proposed. Currently, the construction of multi-source cloud removal datasets typically employs single-polarization or dual-polarization backscatter SAR feature images, lacking a comprehensive description of target scattering information and polarization characteristics. This paper constructs a high-resolution remote sensing dataset, AIR-POLSAR-CR1.0, based on optical images, backscatter feature images, and polarization feature images using the fully polarimetric synthetic aperture radar (PolSAR) data. The dataset has been manually annotated to provide a foundation for subsequent analyses and processing. Finally, this study performs a performance analysis of typical cloud removal deep learning algorithms based on different categories and cloud coverage on the proposed standard dataset, serving as baseline results for this benchmark. The results of the ablation experiment also demonstrate the effectiveness of the PolSAR data. In summary, AIR-POLSAR-CR1.0 fills the gap in polarization feature images and demonstrates good adaptability for the development of deep learning algorithms. Full article

Public discourse on immigration and social services access has been contentious in immigrant-receiving countries. Scholars have examined immigrants’ marginalization as a form of civic stratification, where boundaries based on documentation status affect immigrants’ experiences and benefits granted by the state. This scholarship lacks a framework outlining existing documentation status categories and does not fully answer three research questions I pose in this article: (1) what is the alignment of documentation status categories relative to each other, (2) how does policy (re)configure those categories over time, and (3) how have documentation status categories shaped access to health care in the United States? This article answers those questions and argues that the documentation status continuum (DSC) framework fills these gaps. In the DSC, undocumented immigrants are at one end and citizens are at the other, with many documentation statuses in between. Public policy creates these statuses and generates stratification through allocating benefits based on one’s DSC position. Policy also shapes movement along the continuum, which shapes benefits eligibility. Using the 2006 Massachusetts Health Reform and national 2010 Affordable Care Act (ACA) Reform as policy examples and interviews conducted with 207 immigrants, healthcare professionals, and immigrant organization employees in Boston, this article demonstrates how healthcare access is stratified along the DSC between citizens and noncitizens. This has implications for various outcomes that social scientists examine amid increasing anti-immigrant sentiment in the US and beyond. Full article

Despite nearly a century of research on water-related issues, water erosion remains one of the greatest threats to soil health and soil ecosystem services around the world. Yet, to date, data on water erosion needed to develop mitigation strategies are scarce, especially in the Sahelian regions. The current study therefore sets out to estimate annual soil losses caused by water erosion and to analyze trends over the period of 1981–2020 in the Mékrou watershed, located in the Middle Niger river sub-basin in West Africa. The Revised Universal Soil Loss Equation, remote sensing, and the Geographic Information System (GIS) were deployed in this study. Several types of data were used, including rainfall data, sourced from meteorological stations and reanalysis datasets, which capture the temporal variability of erosive forces. Soil properties, including texture and organic matter content, were derived from FAO global soil databases to assess soil erodibility. High-resolution digital elevation models (30 m) provided detailed topographic information, crucial for calculating slope length and steepness factors. Land use and land cover data were extracted from satellite imagery, enabling the analysis of vegetation cover and anthropogenic impacts over four decades. By integrating and treating these data, this study reveals that the estimated average annual amount of water erosion in the Mékrou watershed is 6.49 t/ha/yr over 1981–2020. The dynamics of the ten-year average are highly variable, with a minimum of 3.45 t/ha/yr between 1981 and 1990, and a maximum of 8.50 t/ha/yr between 1991 and 2000. Even though these average soil losses in the Mékrou basin are below the tolerable threshold of 10 t/ha/yr, mitigation actions are needed for prevention. In addition, the spatial dynamics of water erosion are noticeably heterogeneous. The study reveals that 72.7% of the surface area of the Mékrou watershed is subject to slight water erosion below the threshold, compared with 27.3%, particularly in the mountainous south-western part, which is subject to intense erosion above the threshold. This research is the first study of soil erosion quantification with the RUSLE method and GIS in the Mékrou watershed, and fills a critical knowledge gap of the water erosion in this watershed, providing insights into erosion dynamics and supporting future sustainable land management strategies in vulnerable Sahelian landscapes. Full article

Background: This study presents a comprehensive analysis of 135 cases of vestibular schwannoma (VS) treated between 2006 and 2022 at the National Institute of Neurology and Neurovascular Diseases in Bucharest, Romania. The investigation focuses on the clinical presentation, treatment outcomes, and demographic trends of VS patients, highlighting region-specific insights that fill critical gaps in Eastern European data. Methods: Patients were treated with either open surgery (93.3%) or gamma knife radiosurgery (6.6%). The study identifies predominant symptoms, including hearing impairment, facial palsy, and balance disorders, with variations observed across age and gender subgroups. Comorbidities such as hypertension and obesity were prevalent, and they influenced perioperative risks. Results: Post-treatment outcomes showed a significant correlation between clinical symptoms and treatment modalities, with a majority achieving favorable results. The findings emphasize the need for tailored approaches in VS management and underscore the importance of region-specific factors in influencing clinical outcomes. Conclusions: This study contributes to refining treatment strategies and improving healthcare delivery for VS patients in Romania and beyond. Full article

This study examines how institutional ownership concentration, quantified by the Herfindahl-Hirschman Index (HHI), influences the financial consequences of CSR initiatives within the U.S. restaurant industry. This study distinguishes between CSR activities that are integral to core operations (operation-related CSR) and those that are not (non-operation-related CSR), analyzing their impacts on both market-based (ROA) and accounting-based performance (Tobin’s q) measures. Employing panel regression analysis, findings reveal that higher institutional ownership concentration enhances the alignment of operation-related CSR with improved financial performance. Conversely, the institutional ownership concentration does not significantly moderate the impact of non-operation-related CSR on both performance measures. Given the restaurant industry’s high consumer visibility and operational reliance on social and environmental factors, this study fills gaps in CSR literature by offering insights for aligning CSR strategies with institutional owners’ expectations. The results provide actionable guidance for policymakers and industry practitioners to optimize organizational outcomes from CSR activities. Full article

The surge in technological advancements has resulted in concerns over its misuse in politics and entertainment, making reliable detection methods essential. This study introduces a deepfake detection technique that enhances interpretability using the network dissection algorithm. This research consists of two stages: (1) detection of forged images using advanced convolutional neural networks such as ResNet-50, Inception V3, and VGG-16, and (2) applying the network dissection algorithm to understand the models’ internal decision-making processes. The CNNs’ performance is evaluated through F1-scores ranging from 0.8 to 0.9, demonstrating their effectiveness. By analyzing the facial features learned by the models, this study provides explainable results for classifying images as real or fake. This interpretability is crucial in understanding how deepfake detection models operate. Although numerous detection models exist, they often lack transparency in their decision-making processes. This research fills that gap by offering insights into how these models distinguish real from manipulated images. The findings highlight the importance of interpretability in deep neural networks, providing a better understanding of their hierarchical structures and decision processes. Full article

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disorder, affecting both children and adults, which often leads to significant difficulties with attention, impulsivity, and working memory. These challenges can impact various cognitive and perceptual domains, including music perception and performance. Despite these difficulties, individuals with ADHD frequently engage with music, and previous research has shown that music listening can serve as a means of increasing stimulation and self-regulation. Moreover, music therapy has been explored as a potential treatment option for individuals with ADHD. As there is a lack of integrative reviews on the interaction between ADHD and music, the present review aimed to fill the gap in research. Following PRISMA guidelines, a comprehensive literature search was conducted across PsychInfo (Ovid), PubMed, and Web of Science. A narrative synthesis was conducted on 20 eligible studies published between 1981 and 2023, involving 1170 participants, of whom 830 had ADHD or ADD. The review identified three main areas of research: (1) music performance and processing in individuals with ADHD, (2) the use of music listening as a source of stimulation for those with ADHD, and (3) music-based interventions aimed at mitigating ADHD symptoms. The analysis revealed that individuals with ADHD often experience unique challenges in musical tasks, particularly those related to timing, rhythm, and complex auditory stimuli perception, though these deficits did not extend to rhythmic improvisation and musical expression. Most studies indicated that music listening positively affects various domains for individuals with ADHD. Furthermore, most studies of music therapy found that it can generate significant benefits for individuals with ADHD. The strength of these findings, however, was limited by inconsistencies among the studies, such as variations in ADHD diagnosis, comorbidities, medication use, and gender. Despite these limitations, this review provides a valuable foundation for future research on the interaction between ADHD and music. Full article