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Assessment of groundwater quality and health risk was conducted in the Shenfu coal mine area in Ordos basin, northwestern China. Statistical analysis, Piper and Chadha diagrams were used to reveal the hydrogeochemical characteristics of groundwater via physicochemical analysis of 44 collected samples. The suitability of groundwater was assessed for domestic and irrigation purposes, and the fuzzy comprehensive method was adopted to assess the overall groundwater quality for further discussion on groundwater management. The model recommended by the USEPA was selected to estimate the non-carcinogenic risks caused by NO3−, NO2−, NH4+, F−, Fe and Mn through oral ingestion and direct dermal contact. The results revealed that the predominant hydrochemical types of groundwater were SO4∙Cl–Ca∙Mg and HCO3–Ca∙Mg types and the major cations and anions followed the orders of Ca2+ > Na+ > Mg2+ >K+ and HCO3− > SO42− > Cl−, respectively. Groundwater is generally acceptable for irrigation. However, for domestic purposes, 47.73% of the collected samples are of excellent and good quality and are suitable for direct consumption. Both adults and children face non-carcinogenic risks because of exposure to contaminants such as nitrate, nitrite and fluoride. The risk to children is higher than that to adults, which is consistent with other studies. Nitrite contributes most to the risks, followed by nitrate and fluoride. Home-use water quality improvement devices and rainwater harvesting are suggested to enhance the groundwater quality protection and management in this area. The research also indicates that health risk assessment should always accompany general water quality assessment to ensure the reliability of the water quality assessment.

The gut microbiota serves as a critical “organ” in the life cycle of animals, particularly in the intricate interplay between herbivorous pests and plants. This review summarizes the pivotal functions of the gut microbiota in mediating the insect–plant interactions, encompassing their influence on host insects, modulation of plant physiology, and regulation of the third trophic level species within the ecological network. Given these significant functions, it is plausible to harness these interactions and their underlying mechanisms to develop novel eco-friendly pest control strategies. In this context, we also outline some emerging pest control methods based on the intestinal microbiota or bacteria-mediated interactions, such as symbiont-mediated RNAi and paratransgenesis, albeit these are still in their nascent stages and confront numerous challenges. Overall, both opportunities and challenges coexist in the exploration of the intestinal microbiota-mediated interactions between insect pests and plants, which will not only enrich the fundamental knowledge of plant–insect interactions but also facilitate the development of sustainable pest control strategies.

The maintenance of iron homeostasis is essential for proper endocrine function. A growing body of evidence suggests that iron imbalance is a key factor in the development of several endocrine diseases. Nowadays, ferroptosis, an iron-dependent form of regulated cell death, has become increasingly recognized as an important process to mediate the pathogenesis and progression of type 2 diabetes mellitus (T2DM). It has been shown that ferroptosis in pancreas β cells leads to decreased insulin secretion; and ferroptosis in the liver, fat, and muscle induces insulin resistance. Understanding the mechanisms concerning the regulation of iron metabolism and ferroptosis in T2DM may lead to improved disease management. In this review, we summarized the connection between the metabolic pathways and molecular mechanisms of iron metabolism and ferroptosis in T2DM. Additionally, we discuss the potential targets and pathways concerning ferroptosis in treating T2DM and analysis the current limitations and future directions concerning these novel T2DM treatment targets.

The purpose of this study was to compare the joint, racket, and ball kinematics between the different levels of male tennis players in tennis serve-return at slow and high serve speeds. Thirty male tennis players were divided into an advanced group ( n  = 15) and an intermediate group ( n  = 15) based on skill level. The advanced group and intermediate group matched shake hand-grip players performed serve-receive test at the different serve speeds. Kinematic data were collected on the trunk, upper limbs, racket and ball to compare the differences between the advanced and intermediate groups at different serve speeds. At both serve speeds, the AG has faster racket speed and ball speed, and is closer to the baseline placement ( p <0.05). It is characterized by a faster trunk turn, greater speed with the shoulder joint forward and outward and greater angle of wrist flexion and extension during the stroke ( p <0.05), and greater speed with the forearm inward and internal rotation during the follow-through swing, at the same time, there is a difference in the angle of the racket to the ground between the advanced group and an intermediate group( p <0.05). Elbow internal rotation and abduction angular velocity in the backswing phase, Timing of peak racket resultant velocity and Wrist extension in the stroke phase, and shoulder and elbow joint activities in the follow-through phase were significantly correlated with the ball speed of the return. With faster serve speeds, higher quality serve-receive can be achieved by shortening the duration of the backswing lifting the upper arm and rotating the forearm inward quickly, accelerating the speed of movement of wrist joints at the impact phase, adjusting the speed of shoulder adduction and elbow internal rotation to complete the follow-through swing. These findings help to improve elite players’ serve-receive skills at faster serve speed.

With the primary objective of creating playlists that suggest songs, interest in music genre categorization has grown thanks to high-tech multimedia tools. To develop a strong music classifier that can quickly classify unlabeled music and enhance consumers’ experiences with media players and music files, machine learning and deep learning ideas are required. This study presents a unique method that blends convolutional neural network (CNN) models as an ensemble system to detect musical genres. The method makes use of discrete wavelet transform (DWT), mel frequency cepstral coefficients (MFCC), and short-time fourier transform (STFT) characteristics to provide a comprehensive framework for expressing stylistic qualities in music. To do this, each model’s hyperparameters are generated using the capuchin search algorithm (CapSA). Preprocessing the original signals, feature description utilizing DWT, MFCC, and STFT signal matrices, CNN model optimization to extract signal features, and music genre identification based on combined features make up the four main components of the technique. By integrating many signal processing techniques and CNN models, this study advances the field of music genre classification and provides possible insights into the blending of diverse musical components for improved classification accuracy. The GTZAN and Extended-Ballroom datasets were the two used in the studies. The average classification accuracy of 96.07 and 96.20 for each database, respectively, show how well our suggested strategy performs when compared to earlier, comparable methods.

In a flat band superconductor, the charge carriers’ group velocity v F is extremely slow. Superconductivity therein is particularly intriguing, being related to the long-standing mysteries of high-temperature superconductors 1 and heavy-fermion systems 2 . Yet the emergence of superconductivity in flat bands would appear paradoxical, as a small v F in the conventional Bardeen–Cooper–Schrieffer theory implies vanishing coherence length, superfluid stiffness and critical current. Here, using twisted bilayer graphene 3 – 7 , we explore the profound effect of vanishingly small velocity in a superconducting Dirac flat band system 8 – 13 . Using Schwinger-limited non-linear transport studies 14 , 15 , we demonstrate an extremely slow normal state drift velocity v n  ≈ 1,000 m s –1 for filling fraction ν between −1/2 and −3/4 of the moiré superlattice. In the superconducting state, the same velocity limit constitutes a new limiting mechanism for the critical current, analogous to a relativistic superfluid 16 . Importantly, our measurement of superfluid stiffness, which controls the superconductor’s electrodynamic response, shows that it is not dominated by the kinetic energy but instead by the interaction-driven superconducting gap, consistent with recent theories on a quantum geometric contribution 8 – 12 . We find evidence for small Cooper pairs, characteristic of the Bardeen–Cooper–Schrieffer to Bose–Einstein condensation crossover 17 – 19 , with an unprecedented ratio of the superconducting transition temperature to the Fermi temperature exceeding unity and discuss how this arises for ultra-strong coupling superconductivity in ultra-flat Dirac bands. The authors investigate the effect of small velocity in a superconducting Dirac flat band system, finding evidence for small pairs and that superfluid stiffness is not dominated by kinetic energy.

The future of islands has been the subject of international concern, scientific debate and media interest in the last decade. As a result of global warming and sea level rise, increasingly more research and speculations about the morphology and positions of island coastlines have been produced. However, some assumptions are not well documented due to the lack of large-scale research and data support. This paper contributes to filling this gap by extracting and assessing coastline changes on Southeast Asian islands overall during 2000–2015 based on Landsat remote sensing images. The results are as follows: ① the coastline, defined by the mean high water line (MHWL), of Southeast Asia remained relatively stable but showed considerable variability in space, especially in estuaries, bays and straits. ② A total of 9035 islands were extracted, among which approximately 10% of islands witnessed locational changes in coastlines, resulting in net reductions of nearly 86 km2 in area and 50,000 km in centroid displacement. Additionally, the coastline length increased by 532 km from 2000 (148,508 km) to 2015 (149,040 km). Natural coastlines decreased by 2503 km, while artificial coastlines increased by 3035 km. Among the total coastlines, 11% changed: 5% exhibited deposition, while 6% experienced retreat. ③ The temporal and spatial changes in coastlines were the result of interactions between natural processes and human activities. Climatic and environmental changes had wide impacts, while human activities caused more dramatic local changes. In addition, the sizes, shapes and landforms of the islands played significant roles in coastline changes. ④ Coastal erosion and expansion often coexisted in dynamic equilibrium under the influence of coastal hydrodynamics, such as cyclical tides and near-shore sediment transport. Our findings reveal spatial–temporal variations in island coastlines in Southeast Asia from 2000–2015 and provide critical information for the current study of islands. This work has great significance for the study of global climate change impacts and the integrated management of island coastal zones.

Although organic nanomaterials and inorganic nanoparticles possess inherent flexibility, facilitating functional modification, increased intracellular uptake and controllable drug release, their underlying cytotoxicity and lack of specificity still cause safety concerns. Owing to their merits, which include natural biocompatibility, structural stability, unsurpassed programmability, ease of internalization and editable functionality, tetrahedral DNA nanostructures show promising potential as an alternative vehicle for drug delivery and biomedical treatment. Here, we describe the design, fabrication, purification, characterization and potential biomedical applications of a self-assembling tetrahedral DNA nanostructure (TDN)–based multifunctional delivery system. First, relying on Watson-Crick base pairing, four single DNA strands form a simple and typical pyramid structure via one hybridization step. Then, the protocol details four different modification approaches, including replacing a short sequence of a single DNA strand by an antisense peptide nucleic acid, appending an aptamer to the vertex, direct incubation with small-molecular-weight drugs such as paclitaxel and wogonin and coating with protective agents such as cationic polymers. These modified TDN-based complexes promote the intracellular uptake and biostability of the delivered molecules, and show promise in the fields of targeted therapy, antibacterial and anticancer treatment and tissue regeneration. The entire duration of assembly and characterization depends on the cargo type and modification method, which takes from 2 h to 3 d. In this protocol, the authors describe the design, fabrication, purification, characterization and potential biomedical applications of a self-assembling TDN-based multifunctional delivery system.

Aim/Purpose: This study investigates the key factors influencing preschool teachers’ sustained use of Artificial Intelligence-Generated Content (AIGC) technology in educational settings. While prior research has extensively examined initial adoption, little attention has been given to understanding the continuous intention of preschool teachers with AIGC. To bridge this gap, this study integrates the Technology Acceptance Model (TAM), Expectation-Confirmation Model (ECM), and Flow Theory to develop a comprehensive framework that captures cognitive, affective, and experiential factors shaping continued AIGC adoption. Background: AIGC has demonstrated immense educational potential, providing personalized learning experiences, real-time feedback, and intelligent student progress tracking. However, most existing research focuses primarily on system usability and feasibility, neglecting the motivational and psychological aspects that determine continuous intention to use AIGC. Specifically, satisfaction, expectation confirmation, and flow experience have been largely overlooked as key determinants of sustained technology use. Given that preschool educators face unique pedagogical challenges, such as adapting AIGC content to young learners and maintaining engagement, understanding the drivers of long-term AIGC use is essential for optimizing its integration into preschool education. Methodology: This study employs a mixed-method approach to ensure a rigorous and comprehensive analysis. A total of 433 preschool teachers participated in the survey, and Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to test the hypothesized relationships. To complement structural modeling, Artificial Neural Network (ANN) modeling was applied to uncover non-linear relationships that traditional statistical methods might overlook. By integrating PLS-SEM and ANN, this study provides a more robust, predictive, and holistic understanding of the factors driving sustained AIGC adoption. Contribution: This study makes significant theoretical and practical contributions. Theoretically, it extends TAM and ECM by incorporating Flow Theory. Unlike prior studies focusing primarily on perceived usefulness and ease of use, this research identifies confirmation and satisfaction as the strongest predictors of continued intention to use AIGC. Practically, the findings provide valuable insights for policymakers, school administrators, and ed-tech developers, offering recommendations for designing more engaging, sustainable, and user-friendly AIGC solutions tailored for preschool education. Findings: The results indicate that satisfaction (β = 0.280, p < 0.001) is the strongest predictor of continued AIGC use, followed by attitude (β = 0.262, p < 0.001) and flow experience (β = 0.223, p < 0.001). Expectation confirmation significantly enhances perceived usefulness (β = 0.505, p < 0.001) and satisfaction (β = 0.349, p < 0.001), reinforcing the importance of aligning AIGC tools with teachers’ expectations. ANN analysis further highlights confirmation (95.28%) and satisfaction (82.41%) as the most influential factors, whereas perceived ease of use (22.35%) has a relatively minor impact. These findings suggest that positive user experience, engagement, and expectation fulfillment are key drivers of long-term AIGC adoption. Moreover, ANN analysis revealed complex nonlinear relationships, demonstrating that traditional statistical methods might underestimate the true impact of psychological and experiential factors on technology retention. Recommendations for Practitioners: For practitioners, this study provides several actionable recommendations. First, AIGC tools should be designed to enhance engagement and intrinsic motivation, integrating gamification elements, interactive features, and adaptive learning support to sustain user interest. Second, ongoing professional development programs should be implemented to train teachers on the pedagogical applications of AIGC, addressing any concerns related to usability or long-term feasibility. Third, AIGC platforms should incorporate customization features, allowing educators to tailor content based on their specific classroom needs and teaching styles. By addressing these factors, AIGC adoption in preschool education can be more sustainable and impactful. Recommendation for Researchers: For researchers, this study opens multiple avenues for future exploration. First, future research should adopt a longitudinal approach to examine how preschool teachers’ attitudes and behaviors toward AIGC evolve over time. Second, more research is needed to explore the role of teacher personality traits and digital literacy levels in shaping AIGC adoption patterns. Third, cross-cultural studies could provide deeper insights into how different educational systems and socio-cultural contexts influence preschool teachers’ responses to AIGC technologies. Furthermore, AI-driven predictive analytics should be explored to model behavioral trends and optimize AIGC implementations across diverse learning environments. Impact on Society: This study has significant implications for educational equity, teacher workload, and early childhood learning experiences. By empowering preschool teachers with AIGC, this research promotes more inclusive and accessible preschool education, reducing disparities in educational resources and opportunities. Additionally, AI-driven teaching solutions can alleviate teacher workload, enabling educators to focus on creative and interactive pedagogical strategies rather than administrative tasks. As AIGC continues to evolve, its potential to transform preschool education into a more engaging, adaptive, and learner-centered experience becomes increasingly evident. Future Research: While this study provides valuable insights into preschool teachers’ sustained use of AIGC, several areas require further exploration. First, objective usage data should be incorporated into future research rather than relying solely on self-reported surveys to enhance validity. Second, longitudinal studies should examine how teachers’ continuous intention to use AIGC evolves over time in response to technological advancements and policy shifts. Third, as this study focuses on preschool educators, future research should explore whether the identified factors apply to primary and secondary education teachers. Additionally, ethical concerns, AI trust, and data privacy issues should be further investigated, as they may significantly impact the long-term adoption of AIGC in educational settings.

In the past, it was widely accepted that herbivory decreased with latitude. However, several empirical studies have failed to support this hypothesis, leading to strong debate. In addition to providing a quantitative assessment of the latitudinal gradient in herbivory, our study aims to provide new information about the role of climatic factors in shaping the latitudinal gradient in herbivory. Through the collection and analysis of published data on herbivory, we tested the latitudinal herbivory hypothesis (LH hypothesis) and the relationship of temperature and precipitation to herbivory. Based on 1890 data points distributed world‐wide from more than 1000 plant species, our results show that herbivory decreased with latitude and increased with temperature only in the Northern Hemisphere. In contrast, in the Southern Hemisphere, herbivory did not have a relationship with latitude and tended to decrease with temperature. Synthesis. This study shows the LH hypothesis is supported only in the Northern Hemisphere and highlights the importance of temperature in explaining the pattern of herbivory at the global scale. These possible hemispheric asymmetries in herbivory should not be overlooked in future studies.