Search Results (2,335)

School-based neuromuscular training interventions have the potential to mitigate dynapenia in the paediatric population and enhance movement skill outcomes; however, translating research into practice in primary school settings has been slow due to the expertise and professional learning required for implementation. This review describes the new teacher-supported intervention ‘Kids Innovative Neuromuscular Enhancement & Teacher-supported Instructional Coaching with AI’ (Kinetic AI) and presents evidence supporting its use in primary school settings. The Scale for the Assessment of Narrative Review Articles (SANRA) was used to guide the narrative and conceptual review methodology employed to synthesise peer-reviewed literature on paediatric dynapenia, school-based neuromuscular training, and AI technology-supported instructional models. This synthesis informed the development of a conceptual approach to neuromuscular training delivery in primary schools. The newly developed Kinetic AI conceptual model provides a pathway to embed neuromuscular training within active class breaks, offering adaptive feedback and targeted teacher support to facilitate implementation. This approach has the potential to bridge gaps between research, access, and practice. The Kinetic AI application is designed to support children’s muscular fitness and movement skills through school-based neuromuscular training, while addressing barriers to research translation and teacher expertise. When applied during school breaks, this approach has the potential to reduce the risk of dynapenia and contribute to scalable improvements in paediatric health and wellbeing. Full article

Biopolymer-based packaging films possess outstanding performances and are being developed as the alternatives to traditional petroleum-based plastic packaging films with many non-ignorable shortcomings. In this study, chitosan, succinylated pullulan (SP), and berberine (BBR) were combined to fabricate novel biopolymer-based composite films (CSSPB) via the layer-by-layer assembly method. The effects of the incorporation of BBR on the physicochemical properties of the film were investigated. It was found that after BBR was added, the tensile strength (TS), elongation at break (EAB), hydrophobicity, and antioxidant capacities of the film were enhanced. The chemical bonding, crystalline properties, elemental composition, and thermal stability of the films were also characterized by Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA), respectively. The in vitro antifungal tests revealed the antifungal activities of the films with a relatively high BBR content against Colletotrichum gloeosporioides (CG). In the preservation experiments, the CSSPB films exhibited preservation effects on fresh-cut apples, which manifested as delaying browning, weight loss, an increase in the soluble solids content, and a decrease in hardness. The new CSSPB composite films were opined to hold application potential in the field of food packaging. Full article

As a reliable and high-potential wireless communication technology for the Internet of Things (IoT), LoRa excels in long-distance and low-power transmission. The star topology adopted by traditional LoRaWAN suffers from poor deployment flexibility and insufficient scalability in scenarios with complex terrain or harsh environments. LoRa-Mesh networks can effectively solve coverage challenges through characteristics such as multi-hop and self-organization; however, the relay and forwarding requirements of nodes also introduce new challenges in energy consumption management. To address the energy consumption management challenges of LoRa-Mesh, this paper proposes a Data-Driven Energy Saving (DDES) protocol. It flexibly sets and dynamically fine-tunes node sleep durations based on data changes, constructs an efficient energy-saving framework through uplink data streams, and implements precise control over nodes via downlink post-analysis messages to achieve on-demand energy saving. Simulation results in the smart agriculture scenario of soil moisture monitoring and irrigation show that compared with protocols without a sleep mechanism, the battery life of the LoRa-Mesh network using the DDES protocol is extended by approximately 20 times. The proposed protocol breaks through the limitations of fixed sleep schemes, realizes refined and flexible division of sleep regions, and exhibits significant advantages in LoRa network energy saving. Full article

Diagnosis of cancer is one of the hardest problems faced in modern medicine and involves integrating different data sources such as medical images, genomic profiles and clinical records. Traditional machine learning methods have difficulty handling the high-dimensional and complex correlation properties of multimodal medical data. In view of this, we propose a new Quantum-Enhanced Multimodal Fusion Network (QEMFN) framework to break through traditional image–text matching based on quantum computing principles for CT imaging with genomic sequencing data and EHR information. Our approach utilizes variational quantum circuits for feature encoding, quantum kernel methods for crossmodal attention, and hybrid quantum–classical architectures for final classification. We realize the framework using Google Cirq quantum computing library and validate it on publicly available datasets including TCIA (The Cancer Imaging Archive), TCGA (The Cancer Genome Atlas), and MIMIC-III clinical database. The matched multimodal cohort comprises 847 lung cancer patients, 623 colorectal cancer patients, and 401 liver cancer patients with complete imaging, genomic, and clinical records, assembled via de-identified patient ID linkage across the three archives. The experiment takes steps toward the realization of quantum-enhanced diagnostic systems and offers a path for subsequent experimental confirmation. We theoretically analyze the potential quantum advantage, present detailed implementation details using Cirq, and describe a roadmap to clinical translation for quantum-enhanced diagnostic tools. Full article

The Arganaty Massif in the Eastern Balkhash region (Kazakhstan) is located in a key sector of the Central Asian Orogenic Belt, but its petrogenesis and relationship to neighboring Late Palaeozoic intrusions remain poorly constrained. This study presents the first U–Pb zircon age and whole-rock geochemical data for the Arganaty granites, combined with a comparison with massifs of the Northern Balkhash region and Alatau Mountains (East Kazakhstan and Western Xinjiang, NW China). The Arganaty granites have a concordant U–Pb age of 281.5 ± 2.1 Ma. They are high-K calc-alkaline, metaluminous to slightly peraluminous I-type granites with low Mg# (0.22–0.33) and Nb/Ta ratios (10.2–17.3). Geochemical comparison indicates close affinity to the Lepsy complex intrusions and eastern plutons of Alatau mountains, rather than to the Katbar complex of Northern Balkhash. The new age and geochemical data show that the Arganaty granites formed in a post-collisional setting after the closure of the Junggar–Balkhash Ocean. Their mixed crust–mantle signature and depth estimates (~30 km) are consistent with lower crustal melting triggered by slab break-off. These results clarify the post-collisional magmatic evolution of the region and contribute to the understanding of Late Palaeozoic crustal growth in the CAOB. Full article

The strong non-linearity and multi-scale coupling characteristics of massive heterogeneous components in modern power systems pose severe challenges to traditional numerical simulation methods, rendering them inadequate for urgent online real-time assessment. This paper systematically reviews state-of-the-art hybrid transient stability simulation technologies that deeply integrate physics and data. It first dissects the critical bottlenecks of traditional numerical simulations—specifically computational inefficiency, convergence fragility, and model fidelity gaps—to elucidate the necessity of evolving toward a new physics–data integration paradigm. Subsequently, the review categorizes current methodologies into three technical dimensions: artificial intelligence (AI)-enhanced numerical solvers, AI-based surrogate modeling, and physics-embedded AI modeling. These approaches are synthesized to demonstrate their unique advantages in breaking through computational speed limits, enhancing numerical robustness, and effectively bridging the fidelity gap between simulation models and physical reality. Finally, addressing existing limitations regarding physical consistency and generalization, the paper proposes future research directions, including constructing network architectures with hard physical constraints, enhancing adaptability to complex grid scenarios, and developing self-evolving intelligent simulation frameworks to ensure future grid security. Full article

In the context of digitalization and growing demands for environmental responsibility, creative industries are seeking ways to reduce their material footprint. The purpose of this study is to evaluate the role of digital technologies, such as virtual prototyping and digital fashion, in shaping new sustainability standards. To achieve this, a systemic multidisciplinary approach was applied, combining comparative analysis, quantitative assessment of key indicators (MIRR, CFCI, VSR), and the calculation of the Integral Sustainability Index (ISI).The results show that virtual prototyping reduces material costs by 45–65% and the number of physical prototypes by 3–5 times; however, its energy efficiency depends on project complexity and is achieved only after the ‘energy break-even point.’ Digital fashion practices demonstrate the potential to reduce the carbon footprint, but only when utilizing energy-efficient digital infrastructure. The integrated assessment revealed an increase in the overall level of sustainability (with $ISI$ rising from 0.52 to 0.71) during the transition to digital processes. The main conclusion is that digital technologies establish new sustainability standards, yet their positive impact is realized only through the conscious design of technological systems, business models, and institutional environments focused on balancing environmental, economic, and social goals. Full article

This paper focuses on the critical issue of change point detection in panel linear regression models and proposes a novel jump information criterion (JIC) for efficient solution. The core innovation of this criterion lies in reconstructing the traditional change point hypothesis testing problem into a parameter estimation problem: under the null hypothesis ($H_0$, i.e., no change point exists in the model) and the alternative hypothesis ($H_1$, i.e., a change point exists in the model), the number of potential change points is set to 0 and 1 for modeling and solution, respectively. To verify the theoretical reliability of the proposed method, this paper systematically establishes the consistency of the change point count estimator through rigorous mathematical deductions and further derives its optimal convergence rate. In terms of numerical validation, extensive Monte Carlo simulation experiments and real data empirical analysis both demonstrate that the estimator constructed based on JIC exhibits excellent performance in change point identification accuracy, stability, and computational efficiency, providing a reliable new tool for structural break analysis in panel data models. Full article

Retrieval-augmented generation (RAG) grounds large language models (LLMs) with external evidence. Dynamic knowledge tasks, however, require systems to decide not only what to retrieve but also when to refresh, how to arbitrate conflicts, and how to preserve an auditable record of the evidence used to answer a query. We present LedgerRAG, a trigger-aware retrieval chain framework that maintains an explicit claim-level evidence ledger and uses coverage, temporal validity, authority, and conflict signals to control retrieval, refresh, and stopping decisions. We expand the evaluation with a query-level BM25 baseline, a dense retriever setting, and task-aligned proxy baselines representing graph-style retrieval, temporal-only retrieval, and conflict-focused retrieval. The revised results show that LedgerRAG’s clearest advantage lies in conflict governance and auditable evidence control, achieving near-perfect ConFLICT adjudication (CRAcc = 0.993) under authority-aware routing while yielding more modest gains and explicit trade-offs in regulation-change and streaming settings. Full article

Local Lorentz Invariance is one of the fundamental postulates of General Relativity, making its experimental verification of paramount importance. Given that various frontier theoretical models predict potential symmetry breaking, the Standard Model Extension framework has been established to systematically study such phenomena. Within the Standard Model Extension gravitational sector, the high-order Lorentz-violating terms with mass dimension $d=6$ exhibit a rapid signal decay with distance, providing a distinct detection advantage in short-range gravity experiments. This work is dedicated to optimizing the testing schemes for $d=6$ Lorentz-violating coefficients. Based on a high-precision torsion balance platform, we propose a novel scheme featuring a comb-stripe design. The improvements are twofold: first, the spatial orientation of the experimental apparatus is optimized to leverage the modulation effects of the Earth’s rotation, thereby enhancing the capability to distinguish and constrain different violation parameters; second, the test and source masses are reconfigured into specifically designed stripe patterns to significantly amplify the fringe-field signals sensitive to Lorentz-violating effects. This paper systematically elaborates on the theoretical foundation and design principles of the new scheme. By performing a detailed comparison of the constraint potentials of various stripe configurations, the five-stripe geometry is identified as the optimal experimental configuration. This study provides a new experimental methodology for exploring physics beyond the Standard Model at higher levels of precision. Full article

Cassava brown streak disease (CBSD), caused by cassava brown streak virus (CBSV; Ipomovirus brunusmanihotis) and Ugandan cassava brown streak virus (UCBSV; Ipomovirus manihotis) (family Potyviridae, genus Ipomovirus), is increasingly becoming a threat to cassava production in several parts of Africa, especially in Eastern, Central and Southern Africa. In Kenya, the disease continues to wreak havoc on cassava production leading to a significant reduction in crop yields and economic losses of up to USD 1 billion. Variation in virus populations make the control of CBSD challenging as virus genomic variation can affect the accuracy of diagnostic tests, lead to resistance breaking isolates and jeopardize strategies of breeding for resistance. CBSV and UCBSV populations obtained from cassava fields in Kenya were characterized. In total, 44 new complete sequences of CBSV and UCBSV were assembled and 40 sequences successfully submitted to GenBank. Single Nucleotide Polymorphism (SNP) analysis revealed that the cylindrical inclusion protein (CI) is the most stable region across the genome of CBSV and UCBSV. In contrast, protein 1 (PI) and the coat protein (CP) were the most hypervariable regions. Phylogenetic analysis showed three major geographical groupings for both UCBSV and CBSV isolates, suggesting a continued spread of the viruses through human-mediated movement of infected planting materials. The data obtained in this study can support the development of disease management strategies through improved molecular diagnostic tests and targets for breeding for resistance against CBSD. Full article

Deep marine shale reservoirs are controlled by multi-factor coupling effects, and the genetic mechanism of “sweet spots” exhibits strong complexity, leading to prominent difficulties in quantitative prediction and precise evaluation of sweet spots. Aiming at the problems of an unclear lithofacies-controlled sweet spot evolution law and insufficient accuracy of multi-parameter quantitative evaluation in traditional evaluation methods, this paper takes the Wufeng Formation and Long1 member of the Longmaxi Formation in the LZ block, Southern Sichuan, as the research object. Innovatively integrating machine learning (ML), grey correlation analysis (GRA), and three-dimensiona (3D) geological modeling technologies, a refined prediction model for reservoir sweet spot evaluation indicators under lithofacies constraint conditions is established, and a multi-parameter fusion quantitative evaluation method for deep marine shale gas sweet spots with high prediction accuracy is proposed. The results demonstrate that the LightGBM-based prediction model for sweet spot evaluation indicators achieved excellent performance. Based on a total of 380 preprocessed samples divided into training and test sets in a 7:3 ratio, the coefficient of determination (R²) of the model exceeded 0.9 in both the test and validation datasets. The “sweetness index”, a comprehensive evaluation index of reservoir sweet spots constructed via GRA-based multi-factor fusion, shows a correlation coefficient of 0.91 with respect to actual gas well production, presenting a high fitting degree. The 3D sweet spot geological model reveals that Class I sweet spots are mainly developed in the 1st to 3rd sub-layers of the Long1 member, while Class II sweet spots are distributed in the 5th and 6th sub-layers, which is highly consistent with the actual development law of the gas field. This study breaks through the limitations of single evaluation methods and weak lithofacies control consideration in traditional sweet spot evaluation and forms a set of innovative technical process integrating “precision prediction—multi-factor fusion—3D characterization”. It provides a new technical approach for efficient and accurate evaluation of deep marine shale reservoir sweet spots and has important guiding significance for the efficient development of shale gas. Full article

Epilepsy affects more than 50 million individuals worldwide, and approximately one-third of patients remain refractory to existing antiseizure medications. Advances in gene therapy and genome editing have opened new possibilities for disease-modifying interventions that directly target the molecular and circuit-level mechanisms underlying epileptogenesis. Recent progress in central nervous system tropic viral vectors, non-viral delivery systems, and programmable genome-editing technologies has enabled precise manipulation of neuronal and glial function in preclinical epilepsy models. Strategies range from restoration of haploinsufficient genes implicated in monogenic epilepsies, such as SCN1A in Dravet syndrome, to modulation of neuronal excitability through engineered ion channels, neuropeptides, and astrocyte-based approaches. In parallel, CRISPR-derived platforms, including transcriptional activation and repression systems, base editing, and prime editing, offer new avenues for regulating gene expression in post-mitotic neurons without introducing double-strand DNA breaks. Despite these advances, significant translational challenges remain, including efficient and cell-type-specific delivery, long-term safety, and the risk of network-level side effects in the epileptic brain. This review critically examines recent gene therapy and genome-editing approaches for epilepsy, highlights key technological and biological barriers to clinical translation, and discusses emerging strategies that may enable durable and targeted treatments for drug-resistant epilepsies. Full article

Microwave pretreatment of native soybeans in the preparation of yuba remains underexplored, and the impact of this treatment on the resulting yuba quality is still unclear. In this study, soybeans were subjected to microwave pretreatment for 30–120 s before conventional soaking. CLSM revealed soybean microstructural changes, including cell-wall degradation and improved dispersion of proteins and lipids. FTIR and SDS-PAGE results of yuba indicated that hydrogen bond cleavage and the formation of new cross-links reduced protein coiling and polar group exposure, while stabilizing aliphatic chains, ultimately yielding a stronger and more compact yuba network structure. Mechanical and rehydration results further indicated that microwave treatment positively affected yuba quality. The 90 s pretreatment was identified as the optimal condition, exhibiting the highest elongation at break (126.36% increase) and rehydration capacity, along with improved color attributes, including higher lightness (L*) and yellowness (b*) values. These changes are likely attributable to disulfide-mediated protein reorganization, which creates greater spatial availability and thereby facilitates lipid incorporation. This study elucidates how microwave pretreatment drives the reorganization of soybean protein and lipid components, thereby influencing their distribution during film formation and providing a foundation for the tailored design of yuba with targeted mechanical properties. Full article

To achieve high-precision and real-time quantitative evaluation of gas production in underground gas storage (UGS), this study focused on 11 typical injection-production wells in the Sudong UGS group. To address the common challenges posed by deviated well structures and complex wellbore temperature field distributions, a gas flow-rate calculation method based on Distributed Temperature-Sensing (DTS) data was developed. By standardizing the processing of multi-well temperature data, deviated wellbore trajectories were straightened to convert measured depth (MD) to true vertical depth (TVD). By incorporating a geothermal correction mechanism, temperature anomalies closely related to fluid flow were extracted, and a spatially unified temperature field model was constructed. On this basis, a “Dual-Point Temperature Difference Method” is proposed as a novel approach for single-well production evaluation. Based on thermodynamic phenomena such as the Joule–Thomson effect and expansion cooling, two critical sensing points, upstream and downstream of the production layer, were selected, with their temperature anomaly difference (∆T) serving as a sensitive indicator of flow rate variations. Combined with downhole pressure parameters and synchronized wellhead metering data, a nonlinear quantitative relationship model between ∆T and gas production rate Q was established, enabling accurate conversion of wellbore thermal response to macroscopic flow parameters. The results indicated that the gas production rates calculated by this method align well with traditional wellhead metering data, with errors maintained within engineering tolerances. Notably, the method demonstrates higher reliability and corrective capabilities in wells with drifting or faulty meters. This achievement breaks the reliance of traditional methods on specific layers or mechanical meters. It enables the effective application of multi-well, full-section, and non-contact temperature data in gas volume assessment. This research provides new technical support for dynamic monitoring, efficient operation, and remaining gas evaluation of UGS, offering significant prospects for engineering applications. Full article