Search Results (151,895)

The paper studies the behavior of homogeneous isotropic materials by performing appropriate numerical analyses and utilizing suitable FEMs to reproduce the Brazilian splitting test. Starting with a theoretical approach and adopting suitable numerical simulations, a new formula that is able to characterize the Young’s modulus is presented. To this end, in addition to the analysis of the specimen’s response in terms of stresses and strains, the real displacement field resulting from the real kinematical constraints on the specimen is determined. Therefore, the Brazilian test is taken as a reference test and the specimen’s behavior is derived by taking advantage of both the theoretical approach and numerical simulations developed in the ANSYS 2021 R1 environment. The latter allows us to define a new mathematical relation representing the missing part of the kinematical field. Furthermore, a new formula which explicitly relates the Young’s modulus of the material to the geometrical characteristics of the specimen, to the acting force, and to a measured selected displacement is proposed. Future developments will include adopting the proposed formulas for the identification of other mechanical parameters of the material, e.g., by adopting a full-field contactless approach to displacement measurement and studying the behavior of specimens with different geometrical characteristics. Full article

Low Earth orbit (LEO) signal of opportunity (SOP) positioning relies on the accumulation of epochs obtained through prolonged observation periods. The contribution of an LEO satellite single epoch to positioning accuracy is influenced by multi-level characteristics that are challenging for traditional models. To address this limitation, we propose an Agent-Weighted Recursive Least Squares (RLS) Positioning Framework (AWR-PF). This framework employs an agent to comprehensively analyze individual epoch characteristics, assess their credibility, and convert them into adaptive weights for RLS iterations. We developed a novel Markov Decision Process (MDP) model to assist the agent in addressing the epoch weighting problem and trained the agent utilizing the Double Deep Q-Network (DDQN) algorithm on 107 h of Iridium signal data. Experimental validation on a separate 28 h Iridium signal test set through 97 positioning trials demonstrated that AWR-PF achieves superior average positioning accuracy compared to both standard RLS and randomly weighted RLS throughout nearly the entire iterative process. In a single positioning trial, AWR-PF improves positioning accuracy by up to 45.15% over standard RLS. To the best of our knowledge, this work represents the first instance where an AI algorithm is used as the core decision-maker in LEO SOP positioning, establishing a groundbreaking paradigm for future research. Full article

The Maillard reaction refers to the reaction between carbonyl compounds with reducing properties and amino-containing compounds that undergo condensation and polymerization to produce melanoidins. In flour product processing, the Maillard reaction is a critical chemical reaction influencing color, flavor, nutrition, and safety. A moderate Maillard reaction contributes to desirable color and flavor profiles in flour products, whereas an excessive reaction leads to amino acid loss and the formation of harmful substances, posing potential health risks. This review summarizes the substrate sources, reaction stages, influencing factors, impact on quality, and mitigation strategies of harmful products, aiming to provide a reference for regulating the Maillard reaction in flour product processing. Currently, most existing mitigation strategies focus on inhibiting harmful products, while research on the synergistic optimization of color and flavor remains insufficient. Future research should focus on elucidating the molecular mechanisms of reaction pathways, understanding multi-factor synergistic effects, and developing composite regulation technologies to balance the sensory quality and safety of flour products. Full article

Potato (Solanum tuberosum L.) is one of the most important food crops in the world, ranking fourth after rice, maize, and wheat. Potatoes are exposed to biotic and abiotic environmental factors, which lead to economic losses and increase the possibility of food security threats in many countries. Traditional potato breeding faces several challenges, primarily due to its genetic complexity and the time-consuming nature of the process. Therefore, gene editing—CRISPR-Cas technology—allows for more precise and rapid changes to the potato genome, which can speed up the breeding process and lead to more effective varieties. In this review, we consider CRISPR-Cas technology as a potential tool for plant breeding strategies to ensure global food security. This review summarizes in detail current and potential technological breakthroughs that open new opportunities for the use of CRISPR-Cas technology for potato breeding, as well as for increasing resistance to abiotic and biotic stresses, and improving potato tuber quality. In addition, the review discusses the challenges and future perspectives of the CRISPR-Cas system in the prospects of the development of potato production and the regulation of gene-edited crops in different countries around the world. Full article

This comprehensive study investigates the spatial distribution of metals in surface water, their accumulation in fish tissues, and their impact on the gut microbiome dynamics of fish in the Qi River, Huanggang City, Hubei Province. Three distinct sampling regions were established: the mining area (A), the transition area (B), and the distant area (C). Our results revealed that metal concentrations were highest in the mining area and decreased with increasing distance from it. The bioaccumulation of metals in fish tissues followed the order of gut > brain > muscle, with some concentrations exceeding food safety standards. Analysis of the gut microbiota showed that Firmicutes and Proteobacteria dominated in the mining area, while Fusobacteriota were more prevalent in the distant area. Heavy metal pollution significantly altered the composition and network structure of the gut microbiota, reducing microbial associations and increasing negative correlations. These findings highlight the profound impact of heavy metal pollution on both fish health and the stability of their gut microbiota, underscoring the urgent need for effective pollution control measures to mitigate ecological risks and protect aquatic biodiversity. Future research should focus on long-term monitoring and exploring potential remediation strategies to restore the health of affected ecosystems. Full article

Direct Air Capture (DAC) is gaining worldwide attention as a negative emissions strategy critical to meeting climate targets. Among emerging DAC materials, pyrolysis chars (PCs) and gasification chars (GCs) derived from biomass present a promising pathway due to their tunable porosity, surface chemistry, and low-cost feedstocks. This review critically examines the current state of research on the physicochemical properties of PCs and GCs relevant to CO₂ adsorption, including surface area, pore structure, surface functionality and aromaticity. Comparative analyses show that chemical activation, especially with KOH, can significantly improve CO₂ adsorption capacity, with some PCs achieving more than 308 mg/g (100 kPa CO₂, 25 °C). Additionally, nitrogen and sulfur doping further improves the affinity for CO₂ through increased surface basicity. GCs, although inherently more porous, often require additional modification to achieve a similar adsorption capacity. Importantly, the long-term stability and regeneration potential of these chars remain underexplored, but are essential for practical DAC applications and economic viability. The paper identifies critical research gaps related to material design and techno-economic feasibility. Future directions emphasize the need for integrated multiscale research that bridges material science, process optimization, and real-world DAC deployment. A synthesis of findings and a research outlook are provided to support the advancement of carbon-negative technologies using thermochemically derived biomass chars. Full article

Background/Objectives: Traumatic brain injury (TBI), especially mild TBI (mTBI), is frequently caused by traffic accidents, falls, or sports injuries. Although computed tomography (CT) is the gold standard for diagnosis, overuse can lead to unnecessary radiation exposure, increased healthcare costs, and emergency department saturation. Blood-based biomarkers have emerged as potential tools to optimize CT scan use. This systematic review aims to evaluate recent evidence on the role of specific blood biomarkers in guiding CT decisions in patients with mTBI. Methods: A systematic search was conducted in the PubMed, Cochrane, and CINAHL databases for studies published between 2020 and 2024. Inclusion criteria focused on adult patients with mTBI evaluated using both CT imaging and at least one of the following biomarkers: glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and S100 calcium-binding protein B (S100B). After screening, six studies were included in the final review. Results: All included studies reported high sensitivity and negative predictive value for the selected biomarkers in detecting clinically relevant intracranial lesions. GFAP and UCH-L1, particularly in combination, consistently identified low-risk patients who could potentially forgo CT scans. While S100B also showed high sensitivity, discrepancies in cutoff values across studies highlighted the need for harmonization. Conclusions: Blood biomarkers such as GFAP, UCH-L1, and S100B demonstrate strong potential to reduce unnecessary CT imaging in mTBI by identifying patients at low risk of significant brain injury. Future research should focus on standardizing biomarker thresholds and validating protocols to support their integration into clinical practice guidelines. Full article

Cultured meat is emerging as a sustainable alternative to conventional animal agriculture, with scaffolds playing a central role in supporting cellular attachment, growth, and tissue maturation. This review focuses on the development of gel-based hybrid biomaterials that meet the dual requirements of biocompatibility and food safety. We explore recent advances in the use of naturally derived gel-forming polymers such as gelatin, chitosan, cellulose, alginate, and plant-based proteins as the structural backbone for edible scaffolds. Particular attention is given to the integration of food-grade functional additives into hydrogel-based scaffolds. These include nanocellulose, dietary fibers, modified starches, polyphenols, and enzymatic crosslinkers such as transglutaminase, which enhance mechanical stability, rheological properties, and cell-guidance capabilities. Rather than focusing on fabrication methods or individual case studies, this review emphasizes the material-centric design strategies for building scalable, printable, and digestible gel scaffolds suitable for cultured meat production. By systemically evaluating the role of each component in structural reinforcement and biological interaction, this work provides a comprehensive frame work for designing next-generation edible scaffold systems. Nonetheless, the field continues to face challenges, including structural optimization, regulatory validation, and scale-up, which are critical for future implementation. Ultimately, hybrid gel-based scaffolds are positioned as a foundational technology for advancing the functionality, manufacturability, and consumer readiness of cultured meat products, distinguishing this work from previous reviews. Unlike previous reviews that have focused primarily on fabrication techniques or tissue engineering applications, this review provides a uniquely food-centric perspective by systematically evaluating the compositional design of hybrid hydrogel-based scaffolds with edibility, scalability, and consumer acceptance in mind. Through a comparative analysis of food-safe additives and naturally derived biopolymers, this review establishes a framework that bridges biomaterials science and food engineering to advance the practical realization of cultured meat products. Full article

The cornea, the transparent anterior window of the eye, critically refracts light and protects intraocular structures. Corneal pathologies, including trauma, infection, chemical injury, metabolic diseases, genetic conditions, and age-related degeneration, can lead to significant visual impairment. While penetrating keratoplasty or full-thickness corneal transplantation remains a standard and effective intervention for severe corneal dysfunction, limitations in donor tissue availability and the risk of immunogenic graft rejection necessitate alternative therapeutic strategies. Furthermore, for cases of isolated epithelial disfunction, a full-thickness cornea graft may not be required or effective. This review examines the potential of corneal epithelial constructs derived from autologous stem cells with functional barrier properties for corneal reconstruction and in vitro pharmacotoxicity testing. In this review, we delineate the current limitations of corneal transplantation, the advantages of stem cell-based approaches, and recent advances in generating engineered corneal epithelium. Finally, we address remaining technical challenges and propose future research directions aimed at clinical translation. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

The high-proportion integration of renewable energy sources, represented by wind power and photovoltaics, into active distribution networks (ADNs) can effectively alleviate the pressure associated with advancing China’s dual-carbon goals. However, the high uncertainty in renewable energy output leads to increased system voltage fluctuations and localized voltage violations, posing safety challenges. Consequently, research on optimal dispatch for ADNs with a high penetration of renewable energy has become a current focal point. This paper provides a comprehensive review of research in this domain over the past decade. Initially, it analyzes the voltage impact patterns and control principles in distribution networks under varying levels of renewable energy penetration. Subsequently, it introduces optimization dispatch models for ADNs that focus on three key objectives: safety, economy, and low carbon emissions. Furthermore, addressing the challenge of solving non-convex and nonlinear models, the paper highlights model reformulation strategies such as semidefinite relaxation, second-order cone relaxation, and convex inner approximation methods, along with summarizing relevant intelligent solution algorithms. Additionally, in response to the high uncertainty of renewable energy output, it reviews stochastic optimization dispatch strategies for ADNs, encompassing single-stage, two-stage, and multi-stage approaches. Meanwhile, given the promising prospects of large-scale deep reinforcement learning models in the power sector, their applications in ADN optimization dispatch are also reviewed. Finally, the paper outlines potential future research directions for ADN optimization dispatch. Full article

The development of gas sensors with high sensitivity and low operating temperatures is essential for practical applications in environmental monitoring and industrial safety. SnO₂-based gas sensors, despite their widespread use, often suffer from high working temperatures and limited sensitivity to H₂ gas, which presents significant challenges for their performance and application. This study addresses these issues by introducing a novel SnO₂-based sensor featuring a three-dimensional (3D) nanostructure, designed to enhance sensitivity and allow for room-temperature operation. This work lies in the use of a 3D anodic aluminum oxide (AAO) template to deposit SnO₂ nanoparticles through ultrasonic spray pyrolysis, followed by modification with platinum (Pt) nanoparticles to further enhance the sensor’s response. The as-prepared sensors were extensively characterized, and their H₂ sensing performance was evaluated. The results show that the 3D nanostructure provides a uniform and dense distribution of SnO₂ nanoparticles, which significantly improves the sensor’s sensitivity and repeatability, especially in H₂ detection at room temperature. This work demonstrates the potential of utilizing 3D nanostructures to overcome the traditional limitations of SnO₂-based sensors. Full article

For mineral exploration, an increasing number of geophysical instruments have adopted unmanned aerial vehicles (UAVs) as their carrier platforms. The effective fusion of multi-modal geophysical information will be conducive to further enhancing the reliability of exploration results. However, the integration degree of UAVs and geophysical equipment is still low, and the advantages of UAVs as robots have not been fully exploited. In addition, the existing fusion methods are still difficult to use to establish the spatial distribution model of ore-bearing rock. Therefore, we reviewed the development status of UAVs and the geophysical instruments. We believe that only by integrating the system, designing the observation plan in accordance with the requirements of the fusion method, and treating the hardware part as an external extension of the algorithm, can high-matching data be provided for fusion. Subsequently, we analyzed the progress of the fusion methods, leading us to believe that the cross-dimensional and cross-abstract-level issues are major challenges in the algorithm aspect. Meanwhile, the fusion should be carried out simultaneously with the generation of the ore-bearing rock model, that is, to establish an integrated system of fusion and generation. It is hoped that this research can promote the development of UAV-borne multi-modal observation technology. Full article

Background/Objectives: Growth prediction may be used by the clinical orthodontist in growing individuals for diagnostic purposes and for treatment planning. This process appraises chronological age and determines the degree of skeletal maturity to calculate residual growth. In developmental deviations, overlooking such diagnostic details might culminate in erroneous conclusions, unstable outcomes, recurrence, and treatment failure. The present review aims to systematically present and explain the available means for predicting growth in humans. Traditional, long-known, popular methods are discussed, and modern digital applications are described. Materials and methods: A search on PubMed and the gray literature up to May 2025 produced 69 eligible studies on future maxillofacial growth prediction without any orthodontic intervention. Results: Substantial variability exists in the studies on growth prediction. In young orthodontic patients, the study of the lateral cephalometric radiography and the subsequent calculation of planes and angles remain questionable for diagnosis and treatment planning. Skeletal age assessment is readily accomplished with X-rays of the cervical vertebrae and the hand–wrist region. Computer software is being implemented to improve the reliability of classic methodologies. Metal implants have been used in seminal growth studies. Biochemical methods and electromyography have been suggested for clinical prediction and for research purposes. Conclusions: In young patients, it would be of importance to reach conclusions on future growth with minimal distress to the individual and, also, reduced exposure to ionizing radiation. Nevertheless, the potential for comprehensive prediction is still largely lacking. It could be accomplished in the future by combining established methods with digital technology. Full article

Climate change is generally having a negative impact on forest health by inducing drought stress and favouring the spread of pest species, such as bark beetles. The terrestrial monitoring of bark beetle infestation is very time-consuming, especially in the early stages, and therefore not feasible for extensive areas, emphasising the need for a comprehensive approach based on remote sensing. Although numerous studies have researched the use of optical data for this task, radar data remains comparatively underexplored. Therefore, this study uses the weekly and cloud-free acquisitions of Sentinel-1 in the Bavarian Forest National Park. Time series analysis within a Multi-SAR framework using Random Forest enables the monitoring of moisture content loss and, consequently, the assessment of tree vitality, which is crucial for the detection of stress conditions conducive to bark beetle outbreaks. High accuracies are achieved in predicting future bark beetle infestation (R² of 0.83–0.89). These results demonstrate that forest vitality trends ranging from healthy to bark beetle-affected states can be mapped, supporting early intervention strategies. The standard deviation of 0.44 to 0.76 years indicates that the model deviates on average by half a year, mainly due to the uncertainty in the reference data. This temporal uncertainty is acceptable, as half a year provides a sufficient window to identify stressed forest areas and implement targeted management actions before bark beetle damage occurs. The successful application of this technique to extensive test sites in the state of North Rhine-Westphalia proves its transferability. For the first time, the results clearly demonstrate the expected relationship between radar backscatter expressed in the Kennaugh elements K₀ and K₁ and bark beetle infestation, thereby providing an opportunity for the continuous and cost-effective monitoring of forest health from space. Full article