Search Results (9,014)

Plant variety/genotype identification has many applications in establishing the identity of plants, including the protection of intellectual property rights and the management of ex situ conservation of genetic resources. The variety may be important for operational reasons based on field performance or post-harvest processing. Blueberry (Vaccinium corymbosum L.), an economically important crop, is propagated by cuttings and commercially important accessions require an accurate variety traceability regime for the maintenance of purity, protection and policing ownership. Genome sequencing methods have improved and are feasible for use, making examination of the whole genome for all possible information on the genotype the ultimate way to distinguish plant varieties. We identified 5.3–5.5 million high-confidence homozygous SNPs with over 99% accuracy, enabling the distinction of 41 blueberry varieties. We developed a novel data-noise identification and filtering framework, which correctly determined the identity of ten unknown samples to be the Masena variety with 100% accuracy. The approach of using a data-noise filtration step minimized the impact of sequencing errors and coincident sequencing of only one allele of any heterozygous base. This SNP-based protocol with the establishment of sequence databases for all varieties of important plant species can potentially be adopted in providing reliable variety identification in critical industrial or legal applications. Full article

Cooperative path planning is recognized as a critical technology for Autonomous Underwater Vehicle (AUV) clusters to execute complex marine operations. Through multi-AUV cooperative decision-making, perception limitations of individual robots can be mitigated, thereby significantly enhancing the efficiency of tasks such as deep-sea resource exploration and submarine infrastructure maintenance. However, the underwater environment is characterized by severe disturbances and limited communication, making cooperative path planning for AUV clusters particularly challenging. Currently, this field is still in its early research stage, and there exists an urgent need for the integration of scattered technical achievements to provide theoretical references and directional guidance for relevant researchers. Based on representative studies published in recent years, this paper provides a review of the research progress in three major technical domains: heuristic optimization, reinforcement and deep learning, and graph neural networks integrated with distributed control. The advantages and limitations of different technical approaches are elucidated. In addition to cooperative path planning algorithms, the evolutionary logic and applicable scenarios of each technical school are analyzed. Furthermore, the lack of realism in algorithm training environments has been recognized as a major bottleneck in cooperative path planning for AUV clusters, which significantly limits the transferability of algorithms from simulation-based validation to real-sea applications. This paper aims to comprehensively outline the current research status and development context of the field of AUV cluster cooperative path planning and propose potential future research directions. Full article

African swine fever (ASF), induced by the African swine fever virus (ASFV), is an acute hemorrhagic disease characterized by high fever, systemic hemorrhages, and elevated mortality. Current diagnostic techniques including PCR and ELISA present limitations in field applications due to requirements for specialized equipment and prolonged processing duration. Therefore, rapid and accurate detection of ASFV has become a key link in ASF prevention and control. This study established a rapid and precise visual diagnostic approach by integrating the CRISPR/AapCas12b system with lateral flow strip (LFS) technology, specifically targeting the B646L gene encoding the major capsid protein p72. The CRISPR/AapCas12b-LFS platform achieved a sensitivity threshold of 6 copies/µL for B646L gene detection, completing analysis within an hour. Validation study confirmed exceptional specificity against common porcine pathogens including PRRSV, CSFV, PRV, PPV4, and PCV3. The developed assay demonstrated complete concordance with real-time PCR results when analyzing 34 clinical specimens including three heart samples, three liver samples, three spleen samples, three lung samples, three kidney samples, three lymph node samples, five serum samples, five blood samples, and five oral swab samples for ASFV detection. Overall, this method is sensitive, specific, and practicable onsite for ASFV detection, showing a great application potential for monitoring ASFV in the field. Full article

Within the field of assisted reproductive technologies (ARTs), embryologists regularly face the critical task of identifying embryos with the highest likelihood of implantation and survival. To help aid and standardize this practice, many embryo selection strategies have been developed to give the best chance of pregnancy success. Over the years, there has been a large increase in experimental studies conducted within this area of research. This increase has allowed for the formation of significant and plausible theories of embryo development, especially in cases where the most prominent factors seem identical. These advancements have both expanded the typical process of traditional treatments and have even paved the way for new techniques. The exact combination of all these relevant factors has not been fully elucidated into a single all-encompassing scheme for embryo decision. Morphological, genetic, and developmental indicators are well-studied individually, but the exact methods that should be prioritized in each scenario may change with respect to an individual patient. Deciding whether factors like age, egg quality, lifestyle choices, or previous medical history should alter methods of embryo ranking can result in conflict, especially in the case where a choice is being made between two similar embryos. This article reviews the conventional methods along with emerging technologies that provide the tools for embryologists to evaluate and rank embryos with high implantation potential (HIP). By showcasing these methods, including their respective benefits and drawbacks, this article provides information to allow clinicians to make effective decisions by integrating multiple approaches to embryo selection. Full article

Model Predictive Control (MPC) predicts the vehicle’s motion within a fixed time window, known as the prediction horizon, and calculates potential collision risks with obstacles in advance. It then determines the optimal steering input to guide the vehicle safely around obstacles. For example, when a sudden obstacle appears, sensors detect it, and MPC uses the vehicle’s current speed, position, and heading to predict its driving trajectory over the next few hundred milliseconds to several seconds. If a collision is predicted, MPC computes the optimal steering path among possible avoidance trajectories that are feasible within the vehicle’s dynamics. The vehicle then follows this input to steer away from the obstacle. In the proposed method, MPC is combined with Adaptive Artificial Potential Field (APF). The APF dynamically adjusts the repulsive force based on the distance and relative speed to the obstacle. MPC predicts the optimal driving path and generates control inputs, while the avoidance vector from APF is integrated into MPC’s constraints or cost function. Simulation results demonstrate that the proposed method significantly improves obstacle avoidance response, steering smoothness, and path stability compared to the baseline MPC approach. Full article

Iron-based catalysts for peroxymonosulfate (PMS) and peroxydisulfate (PDS) activation represent a cornerstone of advanced oxidation processes (AOPs) in environmental remediation, prized for their cost-effectiveness, environmental compatibility, and high catalytic potential. These catalysts, including zero-valent iron, iron oxides, and iron-organic frameworks, activate PMS/PDS through heterogeneous and homogeneous pathways to generate reactive species such as sulfate radicals (SO₄•⁻) and hydroxyl radicals (•OH). However, their large-scale implementation is constrained by inefficient iron cycling, characterized by sluggish Fe³⁺/Fe²⁺ conversion and significant iron precipitation, leading to catalyst passivation and oxidant wastage. This comprehensive review systematically dissects innovative strategies to augment iron cycling efficiency, encompassing advanced material design through elemental doping, heterostructure construction, and defect engineering; system optimization via reductant incorporation, bimetallic synergy, and pH modulation; and external field assistance using light, electricity, or ultrasound. We present a mechanistic deep-dive into these approaches, emphasizing facilitated electron transfer, suppression of iron precipitation, and precise regulation of radical versus non-radical pathways. The performance in degrading persistent organic pollutants—including antibiotics, per- and polyfluoroalkyl substances (PFASs), and pesticides—in complex environmental matrices is critically evaluated. We further discuss practical challenges related to scalability, long-term stability, and secondary environmental risks. Finally, forward-looking directions are proposed, focusing on rational catalyst design, integration of sustainable processes, and scalable implementation, thereby providing a foundational framework for developing next-generation iron-persulfate catalytic systems. Full article

With the continuous increase in global energy consumption and the escalating severity of climate change, the development of high-performance thermal insulation materials is crucial for reducing energy waste and carbon emissions. In this work, a facile method was proposed to prepare thermal-insulating glass fiber wool/methyltrimethoxysilane aerogel (GFWA) composites through vacuum-assisted impregnation. The obtained results indicated that GFWA composites exhibited excellent thermal insulation and hydrophobic properties, with GFWA-30 containing 30 wt.% glass fiber wool having a thermal conductivity of 35.3 mW/m·K and a water contact angle of 125.8°. Additionally, the Young’s modulus of this composite was 21.2% higher than that of MTMS aerogel. In terms of thermal safety performance, compared to methyltrimethoxysilane aerogel, the GFWA-30 composite showed reductions of 21.6%, 18.8%, and 27.95% in peak heat release rate, total heat release, and gross calorific value, respectively. This study offers a simple and feasible approach to fabricating high-performance thermal insulation materials, which display huge potential for widespread application in the fields of building insulation and other fields with thermal insulation requirements. Full article

Organic field-effect transistors (OFETs) have emerged as a transformative platform for high-performance sensing technologies, yet their full potential can be realized only through coordinated performance optimization. This article provides a comprehensive review of recent strategies employed in polymer OFETs to enhance key parameters, including carrier mobility (μ), threshold voltage (Vth), on/off current ratio (I_on/I_off), and operational stability. These strategies encompass both physical and chemical approaches, such as annealing, self-assembled monolayers (SAMs), modification of main and side polymer chains, dielectric-layer engineering, buffer-layer insertion, and blending or doping techniques. The development of high-performance devices requires precise integration of physical processing and chemical design, alongside the anticipation of processing compatibility during the molecular design phase. This article further highlights the limitations of focusing solely on high mobility and advocates a balanced optimization across multiple dimensions—mobility, mechanical flexibility, environmental stability, and consistent functional performance. Adopting a multi-scale optimization framework spanning molecular, film, and device levels can substantially enhance the adaptability of OFETs for emerging applications such as flexible sensing, bioelectronic interfaces, and neuromorphic computing. Full article

Nowadays, the explosive growth of knowledge in the epigenetics field has highlighted DNA methyltransferase 1 (DNMT1) as a key regulator of genomic methylation patterns and a promising therapeutic target in several diseases. In light of the increasing clinical interest in epigenetic enzymes, the present study aimed to develop a robust computational framework for the discovery of novel DNMT1 inhibitors, merging both structure and data-driven strategies. Particularly, the study compiled a dataset of established DNMT1 inhibitors and calculated a series of molecular properties, thus enabling the training of a machine learning model to capture critical structure–activity relationships (SARs). When benchmarked against known active compounds, the model effectively discriminated between putative inhibitors and non-inhibitors with high accuracy. In parallel, molecular docking was conducted to screen additional uncharacterized compounds, estimating their binding affinity to human DNMT1. Their respective properties were then extracted and fed into the aforementioned model to predict their inhibitory potential. Our comparative evaluation against known human DNMT1 inhibitors demonstrated high predictive accuracy, confirming the reliability of the proposed integrated approach. By uniting molecular docking with data-driven SAR modelling, this workflow offers an expedited fast-track avenue for identifying promising human DNMT1 inhibitors while reducing experimental overhead. The results highlight the effectiveness of combining cheminformatics, machine learning, and in silico techniques to guide rational drug design, and accelerate the discovery of novel epigenetic inhibitors. Full article

The human skin microbiome, defined as a multifaceted ecosystem comprising bacteria, fungi, viruses, and mites, plays a pivotal role in maintaining skin homeostasis and regulating immune responses. In recent years, an increasing amount of evidence has illuminated the considerable influence exerted by microbiomes on the pathophysiology of dermatological ailments. This review provides a comprehensive synthesis of contemporary findings concerning the microbiome’s role in acne, aging, hyperpigmentation, and hair disorders, while also addressing the emerging concept of the gut–skin axis and how it could interfere in these skin disorders. Alterations in microbial composition, referred to as dysbiosis, have been associated with inflammatory processes and barrier dysfunction, thereby contributing to the severity and chronicity of diseases. Distinct microbial profiles have been identified as correlating with specific skin conditions. For instance, variations in Cutibacterium acnes phylotypes have been associated with the development of acne, whereas alterations in Corynebacterium and Staphylococcus species have been linked to the processes of aging and pigmentation patterns. Furthermore, the composition of the microbiome is examined in relation to its impact on cosmetic outcomes. It also engages with increasing interest in the modulation of microbiota through the topical application of bioactive compounds. The incorporation of prebiotics, probiotics, and postbiotics into cosmetic formulations constitutes a novel strategy aimed at enhancing skin health. In the domain of dermatological therapies, postbiotics have emerged as a significant class of substances, particularly due to their remarkable stability, safety, and immunomodulatory properties. These characteristics position them as promising candidates for incorporation into dermatological treatments. Recent studies have underscored the significance of microbiome-informed strategies within the domains of therapeutic and preventive dermatology, emphasizing the potential of such approaches to positively influence patient outcomes. As our understanding of this field continues to evolve, skin microbiomes are poised to emerge as a pivotal area of focus in the realm of personalized skin care and treatment. This development presents novel and innovative approaches for the management of skin conditions, characterized by enhanced specificity and efficacy. Full article

RNA methylation, particularly N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), functions as a pivotal post-transcriptional regulatory mechanism and plays a central role in plant growth, development, and stress responses. This review provides a systematic summary of recent advances in RNA methylation research in cucurbit crops. To date, high-throughput technologies such as MeRIP-seq and nanopore direct RNA sequencing have enabled the preliminary construction of RNA methylation landscapes in cucurbit species, revealing their potential regulatory roles in key agronomic traits, including fruit development, responses to biotic and abiotic stresses, and disease resistance. Nevertheless, this field remains in its early stages for cucurbit crops and faces several major challenges: First, mechanistic understanding is still limited, with insufficient knowledge regarding the composition and biological functions of the core protein families involved in methylation dynamics—namely, “writers,” “erasers,” and “readers.” Second, functional validation remains inadequate, as direct evidence linking specific RNA methylation events to downstream gene regulation and phenotypic outcomes is largely lacking. Third, resources are scarce; compared to model species such as Arabidopsis thaliana and rice, cucurbit crops possess limited species-specific genetic data and genetic engineering tools (e.g., CRISPR/Cas9-based gene editing systems), which significantly hampers comprehensive functional studies. To overcome these limitations, future research should prioritize the development and application of more sensitive detection methods, integrate multi-omics datasets—including transcriptomic and methylomic profiles—to reconstruct regulatory networks, and conduct rigorous functional assays to establish causal relationships between RNA methylation modifications and phenotypic variation. The ultimate objective is to fully elucidate the biological significance of RNA methylation in cucurbit plants and harness its potential for crop improvement through genetic and biotechnological approaches. Full article

The mechanical performance of cast iron is strongly governed by the morphology of its graphite phase, yet establishing a quantitative link between microstructure and macroscopic properties remains a challenge. In this study, a three-dimensional finite element method (FEM)-based micromechanical framework is proposed to analyze and predict the mechanical behavior of cast iron with representative graphite morphologies, spheroidal and flake graphite. Realistic representative volume elements (RVEs) are reconstructed based on experimental microstructural characterization and literature-based X-ray computed tomography data, ensuring geometric fidelity and statistical representativeness. Cohesive zone modeling (CZM) is implemented at the graphite/matrix interface and within the graphite phase to simulate interfacial debonding and brittle fracture, respectively. Full-field simulations of plastic strain and stress evolution under uniaxial tensile loading reveal that spheroidal graphite promotes uniform deformation, delayed damage initiation, and enhanced ductility through effective stress distribution and progressive plastic flow. In contrast, flake graphite induces severe stress concentration at sharp tips, leading to early microcrack nucleation and rapid crack propagation along the flake planes, resulting in brittle-like failure. The simulated stress–strain responses and failure modes are consistent with experimental observations, validating the predictive capability of the model. This work establishes a microstructure–property relationship in multiphase alloys through a physics-informed computational approach, demonstrating the potential of FEM-based modeling as a powerful tool for performance prediction and microstructure-guided design of cast iron and other heterogeneous materials. Full article

Smart Electromagnetic Skins (SESs) provide a cost-effective and efficient alternative to increasing the number of Base Stations (BSs) for improving the performance of next-generation communication networks and contribute to the implementation of Smart Radio Environments (SREs). SESs generalize the concept of ReflectArrays (RAs) because they redirect the incident field in a non-specular direction. However, as the difference between the pointing and specular directions increases, specular and parasitic effects arise, which affect the radiation pattern, energy efficiency, and pointing direction. The techniques generally adopted for SES design, using homogenized-effective-medium model, are unable to overcome this drawback efficiently. Starting with initial SES design based on the Phase-Gradient (PG) approach, the suppression of the higher order modes has been achieved by incorporating volumetric charge-current distributions when defining radiation modes, using theory of electromagnetic-multipoles. This approach reveals formation of anapoles in single-layer SESs/RAs for first time ever. By combining both local and non-local approaches in super-cell design, higher-order symmetry-breaking of unit cells is utilized to exploit anapole formation as a parasitic mode suppression method. Numerical analysis of SESs with increasing size confirms the effectiveness of the proposed approach, which allows for a drastic reduction in parasitic modes while leaving the performance of the desired mode unchanged. Adopting a multipole perspective enhances the understanding of SES radiation mechanisms, unlocks their unexploited performance potential, and opens new opportunities for multifunctional design. Full article

We study stochastic variants of the Kairat-II and Kairat-X equations in (3 + 1) dimensions, two canonical models in soliton theory. Random fluctuations are incorporated through a Wiener process, yielding a multiplicative stochastic embedding of the wave fields. By combining the enhanced direct algebraic technique with the new projective Riccati equation approach, we obtain closed-form stochastic soliton solutions and analyze how noise modulates their amplitude and localization. The solutions are illustrated with consistent 3D surface plots (mean field vs. sample paths) and 2D time traces to highlight wave geometry and variability. In addition, we employ the energy balance approach to separate kinetic and potential contributions and to verify an energy balance relation for the derived solutions, thereby clarifying their physical plausibility and stability under noise. The results provide exact, easily verifiable benchmarks for stochastic nonlinear wave models and a practical template for incorporating randomness into nonlinear dispersive systems. Full article

Forest height is a critical parameter for understanding ecosystem functions, assessing carbon stocks, and supporting sustainable forest management. Its accurate measurement is essential for climate change mitigation and understanding the global carbon cycle. While traditional methods like field surveys and airborne LiDAR provide accurate measurements, their high costs and limited spatial coverage make them impractical for the large-scale, dynamic monitoring required for effective sustainability initiatives. This research presents a multi-source remote sensing fusion approach to tackle this problem. For regional forest height inversion, it includes Sentinel-1 SAR, Sentinel-2 multispectral images, ICESat-2 lidar, and SRTM DEM data. Sentinel-1 + ICESat-2 + SRTM, Sentinel-2 + ICESat-2 + SRTM, and Sentinel-1 + Sentinel-2 + ICESat-2 + SRTM were the three data combination methods built using Shangri-La Second-class Category Resource Survey data as ground truth. An accuracy assessment was performed using three machine learning models: Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Based on the results, the ideal configuration using the LightGBM model and the following sensors: Sentinel-1, Sentinel-2, ICESat-2, and SRTM yields a correlation coefficient of 0.72, an RMSE of 5.52 m, and an MAE of 4.08 m. The XGBoost model obtained r = 0.716, RMSE = 5.55 m, and MAE = 4.10 m using the same data combination as the Random Forest model, which produced r = 0.706, RMSE = 5.63 m, and MAE = 4.16 m. The multi-source comprehensive fusion technique produced the greatest results; however, including either Sentinel-1 or Sentinel-2 enhances model performance, according to comparisons across multiple data combinations. This work presents an efficient technological strategy for monitoring forest height in complex terrains, thereby providing a scalable and robust methodological reference for supporting sustainable forest management and large-scale ecological assessment. The proposed multi-source spatiotemporal fusion framework, coupled with systematic model evaluation, demonstrates significant potential for operational applications, especially in regions with limited LiDAR coverage. Full article