Search Results (1,454)

Brassica juncea var. multiceps (Xuelihong), a variety of B. juncea (L.) Czern., holds considerable nutritional and economic value. However, its complete chloroplast genome and the evolutionary relationships within Brassicaceae remain poorly characterized. Using Illumina NovaSeq 6000 high-throughput sequencing, we assembled and annotated the full chloroplast genome sequence of B. juncea var. multiceps. The genome is 153,483 bp in length, with 36.36% GC content, and encodes 132 genes. Codon usage analysis identified leucine (Leu) as the dominant amino acid. Thirty-one codons had relative synonymous codon usage (RSCU; a metric for codon preference) values greater than one, with 93.55% of these preferred codons ending in A/U. We detected 37 dispersed repeats (14 forward, 18 palindromic, 3 reverse, and 2 complementary) and 315 simple sequence repeats (SSRs), with mononucleotide SSRs dominating (72.70%). Analysis of the Ka/Ks ratio, a measure of selection pressure (where values greater than one indicate positive selection), indicated that ycf1, ycf2, and nadhF genes may have undergone positive selection. The nucleotide diversity analysis revealed five hypervariable hotspot-genomic regions with high mutation rates, which are critical for phylogenetic studies. Phylogenetic analysis of 27 Brassicaceae species revealed that B. juncea var. multiceps is closely related to B. juncea. Notably, this is the first complete chloroplast genome of B. juncea var. multiceps, with unique hypervariable regions not reported in other B. juncea varieties. These findings clarify evolutionary relationships in Brassicaceae, provide molecular markers for the genetic breeding of B. juncea var. multiceps, and enhance our understanding of chloroplast genome adaptation in Brassica. Full article

Vehicular Ad-Hoc Networks (VANETs) play a critical role in Intelligent Transportation Systems (ITS), enabling communication between vehicles and roadside infrastructure. This paper proposes an Adaptive Context-Aware VANET Routing (ACAVR) protocol designed to handle the challenges of high mobility, dynamic topology, and variable vehicle density in urban environments. The proposed protocol integrates context-aware routing, dynamic clustering, and geographic forwarding to enhance performance under diverse traffic conditions. Simulation results demonstrate that ACAVR achieves higher throughput, improved packet delivery ratio, lower end-to-end delay, and reduced routing overhead compared to existing routing schemes. The proposed ACAVR outperforms benchmark protocols such as DyTE, RGoV, and CAEL, improving PDR by 12–18%, reducing delay by 10–15%, and increasing throughput by 15–22%. Full article

The accurate forecasting of surface air temperature (T2M) is crucial for climate analysis, agricultural planning, and energy management. This study proposes a novel forecasting framework grounded in structured temporal decomposition. Using the Kolmogorov–Zurbenko (KZ) filter, all predictor variables are decomposed into three physically interpretable components: long-term, seasonal, and short-term variations, forming an expanded multi-scale feature space. A central innovation of this framework lies in training a single unified model on the decomposed feature set to predict the original target variable, thereby enabling the direct learning of scale-specific driver–response relationships. We present the first comprehensive benchmarking of this architecture, demonstrating that it consistently enhances the performance of both regularized linear models (Ridge and Lasso) and tree-based ensemble methods (Random Forest and XGBoost). Under rigorous walk-forward validation, the framework substantially outperforms conventional, non-decomposed approaches—for example, XGBoost improves the coefficient of determination ($R^2$) from 0.80 to 0.91. Furthermore, temporal decomposition enhances interpretability by enabling Ridge and Lasso models to achieve performance levels comparable to complex ensembles. Despite these promising results, we acknowledge several limitations: the analysis is restricted to a single geographic location and time span, and short-term components remain challenging to predict due to their stochastic nature and the weaker relevance of predictors. Additionally, the framework’s effectiveness may depend on the optimal selection of KZ parameters and the availability of sufficiently long historical datasets for stable walk-forward validation. Future research could extend this approach to multiple geographic regions, longer time series, adaptive KZ tuning, and specialized short-term modeling strategies. Overall, the proposed framework demonstrates that temporal decomposition of predictors offers a powerful inductive bias, establishing a robust and interpretable paradigm for surface air temperature forecasting. Full article

Secure authentication in vehicular ad hoc networks (VANETs) remains a fundamental challenge due to their dynamic topology, susceptibility to attacks, and scalability constraints in multi-hop communication. Existing approaches based on elliptic curve cryptography (ECC), blockchain, and fog computing have achieved partial success but suffer from latency, resource overhead, and limited adaptability, leaving a gap for lightweight and hardware-rooted trust models. To address this, we propose a multi-hop mutual authentication protocol leveraging Physical Unclonable Functions (PUFs), which provide tamper-evident, device-specific responses for cryptographic key generation. Our design introduces a structured sequence of phases, including pre-deployment, registration, login, authentication, key establishment, and session maintenance, with optional multi-hop extension through relay vehicles. Unlike prior schemes, our protocol integrates fuzzy extractors for error tolerance, employs both inductive and game-based proofs for security guarantees, and maps BAN-logic reasoning to specific attack resistances, ensuring robustness against replay, impersonation, and man-in-the-middle attacks. The protocol achieves mutual trust between vehicles and RSUs while preserving anonymity via temporary identifiers and achieving forward secrecy through non-reused CRPs. Conceptual comparison with state-of-the-art PUF-based and non-PUF schemes highlights the potential for reduced latency, lower communication overhead, and improved scalability via cloud-assisted CRP lifecycle management, while pointing to the need for future empirical validation through simulation and prototyping. This work not only provides a secure and efficient solution for VANET authentication but also advances the field by offering the first integrated taxonomy-driven evaluation of PUF-enabled V2X protocols in multi-hop Wi-Fi environments. Full article

Background/Objectives: Dupuytren’s contracture is a chronic fibroproliferative disorder of the palmar fascia that leads to progressive flexion deformities and functional impairment. The Southampton Dupuytren’s Scoring Scheme (SDSS) is a disease-specific patient-reported outcome measure designed to quantify disability in this condition. This study aimed to translate, culturally adapt, and evaluate the psychometric properties of the Serbian version of the SDSS. Methods: A cross-sectional study was conducted at the Institute for Orthopedic Surgery “Banjica”, Belgrade, from January 2024 to March 2025. Sixty-eight patients with Dupuytren’s contracture completed the Serbian SDSS, the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire, the 12-Item Short Form Health Survey (SF-12), and a Visual Analogue Scale (VAS) for pain. Translation followed standardized forward–backward procedures. Internal consistency was assessed with Cronbach’s alpha, construct validity with confirmatory factor analysis (CFA), and convergent validity with Pearson’s correlation coefficients. Results: The Serbian SDSS demonstrated excellent internal consistency (Cronbach’s α = 0.914). CFA supported a unidimensional five-item structure with strong factor loadings (0.76–0.93) and acceptable fit indices (χ² = 10.094, df = 5, p = 0.073; IFI = 0.979; CFI = 0.978; TLI = 0.956). Convergent validity was confirmed by strong correlations with DASH (r = 0.779) and VAS (r = 0.702) and a strong negative correlation with SF-12 PCS (r = −0.802). Conclusions: The Serbian SDSS is a valid and reliable instrument for assessing functional disability in patients with Dupuytren’s contracture and offers a robust, patient-centered measure for clinical and research use. Full article

In practical Synthetic Aperture Radar (SAR) applications, new-class objects can appear at any time as the rapid accumulation of large-scale and high-quantity SAR imagery and are usually supported by limited instances in most cooperative scenarios. Hence, powering advanced deep-learning (DL)-based SAR Automatic Target Recognition (SAR ATR) systems with the ability to continuously learn new concepts from few-shot samples without forgetting the old ones is important. In this paper, we tackle the Few-Shot Class-Incremental Learning (FSCIL) problem in the SAR ATR field and propose a Forward-Compatible Prototype Classifier (FCPC) by emphasizing the model’s forward compatibility to incoming targets before and after deployment. Specifically, the classifier’s sensitivity to diversified cues of emerging targets is improved in advance by a Virtual-class Semantic Synthesizer (VSS), considering the class-agnostic scattering parts of targets in SAR imagery and semantic patterns of the DL paradigm. After deploying the classifier in dynamic worlds, since novel target patterns from few-shot samples are highly biased and unstable, the model’s representability to general patterns and its adaptability to class-discriminative ones are balanced by a Decoupled Margin Adaptation (DMA) strategy, in which only the model’s high-level semantic parameters are timely tuned by improving the similarity of few-shot boundary samples to class prototypes and the dissimilarity to interclass ones. For inference, a Nearest-Class-Mean (NCM) classifier is adopted for prediction by comparing the semantics of unknown targets with prototypes of all classes based on the cosine criterion. In experiments, contributions of the proposed modules are verified by ablation studies, and our method achieves considerable performance on three FSCIL of SAR ATR datasets, i.e., SAR-AIRcraft-FSCIL, MSTAR-FSCIL, and FUSAR-FSCIL, compared with numerous benchmarks, demonstrating its superiority and effectiveness in dealing with the FSCIL of SAR ATR. Full article

Vision navigation systems provide significant advantages in agricultural scenarios such as pesticide spraying, weeding, and harvesting by interpreting crop row structures in real-time to establish guidance lines. However, the delay introduced by image processing causes the path and pose information relied upon by the controller to lag behind the actual vehicle state. In this study, a hierarchical delay-compensated cooperative control framework (HDC-CC) was designed to synergize Model Predictive Control (MPC) and Sliding Mode Control (SMC), combining predictive optimization with robust stability enforcement for agricultural navigation. An upper-layer MPC module incorporated a novel delay state observer that compensated for visual latency by forward-predicting vehicle states using a 3-DoF dynamics model, generating optimized front-wheel steering angles under actuator constraints. Concurrently, a lower-layer SMC module ensured dynamic stability by computing additional yaw moments via adaptive sliding surfaces, with torque distribution optimized through quadratic programming. Under varying adhesion conditions tests demonstrated error reductions of 74.72% on high-adhesion road and 56.19% on low-adhesion surfaces. In Gazebo simulations of unstructured farmland environments, the proposed framework achieved an average path tracking error of only 0.091 m. The approach effectively overcame vision-controller mismatches through predictive compensation and hierarchical coordination, providing a robust solution for vision autonomous agricultural machinery navigation in various row-crop operations. Full article

Aqueous zinc-ion batteries (ZIBs) have attracted growing interest as promising candidates for large-scale and flexible energy storage due to their intrinsic safety, low cost, and environmental sustainability. However, several persistent issues—such as uncontrolled Zn dendrite growth, hydrogen evolution-induced anode corrosion, and cathode dissolution—continue to hinder their commercial deployment. To address these challenges, hydrogel polymer electrolytes (HPEs) have emerged as an effective strategy. Their unique three-dimensional polymer networks not only retain water and confine ion transport, but also provide a solid–liquid hybrid environment that enhances ionic conductivity and interfacial compatibility. These features enable HPEs to suppress side reactions and improve both electrochemical stability and mechanical adaptability, which are especially valuable for flexible ZIB devices. This review first summarizes fundamental energy storage mechanisms in aqueous ZIBs, including reversible Zn²⁺ insertion/extraction, proton co-insertion, and cathode phase evolution. It then highlights recent progress in HPE design, with emphasis on polyacrylamide (PAM), polyvinyl alcohol (PVA), and polyacrylic acid (PAA)-based systems, with strategies for dendrite suppression, interfacial regulation, and mechanical robustness. Finally, current challenges and future directions are discussed, with a forward-looking perspective on scalable fabrication methods, advanced electrolyte design, and deeper mechanistic understanding necessary to fully realize the potential of HPE-enabled aqueous ZIBs. Full article

Renewable energy integration is no longer a solely technical endeavor; it necessitates a multidimensional transformation that spans technological, economic, environmental, social, and regulatory dimensions. This review presents a visual and strategic framework for addressing the complex challenges of integrating solar, wind, hydro, geothermal, and biomass energy systems. The objective is to redefine traditional approaches by linking specific integration barriers to tailored strategies and measurable outcomes. The study uses comparative analysis, regional case studies, and a variety of visual tools—such as flowcharts, spider charts, and challenge–strategy–outcome maps—to spatially express interdependencies and trade-offs. These tools enable stakeholders to determine the best integration pathways based on performance measures, regional restrictions, and system synergies. The results reveal that visual mapping not only clarifies complex system dynamics, but also enhances stakeholder collaboration by translating technical data into accessible formats. The framework supports adaptive planning, smart grid adoption, and community-centered microgrid development. In conclusion, the study provides a forward-looking strategy for developing resilient, inclusive, and intelligent renewable energy systems. It highlights that future energy resilience will be built on integrated, regionally informed, and socially inclusive design, with technology, policy, and community engagement combined to drive sustainable energy transitions. Full article

Antibodies are a cornerstone of the adaptive immune response, serving as key defenders against viral infections; however, they can also act as a double-edged sword, contributing to immune-mediated pathologies. This review advances a “Yin-Yang” framework to integrate the dual activities of antibodies. The protective ‘Yin’ functions are driven by high-affinity antibodies generated through processes like somatic hypermutation and class-switch recombination. These antibodies execute viral neutralization, activate the complement system, and engage Fc receptors (FcRs) to drive antibody-dependent cellular cytotoxicity (ADCC) and phagocytosis. These mechanisms form the immunological basis of effective vaccines, which aim to elicit durable and functionally specialized antibody isotypes like IgG and mucosal IgA. Conversely, the pathogenic ‘Yang’ of the response can be detrimental. This includes antibody-dependent enhancement (ADE) of infection, notably observed with flaviviruses, and the development of autoimmunity through mechanisms like molecular mimicry and bystander activation, which can lead to conditions such as multiple sclerosis and Guillain-Barré Syndrome. The balance between protection and pathology is tipped by a confluence of factors. These include viral evasion strategies like antigenic mutation and glycan shielding, as well as host-based determinants such as genetic polymorphisms in FcRs, immune history, and the gut microbiome. Understanding these molecular determinants informs the rational design of next-generation interventions. Promising strategies, such as Fc-region glyco-engineering and the design of tolerogenic vaccines, aim to selectively promote protective functions while minimizing pathological risks, offering a clear path forward in combating viral threats. Full article

This review seeks to understand what urban planning and management can do to reduce disaster risk and help cities adapt to the impacts of climate change. To achieve this, it examines various streams of the literature, as the topic sits at the intersection of several distinct but relevant disciplinary fields. These include urban planning in hazardous areas, recovery planning, disaster risk reduction (an umbrella term encompassing disciplines from engineering to geography and sociology), and, more recently, climate change adaptation. To navigate this vast body of knowledge, a conceptual framework is proposed to guide the selection of the relevant literature, and the strategy for this selection is detailed in the methodological section. This review adopts elements of both critical and theoretical approaches: it does not aim to be comprehensive or to systematically search each disciplinary domain addressed. While acknowledging the limitations and potential biases in the selection of articles and books, the review reflects an evolution in the discourse on urban planning for resilience. The discussion explores how the concept of resilience has emerged as a valuable bridge between disaster risk reduction, sustainability, and climate change adaptation—especially as cities face increasing exposure and vulnerability to stresses that are now more frequently compounded, multi-hazard, and cascading. The conclusion outlines the gaps and challenges that researchers, practitioners, and policy makers need to address moving forward. Full article

Large language model (LLM)-based agents are rapidly emerging as transformative tools across biomedical research and clinical applications. By integrating reasoning, planning, memory, and tool use capabilities, these agents go beyond static language models to operate autonomously or collaboratively within complex healthcare settings. This review provides a comprehensive survey of biomedical LLM agents, spanning their core system architectures, enabling methodologies, and real-world use cases such as clinical decision making, biomedical research automation, and patient simulation. We further examine emerging benchmarks designed to evaluate agent performance under dynamic, interactive, and multimodal conditions. In addition, we systematically analyze key challenges, including hallucinations, interpretability, tool reliability, data bias, and regulatory gaps, and discuss corresponding mitigation strategies. Finally, we outline future directions in areas such as continual learning, federated adaptation, robust multi-agent coordination, and human AI collaboration. This review aims to establish a foundational understanding of biomedical LLM agents and provide a forward-looking roadmap for building trustworthy, reliable, and clinically deployable intelligent systems. Full article

The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James Webb Space Telescope (JWST) have made information about distant objects more easily accessible, resulting in extensive amounts of valuable data. As part of this work-in-progress study, we are working to create an atmospheric absorption spectrum prediction model for exoplanets. The eventual model will be based on both collected observational spectra and synthetic spectral data generated by the ROCKE-3D general circulation model (GCM) developed by the climate modeling program at NASA’s Goddard Institute for Space Studies (GISS). In this initial study, spline curves are used to describe the bin heights of simulated atmospheric absorption spectra as a function of one of the values of the planetary parameters. Bayesian Adaptive Exploration is then employed to identify areas of the planetary parameter space for which more data are needed to improve the model. The resulting system will be used as a forward model so that planetary parameters can be inferred given a planet’s atmospheric absorption spectrum. This work is expected to contribute to a better understanding of exoplanetary properties and general exoplanet climates and habitability. Full article

Liquid microlenses and their arrays (LMLAs) have emerged as a transformative platform in adaptive optics, offering superior reconfigurability, compactness, and fast response compared to conventional solid-state lenses. This review summarizes recent progress from an application-oriented perspective, focusing on actuation mechanisms, fabrication strategies, and functional performance. Among actuation mechanisms, electric-field-driven approaches are highlighted, including electrowetting for shape tuning and liquid crystal-based refractive-index tuning techniques. The former excels in tuning range and response speed, whereas the latter enables programmable wavefront control with lower optical aberrations but limited efficiency. Notably, double-emulsion configurations, with fast interfacial actuation and inherent structural stability, demonstrate great potential for highly integrated optical components. Fabrication methodologies—including semiconductor-derived processes, additive manufacturing, and dynamic molding—are evaluated, revealing trade-offs among scalability, structural complexity, and cost. Functionally, advances in focal length tuning, field-of-view expansion, depth-of-field extension, and aberration correction have been achieved, though strong coupling among these parameters still constrains system-level performance. Looking forward, innovations in functional materials, hybrid fabrication, and computational imaging are expected to mitigate these constraints. These developments will accelerate applications in microscopy, endoscopy, AR/VR displays, industrial inspection, and machine vision, while paving the way for intelligent photonic systems that integrate adaptive optics with machine learning for real-time control. Full article

Pultrusion is a manufacturing process used to produce fiber-reinforced polymer composites with excellent mechanical, thermal, and chemical properties. The resulting materials are lightweight, durable, and corrosion-resistant, making them valuable in aerospace, automotive, construction, and energy sectors. However, conventional thermoset composites remain difficult to recycle due to their infusible and insoluble cross-linked structure. This review explores integrating vitrimer technology a novel class of recyclable thermosets with dynamic covalent adaptive networks into the pultrusion process. As only limited studies have directly reported vitrimer pultrusion to date, this review provides a forward-looking perspective, highlighting fundamental principles, challenges, and opportunities that can guide future development of recyclable high-performance composites. Vitrimers combine the mechanical strength (tensile strength and modulus) of thermosets with the reprocessability and reshaping of thermoplastics through dynamic bond exchange mechanisms. These polymers offer high-temperature reprocessability, self-healing, and closed-loop recyclability, where recycling efficiency can be evaluated by the recovery yield retention of mechanical properties and reuse cycles meeting the demand for sustainable manufacturing. Key aspects discussed include resin formulation, fiber impregnation, curing cycles, and die design for vitrimer systems. The temperature-dependent bond exchange reactions present challenges in achieving optimal curing and strong fiber–matrix adhesion. Recent studies indicate that vitrimer-based composites can maintain structural integrity while enabling recycling and repair, with mechanical performance such as flexural and tensile strength comparable to conventional composites. Incorporating vitrimer materials into pultrusion could enable high-performance, lightweight products for a circular economy. The remaining challenges include optimizing curing kinetics, improving interfacial adhesion, and scaling production for widespread industrial adoption. Full article