Search Results (3,753)

As a key task in remote sensing analysis, semantic segmentation of remote sensing images (RSI) underpins many practical applications. Despite its importance, obtaining dense pixel-wise annotations remains labor-intensive and time-consuming. Unsupervised domain adaptation (UDA) offers a promising solution by utilizing knowledge from labeled source domains for unlabeled target domains, yet its effectiveness is often compromised by significant distribution shifts arising from variations in imaging conditions. To address this challenge, we propose a depth-aware adaptation network (DAAN), a novel two-branch network that explicitly leverages complementary depth information from a digital surface model (DSM) to enhance cross-domain remote sensing segmentation. Unlike conventional UDA methods that primarily focus on semantic features, DAAN incorporates depth data to build a more generalized feature space. This network introduces three key components: an adaptive feature aggregator (AFA) for progressive semantic-depth feature fusion, a gated prediction selection unit (GPSU) that selectively integrates predictions to mitigate the impact of noisy depth measurements, and misalignment-focused residual refinement (MFRR) module that emphasizes poorly aligned target regions during training. Experiments on the ISPRS and GAMUS datasets demonstrate the effectiveness of the proposed method. In particular, DAAN achieves an mIoU of 50.53% and an F1 score of 65.75% for cross-domain segmentation on ISPRS to GAMUS, outperforming models without depth information by 9.17% and 8.99%, respectively. These results demonstrate the advantage of integrating auxiliary geometric information to improve model generalization on unlabeled remote sensing datasets, contributing to higher mapping accuracy, more reliable automated analysis, and enhanced decision-making support. Full article

Microplastic pollution has emerged as a major environmental concern due to its persistence, widespread distribution and potential risks to ecosystems and human health. Among the various types of microplastics, fibrous microplastics (FMPs) account for 60% to 90% of all detected microplastic particles in surface waters, primarily originating from synthetic textile production, laundering, and wastewater discharge. Their elongated morphology, high aspect ratio, and complex surface chemistry differentiate them significantly from microplastic fragments or beads, creating unique challenges for effective removal in water treatment systems. In recent years, engineered biochar has attracted increasing attention as a promising and sustainable material for microplastic removal due to tunable pore structure, surface chemistry, and adsorption capacity. However, existing reviews largely discuss microplastic removal in general terms, with limited attention to the distinctive properties of textile FMPs and their implications for biochar design and performance. This review provides a comprehensive and focused analysis of the functional characteristics of biochar that enable the effective removal of textile FMPs in water systems. First, the environmental significance and physicochemical characteristics of textile-derived FMPs are summarized. Next, the major mechanisms governing biochar–microplastic interactions, including physical interception, adsorption, and aggregation processes, are discussed. The review then examines key functional characteristics of engineered biochar, such as pore structure, surface functional groups, hydrophobicity, and composite modifications, that enhance the sequestration of FMPs. Finally, current technological challenges, research gaps, and future directions for developing scalable biochar-based solutions for textile microplastic mitigation are discussed. By linking the unique properties of textile FMPs with the functional design of biochar, this review provides a framework to guide the development of more effective and sustainable treatment strategies for reducing microplastic contamination in aquatic environments. Full article

Production greenhouse complexes increasingly require automation and digitalization to address rising labor costs, improve productivity, and support sustainable resource use. However, most existing solutions target isolated tasks and lack a unified framework for continuous monitoring and production-oriented accounting at facility scale. This paper proposes a system-level architecture that integrates robotic monitoring platforms, AI-based perception, and cloud-based data management into a coherent operational framework. The robotic monitoring platforms operate on rails and concrete surfaces and are capable of elevating cameras and sensors up to 5 m to support plant-health assessment, environmental monitoring, and production accounting. Aggregated data are incorporated into a digital twin that supports spatial traceability, historical analysis, and decision support. The proposed approach enables continuous inspection, improves early detection of crop stress, reduces repetitive manual scouting, and supports targeted interventions. The framework provides a scalable foundation for sustainable, data-driven greenhouse management and practical deployment of robotic monitoring systems in industrial production environments. Full article

This paper investigates the formation mechanism and key influencing factors of freckle defects that arise during the directional solidification of a novel third-generation nickel-based single crystal superalloy turbine blade. A combined experimental and multi-physics numerical simulation approach was adopted. The results indicate that freckle formation primarily originates from solutal convection, which subsequently triggers a cascade of processes, including the development of convection-induced segregation channels, flow-driven dendrite fragmentation, and the migration and aggregation of dendrite fragments. The severity of freckling is closely dependent on both the casting’s position within the furnace and its local geometric characteristics. Castings located in regions with poorer heating conditions exhibit lower temperature gradients and slower solidification rates, significantly increasing their susceptibility to freckle formation. Similarly, on a given casting, the side subjected to less favorable heating is more prone to freckle initiation. The freckle number varies non-monotonically along the blade height, increasing from 3 to a maximum of 16, with a temporary decrease near the platform and a final reduction near the top. This trend is mainly attributed to thickness-dependent channel segregation, as well as freckle propagation into the interior and coalescence at higher positions. This study provides a crucial theoretical basis for understanding the formation mechanism of freckle defects in nickel-based single crystal superalloys and offers valuable guidance for optimizing blade manufacturing processes, reducing solidification defects, and enhancing blade quality and service performance. Full article

Traditional fuzzy decision-making often relies on manual expert calibration, which is labor-intensive and susceptible to subjective bias. This study addresses these limitations by proposing a novel framework that transforms the intrinsic probabilistic outputs of Large Language Models (LLMs) into Triangular Fuzzy Numbers (TFNs). We introduce a multi-temperature sampling strategy coupled with weighted quantile aggregation and an adaptive interval adjustment mechanism to systematically map model stochasticity to fuzzy possibility distributions. Empirical validation on a structured prototype dataset demonstrates that the proposed method achieves high consistency with expert consensus, with GPT-4.2 exhibiting superior central accuracy and Gemini-2.5 excelling in uncertainty coverage. Furthermore, in complex unstructured scenarios involving business public opinion, the integration of Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) significantly corrects cognitive biases and converges uncertainty boundaries. This research establishes a rigorous pathway from generative AI probabilities to fuzzy decision theory, offering a robust automated solution for quantitative risk assessment and intelligent decision support. Full article

This study addresses the instability of statistical modeling for small-sample maximum friction torque data under multiple temperature conditions. Within the Weibull distribution framework, a sample-aggregation method is proposed, and a unified modeling scheme separating central tendency from dispersion structure is established. This approach enables equivalent aggregation of data across different temperature levels while preserving structural consistency, thereby improving parameter estimation stability and statistical efficiency. To overcome the tendency of single-criterion optimization to fall into local optima under small-sample conditions, a secondary identification criterion combining residual minimization with a Levene-based statistical consistency test is introduced, and a dual-level search strategy is used to obtain a more robust global optimal solution. The parameter estimation results indicate that direct estimation based on small samples produces unstable parameters, with the coefficient of variation of the shape parameter reaching approximately 7.4%. In contrast, the sample-aggregation method shows that the scale parameter increases with temperature, while the location parameter first decreases and then increases due to the combined influence of central tendency and dispersion. The parameters obtained by the aggregation method exhibit more stable and regular variation trends with temperature. The results demonstrate that the proposed method significantly improves parameter stability and statistical efficiency for small-sample maximum friction torque data and provides a practical statistical modeling approach for multi-condition small-sample engineering data. Full article

Vanillin is widely used in foods, but its poor water dispersibility and limited stability reduce its flavor performance during processing and storage. In this study, soy protein isolate (SPI) was used as a food-grade carrier to prepare soy protein isolate–vanillin (SPIV) complexes via a pH-shifting strategy. SPI and vanillin were first adjusted to pH 9.0, where SPI unfolded and vanillin was deprotonated and dispersed in the solution and then readjusted to pH 7.0 to form SPIV complexes. Vanillin was incorporated into SPI at different loading levels of 0.5, 1.0, 2.5, and 5.0 mg/mL, corresponding to 9–50 wt.% relative to SPI. The binding efficiency of vanillin decreased from 91.03 wt.% to 69.43 wt.% with increasing vanillin loading. Moderate loading preserved the globular morphology of SPI, whereas excessive loading (≥33.33 wt.%) induced vanillin nanocrystal formation and aggregation. Spectroscopic analyses and molecular docking indicated that vanillin interacted with soy proteins through a combination of covalent and noncovalent interactions. Compared with free vanillin, SPIV showed improved color, light, and thermal stability. Among the tested samples, SPIV2 exhibited the most favorable interfacial behavior and application performance, producing more stable emulsions and higher flavor scores in simplified beverage and soy milk models. These findings establish a loading-dependent structure–function relationship in SPIV complexes and provide practical guidance for the design of soy protein-based carriers for flavor stabilization and delivery. Full article

Vehicular ad hoc networks (VANETs) operating in dense urban environments are characterized by highly dynamic topology, fluctuating traffic conditions, and stringent latency requirements, which significantly complicate reliable data routing and packet forwarding. To address these challenges, this paper proposes an Intelligent Fuzzy Protocol (IFP) for adaptive vehicle-to-vehicle data routing under uncertain and rapidly changing traffic scenarios. The proposed protocol integrates fuzzy logic decision making with the real-time vehicular context, including vehicle velocity, traffic congestion level, distance to road junctions, and data urgency, to dynamically select appropriate forwarding actions. IFP employs a structured fuzzy inference engine comprising fuzzification, rule evaluation, inference aggregation, and centroid-based defuzzification to determine routing and forwarding decisions in a decentralized manner. To further enhance performance robustness, the fuzzy membership parameters and rule weights are optimized using metaheuristic techniques, namely, genetic algorithms (GAs) and particle swarm optimization (PSO). Extensive simulations are conducted using NS-3 coupled with SUMO under realistic urban mobility scenarios and varying network densities. The simulation results demonstrate that IFP significantly outperforms conventional routing approaches in terms of end-to-end delay, packet delivery ratio, and routing overhead. In particular, the optimized IFP variants achieve notable reductions in latency and improvements in delivery reliability under high-congestion conditions, while maintaining low computational and communication overhead. These findings confirm that IFP offers an interpretable, scalable, and energy-aware routing solution suitable for large-scale intelligent transportation systems and next-generation vehicular networks. Full article

Neurodegenerative diseases (NDs), including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis (ALS), represent a growing global health challenge characterized by progressive neuronal loss and a lack of definitive disease-modifying treatments. This review explores the emerging potential of targeting non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and exosomal RNAs, to modulate pathogenic molecular pathways and address the underlying molecular origins of neurodegeneration. We evaluate the integration of advanced computational techniques for RNA structure prediction and gene regulatory network analysis, alongside chemical engineering strategies—such as Locked Nucleic Acids (LNAs) and phosphorothioate modifications—aimed at enhancing the stability and specificity of RNA-based molecules. Furthermore, we analyze cutting-edge delivery and editing technologies, including nanotechnology-driven solutions for precise neuronal targeting and the CRISPR/Cas13 system for direct ncRNA manipulation.The findings indicate that while challenges in delivery efficiency and long-term efficacy persist, the synergy of chemical engineering and computational modeling significantly improves the therapeutic profile of ncRNAs, with exosomal pathways offering a novel route for intercellular signaling modulation and biomarker discovery. Therapeutic interventions directed at specific clinical targets, such as miR-34a and BACE1-AS, demonstrate the capacity to influence protein aggregation and neuroinflammatory cascades. Although ncRNA-based therapies are currently in nascent stages, ongoing technological advancements in RNA editing and nanotechnology offer a transformative framework that could redefine the future of ND treatment and successfully halt disease progression rather than merely managing symptoms. Full article

Wi-Fi networks used in smart grids are essential for enabling communication between smart meters and data aggregation units. A key challenge, however, is the ability to hide the existence and traffic patterns of these communications, so that sensitive information exchanges cannot be easily detected or intercepted. Unfortunately, most existing solutions do not provide support for traffic prioritization and steganographic channel encryption. In this paper, we propose a novel covert channel with Quality of Service (QoS) and encryption support for smart grid environments based on the IEEE 802.11 standard. We introduce an original steganographic approach that leverages the backoff mechanism, the Enhanced Distributed Channel Access (EDCA) function, frame aggregation, and the StegoPaddingCipher algorithm. This design ensures QoS-aware traffic handling while enhancing security through encryption of the transmitted covert data. The proposed protocol was implemented and evaluated using the ns-3 simulator, where it achieved excellent performance results. The system maintained high efficiency even under heavily saturated network conditions with additional background traffic generated by other nodes. The proposed covert channel offers an innovative and secure method for transmitting substantial volumes of QoS-related data within smart grid environments. Full article

Traditional cognitive diagnosis models typically rely on expert-annotated Q-matrices to define the relationship between exercises and knowledge concepts. This process is not only highly subjective and costly, but also prone to introducing noise and bias, which directly affects diagnostic accuracy. Meanwhile, most existing deep learning-based methods overlook the rich semantic information contained in concept descriptions, making it difficult to deeply model the intrinsic relationships among knowledge points, resulting in limited interpretability of the models. To address these issues, this paper proposes a cognitive diagnosis model that incorporates key textual information from concept descriptions to refine the Q-matrix (KECQCD). The core innovation of the model lies in leveraging the pre-trained language model RoBERTa to encode concept texts, fusing semantic features with identifier embeddings through a gating mechanism to construct semantically-enhanced concept representations. It designs a concept-exercise heterogeneous information network and employs a graph attention mechanism to adaptively aggregate node features, explicitly modeling high-order knowledge dependencies. Furthermore, a multi-task joint learning framework is established to predict student performance while dynamically correcting association errors in the initial Q-matrix. Experimental results on the public Junyi dataset show that the KECQCD model significantly outperforms mainstream baseline models across multiple metrics, including accuracy (ACC), area under the curve (AUC), and root mean square error (RMSE). Ablation studies confirm the effectiveness of each core module, and diagnostic consistency (DOA) evaluation further demonstrates the enhanced interpretability of the model’s outcomes. This research offers a new solution for building accurate, reliable, and interpretable cognitive diagnosis systems, contributing positively to the advancement of personalized intelligent education. Full article

Improving the interfacial compatibility between cellulose and poly(butylene adipate-co-terephthalate) (PBAT) is critical for enhancing the performance of PBAT-based composites. Here, microcrystalline cellulose (MCC) was homogeneously silanized at the molecular chain level using t-hexyldimethylchlorosilane (TDMS-Cl) as the modifier, yielding t-hexyldimethylsilylated cellulose (TDMS-Cell). TDMS-Cell/PBAT composite films were then prepared by solution blending and casting in tetrahydrofuran (THF). Structural characterizations confirmed the successful grafting of TDMS-Cl onto cellulose chains, resulting in TDMS-Cell with a degree of substitution of approximately 2. Microstructural observations combined with thermal analysis revealed that TDMS-Cell exerted a dual effect on the crystallization behavior of PBAT: it acted as a heterogeneous nucleating agent that increased the crystallization temperature, while the pronounced steric hindrance simultaneously suppressed crystal growth. Mechanical testing showed that simultaneous strengthening and toughening were achieved at an optimal TDMS-Cell loading of 3–5 wt%. Specifically, the tensile strength increased from ~16 MPa for neat PBAT to 21 MPa (31.25% improvement), and the elongation at break increased from ~700% to 964% (37.7% improvement). In addition, the incorporation of an appropriate amount of TDMS-Cell effectively enhanced the surface hydrophobicity of the composite films. At higher filler loading, however, solvent evaporation-induced phase separation led to self-aggregation of TDMS-Cell, which in turn deteriorated both the mechanical properties and surface hydrophobicity of the composites. Overall, this work systematically elucidates the structure–property relationships of silanized cellulose/PBAT composites in a homogeneous solution system, providing a rational basis for interfacial design and property optimization of PBAT/biomass-based composite materials. The prepared TDMS-Cell/PBAT composite films with balanced mechanical strength, tunable crystallization behavior, and improved surface hydrophobicity exhibit great potential for practical applications in high-performance flexible packaging materials, functional film substrates, lightweight composite structural components, and tunable hydrophobicity coating substrates. Full article

The rapid proliferation of Conversational Artificial Intelligence Assistants (CAIs) has transformed access to mental health information through freely accessible web interfaces, mobile applications, and public APIs (Application Programming Interfaces), yet systematic methodologies for their evaluation remain limited. This paper introduces SELCAI-MH, a multicriteria framework for CAI evaluation and selection. This framework integrates four complementary multicriteria methods: Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Complex Proportional Assessment Method (COPRAS), and Combinative Distance-based Assessment (CODAS), capturing distance-based, compromise-based, proportional, and negative-ideal logics, and proposes SOLAG, an aggregation method that produces a consensus ranking across methods. SELCAI-MH employs a dual evaluation mechanism combining psychiatric expert assessment with AI-based scoring, expert-derived criterion weights, and domain-relevant conversational datasets. The framework is applied to nine internet-accessible CAIs: proprietary platforms (ChatGPT 5.2, Claude Sonnet 4.5, Gemini 1.5 Flash, Perplexity Sonar, Bing AI/Copilot) and open-source Llama variants deployed via cloud inference endpoints. Using a set of anxiety-related questions and CAI responses, evaluated across seven criteria, Claude Sonnet 4.5 emerged optimal, followed by ChatGPT 5.2 and Gemini 1.5 Flash. SOLAG produced highly consistent rankings across the four multicriteria decision-making (MCDM) methods (Spearman ρ ≥ 0.98). Overall, SELCAI-MH provides a structured and reproducible decision-support framework for selecting accessible CAIs in sensitive mental health contexts. Full article

Hybrid material-derived adsorbents have demonstrated exceptional efficacy in a variety of fields, including environmental cleanup and manufacturing operations. In this study, zinc oxide nanoparticles modified with carbon (ZnO-C) as hybrid adsorbent materials were synthesized using both expired zinc chloride and corncob extract. Hybrid ZnO-C adsorbents were employed for the removal of heavy metals, Co(II), and Ni(II) ions, from wastewater via adsorption. Transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and energy dispersive spectroscopy (EDS) were among the methods used to fully characterize the structural and morphological properties. To maximize the adsorption process for every metal ion, kinetic and equilibrium studies were carried out. Results revealed that the ZnO-C material formed crystalline, spherical granules with nanoparticle sizes ranging from 25 nm, embedded within a carbon matrix. Additionally, these spherical zinc oxide particles tended to aggregate into clusters. FTIR analysis indicated that the surface of ZnO-C was rich in hydroxyl (OH) groups and zinc oxide, which play a crucial role in the adsorption mechanism. The capacity of ZnO/CC-NPs to adsorb cobalt and nickel ions from aqueous solutions was investigated, examining the influences of initial ion concentration, pH levels, contact duration, and temperature. The findings highlight the high efficiency of ZnO/CC-NPs as an adsorbent, promoting the reuse of waste materials and supporting environmental sustainability efforts. Full article

Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture. Full article