Search Results (166)

The Segment Anything Model 2 (SAM2) has achieved state-of-the-art performance in pixel-level object segmentation for both static and dynamic visual content. Its streaming memory architecture maintains spatial context across video sequences, yet struggles with long-term tracking due to its static inference framework. SAM 2’s fixed temporal window approach indiscriminately retains historical frames, failing to account for frame quality or dynamic motion patterns. This leads to error propagation and tracking instability in challenging scenarios involving fast-moving objects, partial occlusions, or crowded environments. To overcome these limitations, this paper proposes SAM2Plus, a zero-shot enhancement framework that integrates Kalman filter prediction, dynamic quality thresholds, and adaptive memory management. The Kalman filter models object motion using physical constraints to predict trajectories and dynamically refine segmentation states, mitigating positional drift during occlusions or velocity changes. Dynamic thresholds, combined with multi-criteria evaluation metrics (e.g., motion coherence, appearance consistency), prioritize high-quality frames while adaptively balancing confidence scores and temporal smoothness. This reduces ambiguities among similar objects in complex scenes. SAM2Plus further employs an optimized memory system that prunes outdated or low-confidence entries and retains temporally coherent context, ensuring constant computational resources even for infinitely long videos. Extensive experiments on two video object segmentation (VOS) benchmarks demonstrate SAM2Plus’s superiority over SAM 2. It achieves an average improvement of 1.0 in J&F metrics across all 24 direct comparisons, with gains exceeding 2.3 points on SA-V and LVOS datasets for long-term tracking. The method delivers real-time performance and strong generalization without fine-tuning or additional parameters, effectively addressing occlusion recovery and viewpoint changes. By unifying motion-aware physics-based prediction with spatial segmentation, SAM2Plus bridges the gap between static and dynamic reasoning, offering a scalable solution for real-world applications such as autonomous driving and surveillance systems. Full article

The influence of structural water in alumina (Al₂O₃) and silica (SiO₂) coated titanium dioxide (TiO₂) pigments on the photodegradation behavior of polypropylene (PP) composites was investigated. Four commercial rutile TiO₂ pigments with varying surface inorganic coatings were incorporated into PP plaques and subjected to accelerated UV weathering to simulate outdoor exposure. Photodegradation was assessed through gloss retention measurements, the carbonyl index (CI), and stress at break retention, while pigment morphology and composition were analyzed using transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS). Surface charge and water content were determined through the zeta potential (ζ), Karl Fischer titration, thermogravimetric analysis (TGA), and Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS). The results showed that low-alumina coating alone led to the lowest photodegradation resistance, the highest CI, and the lowest stress at break retention. In contrast, increasing alumina content enhanced photostability, reaching its maximum for combined alumina–silica coatings, which mitigated electron–hole pair migration. PP composites with high alumina–silica-coated TiO₂ exhibited higher gloss retention (36%) compared to low-alumina samples (21%). Furthermore, statistical analysis using ANOVA revealed significant differences in coating content and ζ potential among the pigment grades. These findings provide novel insights into oxide-water interactions and the impact of structural water on the photodegradation of polymer composites. Full article

NiMn₂O₄ thermosensitive ceramics using Bi₂O₃ as a low-temperature ablating aid were prepared by laser melting deposition. Analyzing the structural, morphological, and electrical properties of the ceramics revealed important roles of Bi₂O₃. The room-temperature resistance decreased gradually with the increasing of the Bi₂O₃ content, the thermal constant of the ceramics varied from 2870.1 to 3853.2 K, and the activation energy varied from 0.2473 to 0.3320 eV. Furthermore, the alleviation of the aging issue was attributed to the grain growth and the densification of the ceramics due to the addition of Bi₂O₃ and the corresponding cationic redistribution. As a result, an optimized resistance drifting (∆R/R = 5.72%) of the ceramic was obtained with the addition of Bi₂O₃. Full article

Understanding the maintenance of soil protists within forest ecosystems is crucial for comprehending ecosystem responses to climate change. A comprehensive analysis of soil samples from the Fengtongzhai National Reserve in China, utilizing high-throughput sequencing and network analysis, indicates that topsoil protistan communities predominantly comprise consumers, parasites, and plant pathogens. The principal phyla identified include Stramenopiles, Alveolates, Rhizaria (SAR), Cercozoa, Apicomplexa, and Ciliophora, with Monocystis, Rhogostoma, Cercomonas, and Globisporangium as the most prevalent genera. Although α diversity metrics did not reveal significant differences across various forest types, β diversity demonstrated notable distinctions, primarily influenced by soil pH, organic carbon content, and moisture levels. Complex co-occurrence networks were particularly evident in deciduous broadleaved and evergreen broadleaved mixed forests. The stability of these networks was higher in plantation forests compared with natural forests, with no significant differences observed among the three natural forest types studied. This finding challenges the reliability of using soil protists as indicators for forest soil health assessments. Stochastic processes, especially ecological drift, play a significant role in shaping these communities. In conclusion, the findings suggest that the mechanisms underlying the enhanced stability of co-occurrence networks of soil protists in plantations require further investigation. Additionally, the specific responses of soil protists to forest type highlight the necessity of incorporating multidimensional indicators in the evaluation of forest soil health and the effectiveness of ecological restoration efforts. Full article

In the digital age, climate change content on social media is frequently distorted by misinformation, driven by unrestricted content sharing and monetization incentives. This paper proposes a novel AI-based framework to evaluate the data quality of climate-related discourse across platforms like Twitter and YouTube. Data quality is defined using key dimensions of credibility, accuracy, relevance, and sentiment polarity, and a pipeline is developed using transformer-based NLP models, sentiment classifiers, and misinformation detection algorithms. The system processes user-generated content to detect sentiment drift, engagement patterns, and trustworthiness scores. Datasets were collected from three major platforms, encompassing over 1 million posts between 2018 and 2024. Evaluation metrics such as precision, recall, F1-score, and AUC were used to assess model performance. Results demonstrate a 9.2% improvement in misinformation filtering and 11.4% enhancement in content credibility detection compared to baseline models. These findings provide actionable insights for researchers, media outlets, and policymakers aiming to improve climate communication and reduce content-driven polarization on social platforms. Full article

This study investigates the dehydrogenation of perhydrobenzyltoluene, a Liquid Organic Hydrogen Carrier (LOHC), using sulfur-doped bimetallic PtMo/Al₂O₃ catalysts. Based on previous research that highlighted the superior performance of PtMo catalysts over monometallic Pt catalysts, this work focuses on minimizing byproduct formation, specifically methylfluorene, through sulfur doping. Catalysts with low platinum content (<0.3 wt.%) were synthesized using the wet impregnation method by varying sulfur concentrations to study their impact on catalytic activity. Characterization techniques, including CO–DRIFT and CO–TPD, revealed the role of sulfur in selectively blocking low-coordinated Pt sites, thus improving selectivity and maintaining high dispersion. Catalytic tests revealed that samples with ≥0.1 wt.% sulfur achieved up to a threefold reduction in methylfluorene formation compared to the unpromoted PtMo/Al₂O₃ sample, with a molar fraction below 2% at 240 min. In parallel, these samples reached a degree of dehydrogenation (DoD) above 85% within 240 min, demonstrating that improved selectivity can be achieved without compromising catalytic performance. Full article

In plasma-enhanced chemical vapor deposition (PECVD) processes, thin films can accumulate on the inner chamber walls, resulting in particle contamination and process drift. In this study, we investigate the physical and chemical properties of these wall-deposited films to understand their spatial variation and impact on chamber maintenance. A 6-inch capacitively coupled plasma (CCP)-type PECVD system was used to deposit SiO₂ films, whilst long silicon coupons were attached vertically to the chamber side walls to collect contamination samples. The collected contamination samples were comparatively analyzed in terms of their chemical properties and surface morphology. The results reveal significant differences in hydrogen content and Si–O bonding configurations compared to reference films deposited on wafers. The top chamber wall, located near the plasma region, exhibited higher hydrogen incorporation and larger Si–O–Si bonding angles, while the bottom wall exhibited rougher surfaces with larger particulate agglomerates. These variations were closely linked to differences in gas flow dynamics, precursor distribution, and the energy state of the plasma species at different chamber heights. The findings indicate that top-wall contaminants are more readily cleaned due to their high hydrogen content, while bottom-wall residues may be more persistent and pose higher risks for particle generation. This study provides insights into wall contamination behavior in PECVD systems and suggests strategies for spatially optimized chamber cleaning and conditioning in high-throughput semiconductor processes. Full article

In the era of information explosion, recommendation systems play a crucial role in filtering vast amounts of content for users. Traditional recommendation models leverage knowledge graphs, sentiment analysis, social capital, and generative AI to enhance personalization. However, existing models still struggle to adapt dynamically to users’ evolving interests across multiple content domains in real-time. To address this gap, the cross-domain adaptive recommendation system (CDARS) is proposed, which integrates real-time behavioral tracking with multi-domain knowledge graphs to refine user preference modeling continuously. Unlike conventional methods that rely on static or historical data, CDARS dynamically adjusts its recommendation strategies based on contextual factors such as real-time engagement, sentiment fluctuations, and implicit preference drifts. Furthermore, a novel explainable adaptive learning (EAL) module was introduced, providing transparent insights into recommendations’ evolving nature, thereby improving user trust and system interpretability. To enable such real-time adaptability, CDARS incorporates multimodal sentiment analysis of user-generated content, behavioral pattern mining (e.g., click timing, revisit frequency), and learning trajectory modeling through time-aware embeddings and incremental updates of user representations. These dynamic signals are mapped into evolving knowledge graphs, forming continuously updated learning charts that drive more context-aware and emotionally intelligent recommendations. Our experimental results on datasets spanning social media, e-commerce, and entertainment domains demonstrate that CDARS significantly enhances recommendation relevance, achieving an average improvement of 7.8% in click-through rate (CTR) and 8.3% in user engagement compared to state-of-the-art models. This research presents a paradigm shift toward truly dynamic and explainable recommendation systems, creating a way for more personalized and user-centric experiences in the digital landscape. Full article

The phyllosphere microbiome of aquatic macrophytes constitutes an integral component of freshwater ecosystems, serving crucial functions in global biogeochemical cycling and anthropogenic pollutant remediation. In this study, we examined the assembly mechanisms of epiphytic bacterial communities across four phylogenetically diverse macrophyte species (Scirpus validus, Hippuris vulgaris, Nymphoides peltatum, and Myriophyllum spicatum) inhabiting Ningwu Mayinghai Lake (38.87° N, 112.20° E), a vulnerable subalpine freshwater system in Shanxi Province, China. Through 16S rRNA amplicon sequencing, we demonstrate marked phyllospheric microbiome divergence, as follows: Gammaproteobacteria dominated S. validus, H. vulgaris and N. peltatum, while Alphaproteobacteria dominated in M. spicatum. The nitrate, nitrite, and pH value of water bodies and the chlorophyll, leaf nitrogen, and carbon contents of plant leaves are the main driving forces affecting the changes in the β-diversity of epiphytic bacterial communities of four plant species. The partitioning of assembly processes revealed that deterministic dominance governed S. validus and M. spicatum, where niche-based selection contributed 67.5% and 100% to community assembly, respectively. Conversely, stochastic processes explained 100% of the variability in H. vulgaris and N. peltatum microbiomes, predominantly mediated by dispersal limitation and ecological drift. This investigation advances the understanding of microbial community structural dynamics and diversity stabilization strategies in aquatic macrophyte-associated microbiomes, while establishing conceptual frameworks between plant–microbe symbiosis and the ecological homeostasis mechanisms within vulnerable subalpine freshwater ecosystems. The empirical references derived from these findings offer novel perspectives for developing conservation strategies aimed at sustaining biodiversity equilibrium in high-altitude lake habitats, particularly in the climatically sensitive regions of north-central China. Full article

V_0.5/Pt/TiO₂ tandem catalysts exhibit both an outstanding low-temperature NH₃ conversion rate and high N₂ selectivity in NH₃-SCO reactions, but the mechanism of high N₂ selectivity remains unclear. In this work, V_x/Pt/TiO₂ tandem catalysts were synthesized through a two-step impregnation–deposition method. The modulating effect of the V loading mount on NH₃-SCO performance was evaluated, and the relevant reaction mechanism was explored systematically. The results demonstrated that the synergistic effect of tandem NH₃ oxidation and NH₃-SCR reactions could be regulated by changing the V loading amount, thereby modulating N₂ selectivity. Compared with other V_x/Pt/TiO₂ catalysts and previously reported SCO catalysts, the V_0.5/Pt/TiO₂ catalyst with a V loading amount of 0.5 wt.% exhibited outstanding NH₃-SCO performance, which achieved complete NH₃ conversion and >90% of N₂ selectivity within a range of 250–450 °C. XPS, NH₃-TPD, and O₂-TPD results suggested that the increase in the V loading amount from 0.1 wt.% to 0.5 wt.% was conducive to increasing the relative contents of Pt⁰ and V⁵⁺ species, as well as the amount of acid sites, oxygen species, and oxygen vacancies. Consequently, the synergistic effect of tandem NH₃ oxidation and NH₃-SCR reactions was significantly enhanced, enabling the catalyst to exhibit excellent N₂ selectivity. A further increase in the V loading amount from 0.5 wt.% to 0.9 wt.% would bring about the opposite effect to the above, resulting in a decline in catalytic performance. In situ DRIFTS results showed that a V loading amount of 0.5 wt.% was beneficial for -NH₂ species to participate in NH₃-SCO reactions, thereby boosting N₂ selectivity. Full article

Graphene-based textile pressure sensors are emerging as promising candidates for wearable sensing applications due to their high sensitivity, mechanical flexibility, and low energy consumption. This study investigates the design, fabrication, and electromechanical behaviour of graphene-coated nonwoven textile-based piezoresistive pressure sensors, focusing on the impact of different electrode materials and fabrication techniques. Three distinct sensor fabrication methods—drop casting, electrospinning, and electro-spraying—were employed to impregnate graphene onto nonwoven textile substrates, with silver-coated textile electrodes integrated to enhance conductivity. The fabricated sensors were characterised for their morphology (SEM), chemical composition (FTIR), and electromechanical response under cyclic compressive loading. The results indicate that the drop-cast sensors exhibited the lowest initial resistance (~0.15 kΩ) and highest sensitivity (10.5 kPa⁻¹) due to their higher graphene content and superior electrical connectivity. Electro-spun and electro-sprayed sensors demonstrated increased porosity and greater resistance fluctuations, highlighting the role of fabrication methods in sensor performance. Additionally, the silver-coated knitted electrodes provided the most stable electrical response, while spun-bonded and powder-bonded nonwoven electrodes exhibited higher hysteresis and resistance drift. These findings offer valuable insights into the optimisation of graphene-based textile pressure sensors for wearable health monitoring and smart textile applications, paving the way for scalable, low-power sensing solutions. Full article

This paper addresses the pervasive problem of fake news propagation in social networks. Traditional text-based detection models often suffer from performance degradation over time due to their reliance on evolving textual features. To overcome this limitation, we propose a novel recommender system that leverages the power of knowledge graphs and graph attention networks (GATs). This approach captures both the semantic relationships within the news content and the underlying social network structure, enabling more accurate and robust fake news detection. The GAT model, by assigning different weights to neighboring nodes, effectively captures the importance of various users in disseminating information. We conducted a comprehensive evaluation of our system using the FakeNewsNet dataset, comparing its performance against classical machine learning models and the DistilBERT language model. Our results demonstrate that the proposed graph-based system achieves state-of-the-art performance, with an F1-score of 95%, significantly outperforming other models. Moreover, it maintains its effectiveness over time, unlike text-based approaches that are susceptible to concept drift. This research underscores the potential of knowledge graphs and GATs in combating fake news and provides a robust framework for building more resilient and accurate detection systems. Full article

Traditional text classification models predominantly rely on static text representations, failing to capture temporal variations in language usage and evolving semantic meanings. This limitation reduces their ability to accurately classify time-sensitive texts, where understanding context, detecting trends, and addressing semantic shifts over time are critical. This paper introduces a novel time-aware short text classification model incorporating temporal information, enabling tracking of and adaptation to evolving language semantics. The proposed model enhances contextual understanding by leveraging timestamps and significantly improves classification accuracy, particularly for time-sensitive applications such as News topic classification. The model employs a hybrid architecture combining Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks, enriched with attention mechanisms to capture both local and global dependencies. To further refine semantic representation and mitigate the effects of semantic drift, the model fine-tunes GloVe embeddings and employs synonym-based data augmentation. The proposed approach is evaluated on three benchmark dynamic datasets, achieving superior performance with classification accuracy reaching 92% for the first two datasets and 85% for the third dataset. Furthermore, the model is applied to a different-fields categorization and trend analysis task, demonstrating its capability to capture temporal patterns and perform detailed trend analysis of domain-agnostic textual content. These results underscore the potential of the proposed framework to provide deeper insights into the evolving nature of language and its impact on short-text classification. This work advances natural language processing by offering a comprehensive time-aware classification framework, addressing the challenges of temporal dynamics in language semantics. Full article

Quinoa (Chenopodium quinoa, Amaranthaceae) is a pseudocereal native to the Andes of South America that contains high protein content and adequate nutrient levels. Peru possesses an abundant morphological diversity of quinoas and is among the top producers and exporters worldwide of this precious crop. However, knowledge about the genetic and population components of quinoa from the Peruvian Andes is still limited. Here, we used 13 microsatellite markers to determine the genetic diversity and population structure of 105 landraces of quinoa cultivated in 11 provinces of Ayacucho, the southern Peruvian Andes. A total of 285 bands were manually scored, generating a 105 × 285 presence/absence data set. Principal coordinate analysis, similar to a dendrogram using the UPGMA clustering algorithm, showed that quinoa from Ayacucho is grouped into three clusters without a clear geographic component. Estimation of the genetic diversity indices was conducted considering the three populations (C1: south 1, C2: south 2, C3: north) determined by STRUCTURE analysis, showing mean expected heterozygosity was 0.08, which may be attributed to high rates of inbreeding and genetic drift, as Ayacucho suffered decades of sociopolitical violence, promoting the migration of farmers. The highest population divergence (F_ST) was exhibited for C2 and C3 (0.03), whereas the lowest was for C1 and C3 (0.02). Analysis of molecular variance revealed the greatest variation within populations (80.07%) and indicated that variability between populations is 19.93%. Microsatellite markers were effective; however, more studies of the genetic components of quinoa from other Peruvian Andean localities are still needed. We expect that this work will help pave the way towards the development of modern breeding programs of quinoa in Peru, with accurate strategies for the conservation of this nutritious crop. Full article

In the presented work, titanosilicate with the MWW structure (Ti-MWW) was hydrothermally synthesized using boron and titanium precursors, with piperidine as a structure-directing agent. The resulting layered zeolite precursor, with a Si/Ti molar ratio of 50, was treated in an HNO₃ solution to remove extraframework Ti and B species. The acid-modified zeolite was functionalized with transition metal cations (Cu²⁺, Fe²⁺, Mn²⁺) and trinuclear oligocations (Fe(3) and Mn(3)). The application of this catalytic system is supported by the presence of titanium in the catalytic support structure—similar to a commercial system, V₂O₅–TiO₂. The obtained samples were characterized with respect to their structure (P-XRD, DRIFT), textural parameters (low-temperature N₂ sorption), surface acidity (NH₃-TPD), transition metal content (ICP-OES) and form (UV–vis DRS) as well as catalyst’s reducibility (H₂-TPR). Ti-MWW zeolite samples modified with transition metals were evaluated as catalysts for the selective catalytic reduction of NO with ammonia (NH₃-SCR). The effective temperature range for the NO conversion varied depending on the type of active phase used to functionalize the porous support. The catalytic performance was influenced by transition metal content, its form, and accessibility for reactants as well as interactions between the active phase and titanium-containing support. Among the catalysts tested, the copper-modified Ti-MWW zeolite showed the most promising results, maintaining 90% NO conversion rates across a relatively broad temperature range from 200 to 325 °C. This catalyst meets the requirements of modern NH₃-SCR installations, which aim to operate in the low-temperature region, below 250 °C. Full article