Search Results (2,102)

Microplastics (MPs), as an emerging persistent contaminant, pose a potential threat to ecosystems and human health. The adsorptive removal of MPs from aqueous environments using magnetic nanoparticles has become a particularly promising remediation technology. Nevertheless, there remain significant knowledge gaps regarding its adsorption mechanism, especially how the key physical properties of magnetic nanoparticles regulate their adsorption behavior towards MPs. This study first investigated the relationship between the particle size of Fe₃O₄ nanoparticles and their adsorption efficacy for MPs. The results demonstrated a non-monotonic, size-dependent adsorption of MPs by Fe₃O₄ nanoparticles, with the adsorption efficiency and capacity following the order: 300 nm > 15 nm > 100 nm. This non-linear relationship suggested that factors other than specific surface area (which would favor smaller particles) are significantly influencing the adsorption process. Isotherm analysis indicated that the adsorption is not an ideal monolayer coverage process. Kinetic studies showed that the adsorption process could be better described by the pseudo-second-order model, while intra-particle diffusion played a critical role throughout the adsorption process. Furthermore, the effect of pH on adsorption efficiency was examined, revealing that the optimal performance occurs under neutral to weak acidic conditions, which is consistent with measurements of surface charges of nanoparticles. These findings suggest that the adsorption is not determined by specific surface area but is dominated by electrostatic interactions. The size-dependent adsorption of MPs by Fe₃O₄ nanoparticles provides new insights for the modification of magnetic adsorbents and offers a novel perspective for the sustainable and efficient remediation of environmental MPs pollution. Full article

Compression of clayey sediments not only causes land subsidence but also results in geogenic high fluoride groundwater. The distribution characteristics and enrichment mechanisms of fluoride in alluvial−lacustrine facies clayey sediments in the land subsidence area of Cangzhou Plain, China, were investigated using sample collection, mineralogical research, and hydrogeochemical and isotopic analysis. The results show that F⁻ concentration of groundwater samples ranged from 0.31 to 5.54 mg/L in aquifers. The total fluoride content of clayey sediments ranged from 440 to 792 mg/kg and porewater F⁻ concentration ranged from 0.77 to 4.18 mg/L. Clay minerals containing fine particles, such as muscovite, facilitate the enrichment of fluoride in clayey sediments, resulting in higher total fluoride levels than those in sandy sediments. The clay porewater F⁻ predominantly originated from the dissolution of water-soluble F and the desorption of exchangeable F from sediments. The F⁻ concentration in porewater was further influenced by ionic interactions such as cation exchange. The stable sedimentary environment and intense compression promoted the dissolution of F–bearing minerals and the desorption of adsorbed F in deep clayey sediments. The similar composition feature of δ²H−δ¹⁸O in deep groundwater and clay porewater samples suggests a significant mixing effect. These findings highlight the joint effects of hydrogeochemical and mineralogical processes on F behavior in clayey sediments. Full article

Peritoneal carcinomatosis (PC) and malignant pleural effusions (MPE) are two common complications of cancers metastatic to the respective body cavities. A PC diagnosis indicates metastasis to the tissue lining the abdominal cavity and is most common in patients with gastrointestinal and gynecological cancers. It is often accompanied by ascites, an accumulation of serous fluid in the abdomen. MPE presents as the accumulation of fluid in the space between the lungs and chest wall. It is a common terminal event in patients diagnosed with breast cancer, lung cancer, lymphoma, and mesothelial cancers, and less commonly, in a wide variety of other epithelial cancers. Due to the aggressive nature of cavitary tumors, the outcome of current treatments for both PC and MPE remains bleak. Although PC and MPE are characteristically affected by different sets of primary tumors (lung/breast/mesothelioma for MPE and gynecologic/gastrointestinal for PC), their environments share common cytokines and cellular components. Owing to the unique cytokine and chemokine content, this environment promotes aggressive tumor behavior and paradoxically both recruits and suppresses central memory and effector memory T cells. The cellular and secretomic complexity of the cavitary tumor environment renders most currently available therapeutics ineffective but also invites approaches that leverage the robust T-cell infiltrate while addressing the causes of local suppression of anti-tumor immunity. Interactions between the heterogeneous components of the tumor environment are an area of active research. We highlight the roles of the immune cell infiltrate, stromal cells, and tumor cells, and the soluble products that they secrete into their environment. A more comprehensive understanding of the cavitary tumor environment can be expected to lead to better immunotherapeutic approaches to these devastating conditions. Full article

Background/Objectives: The process of designing and fabricating bone tissue engineering scaffolds is a multi-faceted and intricate process. The scaffold is designed to attach cells to the required volume of regeneration to subsequently migrate, grow, differentiate, proliferate, and consequently develop tissue within the scaffold which, in time, will degrade, leaving just the regenerated tissue. The fabrication of tissue scaffolds requires adapting the properties of the scaffolds to mimic, to a large extent, the specific characteristics of each type of bone tissue. However, there are some significant limitations due to the constrained scaffolds’ architecture and structural features that inhibit the optimization of bone scaffolds. Methods: To overcome these shortcomings, new computational approaches for scaffold design have been adopted through currently adopted computational methods such as finite element analysis (FEA), computational fluid dynamics (CFD), and fluid–structure interaction (FSI). Results: This paper presents a narrative review of the state of the art in the field of parametric numerical modeling and computational fluid dynamics geometry-based models used in bone tissue engineering. Computational methods for scaffold design improve the process of constructing scaffolds and contribute to tissue engineering. Conclusions: This paper highlights the benefits of computational methods on employing scaffolds with different architectures and inherent characteristics that can potentially contribute to a favorable environment for hosting cells and predict their behavior and response. By recognizing these benefits, researchers can enhance and optimize scaffold properties for future advancements in tissue engineering research that will lead to more accurate and robust outcomes. Full article

Background/Objectives: Polybia-MP1 (MP1) exhibits antimicrobial and anticancer properties. To improve selectivity toward acidic tumor microenvironments, we designed HMP1, a histidine-substituted analog of MP1, aiming to introduce pH-responsive behavior within physiological and pathological pH ranges. Methods: HMP1 was synthesized by replacing all lysine residues in MP1 with histidines. We characterized its surfactant properties and interactions with lipid monolayers composed of DPPC under varying pH and ionic strength conditions. Langmuir monolayer experiments were used to evaluate peptide-induced morphological changes and lipid packing effects at physiologically relevant lateral pressures. Results: HMP1 displayed pH-dependent activity between pH 5.5 and 7.5, inducing significant morphological reorganization of lipid domains without reducing the condensed phase area. Ionic strength modulated these effects, with distinct behaviors observed at low and physiological saline conditions. HMP1 preferentially interacted with cholesterol-enriched membranes, while MP1 did not induce comparable effects under the same conditions, as previously reported, at physiological lateral pressures. HMP1 also exhibited non-hemolytic properties and lower cytotoxicity compared to MP1. Conclusions: The lysine-to-histidine substitution conferred pH sensitivity to HMP1, enabling selective modulation of membrane organization based on lipid composition, packing, pH, and ionic environment. These findings highlight HMP1’s potential in targeted therapeutics and pH-responsive drug delivery systems. Full article

NO₂ is a toxic gas mainly generated by combustion processes, such as vehicle emissions and industrial activities. It is a key contributor to smog, acid rain, ground-level ozone, and particulate matter, all of which pose serious risks to human health and the environment. Conventional resistive gas sensors, typically based on metal oxide semiconductors, detect NO₂ by resistance modulation through surface interactions with the gas. However, they often suffer from low responsiveness and poor selectivity. This study investigates NO₂ detection using nanoporous zinc oxide thin films integrated into a resistor structure and floating-gate field-effect transistor (FGFET). Both Silvaco-Atlas simulations and experimental fabrication were employed to evaluate sensor behavior under NO₂ exposure. The results show that FGFET provides higher sensitivity, faster response times, and improved selectivity compared to resistor-based devices. In particular, FGFET achieves a detection limit as low as 89 ppb, with optimal performance around 400 °C, and maintains stability under varying humidity levels. The enhanced performance arises from quantum well effects at the floating-gate Schottky contact, combined with NO₂ adsorption on the ZnO surface. These interactions extend the depletion region and confine charge carriers, amplifying conductivity modulation in the channel. Overall, the findings demonstrate that FGFET is a promising platform for NO₂ sensors, with strong potential for environmental monitoring and industrial safety applications. Full article

Extended Reality (XR) technologies—including Virtual Reality, Augmented Reality, and Mixed Reality—are increasingly applied in the food industry to simulate sensory environments, support education, and influence consumer behavior, while robotics addresses labor shortages, hygiene, and efficiency in production. This review uniquely synthesizes their convergence through digital twin frameworks, combining XR’s immersive simulations with robotics’ precision and scalability. A systematic literature review and keyword co-occurrence analysis of over 800 titles revealed research clusters around consumer behavior, nutrition education, sensory experience, and system design. In parallel, robotics has expanded beyond traditional pick-and-place tasks into areas such as precision cleaning, chaotic mixing, and digital gastronomy. The integration of XR and robotics offers synergies including risk-free training, predictive task validation, and enhanced human–robot interaction but faces hurdles such as high hardware costs, motion sickness, and usability constraints. Future research should prioritize interoperability, ergonomic design, and cross-disciplinary collaboration to ensure that XR–robotics systems evolve not merely as tools, but as a paradigm shift in redefining the human–food–environment relationship. Full article

This paper presents an innovative end-to-end framework for conformal antenna array design and beam steering in Low Earth Orbit (LEO) satellite-based IoT communication systems. We propose a multi-stage learning architecture that integrates machine learning (ML) for antenna parameter prediction with reinforcement learning (RL) for adaptive beam steering. The ML module predicts optimal geometric and material parameters for conformal antenna arrays based on mission-specific performance requirements such as frequency, gain, coverage angle, and satellite constraints with an accuracy of 99%. These predictions are then passed to a Deep Q-Network (DQN)-based offline RL model, which learns beamforming strategies to maximize gain toward dynamic ground terminals, without requiring real-time interaction. To enable this, a synthetic dataset grounded in statistical principles and a static dataset is generated using CST Studio Suite and COMSOL Multiphysics simulations, capturing the electromagnetic behavior of various conformal geometries. The results from both the machine learning and reinforcement learning models show that the predicted antenna designs and beam steering angles closely align with simulation benchmarks. Our approach demonstrates the potential of combining data-driven ensemble models with offline reinforcement learning for scalable, efficient, and autonomous antenna synthesis in resource-constrained space environments. Full article

Based on the steel sheet pile cofferdam project for the main bridge piers of a cross-sea bridge, finite element numerical simulations were conducted to analyze the influence of construction sequences in marine environments, as well as the effects of initial water levels and support positions under various construction conditions on the stress and deformation behavior of steel sheet piles. Using a staged construction simulation with a Mohr–Coulomb soil model and stepwise activation of loads/excavation, this study delivers practically relevant trends: staged dewatering halves the sheet pile head displacement (top lateral movement <0.08 m vs. ~0.16 m in the original scheme) and mobilizes the support system earlier, while slightly increasing peak bending demand (~1800 kN·m) at the bracing elevation; the interaction between water head and brace elevation is explored through fitted response curves and summarized in figures/tables, and soil/structural properties are tabulated for reproducibility. The results indicate that a well-designed dewatering process, along with the coordination between water levels and internal support positions, plays a critical role in controlling deformation. The findings offer valuable references for the design and construction of sheet pile cofferdams in marine engineering under varying construction methods and water level conditions. Full article

This research aimed to evaluate the emotional intelligence of future teachers and its relationship with attitudes towards people with disabilities, sexism, and bullying behaviors. Using a non-experimental design under a quantitative approach, 1004 future teaching subjects were selected by convenience sampling from different Faculties of Education in Spanish universities. The following instruments were applied online: for emotional intelligence, the TMMS-24; for bullying assessment, the Bullying Questionnaire-CAME; the General Scale of Attitudes towards People with Disabilities (2016) and the Ambivalent Sexism Inventory (ASI). Results indicated that both male and female future teachers exhibited low levels of emotional intelligence. A correlation was observed between greater emotional intelligence and more positive attitudes towards people with disabilities, as well as a lower tendency towards sexism. Additionally, a relationship was found between greater emotional attention and a lower tendency to bully from the victim’s perspective. These findings highlight the importance of developing emotional intelligence in future teachers to improve their interactions with students and promote a more inclusive and respectful school environment. Full article

Ferritic/martensitic (F/M) steel is a candidate material for key structures in fourth-generation nuclear energy systems (such as fusion reactors and fast reactors). Irradiation hardening behavior is a core index to evaluate the material’s stable performance in a high-neutron-irradiation environment. In this study, based on 2048 composition and property data, a correlation model between key elements and their interactions and irradiation hardening in F/M steel was constructed using a Bayesian optimization neural network, which realized quantitative prediction of the effect of composition on hardening behavior. Studies have shown that the addition of about 9.0% Cr, about 0.8% Si, Mo content higher than about 0.25%, and the addition of Ti, Mn can effectively suppress the irradiation hardening of F/M steel, while the addition of N, Ta, and C will aggravate its irradiation hardening, and the addition of W and V has little effect on the irradiation hardening of F/M steel. There is an interaction between the two elements. C-Cr has a strong synergistic mechanism, which will cause serious hardening when the content is higher than 0.05% and the Cr content is higher than 10%. Cr-Si has a strong antagonistic mechanism, which can achieve the comprehensive irradiation hardening effect in the 9Cr-0.8Si combination. N-Mn needs N controlled lower than 0.01%. Mo-W needs to control Mo content higher than 0.5% to alleviate irradiation hardening. There is a weak synergistic effect in Si-V; when the content is between 0.3% and 0.8% and the V content is between 0.2% and 0.3%, it can assist in optimizing the composition of F/M steel. Through the optimization of multi-element combination, the composition of F/M steel with lower irradiation hardening can be designed. Full article

The potential of elastomeric composites reinforced with natural fillers to replace traditional synthetic materials in applications involving exposure to acidic environments offers both economic and environmental advantages. On the one hand, these materials contribute to cost reduction and the valorization of organic waste through the development of value-added products. On the other hand, the presence of wood waste in the composite structure enhances biodegradation potential, making these materials less polluting and more consistent with the principles of the circular economy. The present study aims to evaluate the behavior of composites based on Ethylene Propylene Diene Monomer (EPDM) synthetic rubber, reinforced with silica and wood sawdust, in a weakly acidic yet strongly corrosive environment—specifically, acetic acid solutions with concentrations ranging from 10% to 30%. The study also investigates the extent to which varying the proportions of the two fillers affects the resistance of these materials under such environmental conditions. Physico-chemical, structural, and morphological analyses revealed that the materials underwent chemical modifications, such as acetylation of hydroxyl groups. This process reduced the hydrophilic character of the sawdust and, combined with the formation of stable interfaces between the elastomeric matrix and the fillers during vulcanization, limited acid penetration into the composite structure. The composites in which 20 phr or 30 phr of wood sawdust were used-replacing equivalent amounts of silica from the initial 50 phr formulation-demonstrated the highest resistance to the corrosive environments. After 14 days of exposure to a 20% acetic acid solution, the composite containing 30% wood sawdust exhibited a decrease in cross-link density of only 1.44%, accompanied by a reduction in Young’s modulus of just 0.95%. At the same time, tensile strength and specific elongation increased by 22.57% and 26.02%, respectively. FTIR and SEM analysis confirmed good rubber–filler interactions and the stability of the composite structure under acidic conditions. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

This study evaluates the tribological properties of poly-DL-lactic acid (PDLLA) nanosheets attached to shark-skin surfaces with varying textures. The main goal was to assess friction reduction in samples with different surface textures and investigate the influence of PDLLA nanosheets on tribological behaviors. Biomimetic shark skin was created using a polydimethylsiloxane (PDMS)-embedded stamping method (PEES) that replicates shark skin’s unique texture, which reduces friction and drag in aquatic environments. PDLLA nanosheets, with a controlled thickness of several tens of nanometers, were fabricated and attached to the PDMS surfaces. The morphological characteristics of the materials were analyzed before and after attaching the PDLLA nanosheets using scanning electron microscopy (SEM), revealing the uniformity and adherence of the nanosheets to the PDMS surfaces. Friction tests were conducted using force transducers to measure the friction coefficients of biomimetic shark skin, biological models, and flat PDMS and silicon substrates, allowing a comprehensive comparison of frictional properties. Additionally, sliding tests with human fingers were performed to assess friction coefficients between various fingerprint shapes and sample surfaces. This aspect of the study is critical for understanding how human skin interacts with biomimetic materials in real-world applications, such as wearable devices. These findings clarify the relationship between surface texture, nanosheets, and their tribological performance against human skin, thereby contributing to the development of materials with enhanced friction-reducing properties for applications such as surface coatings, substrates for wearable devices, and wound dressings. Full article

A significant problem in cybersecurity is to accurately detect malicious network activities in real-time by analyzing patterns in socket-level packet transmissions. This challenge involves distinguishing between legitimate and adversarial behaviors while optimizing detection strategies to minimize false alarms and resource costs under intelligent, adaptive attacks. This paper presents a comprehensive framework for network security by modeling socket-level packet transmissions and extracting key features for temporal analysis. A long short-term memory (LSTM)-based anomaly detection system predicts normal traffic behavior and identifies significant deviations as potential cyber threats. Integrating this with a zero trust signaling game, the model updates beliefs about agent legitimacy based on observed signals and anomaly scores. The interaction between defender and attacker is formulated as a Stackelberg game, where the defender optimizes detection strategies anticipating attacker responses. This unified approach combines machine learning and game theory to enable robust, adaptive cybersecurity policies that effectively balance detection performance and resource costs in adversarial environments. Two baselines are considered for comparison. The static baseline applies fixed transmission and defense policies, ignoring anomalies and environmental feedback, and thus serves as a control case of non-reactive behavior. In contrast, the adaptive non-strategic baseline introduces simple threshold-based heuristics that adjust to anomaly scores, allowing limited adaptability without strategic reasoning. The proposed fully adaptive Stackelberg strategy outperforms both partial and discrete adaptive baselines, achieving higher robustness across trust thresholds, superior attacker–defender utility trade-offs, and more effective anomaly mitigation under varying strategic conditions. Full article