Search Results (38,245)

Wireless communication advancements have significantly improved connectivity and user experience with each generation. The recent release of the framework M.2160 for the upcoming sixth generation (6G or IMT-2030) cellular wireless standard by ITU-R has significantly heightened expectations, particularly for Internet of Things (IoT) driven use cases. However, this progress introduces significant security risks, as technologies like O-RAN, terahertz communication, and native AI pose threats such as eavesdropping, supply chain vulnerabilities, model poisoning, and adversarial attacks. The increased exposure of sensitive data in 6G applications further intensifies these challenges. This necessitates a concerted effort from stakeholders including ITU-R, 3GPP, ETSI, OEMs and researchers to embed security and resilience as core components of 6G. While research is advancing, establishing a comprehensive security framework remains a significant challenge. To address these evolving threats, our research proposes a dynamic security framework that emphasizes the integration of explainable AI (XAI) techniques like SHAP and LIME with advanced machine learning models to enhance decision-making transparency, improve security in complex 6G environments, and ensure effective detection and mitigation of emerging cyber threats. By refining model accuracy and ensuring alignment through recursive feature elimination and consistent cross-validation, our approach strengthens the overall security posture of the IoT–6G ecosystem, making it more resilient to adversarial attacks and other vulnerabilities. Full article

The integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variations. Power Conditioning Systems (PCSs) interconnected with PV installations are increasingly considered for voltage control to address these challenges. This study proposes a Machine Learning (ML)-based control method for sub-transmission grids, integrating long-term LRT tap-changing with short-term reactive power control of PCSs. The approach estimates the voltage at each grid node using a Deep Neural Network (DNN) that processes measurable substation data. Based on these estimated voltages, the method determines optimal LRT tap positions and PCS reactive power outputs using Deep Reinforcement Learning (DRL). This enables real-time voltage monitoring and control using only substation measurements, even in grids without extensive sensor installations, ensuring all node voltages remain within specified limits. To improve the model’s transparency, Shapley Additive Explanation (SHAP), an Explainable AI (XAI) technique, is applied to the DRL model. SHAP enhances interpretability and confirms the effectiveness of the proposed method. Numerical simulations further validate its performance, demonstrating its potential for effective voltage management in modern power grids. Full article

The rise in intrusions on network and IoT systems has led to the development of artificial intelligence (AI) methodologies in intrusion detection systems (IDSs). However, traditional AI or machine learning (ML) methods can compromise accuracy due to the vast, diverse, and dynamic nature of the data generated. Moreover, many of these methods lack transparency, making it challenging for security professionals to make predictions. To address these challenges, this paper presents a novel IDS architecture that uses deep learning (DL)-based methodology along with eXplainable AI (XAI) techniques to create explainable models in network intrusion detection systems, empowering security analysts to use these models effectively. DL models are needed to train enormous amounts of data and produce promising results. Three different DL models, i.e., customized 1-D convolutional neural networks (1-D CNNs), deep neural networks (DNNs), and pre-trained model TabNet, are proposed. The experiments are performed on seven different datasets of TON_IOT. The CNN model for the network dataset achieves an impressive accuracy of 99.24%. Meanwhile, for the six different IoT datasets, in most of the datasets, the CNN and DNN achieve 100% accuracy, further validating the effectiveness of the proposed models. In all the datasets, the least-performing model is TabNet. Implementing the proposed method in real time requires an explanation of the predictions generated. Thus, the XAI methods are implemented to understand the essential features responsible for predicting the particular class. Full article

In this educational review, the basic physics underlying the use of ultra-high contrast (UHC) bipolar filter (BLAIR) sequences, including divided subtracted inversion recovery (dSIR), is explained. These sequences can increase the contrast produced by small changes in T₁ by a factor of ten or more compared with conventional IR sequences. In illustrative cases, the sequences were used in multiple sclerosis (MS) patients during relapse and remission and were compared with positionally matched conventional (T₂-weighted spin echo, T₂-FLAIR) images. Well-defined focal lesions were seen with dSIR sequences in areas where little or no change was seen with conventional sequences. In addition, widespread abnormalities affecting almost all of the white matter of the brain were seen during relapses when there were no corresponding abnormalities seen on conventional sequences (the whiteout sign). Grayout signs, in which there is a loss of contrast in gray matter or between gray matter and CSF, were also seen, as well as high signal boundaries around lesions. Disruption of the usual high signal boundary between white and gray matter was seen in leucocortical lesions. Lesions in the spinal cord were better seen or only seen with dSIR sequences. Generalized change was observed in the optic nerve with the dSIR sequence in a case of optic neuritis. UHC BLAIR sequences may be of considerable value for recognition of abnormalities in clinical practice and in research studies on MS. Full article

Mean and slowly varying wave loads on floating offshore wind turbines (FOWTs) need to be estimated accurately for the design of mooring systems. The low-frequency drift forces are underestimated by potential flow theory, especially in steep waves. Viscous forces on columns is an important contributor which could be included by adding the quadratic drag of Morison formulation to the potential flow solution. The drag coefficients in Morison equation can be determined based on an empirical formula, CFD study, or model testing. In the WINDMOOR project, a FOWT support structure, composed of three columns joined at the bottom by pontoons and at the top by deck beams, is studied using CFD. In order to extract the KC-dependent drag coefficients, a series of simulations for the fixed structure in regular waves is performed with the CFD code STAR-CCM+. In this study, the forces along each column of the FOWT are analyzed using the results of CFD as well as potential flow simulations. The hydrodynamic interactions between the columns are addressed. A methodology is proposed to process the CFD results of forces on the columns and extract the contribution of viscous effects. Limitations of the Morison drag model to represent extracted viscous forces in steep waves are investigated. The obtained drag coefficients are compared with the available data in the literature. It is shown that accounting for potential flow interactions and nonlinear flow kinematics could, to a large degree, explain the previously reported differences between drag coefficients for a column in waves. Moreover, it is shown that the proposed model can capture the contribution of viscous effects to mean drift forces for fixed columns in waves. Full article

Shrub encroachment is a global ecological issue. The changes in growth dynamics between shrub seedlings and herbs are pivotal in determining shrub encroachment, yet their response to varying rainfall regimes remains unclear. We conducted a precipitation manipulation experiment (three precipitation (P) amount treatments: P−25% (225 mm), P (300 mm), P+25% (375 mm); three drought interval treatments: DI4, DI6, DI8) on a mixture of Caragana microphylla (shrub) seedlings and four herbs (Neotrinia splendens, Campeiostachys dahurica, Lolium multiflorum and Medicago sativa), analyzing their ecophysiological and growth responses. The results showed the following: (1) Under P−25%, herb growth was inhibited, while shrub seedlings thrived. Compared to P, C. microphylla significantly increased by 138% in aboveground biomass (AGB), while herb AGB decreased by 10%. (2) Under P+25%, herbs exhibited superior growth to shrub seedlings. Compared to P, four herbs significantly increased by 53% in AGB, while C. microphylla growth did not significantly respond. (3) Under DI8, shrub seedlings exhibited superior growth compared to herbs. Compared to DI4, C. microphylla significantly increased by 90% in AGB, while herb growth did not significantly respond. Our results indicate that drier conditions suppressed herb growth while promoting shrubs. However, increased precipitation amounts stimulated herb growth but not shrubs. These results could explain the process of shrub encroachment and provide a theoretical basis for predicting the pattern of shrub expansion under future rainfall regimes. Full article

Material recovery operations like recycling are now a common part of many product categories, and yet quantifying recycling potential is still a largely unresolved issue. Prior research into this matter focused on market value as an indicator of the readiness of recycling technologies. Although this is an effective measure, it fails to recognize the environmental, societal, and other impacts of recycling operations. Aiming to expand the evaluated factors of recycling potential, this article centers on assessing recyclability from a toxicity and human health perspective. The article describes the development of a toxicity index for recyclability, which is explained and demonstrated in a comparative study of four building material categories. Findings indicate that post-consumer content in synthetic products reduces toxicity and health-related impacts, while recycled content in extracted natural materials increases their toxicity and health impacts. The article concludes with a discussion about the implications of the findings, survey limitations, and future work. Full article

Background: It is generally accepted that water as a plasticizer can decrease the glass transition temperatures (T_gs) of amorphous drugs and drug delivery systems, resulting in physical instabilities. However, a recent study has reported an anti-plasticizing effect of water on amorphous lidocaine (LID). In co-amorphous systems, LID might be used as a co-former to impair the plasticizing effect of water. Method: Flurbiprofen (FLB) was used to form a co-amorphous system with a mole fraction of LID of 0.8. The effect of water on the stability of co-amorphous FLB-LID upon hydration was investigated. The crystallization behaviors of anhydrous and hydrated co-amorphous FLB-LID systems were measured by an isothermal modulated differential scanning calorimetric (iMDSC) method. The relaxation times of the co-amorphous FLB-LID system upon hydration were measured by a broadband dielectric spectroscopy (BDS), and the differences in Gibbs free energy (ΔG) and entropy (ΔS) between the amorphous and crystalline phases were determined by differential scanning calorimetry (DSC). Results: It was found that the crystallization tendency of co-amorphous FLB-LID decreased with the addition of water. Molecular mobility and thermodynamic factors were both investigated to explain the difference in crystallization tendencies of co-amorphous FLB-LID upon hydration. Conclusions: The results of the study showed that LID could be used as an effective co-former to decrease the crystallization tendency of co-amorphous FLB-LID upon hydration by enhancing the entropic (ΔS) and thermodynamic activation barriers (TΔS)³/ΔG²) to crystallization. Full article

Agricultural cooperatives are the main vehicle for farmers to engage in green agriculture. With the digital transformation in rural areas, it is crucial to explore how cooperative members can effectively access online information and integrate it into green production decision-making processes. Based on the survey data of 530 members of rice planting cooperatives in Heilongjiang Province in China, this paper selected eight green production behaviors commonly used by rice farmers as explained variables, and constructed an ordered probit model. Using the social capital theory, the impact and mechanism of internet use on cooperative members’ green production behavior were examined. The results showed the following: (1) Internet use facilitates the cooperative members’ green production behavior. This conclusion remains valid even after addressing the endogeneity test and robustness test. (2) The heterogeneity analysis revealed that the internet is particularly effective in enhancing the green production behaviors of farmers who are less educated, middle-aged, and those with strong connections to cooperatives. (3) A further mechanism test indicates that internet use not only significantly influences farmers’ trust in cooperatives but also aids them in comprehending the cooperative’s production specifications, thereby further advancing the improvement in green production behaviors. (4) Members’ satisfaction with cooperative sales can serve as a substitute for the internet in influencing their green production behavior. Full article

Capsaicin (CAP), the pain-inducing compound in chili peppers, exerts its effects mainly through the transient receptor potential vanilloid channel 1 (TRPV1), which mediates pain perception and some metabolic functions. CAP has also been demonstrated to improve performance in power sports (but not endurance sports) and does so mainly for females. CAP may also have anti-cancer effects. Many mechanisms have been explored to explain these phenomena, particularly the effects of TRPV1 activation for calcium influx, glucose transporter (GLUT) upregulation and inhibition of insulin (INS) production, but two important ones seem to have been missed. We demonstrate here that CAP binds to both INS and to the estrogen receptor (ESR1), enhancing estradiol binding. Other TRPV1 agonists, such as vanillin, vanillic acid and acetaminophen, have either no effect or inhibit estrogen binding. Notably, TRPV1, ESR1 and INS share significant regions of homology that may aid in identifying the CAP-binding site on the ESR1. Because activation of the estrogen receptor upregulates GLUT expression and thereby glucose transport, we propose that the observed enhancement of performance in power sports, particularly among women, may result, in part, from CAP enhancement of ESR1 function and prevent INS degradation. Chronic exposure to CAP, however, may result in downregulation and internalization of ESR1, as well as TRPV1 stimulation of glucagon-like peptide 1 (GLP-1) expression, both of which downregulate GLUT expression, thereby starving cancer cells of glucose. The binding of capsaicin to the ESR1 may also enhance ESR1 antagonists such as tamoxifen, benefiting some cancer patients. Full article

Environmental particulate matter (PM) exposure has been widely recognized for its significant adverse effects on human health. Monitoring PM levels is one of the essential parameters of air quality assessment. However, PM mass concentration alone does not sufficiently explain its toxicological impacts and effects on health. This study highlights the importance of oxidative potential (OP) as a promising metric for evaluating PM toxicity. It focuses on standardizing the dithiothreitol (DTT) assay as a tool for OP measurement. In order to investigate the impact of various extraction techniques, reagent concentrations, and assay conditions, four previously established protocols were tested without modification, while a novel protocol was introduced based on an extensive literature review. Results revealed strong positive correlations between the new and most established protocols. These findings highlight the significance of the new protocol in advancing the development of standardized methodologies for applying the DTT assay and demonstrating its reliability and relevance. While developing a standardized DTT assay involves addressing numerous parameters—from filter extraction to assay application—this research provides a solid base for achieving consistency in OP measurements and overcoming this critical issue. Full article

Wheat (Triticum aestivum L.) omega-5 gliadin, a major allergen responsible for wheat-dependent exercise-induced anaphylaxis in humans, is encoded by genes located at the Gli-B1 locus on chromosome 1B, which exhibits genetic polymorphism. Gli-B1 alleles have generally been identified based on the electrophoretic mobilities of the encoded gamma-, omega-1,2, and omega-5 gliadins in acid polyacrylamide gel electrophoresis. However, the similar mobilities of omega-5 gliadin variants make it difficult to distinguish them among different wheat varieties. In this study, we optimized reverse-phase–ultra-performance liquid chromatography (RP-UPLC) conditions to separate omega-5 gliadins in the reference wheat cultivar Chinese Spring and its nullisomic–tetrasomic lines for chromosome 1B. Five chromatographic peaks corresponded to omega-5 gliadin, and the average relative standard deviation to each peak retention time ranged from 0.31% to 0.93%, indicating that the method is accurate and reproducible for fractionating omega-5 gliadins in gliadin extracts from wheat flour. Using the optimized RP-UPLC method, we analyzed omega-5 gliadins in 24 wheat varieties with the Gli-B1f allele. The result showed that the wheat varieties were sorted into eight groups according to the composition of omega-5 gliadin, indicating that the classification of Gli-B1 alleles based on A-PAGE could not explain the composition of omega-5 gliadin in wheat. We reclassified 73 wheat varieties containing 16 unique Gli-B1 alleles into 31 groups based on the chromatographic patterns of their omega-5 gliadins. Our results provide information on the specific Gli-B1 alleles of wheat varieties belonging to each group and demonstrate the potential for RP-UPLC to facilitate genetic studies of wheat varieties. Full article

Against the backdrop of rapid global urbanization, studying the spatiotemporal evolution of cross-regional tourism corridors can effectively guide decision-making for sustainable tourism development. However, previous studies have overlooked the role of geographical space in the construction of tourism corridors, particularly the spatiotemporal characteristics of ecological and socio-economic factors. Taking the central region of the Yangtze River Delta (YRD) in China as a case study, this research utilizes remote sensing images, POI data, and other datasets from 2000, 2010, and 2020. Through a combination of landscape value assessment, resistance surface evaluation system, MCR model, and geographical detector, the study examines the spatiotemporal evolution characteristics of cross-regional tourism corridors and their potential influencing factors. The results indicate that (1) between 2000 and 2020, the areas with prominent landscape value in the core region of the YRD decreased, while areas with less prominent landscape value significantly increased. However, the overall distribution became increasingly fragmented. The resistance values in the main low-resistance areas of the study region continuously increased, and the gap between high- and low-resistance areas narrowed. (2) Over the 20-year period, the total length of the corridors in the study area increased by 333.3%, with the number of corridors rising from 91 to 435. The number of source points grew from 14 to 31, and corridor density increased from 0.04% to 0.19%. The growth rate was fastest from 2000 to 2010 and then gradually slowed down. (3) In terms of influencing factors, population density and road length together explained 62.3% of the variation in corridor length evolution. The evolution of corridor number and source points was mainly influenced by public infrastructure levels and road density, while the evolution of corridor density was primarily driven by road length and public infrastructure. In conclusion, we analyze the spatiotemporal evolution characteristics and influencing factors of cross-regional tourism corridors from the perspective of tourism geography at multiple scales. The findings provide significant insights into promoting the integration of cross-regional tourism resources, achieving sustainable development of all-region tourism, and optimizing the spatial allocation of territorial resources. Full article

The refinement of acquired well logs has traditionally relied on predefined rock physics models, albeit with their inherent limitations and assumptions. As an alternative, effective yet less explicit machine learning (ML) techniques have emerged. The integration of these two methodologies presents a promising new avenue. In our study, we used four ML algorithms: Random Forests (RF), Gradient Boosting Decision Trees (GBDT), Multilayer Perceptrons (MLP), and Linear Regression (LR), to predict porosity and clay volume fraction from well logs. Throughout the entire workflow, from feature engineering to outcome interpretation, our predictions are guided by rock physics principles, particularly the Gardner relations and the Larionov relations. Remarkably, while the predictions themselves are satisfactory, SHapley Additive exPlanations (SHAP) analysis uncovers consistent patterns across the four algorithms, irrespective of their distinct underlying structures. By juxtaposing the SHAP explanations with rock physics concepts, we discover that all four algorithms align closely with rock physics principles, adhering to its cause–effect relationships. Nonetheless, even after intentionally excluding crucial controlling input features that would inherently compromise prediction accuracy, all four ML algorithms and the SHAP analysis continue to operate, albeit in a manner that seems irrational and starkly contradicts the fundamental principles of rock physics. This integration strategy facilitates a transition from solely mathematical explanations to a more philosophical interpretation of ML-based predictions, effectively dismantling the traditional black box nature of these ML models. Full article

While traditional self-supervised learning methods improve performance and robustness across various medical tasks, they rely on single-vector embeddings that may not capture fine-grained concepts such as anatomical structures or organs. The ability to identify such concepts and their characteristics without supervision has the potential to improve pre-training methods, and enable novel applications such as fine-grained image retrieval and concept-based outlier detection. In this paper, we introduce ConceptVAE, a novel pre-training framework that detects and disentangles fine-grained concepts from their style characteristics in a self-supervised manner. We present a suite of loss terms and model architecture primitives designed to discretise input data into a preset number of concepts along with their local style. We validate ConceptVAE both qualitatively and quantitatively, demonstrating its ability to detect fine-grained anatomical structures such as blood pools and septum walls from 2D cardiac echocardiographies. Quantitatively, ConceptVAE outperforms traditional self-supervised methods in tasks such as region-based instance retrieval, semantic segmentation, out-of-distribution detection, and object detection. Additionally, we explore the generation of in-distribution synthetic data that maintains the same concepts as the training data but with distinct styles, highlighting its potential for more calibrated data generation. Overall, our study introduces and validates a promising new pre-training technique based on concept-style disentanglement, opening multiple avenues for developing models for medical image analysis that are more interpretable and explainable than black-box approaches. Full article