Search Results (265)

This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG signals corresponding to high and low concentration levels are recorded while participants engage in quizzes to learn and memorize Chinese characters. The attention levels are determined based on performance metrics derived from the quiz results. Following extensive preprocessing, the EEG data undergoes severmal feature extraction steps: removal of artifacts due to eye blinks and facial movements, segregation of waves based on their frequencies, similarity indexing with respect to delay, binary thresholding, and (PCA). These extracted features are then fed into a k-NN classifier, which accurately distinguishes between high and low attention brain wave patterns, with the labels derived from the quiz performance indicating high or low attention. During the implementation phase, the system continuously monitors the user’s EEG signals while studying. When low attention levels are detected, the system increases the repetition frequency and reduces the difficulty of the flashcards to refocus the user’s attention. Conversely, when high concentration levels are identified, the system escalates the difficulty level of the flashcards to maximize the learning challenge. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. Our results indicate that this EEG-based adaptive learning system holds significant potential for personalized education, fostering better retention and understanding of Chinese characters. Full article

Massively parallel or next-generation sequencing (NGS) has enabled the genetic characterization of cancer patients, allowing the identification of somatic and germline variants associated with their diagnosis, tumor classification, and therapy response. Despite its benefits, NGS testing is not yet available in the Chilean public health system, rendering it both costly and time-consuming for patients and clinicians. Using a retrospective cohort of 67 formalin-fixed, paraffin-embedded (FFPE) colorectal cancer (CRC) samples, we aimed to implement the identification, annotation, and prioritization of relevant actionable tumor somatic variants in our laboratory, as part of the public health system. We compared two different library preparation methodologies (amplicon-based and capture-based) and different bioinformatics pipelines for sequencing analysis to assess advantages and disadvantages of each one. We obtained 80.5% concordance between actionable variants detected in our analysis and those obtained in the Cancer Genomics Laboratory from the Universidad de Chile (62 out of 77 variants), a validated laboratory for this methodology. Notably, 98.4% (61 out of 62) of variants detected previously by the validated laboratory were also identified in our analysis. Then, comparing the hybridization capture-based library preparation methodology with the amplicon-based strategy, we found ~94% concordance between identified actionable variants across the 15 shared genes, analyzed by the TumorSec^TM bioinformatics pipeline, developed by the Cancer Genomics Laboratory. Our results demonstrate that it is entirely viable to implement an NGS-based analysis of actionable variant identification and prioritization in cancer samples in our laboratory, being part of the Chilean public health system and paving the way to improve the access to such analyses. Considering the economic realities of most Latin American countries, using a small NGS panel, such as TumorSec^TM, focused on relevant variants of the Chilean and Latin American population is a cost-effective approach to extensive global NGS panels. Furthermore, the incorporation of automated bioinformatics analysis in this streamlined assay holds the potential of facilitating the implementation of precision medicine in this geographic region, which aims to greatly support personalized treatment of cancer patients in Chile. Full article

Background: As a disease with a still largely unknown course, post-COVID requires a comprehensive multidimensional perspective and structured monitoring, as offered by the C19-YRSm. There has not yet been a German version of the scale. Methods: After the translation of the COVID-19 Yorkshire Rehabilitation Scale (modified version, C19-YRSm) into German, we conducted an online survey with it between 23 May 2023 and 10 May 2024 for patients with post-COVID condition. Participation took place twice; people received either only the German version or both the German and the original English versions of the scale at one-week intervals. Based on the results, reliability and validity of the German version of the C19-YRSm were extensively tested. Results: Data of 414 participants were analysed, 156 of whom took part twice at least seven days apart. Cronbach’s alpha coefficients of the subscales of the German version ranged from 0.75 to 0.93. In the English version, it ranged from 0.82 to 0.93. All the subscales correlate with each other with p < 0.001. For the overall scale and for three of the four subscales, the intraclass coefficients, as a measure of the agreement between measurement results at two points in time, showed good consistency. Conclusions: This study confirmed the reliability, applicability, and clinical utility of the C19-YRSm, aligning with previous studies. With its multidimensional structure and excellent quality criteria, the C19-YRS is a valuable asset for clinical practice and research. The validated German C19-YRSm holds significant potential to facilitate tailored interventions, destigmatise post-COVID conditions, and enhance patient care in medical contexts. Full article

Loess Plateau is the region with the most concentrated loess distribution and the deepest loess soil layer in the world, and it is facing serious problems of soil erosion and ecological degradation. The nano carbon modification of soil surface properties is a novel strategy for soil improvement and enhancing the soil’s capacity to sequester carbon, which has been extensively researched. However, the mechanisms underlying the influence of carbon surface structure on the efficacy of loess soil remediation remain unclear. Herein, graphene oxide (GO) with a unique two-dimensional structure and adjustable surface properties was optimized as a model carbon filler to investigate the modification effect on loess. As a result, the addition amount of 0.03% GO significantly reduced the disintegration amount of loess, but, if inhibited for a long time, the disintegration effect would weaken. The highly reduced GO can delay the loess disintegration rate due to its enhanced hydrophobicity, but the inhibitory effect fails over a long period of time. After adjusting the reduce degree with a 50% SA (sodium ascorbate), the water-holding capacity of the modified soil in the high suction range is enhanced. This study reveals the synergistic mechanism of the sheet structure and surface properties of GO on the water stability of loess, providing a reference for the prevention and control of soil erosion and ecological restoration in the Loess Plateau. Full article

The ability to perform instantaneous frequency measurement (IFM) of unknown microwave signals holds significant importance across various application domains. This paper presents a power-independent microwave photonic IFM system. The proposed system implements frequency measurement through the construction of an amplitude comparison function (ACF) curve, achieved by introducing a frequency-dependent time delay via an optical tunable delay line (OTDL) for the signal under test (SUT). System simulation demonstrates the measurement capability across a wide bandwidth of 0.1–40 GHz with high precision, exhibiting frequency errors ranging from −0.03 to 0.04 GHz. The scheme also maintains consistent performance under varying input power levels. Key implementation aspects, including single-sideband modulation selection and system extension methods, are analyzed in detail to optimize measurement accuracy. Notably, the proposed architecture features a simple and compact design with excellent integration potential. These characteristics, combined with its wide operational bandwidth and high measurement precision, make this approach particularly suitable for demanding applications in electronic reconnaissance and communication. Full article

Extraterrestrial exploration presents unique challenges for robotic systems, as traditional rigid rovers face limitations in stowage volume, traction on unpredictable terrain, and susceptibility to damage. Soft robotics offers promising solutions through bio-inspired designs that can mimic natural locomotion mechanisms. Here, we present an optimised, spider-inspired soft jumping robot for extraterrestrial exploration that addresses key challenges in soft robotics: actuation efficiency, controllability, and deployment. Drawing inspiration from spider physiology—particularly their hydraulic extension mechanism—we develop a lightweight limb capable of multi-modal behaviour with significantly reduced energy requirements. Our 3D-printed soft actuator leverages pressure-driven collapse for efficient retraction and pressure-enhanced rapid extension, achieving a power-to-weight ratio of 249 W/kg. The integration of a non-backdriveable clutch mechanism enables the system to hold positions with zero energy expenditure—a critical feature for space applications. Experimental characterisation and a subsequent optimisation methodology across various materials, dimensions, and pressures reveal that the robot can achieve jumping heights of up to 1.86 times its body length. The collapsible nature of the soft limb enables efficient stowage during spacecraft transit, while the integrated pumping system facilitates self-deployment upon arrival. This work demonstrates how biologically inspired design principles can be effectively applied to develop versatile robotic systems optimised for the unique constraints of extraterrestrial exploration. Full article

Under complex operating conditions, vibration signals from rotating machinery often exhibit non-stationary characteristics with non-proportional and closely spaced instantaneous frequency (IF) components. Traditional time–frequency analysis (TFA) methods struggle to accurately extract such features due to energy leakage and component mixing. In response to these issues, an enhanced time–frequency analysis approach, termed Local Entropy Selection Scaling–Reassigning Chirplet Transform (LESSRCT), has been developed to improve the representation accuracy for complex non-stationary signals. This approach constructs multi-channel time–frequency representations (TFRs) by introducing multiple scales of chirp rates (CRs) and utilizes a Rényi entropy-based criterion to adaptively select multiple optimal CRs at the same time center, enabling accurate characterization of multiple fundamental components. In addition, a frequency reassignment mechanism is incorporated to enhance energy concentration and suppress spectral diffusion. Extensive validation was conducted on a representative synthetic signal and three categories of real-world data—bat echolocation, inner race bearing faults, and wind turbine gearbox vibrations. In each case, the proposed LESSRCT method was compared against SBCT, GLCT, CWT, SET, EMCT, and STFT. On the synthetic signal, LESSRCT achieved the lowest Rényi entropy of 13.53, which was 19.5% lower than that of SET (16.87) and 35% lower than GLCT (18.36). In the bat signal analysis, LESSRCT reached an entropy of 11.53, substantially outperforming CWT (19.91) and SBCT (15.64). For bearing fault diagnosis signals, LESSRCT consistently achieved lower entropy across varying SNR levels compared to all baseline methods, demonstrating strong noise resilience and robustness. The final case on wind turbine signals demonstrated its robustness and computational efficiency, with a runtime of 1.31 s and excellent resolution. These results confirm that LESSRCT delivers robust, high-resolution TFRs with strong noise resilience and broad applicability. It holds strong potential for precise fault detection and condition monitoring in domains such as aerospace and renewable energy systems. Full article

Antithrombin (AT) plays a crucial role in the human anticoagulant system and has extensive clinical applications. However, traditional detection methods often require large sample volumes, complex procedures, and lengthy processing times. Methods: We integrated digital microfluidics technology with AT detection to develop a point-of-care testing (POCT) device that is user-friendly and fully automated for real-time AT testing. Results: This device allows for automation and enhanced adaptability to various settings, requiring only a minimal sample volume (whole capillary blood), thereby omitting steps such as plasma separation to save time and improve clinical testing efficiency. Comparisons with conventional AT activity detection methods demonstrate a high degree of consistency in the results obtained with this device. Conclusion: The AT detection system we developed exhibits significant effectiveness and holds substantial research potential, positioning it to evolve into a clinically impactful POCT solution for AT assessment. Full article

Rural areas exhibit a high prevalence of poverty. As significant progress in poverty reduction has been achieved, research on rural livelihoods has transitioned from a focus on poverty eradication to preventing poverty recurrence and fostering development. Development resilience, which has emerged as a pivotal research area in poverty governance, is a crucial metric for assessing rural households’ long-term capacity to avoid falling back into poverty, considering the multi-dimensional aspects of poverty and welfare dynamics. Utilizing data from the Academy of Agricultural Sciences, this study investigates the impact of land inflow on rural household’s development resilience (RHDR). Findings reveal that land inflow significantly enhances RHDR, a conclusion that holds after extensive robustness checks. Mechanism analysis shows that while land inflow initially imposes a financial burden, it eventually acts as an exogenous driver and causes labor force return and economies of scale, boosting RHDR over time. This effect is more pronounced among non-vulnerable households, those with abundant water resources and strong collective awareness. Therefore, it is recommended to refine land inflow systems, reduce barriers to land resource flow, and implement targeted support for vulnerable groups during the initial stages of land inflow to effectively promote rural revitalization through land transfer. Full article

Chromoanagenesis is a catastrophic genomic phenomenon involving sudden, extensive rearrangements within one or a few cell cycles. In osteosarcoma, the most prevalent malignant bone tumor in children and adolescents, these events dramatically alter the genomic landscape, frequently disrupting key tumor suppressor genes like TP53 and RB1, amplifying oncogene expression, and propelling tumor progression and evolution. This review elucidates how key chromoanagenic mechanisms, such as chromothripsis and chromoanasynthesis, arise from replication stress and impaired DNA repair pathways, ultimately contributing to genomic instability in osteosarcoma. Chromothripsis features prominently in osteosarcoma, occurring in up to 62% of tumor regions and driving intratumoral heterogeneity through persistent genomic crises. Next-generation sequencing, optical genome mapping, and emerging technologies like single-cell sequencing empower researchers to detect and characterize these complex structural variants, demonstrating how a single catastrophic event can profoundly influence osteosarcoma progression over time. While targeted therapies for osteosarcoma have proven elusive, innovative strategies harnessing comprehensive genomic profiling and patient-derived preclinical models hold promise for uncovering tumor-specific vulnerabilities tied to chromoanagenesis. Ultimately, unraveling how these rapid, large-scale rearrangements fuel osteosarcoma’s aggressive nature will not only refine disease classification and prognosis but also pave the way for novel therapeutic approaches to enhance patient outcomes. Full article

Microservice deployment methods in edge-native computing environments hold great potential for minimizing user application response time. However, most existing studies overlook the communication overhead between microservices and controllers, as well as the impact of microservice pull time on user response time. To address these issues, this paper proposes a multi-controller service mesh architecture to reduce data transfer overhead between microservices and controllers. Furthermore, we formulate the microservice deployment problem as an optimization problem aimed at minimizing both system communication overhead and microservice deployment cost. To achieve this, we introduce a novel RIME optimization algorithm and enhanced Adaptive Crested Porcupine Optimizer (RIME-ACPO) algorithm that optimizes microservice placement decisions. Notably, this algorithm incorporates a real-time resource monitoring-based load balancing algorithm, dynamically adjusting microservice deployment according to edge server resource utilization to enhance the execution performance of user applications. Finally, extensive simulation experiments were conducted to validate the effectiveness of the proposed algorithm. The experimental results demonstrate that, compared with other algorithms, our algorithm significantly improves user response time, optimizes resource utilization, and reduces the total cost. Full article

Federated learning (FL), as a distributed machine learning framework, enables multiple participants to jointly train models without sharing data, thereby ensuring data privacy and security. However, FL systems still struggle to escape the typical poisoning threat launched by Byzantine nodes. The current defence measures almost all rely on the anomaly detection of local gradients in a plaintext state, which not only weakens privacy protection but also allows malicious clients to upload malicious ciphertext gradients once they are encrypted, which thus easily evade existing screenings. At the same time, mainstream aggregation algorithms are generally based on the premise that “each client’s data satisfy an independent and identically distributed (IID)”, which is obviously difficult to achieve in real scenarios where large-scale terminal devices hold their own data. Symmetry in data distribution and model updates across clients is crucial for achieving robust and fair aggregation, yet non-IID data and adversarial attacks disrupt this balance. To address these challenges, we propose FedOPCS, an optimized poisoning countermeasure for non-IID FL algorithms with privacy-preserving stability by introducing three key innovations: Conditional Generative Adversarial Network (CGAN)-based data augmentation with conditional variables to simulate global distribution, a dynamic weight adjustment mechanism with homomorphic encryption, and two-stage anomaly detection combining gradient analysis and model performance evaluation. Extensive experiments on MNIST and CIFAR-10 show that, in the model poisoning and mixed poisoning environments, FedOPCS outperforms the baseline methods by 11.4% and 4.7%, respectively, while maintaining the same efficiency as FedAvg. FedOPCS therefore offers a privacy-preserving, Byzantine-robust, and communication-efficient solution for future heterogeneous FL deployments. Full article

Facing engineering challenges of real-time and high-precision recognition of strongly heterogeneous formations during directional drilling, it is crucial to address the issues of sparse lithology geological labels and multi-source lithology identification from LWD data. This paper proposes a real-time intelligent recognition method for strongly heterogeneous formations based on multi-parameter logging while drilling and drilling engineering, which can effectively guide directional drilling operations. Traditional supervised learning methods rely heavily on extensive lithology labels and rich wireline logging data. However, in LWD applications, challenges such as limited sample sizes and stringent real-time requirements make it difficult to achieve the accuracy needed for effective geosteering in strongly heterogeneous reservoirs, thereby impacting the reservoir penetration rate. In this study, we comprehensively utilize LWD parameters (six types, including natural gamma and electrical resistivity, etc.) and drilling engineering parameters (four types, including drilling rate and weight on bit, etc.) from offset wells. The UMAP algorithm is employed for nonlinear dimensionality reduction, which not only integrates the dynamic response characteristics of drilling parameters but also preserves the sensitivity of logging data to lithological variations. The K-means clustering algorithm is employed to extract the deep geo-engineering characteristics from multi-source LWD data, thereby constructing a lithology label library and categorizing the training and testing datasets. The optimized CatBoost machine learning model is subsequently utilized for lithology classification, enabling real-time and high-precision geological evaluation during directional drilling. In the Hugin Formation of the Volve field in the Norwegian North Sea, the application of UMAP demonstrates superior data separability compared with PCA and t-SNE, effectively distinguishing thin reservoirs with strong heterogeneity. The CatBoost model achieves a balanced accuracy of 92.7% and an F1-score of 89.3% in six lithology classifications. This approach delivers high-precision geo-engineering decision support for the real-time control of horizontal well trajectories, which holds significant implications for the precision drilling of strongly heterogeneous reservoirs. Full article

Background/Objectives: The annexin A1 (AnxA1) protein has proven important in ocular disease homeostasis and holds great therapeutic promise. However, its role in the context of the healthy retina remains unknown. Therefore, this study used electroretinography (ERG) to investigate the role of endogenous AnxA1 in the retinal function of wild-type (WT) and AnxA1 knockout mice (AnxA1^−/−). Methods: An extensive repertoire of full-field ERG was applied to AnxA1^−/− and WT mice to examine retinal physiology. Morphometric analyses of the retina were conducted. Results: Our results revealed significant differences in the implicit time of a-wave and b-wave between the WT and AnxA1^−/− groups under scotopic conditions. The negative and positive amplitude components of mesopic ON responses were higher in the AnxA1^-/- group than in the WT group. In contrast, the implicit time of mesopic ON responses were significantly higher in the WT group than in the AnxA1^-/- WT group. However, in photopic OFF responses, only the implicit time was significantly longer in the WT group than in the AnxA1^−/− group. In the histomorphometric analysis, the retina of AnxA1^−/− mice shows increased thickness. Conclusions: The absence of AnxA1 alters retinal morphology and physiology. Full article

Modern SiC-based materials are of paramount importance in that they serve as wear-resistant and thermal protectors and as next-generation single-photon sources for photonic and quantum solutions. Efforts are underway to identify more efficient methods of manufacturing SiC-based ceramic materials. The objective of this paper is to provide a description of the optimization of sintering SiC–TiC composites by the electroconsolidation method. The influence of titanium carbide content on the physical and mechanical properties of SiC–TiC composites obtained by spark plasma sintering (SPS) at a pressure of 45 MPa was studied. It was found that compared to sintered silicon carbide, the porosity of composites with 40 mol% TiC decreased from ~30% to 0%, the crack resistance increased from 2.9 to 6.1 MPa × m^0.5, and the hardness increased from 2.9 to 21.5 GPa. The influence of sintering temperature and holding time on SiC–TiC composites’ physical and mechanical properties during sintering at a pressure of 45 MP was also investigated. An increase in temperature from 1900 °C to 2000 °C resulted in an approximately 30% rise in the composite hardness. An extension of the time allotted for the sintering process from 30 to 45 min resulted in a decrease in both the fracture toughness and hardness of the material. The utilization of two- and three-dimensional vector spaces of material features was proposed as a novel methodology for the description of manufacturing process optimization. Full article