Search Results (3,171)

Stroke is a major global cause of death and disability and necessitates both quick diagnosis and treatment within narrow windows of opportunity. CT scanning is still the first-line imaging in the acute phase, but correct interpretation may not always be readily available and may not be resource-available in poor and rural health systems. Automated stroke classification systems can offer useful diagnostic assistance, but clinical application demands high accuracy and explainable decision-making to maintain physician trust and patient safety. In this paper, a ResNet-18 model was trained on 6653 CT brain scans (hemorrhagic stroke, ischemia, normal) with two-phase fine-tuning and transfer learning, XRAI explainability analysis, and web-based clinical decision support system integration. The model performed with 95% test accuracy with good performance across all classes. This system has great potential for emergency rooms and resource-poor environments, offering quick stroke evaluation when specialists are not available, particularly by rapidly excluding hemorrhagic stroke and assisting in the identification of ischemic stroke, which are critical steps in considering tissue plasminogen activator (tPA) administration within therapeutic windows in eligible patients. The combination of classification, explainability, and clinical interface offers a complete framework for medical AI implementation. Full article

Genitourinary (GU) cancers, including prostate, bladder, and renal cancers, represent a significant burden on global health. Conventional treatments, while effective in certain contexts, face limitations due to tumor heterogeneity, therapeutic resistance, and relapse. Recent advances in cancer immunotherapy, particularly in the development of personalized mRNA and DNA-based vaccines, have opened new avenues for precise and durable antitumor responses. These vaccines are being developed to leverage neoantigen identification and next-generation sequencing technologies, with the goal of tailoring immunotherapeutic interventions to individual tumor profiles. mRNA vaccines offer rapid, non-integrative, and scalable, with encouraging results reported in infectious diseases and early-phase cancer trials. DNA vaccines, known for their stability and ease of modification, show promise in generating robust cytotoxic T-cell responses. This review discusses the current landscape, preclinical findings, and ongoing clinical trials of mRNA and DNA-based vaccines in GU cancers, highlighting delivery technologies, combination strategies with immune checkpoint inhibitors, and future challenges, including tumor immune evasion and regulatory hurdles. Integrating immunogenomics and artificial intelligence into vaccine design is poised to further enhance precision in cancer vaccine development. As GU malignancies remain a leading cause of cancer-related morbidity and mortality, mRNA and DNA vaccine strategies represent a promising and rapidly evolving area of investigation in oncology. Full article

Cell detection-tracking tasks are vital for biomedical image analysis with potential applications in clinical diagnosis and treatment. However, it poses challenges such as ambiguous boundaries and complex backgrounds in microscopic video sequences, leading to missed detection, false detection, and loss of tracking. Therefore, we propose an enhanced multiple object tracking algorithm IEGS-YOLO + BoT-SORT, named IEGS-BoT, to address these issues. Firstly, the IEGS-YOLO detector is developed for cell detection tasks. It uses the iEMA module, which effectively combines the global information to enhance the local information. Then, we replace the traditional convolutional network in the neck of the YOLO11n with GSConv to reduce the computational complexity while maintaining accuracy. Finally, the BoT-SORT tracker is selected to enhance the accuracy of bounding box positioning through camera motion compensation and Kalman filter. We conduct experiments on the CTMC dataset, and the results show that in the detection phase, the map50 (mean Average Precision) and map50–95 values are 73.2% and 32.6%, outperforming the YOLO11n detector by 1.1% and 0.6%, respectively. In the tracking phase, using the IEGS-BoT method, the multiple objects tracking accuracy (MOTA), higher order tracking accuracy (HOTA), and identification F1 (IDF1) reach 53.97%, 51.30%, and 67.52%, respectively. Compared with the base BoT-SORT, the proposed method achieves improvements of 1.19%, 0.23%, and 1.29% in MOTA, HOTA, and IDF1, respectively. ID switch (IDSW) decreases from 1170 to 894, which demonstrates significant mitigation of identity confusion. This approach effectively addresses the challenges posed by object loss and identity switching in cell tracking, providing a more reliable solution for medical image analysis. Full article

The present study offers a systematic review of the current state of research on lattice structures manufactured by additive technologies for biomedical applications, with the aim of identifying common patterns, such as the use of triply periodic minimal surfaces (TPMS) for bone scaffolds, as well as technological gaps and future research opportunities. Employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, the review process ensures methodological rigor and replicability across the identification, screening, eligibility, and inclusion phases. Additionally, PRISMA was tailored by prioritizing technical databases and engineering-specific inclusion criteria, thereby aligning the methodology with the scope of this field. In recent years, a substantial surge in interdisciplinary research has underscored the promise of architected porous structures in enhancing mechanical compatibility, fostering osseointegration, and facilitating personalized medicine. A growing body of literature has emerged that explores the optimization of geometric features to replicate the behavior of biological tissues, particularly bone. Additive manufacturing (AM) has played a pivotal role in enabling the fabrication of complex geometries that are otherwise unachievable by conventional methods. The applications of lattice structures range from permanent load-bearing implants, commonly manufactured through selective laser melting (SLM), to temporary scaffolds for tissue regeneration, often produced with extrusion-based processes such as fused filament fabrication (FFF) or direct ink writing (DIW). Notwithstanding these advances, challenges persist in areas such as long-term in vivo validation, standardization of mechanical and biological testing, such as ISO standards for fatigue testing, and integration into clinical workflows. Full article

Pursuing quality and sustainability concerns in construction activities is not new. However, the construction sector continues to face criticism for the outcome of many interventions, and significant progress is still required to realise both objectives. This is particularly pressing in sectors essential for quality of life and wellbeing, such as housing, and in areas frequently neglected in research and practice, such as existing buildings. This paper provides insights into the assessment of quality and sustainability in existing buildings, clarifying these concerns, exploring their interrelationship, emphasising the critical role of the design phase, and synthesising relevant methodologies focused on each objective. Furthermore, a novel methodology is proposed to minimise the risk of poor quality in building rehabilitation processes. Methodologically, the paper includes a review of concepts associated with quality and sustainability in building rehabilitation, a synthesis of existing evaluation tools and methods, and the development of the proposed methodology. The main findings include a definition of construction quality, identification of strong correlations between quality and sustainability, and the recognition that using accessible, flexible, and collaborative tools during the design phase is crucial to achieving both objectives, especially in the context of existing buildings, where practical and operational outcomes remain limited. Full article

The assessment of upper limb (UL) movement patterns plays a critical role in the rehabilitation of individuals with motor impairments resulting from neuromotor disorders, which significantly affect essential activities of daily living (ADLs) such as drinking and eating. However, conventional clinical evaluation methods often lack objective and quantitative insights into the biomechanics of movement. To enable accurate identification of pathological patterns, it is first necessary to establish normative biomechanical and electrophysiological benchmarks in healthy individuals. In this study, a previously developed, low-cost, wearable, and portable prototype device was employed to objectively assess UL movement. The system, specifically designed for clinical applicability, integrates surface electromyography (EMG) sensors and an inertial measurement unit (IMU) to capture muscle activity and kinematic data, respectively. Thirty healthy participants were recruited to perform standardized drinking and eating tasks. The analysis focused on characterizing muscle activation patterns and joint range of motion during different task phases. Results revealed consistent variations in muscle contraction and joint kinematics, allowing the identification of distinct activation profiles for key shoulder muscles. The findings contribute to the establishment of a normative dataset that can serve as a reference for the assessment of clinical populations. Such data are essential for informing rehabilitation strategies and developing predictive models of UL function during ADLs in individuals with neuromotor disorders. Full article

As IoT (internet of things) devices grow in prominence, safeguarding them from cyberattacks is becoming a pressing challenge. To bootstrap IoT security, device identification or authentication is crucial for establishing trusted connections among devices without prior trust. In this regard, radio frequency fingerprinting (RFF) is gaining attention because it is more efficient and requires fewer computational resources compared to resource-intensive cryptographic methods, such as digital signatures. RFF works by identifying unique manufacturing defects in the radio circuitry of IoT devices by analyzing over-the-air signals that embed these imperfections, allowing for the identification of the transmitting hardware. Recent studies on RFF often leverage advanced classification models, including classical machine learning techniques such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), as well as modern deep learning architectures like Convolutional Neural Network (CNN). In particular, CNNs are well-suited as they use multidimensional mapping to detect and extract reliable fingerprints during the learning process. However, a significant limitation of these approaches is that they require large datasets and necessitate retraining when new devices not included in the initial training set are added. This retraining can cause service interruptions and is costly, especially in large-scale IoT networks. In this paper, we propose a novel solution to this problem: RFF using Siamese networks, which eliminates the need for retraining and allows for seamless authentication in IoT deployments. The proposed Siamese network is trained using in-phase and quadrature (I/Q) samples from 10 different Software-Defined Radios (SDRs). Additionally, we present a new algorithm, the Similarity-Based Embedding Classification (SBEC) for RFF. We present experimental results that demonstrate that the Siamese network effectively distinguishes between malicious and trusted devices with a remarkable 98% identification accuracy. Full article

This study aims to develop a performance evaluation system specifically for the design phase of high-rise building projects within the architecture, engineering, and construction industry, where performance is often only measured during construction. The research process included three stages: identification of 21 key performance indicators through a literature review and expert validation; development of standardized indicator sheets detailing calculation protocols and data collection procedures; and creation of a functional dashboard-based evaluation system using Excel. The system was validated through expert review and tested with a simulated project generated using an AI-based language model. The evaluation system proved functional, accessible, and effective in detecting performance issues across five core categories: planning, cost, time, quality, and people. The results from the simulated application highlighted strengths in quality and stakeholder collaboration but also revealed significant gaps in cost and time performance. This study addresses a gap in the existing literature by focusing on performance evaluation during the design phase of construction projects, a stage often underrepresented in performance studies. The resulting system offers a structured, practical tool adaptable to real-world projects. The validation relied on a limited number of expert participants and a simulated project. Future research should recommend broader international validation and real-world application. Full article

Background: Liuwei Dihuang Pills, a classic traditional Chinese medicine formula, has been widely used in clinical practice for its multiple pharmacological effects. However, the systematic characterization and identification of its chemical constituents, especially the aqueous decoction, remain insufficient, which hinders in-depth research on its pharmacodynamic material basis. Thus, there is an urgent need for a comprehensive analysis of its chemical components using advanced analytical techniques. Methods: After screening chromatographic columns, the ACQUITY UPLC™ HSS T3 column (100 mm × 2.1 mm, 1.8 μm) was selected. The column temperature was set to 40 °C, and the mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). A gradient elution program was adopted, and the separation was completed within 20 min. Ultra-high performance liquid chromatography–Orbitrap mass spectrometry (UPLC-Orbitrap-MS) combined with a self-established information database was used for the analysis. Results: A total of 80 compounds were tentatively identified, including 13 monoterpenoids, 6 phenolic acids, 16 iridoids, 11 flavonoids, 25 triterpenoids, and 9 other types. Triterpenoids are mainly derived from Poria cocos and Alisma orientale; iridoids are mainly from Rehmannia glutinosa; monoterpenoids are mainly from Moutan Cortex; and flavonoids are mainly from Dioscorea opposita. Among them, monoterpenoids, iridoids, and triterpenoids are important pharmacodynamic components. The cleavage pathways of typical compounds (such as pachymic acid, catalpol, oxidized paeoniflorin, and puerarin) are clear, and their mass spectral fragment characteristics are consistent with the literature reports. Conclusions: Through UPLC-Orbitrap-MS technology and systematic optimization of conditions, this study significantly improved the coverage of chemical component identification in Liuwei Dihuang Pills, providing a comprehensive reference for the research on its pharmacodynamic substances. However, challenges remain in the identification of trace components and isomers. In the future, analytical methods will be further improved by combining technologies such as ion mobility mass spectrometry or multi-dimensional liquid chromatography. Full article

In distribution networks, single-phase ground faults often lead to abnormal changes in voltage and current signals. Traditional single-modal fault diagnosis methods usually struggle to accurately identify the fault line under such conditions. To address this issue, this paper proposes a fault line identification method based on a multimodal feature fusion model. The approach combines time-frequency images—generated using a Short-Time Fourier Transform (STFT) and Wigner–Ville Distribution (WVD) fusion algorithm with one-dimensional time-series signals for classification. The time-frequency images visualize both temporal and spectral features of the signal and are processed using the RepLKNet model for deep feature extraction. Meanwhile, the raw one-dimensional time-series signals preserve the original temporal dependencies and are analyzed using a BiGRU network enhanced with a global attention mechanism to improve feature representation. Finally, features from both modalities are extracted in parallel and fused to achieve accurate fault line identification. Experimental results demonstrate that the proposed method effectively leverages the complementary nature of multimodal data and shows strong robustness in the presence of noise interference. Full article

Metaphorical instructions are widely used in motor skill learning, yet their impact on learning and memory processes in children remains underexplored. This study examined whether metaphor-based language could enhance children’s acquisition and recall of body posture-related motor skills in an online learning environment. Forty-eight children aged 7 to 9 were randomly assigned to receive either metaphorical or explicit verbal instructions while learning 15 gymnastic postures demonstrated through static images. Following the learning phase, participants completed a free recall task, in which they reproduced the learned postures without cues, and a recognition task involving the identification of previously learned postures. Results indicated that children in the metaphor group recalled significantly more postures than those in the explicit group, with no reduction in movement quality. However, no group differences were observed in recognition accuracy or discrimination sensitivity. These findings suggest that metaphorical instructions may enhance children’s ability to retrieve self-generated motor representations but offer limited advantage when external cues are available. The study provides evidence for the value of metaphor-based strategies in supporting immediate motor memory in digital, child-focused learning settings and highlights the potential task-dependency of instructional language effects on memory outcomes. Full article

This essay focuses on the Greek adoptees’ search for identity and on the agrafa, or the “unwritten” territories, into which this search penetrates. The Greek adoptees represent an underresearched case study of the postwar intercountry adoption movement (1950–1975). Creating a narrative of the self is key to the adoptees’ identity formation, but their personal narrative is often undermined by stereotypes and denunciations that stunt its development. The research presented here has been guided by questions that interrogate the verdict-making or “sentencing” associated with the adoptees’ identity-shaping process: their sentencing to subjugation by stock opinions, the denouncing of their alternative viewpoints about “rescue” adoptions, and the verdict of their entrapment in feel-good master narratives. This essay also explores broader research questions pertaining to modes of interrogating “historic” adoptions from Greece. It is concerned with the why rather than with the how or the who of the oldest, post-WWII intercountry adoption flows. In what forums and genres (narrative, visual, journalistic, scholarly) are Greek adoption facts and legacies articulated, mediated, and/or materialized? How do memories, both positive and negative, underpin current projects of self-identification and transformation? What are the adoptees’ preferred outlets to speak about embodied experiences, and are those satisfactory? Based on a mixed methods approach, the essay ties these steps in identity growth to the Adoptee Consciousness Model, illustrating the five phases of consciousness that the adoptees may experience throughout their lives. Full article

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article

Against the backdrop of intensifying global climate change and advancing the goal of the “dual-carbon” strategy, the built environment is being viewed as a complex socio-technical system in which technological, economic, demographic and institutional subsystems are coupled and evolving at different scales. As a core node in this system, residential buildings not only carry infrastructural functions, but are also deeply embedded in energy flows, material cycles and behavioural structures, which have a significant impact on carbon emissions. Given the high volume of residential buildings in China and the significant differences between urban and rural construction, there is an urgent need to systematically identify and analyse the implicit carbon emissions during the materialisation phase. In this paper, from the perspective of systems engineering, we selected 30 urban and rural residential buildings in provinces and cities from 2005 to 2020 as the research objects, adopted the life cycle assessment (LCA) method to account for the implied carbon emissions in the materialisation stage, and systematically identified the driving factors of carbon emissions based on the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. From this study, we made the following conclusions: (1) the total carbon emissions of residential buildings in urban and rural areas in China continue to rise during the materialisation stage, showing a spatial pattern of “high in the south-east and low in the north-west”, with a significant trend of structural transformation in urban and rural areas and with steel–concrete structures dominating in towns and cities, and bricks and steel being used in rural areas. (2) Resident population and disposable income are generally positive driving factors, while the influence of industrial structure and energy intensity is heterogeneous between urban and rural areas. For overall residential buildings, every 1% increase in resident population and income will lead to a 1.055% and 0.73% increase in carbon emissions, respectively. The study shows that life-cycle-oriented carbon accounting and the identification of multidimensional driving mechanisms are of great policy value in developing urban–rural differentiated emission reduction paths and enhancing the effectiveness of carbon management in the building sector. Full article

Background/Objectives: Disruption of AMPAR trafficking at excitatory synapses contributes to impaired synaptic plasticity and memory formation in several neurological and psychiatric disorders. Arc, an immediate early gene product, has been shown to interact with the AMPAR auxiliary subunit TARPγ2, affecting receptor mobility and synaptic stabilization. Methods: To investigate the in vivo functional effects and protein interactions of the Arc-TARPγ2 interfering peptide RIPSYR, we performed in vivo electrophysiology and spatial memory assessments in male rats. as well as proteomic analyses of peptide-protein interactions in synaptosome lysates. We then used in silico docking to evaluate candidate binding partners. Results: In the present study, in vivo electrophysiological measurements revealed that RIPSYR administration altered early-phase long-term potentiation at CA3 synapses of male rats. Subsequent behavioral testing that assessed spatial memory performance revealed depleted memory retrieval after 24 h, indicating that the peptide has a systemic effect on experience-dependent plasticity. Then, we examined the molecular interactome of RIPSYR using magnetic bead-based immunoprecipitation and subsequent LC-MS identification on synaptosome lysates, and identified additional candidate binding partners, suggesting that the peptide may have broader modulatory effects. RIPSYR binding to the other putative binding partners are investigated by in silico methods. Conclusion: Our results raise the question of how the molecular interactions of RIPSYR contribute to its sum effects on electrophysiology and behavior. Full article