Search Results (599)

Earle’s balanced salt solution (EBSS) is a classical autophagy inducer that provides a special culture environment lacking amino acids and serum, causing cell starvation. However, the production of relevant omics data surrounding EBSS-induced autophagy is still in the early stage. The objective of this study was to identify new potential functional proteins in the autophagy process through omics analysis. We selected EBSS-induced autophagy as our research object and uncovered autophagy-regulatory proteins using RNA-seq analysis. Western blotting showed that EBSS increased LC3B-II protein levels in NRK cells, reaching the maximum amount at 2 h of culture. Then, we used next-generation sequencing to obtain quantified RNA-seq data from cells incubated with EBSS and the bowtie–tophat–cufflinks flow path to analyze the transcriptome data. Using significant differences in the FPKM values of genes in the treated group compared with those in the control group to indicate differential expression, 470 candidate genes were selected. Subsequently, GO and KEGG analyses of these genes were performed, revealing that most of these signaling pathways were closely associated with autophagy, and to better understand the potential functions and connections of these genes, protein–protein interaction networks were studied. Considering all the conclusions of the analysis, 27 candidate genes were selected for verification, where the knockdown of Txnrd1 decreased LC3B-II protein levels in NRK cells, consistent with the results of confocal experiments. In conclusion, we uncovered autophagy-regulatory proteins using RNA-seq analysis, with our results indicating that TXNRD1 may play a role in regulating EBSS-induced autophagy via an unknown pathway. We hope that our research can provide useful information for further autophagy omics research. Full article

Coating degradation monitoring is increasingly important in offshore industries, where protective layers ensure corrosion prevention and structural integrity. In this context, coating thickness estimation provides critical information. The ultrasound pulse-echo technique is widely used for non-destructive testing (NDT), but closely spaced acoustic interfaces often produce overlapping echoes, which complicates detection and accurate isolation of each layer’s thickness. In this study, analysis of the pulse-echo signal from a coated sample has shown that the front-coating reflection affects each main backwall echo differently; by comparing two consecutive backwall echoes, we can cancel the acquisition system’s impulse response and isolate the propagation path-related information between the echoes. This work introduces an ultrasound echo-based methodology for estimating coating thickness by first obtaining the impulse response of the test medium (reflectivity sequence) through a deconvolution model, developed using two consecutive backwall echoes. This is followed by an enhanced detection of coating layer thickness in the reflectivity function using a 1D convolutional neural network (1D-CNN) trained with synthetic signals obtained from finite-difference time-domain (FDTD) simulations with k-Wave MATLAB toolbox (v1.4.0). The proposed approach estimates the front-side coating thickness in steel samples coated on both sides, with coating layers ranging from $60\mathsf{\mu }$m to $740\mathsf{\mu }$m applied over 5 mm substrates and under varying coating and steel properties. The minimum detectable thickness corresponds to approximately $\lambda /5$ for an 8 MHz ultrasonic transducer. On synthetic signals, where the true coating thickness and speed of sound are known, the model achieves an accuracy of approximately $8\mathsf{\mu }$m. These findings highlight the strong potential of the model for reliably monitoring relative thickness changes across a wide range of coatings in real samples. Full article

In the evaluation of vehicle noise, vibration and harshness (NVH) performance, interior noise control is the core consideration. In the early stage of automobile research and development, accurate prediction of interior noise caused by road surface is very important for optimizing NVH performance and shortening the development cycle. Although the data-driven machine learning method has been widely used in automobile noise research due to its advantages of no need for accurate physical modeling, data learning and generalization ability, it still faces the challenge of insufficient accuracy in capturing key local features, such as peaks, in practical NVH engineering. Aiming at this challenge, this paper introduces a forecast approach that utilizes an empirical-informed neural network, which aims to integrate a physical mechanism and a data-driven method. By deeply analyzing the transmission path of interior noise, this method embeds the acoustic mechanism features such as local peak and noise correlation into the deep neural network as physical constraints; therefore, this approach significantly enhances the model’s predictive performance. Experimental findings indicate that, in contrast to conventional deep learning techniques, this method is able to develop better generalization capabilities with limited samples, while still maintaining prediction accuracy. In the verification of specific models, this method shows obvious advantages in prediction accuracy and computational efficiency, which verifies its application value in practical engineering. The main contributions of this study are the proposal of an empirical-informed neural network that embeds vibro-acoustic mechanisms into the loss function and the introduction of an adaptive weight strategy to enhance model robustness. Full article

Background/Objectives: Behavioral variant frontotemporal dementia (bvFTD), the most prevalent clinical subtype within the frontotemporal lobar degeneration spectrum disorders, is characterized by early and prominent changes that significantly disrupt everyday functioning. This study aims to identify the key correlates of functional status in bvFTD by investigating the relative contributions of cognitive deficits, behavioral disturbances, personality changes, and brain perfusion abnormalities. Additionally, it seeks to develop a theoretical framework to elucidate how these factors may interconnect and shape unique functional profiles. Methods: A total of 26 individuals diagnosed with bvFTD were recruited from the 2nd Neurology Clinic of “AHEPA” University Hospital in Thessaloniki, Greece, and underwent a comprehensive neuropsychological assessment to evaluate their cognitive functions. Behavioral disturbances, personality traits, and functional status were rated using informant-based measures. Regional cerebral blood flow was assessed using Single Photon Emission Computed Tomography (SPECT) imaging to evaluate brain perfusion patterns. Penalized Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to identify the most robust correlates of functional impairment, followed by path analyses using structural equation modeling to explore how these factors may interrelate and contribute to functional disability. Results: The severity of negative behavioral symptoms (e.g., apathy), conscientiousness levels, and performance on neuropsychological measures of semantic verbal fluency, visual attention, visuomotor speed, and global cognition were identified as the strongest correlates of performance in activities of daily living. Neuroimaging analysis revealed hypoperfusion in the right prefrontal (Brodmann area 8) and inferior parietal (Brodmann area 40) cortices as statistically significant neural correlates of functional impairment in bvFTD. Path analyses indicated that reduced brain perfusion was associated with attentional and processing speed deficits, which were further linked to more severe negative behavioral symptoms. These behavioral disturbances were subsequently correlated with declines in global cognition and conscientiousness, which were ultimately associated with poorer daily functioning. Conclusions: Hypoperfusion in key prefrontal and parietal regions, along with the subsequent cognitive and neuropsychiatric manifestations, appears to be associated with the pronounced functional limitations observed in individuals with bvFTD, even in early stages. Understanding the key determinants of the disease can inform the development of more targeted, personalized treatment strategies aimed at mitigating functional deterioration and enhancing the quality of life for affected individuals. Full article

To address the challenges of radar multi-target detection in marine environments, this paper proposes an information geometry (IG)-based, two-stage track-before-detect (TBD) framework. Specifically, multi-target measurements are first modeled on the manifold, leveraging its geometric properties for enhanced detection. The designed scoring function incorporates both the feature dissimilarity between targets and clutter, as well as the precise inter-target path associations. Consequently, a novel merit function combining feature dissimilarity and transition cost is derived to mitigate the mutual interference between adjacent targets. Subsequently, to overcome the integrated merit function expansion phenomenon, a two-stage integration strategy combining dynamic programming (DP) and greedy integration (GI) algorithms was adopted. To tackle the challenges of unknown target numbers and computationally infeasible multi-hypothesis testing, a target cancellation detection scheme is proposed. Furthermore, by exploiting the independence of multi-target motions, an efficient implementation method for the detector is developed. Experimental results demonstrate that the proposed algorithm inherits the superior clutter discrimination capability of IG detectors in sea clutter environments while effectively resolving track mismatches between neighboring targets. Finally, the effectiveness of the proposed method was validated using real-recorded sea clutter data, showing significant improvements over conventional approaches, and the signal-to-clutter ratio was improved by at least 2 dB. Full article

Caring for Alzheimer’s patients presents significant global challenges due to complex symptoms and the constant demand for care, which are further complicated by fragmented information and a lack of explicit integration between physical and computational worlds in existing support systems. This article details the construction and validation of OntoCaimer, an ontology designed to support Alzheimer’s patient care systems by acting as a comprehensive knowledge base that integrates disease recommendations with concepts from the physical world (sensors and actuators). Utilizing METHONTOLOGY and REFSENO formalisms, OntoCaimer was built as a modular ontology. Its validation through the FOCA method demonstrated a high quality score $(\hat{\mu }=0.99)$, confirming its robustness and suitability. Case studies showcased its functionality in automating recommendations, such as managing patient locations or environmental conditions, to provide proactive support. The main contribution of this work is OntoCaimer, a novel ontology that formally integrates clinical recommendations for Alzheimer’s care with concepts from cyber-physical systems (sensors and actuators). Its scientific novelty lies in bridging the gap between virtual knowledge and physical action, enabling direct and automated interventions in the patient’s environment. This approach significantly advances patient care systems beyond traditional monitoring and alerts, offering a tangible path to reducing caregiver burden. Full article

With the increasing demand for intelligent mobility assistance among the visually impaired, machine guide dogs based on computer vision have emerged as an effective alternative to traditional guide dogs, owing to their flexible deployment and scalability. To enhance their visual perception capabilities in complex urban environments, this paper proposes an improved YOLOv8-based detection algorithm, termed TFP-YOLO, designed to recognize traffic signs such as traffic lights and crosswalks, as well as small obstacle objects including pedestrians and bicycles, thereby improving the target detection performance of machine guide dogs in complex road scenarios. The proposed algorithm incorporates a Triplet Attention mechanism into the backbone network to strengthen the perception of key regions, and integrates a Triple Feature Encoding (TFE) module to achieve collaborative extraction of both local and global features. Additionally, a P2 detection head is introduced to improve the accuracy of small object detection, particularly for traffic lights. Furthermore, the WIoU loss function is adopted to enhance training stability and the model’s generalization capability. Experimental results demonstrate that the proposed algorithm achieves a detection accuracy of 93.9% and a precision of 90.2%, while reducing the number of parameters by 17.2%. These improvements significantly enhance the perception performance of machine guide dogs in identifying traffic information and obstacles, providing strong technical support for subsequent path planning and embedded deployment, and demonstrating considerable practical application value. Full article

Random number generators play a critical role in ensuring information security, supporting encrypted communications, and preventing data leakage. However, the random number generators widely used in hardware are faced with potential threats such as environmental disturbances and fault injection attacks. Especially in automotive-grade environments, chips encounter threat scenarios involving multidimensional fault injection, which may lead to functional failures or malicious exploitation, endangering the security of the entire system. This paper focuses on a Counter Mode Deterministic Random Bit Generator (CTR-DRBG) based on the AES-128 algorithm and implements a hardware prototype system compliant with the NIST SP 800-22 standard on an FPGA platform. Centering on typical fault modes such as temperature disturbances, voltage glitches, electromagnetic interference, and bit flips, single-dimensional and multidimensional fault injection and simulated fault injection experiments were designed and conducted. The impact characteristics and sensitivities of electromagnetic faults, voltage faults, and temperature faults regarding the output sequences of random numbers were systematically evaluated. The experimental results show that this type of random number generator exhibits modular-level differential vulnerability under physical disturbances, especially in the data transmission processes of encryption paths and critical registers, which demonstrate higher sensitivity to flip-type faults. This research provides a feasible analysis framework and practical basis for the security assessment and fault-tolerant design of random number generators, possessing certain engineering applicability and theoretical reference value. Full article

The increasing penetration of electrified vehicles is accelerating the evolution of on-board and off-board charging systems, which must deliver higher efficiency, power density, safety, and bidirectionality under increasingly demanding constraints. This article presents a system-level review of state-of-the-art charging architectures, with a focus on galvanically isolated power conversion stages, wide-bandgap-based switching devices, battery pack design, and real-world implementation trends. The analysis spans the full energy path—from grid interface to battery terminals—highlighting key aspects such as AC/DC front-end topologies (Boost, Totem-Pole, Vienna, T-Type), high-frequency isolated DC/DC converters (LLC, PSFB, DAB), transformer modeling and optimization, and the functional integration of the Battery Management System (BMS). Attention is also given to electrochemical cell characteristics, pack architecture, and their impact on OBC design constraints, including voltage range, ripple sensitivity, and control bandwidth. Commercial solutions are examined across Tier 1–3 suppliers, illustrating how technical enablers such as SiC/GaN semiconductors, planar magnetics, and high-resolution BMS coordination are shaping production-grade OBCs. A system perspective is maintained throughout, emphasizing co-design approaches across hardware, firmware, and vehicle-level integration. The review concludes with a discussion of emerging trends in multi-functional power stages, V2G-enabled interfaces, predictive control, and platform-level convergence, positioning the on-board charger as a key node in the energy and information architecture of future electric vehicles. Full article

Smart grids leverage smart terminal devices to collect information from the user side, achieving accurate load forecasting and optimized dispatching of power systems, effectively improving power supply efficiency and reliability while reducing energy consumption. However, the development of quantum technology poses severe challenges to the communication security of smart grids that rely on traditional cryptography. To address this security risk in the quantum era, this paper draws on the core idea of quantum private comparison and proposes a quantum-secure identity authentication and key agreement scheme suitable for smart grids. This scheme uses Bell states as quantum resources, combines hash functions and XOR operations, and can adapt to resource-constrained terminal devices. Through a security proof, it verifies the scheme’s ability to resist various attacks; the experimental results further show that the scheme still has good robustness in different noise environments, providing a feasible technical path for the secure communication of smart grids in the quantum environment and having clear practical engineering value. Full article

Background/Objectives: Sepsis remains one of the leading causes of mortality worldwide, characterized by a complex and heterogeneous clinical presentation. Despite advances in patient monitoring and biomarkers, early detection of sepsis in the intensive care unit (ICU) is often hampered by incomplete data and diagnostic uncertainty. In recent years, machine learning models have been proposed as predictive tools, but many function as opaque “black boxes”, meaning that humans are unable to understand algorithmic reasoning, poorly suited to the uncertainty-laden clinical environment of critical care. Even when post-hoc interpretability methods are available for these algorithms, their explanations often remain difficult for non-expert clinicians to understand. Methods: In this clinical perspective, we explore the specific advantages of probabilistic graphical models, particularly Bayesian Networks (BNs) and their dynamic counterparts (DBNs), for sepsis prediction. Results: Recent applications of AI models in sepsis prediction have demonstrated encouraging results, such as DBNs achieving an AUROC of 0.94 in early detection, or causal probabilistic models in hospital admissions (AUROC 0.95). These models explicitly represent clinical reasoning under uncertainty, handle missing data natively, and offer interpretable, transparent decision paths. Drawing on recent studies, including real-time sepsis alert systems and treatment-effect modeling, we highlight concrete clinical applications and their current limitations. Conclusions: We argue that BNs present a great opportunity to bridge the gap between artificial intelligence and bedside care through human-in-the-loop collaboration, transparent inference, and integration into clinical information systems. As critical care continues to move toward data-driven decision-making, Bayesian models may offer not only technical performance but also the epistemic humility needed to support clinicians facing uncertain, high-stakes decisions. Full article

In recent years, with the popularization of intelligent scene monitoring, sound source localization (SSL) has become a major means for indoor monitoring and target positioning. However, existing sound source localization solutions are difficult to extend to multi-source and three-dimensional scenarios. To address this, this paper proposes a three-dimensional sound source localization technology based on eight microphones. Specifically, the method employs a rectangular eight-microphone array and captures Direction-of-Arrival (DOA) information via the direct path relative transfer function (DP-RTF). It introduces spatial entropy to quantify the uncertainty caused by the exponentially growing DOA combinations as the number of sound sources increases, while further reducing the spatial entropy of sound source localization through geometric intersection. This solves the problem that traditional sound source localization methods cannot be applied to multi-source and three-dimensional scenarios. On the other hand, machine learning is used to eliminate coordinate deviations caused by DOA estimation errors of the direct path relative transfer function (DP-RTF) and deviations in microphone geometric parameters. Both simulation experiments and real-scene experiments show that the positioning error of the proposed method in three-dimensional scenarios is about 10.0 cm. Full article

Enhancing the failure tolerance ability of networks is crucial, as node or link failures are common occurrences on-site. The current fault tolerance schemes are divided into reactive and proactive schemes. The reactive scheme requires detection and repair after the failure occurs, which may lead to long-term network interruptions. The proactive scheme can reduce recovery time through preset backup paths, but requires additional resources. Aiming at the problems of long recovery time or high overhead of the current failure tolerance schemes, the Polymorphic Network adopts field-definable network baseline technology, which can support diversified addressing and routing capabilities, making it possible to implement a more complex and efficient failure tolerance scheme. Inspired by this, we propose an efficient Multi-topology Failure Tolerance mechanism in Polymorphic Network (MFT-PN). The MFT-PN embeds a failure recovery function into the packet processing logic by leveraging the full programmable characteristics of the network element, improving failure recovery efficiency. The backup path information is pushed into the header of the failed packet to reduce the flow table storage overhead. Meanwhile, MFT-PN introduces the concept of multi-topology routing by constructing multiple logical topologies, with each topology adopting different failure recovery strategies. Then, we design a multi-topology loop-free link backup algorithm to calculate the backup path for each topology, providing extensive coverage for different failure scenarios. Experimental results show that compared with the existing strategies, MFT-PN can reduce resource overhead by over 72% and the packet loss rate by over 59%, as well as effectively cope with multiple failure scenarios. Full article

Urbanization and traffic congestion caused by the surge in car ownership have exacerbated energy consumption and carbon emissions, and dynamic traffic flow prediction and energy-saving route planning have become the key to solving this problem. Dynamic traffic flow prediction accurately captures the spatio-temporal changes of traffic flow through advanced algorithms and models, providing prospective information for traffic management and travel decision-making. Energy-saving route planning optimizes travel routes based on prediction results, reduces the time vehicles spend on congested road sections, thereby reducing fuel consumption and exhaust emissions. However, there are still many shortcomings in the current relevant research, and the existing research is mostly isolated and applies a single model, and there is a lack of systematic comparison of the adaptability, generalization ability and fusion potential of different models in various scenarios, and the advantages of heterogeneous graph neural networks in integrating multi-source heterogeneous data in traffic have not been brought into play. This paper systematically reviews the relevant global studies from 2020 to 2025, focuses on the integration path of dynamic traffic flow prediction methods and energy-saving route planning, and reveals the advantages of LSTM, graph neural network and other models in capturing spatiotemporal features by combing the application of statistical models, machine learning, deep learning and mixed methods in traffic forecasting, and comparing their performance with RMSE, MAPE and other indicators, and points out that the potential of heterogeneous graph neural networks in multi-source heterogeneous data integration has not been fully explored. Aiming at the problem of disconnection between traffic prediction and path planning, an integrated framework is constructed, and the real-time prediction results are integrated into path algorithms such as A* and Dijkstra through multi-objective cost functions to balance distance, time and energy consumption optimization. Finally, the challenges of data quality, algorithm efficiency, and multimodal adaptation are analyzed, and the development direction of standardized evaluation platform and open source toolkit is proposed, providing theoretical support and practical path for the sustainable development of intelligent transportation systems. Full article

Early-onset colorectal cancer (EOCRC) is emerging as a significant global health concern, particularly among individuals under the age of 50. This alarming trend has coincided with an increase in the consumption of processed foods that often rely heavily on synthetic preservatives. At the same time, these additives play a critical role in ensuring food safety and shelf life. Growing evidence suggests that they may contribute to adverse gut health outcomes, which is a known risk factor in colorectal cancer development. At the same time, synthetic preservatives serve essential roles such as preventing microbial spoilage, maintaining color, and prolonging shelf life. Natural preservatives, on the other hand, not only provide antimicrobial protection but also exhibit antioxidant and anti-inflammatory properties. These contrasting functions form the basis of current discussions on their safety and health implications. Despite their widespread use, the long-term health implications of synthetic preservatives remain inadequately understood. This review synthesizes recent clinical, epidemiological, mechanistic, and toxicological data to examine the potential link between synthetic food preservatives and EOCRC. Particular focus is placed on compounds that have been associated with DNA damage, gut microbiota disruption, oxidative stress, and chronic inflammation, which are the mechanisms that collectively increase cancer risk. In contrast, natural preservatives derived from plants and microbes are gaining attention for their antioxidant, antimicrobial, and possible anti-inflammatory effects. While these alternatives show promise, scientific validation and regulatory approval remain limited. This review highlights the urgent need for more rigorous, long-term human studies and advocates for enhanced regulatory oversight. It advocates for a multidisciplinary approach to developing safer preservation strategies and highlights the importance of public education in making informed dietary choices. Natural preservatives, though still under investigation, may offer a safer path forward in mitigating EOCRC risk and shaping future food and health policies. Full article