Search Results (13,463)

Precise assessment of battery state of health (SoH) is vital for certifying consistent performance in order to enable maintenance of energy storage system. This work compares different deep learning methods to learn and predict the complex and nonlinear dynamics of battery. The models are developed and tested for predicting SoH using sequential degradation data from batteries. The effectiveness of these models is assessed using matrices such as RMSE, MAE and R², along with qualitative analysis. The experiment results show that the BiLSTM model performs better than the others. It has the lowest RMSE (0.90), the lowest MAE (0.72), and the highest R² (0.99), which highlights its enhanced ability to capture long-term temporal dependencies. The proposed models are validated using NASA lithium-ion battery aging dataset (B0005), which is widely used as a benchmark for battery health predictions studies. Overall, the findings indicate that bidirectional network architecture significantly improves the accuracy and consistency of SoH predictions when compared to unidirectional models. Full article

Large language models (LLMs) have shown strong performance in open-domain dialogue, yet they continue to struggle with long-term multi-session conversations (MSC), particularly in non-English languages such as Korean. In this work, we present a comprehensive empirical study on enhancing Korean MSC capabilities of LLMs through dataset construction, memory modeling, and parameter-efficient fine-tuning. We introduce an extended Korean MSC dataset that explicitly distinguishes between persona memory (long-term user attributes) and episode memory (short-term, event-driven information), enabling more effective memory management across sessions. Using this dataset, we evaluate LLM performance on three core MSC tasks: session summarization, memory update, and response generation. Our experiments reveal that Korean MSC is intrinsically more challenging than English MSC and that memory update and response generation require substantial reasoning ability. To address these challenges, we compare LoRA, DPO, MoE, CPT, Layer Tuning, and neuron-level tuning methods. Results consistently show that neuron tuning, guided by a novel language-specific neuron identification method based on activation scores and entropy, achieves superior performance and robustness, particularly in continual learning settings. Overall, our findings highlight neuron-level adaptation as an effective and interpretable approach for improving long-term conversational ability in low-resource languages. Full article

Ascochyta blight remains a major constraint for field pea (Pisum sativum L.) production and a priority for breeding programmes. So far, only moderate levels of incomplete resistance have been identified in pea germplasm and accumulated in pea cultivars by breeding. Resistance identified so far appears to be of complex inheritance, with phenotypic expression strongly affected by plant phenology and morphology and by environ-mental factors. This has slowed down the development and release of resistant elite cultivars. In this work, we describe the development of novel resistant breeding lines derived from targeted intra- and interspecific crosses combined with cycles of selection under high disease pressure at seedling and adult plant stages. The performance of thirteen breeding lines selected for improved resistance and good agronomic traits was further validated in a comparative field trial. Results confirmed the successful combination of competitive yield and good standing ability with good levels of resistance exceeding those of the resistant check. These advanced breeding lines are available on request for research and breeding use. Full article

Photodynamic therapy (PDT) is a promising cancer treatment employing photo-induced reactive oxygen species (ROS) generation by a photosensitizer (PS). Titanium dioxide (TiO₂) is a potential PS due to its superb photocatalytic features and biocompatibility. However, its clinical potential is restricted by its predominant ultraviolet (UV) absorption. To address this limitation, this work introduces TiO₂/carbon dots (CDs) nanohybrid materials for improving the photophysical properties of TiO₂ and its photodynamic performance. TiO₂ and CDs were synthesized through wet chemical and hydrothermal techniques, and subsequently combined via a facile ex situ solvothermal process to produce hybrid materials containing 1–50% w/w CDs. The materials were characterized using XRD, Raman, TEM, FT-IR, zeta potential, TGA, UV-Vis and PL. PDT studies on A431 skin cancer cells indicated improved photosensitizing ability of TiO₂/CDs, with TiO₂/CDs (10%) inducing 47% cell toxicity, versus 20% for TiO₂ after 10 min of red-light irradiation (661 nm, 18 mW/cm², 12.96 J/cm²). Intracellular localization studies revealed enhanced cellular uptake of TiO₂/CDs (10%), compared with TiO₂. In vitro studies on 3T3 healthy fibroblasts confirmed PSs’ safety both with and without light. Overall, this study elucidates the key role of CDs in the photophysical and photodynamic behavior of TiO₂-based systems, providing design guidelines for the next-generation inorganic PSs. Full article

Fake news has caused significant harm and disruption across various sectors of society. With the rapid advancement of the Internet and social media platforms, both academic and industrial communities have shown growing interest in multimodal fake news detection. In this work, we propose MERF (Multimodal Evidence Retrieval and Forensics with LVLM), a unified framework for multimodal fake news detection that leverages the reasoning capabilities of Large Vision-Language Models (LVLMs). While LVLMs outperform traditional Large Language Models (LLMs) in processing multimodal content, our study reveals that their reasoning abilities remain limited in the absence of sufficient supporting evidence. MERF addresses this challenge by integrating web-based content retrieval, reverse image search, and image manipulation detection into a coherent pipeline, enabling the model to generate informed and explainable veracity judgments. Specifically, our approach performs cross-modal consistency checking, retrieves corroborative information for both textual and visual content, and applies forensic analysis to detect potential visual forgeries. The aggregated evidence is then fed into the LVLM, facilitating comprehensive reasoning and evidence-based decision-making. Experimental results on two public benchmark datasets—Weibo and Twitter—demonstrate that MERF consistently outperforms state-of-the-art baselines across all major evaluation metrics, achieving substantial improvements in accuracy, robustness, and interpretability. Full article

The porcine epidemic diarrhea virus (PEDV) causes a gastrointestinal disease generating mortality rates approaching 100% in piglets worldwide. The S glycoprotein of PEDV is the main target for the development of vaccines. Two vaccines approved by the Ministry of Agriculture and Rural Development are used in Mexico: the first vaccine is based on an inactivated virus isolated more than a decade ago, whereas the second vaccine is based on mRNA technology. The most important tool for controlling PEDV outbreaks is vaccination; however, coronaviruses are characterized by the accumulation of multiple mutations, which compromise the immune response elicited by outdated vaccines. In this work, we classified the Mexican strains of PEDV reported so far in GenBank, according to their genotypes. Subsequently, we searched for B and T cell epitopes conserved in Mexican PEDV strains using bioinformatic tools. In addition, we explored whether these epitopes can induce allergies, autoimmunity, and/or toxic effects. Next, we determined the localization of B cell epitopes in the S glycoprotein using the protein crystal and protein modeling of several S glycoproteins. Finally, we carried out molecular docking analysis to assess whether these T cell epitopes could interact with the peptide-binding groove of the Swine Leukocyte Antigens (SLAs). Five conserved B cell epitopes were found to be exposed on the surface of the S glycoprotein, whereas several promiscuous CTL and HTL epitopes were bound, with low free energy, to the peptide-binding grooves of SLA-I and SLA-II, respectively. The best epitopes were used to generate a plasmid carrying the sequence to produce a recombinant protein. This plasmid was used for transfection experiments in PK-15 cell culture. The B cell epitopes reported here were recognized by the sera from pigs infected with PEDV but not by the sera from uninfected animals. These results justify future evaluations of the ability of these epitopes to stimulate cytokine production by T cells, antibody generation, and their neutralizing activity. Full article

Background: Gene expression and cellular identity are regulated by epigenetics that occurs through chromatin modifications, RNA changes, chromatin accessibility, and three-dimensional genome organization. Although DNA methylation has been the focus of most epigenetics studies in the past, other non-methyl epigenetic processes, including histone post-translational modifications (PTMs), epitranscriptomic marks, and chromatin remodeling, are dynamic, reversible, and context-dependent, and thus are difficult to accurately interrogate using endpoint sequencing-based assays, especially in heterogeneous tissues, developing systems, and therapeutic response environments. Scope and Approach: The present review discusses epigenetic modifications other than DNA methylation regarding sensor-based technologies that can measure live, dynamic, and spatially resolved measurements. Epigenetic sensors include any genetically encoded sensors (GECs) based on resonance energy transfer, CRISPR/dCas-derived sensors, or aptamer-based sensors, and hybrid biochemical/imaging sensors that can be used in live or semi-live settings. It lays emphasis on the technologies, which have been developed recently, that allow real-time kinetic measurements, working in three-dimensional and organoid models, and being applied to disease-relevant perturbations. On these platforms, performance properties such as specificity, sensitivity, spatial and temporal resolution, ability to perform dynamic versus locus-specific interrogation, and perturbed endogenous chromatin states are compared. Key Conclusions and Outlook: Together, these sensing strategies are complementary to the traditional methods of measuring epigenomics in that they show epigenetic dynamics unobservable with static measurements. We list the important technical issues, including specificity, quantitation, multiplexing, and chromatin perturbation, and report the barriers and solutions in development and design. Lastly, we provide a conceptual map of how live epigenetic sensing and multi-omics and translational models can be integrated, and how the two methodologies can be used to develop functional epigenetics and guide disease modeling and drug development. Full article

Document authentication remains a pressing challenge in various domains, including financial services, academic credentialing, healthcare, and supply chain management. Existing centralized verification systems are vulnerable to manipulation, inefficiency, and limited transparency. Blockchain technology, with its immutability and tamper-resistant capabilities, offers a strong decentralized alternative; however, many current implementations lack structured, issuer-bound relationships for documents. This paper proposes a blockchain-based model that leverages a hierarchical token structure to authenticate and trace the provenance of high-value digital documents, with a focus on financial records. The model introduces the concept of an issuer-bound parent token and document-linked child tokens, enforcing a structured trust relationship between a legitimate institution and the documents it issues. By combining on-chain cryptographic hashing with off-chain file references, the approach is designed to balance verifiability with scalability. We implement a proof-of-concept using Ethereum-compatible smart contracts on a permissioned blockchain and evaluate it in a consortium-style financial setting. Our functional analyses demonstrate the model’s ability to ensure document integrity, provenance, and resistance to document fraud. This work offers a practical and extensible foundation for secure digital document authentication and verification in financial and other trust-sensitive settings. Full article

The rapid spread of misinformation on social media has emerged as a major societal issue. Over 40% of British social media news-sharers admitted they had shared inaccurate or fake news. The extensive distribution of false information causes public trust deterioration while modifying public opinions and potentially destabilizing social and political systems. There are profound challenges due to this hard-to-detect, hard-to-stop reality and the financials and societal implications are remarkable. As an attempt to limit the challenges created from misinformation this paper introduces some preliminary work on detection of fake news and verification of their reliability based on online content. Large language models (LLMs) are being used along with natural language processing (NLP) techniques to evaluate news articles through their linguistic and contextual characteristics. Several models are compared on how they can typically identify typical indicators of misinformation through the analysis of extensive verified datasets to develop an ability to classify content as authentic or fabricated. This work has been through thorough testing to determine its operational effectiveness and dependability after completion. We present a relatively easy-to-use tool which enables a wide range of people also for those without a background in computer science to easily verify news accuracy before sharing or trusting it. This work could help to stop false information from spreading while promoting fact-based discussions and improving digital literacy skills. The research demonstrates how technology fights the fake news crisis to create an informed digital environment which supports public conversation protection and information integrity in the modern digital age. Full article

The wildfires in the vicinity of the power transmission corridors are famous for their sudden occurrence, rapid growth, and susceptibility to interference from fire-like interferences at night, which can easily lead to line discharge and trip accidents, thus affecting the safe operation of the power system. In order to address the issue of the high false alarm rate and poor generalization performance of wildfire image recognition in complex power transmission corridor environments, a wildfire image recognition method based on an improved AlexNet is proposed in this paper. The proposed method improves the description of flame and smoke properties at different scales by designing a reparameterized multi-scale feature extraction structure, and effectively alleviates the influence of strong light reflection and fire-like interference at night by using lightweight multi-scale attention and hybrid pooling attention mechanisms. A wildfire image dataset is constructed based on 1246 on-site images of the power transmission corridor captured by a visual monitoring device and 600 wildfire images downloaded from the internet, and tested in real-world imbalanced distribution scenarios. The experimental results show that the proposed method can recognize wildfire images with an accuracy of 96.9% and an F1 value of 94.9% on the test dataset, which is much higher than that of the original AlexNet, and has a strong ability to adapt to cross-dataset tests. The research work can provide technical support for online monitoring and operation and maintenance of wildfires in power transmission corridors. Full article

In a solid-state hydrogen storage tank, the storage medium is most often in the form of an intermetallic alloy powder. With each cycle of hydrogen absorption/desorption, the particles swell, move, fragment, and segregate. Understanding and modeling these phenomena are essential in order to guide engineers during the tank design process. However, there are little data in the literature on the mechanical behavior of powders for storage applications. This study focuses on the flowability and compression behavior of an intermetallic powder, with the aim of analyzing particle mobility in a confined environment as well as the transmission of forces to the tank walls. In order to represent the evolution of particle size through fragmentation during cycles, five ${\mathrm{T}\mathrm{i}\mathrm{F}\mathrm{e}}_{\left(1-\mathrm{x}\right)}{\mathrm{M}\mathrm{n}}_{\mathrm{x}}\left(\mathrm{x}\approx 0.1\right)$ powders, differing in their average particle size and polydispersity, are studied. Flowability tests on Granutools^® (Awans, Belgium) instruments show that behaviors differ. Fine-grained samples exhibit rheo-thickening behavior, while coarser samples are quasi-Newtonian. These tests highlight variations in cohesion and internal friction, particularly for polydisperse samples. Stepwise cyclic compression tests (in stages 0-10-20-30 kN) were performed to study the elastic response of the powder under confinement and its ability to transfer stresses to the walls. This work highlights the impact of particle size and polydispersity on stress transfer in a confined space. This work therefore presents the mechanical effects of changes in particle size and polydispersity during absorption/desorption cycles on the overall behavior of the powder storage bed, in terms of flowability, cohesion, and stress transmission, in order to better understand, in the long term, its impact on tank deformation. Full article

Visual mental imagery, the ability to generate and manipulate internal visual experiences without direct sensory input, links perception with memory, planning, and higher cognition. In this targeted narrative review, we synthesize neuroimaging and lesion evidence on the brain basis of visual imagery, with a focus on neuroradiological correlates of the ventral and dorsal visual pathways. Unlike prior cognitive neuroscience reviews that primarily emphasize functional mechanisms, this review is neuroradiology-oriented and integrates lesion patterns and white-matter disconnection to support clinico-radiological interpretation of imagery complaints. Using a dual-stream framework, we contrast ventral occipito-temporal systems that preferentially support object imagery (appearance-based features such as form, faces/objects, and color, with texture remaining under-studied) with dorsal occipito-parietal systems that preferentially support spatial imagery (relations, transformations, and navigation). Across studies, imagery recruitment is strongly task- and stage-dependent: ventral regions are most often engaged during object-focused imagery, whereas parietal regions are prominent during spatial transformation tasks, with evidence for interaction between pathways when demands require both content and spatial operations. Structural and clinico-radiological findings indicate that imagery impairment can arise from focal posterior lesions and posterior neurodegenerative syndromes but also from network disruption affecting long-range connections that support top-down access to posterior representations. Finally, emerging work on aphantasia and hyperphantasia supports a network-level view in which imagery vividness relates to how effectively higher-order systems engage visual representations. We conclude that standardized, stream-sensitive tasks and multimodal approaches combining functional and structural imaging with lesion-based evidence are key to discovering clinically actionable biomarkers of imagery dysfunction. Full article

This paper focuses on the modeling and design of an adaptive vortex generator (AVG). The device is actuated through shape memory alloy (SMA) elements. The interest of the research community in these devices is due to their ability to improve the performance of the aircraft, directly altering and controlling the boundary layer. Their action consists of energizing the flow, thereby hindering separation. The peculiarity of the presented AVG architecture lies in its compactness and adaptability, which allows for its activation just for some specific phases that are not adequately covered by the conventional. This system can enable load alleviation in the cruise phase when a gust occurs (spoiler modality) and stall prevention in high-lift conditions (vane modality). These two working capabilities can be obtained by mounting the AVGs at different angles of incidence, with respect to the direction of the flow. The present paper is structured as follows. First, the project of RADAR, hosting the activities, is presented with specific focus on the main objectives and on the strategy of maturation of the technologies. Then, attention is paid to the simulations of the aerodynamic field produced by the AVG. These outcomes have driven the next part of the work, focusing on the identification of the architecture of the AVG. A dedicated finite element modeling approach was implemented to address the design task, even in the presence of SMA non-linear elements. Three main operational phases were simulated: (1) the stretching of the springs up to their connection to the architecture (pre-load phase); (2) the elastic recovery of the springs and the achievement of equilibrium with the hosting structure; and (3) the activation of the springs through heating to deflect the AVG. The simulations proved the capability of the system to produce the required deflection/deployment, even under the most severe load conditions. In particular, the simulations highlighted the capability of the system to produce a deflection of the vortex generator of 83.5 deg under the most severe load conditions, against the required value of 80 deg. This result was obtained by also keeping the structural safety factor at a value of four, in line with the wind tunnel facility requirement. Another key outcome of the dynamic analysis was the absence of coupling with vortex shedding, since the system resonance frequencies (135 and 415 Hz) are well outside the vortex-shedding frequency range (500–1400 Hz). Full article

Flexible electrode materials with tailored electrical and mechanical properties are essential for reliable electrocardiographic (ECG) sensing. In this work, p-toluenesulfonic-acid-doped polypyrrole (PPy–TSA) films were modified using polymeric and inorganic fillers, as well as their combinations (polyethylene glycol, graphene, carbon nanotubes, and zeolite), to tune their functional performance. The reference PPy–TSA film exhibits typical morphological and chemical characteristics of doped polypyrrole and serves as a reliable baseline for comparison. All composite films retain electrical conductivity within the range required for ECG applications while showing improved mechanical compliance (i.e., enhanced ability to conform to the skin and sustain deformation). Based on the optimized balance between electrical and mechanical properties, flexible ECG electrodes were fabricated using the TSA-doped PPy-based composite film. ECG recordings obtained with the several proposed electrodes show good agreement with those acquired using a commercial ECG electrode, demonstrating the potential of PPy-based composite films for flexible bioelectronic sensing applications. Full article

Background/Objectives: The medical mushroom Ophiocordyceps sinensis (Caterpillar Fungus), known for its ability to enhance “vitality,” is one of the most popular medicines in Asian traditional medical systems. According to the Chinese Pharmacopeia, O. sinensis is standardized for its adenosine content, the precursor of ATP, which mediates numerous physiological and pathological processes in many diseases. The related fungus of order Hypocreales, Cordyceps militaris, and its major bioactive constituents, 3′-deoxyadenosine (cordycepin), also exhibit pleiotropic biological activities. This review aims to provide a rationale for the adaptogenic and resilience-supporting effects of these medicinal fungi and to align food and drug regulation in Western countries. Methods: In this narrative review, we integrated results from chemical, pharmacokinetic, network pharmacology, preclinical, and clinical studies of O. sinensis, C. militaris, and cordycepin using network pharmacology and bioinformatics tools. Results: Across studies, recurrent mechanistic hubs included PI3K–Akt, AMPK–mTOR, MAPK, NF-κB, apoptosis, and adaptive stress-response signaling pathways, linking immune regulation and metabolic homeostasis. Experimental studies confirmed modulation of cytokine production, kinase signaling, and mitochondrial regulators. Clinical meta-analyses demonstrate consistent adjunctive benefits in renal and pulmonary disorders, although heterogeneity in preparation and methodological limitations remains significant. The review reveals controversy regarding the bioavailability of cordycepin in vivo and its concentration in vitro studies, raising the hypothesis that cordycepin may act as a driver, triggering the organism’s adaptive stress response in stress-induced and aging-related diseases. Pharmacokinetic data indicate that systemic cordycepin concentrations after oral administration remain in the nanomolar range, suggesting that some predicted molecular interactions may occur indirectly or through systems-level mechanisms. The review, for the first time, suggests establishing a regulatory category for resilience-supporting physiological modulators to align food and drug regulation in the EU with contemporary systems biology, thereby complementing the work of EFSA, EMA, FDA, and Asian authorities. Conclusions:O. sinensis, C. militaris, and 3-deoxyadenosine share a common adaptogenic mechanism for maintaining homeostasis of cellular and integrated biological system functions. The systems-level network analysis and reductionistic molecular ligand preceptor pharmacology provide complementary approaches for understanding the multi-target bioactivity of these fungi. This review clarifies conceptual and regulatory barriers to recognizing resilience-supporting interventions and informs future regulatory innovation. Full article