Search Results (26,504)

To address the boundary value problem associated with a class of third-order nonlinear differential equations with variable coefficients, this study integrates three key methods: the elastic transformation method (ETM), the similar construction method (SCM), and the elastic inverse transformation method (EITM). Firstly, ETM is employed to transform the original high-order nonlinear differential equations into the Tschebycheff equation, successfully reducing the order of the problem. Subsequently, SCM is applied to determine the general solution of the Tschebycheff equation under boundary conditions, thereby ensuring a structured and systematic approach. Ultimately, the EITM is used to reconstruct the solution of the original third-order nonlinear differential equation. The accuracy of the obtained solution is further validated by analyzing the corresponding solution curves. The synergy of these methods introduces a novel approach to solving nonlinear differential equations and extends the application of Tschebycheff equations in nonlinear systems. Full article

Conventional design methodologies for Frequency Selective Surfaces (FSSs) are often plagued by challenges such as difficulties in determining unit cell structures, a plethora of optimization parameters, and substantial computational demands. In response, researchers have developed deep learning-based approaches for FSS design, highlighting their advantages in terms of high efficiency and low resource consumption. However, these methods are typically confined to designing FSSs within the spectral ranges defined by their datasets, significantly limiting their applicability. This paper systematically analyzes the impact of material and geometric parameters of FSSs on their spectral characteristics, thereby establishing a theoretical foundation for the cross-band transfer learning capability of neural networks. Building on this foundation, we utilized COMSOL (Version 6.0) and MATLAB (Version R2021b) co-simulations to recollect 6000 sets of FSS data in the millimeter-wave band. Using only 23.1% of the data volume, we achieved training results comparable to those obtained with the full dataset in a significantly shorter time frame, with a mean absolute error of 0.07 on the test set. This demonstrates the feasibility of transfer learning and successfully implements cross-band transfer learning of convolutional neural networks from the terahertz band to the millimeter-wave band. The findings of this study provide valuable insights for the integration of deep learning with FSSs, enhancing data utilization efficiency, and further advancing the development of efficient, concise, and universal FSS design methodologies. This advancement extends the scope from solving specific problems to addressing a broader class of issues. Full article

In recent years, the rapid development of the new energy vehicle (NEV) industry has exposed significant deficiencies in intelligent fault diagnosis and information retrieval technologies, especially in intelligent fault information retrieval, which faces persistent challenges including inadequate system adaptability and reasoning bottlenecks. To address these challenges, this study proposes a Retrieval-Augmented Generation (RAG) framework that integrates large language models (LLMs) with knowledge graphs (KGs). The framework consists of three key components: fault data collection, knowledge graph construction, and fault knowledge model training. The primary research contributions are threefold: (1) A domain-optimized fine-tuning strategy for LLMs based on NEV fault characteristics, verifying the superior accuracy of the Bidirectional Encoder Representations from Transformers (BERT) model in fault classification tasks. (2) A structured knowledge graph encompassing 122 fault categories, developed through the ChatGLM3-6B model completing named entity and knowledge relation extraction to generate fault knowledge and build a paraphrased vocabulary. (3) An intelligent fault information retrieval system that significantly outperforms traditional models in NEV-specific Q&A scenarios, providing multi-level fault cause analysis and actionable solution recommendations. Full article

Deep eutectic solvents (DESs) have shown promise as a medium for extracting polar volatile fatty acids (VFAs) and in situ esterification of the extracted molecules using lipases. This solvent enhanced biocatalysis process can potentially streamline VFA separation from fermentation broth by integrating conversion and extraction steps. Two commercial lipases from Aspergillus oryzae (AoL) and Candida rugosa (CrL) were evaluated in reaction systems containing hydrophilic or hydrophobic DESs using a newly optimized lipase assay. The optimal pH for both lipases was around 5.0, with a slight reduction in activity at pH 8.0 and a significant inhibition at pH 2.0. The impact of DES concentration on lipase activity varied depending on the specific DES–lipase pairs. Most hydrophilic DESs show good compatibility with the tested lipases. Specifically for choline chloride/ethylene glycol (1:2) and choline chloride/levulinic acid (1:2), taking into account the influence of pH, CrL activity increased with DES concentration. However, the hydrophobic DES thymol/2,6-dimethoxyphenol (1:2) demonstrated enhanced inhibitory effects on both lipases. Docking simulation helped explain the ligand–protein interactions but showed limited capability in predicting the compatibility of specific DES–lipase pairs due to its constraints in simulating flexible protein structures and the complex interactions between DES components and water. Full article

Abstract: Pose estimation plays a crucial role in on-orbit servicing technologies. Currently, point cloud registration-based pose estimation methods for noncooperative spacecraft still face the issue of misalignment due to similar point cloud structural features. This paper proposes a pose estimation approach for noncooperative spacecraft based on the point cloud and optical image feature collaboration, inspired by methods such as Oriented FAST and Rotated BRIEF (ORB) and Robust Point Matching (RPM). The method integrates ORB feature descriptors with point cloud feature descriptors, aiming to reduce point cloud mismatches under the guidance of a transformer mechanism, thereby improving pose estimation accuracy. We conducted simulation experiments using the constructed dataset. Comparison with existing methods shows that the proposed approach improves pose estimation accuracy, achieving a rotation error of 0.84° and a translation error of 0.022 m on the validation set. Robustness analysis reveals the method’s stability boundaries within a 30-frame interval. Ablation studies validate the effectiveness of both ORB features and the transformer layer. Finally, we established a ground test platform, and the experimental data results validated the proposed method’s practical value. Full article

The ozone layer in the Earth’s atmosphere filters solar radiation and limits the unwanted effects on humans. A depletion of this ozone shield triggered by a violent Sun would permit hazardous levels of UV solar radiation, especially in the UVB range, to bombard Earth’s surface, resulting in potentially significant effects on human health. The concern for these adverse effects intensifies if we consider that the UVB solar radiation is combined with secondary cosmic radiation (SCR) components, such as protons and muons, as well as terrestrial gamma rays. This research aims to delve into the intricate interplay between cosmic and solar radiation on earth at the cellular level, focusing on their synergistic effects on human cell biology. Through a multidisciplinary approach integrating radiobiology and physics, we aim to explore key aspects of biologic responses, including cell viability, DNA damage, stress gene expression, and finally, genomic instability. To assess the impact of the combined exposure, normal human cells (skin fibroblasts, keratinocytes, monocytes, and lymphocytes) were exposed to high-energy protons or gamma rays in combination with UVB. Cellular molecular and cytogenetic biomarkers of radiation exposure, such as DNA damage (γΗ2ΑΧ histone protein and dicentric chromosomes), as well as the expression pattern of various stress genes, were analyzed. In parallel, the MTS reduction and lactate dehydrogenase assays were used as indicators of cell viability, proliferation, and cytotoxicity. Results reveal remaining DNA damage for the co-exposed samples compared to samples exposed to only one type of radiation in all types of cells, accompanied by increased genomic instability and distinct stress gene expression patterns detected at 24–48 h post-exposure. Understanding the impact of combined radiation exposures is crucial for assessing the health risks posed to humans if the ozone layer is partially depleted, with structural and functional damages inflicted by combined cosmic and UVB exposure. Full article

The rapid integration of Internet of Things (IoT) technologies in smart cities enhances urban management, yet public acceptance remains crucial for successful deployment. This study examined gender-based differences in IoT acceptance through a survey of 288 respondents from Warsaw and Plock, analyzed using structural equation modeling (SEM). The results revealed that women demonstrated significantly higher trust in IoT (+0.93, p < 0.001), greater perceived safety (+0.24, p = 0.013), and stronger support for environmental IoT applications (+0.48, p = 0.007) than men. While perceived usefulness was the strongest predictor of IoT acceptance for men (β = 0.523, p < 0.001), safety (β = 0.286, p = 0.001) and environmental awareness (β = 0.507, p < 0.001) drove acceptance among women. These findings highlight the need for gender-sensitive urban technology policies, emphasizing safety and sustainability to foster inclusive smart city development. The research results can be used by city authorities to learn about the requirements and concerns of residents to design a city that meets all residents’ requirements and better communicates IoT technology. Furthermore, the study underscores the importance of targeted education and awareness campaigns to address privacy concerns and promote broader adoption of IoT-driven solutions in urban environments. Full article

The detection, observation, recognition, and statistics of marine plankton are the basis of marine ecological research. In recent years, digital holography has been widely applied to plankton detection and recognition. However, the recording and reconstruction of digital holography require a strictly controlled laboratory environment and time-consuming iterative computation, respectively, which impede its application in marine plankton imaging. In this paper, an intelligent method designed with digital holography and deep learning algorithms is proposed to detect and recognize marine plankton (IDRMP). An accurate integrated A-Unet network is established under the principle of deep learning and trained by digital holograms recorded with publicly available plankton datasets. This method can complete the work of reconstructing and recognizing a variety of plankton organisms stably and efficiently by a single hologram, and a system interface of YOLOv5 that can realize the task of the end-to-end detection of plankton by a single frame is provided. The structural similarities of the images reconstructed by IDRMP are all higher than 0.97, and the average accuracy of the detection of four plankton species, namely, Appendicularian, Chaetognath, Echinoderm and Hydromedusae,, reaches 91.0% after using YOLOv5. In optical experiments, typical marine plankton collected from Weifang, China, are employed as samples. For randomly selected samples of Copepods, Tunicates and Polychaetes, the results are ideal and acceptable, and a batch detection function is developed for the learning of the system. Our test and experiment results demonstrate that this method is efficient and accurate for the detection and recognition of numerous plankton within a certain volume of space after they are recorded by digital holography. Full article

The efficiency and reliability of business processes in commercial banks are critical to financial stability and compliance. However, traditional evaluation methods that rely on retrospective qualitative assessments and static frameworks struggle to address the dynamic complexities inherent in modern banking operations. These approaches lack real-time monitoring, fail to leverage granular event log data, and overlook organizational interdependencies, hindering proactive risk management and optimization. To bridge these gaps, this study proposes a data-driven evaluation framework that integrates three core dimensions: efficiency, quality, and flexibility. We developed a hybrid analytical model by integrating process mining with DEMATEL-AHP to analyze a Chinese bank’s performance guarantee process, comparing pre- and post-centralization workflows. The analysis revealed that post-centralization processes exhibited improved flexibility but reductions in efficiency and quality. Moreover, the social network analysis highlighted structural shifts, including expanded audit participation and reduced departmental cohesion, contributing to inefficiencies. This study advances business process management by demonstrating that a data-driven process evaluation framework offers greater persuasiveness and methodological rigor than traditional qualitative approaches. Full article

Recent advancements in mRNA technology, utilized in vaccines, immunotherapies, protein replacement therapies, and genome editing, have emerged as promising and increasingly viable treatments. The rapid, potent, and transient properties of mRNA-encoded proteins make them attractive tools for the effective treatment of a variety of conditions, ranging from infectious diseases to cancer and single-gene disorders. The capability for rapid and large-scale production of mRNA therapeutics fueled the global response to the COVID-19 pandemic. For effective clinical implementation, it is crucial to deeply characterize and control important mRNA attributes such as purity/integrity, identity, structural quality features, and functionality. This implies the use of powerful and advanced analytical techniques for quality control and characterization of mRNA. Improvements in analytical techniques such as electrophoresis, chromatography, mass spectrometry, sequencing, and functionality assessments have significantly enhanced the quality and detail of information available for product and process characterization, as well as for routine stability and release testing. Here, we review the latest advancements in analytical techniques for the characterization of mRNA-based therapeutics, typically employed by the biopharmaceutical industry for eventual market release. Full article

DNA sequence from a new alloherpesvirus named acipenserid herpesvirus 3 (AciHV-3) was found in sturgeon species that are vulnerable to decline globally. A study was undertaken to develop a better understanding of the virus genome and to develop diagnostic tools to support an epidemiological investigation. A 184,426 bp genome was assembled from PacBio HiFi sequences generated with DNA from a Lake Sturgeon Acipenser fulvescens gonad cell line. The AciHV-3 genome was contiguous with host chromosomal DNA and was structured with telomere-like terminal direct repeat regions, five internal direct repeat regions and a U region that included intact open reading frames encoding alloherpesvirus core proteins. Diagnostic testing conducted with a newly developed and analytically validated qPCR assay established the ubiquitous presence and high titer of AciHV-3 DNA in somatic and germline tissues from wild Lake Sturgeon in the Hudson Bay drainage basin. Phylogenetic reconstructions confirm that the monophyletic AciHV-3 lineage shares a common ancestor with AciHV-1 and that AciHV-3 taxa cluster according to their sturgeon host. The same genotype of AciHV-3 is found in disjunctive Lake Sturgeon populations within and among drainage basins. The results support the hypotheses that AciHV-3 has established latency through germline chromosomal integration, is vertically transmitted via a Mendelian pattern of inheritance, is evolving in a manner consistent with a replication competent virus and has co-evolved with its host reaching genetic fixation in Lake Sturgeon populations in central Canada. Full article

: Pharmacogenomics is revolutionizing precision medicine by enabling tailored therapeutic strategies based on an individual genetic and molecular profile. Circular RNAs (circRNAs), a distinct subclass of endogenous non-coding RNAs, have recently emerged as key regulators of drug resistance, tumor progression, and therapeutic responses. Their covalently closed circular structure provides exceptional stability and resistance to exonuclease degradation, positioning them as reliable biomarkers and novel therapeutic targets in cancer management. This review provides a comprehensive analysis of the interplay between circRNAs and pharmacogenomics, focusing on their role in modulating drug metabolism, therapeutic efficacy, and toxicity profiles. We examine how circRNA-mediated regulatory networks influence chemotherapy resistance, alter targeted therapy responses, and impact immunotherapy outcomes. Additionally, we discuss emerging experimental tools and bioinformatics techniques for studying circRNAs, including multi-omics integration, machine learning-driven biomarker discovery, and high-throughput sequencing technologies. Beyond their diagnostic potential, circRNAs are being actively explored as therapeutic agents and drug delivery vehicles. Recent advancements in circRNA-based vaccines, engineered CAR-T cells, and synthetic circRNA therapeutics highlight their transformative potential in oncology. Furthermore, we address the challenges of standardization, reproducibility, and clinical translation, emphasizing the need for rigorous biomarker validation and regulatory frameworks to facilitate their integration into clinical practice. By incorporating circRNA profiling into pharmacogenomic strategies, this review underscores a paradigm shift toward highly personalized cancer therapies. circRNAs hold immense potential to overcome drug resistance, enhance treatment efficacy, and optimize patient outcomes, marking a significant advancement in precision oncology. Full article

Three-dimensional (3D) spheroid cultures are crucial for modeling salivary gland (SG) morphogenesis and advancing regenerative medicine. This study evaluated the effects of varying ratios of mouse SG-derived epithelial cells co-cultured with human dermal fibroblasts (hDFs), identifying a 2:1 ratio (spheroids containing 67% EpCAM^pos cells with 33% hDFs) as optimal for preserving native SG-derived epithelial cell phenotypes. At this ratio, 67% EpCAM^pos spheroids maintained structural integrity and demonstrated a significant reduction in apoptosis and senescence markers, specifically, cleaved caspase-3 (Cc3) and Serpine1, alongside an enhanced expression of the progenitor marker Keratin 5 (KRT5). This highlights the pivotal role of fibroblasts in supporting epithelial cell function in 3D cultures. These spheroids provide a useful model for developing SG tissues that closely mimic physiological properties. Despite promising results, these findings are preliminary and require further validation under diverse conditions and across different SG models. Full article

Our research investigated whether the carbon emissions trading pilot policy (CET), while mitigating environmental pollution externalities and fostering green economic and social transformation, can also enhance China’s new quality productivity (NQP) as a key driver of economic growth. This study addresses a research gap by examining the CET from an integrated perspective of economic development and environmental protection. We have developed an NQP evaluation indicator system based on three productivity factors, revealing that the CET can elevate NQP levels in pilot provinces through the advancement of green finance (GF) and industrial structure upgrading (ISU). Furthermore, we analyzed the relationship between the CET and NQP from the perspective of low-carbon energy consumption (LCEC), demonstrating that the level of LCEC can reinforce the CET’s positive impact on NQP and moderate the path before and after the mediating process. Our findings offer valuable insights into leveraging market-based environmental regulation tools to support NQP development, thereby facilitating its cultivation and enhancement. Full article

Bacterial cellulose (BC) synthesized by Acetobacter xylinum has gained significant attention due to its unique structural and functional properties. This study focuses on the simple, facile, and cost-effective synthesis of bacterial cellulose films from Acetobacter xylinum and evaluates their impact on selected properties. The BC films were prepared through a series of controlled fermentation, purification, and drying processes, optimizing their porosity and structural integrity with different stabilization forms (the BC films supported by polyester nonwoven (PES NW) fabric) by a static culture method keeping with the sustainability. The selected properties like density, porosity, surface roughness, thermal conductivity, and the wetting properties of surfaces are tested. These properties were chosen because they significantly impact the performance of BC aerogels in the potential application of aerogels in biomedical, insulation, and filtration industries. The results indicated that the synthesized BC aerogels exhibit a highly porous network, lightweight structure, and excellent thermal conductivity, making them suitable for advanced material applications. This research highlights the potential of bacterial cellulose aerogels as sustainable (without any additives/chemicals) and high-performance materials, paving the way for further advancements in bio-based aerogels. Full article