672 papers
This study proposes a multimodal deep learning approach for detecting bacterial wilt in ginger plants, demonstrating that a hybrid CNN + Graph Neural Network (GNN) model outperforms CNN + RNN and CNN + Vision Transformer combinations by achieving a superior accuracy of 96.85%.
This study demonstrates that electrospun nanofibers derived from garlic shell waste effectively enhance the microbiological safety and extend the refrigerated shelf life of red meat by significantly suppressing the growth of pathogenic and spoilage microorganisms.
This study reveals that the CPUG complex in *C. elegans* coordinates RNA interference by utilizing the pseudohelicase MUT 15 to assemble and activate an autoinhibited RDE 8 nuclease module for RNA cleavage, while simultaneously coupling this activity to poly(UG)ylation by MUT 2 to ensure directional processing and prevent re-cleavage.
This study reveals that in *Pseudomonas aeruginosa* adenosine 5'-phosphosulfate reductase, the redox state of the [4Fe-4S] cluster regulates catalytic efficiency by modulating the conformation of the active site and the nucleophilic attack of C256, a mechanism validated through multiscale molecular simulations that align with experimental kinetics.
This study identifies and characterizes specific long non-coding RNAs, including the pan-cancer regulator CAMOR, that are driven by major oncogenic mutations (MYC, KRAS, TP53) and functionally promote carcinogenesis across colorectal, lung, and pancreatic cancers, thereby offering new potential diagnostic and therapeutic targets.
This study identifies a specific panel of myeloid lectins associated with immunosuppressive tumor-associated macrophages in breast cancer and demonstrates that pharmacologically targeting their shared SYK signaling node effectively remodels these macrophages to reduce immunosuppression.
This study reports the synthesis and characterization of a novel zero-dimensional supramolecular copper(II) compound that exhibits selective antibacterial activity against *Shigella flexneri*, potent cytotoxicity against ovarian cancer cells, and enhanced antioxidant properties upon binding to bovine serum albumin, all while maintaining high biocompatibility.
This study utilizes machine-learning potentials to efficiently explore reaction pathways for the Rochow-Müller process on a Cu-Si surface, revealing that while these models can underestimate specific values compared to DFT, they effectively identify promising alternative routes to M2 production by significantly accelerating the search process.
This paper proposes the PACPT-LB framework, a hybrid machine learning approach that proactively predicts handover failures, radio link failures, and ping-pong events in 5G mmWave networks by leveraging a large-scale simulated dataset and optimized model combinations to achieve superior accuracy across diverse mobility conditions.
This study proposes a hybrid MSCNN-LSTM model enhanced with Squeeze-and-Excitation attention to predict hourly Air Quality Index values, demonstrating superior accuracy and stability compared to traditional deep learning models through multi-scale feature extraction and adaptive channel weighting on data from Yiyang City.
This paper proposes a specialized Methane Management System (MMS), grounded in ISO 14001 and ISO 50001 frameworks, to transform methane mitigation from fragmented technical fixes into a comprehensive, organization-wide strategy that drives emission reductions, operational efficiency, and long-term climate resilience across the oil and gas value chain.
This PRISMA-based systematic literature review examines recent technological innovations and policy reforms in urban waste management, highlighting the adoption of digital tools and circular economy principles while identifying significant implementation barriers in developing countries and calling for holistic, context-specific frameworks.
This EU-27 panel analysis reveals that higher education system indicators are primarily driven by their own historical persistence rather than AI attention, with the sole exception that public search interest in generative AI predicts a small increase in subsequent research and development expenditure.
This study proposes and validates an Activity-Based Costing framework that aligns Korean occupational safety and health management costs with the specific, repetitive hazard cycles of modular building installation by linking statutory resources to measurable work activities, thereby enabling traceable, causally driven, and internally consistent cost allocation.
This study establishes a real-time computer vision pipeline for Wire Arc Additive Manufacturing that identifies YOLOv8n as the optimal detector for weld monitoring and proposes a YOLO-guided SAM2 framework for segmentation, while highlighting the current limitations of foundation models in distinguishing continuous welding features under extreme arc glare.
This paper presents a novel heptafurcated optical-fiber sensor capable of simultaneously measuring target distance, tilt magnitude, and tilt-axis azimuth through a six-measurement reconstruction algorithm, achieving high accuracy and offering a compact, low-cost solution for non-contact industrial metrology.
Using data from the 2012 National Health Interview Survey, this study found that while several Complementary and Alternative Medicine therapies like massage and yoga are associated with perceived better sleep across racial and ethnic groups, the effectiveness of other therapies such as chiropractic manipulation and herbal supplements varies significantly by race and ethnicity.
This cross-sectional study of the University of California Health System reveals that neurocysticercosis, which disproportionately affects Hispanic/Latino patients with socioeconomic barriers, is strongly associated with epilepsy and a broad spectrum of neuropsychiatric comorbidities, underscoring the need for greater recognition of this disease in non-endemic U.S. settings.
This paper proposes a novel improvement-path framework and an exact iterative repair algorithm that, by modeling machine idle time as negative waiting time to simplify the problem structure and characterizing queue discontinuity as the sole obstacle to improvement, guarantees finding a globally optimal schedule for the NP-hard single-machine scheduling problem with release times in finite time.
This paper establishes a cross-parameter Ordered Farey Recursion that decomposes the shortest-completion profiles of rational balance languages via explicit index maps, revealing that their local dynamics correspond to classical Christoffel paths and their global structure yields universal completion spectra derived from unimodular coordinate transformations.
This study reports the rare discovery of bilateral accessory biceps muscles located between the levator scapulae and serratus posterior superior in a cadaver, characterizing their distinct morphology, innervation, and clinical significance as unique anatomical variants rather than simple slips of adjacent muscles.
This retrospective study of 1,607 individuals demonstrates that while knee MRI and radiography show systematic differences in epiphyseal staging, MRI offers slightly superior accuracy for bone age assessment and 18-year age threshold classification, with the optimal grading method varying by gender.
This paper proposes the "Lunet Hypothesis," a theoretical framework grounded in the non-trivial zeros of the Riemann zeta function, which resolves wave-particle duality by positing that light's manifestation as a wave or particle depends on its degree of spectral coherence.
ACTINV is an open-source, reproducible solver for nuclear activation and nuclide-inventory calculations that utilizes the Chebyshev rational approximation method and has been rigorously validated against 132 fusion-neutron-source experiments, demonstrating high accuracy and reliability for scientific and regulatory applications.
This 22-year cohort study across Ethiopia, India, and Peru finds that while early childhood human capital does not predict adult mental health, household food insecurity during the COVID-19 pandemic significantly increases depressive and anxiety symptoms in young adults, highlighting the critical need to protect food security during economic shocks in low- and middle-income countries.
This cross-sectional study of 844 Ghanaian patients with chronic non-communicable diseases reveals that the COVID-19 pandemic significantly reduced health service utilization, with a disproportionately higher impact observed in the non-epicentre Northern region compared to the epicentre Ashanti region, driven by factors such as age, income, disease duration, and health insurance status.
The paper introduces SDT-4D, a novel 4D self-supervised denoising framework that utilizes a unified transformer architecture and physics-informed uncertainty quantification to achieve state-of-the-art restoration of volumetric time-lapse fluorescence microscopy data without requiring clean training images.
This study integrates bulk, single-cell, and spatial transcriptomic analyses to identify and validate a reproducible five-gene mitotic transcriptional programme (BIRC5, CDC20, CENPA, CKS1B, and NUF2) that exhibits significantly higher activity in intestinal-type compared to diffuse-type gastric cancer, providing a prioritized framework for future functional and clinical investigation of Lauren subtype biology.
This paper proposes adapting the PageRank algorithm to Social Representations Theory as a more robust and accurate alternative to traditional associative degree metrics for identifying the central nucleus of social thoughts, a method validated through an empirical study on students' perceptions of universities.
This article conceptualizes AI Governance Capability (AIGC) as a multidimensional, higher-order organizational construct grounded in Dynamic Capabilities Theory, arguing that the synergistic integration of governance structures, processes, competencies, strategic alignment, and continuous monitoring is essential for enhancing decision-making quality and organizational resilience, particularly in highly regulated sectors.
This paper introduces a structural equation formulation for general quasi-periodic Gaussian processes that simplifies generation and forecasting while reducing computational complexity from to , offering a scalable and consistent approach for analyzing diverse natural and physiological signals.
This paper introduces the Global Benford Deviation Clustering (GBDC) framework, a novel unsupervised method that transforms global Benford's Law deviations into per-pixel anomaly scores to enable training-free radiometric anomaly detection in multispectral imagery, achieving a baseline mean F1-score of 0.347 on the CloudSEN12 dataset.