Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.

Multi-modal and multi-view imaging is essential for diagnosis and assessment of cervical spondylosis. Deep learning has increasingly been developed to assist in diagnosis and assessment, which can help improve clinical management and provide new ideas for clinical research. To support the development and testing of deep learning models for cervical spondylosis, we have publicly shared a multi-modal and multi-view imaging dataset of cervical spondylosis, named MMCSD.

More Severe Brain Network Hierarchy Disorganization in Treatment-Naive Deficit Compared to Non-deficit Schizophrenia and Underlying Neurotransmitter Associations.

Background And Hypothesis: Deficit schizophrenia (DS) represents a distinct entity characterized by primary and enduring negative symptoms, yet the neurobiological differences between DS and non-DS (NDS) remain undetermined. Using a gradient-based approach, we hypothesize that DS and NDS will exhibit convergent and divergent brain functional hierarchy patterns, each with a specific underlying neurotransmitter architecture.

Study Design: Resting-state functional magnetic resonance imaging images were acquired from 44 treatment-naive DS, 55 treatment-naive NDS, and 60 matched healthy controls (HCs).

Time-varing effect of policy uncertainty on A-share industry returns- A novel Bayesian approach.

The impact of policy uncertainty on A-share industry returns shows significant time-varying characteristics, amplified by industry input-output relationships. Traditional TVP-VAR models overlook network structures, leading to unquantified spillover effects and imprecise systemic risk identification. To address this problem, this study embeds industry input-output tables as matrices into Time-Varying Parameter Spatial Autoregressive Model, and Bayesian methods are innovatively introduced into this model to capture the parameters.

A Novel Black Widow Optimization Algorithm Based on Lagrange Interpolation Operator for ResNet18.

Hyper-parameters play a critical role in neural networks; they significantly impact both training effectiveness and overall model performance. Proper hyper-parameter settings can accelerate model convergence and improve generalization. Among various hyper-parameters, the learning rate is particularly important.

Advancing Carbonaceous Aerosol Remote Sensing across Asia-Oceania: Insights from Geostationary Satellite Retrievals.

Carbonaceous aerosols, comprising mainly black carbon (BC) and organic carbon (OC), have a substantial impact on radiation balance, the climate, and human health. Despite extensive scientific attention, the challenges associated with current approaches for measuring carbonaceous aerosols, especially low spatial and temporal resolutions, limit the understanding of their sources and dynamics. This study presents a comprehensive analysis of carbonaceous aerosols over the Asia-Oceania region using a novel critical reflectance algorithm for geostationary satellite (Himawari-8/9) retrievals.

Study on medical professionals' acceptance of and factors influencing drone delivery for medical supplies.

Introduction: Drone delivery technology provides innovative alternatives for the transportation of medical supplies. Due to their quick reaction, effectiveness, and adaptability, drones can drastically reduce the time it takes to deliver medications, vaccines, and emergency supplies as compared to standard logistics models. However, the real marketing of drone delivery technology has been hampered by medical professionals' concerns about its stability, safety, and dependability.

MHAHF-UNet: a multi-scale hybrid attention hierarchy fusion network for carotid artery segmentation.

Purpose: Carotid plaque is an early manifestation of carotid atherosclerosis, and its accurate segmentation helps to assess cardiovascular disease risk. However, existing carotid artery segmentation algorithms are difficult to accurately capture the structural features of morphologically diverse plaques and lack effective utilization of multilayer features.

Methods: In order to solve the above problems, this paper proposes a multi-scale hybrid attention hierarchical fusion U-network structure (MHAHF-UNet) for segmenting ambiguous plaques in carotid artery images in order to improve the segmentation accuracy for complex structured images.

Bibliometrics of artificial intelligence applications in hepatobiliary surgery from 2014 to 2024.

Background: In recent years, the rapid development of artificial intelligence (AI) in hepatobiliary surgery research has led to an increase in articles exploring its benefits. We performed a bibliometric analysis of AI applications in hepatobiliary surgery to better delineate the contemporary state of AI application in hepatobiliary surgery and potential future trajectories.

Aim: To provide clinical practitioners with a reliable reference point.

Generate vector graphics of fine-grained pattern based on the Xception edge detection.

Harnessing the power of artificial intelligence(AI) approaches to innovatively generating the vector graphics of fine-grained patterns has become an important task in image edge extraction, particularly on the domain of intangible cultural heritage (ICH) images where they are typically fine-grained and having the complex edges. With higher autonomy, the machine learning algorithms are able to accurately extract the image information, understand and convey the concept contained in it. In this paper, we take Qiang embroidery patterns as an example due to containing fine-grained patterns, which is more suitable for the study of image processing and pattern recognition techniques.

A continuous multi-night sleep database for exploring sleep structure similarity.

The sleep structure of healthy adults varies across continuous nights but exhibits periodicity and regularity. To explore the similarities and differences in sleep electroencephalogram structures on an intra- and inter-individual basis across nights, we present an open-access, continuous, multi-night sleep database. This database contains multi-modal sleep monitoring data from 20 healthy participants over three consecutive nights.

Investigating the catalytic degradation performance of the actual coking wastewater DOM by iron-containing sludge-based biochar.

The resource utilization of iron-containing sludge (FeCS) is an urgent problem to be solved in Fenton wastewater treatment process. In this work, biochar was prepared by FeCS and peanut shells as catalyst for the degradation of dissolved organic matter (DOM) in the actual coking wastewater. The results indicated that iron-containing sludge-based biochar (FeCSB) prepared at 800 °C was conducive to the reduction from Fe to Fe, and the relative content of Fe (64.

Synergistic Cooperation of Active Sites in Cu-Incorporated Nitrogen-Doped Porous Carbon for CO Electroreduction to Ethanol.

Selective electroreduction of CO to ethanol has attracted increasing interest. Herein, we report an effective strategy to boost ethanol selectivity from electrocatalytic CO reduction by constructing Cu-incorporated nitrogen-doped porous carbon (Cu-NPC) via pyrolysis of bimetallic CuZn-MOF-74, followed by an etching treatment. Etching processes not only remove large-sized metal particles and create lots of pores in the catalyst but also leave Cu existing mainly as Cu that shows strong electronic interaction with the pyridinic N in NPC.

Combining functional, structural, and morphological networks for multimodal classification of developing autistic brains.

Accumulating neuroimaging evidence suggests that abnormal functional and structural brain connectivity plays a cardinal role in the pathophysiology of autism spectrum disorder (ASD). Here, we constructed brain networks of functional, structural, and morphological connectivity using data from functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural magnetic resonance imaging (sMRI), respectively. The neuroimaging data from a cohort of 50 individuals with ASD and 47 age-, gender- and handedness-matched TDC (age range: 5-18 years) were selected from the Autism Brain Image Data Exchange database.

Target analysis of per- and polyfluoroalkyl substances in surface water: Occurrence, seasonal trends, and risk-based prioritization in the Minjiang River.

Although thousands of per- and polyfluoroalkyl substances (PFAS) are used globally, most research has focused on a limited subset, potentially underestimating their environmental risks. In particular, PFAS pollution in long river systems originating in high mountains and flowing into densely populated plains remains poorly understood. Here, we developed a target analysis method to identify and (semi-)quantify 74 PFAS compounds in surface water samples from the Minjiang River, the largest tributary of the Yangtze River.

The impact of smartphone addiction on creativity among college students: a moderated mediation model of negative emotion and perceived social support.

Introduction: Smartphone addiction has a negative impact on creativity among college students, which has attracted extensive attention from scholars.

Methods: In this study,we surveyed 2900 Chinese college students using the Smartphone Addiction Scale, Negative Emotion Scale, Perceived Social Support Scale, and Creativity Self-evaluation Scale to explore the impact mechanism of smartphone addiction on creativity.

Results: Results from the hierarchical regression analysis indicated that smartphone addiction has negative influence on creativity in college students (b=-0.

Stability Analysis of a Four-Species Periodic Diffusive Predator-Prey System with Delay and Feedback Control.

In this work, we present a novel four-species periodic diffusive predator-prey model, which incorporates delay and feedback control mechanisms, marking substantial progress in ecological modeling. This model offers a more realistic and detailed portrayal of the intricate dynamics of predator-prey interactions. Our primary objective is to establish the existence of a periodic solution for this new model, which depends only on time variables and is independent of spatial variables (we refer to it as a spatially homogeneous periodic solution).

HECM-Plus: Hyper-Entropy Enhanced Cloud Models for Uncertainty-Aware Design Evaluation in Multi-Expert Decision Systems.

Uncertainty quantification in cloud models requires simultaneous characterization of fuzziness (via Entropy, ) and randomness (via Hyper-entropy, ), yet existing similarity measures often neglect the stochastic dispersion governed by . To address this gap, we propose HECM-Plus, an algorithm integrating Expectation (), , and to holistically model geometric and probabilistic uncertainties in cloud models. By deriving -adjusted standard deviations through reverse cloud transformations, HECM-Plus reformulates the Hellinger distance to resolve conflicts in multi-expert evaluations where subjective ambiguity and stochastic randomness coexist.

Spin-multiplexed phase and amplitude manipulations of terahertz waves based on chiral metasurfaces.

The independent manipulation of circularly polarized electromagnetic waves is a significant topic in the field of micro-nano optics, and metasurfaces provide a convenient solution for this target. However, the design of metasurfaces is still complex, often involving both parameter space and polarization space, where the simultaneous control of amplitude and phase is quite challenging. In this paper, we propose a new scheme for the spin-multiplexed control of amplitude and phase based on chiral metasurfaces, which only consider the parameter space.

Transfer learning of neural operators for partial differential equations based on sparse network λ-FNO.

When the solution domain, internal parameters, and initial and boundary conditions of partial differential equation (PDE) are changed, many potential characteristics of the equation's solutions are still similar. This provides the possibility to reduce the cost of PDE operator learning through transfer learning methods. Based on Fourier neural operator (FNO), we propose a novel sparse neural operator network named λ-FNO.

Simultaneous analysis of multi-class antibiotics in bottled water using large-volume direct-injection LC-MS/MS.

A large-volume direct-injection (LVDI) method was developed and validated for the simultaneous analysis of 69 antibiotics in bottled water using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Target antibiotics included 23 sulfonamides, 19 quinolones, 12 macrolides, 11 β-lactams, and 4 tetracyclines. Optimization of ion source parameters led to enhanced signal intensities for 55 antibiotics.

Consistent and Specific Hashing for image set classification.

Image set classification (ISC) has obtained extensive attention with the continuous development of information technology. Different from the traditional single image classification, ISC mainly utilizes image set to improve classification performance instead of an individual image, which could effectively utilize more information from the set to mitigate various appearance variations. Most existing ISC methods learn effective real-value latent discriminative representation, which could suffer from large computational complexity, thereby directly affecting the practical applications for large-scale data scenes.

Multiscale spatiotemporal analysis of carbon dioxide emissions in China from 2012 to 2021 based on kernel normalized difference vegetation index and nighttime light data.

To achieve carbon peaking and carbon neutrality, it is crucial to accurately understand the spatial distribution of carbon dioxide emissions (CE). However, current research primarily focuses on macro perspectives at global and national scales, making it challenging to analyze the detailed spatiotemporal changes in CE. This study combines remote sensing technology with statistical models and, for the first time, integrates the kernel normalized difference vegetation index (kNDVI) and NPP-VIIRS nighttime light data (NTL) to establish the kernel vegetation nighttime light index (kVNTL).

A Case Study of a Hailstorm in the Shanghai Region: Leveraging Multisource Observational Data and a Novel Single-Polarization X-Band Array Weather Radar.

This study investigates a severe summer convective hailstorm that occurred in Shanghai on 18 August 2019, using multisource meteorological datasets, with a particular focus on the innovative application of a single-polarization X-band array weather radar (AWR). Radiosonde data revealed high convective available potential energy and unstable atmospheric indices, while wind profiler radars (WPRs) showed initial easterly moisture transport near the ground and strong southwesterly flow aloft, both contributing significantly to intense convection. Ground-based automatic meteorological stations (AMSs) recorded abrupt temperature drops of approximately 10 °C and wind speed increases exceeding 20 m s, which aligned closely with the rapid expansion of the hailstorm.

Scalable Semantic Adaptive Communication for Task Requirements in WSNs.

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost effectiveness, and ease of deployment. The rapid advancement of the Internet of Things (IoT), Artificial Intelligence (AI), and sixth-generation mobile communication technology (6G) and Mobile Edge Computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices, and changed the image sensing and understanding to novel modes (such as machine-to-machine or human-to-machine interactions). However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission.

FARVNet: A Fast and Accurate Range-View-Based Method for Semantic Segmentation of Point Clouds.

Environmental perception systems provide foundational geospatial intelligence for precision mapping applications. Light Detection and Ranging (LiDAR) provides critical 3D point cloud data for environmental perception systems, yet efficiently processing unstructured point clouds while extracting semantically meaningful information remains a persistent challenge. This paper presents FARVNet, a novel real-time Range-View (RV)-based semantic segmentation framework that explicitly models the intrinsic correlation between intensity features and spatial coordinates to enhance feature representation in point cloud analysis.