bluuebunny/crossref_metadata_2025_split huggingface dataset:
CT-based deep learning radiomics model for preoperative prediction of microvascular invasion in intrahepatic cholangiocarcinoma and its clinical value: a two-center study
Authors: Chen Pengyu , Yang Zhenwei , Ning Peigang , Yuan Hao , Qi Zuochao , Li Qingshan , Yu Haibo 
Published: January 2024

Abstract

Background
To develop and validate a computed tomography (CT) image-based deep learning radiomics model (DLRAD) for preoperative prediction of MVI in ICC patients, and to validate its relationship with prognosis.
Methods
A total of 165 ICC patients were recruited from two centers for retrospective study. Based on the radiomics and deep learning features of arterial phase CT images, dozens of models were constructed and compared using four machine learning methods. The incremental value of different sizes of peritumoral regions to the model was also explored. The performance of the model was evaluated using the area under the curve (AUC), calibration curve and decision curve. Kaplan-Meier curve was used to analyze the relationship between the model prediction results and prognosis.
Results
According to the radiomics features in the intratumoral and 2mm peritumoral regions and the deep learning features in the tumor, the DLRAD model constructed by the LR method showed the best discrimination ability for MVI. The AUC of the internal validation cohort was 0.86. The AUC of the external validation cohort was 0.89. In addition, the MVI predicted by the model was significantly correlated with the overall survival rate of patients (P = 0.005), which was consistent with the actual situation.
Conclusion
The DLRAD model constructed by radiomics and deep learning technology can effectively predict MVI in ICC patients. This provides clinicians with a powerful tool to help them make more accurate treatment decisions.
Analysis of Diagnosis Rate and Factors Influencing the Implementation of Confirmatory Tests in Women with Epithelial Abnormalities of Squamous Cells in Pap Tests: 5 years follow-up using the National Health Insurance Service database.
Authors: Kim Hyeongsu , Hwang Jong Ha 
Published: January 2024

Abstract
Objective: this study aims to investigate the diagnosis rates of cervical cancer and carcinoma in situ between women who did not undergo confirmatory tests within 6 months and those who did, considering influencing factors.
Methods: We identified all Korean women who participate in nationwide cervical cancer screening program from January 2011 and December 2017 using the National Health Insurance Service (NHIS) database.
Results: Among the 21,452,058 eligible patients from 28,619,210 Pap smear tests (2011-2017), 450,349 (1.57%) had squamous cell abnormalities in Pap smear results. Among women notified of squamous cell abnormalities, 124,135 (27.6%) underwent a confirmatory test within 6 months. Among these, there were 4,529 women (3.65%) diagnosed with cervical cancer (C53) and 11,465 women (9.24%) diagnosed with carcinoma in situ (D06). Women who did not undergo confirmatory tests within 6 months were 326,214 (72.4%). Among them, within 5 years, 5,198 women (1.59%) were diagnosed with cervical cancer (C53), and 9,517 women (9.24%) were diagnosed with carcinoma in situ (D06). For cervical cancer (RR 4.11, 95% CI: 3.72-4.54), the diagnosis rate was significantly higher in women over 70. Both cervical cancer (RR 0.73, 95% CI: 0.66-0.79) and carcinoma in situ (RR 0.85, 95% CI: 0.81-0.90) showed significantly lower diagnosis rates in highest economic status.
Conclusions: Encouraging confirmatory tests for abnormal Pap smears is crucial, particularly among the elderly and those with lower economic status.
Application of Cold Atmospheric Plasma (Cap) in the Treatment of Chronic Skin Lesions Associated With Post-surgical Neuropathic Pain in a Domestic Cat
Authors: de Moura Carlos Eduardo Bezerra , Francelino Luiz Emanuel Campos , Silva Guilherme Ramon Vieira da , de Paula Valéria Veras , Nunes Talyta Lins , Façanha Débora Andréa Evangelista , Júnior Clodomiro Alves 
Published: January 2024

Abstract

Cold atmospheric plasma (CAP) has been employed as a therapy against both acute and chronic skin lesions, contaminated or not, and indirectly exerts an influence on adjacent tissues, promoting healing and other benefits, such as nervous tissue repair and differentiation. In this context, this pilot study aimed to evaluate the effects of a CAP jet on the healing of chronic skin lesions caused by a pruritic reaction resulting from post-surgical neuropathic pain. To this end, a single CAP application was performed on a feline patient with recurrent cervical skin lesions associated with specific drug therapy aiming at resolving neuropathic pain. The single plasma application led to significant macroscopic lesion appearance improvement within 10 days, and the owner reported the cessation of intense itching within the first four hours after treatment and a consequent improvement in the animal's quality of life. Therefore, CAP application leads to significant healing of chronically infected skin lesions resulting from post-surgical neuropathic pain.
Watch and Upgrade or Deconstruct and Relocate: Derna Catastrophe Lessons Amid the Climate-change Era of Unpredictable Flash Floods
Authors: Ashoor Abdelwanees , Eladawy Ahmed 
Published: January 2024

Abstract

Water structures' resilience to accelerated global warming impacts is attracting increased attention. Adaptation to unprecedented flash floods in semi-arid regions (e.g., Middle East and North Africa (MENA)) is becoming more challenging under the huge uncertainty of their frequency and intensity, lack of accurate hydrological studies, and water-resilient cities' absence in these wadi systems. Ten to twenty percent of Derna's population (∼90,000 people) were reported dead and missing along with more than 35,000 residents having been displaced (also, 737 completely collapsed, and 2859 partially collapsed houses) following two destruction waves after two-dams failures during unprecedented storm Daniel on the 11th of September 2023. In this study, we deeply investigated the causes and consequences of this significant main dam (i.e., Bu Mansour) breach. We started by discussing the history of dams in the region and the Derna flash flood protection system design background. Then, we rebuilt the most probable scenarios and simulated the dam break analysis validated by ground truthing methods. No matter how well-maintained the main dam was, the unprecedented Daniel storm flood highly exceeded its design capacity. Aside from the widely reported poor maintenance, our investigation of the overflow shafts of the two dams raised a potentially fatal design issue with the main dam. We concluded the most probable dynamics of the two dams' failure through two waves of destruction confirmed by diverse field observations. Ironically, the study concluded that a similar flood induced by the storm could leave much less damage in the case of the absence of any dams (i.e., far less maximum depth and velocity occurrence and fewer flooded areas). Hence, there is an urgent need to reassess similar dams’ design criteria, upgrade, or simply deconstruct similar existing aging dams -including those with small annual storage-to-design capacity ratios- along with relocating residents and infrastructure away from the low-lying areas, all aimed at safeguarding human lives in similar ungauged wadi systems.
Adsorption and removal of perfluorinated compounds from aqueous solution using magnetic fluorinated porous carbons
Authors: Zhan Han-Ying , Lin You-Ming , Qin Rui-Yang , Zhang Zhi-Qi 
Published: January 2024

Abstract
Perfluorinated compounds (PFCs) cause environmental pollution and serious health issues. Therefore, magnetic fluorinated porous carbons (M-FPCs) derived from the carbonization and further fluorination of Fe-Zr MOFs were used as novel adsorbents to investigate the possibility of PFC removal from wastewater. Investigation of the adsorption behavior of PFCs on M-FPCs revealed that the isotherms conformed to the Langmuir model and kinetics fit the pseudo-second-order model. Simulations using the Weber–Morris and Boyd diffusion models indicated that the adsorption of PFCs on M-FPCs involved external mass transfer first, followed by intraparticle diffusion, where film diffusion was the primary controlling process. M-FPCs with maximum adsorption ranging from 518.1–919.3 mg g− 1 for studied PFCs were adopted to remove PFCs from simulated wastewaters of textile mill and leather factory. Up to 98.1–100.0% of PFCs were removed within 15 min, and the residual levels of PFCs reached drinking water standards after treatment, which suggests the promising application of M-FPCs in the removal of PFCs from wastewater.
A Riemannian Genuine Measure of Entanglement for Pure States
Authors: Ramachandran Dharmaraj , Vathsan Radhika 
Published: January 2024

Abstract
While several measures exist for entanglement of multipartite pure states, a true entanglement measure for mixed states still eludes us. A deeper study of the geometry of quantum states may be the way to address this issue, in which context we present a measure for multipartite pure state entanglement (GBR) based on a geodesic distance in the space of quantum states. Our measure satisfies all the desirable properties of a “Genuine Measure of Entanglement” (GME), that exhibits some advantages over other GMEs.
Internet of Things Deployment based on Fog Computing Systems: Security Approach
Authors: ChinaTelecomSichuanBranch LiangyuZhang 
Published: January 2024

Abstract

A smart grid with numerous capabilities has been developed using the Internet of Things (IoT). Due to the high volume of computing of the IoT, there is fog computing, which puts the burden of processing on the cloud computing to lighten the network load, which leading to improved efficiency, energy efficiency, and quality of services optimization. This research provides an intelligent method of IoT-based deploying for intrusion detection by using KDD CUP data to improve accessibility while maintaining security to reduce penetrations. The Social Spider Algorithm (SSA) as a swarm intelligence algorithm has been considered as a solution to improve this problem. The results represented that improvements in intrusion detection and availability have been made by maintaining security over previous methods.
Design of a three level hierarchical hybrid wired-wireless Network-on-Chip architecture
Authors: Varanasi Lakshmi Kiranmai , B.K.N Srinivasarao 
Published: January 2024

Abstract

Three-level hierarchical hybrid wired-wireless Network-on-Chip (NoC) architectures are proposed in this article. Various hybrid wired-wireless configurations have been proposed and examined under various traffic patterns at low, medium, and high traffic loads. In the proposed three-level hierarchy, the bottom and top levels are concerned with subnet topology and wireless hub topology, respectively. The present research introduces a middle level that investigates the number of nodes that will be connected to a subnet's wireless hub. Mesh and fully connected wireless topologies have been chosen for the bottom and top levels of the hierarchy, respectively. Different hybrid wired-wireless configurations have been developed and studied by varying the number of subnets and the number of nodes to be attached to the subnet at the middle level. The objective of investigating the middle level is to reduce the number of subnets and, consequently, the number of wireless nodes in large network architectures without compromising performance.
A Real-Time ANN for Estimation Digital Twin parameters in Health Indication of Flyback Converter
Authors: Koushkaki Ehsan Rahmanian , Rajaei Amirhosein 
Published: January 2024

Abstract

This paper proposes a real-time artificial neural network (ANN) for estimation digital twin parameters in health indication of flyback converter. Indeed, the ANN is approximated digital twin parameters by using voltage input of the flyback converter and comparison of capacitor voltage and inductance current of simulated model and digital twin. Moreover, the ANN structure is picked out multi-layer perceptron (MLP) with back-propagation training algorithm. Furthermore, the ANN is used in a new real-time technique that is trained during of running of the model. However, the digital twin is calculated output voltage and inductance current by 4th order runge-kutta method. The formula of model is determined with state-space averaged (SSA) model technique. Finally, as the output voltage of simulated model and digital twin are the same, the component parameters are compared with initial values and health of converter is detected. The model is tested in some variety scenarios, such as degradation of capacitor, MOSFET, diode and transformer magnetization inductance, and the outcome of them are presented. Also, the method is tested in changing input voltage of converter and duty cycle. The studied model is simulated in MATLAB/Simulink and the digital twin and the ANN block are done in S-function.
Background-aware Siamese Network Tracking based on Salient Feature Fusion
Authors: Zhou Lifang , Dai Jing , Lei Bangjun , Xue Ruixin 
Published: January 2024

Abstract

In the realm of object tracking, complex backgrounds pose significant challenges, often leading to the underperformance of existing trackers that predominantly rely on deep features from the final layer of feature extraction networks. These features, while semantically rich, are not always sufficient to distinguish targets from cluttered environments. To address this limitation, we introduce the Background-aware Siamese Network (BASNet), an innovative approach that enhances the salience of features in intricate scenes. Central to our method is the Dual-Feature Fusion (DFF) model, meticulously crafted to stabilize target representation amidst distracting backgrounds. BASNet leverages the complementary strengths of deep and shallow features, harnessing the former’s semantic depth and the latter’s precision in spatial localization. This fusion not only elevates feature utilization but also mitigates the shortcomings associated with the superficial output of shallow networks. Further refining the target’s spatial definition, our attention-focusing module (AF) plays a pivotal role. It accentuates pertinent features 1 and attenuates noise emanating from the multi-layer feature amalgamation. The module’s ingenuity lies in its cyclic attention operations, which enable each pixel to establish extensive dependencies with all others , thereby spotlighting salient features. For precise target localization, BASNet employs the SIoU loss within its regression branch, a strategic choice that contributes to the model’s accuracy. Extensive experiments demonstrate the competitive performance of our method on six datasets: OTB50, OTB100, UAV123, VOT2016, VOT2018, and VOT2019.
Multi-granularity and Multi-modal Feature Fusion for Indoor Positioning
Authors: Pei Shenglei , Wang Yi , Zhao Hong , Wang Yu 
Published: January 2024

Abstract

Despite the widespread use of indoor positioning technology, current WiFi positioning methods face challenges in accuracy and efficiency due to the complexities of indoor environments and the intricate architecture resulting from pre-deployed Access Points (APs). Moreover, while visual image positioning methods are effective, their costliness stemming from the requirement of using binocular cameras to obtain depth image data hinders their widespread adoption. To tackle these challenges, we propose an indoor positioning method that leverages multi-granularity and multi-modal feature fusion. In the initial, coarse-grained positioning phase, a neural network model is trained using image data to roughly localize indoor scenes. This model adeptly captures the geometric relationships within images, providing a foundation for more precise localization in subsequent stages. In the fine-grained positioning stage, Channel State Information (CSI) features from WiFi signals and Scale-Invariant Feature Transform (SIFT) features from image data are fused, creating a rich feature fusion fingerprint library that enables high-precision positioning. Experimental results show that our proposed method synergistically combines the strengths of WiFi fingerprint and visual positioning, resulting in a substantial enhancement of positioning accuracy. Specifically, our approach achieves an accuracy of 0.4 meters for 45\% of positioning points and 0.8 meters for 67\% of points. Overall, this approach charts a promising path forward for advancing indoor positioning technology.
Persisting influence of continental inheritance on early oceanic spreading
Authors: MOULIN ADRIEN , Jonsson Sigurjon 
Published: January 2024

Abstract
Formation of new lithosphere at mid-oceanic ridges occurs through magmatic crustal accretion and cooling of the asthenosphere, and is essentially controlled by the spreading-rate, ridge segmentation, and eventual arrival of deeply-sourced hot mantle plumes. Its dependence on long-term inheritance is supposedly weak, except in cases where ridge segmentation is preconditioned by the reactivation of continental weak zones during the rifting phase. Here, we provide the first evidence that pre-rift lithospheric thickness variations constitute another forcing that may transmit influence from past Wilson cycles beyond the stage of continental break-up. This long-term control involves differential redistribution of heat/melt sources along young laterally-confined plume-assisted rifts. This is demonstrated here in the case of the Red Sea from the correlation between on-axis volcano-tectonic patterns, distribution of onshore volcanism, and lithospheric thickness variations of the rifted margins.
Comprehensive Framework for Implementing Blockchain-enabled Federated Learning and Full Homomorphic Encryption for Chatbot security System
Authors: Jalali Nasir Ahmad , Chen HongSong 
Published: January 2024

Abstract
Chatbot is an artificial intelligence application that can provide a conversational environment between human and machine. Most organizations and industries are willing to lay out their services through chatbot because it can provide 24/7 customer support. Meanwhile it raises security and privacy challenges like access control, data leakage during transmission, SQL injection attack, language model attack which make the users concerned about their data, performance and accuracy. Therefore this research paper proposed a comprehensive framework integrated blockchain, federated learning and fully homomorphic encryption algorithm with face recognition to solve above mentioned chatbot’s challenges. The experimental result shows that distributed system improves chatbot accuracy (90%) and more transaction in less time with more clients do not affect the performance. In contrast, more iteration and clients will decrease the accuracy, performance and transactions in centralized system. In addition, fully homomorphic encryption improve and speed up data encryption process. It encrypted more data (1792 MB) in a small amount of 1240 time/sec and conversation/transactions can be transferred via a secure network to ensure the confidentiality, integrity and authenticity of users’ data. The implementation of such comprehensive framework in real-life can improve chatbot security that is actively work as a customer agent in organization.
Malnutrition and Muscle Loss Mediate the Association between NT-proBNP and Mortality in Hospitalized Older Adults
Authors: Tao Jun , Zhang Xiaoyan , Wang Niansong , Cheng Dongsheng 
Published: January 2024

Abstract

Background & Aims
The purpose of this study was to assess the association between N-terminal prohormone of type B natriuretic peptide (NT-proBNP) and long-term mortality in hospitalized oldest-old adults and to explore the mediating role of malnutrition and muscle loss.
Methods
This prospective cohort study was conducted among 360 hospitalized patients ≥ 80 years of age (median age 87 [IQR 84–90] years, 24.4% women) in the Department of Geriatrics. The Geriatric Nutritional Risk Index (GNRI) and Mini Nutritional Assessment Short Form (MNA-SF) were used for nutritional assessment, while calf circumference was used as a measure of muscle mass. A Cox proportional hazard model was used to assess the relationship between NT-proBNP levels and mortality. Mediation analysis was used to explore the mediating effects of malnutrition and muscle loss.
Results
The median follow-up was 4.1 years with 159 (44.1%) deaths. Mortality risk increased by 32% per 2-fold increase in NT-proBNP levels (full adjusted hazard ratio: 1.32 [95% CI, 1.20–1.46]). A mediation analysis showed that a lower GNRI score and decreased calf circumference mediated the effects of high NT-proBNP and mortality risk, with an estimated relative effect size of 28.9%, while MNA-SF and calf circumference mediated the effect, with an estimated relative effect size of 25.3%.
Conclusions
NT-proBNP levels were associated with long-term mortality in hospitalized older patients. Moreover, the detrimental effects of NT-proBNP on survival were partly mediated by malnutrition and muscle loss.
Image Encoded Time Series Classification of Small Datasets: An Innovative Architecture Using Deep Learning Ensembles
Authors: Indrasiri Pubudu L. , Kashyap Bipasha , Pathirana Pubudu N. 
Published: January 2024

Abstract

Convolutional neural networks (CNNs) are often favored for their strong learning abilities in tackling automatic intelligent models. The classification of time series data streams spans across many applications of intelligent systems. However, the scarcity of effective Machine Learning architectures to handle limited time-series data adversely affects the realization of some crucial applications. In particular, healthcare-related applications are inherently concerned with limited time series datasets. Indeed, building effective artificial intelligence (AI) models for rare diseases using conventional techniques can pose a significant challenge. Utilizing recent advances in deep learning and signal processing techniques, this study introduces a new ensemble deep learning (DL) approach for time series categorization in the presence of limited datasets. Physiological data, such as ECG and voice, are used to demonstrate the functionality of the proposed DL architecture with data obtained from IoT and non-IoT devices. The proposed framework comprises a self-designed deep CNN-LSTM along with ResNet50 and MobileNet transfer learning approaches. The CNN-LSTM architecture includes an enhanced squeeze and excitation block that improves overall performance.This architecture processes time series data transformed into a 3-Channel image structure via improved recurrence plot (RP), Gramian angular field (GAF), and fuzzy recurrence plot (FRP) methods. The proposed model demonstrated superior classification accuracy on the ECG5000 and TESS datasets compared to other state-of-the-art techniques, validating its efficacy for binary and multiclass classification.