bluuebunny/crossref_metadata_2025_split huggingface dataset:
Is a Large Neck Circumference a Risk Factor for Male Infertility in the Turkish Population?
Authors: Dursunoglu Duygu 
Published: November 2023

Abstract
Background
Currently, the potential role of obesity on semen quality has not yet been fully elucidated. In recent years, alternative anthropometric measurements to body mass index (BMI), which more reflect of body fat distribution, have been established to estimate central adiposity. The association between neck circumference (NC), a novel index of central adiposity, and semen quality has not been explored yet.
Objective
The present study aimed to reveal the potential of NC to predict male infertility.
Subjects and Methods:
This is a cross-sectional study including 4739 male participants aged 17–55 years. Semen parameters included sperm concentration, total count, total motility, progressive motility, rapid progressive motility and vitality. As anthropometric measurements of obesity, BMI and central obesity indices including waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHpR), waist-to-height ratio (WHtR) and NC were examined. The predictive potentials of obesity indices for low semen parameters were evaluated by Receiver Operating Characteristics curve analysis. The associations of obesity indices with semen parameters were analyzed by binary logistic regression analyze after adjusting potential confounders.
Results
Patients with lower semen parameters had higher anthropometric obesity indices than those with normal parameters. The obesity indices had predictive potentials for low semen parameters, excluding WHpR for sperm concentration and, BMI and HC for rapid progressive motility. After adjusting for confounders, the strongest associations were found between HC with sperm count parameters, WC and WHtR with sperm vitality and motility parameters and NC with rapid progressive motility (OR: 2.37, 95% CI: 2.02–2.79, p < .001).
Conclusions
Obesity, particularly central obesity plays important a role in male infertility. The distribution of adipose tissue appears to may have specific roles on sperm functions, resulting in different fertility markers may be affected. A larger NC may be a risk factor for male infertility, specifically low sperm rapid progressive motility.
Opinion of Polish doctors on the use of futile therapy
Authors: Damps Maria , Gajda Maksymilian , Wiktor Łukasz , Byrska Elżbieta Maciejasz , Rybojad Beata , Kowalska Małgorzata , Śniatkowska Alicja Bartkowska , Paprocka Anna Lipińska , Kucewicz Ewa Czech 
Published: November 2023

Abstract

Discontinuation of futile therapy is increasingly discussed in Polish clinical practice. Taking into account the broadly understood well-being of the patient, it is necessary to consider whether all clinical options resulting from medical progress should be used for every patient and on what grounds decisions to limit therapy should be based. Our study aimed to know the opinion of Polish medical staff on the topic discussed. The study was an anonymous survey conducted in paper and electronic versions among doctors of all specialties. An analysis of the collected data was carried out using the methods of descriptive and analytical statistics. The respondents were divided into two groups according to their specialization: intensivists and doctors of other specializations. A total of 323 doctors participated in the study; ​93% of participants were aware of the problem of futile therapy in adults (statistically, significantly more often intensivists, p = 0.002); 95% of respondents supported the idea of discontinuing futile therapy, and over 68% of respondents used the therapy discontinuation protocol. Among the most common reasons for undertaking futile therapy, the respondents declared (more than one answer was possible) fear of legal liability for not taking such action (93.5%), as well as fear of being accused of unethical behavior (62.2%), and fear before talking to the patient/patient’s family and their reaction (57.9%). The respondents also indicated what would facilitate making decisions about limiting futile therapy. These were primarily: precise qualification criteria for limiting therapy and education in this area (95.3%), the patient's declaration of will (87.5%), and a clear legal act (81.3%). The vast majority of study participants supported the idea of limiting futile therapy, and this issue is well-known to Polish doctors.
Novel Insights into the Exact Solutions of the Modified (3+1) Dimensional Fractional KS Equation with Variable Coefficients
Authors: CR Jisha , Jang Bongsoo 
Published: November 2023

Abstract
This study comprehensively explores a unified methodology for deriving exact solutions to the fractional modified (3+1) dimensional Kudryashov-Sinelshchikov (KS) equation featuring variable coefficients. The fractional KS equation, which incorporates fractional local M-derivatives, presents a significant challenge because of its inherent nonlinearity and the introduction of spatially varying coefficients. Of paramount importance are these variable coefficients, as they introduce spatial dependence and intricately capture spatial variations within the equation. This complexity in spatial variation poses a formidable challenge in obtaining exact solutions. Our analytical examination offers profound insights into the intricate interplay between diverse variable coefficients and fractional parameters. This comprehensive analysis greatly enhances our capacity to interpret solutions across various scenarios, enriching our understanding of the nuanced behavior exhibited by the fractional Kudryashov-Sinelshchikov equation. Our investigation encompasses a diverse range of solution forms, including fractional, polynomial, exponential, and others. The outcomes of this study hold profound implications for an extensive array of scientific domains, spanning mathematical physics, fluid dynamics, and nonlinear optics. Furthermore, this research employs advanced data visualization techniques, comprising 3D plots, contour plots, and stream plots, to facilitate a deep comprehension of intricate physical phenomena. These visual aids concurrently illustrate how analytical solutions are influenced by varying conditions.
Ams classification 2010: 45K05, 45G10, 83C15, 35Q35, 35Q68 , 35Q80
Analysis and optimization method of influencing factors for three-dimensional target mixed pixels under zero meteorological view distance
Authors: Wang Jun-Feng , LIU XIAO , CHEN Zhen-Ting , LIU GANG 
Published: November 2023

Abstract

Mixed pixels widely exist in the obtained hyper-spectral data,which directly affects the accuracy of optical remote sensing imaging simulation. Aiming at the three-dimensional target under zero meteorological view distance, this paper comprehensively considers the effects of directional reflection characteristics, geometric shadows and background radiation on the generation of mixed pixels.It proposes a method of modeling mixed pixels by taking each surface of a three-dimensional target as an end element component and using the weight coefficient of the distance between the end element groups to the view center. Through designing experiments to simulate different influence conditions such as directional reflection, geometric shadow and background radiation, this paper verifies the influence of the radiation value of the mixed pixels of the three-dimensional target produced by different mixing conditions in the single-pixel field of view. The experimental results show that directional reflection characteristics, geometric occlusion shadows and background radiation have significant effects on the generation of mixed pixels for the three-dimensional targets. The improved mixed pixel modeling method proposed in this paper considering the influence of multiple factors has an average error of less than 10% in modeling accuracy of three-dimensional targets, which is significantly better than traditional linear modeling methods. It can be seen that the research results of this paper have good reference significance for the research of the three-dimensional target mixed pixel radiation model.
Industrial-scale nonmetal current collectors designed to regulate heat transfer and enhance battery safety
Authors: Mai Liqiang , Li Lun , Yang Jinlong , Tan Rui , Shu Wei , Low CheeTong John , Zhang Zixin , Zhao Yu , Li Cheng , Zhang Yajun , Li Xingchuan , Zhang Huazhang , Zhao Xin , Kou Zongkui , Xiao Yong , Verpoort Francis , Wang Hewu , He Daping 
Published: November 2023

Abstract

Safety concerns have become a long-lived challenge that impedes the industrial implementation of high-energy lithium-ion batteries (LIBs). The major safety issue, known as thermal runaway, is triggered by the local thermal aggregation caused by slower heat dissipation than that of heat generation inside the battery. However, how to improve the internal heat transfer is challenged by the low thermal conductivity of metal current collectors (CCs) and the scalable manufacturing of nonmetal CC foils. Here we report a fast thermoresponsive non-metallic CC that can substitute the benchmark metal CCs, i.e., Al and Cu foils, to regulate heat transfer and considerably enhance battery safety. The non-metallic CC was fabricated using graphene (Gr) oxide through a continuous thermal pressing process to afford a dense and defect-free Gr foil of at a hundred-meter level. This Gr foil demonstrates ultra-high thermal conductivity of up to 1400.8 W m− 1 K− 1, about one order of magnitude higher than those of Al and Cu foils. Importantly, tangible LiNi0.8Co0.1Mn0.1O2|| graphite pouch cells integrated with these fast thermoresponsive foils show faster heat dissipation, eliminating the local heat concentration and circumventing the fast exothermic aluminothermic and hydrogen-evolution reactions, which are critical factors causing the thermal failure propagation of LIB packs with Al CCs. Fast thermoresponsive and light weight Gr CC enable LIBs to operate with increased output energy and survive under extremely harsh abuses.
Integral representations for products of two solutions of theAiry equation with shifted arguments and their applications in physics
Authors: Bazarov Kirill V. , Tolstikhin Oleg I. 
Published: November 2023

Abstract
Integral representations for acomplete set of linearly independent products of two solutions of the Airy equation whose arguments differ by z_0 are obtained using the Laplace contour integral method. This generalizes similar integral representations for the case z_0=0 obtained by Reid.The relation to other previous results is discussed. The results are used to obtain the outgoing-wave Green's function for an electron in a static electric field in a closed analytic form.
Mathematics Subject Classification (2010). Primary 33C10; Secondary 30E20
Detection of Osteosarcoma using Deep Feature Extraction with Federated Learning and MLP from Histopathological Images
Authors: Chowdhury Anjir Ahmed , Mahmud S M Hasan , Elahe Md. Fazla , Jahan Hosney , Shoombuatong Watshara , Mahmud Mufti 
Published: November 2023

Abstract
Background: The most common type of cancerous bone tumour is Osteosarcoma. Microscopic analysis of coloured slides is mostly used for Osteosarcoma diagnosis. As Osteosarcoma is highly heterogeneous, pathologists struggle to get a consensus diagnosis. Moreover, manual analysis of microscopic images is laborious, time-consuming, and vulnerable to bias.
Method: In this research, a framework that extracts features based on CNN is developed to predict Osteosarcoma to address previous studies' performance gaps and shortcomings. The dataset used for this experiment is obtained from pathology archives at Children's Medical Center in Dallas, which includes 1144 histology glass slides. First, data augmentation is used to extend the dataset size. This study found Blur, Contrast, and Random augmentation perform well for the Osteosarcoma dataset. Next, we primarily considered six pre-trained transfer learning (TL) models and modified the models to extract features. Then, we examined feature selectors like Genetic Algorithm, Principal Component Analysis, Recursive Feature Elimination and Baruto and found PCA selects the optimal feature set. Finally, we fed the selected features into a fine-tuned MLP called Grid search MLP (GsMLP) and utilised federated learning (FL) to ensure the diversity and privacy of the dataset.
Results: Experiments have revealed that the modified Xception model performs better in extracting features for Osteosarcoma prediction. Also, the proposed framework performed better than state-of-the-art techniques such as Convolutional Neural Network, VGG19, Random Forest, and AdaBoost with 98.20% accuracy and 97.5% Receiver Operating Characteristic - Area Under the Curve.
Rituximab improves Clinical Outcomes of CAR-T therapy for r/r B-ALL via sensitizing Leukemia Cells to CAR-T-mediated cytotoxicity and reducing CAR-T exhaustion
Authors: Li yangzi , Cui Qingya , Liu Sining , Liu Lingling , Li Mengyun , Zhu Xiaming , Kang Liqing , Yu lei , Cui Wei , Tang Xiaowen , Wu Deipei 
Published: November 2023

Abstract

Despite chimeric antigen receptor (CAR) T-cell therapy has achieved great advances in recent year, approximately 50% of relapsed/refractory B cell acute lymphoblastic leukemia (r/r B-ALL) patients treated with CAR-T experience relapse 6 months post CAR-T treatment. CD20 express on 30 to 50% of B-ALL, which makes CD20 Monoclonal Antibody as one of the potential therapy strategies to decrease the tumor burden and improve the efficacy of CAR-T therapy. Adding Rituximab to chemotherapy protocol had been demonstrated to improve the outcome for CD20-positive ALL. However, rare study explored the influence of Rituximab combined with CAR-T therapy. We retrospectively analyzed 20 r/r B-ALL patients who received CAR-T therapy, all of whom had failed multiple lines of therapy. Before CAR-T infusion, we administered Rituximab to 10 patient with high CD20 expression at a dose of 375mg/m2 for 1 day. Meanwhile, we selected 10 patients with the comparable features who underwent CAR-T treatment without Rituximab in the same period as the control group. In vitro, the surface molecule expression and killing of CAR-T post Rituximab-treated B-ALL cells co-incubated with CAR-T cells were detected by flow cytometry. Rituximab combined with CAR-T therapy was effective for improving the long-term outcome of B-ALL patients who have failed multiple lines of therapy. In vitro, we observed that rituximab potentially improved CAR-T efficacy by sensitizing Leukemia Cells to CAR T-mediated cytotoxicity and reducing CAR-T exhaustion.
Primary low-grade endometrial stromal sarcoma of the ovary with mesenteric metastasis: An unusual case report
Authors: Yuan Jun , Fu Yajun , Jiang Xiaohong , Li Na 
Published: November 2023

Abstract
Background
Low-grade endometrial stromal sarcoma (LGESS) accounts for only 0.2% of gynecological malignancies. Primary low-grade extrauterine endometrial stromal sarcoma (LGEESS) is rare, its morphology varies according to the location of the tumor, posing a great diagnostic challenge for clinical pathologists.
Case presentation: A 46-year-old female patient with bilateral ovarian cysts and multiple uterine fibroids underwent ovarian cystectomy and uterine myomectomy in 2013. Color Doppler ultrasound showed multiple uterine fibroids and bilateral ovarian cysts (left: 74×74×64 mm, right: 26×22 mm). Laboratory tests showed an elevated squamous cell carcinoma (SCC) associated antigen. The patient subsequently underwent a total hysterectomy and bilateral salpingo-oophorectomy (BSO). The intraoperative pathological results were diagnosed as benign ovarian cysts. Combined with morphological, immunohistochemistry, and molecular results of the paraffin section, LGEESS is the final diagnosis.
Conclusion
We made a comprehensive case report on the clinicopathological features of LGEESS to provide references for clinical pathologists to avoid misdiagnosis or missed diagnosis in the work.
Chinese Ancient Paintings Inpainting Based on Edge Guidance and Multi-Scale Residual Blocks
Authors: Sun Zengguo , Lei Yanyan , Wu Xiaojun 
Published: November 2023

Abstract
Chinese paintings have great cultural and artistic significance, known for their delicate lines and rich textures. Unfortunately, many ancient paintings have been damaged due to historical and natural factors. The deep learning methods that are successful in restoring natural images cannot be applied to ancient paintings inpainting. Thus, we propose a model named Edge-MSGAN for inpainting Chinese ancient paintings based on edge guidance and multi-scale residual blocks. The Edge-MSGAN utilizes edge images to direct the completion network for generating entire ancient paintings. It then applies the multi-branch color correction network to adjust the colors. Furthermore, the model uses multi-scale channel attention residual blocks to learn the semantic features of ancient paintings at various levels. At the same time, by using polarized self-attention, the model can improve its concentration on significant structures, edges, and details, which leads to paintings that possess clear lines and intricate details. Finally, we have created a dataset for ancient paintings inpainting, and have conducted experiments to evaluate the model’s performance. After comparing the proposed model with the state-of-the-art models from qualitative and quantitative aspects, it is found that our model is better at inpainting the texture, edge, and color of ancient paintings.
Ransomware Detection with CNN and Deep Learning Based on Multiple Features of Portable Executable Files
Authors: Yang Chia-Cheng , Hsu Jia-Ming , Leu Jenq-Shiou , Hsieh Wen-Bin 
Published: November 2023

Abstract
Due to the epidemic, many industries need to rely on computers to perform necessary tasks. In case of a ransomware attack, the consequences are unimaginable. In order to avoid ransomware attacks, many researchers have proposed large number of methods to distinguish between benign programs and ransomware, but ransomware keeps changing, making these methods gradually ineffective and leaving the virus attacks unresolved. This study collected 1200 samples of ransomware with 80 families, includes packed, encrypted, and variants, so that the models could protect against variants of ransomware. Based on the DLL, Subsystem, Subsystem Version, N-gram as features, we made three deep learning models that could support variable shape size. After experiments, the best model Accuracy was 99.77%, Recall was 99.72%, and Precision was 99.81% We also designed a program that could load the completed models, and provide users with the ability to scan their own computers so that they could be protected from ransomware.
2010 MSC: 00-01, 99-00
Forest Fire Monitoring System Supported by Unmanned Aerial Vehicles and Edge Computing: A Performance Evaluation Using Petri Nets
Authors: Sabino Arthur , Lima Luiz Nelson , Brito Carlos , Feitosa Leonel , Caetano Marcos F. , Barreto Priscila Solis , Silva Francisco Airton 
Published: November 2023

Abstract

Forest fires cause damage to life, biodiversity, and properties, affecting natural ecosystems, healthy and only. During the practical application of fire prevention, numerous detection techniques have been thoroughly researched to prevent devastating fires. The techniques improve early fire detection and accelerate emergency response, reducing damage and optimizing system containment operations. Wildfire detection requires a robust infrastructure for equipment, maintenance, and ongoing monitoring. Effective cooperation between components and integration of technologies is key to a consistent and comprehensive response to fires. This article proposes a fire monitoring system using drones, cameras, and edge computing technologies. We use Stochastic Petri Nets (SPN) to model the structure and evaluate the system's performance. The model is parameterizable, allowing adjustments to the components' resource capabilities and service time. Twenty-four parameters can be defined, making it possible to evaluate a wide variety of different scenarios. The results obtained in different scenarios in this work have the potential for auxiliary administrators of monitoring systems to estimate more than six analyses and plan more optimized architectures as needed.
DV-Hop localization algorithm based on SSA and PSO cooptimization
Authors: 余 修武 , 曹 群丹 , 刘 永 
Published: November 2023

Abstract
The ability to obtain the exact location of nodes in a wireless sensor network is crucial for practical applications. The data is not meaningful if the detected data does not accompany its location. Distance-free localization techniques are beneficial to overcome the hardware limitations of sensor nodes and to avoid expensive distance-based techniques. DV-Hop is a distance-free localization algorithm that is well known for its simplicity. However, due to its poor accuracy and its low safety profile. In this paper, the particle swarm algorithm (PSO) and the squirrel search algorithm (SSA) are used to predict the unknown node bits. The particle swarm algorithm has the advantage of individual memory, and through the particle bit update and individual flying mouse position adjustment, the mouse search algorithm is more memorable when optimized. The performance of our proposed algorithm is compared with the performance of existing localization algorithms (including DV-Hop, PSODV-Hop, GADV-Hop, and SSADV-Hop). Extensive simulations show that our proposed new wireless sensor network; Particle population algorithm;Squirrel search algorithm; DV-Hopalgorithm far outperforms other existing algorithms because it achieves higher localization accuracy and effectively reduces the localization error.
Channel Spatio-temporal Convolutional Network for Pedestrian Trajectory Prediction
Authors: Lu Zhonghao , Luo Yonglong , Xu Lina , Hu Ying , Zheng Xiaoyao , Sun Liping 
Published: November 2023

Abstract

Accurate and timely prediction of the future path of agents in the vicinity of an agent is the core of avoiding conflict in automated applications. The traditional method based on the RNN model requires high computational cost in the prediction process, especially for long-series prediction. In order to obtain a more efficient and accurate prediction trajectory, a channel spatio-temporal convolutional network framework, CSTCN, is proposed in this paper. The framework models the spatial environment as a block of data input to the CSTCN and captures spatio-temporal interactions using an improved temporal convolutional network. Compared with the traditional model, the spatial and temporal modeling of the proposed model is calculated in each local time window so that it can be executed in parallel to obtain higher computational efficiency. Experimental results on five trajectory prediction benchmark datasets demonstrate that the proposed model is superior to the other seven state-of-the-art models in efficiency and accuracy.