bluuebunny/crossref_metadata_2025_split huggingface dataset:
Conditional survival and annual hazard of death in elderly patients with esophageal cancer receiving definitive chemoradiotherapy
Authors: Chang Xiao , Dong Xin , Zhao Yuting , Deng Wei , Yang Dan , Jiang Leilei , Long Liuhua , Teng Yue , Shi Anhui , Yu Huiming , Yu Rong , Wang Weihu 
Published: September 2023

Abstract

Background:Definitive chemoradiotherapy is one of the primary treatment modalities for elderly patients with esophageal cancer (EC). It remains inadequately studied how prognosis evolves over time and how non-EC deaths affect overall survival (OS). We examined the conditional survival and annual hazard of death in elderly patients with EC after chemoradiotherapy.­Methods:The data of patients with EC aged 65 or older registered in the Surveillance, Epidemiology, and End Results database during 2000–2019 were collected. Conditional survival was defined as the probability of survival given a specific time survived. Annual hazard of death was defined the yearly event rate.Results:Among 3739 patients, the 3-year conditional OS increased by 7%-10% annually. Non-EC causes accounted for 18.8% of deaths, with cardio-cerebrovascular diseases as the leading cause. Death hazard decreased from 40% to 10% in the first 6 years and then increased to 20% in the tenth year. The hazard of death from non-EC causes exceeded that from EC causes, starting 5 years after treatment. Older patients and those in early stages had higher risks of death from non-EC causes (HR = 1.36, 95% CI: 1.15-1.62, P < 0.001 and HR = 0.82, 95% CI: 0.68-0.98, P = 0.035, respectively). Tumor histology had no significant impact on non-EC death risk (HR = 1.17, 95% CI: 0.98-1.39, P = 0.081).Conclusions:For elderly patients with ECtreated with chemoradiotherapy, survival probability increases with time. Stress on the management of comorbidities can extend the survival of older patients and those with early-stage disease.
Non-Simple Concentrations for Nonlinear Magnetic Schrodinger Equations with Constant Electric Potentials
Authors: Wang Liping , Zhao Chunyi 
Published: September 2023

Abstract

The paper investigates the influence of magnetic fields on the non-simple concentration phenomenon in the complex-valued nonlinear Schr¨odinger equations with a constant electric potential (iε∇ + A)2u + u − |u|p−1u = 0. We demonstrate that a multi-peak solution always exists at a non-degenerate local maximum or minimum point of the Frobenius norm ∥B∥F, where B is the magnetic field corresponding to the magnetic potential A. It is surprising that at such a minimum point, we can find a two-peak solution, which is distinct from the real-valued case. This is unexpected given that the non-existence of a multi-peak solution at a non-degenerate local minimum point of the electric potential has been proven in [25].
2020 MSC 35J10, 35A01, 35B25
High-Performance Hydrogen Gas Sensor System Based on Transparent Coaxial Cylinder Capacitive Electrodes and a Volumetric Analysis Technique
Authors: Jung Jae K. , Lee Ji H. 
Published: September 2023

Abstract
A high-performance H2 gas sensor system based on capacitive electrodes and a volumetric analysis technique were developed. Coaxial capacitive electrodes were fabricated by placing a thin copper rod in the center and by adhering a transparent conductive film on the exterior surface of a graduated cylinder. Thus, H2 from a polymer specimen lowered the water level in the cylinder between the two electrodes, producing measurable changes in capacitance that allowed for the measurement of the H2 concentration emitted from the specimen enriched by H2 under high-pressure conditions. The sensing system detected diffused/permeated hydrogen gas from a specimen and hydrogen gas leaks caused by imperfect sealing. The hydrogen gas sensor responded almost instantly at 1 s and measured hydrogen concentrations ranging from 0.15ppm to 1500 ppm with controllable sensitivity and a measurable range. In addition, performance tests with polymer specimens used in hydrogen infrastructure verified that the sensor system was reliable; additionally, it had a broad measurement range to four decimal places. The sensor system developed in this study could be applied to detect and characterize pure gases (He, N2, O2 and Ar) by real time measurement.
Dragon Fly Algorithm based Approach for escalating the security among the nodes in Wireless Sensor Network based System
Authors: Sharma Shweta , Kaur Amandeep , Gupta Deepali , Juneja Sapna , Saini Shilpa , Kumar Mukesh 
Published: September 2023

Abstract

For specific intercommunication applications, latest advancements in technology and manufacturing have made it feasible to create substantial, significant, energy-efficient and cost-effective sensor nodes having the features like adaptability, self fulfillness, and self evolvability. In sensor networks for sustainable development, it is looked at how sensor network technologies increase social development and living quality while having little to no detrimental effects on the environment or the planet's natural resources. A wide range of applications, including the military, healthcare, traffic monitoring, and remote image sensing, benefit from the use of wireless sensor networks (WSNs). Due to the limits of sensor networks, different levels of security are required for these crucial applications, making it challenging to employ traditional algorithms. Security has arisen as one of the main problems with IoT and smart city applications, and sensor networks are also considered of as the backbone of IoTs and smart cities. Complex issues like energy consumption are covered by the WSN, making it a useful tool for selecting clusters. By injecting false and harmful data into the network and launching internal attacks, hostile nodes might quickly jeopardize its integrity. Rogue nodes will be identified using trust-based security to offer a powerful and mobile defense. Trust evaluation models are a key technique for enhancing security in Wireless Sensor Networks by enhancing dependability (cooperation) among sensor nodes. This study recommends the cutting-edge trust algorithm DFA U-Trust to address the security requirements of WSNs.
A method of reducing the cogging torque in a flux-torque regulation hybrid excitation machine with axial-radial magnetic circuit
Authors: Qiu Hongbo , Duan Shuaishuai , Yang Cunxiang 
Published: September 2023

Abstract
In order to solve the problem of insufficient torque for flux weakening in permanent magnet machines, a novel flux-torque regulation hybrid excitation machine with axial-radial magnetic circuit (FTRHEM) is proposed. To solve the problem that the cogging torque of the proposed machine is caused by multiple factors and the coupling relationship is complicated, an analytical expression for the cogging torque is derived first. Then, the influence of the axial excitation device on the cogging torque is determined. The influence mechanism of the relative positions between the axial excitation device and stator on the cogging torque is revealed. A dislocation method is proposed to reduce the cogging torque, and its effectiveness is verified by the finite element analysis (FEA). Furthermore, the reduction of the cogging torque in FTRHEM with different pole-slot combinations by this method is comprised and analyzed, which demonstrates the feasibility of the method. Finally, the FEA results are combined with experiments to verify the accuracy of simulations.
Oxidation of polystyrene nanoparticles using ozonation under catalytic and non-catalytic conditions
Authors: P Aiswriya V. , Ajayaghosh Akhilghosh Kochuparambil , Farissi Salman , Muthuchamy Muthukumar , Muthukumar Anbazhagi 
Published: September 2023

Abstract
Plastic pollution surfaced as a major issue across the globe affecting all compartments of the environment alike. The viciousness of plastics further elevates as they breaks down to form micro and nano sized particles which can easily enter into the body of organisms through water, air and food. These particles persist in the water stream even after conventional waste water and drinking water treatment processes and can easily get accumulate in living organisms including humans. Advanced oxidation processes (AOPs) emerged as an effective strategy for the disintegration leading to even complete mineralisation of many persistant pollutants present in the environment. Various AOP methods like photo-degradation, ozonation, electro-fenton and anodic oxidation are studied for their applicability in degrading synthetic polymer particles also. The present study evaluates the oxidative removal of polystyrene nanoparticles by ozone based oxidation in aqueous medium. Catalytic ozonation was also carried out using TiO2 and V2O5 as catalysts. Particles of 200-400nm size were synthesised by nanoprecipitation technique, suspended in aquadest of variable pH conditions (5, 7 and 9) and subjected to ozonation for 60 minutes. DLS analysis revealed about 75% size reduction of particles upon ozonation. The reduction of organic carbon content in the samples after treatment was evaluated through TOC analysis and highest TOC removal rate of 97.78% was obtained with TiO2 as catalyst at pH 7. TiO2 was an effective catalyst under all pH conditions while catalytic activity of V2O5 found to be increasing with the increase in pH. The results relieved the possibility of using ozonation as a method of nanoplastic remediation from water.
Tai-Chi Hydrogel with Chinese Philosophy and Photothermal Properties for Accelerated Diabetic Wound Healing
Authors: Zheng Ruizhu , Liu Li , Wang Hao , He Pengyu , Qi Fuyu , Hu Sanming , Long Xiao , Shi Zhijun , Yang Guang 
Published: September 2023

Abstract

Bacterial cellulose (BC) and photothermal hydrogels are widely used currently in wound repair. However, modified and functionalized BC maybe damage the advantages of natural BC that limits its application as a wound dressing, and photothermal hydrogels have problem of secondary damage about overheating during photothermal therapy process. Therefore, we develop a Tai-Chi hydrogel inspired by the Chinese philosophy of “Tai Chi”, which is consist of BC, namely Yin, and polyvinyl alchol/polydopamine (PVA/PDA), namely Yang. Two hydrogels of Tai-Chi hydrogel have opposite properties to selectively cover on healthy skin and the wound, to adjust/balance the wound temperature under NIR irradiation. In vitro experiments demonstrate that mild warm caused by Yang hydrogel under NIR irradiation promote polarization of RAW 264.7 macrophages to M2 phenotype. Tai-Chi hydrogel itself has a significant therapeutic effect on diabetic wound by regulating inflammatory microenvironment. In addition, Tai-Chi hydrogel in combination with NIR irradiation exhibited remarkably therapeutic effect by promoting re-epithelialization and angiogenesis, accelerating collagen deposition and macrophages polarization to M2 phenotype. This work firstly presents the novel strategy in designing functional materials with opposite properties inspired by the philosophy of "Yin-Yang" in “Tai Chi” as diabetic wound dressing.
The spatiotemporal variations of microbial community in relation to water quality in a tropical drinking water reservoir, southmost China
Authors: Wu Di , Zou Ying , Xiao Juan , Mo Ling , Lek Sovan , Guo Zhiqiang , Fu Qiongyao 
Published: September 2023

Abstract

It is well known that water quality has great significance on microbial community composition in aquatic environments. In this study, a typical tropical reservoir was used as a model system to test the hypothesis that the variations of water quality across time and space resulted in significant differences in microbial community composition. Specifically, we detected water column indicates the microbial community composition of nine sampling sites over two seasons using Illumina TruSeq sequencing in Songtao Reservoir, Hainan Province, Southmost China. The study indicated that the dominant phylum was Actinobacteria, Proteobacteria, Bacteroidetes, and Cyanobacteria. The diversity parameters showed that the microbial community composition had significant spatiotemporal variations, including the significantly higher Shannon index and Simpson index upstream than those midstream and downstream. Besides, there were significantly higher Chao1 index, Shannon index, and Simpson index in winter than in summer. Principal coordinates analysis (PCoA) showed the microbial structural composition had significant seasonal differences. Our results further revealed that the eutrophication level upstream was higher than that of midstream and downstream. The redundancy analysis (RDA) diagram indicated that the abundance of microbiology species significantly correlated with temperature, total phosphorus, Se, and Ni. Furthermore, the mantel test showed that the temperature and total phosphorus significantly affected the community structure composition of archaea and bacteria. Overall, our finding here partially validated our hypothesis that the spatiotemporal variations of microbial community composition are significantly related to water quality, which has been unknown previously in tropical waterbodies. The study provided critical information for the management of reservoir microbial community and drinking water safe.
Development and validation of the biosafety incident response competence scale for clinical nursing staff
Authors: Wu Chao , Zhang Hongli , Zhang Yinjuan , Hu Mengyi , Lin Yawei , Sa Zhen , Li Shuwen , Zhang Yulian , Lang Hong-juan 
Published: September 2023

Abstract
Aims: This study aimed to develop the biosafety incident response competence scale and evaluate its validity and reliability among clinical nurses.
Design: A scale development and validation study was completed in the following three phases: (1) the construction of a multidimensional conceptual model, (2) the preliminary exploration of the items, and (3) further exploration and evaluation of the items.
Methods: The multidimensional conceptual model was developed through a literature review and the Delphi method. A total of 1,712 clinical nurses participated in the preliminary item exploration, while 1,027 clinical nurses were involved in the further item evaluation from July 2023 to August 2023. The item analysis, exploratory factor analysis and confirmatory factor analysis were conducted to evaluate the construct validity. The reliability was measured by internal consistency, split-half reliability and test–retest reliability.
Results: The final scale is composed of 4 factors and 29 items, scored on a 5-point scale. The factors included monitoring and warning abilities, nursing disposal abilities, biosafety knowledge preparedness, and infection protection abilities. The explanatory variance of the 4 factors was 75.100%. The validity and reliability of the scale are well validated. The internal consistency, split-half reliability and test-retest reliability were 0.974, 0.945 and 0.840 respectively. The scale has good structural validity and content validity. The content validity was 0.866.
Conclusions: The biosafety incident response competence scale for nurses exhibits satisfactory reliability and validity, making it a valuable tool for assessing clinical nurses' abilities in responding to biosafety incidents.
Patient or Public Contribution: Clinical nurses participated in the exploration and evaluation of the nurse's biosafety incident response competence scale.
Modeling and Control for a Long-Stroke 4-PPR Compliant Parallel Mechanism
Authors: Ren Jianze , Zhang Chi , Yang Miao , Yuan Liming , Sang Na , Yao Jianhua 
Published: September 2023

Abstract
Long-stroke compliant parallel mechanisms(CPMs) are widely used in precision applications.
However, stress stiffening and sensitivity to external disturbances in CPMs present challenges in the
design of controller. In this paper, the nonlinear stiffness model of the stage is established which is incorporated into
the dynamic model . In particular, the method of adaptive nonsingular fast terminal sliding mode
control(ANFTSMC) is developed based on the dynamic model. This method addresses the problems
of the system parameter uncertainty and the slow convergence of traditional sliding mode control(SMC) at the
equilibrium point. The stability of the presented ANFTSMC strategy has been proved based on the Lyapunov
analysis. Finally, the proposed control architecture is implemented on the designed 4-prismatic-prismatic-revolute(4-PPR)
CPM. The results demonstrate that the developed method exhibits excellent tracking accuracy and robustness compared
to the traditional linear sliding mode control(LSMC) and proportional-integral-derivative(PID) control.
Examining the strength dependency of frequency-magnitude distribution in small Earthquakes: implications for stress state criticality
Authors: Matsumoto Satoshi , Iio Yoshihisa , Sakai Shinichi , Kato Aitaro 
Published: September 2023

Abstract

The distribution of earthquake sizes, characterized by the power law decay as “b-value”, exhibits spatiotemporal variations detected by seismic networks. These changes are sometimes evident before a large earthquake. Understanding these variations is key to developing a model for large-earthquake generation. Previous studies have shown that changes in the b-value are caused by the tectonic stress regime. Furthermore, lab experiments have demonstrated b-value dependency of acoustic emissions on the criticality of the failure condition. However, the factors controlling the b-values during natural seismic activity are unclear. In this study, changes in the b-value in small earthquake sequences were investigated, focusing on failure criticality. Based on our high-precision focal mechanism dataset, we conclude that the b-value decreases as it nears a critical failure condition, providing a physical explanation for the reduction in b-value before a major earthquake. Our findings elucidate fault failure models, facilitating improvement in earthquake alerts and disaster mitigation.
Industry 4.0 Transformation: Adaptive Coati Deep Convolutional Neural Network-based Oral Cancer Diagnosis from Histopathological Images
Authors: Dharani R. , Revathy S. 
Published: September 2023

Abstract
Oral cancer is common cancer that appears in the mouth, posing a significant threat to public health due to its high mortality rate. Oral Squamous Cell Carcinoma (OSCC) is the most prevalent type of oral cancer, accounting for most cases, and it holds the seventh position among all types of cancers worldwide. Detecting OSCC early on is crucial to increase the chances of successful treatment and improve patients' survival rates. However, traditional diagnosis methods such as biopsy, where small tissue samples are extracted from the affected area and tested under a microscope, are time-consuming and require expert analysis. Moreover, due to the heterogeneity of OSCC, accurate diagnosis is challenging, and there is a need for alternative approaches to enhance the detection result of OSCC images. Therefore, this work develops two new approaches for segmenting and identifying OSCC with deep learning techniques named Mask Mean Shift CNN, named MMShift-CNN. The proposed MMShift-CNN approach attained the highest results in segmenting the OSCC region from the input image by retrieving color, texture, and shape features. The novel proposed method attained better performance with accuracy, F-measure, MSE, precision, sensitivity, and specificity of 0.9883, 0.9883, 0.0117, 0.999, 0.9867, and 0.99, respectively. These results reveal the efficiency of the proposed approach in accurately detecting oral cancer and potentially improving the efficiency of oral cancer diagnosis.
Predictive Modelling of Under-Five Mortality Determinants Using Machine Learning Techniques
Authors: Saroj Rakesh Kumar , Yadav Pawan Kumar , Visi Vizovonuo 
Published: September 2023

Abstract

Background
Under-five mortality is one of the major public health issues and directly influences the population’s health, social development, and economic status of countries. Thus, early detection is essential to find what efficient prevention can take to save it. Therefore, this study will explain how machine-learning techniques can help predict the important determinants of under-five mortality in India.
Methods
This study used data from the National Family Health Survey-V of India. We performed the tenfold cross-validation to assess the model’s capability in the dataset. The decision tree, random forest, logistic regression, neural network, ridge regression, k-nearest neighbor, and naive Bayes models were used in under-five mortality data, and metrics like confusion matrix, accuracy, recall, precision, F1-score, Cohen kappa, and the area under receiver operative characteristics (AUROC) were used to assess the predictive power of the models. The chi-square scores, recursive feature elimination, extra tree classifier, random forest importance, sequential feature selector, and traditional logistic regression were used to predict the important features(factors) of under-five mortality. All computational algorithms were done with the help of SPSS-27 and Jupiter notebook (inbuilt Python 3.3) software.
Result
The result reveals that the random forest model was the best predictive model compared to other ML models for under-five mortality. The Random Forest model’s precision was estimated to be 98.88% for all factors and 96.25% for important selected variables. After that, neural network accuracy was 96.52%, and accuracy was 94.83% with important variables. Traditional logistic regression accuracy was 93.99% and 93.51%, respectively. The number of living children, breastfeeding status, birth in the last five years, children ever born, time, antenatal care, region, size of children, number of household members, and birth order, were important factors of under-five mortality after using the feature selection methods.
Conclusions
This is the first study of India to use machine learning approaches to find the important ML predictive model and determine the causative factors for under-five mortality. The random forest model predicted the most important factors with the highest accuracy of under-five mortality. This machine-learning approach can be used as reference concepts to understand students, non-computing professionals, healthcare professionals, and decision-makers in various real-world situations and application areas, particularly from the technical point of view.