bluuebunny/crossref_metadata_2025_split huggingface dataset:
The relationship between occupational stress, gene polymorphism, and dyslipidemia among coal miners: a case-control study.
Authors: Yang YongZhong , Zheng Ziwei , Chen Yuanyu , Wang Xuelin , Wang Hui , Si Zhikang , Meng Rui , Wu Jianhui 
Published: July 2022

Abstract
Background. Dyslipidemia is one of the known risk factors for cardiovascular disease, and its prevalence is increasing worldwide.In order to improve the prevention strategy of dyslipidemia and reduce its prevalence, this study used occupational stress(OS) and genes as research variables to explore their association with dyslipidemia.Methods. A case-control study was selected, and Han employees of a coal mining enterprise in China who participated in occupational health examinations from July to September 2020 were included as the research objects.We selected 445 dyslipidemia patients and 445 age and sex-matched healthy controls. LYPLAL1, APOC3 and SOD2 gene polymorphisms were detected by Polymerase Chain Reactionrestriction-Ragment Length Polymorphism Technique.The effects of OS, genetic polymorphism and interaction on dyslipidemia were analyzed by logistic regression, Andersson additive interaction model, crossover analysis, and GMDR model.Results. Monofactor analysis showed that smoking, drinking, physical activity level, DASH diet score, sleep quality, BMI, hypertension, hyperuricemia, shift work, OS were significantly different between the two groups (P༜0.05).In the APOC3 rs2854116 dominant model, patients with CT/CC genotype had a higher risk of dyslipidemia than those with TT genotype (OR = 1.39, 95% CI: 1.01–1.91). In SOD2 rs4880 recessive model, patients with GG genotype had a lower risk of dyslipidemia than those with AA/AG genotype (OR = 0.28, 95% CI: 0.12–0.67), and the difference was statistically significant. We found that rs12137855 and OS, rs2854116 and OS, rs4880 and OS had joint effects, but no interaction based on the multiplication and addition model was found (Pinteraction>0.05). GMDR model showed that the rs12137855-rs2854116-rs4880-OS four-factor model had the highest cross-validation consistency and training-validation accuracy (P < 0.05), suggesting that there was a high-order interaction between them associated with dyslipidemia.Conclusions. We found that dyslipidemia in coal miners was related to OS and genetic factors. Through this study, we revealed the dual regulation of environmental factors and genetic factors on dyslipidemia. At the same time, this study provides clues for understanding the etiology and mechanism of dyslipidemia, and provides a theoretical basis for the prevention strategies of dyslipidemia in coal miners.
FPGA-Based Cosimulation of S-Box Constitution from Fractional Order Liu System
Authors: Ali Muhammet , TÜRK Ömer 
Published: July 2022

Abstract
Substitution box(S-box) has an important duty in encryption. In recent years plenty of S-box designs by chaotic systems have been studied. In this paper a high speed S-box constitution from fractional order Liu system on FPGA is proposed. The design was implemented by using Xilinx System Generator(XSG) toolbox in MATLAB/Simulink software in single precision floating point numbers format and a low cost Basys 3 FPGA trainer card was used. In addition, numerical fractional order equations was solved by using Grünwald-Letnikov(GL) method with 1024 elements length. The S-box table was constructed about 0.137s. After all performance analyzes as nonlinearity, strict avalanche criterion, differential probability, bits independent criterion, and equiprobable input/output XOR distribution, were performed. The performance analyzes shown that the design was satisfactory.
Biologically interpretable deep learning to predict response to immunotherapy in advanced melanoma using mutations and copy number variations
Authors: Zhang Liuchao , Cao Lei , Li Shuang , Wang Liuying , Song Yongzhen , Huang Yue , Xu Zhenyi , He Jia , Wang Meng , Li Kang 
Published: July 2022

Abstract
Only 30–40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it’s necessary to accurately identify the response of melanoma patients to immune therapy pre-clinically. Here we developed the KP-NET, a deep learning model whose structure is sparse by the KEGG pathways, which can accurately predict melanoma patients’ response to immunotherapy using information at the pathway level that is enriched from gene mutations and copy number variations data prior to immune therapy. The KP-NET demonstrated the best performance on predictive melanoma response to anti-CTLA-4 and anti-PD-1 treatment with corresponding AUROC values of 0.889 and 0.867, and selected some relevant biomarkers, genes such as PLCB2, FGFR4, RHEB and CCNB2, pathways such as Inflammatory mediator regulation of TRP channels, Notch signaling pathway, mTOR signaling pathway and TNF signaling pathway, etc. In conclusion, deep learning model guided by biological information enables accurate prediction of the response of melanoma patients to immunotherapy and relevant biomarkers before clinical treatment, which may be helpful in melanoma precision medicine.
An Efficient Fault Detection and Exclusion Method for Spatial Gradient Monitors
Authors: Jiang Yiping , Li Wang 
Published: July 2022

Abstract
With multiple ground reference stations, the double differenced carrier phase (DDCP) observation can be used to detect a faulty satellite caused by ephemeris fault or ionospheric anomaly, taking advantage of the residual spatial gradient. Assuming only a single satellite can be faulty at a time, it is necessary to distinguish whether a fault comes from a reference satellite (RS) or a non-reference satellite (NRS) in a single DDCP test. Using the current fault detection and exclusion (FDE) method of ephemeris faults in ground-based augmentation system, before an NRS can be tested in real-time by the DDCP, an RS is pre-validated using the previous ephemeris for the newly acquired and re-acquired satellites. This method lacks theoretical support, and the outcome test risks are not obtained. Also, for an ionosphere anomaly, the pre-validation method is not applicable anymore. Considering that existing test theories are designed to test single satellite in one test statistics, this paper proposes an efficient FDE method to test both RS and NRS simultaneously in real-time for both ephemeris fault and ionosphere anomaly. To facilitate application of the new method for purpose of integrity monitoring, the test risks and protection level are bounded.
Using Learning Automata to Solve Dynamic Distributed Constraint Optimization Problems
Authors: Shokoohi Maryam , Afsharchi Mohsen , Shah-Hosseini Hamed 
Published: July 2022

Abstract
Learning-based techniques could be an alternative approach to solve Dynamic Distributed Constraint Optimization Problems (DDCOPs) and are computationally cheaper than sequential DCOP solvers. This paper, proposes a learning-based solution to solve DDCOPs in which the environment is stochastic due to the presence of multiple agents. In our approach the problem is modelled as a multi-agent Markov Decision Process and then a learning automaton, which is a relatively simple method and requires less qualitative data, is employed to learn how to assign values to variables. The proposed method considers two very important issues namely time step dependency and uncertainty about future events upon which we allocate values to variables. Experimental results reveal that the employed method converges and satisfies the constraints of the optimization problems in comparison to the well-known methods.
Acceptance of COVID-19 Booster in the adult population of Pakistan: A cross sectional study
Authors: Amjad Fatima , Parvi Muhammad Zamin , Shamsi Uzma , Hannan Eisha , Jamil Zaigham , Abedin Iman , Nabi Hafiza , Qureshi Imran Ahmad , Sadiq Muhammad Wahhaab , Shaikh Fareeha , Saeed Safa , Kazi Muhammad Ali Ibrahim 
Published: July 2022

Abstract
Background: A year after the COVID-19 pandemic vaccination rollout, vaccine elicited immunity is waning and newer variants e.g. The Delta Variants and Omicron have necessitated the administration of booster doses because of the rise in breakthrough infection rate. The objectives of our study are to determine the prevalence of acceptance status of Covid-19 booster dose in the adult population of Pakistan and its association with knowledge and perceived benefits. Methodology: A cross-sectional study was conducted by online self-administered questionnaire shared to the general population of Pakistan. The form was distributed to 100,000 people out of which 461 responded. The questionnaire was based on the Health Belief Model. Frequencies and proportions for categorical variables, and the Chi-squared test was used to examine differences between COVID-19 booster acceptance and perceived barriers in getting a booster dose and between booster acceptance and knowledge on health benefits of a booster dose. Results: 89.4% reported acceptance of the COVID 19 booster dose . The youngest age group of 18-30 years had acceptance prevalence of this group was 85.4% . Participants with respiratory disease (2.6%) had an acceptance prevalence of 12%, along with participants who identified “other” comorbidities (2.4%) that had a 10% acceptance prevalence. Of all subjects who participated 97% (n=447) had been vaccinated and 32.1% (n=148) had received the booster dose. Knowledge was significant at a p-value <0.01 for acceptance of a booster dose. One of the significant perceived barriers and concerns regarding the Covid-19 booster dose according to chi square test results, was being too busy to get the booster dose. Conclusion: Our research has findings which indicate a relatively large percentage of respondents accepting COVID-19 booster vaccination. More efforts are needed to help people register and educate people about the long-term risks.
WITHDRAWN: Importance of bulk density in the physical properties of carbon nanotubes
Authors: Chen Junjie 
Published: July 2022

Abstract
Carbon nanotubes are a highly effective thermal conductor in the longitudinal direction. However, the effects of different factors on the thermal conductivity of carbon nanotubes are still poorly understood. Little research has been conducted to determine which parameters are important for high thermal conductivity. Carbon nanotube films and fibers were produced, and the parallel thermal conductance technique was employed to determine the thermal conductivity. The effects of different factors on the thermal conductivity of carbon films and fibers were investigated to understand the characteristics of thermal transport in the nanostructured material. The importance of bulk density and cross-sectional area was determined experimentally. The results indicated that the prepared carbon nanotube films and fibers are very efficient at conducting heat. The structure, purity, and alignment of carbon nanotubes play a fundamentally important role in determining the heat conduction properties of carbon films and fibers. Single-walled carbon nanotube films and fibers generally have high thermal conductivity. The presence of non-carbonaceous impurities degrades the thermal performance due to the low degree of bundle contact. Crystallographic defects strongly affect the thermal properties of carbon nanotubes. Such defects lead to phonon scattering, which in turn increases the relaxation rate of the phonons. This reduces the mean free path and reduces the thermal conductivity of carbon nanotube structures. The thermal conductivity may present power law dependence with temperature. The specific thermal conductivity decreases with increasing bulk density. Although the strength of individual carbon nanotube shells is extremely high, weak shear interactions between adjacent shells and nanotubes lead to significant reduction in the effective strength of multi-walled carbon nanotubes and carbon nanotube bundles. Carbon nanotubes can exhibit remarkable electrical conductivity. They also have exceptional tensile strength and thermal conductivity because of their nanostructure and strength of the bonds between carbon atoms. In addition, they can be chemically modified. These properties are expected to be valuable in many areas of technology, such as electronics, optics, composite materials, nanotechnology, and other applications of materials science.
Seismic Damage in India and the Associated Reasons: A case study
Authors: Kain Ruchir , Gupta Vedant , Shrivastava Amit Kumar 
Published: July 2022

Abstract
India has had a rich historical account of seismic occurrences. However, our response to such events in the past has not been much effective in mitigating the dangers to society. Non-Adherence to seismic codes, sporadic revision practices and asynchronous provisions regarding materials and advancements in the market were barriers to earthquake-resistant building practices. We did case studies for three major earthquakes in the last three decades. We observed a similar pattern of practices and damages across the three earthquake occurrences. It was evident that very little change in the operations was noticed in each case. Incompliance with guidelines, the involvement of an inexpert workforce in building projects, and a weak system of checks and inspections led to huge losses as witnessed. We can prevent the large-scale damages witnessed after the onset of seismic events by using correct scientific and engineering principles. Through the active interest of the government and state regulatory bodies and the introduction of strict legal guidelines, our country's building infrastructure could perform better in response to seismic waves. This study presents a comprehensive study of the issues associated with earthquake mitigation strategies in India. We have discussed the far-reaching rationales for the associated damage and disruption of the system due to such calamities. Further, the socio-political reasons for the weak legal infrastructure, a section on the much-hyped Engineers Bill and other practical discussions and suggestions have been presented in this study.
WITHDRAWN: Mathematical modeling of small-scale combustion systems for hydrogen production
Authors: Chen Junjie 
Published: July 2022

Abstract
This study relates to the mathematical modeling of small-scale combustion systems for hydrogen production. The problem is addressed for premixed combustion systems. Computational fluid dynamics simulations are conducted to gain insights into the performance of small-scale combustion systems for hydrogen production. Full detailed descriptions are employed. The factors affecting the performance of small-scale combustion systems are determined for hydrogen production. Particular focus is placed on determining essential factors that affect the performance of small-scale premixed combustion systems. The results indicate that the predictions are in satisfactory agreement with the data obtained from experimental measurements. The thermal conductivity of the channel walls is fundamentally important. The wall thermal conductivity is vital in determining the flame stability of the burner. Further improvement in flame stability can be achieved by using anisotropic walls. Combustion at the microscale can offer many advantages. Faster ignition and more efficient heat transfer can be achieved, but the design is challenging due to the loss of flame stability. The change in specific enthalpy is positive for the exothermic reaction and negative for the endothermic reaction. The change in specific sensible enthalpy is always positive. Design recommendations are made to improve thermal performance for the reactor. Suggested directions of future research are also indicated.
Hologenomic insights demonstrate co-evolution between an intestinal Mycoplasma and its salmonid host
Authors: Rasmussen Jacob , Kiilerich Pia , Waagbø Rune , Lock Erik-Jan , Lise Madsen , Gilbert M Thomas P , Kristiansen Karsten , Limborg Morten 
Published: July 2022

Abstract
The study of co-evolution between host and microbes has the potential to transform how we understand evolutionary adaptations, yet genuine co-evolutionary relationships are challenging to show. The host's intestinal environment shapes the gut microbiota through the co-existence of bacteria and host. This co-existence influences the fitness of both bacteria and host. How this affects the co-evolution occurring in animals is largely unexplored, mainly due to the complexity of the environment, low host selection, and microbial communities.
We present the first metagenomic derived genome of gut microbiota from wild Atlantic salmon (Salmo salar), a new wild model organism with the microbiota of low complexity and known population structures amenable for investigating co-evolution. Our data reveal a strong host selection of a core gut microbiota dominated by a single Mycoplasma species. Interestingly, we found a concordance between the population structure of the Atlantic salmon host and nucleotide variability of the intestinal Mycoplasma populations conforming to expectations from co-evolution between host and microbe.
Our results show that the stable microbiota of Atlantic salmon has evolved with its salmonid host populations while potentially providing adaptive traits to the salmon host populations, including defence mechanisms and protein synthesis. We highlight Atlantic salmon as a novel model for studying co-evolution between vertebrate hosts and their microbiota.
Weather Prediction based on Wireless Sensor Network and Internet of Things with Analysis using Hybrid SSOA with MA
Authors: S Priya , A.S. Radhamani 
Published: July 2022

Abstract
Weather forecasting is a growing area that predicts the weather that will occur in a particular place at a particular time. Weather forecasting is considered to be the most important part of research where many real-time issues arise. These are the requirements for wireless sensor networks to detect the weather. In this article we propose using the Mayfly Algorithm (MA) hybrid algorithm with Shuffled Shepherd Optimization Algorithm (SSOA). Experimental tasks are carried out with a set of climate data. Based on climatic parameters such as temperature, humidity and clouds, the data is divided into heat, wind and rain. From the result obtained, it is predicted that the weather will prove the proposed qualification methods at the level of accuracy. The result shows that the hybridized SSOA and MA method is efficient and accurate in predicting weather conditions. This experimental result is carried out by using Wireless Sensors Network and IoT on agricultural land.
A Highly Speed Packet Classification Design Using Parallel Decision-Making Technique
Authors: MIDDE ADISESHAIAH , Maruvada Sailaja 
Published: July 2022

Abstract
Due to advances in high-speed data communication, packet classification plays a major role in computer networks to improve network security. Field Programmable Gate Array (FPGA) devices can be easily used for the packet classification process due to high speed and flexibility. Using FPGA high throughput is achievable when compared to the embedded devices. To enhance throughput, a novel pipelined FPGA design for packet categorization is discussed in this work.To evaluate the performance various metrics such as Look Up Tables(LUT), Flip Flops(FF), Area, and Power Consumption will be estimated and compared with conventional techniques for Various rule sets. On a Xilinx Ultra Scale FPGA, the proposed technique is implemented. The classifier’s throughput may exceed 802 million packets per second with less latency of 31 ns.
Representing Physician Suicide Claims as Nanopublications: Proof-of-Concept Study Creating Claim Networks
Authors: Leung Tiffany I , Kuhn Tobias , Dumontier Michel 
Published: July 2022

Background
In the poorly studied field of physician suicide, various factors can contribute to misinformation or information distortion, which in turn can influence evidence-based policies and prevention of suicide in this unique population.

Objective
The aim of this paper is to use nanopublications as a scientific publishing approach to establish a citation network of claims in peer-reviewed publications about the rate of suicide among US physicians.

Methods
A list of articles from a previously published scoping literature review on physician suicide was used to identify those articles that commented on or investigated suicidal behaviors of physician populations, including students, postgraduate trainees, and practicing physicians. The included articles were from peer-reviewed publications and asserted a claim about the annual rate of physician suicide. Manual data extraction was performed to collect article (or resource) type, title, authors, digital object identifier or URI, publication year, claim (about annual physician suicide rate), data of last access of the article (eg, for a webpage), and citations supporting the claim. Additional articles, websites, or other links were only added to the set of claims if they were cited by a peer-reviewed article already included in the data set. A nanopublication was created for each article or resource using Nanobench with an investigator-developed literature-based claim nanopublication template.

Results
A set of 49 claims concerning the rate of US physician suicide was represented as nanopublications. Analysis of the claim network revealed that (1) the network is not fully connected, (2) no single primary source of the claim could be identified, and (3) all end-point citations had a claim with no further citation, had no apparent claim, or could not be accessed to verify the claim. The nanopublication strategy also enabled the capture of variant claims published on a website.

Conclusions
Nanopublications remain to be adopted in broader scientific publishing in medicine, especially in publishing about physician mental health and suicide. This proof-of-concept study highlights an opportunity for more coordinated research efforts in the subject of physician suicide. Our work integrates these various claims and enables the verification of nonauthoritative assertions, thereby better equipping researchers to advance evidence-based knowledge and to make informed statements in the advocacy of physician suicide prevention. Representing physician suicide rate claims as nanopublications can be extended and improved in future work.