bluuebunny/crossref_metadata_2025_split huggingface dataset:
WITHDRAWN: Hsa_circ_0079557 promotes CRC proliferation through miR-502-5p/CCND1 axis
Authors: Yu Chao , Huang Xue , Huang Renli , Wang Peiqi , Cai Zongda , Guo Zeyi , Lan Qingnan , Cao Haodi , Yu Jinlong 
Published: November 2022

Abstract
Background
Recent studies have shown that circular RNAs play an important regulatory role in cancer initiation and progression. However, the regulatory mechanisms of circRNAs are not well defined, and exploring the roles and mechanistic progress of circRNAs in tumorigenesis may help identify novel diagnostic markers and therapeutic targets. Here, we aimed to investigate the role and regulatory mechanism of hsa_ circ_ 0079557 in colorectal cancer (CRC).
Methods
The raw gene expression profile datasets were downloaded from Gene Expression Omnibus (GEO) and combined to form a new dataset, from which hsa_circ_0079557 was found to be relatively high expression in tumors. Quantitative real time polymerase chain reaction (qRT-PCR), flow cytometry, colony formation, CCK-8, transwell, scratch wound healing assays, nude mice experiments and Immunohistochemistry (IHC) were performed to assess its role in vitro and vivo. fluorescence in situ hybridization (FISH) and Dual luciferase reporter assay were performed to confirm the relationship between hsa_circ_0079557 and the identified target miRNA. The “TargetScan” web-based tool was used to predict the downstream target proteins, and their expressions were determined using western blot.
Results
hsa_circ_0079557 was found to be relatively upregulated in CRC tissues and cell lines. Suppression of hsa_circ_0079557 expression inhibited the proliferation in vitro and vivo. Furthermore, hsa_circ_0079557 was found to act as a “molecular sponge” for miR-502-5p and upregulated the expression of CCND1.
Conclusion
We identified a new circRNA which is highly expressed in CRC and proposed a potential novel pathway of hsa_circ_0079557/miR-502-5p/CCND1 in CRC.
Determinants of Financial Inclusion of Female Entrepreneurs in Somalia.
Authors: Mohamed Abdinur Ali 
Published: November 2022

Abstract
Financial inclusion is a vital driver of business development that enables the business to build capital over time and seize investment opportunities. The purpose of this study is to examine the determinant of the financial inclusion of female entrepreneurs in Somalia. We measure financial inclusion to the ownership of the formal account, Saving to the account, and access to borrowing from the bank. The determinants considered in this study; are age, education, business age, the form of the business, and the type of the business. This study used a cross-sectional research design to observe the population at one designated point in time and collect the data from a sample of 385 women entrepreneurs using a structured questionnaire. This study employs Probit regression models and presents the marginal effect of each observation. We found that 86% of the female entrepreneurs have a bank account, 59% saved money in a bank account in the last 12 months, and only 19% received loans from the bank. This study shows that female entrepreneurs over 30 years are 4% more like to have an account, 2% less likely to save for the account, and 12% less likely to get a loan from the bank. We also find that the level of education determines female financial inclusion. The female entrepreneur with a university degree is 1% more likely to have the account, 6% more likely to save to the account, and 12% more likely to borrow from the bank than the one that has only completed secondary school or has not attended the formal education. This study confirms that a high education level is associated with the high usage of financial services. We suggest to the government and development institutions work on the improvement of the financial literacy of women entrepreneurs to enable them to access financial services.
Near-infrared-based determination of mass-based material flow compositions in mechanical recycling of post-consumer plastics: Technical feasibility enables novel applications
Authors: Kroell Nils , Chen Xiaozheng , Küppers Bastian , Lorenzo Julius , Maghmoumi Abtin , Schlaak Matthias , Thor Eric , Nordmann Christian , Greiff Kathrin 
Published: November 2022

Abstract

Mass-based material flow compositions (MFCOs) are crucial to assess and optimize mechanical plastic recycling processes. While MFCOs are determined by manual sorting analysis today, in the future MFCOs could be determined inline through near-infrared-based material flow characterization. This study aims to quantify the accuracy of near-infrared-based MFCO determinations to assess its technical feasibility. Therefore, binary mixtures of plastic flakes and post-consumer packaging were pixel-based classified at different material flow presentations (MFPs), and mass-based MFCOs were predicted from the resulting false-color data using different data processing techniques. The results show high correlations between near-infrared-based false-color data and mass-based MFCOs. Through regression models and data aggregation it was possible to predict mass-based MFCOs with mean absolute errors of 0.5% and 1.0% and R2´-scores of 99.9% and 99.4% for plastic flakes and packaging, respectively, across all MFPs. The demonstrated technical feasibility thus paves the way for new sensor technology applications in plastic recycling.
Correspondence on NanoVar’s performance outlined by Jiang T. et al. in “Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation”.
Authors: Than Cheng Yong , Benoukraf Touati 
Published: November 2022

AbstractA recent paper by Jianget al. inBMC Bioinformaticspresented guidelines on long-read sequencing settings for structural variation (SV) calling, and benchmarked the performance of various SV calling tools, including NanoVar. In their simulation-based benchmarking, NanoVar was shown to perform poorly as compared to other tools, mostly due to having low SV recall rates. To investigate the causes for NanoVar's poor performance, we regenerated the simulation datasets (3X to 20X) as specified by Jianget al. and performed benchmarking for NanoVar and Sniffles. Our results did not reflect the findings described by Jianget al. In our analysis, NanoVar displayed more than three times the F1 scores and recall rates as reported in Jiangetal. across all sequencing coverages, indicating a previous underestimation of its performance. We also observed that NanoVar outperformed Sniffles in calling SVs with genotype concordance by more than 0.13 in F1 scores, which is contrary to the trend reported by Jiangetal. Besides, we identified multiple detrimental errors encountered during the analysis which were not addressed by Jianget al. We hope that this commentary clarifies NanoVar's validity as a long-read SV caller and provides assurance to its users and the scientific community.
Importance of Stability Analysis in Control and Sustainability of Fisheries
Authors: Demir Mahir 
Published: November 2022

Abstract
In this study, predator-prey relations of a food web are investigated when two species face harvesting in the food web. A food chain model built on ordinary differential equations is used to investigate such a food web. After ensuring the existence of a solution for the food chain model, all the possible equilibrium points are found and investigated. Then, the stability analysis of important equilibrium points is numerically made. After capturing important values of harvest rates for a sustainable fishery, the predator-prey system is investigated under different levels of harvesting. This investigation shows us how important stability analysis is to controlling fish stocks and capturing sustainable fisheries.
A unified, more intuitive explanation of some quantum phenomena
Authors: Zeng Tian-Hai , Li Kai , Wang Feng , Shao Bin , Liang Shidong 
Published: November 2022

Abstract
There has been wide interest in splitter, quantum tunneling, and double-slit experiments for a long time. The states of single material particles are typically explained as being independent of other objects (and/or electromagnetic fields) and interactions. This independence causes counterintuitive explanations. By analyzing the research conducted separately by Wineland’s group and Haroche’s group, we naturally deduced that the states are dependent on other objects. Based on this dependence, our unified explanation is more intuitive. We design a double-slit thought experiment that can solve the center problem, i.e., which-path information. The dependence can also work for qubits in quantum information. To emphasize and use the dependence in the future, we propose a fundamental postulate that no material particle is free as the zeroth postulate of quantum mechanics.
Analysis of the effect of wheat straw flour on the pore structure of microbial induced carbonate precipitation (MICP) cemented desert aeolian sandy soil based on low field NMR
Authors: Zhou Manxu , Qiang Jin , Li Zihua , Zhu Fuhao 
Published: November 2022

Abstract
In the process of microbial curing of desert aeolian sandy soil, we thought of the water-holding properties of straw flour in view of the high proportion of fine particles and poor water retention of desert aeolian sandy soil, and therefore designed an experiment to add straw flour to enhance the effect of microbial curing of desert aeolian sandy soil. The sand columns prepared under different curing stages were analysed by low-field NMR techniques. The test results show that: (1) the curing product calcium carbonate can effectively fill the pores of the sand and reduce the total porosity, and the addition of straw powder increases the total porosity of the sand column, especially the number of large pores (5–60µm) increases significantly; (2) The addition of straw powder increased the original sand column pore volume and pore size, significantly increasing the water holding capacity of the cured sand column, and the free water content of the sand column was significantly greater than that of the original sand column;(3) The pore filling rate of the sand column decreased after mixing with straw powder, which improved the uniformity of calcium carbonate precipitation within the sand column, but the shear strength was lower than that of the original sand column. The results of the study provide a theoretical basis and data support for optimising the curing effect of desert aeolian sandy soil and its water-holding capacity.
Detail design (DD) of non-tactical deployable 20 GWh (17.20841 kilo ton TNT) plane wave lens configuration fusion device
Authors: Rafique Muhammad Musaddique Ali 
Published: November 2022

Abstract
A simple plane wave lens, using an inert central plastic wave shaper, has been designed. An experiment was done with a 4 in diam, zeroth order design. An iterative technique that uses measured time deviations to connect the next wave shape was developed. Two identical versions of the first iterated shape were fired. Arrival time deviations in these first iterates fell within 50 ns bonds. With greater care and further iterations, lenses bound by 10 ns deviations seem possible.
Psychometrics of a Tool on Intimate Partner Violence Among Female Teachers in a Nigerian State
Authors: MAKINDE GABRIEL IFEOLUWA , Adebayo Ayodeji Matthew , Imoyera Winifred I , Lounsbury David Williams 
Published: November 2022

Abstract
Background
One of the possible strategies for instilling culture of prevention of intimate partner violence (IPV) among rudimentary age-groups is training of teachers on this subject. Aim: To determine teachers’ ability to precisely define their experience of IPV prior to training, this study evaluates the psychometric properties of an adapted WHO/IPV scale assessed on teachers of selected secondary schools in Osun State, Southwestern Nigeria.
Methods
Self-applied responses from a WHO scale on experience of IPV of a sample of 365 female teachers recruited by a two-stage sampling technique was subjected to principal component analysis and ANOVA of independent comparative means.
Results
The PCA yielded three components with respective items correlation range and reliability of: Psychological violence (0.3–0.43, 0.61); sexual violence (0.3–0.52, 0.72) and physical violence (0.3–0.69, 0.73). Experience of psychological and physical violence differed significantly with having an alcoholic spouse while weekly alcoholics differed with the former IPV construct. Having less than three children and voluntary termination of pregnancy differed with sexual violence. Psychological violence was significantly different with ever tried delaying or avoiding getting pregnant. p < 0.05.
Conclusion
This study affirms the validation of the WHO/IPV tool’s delineation across theorised psychological, physical and sexual constructs among secondary school teachers. The tool discriminated against pertinent family, lifestyle, sexual and reproductive characteristics of these pivotal change agents in grassroot prevention of IPV.
Image Steganography of convolutional neural network based on neural architecture search
Authors: Meng Yanger , Li Jingtao 
Published: November 2022

Abstract
Recent studies show that the performance of deep convolutional neural network (CNN) applied to Steganography is better than that of traditional methods. We propose NAS-Stego to solve the problem of static network structure of the model in steganography algorithm based on deep learning.
Different from the existing steganography algorithm based on deep learning, NAS-Stego uses a controller which is a LSTM[18] to generate the architecture of the encoder. We use reinforcement learning and Monte Carlo to train that controller. We have conducted experiments on BOSSBase dataset, and the results show that NAS-Stego has achieved better performance. We use steganalysis analyzer StegExpose to test the anti-steganalysis capability of NAS-Stego, the experiments show that NAS-Stego has achieved good performance.
Ketogenic diet as a metabolic therapy for bipolar disorder: Clinical developments
Authors: Yu Brenda , Ozveren Ruya , Sethi Shebani 
Published: November 2022

Abstract
Background: Bipolar disorder is a neurodevelopmental illness characterized by severe biphasic changes in mood, energy, or thought. Key underlying metabolic pathologies thought to play a role include dysfunction in energy metabolism. The purpose of this article is to review the findings to date of the effects of a low carbohydrate ketogenic diet (KD) on mood symptoms in preclinical and clinical models of bipolar illness. The review highlights the underlying metabolic pathologies of bipolar disorder (BD) and potential therapeutic effects of the KD on these pathologies. The article also explores the potential effects of a KD on metabolic health in BD, including proposed mechanisms of action.Summary: Recent findings support the idea that bipolar disorder, along with other psychiatric disease, may have roots of metabolic dysfunction: cerebral glucose hypometabolism, oxidative stress, as well as mitochondrial and neurotransmitter dysfunction which has downstream effects on synapse connections. A KD provides alternative fuel to the brain aside from glucose and is believed to contain beneficial neuroprotective effects, including stabilization of brain networks, reduction of inflammation and oxidative stress. Several beneficial metabolic effects on insulin resistance, weight, and lipids have been shown. Based on its effectiveness in treating epilepsy, the KD has garnered recent interest in its application for mood disorders as it may imitate the pharmacological effects of mood stabilizers, commonly prescribed agents in the treatment of both BD and epilepsy. Additionally, it may improve metabolic dysfunction often seen in BD and repair deficits in energy metabolism. Limited case studies on KD treatment in BD have been reported; however, studies addressing the potential therapeutic effects of KD on metabolic abnormalities in mental illness are promising. Literature of plausible mechanisms and reports of improvements in psychosis, cognition and mood symptoms have been increasing.Conclusions: Preliminary findings support further testing of a low carbohydrate KD as a potential therapeutic tool in repairing energy metabolism in bipolar illness. Further research and clinical trials are needed to evaluate the efficacy of a KD as a supplemental or co-treatment of bipolar illness and the first open-label trial testing the diet in bipolar illness is currently underway at Stanford.
An Optimal Frequency Regulation in Interconnected Power System through Differential Evolution and Firefly Algorithm
Authors: Mishra Dillip K , Mohanty Asit , RAY PRAKASH K 
Published: November 2022

Abstract
Automatic generation control is extensively used to regulate power plants in a modern area of the power system network. In this paper, automatic generation and frequency control in interconnected power system is presented. A multisource such as thermal, hydro, and gas based power plant is considered in this study, which is carried out by incorporating non-linearity like generation rate constants, HVDC link, and the conventional PID controller design. Further, an optimal setting of the PID controller is performed by employing evolutionary and metaheuristic algorithm-based approach such as differential evolution and firefly algorithm, respectively. The robustness of the proposed controllers is assessed based on comparative analyses to regulate the interconnected power network's frequency profile under different loading conditions. The stability analysis is performed using the Eigen and Nyquist plots to assess the proposed controllers' efficacy. Besides, frequency control study is augmented with comparative assessment through various performance indices such as settling time, peak overshoot and undershoots under different operating conditions. Finally, the proposed control scheme, in the interconnected power system, is validated through a real-time digital simulation platform, i.e., OPAL-RT 5142. The comparison of simulation and real-time results demonstrate the effectiveness of the FA-optimized PID controller in comparison to that of De-optimised PID controller.
An Interpretable Approach to Prediction of Sepsis Using Multivariate Time Series
Authors: Baghaei Kourosh , Rahimi Shahram , Bozorgzad Sean 
Published: November 2022

Abstract
Sepsis is the major cause of death in ICUs and amounts to a large portion of medical expenses. Earlier prediction of sepsis can assist in reducing inpatient mortality and healthcare costs. Despite predictive capabilities of the state-of-the-art approaches, deep models still lack the transparency that provides some sort of reasoning for their predictions. In this work, we studied the predictive performance of an attention-based recurrent neural network and its ability to provide interpretability for the relative contribution of each medical factor to the final prediction. We also qualitatively assess the explainability of the proposed model. This approach sheds light on the opacity issue of deep predictive models. Moreover, it enables the clinical staff to make more reliable decisions based on the status of the patient in ICU thus decreasing the sepsis inpatient mortality.
An approach for joint optimization of probabilistic group test based on cost and time value - taking nucleic acid detection of COVID-19 as an example
Authors: Ma Qianli , Gao Zihui , Ma Baiyu 
Published: November 2022

Abstract
Aiming at people who are susceptible to sudden and infectious diseases, considering the control of viral nucleic acid detection cost and completion time, a probabilistic group test optimization method based on the cost and time value is proposed. Firstly, use different cost functions to express the pooling and testing cost, establish a probability group test optimization model that considers the pooling and testing cost, obtain the optimal combination number of samples for nucleic acid testing, and explore the positive probability and the cost functions of the group testing on the optimization result influences. Secondly, considering the impact of the detection completion time on epidemic control, the sampling ability and detection ability were incorporated into the optimization objective function, then a probability group testing optimization model based on time value is established. Finally, take the nucleic acid detection of the COVID-19 as an example to verify the applicability of the model and obtain the Pareto optimal curve under the minimum cost and shortest completion time. The results show that under normal circumstances, the optimal combination number of samples for nucleic acid detection is about 10. Generally, 10 is used to calculate for the convenience of organization, arrangement and statistics, except for cases where there are special requirements for testing cost and detection completion time.
Grid Integrated Analysis of Hybrid Photovoltaic and Wind Power Generation
Authors: VERMA CHNADRA PRAKASH , Verma K S , SINGH SATYENDRA 
Published: November 2022

Abstract
This paper presents the complex reliability of the PV and the wind power system linked to the grid. The power provided by a wind turbine is designed to suit the linear induction generator. The Archimedese wave swing (AWS) theory is used for the induction generator. A typical connection from dc bus bar through the voltage source converter connects the output terminal of the hybrid system (VSC). This dc power is therefore transformed into ac power by the inverter of the voltage source (VSI).. In order to smooth the rib, the electricity supplied to the power grid of the distribution side is indicated by a supercapacitor (SC). This paper suggests a control system to maintain the equilibrium between the distribution network's generated side and grid. To achieve the purpose, the traditional stabilization tool is added to the device under different conditions and approves the efficiency and performance of the topology used for control and the hybrid photovoltaic and wind turbines.
Estimation of compressive strength of concrete cement using random vector functional link networks: a case study
Authors: Nayak Sarat Chandra , Das Subhranginee , Misra Bijan Bihari 
Published: November 2022

Abstract
Manual estimation of compressive strength of concrete (CSC) is a time consuming and difficult task. It has been an active area of research in the domain of manufacturing engineering. Soft computing techniques are found better to statistical methods applied for prediction of CSC. However, sophisticated prediction models are still missing and need to be explored. Random vector functional link network (RVFLN) significantly reduces the time complexity by assigning input layer weights and bias randomly without further modification. Only, output layer weights are calculated iteratively by gradient methods or non-iteratively by closed form solution like least square methods. It is an efficient algorithm with low time complexity and is able to handle complex domain problems without compromising on accuracy. Motivated from characteristics of RVFLN, in this work we develop a RVFLN-based forecast for estimating compressive strength of concrete cement. A publicly available dataset from UCI repository is used to develop and access the performance of the model. For comparative analysis, few other models such as FLANN, MLP, RBFNN, MLR, and ARIMA are also developed and used for the forecasting considering samples with curing ages at 14, 28, 56, and 91 days. All the models are evaluated in terms of MAPE, ARV, U of Theil’s statistics (UT), NMSE, and execution time. Outcomes of the comparative studies and statistical significance tests are in favor of RVFLN-based forecasting.