Miracle-dz/UniFAR-FTUs huggingface dataset:
Mothers’ Self-Control and Self-Efficacy and Childbirth Complications: A Study at an Indonesian General Hospital

Childbirth complications are still a big problem in developing countries, especially in Indonesia and are one of the causes of high maternal mortality. This study aims to identify the relationship between self-control and self-efficacy as an indirect cause of maternal death with the incidence of childbirth complications. An analytical descriptive design with a cross sectional method was used. 175 mothers in their maternity phase were selected using the consecutive sampling technique. Self-control was measured using the Labour Agentry Scale (LAS), while self-efficacy was measured using the Childbirth Self-Efficacy Inventory (CBSEI) which had been tested for validity and reliability, and data on the incidence of childbirth complications were obtained from hospital medical records. Univariate, bivariate, and multivariate analysis with the help of computer software. The results showed that the variables of self-control and self-efficacy had a very significant relationship with the incidence of childbirth complications (p value 0.000, ? : 0.05). Mothers with poor self-control are at risk of developing complications of childbirth by 9,014 (CI 95%: 0.929 – 87,487) times greater than those with good self-control. Poor self-efficacy is at risk of developing childbirth complications 3,815 (CI 95%: 1,851 – 7,862) times greater than those with good self-efficacy. This study proves that self-control and self-efficacy are variables that influence the existence of labor complications. Antenatal care staff need to prepare mothers from pregnancy and childbirth to have the ability to control themselves and have good self-efficacy in undergoing the delivery process.
Immune-Mediated Neuropathies: Pathophysiology and Management

Dysfunction of the immune system can result in damage of the peripheral nervous system. The immunological mechanisms, which include macrophage infiltration, inflammation and proliferation of Schwann cells, result in variable degrees of demyelination and axonal degeneration. Aetiology is diverse and, in some cases, may be precipitated by infection. Various animal models have contributed and helped to elucidate the pathophysiological mechanisms in acute and chronic inflammatory polyradiculoneuropathies (Guillain–Barre Syndrome and chronic inflammatory demyelinating polyradiculoneuropathy, respectively). The presence of specific anti-glycoconjugate antibodies indicates an underlying process of molecular mimicry and sometimes assists in the classification of these disorders, which often merely supports the clinical diagnosis. Now, the electrophysiological presence of conduction blocks is another important factor in characterizing another subgroup of treatable motor neuropathies (multifocal motor neuropathy with conduction block), which is distinct from Lewis–Sumner syndrome (multifocal acquired demyelinating sensory and motor neuropathy) in its response to treatment modalities as well as electrophysiological features. Furthermore, paraneoplastic neuropathies are also immune-mediated and are the result of an immune reaction to tumour cells that express onconeural antigens and mimic molecules expressed on the surface of neurons. The detection of specific paraneoplastic antibodies often assists the clinician in the investigation of an underlying, sometimes specific, malignancy. This review aims to discuss the immunological and pathophysiological mechanisms that are thought to be crucial in the aetiology of dysimmune neuropathies as well as their individual electrophysiological characteristics, their laboratory features and existing treatment options. Here, we aim to present a balance of discussion from these diverse angles that may be helpful in categorizing disease and establishing prognosis.
Resulting Effect of the p-Type of ZnTe: Cu Thin Films of the Intermediate Layer in Heterojunction Solar Cells: Structural, Optical, and Electrical Characteristics

The microstructural, electrical, and optical properties of Cu-doped and undoped ZnTe thin films grown on glass substrates are covered in this article. To determine the chemical makeup of these materials, both energy-dispersive X-ray (EDAX) spectroscopy and X-ray photoelectron spectroscopy were employed. The cubic zinc-blende crystal structure of ZnTe and Cu-doped ZnTe films was discovered using X-ray diffraction crystallography. According to these microstructural studies, the average crystallite size increased as the amount of Cu doping increased, whereas the microstrain decreased as the crystallinity increased; hence, defects were minimized. The Swanepoel method was used to compute the refractive index, and it was found that the refractive index rises as the Cu doping levels rises. The optical band gap energy was observed to decrease from 2.225 eV to 1.941 eV as the Cu content rose from 0% to 8%, and then slightly increase to 1.965 eV at a Cu concentration of 10%. The Burstein–Moss effect may be connected to this observation. The larger grain size, which lessens the dispersion of the grain boundary, was thought to be the cause of the observed increase in the dc electrical conductivity with an increase in Cu doping. In structured undoped and Cu-doped ZnTe films, there were two carrier transport conduction mechanisms that could be seen. According to the Hall Effect measurements, all the grown films exhibited a p-type conduction behavior. In addition, the findings demonstrated that as the Cu doping level rises, the carrier concentration and the Hall mobility similarly rise, reaching an ideal Cu concentration of 8 at. %, which is due to the fact that the grain size decreases grain boundary scattering. Furthermore, we examined the impact of the ZnTe and ZnTe:Cu (at Cu 8 at.%) layers on the efficiency of the CdS/CdTe solar cells.
An efficient optimizer for the 0/1 knapsack problem using group counseling

The field of optimization is concerned with determining the optimal solution to a problem. It refers to the mathematical loss or gain of a given objective function. Optimization must reduce the given problem’s losses and disadvantages while maximizing its earnings and benefits. We all want optimal or, at the very least, suboptimal answers because we all want to live a better life. Group counseling optimizer (GCO) is an emerging evolutionary algorithm that simulates the human behavior of counseling within a group for solving problems. GCO has been successfully applied to single and multi-objective optimization problems. The 0/1 knapsack problem is also a combinatorial problem in which we can select an item entirely or drop it to fill a knapsack so that the total weight of selected items is less than or equal to the knapsack size and the value of all items is as significant as possible. Dynamic programming solves the 0/1 knapsack problem optimally, but the time complexity of dynamic programming is O(n3). In this article, we provide a feature analysis of GCO parameters and use it to solve the 0/1 knapsack problem (KP) using GCO. The results show that the GCO-based approach efficiently solves the 0/1 knapsack problem; therefore, it is a viable alternative to solving the 0/1 knapsack problem.
Association between Micronutrients and Hyperhomocysteinemia: A Case-Control Study in Northeast China

Hyperhomocysteinemia (HHcy) is an independent risk factor for cardiovascular and cerebrovascular diseases where the plasma homocysteine (Hcy) concentration exceeds 15 µmol/L. HHcy is affected by vitamins B12, B6, and folic acid (fol); however, its relationship with other nutrients is not fully understood. We investigated the nutritional and genetic factors associated with HHcy and the possible dose–response relationships or threshold effects in patients in Northeast China. Genetic polymorphisms and micronutrients were tested with polymerase chain reaction and mass spectrometry, respectively. This trial was registered under trial number ChiCTR1900025136. The HHcy group had significantly more males and higher body mass index (BMI), methylenetetrahydrofolate reductase (MTHFR 677TT) polymorphism proportion, and uric acid, Zn, Fe, P, and vitamin A levels than the control group. After adjusting for age, sex, BMI, vitamin B12, fol, and MTHFR C677T, the lowest Zn quartile reduced the odds ratio of HHcy compared with the highest Zn quartile. The dose–response curves for the association between plasma Zn and HHcy were S-shaped. High plasma Zn concentrations were significantly correlated with high HHcy odds ratios, and the curve leveled off or slightly decreased. Most importantly, HHcy risk decreased with decreasing plasma Zn concentration; the threshold was 83.89 µmol/L. Conclusively, individuals residing in Northeast China, especially those with the MTHFR 677TT polymorphism, must pay attention to their plasma Zn and Hcy levels.
Verproside, the Most Active Ingredient in YPL-001 Isolated from Pseudolysimachion rotundum var. subintegrum, Decreases Inflammatory Response by Inhibiting PKCδ Activation in Human Lung Epithelial Cells

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease which causes breathing problems. YPL-001, consisting of six iridoids, has potent inhibitory efficacy against COPD. Although YPL-001 has completed clinical trial phase 2a as a natural drug for COPD treatment, the most effective iridoid in YPL-001 and its mechanism for reducing airway inflammation remain unclear. To find an iridoid most effectively reducing airway inflammation, we examined the inhibitory effects of the six iridoids in YPL-001 on TNF or PMA-stimulated inflammation (IL-6, IL-8, or MUC5AC) in NCI-H292 cells. Here, we show that verproside among the six iridoids most strongly suppresses inflammation. Both TNF/NF-κB-induced MUC5AC expression and PMA/PKCδ/EGR-1-induced IL-6/-8 expression are successfully reduced by verproside. Verproside also shows anti-inflammatory effects on a broad range of airway stimulants in NCI-H292 cells. The inhibitory effect of verproside on the phosphorylation of PKC enzymes is specific to PKCδ. Finally, in vivo assay using the COPD-mouse model shows that verproside effectively reduces lung inflammation by suppressing PKCδ activation and mucus overproduction. Altogether, we propose YPL-001 and verproside as candidate drugs for treating inflammatory lung diseases that act by inhibiting PKCδ activation and its downstream pathways.
Effects of Glutamine, Curcumin and Fish Bioactive Peptides Alone or in Combination on Intestinal Permeability in a Chronic-Restraint Stress Model

Irritable bowel syndrome (IBS), a multifactorial intestinal disorder, is often associated with a disruption in intestinal permeability as well as an increased expression of pro-inflammatory markers. The aim of this study was to first test the impact of treatment with glutamine (Gln), a food supplement containing natural curcumin extracts and polyunsaturated n-3 fatty acids (Cur); bioactive peptides from a fish protein hydrolysate (Ga); and a probiotic mixture containing Bacillus coagulans, Lactobacillus acidophilus, Lactobacillus gasseri and Lactobacillus helveticus. These compounds were tested alone on a stress-based IBS model, the chronic-restraint stress model (CRS). The combination of Gln, Cur and Ga (GCG) was also tested. Eight-week-old C57Bl/6 male mice were exposed to restraint stress for two hours every day for four days and received different compounds every day one week before and during the CRS procedure. Plasma corticosterone levels were measured as a marker of stress, and colonic permeability was evaluated ex vivo in Ussing chambers. Changes in the gene expression of tight junction proteins (occludin, claudin-1 and ZO 1) and inflammatory cytokines (IL1β, TNFα, CXCL1 and IL10) were assessed using RT-qPCR. The CRS model led to an increase in plasma corticosterone and an increase in colonic permeability compared with unstressed animals. No change in plasma corticosterone concentrations was observed in response to CRS with the different treatments (Gln, Cur, Ga or GCG). Stressed animals treated with Gln, Cur and Ga alone and in combination showed a decrease in colonic permeability when compared to the CRS group, while the probiotic mixture resulted in an opposite response. The Ga treatment induced an increase in the expression of the anti-inflammatory cytokine IL-10, and the GCG treatment was able to decrease the expression of CXCL1, suggesting the synergistic effect of the combined mixture. In conclusion, this study demonstrated that a combined administration of glutamine, a food supplement containing curcumin and polyunsaturated n-3 fatty acids, and bioactive peptides from a fish hydrolysate was able to reduce colonic hyperpermeability and reduce the inflammatory marker CXCL1 in a stress-based model of IBS and could be of interest to patients suffering from IBS.
Quantum Cryptanalysis of Multivariate Permutation Problem

Quantum computation is a new computational model based on quantum mechanical principle. Shor invented the polynomial time algorithms for the prime factorization and discrete logarithm problem, which indicated that the cryptosystems based on them are totally unsafe in the quantum world. Grover constructed an algorithm that finds a solution in only O( 2 n )steps whereas the exhaustive search algorithm needs O(2 n ) steps on average. In this paper we investigate the cryptanalysis of a new cryptography problem----multivariate permutation problem (MPP), which could be used to design public-key cryptosystem, with the help of the two quantum algorithms. Specially, we discuss the strength of a private key of the REESSE1+ public-key cryptosystem, whose security is based on the hardness of MPP. Besides, some suggestions are also given about the implementation of the REESSE1+.
Factors Influencing Intention to Use Online Consumer Reviews: The Case of Vietnam

Online consumer reviews have been shown to play an important role that can influence consumers' attitudes, behaviors, and purchasing decisions. This study seeks to understand factors that affect user intention to use online consumer reviews. To examine this area, this paper theorizes multiple constructs that may influence intention to use. The subjects in this study were 466 online Vietnamese consumers. The data were analyzed using structural equation modeling, and five of the seven hypotheses were found to be significant. This study found several results not found in prior studies. The construct perceived online review importance is found to be important in influencing intention to use online reviews. Both perceived usefulness and perceived credibility influenced consumers' perceptions on the importance of online reviews. The study findings contribute to the research field of online consumer reviews and provide new insights into intention to use online consumer reviews for a developing country, different from prior studies that have focused on developed countries.
Investigation and Modelling of Economic Systematic Risk and Capital Requirement: A Monte Carlo Simulation

This paper tests the ability of the regulatory capital requirement to cover credit losses at default, as carried out by the economic (optimal) capital requirement in Tunisian banks. The common factor in borrowers that leads to a credit default is systematic risk. However, the sensitivity to these factors differs between borrowers. To this end, we derived two kinds of sensitivity to systematic risk: the first is recognised by the Basel Committee; the second is derived from an economic approach. Hence, we can observe the impact of sensitivity to systematic risk on capital requirements. Empirically, we studied a sample of 100 individual borrowers from a Tunisian deposit bank that had credit in January 2020. We estimated the default probability for each borrower and then simulated their systematic risk sensitivity using the Monte Carlo approach, and compared them with the regulatory risk sensitivity. Then, we tested their effects on the economic and regulatory capital requirements. The results indicate that regulatory capital overestimates economic capital. This is due to the overestimation of borrowers’ contagion in terms of default risk, as shown by the superiority of their regulatory sensitivity systematic risk compared to the simulated risk. This leads banks to devote more capital than is really necessary to reach the regulatory standard. Hence, there was an increase in capital costs and the possibility of an arbitrage opportunity.
Treatment with Antiviral Drugs Will Significantly Inhibit the HIV-1 RNA POL Gene Expression and Viral Load in AIDS Patients

Objective This study is to investigate the difference in HIV-1 RNA pol gene expression in AIDS patients before and after antiviral treatment and its effect on the expression level of CD4+/CD8+ T cells in peripheral blood. Methods The participants included 200 AIDS patients who had undergone antiviral medication, and the quantity of HIV-1 RNA pol gene was determined using nested polymerase chain reaction (nPCR). The levels of CD3+, CD4+, and CD8+ T lymphocytes in peripheral blood were measured by flow cytometry before and after therapy. The receiver operating characteristics (ROC) curve was used to assess the impact of HIV-1 RNA pol gene expression and the CD4+/CD8+ ratio on the prognosis of AIDS patients. Results After three months of therapy, the levels of HIV-1 RNA and viral load in the patients showed a drastic decline, while the levels of CD4+/CD8+ were markedly elevated (P < 0.05). Logistic analysis revealed that patients' viral loads were positively correlated with HIV-1 RNA and negatively correlated with CD4+/CD8+ (P < 0.05). The alanine aminotransferase (ALT), white blood cell (WBC) count, Serum creatinine (Cr), total cholesterol (TC), triglyceride (TG), and platelet (PLT) levels significantly increased following a 24-month therapy, while no significant changes were observed in the level of aspartate aminotransferase (AST), red blood cell (RBC), and neutrophil (NEU) (%). (P > 0.05). Conclusion Antiviral drugs significantly inhibit the HIV-1 RNA POL gene expression and viral load in AIDS patients but upregulate the expression level of CD4+/CD8+ T cells in peripheral blood.
Multistate Prediction for In-Service Gas Turbine via Adaptive Diffusion Graph Network

State estimation is integral to the continuous monitoring and management of the energy consumption in gas turbines. Recent research about multistate prediction adopts separate predictions and uses few connections between signals. The complex physical mechanism of the gas turbine makes it difficult to extract and obtain the spatial information between states as prior knowledge, and the spatial information has irregularities as the operating conditions change. To overcome these limitations, a graph structure with a fusion strategy was adopted to solve the irregularity of multisource sensor data and then the adaptive adjacency-matrix diffusion graph network (AdapDGN) was proposed. First, an adaptive adjacency matrix module learned through a query and key and based only on the input data can capture the temporal trends of spatial information. Second, the diffusion graph recurrent module can remember the history sequences. The two aforementioned modules are combined in a unified framework, and the entire framework is learned end to end. The performance of AdapDGN for multistate prediction is demonstrated on a real-world sensor network of a gas turbine control system under variable working conditions. The results show a 2% improvement in prediction accuracy of the fusion strategy to the separate prediction, and the learned connections between signals under diverse working conditions are also visualized.
An Improved Molecular Solution for the Partition Problem

Now the algorithms based on DNA computing for partition problem always converted the elements into binary number and then carry on mathematics operations on them. In this paper, our main purpose is to give an improved molecular solution for the partition problem. Our new algorithm does not need mathematics operations. So the algorithm is easier and the number of the operation is reduced. Besides, the time used in the biological experiment is less than before. In order to achieve this, we design a new encoding method. We also design a special parallel searcher to search the legal DNA strands in the solution space.
Research on application icons generation based on GAN improved by self-attention mechanism

Aims The software interface design is changing and updating and has spawned a large number of interface icon design requirements. Using deep learning and generative adversarial network(GAN) to assist icon design can simplify the process of icon design and enrich the creativity of icon conception. Methods The Python tool was used to capture nearly 2000 icon data and perform a simple filtering. After the steps of icon preprocessing, data transformation, structure design of generating network and discriminant network, definition of loss function, model recording and saving, a generation adversarial network suitable for icon design is successfully constructed. Results In this study, icon generation is well explored by combining deep learning and generative adversarial networks. Although the change of function loss and the output icon quality are not ideal in the preliminary practice, higher quality and new icons are obtained by improving the icon recognition algorithm, and the function loss value is reduced. The research has certain guiding significance for more personalized, higher quality and efficient icon generation and design in the future. Conclusion It is feasible to apply deep learning and GAN into icon design. The newly generated icons have certain artistic expression and instructional intention, and can be used as ideas and creativity in icon design process.
Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey

System logs are almost the only data that records system operation information, so they play an important role in anomaly analysis, intrusion detection, and situational awareness. However, it is still a challenge to obtain effective data from massive system logs. On the one hand, system logs are unstructured data, and, on the other hand, system log records cannot be directly analyzed and calculated by computers. In order to deal with these problems, current researchers digitize system logs through two key steps of log parsing and feature extraction. This paper classifies, analyzes, and summarizes the current log analysis research in terms of log parsing and feature extraction by investigating articles in recent years (including ICSE, TKDD, ICDE, IJCAI, ISSRE, ICDM, ICWS, ICSME, etc.). Finally, in combination with the existing research, the research prospects in the field are elaborated and predicted.