Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Risks and Countermeasures of "Cities Besieged by Research on Risks and Countermeasures of "Cities Besieged by Waste" in China—An Empirical Analysis Based on DIIS Waste" in China—An Empirical Analysis Based on DIIS

Abstract Nowadays, China's fast-growing cities are suffering from the problem of "Cities Besieged by Waste". The true portrayal of China today is people live in cities, while the cities are trapped in waste. China is still in this dilemma that a lot of garbage is difficult to memorial archway, clear, dispose, and manage, although local governments have promulgated corresponding policies in recent years. Tragedies caused by "Cities Besieged by Waste" are still everywhere. The risk situation and countermeasures of "Cities Besieged by Waste" should not be ignored, but should be focused on instead. Based on the DIIS (Data-Information-Intelligence-Solution) framework, this study uses Google Earth and GIS (Geographic Information System) to identify the number, area, and formation of informal landfills in 31 provincial capitals to finally generate the data of the "garbage stock". Besides, based on the DSR (Driving force-Status-Response) risk assessment model, this study identifies the risk of "Cities Besieged by Waste" of 31 provincial capitals. By analyzing the current situation of risk, identifying the source of risk, and clarifying the mechanism of risk occurrence and development, this study works on providing intellectual contribution for government departments to reduce the risk of "Cities Besieged by Waste".
Becker Digital

Haemophilus inﬂuenzae type b is an important cause of meningitis and other serious invasive diseases and initiates infection by colonizing the upper respiratory tract. Among the major adhesins in H. inﬂuenzae type b is a nonpilus protein called Hsf, a large protein that forms ﬁber-like structures on the bacterial surface and shares signiﬁcant sequence similarity with the nontypeable H. inﬂuenzae Hia autotransporter. In the present study, we characterized the structure and adhesive activity of Hsf. Analysis of the predicted amino acid sequence of Hsf revealed three regions with high-level homology to the HiaBD1 and HiaBD2 binding domains in Hia. Based on examination of glutathione S -transferase fusion proteins corresponding to these regions, two of the three had adhesive activity and one was nonadhesive in assays with cultured epithelial cells. Structural modeling demonstrated that only the two regions with adhesive activity harbored an acidic binding pocket like the binding pocket identiﬁed in the crystal structure of HiaBD1. Consistent with these results, disruption of the acidic binding pockets in the adhesive regions eliminated adhesive activity. These studies advance our understanding of the architecture of Hsf and the family of trimeric autotransporters and provide insight into the structural determinants of H. inﬂuenzae type b adherence. Haemophilus
Study protocol for evaluating the current status and needs assessment of health-related characteristics among students at Albert-Ludwigs-University Freiburg

Today, university students face study conditions that increase the risk of sedentary behavior, unhealthy eating, and the likelihood of stress, anxiety, and depression. Although the situation has likely worsened in recent months due to the COVID-19 pandemic, even in the pre-Corona era, several investigations have demonstrated that the students’ health behaviors could increase the incidence of metabolic risk factors and non-communicable diseases, however, further and detailed information is needed to develop tailored counter-strategies. Therefore, in the present survey, the current health status of students at the Albert-Ludwigs-Universität Freiburg (ALU) will be recorded by various questionnaires. In addition, barriers that could potentially influence their health behavior will be identified, and information on the use and awareness of existing health services will be obtained in order to derive concrete needs for further health promotion activities. The study is designed as a monocentric and prospective study at the ALU; the survey of students’ situation and needs assessments will be conducted in the areas of nutrition, exercise, relaxation and stress reduction, self-management, psychosocial health and socio-demographic correlations via an anonymous and self-administered online questionnaire. Subsequently in two-year intervals, the survey will be repeated. Data will be collected over a period of 4 years. One goal of this survey is to gain more insight into the health situation of university students; another goal is to use the knowledge gained to integrate holistic health measures into the university landscape in a structured and sustainable manner. These health measures will be implemented by using the newly developed student health care management at the ALU (www.sgm.uni-freiburg.de). Every two years, after evaluation of the surveys, the effect of the health measures can be checked and adjusted. Trial registration: ETK: 20–1082; DRKS-ID: DRKS00024088.
Investigation of Influence Related to Multi-GPUs and Hyperparameters on the ViT Model Based on PyTorch Data Parallelism

: Neural networks, which are highly effective models for addressing various real-world challenges, face the challenge of excessive computational requirements caused by the substantial size of the models and the extensive datasets employed during training. In this paper, the purpose is to examine the effects of hyperparameters and factors such as the number and model of Graphics Processing Units (GPUs) on training time and accuracy using the Vision Transformer (ViT) model as a case study within parallel algorithms. The experiments conducted in this study employed PyTorch as the framework and CIFAR-10 as the dataset. The learning rate chosen for the experiments was set to 0.001, while two batch sizes, namely 64 and 32, were explored. Additionally, the study investigated different epoch values, including 1, 5, 10, and 20. To handle the computational workload effectively during training, data parallelism was employed as an approach. In the realm of optimizing the performance of parallel algorithms, numerous methods have been proposed to enhance their efficiency. Consequently, this research places particular emphasis on hyperparameter tuning. The primary objectives of this paper are to examine the impact of GPUs on both training time and model accuracy, as well as to analyze the influence of hyperparameter configuration on the accuracy of the model.
SLiTer: A Static Analysis Tool to Detect Security Smells in Terraform Configurations

Infrastructure as Code (IaC) is widely embraced for its ability to facilitate system infrastructure management, ensuring ease of modification and reproducibility. However, the inherent susceptibility of IaC configurations to security vulnerabilities necessitates specialized tools for code analysis. Building upon the work of Rahman et al. , who identified 7 security smells present in IaC scripts and introduced SLIC, a static analysis tool for identifying security smells in Puppet scripts, this paper presents SLiTer — a tool designed to detect the same security smells in Terraform files. By doing so, we developed two Rule Engines to serve distinct purposes: the first faithfully translated SLIC rules to establish a baseline, while the second incorporated modifications to enhance accuracy when applied to Terraform configurations. Evaluating SLiTer on 105 Terraform files from 15 directories revealed the most prevalent security smell as ”Hard-coded secret,” aligning with findings in the original work. SLiTer may prove valuable for practitioners seeking to identify general security smells in Terraform configurations, complementing other tools like Sonar or tfparse for provider-specific issues.
INDC International Nuclear Data Committee INTERNATIONAL NUCLEAR DATA EVALUATION NETWORK (INDEN) ON THE EVALUATED DATA OF STRUCTURAL MATERIALS

A new evaluation of the 56 Fe isotope neutron induced reaction cross sections is ongoing at the CEA Cadarache, with the goal to cover the whole energy range, from the Resolved Resonances Region (RRR) to the continuum, with consistent uncertainty treatment. The motivation was the CIELO project that showed that it is very difficult to extend the RRR to higher energies (above the first inelastic level) than the original Froehner evaluation (1977). The CONRAD code is used, which is an object-oriented evaluation platform developed at CEA Cadarache.
Study of ambidexterity in recent 30 years: a scientometric analysis using CiteSpace

In recent years, there has been rapid progress in the literature on am-bidexterity, but a clear and systematic scientometric analysis is rare. Based on CiteSpace, this study conducted a scientometric analysis of 304 academic publications related to ambidexterity from 1990 to 2023. By identifying and visualizing the collaborative networks, co-citation networks, co-occurrence networks and emerging trends, this study aims to provide a valuable insight for the development of the ambidexterity research field.
Main and papain-like proteases as prospective targets for pharmacological treatment of coronavirus SARS-CoV-2

The pandemic caused by the coronavirus SARS-CoV-2 led to a global crisis in the world healthcare system. Despite some progress in the creation of antiviral vaccines and mass vaccination of the population, the number of patients continues to grow because of the spread of new SARS-CoV-2 mutations. There is an urgent need for direct-acting drugs capable of suppressing or stopping the main mechanisms of reproduction of the coronavirus SARS-CoV-2. Several studies have shown that the successful replication of the virus in the cell requires proteolytic cleavage of the protein structures of the virus. Two proteases are crucial in replicating SARS-CoV-2 and other coronaviruses: the main protease (Mpro) and the papain-like protease (PLpro). In this review, we summarize the essential viral proteins of SARS-CoV-2 required for its viral life cycle as targets for chemotherapy of coronavirus infection and provide a critical summary of the development of drugs against COVID-19 from the drug repurposing strategy up to the molecular design of novel covalent and non-covalent agents capable of inhibiting virus replication. We overview the main antiviral strategy and the choice of SARS-CoV-2 Mpro and PLpro proteases as promising targets for pharmacological impact on the coronavirus life cycle.
Memory Based Temporal Network Prediction

Temporal networks are networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast and mitigate the spread of epidemics and misinformation on the network. Most existing methods for temporal network prediction are based on machine learning algorithms, at the expense of high computational costs and limited interpretation of the underlying mechanisms that form the networks. This motivates us to develop network-based models to predict the temporal network at the next time step based on the network observed in the past. Firstly, we investigate temporal network properties to motivate our network prediction models and to explain how the performance of these models depends on the temporal networks. We explore the similarity between the network topology (snapshot) at any two time steps with a given time lag/interval. We ﬁnd that the similarity is relatively high when the time lag is small and decreases as the time lag increases. Inspired by such time-decaying memory of temporal networks and recent advances, we propose two models that predict a link’s future activity (i.e., connected or not), based on the past activities of the link itself or also of neighboring links, respectively. Via seven real-world physical contact networks, we ﬁnd that our models outperform in both prediction quality and computational complexity, and predict better in networks that have a stronger memory. Beyond, our model also reveals how different types of neighboring links contribute to the prediction of a given link’s future activity, again depending on properties of temporal networks.
RMDGCN: Prediction of RNA methylation and disease associations based on graph convolutional network with attention mechanism

RNA modification is a post transcriptional modification that occurs in all organisms and plays a crucial role in the stages of RNA life, closely related to many life processes. As one of the newly discovered modifications, N1-methyladenosine (m1A) plays an important role in gene expression regulation, closely related to the occurrence and development of diseases. However, due to the low abundance of m1A, verifying the associations between m1As and diseases through wet experiments requires a great quantity of manpower and resources. In this study, we proposed a computational method for predicting the associations of RNA methylation and disease based on graph convolutional network (RMDGCN) with attention mechanism. We build an adjacency matrix through the collected m1As and diseases associations, and use positive-unlabeled learning to increase the number of positive samples. By extracting the features of m1As and diseases, a heterogeneous network is constructed, and a GCN with attention mechanism is adopted to predict the associations between m1As and diseases. The experimental results indicate that under a 5-fold cross validation, RMDGCN is superior to other methods (AUC = 0.9892 and AUPR = 0.8682). In addition, case studies indicate that RMDGCN can predict the relationships between unknown m1As and diseases. In summary, RMDGCN is an effective method for predicting the associations between m1As and diseases.
Elucidating the Temporal Patterns of Gene Expression in the Inferred Regulatory Interactions of GmCOL1b in Glycine max

The CONSTANS ( CO ) gene in Arabidopsis thaliana has a central role in photoperiodic regulation of flowering. However, the roles of CO genes in mediating flowering in soybeans ( Glycine max ) remain uncertain. We previously inferred regulatory interactions of a soybean CO homolog, GmCOL1b , using in-house RNA-seq data and the network inference algorithm package CausNet. Here, we identify potential GmCOL1b downstream genes and experimentally verify them by expressing GmCOL1b in soybean protoplast cells. Temporal expression patterns of these genes indicate the regulatory effects of GmCOL1b on the expression of the circadian clock genes GmLCL1 and GmLCL4 and the flowering regulator GmTEM1a .
Hepatoprotective activity of QBD-based optimized N-acetyl cysteine solid lipid nanoparticles against CCL4-induced liver injury in mice

Purpose: In the present study, N -acetyl cysteine ( NAC)-Solid Lipid Nanoparticles (SLNs) were developed employing the Quality by Design (QBD) approach for the application of hepatoprotective activity. Methods: Using Box-Behnken Design (BBD) three independent variables (Soya lecithin, polysorbate content, and homogenization speed) and four dependent variables (% entrapment efficiency (EE), % drug release (DR), zeta potential (ZP), and particle size (PS)) were chosen for the study. The formulations were prepared by the hot homogenization method and characterized with SEM, FTIR, DSC, and XRD and evaluated their % EE, % DR, PS, and ZP. Developed SLNs were tested for their hepatoprotective activity by an in vivo mice model and compared the effectiveness with free NAC and Silymarin. Results: The optimized NAC-SLNs were found optimum with spherical and intact chemical structure (88.95% EE, 97.15% DR, -43.01 mv ZP, < 200 nm of PS) exhibiting Higuchi model of drug release. In terms of MDA levels, NAC-SLNs had a strong protective impact MDA level (23.09±0.01–21.84±0.01 u mole/mg protein) and were efficient in increasing GPx (16.89±0.01–20.71±0.02 unit/ mg protein), GSH (18.94±0.57–24.21±1.00 unit/mg protein), which were reduced in the CCl 4 -intoxicated group. NAC-SLNs were more effective than NAC at inhibiting the liver enzymes SGOT (150.01±1.5–132.01±0.6 mg/dL), SGPT (100.73±1.1–91.98±2.8 mg/ dL), ALP (147.07±0.8–124.79±0.5 mg/dL), and LDH (290.37±3.04–228.25±2.03U/L). Conclusion: The study concludes that NAC-SLNs therapy was not only substantially more effective than NAC, but it also had effects equivalent to a well-known hepatoprotective and antioxidant drug Silymarin.
A novel approach to optimize LSTM Hyperparameter using the Chimp Optimization Algorithm for the Pressure Ventilator Prediction

: Mechanical ventilation plays a vital role in the treatment of patients suffering from severe lung disease. In times of a pandemic, it becomes crucial to develop ventilators that can automatically adjust parameters during the treatment process. To address this need, a study was conducted to predict the pressure exerted on the patient by the ventilator. This prediction was based on various factors, including the ventilator's parameters and the patient's condition, using a specialized model known as Long Short-Term Memory (LSTM). In order to optimize the LSTM model and improve the accuracy of the prediction results, an algorithm called Chimp Optimization Algorithm (ChoA) was employed. The combination of LSTM and ChoA resulted in the creation of the LSTM-ChoA model, which effectively addressed the challenge of selecting appropriate hyperparameters for the LSTM model. Experimental findings demonstrated that the LSTM-ChoA model outperformed other optimization algorithms such as whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO), as well as regression models including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor
Development of 360-Degree Evaluation Model on Medical Curriculum with

A 360-degree evaluation model of the medical curriculum was developed by adhering to the Kirkpatrick Hierarchy. In order to bridge the research gap pertaining to instrument development of 360-degree evaluation, a set of tools derived from various stakeholders’ perspectives was used. This mixed-method study in the sequential exploratory design involved 797 participants for two years, 2017-2019. The three study phases involved were: the qualitative phase (18 informants), the item construction phase with exploratory factor analysis (EFA) (298 participants), and the validation phase via confirmatory factor analysis (CFA) (481 participants by random sampling). In-depth interview sessions were conducted with two stakeholder groups; lecturers and preceptors. Focus group discussion (FGD) sessions were conducted with two other groups; medical students and patients at the teaching hospital. The item construction phase was executed based on the themes that emerged from qualitative findings. Lastly, the final validation stage was performed based on the EFA results. In total, 23 themes were derived from four stakeholder groups. In the item construction phase, 13 and 10 factors were identified for lecturer and student instruments, respectively. To build the preceptor and patient tools, 10 scales were used to validate the item constructs. Thus, the 360-degree evaluation model has 4 levels of Kirkpatrick Hierarchy with 4 instrument models. The 360-degree evaluation model is valid, well-constructed, and accurately reflects the indicator variables. The evaluation model is feasible and acceptable to assess the medical curriculum
Himpathy and status: Attitudes to social hierarchy predict reactions to sexual harassment

In three studies, we tested whether hierarchical preferences could explain differences in punishment recommendations for sexual harassment. Building on research that suggests punishment is used to regulate social hierarchies, we argue that individuals who are motivated to maintain existing hierarchies will treat male perpetrators of sexual harassment with greater leniency, especially when judging perpetrators of high social status. Conversely, we predict that egalitarians—who are motivated to reduce group-based hierarchies—will judge male perpetrators more harshly, especially those of high social status. Given competing theories in the existing literature, we make no predictions about how perpetrator status will affect punishment recommendations overall. Supporting our hypotheses, we found that individuals high on gender system justification and social dominance orientation recommended more lenient punishments to perpetrators. Moreover, an integrative data analysis uncovered an interaction between social dominance orientation and perpetrator status. This interaction was primarily driven by egalitarians, who provided more lenient punishment recommendations to low status perpetrators when compared to high status perpetrators. Contrary to our predictions, we did not find strong evidence that individuals high on social dominance orientation provided harsher judgements to low status perpetrators. Nor did we find strong evidence for a main effect of perpetrator status on punishment recommendations. Taken together, these findings suggest that people punish sexual harassment to bolster or attenuate power structures. This is particularly true of egalitarians, whose emphasis on social equality leads them to judge high status perpetrators of sexual harassment with particular severity.
Eﬃcient Separability of Regular Languages by Subsequences and Suﬃxes

When can two regular word languages K and L be separated by a simple language? We investigate this question and consider separation by piecewise-and suﬃx-testable languages and variants thereof. We give characterizations of when two languages can be separated and present an overview of when these problems can be decided in polynomial time if K and L are given by nondeterministic automata.
Feasibility Study of Leaching Mining of Deeply Buried Polyhalite and Kinetic Leaching Modeling

Polyhalite resources are rich in China,but they have not yet been exploited because the polyhalite ore is difficult to dissolve and deep-buried.According to the geological conditions of polyhalite deposit formation,the deposit has good sealed wall rocks and basic geological conditions of leaching mining.The technological key to leaching mining of underground polyhalite is the extraction of effective constitutes from polyhalite.Theoretical and experimental studies show that inorganic solutions such as CaCl2 solution may improve the dissolubility of polyhalite.Leaching mining is probably an effective way for the exploitation of deeply buried polyhalite.The technological factors influencing the dissolution process of polyhalite such as the concentration of the leaching agent CaCl2 solution,grain size of ore,infiltration rate and infiltration path were studied through experiments.The kinetic study of the dissolution process shows that the process of leaching polyhalite with calcium chloride solution accords with the kinetic model of metal leaching model constructed by В. С. Голубев-Г. Н. Крuчев. That is C=CH1-e-γ(χ-vBt)U-vB.There is an exponential relationship between the concentration of K+ in the leaching solution and the ratio of the infiltration rate to the length of the infiltration path.The constant of the leaching rate of K and the time required from full leaching-out of K to its entrance into the liquid phase were obtained according to the experimental data.When the infiltration rate increased from 0.159 to 0.318 m/h,the leaching rate constants were 0.03132 h-1 and 0.04383 h-1 respectively;the time required from the full extraction of metal ions to their entrance into the liquid phase decreased from 297.528 to 218.997 h.In-situ leaching mining of deeply buried polyhalite can alleviate severe shortages of potash resources in China.