Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Human Decision Making in an Interactive Landslide Simulator Tool via Reinforcement Learning

Prior research has used an Interactive Landslide Simulator (ILS) tool to investigate human decision making against landslide risks. It has been found that repeated feedback in the ILS tool about damages due to landslides causes an improvement in human decisions against landslide risks. However, little is known on how theories of learning from feedback (e.g., reinforcement learning) would account for human decisions in the ILS tool. The primary goal of this paper is to account for human decisions in the ILS tool via computational models based upon reinforcement learning and to explore the model mechanisms involved when people make decisions in the ILS tool. Four different reinforcement-learning models were developed and evaluated in their ability to capture human decisions in an experiment involving two conditions in the ILS tool. The parameters of an Expectancy-Valence (EV) model, two Prospect-Valence-Learning models (PVL and PVL-2), a combination EV-PU model, and a random model were calibrated to human decisions in the ILS tool across the two conditions. Later, different models with their calibrated parameters were generalized to data collected in an experiment involving a new condition in ILS. When generalized to this new condition, the PVL-2 model’s parameters of both damage-feedback conditions outperformed all other RL models (including the random model). We highlight the implications of our results for decision making against landslide risks.
Enolase 1 Correlated With Cancer Progression and Immune-Infiltrating in Multiple Cancer Types: A Pan-Cancer Analysis

Enolase 1 (ENO1) is an oxidative stress protein expressed in endothelial cells. This study aimed to investigate the correlation of ENO1 with prognosis, tumor stage, and levels of tumor-infiltrating immune cells in multiple cancers. ENO1 expression and its influence on tumor stage and clinical prognosis were analyzed by UCSC Xena browser, Gene Expression Profiling Interactive Analysis (GEPIA), The Cancer Genome Atlas (TCGA), and GTEx Portal. The ENO1 mutation analysis was performed by cBio Portal, and demonstrated ENO1 mutation (1.8%) did not impact on tumor prognosis. The relationship between ENO1 expression and tumor immunity was analyzed by Tumor Immune Estimation Resource (TIMER) and GEPIA. The potential functions of ENO1 in pathways were investigated by Gene Set Enrichment Analysis. ENO1 expression was significantly different in tumor and corresponding normal tissues. ENO1 expression in multiple tumor tissues correlated with prognosis and stage. ENO1 showed correlation with immune infiltrates including B cells, CD8+ and CD4+ T cells, macrophages, neutrophils, and dendritic cells, and tumor purity. ENO1 was proved to be involved in DNA replication, cell cycle, apoptosis, glycolysis process, and other processes. These findings indicate that ENO1 is a potential prognostic biomarker that correlates with cancer progression immune infiltration.
Instagram post popularity trend analysis and prediction using hashtag, image assessment, and user history features

Instagram is one of the most popular social networks for marketing. Predicting the popularity of a post on Instagram is important to determine the influence of a user for marketing purposes. There were studies on popularity prediction on Instagram using various features and datasets. However, they haven't fully addressed the challenge of data variability of the global dataset, where they either used local datasets or discretized output. This research compared several regression techniques to predict the Engagement Rate (ER) of posts using a global dataset. The prediction model, coupled with the results of the popularity trend analysis, will have more utility for a larger audience compared to existing studies. The features were extracted from hashtags, image analysis, and user history. It was found that image quality, posting time, and type of image highly impact ER. The prediction accuracy reached up to 73.1% using the Support Vector Regression (SVR), which is higher than previous studies on a global dataset. User history features were useful in the prediction since the data showed a high variability of ER if compared to a local dataset. The added manual image assessment values were also among the top predictors
An efficient intrusion detection system by using behaviour profiling and statistical approach model

Unauthorized access in a personal computer or single system of a network for tracking the system access or theft the information is called attack/ hacking. An Intrusion detection System defined as an effective security technology, it detect, prevent and possibly react to computer related malicious activities. For protecting computer systems and networks from abuse used mechanism named Intrusion detection system. The aim of the study is to know the possibilities of Intrusion detection and highly efficient and effective prevent technique. Using this model identified the efficient algorithm for intrusion detection Behaviour Profiling Algorithm and to perform dynamic analysis using Statistical Approach model using log file which provides vital information about systems and the activities on them. The proposed algorithm implemented model it produced above 90%, 96% and 98% in the wired, wireless and cloud network respectively. This study concluded that, the efficient algorithm to detect the intrusion is behaviour profiling algorithm, while join with the statistical approach model, it produces efficient result. In further research, possibility to identify which programming technique used to store the activity log into the database. Next identify which algorithm is opt to implement the intrusion detection and prevention system by using big data even the network is wired, wireless or cloud network
Cerebral microbleeds: from depiction to interpretation

Cerebral microbleeds (CMBs) are defined as hypointense foci visible on T2*-weighted and susceptible-weighted MRI sequences. CMBs are increasingly recognised with the widespread use of MRI in healthy individuals as well as in the context of cerebrovascular disease or dementia. They can also be encountered in major critical medical conditions such as in patients requiring extracorporeal mechanical oxygenation. The advent of MRI-guided postmortem neuropathological examinations confirmed that, in the context of cerebrovascular disease, the vast majority of CMBs correspond to recent or old microhaemorrhages. Detection of CMBs is highly influenced by MRI parameters, in particular field strength, postprocessing methods used to enhance T2* contrast and three dimensional sequences. Despite recent progress, harmonising imaging parameters across research studies remains necessary to improve cross-study comparisons. CMBs are helpful markers to identify the nature and the severity of the underlying chronic small vessel disease. In daily clinical practice, presence and numbers of CMBs often trigger uncertainty for clinicians especially when antithrombotic treatments and acute reperfusion therapies are discussed. In the present review, we discuss those clinical dilemmas and address the value of CMBs as diagnostic and prognostic markers for future vascular events.
Experimental and Numerical Investigation of Spray-Drying Parameters on the Dried Powder Properties of Ginkgo biloba Seeds

The operating parameters for spray drying play a significant role in determining the properties of the dried product. Ginkgo biloba (GB) seeds are a popular food and additive in the food and medicine market; in this study we spray dried these seeds. According to the L1644 orthogonal experimental design, a total of 16 tests was performed in a compact spray dryer. Parameters, i.e., inlet air temperature, air flow rate, outlet air temperature, and feed concentration, were selected as affecting factors. The dried product was evaluated with respect to residual moisture content, color difference, mean particle size, and protein content. The effects of these four parameters on the product properties were investigated. The experimental results showed that operating air temperature, especially the outlet air temperature, significantly affected the residual moisture and protein content in the dried product. High outlet air temperature can reduce the product quality. On the other hand, the mean particle size of GB powder was mainly affected by the feed concentration. Increasing feed concentration increased mean particle size. Color difference of GB seed powder was significantly affected by the inlet and outlet air temperatures and feed concentration. Furthermore, the optimal conditions of GB seed spray drying, i.e., inlet air temperature 190°C, air flow rate 120 m3/h, outlet air temperature 110°C, feed concentration 10%, were obtained from an L1644 orthogonal experiment. At last, correlation equations for GB seed powder properties with operating parameters were obtained using multivariable regression analysis.
POLYSACCHARIDES AND pH EFFECTS ON SODIUM-MONTMORILLONITE: FLOCCULATION, DISPERSION, AND RHEOLOGICAL PROPERTIES

Addition of polysaccharides to soils or their formation in situ through microbial activity increases the stability of soil aggregates. Like other components of soil organic matter, polysaccharides are expected to affect strongly the flocculation and dispersion behavior of clays, which are the most important soil component. The influence of polysaccharide concentration, molecule characteristics, and pH, on flocculation and rheological properties of Na-montmorillonite were studied. Flocculation values (FV) increased with increasing concentrations of the anionic polysaccharide polygalacturonic acid (PGA). The pattern was similar at pH 4 and 8: a relatively sharp increase in FV with small additions of PGA up to a concentration of 10 mg L−1 (≈136.0 and 160 mmol L−1) and a slight to moderate increase thereafter. At pH 6 a different pattern was observed. The FV changes at the lower PGA concentrations (up to 10 mg L−1) were similar to those observed for pH 4. At higher concentrations, a further sharp increase in the FV was found. Addition of 1 mg L−1 PGA at pH 10 resulted in a FV increase to 37.0 mmol L−1. For higher PGA concentrations, the FV remained basically unchanged. Results obtained by measurements of the rheological properties of suspensions to which PGA was added provided additional proof for the clay-PGA interaction and followed similar trends. The Na-montmorillonite suspension exhibited non-Newtonian flow properties. When PGA was added to the Na-montmorillonite suspension, the flow behavior gradually changed from non-Newtonian to Newtonian as the PGA concentration increased. The mechanisms proposed to explain the interactions between Na-montmorillonite and the anionic polysaccharide (PGA) are (i) edge charge reversal and (ii) mutual flocculation (heteroflocculation). These mechanisms are similar to those proposed previously for suspensions of Na-montmorillonite to which humic substances (HS) were added. The dissimilarities are, apparently, a result of the differences between structure and characteristics of the molecules of these organic materials. Additions of noncharged polysaccharides (dextrans) resulted in completely different responses. Additions of the low molecular weight dextran (T-40, MW 40000) did not result in significant changes in the FVs of Na-montmorillonite. In contrast, two neutral polysaccharides tested (T-500, MW 500,000 and T-2000, 2,000,000) enhanced clay flocculation and consequently caused a sharp decrease in the FV.
A fundamental feature scale model for low pressure deposition processes

The integro‐differential equations which govern free molecular flow and low pressure chemical vapor deposition in long rectangular trenches are reviewed. The model equations are used to simulate the deposition of tungsten by hydrogen reduction of tungsten hexafluoride, with reactive sticking coefficients determined by local deposition conditions. Numerical solution of the governing equations provides film profiles and deposition rate profiles as a function of position in the trench at any time until the trench mouth closes. The impact of the operating conditions on step coverage is discussed in relation to reactive sticking factors. Calculated tungsten step coverages for three selected realistic initial trench shapes highlight the importance of establishing a consistent method for reporting step coverages. We introduce the percentage of feature fill as a measure of step coverage. To allow evaluation of the quantitative predictive ability of a model, cross‐sectional scanning electron micrographs are requir.
SILDENAFIL AMELIORATES CISPLATIN-INDUCED NEPHROTOXICITY IN WISTAR RATS

The purpose of this study was to investigate the possible protective effect of SIL against CP-induced nephrotoxicity. Twenty four male Wistar albino rats were classified into four equal groups; normal group, SIL group which received SIL twice (10 mg/kg; i.p. twice; 30 minutes and just before saline administration), CP group which received a single dose of CP (7.5 mg/kg; i.p.) and treated with saline, and CP + SIL group which was treated with the same previous doses. Treatment with SIL was given 2 days before and 4 days after CP administration. On day 4 after CP administration, inulin and paraaminohippurate (PAH) clearances were performed and blood was withdrawn for determination of urea and creatinine. Thereafter, rats were euthanized and kidneys were dissected out for determination of the tissue levels of reduced glutathione (GSH), malondialdehyde (MDA), superoxide dismutase (SOD) and glutathione peroxidase (GPx) as well as TNF-α level. CP induced renal injury was evidenced decrease in both inulin and PAH clearances and manifested by significant increase in urea and creatinine levels. Moreover, renal injury was associated with decreased renal tissue activities of SOD, and GPx as well as GSH level. Moreover, renal tissue contents of MDA and TNF-α level were increased. Alterations in these biochemical indices of oxidative stress and inflammation due to CP were attenuated by SIL treatment. In conclusion, SIL treatment can protect rats from CP-induced nephrotoxicity through improving glomerular filtration rate and renal blood flow. This ameliorative effect can be partially attributed to its antioxidant and antiinflamatory effects.
Model-Based Optimisation with Tree-structured Parzen Estimation for Discrete Event Simulation at Container Terminals

: Traditionally discrete event simulation serves as a tool for estimating the impact of managerial decisions at container terminals. For complex situations, simulation-based optimisation can be employed to lower the amount of executed simulation experiments and therefore reduce the time to obtain results. The search is guided by the already run experiments. This paper examines the applicability of the Tree-structured Parzen Estimator for optimizing simulation models of container terminals. The reasoning is based both on prior literature in the field of simulation and machine learning, as well as a numerical study. Within the experiments, the performance of the Tree-structured Parzen Estimator is compared with Simulated Annealing and Random Search. The Tree-structured Parzen Estimator displays a more explorative and robust search behaviour than Simulated Annealing.
The DLPS: A New Framework for Benchmarking Blockchains

Distributed Ledger Technologies (DLT) promise to revolutionize business ecosystems by permitting secure transactions without intermediaries. A widely recognized challenge that inhibits the uptake of DLT is scalability and performance. Hence, quantifying key metrics such as throughput and latency is crucial for designing DLT-based infrastructures, applications, and ecosystems. However, current benchmarking frameworks for blockchains 1 do not cover the whole benchmarking process; impeding transparent comparisons of different DLT networks. In this paper, we present the Distributed Ledger Performance Scan (DLPS), an open-source 2 framework for end-to-end performance characterizations of blockchains, addressing the need to transparently and automatically evaluate the performance of highly customizable conﬁgurations. We describe our new framework and argue that it signiﬁcantly improves existing DLT benchmarking solutions. To demonstrate the capabilities of the DLPS, we also summarize the main results obtained from a series of experiments that we have conducted with it, giving a ﬁrst comprehensive comparison of essential scalability properties of several commonly used enterprise blockchains.
Influence of Family Background Characteristics on Parent-Child Sexual Health Communication in South-West Nigeria

Adolescents' sexual health has become a global public health concern especially in the developing countries. Undoubtedly, parent-child communication remains a vital means by which parents transmit cultural values and monitor the sexual health of their children. Indeed, the extent and the quality of the manner in which parents are involved in their children’s lives are critical factors in the prevention of sexual risk-taking behaviour among adolescents. This paper investigated the influence of parents knowledge, occupation and religion on their communication of sexual health issues with their adolescents among parents in South West Nigeria. The study elicited information from 721 selected respondents, comprising fathers and mothers who had at least, an adolescent in the household; through a multi-stage sampling technique. The study found that most respondents, 78.2% had proper perception of sexual health communication. Parent-child sexual health communication was significantly associated with parents’ occupation (χ2=7.212), and religion (χ2= 6.850). In addition, being self-employed and traditional religion were significant predictors of parent-child sexual health communication in their reference category (OR=0.651, P =0.030<0.05 and OR=0.26, P =0.028<0.05 respectively). Socio-cultural factors still greatly impedes parentchild sexual health communication, despite parents’ proper perception of the need for it. Emerging interventions by all stakeholders need to focus on repositioning parents for early positive parent-child family conversations on sexual health issues coupled with work life and involvement in religious activities that promote parents’ physical and mental preparedness for communication on sexual health
A Clinical Journey Mobile Health App for Perioperative Patients: Cross-sectional Study

Background Mobile eHealth apps are important tools in personal health care management. The Patient Journey app was developed to inform patients with musculoskeletal disorders during their perioperative period. The app contains timely information, video exercises, and functional tasks. Although the Patient Journey app and other health apps are widely used, little research is available on how patients appreciate these apps. Objective The primary aim of this study was to evaluate the user-friendliness of the Patient Journey app in terms of its usability and the attitudes of users toward the app. The secondary aim was to evaluate positive and negative user experiences. Methods A web-based questionnaire was sent to 2114 patients scheduled for surgery for a musculoskeletal disorder. Primary outcomes were usability (measured with the System Usability Scale) and user attitudes regarding the Patient Journey app (assessed with the second part of the eHealth Impact Questionnaire). The secondary outcomes were evaluated with multiple choice questions and open-ended questions, which were analyzed via inductive thematic content analyses. Results Of the 940 patients who responded, 526 used the Patient Journey app. The usability of the app was high (System Usability Scale: median 85.0, IQR 72.5-92.5), and users had a positive attitude toward the Information and Presentation provided via the app (eHealth Impact Questionnaire: median 78.0, IQR 68.8-84.4). The app did not adequately improve the users’ confidence in discussing health with others (eHealth Impact Questionnaire: median 63.9, IQR 50.0-75.0) or motivation to manage health (eHealth Impact Questionnaire: median 61.1, IQR 55.6-72.2). Three core themes emerged regarding positive and negative user experiences: (1) content and information, (2) expectations and experiences, and (3) technical performance. Users experienced timely information and instructions positively and found that the app prepared and guided them optimally through the perioperative period. Negative user experiences were overly optimistic information, scarcely presented information about pain (medication), lack of reference data, insufficient information regarding clinical course deviations and complications, and lack of interaction with clinicians. Conclusions The Patient Journey app is a usable, informative, and presentable tool to inform patients with musculoskeletal disorders during their perioperative period. The qualitative analyses identified aspects that can further improve the user experiences of the app.
Factors associated with length of stay in hospital among the elderly patients using count regression models

Background: Aging is a major challenge not only for high-income countries but also for middle- and low-income countries. The length of stay (LOS) in hospitals is one of the major concerns of elderly patients, which should be taken into consideration. We aimed to investigate the factors affecting LOS of elderly patients admitted to a referral hospital of northeast of Iran. Methods: A relatively large population of 7130 hospitalized elderly patients (over 65 years old) who referred to Ghaem hospital (Mashhad, Iran) from March 20, 2016 to March 19, 2017 were selected. The demographic and medical records data of patients were extracted from the hospital database. Univariate analyses as well as count regression models, including poisson regression and negative binomial regression, were conducted to assess the influential factors on the LOS and the number of admissions considered for potential confounders using SAS software. In this study α =0.05 was considered as statistically significant. Results: The mean age of participants was 76.57±7.29 years, and 54.8% were male and 45.2% were female. The mean LOS was 8.11±13.97 days and the mean number of admissions 1.5±1.73 times. The negative binomial regression model had better fitness than Poisson's model. Findings indicated that emergency hospitalization (RR: 0.21), admission to the CCU (RR: 0.33), and male gender (RR: 0.92) were statistically reducing factors for LOS among elderly patients, respectively. Discharge status (deceased, RR: 1.50), patients with diagnosis of injuries and poisoning (RR: 1.34), and native residence (RR: 1.10) were factors that statistically increased the length of stay among hospitalized elderly patients. Conclusion: LOS in hospitals is affected by multiple factors and the negative binomial regression model is a better statistical method for estimating the influencing factors.
Towards Degree Distribution of a Duplication-Divergence Graph Model

We present a rigorous and precise analysis of degree distribution in a dynamic graph model introduced by Solé, Pastor-Satorras et al. in which nodes are added according to a duplication-divergence mechanism. This model is discussed in numerous publications with only very few recent rigorous results, especially for the degree distribution. In this paper we focus on two related problems: the expected value and variance of the degree of a given node over the evolution of the graph and the expected value and variance of the average degree over all nodes. We present exact and precise asymptotic results showing that both quantities may decrease or increase over time depending on the model parameters. Our findings are a step towards a better understanding of the graph behaviors such as degree distributions, symmetry, power law, and structural compression.