Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of Nasal Testosterone Gel (Natesto®) Stratified by Baseline Endogenous Testosterone Levels

Abstract Objective Pharmacokinetic and efficacy data from a phase 3 testosterone nasal gel (TNG) study were stratified by baseline endogenous testosterone level in patients with testosterone deficiency. Total testosterone (TT), LH, and FSH levels, as well as erectile function, mood, and lean body mass for each group were compared. In a subset of patients with very low baseline endogenous testosterone levels (<100 ng/dL), we investigated whether TNG is a suitable treatment option. Materials and Methods Patients with testosterone deficiency (serum TT <300 ng/dL) were treated with TNG for 3 months, followed by safety extension periods of 90 and/or 180 days. Pharmacokinetic parameters were calculated from serum hormone levels on days 30 and 90, along with efficacy measurements, which were analyzed by comparison with baseline values. Baseline and/or predose TT values were used for patient stratification. Results Prestudy and predose endogenous testosterone concentrations correlated. The maximal concentration of TT was nearly identical across all cohorts at days 30 and 90, whereas the average concentration over 24 hours had a slight positive dependence relative to predose levels. LH levels remained in the normal range but were decreased more in patients with higher starting baseline levels. These findings indicate that TNG works with an active hypothalamic-pituitary-gonadal axis that responds to each dose of TNG throughout the treatment period. Patients with the lowest endogenous testosterone levels received maximum exposure impact from each TNG dose. Patients with severe testosterone deficiency had similar efficacy improvements as the remainder of the study population. Conclusion All testosterone-deficient cohorts were successfully treated with TNG.
Password Hashing Algorithms - From Past to Future

Password is a simple technique users do use to protect and authenticate their private information. It is employed popularly as an effective way in most computer applications and services in the early information security. Afterwards, due to the weakness of length and some bad habits of users make password be vulnerable to attacks, the key stretching and especially, password hashing techniques were presented to fix the problems and provide a stronger protecting. Both have hash functions within as major elements. However, hash functions just focus on hashing and storing password, not security at all, that leads to the need of more effective and secure techniques. Several advanced password hashing schemes were proposed, such as PBKDF2, bcrypt, and scrypt. Recently, Password Hashing Competition (PHC) was occurred to give more supplementary choices, besides the three mentioned schemes above, that are suitable for widespread adoption of modern information era. Along with other effective algorithms, the final PHC winner, Argon2, is a remarkable secure design which shows high performance and high resistance against most known attacks. In this paper, we give a general overview about password hashing techniques from beginning to present, measures and evaluates security aspects of recent password hashing schemes (through Password Hashing Competition), and also makes a prediction of future password hashing in information era so far.
BigPromises: using organisational mindfulness to integrate big data in emergency management decision making

Abstract Many governmental agencies are currently engaged in activities to improve access to information which are encouraging recent Big Data (BD) trends. However, determining and demonstrating the benefit to emergency management information systems (EMIS) users has yet been realised. EMIS are critical to navigating a complex network of disparate IS used for real time, coordinated decision-making among agencies. Two significant challenges to incorporating BD analytical, visual, and predictive capabilities are mutual acceptance and system integration. This paper looks at the underlying principles of organisational mindfulness (OM) demonstrated in the convergence of EM processes, systems, and organisations in decision-making. The investigation proposes a design science research in information systems (DSRIS) approach to build an IS artefact for EMIS evaluation. Critical immediacy of information and decisions require adherence to processes which build capabilities to support EMIS users. This adds a layer of complexity to ISDT for EMIS, yet may equally lead to broader BD acceptance and integration.
Consistency in biomarkers expression between matched tissue microarray cores from primary gallblader and ovarian cancers

Introduction. Tissue microarray (TMA) technique has been widely used, especially in immunohistochemical assays of new prognostic and predictive markers. The main objections raised by its opponents are the small amount of sampled material and the associated risk of inadequate assessment of analysed expression, resulting from the potential heterogeneity of tumour tissue. Material and methods. This study evaluated the compatibility of biomarker expression in two independent tissue cores, 1.5 mm in diameter, obtained by TMA technique from patients with gallbladder cancer (ERb, cytoPgR, HER2, CTGF) and ovarian cancer (PTEN, BCL2, PIK3CA, IGF1R). Comparison of the expression of individual biomarkers between cores was performed using the intraclass correlation coefficient (ICC), assuming a kappa Results. Evaluation of biomarker expression in the primary tumour was performed in 60 patients with gallbladder cancer and in 64 patients with high-grade serous ovarian cancer. Additionally, in patients with follicular cancer, the expression of the tested markers was assessed in the epithelium free from neoplastic malignancy. In both tumours, a good or sufficient level of homogeneity was observed in the expression of the analysed biomarkers between tissue cores. The correlation coefficient for the expression of individual markers in gallbladder cancer and adhering healthy tissue was: 0.68 (95% CI: 0.53–0.79)/0.62 (95% CI: 0.39–0.78) for ERb, 0.44 (95% CI: 0.23–0.61)/0.77 (95% CI: 0.61–0.87) for cytoPgR, 0.77 (95% CI: 0.65–0.85)/0.66 (95% CI: 0.44–0.80) for HER2, and 0.68 (95% CI: 0.53–0.79)/0.62 (95% CI: 0.39–0.78) for CTGF. In patients with ovarian cancer, the correlation coefficient within the primary tumour was 0.82 (95% CI: 0.71–0.89) for PTEN, 0.84 (95% CI: 0.75–0.90) for BCL2, 0.71 (95% CI: 0.56–0.81) for PIK3CA, and 0.77 (95% CI: 0.65–0.85) for IGF1R. Conclusions. Tissue microarray technique allows reliable assessment of the expression of tissue biomarkers within the primary tumour of gallbladder cancer and ovarian cancer.
A Contemporary’s Botany Worksheet Proposal Supported by Bloom’s Taxonomy

Worksheet is a typical device for educators directing understudies learning a subject. This examination plans a programmed worksheet generator for building a worksheet as indicated by the aftereffects of understudy's online test. We center around the worksheet of herbal science perception and investigate 47 unique exercises in the worksheet. The connection between the worksheet movement and its subjective capacity can be utilized to choose what action ought to be built dependent on understudies' test outcome. A basic precedent is shown toward the finish of this paper.
Industrial Intelligent Forecast of TFT-LCD Based on R-SVM

TFT-LCD is a kind of thin film transistor liquid crystal display. The sampling test method is used to estimate the quality of the whole sample, but this method is not comprehensive and has no timeliness. The author hope that machine learning can be used to make a reasonable prediction of product quality through each process data. This paper mainly adopts the combination of SVM and random forest to form a new method: random SVM(R-SVM). Multiple SVM models were established by random sampling and number of features, and the predicted values of multiple models were averaged to obtain the final results. The evaluation standard is the mean square error(MSE). Experimental results show that R-SVM is better than traditional machine learning algorithms. As is known to as all the random forest performs best in the traditional machine learning algorithm. The MSE of our experimental results is 0.6 percentage lower than that of the random forest. The research method of this paper have brought new research ideas for industrial data prediction for the future. It also provides an opportunity for the combination of random forest and other traditional algorithms.
The role of extrinsic noise in biomolecular information processing systems : an in silico analysis

The intrinsic stochasticity of biomolecular systems is a well studied phenomenon. Less attention has been paied to other sources of variability, so called extrinsic noise. While the precise definition of extrinsic noise depends on the system in question, it affects all cells and its significance has been demonstrated experimentally. Information theory provides a rigorous mathematical framework for quantifying both the amount of information available to a signalling system and its ability to transmit this information. Intracellular signal transduction remains a relatively unexplored frontier for the application of information theory. In this thesis, we rely on a metric called mutual information to quantify information flow in models of biochemical signalling systems. After briefly discussing the theoretical background and some of the practical difficulties of estimating mutual information in Chapter 2, we apply it in the context of simplified models of intracellular signalling, referred to as motifs. Using a comprehensive set of two-node motifs we explore the effects of extrinsic noise, model parameters and various combinations of interaction, on the system’s ability to transmit information about an input signal, represented by a telegraph process. Our results illustrate the importance of the system’s response time and demonstrate a trade-off in transmitting information about the current state of the input or its average intensity over a period of time. In Chapter 4, we address the problem of determining the magnitude of extrinsic noise in the presence of intrinsic stochasticity. Using the Approximate Bayesian Computation sequential Monte Carlo algorithm, together with published experimental data, we infer parameters describing extrinsic noise in a model of E. coli gene expression. Lastly, in Chapter 5, we construct and analyse models of bacterial twocomponent signalling, bringing together insights gleaned from earlier work. The results show how the abundances of different molecular species in the system may transmit information about the input signal despite its stochas-
Visual Analytics for Spatiotemporal Cluster Analysis

Traditionally, visualization is one of the most important and commonly used methods of generating insight into large scale data. Particularly for spatiotemporal data, the translation of such data into a visual form allows users to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena that would not be apparent in tabular form. However, several critical challenges arise when visualizing and exploring these large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex. To simplify the visual representations, analytical methods such as clustering and feature extraction are often applied as part of the classification phase. The automatic classification can then be rendered onto a map; however, one common issue in data classification is that items near a classification boundary are often mislabeled. This thesis explores methods to augment the automated spatial classification by utilizing interactive machine learning as part of the cluster creation step. First, this thesis explores the design space for spatiotemporal analysis through the development of a comprehensive data wrangling and exploratory data analysis platform. Second, this system is augmented with a novel method for evaluating the visual impact of edge cases for multivariate geographic projections. Finally, system features and functionality are demonstrated through a series of case studies, with key features including similarity analysis, multivariate clustering, and novel visual support for cluster com-
Quality of life for Alaskan individuals with FASD and their families

Fetal alcohol spectrum disorder (FASD) is a lifelong disability caused by prenatal exposure to alcohol. The effects of FASD include a range of physical, mental, behavioral, and learning disabilities. These disabilities impact quality of life, not only for the affected individual, but for family members. The effects from FASD ripple into schools, the correctional system, and throughout rural and urban communities. Although there are no reliable statistics available on FASD in Alaska, many professionals in the field believe Alaska to have the highest rate of FASD in the United States. This research has explored the ways in which prenatal exposure to alcohol affects quality of life for Alaskan individuals and their families. For this study, I have defined quality of life as the multi-faceted evaluation of the individual's personal experiences and life satisfaction, including health, psychological and social indicators. Since the identification of fetal alcohol syndrome (FAS) and FASD, many studies have analyzed the effects of prenatal alcohol exposure, as well as possible interventions. Few studies have investigated how prenatal alcohol exposure affects the individual's quality of life and even fewer studies have analyzed how raising one or more children with FASD affects the family. To address the gap in the literature, this research applied social constructivist theory and employed a qualitative design, using semi-structured interviews to explore individuals' and parents' life stories and perceptions on how FASD has affected their lives. I interviewed eight individuals with FASD and 14 adoptive or long-term foster parents. Findings indicate that FASD impacts almost every facet of the lives of both the individuals affected and their families. All individuals with FASD interviewed for this project suffered serious adverse childhood experiences in addition to their prenatal exposure to alcohol. All encountered academic and social difficulties at ii school. Individuals struggled in their transitions to adulthood, with some individuals needing assistance from parents or social services throughout their lifespan. Parents expressed their ongoing need for structure within the home and the continual need to advocate for suitable services for their children. They described how the ongoing stress of raising their child(ren) with FASD affected their social lives, employment and even their marriages. The perspectives and insight of these individuals with FASD and their parents can provide other family members, service providers and legislators a better understanding of how FASD affects quality of life and assist decision makers in making informed choices on how to best provide expanded or improved supportive services to these individuals and families whose everyday struggles go largely unrecognized by the general public.
Constructivism And The Technology Of Instruction A Conversation

Thank you very much for downloading constructivism and the technology of instruction a conversation. As you may know, people have look hundreds times for their chosen readings like this constructivism and the technology of instruction a conversation, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their laptop.
Online short course for learning management system training in an African university of technology

With the use of technology in the classroom growing every year, lecturers are expected to blend facilitations by making use of learning management systems. At the Central University of Technology, Free State this poses a problem for the learning designers in charge of training lecturers in educational technologies as there is limited space and time to not only teach lecturers the skills, but also to apply them in their own contexts. The purpose of this paper is to present participant perspectives of an online short course in which they learned how to use Blackboard and other educational technologies needed for blending teaching and learning. An exploratory design was implemented with descriptive statistics involving quantitative analysis of the collected data to determine the participants’ perspective on their skills gained. An online survey tool was used to collect the data. Eighty-four percent of particpants felt that they were able to communicate via the system’s announcements, 82% felt confident that they would be able to customize the space and 94% felt that they were now able to add content, such as files. When asked if they felt like they learned the skills they expected from the online course, 94% of particpants replied positively. Keywords: e-learning; learning management system; Blackboard; instructional design; online course; educational technology.
Artificial Intelligence: Current Issues and Applications

This chapter explains the Artificial Intelligence (AI) techniques in terms of Artificial Neural Networks (ANNs), fuzzy logic, expert systems, machine learning, Genetic Programming (GP), Evolutionary Polynomial Regression (EPR), and Support Vector Machine (SVM); the AI applications in modern education; the AI applications in software engineering development; the AI applications in Content-Based Image Retrieval (CBIR); and the multifaceted applications of AI in the digital age. AI is a branch of science which deals with helping machines find the suitable solutions to complex problems in a more human-like manner. AI technologies bring more complex data-analysis features to the existing applications in various industries and greatly contribute to management’s organization, planning, and controlling operations, and will continue to do so with more frequency as programs are refined.
Dynamic Analysis and Simulation of 6-DOF Industrial Robot

The 6-DOF industrial robot was studied and Newton-Euler method was used to analyze the robot's dynamics.Based on the analyzed result,inverse dynamic equation of the robot was obtained.The dynamic simulation model was built by ADAMS software.The result of simulation not only validates the theoretical computation but also lays a foundation for the research on dynamic characteristics of robots and their control methods.
Attitude towards and Interest in Dog-Assisted Interventions of Students in Higher Education

The mental health of students is a critical issue facing institutions of higher education, as a majority of college and university students report suffering from stress and anxiety. Studies have shown that interacting with animals can enhance the mental health. Consequently, animal-assisted interventions can be used to cover the concerns of students. This study evaluated German university students´ attitude towards dogs, animal-assisted interventions and their interest in its use at their own university. Additionally, possible predictors were investigated. Another aim was the validation of the questionnaire. 560 university students answered a questionnaire consisting of the Coleman Dog Attitude Scale (C-DAS), a modification of the Attitude Towards Animal-Assisted Therapy Scale and a translated and modified version of the Cuestionario de Actitudes ante las Intervenciones Asistidas por Perros (CAINTAP). Results showed a slightly positive attitude towards dogs, animal-assisted interventions and interest in its use at the university. Furthermore, students´course of studies and sex have proven to be predictors. Thus, especially female students of educational science would welcome an implementation of animal-assisted interventions at the university. The questionnaire showed high quality with a Cronbach’s alpha α = . 936.
Nonreflecting Boundary Conditions Obtained From Equivalent Sources For Time-Dependent Scattering Problems

In many engineering applications, scattering of acoustic or electromagnetic waves from a body of arbitrary shape is considered in an infinite medium. Solving the underlying partial differential equations with a standard numerical method such as finite elements or finite differences requires truncating the unbounded domain of definition into a finite computational region. As a consequence, an appropriate boundary condition must be prescribed at the artificial boundary. Many approaches have been proposed for this fundamental problem in the field of wave scattering. All of them fall into one of three main categories. The first class of methods is based on mathematical approximations or physical heuristics. These boundary conditions are easy to implement and run in short computing times. However, these approaches give rise to spurious reflections at the artificial boundary, which travel back into the computational domain and corrupt the solution. A second group consists of accurate and convergent methods. However, these formulations are usually harder to implement and often more expensive than the computation of the interior scheme itself. Finally, there are methods which are accurate and fast. The drawback of these approaches lies in the fact that the outer boundary must be taken to be either a sphere, a plane, or a cylinder. For many applications of interest, this may require use of a computational domain much larger than actually needed, which leads to an expensive overall numerical scheme. This work introduces a new methodology in order to compute the fields at the artificial boundary. Like the second class of methods described above, the proposed algorithm is accurate and numerically convergent, yet its computational cost is less than the underlying portion of the volumetric calculation. And, unlike the third category, this new approach allows us to choose the artificial boundary to be arbitrarily close to the scatterer. This method is based on a novel concept of "equivalent source' representations which allows a highly accurate and fast evaluation of the boundary condition.
Intelligent Search on Integrated Knowledge Base of Traditional Chinese Medicine

To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machine-understandable resources so that intelligent search can be supported. TCMSearch, a deployed intelligent search engine for traditional Chinese medicine (TCM), is presented. The core of the system is an integrated knowledge base that uses a TCM domain ontology to represent the instances and relationships in TCM. Machine-learning techniques are used to generate semantic annotations for texts and semantic mappings for relational databases, and then a semantic index is constructed for these resources. The major benefit of representing the semantic index in RDF/OWL is to support some powerful reasoning functions, such as class hierarchies and relation inferences. By combining resource integration with reasoning, the knowledge base can support some intelligent search paradigms besides keyword search, such as correlated search, semantic graph navigation and concept recommendation.