Miracle-dz/UniFAR-FTUs huggingface dataset:
Public Procurement Act 2007 and Contracting Business in Nigeria (Case study of Public Sector organizations)

In Nigeria, the Public Procurement Act 2007 (PPA 2007) was enacted to drive and sanitize the implementation of the procurement of goods and services required. However, the effect of public procurement Act 2007 on contracting firms when tendering for a construction projects are capable of impeding the contracting business in Nigeria. In the context of eligibility documents, bidding expenses and contract administration dynamics, the impacts of implementation of the PPA 2007 requires investigation. The answer to this research question is necessary for government to evolve proactive laws, set up regulatory mechanisms to avoid the collapsing of indigenous contracting firms. Survey research design was employ to obtain data, using questionnaire distributed to contracting firms in North-Western region of Nigeria, stratified random sampling method was used to draw the sample size of 272 out of 852 of the population. The primary analytical technique used was Structural Equation Modeling (SEM) to yield information on Goodness-of-Fit, model development and comparison, and confirmatory strategies. SPSS Amos 21 was used for data analysis and model. The results of this study demonstrate that the Public Procurement Act has very high impact on contracting business in terms of the bidding process, documentation and administration of contract. Also, the act has made some impact by increasing transaction costs of doing contracting business in Nigeria despite the fact that it ensures efficiency and effectiveness in awarding of contracts to contractors and consultants. The paper concludes that contracting firms should always abide by the rules and regulation guiding the bidding process in MDAs, this will increase their chances of winning tender in the bidding process. Minimum requirement should be solicited from contracting firms by the public organization (MDAs). Finally an amendment of the Act should be carried out by the legislators in order to consider procurement timeframe flexibility, introduction of eprocurement system section, and other necessary changes.
Are Computational Thinking Skills Measurable? An Analysis

Acquisition of digital literacy constitutes, not only knowledge on the use of software and or hardware, but also knowledge of sophisticated skills (cognitive, motoric, sociological and emotional) which are essential for functioning effectively [1]. Computational Thinking (CT) skills and their acquisition by learners and assessing their competency level is important. Even though many countries have integrated CT into their education curricula, comprehensive assessment tools for these skills are still lacking [2]. This paper presents the analysis of various assessment tools and demonstrated the use of customised version of Cognitive Skills Framework (CSF), also known as Skill-based Theoretical Framework [1], to assess CT skills. The study was conducted among first year computer science international students with basic or no programming skills. Scratch programming language was used to develop projects and content delivered using design-based approach. The study showed that, as the learners progressed in the course, their competency also increased. The research concluded that CSF has the capability of assessing CT skills comprehensively.
An International Comparison of Blended Learning

This paper presents an investigation into the implementation of blended learning (BL) in several countries where it has been used in schools to enhance the learning outcomes of students through the utilisation of technology in education. BL means integrating traditional tools of teaching with technical tools such as online learning through websites, CDs, and videos. To implement a sustainable BL environment, an IT infrastructure must be built including PCs and network access. This enables students to connect to the internet during the class to access educational web-based content as well as to share discussions about subjects with their peers in and out of the class. Developed countries in different regions such as USA, Europe, and the Far East have applied BL in their educational systems.
Supplement to “Dynamic Belief Elicitation”

This supplement includes additional results omitted from the main paper. In Section S.1, we provide an algorithm that computes the payoffs for a simple instance of strategyproof protocol in the general setting of Section 4. In Section S.2, we show how to construct elicitation protocols for information structures involving potentially infinitely many time periods using menus with random deadlines. Sections S.3–S.5 are relevant to situations in which expert knowledge is solicited or evaluated for the purpose of helping decision makers. In Section S.3, we show that, subject to regularity conditions, the knowledge of high-order beliefs elicited by the protocols we study is sufficient to solve essentially any dynamic decision problem. In Section S.4, we argue that knowledge of these high-order beliefs is much needed when the decision environment is dynamic: we ask what decision problems can be solved using the classical methods that elicit only first-order beliefs, and show they form a degenerate class. Finally, in Section S.5, we illustrate our results in the context of simple principal-agent problems.
Approximate Generalized Inverses with Iterative Refinement for ε-Accurate Preconditioning of Singular Systems

We introduce a new class of preconditioners to enable flexible GMRES to find a least-squares solution, and potentially the pseudoinverse solution, of large-scale sparse, asymmetric, singular, and potentially inconsistent systems. We develop the preconditioners based on a new observation that generalized inverses (i.e., $\boldsymbol{A}^{g}\in\{\boldsymbol{G}\mid\boldsymbol{A}\boldsymbol{G}\boldsymbol{A}=\boldsymbol{A}\}$) enable the preconditioned Krylov subspaces (KSP) to converge in a single step. We then compute an approximate generalized inverse (AGI) efficiently using a hybrid incomplete factorization (HIF), which combines multilevel incomplete LU with rank-revealing QR on its final Schur complement. We define the criteria of $\epsilon$-accuracy and stability of AGI to guarantee the convergence of preconditioned GMRES for consistent systems. For inconsistent systems, we fortify HIF with iterative refinement to obtain HIFIR, which effectively mitigates the potential breakdowns of KSP and allows accurate computations of the null space vectors. By combining the two techniques, we then obtain a new solver, called PIPIT, for obtaining the pseudoinverse solutions for systems with low-dimensional null spaces. We demonstrate the robustness of HIF and HIFIR and show that they improve both accuracy and efficiency of the prior state of the art by orders of magnitude for systems with up to a million unknowns.
Numerical Investigation of Heat Transfer of Flow Over the Cylinder with High Conductivity Fins

Forced convective heat transfer, Cylinder fins, Nusselt number, Reynolds number. The laminar forced convection across a heated cylinder is an important problem in heat transfer. In this study, the problem of forced convective heat transfer of flow over horizontal cylinder with equally spaced fins with high conductivity has been numerically investigated on the outer surface. The effect of the number of fins and their height on the average Nusselt number was investigated for Reynolds numbers within the domain 5-2000. The effect of the fins on the forced convective heat transfer over isothermal horizontal cylinder was studied for numerous combinations of fins (F=0-14), the height of fins (H=0.5-3) and different Reynolds number values (ReD = 5-2000). Changing the average Nusselt number for a given value of Reynolds number due to the increased number of fins, the average Nusselt number at the same Reynolds number was normalized by the Nusselt number of finless cylinder and it was conducted because of the focus on the relatively effect of the increased fins. The results showed that there is an optimum number of fins to obtain the maximum Nusselt number for a given value of fins height and Reynolds number. Furthermore, the short fins decrease the Nusselt number at low Reynolds number.
Radiation Resistant Camera System for Monitoring Deuterium Plasma Discharges in the Large Helical Device

Radiation resistant camera system was constructed for monitoring deuterium plasma discharges in the Large Helical Device (LHD). This system has contributed to safe operation during two experimental campaigns without serious problems due to radiation (neutrons and gamma-rays). The cameras steadily functioned even in the plasma discharge with the maximum neutron emission rate in FY 2017, though some bright specks temporarily appeared on the images. The cameras have been installed in shield boxes which consist of lead boxes covered with 10% borated polyethylene blocks in all directions. For optimizing the design of the shield box, the radiation flux distribution was calculated by MCNP-6 code, which reveals the reduction of the radiation flux and the change of the energy spectra in the shield box. Thanks to the optimization, significant extension of the lifetime of the cameras has been realized. Investigation of the influence of the radiation on the CCD image sensor shows that the number of bright specks generally increases with the radiation flux to the camera, which also indicates that some bright specks disappear by the self-annealing process on the image sensor. This phenomenon also highly contributes to the further extension of the lifetime of the radiation resistant cameras. c © 2020 The Japan Society of Plasma Science and Nuclear Fusion Research
Allan time

Stability tests based on the Allan variance method have become a standard procedure for the evaluation of the quality of radio-astronomical instrumentation. They are very simple and simulate the situation when detecting weak signals buried in large noise fluctuations. Here a practical example will be presented on the basis of the Callisto frequency agile radio spectrometer. The result will be a numerical value called Allan-time which defines the suggested period of calibration.
Descriptive Modeling Uses K-Means Clustering for Employee Presence Mapping

: Human resource is valuable asset for an agency. The success of an institution is not only determined by the quality of its human resources, but also by the level of discipline. The discipline of an employee in an institution can be seen and measured by the level of attendance in doing a job, because the level of attendance is one of the factors that determine productivity. The current problem is the management level of the company that has difficulty in monitoring and controlling the employee attendance data. There needs to be a mapping and grouping to find out patterns of absence. Mapping or patterns that are obtained help management levels to monitor employees, take approaches and take action so as to improve employee discipline. In this study, it was used descriptive modeling with the implementation of the k-means clustering method. The results of the mapping obtained help the management level in controlling and monitoring as a reference for the next policy maker.
A Class of Generalized Dynamic Correlation Models

This paper proposes a class of parametric correlation models that apply a twolayer autoregressive-moving-average structure to the dynamics of correlation matrices. The proposed model contains the Dynamic Conditional Correlation model of Engle (2002) and the Varying Correlation model of Tse and Tsui (2002) as special cases and offers greater flexibility in a parsimonious way. Performance of the proposed model is illustrated in a simulation exercise and an application to the U.S. stock indices.
News Impact on Stock Trend

Stock market trend can be predicted with the help of machine learning techniques. However, the stock market changes is uncertain. So it is very difficult and challenging to forecast stock price trend. The main goal of this paper is to implement a model for stock value trend prediction using share market news by machine learning techniques. Although this kind of work is implemented for the stock markets of various developed countries, it is not so common to observe such kind of analysis for the stock markets of underdeveloped countries. The model for this work is built on published stock data obtained from DSE (Dhaka Stock Exchange, Bangladesh), a representative stock market of an underdeveloped country. The empirical result reveals the effectiveness of Convolutional Neural Networks with LSTM model.
4 Scenarios , Targets , Gaps , and Costs

With substantial increases in the global demand for energy services expected over the next century, energy technology holds the key to effective limitation of greenhouse gas emissions. Carbon-based fuels and their associated technologies supplied 88 percent of the world’s energy in 1995. Over the coming decades, technologies that provide energy services but emit little or no CO2 into the atmosphere will compete in the global marketplace with ever-improving carbon-emitting fossil-fuel technologies. How successfully they compete will determine future emissions. Figure 4.1 illustrates the nature of the challenge. The middle curve depicts the carbon dioxide emissions associated with the Intergovernmental Panel on Climate Change (IPCC) central scenario, denoted IS92a. This IPCC reference case scenario is based on analysis of trends in global population, economic growth, land use, and energy systems. The underlying energy and land use changes produce carbon emissions that grow steadily throughout the century from 7 petagrams of carbon (PgC) per year in 1990 to almost 20 PgC per year in 2100. Insert Figure 4.1 about here
The Wellbeing of Nations: A Multidimensional Mixture Distribution Analysis of Poor Nation-Rich Nation Status

Recent concerns about the measurement of wellbeing have led to the progress of nations to be classified and studied in a multidimensional context, unfortunately this compounds the difficulties encountered in categorizing groups and assessing progress. Here a feasible methodology for defining classes in terms of the commonality of behaviours of the actors in a multidimensional setting is presented and techniques for assessing poverty, inequality, polarization and mobility within and between groups are proposed and implemented for 164 countries over the period 1990-2014 in that many variable setting without arbitrarily defining frontiers. The analysis detected a slowly evolving, relatively immobile world, over the period the poor group appears to have diminished in size (which may be interpreted as a reduction in the poverty rate and is reflective of some upward mobility). There appears to have been some reduction in inequality both within and between groups over the period though they appear to have become more polarized.
Enhancement of the Gas Extraction for Reservoir Identification in a New Mud Logging System

The TRU-Vision system, developed by Baker Hughes, analyzes the formation gas extracted from the drilling mud to estimate the hydrocarbons content in the drilled rock formation. Several separation processes were surveyed in order to improve the gas extraction at the gas trap, namely, mechanical stirring, vacuum, air sparging, membranes, ultrasounds and cyclones. Stirring devices (a propeller, a flat-blade turbine, two baffles sets), a vacuum generator, and an air bubble generator were designed and assembled to increase the efficiency and stability of the TRU-Vision response.
AAAI 2020 Tutorial on Fairness and Bias in Peer Review and other Sociotechnical Intelligent Systems (Part II on Peer Review)

Peer review is the backbone of scholarly research, but it faces a number of challenges pertaining to bias and unfairness. There is an urgent need to improve peer review. This AAAI tutorial (part 2) discusses several problems, empirical studies, proposed solutions, and open problems in this domain. This document serves to provide a summary and references for the tutorial.
Efficacy of surgical treatment in patients with cervical spondylotic myelopathy

Background/Aim. Treatment options for cervical spondylotic myelopathy (CSM) are the topic for discussion due to the lack of controlled randomized prospective studies. Also, the natural history of CSM is unpredictable and the efficacy of surgical decompression is still controversial. The aim of this prospective study was to describe the results of surgical treatment of patients with CSM in a single institution. Methods. Fifty-nine patients with symptomatic CSM were enrolled in this single-center prospective study. At the end of the follow-up period of 12 months, 50 patients were analyzed. All patients were operated on; surgical decompression was performed by anterior or posterior surgical approach. Outcome evaluations were obtained preoperatively and 12 months postoperatively by using the following outcome measures: the modified Japanese Orthopedic Association (mJOA) scale, the Nurick score and the Neck Disability Index (NDI). The functional recovery ratio was calculated postoperatively by using Hirabayashi? s formula. Results. According to our results, significant improvements were detected in all outcome variables (mJOA score, Nurick score and NDI). Also, a statistically significant improvement was observed in all three categories of patients according to the preoperative mJOA score (mild, moderate, severe). Twenty-three patients (46%) had a satisfactory functional recovery, while twenty-seven (54%) had an unsatisfactory functional recovery rate. Conclusion. Surgical treatment of CSM is a very effective treating method and it resulted in a significant improvement in all outcome measures for a 1-year follow-up period. New studies could be recommended to evaluate the course of the disease, define the optimal surgical strategy, and better determine surgical outcome predictors.
How to Choose Tuning Parameters in Lasso and Ridge Regression?

This paper gives a literature review of choosing tuning parameters for ridge regression and lasso. These regularized regressions introduce a little bias in return for a considerable decrease in the variance of the predicted values, thus increasing prediction accuracy. We can use AIC or BIC to select the tuning parameter for linear predictive models. However, for general predictive models, cross-validation and bootstrap work better because they directly estimate prediction error. Though, cross-validation is more widely used than bootstrap. An empirical example is employed to illustrate how cross-validated lasso works. It shows that lasso may solve the multicollinearity problem.
A GENERAL PURPOSE SCALE-INDEPENDENT MCMC ALGORITHM

We develop a new eﬀectively adaptive, general purpose MCMC for arbitrary continuous distributions and correlation structure. We call this MCMC the t-walk . The t-walk maintains two independent points in the sample space, and all moves are based on proposals that are then accepted with a standard Metropolis-Hastings acceptance probability on the product space. Hence the t-walk may be viewed as an adaptive MCMC sampler that maintains a set of two points in the state space and moves them with some structure. However the t-walk is strictly not adaptive on the product space, but does display beneﬁcial self-adjusting behavior on the original state space. Four proposal distributions, or ‘moves’, are given resulting in an algorithm which is eﬀective in sampling distributions of arbitrary scale, without the requirement for further tuning of parameters. Several examples are presented showing good mixing and convergence characteristics, varying in dimensions from 1 to 200 and with radically diﬀerent scale and correlation structure, using exactly the same sampler.
PID-controlled Moving Objects Spatio-Temporal Model Algorithm for Identifying the Location of a Mobile Object in Real-time

Triangulation is a typical method to locate or identify the location, which requires inherently at least three pre-recognized reference points. In some cases, owing to out of reachability to communication facility the target node can not reachable always to three base stations. This paper presents a predictive method, which can estimate the location of the moving target node in real time even though the target could not get in touch with all three base stations. The method is based on the PID-controlled Moving Objects Spatio-Temporal Model Algorithm. This can predict the moving direction of the moving target, and then combine with the past target position information to judge accurately the location
Seasonal variations of temperature and rainfall characteristics in the northeastern part of Bangladesh around Sylhet

Utilizing surface and satellite observations, we showed some characteristic climatic aspects in and around Sylhet area, northeastern part of Bangladesh. The 14-year climatological rainfall in pre-monsoon and monsoon seasons was calculated from TRMMPR surface rain data. In pre-monsoon season, rainfall amount in the upper catchment of the Meghna river in India was high. The location of rainfall maximum on the Meghalaya Plateau in pre-monsoon season shifted westward relative to the monsoon season. Analysis of year-to-year variation of rainfall amount in BMD Sylhet observatory showed that the pre-monsoon rainfall contributed the year-to-year variation of the annual rainfall in Sylhet as much as the monsoon rainfall. Rainfall within 20-99 mm/h intensity range strongly affected the total rainfall for pre-monsoon season, while it was 50-499 mm/h for monsoon season. The analysis of the maximum temperature variation indicated that the temperature variation in Sylhet was largely different from that in other part of Bangladesh, especially in the rainy seasons. This was likely to be due to the cold air mass originated from the Meghalaya Plateau associated with strong rainfall activity in pre-monsoon and monsoon seasons. Recent warming trend in daily maximum temperature was prominent in Sylhet compared with other sites.
Pharmacokinetics of Quinocetone and its Major Metabolites in Swines

Pharmacokinetics of quinocetone and its three main metabolites in swine were investigated in this study.Quinocetone was administered to 7 healthy cross-bread swine orally at a dosage of 40 mg/kg,The concentration of quinocetone and its major metabolites in plasma were detected using the high-performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS) method,the concentration-time data were analyzed with WinNonlin5.2 program.After oral administration,quinocetone and its three major metabolites were detected.Plasma drug concentration-time data of quinocetone after oral administration was found to be fitted to one compartment open model with first order absorption and its pharmacokinetic parameters were as follows: T1/2Ka=(0.97±0.08)h,T1/2λz=(2.79±0.16)h,CL=(26.03±0.65)L/h·kg,Cmax=(0.26±0.01)μg/mL,Tmax=(2.23±0.06)h,AUC=(1.54±0.04)h·μg/mL.Plasma drug concentration-time data of demonoxyquinocetone and desoxyquinocetone were analyzed by non-compartmental model and its pharmacokinetic parameters were respectively as follows:demonoxyquinocetone,Tmax=(6.33±1.37)h,Cmax=(8.81±2.08) ng/mL,T1/2λz=(3.03±1.27)h,AUC=(0.07±0.01)h·ng/mL,MRT=(6.58±0.40)h;desoxyquinocetone,Tmax=(10.29±0.29)h,Cmax=(6.20±1.11)ng/mL,T1/2λz=(5.84±2.78)h,AUC=(0.15±0.01)h·ng/mL,MRT=(3.64±0.72)h.MQCA were detected in several samples.Quinocetone was rapidly absorbed and metabolized in swine after oral administration;three metabolites were detected,but their concentration were low.
TRADE INTEGRATION IN LATIN AMERICA: A NETWORK PERSPECTIVE

TRADE INTEGRATION IN LATIN AMERICA: A NETWORK PERSPECTIVE . 3 A. Introduction . 3 B. How Integrated is Latin America in the World Trade Network? 4 Overview of the Trade Network . 5 Integration on the Extensive Margin (Degree) . 7 Integration on the Intensive Margin (Strength) . 10 Other Centrality Measures . 11 Integration Within Latin America . 12 C. Integration in Actual versus Benchmark Trade Networks . 16 Binary (Unweighted) Networks: Actual vs. Benchmark . 16 Weighted Networks: Actual vs. Predicted . 19 D. Conclusions . 21