Miracle-dz/UniFAR-FTUs huggingface dataset:
Flight Ticket Prediction using Random Forest Regressor Compared with Decision Tree Regressor

The objective of the research study is to predict and analyze the flight ticket fare using machine learning algorithm Random Forest Regressor in comparison with Decision tree Regression algorithm. The Random Forest regressor (N=10) and Decision tree regressor algorithm (N=10) with the estimated sample sizes are taken and the prediction accuracy was calculated by using two groups and a total of 20 samples taken for both algorithms in this work. The samples were processed as 10 per group using a G-Power value of 0.8, threshold 0.05% and CI at 95%. The prediction Accuracy of both the algorithms are identified as the Random Forest Regressor with 86.70% and the Decision Tree Regressor with 79.69%. The statistical significance difference between GradientBoosting Regressor and AdaBoostRegressor was found to be 0.00 in the 2-tailed test (p<0.05). After all the Procedures the Prediction of Flight Prices using Random Forest Regressor seems to appear more accurate while compared with the Decision tree Regressor.
Part 2: Exposing the Most Common Myths in Validity of Effort Testing

Part 1 of this article described multiple variables in the Gordian Knot known as workers’ compensation. These variables included and were aggravated by functional capacity evaluation (FCE) validity of effort testing. The weaknesses in using intake interviews, pain questionnaires and various clinical tests and observations as the basis for conclusions related to validity of effort were pointed out. The remaining element of the FCE validity of effort testing, various common assessments of physical strength, were referred to as myths on the same order as the legendary Gordian Knot. The conclusion of
A Probabilistic Approach to Model and Optimize Geothermal Drilling

Data-driven approaches are key for modeling and optimizing the operation of geothermal wells. However, data collected at different drilling sites are usually not standardized and contain missing or erroneous values which hinder the development of reliable models that can be used across a broad range of conditions and geological contexts. In this work, we present an end-to-end workflow for analyzing, modeling, and optimizing geothermal drilling based on probabilistic methods to account for data uncertainty and heterogeneity. The components of the workflow include: 1) self-organizing maps to visualize well operation data and process trajectories, 2) process mining to analyze drilling event logs and discover process models, 3) Bayesian models to predict relevant operation and performance metrics from incomplete and uncertain drilling data, and 4) algorithms to optimize well drilling under specific operational constraints. The workflow has been implemented as a web-based tool that facilitates geothermal drilling planning and operation tasks. Models used in the workflow have been trained and tested on the database of 113 geothermal wells representing various geological settings which was built in the framework of the EDGE project.
Macroeconomic implications of decarbonization, policy tradeoffs and sequences

Decarbonization policies exhibit strong sequencing patterns within sectors and countries and across them. This paper explains these sequences using a Solow-Swan growth model with two distinguishing features. One is a variable elasticity of substitution production function with fossil and low carbon inputs. The other is inclusion of two-types of decarbonization policies—a low carbon investment subsidy and carbon pricing. Decarbonization’s macroeconomic implications arise from the short run decarbonization-output tradeoff and differences in policy costs between the subsidy and carbon pricing that vary with progress in decarbonization. The sequence of a low carbon investment subsidy before carbon pricing and initial use of decarbonization policies in those sectors and countries where low carbon inputs are stronger substitutes for the incumbents manage these macroeconomic implications of decarbonization. This explanation of observed policy sequences complements others based on microeconomic considerations of efficiency in imperfect markets, distributional fairness, and economic interests in change. The author thanks Olivier Blanchard, Joe Gagnon, Patrick Honohan, Jean Pisani-Ferry, Adam Posen and Rick van der Ploeg for helpful discussions and suggestions.
DDoS Attacks Detection using Machine Learning

The advancement in information technology has created a new era named as Internet of Things (IoT). This new technology has allowed things to be connected to the Internet, for example smart TVs, printers, cameras, smartphones, smartwatches, etc. This trend has enhanced the lifestyle of the users of these devices, and it provides new services and applications to them. The fast growth of IOT has resulted in inclusion and connection of these devices a predominant procedure. Though there are many advantages due to usage of IoT devices, there are different challenges as well due to its usage. Among the many existing challenges, Distributed Denial of Service(DDoS) attack is a relatively simple but very powerful technique to attack intranet and Internet resources. Usually, in this attack, the legitimate users are deprived of using web-based services by many compromised machines. DDoS attacks can be implemented in network, transport and application layers using different protocols, such as TCP, UDP, ICMP and HTTP. The CIC-DDoS2019 dataset consists of 11 different DDoS attacks and benign traffic with 88 features. In this report, data for six DDoS attacks and benign data has been used. Info Gain Attribute Evaluator was used to extract the twenty-four most important features. The Machine Learning (ML) algorithms studied are Bayesian Network (BayesNet) , K-Nearest Neighbors (KNN) , J48. The experiments have been performed using the Waikato Environment for Knowledge Analysis (WEKA) tool with five-fold validation. Accuracy, Precision, Recall, F-measure, and execution time have been used as the performance metrics. From the results obtained, J48 performed better among all the algorithms in terms of accuracy, precision, recall and F-measure.
Blockchain and Self-Sovereign Identity for Public Administration

The e-Government is based on the use of ICT and represents a strategic goal in government agendas. Recently, a lot of attention has been directed towards blockchain technology. It has characteristics that could have a strong impact in several sectors, includingthepublicone. TheSelf-SovereignIdentity, foritspart, isatechnologythatcouldsolvesomecriticalissues highlighted with the use of current digital identity systems.
Duplicate Bug Report Summarization

As bug reports are often duplicated, researchers have proposed detecting and classifying duplicate bug reports. Researchers also utilized the duplicated relationships of bug reports to summarize a bug report. However, to the best of our knowledge, the previous bug report summarization is limited to single document summarization techniques. In this paper, we propose applying a multi-document summarization technique to duplicate bug reports so as to obtain more informative summaries. In our work, we demonstrate the results of summarizing duplicate bug reports with our multi-document summarization technique and discuss our future research direction.
Effect of fair value accounting on earnings quality of Nigeria deposit money banks

The effect of fair values on the earnings quality of selected banks in Nigeria is the main thrust of this work. To achieve this, the relationship between fair values and earnings quality was examined to determine its nature, and the effect of fair value adoption on earnings quality. Ten banks were purposively selected for the study using the data on their annual financial statements for period, 2012- 2016, for analyses. The dependent variable is earnings quality (EQ), proxied by predictability (PRED) of earnings; while the independent variables are fair value through other comprehensive income (FVTOCI), log of total asset values (SIZE) and leverage (LEV). Employing ex-post facto research design using the panel data regression and correlation tests for analyses. The results showed that all the independent variables (FVTOCI, SIZE and LEV) have significant relationship with the dependent variable (EQ). The F-statistic, 0.000070 is significant at 5% level of significance. The adjusted R2 indicated that about 49.61% variation in the EQ is as a result of FVTOCI, SIZE and LEV. The remaining 50.39% variation is due to variables not reflected in this model. It is recommend that management of banks should consider the state of the stock market and the economic climate while adopting fair value. , Financial Statement regulators need to be forthcoming on conditions that favour the application of fair values in order not to impair earnings quality of banks
Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations.
Optimism Bias and Perceptions of Behavioral Factors for Preventing Severe COVID-19 Complications

We examine the presence of optimism bias in individuals’ perceptions of factors associated with preventing severe complications from COVID-19. Consistent with optimism bias, we find for several factors, that engaging in a behavior is associated with viewing it as more influential for preventing illness severity. Specifically, we find that individuals who exercise more, eat healthier, and take dietary supplements rate these behaviors as more important for preventing severe complications from COVID-19. JEL Classification: I12
Brain–Computer Interfaces Handbook Technological and Theoretical Advances

This chapter focuses on the different implementations of brain–computer interface (BCI) based on steady-state visually evoked potentials (SSVEPs). In offline BCI, feature extraction and classification are performed at the end of the session, when all trials are available, whereas in online settings, they are performed several times during each trial, usually for each available epoch recorded by the electroencephalogram (EEG) device, enabling real-time Sylvain
Coagulation and proteolysis of high-protein milks in the gastric environment.

Gastric digestion of 2 commercial ultrafiltered milks and milk enriched with skim milk powder (to simulate concentration by reverse osmosis) was investigated and compared with the digestion of nonconcentrated milk. Curd formation and proteolysis of high-protein milks in simulated gastric conditions were studied using oscillatory rheology, extrusion testing, and gel electrophoresis. The presence of pepsin in the gastric fluid triggered coagulation at pH >6 and the elastic modulus of gels from high-protein milks was ∼5 times larger than the gel from reference milk. Despite similar protein concentrations, the coagulum from milk enriched with skim milk powder showed higher resistance to shear deformation than the coagula from ultrafiltered milks. The gel structure was also more heterogeneous. During digestion, the degradation of coagula from high-protein milks was slowed down compared with the coagulum from reference milk, and intact milk proteins were still detected after 120 min. Differences in the digestion patterns of coagula from high-protein milks were observed and were associated with the proportion of minerals bound to caseins and the denaturation rate of whey proteins.
Comparing probabilistic methods for outlier detection

This paper compares the use of two posterior probability methods to deal with outliers in linear models. We show that putting together diagnostics that come from the mean-shift and variance-shift models yields a procedure that seems to be more effective than the use of probabilities computed from the posterior distributions of actual realized residuals. The relation of the suggested procedure to the use of a certain predictive distribution for diagnostics is derived.
Expanding Service Capabilities Through an On-Demand Workforce

Call Center Staffing: On-Demand Is in Demand Traditional call centers face challenges in quickly adapting their service capacity to meet fluctuations in demand, even with short staffing periods. In their research article “Expanding Service Capabilities Through an On-Demand Workforce,” Sun and Liu propose a solution for call centers to improve service levels and reduce operating expenses. They develop a two-stage decision model that determines the optimal combination of permanent and on-demand staff, along with an optimal on-demand staffing and call scheduling policy that minimizes costs. By utilizing diffusion approximation, they derive approximate solutions for the second-stage problem. The optimal staffing rule employs switching boundaries to determine when to bring in or dismiss on-demand agents, while the scheduling rule employs a nested threshold rule that prioritizes customer urgency. Interestingly, the call scheduling rule exhibits an intriguing pattern arising from the interaction between on-demand staffing and call scheduling decisions. Their research findings highlight significant cost savings achievable through the implementation of an on-demand workforce.
Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.
Educational Approach of Leprosy in Primary School Students: An Approach to Social Stigma Reduction

The effort of eradicating leprosy in Indonesia has taken place for a long time, but new cases are detected up to now. The government has launched numerous efforts in light of eliminating leprosy. However, the results were not satisfactory. Medical and community approaches are equally important to be carried out simultaneously, in order to obtain comprehensive treatment. One of the aspects that needs to be emphasized is the stigma reduction, towards leprosy patients as well as the patients' self-stigma. KATAMATAKU Universitas Indonesia conducted an action research whose objective was increasing awareness, considering to complement the comprehensive leprosy management. The first collaboration was carried out with the Gemala Ananda School in Jakarta, Indonesia, specifically for fourth-grade students. The tiered educational model was chosen in order to produce impactful activities. After acquiring the education provided by the KATAMATAKU team, students were asked to re-explain the information to their closest social circle about leprosy. By this educational intervention trial, it is prospected that a better educational model could be applied to reduce the stigma of leprosy in Indonesia.
Enzymatic and catalytic behaviour of low-dimensional gold nanomaterials in modular nano-composite hydrogels

Inorganic nanoparticles have long been applied as catalysts and nanozymes with exceptional rate constants arising from their large surface areas. While it is understood that high surface area-to-volume ratios and low average atomic coordination are responsible for their exceptional catalytic properties, these facets remain under exploited in the design of gold nanoparticle catalysts and nanozymes. Here we have developed 3D, 2D, and quasi-1D gold nanoparticles for use as catalysts in reducing 4-nitrophenol by sodium borohydride. Each morphology was characterised with transmission electron microscopy and UV–vis absorption spectroscopy, while the highest catalytic activity was achieved when the perimeter-to-surface area, or amount of ‘edge’, was maximised. The particles were then applied as nanozymes in modular nano-composite hydrogels. Independent hydrogel tiles containing either the substrate or catalyst were bonded in stacks, which allowed reagent transport across their interface for the colourimetric detection of hydrogen peroxide. This work presents novel insight into the catalytic activity of low-dimension nanoparticles and their potential application in nanozyme-based diagnostic devices.
Firefly algorithm optimization based WSN localization algorithm

Regarding the relatively big errors with running the DV-Hop localization algorithm in a network topology environment, with which the nodes were randomly distributed, the firefly algorithm(FA) was used to correct the position estimated by DV-Hop, a firefly optimization localization algorithm based on DV-Hop was proposed. The mathematical model was built with the smallest error for all of unknown nodes, and the firefly algorithm was used to obtain the optimal solution of the model without additional devices and increasing the traffic. The simulation results show that the average localization error of the FDV-Hop algorithm is significantly improved compared with the original algorithm.
A Comparative Study of Reading Text Difficulty of CET-6 and IELTS Based on Coh-Metrix

: This study aims to compare the text complexity of reading texts from CET-6 and IELTS using Coh-Metrix, SPSS, and Range. The study found that IELTS reading texts are generally more complicated than those of CET-6 in terms of readability, and there are inherent differences between the two sets of texts. Compared with IELTS reading texts, those of CET-6 exhibit higher narrativity, and higher connectivity, while lower word concreteness, and lower referential cohesion. This reflects the nature of the complexity of a text, namely, referential cohesion can help readers better understand the articles with difficult topics; Moreover, there is no positive relationship between word concreteness and lexical resources.
Removal of atrazine from aqueous solution onto commercial Activated Carbons

The main objective of this work was to study the adsorption of atrazine (ATZ) from aqueous solution onto commercial activated carbon forms: powdered (PAC) and granular (GAC) activated carbon, in batch system. The experimental adsorption data was modeled by kinetic and equilibrium models. The pseudo-first (PFO) and pseudo second-order (PSO) kinetic models were applied in order to describing the mechanism of adsorption. The data were found to be best fitted to the PSO model. The equilibrium study was performed using four models including Langmuir, Freundlich, Sips and Redlich-Peterson. The results revealed that the Freundlich isotherm fitted well the equilibrium data of ATZ onto PAC, while the Langmuir isotherm fitted well the equilibrium data of ATZ onto GAC. The suitability of the Freundlich for the adsorption of ATZ onto PAC and Langmuir isotherm for the adsorption of ATZ onto GAC was confirmed by Sips and Redlich-Peterson models. Based on isotherm parameter determination, PAC and GAC adsorbents capacities of 35.82 and 18.50 mg g -1 were obtained, respectively. The results of the present study showed the efficiency of using PAC and GAC as an adsorbent for the removal of ATZ from aqueous solution with PAC as the preference due to their high adsorption capacity.
Superconductivity between standard types: Multiband versus single-band materials

In the nearest vicinity of the critical temperature, types I and II of conventional single-band superconductors interchange at the Ginzburg-Landau parameter $\ensuremath{\kappa}=1/\sqrt{2}$. At lower temperatures this point unfolds into a narrow but finite interval of $\ensuremath{\kappa}$'s, shaping an intertype (transitional) domain in the $(\ensuremath{\kappa},T)$ plane. In the present work, based on the extended Ginzburg-Landau formalism, we show that the same picture of the two standard types with the transitional domain in between applies also to multiband superconductors. However, the intertype domain notably widens in the presence of multiple bands and can become extremely large when the system has a significant disparity between the band parameters. It is concluded that many multiband superconductors, such as recently discovered borides and iron-based materials, can belong to the intertype regime.