Miracle-dz/UniFAR-FTUs huggingface dataset:
Situations of using white lie during patient care: a qualitative study into nurses’ perspectives

Background: Keep the patients well and fully informed about diagnosis, prognosis, and treatments is one of the patient’s rights in any healthcare system. Although all healthcare providers have the same viewpoint about rendering the truth in treatment process, sometimes the truth is not told to the patients and instead, healthcare staff use “white lie”. This study aimed to explore the nurses’ experience of white lies during patient care.Methods: This qualitative and descriptive study was conducted during June to December 2018. Eighteen hospital nurses were purposively recruited with maximum variation from ten teaching and public hospitals affiliated to Tehran University of Medical Sciences. Purposeful sampling was used and data were collected by semi-structured interviews that were continued until data saturation. Data was classified and analyzed by content analysis approach.Results: The data analysis in this study resulted in four main categories and eleven subcategories. The main categories included hope crisis, bad news, cultural diversity, and nurses’ limited professional competence. Conclusion: Results of the present study showed that, the use of white lie by nurses during patient care may be due to a wide range of patient, nurse and organizational-related factors. Communication was the main factor that influenced information rendering. Nurses’ communication with patients should be based on mutual respect, trust and adequate cultural knowledge, and also nurses should provide precise information to patients, so they can make accurate decisions regarding their health care.
Channel leadership and recycling channel in closed-loop supply chain: The case of recycling price by the recycling party

Due to the fast growing of the waste electrical and electronic equipment (WEEE), the business values of closed-loop supply chains (CLSCs) have been well recognized. In this paper, we investigate the performance of the CLSCs under different combinations of the recycling channel and the channel leadership when the recycling price is determined by the recycling party. Specially, we consider a CLSC consisting of two channel members, i.e., a manufacturer and a retailer. Each member acting as the channel leader has three different channels to collect the used products, and they are (ⅰ) the manufacturer (M-channel), (ⅱ) the retailer (R-channel) and (ⅲ) the third-party (T-channel). Given the recycling party determines the recycling price, mathematical models are developed to investigate the performance of the CLSC under different combinations of the channel leadership and the recycling channel. Through a comparison analysis, we find that M-channel is the most effective recycling channel. Moreover, once the M-channel be adopted, the retailer-led structure is as good as manufacture-led structure. We find that the recycling channel structure could be more important than the channel leadership in the CLSC. Finally, we illustrate that the CLSC can be coordinated by a two-part tariff contract.
Dioxetanes as Mechanoluminescent Probes in Thermoplastic Elastomers

Mechanoluminescent 1,2-dioxetane units, which emit light upon scission of the strained 4-membered ring, were incorporated into the main chain of segmented copolymers with poly(tetramethylene oxide) soft segments and various hard segments. Test samples of the polymers emitted light upon applying strain. Polymers with hydrogen bonding urethane or amide groups in the hard segments showed stronger luminescence than polyester segmented copolymers of similar molecular weight. Increasing strain rate led to stronger luminescence. The positive effect of molecular weight on emission intensity in polyurethanes was interpreted in terms of decreased chain slippage when multiple hard segments anchor the polymer chain in different hard blocks. The results demonstrate that even chains with more than 50 hard segments slippage can be further reduced by increasing the degree of polymerization.
Experimental Evaluation of Clickbait Detection Using Machine Learning Models

The exponential growth of social media has been instrumental in directing the news outlets to rely on the stated platform for the dissemination of news stories. While social media has helped in the fast propagation of breaking news, it also has allowed many bad actors to exploit this medium for political and monetary purposes. With such an intention, tempting headlines, which are not aligned with the content, are being used to lure users to visit the websites that often post dodgy and unreliable information. This phenomenon is commonly known as clickbait. A number of machine learning techniques have been developed in the literature for automatic detection of clickbait. In this work, we consider six state of the art and classical machine learning algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes Classifier (NBC), Long Short Term Memory (LSTM), Parallel Convolutional Network (PNN), and Bidirectional Encoder Representations from Transformers (BERT) for automated clickbait detection. We also use four performance evaluation metrics, namely accuracy, precision, recall and F1-score to evaluate the performance of the selected set of machine learning algorithms on a real world data set. The results show that BERT is the best performing learning algorithm on three out of four evaluation metrics, and it achieves an average performance superiority of 3%–4% over all the other algorithms. Furthermore, it is observed that PNN has the worst performance among the selected algorithms.
Heat capacities and low temperature thermal transitions of 1-hexyl and 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide

Several previous measurements of the isobaric heat capacity of the ionic liquid 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide ([Hmim][Tf2N]) differ relative to the IUPAC recommended value by ± 8 %. Specifically, the results obtained by differential scanning calorimetry (DSC) showed relative difference from each other and from values determined by adiabatic calorimetry by up to 12 % and by 6 % on average. The aim of this work was to explore the reason for these discrepancies in DSC measurements. Accordingly, measurements of the isobaric heat capacity and low temperature thermal transitions of [Hmim][Tf2N] and 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide ([Omim][Tf2N]) made by DSC are reported here. The isobaric heat capacities for both ionic liquids were measured on samples of (5 to 9) g over the temperature ranges (303 to 373) K for [Hmim][Tf2N] and (288 to 373) K for [Omim][Tf2N] using steps of 10 K and a scan rate of 0.025 K·min−1. These heat capacity measurements were c.
Spectrophotometric Quantification of Sulfur in Sulfide-containing Wastewater Treated with Microbial

A spectrophotometric method for indirect quantification of microorganism-produced sulfur in wastewater was developed. Sulfur in wastewater was extracted by chloroform, and then evaporated to dryness. The residue was dissolved in ethanol then treated with exceeded bisulphite to convert thiosulfate, the later was reacted with exceeded iodin and the rest was further determined by spectrophotometric method. The results revealed that the present innovative method was characterized by convenience, high sensitivity, high recovery range and well recurred, the limit of detection was as low as 0.03 mg·L-1 and the recovery ranged from 97.8% to 100.2%. Furthermore, the spectrophotometric method was well testified by a liquid chromatographic method with no significant difference. And the innovative method was characterized by simple apparatus, high sensitivity, and low cost in comparison with the HPLC method.
The Association Between Abnormal Eating Behaviors, Body Mass Index, and Waist-to-Height Ratio Among University Students in Malaysia

Objectives: Transitioning from the home to a new environment exposes university students to unhealthy eating behaviors, which may impact their body mass index (BMI). The purpose of this study was to determine the frequency of these abnormal eating behaviors among the students of the Malaysian Allied Health Sciences Academy (MAHSA) University, Malaysia, gender difference in eating behaviors, and to explore the association of such behaviors with body mass index (BMI), waist circumference, and waist-to-height ratio. Methods: In this cross-sectional study, 300 students from MAHSA University were evaluated for eating habits using the Eating Attitude Test 26 (EAT-26) questionnaire. Their height, weight, and waist circumference were measured, and their BMI and waist-to-height ratio was calculated. Data were analyzed using Statistical Package for the Social Sciences (SPSS), Version 24. Results: Of the 300 students enrolled in the study, 6% scored >20 on the EAT26, therefore appearing to have abnormal eating behaviors. Most of the students (62.0%) were found to be in the “normal” weight category according to their BMI measurements. A significant positive correlation was found between EAT-26 score and BMI, and between EAT-26 score and waist-to-height ratio. Female students had higher EAT-26 scores compared to the male students (p=0.01). Conclusions: Female students are at higher risk of developing abnormal eating behaviors than male students, according to this study. Among Malaysian students, the higher the risk of abnormal eating behaviors, the greater the likelihood of having a higher BMI and waist-to-height ratio.
Disturbance-Observer-Based Direct Torque Control of Five-Phase Permanent Magnet Motor Under Open-Circuit and Short-Circuit Faults

Multiphase permanent magnet (PM) motors are popularly adopted for their high torque density, high efficiency, and good fault tolerance. Open-circuit and short-circuit faults can result in high fluctuating torque and deteriorated dynamic performance. This article proposes a novel direct torque control (DTC) approach based on disturbance observer (DOB) to minimize the torque pulsations and enhance dynamic performance of a five-phase PM motor under open-circuit and short-circuit fault conditions. The novelty of the proposed strategy is the development of fault-tolerant switching table based on closed-loop mode of third harmonic current regulation and the incorporation of DOB. Based on the switching table, the proposed method can restrain the torque fluctuations caused by open-circuit fault and suppress y-axis current. The DOB is introduced to eliminate the external disturbances, which include torque fluctuations caused by short-circuit current and unmodel dynamic. Then, the proposed strategy is applicable to the PM motor, whose back electromotive force contains third harmonic, under open-circuit or short-circuit fault condition. This fault-tolerant DTC strategy can not only smooth fluctuating torque and ensure that its dynamic performance reaches the healthy performance, but also enhance the robustness against the external disturbances under both open-circuit and short-circuit fault conditions. The simulation and experimental results are presented to validate the proposed strategy.
Effects of CO2 Concentration and Electric Current on the Ionic Conductivity of Anion Exchange Membranes for Fuel Cells

The dependences of the CO2 concentration and the effects of electric current on the ionic conduction properties of anion exchange membranes (AEMs) in fuel cells were investigated. The ionic conductivity of AEMs was measured by AC impedance method at 40°C in various atmospheres. After the initial ion exchange process, contact with air containing 38% CO2 caused the ionic conductivity of AEMs to decrease to 40% of the value measured immediately after the initial ion exchange process. However, exposing the AEMs in a CO2-free air atmosphere increased the ionic conductivity to 50% of the value measured immediately after the initial ion exchange process. Then 89 C cm−2 of direct current (DC) was applied in a CO2-free atmosphere to the AEMs. The ionic conductivity increased close to the value measured immediately after the initial ion exchange process. These results are discussed by assuming that bicarbonate (HCO3−) is produced in the AEMs.
Analysis of ECG Signal based on Feature Fusion and Two-Fold Classification Approach

Automated analysis of electrocardiogram (ECG) signal for home health care system can ensure early stage detection of cardiac arrhythmia. Most of the published works are based on advanced signal processing algorithms, large number of ECG data, complex classifiers for arrhythmia diagnosis. Hence it is difficult to implement these algorithms in portable health monitoring device. This study introduces two-fold feature selection and classification approach for easier and faster arrhythmia identification. The discrimination of different ECG data is employed by fusion of different non-linear, temporal and statistical parameters of ECG signals. The significances of individual feature in each stage of detection process are evaluated and ranked by introducing Euclidean score. Further, the eligible features are selected to construct of an integrated feature called fused feature. These fused features are efficient to accurately classify ECG beats with lesser computational burden. The proposed method is validated using MIT-BIH arrhythmia database. Experimental result of each stage classification shows better performance of the fused feature compare to the individual feature combinations. Furthermore, proposed work produces encouraging result comparing with existing work.
How Dense of a Circumstellar Medium Is Sufficient to Choke a Jet?

The progenitor stars of stripped-envelope high-velocity supernovae (Ic-BL SNe) can explode inside a dense circumstellar medium (CSM) that extends out to many times the progenitor radius. This complicates the question of whether all Ic-BL SNe harbor a jet, which can tunnel through the star and be viewed on-axis as a long-duration gamma-ray burst (GRB). More specifically, a sufficiently dense CSM might “choke” the jet, redistributing its energy quasi-spherically. In this study, we numerically calculate the CSM density necessary for jet choking. For typical GRBs, we determine the jet is not choked in the CSM unless this requires several solar masses of CSM to be situated within 1013 cm of the progenitor, a much higher density than any CSM observed. We conclude that typical GRB jets are not choked in the CSM. However, in many cases the CSM has sufficient mass to decelerate the jet to a modest Lorentz factor (Γ ∼ 10), which should lead to a long coasting phase for the jet, observable as a long plateau (potentially up to a few days) in the afterglow light curve. For extreme cases of low-energy GRBs in a high-mass CSM, the jet will decelerate to nonrelativistic velocities, causing it to spread modestly to a larger opening angle (θj ≈ 20°) before breaking out of the CSM. Even in these extreme examples, the jet does not have time to redistribute its energy quasi-spherically in the CSM before breakout.
Optimization strategy based on deep reinforcement learning for home energy management

With the development of a smart grid and smart home, massive amounts of data can be made available, providing the basis for algorithm training in artificial intelligence applications. These continuous improving conditions are expected to enable the home energy management system (HEMS) to cope with the increasing complexities and uncertainties in the enduser side of the power grid system. In this paper, a home energy management optimization strategy is proposed based on deep Q-learning (DQN) and double deep Q-learning (DDQN) to perform scheduling of home energy appliances. The applied algorithms are model-free and can help the customers reduce electricity consumption by taking a series of actions in response to a dynamic environment. In the test, the DDQN is more appropriate for minimizing the cost in a HEMS compared to DQN. In the process of method implementation, the generalization and reward setting of the algorithms are discussed and analyzed in detail. The results of this method are compared with those of Particle Swarm Optimization (PSO) to validate the performance of the proposed algorithm. The effectiveness of applied data-driven methods is validated by using a real-world database combined with the household energy storage model.
Factors leading to retention and organizational turnover in software engineering : a conceptual model

Since several years the software engineering industry is facing high financial and intellectual loss due to employees frequently leaving the companies. Therefore it is important to understand the reasons and intentions of employees to counteract these factors and foster retention. There is a certain body of knowledge regarding retention and turnover from other domains, however the knowledge regarding software engineering is rather limited. This thesis proposes a conceptual model for turnover in the IT domain. It is based on knowledge collected in a comprehensive snowball literature research based on a tentative start set of papers from several domains. The model was then tested in the perspective of a company that is known as a good place to work by conducting an explorative questionnaire case study with several employees of said company. The results of the survey showed some support of the propositions, but there is no statistically significant evidence of the relationship between the factors and turnover or retention mainly because of the small size of the population sample. Nevertheless, several insights could be gained from the survey results, such as identifying social support as one of the most important personal aspects for many participants. These findings led to a revision of the model to better reflect the situation perceived from the survey results. This revised model requires another study to support it, though. Based on the findings of the study, the thesis closes with some suggestions regarding future research in the context of this project.
A Trust-Region Parallel Bayesian Optimization Method for Simulation-Driven Antenna Design

This article presents a trust-region parallel Bayesian optimization (TRPBO) method for simulation-driven antenna design problems. In this method, a multipoints acquisition function and trust region (TR)-based design space were developed to accelerate the solution efficiency of antenna design optimizations. For each optimization cycle in TRPBO, the acquisition function is applied to choose multiple updating points in a dynamic sampling space managed by a gradient-free TR, and then the responses at the updating points are calculated using parallel electromagnetic simulations. The proposed TRPBO was illustrated and tested on several benchmark problems, and then the TRPBO was applied to perform the design optimizations of a circularly polarized antenna, two wideband antennas, and an array antenna. Compared with the state-of-the-art methods, the proposed TRPBO achieves better optimization results with significantly less calculation time. The measured results of three fabricated antenna prototypes further show great potential of the TRPBO for solving simulation-driven antenna design problems.
Optimizing the production conditions of red Jujube broken rice milk using the response surface methodology

Production conditions of red jujube rice milk using wet-milled broken rice through a process of alkaline protease digestion combined with phosphorylation, emulsion stabilizer and high pressure homogeneous(HPH) were optimized. Sucrose ester at 0.06% and monostearin at 0.08% were used as emulsions. Using the centrifugal sedimentation rate as a response value, effects of the sodium tripolyphosphate, CMC-Na, and sodium alginate content and the HPH pressure on the rice milk stability were determined. Based on SAS data analysis, we confirmed that the optimum conditions were sodium tripolyphosphate at 0.13%, CMC-Na at 0.1%, sodium alginate at 0.12%, and the homogenization pressure at 50 MPa. Under these production conditions, the red jujube rice milk beverage based on the recipe of 5% red jujube pureed, maltodextrin 6%, and 7.5% xylitols was smooth and uniform without sediment.
A Plaster Cast Method for Determining Soil Bulk Density

Abstract The plaster cast method for determining bulk density is an excavation method that uses dental plaster to form a cast of the excavation volume. Bulk density measurements using the plaster cast method were compared against measurements using the clod and core methods. Results from the plaster cast method were not significantly different (P< 0.05) from the clod and core methods for coarse-textured soils with loose consistence and minimal macropores. The plaster cast method is especially useful under soil conditions that limit the use of the clod or core method, such as soils with loose consistence, abundant rock or gravel content, thin horizons, or sloping topography, but may not be suitable for use in soils with abundant macropores.
Impact of Covid 19 – Lockdown on Private Schools in Nigeria: Evidence in FCT

The COVID-19 pandemic is the most widely discussed health phenomenon in recent history. This global pandemic has spread almost all countries and continents of the world. The pandemic has undoubtedly impacted on global economic growth leading to review of the global outlook by most rating agencies. The policy makers were confronted with decision dilemma of either saving lives or continue the pursuit of political and economic gains. The bitter bill of shutting down businesses, schools inclusive, crystalizes the dilemma. These interruptions will not just be a short-term issue, but can also have long-term consequences for the affected stakeholders. School lockdown disruptions to daily routine affects psychological wellbeing, academic, education approaches and finance models. The degree of impact on stakeholders varies. Covid19 is novel with little or no evidence on its impact hence the need to investigate the phenomenon. A total of 291 participants located in the 5 local government areas were involved in the survey. The survey data was tested for validity, reliability and value. Interview complimented the quantitative data. Even though a short period of schooling lockdown, the effect provides a guide to reasonably peep into likely possible outcomes as an extremely rough measure of the impact of the current school closures. The real quantitative impact will be better analysed post lockdown and covid19 era. This paper adopts regression research design in the analysis, specifically, smart PLS modelling was used. The study has implications on students, parents, leadership of schools, and operators of businesses on campus. The study further provides concept deployment, evidence from Federal Capital Territory (FCT), Nigeria and useful addition to theory, literature and practice. The study found that 10 of the 12 hypotheses were supported and 2 unsupported. In addition, change oriented leadership moderated the relationships. The underpinning theory, change oriented leadership, the methodology, and implications of quantitative and qualitative data were discussed. The paper concludes that there is a significant positive relationship between change oriented leadership and the independent variable thereby confirming the model theorised. Also the compliments of interview result reinforced the postulation. In conclusion, the present study has added valuable theoretical, practically and methodologically to the growing body of knowledge in the field of social sciences and management.