Miracle-dz/UniFAR-FTUs huggingface dataset:
AT&T and T-Mobile Merger Analysis Using a Time Series Forecasting Approach

The object of the present study is to examine why AT&T and T-Mobile attempted to merge their entities in March 2011. We will use time series forecasting from the past ten years of data for revenues and number of users for both AT&T and T-Mobile to predict the next ten years of values. We develop a classical ARIMA model to forecast the average revenue per user for both companies for the next decade. These results will show why the companies attempted to merge, while allowing us to further elaborate on the exact solutions that a merger would have presented.
Lung Ultrasound Improves Outcome Prediction over Clinical Judgment in COVID-19 Patients Evaluated in the Emergency Department

In the Emergency Department (ED), the decision to hospitalize or discharge COVID-19 patients is challenging. We assessed the utility of lung ultrasound (LUS), alone or in association with a clinical rule/score. This was a multicenter observational prospective study involving six EDs (NCT046291831). From October 2020 to January 2021, COVID-19 outpatients discharged from the ED based on clinical judgment were subjected to LUS and followed-up at 30 days. The primary clinical outcome was a composite of hospitalization or death. Within 393 COVID-19 patients, 35 (8.9%) reached the primary outcome. For outcome prognostication, LUS had a C-index of 0.76 (95%CI 0.68–0.84) and showed good performance and calibration. LUS-based classification provided significant differences in Kaplan–Meier curves, with a positive LUS leading to a hazard ratio of 4.33 (95%CI 1.95–9.61) for the primary outcome. The sensitivity and specificity of LUS for primary outcome occurrence were 74.3% (95%CI 59.8–88.8) and 74% (95%CI 69.5–78.6), respectively. The integration of LUS with a clinical score further increased sensitivity. In patients with a negative LUS, the primary outcome occurred in nine (3.3%) patients (p < 0.001 vs. unselected). The efficiency for rule-out was 69.7%. In unvaccinated ED patients with COVID-19, LUS improves prognostic stratification over clinical judgment alone and may support standardized disposition decisions.
Improving FE Pass Rates through Changes in Attitude

All engineering students who attend the Virginia Military Institute (VMI) are required to take, but not necessarily pass, the Fundamentals of Engineering (FE) exam prior to graduation. It has been considered acceptable for pass rates to fall slightly below the national average due to the mandatory FE requirement and time constraints which include a rigorous core curriculum, seven physical education classes, eight ROTC classes, mandatory military training, and a "Spartan" lifestyle in barracks. The VMI Civil and Environmental Engineering (CEE) department has recently decided to implement new strategies in an attempt improve the FE pass rate. This paper presents some of the strategies used in the past to prepare students for the FE exam and how changing students' attitude improves performance on the exam.
Individual Stress Burden and Mental Health in Health Care Workers during the COVID-19 Pandemic: Moderating and Mediating Effects of Resilience

Abstract Introduction: The COVID-19 pandemic generated a significant burden on the German health care system, affecting the mental health of health care workers (HCW) in particular. Resilience may serve as an essential protective factor for individuals’ well-being. Objective: Our objective was to identify demographic and work-related correlates of individual resilience and to investigate the association between pandemic-related stress, resilience and mental health using different resilience models. Methods: Our sample comprised 1034 German HCW in different medical professions who completed an online survey from 20 April to 1 July 2020. Resilience was assessed using the Resilience Scale-5 (RS-5). The pandemic-related self-reported stress burden was captured by a single item, while depression and anxiety symptoms were measured with the PHQ-2 and GAD-2, respectively. Additionally, various sociodemographic and work-related factors were assessed. Results: Overall, we found high levels of resilience in the sample compared to a German sample before the pandemic, which were significantly associated only with the older age of participants and having children in both univariate and multivariate analyses. Regarding mechanisms of resilience, moderation analysis revealed that low individual resilience and high pandemic-related stress burden independently contributed to both anxiety and depression symptoms while resilience additionally moderated the relationship between stress burden and anxiety symptoms. The link between self-reported stress burden and mental health symptoms was also partially mediated by individual resilience. Conclusion: Taken together, the findings based on the present sample during the COVID-19 pandemic suggest that resilience plays a central role in the mental health of healthcare workers and that resilience-building interventions should be expanded, especially with a focus on younger employees.
Developing a blended learning model in an EFL class

The application of blended learning approach in foreign language teaching has become quite prevalent in language classes worldwide. As opposed to pure e-learning which involves only electronic media in the learning process, blended learning supplements traditional face-to-face teaching and learning environment with different kinds of technology-based instruction. The basic objective of the present study is to outline a framework of blended learning model in college English teaching applicable in China EFL class environment, and a case study was conducted to investigate its effectiveness. A total of 96 non-English major students who study intensive English at a China's key university participated in the study. The experimental group studied the target course through blended learning strategies, and their performance was evaluated through comparing the results got from a pretest, progress test and post-test, respectively, to check the effectiveness of the model. Questionnaires and semi-structured interviews were also conducted to identify students' evaluation of the blended learning model. It was observed that the participants of the study perceived the model to be useful and helpful for the improvement of their English proficiency. In addition, it was found that the participants in the experimental class received higher scores for their post-test at the end of the study. Based on the main findings, modifications have been made to the model and further suggestions and implications are provided.
Jellyfish as an Alternative Source of Bioactive Antiproliferative Compounds

Jellyfish are commonly considered a nuisance for their negative effects on human activities (e.g., fisheries, power plants and tourism) and human health. However, jellyfish provide several benefits to humans and are commonly eaten in eastern countries. Additionally, recent studies have suggested that jellyfish may become a source of high-value molecules. In this study, we tested the effects of the methanolic extracts and enriched fractions, obtained by solid-phase extraction fractionation, from the scyphomedusae Pelagia noctiluca, Rhizostoma pulmo, Cotylorhiza tuberculata and the cubomedusa Caryddea marsupialis on different human cancer cell lines in order to evaluate a potential antiproliferative activity. Our results indicated that fraction C from Caryddea marsupialis-(CM) and C. tuberculata oral arms (CTOA) were the most active to reduce cell viability in a dose-dependent manner. LC/MS based dereplication analyses highlighted that both bioactive fractions contained mainly fatty acids and derivatives, with CM additionally containing small peptides (0.7–0.8 kDa), which might contribute to its higher biological activity. The mechanism of action behind the most active fraction was investigated using PCR arrays. Results showed that the fraction C of CM can reduce the expression of genes involved in apoptosis inhibition in melanoma-treated cells, which makes jellyfish a potential new source of antiproliferative drugs to be exploited in the future.
Instrumentation and Computational Modeling for Evaluation of Bridge Substructures Across Waterways

This State Study 229 was proposed as the Phase I study for implementing sensing technologies and computational analysis to assess bridge conditions and support decision-making for bridge maintenance in Mississippi. The objectives of the study are to: (1) prioritize the major detrimental bridge substructure deterioration in Mississippi waterway and their measurable parameters; (2) identify instrumentation technology for wirelessly in-situ monitoring of these parameters; (3) develop computational framework that can correlate the identified bridge substructure deterioration with the structure performance; (4) integrate research findings into education and professional preparation for students and professionals; and (5) prepare the field implementation for the next phase study and identify further needed research in above areas.
Study of Desiccation Crack Behavior of Fly Ash - Bentonite (FAB) Composite in Landfill Liner System

The addition of bentonite to fly ash can lower the permeability coefficient of the composite landfill liner. Also, the montmorillonite bentonite can bind quite a lot of water molecules during the hydration process. In this study, the effect of mixture, water content, and salinity on the desiccation cracking phenomenon in the bentonite - fly ash mixture as a landfill liner system was investigated by conducting tests such as desiccation test, water content test, and standard proctor and permeability test. The variation in bentonite levels: 0%, 15%, 20%,25% and 25% + 1 N NH4Cl solution. The result shows that the appropriate mixture is fly ash + 15% bentonite with permeability of 4,065 x 10-7. The mixture complies with local regulations where the permeability coefficient of the bottom layer of the landfill should be less than 10–6 cm/s and have a low cracking value (CIF). Furthermore, the addition of saline solution to the composite can rise the value of the optimum moisture content. However, saline conditions can decrease the value of CIF drastically
Effects of oxygen scavengers (Sodium sulfite, sodium bisulfite, sodium dithionite, and sodium metabisulfite) on growth and accumulation of biomass in the green alga Asterarcys quadricellulare

The present investigation aims to know the effects of oxygen scavengers such as sodium sulfite (Na 2 SO 3 ), sodium bisulfite (NaHSO 3 ), sodium dithionite (Na 2 S 2 O 4 ), and sodium metabisulfite (Na 2 S 2 O 5 ) on growth and accumulation of biomass (chlorophylls) in green alga Asterarcys quadricellulare ( A. quadricellulare ). Alga is grown in tris-acetate-phosphate (TAP) medium, along with various concentrations of NaHSO 3 , Na 2 SO 3 , Na 2 S 2 O 4 , and Na 2 S 2 O 5 individually under in vitro conditions. To evaluate the effects of scavengers, in vitro grown algal cells were used to estimate the chlorophyll a and b, along with total chlorophylls. Augmented growth and total chlorophyll content (63.23 mg/L) was noticed in A. quadricellulare cultures grown in TAP with 3.2 mM of sodium sulfite medium. TAP with 0.4 mM sodium bisulfite medium enhanced the growth and total chlorophyll content (49.38 mg/L) in this alga. Similarly, improved growth and total chlorophyll content (44.46 mg/L) was observed in A. quadricellulare grown in TAP with 0.4 mM of sodium dithionite medium. TAP with 0.08 mM sodium metabisulfite medium augmented the growth and total chlorophyll content (41.01 mg/L) in this alga. Higher doses of scavengers inhibit the growth and reduced the total chlorophyll contents in A. quadricellulare . The present work will be helpful to standardize the oxygen scavenger resistance levels in various algal species for anaerobic experiments including in vitro production of hydrogen.
Research on the remaining useful life prediction method of lithium‐ion batteries based on aging feature extraction and multi‐kernel relevance vector machine optimization model

Lithium‐ion batteries are used in a wide range of applications due to their cleanliness and stability, and the health management of lithium‐ion batteries has become a necessity. The most important aspect of health management is the prediction of the remaining useful life (RUL) of the battery. Therefore, a RUL estimation model based on the aging factor of the charging process and an improved multi‐kernel relevance vector machine is proposed in order to achieve high accuracy estimation of the RUL of lithium‐ion batteries. First, eight aging features highly correlated with lithium‐ion batteries capacity degradation are extracted based on charging current, voltage, and temperature data, then, their correlation is proved using gray relation analysis. Secondly, the improved gray wolf constrained optimization algorithm is used to determine the kernel function combination coefficients of the multi‐kernel relevance vector machine, and the RUL prediction model of the improved multi‐kernel relevance vector machine is established. Finally, using the battery dataset from NASA, aging data of three datasets, 24°C, 43°C, and 4°C, with a total of 11 batteries, were selected for validation. The validation results show that the improved multi‐kernel relevance vector machine prediction model has higher prediction accuracy and more robust long‐term prediction capability, with RUL prediction error less than 10 cycles and MAE less than 0.05, both of which are better than that of the single‐kernel relevance vector machine model and other multi‐kernel relevance vector machine models.
Losartan Potassium and Verapamil Hydrochloride Compound Transdermal Drug Delivery System: Formulation and Characterization

In this study, we developed a sustained-release transdermal delivery system containing losartan potassium (LP) and verapamil hydrochloride (VPH). LP and VPH have low bioavailability and long half-life. Therefore, the development of an optimum administration mode is necessary to overcome these drawbacks and enhance the antihypertensive effect. A transdermal diffusion meter was used to determine the optimal formulation of LP-VPH transdermal drug delivery systems (TDDS). Based on in vitro results, a sustained-release patch was prepared. Physical characteristics, including quality, stickiness, and appearance, were evaluated in vitro, while pharmacokinetics and skin irritation were evaluated in vivo. The results showed that 8.3% polyvinyl alcohol, 74.7% polyvinylpyrrolidone K30, 12% oleic acid-azone, and 5% polyacrylic acid resin II provided an optimized TDDS product for effective administration of LP and VPH. Furthermore, in vitro and in vivo release tests showed that the system continuously released LP and VPH for 24 h. The pharmacokinetic results indicated that although the maximum concentration was lower, both the area under the curve from 0–time and the mean residence time of the prepared patch were significantly higher than those of the oral preparations. Furthermore, the prepared LP-VPH transdermal patch showed good stability and no skin irritation. The developed LP-VPH TDDS showed a sustained-release effect and good characteristics and pharmacokinetics; therefore, it is an ideal formulation.
Terrestrial Gamma‐Ray Flashes With Accompanying Elves Detected by ASIM

The Atmosphere‐Space Interactions Monitor was designed to monitor Terrestrial Gamma‐ray Flashes (TGFs) and Transient Luminous Events (TLEs) from space, enabling the study of how these phenomena are related. In this paper, we present observations of 17 TGFs with accompanying Elves. TGFs are short and highly energetic bursts of gamma photons associated with lightning discharges, whereas Elves are TLEs that are observed as concentric rings of ultraviolet (UV) and visible light at ionospheric altitudes, produced by the excitation of N2 molecules when an electromagnetic pulse hits the base of the ionosphere. Elves were identified when optical detections in the UV band could be clearly distinguished from other optical signals from lightning strokes. The TGFs they accompanied had short durations and were associated with particularly high peak current lightning. Lightning sferics associated with these events were detected by the global lightning network GLD360 and the World Wide Lightning Location Network, and they were, with the exception of one event, observed over ocean or coastal regions. It is likely that these events were associated with Energetic In‐cloud Pulses. We show that short duration TGFs tend to be associated with higher peak currents than long duration TGFs.
Assessing Learners’ Conceptual Understanding of Introductory Group Theory Using the CI2GT: Development and Analysis of a Concept Inventory

Prior research has shown how incorporating group theory into upper secondary school or undergraduate mathematics education may positively impact learners’ conceptual understanding of mathematics in general and algebraic concepts in particular. Despite a recently increasing number of empirical research into student learning of introductory group theory, the development of a concept inventory that allows for the valid assessment of a respective conceptual understanding constitutes a desideratum to date. In this article, we contribute to closing this gap: We present the development and evaluation of the Concept Inventory of Introductory Group Theory—the CI2GT. Its development is based on a modern mathematics education research perspective regarding students‘ conceptual mathematics understanding. For the evaluation of the CI2GT, we follow a contemporary conception of validity: We report on results from two consecutive studies to empirically justify that our concept inventory allows for a valid test score interpretation. On the one hand, we present N=9 experts‘ opinions on various aspects of our concept inventory. On the other hand, we administered the CI2GT to N=143 pre-service primary school teachers as a post-test after a two weeks course into introductory group theory. The data allow for a psychometric characterization of the instrument, both from classical and probabilistic test theory perspectives. It is shown that the CI2GT has good to excellent psychometric properties, and the data show a good fit to the Rasch model. This establishes a valuable new concept inventory for assessing students’ conceptual understanding of introductory group theory and, thus, may serve as a fruitful starting point for future research into student learning of abstract algebra.
Predictive Value of CT-Based Radiomics in Distinguishing Renal Angiomyolipomas with Minimal Fat from Other Renal Tumors

Objectives This study is aimed at determining whether CT-based radiomics models can help differentiate renal angiomyolipomas with minimal fat (AMLmf) from other solid renal tumors. Methods This retrospective study included 58 patients with a postoperative pathologically confirmed AMLmf (observation group) and 140 patients with other common renal tumors (control group). Non-contrast-enhanced CT and contrast-enhanced CT data were evaluated. Radiomics features were extracted from manually delineated volume of interest (VOIs). The least absolute shrinkage and selection operator (LASSO) regression was used for feature screening. Five classifiers, including logistic regression, multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbor (KNN), and logistic regression (LR), were used, with leave-out validation (128 training, 60 testing). The diagnostic performance of the classifier was evaluated and compared by receiver operating characteristic curve (ROC) analysis. Results Among the 1029 extracted features, prediction models of AMLmf were composed, by 2, 10, 4, and 9 selected features for precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP), respectively. Models of CMP and NP achieved adequate performance after using MLP classifier, with prediction accuracy of 0.767 (AUC 0.85, sensitivity 0.76, and specificity 0.78) and 0.783 (AUC 0.83, sensitivity 0.79, and specificity 0.78), respectively. MLP model of features selected from the combination of the all features had the best diagnostic performance (accuracy 0.8500, sensitivity 0.8095, specificity 0.9444, and AUC 0.9193). Conclusions Radiomics features may help to distinguish benign AMLmf from common malignant kidney masses, which may contribute to the selection of interventions for renal tumors.
Comparison of different waste bin monitoring approaches: An exploratory study

Waste bin monitoring solutions are an essential step towards smart cities. This study presents an exploratory analysis of two waste bin monitoring approaches: (1) ultrasonic sensors installed in the bins and (2) visual observations (VO) of the waste collection truck drivers. Bin fill level data was collected from a Portuguese waste management company. A comparative statistical analysis of the two datasets (VO and sensor observations) was performed and a predictive model based on Gaussian processes was applied to enable a trade-off analysis of the number of collections versus the number of overflows for each monitoring approach. The results demonstrate that the VO are valuable and reveal that significant improvements can be achieved for either of the monitoring approaches in relation to the current situation. A monitoring approach based on VO combined with a predictive model is shown to be viable and leads to a considerable reduction in the number of collections and overflows. This approach can enable waste collection companies to improve their collection operations with minimal investment costs during their transition to fully sensorized bins.
Forcasting Stock Price PT. Indonesian Telecomunication with ARCH-GARCH Model

This research discusses the modeling of time series using R software, focusing on forecasting the stock price of PT. Indonesian telecommunications with ARCH-GARCH model. The data used daily closing data on stock prices from January 6, 2020, to January 6, 2021 was obtained from the website www.finance. yahoo.com. The goal is to find out the best model arch-garch on PT. Indonesian telecommunications to find out the results of stock price forecasting the next day using the ARCH-GARCH model. The best model was ARIMA (2,1,3). The results of the ARCH-LM test showed the data contained heteroskedasticity effects or ARCH elements. The research models proposed in this study are ARCH (1) and ARCH-GARCH (1,1). The smallest AIC and BIC values of these two models are ARCH-GARCH (1,1) which is the best model for forecasting the stock price of PT. Indonesian telecommunications for the next 10 days. The study attempts to conduct stock price forecasting with the ARCH-GARCH model. The result of the forecasting of the share price of PT. Indonesian telecommunications from January 07, 2021 to January 20, 2021 respectively except for holidays is IDR 3374.884, IDR 3379.617,IDR 3378.305, IDR 3376.610, IDR 3380.050, IDR 3376.372, IDR 3379.071, IDR 3377.964, IDR 3377.515, IDR 3379.002. Forecasting results are close to factual data for forecasting the next 10 days so that they can be taken into consideration in investing by investors.