Miracle-dz/UniFAR-FTUs huggingface dataset:
Selected Aspects Of Design Lifespan Of Flexible Pavement With Anti-Fatigue Course

Abstract The aim of the study is to compare flexible pavement design lifespans and the main factors which create their values for a standard structure and one with an anti-fatigue course AF at different parameter values of pavement and its load, relevant to their design processes. Depending on the mixture used for the anti-fatigue course or the course thickness, durability improvement of the pavement (compared to the durability of a standard structure) can be obtained by extending the design lifespan of the asphalt base course or by extending the design lifespan of the AF course. On sections with predominantly slow traffic, the lifespan decreases significantly compared to sections with typical vehicle speed - the relative decrease is greater if anti-fatigue course is applied.
Application of Xanthan-Gum-Based Edible Coating Incorporated with Litsea cubeba Essential Oil Nanoliposomes in Salmon Preservation

Salmon is prone to be contaminated by Vibrio parahaemolyticus (V. parahaemolyticus), leading to the deterioration of salmon quality and the occurrence of food-borne diseases. In this study, we aimed to develop a novel xanthan-gum-based edible coating embedded with nano-encapsulated Litsea cubeba essential oil (LC-EO) for salmon preservation at 4 °C. First, the results of the growth curves and scanning electron microscopy (SEM) showed that LC-EO displayed potent antibacterial activity against V. parahaemolyticus; the optimal concentration of LC-EO in the liposomes was 5 mg/mL, and the maximal encapsulation efficiency (EE) was 37.8%. The particle size, polydispersity coefficient (PDI), and zeta potential of the liposomes were 168.10 nm, 0.250, and −32.14 mV, respectively. The rheological test results of xanthan-gum-based edible coatings incorporating liposomes showed that the prepared coating was suitable for applying on food surfaces. The results in the challenge test at 4 °C demonstrated that the treatment of 1:3 (liposome: xanthan gum, v/v) coating performed the best preservative properties, the coating treatment delayed the oxidation of salmon, and controlled the growth of V. parahaemolyticus. These findings suggest that the coatings formulated in this study could be used as a promising approach to control V. parahaemolyticus contamination and maintain salmon quality.
BERENICE Final Analysis: Cardiac Safety Study of Neoadjuvant Pertuzumab, Trastuzumab, and Chemotherapy Followed by Adjuvant Pertuzumab and Trastuzumab in HER2-Positive Early Breast Cancer

Simple Summary A combination of pertuzumab, trastuzumab, and chemotherapy is a standard treatment for patients with a type of breast cancer called HER2-positive. Before the BERENICE study, little was known about the safety and effectiveness of pertuzumab with trastuzumab after surgery. Cardiac safety was a particular concern, especially when the chemotherapy given before surgery included drugs called anthracyclines. BERENICE was designed to assess the cardiac safety of pertuzumab with trastuzumab before surgery in combination with two different types of anthracycline-based chemotherapies. This paper describes additional safety and effectiveness data from BERENICE after patients had undergone surgery and when they had finished treatment. The incidence of cardiac side effects was low regardless of anthracycline use. The cardiac safety of pertuzumab and trastuzumab is now well characterized based on our study and others. Available information supports the use of pertuzumab–trastuzumab-based therapies as a standard treatment in HER2-positive early breast cancer. Abstract BERENICE (NCT02132949) assessed the cardiac safety of the neoadjuvant–adjuvant pertuzumab–trastuzumab-based therapy for high-risk, HER2-positive early breast cancer (EBC). We describe key secondary objectives at final analysis. Eligible patients received dose-dense doxorubicin and cyclophosphamide q2w × 4 ➝ paclitaxel qw × 12 (Cohort A) or 5-fluorouracil, epirubicin, cyclophosphamide q3w × 4 ➝ docetaxel q3w × 4 (B) as per physician’s choice. Pertuzumab–trastuzumab (q3w) was initiated from the taxane start and continued post-surgery to complete 1 year. Median follow-up: 64.5 months. There were no new cardiac issues and a low incidence of Class III/IV heart failure (Cohort B only: one patient (0.5%) in the adjuvant and treatment-free follow-up (TFFU) periods). Fourteen patients (7.7%) had LVEF declines of ≥10% points from baseline to <50% in Cohort A, as did 20 (10.5%) in B during the adjuvant period (12 (6.2%) in A and 7 (3.6%) in B during TFFU). The five-year event-free survival rates in Cohorts A and B were 90.8% (95% CI: 86.5, 95.2) and 89.2% (84.8, 93.6), respectively. The five-year overall survival rates were 96.1% (95% CI: 93.3, 98.9) and 93.8% (90.3, 97.2), respectively. The final analysis of BERENICE further supports pertuzumab–trastuzumab-based therapies as standard of care for high-risk, HER2-positive EBC.
Prediction of Breast Cancer using Machine Learning Approaches

Background: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. Objective: This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data. Material and Methods: In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer. Results: RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network. Conclusion: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.
Times Series Analysis Of Road Safety Trends At The Regional Level In Poland

Abstract The paper presents possibility of applying a structural times series modeling with explanatory and intervention variables as a tool capable for explaining the changes in the monthly number of fatalities and seriously injured in traffic accidents. The analysis covers regional level and takes into consideration traffic accidents data of two regions: Pomorskie and Warmia-Mazury. In addition short-term forecasts for the two regions were developed and verified.
SCS-Gan: Learning Functionality-Agnostic Stylometric Representations for Source Code Authorship Verification

In recent years, the number of anonymous script-based fileless malware attacks and software copyright disputes has increased rapidly. In the literature, automated Code Authorship Analysis (CAA) techniques have been proposed to reduce the manual effort in identifying those attacks and issues. Most CAA techniques aim to solve the task of Authorship Attribution (AA), i.e., identifying the actual author of a source code fragment from a given set of candidate authors. However, in many real-world scenarios, investigators do not have a predefined set of authors containing the actual author at the time of investigation, i.e., contradicting AA's assumption. Additionally, existing AA techniques ignore the influence of code functionality when identifying the authorship, which leads to biased matching simply based on code functionality. Different from AA, the task of (extreme) Authorship Verification (AV) is to decide if two texts were written by the same person or not. AV techniques do not need a predefined author set and thus could be applied in more code authorship-related applications than AA. To our knowledge, there is no previous work attempting to solve the AV problem for the source code. To fill the gap, we propose a novel adversarial neural network, namely SCS-Gan, that can learn a stylometric representation of code for automated AV. With the multi-head attention mechanism, SCS-Gan focuses on the code parts that are most informative regarding personal styles and generates functionality-agnostic stylometric representations through adversarial training. We benchmark SCS-Gan and two state-of-the-art code representation models on four out-of-sample datasets collected from a real-world programming competition. Our experiment results show that SCS-Gan outperforms the baselines on all four out-of-sample datasets.
ORAI1-Regulated Gene Expression in Breast Cancer Cells: Roles for STIM1 Binding, Calcium Influx and Transcription Factor Translocation

A remodeling of calcium homeostasis, including calcium influx via store-operated calcium entry (SOCE), is a feature of breast cancers. SOCE is critical to maintain calcium balance in the endoplasmic reticulum calcium store and is an important mechanism for calcium signaling in a variety of cell types, including breast cancer cells. The canonical mechanism of SOCE is stromal interacting molecule 1 (STIM1)-mediated activation of ORAI. Elevated ORAI1 expression is a feature of basal breast cancer cells. However, the role of ORAI1 in the regulation of transcription in breast cancer cells of the basal molecular subtype is still unclear. Using CRISPR-Cas9 gene editing, ORAI1 protein expression was disrupted in MDA-MB-231 and MDA-MB-468 basal breast cancer cells. The ORAI1 wild-type and mutants were reintroduced into ORAI1 knockout cells to study the role of ORAI1 in gene transcriptional regulation. In the absence of calcium store depletion, ORAI1 regulated PTGS2 in MDA-MB-231 cells, and this was dependent on ORAI1 pore function and STIM1 binding. The activation of SOCE by thapsigargin resulted in ORAI1-dependent increases in IL6 transcription in MDA-MB-468 cells; this was also dependent on ORAI1 pore function and STIM1 binding and was associated with the translocation of NFAT1. Given the upregulation of ORAI1 in basal breast cancer cells, our results provide further evidence that ORAI1 may contribute to cancer progression through regulation of gene expression.
Friction factor of annular Poiseuille flow in a transitional regime

Annular Poiseuille flows in a transitional regime were investigated by direct numerical simulations with an emphasis on turbulent statistics including the friction factor that are affected by the presence of large-scale transitional structures. Five different radius ratios in the range of 0.1–0.8 and several friction Reynolds numbers in the range of 48–150 were analyzed to consider various flow states accompanied by characteristic transitional structures. Three characteristic structures, namely, turbulent–laminar coexistence referred to as “(straight) puff,”“helical puff,” and “helical turbulence” were observed. The selection of the structures depends on both the radius ratio and the Reynolds number. The findings indicated that despite the transitional state with a turbulent–laminar coexistence, the helical turbulence resulted in a friction factor that was as high as the fully turbulent value. In contrast, with respect to the occurrence of streamwise-finite transitional structures, such as straight/helical puffs, the friction factor decreased in a stepwise manner toward a laminar level. The turbulent statistics revealed asymmetric distributions with respect to the wall-normal direction wherein the profiles and magnitudes were significantly influenced by the occurrence of transitional structures.
Structural Behavior of Column-Bent Cap Beam-Box Girder Systems in Reinforced Concrete Bridges Subjected to Gravity and Seismic Loads Part I: Pre-test Analysis and Quasi-Static Experiments

iii ACKNOWLEDGMENTS . v TABLE OF CONTENTS . vii LIST OF TABLES . xi LIST OF FIGURES . xiii 1 INTRODUCTION.1 1.1 General . 1 1.2 Problem Statement and Objectives . 5 1.3 Experimental Framework . 5 1.4 Computational Framework . 7 1.5 Organization of the Report . 8 2 BACKGROUND . 11 2.1 Effective Width.11 2.1.1 Concept of Effective Flange Width . 11 2.1.2 Effective Width of Flanged Beams in Building Codes . 14 2.1.3 Effective Width of Flanged Beams in Bridge Codes . 17 2.2 Review of Literature . 21 2.2.1 Effective Width for Bridge Girders . 22 2.2.2 Bridge Subassembly and Component Testing . 24 2.2. 3 Performance-Based Design of Bridges . 29 3 DEVELOPMENT OF EXPERIMENTAL PROGRAM . 31 3.1 Bridge Terminology . 31 3.2 Prototype . 33 3.3 Subassembly Development . 35 3.3.1 Subassembly Geometry . 35 3.3.2 Boundary Conditions . 37 3.4 Specimen Design.39 3.5 Test Set-Up . 42 3.6 Experimental Program . 46
HIV Latency in Myeloid Cells: Challenges for a Cure

The use of antiretroviral therapy (ART) for Human Immunodeficiency Virus (HIV) treatment has been highly successful in controlling plasma viremia to undetectable levels. However, a complete cure for HIV is hindered by the presence of replication-competent HIV, integrated in the host genome, that can persist long term in a resting state called viral latency. Resting memory CD4+ T cells are considered the biggest reservoir of persistent HIV infection and are often studied exclusively as the main target for an HIV cure. However, other cell types, such as circulating monocytes and tissue-resident macrophages, can harbor integrated, replication-competent HIV. To develop a cure for HIV, focus is needed not only on the T cell compartment, but also on these myeloid reservoirs of persistent HIV infection. In this review, we summarize their importance when designing HIV cure strategies and challenges associated to their identification and specific targeting by the “shock and kill” approach.
Deep Learning Based Disease, Pest Pattern and Nutritional Deficiency Detection System for “Zingiberaceae” Crop

Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of 99% for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of 96%. For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy (96%). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets.
A prediction model for keyhole geometry and acoustic signatures during variable polarity plasma arc welding based on extreme learning machine

Purpose The purpose of this paper is to investigate the relationship between the keyhole geometry and acoustic signatures from the backside of a workpiece. It lays a solid foundation for monitoring the penetration state in variable polarity keyhole plasma arc welding. Design/methodology/approach The experiment system is conducted on 6-mm-thick aluminum alloy plates based on a dual-sensor system including a sound sensor and a charge coupled device (CCD) camera. The first step is to extract the keyhole boundary from the acquired keyhole images based on median filtering and edge extraction. The second step is to process the acquired acoustic signal to obtain some typical time domain features. Finally, a prediction model based on the extreme learning machine (ELM) technique is built to recognize different keyhole geometries through the acoustic signatures and then identify the welding penetration status according to the recognition results. Findings The keyhole geometry and acoustic features after processing can be closely related to dynamic change information of keyhole. These acoustic features can predict the keyhole geometry accurately based on the ELM model. Meanwhile, the predict results also can identify different welding penetration status. Originality/value This paper tries to make a foundation work to achieve the monitoring of keyhole condition and penetration status through image and acoustic signals. A useful model, ELM, is built based on these features for predicting the keyhole geometry. Compared with back-propagating neural network and support vector machine, this proposed model is faster and has better generalization performance in the case studied in this paper.
Can social media help end the harm? Public information campaigns, online platforms, and paramilitary-style attacks in a deeply divided society

Online platforms can help public information campaigns reach target audiences who are unlikely to engage with content distributed via traditional media. This paper adds to this emergent literature, as the first study of the Ending the Harm campaign, which is designed to change public discourse about paramilitary-style attacks in Northern Ireland. Campaign effects were explored through interviews (N = 7) conducted with key stakeholders, as well as the results of a quantitative survey of residents (N = 805) in areas most affected by these attacks. Results indicate that exposure to the ETH advertisements correlated with a belief that PSAs were unjustified. Platforms like Snapchat helped the campaign reach younger demographics (16–34 years old). Nevertheless, it was unclear whether self-reported changes in attitude toward PSAs would lead to sustained behavioral changes.
Teachers’ Attitudes Towards Inclusive Education in Kosovo and the Role of Efficacy

The aim of the study is to understand teachers’ attitudes towards inclusive education. The study included in total (N = 293) participants. The reported results indicated that teachers in Kosovo have an average score approaching the median point (M = 29.36, DS = 6.30). Moreover, the reported results showed differences between groups of teachers’ attitudes towards inclusion [F (2, 290) = 3.831, p = . 02]. Experienced teachers who have children with SEN in their classes had a more positive attitudes towards inclusion.
Evaluation of performance and stability of new sources for tolerance to post-emergence herbicides in lentil (Lens culinaris ssp. culinaris Medik.)

ABSTRACT Context. Yield losses in lentil (Lens culinaris ssp. culinaris Medik.) caused by weeds are estimated at 20–80%. In the absence of effective broad-spectrum herbicides, the menace of weed has emerged as a serious yield constraint in lentil. Aims. Identification of tolerance to two broad-spectrum post-emergence herbicides, imazethapyr and metribuzin. Methods. 221 lentil accessions were screened at the experimental research station of the International Center for Agricultural Research in the Dry Areas, Terbol (Lebanon) over four crop seasons (2014/15–2018/19). During the 2014/15 season, a preliminary screening experiment was conducted with 221 lentil accessions in an alpha lattice design with two replications, where accessions were treated at the pre-flowering stage at 150% of the recommended dose of imazethapyr (112.5 g active ingredient/ha) or metribuzin (315 g active ingredient/ha). Based on the preliminary results, 38 accessions were selected for further evaluation. In subsequent testing, the selected accessions were screened at 100% and 150% of the recommended doses of imazethapyr or metribuzin. Key results. Herbicide treatments delayed the flowering time and maturation of tested lentil accessions. The plant height of selected tolerant accessions was reduced by 15% and 8% in the plots treated by imazethapyr and metribuzin, respectively. Reduction index (RIsy) was an effective tool to select for herbicide tolerance. Stability analysis indicated that two accessions (IG4400 and IG5722) performed under high rainfall environments while two other accessions (IG323 and IG4605) were adapted to low rainfall environments. Conclusions. Four accessions (IG323, IG5722, IG4400, IG4605) were identified as independently tolerant to metribuzin and imazethapyr. Implications. The identified herbicide-tolerant accessions can be used to develop herbicide-tolerant cultivars.
Early Grade Prediction Using Profile Data

 Abstract —Universities are reputable institutions for higher education and therefore it is crucial that the students have satisfactory grades. Quite often it is seen that during the first few semesters many students dropout from the universities or have to struggle in order to complete the courses. One way to address the issue is early grade prediction using Machine Learning techniques, for the courses taken by the students so that the students in need can be provided special assistance by the instructors. Machine Learning Algorithms such as Linear Regression, Decision Tree Regression, Gaussian Naïve Bayes, Decision Tree Classifier have been applied on the data set to predict students’ results and to compare their accuracy. The evaluated profile data have been collected from the students of 10th semester or above of the Computer Science department, BRAC University, Dhaka, Bangladesh. The Decision Tree Classifier technique has been found to perform the best in predicting the grade, closely followed by Decision Tree Regression and Linear Regression has performed the
A Unified Local–Global Feature Extraction Network for Human Gait Recognition Using Smartphone Sensors

Smartphone-based gait recognition has been considered a unique and promising technique for biometric-based identification. It is integrated with multiple sensors to collect inertial data while a person walks. However, captured data may be affected by several covariate factors due to variations of gait sequences such as holding loads, wearing types, shoe types, etc. Recent gait recognition approaches either work on global or local features, causing failure to handle these covariate-based features. To address these issues, a novel weighted multi-scale CNN (WMsCNN) architecture is designed to extract local to global features for boosting recognition accuracy. Specifically, a weight update sub-network (Ws) is proposed to increase or reduce the weights of features concerning their contribution to the final classification task. Thus, the sensitivity of these features toward the covariate factors decreases using the weight updated technique. Later, these features are fed to a fusion module used to produce global features for the overall classification. Extensive experiments have been conducted on four different benchmark datasets, and the demonstrated results of the proposed model are superior to other state-of-the-art deep learning approaches.