Miracle-dz/UniFAR-FTUs huggingface dataset:
Maghemite Nanofluid Based on Natural Ester: Cooling and Insulation Properties Assessment

The objective of this work is to study the effect that the addition of magnetic nanoparticles to a natural ester has on its properties and its cooling capacity. Some samples of ferrofluid (natural ester with maghemite) have been prepared using different concentrations. These have been characterized by measuring their thermo-hydraulic and dielectric properties, to find an optimal concentration. Then, the cooling capacities of the optimal nanofluid and the base fluid have been tested in a transformer immersed in these liquids. The experimental platform allowed the measurement of temperatures in different locations at different load levels. Parallel simulations of these tests have been carried out with a Computational Fluid Dynamics model of the experimental platform. The results show an improvement of the insulating capacity of the base fluid with the addition of maghemite nanoparticles, and an enhanced cooling capacity.
Improved Message Payload and Security of Image Steganography using 3-3-2 LSB and Dual Encryption

This research proposed a steganography technique on RGB true color image using LSB 3-3-2 method. LSB 3-3-2 is a message embedding technique on 3 Least Significant Bit (LSB) on the red and green channel, while in the blue channel only 2 LSB. Embedding messages on blue channels is less than red and green, which aims to maintain the quality of imperceptibility of stego image with maximum message payload is 2.67 bits per pixel (bpp). To improve message security, dual encryption methods are used, ie Caesar and Vigenere ciphers combined with bitwise and operator and bitwise or operators on the embedding process. Based on the experimental results obtained a satisfactory average value of PSNR is about 43dB.
Game-Based Learning - Developing a Business Game for Interactive Architectural Visualization

We explored how game-based learning including open forms of teaching can improve teamwork, social skills and collaboration of inhomogeneous student groups. As open forms of teaching we used blended learning, open space technology (OST) and selected elements of gamification. Game-based learning approaches posit that learners who interact with educational materials with playful and dynamic tasks will learn better. To better understand improvement of teamwork, social skills and collaboration a specific business game with a defined amount of play tokens is introduced. Within this business game all participants (n = 22) will have to form interdisciplinary project teams to manage a professional 3D-visualization task. In three subsequent project stages (concept, development and implementation) all projects are continually evaluated and the teams are appropriately rewarded with play tokens. With these play tokens every team can purchase missing know-how or hire additional workforce. The final visualization project is evaluated by specific design criteria (qualitative) and by the remaining amount of play tokens (quantitative). The concept of a business game was a visible success. Open forms of teaching, such as blended learning and open space technology promote the active acquisition of basic knowledge, technical understanding and at least increase transfer competence. A continuous and transparent evaluation makes the individual learning progress directly tangible. Students who took part in the business game reported more enjoyment in learning the 3D visualization pipeline than students from the years before. The business game induced greater interest to the students in acquiring new content more easily and quickly than in the other courses. Also their high degree of personal responsibility led to outstanding new ideas and their own initiatives.
Classification of Nutrient Deficiency in Black Gram Using Deep Convolutional Neural Networks

This paper investigates the use of various deep convolutional neural networks (CNNs) with transfer learning to identify nutrient deficiencies from a leaf image. Experiments were conducted with a dataset containing 4,088 images of black gram (Vigna mungo) leaves grown under seven different treatments, i.e., complete nutrient treatment and six nutrient deficiency treatment, including calcium (Ca), iron (Fe), magnesium (Mg), nitrogen (N), potassium (K), and phosphorus (P) deficiencies. Experimental results indicate that a deep CNN model known as ResNet50 was the best among all experimented models with a test accuracy of 65.44% and a F-measure of 66.15%. In addition, We found that the ResNet50 model obviously outperformed a block-based method and the human performance reported in a literature.
Exploring Factors Influence Behavioral Intention to Use E-Government Services Using Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)

This study used Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model to understanding factors affecting behavioral intention to use e-Government services. UTAUT2 model more completely than UTAUT with added variables such as hedonic motivation, price value, and habit. We modify some variables in UTAUT2. Price value variable was substituted by public value variable. Whereas the hedonic motivation variable was eliminated because public service is not business activity. We complete the model with antecedent trust variable. We have 203 respondents from the citizen that spread in Jakarta and surroundings to answer through online questionnaires. The data analysis showed that public value, habit and effort expectancy have positive impact on behavioral intention to use e-government services.
Mask R-CNN for Object Detection in Multitemporal SAR Images

Synthetic aperture radar (SAR) has unique advantages in ocean monitoring. Ship object detection in multitemporal SAR images has great potentials in various applications. In this study, we aim to improve the accuracy of deep learning method for ship detection in SAR images. We have proposed a framework based on the Mask R-CNN. The training process utilizes SAR dataset SSDD, and the model incorporates ImageNet weights which are adequately trained for transfer learning. Experiments based on both Single-Mask and Multiple-Masks have been designed and conducted with data preparing methods such as denoising and data augmentation. The results showed that Mask R-CNN model outperformed other start-of-art models for SAR image-based ship detection. Specifically, the accuracy has been improved to 96.8% within 116ms.
Seven Segment Display Detection and Recognition using Predefined HSV Color Slicing Technique

Detection and recognition of LED seven segment panels present a particular challenge of locating characters among background clutter. This study proposed a framework based on a predefined HSV color slicing technique. The results demonstrate the framework's superiority over other color slicing methods with 94.46% precision, 92.24% recall, and 87.17% accuracy rates. The digit detection and recognition with the proposed predefined HSV color slicing is simple and straightforward - making it an attractive solution for the future deployment for edge-computing.
Hybrid Chain-Hypergraph P Systems for Multiobjective Ensemble Clustering

Clustering is a classic combined optimization problem that is widely used in pattern recognition, image processing, market analysis and so on. However, the efficiency of clustering algorithms decreases as the amount of data increases. In addition, most of the existing methods optimize only one objective and therefore may be suitable only for datasets with certain features. To address these limitations, in this paper, we develop a new hybrid chain-hypergraph P system (named HCHPS), which makes full use of the parallelism of P systems as well as the advantages of chain and hypergraph topology structures for accurate and efficient clustering. Our new P system comprises three types of subsystems, i.e., reaction chain membrane subsystems, local communication membrane subsystems and global ensemble membrane subsystems. Each type of subsystems is implemented end-to-end in HCHPS with new rules and membrane structures in parallel. In particular, to obtain efficient clustering center objects and make the algorithm robust to data with various features, the reaction chain membrane subsystems perform three different multiobjective strategies simultaneously by new chain evolution rules. To increase the population diversity of cluster centers, the local communication membrane subsystems utilize transport rules between membranes for coevolution of nondominated objects. The global ensemble membrane subsystems conduct a new dense representation multisize ensemble strategy to further improve the accuracy of the final results. Evaluations on two artificial data sets and 17 real-life data sets demonstrate the robustness of the proposed method in correctly clustering data sets with different dimensions and shapes. Our experimental results outperform those of both baseline and state-of-the-art methods. Moreover, benefiting from the parallelism, HCHPS is less time consuming than other methods, featuring an average completion time of 28.07 seconds on the 17 real-life data sets. Moreover, an ablation study shows that our proposed components are critical for effective cluster analysis.
Weighted Optical Flow Prediction and Attention Model for Object Tracking

Object tracking has been a hot computer vision topic for many years. Although great process has been made, it still has large room to improve because of the complexity of the natural scene and the multiple interference. In this work, we improve the object tracking performance in two ways. First, a sequential scoring model is proposed to integrate the optical flow information of history video frames into the feature map of current frame. Second, an attention model with optical flow information is used for further improvement by differentiating the contribution of different positions in the template to the final response map. On the other hand, the entire model are end-to-end trainable. We test the methods on OTB (Object Tracking Benchmark) and VOT (Visual Object Tracking) tracking datasets. The experimental results demonstrate that the improved tracking accuracy and robustness to occlusion, strenuous motion and vanishing objects.
Quality Measurement of Software Based on Characteristics of Functionality, Reliability, and Maintainability

The purpose of this study is to determine the level of quality of software “application of software cost estimation ” which is a product of previous research that will be opened to the public. Testing uses the ISO 9126–1 standard that applies internationally. The ISO standard is chosen because it has complete quality characteristics. The characteristics tested in this study are functionality, reliability, and maintainability. These three characteristics are the factors that most influence the quality of the application. The results of this study are quality measurement values based on ISO 9126–1 and recommendations for improving application quality. After measuring the application/software quality, we know that the characteristics of functionality and reliability are very good, while the maintainability characteristics are still in the bad category. Required to add some features such as to record the activity log, to diagnose the application, and to test features which are installed in the application. Finally, the procedures, methods, and metrics used in this study can be adopted for other software quality testing. Adjustments, as needed, are needed with the condition of the software object being tested.
Rectal Diclofenac administration for prevention of post-Endoscopic Retrograde Cholangio-Pancreatography (ERCP) acute pancreatitis. Randomized prospective study.

INTRODUCTION Post-Endoscopic Retrograde Cholangio-Pancreatography pancreatitis (PEP) is a relevant (1-4%) complication of biliopancreatic operative endoscopy. Rectal nonsteroidal anti-inflammatory drugs (specifically, 100 mg of diclofenac) have shown promising prophylactic activity in PEP. The aim of our prospective study is to report whether prophylactic oral versus rectal suppository versus intramuscular diclofenac versus placebo are able to reduce the incidence and the severity of ERCP-induced pancreatitis. MATERIALS AND METHODS In this randomized, double-blinded, prospective study, 100 patients (49 male, 51 female), similar with regard to indication for ERCP, were enrolled between January 2016 and November 2017 to undergo ERCP in the Section of General and Thoracic Surgery of University Hospital of Palermo. They were randomized into five groups, respectively 20 patients with placebo by mouth; 20 patients with 50 mg diclofenac sodium enteric-coated capsules by mouth; 20 with 100 mg rectal suppository diclofenac, 20 with 75 mg/3 ml intramuscular diclofenac sodium, 20 with 75 mg/3 ml intramuscular diclofenac sodium and 20 with 75 mg/3 ml intravenous diclofenac. All drugs were administered 30 to 90 minutes before ERCP. All clinical data were collected one day before and 2, 12 and 24 hour after ERCP. RESULT Data were prospectively collected and to demonstrate the preventive effect of rectal diclofenac on PEP, a two-by-two table and chi-square test with Yates correction were used: the incidence of PEP was significantly lower (p < 0.001) in the rectal diclofenac group respect to other groups and, in the same way, the incidence of post-ERCP pain was significantly lower in the rectal diclofenac group than in the other groups (p = 0.001) and patients discharge was consequently earlier (p < 0.01). CONCLUSION 100 mg dose rectal diclofenac administered 30-60 minutes before ERCP can effectively prevent PEP.
Characterization of the Impact of Drug Metabolism on the Gas-Phase Structures of Drugs Using Ion Mobility-Mass Spectrometry.

Conventional strategies for drug metabolite identification employ a combination of liquid chromatography-mass spectrome-try (LC-MS), which offers higher throughput but provides limited structural information, and nuclear magnetic resonance spectroscopy, which can achieve the most definitive identification but lacks throughput. Ion mobility-mass spectrometry (IM-MS) is a rapid, two-dimensional analysis that separates ions on the basis of their gas-phase size and shape (reflected by collision cross section, CCS) and their mass-to-charge (m/z) ratios, respectively. The rapid nature of IM separation com-bined with the structural information provided by CCS make IM-MS a promising technique for obtaining more structural information of drug metabolites without sacrificing analytical throughput. Here, we present an in vitro-biosynthesis coupled with IM-MS strategy for rapid generation and analysis of drug metabolites. Drug metabolites were generated in vitro using pooled subcellular fractions derived from human liver and analyzed using a rapid flow injection-IM-MS method. We meas-ured CCS values for 19 parent drugs and their 37 metabolites generated in vitro (78 values in total), representing a wide va-riety of metabolic modifications. Post-IM fragmentation and computational modeling were used to support metabolite iden-tifications and explore the structural characteristics driving behaviors observed in IM separation. Overall, we found the effects of metabolic modifications on the gas-phase structures of the metabolites to be highly dependent upon the structural charac-teristics of the parent compounds and the specific position of the modification. This in vitro-biosynthesis coupled with rapid IM-MS analysis workflow represents a promising platform for rapid and high-confidence identification of drug metabolites, applicable at a large scale.
Premarital parenthood and newlyweds' marital trajectories.

An increasing number of couples in the United States are entering their first marriage having already had a child together, raising important questions about whether and how these couples' marriages differ from newlywed couples who enter marriage without children. The current study used 5 waves of data collected over the first 4.5 years of marriage from a sample of ethnically diverse, first-married newlywed couples living with low incomes to examine the effects of premarital parenthood on couples' self-reported satisfaction, observed communication, and marital dissolution over time. Among couples who entered marriage with a shared biological child (premarital parents), satisfaction levels were lower and communication was less effective, less positive, and more negative than couples entering marriage without children. Rates of change in marital functioning did not differ between groups, but the rate of marital dissolution was twice as high among premarital parents (19.1%) relative to couples who were not parents at the start of marriage (9.5%). These between-groups differences remained robust after controlling for several demographic differences (race, age, education, household income, work status, relationship length, premarital cohabitation), and initial differences in communication did not account for between-groups differences in marital satisfaction or dissolution. Together, these findings indicate that newlywed marriage unfolds in similar ways for low-income couples with and without children at the start of marriage, but couples raising children at the time of marriage have greater vulnerability to marital distress and dissolution. Further research is needed to characterize this risk and how interventions can offset it. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Influence of Test Environment, Age, Sex, and Sport on Baseline Computerized Neurocognitive Test Performance

Background: Baseline computerized testing is a common component of concussion assessments, and the testing environment has been suggested to influence test performance and validity. Purpose: To compare concussion baseline computerized neurocognitive test performance and validity among adolescent athletes based on testing environment (group, individual), age group (10-12, 13-15, 16-18 years), sex (male, female), and sport type (collision/combat, contact, noncontact). Study Design: Cohort study; Level of evidence, 3. Methods: Through a concussion community outreach program, participants completed baseline computerized neurocognitive testing using Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT). A total of 2845 athletes aged 10 to 18 years completed the baseline assessment. A total of 2241 (79%) athletes completed baseline testing in a group environment, and 604 (21%) completed testing in an individual environment. A random subsample of 500 athletes from each group was selected for statistical comparison. Results: No significant differences were supported in baseline computerized neurocognitive test performance between the group and individual administration environments. Test validity was statistically similar across testing environment, age group, and sex. However, patients of older age (16-18 years), female sex, and collision/combat and contact sports performed better on ImPACT. There were differences in total symptom severity scores (t = 2.19, df = 998, P = .03), with participants in the group testing environment reporting lower total symptom severity scores than those in the individual testing environment. The rates of invalid tests were low across all age groups, averaging from 4.0% in the 10- to 12-year age group to 4.8% in the 13- to 15-year age group. Conclusion: The findings indicated that concussion baseline neurocognitive test performance is similar when administered in group and individual testing environments. However, differences based on age group, sex, and sport type should be considered when interpreting baseline computerized neurocognitive test scores. The finding of higher symptom scores in older adolescents in the individual testing environment suggests that they may be less forthcoming about symptoms in a group setting.
Trifluridine/Tipiracil plus Oxaliplatin Improves PD-1 Blockade in Colorectal Cancer by Inducing Immunogenic Cell Death and Depleting Macrophages

The induction of immunogenic cell death (ICD) in tumors enhances immune checkpoint blockade. Trifluridine/tipiracil plus oxaliplatin induces T cell–dependent ICD in murine colon cancer and depletes immunosuppressive macrophages. Trifluridine/tipiracil (FTD/TPI) is a new antimetabolite agent used to treat chemorefractory metastatic colorectal cancer. FTD/TPI induced immunogenic cell death (ICD) in vitro in the microsatellite-stable (MSS) CT26 mouse colon carcinoma cell line, as well as in various human MSS colorectal cancer cell lines (SW620, Caco-2, and Colo-320). The combination of FTD/TPI with oxaliplatin synergized to promote ICD. In vivo, the combination was able to induce ICD, but not the single agents, although all treatment groups showed T-cell dependency. In addition, FTD/TPI and oxaliplatin did not affect regulatory T cells or myeloid-derived suppressor cells but eliminated type-2 tumor-associated macrophages (TAM2), resulting in higher cytotoxic CD8+ T-cell infiltration and activation. This effect was concomitantly associated with PD-L1 expression on tumor cells and PD-1 induction on CD8+ T cells, leading to secondary T-cell exhaustion. Finally, although anti–PD-1 was unable to synergize with FTD/TPI or oxaliplatin monotherapy, concomitant administration of anti–PD-1 to FTD/TPI and oxaliplatin enhanced the antitumor efficacy of the double chemotherapy. Our study showed a novel immunomodulatory role of FTD/TPI and oxaliplatin in depleting TAM2. The combination of oxaliplatin and FTD/TPI induced ICD in vivo, providing a rationale for the use of these drugs to eliminate immunosuppressive cells and boost checkpoint efficacy in patients with metastatic colorectal cancer.
Risk of pneumonia among residents living near goat and poultry farms during 2014-2016

In the Netherlands, an association was found between the prevalence of pneumonia and living near goat and poultry farms in 2007–2013. This association then led to regulatory decisions to restrict the building of new goat farms and to reduce emissions of poultry farms. Confirmation of these results, however, is required because the period of previous analyses overlapped a Q-fever epidemic in 2007–2010. To confirm the association, we performed a population-based study during 2014–2016 based on general practitioner (GP) data. Electronic medical records of 90,183 persons were used to analyze the association between pneumonia and the population living in the proximity (within 500–2000 m distance) of goat and poultry farms. Data were analyzed with three types of logistic regression (with and without GP practice as a random intercept and with stratified analyses per GP practice) and a kernel model to discern the influence of different statistical methods on the outcomes. In all regression analyses involving adults, a statistically significant association between pneumonia and residence within 500 meters of goat farms was found (odds ratio [OR] range over all analyses types: 1.33–1.60), with a decreasing OR for increasing distances. In kernel analyses (including all ages), a population-attributable risk between 6.0 and 7.8% was found for a distance of 2000 meters in 2014–2016. The associations were consistent across all years and robust for mutual adjustment for proximity to other animals and for several other sensitivity analyses. However, associations with proximity to poultry farms are not supported by the present study. As the causes of the elevated pneumonia incidence in persons living close to goat farms remain unknown, further research into potential mechanisms is required for adequate prevention.