Miracle-dz/UniFAR-FTUs huggingface dataset:
Yield evaluation of guar genotypes (Cyamopsis tetragonoloba L. Taub.) selected for high-density planting and mechanical harvesting

Reis, C. M. G. , Almeida, C. M. , Peças, L. F. V. & Almeida, J. P. F. (2021). Yield evaluation of guar genotypes (Cyamopsis tetragonoloba L. Taub.) selected for high-density planting and mechanical harvesting. Bulg. J. Agric. Sci., 27 (5), 926–932 Guar (Cyamopsis tetragonoloba) is an annual and self-pollinated crop with a spring-summer cycle that belongs to Fabaceae family. Guar has been assuming increasing importance among agro-industrial crops due to the high galactomannan content of its seeds. Fifteen unbranched genotypes selected for a high-density planting and mechanical harvesting were evaluated for agronomic and yield traits. Ten morphological traits were studied for the analysis of variance, correlation, path coefficient analysis and principal components. The analysis of variance exhibited statistically significant differences among genotypes for all traits studied. The seed plant yield ranged from 13.5 to 25.0 g with mean 19.2 g. The highest significant positive correlation was observed between seed plant yield and number of pods per plant (r=0.806). Likewise, the path coefficient analysis showed that the number of pods per plant (0.72) exerted significantly positive direct effect on seed yield per plant. The PCA showed three components with eigenvalues greater than one, which explained 80.7% of the total variation. Among the fifteen genotypes studied, CB06, CB09, CB12 and CB14 were the most promising for a high-density planting and mechanical harvesting. The information presented in this manuscript is useful in breeding and development of guar genotypes for high-density planting and mechanical harvesting in Mediterranean environment.
Running head: QUALITY OR QUANTITY Quality or quantity? A multi-study analysis of emotion regulation skills deficits associated with Borderline Personality Disorder

Linehan’s (1993) biosocial theory posits that people with Borderline Personality Disorder (BPD) have emotion regulation skills deficits characterized by 1) less frequent use of adaptive emotion regulation strategies, 2) more frequent use of maladaptive strategies, or 3) poorer quality strategy implementation (i.e., strategies implemented less skillfully). We tested these possibilities among participants with BPD, Major Depressive Disorder (MDD), or no disorder (controls). Study 1 participants (N = 272) were recruited online; Study 2 participants (N = 90) completed in-person diagnostic assessments. The BPD groups reported greater use of maladaptive strategies than the MDD (d = .35) and control (d = 1.54) groups and lower quality implementation than the MDD (d = −.33) and control groups (d = −.97). BPD participants reported similar use of adaptive strategies as the MDD group (d = −.09) but less use than controls (d = −.47). BPD may be uniquely characterized by overuse of maladaptive strategies and poorer quality emotion regulation implementation.
Fostering Civil Discourse Online: Linguistic Behavior in Comments of #MeToo Articles Across Political Perspectives

Linguistic style and affect shape how users perceive and assess political content on social media. Using linguistic methods to compare political discourse on far-left, mainstream and alt-right news articles covering the #MeToo movement, we reveal rhetorical similarities and differences in commenting behavior across the political spectrum. We employed natural language processing techniques and qualitative methods on a corpus of approximately 30,000 Facebook comments from three politically distinct news publishers. Our findings show that commenting behavior reflects how social movements are framed and understood within a particular political orientation. Surprisingly, these data reveal that the structural patterns of discourse among commenters from the two alternative news sites are similar in terms of their relationship to those from the mainstream – exhibiting polarization, generalization, and othering of perspectives in political conversation. These data have implications for understanding the possibility for civil discourse in online venues and the role of commenting behavior in polarizing media sources in undermining such discourse. 1
Design of Center-TRACON Automation System

A system for the automated management and control of terminal area traffic, referred to as the Center-TRACON Automation System (CTAS), is being developed at NASA Ames Research Center. In a cooperative program, NASA and FAA have efforts underway to install and evaluate the system at the Denver area and Dallas/Ft. Worth area air traffic control facilities. This paper will review CTAS architecture, and automation functions as well as the integration of CTAS into the existing operational system. CTAS consists of three types of integrated tools that provide computer-generated advisories for both en-route and terminal area controllers to guide them in managing and controlling arrival traffic efficiently. One tool, the Traffic Management Advisor (TMA), generates runway assignments, landing sequences and landing times for all arriving aircraft, including those originating from nearby feeder airports. TMA also assists in runway configuration control and flow management. Another tool, the Descent Advisor (DA), generates clearances for the en-route controllers handling arrival flows to metering gates. The DA's clearances ensure fuel-efficient and conflict free descents to the metering gates at specified crossing times. In the terminal area, the Final Approach Spacing Tool (FAST) provides heading and speed advisories that help controllers produce an accurately spaced flow of aircraft on the final approach course. Data bases consisting of several hundred aircraft performance models, airline preferred operational procedures, and a three dimensional wind model support the operation of CTAS. The first component of CTAS, the Traffic Management Advisor, is being evaluated at the Denver TRACON and the Denver Air Route Traffic Control Center. The second component, the Final Approach Spacing Tool, will be evaluated in several stages at the Dallas/Fort Worth Airport beginning in October 1993. An initial stage of the Descent Advisor tool is being prepared for testing at the Denver Center in late 1994. Operational evaluations of all three integrated CTAS tools are expected to begin at the two field sites in 1995.
Augmented Reality Mobile Application Co-Design Experience: Delineating the Nuances from the Lens of a System Developer

With the rapid development of educational tools, co-design has been on the rise. Co-design is instrumental in successful product development as it merges two key perspectives, namely consumers’ insights and professionals’ knowledge. The roles of users, designers, and developers are now blurred as educators have begun to construct their own tools for teaching based on problems and ideas conceived in their classrooms, thereby assuming roles as designers and to some extent, as developers. A more common practice in recent days is educators’ co-designing of tools with system developers to achieve their targeted goal. This study attempted to explore the process of co-designing an augmented reality (AR) mobile application and the role of a system developer in mediating the design process with non-designers, namely academic members of the Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. This study further aimed to delineate the nuances of the AR mobile application co-design experience from the perspective of the system developer. Semi-structured interviews, observations, and document analyses were conducted to examine the detailed process of co-designing and developing the AR application as well as to understand the developer’s interaction with the consumers. The main findings of this study suggest that the waterfall model of the Software Development Life Cycle was in use during the co-design process. This cycle generally involves five stages, namely (i) planning, (ii) analysis, (iii) design, (iv) implementation, and (v) maintenance, which are iterative in nature. The designer’s role in mediating the co-design process encompassed balancing their knowledge and experience with the needs of consumers that do not necessarily match the designer’s expectations. This was achieved through (i) precise communication, (ii) commitment to the delivery and quality of the AR mobile application, as well as to building cohesive working relationships, and (iii) motivation to work with co-designers during the development process. The findings shed light on the value of co-design and the complex role of designers in mediating the design process with non-designers, which when accounted for, can lead to more feasible project development.
Gamma SAR processor and interferometry software

Based on the previous work of C. Werner and U. Wegmuller in the frame of their research at the University of Zurich and Jet Propulsion Laboratory, as well as up to date algorithms published in the literature Gamma Remote Sensing developed its advanced SAR processing and interferometry software packages. The SAR processor and the interferometry software are modular software packages written in the widely supported ANSI-C language. The well documented structured code and stepwise processing approach permit users to experiment with new algorithms and applications. Data of both airborne and spaceborne sensors including ERS, JERS-1, SIR-C, and RADARSAT StripMap mode, and the Dornier DOSAR have been successfully processed interferometrically. The processor is currently being certified by ESA for ERS processing and RSI for RADARSAT. The overall design of the software, algorithms used, special features implemented, typical sequences for processing with the Modular SAR Processor (MSP) and the Interferometric SAR Processor (ISP), as well as a few examples for results will be presented.
PREDICTIVE MODELING OF REAL TIME STOCK DATA

: Stock price prediction is one of the major problems faced by many investors. The volatile nature of the stock market increases the risk of investing our money in fear of losing it. Fluctuating stock prices affects the investor’s belief and thus there is a need to predict the future stock price to make more informed and accurate investment decisions. Also, one of the most quickly expanding research areas in today’s world is Sentiment Analysis. Social Media sites like Twitter have become an origin of a wide variety of information. This is because the people of the modern world prefer blogs or social media pages to post their ideas or opinions about a particular product, service, or organization. This paper aims to propose a prediction system using the Machine Learning algorithms; ARIMA, LSTM & Linear Regression and makes use of a popular social media platform, Twitter to implement a system that gets insights about the sentiments from the fetched Tweets to predict stock prices. These Machine Learning algorithms are applied to the historical stock data of the past 2 years. Parameters considered are open, close, low, high, and volume.
Insect-based diets effects on turkey meat quality

Lalev, M. , Mincheva, N. , Oblakova, M. , Hristakieva, P., Ivanova, I. , Atanassov, A. & Petrova, A. (2021). Insect-based diets effects on turkey meat quality. Bulg. J. Agric. Sci., 27 (5), 980–989 Data on the effects of insect-based diets on turkey meat quality is limited. To address this, the current study aimed to assess the effects of 10% inclusion of insect meals from Black soldier fly (Hermetia illucens) and Silkworm (Bombyx mori) on turkey meat quality by analyzing physicochemical (pH, water holding capacity, cooking loss, color) and nutritional parameters (protein, amino acids, lipid and mineral content). Hybrid female turkeys at 56 day of age were divided into five dietary treatments: Control (soybean meal), Silkworm meal (SW), Silkworm meal with probiotic mix ‘Zoovit’ (SWpro), Black soldier fly defatted (BSFd) and Black soldier fly whole larvae (BSFw) meals. The experiment lasted for 74 days, from 56-130 days of a turkey’s age. Overall results suggest that 10% inclusion of insect meal have positive effects on turkey meat, especially on technical parameters, such as water holding capacity and cooking loss. Breast and thigh meat responded differently to the diets. Breast meat had significant physicochemical responses to the new diets (improved water holding capacity, cooking loss), but lacked nutritional – no change in protein, lipid, or mineral content. In contrast, in the thigh, both BSF diets increase the level of lipids and Iron. Insect diets also improve omega-3 and omega-6 fatty acid levels. The applicability of the results is demonstrated by the fact that insect meals can alter turkey meat quality in a tissue-specific manner.
Early Development of ADHD and ODD Symptoms from the Toddler to Preschool Years

Objective: ADHD and ODD are commonly co-occurring, but often studied individually. This study evaluated common trajectories of these disorders and explored how they co-develop in early childhood. Method: Community parents (N = 273) completed online surveys about their 2-year-old. Children’s inattention, hyperactivity/impulsivity, and oppositional defiant disorder symptoms over 2 years were examined using latent class, dual trajectory, and cross-lagged analyses. Results: Most children followed low symptom trajectories. A small portion showed high, moderate, or increasing trajectories. The hyperactive/impulsive domain of ADHD showed a declining symptoms group. Children in high ODD groups were likely to be in high ADHD symptom groups; the converse was true but probabilities were lower. Hyperactive/impulsive symptoms predicted ODD symptoms across time, more than vice versa. Conclusion: The study extends the small body of literature assessing early development of ADHD and ODD. Findings suggest that earlier intervention for symptoms of ADHD may mitigate risk of developing ODD.
Dynamic Rupture Modeling of Induced Earthquakes in Oklahoma

Since 2008, seismicity has increased in Oklahoma due to high-volume fluid injection. We examine the impact of pore pressure and tectonic factors on the magnitude and the potential for larger earthquakes Dynamic rupture models are performed on 10 km faults with varying input parameters. A neural network trained to approximate the results of the models predicts the moment magnitude of a rupture with 74% accuracy. Fault roughness and normal stresses are negatively correlated with moment magnitude, while the pore pressure has no correlation. We use the trained neural network with a Markov Chain Monte Carlo (MCMC) to find distributions of tectonic conditions. Using the inverse method on induced seismicity data from the Fairview earthquake sequence provides information about frictional parameters and fault geometry that cannot be found using other methods. The results suggest that induced earthquakes are tectonic earthquakes that have been advanced in time, rather than new events.
Regression forest region recognition enhances multi-atlas spleen labeling

Complex and variable abdominal anatomy poses considerable challenges for automated segmentation of the spleen. Herein, we propose to use “regression forests region recognition” (RFRR) to identify bounding boxes around a region of interest (e.g., the spleen) prior to image-based registration. Within these pre-identified regions, we apply multi-atlas segmentation using deformable registration. By focusing on reduced regions of interest, fewer structures confound registration and errors are minimized. The primary contributions of this work are: (1) to extend the existing regression forest framework to include local features; (2) to combine automated regional localization with multi-atlas segmentation, and (3) to accurately segment spleen volumes on clinically acquired computed tomography (CT) data. This approach achieves Dice similarity coefficient (DSC) of 0.82±0.13 and mean surface distance of 7.49±10.62 mm verses a traditional multi-atlas method (without reduced regions of interest) with mean DSC of 0.66±0.21 and mean surface distance of 8.67±13.35 mm.
UniSUF: a unified software update framework for vehicles utilizing isolation techniques and trusted execution environments

Today’s vehicles depend more and more on software, and can contain over 100M lines of code controlling many safety-critical functions, such as steering and brakes. Increased complexity in software inherently increases the number of bugs affecting vehicle safety-critical functions. Consequently, software updates need to be applied regularly. Current research around vehicle software update solutions is lacking necessary details for a versatile, unified and secure approach that covers various update scenarios, e.g., over-the-air, with a workshop computer, at factory production or using a diagnostic update tool. We propose UniSUF, a Unified Software Update Framework for Vehicles, well aligned with automotive industry stakeholders. All data needed for a complete software update is securely encapsulated into one single file. This vehicle unique file can be processed in multitudes of update scenarios and executed without any external connectivity since all data is inherently secured. To the best of our knowledge, this comprehensive, versatile and unified approach cannot be found in previous research and is a contribution to an essential requirement within the industry for handling the increasing complexity related to vehicle software updates.
’ s response to : RC # 1 and RC # 2 of Anonymous Referee # 1 and # 2

RC#1 and RC#2 of Anonymous Referee #1 and #2 https://doi.org/10.5194/acp-2021-517-RC1; https://doi.org/10.5194/acp-2021-517-RC2 Jonas Witthuhn1, Anja Hünerbein1, Florian Filipitsch2, Stefan Wacker2, Stefanie Meilinger3, and Hartwig Deneke1 1Leibniz Institute of Tropospheric Research (TROPOS), Leipzig, Germany 2German Weather Service (DWD), Meteorological Observatory Lindenberg, Tauche, Germany 3University of Applied Sciences Bonn-Rhein-Sieg, Sankt Augustin, Germany Correspondence: Jonas Witthuhn (jonas.witthuhn@tropos.de)
Non-English Majored Freshmen’s Perspectives on Benefits and Challenges of Using E-Learning: a Study at Van Lang University

Since the first outbreak of Covid-19 was first identified in Wuhan, China in December 2019, the pandemic has been spreading all over the globe. It has been causing disastrous impacts on many aspects of society including economy, politics, etc., especially education. Vietnam is not an exception. Since the pandemic started in Vietnam in January 2020, education in Vietnam has changed a lot as a result. Many educational institutions have had different strategies for their training programs. Some allowed students to stay at home for a longer holiday whereas other schools carried out online learning courses for the students to avoid being behind the schedules. There have been a number of different perspectives on the effectiveness of online learning courses among parents and students because this is the first time that all of the courses have been completely delivered online, and Van Lang University (hereafter VLU) is an example. VLU students were asked to study online the whole second semester of the academic year 2020-2021. At first, there were many problems arising such as technical problems, difficulties related to new teaching-learning methods, etc., but later, the situation was more positively improved.
Effect of Structured Postpartum Nursing Intervention Involving COVID-19 Precautions on Mother ' s Knowledge , Practice , Fear level and Neonatal care 1

The study aimed to evaluate the effect of structured postpartum nursing intervention involving COVID-19 precautions on mother's knowledge, practice, fear level and neonatal care. The study sample was purposive composed of 75 mothers during postpartum period. Study design was Quasi Experimental ( pre-post). Setting: the study conducted at the obstetrics and gynecology departments affiliated to University Hospital and Shebin El-Kom teaching hospital, Menoufia Governorate, Egypt. Five tools were used for data collection: a structured interviewing questionnaire, an observational checklist of mother practice of maternal and neonatal care, COVID-19 precautions knowledge questionnaire, Observational checklist of COVID-19 practice, and the COVID-19 fear scale. Study results revealed a statistically significant difference in the mothers' knowledge and practice regarding COVID-19 precautions and decrease in fear level on post and follow up intervention compared to pre intervention which indicated that the nursing intervention involving COVID-19 precautions had positive effect on mothers’ knowledge, practice and fear level. So, integrating postpartum nursing intervention involving COVID-19 precautions as a routine care for all postpartum mothers is recommended.
Research Internship 2021-2022 Topic Formally Veriﬁed Compilation of C and Rust

CompCert 1 is a compiler for the C programming language for the assembly languages of several processor architectures. In contrast to compilers such as Visual C++, GCC, or LLVM, its compilation phases are proved mathematically correct, and thus the compiled program always matches the source program: the formal correctness of CompCert states that if the compiler succeeds to produce an executable, then the observable behaviors of the executable are also observable on the source program [1, 2]. Other compilers may contain bugs that in some cases result in incorrect code being generated. The possibility of compilation bug cannot be tolerated in certain applications with high safety requirements, and then costly solutions such as disabling all optimizations are used to get assembly code that is close to the source. In contrast, CompCert, despite not optimizing as well as gcc -O3 or clang -O3, allows using optimizations safely [3, 4]. CompCert is itself written as a combination of the Coq interactive theorem prover 2 and the OCaml programming language 3 . CompCert-KVX 4 is a variant of CompCert developed at Verimag which provides the ﬁrst formally veriﬁed eﬃcient optimizations for superscalar and VLIW processors [5, 6, 7, 8, 9]. We propose several internship topics around formally veriﬁed compilation (to be discussed according to the interests of the students), through modiﬁcations to CompCert-KVX or in independent tools. Here are examples of hot topics for us:
Impact of model-agnostic nonconformity functions on efficiency of conformal classifiers: an extensive study

The property of conformal predictors to guarantee the required accuracy rate makes this framework attractive in various practical applications. However, this property is achieved at a price of reduction in precision. In the case of conformal classiﬁcation, the system can output multiple class labels instead of one. It is also known, that the choice of nonconfor-mity function has a major impact on the eﬃciency of conformal classiﬁers. Recently, it was shown that diﬀerent model-agnostic nonconformity functions result in conformal classiﬁers with diﬀerent characteristics. For a Neural Network-based conformal classiﬁer, the inverse probability (or hinge loss) allows minimizing the average number of predicted labels, and margin results in a larger fraction of singleton predictions. In this work, we aim to further extend this study. We perform an experimental evaluation using 8 diﬀerent classiﬁcation algorithms and discuss when the previously observed relationship holds or not. Additionally, we propose a successful method to combine the properties of these two nonconformity functions.