Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of chip budded cassava plants for growth, tuber yield and starch content

The experiment was conducted at Tapioca and Castor Research Station, Yethapur to evaluate the performance of chip bud plants under field conditions for assessing the vegetative growth and tuber yield parameters. The experiment was laid out in Randomized Block Design with three replications. Cassava chip budded plants were produced through Rapid Multiplication Technique (RMT) using protray method under net house and a total of ten treatments were imposed on the plating material. At the time of harvest i.e., eight months after planting, maximum plant height (141.63 cm) was recorded in chip budded plants raised in pot mixture containing Pseudomonas (T10) and the minimum plant height (111.42 cm) was recorded in Mini sett plants raised in Vermicompost containing Pseudomonas. The maximum (8.6 cm) and minimum stem girth (6.8 cm) was recorded in chip budded plants raised in pot mixture containing Pseudomonas (T10) and Mini sett plants raised in pot mixture containing Pseudomonas (T9) respectively. The maximum tuber yield per plant (7.26 kg) was recorded in chip budded plants raised in Cocopeat containing Pseudomonas (T4) which was significantly different over the other treatments. The minimum tuber yield per plant (4.58 kg) was recorded in Control (Farmers practice Propagation by raised bed system) (T1). The pooled mean analysis revealed that maximum starch content was recorded (25.49%) in chip budded plants raised in vermicompost containing Pseudomonas (T7) which was on par (24.91%)with chip budded plants raised in pot mixture containing Pseudomonas (T10).
When the Boss Comes to Town: The Effects of Headquarters' Visits on Facility-Level Misconduct

We study the effects of visits by headquarters’ managers on facility-level misconduct. These visits are central to monitoring, but are difficult to observe for a large sample of firms. We use the staggered introduction of airline routes to identify exogenous reductions in travel time between headquarters and facilities as our measure of visits and test whether the reductions affect misconduct. We find that for the at-risk sample travel-time reductions decrease the number of violations by 2% and penalties by 23.4%, suggesting that management focuses on reducing costlier violations as opposed to simply reducing the number of violations. These effects are concentrated in firms with weaker control systems, suggesting that strong controls can act as substitutes for visits. Furthermore, the introduction of broadband internet attenuates, but does not eliminate the effect of reductions on misconduct. Finally, we find that reductions result in greater misconduct when firms are subject to performance pressure.
USING PATH ANALYSIS TO PARTITION THE VARIABILITY IN SLAUGHTER PERFORMANCE IN THREE LINES OF JAPANESE QUAIL DIFFERING IN GENETI

Chicks of two selected lines of Japanese quail for high six weekbody weight and high 1-6 week growth rate (HBW6 and HGR0-6) over three generations were used to partition the variability of growth and carcass traits. A randombred control line (RC) was kept in order to facilitate comparison between lines. The RC had the lowest boneless meat% (BLM), fat% and live body weight at slaughter (LBW6) being 48.13%, 16.09% and 184.65g, respectively. Although HGR had the highest BLM% and dressing%, birds of this line had significantly poorer feed conversion (FC) and performance index (PI) than other lines. On the other hand, birds of the HBW line had the highest LBW6 and PI but had higher carcass' fat% (200.25g, 3.55 and 20.51%, respectively). Sex significantly affected BLM%, carcass %, dressing%, LBW6, FC and PI. Although males had significantly higher carcass%, their PI were poorer than females, this may be due to the superiority of females growth than males at different ages of growth and this difference may be attributed primarily to the relatively large ovaries, liver and intestines of the females. When these parts were excluded, lower carcass% for female was obtained. Except triglycerides (TG), line significantly affected all plasma constituents. The RC line had the highest growth hormone (GH) and the lowest albumin (Alb), total lipids (TL), total protein (TP), triiodothyronine (T3) and ratio of T3 to thyroxine (T3/T4). Whereas, HGR had the lowest GH and the highest Alb, TP, T3 and T4. Females had significantly higher Alb, TP and T3/T4 than males, however, males had higher T3 than females. The results of path analyses revealed that the studied plasma constituents measured at three weeks of age in both sexes could be used to predict carcass traits and growth productive performance in Japanese quail. Each of GH, TG, TL, Alb, TP and T3/T4 was the first contributors of studied productive traits in line by sex groups indicating direct effects which ranged from 0.530 to 0.763, 0.504 to 0.945, 0.887 to 0.906, 0.513 to 0.990 and 0.609 to 0.892, respectively. Plasma constituents showed higher indirect coefficients of determination for their effects on all studied productive traits than their direct effects. This suggests that a part of the variations in these traits could be attributed to a trait or more, not handled in this study and may diminish the random error variation when considered.
FACTORS OF ANTICIPATORY SOCIALIZATION: A STUDY WITH STUDENTS OF ACCOUNTING SCIENCES

Motivation, environmental perception, involvement and professional commitment are characterized as sociable elements of an individual. In this context, this study investigates the factors of anticipatory socialization of university students of accounting from two federal universities in the state of Paraná under the conceptual and methodological support of the Astin model. The data were collected through a questionnaire adapted from the study of Ahmad, Anantharaman and Ismail (2012), applied to 71 graduating students in the year 2016, and were treated with the statistical techniques of descriptive analysis and Structural Equation Modeling Article presented in XIX SEMEAD – Seminários em Administração (Business Seminar), November 2016. Submission on 1/21/2018. Review on 4/18/2018. Accepted on 5/3/2018. Edenise Aparecida dos Anjos, Edicreia Andrade dos Santos, Ivanildo Viana Moura, Lauro Brito de Almeida, Rogério João Lunkes Revista Catarinense da Ciência Contábil, ISSN 1808-3781 – eISSN 2237-7662, Florianópolis, SC, v. 17, n. 52, p. 55-67, Sept. /Dec. 2018 via Partial Least Square (PLS). The research findings suggest that the motivation and environmental perception factors do not affect in a statistically significant way on the involvement and, consequently, the professional commitment. However, it was confirmed that the motivation positively and significantly affects the professional commitment. Thus, these results instigate future research to include other cognitive characteristics of students that may influence them in the face of professional commitment.
THE CONTEXT OF SUFFERING: EMPIRICAL INSIGHTS INTO THE PROBLEM OF EVIL

While the evidential problem of evil has been enormously influential within the contemporary philosophical literature—William Rowe’s 1979 formulation in “The Problem of Evil and Some Varieties of Atheism” being the most seminal—no academic research has explored what cognitive mechanisms might underwrite the appearance of pointlessness in target examples of suffering. In this exploratory paper, we show that the perception of pointlessness in the target examples of suffering that underwrite Rowe’s seminal formulation of the problem of evil is contingent on the absence of broader context. In other words, we show that when such suffering is presented alongside broader contextual information, the appearance of pointlessness, on average, significantly diminishes. In §1 we briefly elucidate Rowe’s formulation of the problem of evil and the thought experiment that motivates a key premise. In §2 and §3 respectively, we briefly explain our hypothesis regarding Rowe’s case and our methods for testing these hypotheses. In §4, we elucidate our results, and in §5 we explore some of the philosophical implications of our findings and gesture towards some areas for future research. Finally, in §6, we briefly connect our research to some of the established philosophical literature on suffering and narrative before concluding. CHURCH, WARCHOL, AND BARRETT 2
Generalizability of cardiovascular reactivity to stress among doctoral students between laboratory and natural settings

GENERALIZABILITY OF CARDIOVASCULAR REACTIVITY TO STRESS AMONG DOCTORAL STUDENTS BETWEEN LABORATORY AND NATURAL SETTINGS James P. Davig This study extends previous work investigating the generalizability of cardiovascular reactivity to stress obtained in response to laboratory-based tasks to cardiovascular reactivity measured in response to a natural occurring event. Cardiovascular reactivity (HR,SBP,DBP) to stress was measured with ambulatory monitoring equipment in 6 male and 16 female healthy Caucasian graduate students in two settings: during five standardized laboratory stressors, and during the proposal or defense of a thesis or dissertation. Results indicate that for all cardiovascular parameters, the magnitude of change was greatest in response to the natural event, and that among the laboratory tasks, the greatest degree of reactivity was observed in response to the speech task. However, the cardiovascular reactivity associated with the speech task was not the best predictor of the natural speech task, despite the structural similarity of these two tasks. Overall, greater correlations were noted among all of the tasks based on absolute values of HR and BP versus residualized change scores for HR and BP, and greater correlations were also observed among anticipation periods than among task periods. Partial support was provided for the hypothesis that the generalizability of cardiovascular reactivity to stress would be enhanced using an aggregation of multiple tasks rather than single tasks. This effect was observed for HR but not BP. Consistent with the literature in this field, only limited generalizability of cardiovascular response to stress was observed. Generous financial support was provided by the West Virginia University Department of Psychology Alumni Fund as well as the Office of Academic Affairs and Research. Acknowledgements Without Kevin’s guidance, support, and expertise, this project would not have succeeded. Words cannot convey my appreciation for both the assistance he has provided, and the excellent clinical, academic, and personal example he has provided for me. Also instrumental in this project has been the assistance of Jeff Goodie; I don’t know what I would’ve done without you. Sincere thanks also to the members of my committee, all of whom truly added to the substance and quality of the final product. Graduate school would be a much more tolerable experience if all students were surrounded by such support. Finally, this experience has imprinted on me the importance of emotional support. Friends and mentors who sustained me throughout graduate school deserve mention; in addition to those listed above, special thanks to Scott Schauss, Lynda Szymanski, Rick Seime, Virginia Goetsch, and Leslie and Susan Dozsa. Thank you to my extended family for all of the trips you made to visit us. Most importantly, thank you to my wife, Kris, and children, Grace and Peter, for keeping me sane. It could not have been an easy job. Also, thanks Knute.
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation

Cardinality estimation is core to the query optimizers of DBMSs. Non-learned methods, especially based on histograms and samplings, have been widely used in commercial and open-source DBMSs. Nevertheless, histograms and samplings can only be used to summarize one or few columns, which fall short of capturing the joint data distribution over an arbitrary combination of columns, because of the oversimplification of histograms and samplings over the original relational table(s). Consequently, these traditional methods typically make bad predictions for hard cases such as queries over multiple columns, with multiple predicates, and joins between multiple tables. Recently, learned cardinality estimators have been widely studied. Because these learned estimators can better capture the data distribution and query characteristics, empowered by the recent advance of (deep learning) models, they outperform non-learned methods on many cases. The goals of this paper are to provide a design space exploration of learned cardinality estimators and to have a comprehensive comparison of the SOTA learned approaches so as to provide a guidance for practitioners to decide what method to use under various practical scenarios.
EFFECT OF RISK ANALYSIS ON PERFORMANCE OF LARGE CONSTRUCTION PROJECTS IN KUWAIT

Since construction projects are usually associated with high range of risks, risk analysis is an essential part within the decision-making processes in this industry. This study aims at finding the effect of appropriate risk analysis on enhancing the performance of the large construction projects in Kuwait. Through this correlational descriptive and qualitative study; firstly, a questionnaire was used to collect the main data where it was distributed on a sample of (150) engineers working in the large construction projects in Kuwait. Moreover, an interview was conducted with (15) project managers in the large construction projects in Kuwait. The results of the questionnaire showed that risks analysis aspects are highly implemented in Kuwait and that the performance indices for the construction projects are high. Moreover, it was found that there is a statistically significant positive effect of risk analysis on the performance of the large construction projects in Kuwait. The results showed that there were statistically significant differences in the effect of risk analysis on the performance of the construction projects attributed to the project type in favor of the private projects. Finally, the results of the interview showed that client satisfaction and maintain resources are the main effects of appropriate risk analysis tools, where project managers assured that re-allocating risks and changing roles are the main mechanisms to lessen the risks within the large construction projects in Kuwait.
Daily prediction modeling of forest fire ignition using meteorological drought indices in the Mexican highlands

Aleida Yadira Vilchis-Francés , Carlos Díaz-Delgado , Rocío Becerril Piña , Carlos Alberto Mastachi Loza , Miguel Ángel Gómez-Albores , Khalidou M Bâ (1) We analyzed the behavior of forest fires for daily prediction purposes in one of the regions with the highest fire incidence in Mexico. The main objective was to build logistic regression models (LRMs) for daily prediction of forest fire ignition based on meteorological drought indices. We built 252 LRMs for seven types of vegetation cover of greater representativeness and interest for the study area. Three dynamic variables were considered to estimate daily dryness in combustible fuels based on the effective drought index and the standardized precipitation-evapotranspiration index. Additionally, two weather data sources were included in drought indices: conventional weather stations (CWS) and automatic weather stations (AWS). Prediction efficiency assessment for LRMs was done through the relative operating characteristic (ROC) and model precision efficiency (MPE). The results show that LRMs using data from CWS performed relatively better than those based on data from AWS, as the former data sources have higher spatial density and thus generate predictions with higher accuracy (ROC ≥ 0.700, MPE ≥ 0.934). For both data sources, the use of standardized precipitation-evapotranspiration index as a fuel dryness estimator is recommended, as it reflects an atmospheric moisture balance between precipitation and reference evapotranspiration (ROC ≥ 0.734, MPE = 1). Such predictive models can be used as inputs in early warning systems for forest fire prevention or mitigation.
Blur Removal in Natural Digital Images Using Self-Reference Generative Networks

—The blur detection in a single image is considered as a pivotal issue in digital image processing applications, especially when the blurring is spatially-varying. This study introduces self-reference generative network (SR-GN) algorithm in order to deblur a digital natural blurred image. The proposed method divides the contaminated image into two main parts, which are the clear part and blur parts. The blur parts which caused by the shadowy channel are less sparse while the other parts have some dark patches, which can be cleaned by avenging them. In addition, due to the global performance of the proposed algorithm on public database, multicomponent loss function is utilized in order to perform further classification to the original patches of the blurred image to distinguish it from the contaminated counterparts as the experimental results demonstrated. The experimental results show that the proposed technique has an improvement in visual quality, and objective results as well in comparison to related advanced blur removal algorithms.
Population dynamics of aphid and coexisting predators in tomato agroecosystem

Tomato (Solanum lycopersicum Mill) is the second most important vegetable crop after potato (FAOSTAT, 2018). Globally, tomato production stands at 182.26 million tonnes (mt) from 4.76 million hectares (mha) (FAOSTAT, 2018). India is the second-largest producer of fresh tomatoes after China (61.63 mt) and ensures a total production of 19.37 mt from 0.79 mha (FAOSTAT, 2018). Tomato productivity is hampered by several abiotic and biotic stressors such as unfavourable environment, degraded soils, weeds, insectpests and diseases (Osei et al., 2010). Global crop losses owing to pest infestation are appraised to be around 34.4% of attainable tomato yield and without crop protection measures losses would escalate to 77.7% (Zalom, 2003). Aphids generally attack all the vegetative plant parts in larger numbers, settle down on the underside of leaves and suck the cell sap from phloem sieves (Watt & Hales, 1996). Outrageous aphid population often results in stunting and curling of leaves, reduces plant vigour rendering the plant more susceptible to secondary infestation which may exterminate the plant completely (Berlandier and Sweetingham, 2003). Population dynamics studies are imperative for proper knowledge of pest ecology in key agricultural crops. Aphids as well as their predators are affected by prevailing weather factors viz., temperature, relative humidity, rainfall, sunshine, and wind speed. Favourable weather conditions help in population build-up and ultimately increases pest infestation. (Khokhar et al., 2019). Aphids are being widely studied in several crops, but little is known about population dynamics and its predatory complex in tomato. An exact determination of changes in aphid and its predators’ population with meteorological factors is a prime requisite for Integrated Pest Management (IPM). We aimed to utilize these research gaps and study the population dynamics of the aphid complex and its predators in tomato. Based on the literature review, we hypothesized that prevailing weather conditions greatly influence the aphid population which in turn might affect its predators and their feeding rates. This study explains the trends in aphid and their predators’ population and helps us to predict the crop stages when pest management tactics can be initiated. ABSTRACT
COVID-19’s Impact on Substance Use and Well-Being of Younger Adult Cannabis Users in California: A Mixed Methods Inquiry

Few qualitative studies have examined the impact of COVID-19 on cannabis and alcohol use, and overall well-being among cannabis users. Cannabis users (aged 26-32) were surveyed quantitatively (n=158) and interviewed qualitatively (n=29) in April 2020–May 2021 in Los Angeles. 63.3% of the quantitative sample reported increasing use of either cannabis (29.1%) or alcohol (15.2%) or both (19.0%) following the COVID-19 outbreak. Qualitative data revealed that increases in cannabis and alcohol use were largely attributed to changes in employment and staying at home resulting in fewer impediments and boredom. Themes of loneliness and utilization of various coping strategies were more pronounced among those who increased cannabis and/or alcohol use. For some, increases in cannabis/alcohol use were temporary until participants adjusted to “a new normal” or embraced more adaptive coping strategies. Results suggest monitoring cannabis/alcohol use trends and identifying coping strategies to reduce the pandemic’s impact on substance use and mental health.
A Novel Method for Multivariant Pneumonia Classification based on Hybrid CNN-PCA Based Feature Extraction using Extreme Learning Machine with Chest X-Ray Images

In this era of COVID19, proper diagnosis and treatment for pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite difficult and time-consuming to diagnose due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgment. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network - principle component analysis (CNN-PCA) based feature extraction (ECP), and ECP with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc.
COMPARE: Accelerating Groupwise Comparison in Relational Databases for Data Analytics

Data analysis often involves comparing subsets of data across many dimensions for finding unusual trends and patterns. While the comparison between subsets of data can be expressed using SQL, they tend to be complex to write, and suffer from poor performance over large and high-dimensional datasets. In this paper, we propose a new logical operator COMPARE for relational databases that concisely captures the enumeration and comparison between subsets of data and greatly simplifies the expressing of a large class of comparative queries. We extend the database engine with optimization techniques that exploit the semantics of COMPARE to significantly improve the performance of such queries. We have implemented these extensions inside Microsoft SQL Server, a commercial DBMS engine. Our extensive evaluation on synthetic and real-world datasets shows that COMPARE results in a significant speedup over existing approaches, including physical plans generated by today's database systems, user-defined functions (UDFs), as well as middleware solutions that compare subsets outside the databases.