Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of Organic Acids and Kanamycin as Feed Additives in Improving the Immune Status of Broiler chickens Infected with Salmonella Typhimurium

This investigation was performed to demonstrate the effect of continuous feeding of organic acids and antibiotic supplemented diets on some immunological and biochemical parameters in healthy and Salmonella Typhimurium experimentally infected broiler Chickens. One hundred and twenty one-day old Ross 308 chicks were divided into six equal groups. Group 1 negative control. Group 2: infected broilers with 0.5 mL of 1x108 CFU/mL S. Typhimurium per os as a positive control. Group 3: infected chickens and treated intramuscularly with 0.1 mL / kg body weight kanamycin for 5 successive days. Group 4 administered with propionic acid by a dose of 2 kg/ ton diet then infected with S. Typhimurium. Group 5: treated with formic acid by 1 mL/ L drinking water then infected with S. Typhimurium. Group 6: administered with propionic acid by a dose of 2 kg/ ton diet + 1 mL formic acid / L drinking water then infected with S. Typhimurium. The results revealed that group 2 infected by Salmonella showed loss of appetite, depression, and diarrhea, with mortality rate up to 20%. Group 3 showed mild clinical signs with 15% mortality rate. Groups treated with organic acid showed mild clinical signs with 10% mortality rate. S. Typhimurium infected chickens (G2) revealed leukocytosis, heterophilia, monocytosis, significant increase in uric acid, creatinine, liver enzymes activities, IgA and IgG and significant decrease in albumin and total antioxidant capacity (TAOC). While treatment of Salmonella infection with kanamycin, organic acids alone or combination of propionic acid and formic acid resulted in a positive effect in treatment, a significant improvement in some immunological and biochemical parameters and ameliorating the severity of infection. In conclusion, the combination of organic acids revealed the best results as it returned leukogram, biochemical and immunological parameters nearly to their normal levels and decreased bacterial colonization in poultry.
Conversion of Gesture to Text Message Using Neural Network

: The use of gestures as means to convey information is an important part of human communication. The hand gesture recognition are important methods for nonverbal communication for human beings. The non-verbal communication is not only used for physically challenged person but also it can be efficiently used for various applications such as 3D gaming, aviation, surveying, etc. To solve the difficulties faced by hearing impairment, it is necessary to develop a system which translates the sign language into text which can easily be recognized by the impaired people.For recognizing the gestures, there are several algorithms are available. There are several approaches for gesture recognition using MATLAB. Artificial Neural networks are flexible in a changing environment. It also describes the process of gesture recognition using neural network. It’s a highly efficient method that has been proven able to distinguish and classify with an amazing rate of performances. Then, it will be possible to train the network with a full set of examples and finally to use in real time conditions.
Automated Arabic-Arabic sign language translation system based on 3D avatar technology

Arabic sign language (ArSL) is the natural language of the deaf community in Arabic countries. Deaf people have a set of difficulties due to poor services available. They have problems accessing essential information or receiving an education, communicating with other communities, and engaging in activities. Thus, a machine translation system of Arabic to ArSL has been developed using avatar technologies. Firstly, a dictionary of ArSL was constructed using eSign editor Software. The constructed dictionary has three thousand signs. It can be adopted for the translation system in which written text can be transformed into sign language. The dictionary will be available as a free resource for researchers. It is complex and time-consuming, but it is an essential step in the machine translation of whole Arabic text to ArSL with 3D animations. Secondly, the translator has been developed. It performs syntactic and morphological analysis and then applies a set of rules to translate an Arabic text into ArSL text based on the structure and grammar of ArSL. The system is evaluated according to the parallel corpus that consists of 180 sentences using the metric for evaluation of translation with explicit ordering metric for evaluation of translation with explicit ordering(METEOR) our system achieves a relative score of (86%).
A Scienti c Perspective of the Three Harmonies Principle of Taijiquan

Copyright © 2022 by the author(s). Published by Bilingual Publishing Co. This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. (https://creativecommons.org/licenses/by-nc/4.0/). *Corresponding Author: C.P. Ong, Independent Reseacher, Potomac, Maryland, USA; Email: cpTaiji@gmail.com A Scienti c Perspective of the Three Harmonies Principle of Taijiquan
Effect of Combination of HBM and Certainty Sampling on Workload of Semi-Automated Grey Literature Screening

With the rapid increase of unstructured text data, grey literature has become an important source of information to support research and innovation activities. In this paper, we propose a novel semi-automated grey literature screening approach that combines a Hierarchical BERT Model (HBM) with active learning to reduce the human work-load in grey literature screening. Evaluations over three real-world grey literature datasets demonstrate that the proposed approach can save up to 64.88% of the human screening workload, while maintaining high screening accuracy. We also demonstrate how the use of the HBM model allows salient sentences within grey literature documents to be selected and highlighted to support workers in screening tasks.
Prilog razvoju poslovne strategije Poste : magistarski rad

Umnozeno za odbranu. - Univerzitet u Beogradu, Saobracajni fakultet, odbranjeno 26.02. 2001. - Bibliografija: listovi 117-119. - Sadrzaj ; Abstract: A contribution to the development of corporate level strategy of the post office.
Do Hiring Subsidies Work in Recessions ? Evidence from France (cid:3)

This paper evaluates a French program of hiring subsidies targeted to (cid:133)rms with less than 10 employees and to workers paid below 1.6 times the minimum wage. Using an almost perfect natural experiment and rich administrative data covering all French (cid:133)rms, we (cid:133)nd that the program had a strong and fast impact on employment, corresponding to a wage elasticity of labor demand around (cid:0) 2 . Although the subsidy was not conditional on net job creation, it did not induce (cid:133)rms to substitute new workers for incumbent workers in order to bene(cid:133)t from the subsidy. Nevertheless, we (cid:133)nd that subsidies conditional on net job creation above the employment growth rate threshold of minus 4 percent would have maximized job creation at constant budget, creating 40 percent more jobs than the non conditional subsidy, if the take-up rate had remained identical for both types of subsidy.
A FRAMEWORK FOR ETL DESIGN EVALUATION BASED ON QUALITY FRAMEWORK

Using Extraction, transformation, and loading ETL activities, an association might collect information from both inside and outside sources, change it, and afterward populate an information stockroom with the consequences of these cycles. As a model for business processes, the Business Process Modelling and Notation has been given. ETL procedures are expressed on a conceptual level. In this study, an alternative strategy is investigated. ETL is specified using relational algebra (RA), which has been enhanced with update procedures. A data warehousing architecture's Extraction, Transformation, and Loading (ETL) process is critical. ETL operations are often time-consuming and sophisticated. The design step is very crucial (though sometimes overlooked) in ETL development since it has an influence on the future phases, namely implementation. As well as execution When ETL quality is addressed throughout the design process, it is possible to take steps that will have a good and long-term impact.Process efficiency is improved at a cheap cost. Briand et alframework . 's (theoretical validation) was used. Internal metrics that we believe are linked to process efficiency are established in detail (framework for system artefacts). We also present empirical support for this relationship, as well as an interpretation of the data.the efficiency indicators that have a greater influence. ETL design quality has been addressed before, but this is the first time that measurements across ETL models have been used to anticipate the performance of these models.
Simulation, Control, and Evaluation of Actuated Car Doors

The simulation, control, and evaluation of actuated car doors with one or more degrees of freedom (DOF) are comprehensively described. Virtual Reality Prototyping (VRP) with haptic feedback is proposed for the efficient development and evaluation of such doors. As universal framework for the simulation of actuated mechanisms, Active Admittance Control is introduced. For the support of the haptic interaction, generic path planning, collision avoidance, and intention recognition methods are developed. On this foundation, a variety of actuated car doors is simulated and evaluated at VRP test beds. The results are directly transferred to prototypes with two DOF and one DOF. Comprehensive user studies reveal the effectiveness of the proposed support methods and the predominant appreciation of these actuated car doors.
Investigating Student Perceptions of Team-based Brainstorming During Conceptual Design: Challenges and Recommendations

researching educational approaches to enhance creativity in engineering design teams. His interests include ideation tasks, idea selection, and metrics for creative capacity. He is the leader of the Critical Research in Engineering and Technology Education (CREATE) group at Cal Poly. His research interests include critical pedagogies; efforts for diversity, equity, and inclusion in engineering, engineering design theory and practice; conceptual change and understanding; and school-to-work transitions for new engineers. His current work explores a range of engineering education and design contexts, including the role of power in brainstorming activities, epistemological and conceptual development of undergraduate learning assistants, as well as the experiences of recent engineering grad-uates as they navigate new organizational cultures.
HOW FIRM APPROPRIATELY APPLY NEW EXTERNAL KNOWLEDGE: THE WATERFALL MODEL OF ABSORPTIVE CAPACITY AND INNOVATION

This article was an attempt to brief a new model of absorptive capacity and develop the concept of absorptive capacity which is a critical factor in applying, accumulating and linking new external knowledge to innovation. Through a comprehensive review of literature on absorptive capacity, authors ended up with the idea that the stream of new external knowledge in the waterfall model continually fosters all abilities of absorptive capacity. In addition, following knowledge spillover in each stage, abilities perform their activities. Knowledge accumulation is also another capability, which in this process, plays the role of linking absorptive capacity and innovation.
Minimax Regret on Patterns Using Kullback-Leibler Divergence Covering

This paper considers the problem of finding a tighter upper bound on the minimax regret of patterns, a class used to study large-alphabet distributions which avoids infinite asymptotic regret and redundancy. Our method for finding upper bounds for minimax regret uses cover numbers with Kullback-Leibler (KL) divergence as the distance. Compared to existing results by Acharya et al. (2013), we are able to improve the power of the exponent on the logarithmic term, giving a minimax regret bound which matches the best known minimax redundancy bound on patterns.
Modeling the Bioavailability of Circulating Gold Nanoparticles

Experiments in animal models are underway to investigate the therapeutic bioavailability of nanoparticles used in the treatment of cancerous tumors in a technique known as nanoparticle-assisted photothermal cancer therapy. This paper discusses the devel-opment of mathematical models to estimate in near real-time the circulation parameters of area-under-the-curve (AUC) and half-life towards a clinical application.
THE EFFECT OF CORPORATE GOVERNANCE, CASH FLOW, AND DIVIDEND POLICY ON COMPANY PERFORMANCE WITH FAMILY OWNERSHIP AS MODERATING VARIABLES ON MANUFACTURING COMPANIES LISTED ON INDONESIA STOCK EXCHANGE

The purpose of this research is to examine the effect of corporate governance, cash flow, and dividend policy on firm performance in manufacturing companies listed on Indonesia Stock Exchange period of 2016 – 2020. This research uses firm age as a control variable and family ownership as a moderating variable. The technique for the quantitative data uses a statistical tool, i.e. Moderated Regression Analysis (MRA). The results shows that cash flow has a positive effect on firm performance. Meanwhile, corporate governance, and dividend policy have no effect on firm performance the implication is the members of corporate governance may come from non-family members or majority are non-family member so the family can not force regulation as well as dividend policy. Then family ownership is able to strengthen the relationship between corporate governance on firm performance since family members can enforce regulations and procedures. However, family ownership is not able to strengthen or weaken the relationship between cash flow, and dividend policy on firm performance because cash flow and dividend policy are based on company’s daily operation.
The economic benefit of liner ships with lower speed

In recent years, the proportion of fuel costs in the total shipping costs grows up with the increase in international crude oil price. The rising cost seriously restricts the development of shipping enterprises. Many researches and cases show that reducing the ship’s fuel consumption by cautiously and reasonably decelerating the ship is the most simple and effective mean to control the shipping cost. However, the transportation time is longer when the speed is reduced. In other words, shipper would have to pay more for per unit transportation time. When the increase in transit time exceeds a certain range, liner companies are likely to lose customers. This article aims to maximize the profit of a liner company, which consisted of the variety of fuel cost, fixed cost, operating cost, and other related factors. And at the same time, the change in the shipper’s transport time cost and freight cost is considered. Then, a speed–freight optimization model based on the bat algorithm is established using MATLAB toolbox, in which the liner line is segmented. Finally, this article uses the model to optimize the route of the Far East to North America. The results show that the model can guarantee the quality of transport and reduce transport cost by adjusting speed at the same time.
ETF Portfolio Construction via Neural Network trained on Financial Statement Data

Recently, the application of advanced machine learning methods for asset management has become one of the most intriguing topics. Unfortunately, the application of these methods, such as deep neural networks, is difficult due to the data shortage problem. To address this issue, we propose a novel approach using neural networks to construct a portfolio of exchange traded funds (ETFs) based on the financial statement data of their components. Although a number of ETFs and ETF-managed portfolios have emerged in the past few decades, the ability to apply neural networks to manage ETF portfolios is limited since the number and historical existence of ETFs are relatively smaller and shorter, respectively, than those of individual stocks. Therefore, we use the data of individual stocks to train our neural networks to predict the future performance of individual stocks and use these predictions and the portfolio deposit file (PDF) to construct a portfolio of ETFs. Multiple experiments have been performed, and we have found that our proposed method outperforms the baselines. We believe that our approach can be more beneficial when managing recently listed ETFs, such as thematic ETFs, of which there is relatively limited historical data for training advanced machine learning methods.
Tableless Calculation of Circular Functions on Dyadic Rationals

I would like to tell a story. A story about a beautiful mathematical relationship that elucidates the computational view on the classic subject of trigonometry. All stories need a language, and for this particular story an algorithmic language ought to do well. What makes a language algorithmic? From our perspective as the functional programming community, an algorithmic language provides means to express computation in terms of functions , with no implementation-imposed limitations. We de-velop a new algorithm for the computation of trigonometric functions on dyadic rationals, together with the language used to express it, in Scheme. We provide a mechanically-derived algorithm for the computation of the inverses of our target functions. We address efﬁciency and accuracy concerns that pertain to the implementation of the proposed algorithm either in hardware or software.
DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles

A crucial component in the curation of KB for a scientific domain (e.g., materials science, food & nutrition, fuels) is information extraction from tables in the domain’s published research articles. To facilitate research in this direction, we define a novel NLP task of extracting compositions of materials (e.g., glasses) from tables in materials science papers. The task involves solving several challenges in concert, such as tables that mention compositions have highly varying structures; text in captions and full paper needs to be incorporated along with data in tables; and regular languages for numbers, chemical compounds, and composition expressions must be integrated into the model. We release a training dataset comprising 4,408 distantly supervised tables, along with 1,475 manually annotated dev and test tables. We also present DiSCoMaT, a strong baseline that combines multiple graph neural networks with several task-specific regular expressions, features, and constraints. We show that DiSCoMaT outperforms recent table processing architectures by significant margins. We release our code and data for further research on this challenging IE task from scientific tables.
Anomaly Detection with Adversarially Learned Perturbations of Latent Space

Anomaly detection is to identify samples that do not conform to the distribution of the normal data. Due to the unavailability of anomalous data, training a supervised deep neural network is a cumbersome task. As such, unsupervised methods are preferred as a common approach to solve this task. Deep autoencoders have been broadly adopted as a base of many unsupervised anomaly detection methods. However, a notable shortcoming of deep autoencoders is that they provide insufficient representations for anomaly detection by generalizing to reconstruct outliers. In this work, we have designed an adversarial framework consisting of two competing components, an Adversarial Distorter, and an Autoencoder. The Adversarial Distorter is a convolutional encoder that learns to produce effective perturbations and the autoencoder is a deep convolutional neural network that aims to reconstruct the images from the perturbed latent feature space. The networks are trained with opposing goals in which the Adversarial Distorter produces perturbations that are applied to the en-coder's latent feature space to maximize the reconstruction error and the autoencoder tries to neutralize the effect of these perturbations to minimize it. When applied to anomaly detection, the proposed method learns semantically richer representations due to applying perturbations to the feature space. The proposed method outperforms the existing state-of-the-art methods in anomaly detection on image and video datasets.
R^2VOS: Robust Referring Video Object Segmentation via Relational Multimodal Cycle Consistency

Referring video object segmentation (R-VOS) aims to segment the object masks in a video given a referring linguistic expression to the object. It is a recently introduced task attracting growing research attention. However, all existing works make a strong assumption: The object depicted by the expression must exist in the video, namely, the expression and video must have an object-level semantic consensus. This is often violated in real-world applications where an expression can be queried to false videos, and existing methods always fail in such false queries due to abusing the assumption. In this work, we emphasize that studying semantic consensus is necessary to improve the robustness of R-VOS. Accordingly, we pose an extended task from R-VOS without the semantic consensus assumption, named Robust R-VOS ( R 2 -VOS). The R 2 -VOS task is essentially related to the joint modeling of the primary R-VOS task and its dual problem (text reconstruction). We embrace the observation that the embedding spaces have relational consistency through the cycle of text-video-text transformation, which connects the primary and dual problems. We leverage the cycle consistency to discriminate the semantic consensus, thus advancing the primary task. Parallel optimization of the primary and dual problems are enabled by introducing an early grounding medium. A new evaluation dataset, R 2 -Youtube-VOS, is collected to measure the robustness of R-VOS models against unpaired videos and expressions. Extensive experiments demonstrate that our method not only identiﬁes negative pairs of unrelated expressions and videos, but also improves the segmentation accuracy for positive pairs with a superior disambiguating ability. Our model achieves the state-of-the-art performance on Ref-DAVIS17, Ref-Youtube-VOS, and the novel R 2 -Youtube-VOS dataset.
The Binary Enots Wolley Sequence

It is an open conjecture that the Enots Wolley sequence is surjective onto the set of positive integers with a binary weight of at least 2. In this paper, this property is proved for an analog of the Enots Wolley sequence which operates on the binary representation of a number rather than the prime factorization.