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We study the fact-checking problem, which aims to identify the veracity of a given claim. Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its accompanied dataset. The task consists of the subtasks of retrieving the relevant documents (and sentences) from Wikipedia and validating whether the information in the documents supports or refutes a given claim. This task is essential and can be the building block of applications such as fake news detection and medical claim verification. In this article, we aim at a better understanding of the challenges of the task by presenting the literature in a structured and comprehensive way. We describe the proposed methods by analyzing the technical perspectives of the different approaches and discussing the performance results on the FEVER dataset, which is the most well-studied and formally structured dataset on the fact extraction and verification task. We also conduct the largest experimental study to date on identifying beneficial loss functions for the sentence retrieval component. Our analysis indicates that sampling negative sentences is important for improving the performance and decreasing the computational complexity. Finally, we describe open issues and future challenges, and we motivate future research in the task.

Abstract: Different colored digital images can be represented in a variety of color spaces. Red-Green-Blue is the most commonly used color space. That can be transformed into Luminance, Blue difference, Red difference. These color pixels' defined features provide strong information about whether they belong to human skin or not. A novel color-based feature extraction method is proposed in this paper, which makes use of both red, green, blue, luminance, hue, and saturation information. The proposed method is used on an image database that contains people of various ages, races, and genders. The obtained features are used to segment the human skin using the Support-Vector- Machine algorithm, and the promising performance results of 89.86% accuracy are then compared to the most commonly used methods in the literature. Keywords: Skin segmentation, SVM, feature extraction, digital images

Purpose The circular economy (CE) is an evolving subject transitioning from conceptualization to empirical testing. Over the past decade, researchers have done an exhaustive study to understand the concept of CE and its realized values both financially and environmentally on organizations that have traditional business models based on linear consumption. For understanding the transitional phenomena completely, the paper aims to review the current and emerging research trends in CE to ascertain future direction. Design/methodology/approach The research was conducted on 91 articles published in the study area during the past decade (2016–2021) in renowned peer-reviewed journals. The criteria set to review literature are based on the following assortment: CE drivers, CE barriers, definitions by different authors, yearly distribution of the publication, research publisher and journals, google citation and methodology used in the selected research articles. Findings The study suggests that researchers from the selected years are keen to understand the transition and its critical factors by bringing forward frameworks and incorporating CE with digital technologies. The digital technology implied are Industrial Technology (IR) 4.0, Big Data, Internet of Things (IoT), Artificial Intelligence, Blockchain and Data Analytics to increase organizational and environmental performance. CE researchers need to use empirical testing in different sectors to understand and bring forward more improvised business models and practices according to the dynamics of the industry. Originality/value The literature review suggests gaps exist to integrate the micro, meso and macro levels to get CE implementation's system-wide benefits. The study has also identified that many CE frameworks available in the literature for implementation must be empirically tested to yield performance results.

AbstractAtlantic salmon aquaculture is expanding, and with it, the need to find suitable replacements for conventional protein sources used in formulated feeds. Torula yeast (Cyberlindnera jadinii), has been identified as a promising alternative protein for feed and can be sustainably cultivated on lignocellulosic biomasses. The present study investigated the impact of torula yeast on the growth performance and gut microbiome of freshwater Atlantic salmon. A marine protein base diet and a mixed marine and plant protein base diet were tested, where conventional proteins were replaced with increasing inclusion levels of torula yeast, (0%, 10%, 20%). This study demonstrated that 20% torula yeast can replace fish meal without alteration to growth performance while leading to potential benefits for the gut microbiome by increasing the presence of bacteria positively associated with the host. However, when torula yeast replaced plant meal in a mixed protein diet, results suggested that 10% inclusion of yeast produced the best growth performance results but at the 20% inclusion level of yeast, potentially negative changes were observed in the gut microbial community, such as a decrease in lactic acid bacteria. This study supports the continued investigation of torula yeast for Atlantic salmon as a partial replacement for conventional proteins.

This research aims to find out the evaluation of the performance of Islamic religious education teachers at Sma Negeri 7 Muaro Jambi. This research uses descriptive qualitative research methods. Data is collected by observation, interview, and documentation. The results showed that Sma Negeri 7 Muaro Jambi implemented the implementation of teacher performance evaluation program management held in each semester. The evaluation and monitoring activities are carried out directly by the principal and the Ministry of Religious Affairs. The evaluation program for the performance management of Islamic religious education teachers went quite well. Teachers always report their performance results shown through the learning outcomes of students who continue to experience improvement.

Debates have arisen as to whether non-human animals actually can learn abstract non-symbolic numerousness or whether they always rely on some continuous physical aspect of the stimuli, covarying with number. Here we investigated archerfish (Toxotes jaculatrix) non-symbolic numerical discrimination with accurate control for co-varying continuous physical stimulus attributes. Archerfish were trained to select one of two groups of black dots (Exp. 1: 3 vs. 6 elements; Exp. 2: 2 vs. 3 elements); these were controlled for several combinations of physical variables (elements’ size, overall area, overall perimeter, density and sparsity), ensuring that only numerical information was available. Generalization tests with novel numerical comparisons (2 vs. 3, 5 vs. 8 and 6 vs. 9 in Exp. 1; 3 vs. 4, 3 vs. 6 in Exp. 2) revealed choice for the largest or smallest numerical group according to the relative number that was rewarded at training. None of the continuous physical variables, including spatial frequency, were affecting archerfish performance. Results provide evidence that archerfish spontaneously use abstract relative numerical information for both small and large numbers when only numerical cues are available.

Abstract. The marine environment provides many ecosystems that support habitats biodiversity. Benthic habitats and fish species associations are investigated using underwater gears to secure and manage these marine ecosystems in a sustainable manner. The current study evaluates the possibility of using deep learning methods in particular the You Only Look Once version 3 algorithm to detect fish in different environments such as; different shading, low light, and high noise within images and by each frame within an underwater video, recorded in the Atlantic Coast of Morocco. The training dataset was collected from Open Images Dataset V6, a total of 1295 Fish images were captured and split into a training set and a test set. An optimization approach was applied to the YOLOv3 algorithm which is data augmentation transformation to provide more learning samples. The mean average precision (mAP) metric was applied to measure the YOLOv3 model’s performance. Results of this study revealed with a mAP of 91,3% the proposed method is proved to have the capability of detecting fish species in different natural marine environments also it has the potential to be applied to detect other underwater species and substratum.

Background. Even in today’s environment, when there is a plethora of information accessible, it may be difficult to make appropriate choices for one’s well-being. Data mining, machine learning, and computational statistics are among the most popular arenas of training today, and they are all aimed at secondary empowered person in making good decisions that will maximize the outcome of whatever working area they are involved with. Because the degree of rise in the number of patient roles is directly related to the rate of people growth and lifestyle variations, the healthcare sector has a significant need for data processing services. When it comes to cancer, the prognosis is an expression that relates to the possibility of the patient surviving in general, but it may also be used to describe the severity of the sickness as it will present itself in the patient's future timeline. Methodology. The proposed technique consists of three stages: input data acquisition, preprocessing, and classification. Data acquisition consists of input raw data which is followed by preprocessing to eliminate the missed data and the classification is carried out using ensemble classifier to analyze the stages of cancer. This study explored the combined influence of the prominent labels in conjunction with one another utilizing the multilabel classifier approach, which is successful. Finally, an ensemble classifier model has been constructed and experimentally validated to increase the accuracy of the classifier model, which has been previously shown. The entire performance of the recommended and tested models demonstrates a steady development of 2% to 6% over the baseline presentation on the baseline performance. Results. Providing a good contribution to the general health welfare of noncommercial potential workers in the healthcare sector is an opportunity provided by this recommended job outcome. It is anticipated that alternative solutions to these constraints, as well as automation of the whole process flow of all five phases, will be the key focus of the work to be carried out shortly. Predicting health status of employee in industry or information trends is made easier by these data patterns. The proposed classifier achieves the accuracy rate of 93.265%.

Mineral oil has been used as an insulating fluid in the power industry. However, surplus waste oil poses serious environmental threats because of disposal concerns. Waste to biofuel is an excellent way to deal with waste material from various sources. In this study, the trans-esterification method was utilised to convert the waste-insulating mineral oil into a quality bio-fuel. Waste-insulating transformer oil was converted to biodiesel, and it was tested according to ASTM standards. Four different blends of waste-insulating biodiesel with diesel in 25 per cent (WIOBD25), 50 per cent (WIOBD50), 75 per cent (WIOBD75), and 100 per cent fractions (WIOBD100), were used for performance testing in a direct injection compression ignition (DICI) engine. The combustion parameters such as BSFC, EGT, and BTE were evaluated with varying crank angles and constant engine speed. The waste-insulating biodiesel performance results are then compared with diesel fuel. BSFC increased as the biofuel mixture in diesel was raised, and the brake thermal efficiency (BTE) was significantly reduced compared to diesel for all WIOBD diesel mixtures. Due to the combustion process, a high pressure and heat release rate (HRR) were noticed inside the cylinder with the waste-insulating oil-derived biodiesel samples. WIOBD biodiesel blends produced lower levels of hydrocarbon, carbon monoxide, and smoke emissions than diesel fuel, but greater levels of nitrogen oxides (NOx) were produced than diesel fuel. In addition to lower emissions combined with improved engine performance, the WIOBD25 fuel blend has been found to be experimentally optimal for practical application. As a result, the test findings indicated that WIOBD biodiesel might be used as a substitute for conventional diesel fuel.

This developmental-descriptive research sought to design and developed a computer-based system, to be known as the Senior Citizens Information System, to provide real-time information dissemination among senior citizen members in the Municipality of Batad, Iloilo pertaining to their membership status and monetary assistance monitoring. The Rapid Application Development model was employed as the SDLC in the development of the system product. More so, the three-tier architecture was used as the architectural model. There were 120 senior citizens as user-respondents and five IT experts for evaluation. The system product was evaluated using a researchers-made survey instrument to determine if level of usability and performance. Results of the investigation shows that the system product was perceived as very good with its level of usability as well as its performance.