Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross‐species transmission of deltacoronavirus and the origin of porcine deltacoronavirus

Deltacoronavirus is the last identified Coronaviridae subfamily genus. Differing from other coronavirus (CoV) genera, which mainly infect birds or mammals, deltacoronaviruses (δ‐CoVs) reportedly infect both animal types. Recent studies show that a novel δ‐CoV, porcine deltacoronavirus (PDCoV), can also infect calves and chickens with the potential to infect humans, raising the possibility of cross‐species transmission of δ‐CoVs. Here, we explored the deep phylogenetic history and cross‐species transmission of δ‐CoVs. Virus–host cophylogenetic analyses showed that δ‐CoVs have undergone frequent host switches in birds, and sparrows may serve as the unappreciated hubs for avian to mammal transmission. Our molecular clock analyses show that PDCoV possibly originated in Southeast Asia in the 1990s and that the PDCoV cluster shares a common ancestor with Sparrow‐CoV of around 1,810. Our findings contribute valuable insights into the diversification, evolution, and interspecies transmission of δ‐CoVs and the origin of PDCoV, providing a model for exploring the relationships of δ‐CoVs in birds and mammals.
The effect of emotion regulation training on family relationships of hyperactive children

INTRODUCTION: One of the psychiatric disorders related to childhood is attention deficit hyperactivity disorder that can negatively affect the family interactions of these children. Parents of these children, especially the mother as the main caregiver, need comprehensive training to make a positive difference in their attitudes and behaviors with these children. The purpose of this study was to investigate the effect of emotion regulation training on family relationships in hyperactive children. METHODS: This was a randomized clinical trial study with a control group performed on eighty mothers of hyperactive children who were selected randomly. The intervention group members attended emotion regulation skills training sessions for one session of 90 min each week for 8 weeks. During the training course in the intervention group, the control group did not receive any training. Data collection tools included demographic information questionnaires, Mother–Child Relationship Evaluation, and Rahim Organizational Conflict Inventory-II. Data analysis was performed by descriptive statistics and the analysis of covariance. RESULTS: The mean scores of the mother–child relationship in the intervention group showed a significant improvement in the subscales of acceptance, overprotection, facilitation, and rejection (P < 0.0001). The results of this research also showed improvements in all the five communication styles of integrating, avoiding, compromising, obliging (P < 0.0001), and dominating (P < 0.012) between spouses. CONCLUSION: It seems that the emotion regulation training approach in mothers with hyperactive children improved both the mother–child relationship and the interactive spousal styles and can be considered by the managers of treatment and rehabilitation field as an adjunctive therapy for the families of these children.
Medical Research Council framework for development and evaluation of complex interventions: A comprehensive guidance

Complex intervention framework is extensively used in health service delivery, community, health and social policy, such as education, with important health outcomes. Such interventions are sensitive and flexible to the regional context and culture. Complex interventions introduce the use of two or more information sources or two or more research methods to answer a research question. [1,2] Using more than two research methods with triangulation techniques increases the validity of the research findings. Such studies are considered as mixed methods. [2] Complex intervention framework includes the steps which are not necessarily linear and there is also no clear boundary between simple and complex interventions. Such steps include plan development, feasibility and pilot, evaluation, and implementation. These steps have no linear or cyclic order. [3] This framework emphasizes that any intervention should be presented to the patients tailored to the context and culture of that society (political, social, and geographical). Even if the intervention can be presented equally in different societies, the context of that society cannot be ignored in the face of the intervention because one intervention may have different effects on two different societies. In fact, this framework introduces the mechanisms which are sufficiently relevant to that society in order to achieve some changes. In addition, the context of the society after the intervention should be considered in interpreting the findings. [4] Meanwhile, Lakshman et al. considered the use of this framework to infants' nutritional behaviors in lactation with the analysis of cost-effectiveness of a complex intervention in his study as a challenging, long and costly intervention. He even suggested conducting interventional research with or without a framework to evaluate their effectiveness alongside costs. [5]
Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks

The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity among these factors causes a bias in the prediction models. The aim of this study was to explore the potential of a hybrid model to predict the eating behaviors. The hybrid model of structural equation modelling (SEM) and artificial neural networks (ANN) was applied to evaluate the prediction model. The SEM analysis was used to check the relationship of the emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) and their effect on different categories of eating behavior patterns (EBP). In the second step, the input and output required for ANN analysis were obtained from SEM analysis and were applied in the neural network model. 340 university students participated in this study. The hybrid model (SEM-ANN) was conducted using multilayer perceptron (MLP) with feed-forward network topology. Moreover, Levenberg–Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The tangent/sigmoid function was used for the input layer, while the linear function was applied for the output layer. The coefficient of determination (R2) and mean square error (MSE) were calculated. Using the hybrid model, the optimal network happened at MLP 3-17-8. It was proved that the hybrid model was superior to SEM methods because the R2 of the model was increased by 27%, while the MSE was decreased by 9.6%. Moreover, it was found that BSC, BAS, and EES significantly affected healthy and unhealthy eating behavior patterns. Thus, a hybrid approach could be suggested as a significant methodological contribution from a machine learning standpoint, and it can be implemented as software to predict models with the highest accuracy.
The Third and Fatal Shock: How Pandemic Killed the Millennial Paradigm

Abstract This millennium began with widespread acceptance of a governing paradigm emphasizing small government, free markets, and open borders. Three crises—the 9/11 attacks, the 2008 financial crisis, and the 2020 COVID‐19 pandemic—forced American policy makers to diverge from this paradigm. At the time, these divergences were described as temporary departures from normalcy. In retrospect, it would be more accurate to regard the millennial paradigm itself as the abnormality: a model of governance designed for rare moments of calm. In the last two decades, a different paradigm has emerged. American government has become the ultimate bearer of societal risks. Repeatedly, it has adopted extraordinary measures to protect public safety and the economy. However, the American state lacks the capacity to anticipate and manage these massive risks competently. New capabilities are required, along with a new mentality about governing. Domestic politics will complicate the task of building these capabilities.
Estádios de maturação e qualidade pós-colheita de goiabas 'Pedro Sato'

O presente trabalho objetivou avaliar a influencia dos estadios de maturacao na qualidade pos-colheita de goiabas 'Pedro Sato' (Psidium guajava L.). Os frutos foram colhidos em tres estadios de maturacao, segundo a cor da casca (estadio 1- verde-escura; estadio 2- verde-clara e estadio 3- verde-amarela) e armazenados a temperatura controlada de 25±1oC e 85±5% umidade relativa do ar. No momento da colheita, os tres estadios apresentaram valores distintos de cor da casca, firmeza da polpa e da relacao solidos soluveis totais/ acidez titulavel (SST/AT). O maximo periodo de conservacao foi de 6; 4 e 2 dias apos a colheita para os estadios 1; 2 e 3, respectivamente. Ao final deste periodo, os frutos colhidos no estadio 1 apresentavam-se com a cor da casca mais verde (ho > 101,55), com maior teor de acidez e menor relacao SST/AT que os demais estadios. Os frutos colhidos nos estadios 2 e 3 nao diferiram quanto as caracteristicas fisico-quimicas, ao final do periodo comercializavel, porem apresentaram diferencas significativas quanto as caracteristicas sensoriais, sendo aqueles colhidos no estadio 3 superiores aos demais estadios.
Paternally inherited genetic markers reveal new insights into genetic structuring within Macropus fuliginosus and hybridisation with sympatric Macropus giganteus

Abstract. There are several aspects of biology in which the contribution of males and females is unequal. In these instances the examination of Y chromosome markers may be used to elucidate male-specific attributes. Here, male dispersal patterns and genetic structuring were examined using four Y-microsatellite loci in 186 male western grey kangaroos, Macropus fuliginosus, from throughout the species’ trans-continental distribution. In addition, 52 male grey kangaroos were examined to investigate hybridisation between M. fuliginosus and the eastern grey kangaroo, Macropus giganteus, in their region of sympatry in eastern Australia. Detected Y chromosome diversity was low, resulting from low effective male population size due to skewed sex ratios and a polygynous mating system. As expected, male dispersal was high across the range. However, the Lake Torrens–Flinders Ranges region appears to have significantly restricted male movement between eastern and central/western Australia. There was little evidence to suggest that other barriers (Nullarbor Plain and Swan River Valley) previously identified by nuclear and mitochondrial DNA marker studies restrict male movement. Hence, the admixture events previously identified may be associated with high male dispersal. Within the region of sympatry between M. fuliginosus and M. giganteus in eastern Australia, four M. giganteus individuals were found to possess M. fuliginosus Y-haplotypes. These results confirm the occurrence of hybridisation between male M. fuliginosus and female M. giganteus. Additionally, the introgression of M. fuliginosus Y-haplotypes into M. giganteus populations indicates that at least some male hybrids are fertile, despite evidence to the contrary from captive studies. This study has provided insights into the male contribution to population history, structure and hybridisation in M. fuliginosus, which were not predicted by comparisons between biparentally and maternally inherited markers. This highlights the importance of direct examination of the Y chromosome to provide novel insights into male-mediated processes, especially where the contribution of the sexes may differ.
Detection and Recognition of Arrow Traffic Signals using a Two-stage Neural Network Structure

: This paper develops a trafﬁc light detection and recognition system based on convolutional neural networks for Taiwan road scenes. A two-stage approach is proposed with ﬁrst detecting the trafﬁc light position, followed by the light state recognition. It is speciﬁcally designed to identify the challenging arrow signal lights in many urban trafﬁc scenes. In the detection stage, the map information and two cameras with different focal lengths are used to detect the trafﬁc lights at different distances. In the recognition stage, a new method combining object detection and classiﬁcation is proposed to deal with various light state classes in Taiwan road scenes. Furthermore, an end-to-end network with shared feature maps is implemented to reduce the computation time. Experiments are carried out on the public LISA dataset and our own dataset collected from two routes with urban trafﬁc scenes.
Open Data Analytic Querying using a Relation-Free API

: The large availability of tabular Open Data sources with hundreds of attributes and relations makes the query development a difﬁcult task, where analytic queries are common. When writing such queries, often called SPJG (Select-Project-Join-GroupBy), it is necessary to understand a data model and to write JOIN operations. The most common approach is to use business intelligence frameworks, or recent solutions based on keywords or examples. However, they require the utilization of speciﬁc applications and there is a lack of support for web-based APIs. We present a solution that eases the task of query development for tabular Open Data analytics through an API, using a simpliﬁed query representation where it is not allowed to specify the data relations, and consequently neither the joins over them, called Relation-Free Query . We deﬁne a single virtual schema that captures the database structure, which allows the use of relation-free queries in existent DBMS’s. The concrete queries are exposed by a RESTful API, which is then translated into a database query language using known query generation solutions. The API is available as a microservice. We present a case study to describe solution, using a real world scenario to query in an integrated database of several Brazilian open databases with hundreds of attributes.
Updating Ontology Alignment on the Relation Level based on Ontology Evolution

Ontologies are becoming a popular and convenient way for knowledge representation they can store information about objects and relations between them. However, nothing is constant and new information may appear, therefore those alterations should be reflected both in an ontology as well as in alignment between two ontologies if the knowledge about some domain is distributed in many sources. In the literature, it is possible to find approaches devoted to tracking changes in ontologies, but tools for updating ontology alignment are limited, especially devoted to the level of relations. It became a motivation for this work, thus, the aim of this paper is split into two parts. Firstly, we will present a criterion that tells us that modification in an ontology on the relation level is significant, and how it influences the maintained alignment (also on the relation level). Next, an algorithm for simple revalidation of existing mappings is proposed.
Multi-tier Automated Planning for Adaptive Behavior (Extended Version)

A planning domain, as any model, is never “complete” and inevitably makes assumptions on the environment's dynamic. By allowing the specification of just one domain model, the knowledge engineer is only able to make one set of assumptions, and to specify a single objective-goal. Borrowing from work in Software Engineering, we propose a multi-tier framework for planning that allows the specification of different sets of assumptions, and of different corresponding objectives. The framework aims to support the synthesis of adaptive behavior so as to mitigate the intrinsic risk in any planning modeling task. After defining the multi-tier planning task and its solution concept, we show how to solve problem instances by a succinct compilation to a form of non-deterministic planning. In doing so, our technique justifies the applicability of planning with both fair and unfair actions, and the need for more efforts in developing planning systems supporting dual fairness assumptions.
LEDAC: Optimizing the Performance of the Automatic Classification of Legal Documents through the Use of Word Embeddings

: Nowadays, the number of legal documents processed daily prevents the work from being done manually. One of the most relevant processes is the classiﬁcation of this kind of documents, not only because of the importance of the task itself, but also since it is the starting point for other important tasks such as data search or information extraction. In spite of technological advances, the task of automatic classiﬁcation is still performed by specialized staff, which is expensive, time-consuming, and subject to human errors. In the best case it is possible to ﬁnd systems with statistical approaches whose beneﬁts in terms of efﬁcacy and efﬁciency are limited. Moreover, the presence of overlapping elements in legal documents, such as stamps or signatures distort the text and hinder these automatic tasks. In this work, we present an approach for performing automatic classiﬁcation tasks over these legal documents which exploits the semantic properties of word embeddings. We have implemented our approach so that it is simple to address different types of documents with little effort. Experimental results with real data show promising results, greatly increasing the productivity of systems based on other approaches.
Early immune gene expression responses to Salmonella enteritidis infection in indigenous chickens

Salmonella enteritidis (SE) is a common cause of food-borne disease in humans and loss of growth in poultry. An effective method to inhibit salmonellosis is to increase the genetic resistance of poultry to Salmonella through genetic selection programmes that may be performed using phenotypic or genotypic data. A better understanding of the effects of Salmonella infection on the expression of inflammatory and anti-infectious cytokines and antimicrobial molecules is essential for choosing potential markers in selection programmes. The aim of this study was to investigate the expression of NRAMP1, TLR4, IL8 and IFNg genes in the caecum and spleen of Malaysian village chickens and red jungle fowl 48 h after inoculation with SE. Real-time reverse transcription PCR was used to quantify the fold-change in mRNA expression. The results showed that all the genes were highly expressed 48 h post-infection in the caecum of the village chickens. Overall, these results showed that Malaysian indigenous chickens have appropriate innate immune responses to SE infection.
Personal Documents Classification using a Hybrid Framework at a Mobile Insurance Company: A Case Study

In the information age, coupled with the full range and speed of data, the ease of access to new disruptive technologies brings the relevant problem of document classification. Identifying and categorizing documents is still a very challenging initiative addressed in the literature. This paper analyzes the construction of a document classification hybrid framework in a real business context. The research is based on a case study addressing the construction of a hybrid framework that uses text and image in document classification and how this framework can be useful in an authentic context of a mobile insurance company. Excellent accuracy and precision results were found in the use of both approaches, even considering a possible fraudulent circumstance. From these results we can conclude that using the hybrid framework, using the visual approach as a filter — which is more efficient in verifying the authenticity of documents— and consolidating the results with the textual approach, is a convincing option for deployment in the company in question.
A Guide to Budgeted Tree Search

Budgeted Tree Search (BTS), a variant of Iterative Budgeted Exponential Search, is a new algorithm that has the same performance as IDA* on problems where the state space grows exponentially, but has far better performance than IDA* in other cases where IDA* fails. The goal of this paper is to provide a detailed guide to BTS with worked examples to make the algorithm more accessible to practitioners in heuristic search.
Teaching Business Information Systems in 2020s: Pitfalls, Challenges and Some Methodological Ideas for Testing

: In the paper we present experiences from the organisation and running of a Business Information Systems course for undergraduate students at the Berner Fachhochschule. Aspects related to the teaching style, the content and the process that have been defined for use are presented and discussed, while the rationale for our decisions is also outlined. An experiential approach is fostered that allows students to capitalise on their individual preferences and learning paths, combined with the idea of supporting the acquisition of digital skills that will allow better use in their current or future working environments.
Impact of a New Word Embedding Cost Function on Farsi-Spanish Low-Resource Neural Machine Translation

Neural Machine Translation (NMT) relies heavily on word embeddings, which are continuous representations of words in a vector space, obtained from large monolingual data and, independently, from bilingual data for NMT model training. Word embeddings have proven to be invaluable for performance improvements in natural language analysis tasks that otherwise suffer from data scarcity. This paper deﬁnes a new cost function—demonstrated on Farsi-Spanish low-resource attention-based NMT—that encodes word similarity as distances within a word embedding space. The novelty of this cost function is that it encourages our attentional NMT model to generate words that are close to their references in the embedding space. This approach encourages the decoder to select acceptably similar words when potential candidates are found to be Out-Of-Vocabulary (OOV). Experimental results demonstrate improvements of our attentional NMT model over a community-standard NMT baseline model.
Experiment Workbench: A Co-agent for Assisting Data Scientists

: The analysis of large volumes of data is a ﬁeld of study with ever increasing relevance. Data scientists is the moniker given for those in charge of extracting knowledge from Big Data. Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. The exploration done by data scientists relies heavily on the practitioner experience. These activities are hard to plan and can change during execution – a type of process named Knowledge Intensive Processes (KiP). The knowledge about how a data scientist performs her tasks could be invaluable for her and for the enterprise she works. This work proposes Experiment Workbench (EW), a system that assists data scientists in performing their tasks by learning how a data scientist works in-situ and being a co-agent during task execution. It learns through capturing user actions and using process mining techniques to discover the process the user executes. Then, when the user or her colleagues work in the learned process, EW suggests actions and/or presents existing results according to what it learned towards speed up and improve user wok. This paper presents the foundation for EW development ( e.g. , the main concepts, its components, how it works) and discuss the challenges EW is going to address.