Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Advances in Early Diagnosis of Viruses Associated with Gastroenteritis by Biosensors

Gastroenteritis, as one of the main worldwide health challenges, especially in children, leads to 3–6 million deaths annually and causes nearly 20% of the total deaths of children aged ˂5 years, of which ~1.5 million gastroenteritis deaths occur in developing nations. Viruses are the main causative agent (~70%) of gastroenteritis episodes and their specific and early diagnosis via laboratory assays is very helpful for having successful antiviral therapy and reduction in infection burden. Regarding this importance, the present literature is the first review of updated improvements in the employing of different types of biosensors such as electrochemical, optical, and piezoelectric for sensitive, simple, cheap, rapid, and specific diagnosis of human gastroenteritis viruses. The Introduction section is a general discussion about the importance of viral gastroenteritis, types of viruses that cause gastroenteritis, and reasons for the combination of conventional diagnostic tests with biosensors for fast detection of viruses associated with gastroenteritis. Following the current laboratory detection tests for human gastroenteritis viruses and their limitations (with subsections: Electron Microscope (EM), Cell Culture, Immunoassay, and Molecular Techniques), structural features and significant aspects of various biosensing methods are discussed in the Biosensor section. In the next sections, basic information on viruses causing gastroenteritis and recent developments for fabrication and testing of different biosensors for each virus detection are covered, and the prospect of future developments in designing different biosensing platforms for gastroenteritis virus detection is discussed in the Conclusion and Future Directions section as well.
Demand‐side management using a distributed initialisation‐free optimisation in a smart grid

Due to the integration of the renewable generation and the distributed load that inherently uncertain and unpredictable, developing an efficient distributed management structure of such a complex system remains a challenging issue. Most of the existing works on the demand-side management concentrate on the centralised methods or need a proper initialisation process. This study proposed a demand-side management strategy that can solve the optimisation problem in a distributed manner without initialisation. The objective of the designed demand management system is to maximise the social welfare of a smart grid by controlling the active power economically. The proposed optimisation strategy that generates the optimal power references uses the neighbouring information while considering the local feasible constraints by using a projection operation. Furthermore, the optimisation algorithm is initialisation free, which avoids any initialisation process when plugging-in new customers or plugging-out power units, such as demand loads, battery energy storage systems and distributed generators. The proposed strategy only uses the neighbouring information, so that the proposed approach is scalable and potentially applicable to large-scale smart grids. The effectiveness and scalability of the proposed algorithm are established and verified through case studies.
A Microstrip Dual-Split-Ring Antenna Array for 5G Millimeter-Wave Dual-Band Applications

A microstrip dual-split-ring antenna array for fifth-generation (5G) millimeter-wave dual-band applications is presented in this letter. The microstrip dual-split-ring structure is adopted to generate two resonance points operating at 28/38 GHz dual band, respectively. To improve the radiation characteristics at 38 GHz band, two branches are added to the middle of microstrip dual-split-ring structure. Finally, the proposed 1 × 4 microstrip dual-split-ring antenna array can achieve impedance bandwidths of 12.5% (26–29.5 GHz) and 5.3% (36.7–38.7 GHz), and peak gains of 12.7 and 14.9 dBi at 28/38 GHz dual band, respectively. The proposed antenna array with one signal chain has these advantages of simple structure, wider dual-band impedance and gain bandwidths, higher gain and lower sidelobe level. Therefore, the design scheme will be suitable for the 5G millimeter-wave dual-band applications.
Analysis of application of Six Sigma in refuelling process in Brazilian airline

ABSTRACT Aircraft refuelling is a major cause of flight delays because it is a slow process. Further, if it does not begin as soon as the aircraft is available for ground handlers, there is an increasing risk of it being terminated after the final passenger has boarded. Usually, the process only begins after information regarding the required quantity of fuel is passed through the flight dispatcher, and this information typically requires a certain time to reach the ground handlers. Therefore, it is intended to test a new scenario: to begin refuelling with a minimum level and, if necessary, fill up the remainder with the final fuel figures when received. The aim of this paper is to analyse the application of Six Sigma in this process through Student’s t-test and statistical process control. The collected data in this case study include the amount of fuel supplied and flight delays (which are mainly caused by refuelling). The results demonstrate that the new process is favourable, and that the average length of flight delays is reduced from 14 to 6 min, which is an improvement of 57%. It is concluded that the application of Six Sigma in the aircraft refuelling process saves time and improves on-time performance levels, which is relevant to the scientific literature, thereby aiding in mitigating the risk of fines and penalties.
Precise prediction of photothermally induced irreversible bending deformation based on non-uniform thermal expansion of layer-structure films

Although photothermally induced self-bending films based on nonuniform thermal expansion are created, heat transfer and shape-deformation procedures are not investigated very well, limiting the understanding of such complicated behavior and the achievement of precise shape control. Here, thermally expanded microspheres (TEMs) were added into polydimethylsiloxane (PDMS) to create active layers as PDMS-TEM single-layer films that were attached onto pure PDMS passive layer to create PDMS/PDMS-TEM bi-layer films subsequently. After heating, TEMs in the film undergo phase transition and present irreversible thermal expansion, driving deformation of films. Combined with steady-state (uniform heating) deformation simulations and experiments, a deformation model was established before a non-steady state (light irradiation) heat transfer model was set up to simulate the heat transfer process of films under light irradiation. Then the temperature distribution was coupled with the deformation model to simulate the photothermally induced deformation of the samples. The PDMS-TEM single-layer films also presented bending deformation under illumination due to the non-uniform thermal expansion caused by temperature gradient. For bi-layer films, different degrees of thermal expansion between two layers lead to bending deformation. The films with TEM concentration of 30 wt% can achieve great deformation, and the bending curvature was 0.07 mm−1. The geometric parameters including thickness, layer thickness ratio and aspect ratio had little effect on deformation degrees of the films. Deformation mode was varied with aspect ratio. A long side bending and diagonal bending were achieved in bi-layer films at aspect ratios of 7:1 and 1:1. In combination with experimental results, the heat transfer model and deformation model were constructed to demonstrate the complex deformation process, which provides guidance for the structural design of the photothermally induced shape-changing films in practical applications.
“It’s not good, but it could be worse”: racial microaggressions toward Chinese international students during the COVID-19 pandemic

ABSTRACT Since the 2010s, Portugal has experienced a considerable growth in the number of Chinese international students, who have been attracted by the country’s image of tolerance and openness for diversity. However, as it was reported in other contexts, throughout the health crisis, these students were blatantly confronted with racial microaggressions in their daily routines inside and outside their higher education institutions’ facilities. Drawing on evidence from 30 in-depth interviews conducted with Chinese international students in Portugal and following a thematic analysis approach, this paper analyses how they frame and perceive their encounters with racism in their daily lives during the pandemic. We argue that Chinese international students resort on distinct discursive strategies to negotiate their encounters with racism during their sojourns in Portugal throughout the health crisis and to negate their “otherness” as a racial minority in the country.
Evaluating the Affective Aspect of Intuitive Interactions Using Image Schemas

Intuitive use is a desirable feature in product and interface design. Until recently, however, there has been little research on the affective aspects of intuitive use. This paper contributes to the research in this area by proposing a systematic analytical method for assessing the affective aspect of intuitive use; the method uses an ontology of image schemas, computational semantics, and a sentiment analysis to determine the affect associated with interactions. The approach is evaluated through an empirical study involving 40 participants who completed a task with two products. The results show that the approach links the image schemas used for the completion of a task to the affective experiences of the users. The study has the potential to lead to improvements in design and the improved evaluation of intuitive use because it allows experiences to be linked directly to the specific image schemas employed in the design.
Joint Routing and Scheduling Optimization in Time-Sensitive Networks Using Graph-Convolutional-Network-Based Deep Reinforcement Learning

The growing number of Internet of Things (IoT) devices brings enormous time-sensitive applications, which require real-time transmission to effectuate communication services. The ultrareliable and low-latency communication (URLLC) scenario in the fifth generation (5G) has played a critical role in supporting services with delay-sensitive properties. Time-sensitive networking (TSN) has been widely considered as a promising paradigm for enabling the deterministic transmission guarantees for 5G. However, TSN is a hybrid traffic system with time-sensitive traffic and best effort traffic, which require effective routing and scheduling to provide a deterministic and bounded delay. While joint optimization of time-sensitive and non-time-sensitive traffic greatly increases the solution space and brings a significant challenge to obtain solutions. Therefore, this article proposes a graph convolutional network-based deep reinforcement learning (GCN-based DRL) solution for the joint optimization problem in practical communication scenarios. The GCN is integrated into deep reinforcement learning (DRL) to obtain the network’s spatial dependence and elevate the generalization performance of the proposed method. Specifically, the GCN adopts the first-order Chebyshev polynomial to approximate the graph convolution kernel, which reduces the complexity of the algorithm and improves the feasibility for the joint optimization task. Furthermore, priority experience replay is employed to accelerate the convergence speed of the model training process. Numerical simulations demonstrate that the proposed GCN-based DRL algorithm has good convergence and outperforms the benchmark methods in terms of the average end-to-end delay.
Dissection of Paenibacillus polymyxa NSY50-Induced Defense in Cucumber Roots against Fusarium oxysporum f. sp. cucumerinum by Target Metabolite Profiling

Simple Summary Plant growth-promoting rhizobacteria (PGPR) have significant potential to enhance the tolerance of biotic and abiotic stresses and the productivity of crops. However, the mechanism of PGPR in improving plant resistance to pathogens is unclear. Recently, the newly isolated Paenibacillus polymyxa strain NSY50 was shown to considerably suppress the Fusarium wilt of cucumber plants. This study was carried out to explore the underlying mechanism of NSY50 in improving plant resistance to pathogen invasion via target metabolite profiling, and the results indicated that strain NSY50 was able to alleviate Fusarium wilt stress by activating GSH metabolism and improving redox balance. Our research findings enable a deeper understanding of P. polymyxa NSY50-induced enhanced defense against F. oxysporum in cucumber. Abstract To gain insights into the roles of beneficial PGPR in controlling soil-borne disease, we adopted a metabolomics approach to investigate the beneficial impacts of P. polymyxa NSY50 on cucumber seedling roots under the pathogen of Fusarium oxysporum f. sp. cucumerinum (FOC). We found that NSY50 pretreatment (NSY50 + FOC) obviously reduced the production of reactive oxygen species (ROS). Untargeted metabolomic analysis revealed that 106 metabolites responded to NSY50 and/or FOC inoculation. Under FOC stress, the contents of root osmotic adjustment substances, such as proline and betaine were significantly increased, and dehydroascorbic acid and oxidized glutathione (GSH) considerably accumulated. Furthermore, the contents of free amino acids such as tryptophan, phenylalanine, and glutamic acid were also significantly accumulated under FOC stress. Similarly, FOC stress adversely affected glycolysis and the tricarboxylic acid cycles and transferred to the pentose phosphate pathway. Conversely, NSY50 + FOC better promoted the accumulation of α-ketoglutaric acid, ribulose-5-phosphate, and 7-phosphosodiheptanone compared to FOC alone. Furthermore, NSY50 + FOC activated GSH metabolism and increased GSH synthesis and metabolism-related enzyme activity and their encoding gene expressions, which may have improved redox homoeostasis, energy flow, and defense ability. Our results provide a novel perspective to understanding the function of P. polymyxa NSY50, accelerating the application of this beneficial PGPR in sustainable agricultural practices.
Study on enhancement of dominating sets in Ad Hoc wireless networks

An essential component of eﬀective use of Ad Hoc Wireless networks is proper utilization of available resources and network stability. There has been a recent increase of interest in Ad Hoc networks, partly due to the fact that Ad Hoc networks can be implemented without requiring any support from existing wired backbone or costly hardware setup. The eﬀective utilization of the power of the Ad Hoc networks lies in scalability, stability, maintainability and rapid convergence of these networks. The route detection and route formation in the Ad Hoc networks should be done instantaneously. Performing eﬀective route detection on such environments can be best achieved when the broadcasting is improved and the redundancy in route detection is removed. In this thesis work, a cluster based algorithm as well as a localized algorithm for Connected Dominating Set formation have been proposed. The Clustering algorithm forms disjoint logical groups of nodes with a lead node as head of the cluster. The cluster head takes part in the routing and has additional tasks of cluster maintenance. The clusters form a logical backbone and the Route search space is limited to the no. of clusters. But, the method forms suboptimal routes. The Connected Dominating Set based algorithm forms a logical backbone of connected gateways. The broadcast packets are retransmitted by these nodes. The routes are computed by shortest path algorithm. Changes in the backbone does not have impact on the ongoing communications. New routes are computed upon failure of existing routes. The results obtained using this algorithm are compared with other methods across a range of diﬀerent scenarios with 100 nodes upon a simulation area of 100 X 100 twips. The Connected Dominating Set algorithm distributes the dominating nodes equally along the entire range while others are being biased towards higher “id” nodes. The Connected Dominating Set based algorithm is excelling the cluster algorithm in Search space size.
Uma Técnica de Análise Estática para Detecção de Canais Laterais Baseados em Tempo

Canais laterais baseados em tempo são vulnerabilidades ligadas à implementação de sistemas criptográficos e que permitem ao adversário conhecer acerca de uma informação sigilosa através de minuciosas observações do tempo de execução do programa. Mascaramento e sistemas de tipos já foram propostos objetivando mitigar esse problema. Este artigo propõe uma alternativa focada em análise estática de fluxo de informação. Aplicou-se essa análise na biblioteca NaCl e em porções da OpenSSL onde foi possível validar a boa qualidade da primeira e reportar vários traços vulneráveis na segunda.
Emission-Intensity-Based Carbon Tax and Its Impact on Generation Self-Scheduling

We propose an emission-intensity-based carbon-tax policy for the electric-power industry and investigate the impact of the policy on thermal generation self-scheduling in a deregulated electricity market. The carbon-tax policy is designed to take a variable tax rate that increases stepwise with the increase of generation emission intensity. By introducing a step function to express the variable tax rate, we formulate the generation self-scheduling problem under the proposed carbon-tax policy as a mixed integer nonlinear programming model. The objective function is to maximize total generation profits, which are determined by generation revenue and the levied carbon tax over the scheduling horizon. To solve the problem, a decomposition algorithm is developed where the variable tax rate is transformed into a pure integer linear formulation and the resulting problem is decomposed into multiple generation self-scheduling problems with a constant tax rate and emission-intensity constraints. Numerical results demonstrate that the proposed decomposition algorithm can solve the considered problem in a reasonable time and indicate that the proposed carbon-tax policy can enhance the incentive for generation companies to invest in low-carbon generation capacity.
Enabling URLLC in 5G NR IIoT Networks: A Full-Stack End-to-End Analysis

This paper addresses the problem of enabling inter-machine ultra-reliable low-latency communication (URLLC) in 5th generation (5G) NR Industrial Internet of Things (IIoT) networks. In particular, we consider a common Standalone Non-Public Network (SNPN) architecture proposed by the 5G Alliance for Connected Industries and Automation (5G-ACIA), and formalize a full-stack end-to-end (E2E) latency analysis where semi-persistent uplink scheduling is considered in detail and compared with a baseline grant-based approach. Through simulations, we demonstrate that semi-persistent scheduling outperforms the baseline scheme and provides an E2E latency below 1 ms, thereby representing a desirable solution to allocate resources for URLLC. Notably, we provide numerical guidelines for dimensioning 3GPP-compliant IIoT networks for both periodic and aperiodic traffic applications, and as a function of the number of machines in the factory and of the offered traffic.
Great expectations: The construct validity of the violation‐of‐expectation method for studying infant cognition

The violation-of-expectation method has been used in thousands of studies examining the breadth and depth of preverbal infants' knowledge and cognitive capacities. In this commentary, we review evidence that supports the violation-of-expectation method as an important tool for infant research that has strong construct validity. We show that this method reveals infants' expectations, produces reliable results across study designs and stimuli, and is grounded in theories of learning. We support and encourage the use of this method to continue to unveil the scope and sophistication of infant cognition. ’ active predictions,
GsRSS3L, a Candidate Gene Underlying Soybean Resistance to Seedcoat Mottling Derived from Wild Soybean (Glycine soja Sieb. and Zucc)

Soybeans are a major crop that produce the best vegetable oil and protein for use in food and beverage products worldwide. However, one of the most well-known viral infections affecting soybeans is the Soybean Mosaic Virus (SMV), a member of the Potyviridae family. A crucial method for preventing SMV damage is the breeding of resistant soybean cultivars. Adult resistance and resistance of seedcoat mottling are two types of resistance to SMV. Most studies have focused on adult-plant resistance but not on the resistance to seedcoat mottling. In this study, chromosome segment-substituted lines derived from a cross between Suinong14 (cultivated soybean) and ZYD00006 (wild soybean) were used to identify the chromosome region and candidate genes underlying soybean resistance to seed coat mottling. Herein, two quantitative trait loci (QTLs) were found on chromosome 17, and eighteen genes were found in the QTL region. RNA-seq was used to evaluate the differentially expressed genes (DEGs) among the eighteen genes located in the QTLs. According to the obtained data, variations were observed in the expression of five genes following SMV infection. Furthermore, Nicotiana benthamiana was subjected to an Agrobacterium-mediated transient expression assay to investigate the role of the five candidate genes in SMV resistance. It has also been revealed that Glyma.17g238900 encoding a RICE SALT SENSITIVE 3-like protein (RSS3L) can inhibit the multiplication of SMV in N. benthamiana. Moreover, two nonsynonymous single-nucleotide polymorphisms (SNPs) were found in the coding sequence of Glyma.17g238900 derived from the wild soybean ZYD00006 (GsRSS3L), and the two amino acid mutants may be associated with SMV resistance. Hence, it has been suggested that GsRSS3L confers seedcoat mottling resistance, shedding light on the mechanism of soybean resistance to SMV.
Learner Engagement in Using Kahoot! within a University English Proficiency Course

Background / purpose – Game-based learning is becoming increasingly popular among educators in higher institutions. However, it can be a daunting task for educators to ascertain which of the currently available game-based learning platforms best suits their learners’ needs. This study explored the suitability of Kahoot! as one of the most commonly used game-based learning platforms in education. The aim of study was to determine whether Kahoot! can be used to engage learners and reinforce learning for academic purposes in higher education institutions. Materials / methods – Data were collected among 80 science and technology undergraduate students enrolled to an “English for Academic Communication” course, which is compulsory for English language proficiency at the participant university. The students were exposed to the use of Kahoot! through several predetermined quizzes conducted during their lessons over a single 14-week semester. The study employed a two-part, 24-item, Likert-type scale administered via Google Forms. The questionnaire was assessed using Cronbach alpha reliability analysis, which yielded a score of . 97, confirming the questionnaire to be a reliable instrument. Results – The study’s findings revealed that the participant students found Kahoot! to be fun, enjoyable, engaging, and interesting to use. Conclusion – The participant students also believed that Kahoot! was able to consolidate and reinforce learning in academic courses, which is integral to the higher education context in terms of creating better classroom dynamics.