Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Defect Classification Approach Based on the Fusion Matrix of Multi-Eigenvalue

Defect recognition plays an important part in the health monitoring of in-service equipment. Surface defects and sub-surface defects of key components have different effects on the safety of the equipment. Sub-surface defects are difficult to be identified, and it is more likely to cause unpredictable damage than surface defects. However, the existing characteristic cannot accurately identify the defect type. Therefore, it is critical to find features that are useful for defect classification. The fusion of multiple feature values facilitates defect classification. For obtaining multi-feature fusion eigenvalues, this article introduces a fusion matrix based on Fisher’s discriminant criterion and correlation analysis, which realizes the fusion of two dimensions of the whole and the local and different aspects. The BP neural network classifier is then used to discriminate and classify surface defects and sub-surface defects. The recognition accuracy of surface defects and sub-surface defects can reach 98.1%. The classification accuracy of this approach is significantly improved compared with the method based on a single feature value and other state-of-the-art methods. The approach can also be used to identify defects with different cross-sectional shapes, indicating that this approach is suited for nature defects.
Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients

The discrimination of the interictal and preictal states in epilepsy contributes to the construction of an efficient system of seizure prediction. Here, we performed the classification of the interictal and preictal states for EEG signals of the scalp. The energies of the levels obtained by the signal decomposition of the Wavelet Discrete Transform were used as features for classification. The kNN and SVM classifiers were used in the analysis of the individual EEG channels, which gave indications that the occipital lobe region channels are the most relevant to differentiate between the interictal and preictal states. Using these channels, the classification into two states achieved accuracy of 97.29%, sensitivity of 96.25% and specificity of 98.33%. In addition, the different frequency ranges obtained by Wavelet for the classification were analyzed, and it was observed that the range of 32 Hz to 128 Hz presented greater relevance in the task.
Electrochemical and optical biosensors for biological sensing applications

: Biosensors are analytical tools that play an important role in advanced applications, especially in the monitoring of biological molecules related to health conditions. A variety of biosensors have been described in this review to emphasize the important applications in the biological ﬁeld. These biosensors are categorized into four groups depending on the bioreceptor molecules, including enzymatic biosensors, immuno-based biosensors, DNA-based biosensors, and other interesting biosensors. Many platforms and materials have been developed with various beneﬁts. The detection principles of the biosensors in this review are focused on optical and electrochemical techniques due to their characteristic advantages. Information of using novel materials for fabrication of biosensors is also provided.
Mean-Square Consentability of Multiagent Systems With Nonidential Channel Fading

In this article, we are concerned with the consensus problem of multiagent system where the channels from each agent's control input to the plant are fading nonidentically. Two problems have been extensively studied. One is the consentable problem when the current state information is available for the controller. The other is that the state information is delayed for the controller in which we propose a novel predictorlike control protocol. It is obtained that the multiagent system with system matrices $A=B=1$ is mean-square consentable for both problems. In particular, the mean-square consensus value is the average of all agents’ initial states. Furthermore, for the multiagent system with general dynamics, sufficient conditions have been obtained in terms of two parameterized Riccati inequality/equation for both problems, respectively.
Psychometric evaluation of a questionnaire to evaluate organizational capacity development for faculty development programs

Background: Organizational capacity development is an important outcome of faculty development programs, but there is a lack of an appropriate instrument for its evaluation. Aims and Objectives: The aim of this study was to develop a questionnaire to evaluate the organizational capacity development for faculty development programs and to test its psychometrics. Materials and Methods: The initial capacity development for faculty development questionnaire (CDQ-FD) of 26 items was developed based on a literature review and opinion of experts. Content validity ratio (CVR), content validity index (CVI), content validity index for items (I-CVI), and the content validity index for scales (S-CVI) were computed for content validity. Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) were performed for construct validation. Results: The score for CVR, CVI, I-CVI, and S-CVI was 0.71, 0.83, 0.87, and 0.90, respectively. EFA resulted in a three-factor model with total variance extraction of 64%. Cronbach's alpha and Spearman Brown coefficient were investigated for reliability assessment. The Cronbach's alpha of overall scale was 0.8 and the test-retest reliability of the overall scale was 0.78. The final CDQ-FD contained 21 items and three categories. Conclusions: The CDQ-FD questionnaire appears to be a valid and reliable instrument for the evaluation of organizational capacity development for faculty development in the medical education.
Fast and slow velocity alignments in a Cucker-Smale ensemble with adaptive couplings

We study the emergent dynamics of the Cucker-Smale (C-S for brevity) ensemble under adaptive couplings. For the adaptive couplings, we basically consider two types of couplings: Hebbian vs. anti-Hebbian. When the Hebbian rule is employed, we present sufficient conditions leading to the mono-cluster flocking using the Lyapunov functional approach. On the other hand, for the anti-Hebbian rule, the possibility of mono-cluster flocking mainly depends on the integrability of the communication weight function and the regularity of the adaptive law at the origin. In addition, we perform numerical experiments and compare them with our analytic results.
MMPD: A Novel Malicious PDF File Detector for Mobile Robots

PDF is one of the most commonly used office tools, and can be used by robots to share documents with people. However, it has become the target of hackers to attack a variety of devices since it is cross-platform and can be embed with JavaScript code or URIs. Therefore, how to protect the platform security while robots parsing PDF files becomes a serious problem. In this paper, we design and realize a novel malicious PDF file detector, MMPD, for mobile robots based on deep learning. Experiments show that the F1-Score of MMPD can achieve up to 99.478%. In the meantime, the hardware resource usage is low and no significant performance reduction of the whole system is observed.
Evaluation of External and Internal Egg Quality Traits of Indigenous Sakini Chicken in Different Generations of Selection

Indigenous Sakini chicken is the principal breed in Nepal. Present study was conducted mainly aiming at comparing external and internal egg quality traits in four different generations (G0, G1, G2 and G3) of Sakini chicken and determining relationships among these traits. Total of 154 eggs (G0: 30, G1: 44, G2: 40, G3: 40) were evaluated for external egg traits like egg weight, egg length, egg breadth, shell thickness, shell weight and internal egg traits like yolk weight, yolk height, yolk diameter, albumen weight, albumen height, albumen diameter. The data were recorded and analysed using GenStat 19 edition software. There was significant difference in almost all traits of external and internal traits of egg except egg shell quality, yolk to albumin ratio and yolk percentage. Encouragingly, we had observed an increasing trend for each trait specially Haugh unit, a measure for better quality of egg protein in every generation indicating that selection to be continued unless the uniform performance is demonstrated in the population. Positive and significant (p<0.01) correlations (r = 0.44-0.92) were observed between egg quality traits under study. Findings suggested that selection brings genetic improvement in most of the egg quality traits of indigenous Sakini chicken. However, continuous selection practices to be employed in successive generations to exploit the maximum genetic potential in Sakini chicken.
Biomass to Syngas: Modified Stoichiometric Thermodynamic Models for Downdraft Biomass Gasification

To help meet the global demand for energy and reduce the use of fossil fuels, alternatives such as the production of syngas from renewable biomass can be considered. This conversion of biomass to syngas is possible through a thermochemical gasification process. To design such gasification systems, model equations can be formulated and solved to predict the quantity and quality of the syngas produced with different operating conditions (temperature, the flow rate of an oxidizing agent, etc.) and with different types of biomass (wood, grass, seeds, food waste, etc.). For the comparison of multiple different types of biomass and optimization to find optimal conditions, simpler models are preferred which can be solved very quickly using modern desktop computers. In this study, a number of different stoichiometric thermodynamic models are compared to determine which are the most appropriate. To correct some of the errors associated with thermodynamic models, correction factors are utilized to modify the equilibrium constants of the methanation and water gas shift reactions, which allows them to better predict the real output composition of the gasification reactors. A number of different models can be obtained using different correction factors, model parameters, and assumptions, and these models are compared and validated against experimental data and modelling studies from the literature.
FracTCAM: Fracturable LUTRAM-Based TCAM Emulation on Xilinx FPGAs

In this brief, we present FracTCAM, an efficient methodology for ternary content addressable memory (TCAM) emulation on Xilinx field-programmable gate arrays (FPGAs) by leveraging primitive architectural resources. The proposed methodology exploits the fracturable nature of lookup table random access memories (LUTRAMs) and built-in slice flip-flops for deeper pipelining. Multiple slices can be combined together to build deeper and wider TCAMs using ANDing operations. This results in TCAM implementations that achieve lower resources utilization, lower delay, and power consumption. A comparison with the existing schemes shows that FracTCAM consistently achieves the best performance per area (PA) and performance per area per watt (PAW).
Le saumon atlantique de la rivière Matane : croissance marine, abondance des retours en rivière et indice de l’oscillation nord-atlantique

L’abondance du saumon atlantique (Salmo salar) dans le nord de l’Atlantique a diminue considerablement au cours des dernieres decennies. Jusqu’a maintenant, peu de stocks ont montre des signes de retablissement malgre des efforts importants pour renverser cette situation. Les niveaux decroissants d’abondance des saumons adultes de retour en riviere pourraient etre associes a une augmentation de la mortalite naturelle en mer decoulant d’une reduction de la croissance. La presente etude a pour but de verifier si 2 indices de croissance scalaire en phase marine (la distance intercirculi maximale atteinte lors de la premiere saison estivale en mer et la somme des distances intercirculi de la zone de croissance marine des ecailles) et l’abondance des retours de saumon dans la riviere Matane (Quebec, Canada) appuient cette hypothese. Bien que ces indices presentent des differences interannuelles significatives, ils affichent une tendance a la hausse de 1964 a 1979 et une tendance a la baisse de 1995 a 2012. Ces tendances sont semblables a celles des retours de saumons adultes en riviere et a celles de l’indice de l’oscillation nord-atlantique (IONA). Ces similitudes concordent avec l’hypothese d’une relation inverse entre la croissance marine et la mortalite et, consequemment, les variations d’abondance qui en resultent.
The Impact of Firm Size and Business Strategy on Response to Service Disruptions: Evidence From the U.S. Domestic Airline Industry

In this article, data from the U.S. domestic airline industry to examine the impact of firm size and business strategy on the effect of service disruptions (cancellations and delays) on service quality (customer dissatisfaction) is used. We test our hypotheses by fitting fixed-effects models to longitudinal data obtained from the U.S. Department of Transportation. The findings suggest that there is a significant relationship between service disruptions and service quality. The results show that both airline size and airline business strategy have moderating impacts on the relationship between service disruptions and service quality. We find the negative impacts of delays and cancellations on service quality are more pronounced for network airlines than for low-cost airlines. The impact of service disruptions on service quality is less significant for larger airlines when the rate of cancellations is low. As the rate of cancellations increases, the service quality of larger airlines is increasingly affected, and the effect is more pronounced compared to the effect on the smaller airlines, suggesting a moderating impact of firm size. Overall, our empirical results show that the relationship between service disruptions and service quality is being moderated by both a firm's size and a firm's business strategy.
Analog Self-Timed Programming Circuits for Aging Memristors

Reliable programming crossbar memristors to the required resistive states is the challenge that hinders VLSI deployment of the memristive neural network circuits, as current memristive devices face the variability issues of resistive switching. There is also a need for on-chip control circuitry that detects malfunctioning memristive nodes in the crossbar due to the memristor aging. <italic>Program and Verify</italic> (<inline-formula> <tex-math notation="LaTeX">$P\&V$ </tex-math></inline-formula>) schemes can be used for both controlling resistive switching as well as evaluating the functionality of memristors. In this brief, we propose a novel analog circuit design for the <inline-formula> <tex-math notation="LaTeX">$P\&V$ </tex-math></inline-formula> approach of row-by-row programming bipolar memristors in a 1T1M crossbar configuration. The proposed control circuit (CC) is self-timed and performs both read and program operations, decreasing the overall programming complexity. CC design is verified with Spice simulations using low power 22nm high-k CMOS models and Modified S memristor model for large scale simulations. Parasitic of wire lines under thermal variation and CMOS variability were included for programming 1T1M crossbar partitions of the sizes <inline-formula> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$32\times 32$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$64\times 64$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$128\times 128$ </tex-math></inline-formula>.
Investigating the Multivariate Granger Causality between Remittance, Government Expenditure and Economic Growth in Ethiopia

Several studies argued that international remittances have often been contributed to a significant source of finance to upsurge domestic investment for developing countries. Against this backdrop, this current article analyses a causal interdependency between remittance, government expenditure, and economic growth in Ethiopia covering the period from 1982 to 2017 by applying Toda and Yamamoto and Granger causality tests. Moreover, the Johansen-Juselius and an Auto-regressive Distributed Lag (ARDL) bounds-test approach was utilized to test the long-run co-integration relationship among the variables. Our findings confirmed that the series of variables has been jointly co-integrated in the long-run. The tests of the causality divulge that there was feedback in the causality between the remittance and economic growth in Ethiopia. We found that a unidirectional Granger causality links running from remittance to economic growth, and also a unidirectional causality running from government expenditure to economic growth. These causality results further suggested that the growth hypothesis which was assumed that the remittance and government expenditure was the main driving factor of determinants for economic growth in Ethiopia over the study periods and consequently, economic growth also the function of remittance and government expenditure. Keywords: Remittances; Economic growth; Government Expenditure; Co-integration; Granger Causality; Ethiopia JEL classification: C22; F24; F43; O55 DOI: 10.7176/JESD/11-16-17 Publication date: August 31 st 2020
Fine-Grained Service-Level Passenger Flow Prediction for Bus Transit Systems Based on Multitask Deep Learning

Bus services play a crucial role in urban transit. It is significant to achieve the fine-grained service-level passenger flow prediction (SPFP), namely to predict the total number of passengers for each service of each bus line passing through each station during the next short-term interval. However, it faces great challenges due to complex factors including inter-station and inter-line spatial dependencies, intra-station and inter-service temporal dependencies, and internal/external influences. To address these challenges, we propose a multitask deep-learning (MDL) approach, called MDL-SPFP, to jointly predict the arriving bus service flow, line-level on-board passenger flow and line-level boarding/alighting passenger flow by leveraging well-designed deep neural networks called ARM. The MDL framework can mutually reinforce the prediction of each type of flow, and finally integrate the outputs to achieve the fine-grained service-level prediction. The ARM network combines three modules, Attention mechanism, Residual block and Multi-scale convolution, to well capture various complex non-linear spatio-temporal dependencies and influence factors. Extensive experiments based on a large-scale realistic bus operation dataset are conducted to confirm that our MDL-SPFP approach outperforms 10 state-of-the-art baselines, and improves 22.39% accuracy than the best baseline.
Enhancing data utilisation in the construction project lifecycle through early involvement and integration

The productivity of projects in the construction sector has not developed as well as in other sectors. One factor contributing to this low productivity is the weak digital intensity of the construction sector. Often, the implementation of construction projects is fragmented, and the objectives of the project stakeholders’ conflict with those of the whole project. Existing research has highlighted the importance of the use of building information modelling in the construction industry. In some other sectors, the product data lifecycle management has an essential role in their business, but in the construction industry, product data management has less attention. The motivation for this dissertation is to help the construction industry to gain the benefits that the use of BIM can provide. The focus is on enhancing data utilisation because late and fragmented management of information during the project lifecycle leads to defects and rework. This dissertation’s aim is on how to enhance data utilisation in construction projects by enabling early integration of stakeholders. The technical description of the data transfer format has not been in the scope of this research. This dissertation does not either include descriptions of all the commercial project delivery models. This compilation dissertation consists of qualitative studies with literature reviews, focus group interview, survey, and interviews. It identifies practical obstacles to the wider use of information modelling, how information modelling supports early involvement and integration, and the benefits of early integration. Finally, it produces a model for data and information management for construction projects. The main contribution of this study is that it identifies a need to design a unique project product data model in the early phases of the project, according to the information of all stakeholders. One master data enables data sharing with different applications. Design for X (DfX) can be used for early integration. The client is responsible that the project delivery agreements support the wellorganised use of data and information flow throughout the project’s lifecycle. The most significant barrier for achieving the benefits of BIM is that the data needs and the overall process of BIM utilisation are not planned proactively. The practical implications are written mainly from the infrastructure construction project angle. The main impact is that the project product data model is better to design specially for each unique project that matches the capability level of the project team, available data, software and hardware, project delivery type, and team motivation.
Musculoskeletal manifestations of Lyme borreliosis – a review

Lyme borreliosis (LB) is a zoonotic disease caused by Gram-negative bacteria Borrelia burgdorferi sensu lato. The majority of reported cases of LB originate in the northern hemisphere, mostly in the US and Europe. One of the typical manifestations of LB are musculoskeletal symptoms; they may appear in any of the three LB stages. The diagnosis is based on clinical manifestations and confirmed by serological tests. One course of antibiotic therapy is sufficient for LB to dissipate in most cases, although for some patients, the symptoms subside gradually even after completion of therapy. Patients who have been demonstrated to have specific antibodies but are symptomless should not be treated. In instances where the advised treatment proved to be unsuccessful, patients should be referred to rheumatologist for additional diagnostics. The goal of this review is to update physicians on current scientific knowledge on musculoskeletal manifestations of LB.