Miracle-dz/UniFAR-FTUs huggingface dataset:
MFS usage intention during COVID-19 and beyond: an integration of health belief and expectation confirmation model

Purpose The usage rate of mobile financial services (MFS) has shown an uptick since the emergence of the COVID-19 pandemic in Bangladesh. This study aims to reveal the underpinning reasons for such MFS surge and its continuance by integrating health belief model (HBM) and expectation confirmation model (ECM). Design/methodology/approach The study analyzes 529 MFS users' responses during the second wave of the COVID-19 outbreak in Bangladesh using the partial least square method.Findings Satisfaction is more predictive than perceived usefulness in explaining continuance usage intention. Expectation confirmation also indirectly affects continuance intention. Among the HBM constructs, the indirect effect of perceived severity on continuance intention via perceived usefulness and satisfaction is significant. Besides, the impact of self-efficacy on continuance intention is also significant. Moreover, perceived credibility significantly affects satisfaction and indirectly affected continuance usage intention via satisfaction.Practical implications The study projects boosting customers' satisfaction is critical for the successful retention of existing MFS customers. MFS service providers should emphasize the factors that amplify satisfaction. They must evaluate preadoption factors so that customers can have positive confirmation. Especially, the service providers, the policymakers and the regulators should take an active role in improving the users' self-efficacy and the system's credibility. Undertaking the MFS literacy program, installing hotline service to provide emergency help will boost users' confidence in using the system.Originality/value The study is a unique contribution in the context of Bangladesh. To the best of the authors’ knowledge, no previous MFS studies in Bangladesh explored MFS continuance usage intention during COVID-19 and beyond. Besides, the inclusion of “perceived credibility” in the framework will supplement the earlier studies conducted on this aspect.
CFD-CAA Method for Prediction of Pseudosound and Emitted Noise in Quadcopter Propeller

Subsonic flow air blade machines like UAV propellers generate intensive noise thus the prediction of acoustic impact, optimization of these machines in order to reduce the level of emitted noise is an urgent engineering task. Currently, the development of calculation methods for determining the amplitudes of pressure pulsations and noise characteristics by CFD-CAA methods is a necessary requirement for the development of computer-aided design methods for blade machines, where the determining factors are the accuracy and speed of calculations. The main objective is to provide industrial computer-aided design systems with a highly efficient domestic software to create optimal designs of UAV blade machines that provide a given level of pressure pulsations in the flow part and radiated noise. It comprises: 1) creation of a method for the numerical simulation of sound generation using the correct decomposition of the initial equations of hydrodynamics of a compressible medium and the selection of the source of sound waves in a three-dimensional definition, taking into account the rotation of blades and their interaction with the stator part of the UAV; 2) decomposition of the boundary conditions accounting pseudo-sound disturbances and the complex acoustic impedance at the boundaries of the computational domain 3) development of an effective SLAE solver for solving the acoustic-vortex equation in complex arithmetic (taking into account the boundary conditions in the form of complex acoustic impedance); 4) testing of a new method at all stages of development using experimental data on the generation of pressure pulsations and aerodynamic noise, including a propeller noise measurements.
Chest Radiographic Findings in High-risk Covid-19 Pneumonia Patients

Background/ objectivesThe severity of Covid-19 pneumonia has shown a positive association with co-existing risk factors. However, the exact nature of lung involvement in high-risk Covid-19 patients is yet to be resolved. Therefore, we evaluated the CXR features, temporal progression, and the factors associated with CXR severity in high-risk patients.MethodsChest X-rays (n=289) of Covid-19 infected high-risk adults (n=228) treated at the Base Hospital Homagama were evaluated to record CXR features, their temporal progression, CXR severity score and the patient outcomes. ResultsThe studies patients (48.2% men) were in mean age(SD) of 59(15) years. The most frequent CXR features were patchy ground-glass opacities (49%; GG) and patchy consolidations (42%; CON). They showed bilateral (100%) involvement, superoinferior gradient (100%) and diffuse (27%), peripheral (18%) or perihilar (10%) distribution. CON was the predominant opacity among the non-survivors and GG among the survivors (χ2=14.73; p=0.001). Right lung predominant (28%) asymmetrical lung involvement was more frequent than bilateral symmetrical (16%) or left lung predominance (7%). Progression into fatal disease was significantly higher when the lung involvement is asymmetrical: right predominance: ODDs:0.502; p=0.023; left predominance: ODDs:0.268; p=0.002. The CXRs were frequently normal in early (66%) and progressive (56%; χ2=36.64; p<0.001) stages than in peak or resolving stages. The predictors of CXR severity included age (β:0.140; 95% CI:0.041–0.233; p=0.004), male gender (β:4.140; 95% CI:1.452–6.481; p=0.003), and disease day (β:0.622; 95% CI:0.301–0.942; p<0.001). ConclusionThis study decoded the CXR features of Covid-19 pneumonia in a high-risk cohort while describing their associations.
Recruiting on the Net: insights for employers from prospective employees

PurposeDrawing on the signaling theory and technology acceptance model, the main purpose of this study was to predict prospective employees' intentions to apply for jobs in a firm, with a special focus on the mediating role of attitudes toward corporate websites and the moderating role of perceived value fit.Design/methodology/approachCollecting data from a convenient sample of 318 prospective job candidates, the research hypotheses were tested using structural equation modeling (SEM) with AMOS (version 24) and SPSS Process Macro (version 3.4). FindingsThe test results revealed that prospective employees' attitudes toward corporate websites partially mediate the association of corporate reputations, perceived ease of use and perceived usefulness with their intentions to apply for jobs in an organization. Noticeably, perceived value fit moderated the perceived usefulness–application intentions link in such a way that the impact of perceived usefulness on intentions to apply appears higher for individuals with a low level (than a high level) of perceived value fit.Research limitations/implicationsConsistent with the research findings, a notable theoretical contribution and practical implications for HR professionals have been discussed. This paper ends with outlining some limitations and future research directions.Originality/valueDespite having the salient buffering effects of perceived value fit on the applicant attraction process, empirical study on this theoretical phenomenon is still sparse in a pre-employment context. This may be the first study that demonstrates under what circumstances prospective employees' job pursuit intentions could be optimized in respect of their perceived value fit within a single framework comprised of two theories.
NLMS Adaptive Beamforming Algorithm for smart antenna

The smart antennas are widely used for wireless communication because it has an ability to increase the coverage capacity of a communication system. The main purpose of the smart antenna system is the selection of smart algorithms for adaptive array. By using beam forming algorithms the weight of the antenna array can be adjusted to form certain amount of adaptive beam to track corresponding users automatically and to minimize interference arising from others by introducing nulls in their direction. On this paper, one of the beam forming algorithms called Normalized Least Mean Square Algorithm (NLMS) is presented. The efficiency of NLMS algorithm is compared on the basis of normalized array factor and mean square error (MSE) for mobile communication.
Digital Tool for the Analysis of UV–Vis Spectra of Olive Oils and Educational Activities with High School and Undergraduate Students

UV-vis absorption spectroscopy is one of the most accessible spectroscopic techniques at the high school educational level, and it is usually introduced in analytical chemistry courses due to its high versatility and to the wide range of applications in many fields of chemistry. Within this framework, we have developed an easy-to-use "simulation tool" to identify and quantify the main pigments in a relatively complex food matrix, such as olive oil and seeds' oils. This digital software, freely available, can be used by high school students and first-year undergraduate students to analyze the UV-vis absorption spectrum of olive oils recorded in the bulk without any chemical treatment. In this paper, we are reporting the basic principles of the spectroscopic method and the way to use the "simulation tool" with several examples and explanations that are useful for students and teachers. In the second part of the paper, several examples of activities about the chemistry of olive oil, realized with the fifth classes' students of a high school technical institute (K-12 level) and undergraduate students of an introductory course in spectroscopy in the second year of the Chemistry Degree Course, are reported. These activities were performed partially face-to-face and partially in distance learning mode during the COVID-19 pandemic. The main learning outcomes, methodological issues, and students' feedback resulting from these experiences are reported and commented on, showing the potential of the simulation tool for educational purposes.
Control of Expression of Key Cell Cycle Enzymes Drives Cell Line-Specific Functions of CDK7 in Human PDAC Cells

Inhibition of the dual function cell cycle and transcription kinase CDK7 is known to affect the viability of cancer cells, but the mechanisms underlying cell line-specific growth control remain poorly understood. Here, we employed a previously developed, highly specific small molecule inhibitor that non-covalently blocks ATP binding to CDK7 (LDC4297) to study the mechanisms underlying cell line-specific growth using a panel of genetically heterogeneous human pancreatic tumor lines as model system. Although LDC4297 diminished both transcription rates and CDK T-loop phosphorylation in a comparable manner, some PDAC lines displayed significantly higher sensitivity than others. We focused our analyses on two well-responsive lines (Mia-Paca2 and Panc89) that, however, showed significant differences in their viability upon extended exposure to limiting LDC4297 concentrations. Biochemical and RNAseq analysis revealed striking differences in gene expression and cell cycle control. Especially the downregulation of a group of cell cycle control genes, among them CDK1/2 and CDC25A/C, correlated well to the observed viability differences in Panc89 versus Mia-Paca2 cells. A parallel downregulation of regulatory pathways supported the hypothesis of a feedforward programmatic effect of CDK7 inhibitors, eventually causing hypersensitivity of PDAC lines.
Augmentation of Body-in-White Dimensional Quality Systems through Artificial Intelligence

Reducing the dimensional variability of the body-in-white (BIW) in automotive manufacturing is perhaps the most difficult quality control problem due to complex interdependencies amongst the multiple assembly stations that a BIW must pass through in a bodyshop. As increasing quantities of dimensional data are generated in factories, manufacturers face the challenge and opportunity to derive value from the data by enabling advanced quality control methods that can realize greater dimensional stability. As the BIW moves through the bodyshop, dimensional deviations propagate and amplify to downstream stations affecting final vehicle fit-and-finish and visible quality aesthetics potentially influencing a customers’ purchase decision. Current BIW quality approaches rely on univariate statistical process control (SPC) charts. With the large amounts of complex data produced, such charts often fail to detect quality patterns that may exist in hyper-dimensional spaces. As a stop-gap measure, manufacturers attempt to remediate quality issues by assigning operators in final vehicle assembly to visually identify and manually fix apparent deviations. This paper illustrates the application of artificial intelligence (AI) to develop a real-time monitoring system that seeks to predict and detect early dimensional quality issues and eliminate the need for costly downstream corrective actions. Moreover, beyond early detection and prediction, the proposed system also facilitates diagnosis of root causes and understanding the true nature of quality issues.
Precision Motion Control of Robotized Industrial Hydraulic Excavators via Data-Driven Model Inversion

This work proposes a novel precision motion control framework of robotized industrial hydraulic excavators via data-driven model inversion. Rather than employing a single neural network to approximate the whole excavator dynamics, including input delays and dead-zones, we construct a physics-inspired data-driven model with a modular structure. The data-driven model is then inverted in a modular fashion which benefits the training speed. The data-driven model and its inversion are trained offline in a supervised manner using the real operational data since online learning methods can damage the machine and surroundings. The entire motion control framework consists of the data-driven model inversion that compensates for the excavator dynamics and the proportional control that determines the input of the model inversion to enhance the robustness. The framework is experimentally validated with a commercial 38-ton class hydraulic excavator for digging and grading tasks, achieving a precise control performance (i.e., root-mean-square of the path following error under 2 [cm]) even under severe soil interactions.
Significance of clean water for sustainable good health in Nigeria

The significance of the impact of water-related diseases on human health has been recognized as a major threat to sustainable human development in some international forums. This study is an investigation into the correlation between unclean water and the outbreak of water-related diseases in Nigeria. It was established from the review of previous researches that the concept of clean water and sanitation is critical to the good health and well-being of all individuals. Nigeria still has a long way to go towards achieving the Sustainable Development Goal number 6 (SDG 6) of the United Nations, hence it is high time government at all levels and individuals embraced Water, Sanitation and Hygiene (WASH) agenda 2030.
Methodology for Interactive Labeling of Patched Asphalt Pavement Images Based on U-Net Convolutional Neural Network

Image recognition based on deep learning generally demands a huge sample size for training, for which the image labeling becomes inevitably laborious and time-consuming. In the case of evaluating the pavement quality condition, many pavement distress patching images would need manual screening and labeling, meanwhile the subjectivity of the labeling personnel would greatly affect the accuracy of image labeling. In this study, in order for an accurate and efficient recognition of the pavement patching images, an interactive labeling method is proposed based on the U-Net convolutional neural network, using active learning combined with reverse and correction labeling. According to the calculation results in this paper, the sample size required by the interactive labeling is about half of the traditional labeling method for the same recognition precision. Meanwhile, the accuracy of interactive labeling method based on the mean intersection over union (mean_IOU) index is 6% higher than that of the traditional method using the same sample size and training epochs. In addition, the accuracy analysis of the noise and boundary of the prediction results shows that this method eliminates 92% of the noise in the predictions (the proportion of noise is reduced from 13.85% to 1.06%), and the image definition is improved by 14.1% in terms of the boundary gray area ratio. The interactive labeling is considered as a significantly valuable approach, as it reduces the sample size in each epoch of active learning, greatly alleviates the demand for manpower, and improves learning efficiency and accuracy.
Titanium alloy composited with dual-cytokine releasing polysaccharide hydrogel to enhance osseointegration via osteogenic and macrophage polarization signaling pathways

Abstract Titanium alloy has been widely used in orthopedic surgeries as bone defect filling. However, the regeneration of high-quality new bones is limited due to the pro-inflammatory microenvironment around implants, resulting in a high occurrence rate of implant loosening or failure in osteological therapy. In this study, extracellular matrix-mimetic polysaccharide hydrogel co-delivering BMP-2 and interleukin (IL)-4 was composited with 3D printed titanium alloy to promote the osseointegration and regulate macrophage response to create a pro-healing microenvironment in bone defect. Notably, it is discovered from the bioinformatics data that IL-4 and BMP-2 could affect each other through multiple signal pathways to achieve a synergistic effect toward osteogenesis. The composite scaffold significantly promoted the osteoblast differentiation and proliferation of human bone marrow mesenchyme stem cells (hBMSCs). The repair of large-scale femur defect in rat indicated that the dual-cytokine-delivered composite scaffold could manipulate a lower inflammatory level in situ by polarizing macrophages to M2 phenotype, resulting in superior efficacy of mature new bone regeneration over the treatment of native titanium alloy or that with an individual cytokine. Collectively, this work highlights the importance of M2-type macrophages-enriched immune-environment in bone healing. The biomimetic hydrogel–metal implant composite is a versatile and advanced scaffold for accelerating in vivo bone regeneration, holding great promise in treating orthopedic diseases.
An Experimental Investigation of the Impact of Different Advertising Formats on Children’s Attitudes’ Towards Sports Brands

The study aims to investigate children’s attitude towards different advertising formats including TV ads, in-movie product placement and in-game advertisements, using a web-based between subjects’ experimental design among a sample of Egyptian children aged between 6 to 13 years. Children’s attitude development by advertising efforts is considered under investigated in literature particularly in Arab cultures and as they are considered as powerful influencers to parents’ purchase decisions, the study contributes to a better understanding of children’s attitudes that will guide mangers in making wiser advertising budget allocation. The results showed that there is a significant different impact of the investigated advertising formats on children’s attitude towards sports brands with higher cognitive, affective and behavioral responses resulted from the exposure to traditional video advertising compared to in- movie product placement and in-game advertisements.
Changes in railway unevenness after the installation of in-track vibration countermeasures

When in-track vibration countermeasures, such as resilient rail and slab support are installed, the unevenness perceived by a train will be modified and this is often not accounted for. In this paper, it is investigated, with the aid of an analytical track model, how this perceived unevenness will change and what the relation is with different types of unevenness for the particular case of a floating slab track. The different types of unevenness considered are: track bed unevenness, bent rail, bent slab and variation of slab bending stiffness. The total unevenness perceived by the wheels of a moving train is the result of a combination of these and other types of unevenness. It is found that, depending on the unevenness type, both a reduction or an increase of the perceived unevenness are possible. The increase may be large enough to lead to a substantial reduction in the performance of the vibration countermeasure.
Cortisol Awakening Reaction and Anxiety in Depressed Coronary Artery Disease Patients

Disturbances of HPA axis functioning as represented by cortisol awakening reaction (CAR) belong to the mediating pathways linking psychosocial distress and cardiovascular risk. Both depression and anxiety have been confirmed as independent risk factors for coronary artery disease (CAD). However, data on anxiety and cortisol output in CAD patients are scarce. Based on previous data, we hypothesized that anxiety would be associated with higher cortisol output and a more pronounced morning increase in moderately depressed CAD patients. 77 patients (60 y, 79% male) underwent saliva sampling (+0, +30, +45, +60 min after awakening, midday and late-night sample). Anxiety was measured using the Hospital Anxiety and Depression Scale (HADS) and patients were grouped into anxious versus non anxious subjects based upon the recommended score (≥11). A repeated measures ANOVA yielded a significant time and quadratic time effect referring to the typical CAR. Anxious patients showed a significantly steeper 30 min increase, higher AUCi, lower waking and late-night cortisol levels. The steeper cortisol increase in the anxious group is in line with previous data and may be interpreted as a biological substrate of affect regulation. The lower basal and late-night levels coupled with greater AUCi mirror a more dynamic reactivity pattern compared to depressed subjects without anxiety.
Topology-Aware Learning Assisted Branch and Ramp Constraints Screening for Dynamic Economic Dispatch

Multi-interval or dynamic economic dispatch (D-ED) is the core of various power system management functions. This optimization problem contains many constraints, a small subset of which is sufficient to enclose the D-ED feasible region. This paper presents a topology-aware learning-aided iterative constraint screening algorithm to identify a feasibility outlining subset of network and generating units ramp up/down constraints and create a truncated D-ED problem. We create a colorful image from nodal demand, thermal unit generation cost, and network topology information. Convolutional neural networks are trained for constraint status identification using colorful images corresponding to system operating conditions and transfer learning. Filtering inactive line flow and ramp up/down constraints reduces the optimization problem's size and computational burden, resulting in a reduction in solution time and memory usage. Dropping all inactive branch and ramp constraints may activate some of these originally inactive constraints upon solving the truncated D-ED. A loop is added to form a constraints coefficient matrix iteratively during training dataset preparation and algorithm utilization. This iterative loop guarantees truncated D-ED results feasibility and optimality. Numerical results show the proposed algorithm's effectiveness in constraint status prediction and reducing D-ED size and solution time.