Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 Vaccine Intent Among Health Care Professionals of Queen Elizabeth Hospital, Barbados

Introduction Due to increased exposure risk and the potential impact of COVID-19 infection, health care professionals (HCP) are a target group for COVID-19 vaccination. This study aimed to examine the acceptability of COVID-19 vaccines among HCP at the Queen Elizabeth Hospital, Barbados. Design and Methods A cross-sectional survey of HCP was conducted between February 14 and 27, 2021 using an online questionnaire. The questionnaire included demographic information, knowledge of novel coronavirus, intention to accept the COVID-19 vaccination, vaccine literacy (VL), and perceptions and attitudes regarding COVID-19 vaccines. Mean VL scores were calculated. The relationship between socio-demographic variables and vaccine intent was assessed using a multivariable logistic regression model. Results Of 343 HCPs, 55.1% indicated they would accept the COVID-19 vaccine if it were available; 44.9% expressed hesitancy towards the COVID-19 vaccine. We assessed the impact of socio-demographic factors and previous vaccine behavior on vaccine intent; after adjustment of the multivariable logistic regression model, non-Barbadian nationality and previous flu vaccine uptake were statistically significant predictors of reported intent to take the COVID-19 vaccine. Persons who indicated that they would take the vaccine had a higher mean vaccine literacy score [3.46 95% CI (3.40, 3.52)] than those who were not ready to take the vaccine immediately [3.23 95% CI (3.15, 3.30)]. VL scores were higher among the 29.5% of HCPs who believed vaccines should be mandatory. Conclusion This study highlighted vaccine hesitancy among HCPs in the sole public tertiary hospital of Barbados. As HCP perceptions may help or hinder the campaign to promote vaccine uptake in Barbados, vaccine promotion programs targeting HCPs are needed to ensure the success of the country’s COVID-19 vaccination drive.
Investigating the Immediate Influence of Moderate Pedal Exercises during an Assembly Work on Performance and Workload in Healthy Men

Physical inactivity has increased in prevalence among adults in industrialized and developing countries owing to the fact that the majority of job situations require individuals to remain seated for extended periods of time. This research aims to evaluate the influence of cycling on a stationary bike while executing a keyboard assembly task on the task completion time, error percentage, and physiological and subjective measurements. The physiological measures were electroencephalography (EEG) and electrocardiographic (ECG) signal responses, whereas the subjective measures were subjective workload ratings and subjective body discomforts. Two variables were evaluated, namely assembly methods (with versus without pedal exercises at a moderate intensity) and session testing (pre- versus post-test). Thus, the repeated measures design (i.e., assembly method by session testing of participants) was used. According to the completion time, error %, participant self-reports, and ECG and EEG statistical analysis data, the participants’ performances in the keyboard assembly task did not decrease while they performed pedaling exercises (p > 0.05). Additionally, when participants completed the assembly task while executing the pedaling exercises, the mean inter-beat (RR) intervals significantly reduced (p < 0.05) while the mean heart rate increased (p < 0.05), which mean that pedaling exercises caused physical workloads on the participants. Participant performance was unaffected by performing a workout while performing the assembly activity. Thus, administrations should encourage their employees to engage in short sessions of moderate-intensity exercise similar to the suggested exercise in the study to improve a person’s physical health during work without interfering with the effectiveness of work.
A finite element model investigation of ultrasonic array performance for inspecting polycrystalline materials

Microstructural noise has long hindered ultrasonic NDE of polycrystalline materials. In recent years however, arrays have enabled new possibilities to advance ultrasonic inspection of these materials. A Finite Element (FE) model is used to explore the different phenomena caused by grain scattering which may hinder detection of defects by an array. These include multiple scattering and beam deviation due to anisotropy; two aspects of the physics which are often required to be ignored in analytical models due to theoretical assumptions or computational limitations. We rely on a GPU based FE solver, Pogo, to provide fast computation and thereby enable parametric studies. The impact on array detection performance when varying center-frequency and aperture size is investigated. Preliminary results show that there exists an optimum for both the array aperture and frequency for inspection of these materials.
Trusted Computing Concepts for Resilient Embedded Networks

Trusted Computing concepts have proven well in the conventional oce ICT area. Since interconnected embedded devices have penetrated many application areas with high requirements for safety and security, the risk of ICT-based attacks is considerably growing. Consequently additional security measures are of utmost importance which can give assurance in the correct behavior of the device. Introducing Trusted Computing to embedded architectures on the one hand raises new problem statements which have to be solved, however on the other hand due to the dierent operating environment new possibilities of implementing security features open up. This paper presents a prototype for embedded Trusted Computing on networked ARM-based systems addressing these issues. In detail it covers dierent approaches for the establishment of initial trust into an individual device as well as health monitoring, system attestation and reporting capabilities. In addition a proposal for easy set-up-, update- and maintenanceprocedures will be given. Besides, the fundamental ideas of this work can be easily adopted to other embedded architectures.
A Novel Three-polar Representation for R 3 Surfaces : Performance and Stability for Invariant 3D Faces Description

Résumé We intend here to introduce a new curved surface representation. It is called three-polar since it is constructed by the superposition of the three geodesic potentials generated from three reference points of the surface. By sampling this superposition, invariant points are obtained. The accuracy of the three-polar representation for 3D human faces description is performed in the mean of the Hausdorff distance. A comparison between this representation and the one composed of the level curves around the nose tip is established in the sense of the stability under errors on the nose tip positions.
Modeling of Low-Speed, Front-to-Rear Vehicle Impacts

Front-to-rear crashes between vehicles at speeds well below 20 mph account for a surprisingly large number of significant injuries, usually classified as Whiplash Associated Disorders (WAD). Although an efficient model or process that relates the vehicle-tovehicle collision condi tions and parameters to t he level and characteristics of injury is desirable, the complexity of the probl em m akes such an overall crash-to-injury model impractical. Instead, this paper develops and explores a reasonably effective model of the vehicle-to-vehicle impact that determines the forward≍rearward accelerations, velocities and the contact force as functions of time for both the striking and struck vehicles. T ire drag d ue to braking is included to allow the assessment of its effects.
Recognition of Power Equipment Based on Multitask Sparse Representation

Image analysis of power equipment has important practical significance for power-line inspection and maintenance. This paper proposes an image recognition method for power equipment based on multitask sparse representation. In the feature extraction stage, based on the two-dimensional (2D) random projection algorithm, multiple projection matrices are constructed to obtain the multilevel features of the image. In the classification process, considering that the image acquisition process will inevitably be affected by factors such as light conditions and noise interference, the proposed method uses the multitask compressive sensing algorithm (MtCS) to jointly represent multiple feature vectors to improve the accuracy and robustness of reconstruction. In the experiment, the images of three types of typical power equipment of insulators, transformers, and circuit breakers are classified. The correct recognition rate of the proposed method reaches 94.32%. In addition, the proposed method can maintain strong robustness under the conditions of noise interference and partial occlusion, which further verifies its effectiveness.
Inflorescence Position Influences Phenological Rhythms in Leguminosae Species

We present a case study focusing on the influence of inflorescence position on the relationships between foliar and reproductive phases, focusing on three Leguminoseae tree species in the Chapada Diamantina, Brazil. Both Senna multijuga and Copaifera langsdorffii produce terminal panicles and showed vegetative and reproductive phenophases that were correlated but segregated in time, with the marked concentration of reproductive data. The presence of axillary inflorescences on Inga cayennensis enables the simultaneous production of both flowers and leaves, without the segregation or the correlation of those phases, with a uniform distribution of reproductive data. This study encourages further investigations regarding the effects of inflorescence position on plant phenology.
Calcified Small Bowel Mass

A61-year-oldman after bilateral lung transplantation for chronic obstructive pulmonary disease presentedwith recurrent postprandial abdominal painwith nausea and vomiting.His laboratorywork, including a completemetabolic panel and a complete blood count, was notable for persistently low hemoglobin. Computed tomography of the abdomen/pelvis revealed a 5-mm calcified focus with surrounding soft tissues in the third portion of the duodenum(Figure 1). Upper gastrointestinal endoscopy revealed a 3-cm sessile polyp in the third part of the duodenum. Endoscopic ultrasound showed a hyperechoic focus with acoustic shadowing suggestive of a calcified focus within the superficial layers of the duodenal polyp (Figure 2). A preresection biopsy of the polyp revealed a duodenal adenoma with low-grade dysplasia. Owing to the calcification of the polyp, we removed the lesion en blocwith endoscopic submucosal dissection in case therewas advancedhistology.A calcified nodulewas visible and palpable in the postresection specimen (Figure 3). Postresection histopathology showed a duodenal tubular adenoma with high-grade dysplasia and negative margins. The calcified nodule was not present onfinal histology, but therewas a 7-mmspace in the submucosawith an epithelial lining, likely a dilated gland/duct (Figure 4). It was postulated that the calcified nodule was in the dilated gland and dissolved after fixation and acidic decalcification on staining.
Capturing Peer Interactions While Playing Buaya di Bawah Jambatan (BDBJ) Board Game in Learning Adab and Moral Values

Abstract: This paper examines peer interactions among preschoolers while playing the board game known as Buaya di Bawah Jambatan (BDBJ) particularly in learning adab and moral values. Additionally, this study is guided by two research objectives focusing on the nature (types) of interactions among players during the game and suggestions or responses from the instructors on the game including its physical outlook and processes. This research embarked upon a qualitative study where 4, 5 to 6 years old children attending a pre-school in Shah Alam were observed when playing ‘Buaya di Bawah Jambatan’. The sample was basically a group made up of 2 boys & 2 girls. The teacher assigned for the group became the instructor facilitating the whole process of playing the game. Data collected was mainly from the non-participant observations made by the researchers when the game was played. Video recordings too were made to capture the interactions observed during the whole session. Analysis were made both on the initial observation and the subsequent observations done on the recorded videos. The findings of this research revealed three major themes on peer interactions which include (1) learning to be with peers, (2) developing creative thinking and problem solving and (3) humour and laughter. The study also found that suggestions or responses from the instructor comprises both areas of improvements and strengths of the game. These findings are crucial to help teacher-educators to enhance their pedagogical skills and knowledge, particularly on learning adab and moral values in more fun, engaging and meaningful ways. Hence, learning adab and moral values among children should be more dynamic and relevant to children’s lives rather than applying solely on conventional approaches. It is hoped that this study opens more opportunities, insights, and ideas for other researchers to study peer interactions and its relations to games and learning adab with children in other populations such as among the marginalized community and B40 group as well as children with Special Educational Needs (SEN). Keywords: Peer interaction, adab, moral values, creative thinking and problem solving
Capital Market Relationships and Interfirm Information Spillovers∗

We investigate the role of capital market relationships in facilitating the flow of relevant competitive information between firms. Using patent citations as a proxy for interfirm knowledge spillovers, we find that firms are more likely to cite another firm’s patent if this firm is covered by the same financial analyst. Difference-in-difference analyses exploiting exogenous shocks to analyst coverage overlaps over time suggest that the documented effects are not simply due to dynamic adjustments in firms’ business models and corresponding changes in analyst coverage, but instead are consistent with a plausible causal relationship between analyst coverage overlaps and interfirm information spillovers. The effect is also stronger for analysts with relatively higher industry-specialization, a higher level of experience, a larger coverage portfolio, and higher activity, as well as firm pairs with larger geographic or organizational diversity. Overall, our findings suggest that capital market relationships do not only play an important role in facilitating information transfer and reducing information asymmetries between firms and capital markets, but also facilitate the production of relevant business intelligence through feedback and interfirm information transfers. JEL classification: G24, G30, O31
Multi-microphone correlation-based processing for robust automatic speech recognition

Speech recognition systems suffer from degradation in recognition accuracy when faced with input from noisy and reverberant environments. While most users prefer a microphone that is placed in the middle of a conference table, on top of a computer monitor, or mounted in a wall, the recognition accuracy obtained with such microphones is generally much worse than the accuracy obtained using a close-talking headset-mounted microphone. Unfortunately, headset-mounted microphones are often uncomfortable or impractical for users. Research in recent years on environmental robustness in speech recognition has concentrated on signal processing using the output of a single microphone to correct for differences in spectral coloration between microphones used in the training and testing environments, and to account for the effects of linear filtering and additive noises present in real testing environments. This thesis explores the use of microphone arrays to provide further improvements in speech recognition accuracy. A novel approach to multiple-microphone processing for the enhancement of speech input to an automatic speech recognition system is described and discussed. The system is loosely based on the processing of the binaural hearing system, but with extensions to an arbitrary number of input microphones. The processing includes bandpass filtering and nonlinear rectification of the signals from the microphones to model the effects of the peripheral auditory system, followed by cross-correlation within each frequency band of the outputs of the rectifiers from microphone to microphone. Estimates of the correlated energy within each frequency band are used as the basis for a feature set for an automatic speech recognition system. Speech recognition accuracy in natural environments and using artificially-added noise were compared using the new correlation-based system, conventional delay-and-sum beamforming, and traditional adaptive filtering using the Griffiths-Jim algorithm. It was found that the more computationally-costly correlation-based system provided substantially better recognition accuracy than previous approaches in pilot experiments using artificial stimuli, and in experiments using natural speech signals that were artificially corrupted by additive noise. The correlation-based system provided a consistent, but much smaller, improvement in recognition accuracy (relative to previous approaches) for experiments conducted using speech in two natural environments. It is also demonstrated that the benefit provided by microphone array processing is complementary to the benefit provided by single-channel environmental adaptation algorithms such as codeword-dependent cepstral normalization, regardless of which adaptation procedure is employed.
An adaptive genetic algorithm approach for the mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line that is capable of producing a variety of different product models simultaneously and continuously. The design and planning of such lines involve several long- and short-term problems. Among these problems, determining the sequence of products to be produced has received considerable attention from researchers. This problem is known as the Mixed-Model Assembly Line Sequencing Problem (MMALSP). This paper proposes an adaptive genetic algorithm approach to solve MMALSP where multiple objectives such as variation in part consumption rates, total utility work and setup costs are considered simultaneously. The proposed approach integrates an adaptive parameter control (APC) mechanism into a multi-objective genetic algorithm in order to improve the exploration and exploitation capabilities of the algorithm. The APC mechanism decides the probability of mutation and the elites that will be preserved for succeeding generations, all based on the feedback obtained during the run of the algorithm. Experimental results show that the proposed adaptive GA-based approach outperforms the non-adaptive algorithm in both solution quantity and quality.
impact of the transnationalization process on the consumer market of Ukraine

During the research it was found that globalization in no way affects the content of monopoly capital, which had changed its form because of the existence of transnationalization process. The increasing number of transnational corporations not only enhances engagement with the state at the nowadays process of globalization. Minimization of the negative consequences of transnational corporations on the consumer market, providing the country’s food security suggested the implementation of a strategy of import substitution and reasonable protectionism. It triggers upgrades foreign economic strategies, improves institutional and organizational, and economic efforts of state regulation to ensure the protection of the boundaries of the consumer market. Here are target goals are defined: growth of own production; increase in investment demand; increasing non-raw exports through the export of high-tech, knowledge-based goods, and services; intensification of measures within the system of integrated export support.
Benefits of Information Integration in Budget Planning for Pavement Management

The budget-allocation process in pavement management occurs at several levels of decision making. In normal practice, the central level is concerned with the allocation of the budget to regional agencies to improve the health of the overall road network, while the regions are responsible for the programming and scheduling of road maintenance in their respective networks. The information processed at different management levels differs, and information at one level may not be readily available to the other. As a result, the maintenance funds allocated by central management to regional highway agencies may not be best used. A budgetallocation procedure based on artificial-intelligence agents is presented; it takes into account information integration between the central and regional management levels by using multiagent systems. Agents are used to facilitate the interaction between decision makers at the two management levels and thus provide a means for vertical information integration. A hypothetical problem containing a two-level funds-allocation structure consisting of a central-level management and three regional highway agencies is investigated. Each regional agency has different road network profiles with different local-level objectives. A genetic algorithm is used as the optimization tool in the decision-making process carried out by each agent. The results are compared with conventional allocation approaches and a two-stage optimization approach. It is shown that, with better integration of information at the different management levels, the resulting budget-allocation strategy produces greater maintenance-cost savings for a given target level of pavement performance.
OP-convNet: a patch classification based framework for CT vertebrae segmentation

Accurate vertebrae segmentation from medical images plays an important role in clinical tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease, spondylolisthesis, and post-operative assessment. Although the structures of bone have high contrast in medical images, vertebrae segmentation is a challenging task due to its complex structure, abnormal spine curves, and unclear boundaries. In recent years, deep learning has been widely applied in the segmentation of vertebrae images. In this paper, towards a robust and automatic segmentation system, we present an overlapping patch-based convNet (OP-convNet) model for automatic vertebrae CT images segmentation. Due to the greater memory and processing costs associated with 3D convolutional neural networks, as well as the risk of over-fitting, we employ overlapping patches in segmentation tasks using 2D convNet. In the proposed vertebrae segmentation method, OP-convNet effectively keeps the local information contained in CT images. We divide CT image slices into equal-sized square overlapping patches and applied the RUS-function on these patches for class balancing to minimize computational requirements. Then, these patches are input into the model along with their corresponding ground truth patches. This method has been evaluated on publicly available CT images from the MICCAI CSI workshop challenge. The results indicate that OP-convNet has precision (PRE) of 90.1%, specificity (SPE) of 99.4%, accuracy (ACC) of 98.8%, F-score of 90.1% in terms of the patch-based classification accuracy, and BF-score of 90.2%, sensitivity (SEN) of 90.3%, Jaccard index (JAC) of 82.3%, dice similarity score (DSC) of 89.9% in terms of the segmentation accuracy that outperform previous methods across all metrics.