Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Big-Data-Driven Data Visualization and Visual Communication Design Patterns

This paper provides an in-depth study and analysis of big-data-driven data visualization and visual communication design models. The characteristics of new media and the definition of traditional media are analyzed; the importance of the new media environment is derived through comparison; and the successful cases of new media integration today are analyzed. In this process, we will optimize the traditional science and technology intelligence service model, optimize the various components that make up the science and technology intelligence wisdom service, achieve model optimization and reflect the four characteristics of science and technology intelligence wisdom service, and reconstruct the science and technology intelligence wisdom service using the literature research method. The design based on imagery schema theory is manifest, inclusive, and somewhat innovative and at the same time has a high degree of consistency and internal logical relationship with the visual representation of multidomain heterogeneous data at the cognitive level and displays purpose. This internal logical relationship is systematically organized and deeply analyzed, and the methodology from subpattern extraction and visual interaction design to the deep integration of visual representation is proposed in combination with specific application scenarios and cases.
Vehicle motion analysis based on a monocular vision system

This paper presents a comprehensive methodology for on-road vehicle motion analysis using a monocular vision system. Vehicle motion analysis plays an essential role in various intelligent vehicle applications, such as cruise control, vehicle platooning, and collision avoidance. In this paper, it's proposed to improve the accuracy of vehicle motion analysis by breaking the task into two complementary steps: incoming vehicle detection and vehicle motion analysis. In the vehicle detection, a new vehicle which enters the observation field will be identified by inspecting its vehicle-related features. Once a vehicle is detected, a fine-level motion analysis mechanism is employed to monitor its position and relative speed based on the temporal consistency exploitation. Specifically, a novel 3-D Pulse-Coupled Neural Network (PCNN) model is employed for optical flow calculation and optimization. The improved optical flow is then interpreted to generate reliable vehicle motion estimation. Overall, the proposed method shows excellent performance in terms of both accuracy and efficiency owing to its effective coarse-to-fine processing scheme and multiple-cue consideration.
Disease knowledge and preferences for disclosure of information in patients with rheumatoid arthritis

Objectives: To verify the level of knowledge about rheumatoid arthritis (RA) and the patient’s preferences for disclosure of information by the physician in patients with RA, we compared with a control group of patients without RA. Methods: We evaluated 30 patients diagnosed with RA, according to the ACR criteria, and 30 patients with other diseases pared for age, gender, level of education, and disease duration followed in an Out-Patient Service. Clinical-demographic data as age, gender, level of education, and disease duration were obtained from both groups. We applied questionnaires to evaluate the level of knowledge about RA (10 questions) with answers yes or no, and the patient’s preference for disclosure of information by the physician (4 questions) in a Likert scale from a) strongly agree to e) strongly disagree to both groups. The questionnaires were validated for Portuguese by the method of translation and re-translation. The Ethical Committee at the Pontifical Catholic University approved the study, and we obtained informed consent from all patients. When appropriat-ed, statistical analysis of the data included the chi-square test and student’s t-test. The significance level was 0.05. Results: Over 30 patients with RA, 27 patients (90%) were women, the mean age was 50.8 years old, with a mean of 3.03 years on school, and mean disease duration of 10.6 years. The knowledge about RA as a mean reached 5.36 points in 10 as a correct answer in RA patients and 5.13 in controls. Concerning the patient’s preference for disclosure of information by the physician, the results varied between 23.3% and 90% in the answers strongly agree or agree in the group of RA patients and 6.6% to 90% in controls. In a specific question about "patients with RA should not play a role in managing their disease because the physician is the one in charge," 56.6% of RA patients agreed, and 46,6% of controls too. Only one question about patient’s preference for disclosure of information by the physician “When there is more than one way to treat a problem, I should be told about each one” there was a trend to differ between the controls and the RA patients (p=0.06). The analysis of the results showed no statistical difference in answers to the questionnaires between RA patients and controls. Conclusion: Our results showed that RA patients, compared to control patients with other non-rheumatic diseases, do not show differences to the level of knowledge about rheumatoid arthritis (RA) and the patient’s preferences for disclosure of information by the physician.
Constructing a multiple‐part morphospace using a multiblock method

Popular current methods for quantifying variation in biological shape are well‐suited to analyses of isolated parts (e.g. the same bone from the skeletons of many individuals). An analytical challenge exists for quantifying variation between the shapes of multiple‐part objects where each part has a different position, rotation or scale (e.g. partial or whole articulated skeletons). We investigated regularised consensus principal component analysis (RCPCA) as a multiblock method for quantifying variation in the shape of multiple‐part objects. Multiblock methods are routinely used in other big data research fields such as bioinformatics/medicine, marketing and food research, but have not been widely embraced for evolutionary biology research. We have created the new package morphoBlocks for the r programming language to make RCPCA more accessible for shape evolution research. morphoBlocks provides a complete workflow for formatting, analysing and visualising the variation between multiple‐part objects by integrating functions from a diverse range of other packages. In particular, global components produced by RCPCA provide a consensus space that we present here as a morphospace for multiple‐part objects. morphoBlocks is demonstrated with a case study of manually placed landmarks and automatically placed pseudolandmarks from the partial wing skeletons of 15 extant penguin species and five fossil penguin species. Our case study provides quantitative support for a historical hypothesis about the magnitude and mode of morphological change across the evolutionary history of penguins. RCPCA can be used to analyse two‐ or three‐dimensional datasets with 10s of landmarks, or 100s to 1,000s of semilandmarks or pseudolandmarks, from 10s to 100s of specimens comprised of two or more parts. We use morphoBlocks on a small three‐bone case study and provide a framework for applying this method to much larger studies investigating the ecological or evolutionary significance of multiple‐part objects.
New method to measure interbreath intervals in infants for the assessment of apnoea and respiration

Background Respiratory disorders, including apnoea, are common in preterm infants due to their immature respiratory control compared with term-born infants. However, our inability to accurately measure respiratory rate in hospitalised infants results in unreported episodes of apnoea and an incomplete picture of respiratory activity. Methods We develop, validate and use a novel algorithm to identify interbreath intervals (IBIs) and apnoeas in preterm infants. In 42 preterm infants (1600 hours of recordings), we assess IBIs from the chest electrical impedance pneumograph using an adaptive amplitude threshold for the detection of breaths. The algorithm is refined by comparing its accuracy with clinically observed breaths and pauses in breathing. We develop an automated classifier to differentiate periods of true apnoea from artefactually low amplitude signal. We assess the performance of this algorithm in the detection of morphine-induced respiratory depression. Finally, we use the algorithm to investigate whether retinopathy of prematurity (ROP) screening alters the IBI distribution. Results Individual breaths were detected with a false-positive rate of 13% and a false-negative rate of 12%. The classifier identified true apnoeas with an accuracy of 93%. As expected, morphine caused a significant shift in the IBI distribution towards longer IBIs. Following ROP screening, there was a significant increase in pauses in breathing that lasted more than 10 s (t-statistic=1.82, p=0.023). This was not reflected by changes in the monitor-derived respiratory rate and no episodes of apnoea were recorded in the medical records. Conclusions We show that our algorithm offers an improved method for the identification of IBIs and apnoeas in preterm infants. Following ROP screening, increased respiratory instability can occur even in the absence of clinically significant apnoeas. Accurate assessment of infant respiratory activity is essential to inform clinical practice.
Avalanche transit time microwave oscillators and amplifiers

The state of the art on avalanche transit time diodes is reviewed. Experimental results are reported on two different diode structures; (1) A simple PN junction and, (2) A hyperabrupt junction "Read" structure, both of which employ the avalanche transit time mechanisms originally proposed by Read. These results are compared with the theoretical predictions of Read and Misawa. Preliminary circuit results to date include 19 mw of CW power at 5 Gc with 1.4% efficiency from a Read structure, and 13 mw of CW 10.5 Gc power with 0.5% efficiency from a simple PN junction. PN junction amplifier operation at 11.5 Gc has produced 20 db of gain with 30 mc bandwidth. Noise figures in the 50 to 60 db range were obtained.
Saliency ratio derivation and optimisation for an interior permanent magnet machine with concentrated windings using finite-element analysis

The implementation of concentrated windings in interior permanent magnet (IPM) machines has numerous advantages over distributed windings, with a major disadvantage being the decrease in saliency ratio. Theoretically, this would result in a lower field weakening range which is undesirable for traction applications. Although it is revealed in some studies that concentrated winding results in lower saliency ratio, experimental techniques used for accurately acquiring the saliency ratio of IPM machines with concentrated windings are yet to be fully understood. This study presents a proposed finite-element (FE) method, which is derived from the experimental AC standstill test method. With this method, the d- and q-axis inductances (Ld and Lq) of the IPM machine with fractional-slot concentrated windings can be accurately determined. Limitations of this method are discussed. Subsequently, this method is used to determine Ld and Lq of various winding configurations and rotor designs. From repeated FE testing using Flux2D, the authors derive a set of general rules for optimising the saliency of an IPM machine with fractional-slot, single-layer concentrated winding.
CSI-IANet: An Inception Attention Network for Human-Human Interaction Recognition Based on CSI Signal

In recent years, Wi-Fi infrastructures have become ubiquitous, providing device-free passive-sensing features. Wi-Fi signals can be affected by their reflection, refraction, and absorption by moving objects in their path. The channel state information (CSI), a signal property indicator, of the Wi-Fi signal can be analyzed for human activity recognition (HAR). Deep learning-based HAR models can enhance performance and accuracy without sacrificing computational efficiency. However, to save computational power, an inception network, which uses a variety of techniques to boost speed and accuracy, can be adopted. In contrast, the concept of spatial attention can be applied to obtain refined features. In this paper, we propose a human–human interaction (HHI) classifier, CSI-IANet, which uses a modified inception CNN with a spatial-attention mechanism. The CSI-IANet consists of three steps: i) data processing, ii) feature extraction, and iii) recognition. The data processing layer first uses the second-order Butterworth low-pass filter to denoise the CSI signal and then segment it before feeding it to the model. The feature extraction layer uses a multilayer modified inception CNN with an attention mechanism that uses spatial attention in an intense structure to extract features from captured CSI signals. Finally, the refined features are exploited by the recognition section to determine HHIs correctly. To validate the performance of the proposed CSI-IANet, a publicly available HHI CSI dataset with a total of 4800 trials of 12 interactions was used. The performance of the proposed model was compared to those of existing state-of-the-art methods. The experimental results show that CSI-IANet achieved an average accuracy of 91.30%, which is better than that of the existing best method by 5%.
Incorporating the overmodulation range in space vector pattern generators using a classification alg

Operating voltage-source pulsewidth-modulated (PWM) inverters in the overmodulation region extends their voltage and power ranges. Proper operation in this region is of particular importance in AC motor drives. This paper presents a classification algorithm for the implementation of the space vector modulation (SVM) in the pulse-dropping region. It is demonstrated that the proposed algorithm allows smooth transitions through the entire operating range up to square wave operation. It does not require approximations and guarantees exact positioning of the switching instants. In addition, it is shown that the harmonic distortion can be kept lower than with conventional techniques in most of the overmodulation region. The technique requires less computational time, when compared with the conventional SVM methods. The results of the theoretical and mathematical analysis are verified on a 2-kVA prototype unit using a digital signal processor (DSP)-based controller.
Low-distortion variable-level PWM technique

A multicarrier disposition pulse width modulation (PWM) technique that results in low harmonic distortion for a three-phase PWM inverter with a 5-level line-to-line output voltage waveform is discussed in the paper. Comparison with the conventional 5-level unipolar PWM technique for three-phase inverters reveals that the proposed strategy results in a high performance converter with minimised filter requirements since the amplitude of all harmonics is reduced. The resulting line-to-line voltage waveform has five levels at high and three levels at low amplitude modulation indexes (variable-level). Harmonic analysis is presented, along with simulated and experimental results to support the theoretical considerations.
Does Engagement Partners’ Effort Affect Audit Quality? With a Focus on the Effects of Internal Control System

An audit team includes engagement partners, CPAs, and staff. Among them, partners play a vital role in performing tasks that require expertise and experience, such as analyzing and understanding the industry, and supervising the overall audit process. In detail, the partners establish an audit plan, determine the overall audit time, provide the audit input ratio of the engagement team, and review the audit reports. This study examines for association between the partner’s audit hour ratio and audit quality depending on the client firms’ characteristics. Although the role of partners is important, the information about partner audit hours is limited. However, the Korean government requires audit firms to disclose the partner hour information in the audit report starting in the 2014 fiscal year. By the disclosure, it is possible to examine the association between partner audit hours and audit quality. In this study, the information on partner audit hour is hand-collected from the firms’ business reports. Using 6340 observations from 2014 to 2017, the partner audit hour ratio is associated with audit quality, under the characteristics of client firms. Firms’ risks are adopted for client characteristics, and we focused on the operation of internal control. The internal control operation level is measured by the following: (1) the ratio of internal control personnel and (2) experience of the internal control personnel in the accounting and IT departments. The result suggests that for the firms where internal control is not effectively operated, partners make more effort to enhance audit quality.
Robust digital position control of brushless DC motor with adaptive load torque observer

A new control method for the robust position control of a brushless DC (BLDC) motor using the adaptive load torque observer is presented. It is shown that the augmented state variable feedback can be applied to the BLDC motor system approximately linearised using the field-orientation method. To overcome the problem of an unknown parameter or a parameter variation such as a flux linkage, an adaptive mechanism is employed. As a result, the robustness can be obtained without affecting the overall system response. The load disturbance is detected by the adaptive 0-observer of an unknown and inaccessible input, and is compensated by feedforwarding the equivalent current having the fast response.
In Vitro Comparative Study of Solid Lipid and PLGA Nanoparticles Designed to Facilitate Nose-to-Brain Delivery of Insulin

The brain insulin metabolism alteration has been addressed as a pathophysiological factor underlying Alzheimer’s disease (AD). Insulin can be beneficial in AD, but its macro-polypeptide nature negatively influences the chances of reaching the brain. The intranasal (IN) administration of therapeutics in AD suggests improved brain-targeting. Solid lipid nanoparticles (SLNs) and poly(lactic-co-glycolic acid) nanoparticles (PLGA NPs) are promising carriers to deliver the IN-administered insulin to the brain due to the enhancement of the drug permeability, which can even be improved by chitosan-coating. In the present study, uncoated and chitosan-coated insulin-loaded SLNs and PLGA NPs were formulated and characterized. The obtained NPs showed desirable physicochemical properties supporting IN applicability. The in vitro investigations revealed increased mucoadhesion, nasal diffusion, and drug release rate of both insulin-loaded nanocarriers over native insulin with the superiority of chitosan-coated SLNs. Cell-line studies on human nasal epithelial and brain endothelial cells proved the safety IN applicability of nanoparticles. Insulin-loaded nanoparticles showed improved insulin permeability through the nasal mucosa, which was promoted by chitosan-coating. However, native insulin exceeded the blood-brain barrier (BBB) permeation compared with nanoparticulate formulations. Encapsulating insulin into chitosan-coated NPs can be beneficial for ensuring structural stability, enhancing nasal absorption, followed by sustained drug release.
Self-medication with cannabidiol (CBD) and associated treatments in the self-care of anxiety in women from Mexico City: A qualitative study

Introduction. The popular use of marijuana and its components (THC and CBD) for medicinal purposes is often regarded as irresponsible and ignorant. However, in this paper, it is conceptualized and studied as medical knowledge of self-care, in which it is linked to different therapeutic knowledge from conventional and alternative medicine to treat anxiety. Objective. This article reports on a qualitative research project designed to analyze the practices and risks of self-medication with CBD, and its links with medical treatments and self-care measures for dealing with anxiety in adult women in Mexico City. Method. Eleven adult women, mostly professionals, diagnosed with anxiety, participated in the study. In-depth interviews were conducted to learn how women use therapeutic resources to cope with anxiety in their everyday lives. The interviews were transcribed and analyzed using ATLAS ti 7 software. Results. Self-medication is part of a self-care process with several facets: the experience of symptoms, self-diagnosis, the use of various remedies (dosage and administration), and their evaluation. During this process, women use a range of medication, psychological therapies, herbalism, acupuncture, yoga, and sleep hygiene measures, including CBD, either as an alternative or an adjunct to biomedical pharmacology, with or without medical supervision. Certain risks were identified, such as the use of CBD that may be incorrectly labeled or interact dangerously with other self-prescribed substances. Discussion and conclusion. These results coincide with those of various authors who equate self-care with a first level of care, which has risks that could be reduced by controlling misleading marketing and developing professional medical services, which are trained and accessible to users, who could inform them about the medicinal use, scope and risks of CBD or THC for health.
Exploring the degree of nicotine dependence and willingness to quit smoking in Chinese smoking patients with stroke

Abstract Smoking is closely related to the occurrence of stroke. The degree of nicotine dependence and willingness to quit smoking are key factors that determine whether a smoker can successfully quit smoking. Few studies have been conducted on factors affecting the willingness to quit smoking among smokers who suffered from stroke. Although the smoking rate has declined, it is still the main changeable risk factor for cerebrovascular diseases. We aimed to investigate the current status of nicotine dependence and willingness to quit smoking among patients who suffered from stroke. We also clarified factors that affect the willingness to quit smoking among stroke patients and provide evidence for quitting smoking. Convenience sampling methods were used to conduct cross-sectional studies. A questionnaire survey was administered to 215 stroke patients who smoked. A general information questionnaire as well as patient smoking and cessation status questionnaire were used. Nicotine dependence test scale, smoking attitude factor score, and tobacco harm perception scale were applied for investigation. Logistic regression was used to analyze factors related to patients’ willingness to quit smoking. Statistically significant differences in willingness to quit smoking were found among stroke patients with different ages, household registration status, education levels, marital status, occupation, smoking attitude, and nicotine dependence (P < .05). About 34.4%, 39.1%, and 26.5% of smoking patients had low, moderate, and severe nicotine dependence, respectively. Logistic analysis showed that compared with other occupations, workers, retirees, farmers, and freelancers compared with other people (0.050, 0.081, 0.053, 0.048) had a negative impact on the willingness to quit smoking among stroke patients. Compared with positive attitudes, negative and neutral smoking attitudes (0.190, 0.048) had a negative impact on the willingness to quit smoking among stroke patients. Compared with high nicotine dependence, low and moderate nicotine dependence (4.628, 2.596) had a positive impact on willingness to quit smoking. Smoking patients in neurology department are more willing to quit smoking (67.9%). Factors such as occupation, smoking attitude, and nicotine dependence should be considered when establishing smoking cessation interventions for stroke smokers.