Miracle-dz/UniFAR-FTUs huggingface dataset:
32-42 JPACD-2019-01 Estimation of cladode area-Lucena et al

Determination of photosynthetic area of a plant, leaf or cladode is a fundamental tool in study of transpiration intensity, specific leaf area and leaf area index. The objective of this study was to evaluate Nopalea cochenillifera (L.) Salm-Dyck). cladode area, in a nondestructive way, using digital images and test its relation with the variables: product of length and maximum width and real cladode area through regression models. The design used randomized blocks with three replicates and using the N. cochenillifera forage cactus clone, Giant Sweet. To determine the real cladode area of cactus forage, 432 cladodes in different stages of growth were randomly collected (162 primary cladodes, 127 secondary and 143 tertiary cladodes), all free from damage, disease or pest attacks. All cladodes were photographed with a digital camera (Sony Mark, model DSC-P72) generating a sample of 432 1200 x 2500 pixel digital images of N. cochenillifera cladodes. Linear, gamma and power regression models were adjusted to test the relation between the digital cladode area and the explanatory variables real cladode area and product of length by width. Models were evaluated with the following criteria: Coefficient of model determination, Akaike information criterion, sum of squares of residuals and Willmott index. The power model gave the best performance, with explanatory power higher than 99.5%, while the Willmott index exceeded 0.99. Sum of squares of residuals and Akaike information criterion had lower values. The digital cladode area of N. cochenillifera can be explained by the linear dimensions of cladodes in, and independent of, branching order. The digital cladode area (DCA) of N. cochenillifera can be explained as a function of the power model DCA = LW . considering the product of length by width (LW) with explanatory variable, and by DCA = RCA . considering real cladode area (RCA) with explanatory variable.
Depressive, Anxious, and Post-Traumatic Stress Symptoms Related to Violence during the COVID-19 Pandemic, by Sex, COVID-19 Status, and Intervention-Seeking Conditions among the General Population

Worldwide, three out of four of the general population have reported experiencing violence. Governments should address solutions to violence and its effects on mental health. The study aimed to determine depressive, anxious, and posttraumatic stress symptoms related to the violence experienced during the COVID-19 pandemic in the general population. The study was conducted with 18,449 Mexicans of 33 years (SD = 11.00, range = 18–59), with 12,188 (66.10%) being women, 3559 (19.29%) having COVID-19, 2706 (14.67%) seeking psychological care, and 5712 (30.96%) experiencing violence. Subjects completed the Major Depressive Episode (MDE) Checklist, Generalized Anxiety (GA) Scale, and the Posttraumatic Stress (PTS) Checklists (PCL-5) programmed in a WebApp application. We assessed the dimensionality of the scales through the Confirmatory Factor Analysis (CFA), the measurement invariance, and a structural equation model (SEM). In the total sample, 28.10% fulfilled the MDE criteria, and 42.30% had high levels of GA. In the sample of those experiencing violence, 48.40% met the MDE criteria, 61.70% had high GA symptoms, and 50% met the criteria for a PTS disorder. Experiencing violence was associated with GA and severe PTS symptoms when the discomfort had bothered them for over a month since the onset of these symptoms. Subjects who had experienced violence and had mental health symptoms seemed ready for treatment. Further studies will evaluate the effect of remote psychological care to help reduce the treatment gap.
Exploring the Relationships of Financial Literacy and Financial Behaviour with Health-Related Quality of Life (HRQOL) among the Low-Income Working Population in Malaysia during the COVID-19 Pandemic

This cross-sectional study examined the relationships of financial literacy (FL) and financial behaviour (FB) with health-related quality of life (HRQOL) during the COVID-19 pandemic among low-income working population (20–60 years old) in Malaysia. A self-administered questionnaire survey was used with HRQOL data were gathered using the EuroQol 5-Dimension 5-Level (EQ-5D-5L) tool. A generalised linear model was employed to examine the hypothesised relationships between the constructs. From 1186 respondents, the majority were employed (73.9%), had a monthly household income of less than RM 2500 (74.5%), and did not have any chronic medical conditions (74.5%). The mean (SD) values of FL, FB, and EQ-5D-5L were 5.95 (1.48), 22.08 (4.79), and 0.96 (0.10), respectively. The results of the adjusted model revealed lower age group, Malay ethnicity, Indian ethnicity, and increased FB score as significant determinants of higher EQ-5D-5L scores. With the addition of the chronic medical condition factor into the saturated model, the lower age group, ethnicity, and no chronic medical condition were significant determinants of higher HRQOL. The effects of FB on QOL were confounded by chronic diseases, implying that interventions that focus on improving FB for those with chronic medical condition may help to improve the QOL among the low-income working population.
Research Progress on the Positive and Negative Regulatory Effects of Rhein on the Kidney: A Review of Its Molecular Targets

Currently, both acute kidney injury (AKI) and chronic kidney disease (CKD) are considered to be the leading public health problems with gradually increasing incidence rates around the world. Rhein is a monomeric component of anthraquinone isolated from rhubarb, a traditional Chinese medicine. It has anti-inflammation, anti-oxidation, anti-apoptosis, anti-bacterial and other pharmacological activities, as well as a renal protective effects. Rhein exerts its nephroprotective effects mainly through decreasing hypoglycemic and hypolipidemic, playing anti-inflammatory, antioxidant and anti-fibrotic effects and regulating drug-transporters. However, the latest studies show that rhein also has potential kidney toxicity in case of large dosages and long use times. The present review highlights rhein’s molecular targets and its different effects on the kidney based on the available literature and clarifies that rhein regulates the function of the kidney in a positive and negative way. It will be helpful to conduct further studies on how to make full use of rhein in the kidney and to avoid kidney damage so as to make it an effective kidney protection drug.
Impact of Transportation Electrification on the Electricity Grid—A Review

Transportation electrification is a pivotal factor in accelerating the transition to sustainable energy. Electric vehicles (EVs) can operate either as loads or distributed power resources in vehicle-to-grid (V2G) or vehicle-to-vehicle (V2V) linkage. This paper reviews the status quo and the implications of transportation electrification in regard to environmental benefits, consumer side impacts, battery technologies, sustainability of batteries, technology trends, utility side impacts, self-driving technologies, and socio-economic benefits. These are crucial subject matters that have not received appropriate research focus in the relevant literature and this review paper aims to explore them. Our findings suggest that transitioning toward cleaner sources of electricity generation should be considered along with transportation electrification. In addition, the lower cost of EV ownership is correlated with higher EV adoption and increased social justice. It is also found that EVs suffer from a higher mile-per-hour charging rate than conventional vehicles, which is an open technological challenge. Literature indicates that electric vehicle penetration will not affect the power grid in short term but charging management is required for higher vehicle penetration in the long-term scenario. The bi-directional power flow in a V2G linkage enhances the efficiency, security, reliability, scalability, and sustainability of the electricity grid. Vehicle-to-Vehicle (V2V) charging/discharging has also been found to be effective to offload the distribution system in presence of high EV loads.
Investigation of Spatiotemporal Profiles of Single-Pulse TMS-Evoked Potentials with Active Stimulation Compared with a Novel Sham Condition

Identifying genuine cortical stimulation-elicited electroencephalography (EEG) is crucial for improving the validity and reliability of neurophysiology using transcranial magnetic stimulation (TMS) combined with EEG. In this study, we evaluated the spatiotemporal profiles of single-pulse TMS-elicited EEG response administered to the left dorsal prefrontal cortex (DLPFC) in 28 healthy participants, employing active and sham stimulation conditions. We hypothesized that the early component of TEP would be activated in active stimulation compared with sham stimulation. We specifically analyzed the (1) stimulus response, (2) frequency modulation, and (3) phase synchronization of TMS–EEG data at the sensor level and the source level. Compared with the sham condition, the active condition induced a significant increase in TMS-elicited EEG power in the 30–60 ms time interval in the stimulation area at the sensor level. Furthermore, in the source-based analysis, the active condition induced significant increases in TMS-elicited response in the 30–60 ms compared with the sham condition. Collectively, we found that the active condition could specifically activate the early component of TEP compared with the sham condition. Thus, the TMS–EEG method that was applied to the DLPFC could detect the genuine neurophysiological cortical responses by properly handling potential confounding factors such as indirect response noises.
Review of an Initial Physician Assistant Training Program Orthopaedics Course Curriculum Using Kern's 6-Step Process

Graduating an average of 169 graduates per year, the Interservice Physician Assistant Program (IPAP) is the largest physician assistant training program in the world. The IPAP orthopedics course is a 4-credit hour course that started as approximately 60 hours of lecture, 9 hours of laboratory practice to prepare for an Objective Structured Clinical Examination, and 4 hours of splinting laboratory. After several cohorts performed poorly on the second block exam and student critiques voiced the need for course revision, the curriculum committee formed a sub-committee to review the course content and structure. The primary course instructor was familiar with Kern's 6-step approach to curriculum development and proposed using the Kern’s approach for the curriculum review and revision. This article aims to demonstrate the use of Kern's 6-step approach for review and revision of an introductory orthopedic course curriculum. This article shows how an IPAP curriculum sub-committee executed the 6 steps of the Kern’s process: (1) problem identification and general needs assessment, (2) targeted needs assessment, (3) development of goals and objectives, (4) development of educational strategies, (5) implementation, and (6) evaluation and feedback. To our knowledge, this is the first example of a physician assistant program using Kern's 6-step process to revise a curriculum. Thus far, the revisions show a positive change, indicating that other physician assistant programs could conduct similar reviews using the Kern’s 6-step approach.
Exploring the Factors Associated with Mental Health Attitude in China: A Structural Topic Modeling Approach

Mental health attitude has huge impacts on the improvement of mental health. In response to the ongoing damage the COVID-19 pandemic caused to the mental health of the Chinese people, this study aims to explore the factors associated with mental health attitude in China. To this end, we extract the key topics in mental health-related microblogs on Weibo, the Chinese equivalent of Twitter, using the structural topic modeling (STM) approach. An interaction term of sentiment polarity and time is put into the STM model to track the evolution of public sentiment towards the key topics over time. Through an in-depth analysis of 146,625 Weibo posts, this study captures 12 topics that are, in turn, classified into four factors as stigma (n = 54,559, 37.21%), mental health literacy (n = 32,199, 21.96%), public promotion (n = 30,747, 20.97%), and social support (n = 29,120, 19.86%). The results show that stigma is the primary factor inducing negative mental health attitudes in China as none of the topics related to this factor are considered positive. Mental health literacy, public promotion, and social support are the factors that could enhance positive attitudes towards mental health, since most of the topics related to these factors are identified as positive ones. The provision of tailored strategies for each of these factors could potentially improve the mental health attitudes of the Chinese people.
The Effect of Coil Orientation on the Stimulation of the Pre–Supplementary Motor Area: A Combined TMS and EEG Study

Studies using transcranial magnetic stimulation (TMS) have demonstrated the importance of direction and intensity of the applied current when the primary motor cortex (M1) is targeted. By varying these, it is possible to stimulate different subsets of neural elements, as demonstrated by modulation of motor evoked potentials (MEPs) and motor behaviour. The latter involves premotor areas as well, and among them, the presupplementary motor area (pre–SMA) has recently received significant attention in the study of motor inhibition. It is possible that, similar to M1, different neuronal populations can be activated by varying the direction and intensity of TMS; however, the absence of a direct electrophysiological outcome has limited this investigation. The problem can be solved by quantifying direct cortical responses by means of combined TMS and electroencephalography (TMS–EEG). We investigated the effect of variable coil orientations (0°, 90°, 180° and 270°) and stimulation intensities (100%, 120% and 140% of resting motor threshold) on local mean field potential (LMFP), transcranial evoked potential (TEP) peaks and TMS–related spectral perturbation (TRSP) from pre–SMA stimulation. As a result, early and late LMFP and peaks were larger, with the coil handle pointing posteriorly (0°) and laterally (90°). This was true also for TRSP in the β–γ range, but, surprisingly, θ–α TRSP was larger with the coil pointing at 180°. A 90° orientation activated the right M1, as shown by MEPs elicitation, thus limiting the spatial specificity of the stimulation. These results suggest that coil orientation and stimulation intensity are critical when stimulating the pre–SMA.
01 Aleena Shuja

INTRODUCTION Aleena Shuja1* Naveed Yazdani2 Aleema Shuja3 For years to come, telecommunication and IT sectors will face enormous challenges due to transfigurations happening in digital marketplace. History provides strong evidence on these sectors struggling hard to adapt against transformations occurring in the external environment. Financial services sector is no exception in confronting the digitization and technological disruption resulting in innovative business models for its endured survival. Companies in order to persist against technology based unprecedented transformations need to be proactively prepared (Jacobi & Brenner, 2018). Chaotic disturbance and acceleration expansion, both are perceived as either consequences of globalization that acts as a catalyst for political and socio-economic disruption. Consequently, heterogeneous organizations must leverage the diversity and complexity through innovation and knowledge networks for upholding their resilience, robustness and viable entrepreneurship (Carayannis, 2008). Considered as open systems, organizations adaptably while operating in an environment characterized by uncertainly, turbulence and risk unceasingly strive for coherence and stability with change with a view to achieve high level excellence, efficacy, sustainability and 01 January-June 2020 JISR-MSSE Number 1 Volume 18 10.31384/jisrmsse/2020.18. 1.1
A Unified Domain Adaptation Framework with Distinctive Divergence Analysis

Unsupervised domain adaptation enables knowledge transfer from a labeled source domain to an unlabeled target domain by aligning the learnt features of both domains. The idea is theoretically supported by the generalization bound analysis in Ben-David et al. (2007), which specifies the applicable task (binary classification) and designates a specific distribution divergence measure. Although most domain adaptation models seek theoretical grounds from this particular bound analysis, they do not actually fit into the stringent conditions. In this paper, we bridge the long-standing theoretical gap in literature by providing a unified generalization bound. Our analysis can well accommodate the classification/regression tasks and most commonly-used divergence measures, and more importantly, it can theoretically recover a large amount of previous models. In addition, we identify the key difference in the distribution divergence measures underlying the diverse models and commit a comprehensive in-depth comparison of the commonly-used divergence measures. Based on the unified generalization bound, we propose new domain adaptation models and conduct experiments on real-world datasets that corroborate our theoretical findings. We believe these insights are helpful in guiding the future design of domain adaptation algorithms.
Transforming circumnavigating Kelvin waves that initiate and dissipate the Madden–Julian Oscillation

Analyses of Lagrangian model simulations and atmospheric sonde data reveal a key component of the Madden–Julian Oscillation (MJO): circumnavigating equatorial Kelvin waves with dynamics that transform between dry and moist, which initiate and dissipate MJO convection. The same compositing procedure is applied to simulated and observed MJOs, which uses a coordinate system that moves with the precipitation centre, and treats developing, mature and dissipating stages separately. MJO structure and evolution are similar in the simulation and the observations. To the west of the developing convection, there is a broad region of low‐level (upper‐level) perturbation westerlies (easterlies), which is accompanied by a deep negative temperature perturbation. As this feature enters the MJO‐formation region, convection intensifies on its eastern edge, and the zonal wind perturbations decrease in zonal extent and propagation speed. This process is shown to be a dynamical consequence of a largely dry, first baroclinic Kelvin wave entering a region where large‐scale upward motion is mostly balanced by convective heating. As the MJO matures, a Kelvin wave of opposite sign emerges from its eastern edge, and makes the opposite transition (from moist to dry). The resulting wave, which includes low‐level (upper‐level) easterlies (westerlies) and a deep positive temperature perturbation, rapidly propagates around the world and dissipates MJO convection. The Kelvin wave that initiates MJO convection is shown to originate from the inactive phase of a previous MJO, so that the complete MJO cycle is characterized by the two kinds of Kelvin waves emerging from active and suppressed phases of MJO convection, circumnavigating the Tropics, and triggering the opposite phase.
Quantitative Analysis of a Wealth Tax in the United States: Exclusions, Evasion, and Expenditures

Macroeconomic analyses of wealth taxes typically treat all household wealth as taxable, despite noted administrative difficulties with including owner-occupied housing and noncorporate equity in the tax base. In this paper, we quantify the macroeconomic and budgetary impact of avoidance due to these exclusions from a stylized, broad-based, top-wealth tax in the United States. We use a two-sector, large-scale overlapping generations model where, in the presence of exclusions, avoidance behavior arises endogenously through households’ reallocation of wealth and firms’ reallocation of economic activity. We find that while the macroeconomic and budgetary effects of the housing exclusion are insignificant, the noncorporate equity exclusion introduces a production-level distortion that results in a significant reallocation of economic activity from the corporate to noncorporate sector. We show that the federal revenue loss due to legal avoidance in the latter case can be similar to the amount lost due to illegal evasion via under-reporting wealth, but nonetheless have a quantitatively distinct path of macroeconomic aggregates. Finally, because interest in a wealth tax is linked to its potential for financing federal outlays, we show how variation in macroeconomic and budgetary effects across alternative expenditures affects the amount of new outlays availed by the tax itself. We find that while dedicating new revenue to public infrastructure investment leads to the largest increase in aggregate output, dedicating new revenue to federal debt reduction leads to the largest increase in outlays. JEL Codes: E62, H26, H27
Saliency-versus Scanpath-based patch selection for predicting the perceptual quality of 2D images

In recent years, the use of deep learning-based methods have developed to estimate the quality of 2D images. Most of these methods are trained using small patches with the subjective score of the whole image (i.e. MOS) as target. It was thus often assumed that all patches of the image have the same perceptual impact, which is not "totally" in accordance with our HVS. Indeed, each patch has its own perceptual impact since only some regions of the image are analyzed by observers when they look at the image. To overcome this assumption or at least reduce its impact, different solutions can be proposed. Here, we focus more on the use of the saliency information to estimate the quality of an image through only the most perceptually relevant patches. More precisely, we show which of the saliency-or scanpath-based patch selection approaches accurately predict the quality of 2D images.
On Black Hole Entropy Corrections in the Grand Canonical Ensemble

We study entropy corrections due to thermal fluctuations for asymptotically AdS black holes in the grand canonical ensemble. To leading order, these can be expressed in terms of the black hole response coefficients via fluctuation moments. We also analyze entropy corrections due to mass and charge fluctuations of R-charged black holes, and our results indicate an universality in the logarithmic corrections to charged AdS black hole entropy in various dimensions.
Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository

Data from 255 Thais with chronic pain were collected at Chiang Mai Medical School Hospital. After the patients self-rated their level of pain, a smartphone camera was used to capture faces for 10 s at a one-meter distance. For those unable to self-rate, a video recording was taken immediately after the move that causes the pain. The trained assistant rated each video clip for the pain assessment in advanced dementia (PAINAD). The pain was classified into three levels: mild, moderate, and severe. OpenFace© was used to convert the video clips into 18 facial action units (FAUs). Five classification models were used, including logistic regression, multilayer perception, naïve Bayes, decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Out of the models that only used FAU described in the literature (FAU 4, 6, 7, 9, 10, 25, 26, 27, and 45), multilayer perception is the most accurate, at 50%. The SVM model using FAU 1, 2, 4, 7, 9, 10, 12, 20, 25, and 45, and gender had the best accuracy of 58% among the machine learning selection features. Our open-source experiment for automatically analyzing video clips for FAUs is not robust for classifying pain in the elderly. The consensus method to transform facial recognition algorithm values comparable to the human ratings, and international good practice for reciprocal sharing of data may improve the accuracy and feasibility of the machine learning's facial pain rater.