Miracle-dz/UniFAR-FTUs huggingface dataset:
Extensive Radiological Manifestation in Patients with Diabetes and Pulmonary Tuberculosis: A Cross-Sectional Study

Background Diabetes mellitus (DM) is believed to affect tuberculosis (TB) at multiple levels in disease control and treatment efficacy, but clinical and radiological presentation resulting from interaction of the two diseases is not known. Methods A cross-sectional study was conducted on data obtained from medical records of 438 patients confirmed with TB-DM comorbidity at the Third people’s hospital of Shenzhen from May 01, 2014, to April 30, 2019. Their CT images were reviewed, and patients were divided into subgroups according to lung cavitation: with and without cavities, and number of segments showing pulmonary infiltration: <4 segment, 4–8 segment, >8 segment infiltrates. We then compared clinical parameters between these groups. Results The median age of the patients was 50.0 years (IQR 43.3–56.0) and 86% (n=375) of them were male. Pulmonary cavities were found in 80.8% patients. About 42.7% and 27.2% patients were seen to have infiltration involving 4–8 and >8 lung segments, respectively. Patients presented with cavitation and infiltration involving a greater number of lung segments had significantly higher values of WBC, MONO%, GRA%, CRP, lower LYN% level and higher bacterial burden in sputum (P<0.001). Higher HbA1c and FBG were only observed in patients with lung cavities (P<0.001). There was no difference in positive ELISPOT.TB and PCT level between the groups regardless of presence or absence of lung cavity (P>0.9 and P=0.1 respectively). Lower HGB, ALB and higher PCT were observed in patients with infiltration involving more lung segments. Conclusion Hyper-inflammation in peripheral blood was significantly associated with cavity and the number of lung lesions. Hyperglycemia was significantly associated with the development of lung cavity. Glycemic control and inflammation influenced radiographic manifestations in patients with TB-DM.
FAMILY BUSINESS AND THEIR CONCERN TO CORPORATE SOCIAL RESPONSIBILITY DISCLOSURE

The purpose of this study is to empirically examine the effect of a family business on the disclosure of Corporate Social Responsibility. The sample in this study is a company that can be identified as a family company in a manufacturing company listed on the Indonesia Stock Exchange in 2017 – 2019. The number of samples in this study is 99 samples. This study divides the family business into two variables: the family as the first largest shareholder and the family as the second-largest shareholder. The variable of family ownership as the first largest shareholder is measured using a dummy variable, given a value of 1 if the family is the first largest shareholder and 0 otherwise. The variable of family ownership as the second-largest shareholder is measured using a dummy variable, given a value of 1 if the family is the second-largest shareholder and 0 if other. CSR disclosure variable is measured using the disclosure index. The first control variable is the firm size measured using the natural logarithm of total assets. The second control variable is the firm age measured using years the firm was found until years of the research. Data were analyzed using multiple regression. The results showed that the family as the first largest shareholder and the family as the second-largest shareholder had no significant effect on corporate social responsibility disclosure.
In vitro Antisalmonella and Antioxidant Activity of Hydroethanolic and Aqueous Extracts of Bauhinia rufescens Leaf and Stem Bark Extracts

In Chad, enteric fever remains a major public health problem where it is still endemic due to the precariousness of life hygiene combined with the abusive and inappropriate use of antibiotics. Objective: The aim of this work was to evaluate the in vitro antisalmonella and antioxidant activity of extracts from the leaves and stem bark of B. rufescens. Methods: Phytochemical screening of these extracts was performed by standard methods to justify the observed activities. The antisalmonella activity was evaluated using the liquid microdilution method. Antioxidant activity of these extracts was determined by investigating their 1, 1-diphenyl-2 picrylhydrazyl (DPPH●) antiradical and iron reducing capacities. Results: The Minimum Inhibitory Concentrations (MICs) were varied from 256 to 1024 µg/ml. The 95% hydroethanolic extract of the leaves exhibited higher DPPH● antiradical activity than all extracts and IC50s ≤ 20 μg/ml for all extracts tested. Conclusion: These results showed that the 95% hydroethanolic extract of B. rufescens leaves possess in vitro antisalmonella and antioxidant activities and could be used for in vivo antisalmonella and antioxidant studies.
Real-time power line detection for safe flight of agricultural spraying drones using embedded systems and deep learning

Power line detection is necessary for the safe flight of low-flying UAVs (Unmanned Aerial Vehicles). This paper deals with the power line recognition problem for the safety of agricultural spraying drones in agricultural environments. The dataset of power lines was obtained in an agricultural environment. The training dataset was constructed by labeling powerlines with bounding boxes of 6 sizes, ranging from 0.03 to 0.15 times the image. The model used for training was the tiny-YOLOv3 model. The model was verified using the mean average precision (mAP), which was used to verify the object recognition performance. Depending on the six sizes of bounding boxes, the mAPs were evaluated to be 70.22, 94.00, 86.75, 68.87, 61.65, and 53.40, respectively. The mAP was the highest at the bounding box of 0.05 times the image size, and it was confirmed that this size is most suitable for power line detection. The real-time frames per second (FPS) results of power lines detection are on average 12.5. This paper shows that the location detection of power lines is possible in real-time using deep-learning techniques with embedded systems.
Face-to-face, online, and hybrid designs for mentoring

The purpose of this study was to investigate how face-to-face (F2F) coaching and online mentoring influences teacher professional development and learning. “A TIME for Physics First” (PF) is a 5-year National Science Foundation (NSF) funded project, which focuses on developing 9th grade science teachers to become teacher leaders and build physics content knowledge integrated with inquiry, modeling, and technology. The PF project provides coaching (F2F) and mentoring (online) to support to improve teaching and learning. The overarching question guiding this study is: What affordances do F2F and online venues provide to a professional development project in terms of mentoring? The primary data were interview and field observation. Secondary data included the teacher self-reflection form, the classroom observation Form, monthly blogs, and artifacts. Based on the cross-case analysis of the two cases, I made the following assertions: (1) the types of questions that were asked in F2F and online post-observation discussion were very similar. The quality of those discussions is influenced by the venue and the context, (2) classroom observation is an essential component for coaching and mentoring designs, (3) effective coaching or mentoring includes time for meaningful post-observation discussions, and (4) A hybrid design, employing both F2F and online venues, provides efficient support of teachers. The hybrid mentoring and PLC design include: (1) online pre-observation report, (2) F2F classroom observation, (3) online post-observation discussion with a Mentor Observation Form and a Teacher Reflection Form, and (4) online PLC meetings with periodic F2F meetings. Evidence from the data suggests that hybrid design provides the flexibility and opportunities to optimize high quality professional development, while overcoming the limitations of single venue forms of support provided by coaching and mentoring.
The Implementation of Contextual And Learning (CTL) Model in Critical Thinking Ability on Primary Students' Learning Outcomes

This study aims to determine the effect of contextual learning and learning (CTL) models in critical thinking leadership on student learning outcomes. the results of data analysis and discussion, it can be concluded that there is an effect of applying a contextual approach to the learning outcomes of Civics on Human Rights in fourth grade students of SD Negeri. By using the inferential analysis technique to test the research hypothesis, in this case the Independent Sample T-test formula is used in the SPSS program. Before testing the hypothesis, the normality test and homogeneity test were first carried out. From the results of the analysis obtained data are normally distributed and homogeneous. Because the data that has been analyzed is normally distributed and homogeneous, it is continued with the T- test using the Independent Sample T- test formula with SPSS. The results of the analysis show that the significance of 0.033 is smaller than = 0.05. Thus H0 is rejected and H1 is accepted
Lower and upper bounds for the blow-up time to a viscoelastic Petrovsky wave equation with variable sources and memory term

ABSTRACT Under Dirichlet boundary conditions, we consider here a new type of viscoelastic Petrovsky wave equation involving variable sources and memory term We discuss the blow-up in finite time with arbitrary positive initial energy and suitable large initial values if and the relaxation function g satisfies some conditions. Employing a different method for higher bounded positive initial energy, not only finite time blow-up for solutions proved but also the lower and upper bounds for blowing up time are gotten.
The influence of culture, beliefs and experience on the sustainable end-of-life management of buildings in Nigeria

While it is usually believed that economic incentives are behind waste management practices in developing countries, this article discusses culture, belief and experience as the other major influences in the management practices of building demolition waste in Nigeria. Practitioners in the building demolition sub-sector were interviewed to identify the influences that contribute to the emergence of reuse as a preferable demolition waste management technique in society, in addition to the economic incentives. This study will give an insight into some practices in a pre-industrial society with potential for recognition and contribution of the development of formal waste management systems in developing countries.
The Madden-Julian Oscillation

ABSTRACT There are rich variabilities in the global atmosphere and climate system on the intraseasonal time scale. This review article focuses on the Madden-Julian Oscillation (MJO), which is the dominant mode of tropical intraseasonal variability. The MJO has a tremendous global impact and represents a major source of skill for subseasonal to seasonal predictions. Our knowledge on the MJO has been advanced in recent decades, but challenges remain in understanding, simulating and predicting the MJO and its global influence. Here we review the basic observed features of the MJO, possible mechanisms, the related teleconnections and tropical-extratropical interactions, its interannual variation, and its simulation and predictions.
Review on quick safety assessment of building structures in complex urban environment after extreme explosion events

Extreme explosion events result in demands for emergency rescue service. From the civil engineering perspective, a quick safety assessment of building structures in the explosion’s vicinity will provide the emergency rescue committee with concrete support to make scientific decisions. In this paper, three primary issues, namely, inverse analysis of explosive characteristics, blast wave propagation in complex urban areas and blast-induced damage identification, are reviewed. These are often performed stepwise and form a multi-step whole to assist the emergency rescue service. The paper begins by introducing the inverse analysis of explosives based on craters, building damages and seismic or acoustic records. In this step, explosive characteristics, for example, charge type, original time, yield and location, could be produced and input into blast load calculation in the next step. Then, the existing literature on blast wave propagation and blast load determination is presented with close attention to complex urban environments. It shows that the current study remains in its infancy and relies on advancement in computational fluid dynamics (CFD). Besides, pressure–impulse (P-I) diagrams which predict the structural damage based on the calculated blast loads are illustrated. Onsite damage detection techniques, such as visual inspection, non-destructive testing (NDT) and vibration-based methods, are also discussed. The paper ends with a discussion of the shortcomings of previous work and the outlooks of further work.
Plant-Derived Smoke Solution Alleviates Cellular Oxidative Stress Caused by Arsenic and Mercury by Modulating the Cellular Antioxidative Defense System in Wheat

Heavy metal stress is a significant factor in diminishing crop yield. Plant-derived smoke (PDS) has been used as a growth promoter and abiotic stress alleviator for the last two decades. Although the roles of PDS have been determined in various plants, its role in ameliorating heavy metal stress in wheat has not been reported so far. Therefore, the present work was conducted to investigate the effect of smoke solution extracted from a wild lemongrass Cymbopogon jwarancusa (C. jwarncusa) on physiological and biochemical features of wheat under arsenic (As) and mercury (Hg) stress. The results showed that higher concentrations of As and Hg pose inhibitory effects on wheat seed germination and seedling growth, including shoot/root length and shoot/root fresh weight. Photosynthetic pigments, such as chlorophyll a and b and carotenoids, were significantly decreased under As and Hg stress. Importantly, the levels of H2O2, lipid peroxidation, and TBARS were increased in wheat seedlings. The activity of antioxidant enzymes, such as CAT, was decreased by As and Hg stress, while the levels of SOD, POD, and APX antioxidant enzymes were increased in root and shoot. Interestingly, the application of PDS (2000 ppm), individually or in combination with either As or Hg stress, enhanced wheat seed germination rate, shoot/root length, and shoot/root fresh weight. However, the levels of H2O2, lipid peroxidation, and TBARS were decreased. Similarly, the levels of SOD, POD, and APX were decreased by PDS under As and Hg stress, while the level of CAT was enhanced by PDS under As and Hg stress. Interestingly, the levels of chlorophyll a and b, and total carotenoids were increased with the application of PDS under As and Hg stress. It is concluded that PDS has the capability to alleviate the phytotoxic effects of As and Hg stress in wheat by modulating the antioxidative defense system and could be an economical solution to reduce the heavy metal stress in crops.
Automated detection of low ejection fraction from a one-lead electrocardiogram: application of an AI algorithm to an electrocardiogram-enabled Digital Stethoscope

Abstract Aims Electrocardiogram (ECG)-enabled stethoscope (ECG-Scope) acquires a single-lead ECGs during cardiac auscultation and may facilitate real-time screening for pathologies not routinely identified by cardiac auscultation alone. We previously demonstrated an artificial intelligence (AI) algorithm can identify left ventricular dysfunction (LVSD) [defined as ejection fraction (EF) ≤ 40%] with an area under the curve (AUC) of 0.91 using a 12-lead ECG. Methods and results One hundred patients referred for clinically indicated echocardiography were prospectively recruited. ECG-Scope recordings with the patient supine and sitting were obtained in multiple electrode locations at the time of the echocardiogram. The AI algorithm for the detection of LVSD was retrained using single leads from ECG-12 and validated against ECG-Scope to determine accuracy for low EF detection (≤35%, <40%, or <50%). We evaluated the algorithm with respect to body position and lead location. Amongst 100 patients (aged 61.3 ± 13.8; 61% male, BMI: 30.0 ± 5.4), eight had EF≤40%, and six had EF 40–50%. The best single recording position was V2 with the patient supine [AUC: 0.88 (CI: 0.80–0.97) for EF≤35%, 0.85 (CI: 0.75–0.95) for EF≤40%, and 0.81 (CI: 0.71–0.90) for EF < 50%]. When using an AI model to select the recording automatically, AUC was 0.91 (CI: 0.84–0.97) for EF≤35%, 0.89 (CI: 0.83–0.96) for EF≤40%, and 0.84 (CI: 0.73–0.94) for EF < 50%. Conclusion An AI algorithm applied to an ECG-enabled stethoscope recording in standard auscultation positions reliably detected the presence of a low EF in this prospective study of patients referred for echocardiography. The ability to screen patients with a possible low EF during routine physical examination may facilitate rapid detection of LVSD.
Comparisons of Means for Estimating Sea States from an Advancing Large Container Ship

The paper deals with sea state estimation from a container carrier (9,400 TEU) en-route. Knowledge of the on-site sea state is fundamental input to any kind of inservice decision support system that evaluates performance of, e.g., accelerations, fuel efficiency, and hull girder strength, related to ship-wave interactions in a seaway. In the paper, sea state estimates are produced by three means: the wave buoy analogy, relying on shipboard response measurements, a wave radar system, and a system providing the instantaneous wave height. The presented results show that for the given data, recorded on five different days of continuous operation, the agreement between the estimating means is reasonable; in terms of both absolute (mean) values and hourly trends of integrated sea state parameters.
Sexual politics in the field: gendered research spaces in tourism geographies

Abstract Sexual violence and harassment in field research is an uncomfortable and under-discussed phenomenon in the social sciences. Tourism geographies, being cross-cultural, often require solo fieldwork that exposes one’s gender in geographically remote locations. There is a pressing need to normalise the discussion of sexual politics in the field, specifically concerning occurrences of gender-associated risks in fieldwork and report on the unexpected physical and mental health risks for women researchers. This study takes a feminist theoretical lens to unpack the hidden dimensions of women engaged in ‘voluntary’ risk taking by conducting field research in male-dominated research environments. Taking an exploratory approach within an interpretivist paradigm, this study is based on the fieldwork experiences of 13 women from diverse cultural and academic backgrounds with the participants sharing their subjective realities of researching in tourism geographies. A thematic analysis revealed the two key themes of risk/perceived vulnerabilities and wellbeing/care in the field as paramount for field research spaces for women, along with 11 subthemes. Findings reaffirmed the political nature of tourism geographies fieldwork and the need to challenge sexual politics and patriarchal domination, including for LGBTQ researchers. Further, the results highlighted the intersectionality of race and gender of women’s experiences of sexual violence and other risks in the field. Thus, the findings suggest an urgent need to provide an imperative for fieldworker safety, wellbeing considerations, and alternative ways of researching.
Identification of plant species through leaf vein morphometric and deep learning

Taxonomy language is challenging to comprehend and automated knowledge is required to identify the plant species. The study focused on developing an improved deep neural network: Residual Neural Network-ResNet & and Densely Connected Convolution Network (DenseNet) for the plant identification with plant leaf vein architecture. There was a total of 44 species. Each species had 64 images, each of which was further divided into 52 images for the training data and 12 images for the test data. The Canny edge detection method was deployed to detect the vein architecture of the leaves. For ResNet and DenseNet, the 224 x 224 binary image was used. The size of the feature maps in 4 dense blocks was: 56 x 56, 28 x 28, 14 x 14, and 7 x 7, respectively. MalayaKew (MK) data set was used for the experiment. There was a total of 44 classes and images were divided into the training set and the test set. The training set contained 2288 images, with each class having 52 images. Test class contained 528 images, with each class having 12 images. After preprocessing these images, they were fed to various networks of ResNet and DenseNet. Two algorithms, Stochastic gradient descent (SGD) and Adam optimization, were used in each network. Through SGD, the model ResNet, had 26, 34, 50, 101, and 152 layers. The best accuracy achieved was 89.24% using 50 layers. DenseNet had 121, 169, and 201 layers. The best accuracy achieved was 94.20% using 169 layers. In Adam optimizer, the ResNet model had 26, 34, 50, 101, and 152 layers. The best accuracy achieved was 89.50% using 101 layers. DenseNet had 121, 169, and 201 layers. The best accuracy achieved was 95.72% using 169 layers. Overall, the best performance was achieved using Adam optimizer using the DenseNet model with 169 layers and came out to be 95.72%. This also surpassed the accuracy that was achieved using D-leaf architecture. The proposed deep learning (DL) methods were very accurate in identifying plants.
Direct Interfacing of Parametric Average-Value Models of AC–DC Converters for Nodal Analysis-Based Solution

AC–DC converters are widely used in many power-electronic-based systems. There is an increasing need to simulate such systems using larger time-steps in offline and/or real-time electromagnetic transient (EMT or EMTP) simulators. The so-called parametric average-value models (PAVMs) have been developed to allow larger time-steps and provide fast simulations. However, the application of PAVMs in nodal-analysis-based EMTP programs typically requires a one-time-step delay between the interfacing sources and the network solution (i.e., indirect interfacing), causing inaccuracy and numerical instability at medium-to-large time-steps. This paper presents a direct interfacing method for PAVMs of line-commutated rectifiers (LCRs). The proposed method linearizes the PAVM interfacing equations and incorporates the respective sub-matrices and history terms into the network nodal equations, which eliminates the need for a time-step delay. Simulation studies verify the effectiveness of the proposed method in EMTP-type solution wherein very good accuracy and numerical stability is achieved at fairly large time-steps, which has not been previously possible with conventional methods.