Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pilot Study to Test Feasibility the Effect Structured Education Program in Nursing Patients with Diabetes Kidney Disease

Background: Multidisciplinary team and education programs need to be set up for the treatment of complications related to diabetes. Purpose: We aimed to test a structured educational intervention coordinated by diabetes nurse educator to improve self-management of diabetes in people with diabetic kidney disease. Methods: Patients were randomly assigned to the control group to receive standard clinical management of diabetes with kidney disease or intervention group to receive the structured education. The primary endpoint was the change of renal function and metabolic parameters at 6th month. Results: The control of HbA1c to level of <7% was achieved in 40% and 10% in intervention and control group, respectively. The percentage of achieving low-density lipoprotein cholesterol < 100 mg/dl increased by 31.25% and 6.25% in intervention and control group, respectively. The percentage of achieving systolic blood pressure to <130 mmHg was also increased. At 6 months, 30% attained 3 targets in intervention group, whereas 10% attained 3 targets in the control group. Intervention group significantly improved urine albuminuria status, regression of macroor microabuminuria to normoalbuminuria occurred in 31.25% and 11.25% in intervention and control group, respectively. Conclusion: Type 2 diabetes with diabetes kidney disease receiving a structure education program delivered by diabetes educator team is more likely to attain multiple treatment targets, by accepting treatment changes, and improved their self-care behaviors. *Corresponding author: Chien Huang*, School of Medicine, Department of Nursing and Internal Medicine, Division of Endocrinology and Metabolism, Chung Shan Medical University Hospital, No. 110, First Section, Jian Guo North Road, South District, Taichung 40201, Taiwan, Tel: 886-4-24739595-34723; E-mail: monica119@gmail.com Received December 13, 2019; Accepted December 24, 2019; Published December 31, 2019 Citation: Li CR, Chia TW, Huang CN, Yang YS (2019) A Pilot Study to Test Feasibility the Effect Structured Education Program in Patients with Diabetes Kidney Disease. J Nurs Care 8: 499. Copyright: © 2019 Li CR, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Deep Hierarchical Diagnosis of Skin Lesions

The early detection of skin cancer is very important because it increases signiﬁcantly the patient’s prognosis, and techniques of data classiﬁcation are important tools to accomplish it. Skin lesions can be organized in a hierarchical structure, where in the ﬁrst level the melanocytic and non-melanocytic lesions are splitted, and in each of these sub-groups the benign and malignant lesions are further discriminated. However, this knowledge has been disregarded by most automatic methods. Thus, in this thesis we propose to investigate this issue. In this work, we implement and compare the results of two different approaches to diagnose skin lesions’ images, based in deep learning: (i) a ﬂat approach, where the inference is done in a single decision which involves all the categories, and (ii) a hierarchical approach that explores the hierarchical structure of skin lesions’ organization, where the inference for each example involves more than one decision. Afterwards, a mixed model, which combines these approaches, was implemented in order to capture the strengths of each one. We veriﬁed that the hierarchical model was affected by error propagation of intermediate decisions, in comparison with the ﬂat classiﬁer. The mixed model allowed to improve signiﬁcantly the results of both approaches.
Acquisition of the Long Jump Skill Using Varying Feedback / Usvajanje tehnike skoka u dalj korištenjem povratnih informacija različitih vrsta i frekvencija

The aim of this study was to examine the acquisition of the long jump skill in elementary school children using augmented feedback of varying type and frequency. Eighty-eight boys and girls aged (mean ± SD) 11 ± 0.5 years, without any prior experience in the long jump skill acquisition, were assigned to one of the four study groups: (1) the group receiving only verbal feedback on key errors, (2) the group receiving both verbal and video feedback on key errors, (3) the group receiving both verbal and video feedback on all errors, and (4) the group receiving no feedback. Before and after an 8-week training intervention, long jump distance and relevant kinematic variables were recorded. The results indicated that the group receiving both verbal and video feedback on all errors improved the most in terms of the long jump distance. Varying feedback influenced kinematic parameters differently, as there was no consistent change in the monitored kinematic variables across groups. It was concluded that, when learning a complex motor skill in a typical Physical Education setting, elementary school children are likely to benefit the most when receiving frequent feedback (both verbal and using video analysis, focusing on all errors) in comparison with the situation in which they receive feedback reduced in the type (only verbal) and the frequency (focusing only on key errors). Keywords: bandwidth feedback; kinematics; knowledge of performance; motor learning. --- Cilj ovoga rada bio je ispitati utjecaj razlicitih kolicina i vrsta povratnih informacija (PI) u procesu usvajanja tehnike skoka u dalj kod ucenika osnovne skole. Osamdeset i osam ucenika i ucenica, starosti 11 (±0,5) godina, bez prethodnoga iskustva u treningu skoka u dalj, raspodijeljeno je u jednu od cetiriju skupina ispitanika koje su dobivale PI: (1) verbalno samo na kljucne greske, (2) verbalno i vizualno samo na kljucne greske, (3) verbalno i vizualno na sve greske ili (4) nisu primale PI. Duljina skoka i relevantni kinematicki parametri skoka u dalj izmjereni su prije i nakon 8-tjednog eksperimentalnoga tretmana. Rezultati istraživanja pokazali su kako je grupa koja je primala PI, na svaku gresku verbalnim i vizualnim putem najvise napredovala u smislu duljine skoka u dalj. Razlicite kolicine i vrste PI razlicito djelovale su na promatrane kinematicke parametre te nisu uocene konzistentne promjene između grupa. Zakljuceno je kako djeca osnovnoskolske dobi prilikom usvajanja kompleksne motoricke vjestine imaju vise koristi od vecih frekvencija PI (verbalnih i vizualnih na sve greske) u usporedbi s reduciranim PI prema vrsti (samo verbalne) ili frekvenciji (samo na kljucne greske). Kljucne rijeci: kinematika; motoricko ucenje; poznavanje izvedbe; reducirane povratne informacije.
Classification of motor vibration with machine learning methods and simulating the vibration using statistical models

Reducing costs is an important part in todays buisness. Therefore manufacturers try to reduce unnecessary work processes and storage costs. Machine maintenance is a big, complex, regular process. In addition, the spare parts required for this must be kept in stock until a machine fails. In order to avoid a production breakdown in the event of an unexpected failure, more and more manufacturers rely on predictive maintenance for their machines. This enables more precise planning of necessary maintenance and repair work, as well as a precise ordering of the spare parts required for this. A large amount of past as well as current information is required to create such a predictive forecast about machines. With the classification of motors based on vibration, this paper deals with the implementation of predictive maintenance for thermal systems. There is an overview of suitable sensors and data processing meth-ods, as well as various classification algorithms. In the end, the best sensor-algorithm combinations are shown.
Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model

Air pollution has become a critical issue in human’s life. Predicting the changing trends of air pollutants would be of great help for public health and natural environments. Current methods focus on the prediction accuracy and retain the forecasting time span within 12 hours. Shorter time span decreases the practicability of these perditions, even with higher accuracy. This study proposes an attention and autoencoder (A&A) hybrid learning approach to obtain a longer period of air pollution changing trends while holding the same high accuracy. Since pollutant concentration forecast highly relates to time changing, quite different from normal prediction problems like autotranslation, we integrate “time decay factor” into the traditional attention mechanism. The time decay factor can alleviate the impact of the value observed from a longer time before while increasing the impact of the value from a closer time point. We also utilize the hidden states in the decoder to build connection between history values and current ones. Thus, the proposed model can extract the changing trend of a longer history time span while coping with abrupt changes within a shorter time span. A set of experiments demonstrate that the A&A learning approach can obtain the changing trend of air pollutants, like PM2.5, during a longer time span of 12, 24, or even 48 hours. The approach is also tested under different pollutant concentrations and different periods and the results validate its robustness and generality.
Energy Audit and Feasibility of Solar PV Energy System: Case of a Commercial Building

Energy situation in Malawi is continuing facing critical challenges to satisfy the existing demand. However, energy consumption and energy conservation studies have been neglected to help overcome this problem. In this paper, electric energy audit was conducted for a commercial building in Mchinji, Malawi, in order to identify energy-saving opportunities. The study employed a mixed method research which involved a series of surveys, observation, data collection, and analysis. The current energy consumption was determined and compared with the proposed energy consumption after replacing some equipment. The proposed system saved up to 33.46% of energy. The study also suggested behavior change towards energy saving. Additionally, an alternative energy system was also suggested. Thus, the HOMER software was employed to design, optimize, and analyze a solar-battery-grid-connected energy system. The proposed system has a simple payback period of 9.8 years. The system’s cost of energy was estimated as 0.0372 $/kWh, and the capital cost was $ 150,887.
Enhanced Photocatalytic Hydrogen Production with Synergistic Two-Phase Anatase/Brookite TiO2 Nanostructures

Highly crystalline pure brookite and two-phase anatase/brookite TiO2 nanostructures were synthesized via a simple hydrothermal method with titanium sulfide as the precursors in sodium hydroxide solutions. The control of the phase composition has been demonstrated via solution concentration and reaction time, and the phase transformation mechanism has been elucidated. Photocatalytic activities of the as-synthesized two-phase anatase/brookite TiO2, pure anatase nanoparticles, and pure brookite nanoplates were appraised via photocatalytic hydrogen evolution in aqueous methanol solution. Results have shown that the photocatalytic activity is higher for the two-phase anatase/brookite TiO2 and brookite nanoplates as compared to pure anatase nanoparticles despite the lower surface areas of the two-phase anatase/brookite TiO2 and brookite nanoplates. From the Mott–Schottky analysis, brookite phase is shown to have a more cathodic conduction band edge potential than anatase phase, which leads to more energetically.
Grasping Point Prediction in Cluttered Environment using Automatically Labeled Data

We propose a method to automatically generate high quality ground truth annotations for grasping point prediction and show the usefulness of these annotations by training a deep neural network to predict grasping candidates for objects in a clut-tered environment. First, we acquire sequences of RGBD images of a real world picking scenario and leverage the sequential depth information to extract labels for grasping point prediction. Afterwards, we train a deep neural network to predict grasping points, establishing a fully automatic pipeline from acquiring data to a trained network without the need of human annotators. We show in our experiments that our network trained with automatically generated labels delivers high quality results for predicting grasping candidates, on par with a trained network which uses human annotated data. This work lowers the cost/complexity of creating speciﬁc datasets for grasping and makes it easy to expand the existing dataset without additional effort.
Experiences from Teaching Practical Machine Learning Courses to Master's Students with Mixed Backgrounds

Machine learning education has become more accessible and relevant to students from various backgrounds. Practical courses complement theoretical lectures by focusing on applied machine learning. In this work, we report about our experiences from teaching two machine learning practical courses to master’s students from different study programs; an introductory and an advanced course. We present a summary of the teaching and evaluation methods used in both courses. We summarize our experiences and the feedback collected from the students through a survey. We conclude with our recommendations on teaching and designing practical machine learning courses.
High Flow Nasal Oxygen Therapy in Severe Pneumonia of COVID-19

Original Research Article Coronavirus disease 2019 (COVID-19) has quickly spread across the world and is currently a real public health problem. It is the cause of severe respiratory failure which has caused many deaths. Many therapies aimed at correcting respiratory failure have been used, and it appears that hight flow nasal oxygen (HFNO) therapy has gained space compared to other means of ventilator support. We report in this work our experience with the HFNO in our hospital located in Morocco.
Brief Report: Hispanic Patients’ Trajectory of Cancer Symptom Burden, Depression, Anxiety, and Quality of Life

Abstract Background: Anxiety and depression symptoms are known to increase cancer symptom burden, yet little is known about the longitudinal integrations of these among Hispanic/Latinx patients. The goal of this study was to explore the trajectory and longitudinal interactions among anxiety and depression, cancer symptom burden, and health-related quality of life in Hispanic/Latinx cancer patients undergoing chemotherapy. Methods: Baseline behavioral assessments were performed before starting chemotherapy. Follow-up behavioral assessments were performed at 3, 6, and 9 months after starting chemotherapy. Descriptive statistics, chi-square tests, Fisher’s exact tests, and Mann–Whitney tests explored associations among outcome variables. Adjusted multilevel mixed-effects linear regression models were also used to evaluate the association between HADS scores, follow-up visits, FACT—G scale, MDASI scale, and sociodemographic variables. Results: Increased cancer symptom burden was significantly related to changes in anxiety symptoms’ scores (adjusted β^ = 0.11 [95% CI: 0.02, 0.19]. Increased quality of life was significantly associated with decreased depression and anxiety symptoms (adjusted β^ = −0.33; 95% CI: −0.47, −0.18, and 0.38 adjusted β^= −0.38; 95% CI: −0.55, −0.20, respectively). Conclusions: Findings highlight the need to conduct periodic mental health screenings among cancer patients initiating cancer treatment.
Using Distance Measure based Classification in Automatic Extraction of Lungs Cancer Nodules for Computer Aided Diagnosis

We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying lungs connected components into nodule and not-nodule. We explain also using Connected Component Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some morphological operations. Our tests have shown that the performance of the introduce method is high. Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we tested the method by some images of healthy persons and demonstrated that the overall performance of the method is satisfactory.
Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the model is tested on the NASDAQ stock market indices which is among the most widely followed indices in the United States. Empirical results show that by determining the parameters of ANFIS (premise and consequent parameters) using GA, we can improve performance in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R-Squared) in comparison with using solely ANFIS.
Interpretable Network Representation Learning with Principal Component Analysis

We consider the problem of interpretable network representation learning for samples of network-valued data. We propose the Principal Component Analysis for Networks (PCAN) algorithm to identify statistically meaningful low-dimensional representations of a network sample via subgraph count statistics. The PCAN procedure provides an interpretable framework for which one can readily visualize, explore, and formulate predictive models for network samples. We furthermore introduce a fast sampling-based algorithm, sPCAN, which is significantly more computationally efficient than its counterpart, but still enjoys advantages of interpretability. We investigate the relationship between these two methods and analyze their large-sample properties under the common regime where the sample of networks is a collection of kernel-based random graphs. We show that under this regime, the embeddings of the sPCAN method enjoy a central limit theorem and moreover that the population level embeddings of PCAN and sPCAN are equivalent. We assess PCAN's ability to visualize, cluster, and classify observations in network samples arising in nature, including functional connectivity network samples and dynamic networks describing the political co-voting habits of the U.S. Senate. Our analyses reveal that our proposed algorithm provides informative and discriminatory features describing the networks in each sample. The PCAN and sPCAN methods build on the current literature of network representation learning and set the stage for a new line of research in interpretable learning on network-valued data. Publicly available software for the PCAN and sPCAN methods are available at https://www.github. com/jihuilee/.
Over-the-Air Evaluation of User Body Loss for Popular In-Ear Bluetooth Earbuds

Wireless earbuds have become the preferred electronic device for listening to music and for hands-free talking. The combined radio performance of the earbud and the handset determines the quality of the established communication link and hence the quality of the user experience. This paper presents an extensive comparative study of five common Bluetooth earbuds, where the transmit performance, in terms of total radiated power (TRP), is investigated. The measurements are conducted for the right-side earbud and for the low and high ends of the Bluetooth frequency spectrum. Moreover, two scenarios are considered: (1) the earbud is placed in free space and (2) the earbud is placed in a person’s ear. For the latter case, a total of 12 volunteers are involved in the measurement campaign. Measurements show that the mean performance deterioration, due to the placement of the earbud in the person’s ear, ranges from 3 to 6 dB across the tested earbuds.
Furrow design for improving crop establishment of two-wheel tractor operated strip tillage planters in loam and clay loam soils

: Conservation agriculture (CA) based production systems may help in achieving more sustainable intensification of cropping systems that use less labour and energy and have higher profit margins, in addition to soil conservation and environmental impact mitigation advantages. But these objectives can only be achieved when the right mechanization options, including appropriate crop establishment equipment, are in place to assist in timely field operations. An urgent need exists, therefore, to fine tune and re-adjust the existing two-wheel tractor (2WT) operated seed drills, with specific reference to the design of blade and furrow openers, while at the same time considering performance in different soil types and environments. To this end, experiments were conducted during 2013-2014 and 2014-2015 at two BARI Regional Agricultural Research Stations in Jamalpur and Barisal, Bangladesh, on a loam and clay loam soil, respectively, to evaluate five types of furrow opener for strip tillage. Shoe and modified shoe-type furrow openers were tested and compared with three inverted-T furrow openers with rake angles of 75°, 65° and 55°. The newly designed inverted-T furrow openers were narrower than the shoe-type openers; they also had a longer, hollow shanks and provided better options for adjustment to achieve the desired seeding depth and line spacing. Compared to shoe-type openers, better seeding depth, uniformity and higher degree of seed coverage were recorded with use of the inverted-T furrow opener with a 65° rake angle. This resulted in better seed coverage in the furrow, a higher emergence rate index, and the highest emergence percentage of maize and mung bean. Our research findings can be generalized to smallholder production systems on loam and clay loam soils where farmers utilize 2WT operated seed drills for crop establishment in both traditional and conservation agriculture-based planting systems
Macrophage Activities inMyocardial Infarction andHeart Failure

Heart diseases remain the major cause of death worldwide. Advances in pharmacological and biomedical management have resulted in an increasing proportion of patients surviving acute heart failure (HF). However, many survivors of HF in the early stages end up increasing the disease to chronic HF (CHF). HF is an established frequent complication of myocardial infarction (MI), and numerous influences including persistent myocardial ischemia, shocked myocardium, ventricular remodeling, infarct size, and mechanical impairments, as well as hibernating myocardium trigger the development of left ventricular systolic dysfunction following MI. Macrophage population is active in inflammatory process, yet the clear understanding of the causative roles for these macrophage cells in HF development and progression is actually incomplete. Long ago, it was thought that macrophages are of importance in the heart after MI. Also, though inflammation is as a result of adverse HF in patients, but despite the fact that broad immunosuppression therapeutic target has been used in various clinical trials, no positive results have showed up, but rather, the focus on proinflammatory cytokines has proved more benefits in patients with HF. 1erefore, in this review, we discuss the recent findings and new development about macrophage activations in HF, its role in the healthy heart, and some therapeutic targets for myocardial repair. We have a strong believe that there is a need to give maximum attention to cardiac resident macrophages due to the fact that they perform various tasks in wound healing, self-renewal of the heart, and tissue remodeling. Currently, it has been discovered that the study of macrophages goes far beyond its phagocytotic roles. If researchers in future confirm that macrophages play a vital role in the heart, they can be therapeutically targeted for cardiac healing.