Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of 90/90 Bridge with Ball and Balloon Exercise on Lung Volumes and Quality of Life in Patients with Chronic Obstructive Pulmonary Disease

Background: Chronic obstructive pulmonary disease is the 4th leading cause of death in the world and itis estimated to be the 3rd leading cause of death by 2020. Diaphragmatic dysfunction is one of the majorclinical finding in chronic obstructive pulmonary disease patients which alters the respiratory function andquality of life. Therefore, the objective is to investigate the impact of 90/90 bridge with ball and balloonexercise on lung volumes and quality of life in patients with chronic obstructive pulmonary disease.Method: A total 38 patients were taken, 30 of them met criteria and were divided into two groups usingrandomized sampling technique, 15 in experimental group i.e., 90/90 bridge with ball and balloon exerciseand 15 in control group i.e., diaphragmatic breathing. There was a single dropout in experimental group. Theintervention given for 6 weeks, 5 days in a week, one session for a day i.e., 30 minutes. Both outcomes i.e.,Forced expiratory volume in 1second and St. George Respiratory Questionnaire values were taken beforeand after intervention in this study.Results: The findings suggest that both experimental and control group showed statistically significantimprovement in both outcome measures. Statistically experimental group showed more significant thancontrol group.Conclusion: The study showed that 90/90 bridge with ball and balloon exercise is effective in improvingthe lung volumes in patients with chronic obstructive pulmonary disease, which leads to improvement inquality of life.
Gene Set Enrichment Analysis in RNA-Seq Data

To date, many gene set analysis (GSA) approaches have been developed for identifying diﬀerentially expressed gene sets using microarray data. However, these methods are not directly applicable to RNA-Seq data due to intrinsic diﬀerence between two data structures. When testing the diﬀerential expression of gene sets, there is a critical assumption that the members in each gene set are sampled independently in most GSA methods. It means that the genes within a gene set don’t share a common biological function. The aim of this paper is twofold. First, we propose a powerful yet simple extension to GSA methods based on the de-correlation (DECO) algorithm that properly remove the correlation bias in the expression of each gene set. We then study the performance of our proposed method compared with other GSA methods through a real RNA-Seq dataset and simulation studies under various scenarios combining with four commonly used normalization methods. Second, we discuss the eﬀect of the complex correlation structure of gene sets on four normalization methods. As a result, we found that our proposed method outperforms the others in terms of Type I error rate and empirical power. A compara-tive study on a public data showed that gene sets identiﬁed by our proposed method have better concordance with biological conﬁrmed pathways than other methods.
On the long-run relationship between immigration and growth: empirical evidence from European countries

ABSTRACT This paper has a main ambition. It evaluates the impact of immigration on GDP per worker for ten European countries in order to fill the gap with numerous studies focusing on variables other than growth (wages, unemployment, innovation or public finances). We show that there is no obvious relationship between growth and immigration for a majority of economies. It is only when we allow for the possibility of structural breaks that we find the existence of cointegration between the two variables for six countries. Moreover, our results suggest that the impact of immigration remains rather modest on average.
Predicting Potato Diseases Using Tensorflow in Mobile Apps Android

Detecting disease is something that can help to determine how to cure it. Potato diseases, namely early blight and late blight, are the problem in agriculture. Both diseases have almost the same characteristics so it is difficult to recognize. The technique used by farmers only utilizes their eyesight. In addition, previous studies have mostly used the biological aspect approach. In this study utilizing Machine Learning to detect an object. The development of this application uses the training configuration used is SSD Mobilenet v1. Application test results obtained an average value of 90% can detect and distinguish the types of diseases in the potato plant.
Intrapulse Azimuth Frequency Scanning-Based 2-D Scanning SAR for HRWS Imaging

In this article, a novel 2-D scanning synthetic aperture radar mode for high-resolution and wide-swath imaging is introduced. Instead of the interpulse progressive azimuth beam scanning used in Terrain Observation by Progressive Scans (TOPS) mode, the intrapulse continuous azimuth frequency scanning is employed in this mode. The same azimuth resolution as the multichannel TOPS mode can be obtained through this mode in the case of single channel transmitting and receiving. And the wide-swath imaging in range is still achieved through the cyclically elevation beam scanning from burst to burst. Moreover, the antenna aperture can be fully exploited for transmitting in this mode. Therefore, it will outperform the multichannel TOPS mode especially in the case of active array antenna. The signal model of this mode is derived. Based on the analysis of the characteristics of echo spectrum, the design criteria for this mode are developed, followed by a design example. Finally, an imaging algorithm for this mode is presented. Theoretical analysis and numerical simulations verify the effectiveness of the proposed design criteria and imaging algorithm.
Physiological indices and production of sesame under salt stress and nitrate/ammonium proportions

In semi-arid regions, the occurrence of water with high concentration of salts is common, which compromises the growth and consequently the production of crops. Thus, this study aimed to evaluate the gas exchanges and production of sesame, cv. CNPA G3, irrigated with saline water and fertilized with different proportions of nitrate and ammonium, in an experiment conducted using lysimeters in a greenhouse in the municipality of Campina Grande-PB, Brazil. The treatments were arranged in randomized blocks and analyzed in a 5 x 5 factorial scheme, with three replicates, relative to five levels of electrical conductivity of the irrigation water – ECw (0.6, 1.2, 1.8, 2.4 and 3.0 dS m) and five proportions of nitrate/ammonium NO3 /NH4 + (200/0, 150/50, 100/100, 50/150 and 0/200 mg of N kg of soil). Irrigation with water of salinity level higher than 0.6 dS m promoted negative effect on gas exchanges and production components of the sesame cv. CNPA G3. Fertilization with N exclusively in the form of NH4 + promoted increment in CO2 concentration and reduction in its assimilation rate and instantaneous carboxylation efficiency. The highest rate of CO2 assimilation, transpiration and instantaneous carboxylation efficiency were obtained when the plants were irrigated with water of 0.6 dS m and fertilization with 200/0 of NO3 /NH4 . Increasing levels of water salinity promoted a decrease in the total seed mass, regardless of the proportion of NO3 /NH4 . The sesame cv. CNPA G3 is classified as sensitive to salt stress from the electrical conductivity of water of 0.6 dS m.
Decline of Mongolian Pine Forest by the Combinatory Effect of European Woodwasp Sirex noctilio (Hymenoptera: Siricidae) and Plant Pathogenic Fungi

Interactions between the decline of Mongolian pine woodlands and fungal communities and invasive pests in northeast China, are poorly understood. In this study, we investigated the fungal communities occurring in healthy uninfested, unhealthy uninfested and Sirex noctilio infested Mongolian pine and analyzed the relationship between the decline of Mongolian pine and fungal communities and woodwasp. The population number of S. noctilio was very high in the declining Mongolian pine forest. 26 fungal species identified from three tree samples. Each tree sample harbored a fungal endophyte community with a unique structure. The invasion of woodwasp appear to be promoted by the fungal community in the Mongolian pine woodlands. Pathogenic fungi richness was four times higher in infested and unhealthy uninfested compared to healthy uninfested trees. Sphaeropsis sapinea was the most dominant pathogenic fungus in Mongolian pine forest, and infested healthy Mongolian pine without wounding, but a with lower incidence of without wounding (38.72%) than wound ones (83.22%). Collectively, these data indicated that the fungal disease may have caused the initial reason of the decline of the Mongolian pine forest, and also provided convenient conditions for the successful colonization of woodwasp. The woodwasp attacked stressed Mongolian pine trees and accelerated its decline.
Environmental cost accounting and financial performance: The mediating role of environmental performance

This study aims to investigate both the direct and indirect relationships between Environmental Cost Accounting (ECA), Environmental Performance (EP) and Financial Performance (FP). The samples are companies listed in the industrial sector of Amman Stock Exchange. Subjective data using questionnaire are collected to measure ECA and EP, and objective data are obtained from the companies’ annual reports to measure FP. The author used structural equation modeling to analyze the data. The results showed that ECA positively affected EP and FP, also ECA positively affected EP. Moreover, the results confirmed the mediation role of EP on the direct relationship between ECA and FP. This study contributes to the management accounting literature and contingency theory by using structural equation modeling to examine the above-mentioned relationships, which have been neglected in previous studies, and by analyzing more recent data from a developing country perspective. The results will be useful for practitioners in Jordan, especially for management in the industrial companies and the Ministry of Environment.
Tracing Fiscal Sustainability in Malaysia

One of the concerns in the economic policy circle is the fiscal sustainability. This current research revisit the notion of fiscal sustainability for Malaysia using the Indicator of Fiscal Sustainability (IFS) developed by Croce and Juan-Ramón (2003) where we employ samples of time-series data from 1970 to 2017. The findings reveal that 40 out of 48 years, during which the calculated IFS algorithm is above the threshold of 1, imply Malaysia was fiscally unsustainable. Despite having been fiscally unsustainable, Malaysia’s fiscal stance shows improvement as a result of fiscal consolidation and fiscal reforms during the sample period. This is shown by the improved calculated IFS algorithm on average, which the value improved from 1.465 in 1970–1993 to 1.377 in 1998–2004 and to 1.146 in the 2006–2013. From the policy front, this indicator can serve as a precautionary early warning measure in formulating future fiscal path for Malaysia. This can be executed by targeting debt ratio and shifting the allocation of expenditures away from less efficient toward more growth-enhancing ones, which eventually would regain fiscal space to counter any incoming economic shocks in the future. This can enhance the fiscal transparency and assist in formulating a fiscal policy strategy in Malaysia.
Requirements and Realities of REDD+ Implementation in Nigeria

Graphical Abstract Negotiations and decisions of the 2007 and 2010 Conference of the Parties (COP) sessions at Bali and Cancun enabled participants to develop pilot projects leading into a national strategy on Reducing Emissions from Deforestation and Forest Degradation (REDD+) ‘Readiness’. REDD+ provides incentives for countries to receive payments for restraining greenhouse gas emissions through forest conservation, sustainable forest management and enhancement of forest carbon stocks. The overarching Warsaw Framework on REDD+ set at COP19 in 2013 provided guidelines for how countries can work towards eligibility for results-based payments for carbon savings from REDD+. It included seven points covering governance issues, approaches to reference emissions level estimation, forest inventory and reporting obligations. In this paper we examine the case of REDD+ in Nigeria by assessing how effectively that nation has implemented the Warsaw Framework requirements. We determine that the country’s over-ambitious political motivation for REDD+ affected not only the practical design of its National Strategy but also distracted management from establishing adequate safeguards for the program. Lack of capacity in advanced modeling hindered the establishment of the forest emissions reference level. That step was essential to enabling payments for reported and verified emission reduction outcomes. We reveal that national circumstances like forest governance, capacity, and political factors significantly influenced the effectiveness of pilot programs. From the Nigerian case, we maintain that effective REDD+ pilot programs should ensure that there is: full and active engagement with relevant stakeholders; a comprehensive review of forest management policies and regulations to establish a reliable forest reference level; a rigorous and robust national forest monitoring system; and safeguards to assist implementation and distribute carbon benefit-sharing rights.
Tribological properties of 2 novel Mo/B–based lubricant additives in polyalphaolefin

Two kinds of Mo/B oleic diethanolamide derivatives (coded as YXM and YXB) were synthesised, and their tribological properties were evaluated using a 4‐ball machine. Results indicate that the 2 novel additives show excellent antiwear and extreme pressure properties. When 2.5 wt% YXM was added into the base oil, the wear scar diameter was reduced by 42.2%, and the PB value was increased by 170.4%; when 2.5 wt% YXB was added into the base oil, the wear scar diameter was reduced by 23.1%, and the PB value was increased by 167.1%. The worn surfaces of the lubricated GCr15 steel were analysed by using scanning electron microscopy and X‐ray photoelectron spectroscopy. It is indicated that the variation of tribofilm species produced by a chemical reaction between the additives and the steel surface plays an important role in inhibiting wear and friction. A model was used to analyse the action mechanism. According to the analysis, the tribofilm is composed of an adsorption layer and a reaction layer, and it could effectively protect the steel surfaces from direct contact.
Numerical studies of the statistics of seismic waveform propagation in random heterogeneous media

Incoherent scattered waves are generated when seismic waves propagate through heterogeneous media. These scattered waves contain statistical information, such as random heterogeneity. In order to study the influence of random heterogeneity on seismic waveforms, four series of simulation experiments were performed in the presents study, as follows: (1) Waves were excited by the same source and propagated through four models with diﬀerent parameters; (2) Waves were excited by different frequencies, but spread in the same model (0.25, 0.50, 1.00, 2 MHz); (3) Waves were excited by the same source and propagated through three different velocity fluctuation models (5%, 10% and 15%); (4) Waves were excited by the same source and propagated through three different propagation distances (30, 60, and 90 mm). In the present study, the correlation between coherent waveforms and observed waveforms, the energy distribution with respect to the elapsed time, and the statistical distribution of the waveform correlation coefficient were calculated. Next, the variation of the waveform parameters with the normalized scale parameter ka was investigated. Stronger scattered waves and more complicated waveforms were observed when the ka value increased, indicating the transition from an equivalent homogeneous medium to a heterogeneous medium.
Influences of Summertime Arctic Dipole Atmospheric Circulation on Sea Ice Concentration Variations in the Pacific Sector of the Arctic during Different Pacific Decadal Oscillation Phases

Atmospheric circulation associated with the Arctic dipole (AD) pattern plays a crucial role in modulating the variations of summertime sea ice concentration (SIC) within the Pacific Arctic sector (PAS). Based on reanalysis data and satellite observations, we found that the impacts of atmospheric circulation associated with a positive AD (AD+) on SIC change over different regions of the PAS [including the East Siberian Sea (ESS), Beaufort and Chukchi Seas (BCS), and Canadian Arctic Archipelago (CAA)] are dependent on the phase shifts of Pacific decadal oscillation (PDO). Satellite observations reveal that SIC anomalies, influenced by AD+ during PDO− relative to that during PDO+, varies significantly in summer by 4.9%, −7.3%, and −6.4% over ESS, BCS, and CAA, respectively. Overall, the atmospheric anomalies over CAA and BCS in terms of specific humidity, air temperature, and thereby downward longwave radiation (DLR), are enhanced (weakened) in the atmospheric conditions associated with AD+ during PDO− (PDO+). In these two regions, the larger (smaller) increases in specific humidity and air temperature, associated with AD+ during PDO− (PDO+), are connected to the increased (decreased) poleward moisture flux, strengthened (weakened) convergence of moisture and heat flux, and in part to adiabatic heating. As a consequence, the DLR and surface net energy flux anomalies over the two regions are reinforced in the atmospheric scenarios associated with AD+ during PDO− compared with that during PDO+. Therefore, smaller SIC anomalies are identified over CAA and BCS in the cases related to AD+ during PDO− than during PDO+. Essentially, the changes of the DLR anomaly in CAA and BCS are in alignment with geopotential height anomalies, which are modulated by the anticyclonic circulation pattern in association with AD+ during varying PDO phases. In contrast, the SIC changes over ESS is primarily attributed to the variations in mechanical wind forcing and sea surface temperature (SST) anomalies. The cloud fraction anomalies associated with AD+ during different PDO phases are found not to be a significant contributor to the variations of sea ice anomaly in the studied regions. Given the oscillatory nature of PDO, we speculate that the recent shift to the PDO+ phase may temporarily slow the observed significant decline trend of the summertime SIC within PAS of the Arctic.
Factors affecting the job satisfaction of construction engineers: evidence from Jordan

Abstract Satisfaction of construction engineers affects the development of construction industry, and plays an important role in enhancing their productivity. In this paper, the researchers aim to determine the most significant factors affecting overall job satisfaction of construction engineers in Jordan and to develop a model that measures their satisfaction. To collect the required data, a questionnaire was developed and distributed among engineers working in different construction sites. Cronbach, Kaiser-Meyer-Olkin, and multicollinearity tests were conducted to attest the questionnaire's reliability and validity. Also, stepwise multiple regression analysis and hypotheses testing were used to verify the predictive factors of job satisfaction. As per the survey results, working environment, satisfaction with coworkers, pay and benefits, and satisfaction with supervision are the most significant factors out of ten affecting overall job satisfaction of construction engineers. Accordingly, a proposed model was developed to measure their job satisfaction using these four significant factors, and subsequently, was validated by interviewing experienced construction engineers. The proposed model can be useful in keeping track of job satisfaction of construction engineers not only in Jordan but also in other countries, especially those with similar culture.
Multi-Metric Induced Robust Graph

Graph-based learning model has a wide range of applications in machine learning and computer vision. The key issue of the graph-based applications is to construct an informative graph to effectively represent data correlations. In practice, real-world data is usually contaminated by complex noise beyond Gaussian noise and sparse noise, which degrades learning performance dramatically. To construct a robust graph that represents real-world data distribution well, we propose a novel graph construction method. The proposed method is designed to be robust to complex noise beyond sparse or Gaussian noise by combining different previously given metrics of measuring data errors, which robustly and correctly generates a set of edges. To demonstrate the superiority of the proposed method, we employed the clustering and classification tasks on the real-world datasets. The experimental results demonstrate that the proposed method is superior to the existing methods in both accuracy and robustness to complex noise.
PHYTOCHEMICAL CONSTITUENTS OF THE LEAVES OF LEUCAENA LEUCOCEPHALA FROM MALAYSIA

Objective: This study was conducted to identify the phytochemical constituents of Leucaena leucocephala leaf extracts using gas chromatography-mass spectrometry (GC-MS). Methods: Hexane, petroleum ether, chloroform, ethyl acetate and methanol leaves extract of L. leucocephala were analyzed using GC-MS, while the mass spectra of the compounds found in the extract were matched with the National Institute of Standards and Technology (NIST) library. Results: GC-MS analysis of L. leucocephala leaves revealed the presence of 30 compounds and the major chemical constituents were Squalene (41.02%), Phytol (33.80%), 3,7,11,15-Tetramethyl-2-hexadecen-1-ol (30.86%) and 3,7,11-Tridecatrienenitrile, 4,8,12-trimethyl (25.64%). Some of these compounds have been reported to possess various biological activities such as antioxidant, antimicrobial, hepatoprotective, antiparasitic, insecticide, nematicide, pesticide, anti coronary, antiarthritic, antiandrogenic, hypocholesterolemic, cancer preventive, anti-cancer, analgesic, anesthetic, allergenic and etc. Conclusion: The findings of this study indicating that L. leucocephala leaves possess various potent bioactive compounds and is recommended as a plant of phytopharmaceutical importance.
Deep Learning Based Goods Management in Supermarkets

Goods management in supermarkets is a simple yet time and effort consuming task. To mitigate the problem, we propose a computer vision based goods management system for supermarkets using Tiny YOLOv3 model, Convolutional Neural Network and Keras. The system, with a moving camera, can detect supermarket products such as cans, bottles, bags, etc. as well as their corresponding prices and barcodes thus allowing inventory management, empty shelves and mispricing detection, etc. The system was trained on our own dataset (object detection, 16 classes, using transfer training) and the SVHN dataset (digit recognition). Experiment results show that the system works well with 92.81% mAP (mean Average Precision) for object detection and 92.28% of accuracy for digit recognition.