Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparative study of robots in laparoscopic surgeries

Robots have been used in industry for several decades. Their use in medicine is relatively more recent, starting from the late 1980s. Today, robotic surgical systems are used in urology, gynecology, cardiology, general surgery, and other medical specialties. Robot systems such as Da Vinci and ZEUS have helped accelerate and expand the boundaries of minimally invasive surgery. This has led to more complex yet safer procedures (compared to conventional laparoscopy) through better visualization, improved dexterity, and hand tremor filtering. These systems also provide improved hand-eye coordination and ergonomics resulting in reduced physical stress to the surgeons. This paper is emphasized on providing a review of the different robotic surgical systems. A detailed comparison of the most commonly used systems, as well as their applications and cost implications, are discussed in the paper.
Design Analysis of an Industrial Robot under Development

The usage of robotics in industries and other areas has increased over the years. This has resulted in the increased demand and hence a need for their availability in India. In this paper, the design analysis of an indigenous industrial robot under development is reported. Based on the workspace requirements, sizes of the motors, gearboxes, etc., CAD model of a 6kg payload robot was developed. Its geometric parameters required for the kinematic analysis were determined and thereafter the CAD model of the robot was imported in RoboAnalyzer software. Cartesian motion for the end-effector for a typical pick and place operation was provided as input in RoboAnalyzer and the corresponding joint trajectories were obtained. These joint trajectories were then used as custom input in Autodesk Inventor and inverse dynamics analysis was performed to determine maximum torque at each joint, which helps in the selection of motor and gearbox. Further, the robot links were subjected to stress analysis and the components were tested to be safe. Thereafter the components were fabricated and assembled to obtain a physical prototype of the robot. The steps reported in this paper can be used in the design and verification of a serial robot, developed by individuals and industries.
Are serum GRP78 levels significant in chronic hepatitis C patients? A case-control study

Amac: Glikozla duzenlenen protein 78 (GRP-78) dokudaki endoplazmik retikulum (ER) stresinin temel gostergelerinden birisidir. Hepatit C ile enfekte hastalarda karacigerde ER stresinin gelistigi bilinmektedir. Bu calismada kronik hepatit C (KHC) hastalarinda bir stres belirteci olarak daha once incelenmemis olan serum GRP78 duzeylerinin degerlendirilmesi amaclanmistir. Yontemler: Calismamiz, Enfeksiyon Hastaliklari poliklinigimize basvuran Kronik Hepatit C (KHC) enfeksiyonu tanisi almis hasta grubu (n=60) ve ek kronik hastaligi olmayan saglikli kontrol (n=60) grubundan olusan bir vaka kontrol calismasidir. Serum GRP78 seviyesi Enzyme-Linked Immuno Sorbent Assay (ELISA) ile olculmus, ardindan GRP78 ile alanin aminotransferaz (ALT), aspartat aminotransferaz (AST), HCV-RNA duzeyleri arasinda korelasyon analizi yapilmistir. Bulgular: KHC hastalarinda HCV-RNA duzeyleri ile ALT ve AST duzeyleri arasinda anlamli bir iliski izlenmistir (sirasiyla P<0,001 ve P=0,008). Serum GRP78 hem kontrol hem de HCV alt gruplarinda benzer seviyelerde saptanmistir. Serum GRP78 seviyesi ile AST duzeyleri arasinda anlamli bir pozitif korelasyon saptanirken (P=0,046), serum ALT duzeyleri ile anlamli bir iliski saptanamamistir. Sonuc: HCV ile induklenen karaciger hasarinda ER stresinin gelistigi gosterilmis olmasina ragmen, sonuclarimiz kronik HCV enfeksiyonu sirasinda serum GRP 78 duzeyinde anlamli bir artis olmadigini gostermektedir.
Visual selection of Urochloa ruziziensis genotypes for green biomass yield

The breeding program of Urochloa ruziziensis evaluates many genotypes in initial phases. Evaluations through grades might make the selection less costly. The aim of this study was to verify the efficiency of visual selection for green biomass yield in relation to different selection strategies, such as mass selection by phenotypic mean, BLUP (Best Linear Unbiased Prediction) and at random. For this purpose, 2,309 regular genotypes were evaluated in an augmented block design in two cuts. The evaluators gave grades for plant vigor, and later, the plots were measured for green biomass yield. The coincidences of the selected genotypes were estimated by different selection strategies. Then, 254 clones of the genotypes selected in different strategies were evaluated in a clonal test in a triple lattice design in four cuts. The statistical analyses were performed in SAS using the Mixed procedure. The regular genotype level and clone-mean basis heritabilities were 31.16 and 62.91%, respectively, for green mass yield. The expected selection gains were 21.09% (visual), 25.43% (phenotypic mean), and 27.5% (BLUP). Moreover, the realized heritabilities for these strategies were 15.58, 11.87, and 15.86%, respectively, which might be associated with genotype by environment interaction. Therefore, the visual selection could be a useful strategy in initial phases of a U. ruziziensis breeding program because the efficiency was moderate to high in relation to phenotypic mean and BLUP.
Organizational Politics and its Correlates: A Study on Higher Education System of India

The purpose of this paper is to investigate the relationship of perception of organizational politics (POP) with its antecedents and consequences. The study proposes a model including three levels of antecedents which are never tested in a single study before. Organizational level antecedents include scarcity of resources and workforce diversity. Work environment level includes relationship and role conflict. Individual level includes need for power with gender and age of employees. Organizational commitment, job anxiety and intention to turnover were undertaken as consequences of POP. Primary data is collected through structured questionnaire from 294 faculty members of two central universities in Northern India. The sample constitutes Professors,Associate Professors andAssistant Professors from various departments of the Universities. Data was analyzed using simple linear regression analysis and t-test. The results indicated a strong support for proposed linkage of POP with scarcity of resources, workforce diversity, relationship and role conflict, organizational commitment, job anxiety and intention to turnover. On the other hand, no relationship was found between POP and need for power. This study will be helpful in motivating future research for improving the quality of higher education in India by reducing the reducing the level of POP and its unfavorable consequences by controlling its antecedents.
Glomerular Filtration Rate in Former Extreme Low Birth Weight Infants over the Full Pediatric Age Range: A Pooled Analysis

Various cohort studies document a lower glomerular filtration rate (GFR) in former extremely low birth weight (ELBW, <1000 g) neonates throughout childhood when compared to term controls. The current aim is to pool these studies to describe the GFR pattern over the pediatric age range. To do so, we conducted a systematic review on studies reporting on GFR measurements in former ELBW cases while GFR data of healthy age-matched controls included in these studies were co-collected. Based on 248 hits, 6 case-control and 3 cohort studies were identified, with 444 GFR measurements in 380 former ELBW cases (median age 5.3–20.7 years). The majority were small (17–78 cases) single center studies, with heterogeneity in GFR measurement (inulin, cystatin C or creatinine estimated GFR formulae) tools. Despite this, the median GFR (mL/min/1.73 m2) within case-control studies was consistently lower (−13%, range −8% to −25%) in cases, so that a relevant minority (15–30%) has a eGFR<90 mL/min/1.73 m2). Consequently, this pooled analysis describes a consistent pattern of reduced eGFR in former ELBW cases throughout childhood. Research should focus on perinatal risk factors for impaired GFR and long-term outcome, but is hampered by single center cohorts, study size and heterogeneity of GFR assessment tools.
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy Dovepress

1graduate School of Sports and health Science, Fukuoka University, Fukuoka, Japan; 2Faculty of Medicine, Fukuoka University, Fukuoka, Japan; 3Faculty of nutrition Sciences, nakamura gakuen University, Fukuoka, Japan; 4Faculty of Sports and health Science, Fukuoka University, Fukuoka, Japan; 5Kyushu clinical Pharmacology research clinic, Fukuoka, Japan; 6PS clinic, Fukuoka, Japan; 7Saga University hospital, Saga, Japan
Volatility and risk spillovers between oil, gold, and Islamic and conventional GCC banks

This paper examines time-varying risk spillovers and hedging effectiveness between two major commodity markets (oil and gold) and both the Islamic and conventional bank stock indices for five GCC countries (Bahrain, Kuwait, Qatar, Saudi Arabia and UAE), using the DECOFIGARCH model and the spillover index of Diebold and Yilmaz (2012). The results of the DECO-FIGARCH model show evidence of a weak average conditional correlation between all the GCC bank stock indices and the two commodity markets. Moreover, we find significant risk spillovers between these Islamic and conventional GCC bank stock indices and the commodity markets. The spillovers rise considerably during the 2008-2009 global financial crisis and the 2014-2015 oil price plunge periods Further, oil, gold, and the conventional bank stock index of Saudi Arabia, Kuwait and Qatar are net sources of volatility spillovers into the other markets, while all the Islamic banks and conventional banks of UAE and Bahrain are net volatility recipients of volatility spillovers. Finally, we provide evidence asserting that including gold and oil in a GCC portfolio offers better but different diversification benefits and hedging effectiveness for the GCC banks.
Real Time Multifactor Approximation Based Job Scheduling for Improved QoS

The problem of job scheduling has been well studied towards the QoS development. There are a number of methods developed earlier but suffer to achieve higher performance in scheduling. To improve the performance, an efficient real time multifactor approximation algorithm is presented in this paper. The method first reads the job set and identifies the list of resources required. In the second stage, the possible sequences have been generated. Then for each job sequence identified, the method estimates process waiting time approximation, resource waiting time approximation, yield approximation. Based on the approximated value of different factors considered, the method estimates selection weight. According to the selection weight, the method selects a single job sequence with higher selection weight. The proposed algorithm produces higher performance in scheduling and improves the quality of service of the system. Keywords— Identical Machines, Job Scheduling, Wait Time, Scheduling Yield, Multifactor Approximation.
Acceptance and usage, social influences, instructor’s presence and performance among learners in an ODL institution

Open Distance Learning (ODL) has been implemented in Malaysia for several decades to widen access to education while enriching the quality of learner's experience. Building from previous literature, this article aims to examine the relationship between independent variables of learner acceptance and usage of e-learning and instructor presence and learner performance, as the dependent variable, while social influences act as the mediation variable among learners in a distance learning (ODL) environment. Working adults enrolled for business programmes in one of the ODL institutions in Malaysia were surveyed. A total of 148 participants completed a survey questionnaire measuring their responses on the said variables. The study found that there was a positive relationship between the two independent constructs and learner performance. Social influences fully mediated between the independent and dependent variables. This indicates that peer influence is important during the learning process and the delivery of the teaching will enhance the learner’s competencies and knowledge. These findings indicate that these variables can be included in ODL learning environment. However, other constructs should also be explored. Suggestions and recommendations on the strength and influence of these variables to working adults are discussed and their applicability in other cultures and ODL institutions are elaborated upon. (Abstract by authors)
Study on the Linkage between Between International Crude Oil Price and Sino-U.S. Stock Market against Against the Backdrop of Trade War

The U.S.-China trade war is increasingly having a significant impact on financial markets and the world economy. Based on the daily data from 2017 to June 2019, this paper carries out an empirical study on the mean spillover effect and volatility spillover effect of international crude oil prices and Sino-U.S. stock markets by adopting VAR model and multivariate BEKK-GARCH model. The results show that in terms of mean spillover effect, the trade war has not changed the linkage between Sino-U.S. stock markets and there is no notable spillover effect between international crude oil prices and Sino-U.S. stock markets. While in terms of volatility spillover effect, the impact of the trade war is evident in the risk contagion between international crude oil markets and Sino-U.S. stock markets. The volatility spillover effect between the international crude oil markets and the U.S. stock market is enhanced, while the volatility spillover effect between the Chinese stock markets is weakened. In addition, the risk contagion effect between Sino-U.S. stock markets has also undergone changes in varying degrees.
Narratives of Resistance and Resistance to Narrative: The Tragic Picaro as an Alternative Model for Life Stories

A specific narrative form implies a specific ethics. While the plot of the Bildungsroman values personal growth and fruitful dialogue with the outside world leading to a meaningful whole, the plot of the picaresque – its direct opposite – values episodic fragmentation and a nonchalant or even hostile relationship with our surroundings. Picaresques often deploy unnatural narrative, a form that constructs a reality that blatantly reveals itself to be fictional and constructed. In this paper, I analyse novels by Günter Grass, Salman Rushdie and Abdelkader Benali (The Tin Drum, Midnight’s Children and The Long Awaited) in which this picaresque tradition is modified to become a narrative of transition. There is strong, deliberate intertextuality between these texts, with each novel building on its predecessor to create a narrative form that can solve the trauma of transition (from the life of the German minority in Poland to post war West Germany, from colonial rule to independence and from village life in Morocco to city life in the Netherlands, respectively). Oscillating between the nonchalance of the picaro and the need for belonging that drives the plot of the Bildungsroman, and between the ethics and values each of these imply, the novels establish a tradition of ‘tragic picaro’ that show both the necessity and futility of resisting nationalism and history through narrative. Narrative works towards an end point that is always oppressive as it subordinates all earlier events, so any resistance to oppression through narrative becomes oppressive itself. The unnatural narrative deployed in these novels foregrounds this and facilitates a continuing resistance, not only to the narratives of nationalism and history but, ultimately, to all narrative. As such, the narrative tradition established in these three novels has a use-value as an alternative to dominant stories in the neoliberal realm.
A Preliminary Study on Technological Innovation Capabilities (TICs) and Competitive Advantage in the Automotive Industry in Malaysia

This study investigates the relationship of TIC dimensions on competitive advantage in the automotive industry in Malaysia.The purpose of this paper is to present the research model along side hypotheses development, and the results of a preliminary study on these relationships. A pilot study was conducted, with the aim of assessing the validity and reliability of the instruments that are designed to be replicated in the main survey. The PLS-SEM measurement model was applied to measure the reliability and validity of the items in this study. The results indicate that the measuring instruments are reliable and the data for pilot study showed a strong evidence of construct validity in terms of convergent and discriminate validity and there is no collinearity problem between the first-order constructs.
Preference of Rural Youth Livelihood in Indonesia Rural Areas and Its Factors

Indonesia is facing serious problem of unemployment in rural areas while in agricultural sector, there is lack of labour availability. This research discusses issue between livelihood choices and their determinant factors of rural youth in East Java, Indonesia. The 200 survey data was analysed by multinomial logit for measuring response probabilities of alternative livelihoods which consist of agricultural jobs, non-agricultural employment, non-farm entrepreneur, and urban employment. The outcome of the research is that education of youth has consistently positive and significant correlation with livelihood option which is outside of agricultural sector. The more academic achievements that respondents get, the more reasonable their expectation of success in outside farming relative to agriculture. On the other hand, the larger land holding that their parents have, the lower possibility in choosing a livelihood outside of agriculture over farming. Therefore, it implies that better land access will encourages youth to approach agricultural works.
Performance Evaluation of Distributed Machine Learning for Load Forecasting in Smart Grids

Load forecasting in smart grid is the process of predicting the amount of electrical power to meet the short, medium and long term demands. Accurate load forecasting helps electrical utilities to manage their energy production, operations, control and management. Most of the state-of-the-art forecasting methodologies utilize classical machine learning algorithms to predict the electrical load. There is a need that big data platforms and parallel distributed computing are utilized to their potential in the available solutions. In this paper, the Apache Spark and Apache Hadoop are utilized as big data platforms for distributed computing in order to predict the load using available big data. In this paper, MLib, Spark library for machine learning algorithms, is utilized for distributed computing. Using MLib allows testing the classic regression algorithms such as linear regression, generalized linear regression, decision tree, random forest and gradient-boosted trees in addition to survival regression and isotonic regression. The obtained results show that Spark produces high accuracy while parallelizing the process of load forecasting in highly competent training and test times. Actual big data are used in the load forecasting process.
Deep images at metre wavelengths using the GMRT

We have been studying several fields at low radio frequencies, such as ELAIS-N1, GOODS-N/S and the AKARI deep field, and making deep images at metre-wavelengths using the Giant Metrewave Radio Telescope (GMRT). In this paper we largely summarise the results of the observations of ELAIS-N1 at 325 MHz, and present new results on the ECDFS at 150 and 325 MHz. These fields have been studied extensively at different wave-bands using the most powerful groundbased and space telescopes. The ELAIS-N1 image with the GMRT has an rms noise of ∼40 μJy beam−1 towards the phase centre, while for the ECDFS field the rms noise values are 2.5 mJy beam−1 and 75μJy beam−1 at 150 and 325 MHz respectively. The ECDFS observations also show a flattening of the source counts at ∼1 mJy at 325 MHz, consistent with the results reported earlier for the ELAIS-N1 field. The sub-mJy sources show a larger scatter in their spectral indices which is possibly at least partly due to a population of both starburst galaxies and low-luminosity AGN. These deep observations with the GMRT have helped identify a significant number of very steep-spectrum sources, which could be either relic sources or candidate high-redshift galaxies.
Performance Analysis of Lung Cancer Diagnosis Algorithms on X-Ray Images

Among several types of cancer, lung cancer is regarded as one of the most common and serious. In this respect, early diagnosis is required and beneficial to reduce mortalities caused by this type of cancer. Such diagnosis is typically performed by doctors through manual examinations on X-Ray images. However, manual examinations are labor extensive and time consuming. In this paper, we conduct a study to analyze the performance of some computer-based lung cancer diagnosis algorithms. The algorithms are built using different feature extraction (gray-level co-occurrence matrix, pixel intensity, histogram and combination of the three) and machine learning (Multi-layer Perceptron and K-Nearest Neighbor) techniques and the performance of each algorithm is compared and analyzed. The result of the study shows that the best performance of lung cancer classification is obtained by the computer algorithm that uses the combined features to characterize lung cancer and subsequently classifies the features using Multi-layer Perceptron.
Translation and Validation of the Spanish version of the Chronic Illness

4 Ana M. Trejos-HerreraDepartment of Psychology, Universidad del Norte, Barranquilla, Colombia 5 Correspondence 6 atrejos@uninorte.edu. co 7 8 Ana M. Trejos-Herrera*. , Stefano Vinaccia**. , Marly J. Bahamón***. , Yolima Alarcón-Vásquez***. , 9 Marjorie Rodríguez Giraldo*. Gladys Gaviria García**** 10 11 *Universidad del Norte 12 **Universidad del Sinú 13 *** Universidad Simón Bolívar 14 ****Universidad de la Costa 15 16 17 ABSTRACT. 18
Compressive imaging with total variation regularization and application to auto-calibration of parallel magnetic resonance imaging

An important task in imaging is to recover an image from samples of its Fourier transform. With compressed sensing, this is done by applying a sparsifying transform and solving a `1 minimization problem. One possible transform is the discrete gradient operator, in which case penalizing the `1 norm leads to Total Variation (TV) minimization. We present new recovery guarantees for TV regularization in arbitrary dimensions using two sampling strategies: uniform random Fourier sampling and variable density Fourier sampling. In particular, we determine a near-optimal choice of sampling density in any dimension. Our theoretical and numerical results show that variable density Fourier sampling increases the stability and robustness of TV regularization over uniform random Fourier sampling. As an application, we consider auto-calibration in parallel magnetic resonance imaging (pMRI). We develop a two-step algorithm: firstly, using sparse regularization to recover the coil images; secondly, using linear least squares to obtain the overall image.