Miracle-dz/UniFAR-FTUs huggingface dataset:
VERIFICATION OF NANOINDENTATION DEVICES USING ATOMIC FORCE MICROSCOPY

Nanoindentation has become a popular tool for the exploration of mechanical properties of materials at the nanoscale. However, calibration and traceability issues are often neglected, making the direct comparison of results between different instruments difficult. The direct calibration of the sensors is challenging and requires dedicated instruments leaving indirect calibration as the only option to ordinary users. However, indirect calibration compares only the resulting hardness or modulus of the reference sample and the measured sample. Therefore, it cannot distinguish the source of errors, such as errors in the depth and load sensors, errors in the reference sample properties, contamination of the tip or sample, wear of tip and others. In this contribution we explore the possibility of using measurements of indents by atomic force microscopy for the verification of the depth sensor. Measurements for different nanoindentation devices are presented and compared with a focus on the uncertainties of the method.
Characterization of an optical pulse slicer for gas-phase electric field measurements using field-induced second harmonic generation

This work describes the characterization of an optical pulse-slicer for performing electric field measurements using the Electric Field Induced Second Harmonic generation (or E-FISH) method. This laser-based diagnostic generally favors ultrashort (sub-nanosecond) pulses, given their intrinsically higher intensities and superior measurement time resolution. However, such laser systems can often prove inaccessible due to their high costs. Our response to this problem is to develop a Pockels-cell-based pulse slicing scheme, compatible with the more affordable and ubiquitous class of nanosecond laser sources. Using such a slicer, we demonstrate the ability to slice a 35 mJ, 20 ns (FWHM), 1064 nm pulse from a conventional Nd:YAG laser down to about 3 ns (FWHM) with an energy of 2 mJ . These shorter pulses are in turn used to make electric field measurements in a pulsed electrostatic field (at 5 bar) using E-FISH . In agreement with previous studies, measurements performed with these sliced pulses not only have the expected benefit of improving the time resolution of the field measurement, but also reduce the possibility of laser-plasma interactions due to the lower laser pulse energy required. The current pulse slicer incorporates an off-optimum design Pockels cell, which limits the minimum pulse width and maximum energy that may be realized. It is anticipated that higher energies and a reduction in the pulse width to around 100 ps may be achieved with further optimization, and could present a cost-effective approach to extending the applicability of the E-FISH method.
The complete mitochondrial genome of diamond dove (Geopelia cuneata) and its phylogeny

Abstract The complete mitochondrial genome (mitogenome) of diamond dove (Geopelia cuneata) was first determined in this study. The mitogenome is 17,880 bp in size and composed of 22 transfer RNA genes, 13 protein-coding genes, two ribosomal RNA genes, and a control region. The overall nucleotide composition is 30.11% of A, 13.66% of G, 25.11% of T, and 31.13% of C. The mitochondrial structure and gene order were similar to other Columbidae species. Phylogenetic analysis suggested that G. cuneata was placed as sister to G. striata of the same genus. The data could provide valuable information for further studies on conservation genetics and evolution of G. cuneata.
Phylogenetic and Phylogenomic Analyses for Large Datasets

The phylogenetic tree is a main tool to study the evolutionary relationships among species. Computational methods for building phylogenetic trees from gene/protein sequences have been developed for decades and come of age. Efficient approaches, including distance-based methods, maximum likelihood methods, or classical maximum parsimony methods, are now able to analyze datasets with thousands of sequences. The advanced sequencing technologies have resulted in a huge amount of data including whole genomes. A number of methods have been proposed to analyze the wholegenome datasets, however, numerous challenges need to be addressed and solved to translate phylogenomic inferences into practices. In this paper, we will analyze widely-used methods to construct large phylogenetic trees, and available methods to build phylogenomic trees from whole-genome datasets. We will also give recommendations for best practices when performing phylogenetic and phylogenomic analyses. The paper will enable researchers to comprehend the state-ofthe-art methods and available software to efficiently study the evolutionary relationships among species from large datasets.
EnerGAN: A GENERATIVE ADVERSARIAL NETWORK FOR ENERGY DISAGGREGATION

An efficient, appliance-level approach for energy disaggregation, exploiting the benefits of Generative Adversarial Networks, is presented. The concept of adversarial training supports the creation of fine tuned dissagregators, which produce more detailed load estimations for a specific appliance, compared to state of the art deep learning models. The Generator and Discriminator of the model are appropriately adapted to fit the particularities of NILM problem, whereas a Seeder component is added to provide encoded compact input vectors to the Generator. The experimental evaluation against state of the art techniques indicates promising results.
Demographic, Clinical and Forensic Profiling of Alleged Offenders Diagnosed with an Intellectual Disability

ABSTRACT Forensic psychiatry and forensic psychology study the link between mental health and the law. These forensic behavioral sciences play a vital role when assessing both a person’s competency to stand trial and the degree to which criminal responsibility can be attributed to the perpetrator of alleged offenses by the criminal justice system. Offenders with a suspected intellectual disability are frequently referred for forensic evaluations by courts of law and possible psychosocial rehabilitation thereafter. The aim of this study was to determine the demographic, clinical and forensic characteristics of alleged offenders diagnosed with an intellectual disability who were referred to Forensic Units at a Psychiatric Complex under Sections 77, 78 and 79 of the South African Criminal Procedure Act no. 51 of 1977. A data collection form was used to gather information from the clinical files/records of the 120 participants studied throughout the course of this research project. Participants were mainly Black African males (95.8%), the majority of whom had been diagnosed with a mild intellectual disability (70.8%). These offenders were largely accused of sexual offenses. The majority of the subjects were deemed to be neither competent to stand trial nor criminally responsible for their alleged offenses because of intellectual disability. This study provides valuable information on the relationship between intellectual disability and the types of crimes committed by intellectually disabled individuals.
Biliary atresia: pathology, etiology and pathogenesis

Biliary atresia is a progressive fibrosing obstructive cholangiopathy of the intrahepatic and extrahepatic biliary system, resulting in obstruction of bile flow and neonatal jaundice. Histopathological findings in liver biopsies include the expansion of the portal tracts, with edematous fibroplasia and bile ductular proliferation, with bile plugs in duct lumen. Lobular morphological features may include variable multinucleate giant cells, bilirubinostasis and hemopoiesis. The etiopathogenesis of biliary atresia is multifactorial and multiple pathomechanisms have been proposed. Experimental and clinical studies have suggested that viral infection initiates biliary epithelium destruction and release of antigens that trigger a Th1 immune response, which leads to further injury of the bile duct, resulting in inflammation and obstructive scarring of the biliary tree. It has also been postulated that biliary atresia is caused by a defect in the normal remodelling process. Genetic predisposition has also been proposed as a factor for the development of biliary atresia.
Sex determination of the Acadian Flycatcher using discriminant analysis

-I used five morphometric variables from 114 individuals captured in Arkansas to develop a discriminant model to predict the sex of Acadian Flycatchers (Empidonax virescens). Stepwise discriminant function analyses selected wing chord and tail length as the most parsimonious subset of variables for discriminating sex. This two-variable model correctly classified 80% of females and 97% of males used to develop the model. Validation of the model using 19 individuals from Louisiana and Virginia resulted in 100% correct classification of males and females. This model provides criteria for sexing monomorphic Acadian Flycatchers during the breeding season and possibly during the winter. DETERMINACION DEL SEXO EN EMPIDONAX VIRESCENS UTILIZANDO ANALISIS DISCRIMINATIVO Sinopsis. -De una muestra de 114 individuos, capturados en Arkansas, utilice cinco variables morfometricas para desarrollar un modelo discriminativo para predecir el sexo de individuos del papamoscas Empidonax vicescens. Un andlisis de funci6n discriminativa seleccion6 el largo del ala y del rabo como las variables mds adecuadas para discriminar el sexo de 6stas aves. El modelo de dos variables, clasific6 correctamente el 80% de las hembras y el 97% de los machos. La validaci6n del modelo en donde se utilizaron individuos de Louisiana y Virginia, permiti6 determinar el sexo de 19 individuos examinados. El modelo provee criterios morfometricos, para sexar a esta especie de papamoscas durante la epoca de reproducci6n y posiblemente durante el invierno. Ornithologists have long struggled over the sexing and aging of Empidonax flycatchers. Although researchers have documented several criteria for differentiating species, criteria for sexing these sexually monomorphic birds outside of the breeding season are lacking. Currently, the only reliable method for sexing such monomorphic birds is the presence of a cloacal protuberance in males or brood patch in females (Pyle 1997a). However, this method is restricted to reproductively active birds during the breeding season. Even so, male flycatchers, especially among Empidonax, do not develop large cloacal protuberances, such that males may often be mis-sexed as females during the breeding season. Furthermore, non-mated individuals, as well as birds outside of the breeding season, exhibit neither a cloacal protuberance or a brood patch. Thus, these individuals cannot be readily sexed in the field, complicating estimation of sex-specific demographic parameters. Interspecific differences in morphometric characteristics (e.g., wing morphology and bill shape) have been documented for Empidonax flycatchers (Pyle 1997a:218), suggesting that intersexual differences may exist. Indeed, Phillips et al. (1966) were able to sex correctly 90% of Alder Flycatchers (Empidonax alnorum) and 93% of Least Flycatchers (Empidonax minimus) using wing morphology. Likewise, Pyle (1997b), was able
Deep Soft Interference Cancellation for MIMO Detection

Accurate symbol detection in multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, is a challenging task. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume that the channel is linear, a model which does not reflect many relevant channels, as well as require accurate channel state information (CSI), which may not be available. In this work we propose a multiuser MIMO receiver which learns to jointly detect in a data-driven fashion, without assuming a specific channel model or requiring CSI. In particular, we propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm. The resulting detector, referred to as DeepSIC, is based on integrating dedicated machine-learning (ML) methods into the iterative SIC scheme, and learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear and its parameters to be known. Our numerical evaluations demonstrate that for linear channels with full CSI, DeepSIC approaches the performance of iterative SIC, which is comparable to the optimal performance, while being notably more robust to CSI uncertainty. Finally, we show that DeepSIC accurately detects symbols in non-linear channels, where conventional iterative SIC fails even when accurate CSI is available.
Establishment of a reliable change index for the GAD-7

Aim: It is increasingly important for mental healthcare providers and researchers to reliably assess client change, particularly with common presenting problems such as anxiety. The current study addresses this need by establishing a Reliable Change Index of 6 points for the GAD-7. Method: Sample size included 116 online community participants using Amazon’s Mechanical Turk (MTurk) and archival data for 332 clinical participants. Participants completed measures of the GAD-7 and the MDI in 2 rounds. Using previously established cutoff scores and Jacobson and Truax’s (1991) method, we establish a Reliable Change Index which, when applied to 2 administrations of the GAD-7, indicates if a client has experienced meaningful change. Results: For the GAD-7, the mean score for the clinical sample was 10.57. For the community sample at Time 1, the mean score was 4.14. A Pearson’s correlation was computed to assess the 14-28-day test-retest reliability of the GAD-7, r(110) = . 87, indicating good test-retest reliability. Conclusion: Using the RCI equation, this resulted in an RCI of 5.59. For practical use the RCI would be rounded to 6.
Effect of Breeding on Income at First Commercial Thinning in Silver Birch Plantations

The economic importance of fast-growing tree species like silver birch (Betula pendula Roth.) is increasing due to growing demand for timber. Tree breeding provides the opportunity to increase the timber supply and thus ensure the most efficient use of forest land. Application of the results of a breeding program—the planting of young stands—is costly, and information on (potential) early income for the landowner from this investment is scarce. Therefore, the aim of this study was to characterize the gain from the use of improved silver birch material at the first commercial thinning. Material was collected from an open-pollinated progeny trial of 524 silver birch plus-trees at the age of 14 years in the central part of Latvia. Incomes from the first thinning were calculated at low and high timber prices. Heritability of growth traits (assessed as diameter at breast height) and timber value at first thinning were similar. Both timber market fluctuations and genetics had a notable impact on economic outcome: the internal rate of return for the selected best-performing families was 9.4% and 8.3% in the case of high and low timber prices, respectively; on average, for all families in the trial the figures were 8.1% and 6.7%, respectively. Results indicate profitability for investments in planting of improved regeneration material, even at a young age, in hemiboreal forests.
Automated Malware Detection in Mobile App Stores Based on Robust Feature Generation

Many Internet of Things (IoT) services are currently tracked and regulated via mobile devices, making them vulnerable to privacy attacks and exploitation by various malicious applications. Current solutions are unable to keep pace with the rapid growth of malware and are limited by low detection accuracy, long discovery time, complex implementation, and high computational costs associated with the processor speed, power, and memory. Therefore, an automated intelligence technique is necessary for detecting apps containing malware and effectively predicting cyberattacks in mobile marketplaces. In this study, a system for classifying mobile marketplaces applications using real-world datasets is proposed, which analyzes the source code to identify malicious apps. A rich feature set of application programming interface (API) calls is proposed to capture the regularities in apps containing malicious content. Two feature-selection methods—Chi-Square and ANOVA—were examined in conjunction with ten supervised machine-learning algorithms. The detection accuracy of each classifier was evaluated to identify the most reliable classifier for malware detection using various feature sets. Chi-Square was found to have a higher detection accuracy as compared to ANOVA. The proposed system achieved a detection accuracy of 98.1% with a classification time of 1.22 s. Furthermore, the proposed system required a reduced number of API calls (500 instead of 9000) to be incorporated as features.
Phenomenon of the First Stage Kohlberg’s Theorem in Accounting Lecturer’s Ethical Decision Making

Previous research of ethical decision making (EDM) was describes the phenomenon of the first and second of the Kohlberg’s six stages ethical judgments, those were about orientation of obedience on avoidance sanctions and an egoistic oriented for receiving money income. EDM process have also divided to be ethical awareness, ethical judgments, and ethical intentions, then also ethical behaviour. This study will use moderated regression analysis (MRA) in the evaluations of which one between junior and senior lecturer, also master degree or doctoral degree lecturer allow the rewards and sanctions among the levels of ethical awareness, ethical judgments, and ethical intentions or ethical behaviour in the
Evaluation of Late Gadolinium Enhancement and Cardiac Cine-MRI Based on Artificial Intelligence in a Patient with Selenium Deficiency Cardiomyopathy (Keshan Disease): A Case Report

Keshan disease is an endemic cardiomyopathy with unknown causes. The disease mainly invades the myocardium, manifested by myocardial degeneration, necrosis, repair and other changes. MRI can objectively reflect the above pathological mechanism and characteristics, but there is no relevant literature report at present. A 51-year-old male patient was hospitalized and suspected of suffering from rare cardiomyopathy. Cardiovascular magnetic resonance imaging (CMRI) showed late gadolinium enhancement (LGE) in the areas other than the anterior wall of the left ventricle, accompanied by irregular epicardial LGE mainly in the left ventricular apex. Results of left ventricular function parameters calculated by artificial intelligence (AI) were as follows: end-systolic volume (ESV) 98.14 mL, end-diastolic volume (EDV) 121.59 mL, ejection fraction (EF) 35.13%, end-diastolic dimension (EDD) 47.49 mm, fraction shortening (FS) 24.83%, myocardial mass (MM) 62.74 g, cardiac output (CO) 3.49 L/min, stroke volume (SV) 42.71 mL, and cardiac index (CI) 3.09 L/min.m. According to the results of CMRI-LGE and left-ventricular functional parameters based on AI, Keshan disease manifested as enlarged morphology, abnormal cardiac function parameters, decreased myocardial cell activity, degeneration and necrosis, and myocardial interstitial fibrosis formation, which might help to diagnose the disease after excluding ischemic heart disease and primary cardiomyopathy.
STUDENTS’ MATHEMATICAL PROBLEM-SOLVING ABILITY BASED ON TEACHING MODELS INTERVENTION AND COGNITIVE STYLE

The study aimed to analyze the interaction effect teaching models and cognitive style field dependent (FD)-field independent (FI) to students’ mathematical problem-solving ability (MPSA), as well as students' MPSA differences based on teaching models and cognitive styles. Participants in this study were 145 junior high school students, with details of 50 students learning through the Connect, Organize, Reflect, and Extend Realistic Mathematics Education (CORE RME) model, 49 students use the CORE model, and 46 students use the Conventional model. Data collection tools used are the MPSA test, and the group embedded figure test (GEFT). The MPSA test finds out that there are interaction effect teaching models and cognitive styles on students' MPSA, as well as a significant difference in MPSA students who study through the CORE RME model, CORE model, and Conventional model. Based on cognitive style, between students who study through CORE RME model, CORE model, and Conventional model found that there was no significant difference in MPSA between FI students. Furthermore, there were significant differences in MPSA between FD students and also MPSA of FI students better than MPSA FD students. Therefore, teaching models and student cognitive styles are very important to be considered in the learning process, so students are able to solve mathematical problems.
A Swot Analysis of Factors Influencing the Development of Agriculture Sector and Agribusiness Entrepreneurship

Agribusiness entrepreneurship has significant role in the economy to foster rural development in the country. Kazakhstan has strong potential for the development of agribusiness entrepreneurship due to country’s considerable land resources, as Kazakhstan has the world’s 12th largest arable land area, and fifth in the world in terms of pastureland of 182.2 million hectares, but it uses only about 30% of the pastures (FAO, 2018). The current agribusiness entrepreneurship in Kazakhstan is mainly in the nature of food processing units. They have implication on food security and basic needs of human being. The present article aims at identifying strengths, weaknesses, opportunities, and threats for agribusiness entrepreneurship with combining financial and economic aspects in Kazakhstan. The article proposes among others effective and adequate agribusiness entrepreneurship policies such as price stabilization policy and programmes for addressing factors that hinder the growth and development of agribusiness entrepreneurship in Kazakhstan.
A Survey On Various Types Of Task Scheduling Algorithm In Cloud Computing Environment

: Now a days, Cloud computing has become an significant and most popular computing model that usually supports on demand services. Cloud Computing provides its services on pay-as-you-go basis . By using cloud computing resources expeditiously and by reducing in managing time and cost and increasing the outcome of the project is the main idea of cloud service provider. Therefore, using effective cloud scheduling algorithms is still main concern in cloud computing. Task scheduling is a pivotal part in the field of the cloud environment. In task scheduling user requests for certain task, then tasks are scheduled to certain resources at a specific exemplification of time. Basically task scheduling mainly focuses to diminish the make span and lengthen the resource utilization. Task scheduling is an Non Polynomial-Complete problem. There are lots of subsisting trail-and-error techniques for task scheduling till now but more amelioration and rectification is needed for better execution and to increase the efficiency of task scheduling till now, there is no combined study of task scheduling mechanism in cloud computing which describes its parameter, pros, cons, algorithm. This paper mainly emphasis on explaining Comparison on different Task scheduling algorithm in cloud computing adaptive ——————————  —————————— .
Influence of load mass and drum speed on fabric motion and performance of a heat pump tumble dryer

Abstract The present study investigates fabric motion in the drum and its effect on performance parameters of a domestic heat pump tumble dryer in several operating points, defined by different load masses and drum speeds. Fabric motion in the drum was recorded by using a high-speed camera and evaluated statistically in order to determine the relation between the fabric distribution and the dryer performance parameters. Multiple regression analysis was performed to develop models for condensate mass flow rate as a function of drum speed, load mass, and drying stage. The results enable optimization of dryer operating parameters with respect to energy efficiency, drying time per kilogram of fabric, and condensate mass flow rate.