Miracle-dz/UniFAR-FTUs huggingface dataset:
NUMERICAL STUDY OF THE BENDING OF STEEL SHEETS TRIP 304L

Folding is a common industry practice. In this work we have studied the influence of some parameters such as (the radius of curvature of the matrix, the coefficient of friction between the punch and the specimen, the play between matrix and punch, the thickness of the specimen and the mesh of the specimen) on the deformations of the different zones of the specimen, by a numerical simulation of U-bending using the ABAQUS calculation code. The studied parameters will prove to be of relevant use for the design of the parts from the metal sheets, through the bending forming process. Numerical simulation is a good decision support tool for parts manufacturing companies. For the continuation of this work, a phenomenological model developed by IWAMOTO has been selected and modified to include the evolution of the microstructure (from austenite to martensite) after deformation.
Effect of early skin to skin contact between mother and her neonate on initiation of breast feeding and neonate physiological parameters

The evidence supporting the practice of skin-to-skin contact after birth is robust, indicating multiple benefits for both mother and baby. However, separation of mothers and infants seems to be common practice in many hospitals. Thus, the present study aimed to investigate the effect of early Skin to skin contact immediately after birth between mother and her neonate on initiation of breast feeding and neonate physiological parameters. A quasi-experimental research design was utilized for this study. A purposeful sample of 70 mothers and their neonates who met the inclusion criteria and admitted to the delivery room in Minia General Hospital. They divided randomly into two groups (study and control). One tool was used to collect the required data. A structured interview questionnaire sheet included three parts: 1)-Neonates data, 2)-Personal and obstetric data of mothers and 3)-Neonate vital signs, time of initiating breast feeding and duration of the first breast fed. Results of the present study shows that, decreasing mean time to initiate breast feeding and increasing mean duration of the first breast fed of the study group than the control group with statistically significant differences (P=
External Force/Torque Estimation With Only Position Sensors for Antagonistic VSAs

Recent use scenarios involving human–robot collaboration have revealed that the robots require elastic joints to safely interact with humans. It is also critical to know applied force/torque (f/t) during the interaction for control and motion planning purposes. In this article, we estimate the external f/t values without using any sensors other than low-cost encoders by exploiting the inherent elastic properties of the joint. For estimation, the following two different approaches are used: model based and model free. In the model-based approach, an extended Kalman filter (EKF) and an external force observer (EFOB) are used considering the dynamical behavior of the system to estimate the interaction force. In the model-free approach, the artificial neural network (ANN) utilizes the data gathered from mechanical systems. In comparative analysis, we have, therefore, considered three different estimation methods, two of which are model based and the remaining one is model free (i.e., data driven). Implementing these estimation algorithms experimentally on a variable stiffness joint, we performed an extensive evaluation of their performances. All methods show similar level of performance in terms of the root-mean-square (RMS) error with 0.0847, 0.0841, and 0.1082 N for the EKF, EFOB, and ANN, respectively. Model-based methods do not require continuous data stream through the experimental set up. On the other hand, the ANN does not need an explicit model of the system; therefore, it may become preferable when the detailed model derivation is not possible.
Dual Pilot Scheme (DPS) and Its Application in Massive MIMO

The pilot scheme currently used in 5th generation (5G) cellular networks assigns the same set of orthogonal pilot signals to all cells. This results in inter-cell interference, also known as pilot contamination, which can significantly degrade performance, especially in massive multi-input multi-output (MIMO) systems. To mitigate this interference, we propose a novel Dual Pilot Scheme (DPS) that assigns a slightly modified set of nearly-orthogonal pilot signals. DPS is a general scheme that can be implemented in any wireless communication system, including 5G and beyond. We demonstrate the integration of DPS in a massive MIMO system in both microscopic and macroscopic levels and analytically prove that DPS enables more accurate estimates of the channel state information in the minimum mean-squared error sense, under the independent identically distributed (i.i.d.) and the correlated Rayleigh fading wireless communication channel models. We further validate and demonstrate the advantages of DPS over various channel models of massive MIMO 5G technology by extensive simulations.
Evaluating habitat-fishery interactions: Submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay

This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and submerged aquatic vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that allows for all three possible relationships was estimated and two alternative approaches were used to test whether SAV matters for the crab stock. Our results indicate that a model that incorrectly assumes that habitat is essential to a species can result in model misspecification and biased estimates of the impact of habitat on species productivity. Using a model that assumes an essential relationship, we find that SAV has a significant positive impact on blue crab productivity (p<0.001). However, in a more general model, we failed to reject the null hypothesis that SAV is irrelevant for crabs in the Bay (p>0.05).
Antioxidants and intracerebral haemorrhage

Intracerebral haemorrhage (ICH), a serious cerebrovascular condition, is accompanied by a series of biochemical and pathological events constituting the release of reactive oxygen species (ROS) and reactive nitrogen species (RNS), and thus altering the antioxidant mechanisms and antioxidant enzymatic levels. In studying these antioxidant cascades, cerebrospinal fluid (CSF) analysis offers the most innocuous tool as it is in close connection with the central nervous system (CNS). This study aimed at assessing the difference in the activities of Catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx), glutathione reductase (Gred), glutathione transferase (GTS) activities in CSF samples of healthy and ICH patient. The level of oxidative damage was highlighted by malondialdehyde (MDA) as a parameter of lipid peroxidation. Total nitrite levels were also measured. During ICH, the decrease in the activity of SOD, CAT and GPx is accompanied by a significant increase in the activities of GST and Gred. Total nitrite and MDA levels both increased significantly. In present study we may conclude that ICH alters antioxidants levels and several of these parameters can be used as biomarkers of extreme cerebral oxidative stress.
Spatiotemporal Splitting of Distribution Networks Into Self-Healing Resilient Microgrids Using an Adjustable Interval Optimization

The distribution networks can convincingly break down into small-scale self-controllable areas, namely microgrids (μG), to substitute μGs arrangements for effectively coping with perturbations. This flexible structure not only could potentially possess the strength to recover quickly, but also ensures the supply of vital loads and preserves functionalities under any contingency. To achieve these targets, this article examines a novel spatiotemporal algorithm to split the existing network into a set of self-healing μGs. In this endeavor, after designing the μGs by determining a mix of heterogeneous generation resources and allocating remotely controlled switches, the μGs operational scheduling is decomposed into interconnected and islanded modes. The main intention in the grid-tied state is to maximize the μGs profit while equilibrating load and generation at the islanded state by sectionalizing on-fault area, executing resources rescheduling, network reconfiguration and load shedding when the main grid is interrupted. The proposed problem is formulated as an exact computationally efficient mixed integer linear programming problem relying on the column & constraint generation framework and an adjustable interval optimization is envisaged to make the μGs less susceptible against renewables variability. Finally, the effectiveness of the proposed model is adequately assured by performing a realistic case study.
TEMPERATURE FIELD ANALYSIS AND OPTIMIZATION OF RADIAL 2-DOF HYBRID MAGNETIC BEARING

The loss of magnetic bearing in the process of operation will lead to the temperature rise of the bearing and affect its performance. A permanent magnet is used to provide bias magnetic flux for hybrid magnetic bearing, which can reduce the loss and temperature rise of the magnetic bearing. In this paper, the loss of radial 2-DOF hybrid magnetic bearing (HMB) is analyzed. On this basis, the 3D thermal analysis model of HMB is constructed by using ANSYS Workbench finite element software. The loss is introduced into the temperature field as a heat source, and the temperature distribution of magnetic bearing is calculated. Combined with the results of loss and temperature analysis, the structural parameters were optimized by using genetic particle swarm optimization algorithm (GAPSO). The results show that the loss and temperature rise of the optimized magnetic bearing are significantly reduced.
Prediction of Inorganic Salt and Moisture Content of Process Cheese Using Dielectric Spectroscopy

The development of on-line sensors for compositional analysis during cheese manufacture is desirable for improved quality control. Dielectric properties of a food product are principally determined by its moisture and salt content. This indicates that dielectric spectroscopy may offer a rapid, on-line and non-destructive method for the determination of moisture and salt content of process cheese. However limited information is available in the literature on the dielectric properties of process cheese. Therefore the aims of this study are to investigate the dielectric properties of process cheese samples over a range of compositional parameters and to assess the potential of dielectric spectroscopy to improve process control during process cheese manufacture. Dielectric spectra of process cheese samples were measured using a coaxial line probe between 300 MHz and 3 GHz. A clear tend was observed between higher moisture content and increases in the dielectric constant. Inorganic salt content was found to have a major influence on the loss factor. The dielectric data obtained was used to develop chemometric models for the prediction of moisture and inorganic salt content of two experimental sets of process cheese samples (exp A and exp B). The root mean square error of prediction (RMSEP) for the models developed to predict moisture content were 0.524% (w/w) (exp A), and 0.423% (w/w) (exp B), while the RMSEP of the inorganic salt models were 0.220% (w/w) (exp A), and 0.263% (w/w) (exp B). It was concluded that dielectric spectroscopy has potential application for compositional analysis in process cheese manufacture.
The Value of Naturalness of Urban Green Spaces: Evidence from a Discrete Choice Experiment

The range of benefits for humans and biodiversity conservation provided by urban green spaces (UGSs) receives substantial attention in relation to urban planning and management. However, little is known about the value of nature in UGSs. We developed a graphical measurement scale for the naturalness of UGS, with five steps between largely sealed and largely wilderness, which was embedded in an online survey and a discrete choice experiment. Using mixed logit models, we find that German citizens have a mean willingness to pay of €20.25 per month for an increase in the naturalness of the closest UGS by one step.
Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening

The fusion of high spatial resolution panchromatic (PAN) data with simultaneously acquired multispectral (MS) data with the lower spatial resolution is a hot topic, which is often called pansharpening. In this article, we exploit the combination of machine learning techniques and fusion schemes introduced to address the pansharpening problem. In particular, deep convolutional neural networks (DCNNs) are proposed to solve this issue. The latter is combined first with the traditional component substitution and multiresolution analysis fusion schemes in order to estimate the nonlinear injection models that rule the combination of the upsampled low-resolution MS image with the extracted details exploiting the two philosophies. Furthermore, inspired by these two approaches, we also developed another DCNN for pansharpening. This is fed by the direct difference between the PAN image and the upsampled low-resolution MS image. Extensive experiments conducted both at reduced and full resolutions demonstrate that this latter convolutional neural network outperforms both the other detail injection-based proposals and several state-of-the-art pansharpening methods.
An Assessment of the Use of Ultrasound in the Particle Engineering of Micrometer-Scale Adipic Acid Crystals

Adipic acid was crystallized via batch cooling from aqueous solution under continuous ultrasonic radiation. The product particle characteristics are compared first with those achieved at steady state in a continuous crystallization process operated under continuous insonation and second with the products of dry milling techniques, viz. hammer milling and micronization and high shear wet milling (HSWM) respectively, and of batch reverse antisolvent crystallization (RAS). off-line Malvern MasterSizer measurement, scanning electron microscopy (SEM), and atomic force microscopy (AFM), were used to compare particle size distributions and to investigate the difference in particle habit and to analyze the surface characteristics at sub-micrometer scale. The effect of ultrasonic waves applied to batch and continuous crystallization was found to be effective in producing particle sizes comparable with micronization. Continuous insonation during batch crystallization provides spherical particles, with increased and.
Macroprudential Policy Efficiency: Assessment for the Uncollateralized Consumer Loans in Russia

We use the Russian banks’ 2015-2019 data to evaluate the effectiveness of the macroprudential measures in curbing the booming consumer lending segment. We find that the measures are successful in reducing the overall loan portfolio riskiness and in capital cushion accumulation by banks. In the short-run of up to 1-2 quarters after the measure announcement date banks tend to reduce both the new loan volumes and the average consumer loan portfolio growth rate. Such reduction is more typical with the smallest market players. However, in the longer time horizon up to a year from the measure application date we observe the increase in the average credit growth rates. Such findings correspond to the experience of the emerging markets of Argentine, Colombia, Thailand. In general, we consider that the observed credit growth after the measure implementation is smaller than it could have been without the measures in place. We also expect that the observed lending growth rate brings less financial instability risks and it reflects the potential for the natural loan extension in Russia. Macroprudential Policy Efficiency: Assessment for the Uncollateralized Consumer Loans in Russia November 2020 4
Customer Service Orientation, Emotional Labor Strategy, and Emotional Exhaustion

This study aimed to determine the relationship between customer service orientation with emotional labor strategy and emotional exhaustion among the sales employees. Customer service orientation is the predictor variable that influences emotional labor strategy and emotional exhaustion among the employees. The participants in this study were 88 sales employees in one of the automotive companies in Makassar, Indonesia. The regression analysis result showed a positive effect of customer service orientation on emotional labor and emotional exhaustion among the sales employees. The employees who have high service orientation tend to show deep acting strategy when serving their customers, enabling them to decrease emotional exhaustion.
Combining ability analysis and gene action for blast resistance in rice (Oryza sativa L.)

Ten rice genotypes were crossed in a line x tester mating design using four testers and six lines to produce 24 hybrids. Analysis of variance showed highly significant differences among genotypes for all the studied traits indicating a wide range of genetic diversity, different origin of these parents and prevalence of additive variance. However, mean sum of squares due to parents was significant for the studied traits except days to flowering, flag leaf length, flag leaf width and test weight. The highest GCV and PCV coupled in the character number of effective tiller. While for plant height, Shayamala, Jaldubi and Makdo exhibited highly significant negative GCA effects for short plant height. The best hybrids produced from different crosses were Mahamaya x Mehardhan, IGKV-R-1244 x Mehardhan and MTU-1001 x IR-64. These crosses could be involved in the rice breeding program. The yield attributing character that is number of effective tiller is found significantly correlated with other characters like flag leaf width, panicle length and panicle weight. Significant phenotypic correlation of ET with FLW and PL shows that yield can be increase with improvements in above characters.
A Generative Adversarial Network Enabled Deep Distributional Reinforcement Learning for Transmission Scheduling in Internet of Vehicles

The Cognitive Internet of Vehicles (CIoV) is an intelligent network that embeds the cognitive mechanism in the Internet of Vehicles (IoV) to sense the environment and observe the network states to learn the optimal policies adaptively. However, one of the key challenges in CIoV systems is to design a smart agent that can smartly schedule the packet transmission for ultra-reliable low latency communication (URLLC) under extreme random and noisy network conditions. We propose a software defined network (SDN) based scheduling algorithm that leverages generative adversarial network (GAN) based deep distributional Q-network (GAN-DDQN) for learning the action-value distribution for intelligent transmission scheduling. A reward-clipping technique is proposed for stabilizing the training of GAN-DDQN against the effect of broadly spanning utility values. The extensive simulation results verify that GAN-Scheduling achieves higher spectral efficiency (SE), service level agreement (SLA), system throughput, transmission packet rate with lower transmission delay, and power consumption compared to the existing reinforcement learning algorithms.
Co-immobilization of cholesterol esterase, cholesterol oxidase and peroxidase on PVC strip for serum cholesterol determination

A mixture of commercial cholesterol esterase, cholesterol oxidase and peroxidase was co-immobilized covalently on the surface of a polyvinyl chloride (PVC) sheet (plastic strip) after its chemical modification. The strip retained 75.23% of its initial activity of enzymes with a conjugation yield of 0.37 mg protein cm−2. The strip bound enzymes showed optimum activity at pH 7.2, when incubated at 45 °C for 5 min. The strip was employed for determination of total cholesterol in serum. Minimum detection limit of the method was 0.01 mM in serum. Analytical recovery of added cholesterol in serum (100 mg dL−1 and 200 mg dL−1) was 92.50% and 93.33% respectively. Within and between assays coefficients of variation (CVs) were 1.85% and 2.04% respectively. A good correlation (r = 0.998) was obtained between serum cholesterol values by standard enzymic colorimetric method and the present method. The strip was reused 200-times over a period of 4 months, when stored at 4 °C. The method has the advantage that it provides the reuse of enzymes with enormous ease. Overall results also strengthen our view of using a chemically modified plastic strip as a solid support for covalent immobilization of enzymes.
Energy Optimization for Motorized Spindle System of Machine Tools under Minimum Thermal Effects and Maximum Productivity Constraints

Motorized spindle system is one of the crucial components affecting the machine tools energy performance. Many previous studies have examined its energy optimization problems, however, most such studies focused mainly on parameters optimization to improve material removal energy efficiency or reduce total energy consumption. A missing research area is energy optimization problem considering thermal stability and productivity constraints simultaneously. Against this background, an energy optimization approach of motorized spindle system is presented with consideration of thermal stability and productivity adequately, with the goal of maximization of energy efficiency and material removal rate, and minimization of spindle average temperature which is closely associated with thermal stability. Firstly, the energy characteristics of motorized spindle and its cooling system are mathematically modelled. Then, a multi-objective optimization model is established to take the maximum energy efficiency, minimum spindle average temperature, and maximum material removal rate as objectives. The optimal solution is obtained by solving the proposed optimization model with the Non-dominated Sorted Genetic Algorithm-II (NSGA-II). Finally, a case study is introduced to validate the proposed method and the results indicate that the proposed method is more effective to find optimal decision variables for balancing the considered objectives compared with the existing optimization method.
Maternal TCN1 (rs526934) G>A Gene Polymorphism and its Association with Congenital Heart Disease (CHD)

Background: Congenital heart diseases (CHDs) represent the most common birth defect, affecting 0.6–9.1 per 1000 live births, and the leading cause of infant deaths worldwide. Genes involved in vitamin B12 metabolism such as TCN1 may affect the balance of folate metabolism and ultimately contribute to the development of CHD through the disrupted folate metabolism pathway. Methods: A total of 100 pregnant women carrying foetus with congenital heart defects are considered as case group and an equal number of healthy pregnant women carrying healthy foetus devoid of any defects are considered as control group for the present study. Blood samples were collected from all the study subjects. DNA was extracted and genotyping of TCN1 intron 372 G>A (rs526934) polymorphism was carried out using allele specific PCR, followed by agarose gel electrophoresis. Results: Pregnant women in patient group, with GA genotype has 1.95 times more risk of having foetus with CHD compared to controls. Whereas, GG genotype played a protective role. ‘A’ allele was 2.14 times susceptible towards the disease, while ‘G’ allele showed protective effect. Conclusion: Understanding the possible underlying genetic factors of vitamin B12 metabolism will lead to an increased understanding of the biological mechanisms underlying the maternal TCN1 gene polymorphism effect on CHD. This helps in early diagnosis, management and provide appropriate therapeutic strategies.