Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Neural Network based model for the prediction of Hybrid Electric Vehicles carbon dioxide

• Generation of a HEV database based on the design parameters of different HEV architectures. • Definition of a data management approach for introducing the data into the prediction model. • Definition and automatic calibration of the main Deep Neural Network hyperparameters. • Investigation on the performance of the classification Deep Neural Network and the entire pipeline when tested upon different HEV datasets. • Analysis of the pipeline behavior in case of classification of false positive HEV layouts. a r t i c l e i n f o
Voting-Boosting: A novel machine learning ensemble for the prediction of Infants' Data

Background/Objectives: Owing to the continuous increase of electronic records and recent advances in machine learning, various automated disease diagnosis tools have been developed and proposed in healthcare sector. In the present study, an ensemble methodology using voting and boosting techniques has been proposed for optimal selection of features and prediction of infants' data of India.Methods/Analysis: For feature selection, the best-first search algorithm of wrapper technique has been used in addition to votingboosting. The proposed ensemble consists of combination of heterogeneous classifiers including Random Forest, J48, JRip, CART and Stochastic Gradient Descent (SGD). The effectiveness of the proposed ensemble and single classifiers have been investigated in terms of classification accuracy, precision, f-measure, recall, MCC and PRC area using varied k-fold cross validation. Findings: The results depicted that the proposed Voting-Boosting ensemble (k=15) outperforms the individual classifiers using selected features. Applications / Improvements: The proposed Voting-Boosting ensemble can be extended by using more state-of-the art classification approaches and further utilized for other healthcare datasets for enhancing the performance.
Social users rating behaviors for online shopping

– In this paper we put forth a survey that reinforcing the implementations of the rating behavior among the social consumers in online shopping. The proposed outcome of the project is to provide an enjoyable and everlasting experience to provide interactive features such as chat room and Instant messaging service, email support, feedback and reviews, currency conversion, like or dislike of the user comments and so on. Interpersonal rating behavior diffusion is proposed to deeply understand users of the social circle which mainly include direct friends, mutual friends, and the indirect friends, for deeper understanding’ of rating behaviors . These series of experimentations performed will outperform the disadvantages of the existing system.
A Treatment Algorithm for Spasticity-Correcting Surgery in Patients with Disabling Spasticity: A Feasibility Study

Background: Spasticity is a common secondary consequence of an injury to the Central Nervous System (CNS). It is a complex problem that can cause profound disability. Aim: To evaluate the feasibility of a treatment algorithm for spasticity-correcting surgery in patients with disabling Upper Limb (UL) spasticity due to CNS injuries. Design: Retrospective observational study. Setting: Inpatient clinic. Population: A cohort of 58 patients with disabling spasticity due to CNS injuries aged 18 years and older admitted to the Centre for Advanced Reconstruction of Extremities (C.A.R.E), Sahlgrenska University Hospital, Sweden, between February 2017 and June 2019. Methods: Data were extracted from the medical records of patients who underwent spasticity-correcting surgery, predominantly tendon-lengthening procedures. Depending on residual UL function prior to surgery, patients received treatment according to a high-, lowor non-functioning regimen. Analyses were based on measurements and data acquisition applied as part of routine care. Assessments were made before surgery and six months post-surgery, including measures of body function and activities. The primary outcome differed among treatment regimens, but spasticity was measured across all three groups with the Modified Ashworth Scale (MAS). Results: Patients received treatment according to a high(N = 18), low(N = 27) and non(N = 13) functioning regimen (HFR, LFR and NFR, respectively). Six months post-surgery, patients in all three groups had improved various aspects of bodily functions and activities, including the primary outcome measures; the HFR-group improved unimanual UL functioning, the LFR-group improved bimanual daily activities and the NFR-group improved passive (ease of care) aspects of activities. The mean decrease in spasticity, as measured by MAS, was 2.16 (±1.1) in the high-, 1.88 (±1.3) in the lowand 2.14 (±1.1) in the non-functioning group (p<0.000 for all groups). Conclusion: This study provides data in support of a feasible treatment algorithm for spasticity-correcting surgery in patients with disabling UL spasticity. Clinical Rehabilitation Impact: The algorithm may be used to guide clinician and patient expectations and may facilitate the tailored selection of UL rehabilitation goals based on the individual patient’s capacity for improvement. Citation: Ramström T, Bunketorp-Käll L, Reinholdt C, Wangdell J (2021) A Treatment Algorithm for Spasticity-Correcting Surgery in Patients with Disabling Spasticity: A Feasibility Study. J Surg 6: 1363. DOI: 10.29011/2575-9760.001363 2 Volume 06; Issue 01 J Surg, an open access journal ISSN: 2575-9760
A review Study on Human Resource Practices and Their Impact on Organizational Performance

The purpose of this research paper is to examine, the job satisfaction act as mediator variable mediator between employee intelligence and organizational structure. In this paper organizational structure is the dependent variable; employee intelligence is the independent variable and job satisfaction act as mediator. The results of this study are drawn on the bases of available literature. The results showed that there is a significant positive relationship among employee intelligence and job satisfaction; and there is a significant positive relationship among job satisfaction and organizational structure. In addition, there is no direct link exists between organizational structure and employee intelligence. There is an indirect relation occurs among organizational commitment and employee intelligence that is mediated with job satisfaction.
SITE SPECIFIC STUDY OF SEISMIC HAZARD ANALYSIS – CASE STUDY REVIEW

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract -Seismic Hazard Analysishas become anincreasingly important tool for aiding design and decision making at all levels in both the private sector and government. The level of sophistication applied to Seismic Hazard Analysishas increased dramatically over the past 27 yearssince the technique was first introduced in the literature. Seismic hazard studies are important to reduce the seismic risk.Primary aim is to develop consistent seismic ground motion. If sites located in seismically active regions, then site-specific seismic hazard studies are essential to mitigate the seismic risk of the site. A case study review of Seismic Hazard Analysishas been carried out for the the site located at LNG plant in Naranganj Bangladesh Tectonic framework of Bangladesh indicate that it lies in an active seismic zone.There have been three large magnitude earthquake in the last 150 years whose magnitude ranged from 6.9 to 8.7 . These eathquakes caused moderate damage to buildings and other infrastructures in Bangladesh. The study is been carried out for 5% damping ratio of exceedance in 50 years, which correspond to a 2475 year return period. In this study, the version 2007 of CRISIS software is used to perform probabilistic seismic hazard analysis (PSHA). For PSHA, seismic source database is prepared from the seismic source models of this study using the software. The appropriate ground motion prediction equations (GMPEs) with necessary parameters were defined from the database of the CRISIS.
Decontamination Method for Tuberculosis: A Review

Tuberculosis is a communicable disease spread from one person to another; it is caused by Mycobacterium tuberculosis. It’s a very dangerous bacterial infection liable for severe increase in death cases. It’s a chronic granulomatous infectious disease. Infection happens via aerosol and inhalation of some droplets containing mycobacteria bacilli. The disease also affects animals like cattle; this is often referred to as bovine tuberculosis, which can sometimes be communicated to man. The pulmonary tuberculosis is the most important type of tuberculosis which effects man.
Microstrip Feed Trapezoidal Shape Antenna Array with Defected Ground Structure for S
Band Applications

In this proposal new trapezoidal patch microstrip feed antenna array with ground defected by square shape is designed for detailed antenna parameter study in terms of return loss, VSWR, gain and radiation pattern for S band applications from 2 to 3 GHz. The bandwidth and radiation properties of four radiating element arranged in 2 x 2 array has been improved by defecting half of the ground by etching square shape opposite to the vertical feed point. 30 x 70 x 1.6 mm dimension structure has been fabricated in FR4 substrate for low cost applications and performance analyzed in three different planes. With comparison of four element array with full ground, the proposed array with defected ground has proved the improvement in behavior with return loss of -34.687 dB and ideally fit with VSWR of 1.038. Parametric study with feed length and substrate thickness has also been performed optimized decision of structure dimension. This study reveals that by reducing the substrate thickness and increasing the feed length, we can improve the performance of loss reduction. The front view has been simulated with full ground and defected ground for comparison and the compared results shows that the loss reduction of -22 dB has been achieved with VSWR value of 1.03 from 2.28 for defected ground structure. The designed structure has been simulated with CST software and the comparison of simulated results has conform that the proposed structure can be used for S band application like airport surveillance radars with wide bandwidth of 120 MHz and gain of 3.52 dBi. Comparison has been made between the proposed antenna array and the antennas available in literature with respect to bandwidth gain, reflection coefficient and defection type for better understanding.
Oxygen in the root zone and its effect on plants

The information presented analyzes the state of the art of the evidence in the last 20 years of the importance and the effects of the availability of oxygen in the rhizosphere on the growth and development of plants. Adverse conditions of biotic (microorganisms) and abiotic factors (temperature, waterlogging, salinity, others) that influence the oxygen concentration in the rhizosphere, can lead to a rapid drop in oxygen levels and affect morphology, metabolism and physiology from the roots, with a negative effect on the growth of the plant. Radiation medium oxygenation techniques can be favorable, both in soils and in substrates, in situations where there is a risk of hypoxia, but specific studies are required on the effect of oxygen application in each species, as well as the measurement of the answers to identify the oxygen requirements that maximize the yield and quality of the harvested products, both in the field and in the greenhouse. In the agricultural sector, techniques that are sustainable and economically viable are required, even with the high costs of using new technologies.
Mechanical Coating of Zinc Particles with Bi2O3-Li2O-ZnO Glasses as Anode Material for Rechargeable Zinc-Based Batteries

The electrochemical performance of zinc particles with 250 μm and 30 μm diameters, coated with Bi2O3-Li2O-ZnO glass is investigated and compared with noncoated zinc particles. Galvanostatic investigations were conducted in the form of complete discharge and charging cycles in electrolyte excess. Coated 30 μm zinc particles provide the best rechargeability after complete discharge. The coatings reached an average charge capacity over 20 cycles of 113 mAh/g compared to the known zero rechargeability of uncoated zinc particles. Proposed reasons for the prolonged cycle life are effective immobilization of discharge products in the glass layer and the formation of percolating metallic bismuth and zinc phases, forming a conductive network through the glass matrix. The coating itself is carried out by mechanical ball milling. Different coating parameters and the resulting coating quality as well as their influence on the passivation and on the rechargeability of zinc–glass composites is investigated. Optimized coating qualities with respect to adhesion, homogeneity and compactness of the glass layer are achieved at defined preparation conditions, providing a glass coating content of almost 5 wt % for 250 μm zinc particles and almost 11 wt % for 30 μm zinc particles.
Discovery of cell active macrocyclic peptides with on-target inhibition of KRAS signaling

RAS is the most commonly mutated oncogene in human cancers and RAS-driven tumors are amongst the most difficult to treat. Historically, discovery of therapeutics targeting this protein has proven challenging due to a lack of deep hydrophobic pockets to which a small molecule might bind. The single such pocket available is normally occupied by GDP or GTP which have millimolar cellular concentrations and picomolar affinities for KRAS and hence is challenging to target. The recent FDA approval of sotorasib, a covalent modifier of the KRASG12C mutant protein, has clinically validated KRAS as an oncology target. However, traditional challenges remain for targeting the more common KRAS mutations such as G12D and G12V. As an alternative approach, we investigated the superior binding capacity of macrocyclic peptides to identify KRAS inhibitory molecules. We focused on the recently reported disulfide-cyclized arginine-rich peptide KRpep-2d, discovered through phage display and previously independently confirmed by us as a bona fide KRAS binder. To mitigate intracellular disulfide reduction and loss of binding, we identified an alternate cyclization motif by inverting the configuration of Cys5 and linking it to Cys15 through a thioacetal bridge. The corresponding peptide bound KRAS through cis isomerization of the peptide bond between D-Cys5 and Pro6 as observed through x-ray crystallography. Prototypic molecules displayed measurable cellular inhibition of RAS signaling without membrane lysis and counter-screen off-target activity. An analogue containing azido-lysine confirmed the cell penetrant nature of this molecule using our recently reported NanoClick assay. To improve cellular activity, we sought to improve proteolytic stability. Structure-activity relationship studies identified key scaffold residues critical for binding and revealed that replacement of N- and C-terminal arginine residues with D-arginines and introduction of an α-methyl moiety at Ser10 resulted in a molecule with improved proteolytic stability as indicated by its persistence in whole cell homogenate. The resulting peptide MP-3995 had improved and sustained cell-based efficacy. On-target activity was validated through confirmation of target engagement, lack of signaling in irrelevant pathways, and use of non-binding control peptides. Mechanism of action studies suggested that peptide binding to both GDP- and GTP-states of KRAS may contribute to cellular activity. Although validated as bona fide and on-target inhibitors of cell based KRAS signaling, this series is unlikely to advance to the clinic in its current form due to its arginine-dependent cell entry mechanism. Indeed, we showed a strong correlation between net positive charge and histamine release in an ex vivo assay making this series challenging to study in vivo. Nonetheless, these molecules provide valuable templates for further medicinal chemistry efforts aimed at leveraging this unique inhibitory binding site on KRAS.
Health : Navigating the New Normal Problem

It is often taken for granted that notions of health and disease are generally applicable across the biological world, in that they are not restricted to contemporary human beings, and can be unproblematically applied to a variety of organisms both past and present (taking relevant differences between species into account). In the historical sciences it is also common to normatively contrast health states of individuals and populations from different times and places: e.g., to say that due to nutrition or pathogen load, some lived healthier lives than others. However, health concepts in contemporary philosophy of medicine have not been developed with such cross-lineage, non-human, or diachronic uses in mind, and this generates what I call the ‘new normal’ problem. I argue that the new normal problem shows that current naturalistic approaches to health (when based on biological reference classes) are worryingly incomplete. Using examples drawn from evolutionary archaeology and the human fossil record, I outline an alternative, function-based strategy for naturalizing health that might help address the new normal problem. Interestingly, this might also reconstruct a certain uniqueness for humans in the philosophy and science of health, due to the deep history of obligate enculturation and cultural adaptation that archaeology demonstrates.
Multi Algorithm Of A Single Objective Function Of A Single Phase Induction Motor

The application of four algorithms (GA, PSO, SA, FA) in parameters design of a singlephase induction motor is proposed in this paper. The Four algorithms consider the motor efficiency as an objective function and with five equality and five inequality constraints. Under efficiency as an objective function the performance of the algorithm improved in the order 19.7%, 15.3%, 15.22% and 15.2% as follows, GA, SA, FA and PSO respectively, this means that for the choice of algorithm in terms of efficiency as an objective function GA is preferred. The most performed algorithm is the GA for the single phase induction motor, a code has been provided under MATLAB software. The results show that the GA method gives more suitable design optimization against conventional methods. Keywords—Design Optimization; Single Phase Induction Motor (SPIM); Maximum Efficiency; Genetic Algorithm (GA)
AGE INVARIANT FACE RECOGNITION USING QUADRATIC SUPPORT VECTOR MACHINE – PRINCIPAL COMPONENT ANALYSIS

Age Invariant Face Recognition (AIFR) is a new research area in the domain of face recognition that lately received a lot of interest due to its tremendous potential with relevant applications in real-world. It is very challenging research problem to extract robust features describing aging facial information, usually when broad age difference between face images are perceived. In this paper, by using the quadratic support vector machine and principal component analysis technique of feature selection that is robust to aging we address this challenge and achieved the highest accuracy as compared to other methods such as K-Neighbor Neural Network (KNN), Probabilistic Neural Network (PNN), Backpropagation Neural Network (BPNN). FGNET aging dataset is used for the implementation of proposed method which includes a total of 82 separate subjects comprising 1002 images and each subject includes 6-18 images per subject obtained primarily from scanning photographs of subjects between the age ranging from newborn to 69-year-old subjects. From the images, LBP, Gabor and shape features have been extracted and PCA is used as a method of feature reduction. Four stages have been explored in this work: (a) Face recognition (b) Identify the gender of the object (c) Identify the age of the subject using the backpropagation neural network (BPNN) (d) Age invariant face recognition using KNN, PNN, NN and QSVM-PCA. The highest accuracy is achieved using the Quadratic Support Vector Machine – Principal Component Analysis (QSVM-PCA) classifier.
CSI Estimation Using Artificial Neural Network

We propose using machine learning to estimate channel state information (CSI) for MIMO communication links. The goal is to use information such as atmospheric conditions, amount of path loss, and Doppler shift to improve the accuracy of CSI estimates. We start by designing an algorithm which estimates the CSI based on previously mentioned factors. Using this algorithm, we simulate a dataset of CSI over varying atmospheric conditions, receiver position, and receiver velocity. We then use this dataset to train an artificial neural network, which is able to estimate the CSI by using the current atmospheric condition, receiver position, and velocity.
Unlocking the door to highly active ORR catalysts for PEMFC applications: polyhedron-engineered Pt-based nanocrystals

Proton exchange membrane fuel cells (PEMFCs) are considered to be an important low-carbon energy conversion technology, which has shown impressive performance improvement in the past decades. However, the sluggish kinetics of the oxygen reduction reaction (ORR) at the cathode has been an impediment to the successful commercialization of the technology. Novel highly active electrocatalysts for the ORR are highly desired to meet the US DOE performance targets. In recent years, polyhedron-engineered Pt-alloy nanocrystals have demonstrated unprecedented electrocatalytic activity. Different from previous reviews on shape-manipulated Pt-based ORR catalysts, this review focuses on 3D polyhedron-engineered Pt-based nanocrystal electrocatalysts with an in-depth comparison of different active facet-tailored geometric configurations and their advanced electrocatalytic properties. The review carefully examines and criticizes the aggregated data in this area, and provides an important overview as well as a critical insight into the topic. Understanding the catalytic mechanism taking place with polyhedron-designed Pt-based nanocrystals helps to unlock the door to highly active ORR electrocatalysts for PEMFC applications.
Synthetic Images and Creative AI: A discussion on the nature and production of images in the era of deep learning

The emergence of new AI algorithms in recent years, especially those concerning deep learning, brings new challenges to the sphere of art, changing how artists creatively use computer systems. Although AI is not new in the universe of art, the new scenario makes it possible for algorithms to produce new types of automated images. Given this picture, this paper proposes to shed some theoretical and practical lights on the processes employed in the generation of visual art using AI. We start exploring the very nature of computer images, having as a theoretical framework the ideas of Dietmar Kamper (1936-2001), Hans Belting (1935-), Christoph Wulf (1944-), and Vilém Flusser (1920-1991). Next, building on this conceptual exploration, we describe the process of using deep learning techniques to generate self-por-traits, which are synthetic images pointing to an external index.
Towards Automatic Generation of Smart Summary: Design of a Multioptions Summarizer using Syntactic Chains

The process of document summarization has been studied for more than 60 years in order to achieve an acceptable level at the field of automatic generation of textual summary. But, the demand for generating a smart summary which includes a mixture of helpful forms is still clear. This paper presents an automatic multioptions summarizer based on selecting the best sentences from a given document as a textual summary, converting this summary into a structured form using syntactic chains and then generating three types of summarization: a structured outline, a query by document and a concept map. This summarizer is represented by a novel algorithm which aims at generating a good summary that could reflect the overall picture of a given document by different understandable forms. The implementation of the proposed algorithm has been evaluated with encouraging results.