Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of CRC-Polar Concatenated Codes

Polar code has been proven to obtain Shannon capacity for Binary Input Discrete Memoryless Channel (BIDMC) and its use has been proposed as the channel coding in 5G technology. However, its performance is limited in finite block length, compared to Turbo or LDPC codes. This research proposes the use of various CRC codes to complement Polar codes with finite block length and analyses the performance based on Block Error Rate (BLER) to Es/N0 (dB). The CRC codes used are of degrees 11 and 24, with 3 different polynomial generators for each degree. The number of bits in the information sequence is 32. The list sizes used are 1, 2, 4, and 8. Simulation results show that the concatenation of CRC and Polar codes will yield good BLER vs Es/N0 performance for short blocks of codeword, with rates 32/864 and 54/864. Concatenating CRC codes with Polar codes will yield a BLER performance of 10-2 with Es/N0 values of -9.1 to -7.5 dB when CRC codes of degree 11 is used, depending on the SC list used. The use of CRC codes of degree 24 enables a BLER performance of 10-2 with Es/N0 values of -7 to -6 dB when the SC list used is 1 or 2. The use of CRC codes of degree 24 combined with SC list with sizes 4 or 8 will improve the BLER performance to 10-2 with Es/N0 values of -8 to -7.5 dB
Assessment of the GPM IMERG and CHIRPS precipitation estimations for the steppe region of the Crimea

This paper compares the spatial distribution datasets on monthly precipitation totals derived from the Famine Early Warning System Network FEWS NET service (CHIRPS 2.0 product) and the International Mission of the Global Precipitation Measurement GPM (IMERG v06 product) with ground-based observations of a stationary weather stations located in the steppe region of the Crimean Peninsula in order to assess the representativeness of the precipitation spatial distribution and the applicability of the datasets for water balance calculations and agricultural crop dynamics modeling. A close convergence was observed between the estimated monthly precipitation totals and the precipitation gauge data during the study period (January 2017 – July 2020), with mean correlation coefficients of 0.75 and 0.73 for the GPM IMERG and CHIRPS, respectively. Both products generally overestimated the precipitation values compared to the measured data, with GPM IMERG (final run) exhibiting the greatest overestimations (1.3-2.1 times the weather station values). Our results demonstrate the requirement of GPM-derived precipitation estimations (particularly those from the GPM_3IMERDL v06 daily accumulated late run dataset) to be additionally verified and calibrated based on data from regional weather stations or the CHIRPS 2.0 product (if available).
Software for preprocessing voice signals

ABSTRACT One of the most important tasks of modern science is the development of software tools for human communication with devices (for example, a computer) in natural language, where speech input and output of information is carried out in the most user-friendly way. To create such tools, it is required to solve speech recognition problems. On the basis of many experimental studies, it can be concluded that the quality of speech recognition depends on the results of preliminary signal processing. Improving the quality of speech recognition requires new efficient and high-speed signal preprocessing methods and algorithms.This article proposes a new approach and algorithm for the formation of signs of speech signals. Based on these features obtained by the proposed algorithm, the identification problem is solved. The article also provides a description of the software module for each stage of preprocessing of speech signals. The developed software is a voice-based identification tool.
Solution-processed Au–Ag core–shell nanoparticle-decorated yarns for human motion monitoring

Wearable strain sensors based on Au–Ag core–shell nanoparticle decorated yarns were fabricated by a solution-based approach. Smart wearable systems with our strain sensors that can monitor various human motions, including finger motion, marching and squatting, and transmit the information of human motion in real time to an LED light was demonstrated.
Comparison of affinity degree classification with four different classifiers in several data sets

The affinity notion has been widely used in research fields. Thus, in this research, affinity is employed to find the degree between two data sets and classify through prediction. But, as Affinity Degree (AD) classification is a new technique, the comparison with different classification types is needed to test the compatibility technique. Herein, this study compares various machine learning techniques and determines the most efficient classification technique based on the data set. Four different classification algorithms, K-Nearest Neighbour (KNN), Naive Bayes (NB), Decision Tree (J48), and Support Vector Machine (SVM), were used as other techniques to compare with AD classification. Three different data sets, breast cancer, acute inflammation, and iris plant, were used for experiment purposes. The results show J48 has the best rate in performance measures compare to the other four classifiers. However, the results of AD classification show the significance that more studies can improve it.
The Effect of Growth Mindset on Work-Family Enrichment in Information Age: Examining a Moderated-Mediation Model

With the development of the information age, the research of psychology is deepening day by day, and the understanding of people and their growth is becoming more and more profound. This study examines the effect of growth mindset on employee to their work-family enrichment using learning goal orientation as mediator and also looks at the moderating role of job complexity. Results of 220 samples revealed that learning goal orientation mediated the influence of growth mindset on work-family enrichment. Furthermore, job complexity moderated the relationship between growth mindset and learning goal orientation. Researchers rarely study the relationship between the growth mindset and work-family enrichment. However, in the information age, people with growth mindset could get more work-family enrichment. On the basis of the findings, it can be concluded that learning goal orientation is a mediator in this relationship and the job complexity will enhance the effect of growth mindset on learning goal orientation.
Breeding Objective, Selection Criteria and Breeding Practice of Indigenous Goat Types in Ancher and Gemechis Districts, Eastern Ethiopia

This study was aimed to generate basic information on breeding objective, selection criteria and breeding practice of indigenous goat type under farmer’s management condition in Ancher and Gemechis districts, Eastern Ethiopia. The study was performed based on household survey. About 240 households (120 from each district) were used for household survey. Appearance, color, testicular characteristics and horn were most important traits considered by farmers to choose breeding bucks in Ancher and Gemechis district. Appearance and coat color ranked as first and second, respectively followed by better milk yield in Ancher and twinning ability in Gemechis district as third criteria for selection breeding does. The average age at first sexual maturity of male in Ancher and Gemechis district were 7.6±0.07 and 7.4±0.06 months, respectively, in the meantime female goats were the average age at first sexual maturity in Ancher and Gemechis districts was 7.8±0.07 and 7.6±0.06 months, respectively. The mean of age at first kidding for Ancher and Gemechis districts goat 12.9±0.15 and 12.5±0.14 months, respectively. Mean estimate of effective population size and mean rate of inbreeding was 2.4 and 0.21, respectively when a household flock is herded alone and under random mating. Therefore, any breed improvement strategies that are intended to be implemented in the study area and otherwise- where should consider the traditional breeding practices and breeding objectives of the community.
Performance and Obstacles of SMEs: An Empirical Evidence from BRICS Countries

This study empirically examines the performance and obstacles of SMEs in BRICS economies. For empirical evaluation, Ordinary Least Square technique has applied by taking the time period between “2000-2017”. Performance has taken as dependent variable and obstacles; firm characteristics and global factor have taken as explanatory variables. Estimated results show that ownership and size have a positive impact on SMEs growth and performance. Age has a negative and significant impact on the performance and growth of SMEs. Technology has a positive and significant impact on the performance of SMEs. Obstacles i.e. courts, crime, access to finance, practices of competitors and electricity has a negative and significant impact on the performance of SMEs. Access to land, infrastructure and workforce has a positive and significant impact on SMEs performance. It becomes very important for the policymakers or investigators to pay attention towards making SMEs more competent, capable and productive in order to attain the goal of sustainable development and progress.
Compliance with Hand Hygiene
Among Healthcare Workers in Preventing Healthcare Associated Infections

1 Graduate nursing student, School of Medicine University of Zagreb, Croatia 2 University of Applied Health Sciences, Zagreb, Croatia 3 Andrija Stampar Teaching Institute of Public Health, Zagreb, Croatia 4 Department of Neurosurgery, University Hospital Centre Sestre milosrdnice, Zagreb, Croatia 5 Department of Anaesthesiology and Intensive Medicine, University Hospital Centre Zagreb, Zagreb, Croatia 6 Croatian Institute of Emergency Medicine, Zagreb, Croatia
Amazon SageMaker Debugger: A System for Real-Time Insights into Machine Learning Model Training

Manual debugging is a common productivity drain in the machine learning (ML) lifecycle. Identifying underperforming training jobs requires constant developer attention and deep domain expertise. As state-of-the-art models grow in size and complexity, debugging becomes increasingly difficult. Just as unit tests boost traditional software development, an automated ML debugging library can save time and money. We present Amazon SageMaker Debugger, a machine learning feature that automatically identifies and stops underperforming training jobs. Debugger is a new feature of Amazon SageMaker that automatically captures relevant data during training and evaluation and presents it for online and offline inspection. Debugger helps users define a set of conditions, in the form of built-in or custom rules, that are applied to this data, thereby enabling users to catch training issues as well as monitor and debug ML model training in real-time. These rules save time and money by alerting the developer and terminating a problematic training job early.
Employee Perception Towards Information System Portal for Lecturers

The study aims to identify the perception of users towards the Lecturer Information System Portal at TATI University College. A model of the lecturer portal system success, consisting of four success measures namely system quality, information quality, user satisfaction, and system success is presented and validated. A questionnaire is distributed to lecturers at four academic faculties and results are statistically analyzed using descriptive frequencies, reliability, and multiple regressions. Results revealed a positive impact of all factors investigated namely system quality and user satisfaction; information quality and user satisfaction; and user satisfaction and success.
AN ALTERNATIVE CREDIT GUARANTEE SCHEME FOR FINANCING MSEs IN ISLAMIC BANKING

The objective of this paper is to propose a model for a financing guarantee scheme (FGS) funded by the voluntary sector (through zakat, infaq, and sadaqah, or ZIS funds) to support small and medium-sized enterprises (SMEs). We argue that the existing credit guarantee scheme (CGS) relies on government funding, but that in the current Covid-19 pandemic it is experiencing unstable financial capability. Hence, the sustainability of the CGS program is questionable. We employ the input-output (I-O) approach to identify the expected impact if the proposed FGS is applied. We further substantiate the proposal with seven Indonesian Syariah experts’ opinions, of whom five suggest that the alternative scheme is acceptable. The simulation using I-O shows that if IDR1 trillion (out of IDR10 trillion from ZIS funds in 2019) is disbursed through the scheme, this will increase economic growth by 0.0117%, representing economic activities worth IDR1.235 trillion, and creating 2,151 new jobs. We further discuss the implications of the findings for the Islamic banking industry in the future and for regulators.
Optimization Assisted Convolutional Neural Network for Facial Emotion Recognition

: Facial Expression Recognition (FER) is an important type of visual information that can be used to understand a human‟s emotional situation. FER has attained a significant interest in human-computer interaction, autopilot, medical healing as well as various face expression dependent areas, and it is enormously used in most research areas. Hence, this paper intends to develop an intelligent facial emotion recognition model by following two major processes namely (a) Feature extraction and (b) Classification. Initially, the input image is subjected to extract Local Binary Pattern (LBP) based features. Further, the extracted features are classified using a Convolutional neural network (CNN). Moreover, the weights of CNN are optimally tuned by the Improvised Steering angle and Gear-based ROA (ISG-ROA) algorithm. Finally, the superiority of the ISG-ROA method is compared over existing methods and its improvement is proved effective
Physical Layer Security Against Eavesdropping in Internet of Drone Communication Systems: An Overview

—Drones or unmanned aerial vehicles (UAVs) communication technology, which has recently been thoroughly studied and adopted by 3GPP standard (Release 15) due to its dynamic, ﬂexible, and ﬂying nature, is expected to be an integral part of future wireless communications and Internet of drones (IoD) applications. However, due to the unique transmission characteristics and nature of UAV systems including broadcasting, dominant line of site and poor scattering, providing conﬁdentiality for legitimate receivers against unintended ones (eavesdroppers) appears to be a challenging goal to achieve in such scenarios. Besides, the special features of UAVs represented by having limited power (battery-operated) and processing (light RAM and CPU capabilities), makes applying complex cryptography approaches very challenging and inefﬁcient for such systems. This motives the utilization of alternative approaches enabled by physical layer security (PLS) concept for securing UAV-based systems. Techniques based on PLS are deemed to be promising due to their ability to provide inherent secrecy that is complexity-independent, where no matter what computational processing power the eavesdropper may have, there is no way to decrypt the PLS algorithms. This work is dedicated to highlight and overview the latest advances and state of art researches on the ﬁeld of applying PLS to UAV systems in a uniﬁed and structured manner. Particularity, it discusses and explains the different, possible PLS scenarios and use cases of UAVs, which are categorized based on how the drone is utilized and employed in the communication system setup. The main classiﬁed categories include the deployment of the ﬂying, mobile UAV as a 1) base station (BS), 2) user equipment (UE), 2) relay, or 4) jammer. Then, recommendations and future open research issues are stated and discussed.
Adopting Technology in the Management of Human Resources (e-HRM) in Higher Education Institutions

This paper discusses the paradox of technology and human resource functions in higher education institutions (HEIs). The paper identifies areas where technology has enabled human resource as well as non-HR managers to have access to tools that have been found to ease communication as well as facilitating self-service. While the importance of technology is not disputable, there remain three important questions: (1) what are the benefits of e-HRM in the execution of HRM functions in higher education institutions? (2) What are the technology-related challenges faced by managers in these institutions? And (3) how can the human touch be maintained in the core HRM functions while using technology in these institutions? A qualitative method was augmented by integrative synthesis to collect data, through interviews, summaries from existing research literature, observation and personal experience. Results were analyzed using content and thematic analyses. Technology was found to be mostly used in instruction, advertisement, recruitments and payroll management. These institutions still employed a dual system of traditional HRM and e-HRM, but found technology very effective in accomplishing HRM functions. There were however, dependability and infrastructural technology-related challenges. The paper concludes that regardless of such challenges, technology was just a scarecrow, and did not pause any danger on human relationships.
The Impact of a Care Transitions Intervention on People With Brain Injuries and Their Caregivers

As our population ages, the prevalence of brain injuries increases due to strokes and falls. Brain injuries are a leading cause of rehospitalization for patients, and most brain injury survivors experience depression soon after hospital discharge. This study assessed the difference between: 1) survivors of brain injury’s baseline and 30-day depression, functional ability, and quality of life and caregivers’ depression and caregiver burden among those that received the Care Transitions intervention, and 2) 30-day hospital readmissions between survivors of brain injury that received Care Transitions and a control group. The study used a quasi-experimental pre-posttest design. Participants included people with brain injuries who received the Care Transitions intervention (n = 22) and their caregivers (n = 20) compared to a services-as-usual control group of brain injury survivors (n = 27). Care Transitions is a 90-day family-focused, home visitation, coaching hospital-to-home transition intervention. Outcomes were self-reported baseline and 30-day depression, functional independence, quality of life, and caregiver burden. Hospital record data was used to report readmissions. Study results showed that there were statistically significant differences between depression, functional ability, and caregiver burden between preand post-survey scores among Care Transitions participants. Care transitions’ participants experienced lower brain injury-related hospital readmissions than the services as usual control group. Social work hospital discharge planning needs to continue beyond the hospital and include home visitation to ensure patient and caregiver needs are met post-hospital discharge.
Influence of Surface Characteristics of Different Implant Abutment Materials on Growth of Porphyromonas Gingivalis

1 Department of Prosthodontics, Lithuanian University of Health Sciences, Kaunas 50106, Lithuania 2 Department of Electrochemical Material Science Center for Physical Sciences and Technology (FTMC), Vilnius, Lithuania; Faculty of Chemistry and Geosciences, Vilnius University, Vilnius 10257, Lithuania 3 Department of Dental and Oral Pathology, Lithuanian University of Health Sciences, Kaunas 50140, Lithuania 4 Department of Biomedical and Dental Sciences and Morphological and Functional Imaging, Messina University, 98100 Messina, Italy 5 Department of Maxillofacial Surgery, Lithuanian University of Health Sciences, Kaunas 50140, Lithuania
Application of Multi-Walled Carbon Nanotubes based Materials in Heavy Metals Adsorption

Rapid population growth and lack of access to safe and clean potable water have recently been of major global concerns. The bioaccumulation of the substantial water contaminants in the living cells brings genuine health issue to the human and generates significant environmental impact. Carbon Nanotubes (CNTs) is one of the advanced technologies recently applied for the wastewater treatment due to outstanding and diverse physio-chemical properties offered by CNTs. The large surface area, flexibility in number of activated functional groups, reasonable adsorption behavior in water, and excellent removal efficiency of heavy metals are of those properties which introduced CNTs as a unique adsorbent. In this work the adsorption behavior of metal ions using Multi-Walled CNTs (MWCNTs) was investigated to detect and evaluate the capacity for Cobalt (CO) and Nickel (Ni) on MWCNTs. A multiple characterization techniques were applied such as Scanning Electron Microscope (SEM), Ultraviolet-Visible Spectroscopy (UV-VIS), Energy Dispersive Spectroscopy (EDS), Raman spectroscopy and Fourier Transformer Infrared (FT-IR) on the samples to illustrate the morphology and composition changes, also to locate defects, porosity, and structure changes of the sample. FT-IR spectrum shows the existence of carboxyl group after treatment with (Acidic oxidation). SEM micrographs show the occurrence of surface modification on the MWCNTs structure after treatment. The results show that CNTs are excellent and effective adsorbent for eliminating harmful media in water. Both heavy metals are strongly dependent on pH, oxidized MWCNT concentration and ionic strength. Oxidized MWCNTs may be a promising candidate for concentration of heavy metal ions from industrial wastewater.