Miracle-dz/UniFAR-FTUs huggingface dataset:
A SimpleEncryption Approach to 5G Multicarrier Based Waveform Utilizing Subcarrier Frequency Diversity with Improved BER Performance

The security in 5G Internet of Things (IoT) is considerably different as the low power constraints and complexity features of many IoT devices are limiting the use of conventional cryptographic techniques. The fast Fourier transform (FFT) based multicarriers like orthogonal frequency division multiple access (OFDMA) and the similar non orthogonal counterparts are the preferred waveforms in 5G due to their resilience to fading and ease of implementation. Here a scheme to chaotically rearrange the frequency subcarriers is proposed which will not only provide a relatively simple physical layer security (PLS) with minute changes in existing OFDM based transceiver as per the low complexity, low power requirements of IoT but also improve the bit error rate (BER) performance by utilizing the diverse nature of the channel. The scheme provides an alternate approach to conventional higher layer security, by doing so at physical layer which will be suitable for most FFT based waveforms which have been proposed in 5G especially narrow band IoT (NB IoT) , massive machine-type communication (mMTC) and ultra-reliable low latency communicationis (URLLC). maintain uniformity in the final print version of the journal. Both form and content of the paper has to be as per these guidelines else your paper will not be published even though its content has been accepted.
One Ring to Revascularize Them All: Assessing The End Results of Coronary Bioresorbable Vascular Scaffolds as Revascularization Approach for Chronic Total Occlusions

Following the era of percutaneous coronary intervention (PCI), the occurrence of revascularization in chronic total occlusions (CTO) is correlated with positive and longstanding echocardiographic and clinical outcomes. The beneficial outcomes of bioresorbable vascular scaffolds (BVS) treatment to manage CTO are currently inconclusive, since patients presenting with CTO were frequently ruled out from a vast number of randomized clinical trials (RCTs) which assess BVS. This systematic review is aimed to review and recapitulate available reports on the clinical outcomes of CTO with BVS treatment. Available data in the Cochrane Library, EMBASE, MEDLINE, and, clinicaltrials.gov are being examined to gather investigations on BVS-treated CTO. Outcomes of concern involved vessel restenosis, scaffold thrombosis, target lesion revascularization, myocardial infarction, major adverse cardiac events (MACE), and all-cause mortality. Thirteen papers have met the criteria for inclusion. All papers were written based on observational studies cumulative population samples of 1,077. Two papers were found to involve retrospective comparison of drug-eluting stents (DES) group with BVS group. The investigations had varying group size and duration of follow-up. This review presented beneficial results for BVS-treated CTO. In double-arm studies, the recorded MACE incidence diverged from 0% to 6.7% with no notable differences between DES and BVS populations. While reports on the implantation of the first-generation BVS in CTO populations are infrequent and recruited only insufficient observational studies samples, the available data is promising. The data shows satisfactory results which are analogous to second-generation DES. However, additional investigation by means of RCTs and the application of more novel scaffolds is necessitated.
Developing L2 speaking skills in English-medium EFL higher education

ABSTRACT Higher education students in English medium instruction in non English-dominant countries use L2 to share ideas and demonstrate learning thorough the medium of English and are often required to enhance their ability to communicate in this language while studying. However, the conditions provided for these students in university courses may or may not be conducive to the development of their skills to effectively communicate in speech in academic contexts. This exploratory study presents preliminary results of a larger research project, conducted to design, implement and evaluate a theoretically-driven, genre-based L2 speaking course for academic communication. Drawing on skill acquisition theory and activity theory, in order to understand how aspects of the learning activity enable or hinder the development of genre-based L2 speaking, this study captures the experiences of ten participants – Polish undergraduate English Philology students – with various facets of the course. The results bring to the fore the teaching and learning strategies that the students most value and most frequently use within their educational context. Additionally, the study, being part of a broader design-based research project, offers draft principles for course design.
Accounting for imported cases in estimating the time-varying reproductive number of COVID-19 in Hong Kong

Abstract Estimating the time-varying reproductive number, Rt, is critical for monitoring transmissibility of an infectious disease. The impact of imported cases on the estimation is rarely explored. We developed a model to estimate separately the Rt for local cases and imported cases, with accounting for imperfect contact tracing of cases. We applied this framework to data on COVID-19 outbreaks in Hong Kong. The estimated Rt for local cases rise above 1 in late March, 2020, which was undetected by other commonly used methods. When imported cases accounted for a considerable proportion of all cases, their impact on estimating Rt is critical.
Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.
Chronic Testosterone Administration During Pregnancy Causes Autistic-like Traits in Rat Offspring: Possible Relationships with Cerebral Oxytocin, Dopamine, Serotonin and IGF-1 Levels.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects boys three times more frequently than girls. Previous studies have suggested that higher fetal testosterone exposure can cause autism-like behaviors in males. The mechanism of effect of fetal testosterone on autism development is not well known. In the present study, we investigated the relationship between prenatal testosterone exposure and ASD in an experimental study with the use of behavioral tests, histopathological examinations and biochemical measurements performed to compare offspring with and without exposure to testosterone. Female rats were randomly distributed into Group 1 (control, n = 6) and Group 2 (Testosterone undecanoate, n = 6). Female rats were caged with a fertile male (three female/one male) for 2–3 days during the oestrus period. Group 1 rats were given 1 ml/kg % 0.9 NaCl saline on the 10th day of pregnancy, while Group 2 rats were given 250 mg/kg testosterone undecanoate. The dams were allowed to raise their litters until weaning on postnatal day 21 (P21). On P21 , forty littermates (10 male control, ten female control, ten male Testosterone-exposed, and ten female Testosterone-exposed) were randomly separated and housed. These animals underwent behavioral testing in adulthood on P50. Subsequently, the rats were sacrificed. Biochemical analyses [Brain tissue 5-Hydroxyindoleacetic acid (5-HIAA), homovanillic acid (HVA), Insulin-like growth factor-1 (IGF-1), and oxytocin] and histopathological analyses were performed. Analysis of the behavioral tests (three-chamber social test, open field test) revealed significant differences among the groups, with the testosterone-exposed groups demonstrating autistic traits at a greater degree. Histologically, hippocampal CA1 and CA3 regions displayed significant alterations such as gliosis and neuronal cell death in the testosterone-exposed groups compared to controls. Brain levels of tissue 5-HIAA, HVA, IGF-1 increased while oxytocin level decreased in the testosterone-exposed groups. These results suggest a possible link between chronic prenatal testosterone exposure and neurodevelopmental disorders such as ASD. Testosterone exposure and autism-like traits can be linked to dopamine, serotonin, IGF-1 increase, and oxytocin decrease.
A PWM/PFM Dual-Mode DC-DC Buck Converter with Load-Dependent Efficiency-Controllable Scheme for Multi-Purpose IoT Applications

This paper presents a dual-mode DC-DC buck converter including a load-dependent, efficiency-controllable scheme to support multi-purpose IoT applications. For light-load applications, a selectable adaptive on-time pulse frequency modulation (PFM) control is proposed to achieve optimum power efficiency by selecting the optimum switching frequency according to the load current, thereby reducing unnecessary switching losses. When the inductor peak current value or converter output voltage ripple are considered in some applications, its on-time can be adjusted further. In heavy-load applications, a conventional pulse width modulation (PWM) control scheme is adopted, and its gate driver is structured to reduce dynamic current, preventing the current from shooting through the power switch. A proposed dual-mode buck converter prototype is fabricated in a 180 nm CMOS process, achieving its measured maximum efficiency of 95.7% and power density of 0.83 W/mm2.
Morphology and Molecular Studies of Contracaecum Larvae (Nematoda: Anisakidae) in Some Fish species from Sulaimani Province, Kurdistan Region, Iraq

Contracaecum is a genus of nematodes belonging to the family Anisakidae, that parasitise many fishes which act as an intermediate or paratenic hosts, while the piscivorous birds and mammals are definitive hosts. A total of 44 third larval stage of Contracaecum were collected from 13 infected freshwater fishes belonging to five different species in different water bodies in Sulaimani Province, Kurdistan Region, Iraq from January to the end of December 2018. In this investigation, 966 fishes were collected including six species of Nemacheilidae, five species of Cyprinidae, three species of Leuciscidae, two species of Xenocyprididae, one species of each of Bagridae, Heteropneustidae, Mastacembelidae, Mugilidae, Siluridae and Sisoridae. This study revealed that five fish species (Cyprinus carpio, Luciobarbus barbulus, L. esocinus, L. xanthopterus and Mastacembelus mastacembelus) were infected with Contracaecum larvae with the prevalence of 2.05%, 0.92%, 1.92%, 19.35% and 1.06%, respectively. The Contracaecum larvae were morphologically studied by compound light microscope and the molecular analyses was done by amplification, sequencing and comparing different gene loci (ITS1, ITS2 and COX2) of isolated third larval stage of Contracaecum. The ITS1, ITS2 and COX2 were amplified by polymerase chain reaction (PCR) and sequenced. The sequences of ITS1, ITS2 and COX2 reveal that all Contracaecum larvae from all infected fishes represented exactly one species (Contracaecum rudolphii B) based on compering and identity percentage in Gene Bank database. Phylogenetic analysis of the genotype (for ITS1) was described. The genetic characterization of the Contracaecum larvae in the present study is available in the GenBank database and they were deposited in GenBank and their accession numbers were demonstrated.
An Implementation of IoT and Data Analytics in Smart Agricultural System – A Systematic Literature Review

India is a country that depends on agriculture, where about half the population relies heavily on agriculture for their livelihood. However, most of the practices undertaken in the agricultural process are not for profit and yield favorable. It should upgrade with current technologies to boost seed quality, check soil infertility, check the water level, environmental changes, and market price prediction, and achieve in agriculture sensitivity of faults and background understanding. The advancement in technology and developments is seen as a significant aspect in their financial development and agricultural production growth. The Internet of Things (IoT), Wireless Sensor Networks (WSN), and data analytics accomplish these upgrades. These technologies help in providing solutions to agricultural issues such as resource optimization, agricultural land monitoring, and decision-making support, awareness of the crop, land, weather, and market conditions for farmers. Smart agriculture is based on data from sensors, data from cloud platform storage and data from databases, all three concepts need to be implemented. The data are collected from different sensors and stored in a cloud-based back end support, which is then analyzed using proper analytics techniques, and then the relevant information is transferred to a user interface, which naturally supported the decision to conclude. The IoT applications mainly use sensors to monitor the situation, which collects a large size of data every time, so in the case of the Internet of Things (IoT) application, sensors contribute more. Data analytics requires data storage, data aggregation, data processing and data extraction. To retrieve data and information from database, we must use data mining techniques. It acts a significant position in the selection-making process on several agricultural issues. The eventual objective of data mining is to acquire information form data transform it for some advanced use into a unique human-comprehensible format. Big data's role in Agriculture affords prospect to increase the farmers' economic gain by undergoing a digital revolution in this aspect that we examine with precision. This paper includes reviewing a summary of some of the conference papers, journals, and books that have been going in favor of smart agriculture. The type of data required for smart farming system are analyzed and the architecture and schematic diagram of a proposed intelligent farming system are included. It also involves implementing different components of the smart farming system and integrating IoT and data analytics in the smart farming system. Based on the review, research gap, research agendas to carry out further research are identified.
Seasonal Variations in Carbon, Nitrogen, and Phosphorus Stoichiometry of a Robinia pseudoacacia Plantation on the Loess Hilly Region, China

Seasonal variations in stoichiometry are a crucial regulatory mechanism for plant communities that respond to environmental changes. However, the seasonal characteristics of stoichiometry in plants, litter, and soil are poorly understood, especially in plantation ecosystems. Therefore, we explored the seasonal variations of C, N, and P contents and ratios between plants, litter, and soil of a Robinia pseudoacacia plantation on the Loess Plateau in China in 2017. The results indicate that the C, N, P contents and ratios in plants, litter and soil showed different seasonal patterns. The N and P contents of tree and shrub leaves substantially decreased over the growing season, while the C:N, C:P, and N:P ratios exhibited the opposite trend. The utilization efficiency of the N and P elements by trees and shrubs gradually increased with the change of the growing season. These results suggest that the C:N:P stoichiometry of plants was more sensitive to seasonal changes than the litter and soil; therefore, the potential impacts of time should be considered when using stoichiometry to explore the utilization of plant nutrients. Additionally, the P content between tree leaves and soil and the N content between herb leaves and soil were significantly positively correlated, indicating that the growth of the tree and herb layer in the R. pseudoacacia plantation in the area was restricted by P and N, respectively. Meanwhile, the N content in the leaves between trees and herbs showed a significant negative correlation, indicating that N competition existed between R. pseudoacacia and understory herbs, which was not conducive to the effective use of environmental resources by the R. pseudoacacia plantation ecosystem. This study contributes to vegetation restoration and plantation management on the Loess Plateau and provides basic information for global stoichiometric analyses.
Investigation on the performance properties of polytrimethylene terephthalate (PTT) based staple fibers and cotton blended OE-rotor yarns

Abstract In this study, firstly, it is aimed to spin PTT (Polytrimethylene Terephthalate) based staple Sustans® fibers together with cotton in Open-End rotor yarn production system. In this way, it is aimed to transfer the positive properties of Sustans® fibers such as good elasticity, good dimensional stability, low static electrification and soft hand to the staple yarn structure with a high efficiency spinning technique. In this context, using 5 different ratios of Sustans®/Cotton blends and 100% Cotton, 6 different rotor yarns with Ne 8.25 (71.6 tex) were produced and the quality characteristics of these yarns were tested. In the study, Sustans®/Cotton blends were successfully spun with the Open-End rotor system. As a result, compared to 100% cotton, it was observed that the elongation at break, unevenness, yarn defects and hairiness values were better for yarns containing Sustans® fibers, while the strength values were slightly lower. The optimum blend ratio was concluded to be 50/50% Sustans®/Cotton for the open-end spinning technique.
The Influence of Policy, Cultural and Historical Contexts on Social Work and Human Service Practice Responses with People Seeking Asylum in Germany and Australia

ABSTRACT This article explores the findings of a qualitative study comparing social work and human service practice with people who have sought asylum in Germany and Australia. Globally social workers are concerned with the record high of people forcibly displaced worldwide and the Global North host-nations’ increasingly hostile, discriminatory and restrictive policies towards people seeking asylum. This policy context is antithetical to the professional values of social workers and human services practitioners. The findings of this study reveal how the policy directly impacts social work and human service practice, creates unique ethical challenges and dilemmas and, subsequently, diverse practice responses. The paper also suggests that the country of practice influences how practitioners perceive and respond to ethical challenges and dilemmas. Through doing so, the findings challenge traditional understandings of critical practice theories that suggest that practice focused solely on the individual level is inadequate for work with people seeking asylum. Due to the diversity of contexts and their influences, we encourage social work and human services to view practice on a personal-political continuum when working with people seeking asylum.
Deep Reinforcement Learning for Semisupervised Hyperspectral Band Selection

Band selection is an important step in efficient processing of hyperspectral images (HSIs), which can be seen as the combination of powerful band search technique and effective evaluation criterion. The existing deep-learning-based methods make the network parameters sparse to search the spectral bands using threshold-based functions or regularization terms. These methods may lead to an intractable optimization problem. Furthermore, these methods need to repeatedly train deep networks for evaluating candidate band subsets. In this article, we formalize hyperspectral band selection as a reinforcement learning (RL) problem. Band search is regarded as a sequential decision-making process, where each state in the search space is a feasible band subset. To evaluate each state, a semisupervised convolutional neural network (CNN), called EvaluateNet, is constructed by adding the intraclass compactness constraint of both limited labeled and sufficient unlabeled samples. A simple stochastic band sampling method is designed to train EvaluateNet, making it possible to efficiently evaluate without any fine-tuning. In RL, new reward functions are defined by taking the EvaluateNet and the penalty of repeated selection into account. Finally, advantage actor–critic algorithms are designed to explore in the state space and select the band subset according to the expected accumulated reward. The experimental results on HSI data sets demonstrate the effectiveness and efficiency of the proposed algorithms for hyperspectral band selection.
Staying connected while physically apart: Digital communication when face-to-face interactions are limited

Theoretical and empirical work on digital media use and social connectedness has often considered face-to-face communication to be an available option. But how do various digital media uses relate to social connectedness when face-to-face communication is not, or much less, possible? Drawing on survey data from 2925 US adults during the early months of the COVID-19 pandemic, we find that different digital communication methods display different relationships with social connectedness under stay-at-home circumstances with limited in-person interactions outside the home. Overall, digital communication relates to lower social connectedness. In line with notions from social presence theory, especially digital media lower in social presence (e.g. email, social media, and online games, and to some extent text messaging) relate negatively to social connectedness, while this is not the case for higher social presence media (e.g. voice and video calls). Our study has implications for theorizing about digital media use and social connectedness in times when face-to-face communication is less available.