Miracle-dz/UniFAR-FTUs huggingface dataset:
Down-Link NOMA With Successive Refinement for Binary Symmetric Source Transmission

This paper focuses on a lossy transmission of binary symmetric source (BSS) with down-link non-orthogonal multiple access (DL-NOMA). The transmitted binary sequences are lossy-compressed descriptions of the same BSS, which are overlapped in a specified signal format, corresponding to the receivers with pre-determined distortion requirements. Despite a higher spectrum efficiency achieved by DL-NOMA, redundancy may still remain if the overlapped descriptions are correlated. In this work, a system combining DL-NOMA and successive refinement, referred to as DN-SR, is proposed. Specifically, instead of re-constructing the source by a single description, the receiver can achieve a low distortion by first recovering the basic description, and then refining it with the help of another refinement description. If the two descriptions are made independent, transmission efficiency of the conventional DL-NOMA can be further improved. As the main contribution, this paper derives the outage probability of DN-SR in closed-form, assuming block Rayleigh fading channels. The advantage of DN-SR is numerically studied in terms of the system outage probability, compared to both the conventional DL-NOMA, and a modified DL-NOMA which exploits the correlation between descriptions, referred to as DN-CE. Finally, the optimal power allocation to the two descriptions is investigated for DN-SR, aiming to minimize the system outage probability.
Effect of concentrate finishing on the carotenoid content of perirenal fat in grazing sheep: its significance for discriminating grass-fed, concentrate-fed and concentrate-finished grazing lambs

Carotenoid pigments are good biomarkers of grass-feeding in sheep. However, as grazing lambs may be concentrate-finished, we investigated the effect of concentrate finishing after grazing on the carotenoid concentration of fat in sheep, and its significance for discriminating lamb production systems. In experiment 1 with 32 lambs, four feeding treatments were compared: G = grazing, C = feeding a concentrate-based diet, GSC = short concentrate-finishing period after grazing, and GLC = long concentrate-finishing period after grazing. The concentrate supply was regulated to have similar average daily gain for all treatments. In experiment 2, 42 ewes were given a low carotenoid diet after pasture feeding, for 0, 8, 22, 42, 84 or 126 days. The feeding level was adjusted to maintain body weight and body condition score. The nature and concentration of carotenoids was analysed by HPLC on diets and lambs’ perirenal fat. The reflectance spectrum of perirenal fat was measured at slaughter. Lutein was the only carotenoid in lamb fat. In experiment 1, the intensity of light absorption by lutein was linearly related with its concentration in the fat ( P via a dilution of existing fat with whiter fat rather than through pigments coming out of the fat. The concentration of lutein and the intensity of its light absorption in the fat decreased with lamb body-weight gain during the finishing period ( P P P
Runtime Performance Optimization of 3-D Microprocessors in Dark Silicon

Because the increasing power density is limited by the thermal constraint, multi-core integrated systems have stepped into the dark silicon era recently, meaning not all parts of the system can be powered on at the same time. Dark silicon effects are, especially severe for 3-D microprocessors due to the even higher power density caused by the stacked structures, which greatly limit the system performances. In this article, we propose a greedy based core-cache co-optimization algorithm to optimize the performance of 3-D microprocessors in dark silicon at runtime. The new method determines many runtime settings of the 3-D system on the fly, including the active core and cache bank positions, active cache bank number, and the voltage/frequency (V/f) level of each active core, which optimizes the performance of the 3-D microprocessor under thermal constraint. Because the core-cache settings are co-optimized in the 3-D space and the power budgets are computed dynamically according to the running state of the 3-D microprocessor, the new method leads to a higher system performance compared with the existing methods. Experiments on two 3-D microprocessors show the greedy-based core-cache co-optimization algorithm outperforms the state-of-the-art 3-D dark silicon microprocessor performance optimization method by achieving a higher processing throughput with guaranteed thermal safety.
Catalogue of morphological scale deformities from 13 species of freshwater fish from the Kaniv Reservoir (Dnieper), Ukraine

Among the morphological anomalies that have been reported in fish so far, abnormalities in scale shape and structure have been described from a limited number of species worldwide. The aim of the present study was to extend this knowledge by identifying and describing such scale abnormalities in freshwater fish from the Kaniv Reservoir, Ukraine. Scale deformities were common in most of the species investigated (Cyprinidae, 10 spp.; Percidae, 2 spp.; Esocidae, 1 sp.), with some differences in the number of cases among them. A wide variety of scale deformities was found, classified into two major categories, slight and severe. In all, 211 abnormal scales (172 slight and 39 severe deformities) were observed and described. The scale deformities described herein could help direct future work on the relationships between the condition of the environment and fish health.
Attention-based Multi-modal Sentiment Analysis and Emotion Detection in Conversation using RNN

The availability of an enormous quantity of multimodal data and its widespread applications, automatic sentiment analysis and emotion classification in the conversation has become an interesting research topic among the research community. The interlocutor state, context state between the neighboring utterances and multimodal fusion play an important role in multimodal sentiment analysis and emotion detection in conversation. In this article, the recurrent neural network (RNN) based method is developed to capture the interlocutor state and contextual state between the utterances. The pair-wise attention mechanism is used to understand the relationship between the modalities and their importance before fusion. First, two-two combinations of modalities are fused at a time and finally, all the modalities are fused to form the trimodal representation feature vector. The experiments are conducted on three standard datasets such as IEMOCAP, CMU-MOSEI, and CMU-MOSI. The proposed model is evaluated using two metrics such as accuracy and F1-Score and the results demonstrate that the proposed model performs better than the standard baselines.
Dredging-induced turbid plumes affect bio-irrigation and biogeochemistry in sediments inhabited by Lanice conchilega (Pallas, 1766)

Building man-made structures in coastal seas are often preceded by dredging operations, inducing turbid plumes of suspended sediment. To study the effects of such high-concentration sediment plumes on the suspension-feeding polychaete Lanice conchilega, a laboratory experiment was performed, in which individuals of L. conchilega were exposed to natural seawater with a suspended sediment concentration (SSC) of ∼ 0.3 g l−1 and treatments with elevated SSC of 5 and 1 g l−1, representing concentrations in a dredging plume at the moment of sediment release and after initial dilution, respectively. We measured clearance rates of sediment particles, biogeochemical fluxes, and bio-irrigation. While clearance rates and nitrite efflux significantly increased in both treatments with elevated SSC compared with the control, bio-irrigation increased at 1 g l−1 but was lowest at 5 g l−1. It is suggested that piston pumping is intensified under intermediate concentrations to remove sediment, but ceases under high concentrations are due to sediment ingestion. By transporting oxygen into the sediment, bio-irrigation enhances aerobic microbial processes, among which nitrification. We conclude that short-term extreme suspended sediment concentrations can have a significant impact on the biogeochemistry of the seabed through changes in behaviour of L. conchilega.
A Study on the Transformation of Accounting Based on New Technologies: Evidence from Korea

This study identifies the new accounting technologies into Cloud, Artificial Intelligence, Big Data, and Blockchain, and introduces the case of Korean companies applying new technologies to their accounting process. The purpose of this study is to help understand accounting technologies and provide examples of the adoption of these technologies in actual practice. To achieve this aim of the study, a systematic review of the literature of the major academic publications and professional reports and websites was used as a research methodology. In order to select the cases, it performed the analytical process of reviewing Korean major business and economic newspaper articles. This study provides evidence from Korea to companies contemplating the transformation of their accounting process using technology. Such companies can consider the cases presented in this study as a benchmark. It also offers guidance for the application of technologies to accounting practices for businesses and related researchers. The technology transformation is expected to be accelerated, especially after COVID-19. Therefore, it is necessary to understand and explore ways to effectively apply them. Further, while new technologies offer many opportunities, associated risks and threats should be addressed.
An Improved Binary Search Anti-collision Protocol for RFID Tag Identification

: Radio frequency identification (RFID) has been widespread used in massive items tagged domains. However, tag collision increases both time and energy consumption of RFID network. Tag collision can seriously affect the success of tag identification. An efficient anti-collision protocol is very crucially in RFID system. In this paper, an improved binary search anti-collision protocol namely BRTP is proposed to cope with the tag collision concern, which introduces a Bi-response mechanism. In Bi-response mechanism, two groups of tags allowed to reply to the reader in the same slot. According to Bi-response mechanism, the BRTP strengthens the tag identification of RFID network by reducing the total number of queries and exchanged messages between the reader and tags. Both theoretical analysis and numerical results verify the effectiveness of the proposed BRTP in various performance metrics including the number of total slots, system efficiency, communication complexity and total identification time. The BRTP is suitable to be applied in passive RFID systems.
Formation of rivers and mountains drives diversification of primitively segmented spiders in continental East Asia

Complex topography in continental East Asia mirrors geological events such as formation of rivers and mountains, but to what extent these events drive diversification remains underexplored. We address this question by testing vicariant hypotheses using the primitively segmented spider genera Sinothela and Ganthela, focusing on diversification within Sinothela.
Molecular versus morphological approaches to diet analysis of the caracal (Caracal caracal)

Abstract Diet analysis is an essential part in understanding the biology of a species and functioning of ecosystems. Traditional morphological identification of undigested remains in the scats and molecular analyses of prey species’ DNA have previously been used to assess diet. In the present study, caracal diet in the Abbasabad Wildlife Refuge, Central Iran, was investigated using both molecular and morphological methods. We collected 22 scat samples from caracal dens in the region. Feces were washed on sieves and their remaining components were morphologically identified. We also targeted a 307-base pair fragment of the cytochrome b gene to amplify and sequence the species’ DNA. Morphological analyses revealed that 76% of the diet comprised rodent species. We identified a total of nine prey taxa using the molecular method, including six rodents, one hare, one hedgehog and one wild goat. There was a general agreement between the molecular and morphological results; however, molecular methods tended to allow a better identification of the prey species. Therefore, the DNA-based approach acts as a valuable complement to current morphological methods in the study of a rare felid’s diet when no hair reference library exists.
Multi-objective Optimization Design of Variable-Saliency-Ratio PM Motor Considering Driving Cycles

In this article, a variable-saliency-ratio permanent magnet motor is designed and optimized considering driving cycles. To satisfy the demands of multi-operation conditions of EVs, a variable resistance is adopted and a new variable-saliency-ratio design concept is proposed to enhance output torque, widen speed range and lower irreversible demagnetization risk effectively. A multiobjective optimization considering driving cycles is proposed, where the design objective of saliency ratio is firstly selected and optimized with multiobjective genetic algorithm method. Then, the operation performances of the motor in different operation conditions are investigated in detail. Finally, a prototype motor is built and tested. Both the simulation and experimental results verify the validity of the proposed method.
Bringing New Meaning to the Term “Adaptive Trial”: Challenges of Conducting Clinical Research During the Coronavirus Disease 2019 Pandemic and Implications for Implementation Science

Abstract Although implementation of evidence-based practices takes an average of 17 years, in the context of the global pandemic, coronavirus disease 2019 (COVID-19) interventions were adopted in a greatly compressed time frame. This rapid uptake creates major challenges for conducting COVID-19 clinical research studies, because quickly evolving standards make it difficult to adapt in real time. The rapid dissemination and implementation of COVID-19 interventions is the realization of goals long pursued by the implementation science community. However, the downside of the rapid implementation is that low-quality evidence with little to no scientific vetting may be quickly integrated into clinical care, resulting in lost opportunities to advance our scientific understanding about how to manage infected patients. In the future, novel adaptive designs embedded into electronic health records (Embedded Quantified, Integrated-into-Practice Trial [EQuIPT] designs) that allow for easier and better access to clinical trials may simultaneously improve care and advance healthcare innovations.
The Labor-Intensive Path: Wages, Incomes, and the Work Year in Japan, 1610–1890

I use new evidence from servant contracts, 1610–1890, to estimate male farm wages and the length of the work year in Japan. I show Japanese laborers were surprisingly poor and could only sustain 2–3 adults relative to 7 adults for the English. Japanese wages were the lowest among pre-industrial societies and this was driven by Malthusian population pressures. I also estimate the work year and find peasants worked 325 days a year by 1700, predating the “industrious” revolution in Europe. The findings imply Japan had a distinct labor-intensive path to industrialization, utilizing cheap labor over a long work year.
Could Mergers Become More Sustainable? A Study of the Stock Exchange Mergers of NASDAQ and OMX

This study investigates whether the merger of NASDAQ and OMX could reduce the portfolio diversification possibilities for stock market investors and whether it is necessary to implement national policies and international treaties for the sustainable development of financial markets. Our study is very important because some players in the stock markets have not yet realized that stock exchanges, during the last decades, have moved from government-owned or mutually-owned organizations to private companies, and, with several mergers having occurred, the market is tending gradually to behave like a monopoly. From our analysis, we conclude that increased volatility and reduced diversification opportunities are the results of an increase in the long-run comovement between each pair of indices in Nordic and Baltic stock markets (Denmark, Sweden, Finland, Estonia, Latvia, and Lithuania) and NASDAQ after the merger. We also find that the merger tends to improve the error-correction mechanism for NASDAQ so that it Granger-causes OMX, but OMX loses predictive power on NASDAQ after the merger. We conclude that the merger of NASDAQ and OMX reduces the diversification possibilities for stock market investors and our findings provide evidence to support the argument that it is important to implement national policies and international treaties for the sustainable development of financial markets.
LocMIA: Membership Inference Attacks Against Aggregated Location Data

An increasing amount of users’ locations are aggregated, and the statistical results about the collected data are further released to support mobile applications, such as point-of-interest recommendation and smart transportation. However, such statistical results cause users’ membership privacy leakage. Unfortunately, most studies concerning data aggregation focused on privacy preservation and various attacks rather than the membership inference attacks. Moreover, literature about membership inference attacks mainly aimed at machine learning models and gene sequences rather than the locations in data aggregation. More importantly, these work concerning membership inference attacks assumed that adversaries know the exact data of victims, which is always impossible in practical scenarios. To this end, we propose LocMIA, a more invasive attack system that allows adversaries to launch membership inference attacks against aggregated location data without reliance on any prior knowledge of the locations of victims. The main idea of LocMIA is to train a binary classifier to infer whether a specific victim’s location data is involved in the aggregation group, based solely on the data aggregation’s output (i.e., the statistical results). Finally, experimental results on a real-world check-in data set prove the devastating privacy leaks caused by the proposed LocMIA.
Faith It till You Make It: A CYC Course-Based Qualitative Inquiry

The purpose of this course-based research study was to explore the perceptions of faith-based child and youth care (CYC) students in completing a third-year expressive arts-based CYC practice self-image assignment that invited them to incorporate their personal spiritual beliefs as a component of their CYC identity. The course-base study also sought to examine the potential of arts-based assignment in creating a safe medium to express one’s faith in CYC education. The focus group method was the primary data collection strategy. Thematic analysis resulted in the identification of three themes: a) relational safety within one’s environment, b) fear of judgement from classmates and coworkers, and c) desire for authenticity within one’s CYC identity. The results of this course-based study indicate that for many CYC students, the artsbased CYC practice self-portrait assignment was a safe place to express their faith and spiritual beliefs.
Performance Evaluation of Machine Learning Techniques for Mustard Crop Yield Prediction from Soil Analysis

: Soil is an important parameter affecting crop yield prediction. Analysis of soil nutrients can aid farmers and soil analysts to get higher yield of the crops by making prior arrangements. In this paper, various machine learning techniques have been implemented in order to predict Mustard Crop yield in advance from soil analysis. Data for the experimental set-up has been collected from Department of Agriculture Department, Talab Tillo, Jammu; comprising soil samples of different districts of Jammu region for Mustard crop. For the current study, five supervised machine learning techniques namely K-Nearest Neighbor (KNN), Naïve Bayes, Multinomial Logistic Regression, Artificial Neural Network (ANN) and Random Forest have been applied on the collected data. To assess the performance of each technique under study; five parameters namely accuracy, recall, precision, specificity and f-score have been evaluated. Experimentation has been carried out to make known the most accurate technique for mustard crop yield prediction. From experimental results, it has been predicted that KNN and ANN (among the undertaken ML techniques for the study) found to be most accurate techniques for mustard crop yield prediction.
A review of age estimation methods in marine mammals with special reference to monodontids

This paper presents a critical review of methods for estimating absolute or relative age in marine mammals. Absolute age is achieved by counting growth layer groups (GLGs) in hard structures such as teeth, ear plugs, baleen, bones and claws. Relative age can be obtained by methods such as aspartic acid racemisation, genetic telomeres, bone mineral density, fatty acid signatures and other methods. Each method is discussed in detail. Accuracy and precision, including inter-reader calibration and anomalies, as well as methods of validating GLG deposition rates are also addressed. Each section concludes with methods of age estimation applicable to monodontids, and suggestions on the focus of future age-estimation research.
Prophet: Toward Fast, Error-Tolerant Model-Based Throughput Prediction for Reactive Flows in DC Networks

As modern network applications (e.g., large data analytics) become more distributed and can conduct application-layer traffic adaptation, they demand better network visibility to better orchestrate their data flows. As a result, the ability to predict the available bandwidth for a set of flows has become a fundamental requirement of today’s networking systems. While there are previous studies addressing the case of non-reactive flows, the prediction for reactive flows, e.g., flows managed by TCP congestion control algorithms, still remains an open problem. In this paper, we take the first step to solving this problem in a data center network. To address both theoretical and practical challenges, we introduce a novel learning-based prediction system based on the NUM model, with two key techniques named fast factor learning (FFL) and efficient flow sampling. We adopt novel techniques to overcome practical concerns such as scalability, convergence and unknown system parameters. A system, Prophet, is proposed leveraging the emerging technologies of Software Defined Networking (SDN) to realize the model. Evaluations demonstrate that our solution achieves significant accuracy in a wide range of settings.
Free-Living Diazotrophic Rhizobacteria with Plant Growth Promoting Traits Isolated from the Rhizosphere of Wheat Grown on Saline Soil in West Algeria

*Corresponding Author: Hamoum Hakim, Laboratory of Microbiology and Plant Biology, Faculty of Natural Sciences and Life, University of Mostaganem, Algeria, E-mail: hakim.hamoum@univ-mosta.dz Phone: +213 770 00 88 34 Address: P O Box 36 Lardjem Tissemsilt 38002 ALGERIA Free-Living Diazotrophic Rhizobacteria with Plant Growth Promoting Traits Isolated from the Rhizosphere of Wheat Grown on Saline Soil in West Algeria