Miracle-dz/UniFAR-FTUs huggingface dataset:
Isoform Sequencing Based Transcriptome Resource for Flathead Grey Mullet (Mugil cephalus)

Mugil cephalus, commonly known as flathead grey mullet, is an important candidate brackish water food fish species of herbivorous nature positioned at lower trophic levels of the food chain. The biology, genetics, ecology, and fishing aspects of grey mullet were thoroughly reviewed by Whitfield et al. (2012). In the Indian context, though captive broodstock of grey mullet was developed about two decades back (Abraham et al., 2000), large-scale successful induced breeding trials were realized only recently (Sukumaran et al., 2021). Further, the reproductive period of captive grey mullet is reported to be asynchronous between the Eastern and Western Coast stocks of India. The peak reproductive periods are November and June–August for Eastern and Western grey mullet captive stocks, respectively (Sukumaran et al., 2021). A year-round commercial seed production with captive broodstock could not be realized till date due to constraints in captive maturation and seasonal spawning behavior of the species. The availability of genomic and transcriptomic resources would lead to a better understanding of the regulatory pathways involved in captive maturation and breeding of grey mullet. Such information would also facilitate the conduct of functional studies and derive meaningful inferences out of them for the improvement of grey mullet. Conventionally, transcriptome resources are established by sequencing RNA on an Illumina sequencer wherein the short RNA sequence reads are assembled to generate full-length transcripts. With the development of Pacific Biosciences (PacBio) Single Molecule Real-Time (SMRT) sequencing, the full-length isoform-level transcript sequences could be generated directly without the necessity of assembly. Recently, the PacBio Isoform Sequencing (IsoSeq) based full-length transcript resources were made available for candidate brackish water aquaculture species (Zhang et al., 2019; Katneni et al., 2020; Pootakham et al., 2020) with potential applications to an understanding of economic traits and role in genome annotation. Furthermore, the IsoSeq transcripts can be easily annotated (Zeng et al., 2018) and would help in the discovery of splice sites and structural annotation of the genome. For grey mullet, transcript information based on short-read sequence datasets is available (Byadgi et al., 2016; Dor et al., 2020), but no full-length transcript resource is available. So far, a
Whole blood and peripheral mononuclear cell 1 transcriptional response to prolonged altitude exposure in 2 well-trained runners 3

Recombinant human erythropoietin (rHuEpo) abuse by athletes threatens the integrity of 26 sport. Due to the overlap in physiological response to rHuEpo and altitude exposure, it 27 remains difficult to differentiate changes in haematological variables caused by rHuEpo or altitude and therefore, other molecular methods to enhance anti-doping should be explored. Objective: To identify the haematological and transcriptomic response to prolonged altitude exposure typical of practices used by elite athletes. and Main Outcome Measures: Blood samples were analysed for haemoglobin concentration, haematocrit and reticulocyte percentage. The transcriptomic response in whole blood and 39 peripheral blood mononuclear cells (PBMC) were analysed using gene expression 40 microarrays. Results: A unique set of 29 and 10 genes were identified to be commonly expressed at every 42 altitude timepoint in whole blood and PBMC, respectively. There were no genes identified 43 upon return to sea level in whole blood and only one gene within PBMC. 44 Conclusions: The current study has identified a series of unique genes that can now be 45 integrated with genes previously validated for rHuEpo abuse, thereby enabling the 46 differentiation of rHuEpo from the altitude confounder.
Three‐phase ripple free DCM boost converter with low THD

High total harmonic distortion (THD) and ripple of input current are the two main disadvantages of a single switch discontinuous current mode boost power factor correction (PFC) converter. In this study, THD and input current ripple of conventional DCM boost converter are improved. Reduction of input current THD is achieved by reducing the discharge time of input inductors using magnetic switch concept. Input inductors currents are discharged with a voltage equal to twice of output voltage instead of the output voltage. Also, high input current ripple in discontinuous conduction mode (DCM) operation is solved by using an additional input inductor-capacitor (LC) filter in the conventional converter. This will result in extra weight and volume of the converter. To eliminate the required additional inductors for implementation of the mentioned input LC filter, a ripple cancellation cell using coupled inductors is used and by adjusting mutual inductance in a proper manner, very low input current ripple is achieved. Results from a 400 W prototype converter are presented. Input current THD is below 8% for a wide range of input voltage. Furthermore, the efficiency of the proposed converter is compared with conventional DCM boost PFC converter under the same input current THD. Although the proposed converter applies two switches, the total switching and conduction losses are reduced considerably.
Wide and Deep Fourier Neural Network for Hyperspectral Remote Sensing Image Classification

Hyperspectral remote sensing image (HSI) classification is very useful in different applications, and recently, deep learning has been applied for HSI classification successfully. However, the number of training samples is usually limited, causing difficulty in use of very deep learning models. We propose a wide and deep Fourier network to learn features efficiently by using pruned features extracted in the frequency domain. It is composed of multiple wide Fourier layers to extract hierarchical features layer-by-layer efficiently. Each wide Fourier layer includes a large number of Fourier transforms to extract features in the frequency domain from a local spatial area using sliding windows with given strides.These extracted features are pruned to retain important features and reduce computations. The weights in the final fully connected layers are computed using least squares. The transform amplitudes are used for nonlinear processing with pruned features. The proposed method was evaluated with HSI datasets including Pavia University, KSC, and Salinas datasets. The overall accuracies (OAs) of the proposed method can reach 99.77%, 99.97%, and 99.95%, respectively. The average accuracies (AAs) can achieve 99.55%, 99.95%, and 99.95%, respectively. The Kappa coefficients are as high as 99.69%, 99.96%, and 99.94%, respectively. The experimental results show that the proposed method achieved excellent performance among other compared methods. The proposed method can be used for applications including classification, and image segmentation tasks, and has the ability to be implemented with lightweight embedded computing platforms. The future work is to improve the method to make it available for use in applications including object detection, time serial data prediction, and fast implementation.
Synchrotron Radiation Study of Gain, Noise, and Collection Efficiency of GaAs SAM-APDs with Staircase Structure

In hard X-ray applications that require high detection efficiency and short response times, such as synchrotron radiation-based Mössbauer absorption spectroscopy and time-resolved fluorescence or photon beam position monitoring, III–V-compound semiconductors, and dedicated alloys offer some advantages over the Si-based technologies traditionally used in solid-state photodetectors. Amongst them, gallium arsenide (GaAs) is one of the most valuable materials thanks to its unique characteristics. At the same time, implementing charge-multiplication mechanisms within the sensor may become of critical importance in cases where the photogenerated signal needs an intrinsic amplification before being acquired by the front-end electronics, such as in the case of a very weak photon flux or when single-photon detection is required. Some GaAs-based avalanche photodiodes (APDs) were grown by a molecular beam epitaxy to fulfill these needs; by means of band gap engineering, we realised devices with separate absorption and multiplication region(s) (SAM), the latter featuring a so-called staircase structure to reduce the multiplication noise. This work reports on the experimental characterisations of gain, noise, and charge collection efficiencies of three series of GaAs APDs featuring different thicknesses of the absorption regions. These devices have been developed to investigate the role of such thicknesses and the presence of traps or defects at the metal–semiconductor interfaces responsible for charge loss, in order to lay the groundwork for the future development of very thick GaAs devices (thicker than 100 μm) for hard X-rays. Several measurements were carried out on such devices with both lasers and synchrotron light sources, inducing photon absorption with X-ray microbeams at variable and controlled depths. In this way, we verified both the role of the thickness of the absorption region in the collection efficiency and the possibility of using the APDs without reaching the punch-through voltage, thus preventing the noise induced by charge multiplication in the absorption region. These devices, with thicknesses suitable for soft X-ray detection, have also shown good characteristics in terms of internal amplification and reduction of multiplication noise, in line with numerical simulations.
When IoT Data Meets Streaming in the Fog

IoT and video streaming are the main driving applications for digital data generation today. The traditional way of storing and processing data in the Cloud cannot satisfy many latency critical applications. This is why Fog computing emerged as a continuum infrastructure from the Cloud to end-user devices. Misplacing data in such an infrastructure results in high latency, and consequently increases the penalty for Internet Service Providers (ISPs) incurred by violating the service level agreement (SLA). In past studies, two issues have been investigated separately: the IoT data placement and the streaming cache placement. However, both placements rely on the same Fog distributed storage system. In this paper, we address those issues in a unique model with the aim to minimize the penalty for ISPs incurred by the SLA violation and maximize storage resources usage. We subdivided each Fog node storage space into a storage part and a cache part. First, our model consists in placing IoT data in the storage part of Fog nodes, and then placing streaming data in the cache part of these nodes. The novelty of our model is the flexibility it offers for managing the cache volume, which can, adaptively, spill on the free part dedicated to IoT data. Experiments show that using our model makes it possible to reduce the streaming data penalty of the ISP’s SLA violation by more than 47% on average.
Adaptive Kernel Kalman Filter Based Belief Propagation Algorithm for Maneuvering Multi-Target Tracking

This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for Multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based Multi-target tracking (MTT) BP algorithm.
Credit Strategy Design of Small and Medium-Sized Enterprises

Small and medium-sized enterprises play an important role in promoting social and economic growth, which is an important foundation for the development of China’s national economy. From the perspective of bank interests, this paper uses the invoice data of small and medium-sized enterprises to evaluate their credit risk and formulates corresponding bank credit strategies for these enterprises in different industries and properties. Firstly, the credit risk identification factor system is constructed by using feature engineering, and the quantitative model of enterprise credit risk is built based on back propagation (BP) neural network to predict the default probability of enterprises. On this basis, the k-prototypes clustering algorithm is used to classify enterprises. According to the default probability of each enterprise and the loss rate under different interest rates, a nonlinear programming model, with the maximum expected profit of banks as the goal, is constructed. The simulated annealing algorithm is used to obtain the optimal solution of the credit strategy.
Possible Dynamic Mechanisms of High‐ and Low‐Latitude Wave Trains Over Eurasia and Their Impacts on Air Pollution Over the North China Plain in Early Winter

The Rossby wave train in boreal winter sometimes bifurcates into two branches near the Mediterranean, sometimes not. However, causes for bifurcating of the wave train are still being debated. Additionally, discussions about the differences in the deterioration ability of these wave trains on air pollution are also insufficient. This study uses the empirical orthogonal function (EOF) to obtain two dominant modes of the November‐January season wave trains over Eurasia and discusses their different influences on air pollution over the North China Plain (NCP). From the EOF1 mode, bifurcation of the wave train appears to stem from the Rossby wave reflection near the Mediterranean because the Rossby wave source is located outside of a strong absolute vorticity gradient. From the EOF2 mode, the wave train propagates only along the subtropical westerly jet because the Rossby wave source is located inside of a strong absolute vorticity gradient, so that the wave train tends to be refracted near the Mediterranean. In addition, the wave train associated with the EOF1 is suggested to be related to the eastern Pacific El Niño. The eastern Pacific El Niño tends to force a wave train propagating into East Asia under the charger effect from positive feedback of air‐sea coupling near the far eastern Atlantic and Mediterranean, while the wave train associated with the EOF2 is suggested to be mainly induced by natural internal variability. Furthermore, the results indicate that the bifurcation type wave train tends to cause higher PM2.5 concentrations over the NCP through a strengthened Northeast Asia anomalous anticyclone.
Online Learning-Based Rate Selection for Wireless Interactive Panoramic Scene Delivery

Interactive panoramic scene delivery not only consumes 4∼6× more bandwidth than traditional video streaming of the same resolution but also requires timely displaying the delivered content to ensure smooth interaction. Since users can only see roughly 20% of the entire scene at a time (called the viewport), it is sufficient to deliver the relevant portion of the panoramic scene if we can accurately predict the user’s motion. It is customary to deliver a portion larger than the viewport to tolerate inaccurate predictions. Intuitively, the larger the delivered portion, the higher the prediction accuracy and lower the wireless transmission success probability. The goal is to select an appropriate delivery portion to maximize system throughput. We formulate this problem as a multi-armed bandit problem and use the classical Kullback-Leibler Upper Confidence Bound (KL-UCB) algorithm for the portion selection. We further develop a novel variant of the KL-UCB algorithm that effectively leverages two-level feedback (i.e., both prediction and transmission outcomes) after each decision on the selected portion and show its asymptotical optimality, which may be of independent interest by itself. We demonstrate the superior performance of our proposed algorithms over existing heuristic methods using both synthetic simulations and real experimental evaluations.
R2R3-MYBs in Durum Wheat: Genome-Wide Identification, Poaceae-Specific Clusters, Expression, and Regulatory Dynamics Under Abiotic Stresses

MYB transcription factors (TFs) represent one of the biggest TF families in plants, being involved in various specific plant processes, such as responses to biotic and abiotic stresses. The implication of MYB TFs in the tolerance mechanisms to abiotic stress is particularly interesting for crop breeding, since environmental conditions can negatively affect growth and productivity. Wheat is a worldwide-cultivated cereal, and is a major source of plant-based proteins in human food. In particular, durum wheat plays an important role in global food security improvement, since its adaptation to hot and dry conditions constitutes the base for the success of wheat breeding programs in future. In the present study, a genome-wide identification of R2R3-MYB TFs in durum wheat was performed. MYB profile search and phylogenetic analyses based on homology with Arabidopsis and rice MYB TFs led to the identification of 233 R2R3-TdMYB (Triticum durum MYB). Three Poaceae-specific MYB clusters were detected, one of which had never been described before. The expression of eight selected genes under different abiotic stress conditions, revealed that most of them responded especially to salt and drought stress. Finally, gene regulatory network analyses led to the identification of 41 gene targets for three TdR2R3-MYBs that represent novel candidates for functional analyses. This study provides a detailed description of durum wheat R2R3-MYB genes and contributes to a deeper understanding of the molecular response of durum wheat to unfavorable climate conditions.
D2BF—Data-Driven Beamforming in MU-MIMO with Channel Estimation Uncertainty

Accurate estimation of Channel State Information (CSI) is essential to design MU-MIMO beamforming. However, errors in CSI estimation are inevitable in practice. State-of-the-art works model CSI as random variables and assume certain specific distributions or worst-case boundaries, both of which suffer performance issues when providing performance guarantees to the users. In contrast, this paper proposes a Data-Driven Beamforming (D2 BF) that directly handles the available CSI data samples (without assuming any particular distributions). Specifically, we employ chance-constrained programming (CCP) to provide probabilistic data rate guarantees to the users and introduce ∞-Wasserstein ambiguity set to bridge the unknown CSI distribution with the available (limited) data samples. Through problem decomposition and a novel bilevel formulation for each subproblem, we show that each subproblem can be solved by binary search and convex approximation. We also validate that D2 BF offers better performance than the state-of-the-art approach while meeting probabilistic data rate guarantees to the users.
Sustainable Smart Industry: A Secure and Energy Efficient Consensus Mechanism for Artificial Intelligence Enabled Industrial Internet of Things

In recent years, the Internet of Things (IoT) has been industrializing in various real-world applications, including smart industry and smart grids, to make human existence more reliable. An overwhelming volume of sensing data is produced from numerous sensor devices as the Industrial IoT (IIoT) becomes more industrialized. Artificial Intelligence (AI) plays a vital part in big data analyses as a powerful analytic tool that provides flexible and reliable information insights in real-time. However, there are some difficulties in designing and developing a useful big data analysis tool using machine learning, such as a centralized approach, security, privacy, resource limitations, and a lack of sufficient training data. On the other hand, Blockchain promotes a decentralized architecture for IIoT applications. It encourages the secure data exchange and resources among the various nodes of the IoT network, removing centralized control and overcoming the industry's current challenges. Our proposed approach goal is to design and implement a consensus mechanism that incorporates Blockchain and AI to allow successful big data analysis. This work presents an improved Delegated Proof of Stake (DPoS) algorithm-based IIoT network that combines Blockchain and AI for real-time data transmission. To accelerate IIoT block generation, nodes use an improved DPoS to reach a consensus for selecting delegates and store block information in the trading node. The proposed approach is evaluated regarding energy consumption and transaction efficiency compared with the exciting consensus mechanism. The evaluation results reveal that the proposed consensus algorithm reduces energy consumption and addresses current security issues.
Representation of moist convective processes in Coupled Model Intercomparison Project Phase 5 and Phase 6 models for the simulation of Indian summer monsoon intraseasonal variability

This study assesses the simulation of the Boreal Summer Intraseasonal Oscillation (BSISO), particularly the moist convective processes associated with the BSISO in 10 models from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Most of the models show a slower northward propagation of the BSISO and cannot simulate the origin of positive rainfall anomalies over the equatorial Indian Ocean. The generation of barotropic vorticity to the north of the convection centre leads to the northward shift of moisture convergence resulting in the northward propagation of convection. This mechanism is also insufficient in the models. The models show a lack of consistent evolution of moisture with increasing rain rates. Some of the CMIP6 models such as MPI‐ESM1‐2‐LR, FGOALS‐f3‐L, and NorESM2‐MM exhibit better northward propagation and the associated moist processes. A random relationship between vertical velocity, specific humidity, and precipitation is seen in the observation. On the other hand, the models show a deterministic relationship among these parameters with a much higher fraction of convective rain than large‐scale rain. Only FGOALS‐f3‐L (from the CMIP6 family), which uses convection resolving precipitation parameterization, where the convective and stratiform precipitation is calculated explicitly, produces the variability seen in the observation to a large extent.
An Analysis of Intellectual Property Challenges in Crowdsourcing Platforms for Software Engineering

Crowdsourced Software Engineering (CSE) is an application of online problem-solving model that provides a dynamic way of harnessing crowd intelligence in obtaining creative solutions. It consists of four elements: crowdsourcer, crowd, platform, and tasks. Although CSE offers various advantages to software engineering practices, recent research highlighted that there is still a lack of thorough understanding of Intellectual Property (IP) ownership rights of CSE elements. Therefore, management and control of CSE for obtaining adequate IP from the crowd is inevitable to reduce the risks of using that IP. This paper reviews existing CSE platforms and analyses associated IP challenges encountered by software engineering activities. 51 platforms were preselected and amongst these, analyses were performed on 37 CSE platforms. Exclusion of 11 platforms is on the basis of unavailability and 3 of which are linked to the same webpage. It also shows that 59% of the platforms acquire IP ownership of deliverables submitted by the crowd while 19% do not have a statement which provides clarity on IP ownership in their legal documents. This paper is significant in providing better understanding of IP risks of these CSE platforms and in assisting both crowdsourcers and the crowd in choosing crowdsourcing platforms based on the default IP ownership specification, which is often specified in legal documents of these platforms.