Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and analysis of ultra‐low power 18T adaptive data track flip‐flop for high‐speed application

This paper proposes a novel master slave (MS) flip‐flop design achieved by using only 18 transistors with a single‐phase clock and mixed topology. This design has lowest complexity, so flip‐flop basically focuses on the performance issue such as delay (TCQ) and average power consumption and compared with the other existing logic structured flip‐flops. The proposed circuit is implemented at 65‐nm Complementary Metal‐Oxide Semiconductor (CMOS), and 18‐nm finFET technology node using cadence virtuoso. The proposed flip‐flop architecture have outperformed transmission gate flip‐flop (TGFF) in terms of power (i.e., 74.52%). It is also showing improvement in terms of power as compared to 18‐transistor single‐phase clocking (18TSPC). This work also enhances the speed by reducing the delay minimum of 11.28% and PDP minimum of 37.18%. By using adaptive pass transistors topology to construct flip‐flop, the total area of the proposed flip‐flop reduces by a minimum of 4.78% with respect to 18TSPC, and also with the other flip‐flops reported in this paper. The proposed circuit can work properly within the frequency range up to 2‐GHz clock frequency. Monte Carlo simulations of Power and C to Q delay have been performed for 1000 samples.
β-Cell Knockout of SENP1 Reduces Responses to Incretins and Worsens Oral Glucose Tolerance in High-Fat Diet–Fed Mice

SUMOylation reduces oxidative stress and preserves islet mass at the expense of robust insulin secretion. To investigate a role for the deSUMOylating enzyme sentrin-specific protease 1 (SENP1) following metabolic stress, we put pancreas/gut-specific SENP1 knockout (pSENP1-KO) mice on a high-fat diet (HFD). Male pSENP1-KO mice were more glucose intolerant following HFD than littermate controls but only in response to oral glucose. A similar phenotype was observed in females. Plasma glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1) responses were identical in pSENP1-KO and wild-type littermates, including the HFD-induced upregulation of GIP responses. Islet mass was not different, but insulin secretion and β-cell exocytotic responses to the GLP-1 receptor agonist exendin-4 (Ex4) and GIP were impaired in islets lacking SENP1. Glucagon secretion from pSENP1-KO islets was also reduced, so we generated β-cell–specific SENP1 KO mice. These phenocopied the pSENP1-KO mice with selective impairment in oral glucose tolerance following HFD, preserved islet mass expansion, and impaired β-cell exocytosis and insulin secretion to Ex4 and GIP without changes in cAMP or Ca2+ levels. Thus, β-cell SENP1 limits oral glucose intolerance following HFD by ensuring robust insulin secretion at a point downstream of incretin signaling.
Know Your Enemy, Know Yourself: A Unified Two-Stage Framework for Speech Enhancement

Traditional spectral subtraction-type single channel speech enhancement (SE) algorithms often need to estimate interference components including noise and/or reverberation before sub-tracting them while deep neural network-based SE methods often aim to realize the end-to-end target mapping. In this paper, we show that both denoising and dereverberation can be uniﬁed into a common problem by introducing a two-stage paradigm, namely for interference components estimation and speech recovery. In the ﬁrst stage, we propose to explicitly extract the magnitude of interference components, which serves as the prior information. In the second stage, with the guidance of this estimated magnitude prior, we can expect to better recover the target speech. In addition, we propose a transform module to facilitate the interaction between interference components and the desired speech modalities. Meanwhile, a temporal fusion module is designed to model long-term dependencies without ignoring short-term details. We conduct the experiments on the WSJ0-SI84 corpus and the results on both denoising and dere-verberation tasks show that our approach outperforms previous advanced systems and achieves state-of-the-art performance in terms of many objective metrics.
Accounting for Slot Harmonics and Nonsinusoidal Unbalanced Voltage Supply in High-Speed Solid-Rotor Induction Motor Using Complex Multi-Harmonic Finite Element Analysis

Solid rotor induction machines are still used in high-speed systems. A two-dimensional field-circuit model based on the finite element method and the complex magnetic vector potential has been shown as a very time-effective tool in the analysis of their steady states compared to time-domain models. This continuation work presents a validated computational algorithm that enables the inclusion of the nonsinusoidal and/or asymmetrical voltage supply in the multi-harmonic field-circuit model of these machines that was presented in the previous works by the authors. The extended model accounts for both spatial harmonics due to slotting and/or winding distribution and the time-harmonics due to voltage waveform. The applicability range of the model therefore increases to cases when the machine is supplied with a nonsinusoidal three-phase system of voltages with symmetry or asymmetry that can be decomposed into three symmetrical components. Its short execution time characteristic allows for much more insightful design studies of the contribution of voltage supply- and slotting-related harmonics to the overall efficiency of the machine than is possible with the time-consuming time-domain models. The proposed computational framework has never been presented in the literature. The model is verified positively by the comprehensive time-domain model. It is especially useful in design studies on solid rotor induction motors related to the optimisation of the efficiency of induction motor-based drive systems.
Improved Speech Separation with Time-and-Frequency Cross-Domain Feature Selection

Most deep learning-based monaural speech separation models only use either spectrograms or time domain speech signal as the input feature. The recently proposed cross-domain network (CDNet) demonstrates that concatenated frequency domain and time domain features helps to reach better performance. Although concatenation is a widely used feature fusion method, it has been proved that using frequency domain and time domain features to reconstruct signal makes minor difference compared with only using time domain feature in CDNet. To make better use of frequency domain feature in decoder, we propose using selection weights to select and fuse features from different domains and unify the features used in separator and decoder. In this paper, we propose using trainable weights or the global information calculated from the different domain features to generate selection weights. Given that our proposed models use element-wise fusing in the encoder, only one deconvolution layer in the de-coder is needed to reconstruct signals. Experiments show that proposed methods achieve encouraging results on the large and challenging Libri2Mix dataset with a small increasing in parameters, which proves the frequency domain information is beneficial for signal reconstruction. Furthermore, proposed method has shown good generalizability on the unmatched VCTK2Mix dataset.
Hepatic mRNA Expression Levels of the Oncogenes Alpha-Fetoprotein and Osteopontin as Diagnostics for Liver Cancer in a Murine Model of Diet-Induced Non-Alcoholic Steatohepatitis

Non-alcoholic steatohepatitis (NASH) is associated with an increased risk of hepatocellular carcinoma (HCC). Expression levels of hepatic oncogenes, alpha-fetoprotein (afp) and osteopontin (opn)/secreted phosphoprotein 1 (spp1), were investigated using a model of diet-induced NASH. Mice were randomized to a standard diet or a fast-food diet (FFD) for 17 months. Livers from the FFD cohort exhibited hallmark characteristics of NASH with liver fibrosis, with a subset of animals exhibiting HCC. Expression levels of hepatic afp and opn/spp1 were elevated ~2.5 and ~5-fold, respectively, in the FFD cohort. Hepatic opn/spp1 exhibited a direct (r = 0.65) and significant (p < 0.01) correlation with liver hydroxyproline content. Receiver operating characteristic (ROC) curve analysis for hepatic afp, as a diagnostic for HCC, returned an area under (AU) ROC 0.84, a sensitivity of 87.5%, a specificity of 77% and a threshold of >1.05-fold change in mRNA level. The use of hepatic opn/ssp1 as a diagnostic for HCC returned an AUROC 0.88, a sensitivity of 83.3%, a specificity of 86.7% and a threshold of >2.4-fold change in mRNA level. These data point to a transformation of NASH to an oncotype with hepatic oncogene levels as a diagnostic for NASH.
‘I am finding my path’: A case study of Swedish novice physical education teachers’ experiences when managing the realities and challenges of their first years in the profession

Novice teachers face several challenges during their first years of socialization in schools, often feeling unprepared in managing the full range of teaching duties. If teacher retention and attrition are to be improved, research on the difficulties encountered by novice teachers in diverse contexts and cultures is required. There is a lack of studies regarding physical education (PE) teachers’ induction processes, especially outside Anglophone countries. The aim of this study was, from an occupational socialization perspective, to examine how Swedish novice PE teachers experience, perceive and manage their induction process. Through a single-case study design with embedded multiple units of analysis, we interviewed eight Swedish novice PE teachers. Based on thematic analysis, the results show that Swedish novice PE teachers experience several challenges related to reality shock, marginalization and isolation during induction. We identified three approaches used by the novices as central to successfully managing challenges of induction in the Swedish context: (1) socializing into a community of colleagues, (2) performing the role of the PE teacher as health promoter and (3) maintaining a critical teaching perspective. We conclude that these novice teachers’ socialization relies heavily on the individual, and therefore we argue that the induction process could be further facilitated by formal organizational support. This paper confirms long-standing difficulties reported in other countries, and contributes with new knowledge of how the approaches used when managing challenges of induction are contextually dependent due to the social and political surroundings of education.
Effects of chemical modification and nanotechnology on wood properties

Abstract As a green material, wood is widely used in building decoration, railway construction, and other fields. However, the wood itself has inherent defects of being easy to absorb water and deform, rot, and decrease in strength. The physical and mechanical strengths and stability of artificial fast-growing forest wood are even worse. As wood modification can improve the dimensional stability, durability, strength, and other properties of wood, it has been widely used. Chemical modification is the main method of wood modification. The development of nanotechnology has brought more possibilities for wood modification. Owing to the extensive literature available, this article summarizes the representative achievements of wood chemical modification and nanotechnology. The principle, production process, advantages, and disadvantages of various wood chemical modification methods were analyzed, compared, and evaluated. Finally, according to the application status of wood-modified materials, the problems existing in the current wood chemical modification methods and the application of nanotechnology, and the development trend in the future are analyzed.
The Application of Altman Revised Z-Score Four Variables and Ohlson O-Score as A Bankruptcy Prediction Tool in Small and Medium Enterprise Segments in Indonesia

The small-medium business segment is currently growing in Indonesia, contributing more than one-fifth of Indonesia’s Gross Domestic Product, making this segment is very potential to be developed more. Domestic and foreign banks view this small-medium business segment as a promising segment, and the majority of banks in Indonesia compete to distribute business loans to this segment. However, amid promising prospects, this segment also has its own challenges where the ratio of national non-performing loans exceeds the ratio of non-performing loans to all loans disbursed. This study aims to examine the reliability of the Altman Z-Score and Ohlson O-Score methods as predictors of bankruptcy in Small-Medium Enterprise segment companies in Indonesia and its reliability statements in this segment in reflecting the company’s real conditions as input data for analysis prediction of corporate bankruptcy. The research data were financial report data from companies that were debtors of the Small-Medium Enterprise segment at Bank Mandiri. The results showed that the Z-Score calculation results were able to predict bankruptcy with an accuracy of 51.8%, 37.4%, 36.3%, and 11.4% for one, two, three, and four years before the bankruptcy. Meanwhile, Ohlson O-Score provided better accuracy results of 73.6%, 43.2%, and 58.5% for one, two, and three years before the bankruptcy. Based on these results and also interviews with the internal parties of the bank, it may be concluded that the financial statements for Small-Medium Enterprise segment companies, in general, have not been reliable enough to reflect the company's condition in real terms.
Preliminary Evaluation of Hai‐Nan Lightning Detection Network (HNLDN)

Hai‐Nan Lightning Detection Network (HNLDN) is composed of seven sites that accept very low frequency/low frequency (VLF/LF) signals. A total of seven sites are evenly distributed on Hai‐Nan Island. The baseline distance is between 50 and 150 km, which is about five times longer than in typical short‐baseline VLF/LF imaging networks. Each detection station uses zero‐phase digital filtering to avoid the phase deviation caused by filtering. HNLDN uses an improved peak search method to increase the number of detected of lightning waveforms, applies a One‐dimensional convolutional neural network (1D‐CNN) to identify and classify the waveforms, and to the increase the detection efficiency of intracloud lightning. This article analyzes three typical thunderstorm processes. From inside to outside the network, the detection efficiency of HNLDN relative to the advanced direction‐time lightning detection system (ADTD) is 239%, 181%, and 19%, respectively. It can be seen that the detection efficiency is negatively correlated with the distance from the thunderstorm to HNLDN. The average location error of HNLDN inside the network is 190 m.
Semantic Transportation Prototypical Network for Few-Shot Intent Detection

Few-shot intent detection is a problem that only a few annotated examples are available for unseen intents, and deep models could suffer from the overﬁtting problem because of scarce data. Existing state-of-the-art few-shot model, Prototypical Network (PN), mainly focus on computing the similarity between examples in a metric space by leveraging sentence-level instance representations. However, sentence-level representations may incorporate highly noisy signals from unrelated words which leads to performance degradation. In this paper, we propose Semantic Transportation Prototypical Network (STPN) to alleviate this issue. Different from the original PN, our approach takes word-level representation as input and uses a new distance metric to obtain better sample matching result. And we reformulate the few-shot classiﬁcation task into an instance of optimal matching, in which the key word semantic information between examples are expected to be matched and the matching cost is treated as similarity. Speciﬁcally, we design Mutual-Semantic mechanism to generate word semantic information, which could reduce the unrelated word noise and enrich key word information. Then, Earth Mover’s Distance (EMD) is applied to ﬁnd an optimal matching solution. Comprehensive experiments on two benchmark datasets are conducted to validate the effectiveness and generalization of our proposed model.
Survey of storage insect pests and management of rice weevil, Sitophilus oryzae, using botanicals on sorghum (Sorghum bicolor L.) at Jawi District, Northwestern Ethiopia

Storage pests are the main cause of crop losses. This study was carried out to assess the status of storage insect pest infestation and evaluate the efficacy of botanicals for the management of rice weevil at Jawi District, Ethiopia. A cross-sectional study was conducted to assess storage insect pest infestation. Botanical insecticides were evaluated against Sitophilus oryzae. The treatments were arranged using completely randomized design and replicated three times. Data were collected through questionnaire and experiment and analysed using SAS version 9.0. The result of the study showed that the most important insect pests of stored sorghum were S. oryzae (57%), maize weevil 17% and other pests were 26% and 100% store infestation were recorded. The species of storage insect pests identified were rice weevil, maize weevil, angoumois grain moth, flour beetle, flat grain beetles and saw-toothed grain beetle. The average grain weight loss was 13.63%, physical grain damage 7.22%, germination speed index of 46.36% and germination percentage of 68.75% were recorded. Experimental results revealed that all treatments were exhibited significant differences in mortality rate against S. oryzae. Efficacy of botanical revealed that neem seed powder (50.70%) and garlic (53.33%) were significantly superior in efficacy against S. oryzae after 28th days of exposure. Neem leaf powder, Mexican tea, orange peel and ginger gave moderate mortality rate of 48.55, 26.11, 36.87 and 41.65, respectively, compared with the control. All botanicals have the potential F1 S. oryzae reduction effect. The highest F1 reduction was recorded in neem seed powder (73.14%), neem leaf powder (72.54%) and garlic (66.94%). The highest mean germination of 70, 63.33 and 61.33% were recorded from neem seed powder, garlic and neem leaf powder exposed seeds, respectively. Neem seed powder, garlic and neem leaf powder can be suggested as management options of S. oryzae in storage condition. © 2021 informa UK limited, trading as taylor & francis group CONTACT Abaynew Jemal Jenber abujemal900@gmail.com department of Plant sciences, college of Agriculture and environmental sciences, Bahir dar University, P.o. Box 79, Bahir dar, ethiopia https://doi.org/10.1080/03235408.2021. 1970976 ARTICLE HISTORY Received 28 July 2020 Revised 9 March 2021 Accepted 8 August 2021
A learning to rank framework for future friendship ranking in streaming heterogeneous social networks

Friendship prediction is an important aspect of a social network. Social network users rely a lot on friendship suggestion as it helps them in improving their network of friends. Moreover it has a huge impact in analyzing whom one is influenced by. In order to know this information it is not only essential to identify the potential friends, but also rank them to quantify their influence. To do this, we propose a learning to rank framework using the most popular machine learning technique, LambdaMART with a new boosting algorithm. Our framework provides unparalleled values of normalized discounted cumulative gain measure. We also analyze which feature of the social network is helpful in getting these nonpareil values. Our boosting algorithm yields an analytical solution for the over‐fitting problem with the increasing number of iterations, thereby augmenting the robustness of the framework.
Discovering the Arrow of Time in Machine Learning

Machine learning (ML) is increasingly useful as data grows in volume and accessibility as it can perform tasks (e.g. categorisation, decision making, anomaly detection, etc.) through experience and without explicit instruction, even when the data are too vast, complex, highly variable, full of errors to be analysed in other ways , . Thus, ML is great for natural language, images, or other complex and messy data available in large and growing volumes. Selecting a ML algorithm depends on many factors as algorithms vary in supervision needed, tolerable error levels, and ability to account for order or temporal context, among many other things. Importantly, ML methods for explicitly ordered or time-dependent data struggle with errors or data asymmetry. Most data are at least implicitly ordered, potentially allowing a hidden `arrow of time’ to affect non-temporal ML performance. This research explores the interaction of ML and implicit order by training two ML algorithms on Twitter data before performing automatic classification tasks under conditions that balance volume and complexity of data. Results show that performance was affected, suggesting that researchers should carefully consider time when selecting appropriate ML algorithms, even when time is only implicitly included.
24.2: Invited Paper: Design of multi‐resonance thermally activated delayed fluorescence materials for organic light‐emitting diodes

Two strategies to improve the performance of multiresonant thermally activated delayed fluorescence (MR‐TADF) compounds are explored. These include incorporation of units to turn on aggregation‐induced emission so as to permit use of MR‐TADF compounds at high doping concentrations, and the use of heavy atoms to increase spin‐orbit coupling to enhance reverse intersystem crossing rates. Preliminary photophysical investigations are presented.
A review of assembly line balancing optimisation with energy consideration using meta-heuristic algorithms

Energy utilisation is one of the global trending issues. Various approaches have been introduced to minimise energy utilisation especially in the manufacturing sector, which is the largest consumer sector. One of the approaches includes the consideration of energy utilisation in the Assembly Line Balancing (ALB) optimisation. This paper reviews the ALB with energy consideration from 2012 to 2020. The selected articles were limited to problems solved using meta-heuristic algorithms. The review mainly focusses on the soft computing aspect such as problem variant, optimisation objectives, energy modelling and optimisation algorithm for ALB with energy consideration. Based on the review, the ALB with energy consideration was able to reduce energy utilisation up to 11.9%. It was found that the contribution in future ALB with energy research will be human-oriented, either social factor consideration in optimisation or energy utilisation modelling for workers. In addition, the effort to introduce an algorithm with efficient performance must be pursued because ALB problems have become more complicated. The findings from this review could assist future researchers to align their research direction with the observed trend. This paper also provides the research gap and research opportunities in the future.
Tele-health in the era of COVID-19 pandemic: need, current status, challenges and way forwards

Tele-health is the delivery of health care services by health care professionals, where distance is a critical factor, through using information and communication technologies (ICT) for the exchange of valid and correct information. It is one of the effective option to fight the outbreak of COVID-19. However, its application for patient care during the pandemic times is not without hiccups and problems owing to lack of clarity on its domain and absence of clear guidelines on its use.