Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighted multiple blockwise imputation method for high-dimensional regression with blockwise missing data

Blockwise missing data present a great challenge for data analysis because of their special missing structure. In this article, we propose a novel weighted multiple blockwise imputation method to target the problem of high-dimensional regression with blockwise missing data. Specifically, we first apply a multiple blockwise imputation technique to impute the missing blocks in the design matrix, after which a weighted sum formula is used to integrate the resulting imputation schemes and estimate the regression coefficients. The proposed method can make full use of the available information to perform imputation; hence, it shows superior performance compared to some previous methods. Simulations illustrate the advantageous numerical performance of our method in terms of variable selection, parameter estimation, and prediction ability. A real application is also presented to demonstrate its practical merit.
Label Propagation Clustering Algorithm Based on Adaptive Angle

The direction-based label propagation clustering (DBC) algorithm needs to set the number of neighbors ( k ) and the angle value (degree), which are highly sensitive. Moreover, DBC algorithm is not suitable for datasets with uneven neighbor density distribution. To overcome above problems, we propose an improved DBC algorithm based on adaptive angle and label redistribution (ALR-DBC). The ALR-DBC algorithm no longer input parameter degree, but dynamically adjusts the deviation angle through the concept of high-low density region to determine the receiving range. This flexible receiving range is no longer affected by the uneven distribution of neighbor density. Finally, those points that do not meet the expectations of the main direction are redistributed. Experiments show that the ALR-DBC algorithm performs better than DBC algorithm in most artificial datasets and real datasets. It is also superior to the classical algorithms listed. It also has good experimental results when applied to wireless sensor data annotation.
Influence of CLOCK Gene Variants on Weight Response after Bariatric Surgery

The Circadian Locomotor Output Cycles Kaput (CLOCK) gene has been linked to metabolic dysfunction and obesity. The purpose of this study was to analyze the association between single nucleotide polymorphisms (SNPs) of CLOCK gene with obesity and with long-term weight response after different bariatric surgery (BS) techniques. The cohort includes 375 patients with morbid obesity (MO) and 230 controls. In the association study of SNPs with weight response we combined several variables as phenotype at 6 years after surgery. The study protocol was registered in ISRCTN (ID80961259). The analysis of the selected SNPs was performed by allelic discrimination using Taqman® probes. The genotype association study was performed using the SNPStats program, with comparisons adjusted for sex, age, initial Body Mass Index, type 2 diabetes and hypertension diagnosis, and type of surgery. In the case-control study two of three SNPs were significantly associated with MO. The variant rs1801260 had a protective effect for MO whereas the TT genotype of rs3749474 variant had the strongest association with MO (OR = 2.25 (1.39–3.66); p = 0.0006). In the linear regression analysis both variants showed significant association with long-term weight loss and weight regain after BS, independently of the pre-surgery patient profile.
Infinitely many pairs of primes $p$ and $p+2$

We take the pre-sieved set to be all natural numbers $N=\{1,2,3,\dots\}$ with a sieve system:single sieve,double sieve,. With single sieve, i.e. , remove out the multiple of a prime, we derive all the primes. With double sieve, i.e. , remove out the multiple and the multiple of a prime and $-2$ simultaneously, we get all the prime twins and prove that infinitely many prime twins exist under suitable conditions. Finally, with special 4 sieve, we prove that infinitely many prime twins exist without any restriction.
The Important Role of TaohongSiwu Decoction in Gut Microbial Modulation in Response to High-Salt Diet-Induced Hypertensive Mice

TaohongSiwu decoction (THSWD), a traditional Chinese recipe, has been widely used to treat hypertension since ancient times. However, the mechanisms of its action are still unclear. Herein, we aimed to explore the gut microbial activity of THSWD in high-salt diet-induced hypertensive mice. Eight percent high-salt (NaCl) diet was used to induce hypertension for 4 weeks in a mouse model. Meanwhile, THSWD was used to intervene in the high-salt diet-induced mice, and the efficacy was evaluated by different parameters. Here, we found that THSWD significantly restored blood pressure compared with the model group. Moreover, THSWD effectively protected endothelial function by significantly upregulating the level of nitric oxide (NO) and downregulating the level of endothelin-1 (ET-1), angiotensin I (AngI), and vascular endothelial growth factor (VEGF) in serum compared with the model group. Notably, THSWD significantly upregulated the relative abundance of Dubosiella and downregulated that of Cyanobium_PCC-6307 and DNF00809 at the genus level compared with the model group. The results of PCA and microbial distance calculation further exhibited that THSWD treatment resulted in significant regulation of the microbial community. Furthermore, compared with the model group, THSWD increased the level of vitamin k2 (VK2) in serum. These findings indicate that THSWD could protect blood pressure and endothelial function by regulating gut microbiota and promoting microbial metabolite VK2. These results show the important role of THSWD in regulating the gut microbiota in response to high-salt diet-induced mice.
High-Dimensional Energy Consumption Anomaly Detection: A Deep Learning-Based Method for Detecting Anomalies

With the increase of energy demand, energy wasteful behavior is inevitable. To reduce energy waste, it is crucial to understand users’ electricity consumption habits and detect abnormal usage behavior in a timely manner. This study proposes a high-dimensional energy consumption anomaly detection method based on deep learning. The method uses high-dimensional energy consumption related data to predict users’ electricity consumption in real time and for anomaly detection. The test results of the method on a publicly available dataset show that it can effectively detect abnormal electricity usage behavior of users. The results show that the method is useful in establishing a real-time anomaly detection system in buildings, helping building managers to identify abnormal electricity usage by users. In addition, users can also use the system to understand their electricity usage and reduce energy waste.
Wideband and Ultrathin 2×2 Dipole Array Antenna for 5G mmWave Applications

This letter proposes a wideband (24–40 GHz) and ultrathin (0.76 mm) 2×2 dipole array antenna for fifth-generation (5G) millimeter-wave (mmWave) applications. For the ultrathin array antenna, it is a challenge to match the antenna input impedance because it usually has an extremely low value. Herein, a tightly coupled frequency selective surface is proposed to provide additional resonances, and a capacitively coupled feeding method and H-walls are employed to increase the bandwidth. Furthermore, consistent measurement results are guaranteed by vertical launch connectors, and the calibration process owing to the distortion at the connectors is briefly described. In addition, the cumulative distribution function of effective isotropically radiated power scan patterns is presented at various frequencies. Finally, the performance of the proposed array antenna is compared with those of state-of-the-art mmWave array antennas.
Algorithmique modulaire des équations différentielles linéaires

Les methodes modulaires conduisent a des algorithmes tres efficaces dans de nombreux domaines en calcul formel et notamment dans celui des equations algebriques. Le but de cette these est de montrer comment ces techniques modulaires s'adaptent au cas differentiel et permettent de developper de nouveaux algorithmes (ou d'ameliorer des algorithmes existants) pour l'etude d'equations differentielles lineaires. La premiere partie traite du probleme de la factorisation d'operateurs differentiels en caracteristique positive. Le "miracle" de la caracteristique p est que le probleme peut se reduire a de l'algebre lineaire. En exploitant ce fait, nous developpons un algorithme de factorisation de systemes differentiels. Nous donnons la complexite des differentes etapes de cet algorithme. Enfin, nous le generalisons au cadre de systemes d'equations aux derivees partielles. L'objet de la deuxieme partie est de rendre plus efficace l'algorithme de Beke pour le calcul des solutions exponentielles d'equations differentielles lineaires. Cet algorithme possede deux inconvenients majeurs qui le rendent peu efficace : un probleme combinatoire et un probleme de corps. Nous montrons qu'en combinant des informations "geometriques" locales (les exposants generalises) et des informations "arithmetiques" modulaires (les valeurs propres de la p-courbure), nous pouvons diminuer le nombre de combinaisons considerees habituellement par l'algorithme et reduire le degre des extensions algebriques du corps de base necessaires au calcul des solutions exponentielles. Dans la troisieme partie, nous demontrons qu'une demarche similaire s'applique pour le probleme analogue dans le cas des equations aux differences. Finalement, dans la derniere partie, nous developpons un algorithme entierement modulaire calculant les solutions polynomiales d'equations differentielles lineaires en caracteristique zero. Nous evaluons la pertinence des informations modulaires que l'on peut obtenir pour ce probleme. Nous donnons et comparons les complexites de notre algorithme et des algorithmes existants. Puis, grâce a des comparaisons experimentales, nous exhibons des classes d'equations pour lesquelles notre approche modulaire est mieux adaptee que les algorithmes existants. La plupart de nos algorithmes ont ete implantes dans le logiciel de calcul formel Maple.
Incidence of Arterial and Venous Thromboembolism in Cancer Patients

Background and Aims: The reported incidence of arterial thromboembolism (ATE) and venous thromboembolism (VTE) after cancer varies. A meta-analysis was performed to define the incidence of thromboembolism (TE) in cancer patients. Methods: Articles were searched in PubMed and Embase from inception to November 1, 2022. Studies reporting the incidence data or data from which incidence could be estimated among patients with cancer and the explicit follow-up duration were included. Results: Seventy-four studies involving 5059134 cancer patients were identified. The incidence rate per 1000 person-years was 11.60 (95% CI 7.62-15.58) for ATE, 6.11 (95% CI 3.70-8.53) for myocardial infarction, 9.07 (95% CI 7.48-10.66) for ischemic stroke, 2.11 (95% CI 0.89-3.31) for another ATE, 26.32 (95% CI 24.46-28.18) for VTE, 12.69 (95% CI 11.51-13.87) for deep vein thrombosis, 5.94 (95% CI 5.29-6.59) for pulmonary embolism, and 13.18 (95% CI 9.93-16.42) for another VTE. In addition, the highest incidence of ATE was observed in patients with gastrointestinal cancer, while patients with pancreatic cancer had the highest incidence of VTE. The risk of ATE and VTE increased at the initial stage of cancer, and then declined and became non-significant. Conclusion: This meta-analysis provided overall estimates of ATE and VTE incidence in cancer patients, adding an important insight into the trajectory of the development of TE in cancer patients, which could help reduce the risk of TE in cancer patients in the future. Keywords: cancer, arterial, venous, thromboembolism
Environmental reporting by the Fortune Global 250: exploring the influence of nationality and sector

While research on environmental reporting frequently includes large multinational enterprises, the number of surveys that systematically analyses a whole set of such firms, including the non-reporters, is limited and mainly focuses on the United States. This article presents the state of environmental reporting by the Fortune global 250, all large multinationals with a potentially large impact on other firms. Of these 250 firms, 35% has a recent environmental report, with another 32% publishing other types of environmental information. Reporting frequencies between the financial and non-financial sector differed considerably with 15% and 44% respectively. Besides an analysis of the number and contents of environmental reports, the importance of sectoral differences and firms nationality is also considered. A framework is developed which posits the existence of reporting legislation in firms home countries (some in place/pending, or none) against the direct environmental impact of the sector (large or small). The results of the analysis fit partially in this framework. Only a small number of reporting firms originates from countries with some kind of legislation (particularly the Netherlands), but national societal pressure seems to play a large role (especially in the UK, the Netherlands and Germany). Many operate in sectors with a substantial direct environmental impact (chemicals, pharmaceuticals, oil, and motor vehicles and parts); reporting in sectors with more indirect effects is getting off the ground.
Curcumin Sensitises Cancerous Kidney Cells to TRAIL Induced Apoptosis via Let-7C Mediated Deregulation of Cell Cycle Proteins and Cellular Metabolism

Targeted therapies are the most attractive options in the treatment of different tumours, including kidney cancers. Such therapies have entered a golden era due to advancements in research, breakthroughs in scientific knowledge, and a better understanding of cancer therapy mechanisms, which significantly improve the survival rates and life expectancy of patients. The use of tumour necrosis factor (TNF)-related apoptosis inducing ligand (TRAIL) as an anticancer therapy has attracted the attention of the scientific community and created great excitement due to its selectivity in targeting cancerous cells with no toxic impacts on normal tissues. However, clinical studies disappointingly showed the emergence of resistance against TRAIL. This study aimed to employ curcumin to sensitise TRAIL-resistant kidney cancerous ACHN cells, as well as to gain insight into the molecular mechanisms of TRAIL sensitization. Curcumin deregulated the expression of apoptosis-regulating micro Ribonucleic Acid (miRNAs), most notably, let-7C. Transfecting ACHN cells with a let-7C antagomir significantly increased the expression of several cell cycle protein, namely beta (β)-catenin, cyclin dependent kinase (CDK)1/2/4/6 and cyclin B/D. Further, it overexpressed the expression of the two key glycolysis regulating proteins including hypoxia-inducible factor 1-alpha (HIF-1α) and pyruvate dehydrogenase kinase 1 (PDK1). Curcumin also suppressed the expression of the overexpressed proteins when added to the antagomir transfected cells. Overall, curcumin targeted ACHN cell cycle and cellular metabolism by promoting the differential expression of let-7C. To the best of our knowledge, this is the first study to mechanistically report the cancer chemosensitisation potential of curcumin in kidney cancer cells via induction of let-7C.
"Smart MAV" Media Development Based on Direct Instruction in Learning Indonesian Speaking Skill

This research is conducted to produce and develop audio and visual learning media to improve students understanding of speaking skills. The media is in the form of Smart MAV based on direct instruction. The subject of this research is the students of Indonesian Language Education at Peradaban University. This research belongs to research and development (R&D). This research uses the 4D model. They are: 1) define, 2) design, 3) development, and 4) dissemination. However, the result of this study is only at the development stage, which is product design and validation. Furthermore, data collection activities in this study were carried out through interviews, questionnaires, and observation. The data analysis used descriptive statistics. Based on the research, the result shows Smart MAV based on direct instruction is feasible and valid to use by the validators with a percentage of 93% (excellent), and the results of the material expert validation test with a rate of 83.21% (excellent). These results indicate that the developed media (Smart MAV) is feasible.
Multi-Output Convolutional Neural Network for Automatic Human Head Attributes Classification

: Human head attribute classification (HHAC) is a fundamental and substantial research domain in pattern recognition and computer vision. However, recent HHAC networks do not consider the correlation of common characteristics among the attributes over different regions of the human head. To address the above problem, this study proposes a multi-output convolutional neural network to jointly learn the features of human head attributes with common characteristics. The proposed network contains two convolutional blocks and five output layers, where each output layer learns to predict a specific group of human head attributes. In order to properly learn the correlation among the human head attributes, this study divides these attributes into five groups: hair, face, style, accessories, and appearance. Extensive experiments showed that the proposed network obtained an average classification accuracy of 95.29% and 97.93% on the challenging CelebA and LFWA datasets, respectively. Thus, the proposed network is approximately 2% and 10% superior to the closest competitor (i.e., PS-MCNN) on both datasets. In addition, the proposed network achieved higher classification accuracy compared to the existing networks almost in all human head attributes. That findings demonstrate the effectiveness of the proposed network and the attributes grouping method in learning the correlations among human head attributes correctly.
An Energy-Efficient SIFT Based Feature Extraction Accelerator for High Frame-Rate Video Applications

Visual feature extraction is a key technology of computer vision for intelligent video processing. Efficient feature extraction is a fundamental problem in computer vision applications. Scale-Invariant Feature Transform (SIFT) is one of the most popular feature extraction algorithms because SIFT features are invariant to image scale and rotation and robust to changes in illumination and noise. However, SIFT is a computationally-intensive and power-hungry algorithm, which needs to be accelerated by efficient hardware design to achieve both high-speed feature extraction and high energy efficiency for many high frame-rate video applications at Artificial-intelligent Internet of Things edges. In this work, an energy-efficient SIFT based feature extraction accelerator is proposed. In the Gaussian pyramid and Differences of Gaussian (DoG) pyramid construction process, three design methods are proposed to reduce power consumption and improve information fidelity: a fast and slow dual clock domain design method with a reconfigurable design strategy is proposed to reduce the computation resources; a partial sum reuse design method is proposed to further reduce the computation resources and the amount of computation; a dynamic padding design method is proposed to solve the problem of information loss at image edges and corners after convolution operation. In the keypoint descriptor generation process, an optimized algorithm using circular region and polar coordinates is proposed to parallelize the main orientation assignment and descriptor generation to achieve high-speed processing, while maintaining a comparable matching accuracy with the state-of-the-art designs. The experiment results show that the proposed SIFT hardware accelerator is able to extract features by up to 162 frames per second ( $640\times 480$ pixels) under 100 MHz, with the power consumption of 364.26 mW and energy efficiency of 2.25 mJ/frame based on 180 nm technology, which is suitable for many high frame-rate AIoT applications including autonomous driving cars and unmanned aerial vehicles.
GCN-Unet: A Computer Vision Method with Application to Industrial Granularity Segmentation

In the field of metallurgical industry, identifying the granularity of raw materials is an essential process during transportation. We propose an image segmentation method by the GCN (global convolutional network)-Unet to extract the contour edge of raw materials granules. To obtain legible images of raw materials, a stationary industrial high speed camera is used to photograph the operating belt conveyor from above. Then, a well-trained GCN-Unet model is used to compute the images and output the results with the contour edge of granules and tiny parts of the materials segmented. We combined the U-Net with several global convolutional network models and boundary refinement blocks and compared the prediction results of the GCN-Unet and the U-Net, showing that the GCN-Unet has a better prediction ability with fewer parameters (7,876,675, while the U-Net has 31,101,448 parameters) and a higher calculating speed (about twice faster than the U-Net). Based on the CNN (convolutional neural network), our computer version method can almost replace traditional manual sampling inspection method for the corresponding overall analysis and the automatically identification process.
Decoupling antenna array with X‐shaped strip

Manipulating mutual coupling between antenna array elements is always a critical essential in designing phased arrays. In this article, an X‐shaped strip is applied to decouple a five‐element E‐plane microstrip antenna array, whose adjacent elements' center‐to‐center spacing is only 0.45 λ0. Simulation and measurement results reveal that the proposed array employing the loaded structure exhibits excellent decoupling capability, as in comparison to the reference array, impedance of every port is well matched, mutual coupling between both adjacent elements and nonadjacent ones is efficiently reduced and radiation patterns of every individual patch are markedly corrected. Besides, when beam scanning is performed, the proposed array is equipped with higher gain and lower SLL. The X‐shaped strip predicts a promising application in phased array and a large‐scale array.
Myo-Speech: A System for Recognizing Word Utterances of the Speech Impaired

- We report a new method for identification of unintelligible vocal sounds obtained from two speech impaired volunteers. The ultimate goal of this research is to generate some speech production capabilities for persons with speech impairment by reissuing intelligible versions of words corresponding to the vocal sounds they generate in their typically unsuccessful efforts to produce spoken words. The ability to generate understandable spoken words can have a major positive impact on the quality of life of speech impairment communities around the world. To acquire classifiable signals corresponding to the produced vocal sounds we did not use a conventional microphone as it is not immune to background noise. Rather, we used surface electromyography (sEMG) signals obtained from the sternocleidomastoid muscle in proximity of the vocal cords, with the hypothesis that there should be consistent correlation between the intended words and their myo signals. A preliminary dataset consisting of three Arabic words was acquired from two subjects (one female and one male) in a lab-controlled environment. Average classification accuracy was about 82% using standard machine learning techniques (KNN, NN, and SVM). Preliminary results indicate that classification of this limited vocabulary dataset is feasible with reasonable accuracy to motivate future work involving more subjects and larger datasets.