Miracle-dz/UniFAR-FTUs huggingface dataset:
IRW improves glucose tolerance in high fat diet fed C57BL/6 mice 1 via activation of insulin signaling and AMPK pathways in skeletal 2 muscle 3

IRW, an ovotransferrin-derived peptide, previously showed antihypertensive and anti- 40 inflammatory activities in rodents and in cells. Moreover, IRW prevented angiotensin-II- and 41 tumor necrosis factor (TNF)-α-induced IR in skeletal muscle cells. In this study, we investigated 42 the effects of IRW on body composition, glucose homeostasis and insulin sensitivity in a high-fat 43 diet (HFD) induced obese model. C57BL/6 mice were fed HFD for 6 weeks, after which IRW was 44 incorporated into the diet (45 or 15 mg/kg body weight (BW)) for another 8 weeks. IRW45 reduced 45 BW (p<0.05), fat mass gain (p<0.01), and preserved lean mass (p<0.01) of HFD mice. This was 46 accompanied by enhanced glucose tolerance in the oral glucose tolerance test (OGTT) and reduced 47 fasting blood glucose. In skeletal muscle, IRW45 up-regulated insulin signaling, increased protein 48 kinase B (AKT) phosphorylation (p<0.05) and glucose transporter 4 (GLUT4) abundance in the 49 plasma membrane (P<0.0001). 5'-AMP-activated protein kinase (AMPKα) (p<0.05) and 50 peroxisome proliferator-activated receptor gamma (PPARγ) RNA expression (p<0.05) and protein 51 abundance (p<0.001) were increased in skeletal muscle of IRW45-treated mice, as was the 52 expression of genes involved in myogenesis. Angiotensin converting enzyme-2 (ACE2) activity 53 in the plasma (p<0.01) and angiotensin 2 receptor (AT2R) abundance (P<0.01) in skeletal muscle 54 were enhanced. The thermogenic protein uncoupling protein-1 in WAT was suppressed by HFD 55 and partially restored after IRW supplementation. IRW improves glucose tolerance and body 56 composition in HFD-fed mice. IRW promotes glucose uptake in skeletal muscle via multiple 57 signaling pathways, including insulin signaling and the AMPK pathway, independent of ACE 58 inhibition.
Multi-Node Stochastic Gradient Descent

This dissertation presents the implementation of a multi-node Stochastic Gradient Descent (SGD) algorithm for neural network training. We combine parameter sharding, overlapping computation with communication, and gradient dropping to achieve fast training in clusters of multi-GPU machines. We show how each of these extensions impacts both training throughput and convergence rate for a variety of different multinode configurations. We find that the slow communication between machines (nodes) is a major bottleneck. Without compressed communication, we find it difficult to scale training efficiently over multiple nodes. With sparse communication, however, we achieve impressive convergence rates, beating popular methods like SimuParallelSGD (Zinkevich et al., 2010) and DistBelief (Dean et al., 2012). Future work includes optimising the hyperparameters of our framework to achieve more optimal convergence rate and implementing various tweaks that our experiments indicate might further improve performance. https://github.com/dr-alan-turing/marian-nmt-distributed
Bulletin of Electrical Engineering and Informatics

Received Dec 8, 2021 Revised Apr 4, 2022 Accepted May 8, 2022 In large multiple-input multiple-output (MIMO) systems, we offer a new access strategy for crowd situations. Crowd situations include a high number users who have intermittent access patterns, making orthogonal scheduling impossible. In this paper, the work is driven by the massive MIMO problem at crowded environment. The pilot contamination problem is dependent on the protocol assumptions is observed in the system. We investigate a crowd situation in which the users number and their access behaviours make scheduling broadcasts impossible. The pilot assignment is time-slot randomized, but the data portion is repeated, allowing it to use successiveinterference-cancellation across copies of comparable packets. In result analysis, the transmission of downlink to the user has shown with changing the number of users, fading effect, transmission power and the number of base station antennas.
Deblending using convolutional neural networks

Summary Machine learning has been a booming subject in computer science and its applications have been made in various subjects including geophysics. Convolutional Neural Networks (CNNs) have great potential for solving image processing problems like denoising and interpolation. Deblending, considered as an underdetermined denoising problem, falls into this category. In this report, we use CNN to replace the deblending operator and its performance is analyzed. We use a 4-layer U-Net to perform deblending on synthetically blended shots from a wedge velocity model with point scatterers. We test out different hyper-parameters and the trained model could successfully remove the noise and preserve diffractions from the scatterers with some tolerance. The generality of the model is evaluated by testing the model on an easier 2-layer velocity model. The model can successfully identify and recover most parts of the primaries but fails to deal with some interferences and leaves them muted.
Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network

Bow echoes (BEs) are bow-shaped lines of convective cells that are often associated with swaths of damaging straight-line winds and small tornadoes. This paper describes a convolutional neural network (CNN) able to detect BEs directly from French kilometer-scale model outputs in order to facilitate and accelerate the operational forecasting of BEs. The detections are only based on the maximum pseudoreflectivity field predictor (“pseudo” because it is expressed in mm h−1 and not in dBZ). A preprocessing of the training database is carried out in order to reduce imbalance issues between the two classes (inside or outside bow echoes). A CNN sensitivity analysis against a set of hyperparameters is done. The selected CNN configuration has a hit rate of 86% and a false alarm rate of 39%. The strengths and weaknesses of this CNN are then emphasized with an object-oriented evaluation. The BE largest pseudoreflectivities are correctly detected by the CNN, which tends to underestimate the size of BEs. Detected BE objects have wind gusts similar to the hand-labeled BE. Most of the time, false alarm objects and missed objects are rather small (e.g., <1500 km2). Based on a cooperation with forecasters, synthesis plots are proposed that summarize the BE detections in French kilometer-scale models. A subjective evaluation of the CNN performances is also reported. The overall positive feedback from forecasters is in good agreement with the object-oriented evaluation. Forecasters perceive these products as relevant and potentially useful to handle the large amount of available data from numerical weather prediction models.
Co-overexpression of AVP1 and PP2A-C5 Increases Plant Tolerance to Multiple Stresses

Many eﬀorts have been made in genetically modifying plants to make them more tolerant to various stress conditions. For example, the overexpression of AVP1 or PP2A-C5 increases either salt or drought tolerance. However, in nature, plants are often exposed to multiple stresses. In this study, a hypothesis that the co-overexpression of AVP1 and PP2A-C5 would increase plant tolerance under drought and salt stresses when compared to wild-type, AVP1 overexpressing, and PP2A-C5 overexpressing plants was tested. Three week old arabidopsis were left un-irrigated for the soil to dry to mimic drought conditions. Afterwards, two diﬀerent salt concentrations were used: 1) 100mM NaCl and 2) 200mM NaCl. Five days after salt treatment, plants were re-watered. Two days after re-watering, some plants from the 200mM NaCl treatment group and the control group were collected for real time quantitative PCR analysis, and twenty days after re-watering, survival rates were analyzed for both treatments while biomass was measured for plants of 100mM NaCl treatment. There were no signiﬁcant phenotypic or biomass diﬀerences under 100mM NaCl and drought conditions, and the survival rates were all 100%. Under 200mM NaCl and drought stresses, the survival rate of AVP1/PP2A-C5 co-overexpressing plants was 63%, AVP1 overexpressing plants was 38%, PP2A-C5 overexpressing plants was 19%, and wild-type plants was 0%. The real time quantitative PCR analysis indicated that transcript levels of some drought and salt stress related genes were much lower in AVP1/PP2A-C5 co-overexpressing plants than other genotype plants, indicating that stress levels were much lower in AVP1/PP2A-C5 co-overexpressing plants after the drought and salt treatments. Overall, this research supported the hypothesis that the co-overexpression of AVP1 and PP2A-C5 does increase plant tolerance to multiple stresses of drought and high salinity. Drought plus salt stress experiment AVP1/PP2A-C5 co-overexpressing plants performed better under combined stresses of drought and high salinity AVP1/PP2A-C5 co-overexpressing plants had a lower expression of certain drought and salt stress related genes
Educational Nonlinear Stories with Twine

Multiple studies have hinted at the positive effects of educational games on learner engagement. However, some games, if they exist, may not be readily usable for some lessons even though the same topics are covered. In this workshop, the participants will be introduced to Twine, an open-source development environment for nonlinear stories that requires little to no coding. Tips and tricks to making engaging stories that target desired learning outcomes derived from existing research will be shared. The participants will have the opportunity to apply these insights through a group activity where they create their stories for a lesson. They will then individually create games for their stories and share their works to the rest of the group. An online community will be created after the workshop for the participants to continue exchanging ideas or creations afterwards.
Physiochemical Properties of Mango ( Mangifera indica L.) Seed Kernel's Oil

Mango ( Mangifera indica L.) is one of the important fruits in the Sudan. After consumption of the mango fruit, seed kernels usually remain as waste. With the objective of making use of the remains, we extracted seed kernels of five Sudanese mango cultivars and evaluated their physiochemical properties. The extracted oil content was ranged between 15.2% and 2.73% and their melting point more than 40.1°C. High peroxide value (3.31mg/g) was reported in Abu-samaka cultivar. The saponification value exceeded 190 mg/g in all cultivars while their un-saponifiable matters were found to be in the range of 1.2% to 2.3%. The extracted oils were found to be rich in K, Na, Mg, Ca, Co, Fe and Mn. The main unsaturated fatty acids found were stearic acid (60.88%-67.41%) and palmatic acid (13.78%-16.95%), while the saturated oleic acid was also found (8.32% to 11.43%). All the studied oils showed free radical scavenging and iron chelating activities. The highest DPPH level was observed in Abu-samaka cultivar (67%) whereas the lowest was of Elphons (40%). Phenolic, flavonoid and tannin compounds were also observed. These results suggested that the mango seed kernel oils could be used as a potential composition in industries rather than to left as waste.
Nanocellulosic Materials: Research/Production Activities and Applications

Nanocelluloses have emerged as novel materials and attracted significant interest from both academia and industry. Nanocelluloses can now be produced at pilot plant and pre-commercial scales, and even at commercial scales in some regions in the world. Successful commercial applications of nanocelluloses have entered commercial stages though their full potentials are yet to be developed. In this short communication, the applications of these nanomaterials are high-lighted, including high-volume applications (e.g., paper, textiles), high-value applications (e.g., aerogels and structure materials for aerospace), and novel/emerging applications (e.g., organic light emitting diodes, photonic films). Close collaboration between industries and the academic world would facilitate the development of commercial markets for using nanocelluloses in existing/new areas.
Neuroenhancement using non-invasive brain stimulation

Neuroenhancement using non-invasive brain stimulation Andrea Antal1, Bruce Luber2, Anna-Katherine Brem3,4,28, Marom Bikson5, Andre R Brunoni6,7, Roy Cohen Kadosh8, Veljko Dubljevic9, Shirley Fecteau10, Florinda Ferreri11,12, Agnes Flöel13,14, Mark Hallett15, Roy Hamilton16, Christoph S Herrmann17, Michal Lavidor18, Collen Loo19, Caroline Lustenberger20, Sergio Machado21,22, Carlo Miniussi23, Vera Moliadze24 , Michael A Nitsche25, Simone Rossi26, Paolo M. Rossini27, Emiliano Santarnecchi28, Margitta Seeck29, Gregor Thut30, Zsolt Turi31, Yoshikazu Ugawa32, Ganesan Venkatasubramanian33, Nicole Wenderoth20,34, Anna Wexler35, Ulf Ziemann36,37, Walter Paulus38
A Review on Methodologies Used For Helmet Detection

: Motorcycle accidents have been growing day by day throughout the years because road safety is often neglected by riders worldwide leading to accidents and deaths. Classification of vehicle, Detection of a helmet, and non-helmet, and number plate detection is one of the area of image processing and machine learning. This paper aims to review various methodologies used in object detection and the accuracy of the model. Also, we would like to review some methods that detects automatically, a helmet and non-helmet motorcycle rider using surveillance cameras, so that it would decrease a work load of traffic police, indirectly saves the society, and decreases the death rate due to accidents. This has created a demand towards the leading algorithms used in the detection of the helmet and non-helmet followed by number plate detection. Object detection and Text recognition are the main frameworks consisting of build-in methods which are used to build and train robust, and customized models. This paper shows the various methods that recognizes the motorcycle, motorcycle with rider, also if the rider is wearing a helmet or not, and if not then the methods that are used for number plate detection of vehicle. Most of these methods uses Machine Learning concepts.
Neural Cost-to-Go Function Representation for High Dimensional Motion Planning

—This paper presents c2g-HOF networks which learn to generate implicit cost-to-go functions for a wide range of robotic systems such as manipulators and non-holonomic systems. The c2g-HOF architecture consists of a cost-to-go function over the conﬁguration space represented as a neural network (c2g-network) as well as a Higher Order Function (HOF) network which outputs the weights of the c2g-network for a given input workspace. Both networks are trained end-to-end in a supervised fashion using costs computed from traditional motion planners. Once trained, c2g-HOF can generate a smooth and continuous cost-to-go function directly from workspace sensor inputs (represented as a point cloud in 3D or an image in 2D). At inference time, the weights of the c2g-network are computed very efﬁciently and near-optimal trajectories are generated by simply following the gradient of the cost-to-go function. The experimental results indicate that planning with c2g-HOF is signiﬁcantly faster than other motion planning algorithms, resulting in orders of magnitude improvement when including collision checking. We demonstrate cost-to-go based planning on a 7 DoF manipulator arm where motion planning in a complex workspace requires only 0.13 seconds for the entire trajectory.
Management of Congenital Dislocation of the Knee

Congenital knee dislocation is a rare disorder and the published literature largely consists of small case series. Congenital knee dislocation represents a broad spectrum of severity from hyperextension to frank dislocation. It can be isolated or be present in syndromic infants with concomitant joint disorders such as clubfoot or developmental dysplasia of the hip. Hyperextended knees can be reduced with serial casting alone in the neonatal period and do not require further treatment. Surgical treatment of the congenital knee dislocation results in satisfactory outcomes in the majority of reported cases, although outcomes are highly dependent on the degree of concomitant joint involvement. Treatment decisions are often based on radiographic findings and passive range of motion of the knee. Described procedures include percutaneous or mini-open quadriceps tenotomy, VY quadricepsplasty, and femoral shortening. The current review summarizes the most up-to-date literature on the surgical management of congenital knee dislocation.
BIOSURFACTANT POTENTIALS OF SOME BACTERIA RECOVERED FROM AGRO-INDUSTRIAL WASTES

: Biosurfactants are amphiphilic molecules that accumulate at interfaces, decrease interfacial tensions and form aggregate structures. Biosurfactants are important alternatives to chemical surfactants due to low toxicity, thermo-tolerant, specificity and ability to produce renewable cheaper substrates. The aim of this research is to isolate some bacteria from Agro-industrial wastes and screen for their ability to produce biosurfactant. Three (3) screening methods; blood haemolysis, emulsification index (EI 24 ) and blue agar hydrolysis were used to confirm biosurfactant production. The most outstanding isolates in order of their potential to produce biosurfactants are: Enterobacter cloacae , Bacillus cereus , Bacillus coagulans , Acinetobacteria mallei, Bacillus megaterium, Corynebacterium striatum , Escherichia coli , Corynebacterium pilosum, Bacillus laterosporus , Enterobacter intermedius, Bacillus brevis, Pseudomonas aeruginosa , Bacillus subtilis and Bacillus polymyxa. Enterobacter cloacae is isolated as the most outstanding isolated from Palm kernel, other successful isolates are from Cassava flake, followed by isolates from Poultry dung. It is recommended that more research should be focused on Bacillus species for optimisation and production of biosurfactant production since despite its different origins, it is always successful, also, more promising isolates should be researched from Palm kernel.
Regional inequalities and football: an analysis of the first division of the Brazilian championship between 2003 and 2019

Regional inequalities in Brazil are also related to sport performance and, in particular, to football (soccer) performance. In this work, we use data related to the performance of the Brazilian federative units and macro-regions in the first division of the local football championship between 2003 and 2019 to analyze these inequalities. Results indicate a strong correlation between the performance of the Brazilian macro-regions and their gross domestic product (GDP). Considering the 15 federative units that participated in at least one edition of the Brazilian first division, this correlation is reaffirmed. The presence of occasional outliers (Southern region and Rio de Janeiro, for example) suggests that additional factors (such as tradition and number of fans) may contribute to explaining regional performance in the Brazilian football championship. Measurement issues and the collinearity of these factors with other indicators, however, pose additional difficulties to this kind of analysis.
This Cu 2 SnS 3 (CTS) Nanoparticle Production with Laser Ablation Method and Nonlinear Optical

 Abstract — In this study, Cu 2 SnS 3 (CTS) target have been synthesized using ball milling technique with Copper (Cu), Tin (Sn) and Sulphur (S) powders. After the production of CTS target, pulsed laser ablation (PLA) in liquid method was carried out for nanoparticle form of CTS. Femtosecond (fs) laser system with 800 nm wavelengths, 90 fs pulse duration at 1kHz repetition rate has been adapted to both PLA and z-scan experimental setups. Two different nanoparticle processes were applied to CTS target in PLA method using ethanol medium depending on duration of nanoparticle production. The characterization of crystal structure will put forth in detail and obtained CTS bulk material used to obtain CTS nanoparticle in liquid medium to analysis nonlinear optical properties using femtosecond z-scan method. For these structures, nonlinear absorption coefficient values were observed to be between -6.227 x 10 -11 cm/W and -9.946 x 10 -11 cm/W, while nonlinear refraction indexes were measured to be from 1.331 x 10 -16 cm 2 /W to 7.328 x 10 - 16 cm 2 /W. In addition, third order nonlinear optical susceptibilities for these NPs were calculated to be between 5.486 x 10 -14 (esu) and 8.994 x 10 -14 (esu). All obtained results will be present in
Sudden Shift to Online Learning: COVID-19's Impact on Engineering Student Experiences

In light of the COVID-19 pandemic, engineering education has faced a shift to a partial if not fully virtual environment that has disrupted students' educational experiences. This qualitative study was designed to explore how mechanical engineering students at California Polytechnic University, San Luis Obispo have navigated the shift to virtual learning, what pedagogical practices have been effective online, and how the change has affected students' motivation, learning, and sense of belonging and community. During an hour-long focus group, participants were asked to maintain anonymity by turning off their cameras and by using pseudonyms. They responded to open-ended questions related to teaching strategies, workload, motivation, lab courses, technology issues, building community, academic integrity, and general improvements they wanted to see in future online classes. Summaries were created and analyzed by the two student researchers and discussed among the research team. Further recommendations to professors were made categorized by which theme they applied to best. For example, faculty were advised that students appreciated the effort they put into moving to a virtual environment with such short notice but commented that the quality of online learning could be improved. Our primary findings indicate that the key shortcomings included difficulty building community as well as decreased motivation levels and increased stress levels. © American Society for Engineering Education, 2021
Shape-Tailored Deep Neural Networks With PDEs

We present Shape-Tailored Deep Neural Networks (ST-DNN). ST-DNN are deep networks formulated through the use of partial differential equations (PDE) to be deﬁned on arbitrarily shaped regions. This is natural for problems in computer vision such as segmentation, where descriptors should describe regions (e.g., of objects) that have diverse shape. We formulate ST-DNNs through the Poisson PDE, which can be used to generalize convolution to arbitrary regions. We stack multiple PDE layers to generalize a deep CNN to arbitrarily shaped regions. We show that ST-DNN are provably covariant to translations and rotations and robust to domain deformations, which are important properties for computer vision tasks. We show proof-of-concept empirical validation.