Miracle-dz/UniFAR-FTUs huggingface dataset:
Unitary and Symmetric Structure in Deep Neural Networks

OF DISSERTATION Unitary and Symmetric Structure in Deep Neural Networks Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems. A well-known difficulty in using RNNs is the vanishing or exploding gradient problem. Recently, there have been several different RNN architectures that try to mitigate this issue by maintaining an orthogonal or unitary recurrent weight matrix. One such architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN), which parameterizes the orthogonal recurrent weight matrix through a scaled Cayley transform. This parametrization contains a diagonal scaling matrix consisting of positive or negative one entries that can not be optimized by gradient descent. Thus the scaling matrix is fixed before training, and a hyperparameter is introduced to tune the matrix for each particular task. In the first part of this thesis, we develop a unitary RNN architecture based on a complex scaled Cayley transform. Unlike the real orthogonal case, the transformation uses a diagonal scaling matrix consisting of entries on the complex unit circle, which can be optimized using gradient descent and no longer requires the tuning of a hyperparameter. We compare the performance of The scaled Cayley unitary recurrent neural network (scuRNN) with scoRNN and other unitary RNN architectures. Convolutional Neural Networks (CNNs) is a class of deep neural networks, most commonly applied to analyzing visual imagery. Nowadays, deep neural networks also play an important role in understanding biological problems such as modeling RNA sequences and protein sequences. The second part of the thesis explores deep learning approaches involving recurrent and convolutional networks to directly infer RNA secondary structure or Protein contact map, which has a symmetric feature matrix as output. We develop a CNN architecture with a suitable symmetric parameterization of the convolutional Kernel that naturally produces symmetric feature matrices. We apply this architecture to the inference tasks for the RNA secondary structure or protein contact maps. We compare our symmetrized CNN architecture with the usual convolution network architecture and show that these approaches can improve prediction results while using equal or fewer numbers of machine parameters.
BIOLOGICAL REMEDIATION OF AMMONIA IN THE RIVERNILE BY USING EFFICIENT BACILLUS ISOLATES UNDER STATIC CULTURE CONDITIONS

Applicability of ammonia bioremediation by efficient bacterial isolates was investigated under static batch culture conditions. Bacillus aerius (M.1), Bacillus bingmayongensis (M.2) and Bacillus toyonensis (M.3) were isolated from the Inlet of El-Bahr El-Saedy drinking water treatment plant, Rosetta Branch, Egypt and identified based on their morphological, cultural and biochemical characteristics. Identification of these isolates via genomic 16S rRNA sequencing technique was also performed. To optimize ammonia removing process, effect of some environmental conditions (pH, temperature and ammonia concentrations) were tested. Results indicated that a pH of 6.5 and 7.0, and the temperature between 30 and 35oC were the most optimal values for removing ammonia by the three tested isolates. Ammonia was consumed effectively during the first five incubation days, indicating maximum consumption rate, but it was reduced to its lowest level 25 days after incubation. Ammonia consumption rates were increased by increasing its concentration for all tested isolates, indicating growth activation and no substrate inhibition. Ammonia consumption rate by the ternary mixture reached its maximum of 1.60 mg Lday five days after incubation, and then reduced slowly to 0.38 mg L day 25 days after incubation. It indicated sequential and synergistic effects between the isolates in their ternary mixture during the removal of ammonia.
The Cenozoic leaf morphotypes and palaeoclimate interpretation from the Doi Ton Formation, Mae Sot District, Tak Province, western Thailand

The Cenozoic palaeovegetation and palaeoclimate of Doi Ton, western Thailand, megaflora are reconstructed based on physiognomic climate analysis, including Leaf Margin Analysis (LMA), Leaf Area Analysis (LAA), Leaf Size Index (LSI), Climate Leaf Analysis Multivariate Program (CLAMP), and systematic descriptions of each leaf morphotype. The leaf fossils were divided into 23 dicotyledonous leaf morphotypes and two unknown leaf morphotypes. The mean annual temperature results from LMA indicate 32.3 ± 1.17°C and CLAMP shows 21.2°C. CLAMP also provides temperature data of a warm month mean temperature (WMMT) of 27.4°C and a CMMT of 14.2°C, which is similar to the present climate. The mean annual precipitation is estimated by LAA to be ~ 125 cm. CLAMP suggests precipitation in the 11 months of growing period was 154.9 cm with the three wettest months having precipitation of 73 cm, widely contrasting with 15.5 cm for the three driest months. The precipitation shows the signal of the monsoon effect. The temperature, precipitation, and LSI mirrored the vegetation of the contemporary Doi Ton area which is a semi-evergreen forest in the tropical zone. The palaeoclimatic parameters of Doi Ton are in good agreement with those of south China and northwest India from the Eocene period and the present-day Mae Sot area. Moreover, the Doi Ton flora also closely matches the humid subtropical modern vegetation of south China. Palaeoclimate and vegetation analysis support an Eocene age estimate for the Doi Ton Formation however further independent age estimates are required to test this working hypothesis.
Intelligent Offloading for NOMA-Assisted MEC via Dual Connectivity

Multiaccess edge computing (MEC), which brings computing capability close to the user equipment (UE) within a radio access network (RAN), is a promising technique to meet the challenges of explosive growth in edge data traffic and the number of connected devices. Nevertheless, MEC offloading failures, i.e., interruption of task offloading due to UE mobility and limited battery capacity, are still an important issue. In this article, we aim to minimize the total energy consumption in dual connectivity (DC)- and nonorthogonal multiple access (NOMA)-assisted computation offloading systems. This problem is formulated as a mixed-integer nonlinear programming (MINLP) problem with nonconvex constraints, which is a nondeterministic polynomial-time (NP)-hard and perplexing problem. To address this problem, we propose an iterative optimization algorithm to obtain a suboptimal time scheduling and task allocation solution. Specifically, we solve the nonconvex constrained MINLP problems by exploiting the successive convex approximation (SCA) algorithm and the nonlinear optimization algorithm with the mesh adaptive direct search (MADS) (NOMAD). Furthermore, a deep learning-based intelligent offloading scheme is introduced, called a cyclic branch network, to fully utilize data traffic and the computing capability on the network edge. Numerical results show that the proposed intelligent offloading scheme has comparable performance and dramatically reduces the inference time compared with the traditional optimization algorithm.
Characterization of the complete chloroplast genome of Rosa chinensis ‘Old Blush’ (Rosaceae), an important cultivated Chinese rose

Rosa plants have become model species for woody and ornamental plants with great economical and biological importance. To develop genomic tools for understanding the biological traits evolution and domestication processes of roses, we here present the complete chloroplast genome of Rosa chinensis 'Old Blush' (OB). The total size of the plastome was 156,591 bp with GC content about 37.2%. It contained a large single copy region (LSC) of 85,737 bp and a small single copy region (SSC) of 18,766 bp separated by a pair of inverted repeats (IRs) of 26,044 bp. A total of 113 unique genes including 79 protein-coding genes, 30 tRNA genes and 4 rRNA genes were annotated. Phylogenomic analysis revealed that OB was closer to R. odorata than R. roxburghii. The newly sequenced plastome of OB will provide useful genetic resources for understanding the domestication history of modern roses.
Water Vapour and Temperature Measurements by Raman Lidar in the Frame of the NDACC

In November 2012, the University of BASILicata Raman Lidar system (BASIL) was approved to enter the International Network for the Detection of Atmospheric Composition Change (NDACC). Since then measurements were routinely carried out on a once per week basis. This paper illustrates specific measurement examples from this effort, with a dedicated focus on temperature and water vapour measurements, with the ultimate goal to provide a characterization of the system performance. Case studies illustrated in this paper demonstrate the ability of BASIL to perform measurements of the temperature profile up to 50 km and of the water vapour mixing ratio profile up to 15 km, based on an integration time of 2 hours and a vertical resolution of 150 m, with measurement bias not exceeding 0.1 K and 0.1 g kg−1, respectively. Raman lidar measurements are compared with measurements from additional instruments, such as radiosondings and satellite sensors (IASI and AIRS), and with model re-analyses data (ECMWF and ECMWF-ERA). Comparisons in this paper cover the altitude interval up to 15 km for water vapour mixing ratio and up to 50 km for the temperature. Comparisons between BASIL and the different sensor/model data in terms of water vapour mixing ratio indicate a mean absolute/relative bias of -0.024 g kg−1(or -3.9 %), 0.342 g kg−1(or 36.8 %), 0.346 g kg−1 (or 37.5 %), -0.297 g kg−1 (or -25 %), -0.381 g kg−1 (or -31 %), when compared with radisondings, AIRS, IASI, ECMWF, ECMWF-ERA, respectively. For what concerns the comparisons in terms of temperature measurements, these indicate a mean absolute bias between BASIL and the radisondings, AIRS, IASI, ECMWF, ECMWF-ERA of -0.04, 1.99, 0.48, 0.14, 0.62 K, respectively. Based on the available dataset and benefiting from the circumstance that the Raman lidar BASIL could be compared with all other sensor/model data, it has been possible to estimate the absolute bias of all sensors/datasets, this being 0.004 g kg−1/0.30 K, 0.021 g kg−1/-0.34 K, -0.35 g kg−1/0.18 K, -0.346 g kg−1/-1.63 K, 0.293 g kg−1/-0.16 K and 0.377 g kg−1/0.32 K in terms of water vapour mixing ratio/temperature for BASIL, the radisondings, IASI, AIRS, ECMWF, ECMWF-ERA, respectively.
The effect of Rhizobium seed inoculation on yields and quality of forage and seed of Berseem clover (Trifolium alexandrinum L.) and its impact on soil fertility and smallholder farmer’s income

Afield experiment was conducted to investigate the effects of Rhizobium seed inoculation on forage and seed yield components and the forage quality of berseem clover (Trifolium alexandrinum). The experiment comprised of two treatments, seed inoculation with Rhizobium trifolii and the non-inoculation (control). The results revealed that the seed inoculation significantly affected (P<0.005) and produced maximum number of stems/m (348.2), plant height (24.4cm), green forage yield (39.9 t/ha), dry matter yield (5.54 t/ha), number of heads per m (339), number of seeds per head (24.5), 1000-seed weight (3.851 g) and predicted seed yield (320 kg/ha) as compared to non-inoculated plots. The similar trend of results was also found in the forage quality parameters and seed inoculation produced maximum values of neutral detergent fiber (26.5%), crude protein (28.6%) and water soluble carbohydrates (2.02%). However, the acid detergent fiber (21.0%) and metabolisable energy (10.7%) were found to benon-significant. The use of Rhizobium inoculum also added available nitrogen (N) to soil after the crop increasing available N to0.0414% as compared to 0.0283% in thenon-inoculatedcontrol. The use of Rhizobium trifolii inoculum in combination with the improved variety Agaitti Berseem-2002, produced an additional net income of PKR 111,913 Rs/ha (US$ 1145/ha) compared to non-
STUDY ANALYSIS OF STUDENT NEEDS FOR THE DEVELOPMENT OF SCIENCE EBOOK BASED 3D PAGE-FLIP TO IMPROVE CRITICAL THINKING SKILLS IN TARAKANITA SOLO JUNIOR HIGH SCHOOL

This study aims to analyze the needs of Tarakanita Solo Baru Junior High School students towards the development of the Flip Page-based 3D Science ebook. The research method used is the descriptive analysis by analyzing the needs of students towards the development of the ebook using a questionnaire. The results of the questionnaire research based on the analysis of student needs were 56% answering requiring 3D Flip Page based teaching materials so that learning was more enjoyable, efficient and effective. The Flip Page 3D ebook is one of the technology-based learning media that can facilitate teachers in simplifying abstract material that is difficult for students to understand. 3D Page Flip based ebook can be an effort to overcome various gaps that occur in science learning, especially in class VII Tarakanita Solo Junior High School. Besides that, the 3D Page Flip based e-book is also expected to be a solution that can provide stimulation for students to focus more on the subject, have more interest in science lessons, to improve their critical thinking skills.
The exact solutions of heat equation by RDTM for the fractional order

In this paper,we pursue Fractional Reduced Differential Transform Method (DTM)to the one-dimensional heat equation, so that fractional order partial differential equations (PDEs) can be resolved effectively. The most distinct aspect of this method is that no prescribed assumptions are required and the huge computational exertion is reduced and round-off errors are also evaded. We utilize the proposed scheme on some initial value problem and approximate numerical solutions of linear time fractional PDEs are obtained, which shows that the method is highly accurate and simple to apply. The proposed technique is thus an influential technique for solving the fractional PDEs and fractional order problems occurring in the field of physics, science etc. Numerical results are obtained for verification and demonstration purpose by using mathematica software.
Two-Dimensional Principal Component Analysis-Based Convolutional Autoencoder for Wafer Map Defect Detection

Due to the high complexity and dynamics of the semiconductor manufacturing process, various process abnormality could result in wafer map defects in many work stations. Thus, wafer map pattern recognition (WMPR) in the semiconductor manufacturing process can help operators to troubleshoot root causes of the out-of-control process and then accelerate the process adjustment. This article proposes a novel deep neural network (DNN), two-dimensional principal component analysis-based convolutional autoencoder (PCACAE) for wafer map defect recognition. First, a new convolution kernel based on conditional two-dimensional principal component analysis is developed to construct the first convolutional block of PCACAE. Second, a convolutional autoencoder is cascaded by considering the nonlinearity of data representation. The second convolutional block of PCACAE is constructed based on the encoding part. Finally, the pretrained PCACAE is fine-tuned to obtain the final classifier. PCACAE is successfully applied for feature learning and recognition of wafer map defects. The experimental results on a real-world case demonstrate that PCACAE is superior to other well-known convolutional neural networks (e.g., GoogLeNet, PCANet) on WMPR.
Loading on long span bridges in heavily trafficked areas

ABSTRACT: The critical traffic loading condition for long-span bridges is congestion when vehicles may be closely spaced together. Data on very high traffic flows is limited however, as induction loops which are typically used to collect traffic data, are ineffective when traffic flow breaks down. A number of long span bridges are located in areas of high traffic with periods of recurring congestion occurring daily. While the inter-vehicle gap distances are greater than those occurring during non-recurring full stop accident type events, the high frequency of the recurring congestion events can increase the probability of a critical load case occurring. A location on the N7/M7 national primary route in Ireland is identified where regular congestion occurred due to a lane drop. Traffic monitoring using an Unmanned Aerial Vehicle (UAV) is carried out, generating a significant amount of high quality footage of congested traffic from a bird’s eye view. A method of processing the data is developed to transform the trajectory data into a traffic stream that can be used to simulate load effects on long span structures. The process is applied to 2 notional long span structures. Insights into the load patterns that give rise to critical loading events are identified for both structures.
The Effects of Financial Development on the External Performance of WAEMU Countries: an ARDL Approach

This article assesses the effects of financial development on the external performance of WAEMU countries using an autoregressive model with staggered delays (ARDL) over the period 2000-2017. The model estimates reveal that in the long-term, financial development has a positive and significant effect on the external performance. In the short term, however, the results established a heterogeneity in the contribution of financial development to external performance across countries. This result would result from an imperfection in the financial system, which reduces the interactions between savings and investment and therefore the external performance of the Union economies. Thus, a substantial mobilization of credit from both banking and non-banking financial institutions and the development of the financial system constitute an economic policy of strengthening the role of financial development in the external performance of the Union. Keywords: Financial development, external performance, effects, WAEMU, ARDL DOI: 10.7176/RJFA/11-12-15 Publication date: June 30th 2020
Milk production in dairy cows supplemented with herbal choline and methionine.

The objective of this study was to evaluate the supplementation of herbal choline and methionine to dairy cows on milk production and milk composition during 60 days of lactation, while also identifying the volatile compounds of the herbal methionine and choline. Fourteen compounds were identified for herbal methionine and fifteen in herbal choline including aromas, alcohols, aldehydes and phenolics, some with nutraceutical properties. Twenty-multiparous Holstein cows (body condition score, BCS = 3.1 ± 0.15; mean ± SE) were fed a basal diet (16.3% CP, 1.6% DP and 1.71. Mcal/kg ME). Seven days after calving, cows were randomly assigned to treatments, which consisted of control basal diet or an oral dose of herbal choline (15 g/d) plus herbal methionine (10 g/d). The experiment lasted 60 days with measurements of milk production and composition every 7 days. Supplementation with herbal choline plus herbal methionine improved (P <0.05) milk production (32.96 vs. 34.03 kg/d) and 4% FCM (28.23 vs. 29.91 kg/d). Protein content decreased (P <0.05) on supplemented cows (29.9 vs. 31.7 g/kg). However, no effects on the remaining composition (fat, lactose, total solids and non-fatty solids) was found. Milk production can be improved by supplementing cows with the evaluated herbal sources of choline and methionine.
THE EFFECT OF GRIP POSITION ON UPPER LIMB ANGULAR KINEMATICS DURING TENNIS TOPSPIN DOUBLE-HANDED BACKHAND STROKES

The purpose of this study was to compare the effect of grip on upper limb angular kinematics of sub-elite tennis players during a topspin double-handed backhand while aiming crosscourt. Sixteen sub-elite right-handed tennis players performed double-handed backhand trials using two different non-dominant grips (eastern & continental). Upper limb trajectory data was captured using the Vicon motion capture system (250 Hz). Greater peak angular velocity was observed in the eastern grip at the dominant shoulder (flexion, extension, abduction, adduction) and non-dominant shoulder (extension), elbow (pronation) and wrist (flexion, ulna and radial deviation). Subsequently peak linear velocities for the racket head (horizontal), and upper limb resultant joint centres were greater in the eastern condition. Collectively, these data suggest that using the eastern grip in the non-dominant limb is more optimal for developing racket head speed, and may provide coaches relevant information for athlete development in double-handed backhands.
Analysis of E-Service Quality on Website E-Commerce on E-Customer Satisfaction

—E-commerce has been positively accepted by the community in business to reach customers Easily. E-commerce system must meet all criteria that the community needed to improve the service quality in order to increase of customer satisfaction in online shopping, and at the end it will be affected to an e-commerce website sales. The level of satisfaction is necessarily to be measured to find out how far the quality of services could create customers satisfaction. The purpose of the research is to give empirical contribution about the effect of e-service quality through an e-commerce system to customer e-satisfaction. The sample of the research was the student majoring in business management all varsities in Batam with a survey method. This research used PLS-SEM (Partial Least Square-Structural Equation Model). The result is consistent with previous research stated that e-service quality has a positive effect on customer e-satisfaction in the online system. The conclusion of the research is information quality, e-service quality, usefulness and e-trust have effect on e-satisfaction variable.