Miracle-dz/UniFAR-FTUs huggingface dataset:
DESIGN OF CHANNEL FEED-BACK CODEBOOK AND ADDRESSING POWER LEAKAGE PROBLEM IN MM- WAVE MASSIVE MIMO SYSTEM WITH LENS ANTENNA ARRAYS

Objective The modernistic theory of beamspace MIMO, which involves millimeter wave (mm-Wave) and massive MIMO system depending on lens antenna array (LAA) can efficiently decrease the count of power consuming radio frequency (RF) chains. Hence, it is perceived as an optimistic approach for the 5G technology and its successors. Research in beamspace (BmSp) MIMO has yet to be taken into debate the issues of power leakage in channels, therefore resulting in remarkable deterioration in SNR and the sum-rate (SR). A precoding technique for beam aligning and to tackle power leakage issue has been proposed. Initially a network using phase shifters has been designed, which helps RF chains choose beams and collect leaked power in beamspace MIMO. An algorithm based on rotation for precoding, for the available phase shift networks is proposed to align the gains of the channels in the same direction to maximize SNR received by the users. In systems that employ frequency division duplexing (FDD), it is necessary that the channel is fed back to the base station (BS) via feedback based on codebook. There is no devoted codebook for the LAA dependent mm-Wave systems. The codebook design is proposed to address the gap for these systems. In this codebook, initially a large dimension vectors based on recent idea involving angle coherence time are generated. Then relying on obtained vectors in channel subspace, codebook is created by considering lens and beam selection. After that the channel is quantized and given as feedback to the BS. Results Simulation results conveys that the overhead in feedback is proportional to dominant paths for each user which are few and the proposed approach with uniform linear array (ULA) attains the near optimal performance in SR comparing to ideal case of zero power leakage and also obtains superior Energy efficiency (EE) than existing single beam precoding and Multiple beam via multiple RF (MBMRF) structure precoding methods.
Comparing the responses of rumen ciliate protozoa and bacteria to excess glucose

xi RESUMO . xiii Chapter 1: General Introduction . 1 References. 3 Chapter 2: Literature Review . 4 Introduction. 4 Maintenance . 7 Reserve of carbohydrate . 9 Energy spilling . 12 Occurrence of excess carbohydrate in the rumen . 16 Avoiding energy excess . 17 Prediction of microbial protein . 18 Conclusion . 19 References. 20 Chapter 3: Comparing the responses of rumen ciliate protozoa and bacteria to excess carbohydrate . 30 Introduction. 31 Materials and methods . 33
Preliminary study on the prevalence of gastrointestinal parasites of pigs managed and slaughtered in Umuahia north local government area of Abia State, Nigeria

Fishes are known for their abilities to concentrate heavy metals in their muscles and since they play important role in human nutrition, they need to be carefully screened to ensure that unnecessary high level of some toxic trace metals are not being transfer to man through their consumption. The study was to evaluate heavy metals (chromium, lead, iron and cadmium) in imported and local species of shrimps, crabs and fish using Atomic Absorption Spectroscopy. The selected imported species were horse mackerelkote fish (Trachurus trachurus), mackerel - titus (Scomber scrombus), croaker (Pseudotolithus typus), smooth swim crab (Portunus validus) and pink shrimp (Farfantepenaeus notialis) while the local species used were; Sole fish (Cynoglossus senegalensis), Tilapia (Oreochromis niloticus), brackish water catfish (Chrysichthys nigrodigitatus), freshwater catfish (Clarias gariepinus), lagoon crab (Callinectes amnicola) and freshwater prawn (Macrobrachium macrobrachion). It was observed that there was no significant difference in the metals (Cd, Pb and Cr) between the imported and local fish species and between imported and local crustacean species. However, these metals concentrated more in the local fish species, O. niloticus (1.846 ± 1.00 mg/100g). The mean heavy metals concentration occurred in the following decreasing order: Fe > Cd > Pb > Cr for all the samples. The mean concentrations of the analyzed heavy metals in the samples were below the maximum permissible limits as recommended by the International Atomic Energy Agency (IAEA), United Nations Environment Program (UNEP) and Federal Environmental Protection Agency (FEPA) for heavy metals in fish.Keywords: Fish, Shrimp, Crab, Heavy metal, Chromium, Lead, Iron
Joint Multiview Feature Learning for Hand-Print Recognition

In this article, we propose a joint multiview feature learning (JMvFL) method for hand-print recognition including both finger-knuckle-print (FKP) and palmprint recognition. Unlike most existing hand-print descriptors that are usually handcrafted and only focus on single-view features, our JMvFL method automatically and jointly learns multiview discriminant features of hand-print. Specifically, unlike the existing methods that extract features from raw pixels, we first form a multiview including both texture- and direction-view feature containers for hand-print images. Then, we aim to jointly learn multiview feature codes by enforcing three criteria: 1) the intraclass distance is minimized, and the interclass distance is maximized to make the feature codes of different classes more separate; 2) the information loss between the feature containers and the learned feature codes is minimized; and 3) the variance of interview feature codes is maximized so that the multiview feature codes are more complementary to enhance their overall discriminative power. Extensive experimental results demonstrate the effectiveness of the proposed method on various hand-print recognition tasks, including both FKP and palmprint recognition.
Toward Moving Target Detection in Through-the-Wall Radar Imaging

With the advances in radar technology, through-the-wall radar imaging (TWRI) has become a viable sensing modality that can allow fire-and-rescue personnel, police, and military forces to detect, localize, and identify targets behind opaque obstacles. Many of the existing TWRI approaches detect either stationary or moving targets but not both of them simultaneously. In this article, a method is proposed to detect both stationary and moving targets from a sequence of radar signals. The proposed method decomposes the 3-D radar data, i.e., frequency, space, and time data into a low-rank tensor and two sets of sparse images. One set of images comprises the stationary targets, and the other set of images contains the moving targets. Wall clutter removal and target detection are formulated into an optimization problem regularized by tensor low-rank, joint sparsity, and total variation constraints. Then, an alternating direction technique is developed to reconstruct the sets of stationary and moving target images. Experiments using simulated and real radar signals are conducted. The experimental results illustrate the effectiveness of the proposed method to detect and separate the stationary and moving targets into a pair of sparse images.
Development of Artificial Intelligence to Classify Quality of Transmission Shift Control Using Deep Convolutional Neural Networks

Automatic transmissions are a core component of modern vehicles, and achieving good shift quality is critical to enhance the driving experience. In companies that manufacture transmissions, expert engineers take great care in calibrating parameters for controlling hydraulic pressure with the goal of achieving targeted shift quality. Because we need human experts to evaluate shift quality, it is hard to shorten the time taken to calibrate the systems controlling hydraulic pressure. In this paper, to reduce such development time, we propose a novel framework of a Classifier of Shift Quality; it applies the standard Supervised Deep Learning techniques (CSQ-SDL) to time-series measurement data. The framework consists of five procedures: measurement data collection, labeling by experts, data augmentation, data standardization, and the training of deep convolutional neural networks. Moreover, we also carry out driving experiments in which CSQ-SDL is used to assess the engagement of the lock-up clutch of a specific transmission model. By using raw time-series data measured by ordinary sensors in advance, and labels written later by an expert engineer looking at the raw data, we build binary classifiers and test their performance. It turns out that the accuracy of predicting expert's judgment is high; the area under the curve is 0.94. The result indicates that the proposed method is capable shortening product development times and thus meeting the demands of today's competitive automotive market.
A research on the key factors for classification of diabetes based on random forest

Recently, the number of people visiting the hospital is increasing due to diabetes. According to the Korean Diabetes Association, statistically, 1 in 7 adults over the age of 30 are suffering from diabetes. As such, diabetes is one of the most common diseases among modern people. In this paper, in addition to blood sugar, which is widely used for diabetes awareness, BMI, which is known to be related to diabetes, triglycerides and cholesterol that cause various complications in diabetics it was studied using random forest techniques and decision trees known to be effective for classification. The importance of each element was confirmed using the results and characteristic importance derived using two techniques. Through this, we studied the diabetes-related relationship between BMI, triglyceride, and cholesterol as well as blood sugar, a factor that diabetic patients should pay much attention to.
UniKL STUDENTS’ PERCEPTION ON SYNCHRONOUS LEARNING USING ICT AS LEARNING TOOLS TO LEARN ENGLISH

Blended synchronous learning is not new and higher education is not exceptional It has been used widely by teachers all over the world However, recently, due to the Covid 19 pandemic, teachers and educators are forced to fully use synchronous learning and e-learning to continue teaching and learning process, including Universiti of Kuala Lumpur (UniKL) Nonetheless, it is important to know the students' perception on the use of ICT learning tools such as Zoom Cloud Meeting, Microsoft Teams and VLE as a medium to conduct synchronous learning Therefore, the purpose of this study us to investigate the students' perception on synchronous learning using ICT tools to learn English 250 students were selected to participate in this study The findings indicate that students perceive online learning using ICT tools as beneficial However, the students also believe that online synchronous learning is not as effective as traditional classes © 2020 by Advance Scientific Research
Distribution Network-Constrained Optimization of Peer-to-Peer Transactive Energy Trading Among Multi-Microgrids

This article proposes a two-level network-constrained peer-to-peer (P2P) transactive energy for multi-microgrids (MGs), which guarantees the distribution power network security and allows MGs to trade energy with each other flexibly. At the lower level, a P2P transactive energy is employed for multi-MGs to trade energy with each other. A multi-leader multi-follower (MLMF) Stackelberg game approach is utilized to model the energy trading process among MGs. We prove the existence and the uniqueness of the Stackelberg equilibrium (SE) and provide the closed-form expression for SE. For privacy concerns, we provide several distributed algorithms to obtain SE. At the upper level, the distribution system operator (DSO) reconfigures the distribution network based on the P2P transactive energy trading results by applying the AC optimal power flow considering the distribution network reconfiguration. If there are any network violations, DSO requests trading adjustments at the lower level for network security. We reformulate the DSO operation problem in a mixed-integer second-order cone programming (MISOCP) model and ensure its solution accuracy. Numerical results for a 4-Microgrid system, a modified IEEE 33-bus and 123-bus distribution power system show the effectiveness of the proposed transactive model and its solution technique.
Exploratory Investigation of Disengagements and Crashes in Autonomous Vehicles Under Mixed Traffic: An Endogenous Switching Regime Framework

Autonomous Vehicles (AVs) have a large potential to improve traffic safety but also pose some critical challenges. While AVs may help reduce crashes caused by human error, they still may experience failures of technologies and sensing, as well as decision-making errors in a mixed traffic environment. A disengagement refers to an AV transitioning control from autonomous systems to the trained test driver. The safety critical nature of disengagements makes it imperative to analyze disengagements and crashes together. This study analyzes both crashes and disengagements from real-world AV driving in California to fill the knowledge gap regarding the relationship between disengagements and crashes in a mixed traffic environment. A nested logit model was calibrated using three different outcomes: (1) disengagement with a crash, (2) disengagement with no crash, and (3) no disengagement with a crash. Furthermore, endogenous switching regime models were also calibrated to draw distinctions between the relation of disengagements and crashes while accounting for endogeneity effects. The results show that factors related to AV systems (such as software failures) and other roadway participants increase the propensity of a disengagement without a crash. Furthermore, AVs were observed to disengage less often as the technology matured over time. Marginal effects revealed an 8% decrease. The results thus suggest that disengagements are a part of AVs’ safe performance and disengagement alerts may need to be triggered in order to avoid certain failures with current technology.
DETECTION OF SPECIFIC ANTIBODIES TO THE NUCLEOCAPSID PROTEIN FRAGMENTS OF SEVERE ACUTE RESPIRATORY SYNDROME-CORONAVIRUS AND SCOTOPHILUS BAT CORONAVIRUS-512 IN THREE INSECTIVOROUS BAT SPECIES

Bats are the natural reservoirs of severe acute respiratory syndrome-coronavirus (SARS-CoV). Six Alphacoronavirus and five Betacoronavirus have been detected in many bat species, including SARS-related CoV and Middle East respiratory syndrome (MERS)-related CoV. In Taiwan, SARS-related CoV, belonging to Betacoronavirus, has been detected in Rhinolophus monoceros. Scotophilus bat CoV-512, belonging to Alphacoronavirus, has been detected in Scotophilus kuhlii, Miniopterus fuliginosus, and Rhinolophus monoceros by using reverse transcription polymerase chain reaction (RT-PCR). To understand the infection history of CoV in these three insectivorous bat populations, CoV-specific antibodies were surveyed by using western blot (WB) analysis and indirect enzyme-linked immunosorbent assay (ELISA). The carboxyl terminal fragment of nucleocapsid protein (N3) of SARS-CoV and Scotophilus bat CoV-512 were used as the antigen in the assays. Of the 52 serum samples obtained from Scotophilus kuhlii, 29 samples (56%) were tested positive for Scotophilus bat CoV-512-specific antibodies through ELISA. Of the 63 serum samples obtained from Rhinolophus monoceros, 9 samples were tested positive for only SARS-CoV-specific antibodies, 7 samples were tested positive for only Scotophilus bat CoV-512-specific antibodies, and 16 samples (25.4%) were tested positive for both antibodies through WB analysis. Only 1 of 18 Miniopterus bat serum samples tested positive for Scotophilus bat CoV-512-specific antibodies through ELISA. Lactating female bats had higher positive rates of CoV-specific antibodies than non-lactating female and male bats did. Our findings were crucial for understanding CoV infection history in three insectivorous bat species and important for the control of bat-borne zoonosis diseases.
Spectroscopic Analysis of Expired and Pure Melmet

Spectroscopy is a perfect analyzer to find the elements of all matter. The application of spectroscopy has been used to interpret the effect of antibiotics and other medicines after its life duration. This has been studied by using the diabetics tablet melmet, expired for an year, to visualize the changes physically and chemically [1-3]. It has been observed that the occurrence of changes in color and reduction of smell and also the formation of new peaks and shift by XRD and UV, FTIR characterization respectively [4].
3D terrestrial laser scanning of El Fuerte de Samaipata

This paper concerns El Fuerte de Samaipata – one of Bolivia’s most important monuments inscribed on the UNESCO World Heritage List. The study describes the hardware and software used in the project “Architectural examination and complex documentation of Samaipata (Fuerte de Samaipata/ Bolivia) site from the World Heritage List”, as well as the workflow adopted for the terrestrial laser scanning (TLS) of the site. It also explains the important role that TLS played in the entire project
Mask R-CNN for rock-forming minerals identification on petrography, case study at Monterado, West Kalimantan

This paper explores the experiment of Deep Learning method using Mask Region-Convolutional Neural Network (Mask R-CNN) to identify rock-forming minerals on thin section images from petrographic observation in igneous rocks, which are plagioclase, quartz, K-feldspar, pyroxene, and hornblende. Train and validation dataset consisted of 2 quartz diorites and 1 granodiorite from Monterado, West Kalimantan, 1 quartz diorite and 1 granite from Nangapinoh, West Kalimantan, and 7 andesite and 2 basalts from Bangli, Bali, while test dataset consisted of 3 quartz diorites from Monterado, West Kalimantan. This study uses 4 Mask R-CNN models, which is influenced by the lighting on polarizing microscope and using ResNet-50 architecture (Model A) or ResNet-101 (Model B), and the models that is not affected by the lighting on polarizing microscope and using ResNet-50 architecture (Model C) or ResNet-101 (Model D). From Average Precision scores, it was found that Model B has the highest score (58.0%), followed by Model A (57.8%), Model C (45.8%), and Model D (43.6%). In conclusion, the lighting of polarizing microscope is a major factor to give a better performances of Mask R-CNN models by 12%-14.4%, while the type of backbone architecture on Mask R-CNN models was not too consequential.
Mixed Noise Removal by Residual Learning of Deep CNN

: Due to the huge difference of noise distribution, the result of a mixture of multiple noises becomes very complicated. Under normal circumstances, the most common type of mixed noise is to add impulse noise (IN) and then white Gaussian noise (AWGN). From the reduction of cascaded IN and AWGN to the latest sparse representation, a great deal of methods has been proposed to reduce this form of mixed noise. However, when the mixed noise is very strong, most methods often produce a lot of artifacts. In order to solve the above problems, we propose a method based on residual learning for the removal of AWGN-IN noise in this paper. By training, our model can obtain stable nonlinear mapping from the images with mixed noise to the clean images. After a series of experiments under different noise settings, the results show that our method is obviously better than the traditional sparse representation and patch based method. Meanwhile, the time of model training and image denoising is greatly reduced.
Physiological Responses of Physalis angulata Plants to Water Deficit

It’s known that drought affects crop growth, however, little is known about the physiological responses developed under these conditions by underexploited species, such as Physalis angulata. This study aimed at assessing the physiological responses of Physalis angulata plants after 40 days under different water availability (100%, 80%, 60%, 40% and 20% of pot field capacity). In this research, the effects of the water deficit on the relative water content, water potential, gas exchange, sugars accumulation and activity of nitrate reductase were evaluated. Water relations were affected mainly in plants under severe water deficit, however, the variables remained stable when cultivated at sub-optimal levels of field capacity. Gas exchanges were also affected by water deficit, with reduction in carbon assimilation, internal carbon, stomatal conductance and transpiration, as well as increased leaf temperature and water use efficiency. Plants accumulated sugars as a mechanism of tolerance to severe water deficit, while nitrate reductase activity was reduced. P. angulata plants develop important strategies to tolerate water deficit, contributing to the establishment of crops under low water availability.
Thyroid dysfunction in Congolese subjects with Down syndrome

Objective: The present study aims to assess the proportion and the pattern of thyroid dysfunction in a population of Congolese subjects with Down Syndrome. Methods: A total of 34 subjects with Down syndrome were recruited from Lubumbashi (Democratic Republic of Congo). Thyroid morphology was assessed by sonography. Serum T3, T4, FT4, TSH, Thyroglobulin, Thyroid peroxidase antibody (TPOAb) and antithyroglobulin antibody (TgAb) were measured. Results: The mean age of the patients was 13 years (range: 3 and 47 years). Of 34 Down syndrome subjects, 6 cases (17.6%) were found to have subclinical hypothyroidism. No patient had hyperthyroidism. Among 29 subjects with a thyroid ultrasound imaging, 25 patients (86.2%) were found to have morphological abnormalities of the gland. Regarding thyroid autoantibodies, TPOAb was positive in 1 case (2.9%) and TgAb was positive in 1 case (2.9%). The results were compared between patients with hypothyroidism and with normal thyroid function. There was no statistical difference in terms of age, sex, nutritional status, antibody levels and thyroid volume between these two groups. Conclusions: In our series, the proportion of hypothyroidism is relatively important but it remains largely under the prevalence reported in international studies.
Effect of Specific Nutrients on Ovulation, Oocytes Development, Gene Expression and Coupling Success in Mice

Introduction: The roles of genetic, epigenetic, metabolic and other environmental factors such as nutrition and stress, are becoming evident for a successful and healthy pregnancy. This raises the possibility and question, if and how, we improve the probability of pregnancy and of a healthy fetus? The present study examined the role of metabolic, antioxidant and minerals and the results suggest that these factors may positively influence the oocyte quality and the pregnancy rate. Methods: CD1 female mice aged 15 and 5 weeks were divided into four groups of ten each and treated by intragastric gavage daily for 3 weeks. G1: Vehicle; G2: Carnitines (L-carnitine 0.4 mg and acetyl-L-carnitine 0.12 mg/mouse); G3: Microelements (Zinc 4 ng, Copper 0.8 ng, Iron 7 ng/mouse); G4: G3+G2. At the end of the treatment period superovulation was induced and oocytes were collected to assess their quantity and quality. Further, in vitro fertilization (IVF) experiments were performed to assess the preimplantation embryo development. The birth success rate was also analyzed in old and young female. The mice were in vivo fertilized. qRT-PCR were performed to analyze a possible modulation in key genes of the reproductive process. Results: The number of oocytes was significantly higher in groups 2 and 4 compared to the control group. The oocyte number in group 3 was not affected. The level of degraded oocytes was 29.1% and 19.3% (group 2 and 4) versus 34.3% (control). Concomitantly, the numbers of embryos arriving to successful birth were also increased in G4, both in the old and young group of mice. Preliminary analysis of genes affected evidenced that AMH was up regulated in the ovary and KITL in the uterus in group 2. Conclusion: Results showed that L-carnitine, acetyl-L-carnitine and micronutrients were able to improve both oocytes quality and success rate of pregnancy. Further studies are planned to further examine ways to improve pregnancy and fetal health.