Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Microsatellites for the Philippine Eagle, Pithecophaga jefferyi, Using Next-generation Sequencing

*Corresponding Author: jpquilang@up.edu. ph The Philippine Eagle, Pithecophaga jefferyi, is a critically endangered bird of prey that is endemic to the dipterocarp forests of the Philippines. In this study, data from next-generation sequencing (NGS) were used for the development of microsatellites for the Philippine Eagle. A total of 49,965 microsatellite loci were predicted and 20,960 primer pairs were designed. Forty (40) of these pairs were synthesized and were tested on 20 captive individuals. Twenty (20) of these markers showed high amplification success rates and exhibited polymorphism. A maximum of 24 alleles was detected across the 20 markers with an average of 13.9 alleles, which is remarkably higher than other microsatellite studies in eagles. The resulting values for observed heterozygosity (Ho) ranged from 0 to 1.00 with an average of 0.290, while expected heterozygosities ranged from 0.791 to 0.968 with an average of 0.910.
EFECTO DE LA CONCENTRACIÓN DE 2,4-DICLOROFENOXIACÉTICO EN LA INDUCCIÓN DE CALLOS IN VITRO UTILIZANDO COTILEDONES DE ROCOTO (CAPSICUM PUBESCENS RUIZ & PAV.)

El rocoto (Capsicum pubescens Ruiz & Pav.) es una planta herbacea o arbustico, se diferencia de otras especies del genero Capsicum por su corola de color purpura y semillas negras. Se considera al Peru como su centro de origen, donde es muy empleado en la medicina, industria farmaceutica, y sobre todo en la gastronomia. En el presente trabajo se desarrollo una metodologia para determinar el efecto de la concentracion de 2,4-diclorofenoxiacetico (2,4-D) en la induccion de callos in vitro a partir de cotiledones de rocoto cv. Serrano. Se germinaron in vitro semillas de rocoto, de las cuales a la primera semana se escindieron los cotiledones y fueron colocados en los medios de cultivo MS adicionado con 2,4-D. Se evaluo cinco tratamientos con diferentes concentraciones de 2,4-D. Los mejores resultados fueron obtenidos con los tratamientos que se adicionaron 0.75 y 1 mg·L-1 para la induccion de callos, logrando a los 21 dias 72% de proliferacion de callo. Este estudio pretende ser base para futuros estudios relacionados al cultivo de tejido en rocoto, permitiendo indicar potencial uso de callos de rocotos en la induccion de embriones somaticos y aislamiento de protoplastos.
Rheological and quality characteristics of thyme (Thymus vulgaris) enriched bread

In the present study, the effect of addition of dried thyme at 1, 2, 3 and 4% in wheat flour was examined to prepare functional bread. Addition of thyme significantly increased the water absorption rate and mixing tolerance index of flour. However, dough stability was decreased as the level of supplementation of thyme increased in the flour. Therefore, little modifications were done in baking procedure to prepare thyme bread. Presence of thyme in the formulation had a negative impact on the specific volume of bread. From sensory point of view, 2% level of thyme in the bread was selected as best. Thyme bread was high in total phenolic content and radical scavenging activity. Altogether, our findings indicate that thyme could be added as a functional ingredient in the bakery products.
Analysis of thermo-hydraulic performance of a solar air heater tube with modern obstacles

This paper presents a numerical analysis on turbulent flow and forced-convection characteristics of rectangular solar air heater tube fitted with staggered, transverse, V-shape, modern obstacles on the heated walls. Air, whose Prandtl number is 0.71, is the working fluid used, and the Reynolds number considered equal to 6×103. The governing flow equations are solved using a finite volume approach and the semi-implicit pressure linked equation (SIMPLE) algorithm. With regard to the flow characteristics, the quadratic upstream interpolation for convective kinetics differencing scheme (QUICK) was applied, and a second-order upwind scheme (SOU) was used for the pressure terms. The dynamic thermo-energy behavior of the V-shaped baffles with various flow attack angles, i.e., 50◦, 60◦, 70◦, and 80◦ are simulated, analyzed, and compared with those of the conventional flat rectangular baffles with attack value of 90◦. In all situations, the thermal transfer rate was found to be much larger than unity; its maximum value was around 3.143 for the flow attack angle of 90◦ and y = H/2.
A Study on existing Friend Recommendation Systems in social Networks

With increasing popularity of social media sites, friend recommendation systems have become increasingly important. With increasing number of teenagers, who feel lonely and depressed, friend recommendation systems can help in connecting the user to the right set of friends, who may help them to tackle the above-mentioned problems. Existing homophily-based friend recommendation systems that are used in social media does not provide accurate results. There is a need to design a friend recommendation systems that cater to the needs of the user and also help friends to connect wi th each other in real life. In this paper, various friend recommendation systems are developed to analyse the location, li festyle, and personality. This paper is concluded by proposing a system that combines the above - mentioned systems and is planned to provide personalized friend recommendations.
Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

This article introduces a robust optimal week-ahead generation scheduling approach that takes into account plug in hybrid electric vehicles (PHEVs) considering uncertainty in loads, renewable energy resources, and PHEV charging behavior. Due to the complexity of the scheduling process there is crucial need for a reliable optimal algorithm. The proposed approach can be applied in energy management platforms of decarbonized eco-friendly power systems. Generation scheduling is modeled as a multi-objective optimization problem: (a) minimize generation production cost and (b) minimize emission costs. The focal concern is to (a) handle the scheduling of renewable energy resources against their volatilities, (b) integrating PHEVs with uncertainties related to their state of charge, and (c) stochastic load behavior over a whole week. Two heuristic-based algorithms are used to solve the optimization problem, namely Water Cycle Algorithm and Gravitational Search Algorithm The proposed scheduling approach is implemented in MATLAB Ⓡ Platform, and is tested using two different microgrids sizes, 3 generator, and 10 generator unit systems integrating the effect of week days profile, renewable energy intermittency and different PHEV state of charges using the IEEE Reliability Test System (RTS) data. The results show promising performance of GSA over the WCA in the energy management studies integrating three different types of sources; thermal units, Renewable Energy Resources (RERs), and the PHEVs.
Decentralized task allocation for multiple UAVs with task execution uncertainties *

This work builds on a robust decentralized task allocation algorithm to address the multiple unmanned aerial vehicle (UAV) surveillance problem under task duration uncertainties. Considering the existing robust task allocation algorithm is computationally intensive and also has no optimality guarantees, this paper proposes a new robust task assignment formulation that reduces the calculation of robust scores and provides a certain theoretical guarantee of optimality. In the proposed method, the Markov model is introduced to describe the impact of uncertain parameters on task rewards and the expected score function is reformulated as the utility function of the states in the Markov model. Through providing the high-precision expected marginal gain of tasks, the task assignment gains a better accumulative score than the state of arts robust algorithms do. Besides, this algorithm is proven to be convergent and could reach a prior optimality guarantee of at least 50%. Numerical Simulations demonstrate the performance improvement of the proposed method compared with basic CBBA, robust extension to CBBA and cost-benefit greedy algorithm.
Metode-metode data mining untuk penyelesaian masalah

Data mining yang telah dikembangkan sejak tahun 1990 mempunyai potensial yang besar untuk menemukan informasi-informasi atau pola dari informasi yang belum diketahui. Ini dikarenakan banyaknya data akurat yang tercatat pada bidang kesehatan tetapi data-data tersebut belum terlalu diolah oleh expert. Data mining dapat digunakan menggali informasi dari data-data yang belum diolah tersebut. Metode data mining dapat diklasifikasikan berdasarkan cara pengerjaannya yaitu supervised learning, unsupervised learning dan semisupervised learning. Data mining pada umumnya dapat diklasifikasikan sebagai deskripsi dan prediksi. Proses deskripsi bertujuan untuk menemukan pola yang dapat dipahami manusia dan asosiasi, sedangkan metode prediksi bertujuan untuk meramalkan suatu hal [1].
Research on Multichannel Signals Fault Diagnosis for Bearing via Generalized Non-Convex Tensor Robust Principal Component Analysis and Tensor Singular Value Kurtosis

Tensor decomposition technique has been widely used in multichannel signals processing for its distinct superiority. The early fault feature signal of bearing is weak and easily inundated by ambient noise. In addition, the interference signals generated by other mechanical components also have a serious impact on the result of fault diagnosis. Aiming at the above issues, and built on the methods of generalized non-convex tensor robust principal component analysis (GNCTRPCA) and tensor singular value kurtosis (TSVK), this paper introduces a new fault diagnosis technique for multichannel bearing signals. First, the attractor tensor is formed by reconstructing the acquired multichannel signals in phase space. Second, based on tensor singular value decomposition (TSVD), the tensor robust principal component analysis (TRPCA) can provide a favorable noise reduction performance. However, TRPCA usually lowers the amplitude of useful singular value tubes (SVTs). To tackle this problem, the GNCTRPCA method is proposed to avoid the amplitude reduction. Third, a new TSVK method is introduced to determine the reconstructed order of SVTs, so as to extract the multichannel fault feature signals. Finally, the bearing fault type can be identified by comparing the peak frequencies of the extracted signals with the theoretical fault-related frequencies. Simulation analysis and experiment studies verify the principle and effectiveness of the proposed technique.
A Method to Search Global Maxima by Permanent Monitoring of Voltage and Current of each PV Panel

The maximum power point tracking (MPPT) systems relate to photovoltaic (PV) power plants for maximum energy production. An efficient MPPT systems is needed for large solar plants including several serially and parallel-connected PV panels especially functioning in partial shading conditions. A proportional decrease in electrical power will be a result of inhomogeneous irradiation (partial shading), as well as multiple local maximums may appear. The presence of multiple local maximums is the most difficult obstacle for traditional MPPT which is based on a sequential search of an optimum working point. The present article submits a new algorithm that can be corresponded to the mathematical modeling with elements of Artificial Intelligence (AI). It combines the advantages of AI with those are typical to conventional methods applicable for homogeneous irradiation. A proposed method is to use permanent monitoring of a voltage, a current and temperature of each PV panel placed in the string. An MPPT algorithm determines the position of a global maximum (GM) based on this information and in accordance with the previously obtained math model of individual PV panels. Owing principles of AI, math models should be periodically précised during the service life of the PV plant. Since none of the presented math algorithms can provide localization of GM with the accuracy, required for the modern MPPT, the proposed method desire to be complemented by a conventional approach, let say perturbation and observation or incremental conductance techniques. For example, an algorithm finding zero roots of a power derivative versus current change was used in our work. Proposed algorithm can achieve GM with relatively high speed that is only restricted by digital control ability. Currently, this task would take no more than 50–100 ms maximum. Therefore, the global maximum can be found for any rapidly changing solar irradiation.
A Ceramic PZT-Based PMUT Array for Endoscopic Photoacoustic Imaging

In this paper, we present the design, fabrication, and characterization of a compact <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> piezoelectric micromachined ultrasonic transducer (pMUT) array and its application to photoacoustic imaging. The uniqueness of this pMUT array is the integration of a <inline-formula> <tex-math notation="LaTeX">$4~\mu \text{m}$ </tex-math></inline-formula>-thick ceramic PZT, having significantly higher piezoelectric coefficient and lower stress than sol-gel or sputtered PZT. The fabricated pMUT array has a small chip size of only <inline-formula> <tex-math notation="LaTeX">$1.8\times1.6$ </tex-math></inline-formula> mm<sup>2</sup> with each pMUT element having a diameter of <inline-formula> <tex-math notation="LaTeX">$210~\mu \text{m}$ </tex-math></inline-formula>. The fabricated device was characterized with electrical impedance measurement and acoustic sensing test. Photoacoustic imaging has also been successfully demonstrated on an agar phantom with a pencil lead embedded using the fabricated pMUT array. [2020-0087]
Formation Control and Target Interception for Multiple Multi-rotor Aerial Vehicles

In this paper, we present a distance-based formation control strategy for multiple multi-rotor aerial vehicles participating in a target interception mission. The target moves arbitrarily, following a smooth 3D trajectory, while the intercepting vehicles aim at establishing a predefined enclosing formation around it, by attaining specific distances among them and simultaneously avoiding inter-agent collisions and connectivity breaks. More specifically, we propose a decentralized motion control protocol based on the prescribed performance control notion, which is of low computational complexity and is able to achieve robust and accurate formation establishment. Each agent requires local and relative state feedback, which can be acquired by a common onboard sensor suite without necessitating for explicit network communication. A realistic simulation study with one target and four multi-rotor interceptors was conducted to prove the efficacy of the proposed strategy.
Comparison of optimized motor-inverter systems using a Stacked Polyphase Bridge Converter combined with a 3-, 6-, 9-, or 12-phase PMSM

Stacked Polyphase Bridge Converters (SPB-C) are well suited for integrated motor drives, i.e. for systems that combine an electric motor with its supplying power electronic converter in one compact unit. A SPB-C requires a motor with multiple 3-phase winding systems. In the literature, the SPB-C is typically combined with motors whose winding systems are phase-aligned. This type of winding is magnetically equivalent to a 3-phase winding (with parallel branches). In this paper, the SPB-C with either 2, 3, or 4 modules is combined with a motor that has non-phase-aligned winding systems what results in a 6-, a 9-, or a 12-phase system, respectively. These multiphase systems are compared to 3-phase systems with the same number of modules with regard to efficiency, power density, and the possible degree of modularity and the benefits of the multiphase systems are evaluated.
Control of Parallel Connected Voltage Source Inverters in a Microgrid for Experimental Testing

This article describes the primary and secondary controls of voltage source inverters that are part of a custom-made AC microgrid for testing control strategies. The conceptual and physical structure of the microgrid is described. A cascade voltage-current control and a state feedback control are implemented for inverters with LCL output filters. Also, the conventional droop controls are implemented for power sharing. Simulation and experimentation results are presented of voltage, current and load sharing controls between two inverters of the microgrid, which validate these control strategies for teaching of microgrid control strategies.
3D Map Exploration via Learning Submodular Functions in the Fourier Domain

3D map exploration is one of key technologies in robotics. However, finding an optimal exploration path is a challenge since the environment is unknown. This research proposed the submodular exploration (SE) algorithm to enable an unmanned aerial vehicle (UAV) to explore 3D environments. The algorithm learns the submodular function in the Fourier domain and reconstructs the submodular function in the spatial domain via the compressed sensing techniques. Since the objective function of spatial exploration is reformulated as a maximizing submodular function with path constraints, greedy algorithms can achieve $\frac{1}{2}$ (1−e−1) of the optimum. Experiments conducted with this algorithm demonstrate that the UAV can explore more voxels in the environments than the benchmark approach.
A Collaborative Game-Based Learning Framework to Improve Computational Thinking Skills

Computational Thinking (CT) can amplify learners’ skill sets so that they become excellent problem-solvers. Game-Based Learning and Collaborative Learning are two approaches that may aid in the development of CT skills. This paper describes a framework based on Game and Collaborative Learning which aims to enhance the CT teaching that improves students’ social skills in a fun way. The framework stands out by including collaborative learning features defined in the main literature. Also, the strategy was developed specifically to fit the games’ dynamics. The approach was evaluated via metacognitive and transactive analysis and by a survey. The results showed evidence that the method is able to stimulate interaction among students to apply problem-solving strategies.
Big Data Analytics for Processing Real-time Unstructured Data from CCTV in Traffic Management

Todays many devices generate data everywhere and anytime. Data grow massively and becomes complex thing that needs to be handled. Unstructured data is one type of big data that is difficult to process and consists of unstable attributes. In traffic management, CCTVs are installed to monitor the specific location in the highway. CCTV generates unstructured data in image and video format. These data are difficult to process due to the complexity of the data. This research proposes to implement big data analytics to process real-time unstructured data from CCTV into knowledge displayed in web dashboard. We implement the YOLO framework with YoloV4 Architecture and COCO dataset for traffic flow counting and detecting illegal parking which is categorized as abnormal situation. Unstructured data from CCTV then transformed into semi-structured format in JSON. Data also can be visualized in real time to facilitate local authority to understand the highway situation. Historical data are stored in the NoSQL database to deep more knowledge such as vehicle traffic pattern. The proposed system requires the ROI drawing line as trigger to count the passing vehicles. These experiments are conducted from open CCTV for traffic online in Bali Tower Public Streaming. The prototype result is able to detect the object with 10 fps.
Asymptomatic Bacteriuria and their Antimicrobial Susceptibility Pattern Among Pregnant Women Attending Antenatal Clinics at Mizan Aman Town, Southwestern Ethiopia

Introduction: Failure to identify asymptomatic bacteriuria (ASB) is responsible for its improper clinical management. This may lead to severe health complications during pregnancy. However, little is known about antimicrobial susceptibility of ASB in Ethiopian pregnant women. Therefore, the study was conducted to assess the prevalence of ASB and its antimicrobial susceptibility pattern in the target population. Methods: A cross-sectional study was conducted during September-December of 2017 at Mizan-Aman town, south-western Ethiopia among pregnant women (n=260) attending antenatal clinics. 10ml of clean- catch midstream urine was collected and cultured on Cysteine Lactose Electrolyte Deficient agar. Bacterial isolates were identified using Gram staining, colony morphology, and biochemical analysis. Antibiotic susceptibility was assessed by Kirby-Bauer disc-diffusion method. Logistic regression was employed for association analysis. Results: The prevalence of urinary tract infection was 10.3%. ASB was significantly associated with second trimester (AOR=5.61 [1.40-22.45]; p<0.05) and history of urinary tract infection (AOR=12.47 [3.91-39.82]; p<0.001). Escherichia coli were most prevalent pathogen and gram positive bacteria comprised majority of the overall isolates (63%). The Gram-negatives were more resistant with susceptibility rates of 11.8%, 29.4%, and 35.3% for ampicillin, nalidixic acid and chloramphenicol respectively. The susceptibility rates of Gram-positives were 100%, 80%, 70% for ciprofloxacin and kanamycin, co-trimoxazol and ceftriaxone and gentamycin respectively. Multidrug resistance (≥2 drugs) was seen in 81.5% of isolates. Conclusion: In generally, pregnant women with ASB may have serious consequences on both mother and fetus. Since in this study the locale specific heterogeneity in ASB prevalence, type and number of bacterial species, dominant bacterial species, antibiotic susceptibility pattern and multidrug resistance was observed. Therefore, it is important to screen all antenatal women for asymptomatic bacteriuria at their first prenatal visit; preferably in first trimester as well as antimicrobial susceptibility testing should be performed for management of ASB among pregnant women.
Scalable SiPh-InP Hybrid Switch Based on Low-Loss Building Blocks for Lossless Operation

We design and experimentally demonstrate scalable <inline-formula> <tex-math notation="LaTeX">$2\times2$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> silicon photonic (SiPh) thermo-optic switch exhibiting low loss, low crosstalk, low power penalty, and BER below 10<sup>−10</sup> for payload data transmission. Less than 3.13 dB insertion loss (IL) and approximately 20.5 dB crosstalk is measured in the <inline-formula> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> SiPh banyan switch with thermal phase shifters. We also report on a semiconductor optical amplifier (SOA) in an indium phosphide (InP) technology platform with 25 dB gain and 7 dB noise figure enabling to transmit optical signals with large OSNR. Combining SiPh and InP technologies, we propose a lossless hybrid switch matrix with distributed SOA-based gain capable of transmitting data with near zero loss and low crosstalk over a large switching matrix. In hybrid SiPh/InP switches, the SOA gain compensates for the SiPh switch loss at the cost of amplified spontaneous emission (ASE) noise but mitigated by bandpass optical filters. Lower IL from the SiPh switch requires less gain from the SOAs leading to less OSNR degradation. Experimentally validated building blocks confirmed scalability up to <inline-formula> <tex-math notation="LaTeX">$64\times64$ </tex-math></inline-formula> in SiPh-InP hybrid platform.