Miracle-dz/UniFAR-FTUs huggingface dataset:
Genotypic variability and relationships of Sorghum bicolor accessions from Java Island, Indonesia based on IRAP markers

Martiwi INA, Nugroho LH, Daryono BS, Susandarini R. 2020. Genotypic variability and relationships of Sorghum bicolor accessions from Java Island, Indonesia based on IRAP markers. Biodiversitas 21: 5637-5643. Sorghum (Sorghum bicolor (L.) Moench) is a multifunctional crop with high adaptability to various habitats and thus it is important to assess the genotypic variability of this crop species. This study aimed to reveal the genotypic variability and relationships of sorghum accessions using inter-retrotransposon amplified polymorphism (IRAP) molecular markers. Twenty-three sorghum accessions were collected from smallholder farmers in three provinces of Java Island during the 2019 planting season. The IRAP profiles generated using 10 primer combinations produced 233 scorable bands, with degree of polymorphism 75-96.9%. Cluster analysis using the neighbor-joining (NJ) method showed the formation of three groups corresponds to geographic origin of samples. The NJ dendrogram structure indicated high genotypic variability, and was supported by distribution pattern of accessions on the scatterplot constructed using principal coordinate analysis. The grouping of accessions did not correspond to classification of botanical races. Each group recognized from cluster analysis and principal coordinate analysis consisted of various races namely Bicolor, Guinea, Caudatum, and Kaffir. The fact that each group consisted of various botanical races indicated local adaptation and the maintenance of genotypic identity through farmers’ selection in traditiona l cultivation practices. It could be concluded, therefore, that geographical proximity and the practice of seed exchange between different regions showed considerable influence on the genotypic relationships of sorghum accessions.
Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification

Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details. Our primary challenges come from both small inter-class variations and large intra-class variations. In this article, we propose to combine several innovations to improve fine-grained classification within the use-case of wildlife, which is of practical interest for experts. We utilize geo-spatiotemporal data to enrich the picture information and further improve the performance. We also investigate state-of-the-art methods for handling the imbalanced data issue.
‘MetaNETs’ - Accelerated discovery and design of photonic metamaterials using deep learning

We propose an approach based on neural networks for approximating the electro-magnetic (EM) responses of mesoscale, split-ring resonator photonic metamaterials. We demonstrate that by treating the EM spectral data as time-varying sequences and the inverse problem as a single-input, multi-output model, we force our architecture to learn the geometry of the designs from the training data as opposed to abstract features thereby addressing both the forward and the inverse design problems with great promise.
Learning Multiple Networks via Supervised Tensor Decomposition

We consider the problem of tensor decomposition with multiple side information available as interactive features. Such problems are common in neuroimaging, network modeling, and spatial-temporal analysis. We develop a new family of exponential tensor decomposition models and establish the theoretical accuracy guarantees. An efﬁcient alternating optimization algorithm is further developed. Unlike earlier methods, our proposal is able to handle a broad range of data types, including continuous, count, and binary observations. We apply the method to diffusion tensor imaging data from human connectome project and identify the key brain connectivity patterns associated with available features. Our method will help the practitioners efﬁciently analyze tensor datasets in various areas. Toward this end, all data and code are available at https://CRAN.R-project.org/ package=tensorregress .
ECONOMIC GROWTH EFFECTS OF THE INTERACTION OF TRADE OPENNESS AND INSTITUTIONS QUALITY: EMPIRICAL EVIDENCE FROM SUB-SAHARAN AFRICA

In its various publications, the International Monetary Fund (IMF) has vigorously advocated the need for reforming African trade policy from inward oriented strategies to outward oriented ones. While most countries in Africa have long accepted the reform agenda, there remains marked differences in their expected growth outcomes. This raises some skepticism about the value of trade openness on growth especially in the Sub- Saharan Africa (SSA). This paper therefore investigates how trade openness impacts growth in SSA. In order to further enrich the debate around trade policy orientation and its corresponding effects on growth, we examine the interaction effect of openness and institutions quality on growth. In doing so, panel data set were collected for a sample of 27 SSA countries covering the period 1996-2016.The study employed a dynamic panel estimation technique with a view of evaluating the relative impact of the predictors on growth. The results revealed that trade openness alone bring significant negative impact on growth performance in SSA. However, the simultaneous interaction of trade openness with institutions quality brings about positive and significant impact on growth. This confirmed that positive impact of trade openness on growth is conditional on the quality of institutions. For policy, our results imply that governments in SSA should first strengthen their institutions while adopting a gradual approach to trade liberalization.
Application of microbiological assay to determine the potency of intravenous antibiotics

Demonstration of equivalent amounts of active pharmaceutical ingredient is a basic requirement for intravenous generic drugs prior to administration. Physicochemical methods are often used to determine concentration of antibiotics in biological fluids. However, it does not permit direct quantification of potency of a desired antibiotic. This study demonstrates the application of a microbiological assay to determine the potency and concentration of commercially available pharmaceutical-grade antibiotics used for injections. Concentration-dependent variation of inhibitory effect of four commercial brands of cefuroxime and two of ciprofloxacin were observed against two reference bacteria (Escherichia coli DH5α and Escherichia coli ATCC 8739) on Mueller Hinton agar. Regression analysis was used to assess the in vitro equivalence of generic products sold by different retail companies in Dhaka city. A linear relationship was found between the concentration and response of the bacteria in regression analysis where anti-log of X-intercept and slope showed the concentration and potency, respectively. The study showed excellent results of linearity (r2≥0.89), precision (inter assay variation ≤10% for cefuroxime and ≤20% for ciprofloxacin), accuracy and specificity tests for both types of antibiotics. Pharmaceutical equivalence demonstrated by four cefuroxime and two ciprofloxacin samples showed no significantly distinguishable slopes (P > 0.78 and P > 0.44) and intercepts (P > 0.25 and P > 0.07), respectively. Estimated potency for cefuroxime was 91.1-100.0% and for ciprofloxacin was 68.1-100.0%. Microbiological assay was found to be convenient, rapid, cost-effective, precise and accurate in demonstrating pharmaceutical equivalence of antibiotics in different dosage forms. This technique can be used as an alternative method for testing generic antibiotics prior to their use in animal and human. Stamford Journal of Microbiology, Vol.10 (1) 2020: 25-29
Energy-free Security in Wireless Sensor Networks

—Wireless sensor networks are often deployed in open and uncontrolled environments that make them more vulnerable to security attacks. Cryptographic algorithms can be used to protect the data collected by the sensors against an intruder. The cost in terms of energy to provide enough security can be quite large as these algorithms may be very complex. As communication is the main energy consumer, a way to save energy is to use data compression. We propose to measure the impact of the well-known DES algorithm on the energy consumption for various number of rounds and then, we show that energy-free security may be possible. We combine a cryptographic algorithm with a compression algorithm and show through a model that a node can provide security without consuming more energy. The only counterpart is the time for ciphering and compressing. We get some results from experiments on energy consumption of cryptographic and compression algorithms and establish the level of security that can be achieved in various cases, from a single node to a random network.
Reverse Engineering a Twin-EyeTM Laser Sensor for use as a Fluid Flow Rate Sensor

Current technologies used to measure turbulent flows are large, intrusive, expensive or a combination of these factors. This report briey covers the theory behind turbulence, the structure of turbulent boundary layers and the current technologies for measuring turbulent flows. Also detailed are the steps taken to partially reverse-engineer the Twin-eye laser sensor, intended originally for use in computer mice, to develop it as an SPI-interfaced velocity sensor. Such a sensor provides the ability to perform distributed three-component velocity measurements in a boundary layer. While the sensor was able to communicate with a microcontroller the device was unable to be bought to an operational state even when running demonstration code provided by the manufacturer.
Prevalence of psychiatric disorder

At a time when patients are challenged to cope adaptively with both the diagnosis and treatment of cancer, clinicians need to respond appropriately to the many inevitable questions about the causes of cancer and contributing factors, including ‘Is this my fault? ’. The evidence guiding answers to such questions has been confounded by many methodological challenges, but personality, stress and life events are no longer considered causes of cancer. However, social isolation, untreated depression and social deprivation continue to influence quality of life and reduce cancer survival times. Psychiatry might play a role in promoting lifestyle changes that reduce the risk of cancer, but more importantly it can influence disease progression by optimising patients’ adaptation to the many challenges that cancer brings.
GENDER IDENTIFICATION AND AGE ESTIMATION OF ARABIC SPEAKER USING MACHINE LEARNING

Gender Identification and Age estimation is an important topic in the field of Automatic Speech Recognition (ASR) systems. In the field of robotics, for example, it is important to identify human sex and age for emotion recognition and robot interaction. In this paper, we targeted Arabic speakers by identifying their genders and estimating their ages. Experiments were conducted using six famous learning algorithms such as NB-Tree (Decision Tree), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naïve Bayes (NB). We focused on the accuracy with some important classification measurements. Automatic gender identification and age estimation system is proposed based on extracting MFCC features from Arabic speech. The MFCC features with the machine learning algorithms were applied to determine whether the speech sample is male or female and assign the approximate age slice. Two experiments were done, the first one targeted gender identification. The results of the first experiment showed that the learning algorithms SVM and ANN were superior in gender determination with accuracies 98.5% and 96.5% respectively. The second experiment targeted age estimation. The results of this experiment showed that Decision Tree (NB-Tree), and Random Forest (RF) were superior in age estimation with accuracies 95.9%, and 93.0% respectively.
3D Porous N‐Doped Graphene Frameworks Made of Interconnected Nanocages for Ultrahigh‐Rate and Long‐Life Li–O2 Batteries

The inferior rate capability and poor cycle stability of the present Li–O2 batteries are still critical obstacles for practice applications. Configuring novel and integrated air electrode materials with unique structure and tunable chemical compositions is one of the efficient strategies to solve these bottleneck problems. Herein, a novel strategy for synthesis of 3D porous N‐doped graphene aerogels (NPGAs) with frameworks constructed by interconnected nanocages with the aid of polystyrene sphere@polydopamine is reported. The interconnected nanocages as the basic building unit of graphene sheets are assembled inside the skeletons of 3D graphene aerogels, leading to the 3D NPGA with well‐developed interconnected channels and the full exposure of electrochemically active sites. Benefiting from such an unique structure, the as‐made NPGA delivers a high specific capacity, an excellent rate capacity of 5978 mA h g−1 at 3.2 A g−1, and long cycle stability, especially at a large current density (54 cycles at 1 A g−1), indicative of boosted rate capability and cycle life as air electrodes for Li–O2 batteries. More importantly, based on the total mass of C+Li2O2, a gravimetric energy density of 2400 W h kg−1 for the NPGA–O2//Li cell is delivered at a power density of 1300 W kg−1.
An Evaluation of some Machine Learning Algorithms for the detection of Android Applications Malware

Article history: Received: 08 October, 2020 Accepted: 25 December, 2020 Online: 30 December, 2020 Android Operating system (OS) has been used much more than all other mobile phone’s OS turning android OS to a major point of attack. Android Application installation serves as a major avenue through which attacks can be perpetrated. Permissions must be first granted by the users seeking to install these third-party applications. Some permissions can be subtle escaping the attentions of the users. Some of these permissions can have adverse effects like spying on the users, unauthorized retrieval and transference of the data and so on. This calls for the need of a heuristic method for the identification and detection of malware. In this discourse, testing of classification algorithms including Random forest, Naïve Bayes, Random Tree, BayesNet, Decision Table, Multi-layer perceptron (MLP), Bagging, Sequential Minimal Optimization (SMO)/Support-Vector Machine (SVM), KStar and IBK (also known as K Nearest Neighbours classifier (KNN)) was carried out to decide which algorithm performs best in android malware detection. Two dataset was used in this study and were gotten from figshare. They were trained and tested in the Waikato Environment for Knowledge Analysis (WEKA). The performance metrics used are Root Mean Square Error (RMSE), Accuracy, Receiver Operating Curve (ROC), False positive rate, F-measure, Precision and recall. It was discovered that the best performance with an accuracy of 99.4% was the multi-layer perceptron on the first dataset. Random Forest has the best performance with accuracy, 98.9% on the second dataset. The implication of this is that MLP or random forest can be used to detect android application malwares.
Explosive colorimetric discrimination using a smartphone, paper device and chemometrical approach

In this report we introduce a novel approach for an inexpensive and disposable colorimetric paper sensor array for the detection and discrimination of five explosives – triacetone triperoxide (TATP), hexamethylene triperoxide diamine (HMTD), 4-amino-2-nitrophenol (4A2NP), nitrobenzene (NB), and picric acid (PA). The colorimetric sensor comprised a disposable paper array fabricated using a wax printer and three reagents (KI, creatinine, and aniline) that produced a unique color pattern for each explosive based on chemical interactions between the explosive species and the chemical reagents. The analytes were discriminated from one another as per the color change profiles, which were readily distinguishable after 15 min, using hierarchical clustering analysis (HCA) and principal component analysis (PCA); there were no misclassifications in any of the trials conducted. The colorimetric pattern values were extracted using a smartphone, custom-made software and a closed chamber to circumvent the illumination problems commonly found in other paper approach devices. A semi-quantitative analysis was performed and it was possible to use as low as 0.2 μg of explosives.
Covalent immobilization of β‐galactosidase onto electrospun nanofibers of poly (AN‐co‐MMA) copolymer

Immobilization of β-galactosidase in poly (acrylonitrile-co-methyl methacrylate) poly (AN-co-MMA) Nanofibers was studied by electrospinning, and a spacer-arm i.e., (Polyethyleneimine (PEI)) was covalently attached by the reaction of carbonyl groups of Poly (AN-co-MMA) nanofibers. β-galactosidase was then covalently immobilized through the spacer-arm of the Poly (AN-co-MMA) nanofibers by using glutaraldehyde (GA) as a coupling agent. Nanofibers mode of interaction was proven by FTIR and thermal gravimetric analysis and supported by morphological changes recognized through SEM examination. Factors affecting the modification process such as PEI concentration, reaction time, and reaction temperature have been studied. Its influence on the amount of coupled PEI was consequently correlated to the changes of the catalytic activity and the retained activity of immobilized enzyme, the main parameters judging the success of the immobilization process. Evidences of Poly (AN-co-MMA) nanofibers modification were extracted from morphological changes recognized through SEM examination. The maximum activity (Vmax) and michaelis constant (Km) of immobilized enzyme were found to be 8.8 μmole/min mg protein and 236.7 mM, respectively. Stabilities of the immobilized β-galactosidase were obviously improved. The optimum temperature for β-galactosidase immobilized on the spacer-arm attached nanofiber was 5°C higher than that of the free enzyme and was also significantly broader. The immobilized β-galactosidase had better resistance to temperature inactivation than did the free form. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
Decay (Expansion) of the Universe and Stability of the Solar System in a Fundamental Quantum Description

The expanding (decaying) universe is discussed in the framework of a local quantum field theory, based on a Lagrangian, in which all fermion operators are coupled to bosons. In this formalism the initial phase of the universe has been explained by creation of particles out of the vacuum, accumulation of a system of large mass and immense radius dominated by (e−p+) and (e+p−) pairs, followed by a chirally triggered collapse and annihilation of all (e+p−) pairs (antimatter). The resulting annihilation photons led to strong heating and disintegration of the remaining (e−p+) pairs (matter) during the ”Big Bang”, resulting in exponentially increasing velocities of the decay fragments towards large radii (accelerated expansion). A good description of velocity-distance data from supernovae Ia observation is obtained by adjusting the position of the Solar system to a radius of the universe of ∼ 1200 Mpc. Of importance, at this radius the repulsive and attractive forces compensate each other. Combined with a calculation of its mass, this yields surprising evidence for a stable Solar system and most likely also other cosmic systems, which do not follow the general expansion of the universe. The accelerated expansion interpreted in cosmological models as due to unknown dark energy is understood by the strong radial fall-off of the gravitational potential generated in the early universe.
Novel Architecture for Wireless Power Transfer in Unmanned Arial Vehicles

There has been a rapid growth in research towards developing smart, low-cost, and high-performance unmanned aerial vehicles (UAVs) for enabling wireless data transfer and communication. The practical applications of UAVs in healthcare, surveillance, agriculture and other domains is increasing at a faster pace. Mass production of these UAVs is essential to meet the growing communication requirements. Plans towards standardization and global deployment of Internet-of-Things (IoT) technologies and fifth generation (5G) wireless communication are in progress. Functional diversity is established by the UAVs in enabling wireless communication with IoT and 5G technologies. The different UAVs used in wireless communication is studied and a hierarchical architecture is proposed to facilitate integrating these technologies into next-generation networks. The principles of aerodynamics, wireless communication, power transfer, area of coverage, delay, meteorological impacts and other design parameters and their tradeoffs is analyzed.
Barnoud, J. and Santuz, H. and Craveur, P. and Joseph, Agnel Praveen and Jallu, V. and de Brevern, A.G. and Poulain, P. (2017) PBxplore: a tool to analyze local protein structure and deformability with Protein Blocks. PeerJ

This paper describes the development and application of a suite of tools, called PBxplore, to analyze the dynamics and deformability of protein structures using Protein Blocks (PBs). Proteins are highly dynamic macromolecules, and a classical way to analyze their inherent flexibility is to perform molecular dynamics simulations. The advantage of using small structural prototypes such as PBs is to give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, PBs allow analysis of local protein deformability which cannot be done with other methods and had been used efficiently in different applications. PBxplore is able to process large amounts of data such as those produced by molecular dynamics simulations. It produces frequencies, entropy and information logo outputs as text and graphics. PBxplore is available at https://github.com/pierrepo/PBxplore and is released under the opensource MIT license. Subjects Bioinformatics, Computational Biology, Molecular Biology
Prevalence and Pattern of Tobacco Use in Patients with Mental Disorders in Eastern India

Background:This study looks into the prevalence and pattern of tobacco useamong psychiatric outpatients in eastern India compared to an age and sex matched control group and the association between tobacco use and various socio-demographic and clinical characteristics in this population. Method:An observational study of 795 psychiatric patients was conducted at a government medical college in Kolkata. Sociodemographic and clinical data were gathered using a semi-structured proforma. Diagnosis according to ICD 10 was made using the MINI International Neuropsychiatric Interview. Data regarding nicotine use was collated using the Fagerstrom’s questionnaire. Data were analyzed using Spearman correlation and logistic regression.Results: 58.6% of psychiatric outpatients reported nicotine use (low – moderate – high level of dependence), compared to 35.6% of the control group. In both groups smoking was more common in men whereas smokeless tobacco (SLT) use was commoner in women. Prevalence of smoking among women psychiatric patients (18.7%) was higher compared to other parts of the country. Smoking was highest in patients with depressive disorders followed by bipolar disorder. SLT usewas highest in patients with schizophrenia followed by anxiety disorders. Combined use of smoking and SLT was highest in schizophrenia.Conclusion: In keeping with the global trends nicotine/tobacco use is more common among psychiatric outpatients than the general population. There is a differential pattern of use among psychiatric patients; patients with mood disorders smoke more whereas those with schizophrenia or anxiety disorders are prone to using SLT. Findings of this study should spur more research and allow targeted strategies to promote abstinence is this particularly vulnerable group.