Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Modeling and Experimental Evaluation of Non-Chiral Components of Bergamot Essential Oil with Inhibitory Activity against Human Monoamine Oxidases

Human monoamine oxidases (hMAOs) are well-established targets for the treatment of neurological disorders such as depression, Parkinson’s disease and Alzheimer’s disease. Despite the efforts carried out over the years, few selective and reversible MAO inhibitors are on the market. Thus, a continuous search for new compounds is needed. Herein, MAO inhibitors were searched among the non-chiral constituents of Bergamot Essential Oil (BEO) with the aid of computational tools. Accordingly, molecular modeling simulations were carried out on both hMAO-A and hMAO-B for the selected constituents. The theoretically predicted target recognition was then used to select the most promising compounds. Among the screened compounds, Bergamottin, a furocoumarin, showed selective hMAO-B inhibitory activity, fitting its active site well. Molecular dynamics simulations were used to deeply analyze the target recognition and to rationalize the selectivity preference. In agreement with the computational results, experimental studies confirmed both the hMAO inhibition properties of Bergamottin and its preference for the isoform B.
An online re-linearization scheme suited for Model Predictive and Linear Quadratic Control

This technical note documents the equations for primal-dual interior-point quadratic programming problem solver used for MPC. The algorithm exploits the special structure of the MPC problem and is able to reduce the computational burden such that the computational burden scales with prediction horizon length in a linear way rather than cubic, which would be the case if the structure was not exploited. It is also shown how models used for design of model-based controllers, e.g. linear quadratic and model predictive, can be linearized both at equilibrium and non-equilibrium points, making the presented extension of the controller formulation equivalent to that of the extended Kalman ﬁlter compared to an ordinary Kalman ﬁlter.
Development of small robot for home floor cleaning

The paper describes a small robot for home floor cleaning, and proposes an effective algorithm for navigation based on a simple switch sensor. We list the necessary requirements to be considered in development of a home floor cleaning robot. Based on the requirements, we design and manufacture a cleaning robot, and propose a navigation algorithm for the robot to modify the path correctly without vision or range sensors. The experiment shows that the developed robot can clean up refuse and that the navigation algorithm proposed is very effective on floor cleaning.
A crypto-tourism case study of agnes water/seventeen seventy, Australia

This research note explores the under-investigated assumption that cryptocurrencies are a panacea to stimulate regional tourism demand. Through the application of Rogers’ Diffusion of Innovation Theory, a case study was designed to examine the cryptocurrency effect on two adjacent towns of Agnes Water and 17 70 in Central Queensland, Australia. The findings revealed three major factors that led to merchant receptivity for adopting cryptocurrencies - First, a novelty effect perceived as a good strategy to induce consumer spend. Second, the low entry barriers for merchants adopting cryptocurrencies to conduct transactions. Third, cryptocurrencies incurred zero overhead costs. Nevertheless, the favorable attitudes of these merchants towards cryptocurrencies require a demand-side intention to use such tools during tourist visits to these regional destinations. Other regional tourism destinations will need to consider other ways of integrating such digital innovations to their landscapes.
Occurrence, Antibiotic Susceptibility and Genes Encoding Antibacterial Resistance of Salmonella spp. and Escherichia coli From Milk and Meat Sold in Markets of Bushenyi District, Uganda

The bacteriological safety of food/food products and the menace of antimicrobial resistance amongst enteropathogenic bacteria raise therapeutic management concerns within the public health system. Recently consumers of food/food products purchased from the public market of Bushenyi District presents with Enterobacteriaceae infection-associated symptoms and clinical conditions. We determine the molecular characterization and antibiotic signatures of some enteric bacterial recovered from foods/food products in markets of Bushenyi District, Uganda. Standard molecular biology techniques (Polymerase chain reaction PCR) and microbiological procedures were applied. Meat (MT) and milk (MK) samples were collected from 4 communities/town markets (Kizinda, Ishaka, Bushenyi, kashenyi) between April and September 2020 and analyzed. Our result reveals high differential counts of Salmonella species (175.33 ± 59.71 Log 10 CFU/100 ml) and Escherichia coli (53.33 ± 26.03 Log 10 CFU/100 ml) within the 4 markets with the count of Salmonella species higher than that of E. coli in each sampled market. The PCR further confirmed the detected strains (22.72% of E. coli and 54.29% of Salmonella species) and diverse multiple antibiotic-resistant determinants {TEM: (12 (23.1%) blaTEM-2 gene, 3 (5.8%) blaTEM gene}, 5 (9.6%) blaSHV gene, 3 (5.8%) bla-CTX-M-2, 1 (1.9%) bla-CTX-M-9 }. Other resistance genes detected were {10 (21.7%) strA gene} and 8 (17.4%) aadA gene} indicating a potential antibiotic failure. The need for alternative medicine and therapeutic measure is suggestive. Astute and routine surveillance/monitoring of potential pathogens and food products in the public market remains a core for maintaining future consumer safety.
Rancang Bangun Sistem Monitoring Listrik Prabayar dengan Menggunakan Arduino Uno

Abstrak Kebutuhan akan listrik akan terus meningkat dari tahun ke tahun, hal ini dikarenakan pembuatan produk yang menggunakan listrik sebagai energi nya juga gencar dikeluarkan oleh produsen sehingga kebutuhan akan listrik sudah menjadi kebutuhan yang sangat vital untuk masing-masing individu. Saat ini di negara kita telah dikembangkan listrik prabayar disamping listrik pasca bayar yang telah berjalan, fungsi dari listrik prabayar ini adalah untuk dapat mengontrol penggunaan dari listrik itu sendiri. Namun, penggunaan listrik prabayar tidak sepenuhnya digunakan secara optimal oleh pelanggan, dikarenakan pelanggan tidak mengetahui beban dari alat listrik yang digunakan sehingga tidak dapat melakukan kontrol dari pulsa listrik yang dibeli. Dari permasalahan itu peneliti mengusulkan untuk membuat suatu alat kontrol dengan menggunakan arduino uno sehingga pemilik dapat mengontrol penggunaan listriknya secara real time. Board Arduino berfungsi sebagai sistem kontrol pengambilan data, sebelum data tersebut di olah pada server. Terdapat sistem sensor berfungsi untuk pengambilan data ampere yaitu sensor AC712-20A dan modul relay sebagai saklar elektrik berfungsi untuk memutus daya listrik ketika pulsa tidak mencukupi. Dari hasil pengujian yang telah dilakukan, terdapat kesalahan pengukuran rata-rata sensor ACS712-20A dengan multitester sebesar 26%, sedangkan untuk pengukuran billing listrik prabayar terdapat kesalahan sebesar 6%.
ANDROID APPLICATION FOR S-PARK SYSTEM

Finding a parking space in most metropolitan areas, during the rush hours, is difficult for drivers. The problem arises from not knowing where is the empty space; even if it is known, many vehicles may pursue very limited parking spaces to cause serious traffic congestion. In this project, a prototype of smart parking (S-Park) system is designed and implemented that allows drivers to effectively find and reserve the empty parking spaces using an android application connected to Internet. By periodically polling the parking status from the sensor networks deployed in parking lots, the reservation status is affected by the change of physical parking status. The drivers are allowed to access the server on their Android smartphones. The proposed smart parking policy has the potential to solve the problems of parking systems, as well as reduce traffic congestion caused by parking searching thus making the system two-way efficient, that is, cost wise and time wise.
Precise and Prompt Analyte Detection via Ordered Orientation of Receptor in WSe2-Based Field Effect Transistor

Field-effect transistors (FET) composed of transition metal dichalcogenide (TMDC) materials have gained huge importance as biosensors due to their added advantage of high sensitivity and moderate bandgap. However, the true potential of these biosensors highly depends upon the quality of TMDC material, as well as the orientation of receptors on their surfaces. The uncontrolled orientation of receptors and screening issues due to crossing the Debye screening length while functionalizing TMDC materials is a big challenge in this field. To address these issues, we introduce a combination of high-quality monolayer WSe2 with our designed Pyrene-based receptor moiety for its ordered orientation onto the WSe2 FET biosensor. A monolayer WSe2 sheet is utilized to fabricate an ideal FET for biosensing applications, which is characterized via Raman spectroscopy, atomic force microscopy, and electrical prob station. Our construct can sensitively detect our target protein (streptavidin) with 1 pM limit of detection within a short span of 2 min, through a one-step functionalizing process. In addition to having this ultra-fast response and high sensitivity, our biosensor can be a reliable platform for point-of-care-based diagnosis.
Group Model Building in a Pressure Cooker : A Field Experiment with Public Policy

Group Model Building has established itself as a credible method to uncover the systemic reality of persistent problems in organizations. The contextual nature of the method and the flexibility required during the process mean that it is hard to build up evidence of what works and what does not. One major question is the extent to which the method can be compressed, for example, due to time or budget constraints. We present an experiment, carried out with a public organization, in which we tested two shortcuts. Firstly, we substituted the expertâ€ driven preliminary model for a clientâ€ made model. Secondly, we allowed for a homogenous group because enlarging the group would be too time consuming. We defined several factors at the individual level and at the group level as dependent variables. We conclude that the shortcuts had a negative effect on most variables. We therefore recommend against these changes to the basic model. Copyright Â© 2017 John Wiley & Sons, Ltd.
Cardiovascular and Thermoregulatory Responses to Treadmill Running While Wearing Shirts with Different Fabric Composition

Clothing must be worn in many athletic and occupational situations as part of a uniform or for protection from the environment, but clothing interferes with sweat evaporation and heat dissipation. Thus, the heat dissipation qualities of clothing worn by athletes, soldiers, and others working and/or competing at moderate to high workloads is important. The purpose of this study was to determine if a synthetic shirt purported by the manufacturer to be advantageous in dissipating heat permits more effective heat dissipation than a cotton shirt during exercise in a temperate environment. Methods: Nine active males ran on a treadmill at 65% VO2peak for 45 min in 22 degrees C while wearing cotton sweatpants and either a long-sleeve synthetic shirt (SS), cotton shirt (CS), or no shirt (NS). The first and second trials were counterbalanced, and the third trial was determined by default. At least one week lapsed between trials. Results (means +/- SD): There were no differences in RPE, VO2, change in rectal temperature (Delta T-re), or change in heart rate (Delta HR) between conditions (p > 0.05). Final T-re in SS (38.8 +/- 0.3 degrees C) was lower than CS (39.1 +/- 0.4 degrees C) but not different than NS (38.9 +/- 0.2 degrees C). Mean skin temperature was lower in NS than CS (p 0.05). Thermal sensation was lower in SS and NS than CS. Conclusion: Wearing a synthetic shirt provides a limited thermoregulatory benefit compared to a standard cotton shirt, but comfort is enhanced.
A Compact, Ultrawideband Dual-Polarized Vivaldi Antenna With Radar Cross Section Reduction

A compact, ultrawideband (UWB) dual-polarized Vivaldi antenna with radar cross section (RCS) reduction is proposed and demonstrated. The dual-polarized antenna is constructed by aligning two single-polarized Vivaldi elements into a crossshaped arrangement. By cutting symmetric rectangular inclined slots and curved metal potions out from the radiating surfaces, the RCS of the antenna can be suppressed over a broad frequency band. The antenna possesses a footprint of 0.58<italic> λ<sub>L</sub></italic> × 0.5<italic> λ<sub>L</sub></italic>, where <italic>λ<sub>L</sub></italic> is the free-space wavelength at the lowest working frequency. The antenna is fabricated and characterized, showing a measured <italic>S<sub>ii</sub></italic> < −10 dB covering the UWB band ranging from 1.8 to 6 GHz. Moreover, the measured port isolation is higher than 23 dB, whereas the averaged monostatic RCS is smaller than −30.2 and −28 dBsm under the illumination of <italic>x</italic>- and <italic>y</italic>-polarized plane waves, respectively. The demonstrated antenna is useful in a wide variety of applications such as the near-field measurement systems.
Lesson Learned: Shaping Collaboration Among Tourism Stakeholders During Mount Agung Eruption

The purpose of this study is to investigate the situation during the eruption of Mount Agung and the roles played by tourism stakeholders during the crisis. The findings of the study provide a more in-depth understanding of the lessons acquired by tourism stakeholders in Bali when dealing with catastrophic events, particularly natural disasters. For the purpose of this study, this research included interviews with 19 informants from a variety of tourist stakeholders, as well as those in charge of coping with natural catastrophes. According to the findings of the research, there are several lessons that can be learned from the eruption of Mount Agung, which will help tourism stakeholders be better prepared in dealing with all of the crises that may arise in the future. Keywords: natural disaster, tourism, volcanic, mount agung, stakeholder, crisis management
Intelligent Discrete Sliding Mode Predictive Fault-Tolerant Control Method for Multi-Delay Quad-Rotor UAV System Based on DIECOA

This paper introduces a novel intelligent sliding mode predictive fault-tolerant control method based on the Dynamic Information Exchange Coyote Optimization Algorithm (DIECOA), which is applied to a quad-rotor UAV system with multi-delay and sensor fault. First, the system nonlinearity and sensor fault are dealt with by means of interpolation transformation and system state expansion, and an equivalent system is obtained. Second, the quasi-integral sliding mode surface is used to construct the prediction model so that the initial state of the system is located on the sliding mode surface, and the global robustness is guaranteed. Third, this paper introduces an improved fault and disturbance compensation term, which effectively weakens the adverse effect of time delays and enhances the FTC performance of the system. Fourth, the Dynamic Information Exchange (DIE) strategy is designed to further improve the coyote individual replacement mechanism and speeds up the solution and convergence speed of the method in this paper. Finally, the simulation is carried out on the fault-tolerant simulation platform of the quad-rotor Unmanned Aerial Vehicle (UAV), and the results show the rationality of the method.
Visiting Indian Hospitals Before, During and After Covid

The prevailing COVID-19 situation has brought in temporary and permanent changes in the attitude and lifestyle of people. Starting from Hand sanitizers and face masks, it extends to online classrooms and work from home culture. In case of visiting hospitals and medications, people with pre-existing medical conditions and minor health issues tend to delay or avoid visiting hospitals due to fear of infection, which is dangerous. Further, people or patients tend to access several alternatives and precautions. The alternatives include home remedies, ayurvedic medication, yoga and meditation. On the other hand, hospitals are trying to adapt online consulting and telemedicine. Besides, Cancellation or delay of nonemergency surgeries became inevitable in the lockdown phase. This survey conducted among the people of Erode district, Tamilnadu to study the perception of people concerning visiting hospitals for health issues. The results show that fear of infection, financial and transportation difficulties are the major factors which affected people from visiting hospital. Also, changing trends like Telemedicine and home remedies are likely to be permanently opted by people. In Brief, the outcomes reveal the changing attitude of people towards medication and hospital visiting habits.
Enhancement of Partially Coherent Diffractive Images Using Generative Adversarial Network

We present a deep learning-based generative model for the enhancement of partially coherent diffractive images. In lensless coherent diffractive imaging, a highly coherent X-ray illumination is required to image an object at high resolution. Non-ideal experimental conditions result in a partially coherent X-ray illumination, lead to imperfections of coherent diffractive images recorded on a detector, and ultimately limit the capability of lensless coherent diffractive imaging. The previous approaches, relying on the coherence property of illumination, require preliminary experiments or expensive computations. In this article, we propose a generative adversarial network (GAN) model to enhance the visibility of fringes in partially coherent diffractive images. Unlike previous approaches, the model is trained to restore the latent sharp features from blurred input images without finding coherence properties of illumination. We demonstrate that the GAN model performs well with both coherent diffractive imaging and ptychography. It can be applied to a wide range of imaging techniques relying on phase retrieval of coherent diffraction patterns.
Improved genetic algorithm solution to unit commitment problem

As the electrical industry restructures, many of the traditional algorithms for controlling generating units need modification or replacement. Previously utilized to schedule generation units in a manner that minimizes costs while meeting all demand, the Unit Commitment (UC) algorithm must be updated. This work deals with the Improved Genetic Algorithm (IGA) Solution to UC problem. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A simple GA implementation using the standard reproduction, cross over and mutation operators has been utilized to get optimal solution. In this paper, we have obtained the satisfactory solutions for the UC problem using the varying quality function technique and by adding problem specific operators. The idea has been implemented using technical simulation package MATLAB and the results for the same were obtained. Neyveli Thermal Power Station - II in India, demonstrates the effectiveness of the proposed approach. Numerical results are shown to compare the superiority of the cost solutions obtained using the conventional methods.
Design and analysis of artificial bee-colony-based MPPT algorithm for DFIG-based wind energy conversion systems

ABSTRACT In this article, the proposed maximum power point tracking (MPPT) method is designed by taking rotor speed as an optimization problem, which is solved by artificial bee colony (ABC) algorithm to generate the maximum power output. The main advantage of this algorithm is that its optimal solution is independent of the initial positions and requirement of lesser number of control parameters, which leads to simple and robust MPPT algorithm than other algorithm. Furthermore, the hill climb search and particle swarm optimization-based MPPT algorithm are also discussed and the results obtained by these are compared to verify the effectiveness of proposed algorithm. Simulations for MPPT control along with doubly fed induction-generator-based wind energy conversion system is carried out in MATLAB/Simulink environment. Three statistical methods are used to evaluate the accuracy of each MPPT algorithm. All results are analyzed and compared under randomly selected wind as well as real wind speed configuration. Comparison of both numerical and simulation results under two different varying wind speed conditions strongly suggest that the proposed ABC-based MPPT algorithm is superior than other two MPPT algorithms.
Potential Pleiotropic Genes and Shared Biological Pathways in Epilepsy and Depression Based on GWAS Summary Statistics

Current epidemiological and experimental studies have indicated the overlapping genetic foundation of epilepsy and depression. However, the detailed pleiotropic genetic etiology and neurobiological pathways have not been well understood, and there are many variants with underestimated effect on the comorbidity of the two diseases. Utilizing genome-wide association study (GWAS) summary statistics of epilepsy (15,212 cases and 29,677 controls) and depression (170,756 cases and 329,443 controls) from large consortia, we assessed the integrated gene-based association with both diseases by Multimarker Analysis of Genomic Annotation (MAGMA) and Fisher's meta-analysis. On the one hand, shared genes with significantly altered transcripts in Gene Expression Omnibus (GEO) data sets were considered as possible pleiotropic genes. On the other hand, the pathway enrichment analysis was conducted based on the gene lists with nominal significance in the gene-based association test of each disease. We identified a total of two pleiotropic genes (CD3G and SLCO3A1) with gene expression analysis validated and interpreted twenty-five common biological process supported with literature mining. This study indicates the potentially shared genes associated with both epilepsy and depression based on gene expression, meta-data analysis, and pathway enrichment strategy along with traditional GWAS and provides insights into the possible intersecting pathways that were not previously reported.
Botnet Detection Based on Genetic Neural Network

Botnet have turned into the most serious security dangers on the present Internet framework. A botnet is most extensive and regularly happens in today's cyber-attacks, bringing about the serious risk of our system resources and association's properties. Botnets are accumulations of compromised computers (Bots) which are remotely regulated by its creator (BotMaster) under a typical Command-and-Control (C&C) framework. Botnets cannot just be implemented utilizing existing well-known applications and additionally developed by unknown or inventive applications. This makes the botnet detection a challenging issue. In this paper proposed an anomaly detection model based on genetic neural network system, which joined the significant global searching capability of genetic algorithm with the precise local searching element of back propagation feed forward neural networks to improve the initial weights of neural network.