Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analytical Model and Optimization of a Novel Hybrid Rotor Machine for High Torque Density

This article proposes a novel hybrid rotor permanent magnet (HRPM) machine for the performance of torque and overload capability with the constraint of the rotor diameter. In order to optimize the electromagnetic performance, an analytical model (AM) is built by the magnetic equivalent circuit (MEC) of the rotor and stator considering the saturation and leakage flux effect. A good prediction accuracy is verified by the finite element method (FEM), even in high saturation condition. Based on the AM, the characteristics of the hybrid magnetic pole rotor are analyzed by the equivalent strategy that equivalent PM remanence and thickness of single pole in rotor. Furthermore, a response surface method (RSM) for multi-variate and multi-objective optimization is adapted to improve the electromagnetic performance because of the high torque density and PM material utilization. Finally, a comparison of the torque, PM eddy current loss and anti-demagnetization performance is investigated among surface-mount PM (SPM) machine, spoke-type PM (STPM) machine and HRPM machine. The comparison results show the hybrid PM rotor machine enhances higher torque density and overload capability. Finally, a 12 slot/10 pole HRPM machine is prototyped and tested to validate the analyses.
A Frequency-Domain Quasi-Newton-Based Biparameter Synchronous Imaging Scheme for Ground-Penetrating Radar With Applications in Full Waveform Inversion

Full waveform inversion (FWI) of ground-penetrating radar (GPR) data is becoming a promising technique to facilitate the interpretation of surface-GPR data and the mapping of the subsurface. However, more general FWIs still require a sufficient amount of RAM memory, and it is difficult to produce an accurate and representative reconstruction result due to a large amount of the Hessian matrix calculations and singular value decomposition (SVD). In this article, we developed a novel full-waveform approach of the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm for surface-GPR data that is based on a quasi-Newton framework and the total variation (TV) regularization. The proposed approach uses an L-BFGS algorithm and combines a scale-transformation regularization technique to mitigate the ill-posed problem of inversion, which can impose a biparameter preinformation constraint to ensure the stability of inversion, and adaptive regularization weights are applied to improve the convergence efficiency of inversion. To demonstrate the novelty and effectiveness of the proposed scheme, we tested our FWI algorithm using synthetic data and in-site field data. In the testing, we focus on analyzing the influence of different aspects of the FWI results, including different scale factors, regularization weights, inversion strategies, acquisition configurations, initial models, and the noisy data set. In particular, the FWI experiment is performed to demonstrate the applicability of the proposed algorithm. The results show that the proposed algorithm can effectively reconstruct the biparameter near the subsurface with high accuracy, which makes our approach very attractive for attribute analysis applications and makes the surface-GPR FWI commercially viable.
BIOLOGICAL DIVERSITY OF INVERTEBRATE FAUNA CIRCULATING IN SOME CUCURBIT-BASED MARKET GARDENING AGROSYSTEMS AT MINKO'O (SOUTH REGION, CAMEROON)

Market gardening, one of the predominant activities in developing countries in general and in Cameroon in particular, is nowadays threatened by pests of several animal taxa. These pests cause important yield losses and impose frequent and anarchical applications of large spectrum pesticides that alter product and environment quality. In the perspective of designing a study program on the ecology of major pests of cultivated cucurbit-based agrosystems, the present study aimed at assessing the biological diversity of the invertebrate circulating in these agrosystem. Data were collected at Minko'o, South Region of Cameroon by visual observations in experimental trap gardens from 2015 to 2017. A total of 412 species of 117 families and 20 orders of Invertebrates were identified from a set of 40,741 individuals. This sample comprise Insecta (13 orders), Diplopoda (two orders) and Arachnida (3 orders) and two orders of Gastropoda. The numerically most important orders were Hymenopterans, Hemipterans, Coleopterans, Orthopterans, and Lepidopterans representing 88.67% of the total abundance. The numerically dominant species belonged mainly to these orders. In relation with host plant preferences, all these orders, families and species showed variable level of selectivity as comparisons of their distribution among studied plants appeared different from one taxon to another.
Optimal UAV Caching and Trajectory in Aerial-Assisted Vehicular Networks: A Learning-Based Approach

In this article, we investigate the UAV-aided edge caching to assist terrestrial vehicular networks in delivering high-bandwidth content files. Aiming at maximizing the overall network throughput, we formulate a joint caching and trajectory optimization (JCTO) problem to make decisions on content placement, content delivery, and UAV trajectory simultaneously. As the decisions interact with each other and the UAV energy is limited, the formulated JCTO problem is intractable directly and timely. To this end, we propose a deep supervised learning scheme to enable intelligent edge for real-time decision-making in the highly dynamic vehicular networks. In specific, we first propose a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content placement strategy, we devise a time-based graph decomposition method to jointly optimize the content delivery and trajectory design, with which we then leverage the particle swarm optimization (PSO) algorithm to further optimize the content placement. We then design a deep supervised learning architecture of the convolutional neural network (CNN) to make fast decisions online. The network density and content request distribution with spatio-temporal dimensions are labeled as channeled images and input to the CNN-based model, and the results achieved by the CBTL algorithm are labeled as model outputs. With the CNN-based model, a function which maps the input network information to the output decision can be intelligently learnt to make timely inference and facilitate online decisions. We conduct extensive trace-driven experiments, and our results demonstrate both the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model.
Toward Sensing Emotions With Deep Visual Analysis: A Long-Term Psychological Modeling Approach

With the current global outbreak of COVID-19, an increasing number of people are suffering from negative mental states and mental disorders. We propose a multimodal psychological computational technology in a universal environment. We establish a mental health database following a naturalistic paradigm as well as a long-term ubiquitous interpretable psychological computing model based on prior knowledge and multimodal information fusion. The proposed model achieves state-of-the-art accuracy in both basic and complex emotion detection on the proposed mental health database and effectively solves scientific and accuracy-related problems in long-term complex mental health status recognition and prediction. Regarding psychology and the medicine of mental disorders, we identify the continuous emotional symptoms of three kinds of mental disorders, which have not previously been accurately observed based on multimodal big data. They are accurately and quantitatively described by the newly introduced interpretable psychological computing model. At the same time, we establish the relationship between two complex emotions and the basic emotions, breaking through the cognitive limitations of the traditional psychology field.
Identification of Banana corm weevil Cosmopolites sordidus germar resistance in Musa germplasm

In the present study, a field and in vitro screening approaches were followed to identify the resistant Musa accessions (n=310) against banana corm weevil Cosmopolites sordidus (Germar). The corm was crosssectioned and the tunnels (galleries) made by the grub were recorded. Based on the ratio of galleries, the % was calculated. The infestation level was categorized into resistant, less, moderate and highly susceptible. The results revealed three resistant accessions viz., Kanthali (ABB- Pisang awak), Sambalneyvannan (ABB- Pisang awak) and Bhimkol (BB- wild balbisiana). Around 44 accessions (AA:2, AAA:5, AAB:9, AB:4; BB:5; ABB:15, ABBB:2; Rhodochlayms:2) from different genomes were identified as less susceptible.
Are Cysteine-lipases Involved in the Immune System?

Lipases, esterases and proteases constitute superfamilies of hydrolases not only play an important role in the immune system, but also as catalysts in biotechnology and organic chemistry. Mechanistically, they all involve a similar catalytic triad. The mechanism of lipasecatalysis is defined by the catalytic triad Ser-His-Asp in which activated serine adds nucleophilically to the carbonyl function of an ester or lactone substrate in the rate-determining step with formation of a short-lived oxyanion which then fragments into an alcohol and a covalent acyl-enzyme intermediate, the latter rapidly undergoing reaction with water and liberating the respective carboxylic acid (Figure 1) [1-5].
The evolution of senescence in annual plants: The importance of phenology and the potential for plasticity

Senescence, the decline in survival or fecundity with age, is a predicted outcome of the declining force of selection at late ages. In this chapter we suggest that in semelparous plants, where reproduction is fatal, seasonal limits on the opportunity for reproduction may also influence the timing of senescence. Further, the timing, or phenology, of germination and flowering can influence these seasonal limits. To support this hypothesis, first we use an integrated life-cycle model of the semelparous annual Arabidopsis thaliana to show that under reasonable assumptions about seasonal drivers of mortality, the window for reproduction (and therefore senescence) depends on (1) where in Europe the plant lives and (2) genetic variation that determines life-cycle phenology. In particular, we find that genetic variation that influences the timing of an early lifestage transition (germination timing) can have ramifying effects on the time available for reproduction within each environment. Next, we review recent research on the molecular and genetic basis of semelparity and senescence. This work emphasises the environmental dependence and tight molecular regulation of many of the component processes that ultimately lead to whole-plant senescence. In combination, both the modelling and the review emphasise the possibility of phenotypic plasticity in the regulation of the component processes that underlie survival and fecundity and therefore of senescence. Future work should focus on dissecting the genetic basis of the environmental dependence of the survival/fecundity relationship and creating a better understanding of how the senescence of individual tissues relate to senescence on the wholeplant level.
Electrochemical Impedance Simulation for Single Cell Analysis using a Microelectrode

Electrochemical impedance spectroscopy (EIS) is one of the ways to monitor living cells in terms of morphology and cell-substrate adhesion. In conventional cell analysis, averaged values from many cells on a large area electrode are acquired. Meanwhile, single cell characteristics are obtained by using a microelectrode which is smaller than single cell size. In this study, we investigated the impact of electrode size on single cell EIS analysis by using a computer simulation. The electrode smaller than the cell size would enable independent measurement of the cytoplasm electrical conductivity and the cell-substrate gap resistance. In addition, changes in morphology such as cell height and radius should be monitored at the same time in different frequency range. Thus, our simulation indicates that EIS measurement with microscale electrode may be used in monitoring various physiological changes in cells such as immune response.
Air Pollution Prediction Model Using Artificial Neural Network And Fuzzy Theory

Air pollution is a problem of environmental health risk in big cities. Recently, researchers have proposed using various artificial intelligence technologies to predict air pollution. The proposed model is Cooperative of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), to predict air pollution of Korean cities using Python. Data air pollutant variables were collected and the Air Korean Web site air quality index was downloaded. This paper’s aim was to predict on the health risks and the very unhealthy values of air pollution. We have predicted the air pollution of the environment based on the air quality index. According to the results of the experiment, our model was able to predict a very unhealthy value.
Enhanced Hierarchical Control Framework of Microgrids With Efficiency Improvement and Thermal Management

This article presents an enhanced hierarchical control framework of microgrids, as an effective complementary of conventional hierarchical control, to improve operation efficiency and perform thermal management. The enhanced hierarchical control framework is implemented by two control levels. In primary control level, droop control strategy with active thermal management is proposed to perform identical thermal sharing among paralleled inverters. Furthermore, secondary control level is developed to improve operation efficiency and perform thermal management in system-level according to time-varying load profiles, where finite state machine model is adopted to optimize operation number of inverters. In addition, in grid-connected microgrid, resonance phenomena may be caused by time-varying grid impedance. Hence, resonance mitigation strategy of microgrid is developed in the secondary control level, and small signal model of grid-connected microgrid is established to investigate the resonance phenomenon. Simulation and experimental results show that the proposed hierarchical control framework is able to perform active thermal management, improve operation efficiency, and mitigate harmonic resonance in either autonomous mode or grid-connected mode. The proposed hierarchical control framework is an enhanced complementary for conventional hierarchical control of microgrids.
A COMPACT PHASE SHIFTER WITH WIDE PHASE RANGE USING LOADED TRANSMISSION LINE

This paper presents a 90◦ broadband compact phase shifter which employs loaded λ/2 transmission line. By adding an H-shaped open stub loaded transmission line, the bandwidth of the phase shifter is broadened. Detailed theoretical analysis and circuit configuration are presented to explain the mechanism. The proposed phase shifter is fabricated and measured to validate the design principle. The simulated and measured results show that the proposed phase shifter achieves 6.6 to 19.4 GHz bandwidth with low phase instability ±5◦, very low insertion loss (0.3 dB in 7.5–15.2 GHz), high return loss (10 dB), and a compact size (12.8mm×22mm). Good agreements are observed between the measured and simulated results with small phase deviation. Moreover, the configuration of the proposed phase shifter is simple in both design and fabrication which makes the design suitable for actual applications.
Theory, Design, and Verification of Dual-Circularly Polarized Dual-Beam Arrays With Independent Control of Polarization: A Generalization of Sequential Rotation Arrays

In this article, the theory, design, and validation of planar array antennas capable of producing dual-circularly-polarized (dual-CP) dual-beam radiation with a single feeding network are reported. Importantly, the properties of the formed CP beams with different handedness can be independently controlled. Such a capability is achieved by exploiting both the in-plane rotation-induced Berry phase and excitation phase for all the linearly polarized array elements, which can be regarded as a generalization of the well-known sequential rotation arrays. Microstrip patch arrays with such single-feed dual-CP dual-beam functionalities can be synthesized through a straightforward analytical approach that accurately predicts the radiation patterns for both right- and left-handed CP beams. The proposed technique and design methodology were verified by two proof-of-concept microstrip arrays consisting of <inline-formula> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> linearly polarized circular patch elements operating in the <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-band, one with uniform excitation amplitudes and the other with tapered excitations for sidelobe suppression. Both arrays were fabricated and measured, experimentally achieving a joint <inline-formula> <tex-math notation="LaTeX">$S_{11} < -10$ </tex-math></inline-formula> dB, axial ratio < 3 dB, and 3 dB gain bandwidth of more than 22.5%/20.0% and 16.7%/15.8%, respectively, for the right- and left-handed CP beams. Within this broad operational band, the beam squinting is on average within ±3°. The proposed dual-CP dual-beam arrays have fully planarized structures, low profiles, wide operational bandwidths, and high polarization purity, making them useful candidates for applications including satellite communications, point-to-multipoint communications, and so on.
Examining the Influence of Ethical Leadership on Employee Outcomes: Mediating Role of Psychological Empowerment

This study aims to investigate the association of ethical leadership (EL) and employee outcomes, i.e., organizational citizenship behaviour (OCB) and innovative work behaviour (IWB). The study also examines the mediating role of psychological empowerment (PE) between EL and employee outcomes: OCB and IWB. Through convenience sampling, the data have been collected using a self-administered questionnaire from 382 employees working in different branches of various banks located in Lahore, Pakistan. Results reveal that ethical leadership has a significant and positive influence on psychological empowerment: organizational citizenship behaviour, and innovative work behaviour of employees. However, psychological empowerment partially mediates the association of EL with OCB and IWB. Thus, ethical leadership is a powerful tool that helps employees to improve themselves and consequently elevates the organizations. The practical implications, study limitations, and future directions have also been included.
The Effects of Titanium Dioxide Nanoparticles on the Growth and Development of Sorghum Bicolor (L.) Moenech

Engineered nanoparticles (ENPs) have seen a drastic increase in their use over the past decade in various consumer products. ENPs will therefore enter terrestrial ecosystems and soils with increasing frequencies, yet research into the effects of ENPs on living organisms and crops is greatly lacking. Currently, there is only one major study reported on the effects of a single ENP, silver quantum dots, on Sorghum bicolor , the 5 th largest crop in the world. I examined the effects of a commonly used metal oxide nanoparticle, titanium dioxide (TiO 2 ), on the growth and development of sorghum grown in petri dishes (n=25) with agar media and Murashige and Skoog (MS) media with concentrations of 5, 10, 20, and 40 µg/ml. I measured seedling germination rates, gas exchange rates using a LI-6400 Portable Photosynthesis System, and biomass after 14 days of growth in a growth chamber. There is a significant decrease of on instantaneous water use efficiency rates due to increasing TiO 2 concentrations, but all other gas exchange rates, germination rates, and biomass accumulation were not significant. I also grew sorghum in a greenhouse in potting soil (n = 36 pots) with concentrations of 100, 200, 500, and 1000 mg/kg. I measured sorghum physiology and biomass accumulation at full maturity. There were no measurable effects on physiology or biomass accumulation. While my research indicates no negative effects of this ENP on the growth or development in sorghum, further research will be necessary to identify if TiO 2 ENPs are taken up and translocated by the plant, as well as possible intergenerational effects of ENP exposure. Future research may also be conducted into the possible application of TiO 2 as an antimicrobial agent for use in pesticides or herbicides if no negative effects are found.
Robust Minimum Disturbance Diffusion LMS for Distributed Estimation

This brief proposes a robust distributed estimation algorithm in presence of impulsive noise. Impulsive noises are present both in the measurements and in the communication links in a sensor network. The proposed method is essentially a diffusion LMS algorithm with optimized variable coefficients in the adaptation and combination steps. The optimized coefficients are obtained based on the minimum disturbance principle. Moreover, it is shown that the optimized coefficients of the adaptation step are found by solving a linear system of equations, while the optimized coefficients of the combination step are calculated by an eigenvector of a particular matrix. Moreover, the minimum disturbances calculated theoretically and their upper bounds are derived mathematically. Simulation results show the better performance of the proposed minimum disturbance diffusion LMS algorithm over some state-of-the-art algorithms.
A Sustainable Distributed Building Integrated Photo-Voltaic System Architecture with a Single Radial Movement Optimization Based MPPT Controller

The solar photo-voltaic systems control architecture has a substantial influence over the cost, efficiency, and accuracy of maximum power point tracking under partial shading conditions. In this paper, a novel distributed architecture of a building integrated photo-voltaic system equipped with a single maximum power point tracking controller is presented in order to address the drawbacks associated with respect to cost, complexity and efficiency of the existing photo-voltaic system architectures. In addition, a radial movement optimization based maximum power point tracking control algorithm is designed, developed, and validated using the proposed system architecture under five different partial shading conditions. The inferences obtained from the validation results of the proposed distributed system architecture indicated that cost was reduced by 75% when compared to the commonly used decentralised systems. The proposed distributed building integrated photo-voltaic system architecture is also more efficient, robust, reliable, and accurate.
Investigating the relationship between individual differences and island sensitivity

Catherine Pham1, Lauren Covey2, Alison Gabriele3, Saad Aldosari4 and Robert Fiorentino5 1 Department of Psychology, The Pennsylvania State University, University Park, PA, US 2 Department of Linguistics, Montclair State University, Montclair, NJ, US 3 Second Language Acquisition Laboratory, University of Kansas, Lawrence, KS, US 4 College of Languages and Translation, Al Imam Mohammad Ibn Saud Islamic University, Riyadh, SA 5 Neurolinguistics and Language Processing Laboratory, Department of Linguistics, University of Kansas, Lawrence, KS, US Corresponding author: Lauren Covey (coveyl@montclair.edu)