Miracle-dz/UniFAR-FTUs huggingface dataset:
Insecticide resistance monitoring and diagnostics of resistance mechanisms in cotton‐melon aphid, Aphis gossypii Glover in Central China

Aphis gossypii Glover is one of the most serious pests in China, and the management of this pest mainly relies on insecticides. However, the resistance status of A. gossypii in Central China is still unknown. Therefore, in this study, we firstly determined the susceptibility of 15 A. gossypii field populations from Central China to six chemical insecticides using leaf‐dipping method during 2020 and 2021 and explored the potential resistance mechanisms of these populations. Most of the field populations of A. gossypii have evolved low to high levels of resistance to imidacloprid (RR = 5.98–2352.96‐fold), thiamethoxam (RR = 5.98–2194.96‐fold), flonicamid (RR = 7.18–306.07‐fold), and pirimicarb (RR = 6.90–283.00‐fold); however, 13 tested field populations remained susceptible to chlorpyrifos. For λ‐cyhalothrin, most populations have evolved low to moderate levels of resistance (RR = 5.72–47.38‐fold). The results of correlation analysis indicated that there was a prominent positive correlation between the enzyme activity of cytochrome P450 and the logarithm of LC50 to thiamethoxam and pirimicarb. Carboxylesterase (CarE) was positively correlated with λ‐cyhalothrin, while glutathione S‐transferase (GST) was negatively correlated with λ‐cyhalothrin. Furthermore, we found that multiple resistance associated target site mutations (V62I, R81T, A302S, S431F, M918L) can be detected in the field populations of A. gossypii. Aphis gossypii in Central China have developed different degrees of resistance to tested insecticides and enhanced detoxification of cytochrome P450 and carboxylesterase and co‐existed resistance mutations contribute to the multiple resistance of these field populations. Our results provide valuable information about the status of insecticide resistance in China and are helpful for designing sustainable management strategies of A. gossypii.
A Task-Based Location Deployment Method for UAV-Enabled Multi-region Network

Due to the enhanced mobility, flexibility, and adaptive altitude, unmanned aerial vehicles (UAVs) have been widely used as aerial base station (BS) to provide wireless connections in military and civilian. Here, we present a wireless communication network model, where one UAV serves as the aerial BS, flying to the target region and then hovering to provide communication services for the ground terminals. In view of the limited onboard power of the UAV, we jointly optimize UAV Location deployment and bandwidth allocation as a means of reducing energy consumption. However, the proposed optimization problem is a mix-integer and non-convex optimization problem. By using block coordinate descent (BCD) and successive convex approximation (SCA) algorithm, we proposed a robust algorithm for finding suboptimal solutions. Specifically, UAV location deployment and bandwidth allocation are optimized iteratively, and the convergence of the algorithm is proven. As a result of simulations, the proposed joint location deployment and bandwidth allocation algorithm can achieve better energy consumption performance than the benchmarks.
Longwave multispectral diffractive optic imaging spectrometer

Ann Arbor Sensor Systems is developing and experimenting with a Long Wave Multispectral Diffractive Optic Imaging Spectrometer that operates in the 7 to 14 micron band. The instrument currently under development is for fixed mount applications. The focal plane is an uncooled 320 x 240 30 micron pitch amorphous silicon microbolometer array. With a spectral bandwidth of 220nm the unit is capable of resolving moderate to broad spectral features. This paper will describe the physical characteristics and characterization of the experimental device. Design trades and test data will be presented.
Lane Detection Method Based on Color Feature

Lane detection technology is the basic module of safe and intelligent driving. It is conducive to lane path planning, determine the reasonable driving range, to ensure driving safety. Current research generally adopts machine vision to detect lane lines. Firstly, the region of interest (ROI) is separated from the collected image, and the detection area of the image is reduced. Secondly, HSV color space is used to extract specific color pixels by using the obvious color features of road lane lines. Then, the Laplacian of Gaussian (LOG) filtering method is used to remove the noise and ensure the precision of edge detection. Finally, the algorithm of Hough transform is optimized, and the experimental results show that the method can effectively locate the lane line.
NEUROSCIENTIFIC EVIDENCE AND CARE LEAVING: A MULTIDISCIPLINARY CRITICAL COMMENTARY

While neuroscientific literature suggests that some parts of the brain are not fully developed until the mid-20s, public discourse is skewed toward early child development (ECD) because of its supposed long-term economic benefits. Some researchers have gone so far as to say that society overinvests in remedial programs for disadvantaged adolescents. Such claims resist advocacy efforts for extended care for children in out-of-home care and discourage policy and legislative concerns regarding investing in early adulthood. In this commentary, we unpack the literature on brain development and critically discuss its selective use by legislators and policymakers for investments in ECD. Despite the availability of neuroscientific and economic evidence, it is not prominent in the discourse surrounding supportive interventions like extending care. Using Bourdieu’s theory of social reproduction, we discuss how preference is given to only the type of knowledge that preserves the social structures that work to ensure the multigenerational flow of capital among dominant groups. Also, social institutions act within the dimensions set by the social structure, constantly shaping and reshaping ways of facilitating capital preservation among the upper classes. We conclude that, in addition to moral argument, the current neuroscientific evidence may support investment in extended care programs.
Graph Convolutional Neural Network for EEG Emotion Recognition

Because emotion recognition is vital in improving human-computer interaction, emotion recognition is becoming increasingly critical in artificial intelligence. Deep learning is widely used to analyze frequency or time domain features in emotion recognition research. Few researchers consider the spatial features of Electroencephalogram (EEG) and the correlation between EEG channels. In this paper, the graph convolutional neural network is used to analyze the emotion of EEG signal. Firstly, the edge and point features of EEG were extracted to construct the graph structure, which made full use of the spatial and frequency features of EEG. Then, the time-series information of EEG was extracted and classified by using the long short-term memory neural network. In the DEAP dataset, the accuracy of the proposed method in the valence and arousal dimension are 83.23% and 85.82%, respectively. The research can be applied to the emotional brain computer interface system.
Recovery of Distributions via Moments

Abstract: The problem of recovering a cumulative distribution function (cdf) and corresponding density function from its moments is studied. This problem is a special case of the classical moment problem. The results obtained within the moment problem can be applied in many indirect models, e.g., those based on convolutions, mixtures, multiplicative censoring, and right-censoring, where the moments of unobserved distribution of actual interest can be easily estimated from the transformed moments of the observed distributions. Nonparametric estimation of a quantile function via moments of a target distribution represents another very interesting area where the moment problem arises. In all such models one can apply the present results to recover a function via its moments. In this article some properties of the proposed constructions are derived. The uniform rates of convergence of the approximation of cdf, its density function, quantile and quantile density function are obtained as well.
Alignment of carbon nanotubes in a polymer matrix by mechanical stretching

We report a method to fabricate polymer-based composites with aligned carbon nanotubes, and a procedure to determine the nanotube orientation and the degree of alignment. The composites were fabricated by casting a suspension of carbon nanotubes in a solution of a thermoplastic polymer and chloroform. They were uniaxially stretched at 100 °C and were found to remain elongated after removal of the load at room temperature. The orientation and the degree of alignment were determined by x-ray diffraction. The dispersion and the alignment of the nanotubes were also studied by transmission electron microscopy.
Dislocation Reduction in AlN and GaN Bulk Crystals Grown by HVPE

We analyse the dislocation distribution in GaN and AlN bulk crystals by transmission electron microscopy and X-ray diffraction. The crystals are grown by hydride vapour phase epitaxy onto 6H-SiC[0001] and Si(111) substrates. Two essentially different dislocation populations are observed: (i) a-type dislocations that show efficient dislocation density reduction (down to 4 × 105 cm–2) and (ii) a, (a + c) and c-type dislocations each type in a considerable density with less efficient dislocation reduction. We evaluate the dislocation processes that result in this different behaviour as dependent on the dislocation population.
Electron energy band alignment at the NiO/SiO2 interface

Study of the NiO/SiO2 interface band diagram using optical absorption, photoconductivity, and internal photoemission measurements reveals that deposited Ni oxides have a band gap of 3.2±0.1 eV, with the top of the valence band at 5.3±0.1 eV below the conduction band of SiO2 and showing no measurable sensitivity to the oxygen surplus in the layer up to 20%. Annealing in a reducing ambient (forming gas) at 400 °C results in narrowing of the band gap and an up shift in the occupied electron state edge by ≈1 eV, which is tentatively associated with partial reduction in the NiO layer.
Research on Optimization of cell Nucleus Image Segmentation based on U-Net

To solve the problem of low precision of nucleus segmentation in cell microscopic images, a method of nucleus segmentation based on deep learning is proposed. This paper presents a new network architecture Res18 U-Net++, which is based on the network framework of U-Net++ and introduces the residual module ResNet-18 and spatial attention mechanism. The network structure can make better use of the relationship between feature maps, and use set modules to enhance the reuse of feature information, thus improving the network performance. This work uses a large number of training data sets to train the network. Numerical calculation and experimental results show that this method can quickly and accurately achieve the segmentation of cell nucleus images, and achieve the effect of data enhancement. On this basis, the parameters of the model such as optimizer, loss function, learning rate, iteration value, and batch_size are modified and debugged, and images are preprocessed to try to increase image pixels to achieve better training results. In addition, the network framework proposed in this paper is compared with many network structures in the U-Net family, and the higher IoU value and smaller loss function show the advantages of the network framework proposed in this paper. Finally, validation nuclei segmentation is performed using the generative adversarial network trained on the validation dataset, demonstrating the feasibility of the method. The U-Net cell nucleus image segmentation method based on the convolutional neural network proposed in this paper is helpful for cell biology research and medical image cell nucleus processing.
Data-Adaptive CNN Used for Multi-parameters Estimation in High-Precision Indoor Localization

In recent years, CSI-based WiFi indoor positioning has attracted much interest because CSI (Channel State Infor-mation) measurement becomes more and more convenient. AoA (Angle of Arrival), AoD (Angle of Departure) and ToF (Time of Flight) based on CSI have been widely used in WiFi based indoor positioning. Traditional algorithms such as MUSIC algorithm are difficult to achieve the positioning accuracy required for practical applications due to the influence of multipath. In this paper, we propose a Data-adaptive CNN(DA-CNN) algorithm for high accuracy parameter estimation in indoor localization. The algorithm uses the super net, which can search the neural architecture suitable for CSI data since the neural architecture has significant influence on the accuracy of indoor localization. We further propose a unique CSI processing method, which first removes the CSI phase shift caused by each two parameters of AoA, AoD and ToF in CSI, and then uses this clean data to estimate the other parameter. Simulation results show that DA-CNN can greatly improve the estimation accuracy. Compared with the common neural networks algorithm, the estimation accuracy of AoA and AoD angle can be improved by nearly 40%, and the estimation accuracy of ToF can be improved by nearly 30%.
Classification of Network Data with Machine Learning Methods for Intelligent Intrusion Detection Systems

In information systems, it has become very important to store personal and institutional information and access it safely and quickly when necessary. To ensure the confidentiality of information against unauthorized access, institutions or organizations must protect their important data securely and take various precautions. Intrusion detection systems (IDS) are among these measures. One of the issues that should be carefully considered while creating an IDS is the dataset to be used. In terms of IDS, a dataset is the data obtained from network packets or log records that contain attack data and are necessary to identify attack patterns during the training and testing stages of the system. In this article, widely used machine learning techniques (decision tree, K-nearest neighbor, and support vector machine algorithms) are used to increase the performance of IDSs. The studies were tested on the NSL-KDD dataset, one of the most used datasets in evaluating IDSs. As a result of the tests, it was seen that the highest accuracy rate was 99.7%, and the lowest accuracy rate was 98.7%. The obtained results have shown that the proposed machine learning methods can be used with high sensitivity and accuracy to develop smart IDSs.
Organizational climate and quality of work-life in the creativity of teachers

This study analyzed the effect of organizational climate and quality of work-life on teachers’ creativity throughout state vocational high schools in Serang, Indonesia during the COVID-19 pandemic. This work involved 164 civil servant teachers in state vocational high schools in Serang, Indonesia as the sample. As the major analysis tool, we used a multiple linear regression model. The hypothesis test used the t-test and f-test. The study revealed that: i) Organizational climate positively and significantly influenced the civil servant teachers’ creativity amid the COVID-19 pandemic with a significance value of . 000 (α<.005); ii) The quality of work-life also had a positive and significant effect on the teachers’ creativity during this difficult situation has significance value of . 000 (α<.005); and iii) The quality of their work influenced both organizational climate and the creativity of state vocational high school teachers living in Serang, Indonesia, with a determination coefficient of . 861 or 86.1% during the pandemic.
An Approach to Evaluate Pb Tolerance and Its Removal Mechanisms by Pleurotus opuntiae

Widespread lead (Pb) contamination prompts various environmental problems and accounts for about 1% of the global disease burden. Thus, it has necessitated the demand for eco-friendly clean-up approaches. Fungi provide a novel and highly promising approach for the remediation of Pb-containing wastewater. The current study examined the mycoremediation capability of a white rot fungus, P. opuntiae, that showed effective tolerance to increasing concentrations of Pb up to 200 mg L−1, evidenced by the Tolerance Index (TI) of 0.76. In an aqueous medium, the highest removal rate (99.08%) was recorded at 200 mg L−1 whereas intracellular bioaccumulation also contributed to the uptake of Pb in significant amounts with a maximum of 24.59 mg g−1. SEM was performed to characterize the mycelium, suggesting changes in the surface morphology after exposure to high Pb concentrations. LIBS indicated a gradual change in the intensity of some elements after exposure to Pb stress. FTIR spectra displayed many functional groups including amides, sulfhydryl, carboxyl, and hydroxyl groups on the cell walls that led to binding sites for Pb and indicated the involvement of these groups in biosorption. XRD analysis unveiled a mechanism of biotransformation by forming a mineral complex as PbS from Pb ion. Further, Pb fostered the level of proline and MDA at a maximum relative to the control, and their concentration reached 1.07 µmol g−1 and 8.77 nmol g−1, respectively. High Pb concentration results in oxidative damage by increasing the production of ROS. Therefore, the antioxidant enzyme system provides a central role in the elimination of active oxygen. The enzymes, namely SOD, POD, CAT, and GSH, served as most responsive to clear away ROS and lower the stress. The results of this study suggested that the presence of Pb caused no visible adverse symptoms in P. opuntiae. Moreover, biosorption and bioaccumulation are two essential approaches involved in Pb removal by P. opuntiae and are established as worthwhile agents for the remediation of Pb from the environment.
Equiangular spiral photonic crystal fibers with low bending loss

The leakage losses of a bent equiangular spiral photonic crystal fiber (ES-PCF) have been analyzed in this paper by employing a full-vectorial finite element method. The analysis shows that the confinement and bending losses of ES-PCF are several orders of magnitude less than that of the conventional hexagonal lattice PCF (H-PCF) with similar dimensions. Also, the bending loss increases less rapidly in ES-PCF than in H-PCF for similar bending radii. It has also been found that no mode degeneration between fundamental core mode and first cladding mode occurs in ES-PCF. The reason of higher field confinement in ES-PCF than its hexagonal counterpart is also discussed.
Flow Control of Circular Cylinders with Longitudinal Grooved Surfaces

Flow around circular cylinders with different grooved surfaces (UandVshaped)was investigatedexperimentally. The drag force, mean velocity, and turbulence intensity profiles of the wake behind the cylinders were measured for Reynolds numbers based on the cylinder diameter (D = 60 mm) in the range ReD = 8 £ 103 » 1:4 £ 105 . Flow around the cylinders was visualized using a particle tracer technique to see the flow structure qualitatively. The results were compared with those for a smooth cylinder of the same diameter D. At ReD = 1:4 ££ 105, the U-type groove surface reduced the drag coefficient by 18.6% compared with the smooth cylinder, whereas the drag reduction of V-type grooves was only 2.5%. For the circular cylinder with a U-grooved surface, as the Reynolds number increases, the location of peak turbulence intensity moves downstream, and the turbulence intensity is decreased. The two longitudinal-groovedsurfaces tested in this study show different surface pressure distributions at high Reynolds numbers due to their different flow structures near the cylinder surface. Flow visualization results show that the U-grooved cylinder elongates the vortex formation regionmore than 50% compared with the smooth cylinder. This is consistent with the results of turbulence intensity distributionmeasured along the wake centerline. The large-scale longitudinal vortices formed behind the grooved cylinders seem to be broken into smaller ones due to the existence of longitudinal grooves. On average, the scale of longitudinal vortices behind the U-grooved cylinder is much smaller in size compared with the other two cases.
Modulation of Tropical Instability Wave Intensity by Equatorial Kelvin Waves

AbstractTropical instability waves (TIWs) and equatorial Kelvin waves are dominant sources of intraseasonal variability in the equatorial Pacific Ocean, and both play important roles in the heat and momentum budgets of the large-scale flow. While individually they have been well studied, little is known about how these two features interact, although satellite observations suggest that TIW propagation speed and amplitude are modulated by Kelvin waves. Here, the influence of Kelvin waves on TIW kinetic energy (TIWKE) is examined using an ensemble set of 1/4° ocean model simulations of the equatorial Pacific Ocean. The results suggest that TIWKE can be significantly modified by 60-day Kelvin waves. To leading order, TIWs derive kinetic energy from the meridional shear and available potential energy of the background zonal currents, while losing TIWKE to friction and the radiation of waves. The passage of Kelvin waves disrupts this balance. Downwelling (upwelling) Kelvin waves induce decay (growth) in TIWKE .
Robust Control of Full Bridge Inverter Based on Improved High-Order Sliding Mode Controller

It is difficult to adjust the parameters for the controller in the stability control of a full bridge inverter due to its unknown upper bound. To solve this problem, an improved second-order sliding mode twisting algorithm based on adaptive parameter has been proposed and simulated. By adding the adaptive parameter method into the original second-order sliding mode twisting controller, improved controller not only automatically regulates its parameters according to states of system but also fully compensates the uncertainty of upper bound. The simulation in the output control of full bridge inverter illustrates that the system can reach the target value of voltage and current in finite time. Compared with original second-order sliding mode twisting algorithm, this improved algorithm, though under the existence of unknown upper bound, can effetely guarantee the finite-time stability of the system.
Covid-19 Prediction using Machine Learning Methods: An Article Review

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our systematic literature review demonstrates that ML-powered tools can alleviate the burden on healthcare systems. These tools can analyze significant amounts of medical data and potentially improve predictive and preventive healthcare.