Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Realistic Morphologies of Neurons in Rodent Hippocampus with DGCAN

Dendritic size and branching patterns are important features of neural form and function. However, current computational models of neuronal networks use simplistic cylindrical geometries to mimic dendritic arborizations. One reason for this is that current methods to generate dendritic trees have rigid a priori constraints. To address this, a deep convolutional generative adversarial network (DCGAN) trained on images of rodent hippocampal granule and pyramidal dendritic trees. Image features learned by the network were used to generate realistic dendritic morphologies. Results show that DCGANs achieved greater stability∗ and high generalization, as quantified by kernel maximum mean discrepancy, when exposed to instance noise and/or label smoothing during training. Trained models successfully generated realistic morphologies for both neuron types, with high false positive rate reported by expert reviewers. Collectively, DCGANs offer a unique opportunity to advance the geometry of neural modeling, and, therefore, to propel our understanding of neuronal function. ∗ A “stable/stabilized DCGAN”, as mentioned throughout this work, is a DCGAN which was stable throughout training.
Determinants of Poor Glycemic Control among Diabetes Mellitus Patients in Public Hospitals of the Central Zone, Tigray, North Ethiopia, 2018: Unmatched Case-Control Study

Background: Poor glycemic control is a major determinant for complications of diabetes mellitus. In order to prevent the complications, identifying the determinants of poor glycemic control are imperative. However, only few studies were conducted related to this topic, particularly in Tigray. Hence, this study was aimed at identifying the determinants of poor glycemic control in the Centeral Zone of Tigray, North Ethiopia. Methods: A hospital based case-contrl study was conducted from from August 1 to September 30, 2018. A total of 87 cases and 173 controls selected by systematic random sampling technique were included in the study. Data were entered into Epi data version 3.1. 1 and exported to SPSS version 23 for further analysis. Bivariable logistic regression analysis followed by multivariable logistic regression analysis (AOR, 95% CI and p value
A WPT-Enabled UAV-Assisted Condition Monitoring Scheme for Wireless Sensor Networks

In this paper, a resource allocation and data gathering scenario of an unmanned aerial vehicle (UAV) assisted wireless powered sensor network is investigated, in which the sensor nodes (SNs) are remotely powered by power beacons (PBs) via radio-frequency wireless power transmission (RF-WPT). A time-block structure with two phases is proposed to accommodate operations in the proposed system. During Phase-I, SNs harvest energy from PBs and periodically send its sensed data to the selected cluster heads (CHs). In Phase-II, an UAV collects the data from CHs to be delivered to the data sink for further processing avoiding the need for long range transmission and multi hop communication to the data sink. Then, a closed-form expression for outage probability of the proposed system over Rayleigh and Rician fading channels is derived. Next, outage probability minimization problem is formulated to obtain optimal time allocation for RF-WPT energy harvesting to improve the system performance. Due to the complexity of the problem, Lagrangian duality method is used to develop an asymptotic optimal solution with less execution complexity avoiding complex brute force/ exhaustive search approach. Furthermore, a heuristic method is presented to further lower the computation complexity. Simulation results reveal the superiority of the proposed methods compare to brute force/ exhaustive search approach via analysis, comparison and insights of the system performance results. Finally, the performance superiority of the proposed system is demonstrated with compare to identified baseline WSNs.
Potato Bud Detection with Improved Faster R-CNN

Highlights A series of strategies including feature concatenation, tweaks of ResNet50, and modification of the default anchors with the chaos optimization-based k -means algorithm were proposed to improve the detection performance of the original Faster R-CNN. The improved Faster R-CNN achieved an average precision of 97.71%, which is 5.98% higher than that of the original Faster R-CNN and 14.38% higher than that of YOLOv2. The improved Faster R-CNN greatly boosted the detection performance for potato buds without incurring any noticeable additional computational overhead. Abstract. This article proposes an improved Faster R-CNN model to achieve better detection performance for potato buds, with the goal of preparing for the automated cutting of seed potatoes. Detection results of Faster R-CNNs with eight pretrained networks were compared, and ResNet50 was adopted as the backbone network in Faster R-CNN. On this basis, three model strategies, including feature concatenation, tweaks of ResNet50, and modification of the default anchors with the chaos optimization-based k-means algorithm, were proposed to improve the detection performance for potato buds. Experimental results on the test set demonstrated that the improved Faster R-CNN achieved an average precision (AP) of 97.71%, which is 5.98% higher than that of the original Faster R-CNN and 14.38% higher than that of YOLOv2. In addition, the average running time per image with the improved Faster R-CNN was 0.166 s, the same as that of the original Faster R-CNN. In other words, the improved Faster R-CNN greatly boosted the detection performance for potato buds without incurring any noticeable additional computational overhead, thus satisfying the requirements for real-time processing. Consequently, the improved Faster R-CNN can provide a solid foundation for the automated cutting of seed potatoes. Keywords: Chaos optimization-based k-means, Default anchors, Faster R-CNN, Feature concatenation, Potato bud detection, Tweaks of ResNet50.
Use of the shearlet energy entropy and of the support vector machine classifier to process weak microseismic and desert seismic signals

Low-amplitude signal detection is a key procedure in borehole microseismic and desert seismic exploration. Usually, signals are difficult to detect due to their low amplitude and noise contamination. To solve this problem, we propose a method combining shearlet energy entropy with a support vector machine (SVM) to detect low-amplitude signals. In the proposed method, the signal feature is extracted using shearlet energy entropy. The signal is more sparsely represented in the shearlet domain because of the multi-scale and multi-direction characteristic of the shearlet transform, which favours signal feature extraction. Furthermore, in calculating shearlet energy entropy, we use the correlation of shearlet coefficients to enhance the difference between signal and noise in the shearlet domain. Shearlet energy entropy makes the SVM achieve a more accurate classification result compared with other traditional features such as amplitude and energy. The results of synthetic and field data show that our method is more effective than the STA/LTA and the convolutional neural network for low-amplitude microseismic signal and desert seismic signal detection.
Meshless method using fundamental solution applied to computational simulation of groundwater flow of real aquifers: study case (Guariroba’s APA and Juazeiro do Norte)

ABSTRACT We investigated the influence of fictitious boundary distance, a parameter of MFS, to determine piezometric levels of two unconfined sedimentary aquifers assuming Dupuit-Forchheimer and steady-state flow hypothesis. Two study areas were modelled: Guariroba’s Environmental Protection Area, in Mato Grosso do Sul State, Brazil, and Juazeiro do Norte City, in Ceará State, Brazil. It was observed that in order to use the MFS as a numerical method in modeling groundwater flow, it is necessary to determine the best distance value of the fictitious boundary. This value can be chosen from the use of field data within the analyzed domain, where the relative error is a parameter to be minimized. Applying this methodology and comparing with the results of the MODFLOW application for the same set of initial data, we concluded that the MSF allows to estimate the piezometric level values within the analyzed domains and that the results of the statistical comparison between them point to the need to investigate the representativeness of both methods to determine which one is most appropriate for modelling the groundwater flow in each region.
The Role of Emotions in Moral Judgments: Time-resolved evidence from event-related brain potentials

The influence of emotion on moral judgments has become increasingly prominent in recent years. While explicit normative measures are widely used to investigate this relationship, event-related potentials (ERPs) offer the advantage of a preconscious method to visualize the modulation of moral judgments. Based on Gray and Wegner’s (2009) Dimensional Moral Model, the present study investigated whether the processing of neutral faces is modulated by moral context information. We hypothesized that neutral faces gain emotional valence when presented in a moral context and thus elicit ERP responses comparable to those established for the processing of emotional faces. Participants (N= 26, 13 female) were tested with regard to their implicit (ERPs) and explicit (morality rating) responses to neutral faces, shown in either a morally positive, negative, or neutral context. Higher ERP amplitudes in early (P100, N170) and later (EPN, LPC) processing stages were expected for harmful/helpful scenarios compared to neutral scenarios. Agents and patients were expected to differ for moral compared to neutral scenarios. In the explicit ratings neutral scenarios were expected to differ from moral scenarios. In ERPs, we found indications for an early modulation of moral valence (harmful/helpful) and an interaction of agency and moral valence after 80-120 ms. Later time sequences showed no significant differences. Morally positive and negative scenarios were rated as significantly different from neutral scenarios. Overall, the results indicate that the relationship of emotion and moral judgments can be observed on a preconscious neural level at an early processing stage as well as in explicit judgments.
Trinary Hybrid Cascaded H-Bridge Multilevel Inverter-Based Grid-Connected Solar Power Transfer System Supporting Critical Load

Higher power rating solar photovoltaic systems are emerging in the power system owing to the reduction in the cost of photovoltaic arrays and environmental concerns of the conventional power generation systems. Multilevel inverters are reported at high power levels due to reduced switching frequency and increased efficiency. Cascaded H-bridge (CHB) multilevel inverters have been reported in the literature due to its redundant structure compared to other multilevel topologies. When CHBs with dc sources are in the ratio of 1:3, it is named as a trinary hybrid multilevel inverter, and it generates a maximum number of equal output voltage levels. In this article, a trinary CHB multilevel inverter-based grid-connected solar power transfer system using modified second-order generalized integral control is proposed. A two-stage solar PV system consists of a single-input–multiple-output single-ended primary inductance converter and two CHB structures per phase used to verify the proposed system. A modified second-order generalized integral control is presented for active power control and grid synchronization. The controller is designed to eliminate dc-offset from the sensed load currents and to ensure balanced, sinusoidal, and unity power factor grid currents. Moreover, the shunt-connected system mitigates the reactive power and harmonic demands of the local load, thus maintaining the grid current power quality within the standard prescribed by IEEE-519. The performance of the proposed system is validated with an experimental prototype, and the test results validate the theoretical claims.
Biofábricas para produção de micopesticidas no Brasil: oportunidades de negócio e inovações / Bio-factories for the production of mycopesticides in Brazil: business opportunities and innovations

Increasing the quantity of biological control agents is one of the challenges to be overcome for efficiency in Brazilian agriculture, so it is necessary to increase and improve the bio-factories that make the production of these agents possible. Therefore, research institutions need to support companies through research with production systems, selection of strains and technology transfer so that Brazil can increase the areas of application with biological control agents, with quality and efficiency. The Biological Control Reference Laboratory Unit, CAPSA, Instituto Biológico / APTA / SAA-SP, Campinas-SP, has been carrying out technology transfer work for the production of entomopathogenic fungi since 2000. This unit houses the Entomopathogenic Fungi Collection “Oldemar Cardim Abreu”, registered as a loyal depositary with CGEN / MMA. With the demand for microbiological insecticides growing, some isolates, selected from the collection, for the control of Mahanarva fimbriolata in sugar cane, Deois flavopicta in pastures, such as the IBCB 425 of Metarhizium anisopliae and IBCB 66 of Beauveria bassiana for the control of Cosmopolites sordidus in bananas, Dalbulus maidis in maize, Tetranychus urticae in vegetables, and flowers and Bemisia tabaci in different cultures, are references for microbiological insecticides applied in organic agriculture by Min. 3/2006 SDA / SDC, increasing the demand for these fungi in the market, requiring their mass production. The COMINT course Course on Microbial Control of Insects Entomopathogenic fungi, the Biological Institute has been developing the bio-factories program for the production of these microorganisms, with more than 800 people trained in the course, with 83 companies advised until 2020, only for production through of solid culture, in rice. Through an annual consultancy, the program aims at the implantation of the biofactory, with a project for the construction or adaptation of the structure, training of employees, supply of the strains authorized for bioprospecting and quali-quantitative and stability analyzes of the microbiological insecticides produced. The business opportunities for new ventures and innovations with new production processes and formulations are enormous, providing conditions for startups with an interest in this business, with financing from government or even private bodies. The specific limitation is still the legislation for registration of biological products and the Biodiversity Law.
RATScope: Recording and Reconstructing Missing RAT Semantic Behaviors for Forensic Analysis on Windows

Remote Access Trojan (RAT) attacks have become an extensively prevailing and serious threat to enterprise security. A forensic system targeting RAT attacks is needed to record and reconstruct fine-grained semantic behaviors of RATs. However, existing forensic systems suffer from various issues such as intrusive instrumentation, nontrivial recording overhead, and RAT behavior blindness. In this article, we first conduct a large-scale study of a representative set of real-world RAT families active from 1999 to 2016. This is the first study to understand the landscape of RATs in the literature. Based on the study, we then propose RATScope, an instrumentation-free RAT forensic system targeting Windows platform. Specifically, RATScope offers an audit logging module to efficiently record system logs by leveraging Event Tracing for Windows (ETW), and provides a novel program behavior modeling technique to reconstruct semantic behaviors of RATs accurately. We implement a prototype of RATScope and evaluate the recording overhead and the behavior identification accuracy. The results show that the audit logging module only incurs 3.7 percent runtime overhead on average. Our system can achieve around 90 percent true positive rate in the cross-family experiment, around 80 percent true positive rate in the two-year spanning temporal experiment, and near zero false positive rate.
An Edge Computing Matching Framework With Guaranteed Quality of Service

Edge computing is a new computing paradigm, which aims at enhancing user experience by bringing computing resources closer to where data is produced by Internet of Things (IoT). Edge services are provided by small data centers located at the edge of the network, called cloudlets. However, IoT users often face strict Quality of Service (QoS) constraints for a proper remote execution of their applications on edge. Each user has specific resource requirements and budget limitations for her IoT application, while each cloudlet offers a limited number and types of resources, each with a specific cost. Therefore, a key challenge is how to efficiently match cloudlets to IoT applications and enable a convenient any-time access to edge computing services considering preferences and incentives of users and cloudlets. In this article, we address this problem by proposing a novel two-sided matching solution for edge services considering QoS requirements in terms of service response time. In addition, we determine dynamic pricing of edge services based on the preferences and incentives of cloudlets, IoT users, and the system. The proposed matching is incentive compatible, individually rational, weakly budget balanced, asymptotically allocative efficient, and computationally efficient. We perform a comprehensive assessment through extensive performance analysis experiments to evaluate our proposed matching and pricing solutions.
Velocity-Based Path Following Control for Autonomous Vehicles to Avoid Exceeding Road Friction Limits Using Sliding Mode Method

As tire forces approach road friction limits, vehicles may always exhibit performance degradation and even instability. The actual capacity of the available road friction imposes coupled limits on a vehicle’s longitudinal and lateral accelerations. In this paper, a varying speed method is proposed to design feasible speeds and accelerations, which ensure that the autonomous vehicle will not reach the tire-road friction limits during traversing a clothoid-based path. With the consideration of uncertain traction losses and vehicle parameters, a second-order super-twisting (ST) based speed control strategy is proposed to track above feasible speeds based on varying speed method, and to stabilize the sliding-mode variable of the speed tracking error with relative degree 1. Meanwhile, a second-order quasi-continuous (QC) based path-following control strategy is proposed to follow a desired transition path, and to stabilize the sliding-mode variable of the composite path-following errors with relative degree 2. On this basis, the proposed controllers have been verified to lead good robustness for tracking the ideal speeds and following the desired paths. As compared with the boundary of the autonomous vehicle running at a constant speed, the feasible speed boundary using varying speed method without exceeding the tire-road friction limits can be enlarged up to about 1.59 times, which is decided by the direction change between the entry and exit points of the desired path.
Collaborative Attention-Based Heterogeneous Gated Fusion Network for Land Cover Classification

Existing land cover classification methods mostly rely on either the optical or synthetic aperture radar (SAR) features alone, which ignore the mutual complementary effects between optical and SAR sources. In this article, we compare the distribution histograms of deep semantic features extracted from optical and SAR modalities within land cover categories, which intuitively demonstrates that there are the large complementary potentials between the optical and SAR features. Therefore, we propose a novel collaborative attention-based heterogeneous gated fusion network (CHGFNet), which hierarchically fuses both optical and SAR features for land cover classification. More specifically, the CHGFNet consists of three main components: two-stream feature extractor, multimodal collaborative attention module (MCAM), and the gated heterogeneous fusion module (GHFM). Given optical and SAR patch pairs, two-stream feature extractor introduces multistage feature learning methodology to acquire discriminative optical and SAR features. Then, to explore the inherent complementarity between optical and SAR features, MCAM is embedded into CHGFNet, which provides an efficient stage to capture the correlation between optical and SAR features by jointly calculating the collaborative attention in joint feature space. Finally, to automatically learn the varying contributions of both optical and SAR features for classifying different land categories, GHFM is used to fuse both optical and SAR features. Extensive comparative evaluations demonstrate the advantages of CHGFNet within land cover classification over the state-of-the-art methods on three co-registered optical and SAR data sets.
Turning flight simulation of tilt-rotor plane with fluid-rigid body interaction

Six degrees of freedom turning flight simulation is presented for a tilt-rotor aircraft represented by V-22 Osprey, considering interaction of fluid and rigid body in a coupled manner. A tilt-rotor aircraft has a hovering function like a helicopter by turning axes of rotor toward the sky during takeoff or landing. On the other hand, it behaves as a reciprocating aircraft by turning axes of rotor forward in flight. The tilt-rotor aircraft is known to be susceptible to instable state compared to conventional aircraft. For realizing Digital Flight of turning flight of the aircraft, combination with the Moving Computational Domain (MCD) method and the multi-axis sliding mesh approach is applied. In the MCD method, the whole of the computational domain itself moves with the bodies included inside the domain, which makes an airplane possible to fly freely in the physical space without any restriction of region size. Moreover, this method is also applied to rotation of rotors. The multi-axis sliding mesh approach is computational technique to enable us to deal with multiple rotating axes of different direction, and it is used to rotate two rotors and change flight attitude of the aircraft. As a result of the coupled computation between flow field and rigid body using above approach, the airplane gained lift and propulsion by rotating the rotor and flew in turning by operating flight control surfaces such as flaperons, elevator and rudders. Moreover, the manipulating variables of flight control surfaces needed for turning flight, flight attitude of the aircraft and generated lift were found. Differences of fluid flow between straight flight and turning flight were also captured.