Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Series Analysis of Land Surface Temperature and Drivers of Urban Heat Island Effect Based on Remotely Sensed Data to Develop a Prediction Model

ABSTRACT The local climate of cities is changing, and one of the primary reasons for this change is rapid urbanization. The Lahore district is situated in the Punjab province of Pakistan and is mainly comprised of Lahore city. This city is among the fastest expanding cities in Pakistan. Due to this rapid urbanization, the natural land surfaces are being altered, harming the local environment and thus causing the urban heat island (UHI) effect. For the analysis of the UHI effect, the fundamental and essential step is assessing the land surface temperature (LST). Therefore, the current investigation assessed LST to evaluate the UHI effect of the Lahore district. This study used the remote sensing data retrieved from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. Different new generation algorithms were initially used, but a convolutional neural network (CNN) model was used based on the accuracy. The model was developed by utilizing the past 19 years’ LST values along with elevation, road density (RD), and enhanced vegetation index (EVI) as input parameters for analyzing and predicting the LST. The LST data of the year 2020 was used for the validation of the outcomes of the CNN model. Among the model predicted LST and observed LST, a high correlation was noticed. The mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) for the considered two different periods (January and May) were also computed for both the training and validation processes. The prediction error for most parts of the district was within 0.1 K of the observed values. Hence, the formulated CNN model can be utilized as an essential tool for analyzing and predicting LST and thus for the evaluation of the UHI effect at any location.
Prevention Effect of News Shocks in Anti-Doping Policies

This paper contributes to the debate on anti-doping policies not by evaluating the policy itself but by evaluating the announcement of (new) policy measures. We develop a dynamic model for analyzing the effects of two different types of news shocks: (1) the preannouncement of improved drug testing technological opportunities and (2) the preannouncement of future increases in financial sanctions. We find that the anticipation of policy changes affects the behavior of potentially delinquent athletes. In both scenarios, our simulations show immediately reduced drug abuse among athletes. We conclude that authorities may consider news shocks as an anti-doping strategy.
The Role of Generative Adversarial Network in Medical Image Analysis: An In-depth Survey

A generative adversarial network (GAN) is one of the most significant research directions in the field of artificial intelligence, and its superior data generation capability has garnered wide attention. In this article, we discuss the recent advancements in GANs, particularly in the medical field. First, the different medical imaging modalities and the principal theory of GANs were analyzed and summarized, after which, the evaluation metrics and training issues were determined. Third, the extension models of GANs were classified and introduced one-by-one. Fourth, the applications of GAN in medical images including cross-modality, augmentation, detection, classification, and reconstruction were illustrated. Finally, the problems we needed to resolve and future directions were discussed. The objective of this review is to provide a comprehensive overview of the GAN, simplify the GAN’s basics, and present the most successful applications in different scenarios.
FRL-SLAM: A Fast, Robust and Lightweight SLAM System for Quadruped Robot Navigation

The quadruped robots have been outstood as potential mobile platforms for logistics transportation and hazardous environment explorations because of their extraordinary locomotion capability. quadruped robots are desirable as they can maneuver through stairs, grotto and grassland effortlessly in unstructured scenes. However, quadruped robots often fail to reach designated destinations due to the SLAM module is disturbed if the trunk of a legged robot will swing strongly during locomotion. Therefore, the current quadruped robot navigation algorithms are not satisfactory. In this paper, we proposed FRL-SLAM, a fast, robust and lightweight visual-inertial SLAM system, to address these issues. The FRL-SLAM can accurately estimate the robot pose with a RealSense D435i camera in the case of violent shaking. We estimate the performance of FRL-SLAM on public datasets and real-world quadruped robot and compare against other state-of-the-art algorithms. Our experiments show that FRL-SLAM is as accurate as the state-of-the-art SLAM system and significantly more fast.
Bio-inspired Soft (BIS) Hand for Tele-operated COVID-19 Oropharyngeal (OP) Swab Sampling

The lasting difficulty imposed by the COVID-19 pandemic has made profound challenges to our daily lives. The prominent task to contend with the COVID-19 is the diagnosis of the SARS-CoV-2 virus. The timely diagnosis guides further anti-infection measurements such as effective quarantine and dedicated therapy. Existing diagnosis majorly relies on manual face-to-face Oropharyngeal (OP) and Nasopharyngeal (NP) sampling, which results in high infection risk and heavy working burden to medical staff. Robotic sampling is a promising solution for this challenge because robots are intrinsically immune to viruses and working without fatigue. Following this target, we demonstrate our novel teleoperated OP swab sampling system with excellent safety guarantees, quick deployability, and task efficacy. A bio-inspired soft hand integrated with a soft wrist is developed, which can pick and manipulate the disposable swab as dexterous as a human hand. Stereo imaging is provided to the operator by a terminal binocular camera. The design, working mechanism, and control of our sampling system are discussed. Results show that our proposed robotic sampling system promises safe and effective OP swab sampling to protect medical staff and convenient the sampling process.
Impacts of Layer Sizes in Deep Residual-Learning Convolutional Neural Network on Flower Image Classification with Different class sizes

This paper focuses on evaluating impacts of large -, medium - and small -size deep residual-learning convolutional neural network (DRL-CNN): ResNet50, ResNet35 and ResNet17 models on classifying Oxford-102 flowers image dataset with distinct number of 10, 50 and 102 flower classes. The Flowers image classification assessments rely on precision, recall, F1 scores and accuracy rates, averaged over 10-fold cross validation to ensure unbiased experimented results. Confusion matrix is also considered for more detail of results examination. The comparison results indicate the ResNet35 yields 0.201% and 0.706% few better recognition accuracy consecutively over ResNet50 and 17 according to 10-class dataset. For 50-class, 0.060% and 0.211% bits higher accuracy of ResNet35 than ResNet50 and ResNet17 are respectively generated. Whereas, 0.040% and 0.070% a few bits better performance of ResNet35 than ResNet50 and ResNet17 are sequentially attained on 100-class one. Decreasing rate regarding superiority of ResNet35 over ResNet50 and ResNet17 is indicated when increasing class size. However, less than 0.71% higher classification performance is indicated for ResNet35 than ResNet17 for all cases within the scope of this work. Thus, ResNet17 may be preferred to ResNet35 due to approximate 33% higher amount of parameters used in ResNet35 than ResNet17.
Maximizing the Use of Environmental Constraints: A Pushing-Based Hybrid Position/Force Assembly Skill for Contact-Rich Tasks

The need for contact-rich tasks is rapidly growing in modern manufacturing settings. However, few traditional robotic assembly skills consider environmental constraints during task execution, and most of them use these constraints as termination conditions. In this study, we present a pushing-based hybrid position/force assembly skill that can maximize environmental constraints during task execution. To the best of our knowledge, this is the first work that considers using pushing actions during the execution of the assembly tasks. We have proved that our skill can maximize the utilization of environmental constraints using mobile manipulator system assembly task experiments, and achieve a 100% success rate in the executions.
Modeling and Simulation of Photovoltaic Pumping System Used for Irrigation

this paper presents the modeling and simulation of photovoltaic pumping system which consists of PV panel, DC-DC converter, and DC motor driving a centrifugal pump. These systems are very useful for remote areas with no access to power grid. The output power of PV panel strongly depends on external conditions such as solar irradiance and temperature. The fuzzy logic maximum power point tracking algorithms have been implemented to regulate the duty cycle of the DC-DC converter. A comparison between Photovoltaic pumping system with MPPT connection of the PV panel and Photovoltaic pumping system with direct connection of the PV panel are performed to show the efficient of MPPT controller.
Towards Automatic Operation Motion Tracking Algorithm Based on Inertial Sensors for Multi Joints Machinery

This paper presents a novel method for multi joints machinery motion tracking, which is useful for automatic operation. Heavy machinery such as excavator bulldozer forklift and grader are with multiple arms for particular tasks. These arms with rotation motion will change the dimensional coordinates accordingly. Thus, the motion tracking will give the feedback for automatic control and realize the unmanned operation in the end. Inertial measurement units (IMUs) are designed with 6D MEMS sensors. Calibration method is studied and the installation error is corrected. Extend Kalman filter is designed for dynamic motion tracking. The acceleration chip structure is also studied for later on ASIC design. Experimental result shows that the dynamic angle error is better than 0.1 degree for every joint of a robot arm, which proves the dynamic tracking method.
Quadrotor trajectory planning for visibility-aware target following

In this work, we consider the challenges in the context of the quadrotor trajectory generation for the target-following tasks in cluttered environments where the task may be failed due to the occlusion of the mobile target by structures, i.e. pillars or wall corners. To address this problem, a model predictive control (MPC) based trajectory generation methodology of the quadrotor is proposed to autonomously follow a mobile target with considering flight safety, smoothness, and visibility in cluttered environments. The motion of the quadrotor is formulated as the boundary state constrained primitives (BSCPs), which are constructed offline with the dynamic programming method, and approximated by a pre-trained neural network (NN). Combining with the NN, the proposed method can efficiently generate the following trajectory that explicitly guarantees smoothness and kinodynamic feasibility. The numerical simulation and actual experimental results show that the proposed technique is highly effective. The demonstration video is available at: https://www.bilibili. com/video/BV1iq4y1S7he/.
Design of ternary flip-flop cells using Maximum/Minimum logic operators in carbon nanotube technology

The usage of ternary logic in the digital design leads to the reduction in interconnect complexity and chip area which helps to improve circuit performance in various applications. This work presents the digital logic realization of ternary flip flop structures using ternary Min./Max. operators. The circuit implementation is based on carbon nanotube field effect transistors having the exceptional electrical characteristic of threshold voltage variations by employing CNTs with different diameters. The three-valued logic benefit of enhanced processing capability is employed in designing sequential logic modules. To optimize the performance efficiency of flip-flop designs, the ternary logic family with transistors of suitable on-state driving capability are employed in the realization of NAND/NOR logic cells. Further to analyze the performance of the proposed three valued flip-flops, experimental results are computed considering the 32nm CNTFET Stanford model taking Synopsis HSpice tool. Based on the simulation results, it is shown that presented ternary flip-flop designs provide significant saving (upto 50%) in terms of energy consumption(PDP) as compared to recent counterpart.
Letter to the editor: The clinically relevant MTARC1 p.Ala165Thr variant impacts neither the fold nor active site architecture of the human mARC1 protein

Recent genome-wide association studies (GWAS) have shown a common variant of the human mARC1 protein to be associated with liver disease. Herein, we present the crystal structure of this mARC1 p.Ala165Thr at near-atomic resolution. No relevant differences between the structure of wild type mARC1 and the variant protein are detected.
Changes in both top-down and bottom-up effective connectivity drive visual hallucinations in Parkinson’s disease

Visual hallucinations are common in Parkinson’s disease and are associated with poorer quality of life and higher risk of dementia. An important and influential model that is widely accepted as an explanation for the mechanism of visual hallucinations in Parkinson’s disease and other Lewy-body diseases is that these arise due to aberrant hierarchical processing, with impaired bottom-up integration of sensory information and overweighting of top-down perceptual priors within the visual system. This hypothesis has been driven by behavioural data and supported indirectly by observations derived from regional activation and correlational measures using neuroimaging. However, until now, there was no evidence from neuroimaging for differences in causal influences between brain regions measured in patients with Parkinson’s hallucinations. This is in part because previous resting-state studies focus on functional connectivity, which is inherently undirected in nature and cannot test hypotheses about directionality of connectivity. Spectral dynamic causal modelling is a Bayesian framework that allows the inference of effective connectivity – defined as the directed (causal) influence that one region exerts on another region – from resting-state functional MRI data. In the current study, we utilise spectral dynamic causal modelling to estimate effective connectivity within the resting-state visual network in our cohort of 15 Parkinson’s disease visual hallucinators, and 75 Parkinson’s disease non-hallucinators. We find that visual hallucinators display decreased bottom-up effective connectivity from the lateral geniculate nucleus to primary visual cortex and increased top-down effective connectivity from left prefrontal cortex to primary visual cortex and medial thalamus, as compared to non-hallucinators. Importantly, we find that the pattern of effective connectivity is predictive of the presence of visual hallucinations and associated with their severity within the hallucinating group. This is the first study to provide evidence, using resting state effective connectivity, to support a model of aberrant hierarchical predictive processing as the mechanism for visual hallucinations in Parkinson’s disease.
Localized and Distributed Representations of Person Knowledge for Faces

Modular neural models of face processing posit that face-associated person-knowledge is localized in, or accessed through, the ventral anterior temporal lobe (vATL). However, some studies have suggested that person-knowledge is more widely distributed within a larger face-processing network that includes the mid-fusiform gyrus (‘fusiform face area’). Here, we conducted an fMRI study to distinguish whether person-knowledge is localized or distributed by comparing brain responses evoked by synthetic faces, for which participants had learned person-knowledge (biographical facts) and faces for which the subjects had learned only physical facts. After extensive training, participants were cued to recall a particular biographical or physical fact about the upcoming face. In an alternate passive-viewing task, participants were shown the same faces but were not cued to recall biographical or physical facts. Classification analyses (MVPA) were performed on several a priori chosen face-selective regions (ROIs) in the ventral temporal cortex. Within each ROI tested in isolation, MVPA discriminated faces associated with person-knowledge from faces only associated with physical facts. This result is consistent with a distributed model for person-knowledge. However, when tested in a single model to separate shared and unique information, unique information was limited to the left mid-fusiform and vATL posterior (vATL-p) ROIs. Moreover, the feature weights from these two areas showed that only left vATL-p was specialized for processing biographical facts. This latter result was obtained only when these biographical facts were explicitly retrieved in the cueing task. Thus, our results indicate that the left vATL-p represents explicit recall of face-associated person-knowledge. New & Noteworthy Whether person knowledge for faces is localized in a domain-specific region, vATL, or distributed in many domain-general brain regions, including the mid-fusiform gyrus, is hotly contested. We resolve this debate by using multivariate analyses to partial fMRI signal from different brain regions into unique and shared variance. Our findings show that unique information for person knowledge is found in both the vATL and mid-fusiform but only the vATL represents explicit recall of face-associated person knowledge.
Design of Ultra-Wideband and Constant Gain Antipodal Vivaldi Antenna with Corrugations

This paper presents the design of ultra-wideband and constant gain antipodal Vivaldi antenna with corrugations. The both side edges of proposed antenna are corrugated by slots along the y-axis in order to enhance the gain, especially at lower frequencies. The corrugations are elliptical slots with varying length by exponential function. In addition, bottom edges of conducting arms are also corrugated by elliptical slots along the x-axis. With combination of corrugations on both side edges and bottom edges of conducting arms, the gain of proposed antenna significantly increases, especially at lower end of operating frequency band. By using corrugations, the gain and radiation patterns are improved without increasing the antenna size. Therefore, the overall size of proposed antenna remains compact with dimension of 45 mm × 45 mm. From simulated results, the proposed antenna can operate within the bandwidth covering from 2.97 GHz to above 10 GHz with maximum gain of 6.57 dBi and 1.17 dBi gain variation.
Strip spraying technology for precise herbicide application in carrot fields

Abstract The aim of this empirical field research was to show potential differences due to the precise application of herbicides in the quality and size of the carrot root yield and the amount of working liquid used compared to those for control samples. Empirical verification of the effectiveness of the developed technology, confirmed by statistical analyses of the obtained results, allows for a comparative assessment of this method with the conventional method of herbicide application used in this study. Due to the methodology used, it can be assumed that for carrots, the years were a random factor, and the experiments carried out were a series. It can be assumed that in the analysis of a series of experiments, for each of the examined features there are no differences between the groups and there is no interaction of the groups with the years. The yields of the groups weeded manually, sprayed in a conventional way, and weeded with precise spraying did not differ in a statistically significant way. In the precise application, a 20–30% lower herbicide consumption was observed, which has an impact on the protection of the environment and improves the economic effect of carrot production.
PV Sizing of a Stand Alone Solar Carport System Linked to Charging Stations and its Economic Analysis (A Case Study)

Electricity production from renewable energy sources mainly from solar energy, encouraged the shifting toward clean energy generation while responding to the increased energy demands of the population. This paper aims to design an off-grid photovoltaic (PV) solar carport to supply energy to electric cars. Sizing the off-grid solar carport system is to determine the number and type of solar panels, system voltage, batteries, charge regulator, and inverter size. In addition to sizing fuses and switches, combiner boxes, and the wires connecting the components of the system. In this case, the available parking area for which a solar carport is designed is 165 m2. Thus, 102 solar panels are installed which have an annual energy production of 42150KWh determined by the software System Advisor Model (SAM), where the performance of each solar panel annually decreases by 0.5%. For a daily energy demand of 65KWh, 40 batteries where each battery has a capacity of 220Ah, 4 inverters where each has a power rating of 25KW, and 17 charge controllers are installed. Linking the system components is done through copper wires that their length and cross-section area are sized. The economic analysis of the system concluded that the capital investment cost of the system is 661045.36MAD, the life cycle cost (LCC) is 1460022.64MAD, the levelized cost of energy (LCOE) is 1.60MAD/KWh, and the payback period is 10.5 years. The estimated amount of the mitigated CO2 emissions for the PV system is 657347Kg/year. The results of the developed system can further be used in other applications for solar PV systems sizing.
Grasping Objects Sequentially Using Expanded Segmentation Masks and Linear Combination of RGB-D Heightmaps

The main challenge tackled in this paper is to grasp objects specified by the user sequentially in a cluttered scene. We propose a target-driven Sequential Grasping Network (SGN) to grasp objects in the bin-picking context, which is mainly composed of a Mask R-CNN for instance segmentation and a modified Visual-Pushing-Grasping (VPG) network for grasping evaluation. For the segmentation network, we propose a neighborhood expansion method of the mask based on the size of the target object to facilitate grasps. For the grasping network, we present an approach using the linear combination of the RGB-D heightmaps as the input, this work is to reduce the size of grasping policy network. For the sequential grasping tasks, we have quantitatively evaluated the performance of the proposed SGN on the dataset. The average completion rate of our SGN is 93.3% in the separated scenes and 62.5% in the occluded scenes. The results show that our approach achieves a remarkable performance in the sequential grasping tasks.
Pivoting the Plant Immune System from Dissection to Deployment

Switzerland, 22 to 24 October 2012; www.abim. ch/fileadmin/documents-abim/Presentations_2012/ABIM_2012_6_McDougall_John. pdf.18. L. Zirngibl, Antifungal Azoles (Wiley-VCH, Weinheim,Germany, 1998). 19. K. -J. Schleifer, in Pesticide Chemistry, H. Ohkawa,H. Miyagawa, P. W. Lee, Eds. (Wiley-VCH, Weinheim,Germany, 2007), pp. 77–88.20. C. M. Tice, Pest Manag. Sci. 57,3–16 (2001). 21. C. M. Tice, Pest Manag. Sci. 58, 219–233 (2002). 22. E.D. Clarke,J.S. Delaney, Chimia(Aarau) 57,731–734(2003). 23. C. Lamberth, J. Dinges, in Bioactive Heterocyclic CompoundClasses - Agrochemicals , C. Lamberth, J. Dinges, Eds.(Wiley-VCH, Weinheim, Germany, 2012), pp. 3 –20.24. P. Jeschke, in Modern Methods in Crop Protection Research ,P. Jeschke, W. Kramer, U. Schirmer, M. Witschel, Eds.(Wiley-VCH, Weinheim, Germany, 2012), pp. 73 –128.25. G. Theodoridis, in Fluorine and the Environment