Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring of sustainable land management using remotely sensed vegetation cover and variable tolerable soil erosion targets across New South Wales, Australia

The monitoring of land management practices is vital for the protection of soil resource and the environment. Here, we present a new approach to monitor sustainable land management using widely available remotely sensed data, such as MODIS fractional vegetation cover data. The method is based on the concept of maintaining sufficient vegetation cover to prevent hillslope water erosion beyond tolerable soil erosion targets. The targets were based on long‐term natural erosion rates plus a small constant and are spatially and temporally variable, not static as in most reported studies to date. Where vegetation cover is more than that required to prevent nontolerable erosion under normal conditions for that calendar month, the site (pixel) is deemed to be managed sustainably. Monthly indices are then combined to form a yearly sustainable land management index (SLMI), presented as raster maps with a spatial resolution of 100 m. We explored this new method through case studies over New South Wales (NSW), Australia, over the period 2010 to 2021, with a particular examination of 2020. Results were further stratified by land uses and natural resource management regions, which revealed useful data and trends. The method is offered as an example of the potential use of readily available vegetation cover data to quantitatively assess and monitor levels of sustainable land management across landscapes. We believe it overcomes the limitations of previous methods to monitor vegetation cover and land management from remote‐sensed data alone. Other users are encouraged to adapt the broad approach to meet local requirements.
Effect of miR-21-3p on lung injury in rats with traumatic hemorrhagic shock resuscitated with sodium bicarbonate Ringer’s solution

Background Restricted fluid resuscitation is the most important early method for treating traumatic hemorrhagic shock (THS). This study sought to explore whether micro ribonucleic acid (miR)-21-3p affected resuscitated THS rats by regulating the glycocalyx and inflammation. Methods MiRNAs extracted from the lung tissues were analyzed by miRNA microarray assays. A rat model of THS was induced by hemorrhage from a left femur fracture. The pathological change in the lung tissues and glycocalyx structure was observed by hematoxylin and eosin staining and a transmission electron microscope examination. The miR-21-3p expression in the lung tissues and serum was detected by real-time quantitative polymerase chain reaction (RT-qPCR). The levels of glycocalyx-related factors and inflammation-related factors were determined by enzyme linked immunosorbent assays. The expression of glycocalyx-related proteins, cell junction-related proteins, and the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt)/nuclear factor kappa B (NF-κB) signaling pathway-related proteins was analyzed by Western blot. Results After RT-qPCR verification, the variation trend of miR-21-3p was in line with expected trends. The mean arterial pressure (MAP) and heart rate (HR) were decreased, and the lung injury and damage to the glycocalyx were all aggravated in the THS rats resuscitated with sodium bicarbonate Ringer’s solution (BRS) or sodium lactate Ringer’s solution (LRS). The expression of miR-21-3p was decreased in the THS rats resuscitated with BRS and increased in the THS rats resuscitated with LRS, and the upregulation of miR-21-3p further decreased the MAP and HR, and increased the levels of syndecan-1 (SDC-1), heparanase-1 (HPA1), interleukin (IL)-6, IL-1β, and tumor necrosis factor alpha (TNF-α) in the serum of the THS rats resuscitated with BRS. The upregulation of miR-21-3p also increased the expression of SDC-1, HPA1, β-catenin, matrix metallopeptidase (MMP)2, and MMP9, but decreased the expression of E-cadherin (E-cad) and activated the PI3K/Akt/NF-κB signaling pathway in the THS rats resuscitated with BRS and transfected with miR-21-3p compared to that of the THS rats resuscitated with BRS and transfected with miR-negative control (NC). Conclusions miR-21-3p promoted inflammation and glycocalyx damage by activating the PI3K/Akt/NF-κB signaling pathway, thereby aggravating the lung injury in the THS rats resuscitated with BRS.
Shared Autonomy for Safety Between a Self-reconfigurable Robot and a Teleoperator Using Multi-layer Fuzzy Logic

Autonomous vehicles are designed to elevate the efficiency of assigned tasks and ensure the safety of the environment in which they operate. This paper presents a research study focused on shared autonomy using a multi-layer fuzzy logic framework to build a relationship between an autonomous self-reconfigurable robot and a human user by switching control to the teleoperator to assist the robot when it faces challenging scenarios while keeping a good performance and maintaining a safe environment. A novel multi-layer fuzzy logic decision process with shared autonomy for a safety framework is proposed. It evaluates safety based on the robot's multi-sensor inputs, the teleoperator's attention level, and the configuration state of the self-reconfigurable robot and switches the operation mode, robot speed gain, and configuration state for performance and safety without compromises. The experimental outcome successfully demonstrates the self-reconfigurable robot's capability to navigate safely using shared autonomy in real-world pavement scenarios using the proposed algorithm during autonomous navigation.
Online Extrinsic Correction of Multi-Camera Systems by Low-Dimensional Parameterization of Physical Deformation

In this paper, we propose the online extrinsic correction method that effectively optimizes the extrinsic parameters of multi-camera systems used in visual SLAM. In the typical visual SLAM systems that use multi-camera settings, the intrinsic and extrinsic parameters of the cameras are calculated through offline calibration, which is used as the fixed constraints in online execution. However, the camera rig can be physically deformed by shock or vibration, and the deviation from the offline calibration parameters can adversely affect the accuracy of triangulation and pose estimation. Therefore, it is crucial to maintain the accurate calibration of the camera rigs continuously throughout the execution. The previous online calibration methods optimize the extrinsic camera parameters in a full degree of freedom(DoF) by minimizing the reprojection error, but the limited visual information available online may bias the resulting camera poses. From the observation that the cameras are mounted on a physical body and the patterns that the body can be deformed is restricted and not completely free, we propose to model the pattern of physical rig deformation by external forces in advance, and then use the pre-trained low-dimensional deformation model to robustly and accurately estimate the changed camera poses in real-time. The proposed method consists of two steps. First, the physical model of the camera system is constructed in a simulator and the actual deformations by various external disturbances are recorded, and the deformation patterns are modeled by a PCA algorithm to build a low-dimensional model. In online execution, the camera poses are updated by minimizing the reprojection errors of visual features within the pre-trained low-dimensional parameterization, instead of optimizing all camera poses independently. Through the experiments in synthetic environments, the proposed online extrinsic correction method shows that it produces more accurate and robust camera pose estimation results than the existing method even when inaccurate 3D-2D correspondences exist or 2D feature positions are noisy.
GESRsim: Gastrointestinal Endoscopic Surgical Robot Simulator

Robot-assisted gastrointestinal endoscopic surgery (GES) as a kind of natural orifice transluminal endoscopic surgery (NOTES) is the next-generation minimally invasive surgery (MIS). Besides, rendering certain autonomy to a Gas-trointestinal Endoscopic Surgical Robot (GESR) is promising but highly challenging. Therefore, to accelerate the development and augment the autonomy of GESR, we use CoppeliaSim to develop the first robotic simulator for the GESR system (GESRsim) based on our previous design. The GESRsim provides several 3D models and kinematics of our designed manipulators and endoscopic snake bone. Additionally, we build several scenes for robotic GES training and then utilize different programming interfaces to perform teleoperation. Furthermore, several advanced control algorithms, including visual servoing (VS) and deep reinforcement learning (DRL), are implemented to verify the performance of the GESRsim.
Predictive Modelling for Financial Distress amongst Manufacturing Companies in India

This study develops a model predicting financial distress amongst manufacturing companies in India using logistic regression. 18 financial ratios of 574 companies from 34 industries in manufacturing sector were examined for the period 2005 – 2019 to develop and validate the model. The study can be considered as one of the very few which has examined the financial distress indicators of manufacturing sector in India. EBIT margin, Interest coverage, quick ratio, Cash flow from operations to Sales, Debtors Turnover, Working Capital to Total Assets, Fixed Assets to Total Assets are important determinants of financial health of a business. This study provides useful insights to business managers and lenders to review and monitor financial soundness of business. The findings can also help policy makers to design policies and programs to support distressed industries in India. This study also addresses the urgent need for a country specific model for distress prediction. The model developed shows high predictive ability. Key words: Financial distress, Indian manufacturing sector, distress prediction, logistic regression, distress indicators.
Observations on the relationship between eigenvalues, instrument noise, and detection performance

Hyperspectral data rarely spans the full band space because of factors such as sensor noise, numerical round-off, sparse sampling, and band correlation introduced by data processing. Standard exploitation of data, which often does not consider the possibility of a reduced band space, leads to reduced detection performance. Spectral signature detection performance can be improved by estimating the covariance on a subset of the band space components. The decision about how to limit the band space can be determined by factors such as in-scene estimation of noise. In-scene estimation of noise can be used to optimize spectral signature detection when spectral filtering methods based on covariance inverses are used. We present here a method for determining instrument noise and a new method of covariance inverse regularization which increases spectral filtering performance.
Towards Specialized Hardware for Learning-based Visual Odometry on the Edge

Learning-based visual odometry (VO) has gained increasing popularity in autonomous navigation of small robots. However, most methods in the category require computation resources not normally available on edge systems. We contend that specialized hardware accelerators are ideal solutions to this problem because of their superior energy efficiency. In this paper, we first propose a model to derive compute specifications for VO from physical characteristics of unmanned aerial vehicles (UAVs). These specifications serve as the basis to guide our accelerator design process. Based on the specifications derived from the DJI Mavic Air 2 and Crazyflie 2.0 UAVs, we explore the speed/flight-time design spaces for three target VO algorithms on two NVIDIA Jetson systems. Then, we propose a hardware accelerator architecture and present prototype implementations based on FPGAs. Additionally, we illustrate the algorithm/hardware co-design approach with a series of hardware-aware algorithmic redesigns targeting the FPGA prototypes, and quantify the throughput-accuracy tradeoff of them. Our FPGA implementation of DFVO is 2.7x more energy efficient compared to off-the-shelf embedded computers.
A generalized fractional sub-equation method for nonlinear fractional differential equations

This letter studies some nonlinear fractional differential equations. The sub-equation method is used for finding exact solutions of these equations. Meanwhile, the traveling wave transformation method has been used to convert fractional order partial differential equation to fractional order ordinary differential equation. Calculations in this method are simple and effective mathematical tool for solving fractional differential equations in science and engineering. The power of this manageable method is presented by applying it to several examples. This approach can also be applied to other nonlinear fractional differential equations.
Heating by Raman Backscatter and Forward Scatter

This Letter presents computer simulations of the reflections and heating due to stimulated Raman scattering of intense laser light in large regions of underdense plasma. The heated electron distribution is approximately a Maxwelliam of temperature 1/2m/sub e/v/sub p/ /sup 2/. A simple model of the reflection is presented. Forward Raman scattering was also observed producing extremely energetic electrons. Finally, two-dimensional simulations showed sizable Raman scattering coexisting with heating by 2.omega. /sub p/e instability. Raman scattering may cause a preheat problem with large laser-fusion-reactor targets.
Automated Aerial Screwing with a Fully Actuated Aerial Manipulator

The tasks that unmanned aerial vehicles (UAVs) have taken upon have progressively grown in complexity over the years, alongside with the level of autonomy with which they are carried out. In this work, we present an example of aerial screwing operations with a fully-actuated tilt-rotor platform. Key contributions include a new control framework to automate screwing operations through a robust hole search and in-hole detection algorithm. These are achieved without a-priori knowledge of the exact hole location, and without the use of external tools, such as vision based hole detection or force sensors. Wrench coupling is implemented to account for the platform's kinematic constraints during screwing. The application of a constant contact force and a compliant response to induced disturbances are obtained with the use of admittance control. The full framework is validated with extensive flight experiments that demonstrate the effectiveness of each subsystem, as well as the complete architecture. We also validate the robustness of the detection algorithm against false positives. Within the results we demonstrate the ability to perform the automated task with a 86% success rate over 35 flights, and measured hole search time of 9s (median value).
An Empirical Criterion for Transitive Closure

: The paper establishes a simple and computationally economical criterion for determining whether a given adjacency matrix represents a transitive closure or not. The criterion is based on a new iterative method for finding the transitive closure adjacency matrix. This method is equivalent to but computationally more efficient than the traditional Boolean sum of successive powers of the original adjacency matrix to determine the transitive closure matrix. However, it is computationally less efficient than the Warshall algorithm and the recent more advanced algorithms. Nevertheless, in many cases, in which a given matrix is different from the transitive closure, but may not differ much, a few of the proposed looping steps may suffice to find the transitive closure, avoiding the computational burden of the best-in-class algorithms. Thus, the criterion and the recursive relation are apt in complementing the extant methods to establish an efficient evaluation engine for the determination of the transitive closure.
AIB-MDP: Continuous Probabilistic Motion Planning for Automated Vehicles by Leveraging Action Independent Belief Spaces

While automated research vehicles are already populating the roads, their commercial availability at scale is still to come. Presumably, one of the key challenges is to derive behaviors that are safe and comfortable but at the same time not overcautious, despite considerable uncertainties. These uncertainties stem from imperfect perception, occlusions and limited sensor range, but also from the unknown future behavior of other traffic participants. A holistic uncertainty treatment, for example in a general POMDP formulation, often induces a strong limitation on the action space due to the need for real-time capability. Further, related approaches often do not account for the need for verifiable safety, including traffic rule compliance. The proposed approach is targeted towards scenarios with clear precedence. It is based on an MDP with an action-independent belief (AIB-MDP): We assume that the future belief over the trajectories of other traffic participants is independent of the ego vehicle's behavior. Thus, the future belief can be predicted and simplified in an upstream module, independent of motion planning. This modularization facilitates subsequent ego motion planning in a continuous action space despite the thorough uncertainty consideration. The improved performance compared to state-of-the-art is demonstrated in three example scenarios.
A Stochastic Optimization Method Applied for Single Tuned Passive Filter Planning

Power system harmonics is one of the power quality problems. This study proposed an optimal single-tuned filter to eliminate harmonics in the power system using an optimization tool known as Mixed Integer Distributed Ant Colony Optimization. Two objective functions have been considered: the total of harmonic voltage distortions and transmission losses. The global maximum and minimum were achieved by maintaining the desired range of power factor, the quality factor is maintained within a specific range, avoiding harmonic resonance, and ensuring that capacitors and harmonic voltage do not violate IEEE standards. On a comprehensive passive filter design, this study also presents the advantages of multi-objective approaches over single-objective optimization using the proposed technique.
Intelligent Energy Supply Management Model for Distributed Energy-based Reduce Carbon Emission

In order to intelligently respond to energy demand, it efficiently supplies energy to users in connection with distributed energy generation and energy storage devices. Energy supply management for carbon reduction analyzes model design and results from three perspectives. From the perspective of Energy Storage System (ESS)-linked distributed power, we propose an energy operation model for small unit energy generation analysis, low carbon emission, and an energy demand-responsive next-generation supply system from the perspective of scheduling. In addition, it is expected to achieve energy independence and carbon neutrality by tracking and managing carbon emissions at the city level by optimally operating the energy supply-demand system based on three data analysis strategies.
Child Engagement Estimation in Heterogeneous Child-Robot Interactions Using Spatiotemporal Visual Cues

Robots are increasingly introduced in various Child-Robot Interactions with educational, entertainment or even therapeutic goals. In order to achieve qualitative inter-actions, robots need to adjust their behavior according to children's response. A robot's ability to successfully estimate partner's engagement is of great importance towards this direction. In this research we propose a method to estimate the engagement level of children during heterogeneous and challenging child-robot interactions. Our method uses the spatiotemporal residual $\mathrm{R}(2+1)\mathrm{D}$ blocks to simultaneously leverage the rich RGB and temporal information, which is crucial for the engagement estimation. We present results on three different groups of data, including the PInSoRo open dataset, proving our method's robustness and improvement over previous works.
Optimal Joint TDPA Formulation for Kinematically Redundant Robot Manipulators

The accomplishment of a successful teleoperation task requires guaranteeing system stability and transparency. Communication delay (in particular variable time delay), quantization and discretization negatively affect system stability and might be overcome with Time Domain Passivity Approach (TDPA), a model-free and robust way to cope with energy injection due to communication delay. However, this method degrades the transparency of the teleoperation system and worsens tracking performance, introducing in particular position drift error at the slave side and high frequency vibration (jittering) at the master side. In this work, we propose a new joint passivity controller formulation for kinematically redundant manipulators. Our approach stabilizes the system guaranteeing minimal performance loss by privileging the dissipation of the observed energy in the Jacobian null-space. The residual energy (if any) is dissipated in an orthogonal subspace. This is achieved by the solution of an optimization problem with appropriately defined cost functions and constrained to dissipate the energy observed by the passivity observer, guaranteeing the stability of the system. The effectiveness of our algorithm is tested in simulation with both constant and variable time delays.
Design of a Reconfigurable Robot with Size-Adaptive Path Planner

Area coverage is demanded from the robots utilized in application domains such as floor cleaning. Even though many advanced coverage algorithms have been developed, the area coverage performance is limited due to the inaccessibility of narrow spaces caused by physical constraints. Reconfigurable robots have been introduced to overcome this limitation where reconfigurability could help in assessing narrow spaces. Nevertheless, the state-of-the-art reconfigurable robots are not capable of changing the morphology size and shape as a single entity. Therefore, this paper proposes a novel design of a reconfigurable robot with a size-adaptive coverage strategy. The reconfiguration mechanism is designed in such a way that the robot can independently expand or shrink its size along the principal planar axes, where the behavior allows the change of size and shape. The coverage strategy is based on boustrophedon motion and the A* algorithm modified for accessing narrow areas using the size adaptability. The design of the robot is detailed in the paper, including electro-mechanical aspects, design considerations, and the coverage path planning method. Experiments have been conducted using a prototype of the proposed design to analyze and evaluate the characteristics and the performance of the robot. The results show that the proposed robot design can improve the productivity of a floor cleaning robot in terms of area coverage and coverage time.