Miracle-dz/UniFAR-FTUs huggingface dataset:
MOBILE ROBOT MOTOR BEARING FAULT DETECTION AND CLASSIFICATION ON DISCRETE WAVELET TRANSFORM AND LSTM NETWORK

The impact of motors breakdown and failures on mobile robot motor bearing is an important concern for robot industries. For this reason, predictive motor lifetime and bearing fault classification techniques are being investigated extensively as a method of decreasing motor downtime and enhancing mobile robot reliability. With increasing attention on neural network technologies, many researchers have carried out lots of the relevant experiments and analyses, very plentiful and important conclusions are obtained. In this article, a classification method based on discrete wavelet transform (DWT) and long short-term memory network (LSTM) a proposed to find and classify fault type of mobile robot permanent magnet synchronous motor (PMSM). First, a set of mobile robot motor vibration signal were collected by the sensors. Second, the obtained vibration signal is decomposed into six frequency bands by the DWT. Haar function is selected as the mother function in the processing. The energy of every frequency band was calculated as a classification feature. Thirdly, four classification features with high classification rate are obtained. The feature vector is used as input of the neural network, and the fault type is identified by LSTM classifier with deviation unit. From the results of the experiments provided in the paper, the method can detect the fault type accurately and it is feasible and effective under different motor speed.
A Tutorial for the Analysis of the Piecewise-Smooth Dynamics of a Constrained Multibody Model of Vertical Hopping

Contradictory demands are present in the dynamic modeling and analysis of legged locomotion: on the one hand, the high degrees-of-freedom (DoF) descriptive models are geometrically accurate, but the analysis of self-stability and motion pattern generation is extremely challenging; on the other hand, low DoF models of locomotion are thoroughly analyzed in the literature; however, these models do not describe the geometry accurately. We contribute by narrowing the gap between the two modeling approaches. Our goal is to develop a dynamic analysis methodology for the study of self-stable controlled multibody models of legged locomotion. An efficient way of modeling multibody systems is to use geometric constraints among the rigid bodies. It is especially effective when closed kinematic loops are present, such as in the case of walking models, when both legs are in contact with the ground. The mathematical representation of such constrained systems is the differential algebraic equation (DAE). We focus on the mathematical analysis methods of piecewise-smooth dynamic systems and we present their application for constrained multibody models of self-stable locomotion represented by DAE. Our numerical approach is demonstrated on a linear model of hopping and compared with analytically obtained reference results.
Spatial Surface Reconstruction for Complex Environment Using Color-Depth Sensors

In this paper, the structure-entropy based feature is used to describe the energy of the complex environment and then the structural entropy energy is further used to extract the region of the spatial structural change. Moreover, by finding the maximum entropy energy of the overlapping area, the relative pose between two consecutive frames can be estimated. In the final step, the iterative closest point (ICP) is utilized to determine the rigid transformation matrix for the remaining region. Extensive experimental results show that the proposed method generates more accurate result than that by using the traditional ICP algorithm.
Dust in planetary nebulae

Abstract Infrared spectra from the Spitzer Space Telescope trace the evolution of carbon-rich dust from the asymptotic giant branch (AGB) to young planetary nebulae (PNe). On the AGB, amorphous carbon dominates the dust, but SiC and MgS also appear. In more evolved systems with warmer central stars, the spectra reveal the unidentified 21 μm feature, features from aliphatic hydrocarbons, and spectra from polycyclic aromatic hydrocarbons (PAHs), often with shifted feature positions indicative of the presence of aliphatics. More evolved systems with hot central stars show more typical PAH spectra, along with fullerenes and/or an emission feature known as the big-11 feature at ~11 μm. This features arises from a combination of SiC and PAHs, and it is usually accompanied by a shoulder at 18 μm, which while unidentified might be from cool silicate grains. The strong emission from MgS and SiC in young PNe probably arises from coatings on carbonaceous grains.
Effect of Chromium Substitution on Microstructure and Magnetic Properties of La0.7Sr0.2Ba0.1Mn1-xCrxO3 (x = 0, 0.03, 0.05, 0.07, 0.1)

Structure, morphology, and magnetic properties of perovskite La0.7Sr0. 2Ba0.1Mn1-xCrxO3 (x = 0; 0.03; 0.05; 0.07 and 0.1) synthesized via sol-gel method have been studied. X-ray diffraction studies confirm the Rhombohedral structure with R-3c space groups for all samples. The magnetization measurements clearly show that magnetization decreases with increasing Cr substitution concentration. The partial substitution of Cr3+ in the Mn site weakens the ferromagnetic double exchange interaction of the Mn3+–O–Mn4+ bond. It is caused by the emergence of the Cr3+–O–Cr3+ antiferromagnetic interaction. As known, the structural parameters affect the magnetic properties of perovskite manganese materials in the double exchange interaction. As the Cr3+ concentration increases, the average Mn-O bond length also increases, and the average Mn-O-Mn bond angle decreases, which dramatically weakens the Mn3+–O–Mn4+ double exchange interaction.
VEHICLE DETECTION AND COUNTING SYSTEM

: In today’s world, there are numerous vehicles moving on the road and traffic is unavoidable. By using the number of vehicles moving on the road as data we can evaluate the traffic. Vehicle detection and counting plays a very important role in traffic management. However, classification of the vehicles is a huge challenge. This paper mainly focuses on the methods used in vehicle detection and counting such as OpenCV techniques, and Haar cascade method for the classification of vehicles. The experiment is carried out by testing the video input in various conditions
Loop Closure Detection Algorithm Based on Attention Mechanism

—Loop closure detection is a key component of visual simultaneous localization and mapping (VSLM), which can effectively reduce the cumulative error of the system and improve the accuracy of mapping. The current loop closure detection method using deep learning can obtain accurate scene descriptions, but it is difficult to cope with the challenge of scene changes. This paper obtains complementary feature maps by fusing the shallow and deep image features of the ResNet50 network, and we incorporate a multiscale channel attention mechanism and a spatial attention mechanism after each layer of the ResNet50 network. The model can effectively extract discriminative scene landmarks, suppress the effects of irrelevant local features on similarity and be more robust to the problem of scene change. The method in this paper has been tested on several publicly available datasets and compared with mainstream methods. The experimental results show that the proposed method significantly outperforms mainstream meth-ods in terms of accuracy-recall performance.
Simultaneous temperature sensing using distributed cascading fiber Bragg grating-based single-ended Brillouin optical time-domain analyzer

We report a novel method of simultaneous distributed temperature sensing in optical fibers. The method is based on the cascading fiber Bragg grating (FBG) single-ended Brillouin optical time-domain analyzer (BOTDA). This proposal applies the technology of cascading FBGs to improve the signal-to-noise ratio of the single-ended BOTDA. Experimental results show achievement of a 50 km sensing range with 3 °C temperature resolution and 5 m spatial resolution.
The State of Software Diversity in the Software Supply Chain of Ethereum Clients

—The software supply chain constitutes all the resources needed to produce a software product. A large part of this is the use of open-source software packages. Although the use of open-source software makes it easier for vast numbers of developers to create new products, they all become susceptible to the same bugs or malicious code introduced in components outside of their control. Ethereum is a vast open-source blockchain network that aims to replace several functionalities provided by centralized institutions. Several software clients are independently developed in different programming languages to maintain the stability and security of this decentralized model. In this report, the software supply chains of the most popular Ethereum clients are cataloged and analyzed. The dependency graphs of Ethereum clients developed in Go, Rust, and Java, are studied. These client are Geth, Prysm, OpenEthereum, Lighthouse, Besu, and Teku. To do so, their dependency graphs are transformed into a unified format. Quantitative metrics are used to depict the software supply chain of the blockchain. The results show a clear difference in the size of the software supply chain required for the execution layer and consensus layer of Ethereum. Varying degrees of software diversity are present in the studied ecosystem. For the Go clients, 97% of Geth dependencies also in the supply chain of Prysm. The Java clients Besu and Teku share 69% and 60% of their dependencies respectively. The Rust clients showing a much more notable amount of diversity, with only 43% and 35% of OpenEthereum and Lighthouse respective dependencies being shared.
Adaptive Neural Network Control of Space Flexible Robot Based on Calculated Torque for Non-cooperative Targets

—An adaptive neural network control method based on computed torque is proposed to solve the target acquisition problem of non-cooperative space flexible robots. The dynamic model of free-floating space flexible robot is established, and the model is decomposed into two dynamic submodels, rigid and flexible, using singular perturbation theory. Aiming at the quality uncertainty of non-cooperative targets, a weighted recursive least squares method (WRLSM) is designed to realize online real-time estimation of unknown quality. A computational torque controller based on error estimation model is designed to realize partial decoupling of nonlinear dynamics of space flexible robot; To solve the problem of incomplete decoupling caused by uncertain models, an adaptive neural network is designed to compensate the error model and complete the complete decoupling of the nonlinear model. Aiming at the problem of elastic vibration caused by flexible characteristics, a linear quadratic regulator (LQR) controller is designed to suppress the elastic vibration of. Simulation results verify the effectiveness of the controller.
Automatic Clustering Using FSDE-Forced Strategy Differential Evolution

Clustering analysis is important in datamining for unsupervised data, cause no adequate prior knowledge. One of the important tasks is defining the number of clusters without user involvement that is known as automatic clustering. This study intends on acquiring cluster number automatically utilizing forced strategy differential evolution (AC-FSDE). Two mutation parameters, namely: constant parameter and variable parameter are employed to boost differential evolution performance. Four well-known benchmark datasets were used to evaluate the algorithm. Moreover, the result is compared with other state of the art automatic clustering methods. The experiment results evidence that AC-FSDE is better or competitive with other existing automatic clustering algorithm.
Evaluation of Coastal Forests Recovery in Miyagi Prefecture’s Iwanuma City Following the 2011 Tohoku Earthquake

—In the Sendai Bay Area of Japan’s Miyagi Prefecture, a 10-year coastal forest recovery project began in 2011 following the Tohoku Earthquake earlier that year. We should pay attention to the status of the restoration in order to maintain regional resilience in the aftermath of the earthquake and tsunami as well as to promote the recovery of agriculture and daily life. Therefore, we evaluated the recovery of coastal forests in Miyagi Prefecture following the 2011 Tohoku Earthquake. We analyzed satellite images taken by the Sentinel-2 satellite for several years following the disaster. Using the Google Earth Engine, we searched for and loaded Sentinel-2 images taken from 2016 to 2020. We concluded that the vegetation activity in the coastal forests in Iwanuma City gradually recovered during the 5-year research period. Therefore, we assume that the coastal forests in Miyagi Prefecture’s Iwanuma City have been recovering well following the 2011 Tohoku Earthquake. Nevertheless, some ﬁeld surveys or other proper methods would be needed to conﬁrm the computed results and to make a ﬁnal conclusion, because the normalized difference vegetation index (NDVI) is a relative rather than absolute value.
COVID-19 Vaccination can Aid in the Eradication of Tuberculosis

Received: May 26, 2022 Accepted: Jun 23, 2022 Published Online: Jun 24, 2022 Journal: Journal of Tuberculosis Publisher: MedDocs Publishers LLC Online edition: http://meddocsonline.org/ Copyright: © Khan Z (2022). This Article is distributed under the terms of Creative Commons Attribution 4.0 International License *Corresponding Author(s): Zafran Khan State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China. Email: zafrankhan807@yahoo.com & zafran@gibh.ac. cn
Cyanogenesis in cassava and its molecular manipulation for crop improvement.

While cassava is one of the most important staple crops worldwide, it has received the least investment per capita consumption of any of the major global crops. This is in part due to cassava being a crop of subsistence farmers that is grown in countries with limited resources for crop improvement. While its starchy roots are rich in calories, they are poor in protein and other essential nutrients. In addition, they contain potentially toxic levels of cyanogenic glycosides which must be reduced to safe levels before consumption. Furthermore, cyanogens compromise the shelf life of harvested roots due to cyanide-induced inhibition of mitochondrial respiration, and associated production of reactive oxygen species that accelerate root deterioration. Over the past two decades, the genetic, biochemical and developmental factors that control cyanogen synthesis, transport, storage, and turnover have largely been elucidated. It is now apparent that cyanogens contribute substantially to whole plant nitrogen metabolism and protein synthesis in roots. The essential role for cyanogens in root nitrogen metabolism, however, has confounded efforts to create acyanogenic varieties. This review proposes alternate molecular approaches that integrate accelerated cyanogen turnover with nitrogen reassimilation into root protein that may offer a solution to creating a safer, more nutritious cassava crop.
Interferogram denoising using an iteratively refined nonlocal InSAR filter

ABSTRACT Interferogram denoising is important for height reconstruction and deformation measurement. For high-resolution interferograms over heterogeneous areas, local biases and resolution losses may appear due to the violation of the local stationarity assumption. To address this problem, an iteratively refined nonlocal filter is proposed, whose estimation is refined iteratively by jointly using the amplitude, interferometric phase, coherence, and the pre-estimated patches. For nonlocal interferometric synthetic aperture radar (InSAR) techniques, the similarity of two pixels is computed via their surrounding patches, followed by the maximum likelihood weighted averaging of similar pixels. In this letter, the outliers in the search window is identified before the weight calculation. If the normalized probability density function (NPDF) of the central pixel and other surrounding pixels in the search window is less than the preset threshold, the pixel will be assigned with the minimum weight in the search window. Moreover, the denoising weight is calculated not only depending on the probabilistic patch-based (PPB) similarity, but also the coherence of the pixels in the search window. Both simulated and real data experiments are used to validate the effectiveness of the proposed method.
Moral Injury in People Living in Armed Conflict and Natural Disaster Settings: A Narrative Review

This review explores the moral injury in people living in regions affected by armed conflict and outbreaks. It is based on clinical presentation and measurements of moral injury, its prevalence, its theoretical perspective and implications on psychiatric practices. Poor assessment of moral injury, which is sometimes associated with post traumatic disorder, increases the incidence of substance use disorder, major depressive disorders and suicide in the aftermath of armed conflict and outbreaks. Failure to recognize moral injury impairs its management. This article emphasizes the need for routine screening for moral injury in individuals living in armed conflict and outbreak settings.
Segmentation of Pulmonary Nodules Based on MRBU-Net-WD Model

—It is important to diagnose lung nodules as early as possible in order to improve the cure rate of lung cancer patients. The CT technology is currently the most prevalent method of diagnosis and treatment in clinical medicine. However, the amount of data generated by CT diagnosis and treatment is increasing exponentially. In order to segment lung nodules from medical images, it is crucial to employ computer and artificial intelligence technology. The deep learning model proposed in this paper is MRBU-Net-WD model, a more effective and enhanced version of the U-Net. MRBU-Net-WD is distinguished by combining residual 3D convolution modules with multiscale densely connected modules. Moreover, In addition, in order to avoid the phenomenon of gradient disappearance when the network depth of the model is increasing. The Bi-FPN is therefore introduced to enhance the feature maps of the network at each depth, as well as to provide an effective fusion of features across depths. The weighted Dice loss function has a significant improvement for the pixel imbalance between lung nodules and background images in lung CT images. To test the proposed model, the LUNA-16 dataset has been extensively trained and evaluated. The performance of the comparison methods generally outperforms that of the original U-Net model and several other newly proposed models.
Optimizing maximum carboxylation rate for North America’s boreal forests in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) v.1.3

The maximum carboxylation rate ( V cmax ) is an important parameter for the coupled simulation of gross primary production (GPP) and evapotranspiration (ET) in terrestrial biosphere models (TBMs) such as the Canadian Land Surface 20 Scheme Including biogeochemical Cycles (CLASSIC). Observations of V cmax show it to vary both spatially and temporally, but it is often prescribed as constant in time and space for plant functional types (PFTs) in TBMs, which introduces large errors over North America’s boreal biome. To reduce this uncertainty, we used a Bayesian algorithm to optimize V cmax25 ( V cmax at 25 °C) in CLASSIC against eddy covariance observations at eight mature boreal forest stands in North America for six representative PFTs (two trees, two shrubs, and two herbs). As expected, the simulated GPP and ET using the optimized 25 parameters generally obtained reduced root mean square deviation values compared with eddy covariance observations and corresponding stand-level estimates obtained from gridded global data products. The optimized V cmax25 values for each PFT compared reasonably well with reported estimates derived from leaf-level gas exchange measurements. However, a large spatial variability of V cmax25 was identified, especially for the shrub and herb PFTs. We found that the site characteristics, particularly latitude for the shrub PFTs and air temperature for evergreen needleleaf tree, explained much of the spatial 30 variability, providing a basis to improve V cmax25 parameterizations in TBMs at regional scales
Control Design for Bounded Partially Controlled TPNs Using Timed Extended Reachability Graphs and MDP

This paper is about the design of control sequences for discrete event systems (DESs) modeled with bounded partially controlled timed Petri nets (PC-TPNs) including a set of temporal specifications that correspond to minimal firing durations. Petri nets are well-known mathematical and graphical models that are widely used to describe distributed DESs, including choices, synchronizations, and parallelisms. The domains of application include but are not restricted to manufacturing systems, computer science, and transportation networks. Including the time in the model is important to consider many control problems. This paper is more particularly concerned with control issues in timed context and uncertain environments when unexpected events occur and when control errors disturb the system behavior from the planned policy decided by the controller. To deal with such uncertainties, we propose first to build a timed extended reachability graph (TERG) that includes the time specifications when the PC-TPN behaves with an earliest firing policy. Then, the optimal paths in TERG are found by using an approach based on discrete time Markov decision processes under discounted criterion. Several simulations illustrate the benefit of our method from the computational point of view, in particular for uncertain environments.
Terms-we-Serve-with: A critical feminist intervention in coming to terms with algorithmic systems

In working to expand our vocabulary and capacity to transform algorithmic harms and injustice into positive human experience, it is critical to consider the contractual agreements between people and technology companies. For example, Terms-of-Service (ToS) agreements, cookie policies, content moderation policies, privacy policies, limitations of liability clauses, non-disclosure agreements, and other kinds of user agreements. The small print and legalese in contractual agreements often fail to provide people with meaningful consent and contestability in cases of algorithmic failures, risks, harms, or injustice (Vincent, 2021; Fiesler, Beard, and Keegan, 2020; Vaccaro et al., 2015). Instead of contributing to extractive power structures and information asymmetries, what if contractual agreements could become a living socio-technical artifact that empowers trust through equitable community-driven participation and oversight in the lifecycle algorithmic systems? This question is at the core of the Terms-we-Serve-with (TwSw) framework for evolving and enacting social norms and agreements between people and companies developing AI-driven products and services. The goal of the TwSw intervention proposal is achieving improved transparency and human agency in AI beyond debiasing, explainability, and ethics. Furthermore, it inspires a new legal and regulatory approach to