Miracle-dz/UniFAR-FTUs huggingface dataset:
Air Quality Prediction in Smart City

Technological and industrial advancement is one of the main problems and causes of environmental pollution. Industry, power plants, traffic, factories are the main pollutants in big cities. On the other hand, the same technological advancement can also be used to decrease this bad influence. One of the ways is a smart city sensor network with applications that can predict air quality for the next few days and warn the population in the case of the upcoming severe pollution. The prediction task in this paper is conducted by using state-of-the-art algorithms such as CatBoost, with additional features that help improve the quality of the model. Three models are examined, regular CatBoost, CatBoost with additional variable, and the new approach where CatBoost model is boosted with another model. All the models are compared, while the best performance is achieved via the new approach Boosted CatBoost.
S $^3$ Net: Self-Supervised Self-Ensembling Network for Semi-Supervised RGB-D Salient Object Detection

RGB-D salient object detection aims to detect visually distinctive objects or regions from a pair of the RGB image and the depth image. State-of-the-art RGB-D saliency detectors are mainly based on convolutional neural networks but almost suffer from an intrinsic limitation relying on the labeled data, thus degrading detection accuracy in complex cases. In this work, we present a self-supervised self-ensembling network (S $^3$ Net) for semi-supervised RGB-D salient object detection by leveraging the unlabeled data and exploring a self-supervised learning mechanism. To be specific, we first build a self-guided convolutional neural network (SG-CNN) as a baseline model by developing a series of three-layer cross-model feature fusion (TCF) modules to leverage complementary information among depth and RGB modalities and formulating an auxiliary task that predicts a self-supervised image rotation angle. After that, to further explore the knowledge from unlabeled data, we assign SG-CNN to a student network and a teacher network, and encourage the saliency predictions and self-supervised rotation predictions from these two networks to be consistent on the unlabeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our network quantitatively and qualitatively outperforms the state-of-the-art methods.
Power maximization control of variable‐speed wind generation system using permanent magnet synchronous generator

This paper proposes sensorless output power maximization control of a wind generation system. A permanent magnet synchronous generator (PMSG) is used as a variable speed generator in the proposed system. The generator torque is suitably controlled according to the generator speed and thus the power from a wind turbine settles down on the maximum power point by the proposed MPPT control method, where the information on wind velocity is not required. Moreover, the maximum available generated power is obtained by the optimum current vector control. The current vector of PMSG is optimally controlled according to the generator speed and the required torque in order to minimize the losses of PMSG considering the voltage and current constraints. The proposed wind power generation system can be achieved without mechanical sensors such as wind velocity detector and a position sensor. Several experimental results show the effectiveness of the proposed control method. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(2): 11–19, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20043
Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

Objective This study aimed to develop and validate a machine learning model for predicting bone metastases (BM) in prostate cancer (PCa) patients. Methods Demographic and clinicopathologic variables of PCa patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2017 were retrospectively analyzed. We used six different machine learning algorithms, including Decision tree (DT), Random forest (RF), Multilayer Perceptron (MLP), Logistic regression (LR), Naive Bayes classifiers (NBC), and eXtreme gradient boosting (XGB), to build prediction models. External validation using data from 644 PCa patients of the First Affiliated Hospital of Nanchang University from 2010 to 2016. The performance of the models was evaluated using the area under receiver operating characteristic curve (AUC), accuracy score, sensitivity (recall rate) and specificity. A web predictor was developed based on the best performance model. Results A total of 207,137 PCa patients from SEER were included in this study. Of whom, 6725 (3.25%) developed BM. Gleason score, Prostate-specific antigen (PSA) value, T, N stage and age were found to be the risk factors of BM. The XGB model offered the best predictive performance among these 6 models (AUC: 0.962, accuracy: 0.884, sensitivity (recall rate): 0.906, and specificity: 0.879). An XGB model-based web predictor was developed to predict BM in PCa patients. Conclusion This study developed a machine learning model and a web predictor for predicting the risk of BM in PCa patients, which may help physicians make personalized clinical decisions and treatment strategy for patients.
Design and Development of CPW-Fed Microstrip Antenna for WLAN/WiMAX Applications

In this paper, a novel broadband monopole antenna with an extended rectangular shaped slot based on coplanar waveguide (CPW)-fed is designed and presented. The antenna composed of a planar rectangular patch element embedded with slots, capable of generating two separate resonant modes with good impedance matching. The parametric study is performed to understand the characteristics of the proposed antenna. To verify the simulated design concept, a prototype antenna is designed and fabricated on the FR4 substrate, and characterized experimentally. The overall size of the antenna is 35:24mm £ 26:4mm £ 1:6mm including the flnite ground CPW feeding mechanism and total volume of the antenna is 1.49cm 3 . The antenna operates in broad frequency bands from 3.424GHz to 6.274GHz covering wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) bands. The maximum gain of the proposed antenna is 5.51dBi at 4.78GHz frequency band. The proposed antenna's radiation characteristics are also observed.
Integrating AHP with QFD for robot selection under requirement perspective

Selection of a robot is an important task, as improper selection may adversely affect a firm's production by reducing the quality of the product, thereby reducing productivity as well as profitability. To effectively select a robot for a specified job, several factors have to be considered. The objective of this paper is to explain how, using a combined AHP/QFD model, the authors are able to determine if the deployment of robots in industry helped in performance enhancement from requirement perspective. Incorporating a simple and novel cost factor measure in the proposed integrated AHP/QFD model aids justification of the implementation of a robotic system in a manufacturing firm from an economic point of view also. The proposed integrated approach also identifies technical requirements followed by customer requirements. In this paper, an integrated model combining AHP and QFD has been delineated for the industrial robot selection problem. Seven technical requirement factors have been considered for the case study.
Scattering of obliquely incident guided waves by a stiffener bonded to a plate

Nondestructive testing of aerospace structures often requires their immobilization or disassembly. Structural health monitoring (SHM) can overcome these problems; the use of guided elastic waves (GW) in SHM is of great interest, because they propagate long distance in the structure thickness. Structures being stiffened, optimally positioning sensors implies to determine the number of stiffeners a wave can go through while remaining detectable. Here, the diffraction of GW by a stiffener bonded to a plate is considered. Elastic and geometric invariances along stiffener axis lead to 2D computations involving the three components of wave particle displacement, whatever its incidence on stiffener. A hybrid model is developed combining the semi-analytical finite element method for GW propagation and a finite element method (FE) for the stiffener diffraction. Optimal hybridization is obtained thanks to the development of transparent boundaries of the FE domain. Such boundaries have been obtained for GW normally incident onto a scattering feature, thanks to Fraser's bi-orthogonality relation, which unfortunately does not hold for oblique incidence. A numerical approach is developed to numerically approximate it, then, to derive boundary conditions in the wanted form. Their use minimizes the size of the FE domain and avoids any artificial reflection. They provide a mean for projecting diffracted fields on modes reflected on or transmitted through the stiffener; corresponding coefficients are obtained as functions of the direction of incidence.
Frequency-Independent and Frequency-Dependent Nonlinear Models of TWT Amplifiers

Simple two-parameter formulas are presented for the functions involved in the amplitude-phase and the quadrature nonlinear models of a TWT amplifier, and are shown to fit measured data very well. Also, a closed-form expression is derived for the output signal of a TWT amplifier excited by two phase-modulated carriers, and an expression containing a single integral is given when more than two such earriers are involved. Finally, a frequencydependent quadrature model is proposed whose parameters are obtainable from single-tone measurements.
High‐performance low‐power 27‐GHz CMOS voltage‐controlled oscillator for Ka‐band applications

A new low‐power 27.07‐GHz voltage controlled oscillator is designed for Ka‐band applications in this article. Body bias technique is used for the low‐power and low‐phase noise VCO design. The VCO can be tuned from 26.35 to 27.102 GHz. The measured phase noise is −106.2 dBc/Hz at 1 MHz offset from the carrier frequency. The corresponding figure‐of‐merit (FOM) is calculated to be −183.2 dBc/Hz. The VCO is implemented with the TSMC 0.18 μm one‐poly‐six‐metal 1.5 V mixed signal/RF CMOS technology. The chip size is 0.565 × 0.69 mm2. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:2872‐2875, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26433
Design of a New Micro-Gripper Based on Piezoelectric Bimorphs

Considering assembly and disassembly of micro-gripper, connection and isolation of lead, a new micro-gripper based on piezoelectric bimorphs is designed by using finite element analysis. First, a detailed structure of the micro-gripper is designed based on piezoelectric bimorph cantilevered beams. Second, static and dynamic characteristics of the micro-gripper are analyzed by piezoelectric coupling field analysis technique of ANSYS. For the static performance, there is a linear relationship between displacement and driving voltage of the micro-gripper. The maximum displacement of the micro-gripper finger is about 81.9 μm under the maximum driving voltage 200 V. For dynamic performance, the natural frequency and the response time of the gripper is about 45 Hz and 1.1 ms, respectively. The micro-gripper could meet the designed needs.
Improving the Performance of Popular Supply Chain Forecasting Techniques

This article empirically investigates the extension of the use of an aggregation-disaggregation forecasting approach for intermittent demand (ADIDA) to fast-moving demand data, addressing the need of supply chain managers for accurate forecasts. After a brief introduction to the framework and its background, an experiment is set up to examine its performance on data from the M3-Competition. The relevant forecasting methodology and in-sample optimization techniques are described in detail, as well as the core experimental structure and real data. Empirical results of forecasting accuracy performance are presented and discussed, placing further emphasis on the managerial implications of the framework’s being a simple, cost-efficient, and universally implementable forecasting method self-improving mechanism. Finally, all conclusions are summarized and guidelines for prospective research are proposed.
AN ONLINE ACQUISITION SYSTEM FOR A SECOND ORDER MECHANICAL SYSTEM (R.U.B.E.)

Students often are presented with measured laboratory data which is not tainted by real-world measurement issues such as noise, quantization error and poor sensor sensitivity. The students then perceive the “measurement world” as very much like the analytical theoretical equations that are presented in the classroom environment. This is often done to illustrate a particular theoretical concept (and not “cloud the issue” with real-world, contaminated data). The student then grasps the one concept of interest but is shielded from the actual measurement environment that typically exists. A second order mechanical system is operated in an online, internet controlled experiment where the displacement and accelerations are measured with a variety of different transducers. The selection of transducers spans the range of poor transducer sensitivity, noisy measurements, inadequate resolution and clipped signals. The students must collect the data and process it to numerically integrate and differentiate the displacement and acceleration measurements. A variety of different issues must be addressed in order to cleanse the data of real-world measurement contaminants – filtering, smoothing, bias adjustment are a few of the tools that can be used. The system has variable mechanical parameters—it changes every time it is operated so that no two sets of data are alike (variable input, variable mass, variable stiffness). This forces each student to process his/her own data, as it will be slightly different from data sets collected by other students. The RUBE (Response Under Basic Excitation) is described along with the supporting tools that assist the student in the evaluation of the acquired data.
Comparative Image Quality Analysis of Spatial Filters for Pre-processing of CT Abdominal Images

Aim: To determine the efficient noise reduction filter for abdominal CT images. Background: Image enrichment is the first and foremost step that has to be done in all image processing applications. It is used to enhance the quality of digital images. Digital images are liable to addition of noise from various sources such as error in instrument calibration, excess staining of images, etc., Image de-noising is an enhancement technique used to remove / reduce noise present in an image. Reducing the noise of images and preserving its edges are always critical and challenging in image processing. Materials and Method: In this paper, four different spatial filters namely Mean, Median, Gaussian and Wiener were used on 100 CT abdominal images and their performances were compared against the following four parameters: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), Normalised correlation coefficient (NCC) and Normalised Absolute Error (NAE) to determine the best denoising filter for the abdominal CT images. Result: Based on the experimental parameters, the median filter had the maximum efficiency in managing salt and pepper noise than the other three filters. Both Median and Wiener filters showed efficiency in removing the Gaussian noise. Whereas, the Wiener filter demonstrated higher efficiency in reducing both Poisson and Speckle noise. Conclusion: Based on the results of this study, we can conclude that the median filter can be used to reduce Salt and Pepper noises. Median and Wiener filters are significantly better for Gaussian Noise and the Wiener filter can be used to reduce both Poisson & Speckle noise in abdominal CT images.
Perceptuo-Motor Transparency in Bilateral Teleoperation

In bilateral teleoperation, the operator holds a local robot which determines the motion of a remote robot and continuously receives delayed force feedback. Transparency is a measure of teleoperation system fidelity. The ideal teleoperator system is the identity channel, in which there is neither delay nor distortion. During the last decades transparency was widely analyzed using two-port hybrid representation of the system in Laplace domain. Such representations define hybrid matrix that maps between the transmission channel inputs and outputs. However, in measuring transparency one should consider also the human operator, and therefore we propose a multidimensional measure of transparency which takes into account: i) Perceptual transparency: The human operator cannot distinguish when the teleoperation channel is being replaced by an identity channel. ii) Local Motor transparency: The movement of the operator does not change when the teleoperation channel is replaced by an identity channel. iii) Remote transparency: The movement of the remote robot does not change when the teleoperation channel is replaced by an identity channel. We hypothesize that by selecting filters and training protocol it is possible to obtain perceptually transparent teleoperation (i) and remote motor transparency (iii) without local motor transparency (ii), namely, to transparentize the system. We formally define the transparency error, analyze this process in the linear case, and simulate simplified teleoperation system according to typical experimental results in our previous studies about perception of delayed stiffness. We believe that these tools are essential in developing functional teleoperation systems.Copyright © 2008 by ASME
Epigenetic Small Molecules Rescue Nucleocytoplasmic Transport and DNA Damage Phenotypes in C9ORF72 ALS/FTD

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neurodegenerative disease with available treatments only marginally slowing progression or improving survival. A hexanucleotide repeat expansion mutation in the C9ORF72 gene is the most commonly known genetic cause of both sporadic and familial cases of ALS and frontotemporal dementia (FTD). The C9ORF72 expansion mutation produces five dipeptide repeat proteins (DPRs), and while the mechanistic determinants of DPR-mediated neurotoxicity remain incompletely understood, evidence suggests that disruption of nucleocytoplasmic transport and increased DNA damage contributes to pathology. Therefore, characterizing these disturbances and determining the relative contribution of different DPRs is needed to facilitate the development of novel therapeutics for C9ALS/FTD. To this end, we generated a series of nucleocytoplasmic transport “biosensors”, composed of the green fluorescent protein (GFP), fused to different classes of nuclear localization signals (NLSs) and nuclear export signals (NESs). Using these biosensors in conjunction with automated microscopy, we investigated the role of the three most neurotoxic DPRs (PR, GR, and GA) on seven nuclear import and two export pathways. In addition to other DPRs, we found that PR had pronounced inhibitory effects on the classical nuclear export pathway and several nuclear import pathways. To identify compounds capable of counteracting the effects of PR on nucleocytoplasmic transport, we developed a nucleocytoplasmic transport assay and screened several commercially available compound libraries, totaling 2714 compounds. In addition to restoring nucleocytoplasmic transport efficiencies, hits from the screen also counteract the cytotoxic effects of PR. Selected hits were subsequently tested for their ability to rescue another C9ALS/FTD phenotype—persistent DNA double strand breakage. Overall, we found that DPRs disrupt multiple nucleocytoplasmic transport pathways and we identified small molecules that counteract these effects—resulting in increased viability of PR-expressing cells and decreased DNA damage markers in patient-derived motor neurons. Several HDAC inhibitors were validated as hits, supporting previous studies that show that HDAC inhibitors confer therapeutic effects in neurodegenerative models.
Genetic and species‐level biodiversity patterns are linked by demography and ecological opportunity

The processes that give rise to species richness gradients are not well understood, but may be linked to resource‐based limits on the number of species a region can support. Ecological limits placed on regional species richness should also affect population demography, suggesting that these processes could also generate genetic diversity gradients. If true, we might better understand how broad‐scale biodiversity patterns are formed by identifying the common causes of genetic diversity and species richness. We develop a hypothetical framework based on the consequences of regional variation in ecological limits set by resource availability and heterogeneity to simultaneously explain spatial patterns of species richness and neutral genetic diversity. Repurposing raw genotypic data spanning 38 mammal species sampled across 801 sites in North America, we show that estimates of genome‐wide genetic diversity and species richness share spatial structure. Notably, species richness hotspots tend to harbor lower levels of within‐species genetic variation. A structural equation model encompassing eco‐evolutionary processes related to resource availability, habitat heterogeneity, and contemporary human disturbance supports the spatial patterns we detect. These results suggest broad‐scale patterns of species richness and genetic diversity could both partly be caused by intraspecific demographic and evolutionary processes acting simultaneously across species.
Optical Fiber Distributed Sensing - Physical Principles and Applications

Obtaining the strain data all along the optical fiber, with adequate spatial resolution and strain accuracy, opens new possibilities for structural tests and for structural health monitoring. Formerly, only point sensors, as strain gages or fiber Bragg grating, were available, and information about the response to loads was restricted only to those points on which the sensors were bonded. Unless a sensor was located near the damage initiation point, details about the failure initiation and growth were lost. With a distributed system, the information is given as an array of data with the position in the optical fiber and the strain or temperature data at this point. In this article, the physical principles underlying the different techniques for distributed sensing are discussed, a classification is done based on the backscattered wavelength; this is important to understand its possibilities and performances. The definition of performance for distributed sensors is more difficult than for traditional point sensors because the performance depends on a combination of related measurement parameters. For example, accuracy depends on the spatial resolution, acquisition time, distance range, or cumulated loss prior to measurement location. The field of applications of this new technology is very wide; results of the structural tests of a 40 m long wind turbine blade, detecting the location and load of onset of buckling, and the results of the delamination detection in a composite plate, are presented as examples.