Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Representation Based on the Constraint of Spectral Characteristics for Hyperspectral Anomaly Detection

Anomaly detectors based on representation do not need to make a specific assumption about the statistical distribution of background, which will have better detection performance in anomaly detection. Generally, hyperspectral anomaly detection algorithms of collaborative representation often assume the pixels in the dual-window as the background pixels. Moreover, the constraint of sum-to-one is applied to representation weights for physical meaning. However, in the process of representation, the spectral characteristics of images are not fully utilized. In this article, the spectral angle mapping is applied to adjust the representation weights of the neigh-boring pixels around the tested pixel. Experimental results demonstrated that the proposed collaborative representation based detector using spectral characteristics to adjust weight can highlight anomalies effectively and achieve better detection performance.
Multi-criterial assessment of road crashes for different modes and consequences for land-use in London

Every year, around 1.25 million people die on the roads around the world. Road safety is one of the main concerns of governments worldwide, and some cities are implementing strategies like Vision Zero. In order to enforce such policies, analysis and diagnosis methods should be more robust and involve different tools. The main objective of this paper is to identify the critical areas for crashes of different transportation modes using indicators such as Crash Rate (CR), Casualty Rate (CaR) and monetary cost. Furthermore, the contributing factors for accident diagnosis are sought to be found and the impacts of land use in critical areas are explained. Three critical areas per mode were identified according to the methodologies and results of CR and CaR. Those areas were evaluated in terms of contributory factors to identify typical trending per mode, and this evaluation was performed using Land Use, Infrastructure, and User’s behavior criteria. The findings describe specific contributory factors such as landuse (commercial) for each mode and the general situation in critical areas and a general conclusion regarding conflicts between the different land use and the transport network.
Prediction of Turbidity in Beach Waves Using Nonlinear Autoregressive Neural Networks

The principal focus of this paper is to develop a prediction model to predict the turbidity of beach waves. The prediction model is developed using a nonlinear autoregressive neural network model using three input parameters: water temperature, wave height, and wave period. The beach wave turbidity is predicted without installing any additional sensors. The performance of the developed model is evaluated on three beaches in Chicago Park’s district. The proposed model performance showed better tracking ability for all the three considered beaches. The R2 and mean square errors MSE also confirm the best prediction model’s performance for both training and testing.
Lite-Deep : Improved Auto Encoder-Decoder for Polyp Segmentation

Colorectal cancer(CRC) or colon cancer, is fatal cancer seen in males and females. Colorectal polyps usually develop on the mucosal layer of the colon or rectal part of the large intestine. They may later turn malignant and become cancerous. Diagnosis of colorectal polyps in the initial stages is a key factor in reducing the mortality rate due to CRC. Colonoscopy is considered the golden standard in CRC detection. Automation of polyp detection, localization and segmentation in the screening stage can help the clinicians to a great extent. However, detection, localization and segmentation of polyps of various morphological structures and textures have been proved to be very challenging. Deep neural networks (DNNs) have emerged as a powerful subset of machine learning and recorded a tremendous boost in many visual recognition tasks including medical imaging. Deep learning models often need an immense number of annotated images, which is difficult to collect in the medical domain and these models are computationally expensive and memory intensive. Hence a lot of works are going on to have model compression and acceleration in deep neural networks without significantly decreasing the performance. This work suggests a lightweight deep learning model rooted on auto-encoder decoder architecture for the segmentation of colorectal polyps of various morphological structures and textures. This model can be trained at full length from a considerably less number of images and shows par performance in terms of essential metrics used in semantic segmentation.
Optimization of Fully Convolutional Network for Road Safety Attribute Detection

Even though, deep learning techniques demonstrate an outstanding performance in various applications, success of deep learning techniques depends upon appropriately setting their parameters in achieving most accurate results. Therefore, in this paper, a novel Particle Swarm Optimization (PSO) based technique is introduced to optimize a Fully Convolutional Network (FCN) to recognize road safety attributes. The proposed technique optimizes parameters such as number of convolution layers, activation function, pooling type, attribute image size, number of iterations and learning algorithms. The proposed technique has been evaluated using a custom dataset prepared by extracting images from video data provided by our industry partner. The evaluation results reveal that the proposed technique can automatically determine the most suitable combination of parameters corresponding the highest classification accuracy for road safety attributes by exploring the solution space. Extensive tests in conjunction with the statistical experiments convinced that proposed approach is an appropriate technique for automatically optimizing FCN’s parameters. The best sets of parameters were obtained by the proposed technique for different road safety attributes with a classification accuracy of 92-100%.
PI-Based SRM Speed Control System

There are three types of motors used in electric vehicles currently, namely permanent magnet synchronous motors, induction motors, and Switched Reluctance Motors (SRM). SRM has its advantages over other types of motorcycles. The advantages of SRM are weight, reliability, fault tolerance, and acceleration time. However, the SRM motor needs to be adjusted to have the required speed and transient response. This study aims to simulate and design speed control in SRM. The Proportional Integral (PI) control simulation is firstly conducted by using MATLAB to see the results of the control before it is implemented in SRM. From the results of the SysId Matlab, it is obtained system transfer function in the first order. The transfer function of this system is used to determine the value of Kp and Ki analytically obtained the value of Kp is 0.006159 and Ki is 0.054752. SRM operates at PWM 72-84 or in a duty cycle of 28.23% - 32.94%. This PWM signal is equivalent to the driver input voltage of 1.446 Volts - 1.756 Volts. The implementation is conducted by using an Arduino microcontroller and it only uses a positive control signal that makes the motor rotate in only one direction. From the simulation and implementation that were carried out, it is found that the control system that has been designed is able to track the speed at the setpoint, namely 400 rpm, 600 rpm, and 800 rpm. The settling time, respectively, is 1.2 s, 2 s, and 1.3 s, overshoot is 52.75%, 62.16%, and 13.87%. The steady-state error for each setpoint is 10%, 0.33%, 0.5%.
Data Mining for Revealing Relationship Between Google Community Mobility and Macro-Economic Indicators

Google community mobility reports have helped to evaluate the effectiveness of government-imposed movement control among countries. However, the relationship between the mobility data and the characteristic of regions is less reported. This study aims to reveal hidden information from Google community mobility reports and relate them to all 34 Indonesian provinces' macro-economic indicators. This secondary research implements a data mining approach using the CRISP-DM process framework and Knime Analytics Platform. The community mobility data of residence and workplace are collected as a time series covering Feb 16, 2020, to Jan 31, 2021. Macro-economic indicators are collected from the website of the Indonesian national statistics agency. The clustering method has grouped provinces into three based on their mobility. The findings indicate the relationship between mobility fluctuation during the COVID-19 pandemic and macro-economic indicators, namely human development index and labor force participation rate. In the theoretical aspect, this study has been initiating the investigation of community mobility and macroeconomic. Policymakers in dealing with post-pandemic recovery planning might consider the cluster characteristics for better planning.
A Fast and Robust Seam Tracking Method for Spatial Circular Weld Based on Laser Visual Sensor

Spatial circular welding is an important application in industrial production. However, some feature points extraction methods for spatial circular welds have disadvantages such as large amounts of calculation and poor anti-interference ability. In addition, due to the welding thermal deformation, the spatial circular weld needs to be tracked. Therefore, a fast and robust seam tracking method for spatial circular weld based on laser visual sensors is proposed in this article. First of all, a self-updating template matching (SUTM) method is proposed to get weld feature points. Compared with the method based on geometric features, the proposed SUTM method has the advantages of rapidity and robustness. What is more, an algorithm for spatial circle center fitting based on random sampling consensus (RANSAC) is proposed, which overcomes the interference of outlier points and improves the robustness of circle center detection. At the same time, the tracking error model of the spatial circular weld is established and the welding deviation from the <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-, <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-, and <inline-formula> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula>-directions is obtained. By correcting the deviation in real-time, the fast and robust real-time seam tracking of the spatial circular weld is realized. Through welding test, the proposed method can obtain the position of the spatial fillet circular weld quickly and robustly and complete the high-precision seam tracking.
Access to Ubiquitous Real-Time Information by Bus Passengers in Urban Contexts

Due to higher smartphone uptake and Internet penetration rate, Ubiquitous Real-time Passenger Information (URTPI) has become available to and required by larger segments of the public transit passenger population. URTPI can influence perceptions regarding transport services and affect mode and route choice. The impact of URTPI can be increased by a clear understanding of how the information is actually used. We investigate the demand for URTPI by means of an online survey in the UK. We study passenger behaviour in relation to personal attitudes, social norms and perceived behaviour control. The perceived behavioural control is defined by the attributes of information provision in the case of access to information. Our results confirm that URTPI is very popular among bus users. We find that the frequency of access to URTPI is strongly associated with the attributes of information and to a lesser degree to social norms. Our results highlight that simplifying apps is the best way to foster the use of URTPI, which in turn could help influencing travel behavior.
Research on Spider Sex Recognition From Images Based on Deep Learning

The rapid and accurate identification of spider sex is the first step in spider image recognition. The traditional artificial method used to identify the sex of mature spiders is mainly based on their genital structures (male palps or female epigynum) and highly dependent on the professional background of the identifiers. This article uses computer-based deep learning and transfer learning to identify the sex of spider, explores the design and application of convolutional neural networks in deep learning for spider sex recognition from images, and establishes a neural network model that displays excellent performance in experiments. In addition to optimizing the network model, we select appropriate hyperparameters to improve the accuracy of recognition and reduce the influence of human factors in the identification process. Through a comparison of multiple sets of experiments based on existing sample data collected in the laboratory, we find that the transfer learning method based on Xception can obtain better prediction accuracy than ResNet-152. After data augmentation, the optimization of a combined activation function and the fine-tuning of frozen layers, the prediction accuracy reaches 98.02%, and for an actual measurement of independent samples, the recognition accuracy reaches 92.38%. Therefore, the proposed method can basically replace manual identification and provide a reference for the artificial intelligence-based identification of spider species. Additionally, the model results indicate that male and female dimorphism may exist beyond the non-genital characteristics of spiders.
Multi-factor Load Balancing Algorithm for 5G Power Network Data Center

In 5g era, data center is the core facility of load data exchange, and its internal network traffic shows explosive growth. Aiming at the coexistence of bandwidth sensitive traffic and delay sensitive traffic in power data center network, this paper proposes MFLB (multiple factor load balancing) algorithm. Considering the multi-dimensional statistical characteristics of links, the algorithm integrates six parameters and four dimensions of hops, path delay, average link delay, maximum link delay, maximum residual bandwidth and minimum residual bandwidth to filter paths. The simulation environment is built based on mininet. By comparing with the existing algorithms, the performance of MFLB algorithm in average throughput, standardized total throughput and link delay is significantly improved.
Smart Infrastructure Monitoring using LoRaWAN Technology

An online monitoring system for manhole covers in a smart city environment is proposed. We have used LoRa physical layer for wireless transmission and LoRaWAN protocol for cloud connectivity. The system incorporates sensor nodes, LoRaWAN network and application at the backend. LoRaWAN based IoT has an extremely low power requirement for significant distance transmission. We have employed an accelerometer sensor to monitor the position, removal or harm of manhole covers in sewage systems. When these covers are moved or harmed, the LoRa board transmits the signal, cautioning the nearest gateway associated with The Things Network (TTN), a cloud-based open source LoRaWAN platform. The TTN sends an alarm to the support division of the civic authorities to take corrective actions. Such innovative applications can be displayed for monitoring and controlling different parameters in the smart city.
Modeling Evolutionary Dynamics of Railway Delays with Markov Chains

Railway operators depend on accurate and reliable predictions on future arrival and departure times of trains. This information is also disseminated to passengers to facilitate their journeys. Railway operations are influenced by many variables and are thus highly stochastic and dynamic. Hence modeling the variability of arrival time prediction and its evolution over time and space is of great interest. We propose a set of non-stationary Markov chain approaches to model the stochastic evolution of train delays over a train run. The Markov chains base on a varying combination of arrival, departure, running, and dwell time events. The models are applied to a railway corridor in Switzerland, where we discuss the precision and accuracy of the models. The results indicate a way to improve stochastic railway predictions.
CNN Based Automatic Computer Vision System for Strain Detection and Quality Identification of Banana

Banana is amongst the most appealing and nutritious fruits worldwide, cultivated almost every part of the world round the year. Bangladesh ranks 14th worldwide in producing this appealing fruit putting a substantial mark on the national economic growth. Classification and recognition of specific strains and identifying the quality of different agricultural products has been a challenge for mass production. With the continuous evolution of technological advancements now it has become a beneficial machine vision task to classify different strains and also determine the quality of the fruit to trash the affected ones that will minimize the loss to a great extent. In this paper, we have proposed a Convolutional Neural Network (CNN) based model that classifies five strains of different bananas namely cavendish, lady finger, shabri, green and the red banana and also identifies the rotten ones with great accuracy. We have successfully deployed the two deep learning models to find significant accuracy varying different parameters. We have also utilized the widely accepted precision, recall, F1-score and ROC evaluation metrics. The second model has outperformed the other in terms of accuracy with 93.4±0.8% and identifying the rotten bananas with an accuracy of 98.3±. 8%.
Calculation of pair distribution functions for multiphase systems

The total scattering method is becoming increasingly popular because of its ability to investigate the structures of disordered crystalline and amorphous materials. Also, in recent years, significant development of total scattering instruments and sample environments has allowed for the study of increasingly complex materials, including multiphase samples. The total scattering formalism has already been well described in the paper by Keen [J. Appl. Cryst. (2001), 34, 172–177] but it was limited to the single phase case. In the present paper the formulae for multiple phase samples (consisting of a physical mixture of two or more distinct phases) are derived for the calculation of pair distribution functions for analysis using reverse Monte Carlo and other methods. The equations for conversion between different representations of the pair distribution function are also provided.
ISOLATION OF AEROBIC BACTERIA IN COWS WITH SUBCLINICAL MASTITIS IN TURKEY AND EVALUATION OF THEIR ANTIMICROBIAL SUSPECTIBILITY

This study was conducted to determine the presence and prevalence of aerobic bacteria in cattle with subclinical mastitis (SCM) and evaluation of their antimicrobial suspectibility. In this study, a total of 394 unmedicated cows from 13 dairy farms were randomly selected and screened for SCM using California Mastitis test (CMT) were used. Overall, 294 agents were isolated and identified from milk samples by classic bacteriological methods. Isolated bacteria were tested by Kirby-Bauer antibiogram disc diffusion test to determinate antimicrobial resistance profile. A total of 294 (74.6%) agents were isolated from 394 milk samples; 100 (25.4%) of the milk samples, neither bacteria nor yeast were isolated. Staphylococcus spp and Streptococcus spp. were the most frequently isolated bacteria, found in 106 (36%), 90 (31%) of the samples, respectively. Among the samples, 68 (23.1%) were positive for S. aureus, 34 (11.5%) for Coagulase negative Staphylococcus, 34 (11.5%) for Escherichia coli, 22 (7.5%) for Bacillus spp. , 18 (6.1%) for Corynebacterium spp. , 8 (2.7%) Corynebacterium bovis and Pseudomonas aeruginosa, 6 (2%) for Streptococcus dysagalactiae sp. dysagalactiae, 4 (1.3%) for Staphylococcus chromogenes, Klebsiella pneumonia, Actinomyces spp. The most sensitive 3 antibiotic were Amicasin (78%), Tobramicin (73%) and Gentamicin (72%) against isolated bacteria. In conclusion, because of a high percentage isolation of Staphylococcus and Streptoccocus from cows with subclinical mastitis, we are especially struggle for this two bacteria to prevent cows from mastitis and it is important to test the antimicrobial sensitivity of aetiological agents of mastitis before treatment so as to prevent the development of antibiotic resistance in
Soybean‐Oil‐Based Thermosetting Resins with Methacrylated Vanillyl Alcohol as Bio‐Based, Low‐Viscosity Comonomer

A novel, bio-based, aromatic monomer (methacrylated vanillyl alcohol, MVA) is synthesized using vanillyl alcohol and methacrylic anhydride in the absence of solvents. The resulting MVA is used as a sustainable comonomer to replace styrene in a maleinated acrylated epoxidized soybean-oil (MAESO) resin to produce novel thermosets via free radical polymerization. The influence of MVA loading on the viscosity, gelation time, curing extent, thermomechanical properties, and tensile properties of the MAESO–MVA thermoset is investigated. The synthesized MVA exhibits very low volatility, which is beneficial for the development of construction material with low or zero emission. The viscosity of the MAESO–MVA system can be tailored to meet the commercial requirements. Increasing the MVA content accelerates the crosslinking reaction rate and improves thermal and mechanical properties of the MAESO–MVA system. The glass transition temperature increases with increasing MVA content. Soxhlet extraction experiments show that more than 90% of the components are incorporated into the crosslinking network. Overall, the developed MVA monomer shows promising properties to be used as an effective, green comonomer to replace styrene.
Relationship between Treasury bill rate and NEPSE index in Nepal

This study has examined the relationship between 91-days Treasury bill rate and NEPSE Index with the use of annual time series data taken from Mid-July 1994 to Mid-July 2018 in the context of Nepal. Time series are stationary at first differenced and they are cointegrated in order 1, i.e., I (1). Error Correction Model (ECM) confirms the long-run causality from 91-days Treasury bill rate (INT) to NEPSE Index (IND) at 5 percent level and long-run causality from IND to INT at 10 percent level. This bi-directional causality implies that the interest rate policy of Nepal Rastra Bank considerably influences in the stock market performances in long-run as well as the investors’ decisions in choices between stock market and bank deposits. This finding justifies the theoretical underpinning of impact of cost of capital in business investment decisions and demand for and supply of securities in capital markets. However, Wald statistics for joint short-term coefficients of variables under the interest does not support for shortrun causality between them. This finding justifies the theoretical underpinning that low cost of capital crucially linked to create the demand for and supply of securities through business investment opportunities. Higher interest rate, in other hand, not only discourage the businesses to use credit but also stimulate investors to switch to bank deposits causing decline in stock prices.