bluuebunny/crossref_metadata_2025 huggingface dataset:
Harris Hawks Optimizer for Solving Multiple Sequence Alignment
Authors: Ibrahim Mohammed K. , Yusof Umi Kalsom , Abdullah Rosni
Published: August 2021

In the field of bioinformatics, sequence alignment is crucial for the genes study and drug development. Multiple Sequence Alignment (MSA) is an NP-hard problem in the sequence alignment. In the last three decades, the MSA problem has drawn a great deal of interest from biologists and scientists. It is an essential method to identify the structural and behavioural attributes of biomolecules present in organisms and vital to biological processes. Nature-inspired algorithms are one of the approaches to solve the MSA problem of aligning multiple sequences at a time. They construct powerful tools to overpower traditional optimization techniques to find an accurate solution. This investigative work (HHO-MSA) brings a new nature-inspired algorithm known as the Harris Hawks Optimizer (HHO) Algorithm to solve the MSA problem. HHO’s key influence is the cooperative behaviour and hunting style of the Harris hawks in nature called the surprise pounce. The computational results across five references of the BAliBASE dataset proven the proposed HHO-MSA method’s effectiveness by reaching great results (up to 83.5% optimum solutions). Findings were statistically supported while comparing it with other established literature works to solve the MSA problem.
i-DetectBC: Intelligence detection of breast cancer
Authors: Suradi Saifullah Harith , Abdullah Kamarul Amin , Isa Nor Ashidi Mat
Published: August 2021

Detecting breast cancer lesions at an early stage can help to improve the patients' survival rates. Digital mammograms can be used to detect breast cancer lesions. However, mammographic images suffer from low image quality due to the low exposure factors used. This paper proposes an interactive way of enhancing mammographic images while improving the detection of breast cancer lesions. The Intelligence Detection of Breast Cancer (i-DetectBC) allows the radiologist or clinicians to enhance the original mammographic images by using appropriate algorithms automatically. The i-DetectBC consists of two digital image processing techniques: Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) enhancement and Multilevel Otsu Thresholding segmentation technique. The interface of i-DetectBC can be considered user-friendly with low computational methods to provide fast results, especially when identifying breast cancer types such as benign or malignant. The i-DetectBC has been performed on 322 mammographic images, which were retrieved from the MIAS database. The efficiency of the i-DetectBC is 95.7%, and the error rate is 4.3%. In summary, this i-DetectBC can be helpful in the detection and categorization of breast cancer lesions.
Design, Optimizing and Improvement of Microstrip 5G Patch Antenna Parameters Using Keysight Advanced Designed System (ADS)
Authors: Oo Tun Tun , Africa Aaron Don M. , Navea Roy Francis
Published: August 2021

This paper described the microstrip Patch antenna of 5G mobile phone with calculation, simulation, and optimization method by using the ADS Simulation software. The patch antenna sized could be applied for various applications and designed wideband, multiband frequency operation. Bandwidth improvement of 3500 MHz is progressed after optimization and the patch can operate for wideband application. In this paper, the calculation method of inset feed patch antenna, designed with simulation by using the calculation method. However, some of the calculations are matched the designed frequency. Therefore, we optimized the design with length only. Moreover, the author implemented the designs that the MLIN (Microstrip Line) is based on the patch distributive line and Asymmetric Edge Coupled Microstrip Lines is designed as the MACLIN3 and MLOC mean microstrip location design.
Data Forwarding System with Error Detection and Leader Selection Features
Authors: Obias Karl , Magsino Elmer R. , Abad Alexander , Arada Gerald
Published: August 2021

The concept of vehicular networks utilizes vehicular nodes and roadside units to exchange data to achieve travel comfort and convenience. However, as the number of vehicles on the road increases exponentially, the data dissemination becomes inefficient and lossy due to communication factors, one of which is the availability of too much sources of information. In this paper, a data forwarding system (DFS) is proposed to address communication failure due to bit error during data transmission and in the presence of data collision. The proposed system enables the receiver to detect errors in the received data using Hamming codes and utilizes a retransmission scheme to correct bit errors. DFS also selects a limited number of vehicles, called leaders, that can communicate with the infrastructure to minimize data collision and flooding. DFS, under various number of vehicles, is tested in additive white gaussian noise (AWGN) and Rayleigh channels. Simulation results show that DFS reduces bit error rate (BER) to approximately 54% in the AWGN and 62% in the Rayleigh channel for the maximum number of retransmissions simulated, respectively. Also, with DFS, data upload to infrastructures is reduced by at most 99.5% depending on the maximum allowable number of clusters and vehicles inside the RSU coverage.
Lossless ECG Encoder with Multi-Channel Adaptive Fuzzy Predictor and Enhanced Entropy Encoding in Wireless Body Sensor Networks
Authors: Principe Genesis Marr N. , Arada Gerald P. , Abad Alexander C. , Magsino Elmer R.
Published: August 2021

With the advancement of wireless technology, Wireless Body Sensor Networks, such as Electrocardiograms (ECGs) will serve as state-of-the-art method for electronic-healthcare systems and applications. Like other digital communications, however, ECGs highlight power consumption as the main design constraint and bottleneck as it affects device lifespan and data accuracy. Hence, power reduction and power management techniques and schemes have been developed to eliminate this constraint such as hardware optimization, source and channel coding, signal conditioning, and resolution control. This paper proposes a lossless ECG encoder that combines existing data compression techniques specifically the adaptive fuzzy predictor based on fuzzy decision making and an enhanced entropy encoding that utilizes algorithms in both run length encoding (RLE) and incremental prefix encoding. Simulation results that the proposed scheme outperforms the entropy encoding using Huffman, RLE, and predictive encoding schemes in compression ratio (CR), with the enhanced entropy encoding leading the pre-existing compression techniques by 24.0907 on RLE and 23.6580 on Huffman.
Bibliometric Analysis of Data Mining on Medical Imaging
Authors: Karim Shahiratul A Abd , Nohuddin Puteri N E
Published: August 2021

Recently, data mining application on medical imaging have increased due to expansion usage of them for early disease detection. In this paper, a bibliometric analysis is used to analyze published documents research on medical image mining (MIM) based on Scopus database with search query “Data Mining” and “Medical Imaging”. This paper also interprets 696 publications based on the research productivity, documents and source types, language of publications and subject areas. The most influential journals, countries, institutions, keywords also analyzed. Some of the bibliometric indicators are visualized in network visualization map. As the results, it can be seen that the research growth of MIM has been gradually increased especially in the developed countries such as United States and China. This paper also allows related academicians to understand the status and trends in development in this field.
A cascade hyperbolic recognition of buried objects using hybrid feature extraction in ground penetrating radar images
Authors: Ali H , Ahmad Zaidi A F , Wan Ahmad W K , Zanar Azalan M S , Tengku Amran T S , Ahmad M R , Elshaikh M
Published: August 2021

Ground penetrating radar (GPR) has been acknowledged as effective nondestructive technique for imaging the subsurface. But the process of recognizing hyperbolic pattern of buried objects is subjective and mainly relies upon operator’s knowledge and experience. This project proposed a hyperbolic recognition of buried objects using hybrid feature extraction in GPR subsurface mapping. In this framework, a cascade hyperbolic recognition by means of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are used as hybrid feature recognizing hyperbolic of buried objects. The rationale for an initial focus on cascade hyperbolic recognition is motivated by unique features exhibits by EMD & DWT behaviour in characterizing the hyperbolic pattern which make them particularly well suited to utilities detection in GPR. A series of experiments has been conducted on hyperbolic pattern based on hybrid features using four different geometrical shapes of cubic, cylindrical disc and spherical. Based on the results obtained, the hybrid features of IMF1+ wavelet transform (cH1) shows promising recognition rate in recognizing the hyperbolic that having different geometrical shapes of buried objects.
A Hybrid Encryption and Decryption Algorithm using Caesar and Vigenere Cipher
Authors: Tan Camille Merlin S. , Arada Gerald P. , Abad Alexander C. , Magsino Elmer R.
Published: August 2021

This paper presents a hybrid encryption and decryption algorithm, which combines the concept of Caesar Cipher and Vigenere Cipher algorithms. This was done by comparing the proposed design with some of the well-known ciphers such as Hill Cipher, Caesar Cipher and Vigenere Cipher through MATLAB simulations. Parameters such as letter frequency and graph behavior were evaluated. Results show that the proposed algorithm performs excellently. It has the most evenly distributed graph among other ciphers being compared in terms of their frequency of the ciphertext. Also, the decryption algorithm was able to achieve 99.99% accuracy.
Fusion wind and solar generation forecasting via neural network
Authors: Al Asbahi M M Y M , Mansor M N , Manan M R , Aziz M A A , Kawi R Md , Mohd Faudzi F H
Published: August 2021

Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore. power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction proposed in this paperwork to support a small microgrids system. The system is a combination of hardware of solar panel, wind turbine, hybrid charge controller, current sensor, voltage sensor circuit, battery, Arduino Mega and personal computer that is install with MATLAB along with artificial neural network model for load forecast. The prediction model is known as Feedforward back propagation (FFBP) artificial neural network (ANN), this method utilizes a learning relationship between wind-solar power output and predicted weather. The FFBP model trained ANN to recognize similar pattern and to predict the output power based on train and tested data and the results achieved 99.5 accuracy, 6.25% MAPE and 1.2 % MAD.
Framework Design of Patient Phlebotomy Identification System (PPIS) Mobile Applications for RFID Blood Test Tube
Authors: Lim C C , Markom M A , Teoh L M , Zaharin N F , Aziz I A A , Fadzli F S A , Rahman N H A
Published: August 2021

Phlebotomy procedures is the venepuncture, it's an important tool for diagnosing many medical conditions. However, there are a lot of human error and sample rejection during the phlebotomy procedures due to sample and patient identification. Currently, there are various advance production of medical computing system which able to provide significant benefits to medical staff and society. With this explosion of Smart mobile medical computing systems, a Patient Phlebotomy Identification System (PPIS) is designed to minimize the human error in phlebotomy practice that can caused blood specimen being rejected. The key of PISS is the continuous exchange and transfer of information between doctors and lab technologists. The conceptual framework of PPIS is designed in mobile application that can connect to RFID technology on the blood tube. The patient and blood sample identification data are stored in MySQL databases and real-time access by medical staff thru the PPIS mobile application. This mobile application able to perform data search and identification for work efficiency. This system is user-friendly and eco-friendly.
The Wavelet packet decomposition features applied in EEG based authentication system
Authors: Rosli Fatin Atiqah , Saidatul A. , Abdullah Azian Azamimi , Navea Roy Francis
Published: August 2021

Biometric authentication is recently used for verification someone’s identity according to their physiological and behavioural characteristics. The most popular biometric techniques are fingerprints, facial and voices recognition. However, these techniques have disadvantage in which it can be easily to be imitated and mimicked by hackers to access a device or a system. Therefore, this study proposed electroencephalogram (EEG) as a biometric technique to encounter this problem. The wavelet packet decomposition is explored in this study for feature extraction method. The wavelet packet decomposition feature is represented in the average wavelet, root mean squared (RMS) wavelet and power wavelet were selected as features to extract a meaningful information from the original EEG signal based on the visual representation. These features were applied to classify between familiar and unfamiliar image responses (visual representation) and to recognize 13 subjects by using Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Random Forest (RF). The analysis of the classification between familiar and unfamiliar images responses obtained that gamma frequency (30 – 45 Hz) achieved the highest correct recognition rate (CRR) and KNN obtained the accuracy of 92.8% was obtained with KNN in the classification between familiar and unfamiliar image responses. Using the gamma frequency band, the classification between the EEG responses of the 13 subjects was evaluated using the percentage of false acceptance rate (FAR) and false rejection rate (FRR). From the overall view, the value of FAR is lower than FRR. These values were used in authentication system as threshold for security level. As the result of classification between the subjects, SVM performed better compared as KNN and RF in which the error rate for acceptance of unauthorized person and rejection of authorized person were the lowest.
Implementation of Artificial Neural Network in Electric Motor Control using Brain-Computer Interface
Authors: Navea RF , Alipaspas MA , Guillermo J , Mañosca AP , Awang SA
Published: August 2021

Brain-Computer Interface (BCI) is a technology that integrates analog brain signals to digital computer-based systems for the purpose of analysis, manipulation, and control. With modern machine learning algorithms, Artificial Neural Network (ANN) was used to develop the intelligence in processing brain signals for electric motor control. In this study, supervised and unsupervised learning methods were explored to train the ANN. Guided and unguided thought-task methods were used in manipulating a 5 and an 8 switching control variations of an electric motor. A brain wave filter was designed, and the different brain bands were explored. Significant signal features were obtained and were varied in terms of qualification. Simulation run in an offline and real-time mode. Results show high control accuracies in using the Gamma band with a supervised learning method. Guided thoughts with 5 switching controls, and 4 features gave better results. Control accuracies varies between off-line and real-time implementations.
Image Enhancement System for the Restoration of Old Jawi Malay Manuscripts using Binarization Method
Authors: Devadass Victoriah , Bakar Juhaida Abu , Harun Nor Hazlyna , Zamri Muhammad Faiz
Published: August 2021

Image enhancement is the process of refining a given image, so that the desired image features become easier to be perceived by the human visual system. Ancient manuscripts from hundreds to thousands of years often are often non-comprehendible condition. Several factors include the age of the manuscript, environmental influence, and ink quality. This includes Malay manuscripts, which are normally written in the Jawi scripts. These documents, which were written from the 16th to 19th century, are in possession to date, however, the quality has been degraded. The purpose of this study is to develop a system to protect historical manuscripts which are in worsening form and also to find solution to reduce image noise. Type of enhancement method employed is binarization or thresholding method. As a result of this study, Image Enhancement System (IES) was easy to use and has a great potential for improvement in future because IES provides high entropy value for enhanced image compared to original image.
A Web-Based Campus Navigation System with Mobile Augmented Reality Intervention
Authors: Nordin Noradila , Markom Marni Azira , Suhaimi Farhana Aqilah , Ishak Syaimaa’
Published: August 2021

In this era of information technology, the introduction of new and greatly improved technologies is becoming vital especially with the progressive development of computing power to fulfill current technological needs. Augmented Reality (AR) is a part of the emerging technologies that are gaining great attention and interest with new and better innovations being developed. AR is an immersive technology where it complements real-world objects together with computer-generated objects to give more details and meaning to the objects in the real world. AR has been successfully growing nowadays in many industries, mainly in gaming and entertainment, education and navigation. However, AR has not been standardized and to be widely used in navigation systems even though there are many applications developed for this purpose. This paper proposed a web-based AR-UUM Campus Navigation System using ARToolKit, that is accessible via mobile devices that uses AR to overlay the information as images of the searched location on campus, especially for indoor navigations for lecture halls, tutorial rooms, laboratories, and offices which rarely are covered by the normal maps. Based on the respondents' evaluation, they managed to successfully interact with the AR-UUM Campus Navigation System, however, some improvements are required in terms of the functions expected from the system. Nevertheless, the use of AR in a navigation system shows promising results and has the potential to be widely used due to high interest in augmented reality.
A Deep Learning Based Multiclass Segregation of E-waste using Hardware Software Co-Simulation
Authors: Elangovan S. , Sasikala S. , Arun Kumar S. , Bharathi M. , Naveen Sangath’ E. , Subashini T.
Published: August 2021

Today, the advancement in technology has potentially changed the lifestyle of all the people. Though this innovation is beneficial, it has quite adverse effects on both human health and environmental health. One of the main causes is ‘E-Waste’ produced by electronic gadgets. Globally, the usage of electronic gadgets has increased the quantity of “e-waste” or electronic waste and it has now grown a major problem. An unproper disposal of e-waste is now becoming an environmental and public health issue, as this kind of waste has become the most rapidly increasing segment of the municipal waste stream in the world. But this ever-increasing waste is very complex in nature and is also a rich source of metals such as Neodymium, Indium, Palladium, Tantalum, Platinum, Gold, Silver, Aluminium and Copper which can be recovered from these wastes and brought back into the production cycle and day to day utilization. Hence, there is a need of proper e-waste segregation and management to recover the precious materials from these kinds of wastes. In this project, a Deep learning model implemented using NVIDIA’s Jetson Nano development kit has been proposed, to classify the waste components into two categories based Precious or Non – Precious metals present in the waste. The prototype model developed in turn segregates the waste with good accuracy and less time consumption.
Towards Building Intelligent Robotic Systems to Enhance the Safety of Firefighters
Authors: Kirubakaran M N , Arun Kumar S , Sasikala S , Gohithmugilan S , Muralidhar M
Published: August 2021

Firefighting robot is a human developed robot to safeguard human’s life. Nowadays, the accidents happening during the fire extinguishing is uncountable. The main role of the proposed robot is to detect the fire, to move towards the fire automatically, to move at any direction to extinguish the water and to send a notification to the nearest fire station. The automatic robot movement is fully controlled by a programmable ESP32. The automatic robot which is in a form of vehicle like will move right, left, front and back to detect and extinguish fire. The firefighting robot equipped with a camera is used to capture the live moment happening at a particular time. The live recording from the camera is sent to the nearest fire station which is already programmed. This recording helps the real fire fighters to know the intensity of the fire and they can plan accordingly to equip themselves. Cloud technology is used to send live recording to real fire fighters to identify the intense of the fire. The exact location of where the fire has happened is also sent to enable firefighters to arrive at the location very quickly.
A Conceptual model of E-Taxation satisfaction: How can taxpayers be tickled pink with the smart Web-Based taxation application?
Authors: Haruna Isyaku Uba , Nadzir Maslinda Mohd , Awang Hapini , Mohamed Latifah
Published: August 2021

Governments globally have adopted various types of E-Taxation systems as part of E-Government plans in providing taxpayers a media to lodge tax claims at their ease. As this article is concerned with user satisfaction, a critical analysis of satisfaction literature related to Information Systems (IS), E-Taxation, E-Government, and technology in general, has been thoroughly done. For instance, technology such as Artificial Intelligence is being introduced in practically all aspects of society, including E-Taxation. This led to the understanding of how different researchers have theorized the ‘Satisfaction’ concept in the related areas. We focused on identifying the factors that constitute user satisfaction with the E-Taxation system. As a result, it is discovered that to date, limited studies have been devoted to evaluating user satisfaction with E-Taxation systems. Also, we found the emergence of several dimensions that require further investigation. Therefore, in this article, a conceptual model to evaluate E-Taxation satisfaction among taxpayers is proposed based on DeLone and McLean IS Success Model (D&M). Finally, we urged that the model should be empirically validated so that it will be practical to be adopted to guide local government to improve the quality of services delivered through E-Taxation systems.
A review on image processing for fish disease detection
Authors: Pauzi S N , Hassan M G , Yusoff N , Harun N H , Abu Bakar A H , Kua B C
Published: August 2021

Fish disease is considered the main cause for production and economic losses by fish farmers. Fish disease detection and health monitoring is a demanding task by manual method of human visualization. Therefore, any potential approach that is fast, reliable and possesses high automation supports an interest in this issue. Nowadays, with the current emergence in the technology revolution, image processing has been extensively used in disease detection field, especially in human and plant, aiding the human experts in providing the right treatment. Image processing technique offers opportunities to improve the traditional approach in achieving accurate results. Besides, several steps in image processing are adopted including image acquisition, image pre-processing, image segmentation, object detection, feature extraction and classification. The objective of this paper is to briefly review the work established in the fish disease detection field with the use of numerous classification techniques of image processing, including rule-based expert system, machine learning, deep learning, statistical method and hybrid method. The present review recognizes the need for improvement in these image processing approaches that would be valuable for further advancement in terms of performance.
Graph-LSTM with Global Attribute for Scene Graph Generation
Authors: Shao Tong , Wu Dapeng Oliver
Published: August 2021

Lots of machine learning tasks require dealing with graph data, and among them, scene graph generation is a challenging one that calls for graph neural networks’ potential ability. In this paper, we present a definition of graph neural network (GNN) consists of node, edge and global attribute, as well as their corresponding update and aggregate functions. Based on this, we then propose a realization of GNN model called Graph-LSTM and use it in scene graph generation. The model first extracts the item features in the image as the initial states of the node-LSTM representing subject/object and edge-LSTM representing predicate. Two LSTMs update the states via LSTM’s timestep and aggregate information via message passing. Repeat the update-aggregate until convergence. Meanwhile, the tag feature, i.e., the generated probability distribution of image’s semantic concepts is sent to the LSTM through a semantic compositional network (SCN). The SCN-LSTM is trained in an ensemble style, and hence allows the tag feature to serve as the global attribute providing context information to all individuals. The LSTMs’ final states are input to inference modules and generate the triplet (subject, predicate, object) of the scene graph. Experimental results show that Graph-LSTM outperforms the Message Passing and the attention Graph Covolutional Network methods, proving the effectiveness of the proposed scheme.
The Research of BDPCA Identifying Emotion by EEG
Authors: Yan Chao , Nian Jintao , Lv Chao
Published: August 2021

Electroencephalogram (EEG) data contain wealthy information about the brain’s and body’s pathology and physiological state. It’s not easily to identify the truth that EEG contained. The unsupervised learning method don’t need to take label by human. Without subjective feeling, it greatly improve training accuracy. In this current paper, adopted improved Density Peak Clustering Algorithm (DPCA) to train EEG data. To solved the problem that difficult to determined cluster center number, Bayesian Information Criterion(BIC) was introduced. The algorithm was verified feasibility that in EEG processing by experiment which divided fatigue state level in lab. And used SJTU Emotion EEG Data set (SEED) identifying different emotions. Compared with other cluster algorithms, BDPCA accuracy totally raised about 5%. And BDPCA behavior was steadier in different emotion types.
Assistive control of brain-control multiple robots
Authors: Chi Weiming , Liu Ying , Yang Zhenge , Bi Luzheng
Published: August 2021

Brain-controlled robots have important application values in assisting persons with disabilities. However, few studies focus on brain-controlled multiple robots. This paper proposes an assistive controller based on model prediction with the leader-follower formation strategy for developing brain-controlled multiple robots. The controller is composed of a leader controller and a follower controller. The experimental results of brain-controlled multiple robots show that the proposed method can track the user’s lateral and longitudinal control intention and maintain the stability of the multi-robot formation while ensuring the safety of brain-controlled multiple robots. This work can advance the research and development of brain-controlled multiple robots.
Simulation of LPG tank truck leakage and explosion accident based on CFD
Authors: Kai Ni , Yuepeng Ma
Published: August 2021

Road transportation of hazardous chemicals has a high risk and is likely to cause serious road traffic accidents. In this paper, the most common LPG tanker in hazardous chemical transportation is taken as the research object. Based on theCFD method, we used FLACS software to simulate LPG tank car accident in Italy. The leakage process of LPG tank car after leakage was simulated, and the diffusion process of propane and butane mixture in one minute when the accident occurred. It was further analyzed that the leakage material exploded when it encountered the ignition source, and the shock wave overpressure was the highest at 2.5 seconds after the explosion, with the shock wave pressure exceeded 0.26MPa. Also, the attenuation of theblast wave is higher in the area far away from the explosion source. The research results can provide effective data support for on-site emergency disposal and personnel evacuation of similar accidents.
Machine vision problem for fast recognition of surface defects of thermoelectric cooler components based on deep learning method
Authors: Yu Z Q , Zhao M , Huang J L , Wen T X , Liao T D
Published: August 2021

During thermoelectric coolers (TEC) production, a complex industrial manufacturing process must be experienced, which may cause defects on the surface of the TEC component. To improve the efficiency of TEC component defect inspection, we propose a machine vision technology based on deep learning for surface defect detection. In order to make the deep learning method based on the you only look once (YOLO) model more efficient, first of all, we use a more lightweight network ResNet34 to improve the original network structure. Then, the loss function is improved to complete intersection over union (CIoU) loss. Experiments performed using the proposed model, show an obvious reduction in the number of parameters, the detection speed is as high as 6.5pcs/s, and the detection accuracy is 97.61%. This method lay a good foundation for the further application of deep learning methods in the field of industrial detection. The experimental results verify the feasibility and effectiveness of the model.
A low-cost passenger safety protection system based on laser ranging
Authors: Wang Fuhao , Wang Hongbo , Zhang Xueze , Dong Yu , Lai Zhiping , Wang Jiawei
Published: August 2021

In 2021, the total number of motor vehicles in China has exceeded 370 million. In the process of getting off the car, avoiding accidents between passengers and non-motor vehicles is of great significance for reducing casualties and economic losses. Therefore, this paper designs a low-cost passenger safety protection system based on laser ranging. Following the principle of easy integration and miniaturization, the hardware design and software design of the system have been completed. The mathematical model of arrival time-distance of non-motor vehicle under various speeds is established. Laser-ranging data is used to build the map, and the map can be used for non-motor vehicle identification and speed estimation. The function and precision of the system are tested. The experimental results show that 1) the established model can automatically determine whether passengers can get off the car safely and close the door in dangerous situations; 2) the mapping method based on laser ranging can effectively distinguish between non-motorized vehicles and roadsides. The accuracy of non-motor vehicle speed measurement is about 90%. The designed passenger safety protection system has high practicability.
Contribution Assessment Approach for Command and Control System Based on Force-Sparsed Stacked-Auto Encoding Neural Networks
Authors: Liu Peng , Zhou Zeyu , Li Ning , Zhang Bowei , Han Minglei , Jiao Huiying
Published: August 2021

Aiming at the difficulty of feature extraction and related generation mechanism analysis for Command and Control (C2) System using traditional data mining method, a novel contribution assessment approach based on Force-Sparsed Stacked-Auto Encoding Neural Networks (FS-SAE) is proposed. Combined with big data and complex networks technology, the contribution assessment model to operational system of system (SoS) is built. The emergence relations between the capacity indices of C2 system are formalized. The derivation results show that formalized presentation for the emergence process of performance indices of C2 system based on the proposed model not only reflects the complexity characteristics of non-linear and uncertainty in emergence process, but also gives general-defined meaning for indices structure of C2 system. It provides a feasible method for the commanders to deeply understand, manage and control the complex operation system.
The influence of CLAHE on the accuracy stability of the automatic classification of Mars surface lineament structure based on DEM image
Authors: Li Ziyi , Yan Pengcheng , Liang Jiarui , Tian Xiaolin
Published: August 2021

This article introduces training ResNet to automatically classify the lineament structure of Mars surface (DEM image), and then use CLAHE to pre-process the samples to improve the stability of accuracy. The linear structure of Mars surface is mainly divided into two types: dorsum and vallis, and crater is added as a representative of non-lineament structure. We have prepared a sample set of 300 samples for each class, divided into training set, test set, and validation set at 6:2:2. Without pre-processing, the highest accuracy rate reached 98.33% (for crater), 100.00% (for dorsum), 98.33% (for vallis), 89.44% (for total). However, the accuracy of Dorsum fluctuates greatly and frequently. After CLAHE pre-processing, the fluctuation of the accuracy of dorsum is significantly reduced.
The method of comprehensive performance optimization design for trimaran unmanned vehicle
Authors: Zhang Jincheng , Ke Weishun , Han Xiaofeng , Yang Songlin
Published: August 2021

The comprehensive performance optimization design of the trimaran unmanned vehicle can be summarized as a complex optimization design problem with multiple objective functions under multiple constraints. First, this paper uses the towing resistance test data of a certain ship model to establish a ship type and resistance database using the response surface fitting method. Then, a single total objective function is used in the form of power exponential product to normalize the objective functions of the trimaran unmanned craft’s speed, maneuverability and seakeeping. Secondly, the constraint conditions are the upper and lower limits of the design variables, the penalty function is composed of multiple equality and inequality constraints involved, the fitness function is established, and a more complete comprehensive optimization mathematical model is constructed. Finally, the overall performance of the trimaran unmanned vehicle is optimized by genetic algorithm, particle swarm algorithm and two improved optimization algorithms. Discussed the pros and cons of each optimization algorithm and the sensitivity of the influence factor under the parallel strategy, compared and analyzed the calculation results of each optimization algorithm, selected the best optimization calculation plan, and obtained the best optimization calculation result, which is trimaran how to ensure the comprehensive and optimal navigation performance during the design of unmanned boats provides a practical way and method.
The mechanism of substructure formation and grain growth 316L stainless steel by selective laser melting
Authors: Yang Dengcui , Yin Yanjun , Kan Xinfeng , Zhao Yan , Zhao Zhengzhi , Sun Jiquan
Published: August 2021

This paper aims to investigate the unique substructure and grain growth of 316L stainless steel processed by selective laser melting (SLM) and clarify the mechanism. Results showed that the grain orientation on the x-z plane parallel to the build direction was the same type as the y-z plane, which all grow along the heat flow direction to form elongated columnar grains passing through the multi-layer fusion line. The epitaxial growth direction of grains changed abruptly with the vary of temperature gradient direction. Moreover, to expound the substructure formation based on solidification theory, a finite element model was established to obtain the distribution map of cooling rate G × R and solidified morphology G/R.
Extreme events in globally coupled chaotic maps
Authors: Nag Chowdhury S , Ray Arnob , Mishra Arindam , Ghosh Dibakar
Published: August 2021

Understanding and predicting uncertain things are the central themes of scientific evolution. Human beings revolve around these fears of uncertainties concerning various aspects like a global pandemic, health, finances, to name but a few. Dealing with this unavoidable part of life is far tougher due to the chaotic nature of these unpredictable activities. In the present article, we consider a global network of identical chaotic maps, which splits into two different clusters, despite the interaction between all nodes are uniform. The stability analysis of the spatially homogeneous chaotic solutions provides a critical coupling strength, before which we anticipate such partial synchronization. The distance between these two chaotic synchronized populations often deviates more than eight times of standard deviation from its long-term average. The probability density function of these highly deviated values fits well with the generalized extreme value distribution. Meanwhile, the distribution of recurrence time intervals between extreme events resembles the Weibull distribution. The existing literature helps us to characterize such events as extreme events using the significant height. These extremely high fluctuations are less frequent in terms of their occurrence. We determine numerically a range of coupling strength for these extremely large but recurrent events. On-off intermittency is the responsible mechanism underlying the formation of such extreme events. Besides understanding the generation of such extreme events and their statistical signature, we furnish forecasting these events using the powerful deep learning algorithms of an artificial recurrent neural network. This long short-term memory (LSTM) can offer handy one-step forecasting of these chaotic intermittent bursts. We also ensure the robustness of this forecasting model with two hundred hidden cells in each LSTM layer.
TPWshiny: an interactive R/Shiny app to explore cell line transcriptional responses to anti-cancer drugs
Authors: Zhang Peter , Palmisano Alida , Kumar Ravindra , Li Ming-Chung , Doroshow James H , Zhao Yingdong
Published: August 2021

The NCI Transcriptional Pharmacodynamics Workbench (NCI TPW) is an extensive compilation of directly measured transcriptional responses to anti-cancer agents across the well-characterized NCI-60 cancer cell lines. The NCI TPW data are publicly available through a web interface that allows limited user interaction with the data. We developed ‘TPWshiny’ as a standalone, easy to install, R application to facilitate more interactive data exploration.

With no programming skills required, TPWshiny provides an intuitive and comprehensive graphical interface to help researchers understand the response of tumor cell lines to 15 therapeutic agents. The data are presented in interactive scatter plots, heatmaps, time series and Venn diagrams. Data can be queried by drug concentration, time point, gene and tissue type. Researchers can download the data for further analysis.

Availability and implementation

Users can download the ready-to-use, self-extracting package for Windows or macOS, and R source code from the project website (https://brb.nci.nih.gov/TPWshiny/). TPWshiny documentation and additional information can be found on the project website.
Horizontal Transfer of Microbial Toxin Genes to Gall Midge Genomes
Authors: Verster Kirsten I , Tarnopol Rebecca L , Akalu Saron M , Whiteman Noah K
Published: August 2021

A growing body of evidence has underscored the role of horizontal gene transfer (HGT) in animal evolution. Previously, we discovered the horizontal transfer of the gene encoding the eukaryotic genotoxin cytolethal distending toxin B (cdtB) from the pea aphid Acyrthosiphon pisum secondary endosymbiont (APSE) phages to drosophilid and aphid nuclear genomes. Here, we report cdtB in the nuclear genome of the gall-forming “swede midge” Contarinia nasturtii (Diptera: Cecidomyiidae) via HGT. We searched all available gall midge genome sequences for evidence of APSE-to-insect HGT events and found five toxin genes (aip56, cdtB, lysozyme, rhs, and sltxB) transferred horizontally to cecidomyiid nuclear genomes. Surprisingly, phylogenetic analyses of HGT candidates indicated APSE phages were often not the ancestral donor lineage of the toxin gene to cecidomyiids. We used a phylogenetic signal statistic to test a transfer-by-proximity hypothesis for animal HGT, which suggested that microbe-to-insect HGT was more likely between taxa that share environments than those from different environments. Many of the toxins we found in midge genomes target eukaryotic cells, and catalytic residues important for toxin function are conserved in insect copies. This class of horizontally transferred, eukaryotic cell-targeting genes is potentially important in insect adaptation.
Differences in evolutionary accessibility determine which equally effective regulatory motif evolves to generate pulses
Authors: Xiong Kun , Gerstein Mark , Masel Joanna
Published: August 2021

Transcriptional regulatory networks (TRNs) are enriched for certain “motifs.” Motif usage is commonly interpreted in adaptationist terms, i.e., that the optimal motif evolves. But certain motifs can also evolve more easily than others. Here, we computationally evolved TRNs to produce a pulse of an effector protein. Two well-known motifs, type 1 incoherent feed-forward loops (I1FFLs) and negative feedback loops (NFBLs), evolved as the primary solutions. The relative rates at which these two motifs evolve depend on selection conditions, but under all conditions, either motif achieves similar performance. I1FFLs generally evolve more often than NFBLs. Selection for a tall pulse favors NFBLs, while selection for a fast response favors I1FFLs. I1FFLs are more evolutionarily accessible early on, before the effector protein evolves high expression; when NFBLs subsequently evolve, they tend to do so from a conjugated I1FFL-NFBL genotype. In the empirical S. cerevisiae TRN, output genes of NFBLs had higher expression levels than those of I1FFLs. These results suggest that evolutionary accessibility, and not relative functionality, shapes which motifs evolve in TRNs, and does so as a function of the expression levels of particular genes.
COVID-19-Related Worries, Social Disruptions, and Depressive Symptoms Among Community-Dwelling Older Adults With Disabilities: What Makes the Difference?
Authors: Chao Shiau-Fang , Yu Meng-Hsuan
Published: August 2021

Objectives

This study explores how home- and community-based services use, coronavirus disease 2019 (COVID-19)-related worries, and social disruptions are related to the depressive symptoms of community-dwelling older adults with disabilities, and whether the associations differ by month of interview.

Methods

Data on a sample of 593 older individuals in Taiwan were collected between April and July 2020. Multiple regression analyses were performed to test the hypothesized relationships.

Results

As the number of confirmed cases of COVID-19 in Taiwan continuously declined from April 2020, participants who were interviewed in May, June, and July experienced significantly fewer COVID-19-related worries and social disruptions than those interviewed in April. The month interviewed, representing the pandemic development phase of COVID-19, moderated the relationships between home-based service (HBS) use and COVID-19 worries. The month interviewed also moderated the association between COVID-19-related social disruptions and depressive symptoms.

Discussion

Differences in the level of COVID-19-related worries between HBS users and nonusers were greatest in April, followed by May, and least in June and July, suggesting that the disparities between HBS users and nonusers attenuated over time. Perceived high COVID-19-related social disruptions were weakly and negatively correlated with depressive symptoms in April, but the relationship became moderately positive in May and strongly positive in June and July. These results supported the claim that the associations between COVID-19-related social disruptions and depressive symptoms can vary over time. Professionals who serve disabled older individuals in communities should be aware of their unstated needs and adopt strategies that are appropriate for the current stage of the COVID-19 pandemic to respond better to their needs and emotional state.
Effect of bis-glycinate bound zinc or zinc sulfate on zinc metabolism in growing lambs
Authors: Deters Erin L , VanDerWal Allison J , VanValin Katherine R , Beenken Aubree M , Heiderscheit Katie J , Hochmuth Katherine G , Jackson Trey D , Messersmith Elizabeth M , McGill Jodi L , Hansen Stephanie L
Published: August 2021

To assess the efficacy of bis-glycinate bound Zn, 36 crossbred wethers (34 ± 2 kg) were sorted by body weight into three groups and stagger started on a Zn-deficient diet (18 mg Zn/kg dry matter [DM]; 22.5% neutral detergent fiber [NDF]) for 45 d prior to a 15-d metabolism period (10 d adaptation and 5 d collection). On day 46, lambs were randomly assigned to dietary treatments (four lambs treatment−1group−1): no supplemental Zn (CON) or 15 mg supplemental Zn/kg DM (ZINC) as Zn sulfate (ZS) or bis-glycinate (GLY; Plexomin Zn, Phytobiotics). Blood was collected from all lambs on days 1, 44, 56, and 61. Liver, jejunum, and longissimus dorsi samples were collected after euthanasia on day 61. Gene expression was determined via quantitative real-time polymerase chain reaction. Data were analyzed using ProcMixed of SAS (experimental unit = lamb; fixed effects = treatment, group, and breed) and contrast statements assessed the effects of supplemental Zn concentration (ZINC vs. CON) and source (GLY vs. ZS). After 15 d of Zn supplementation, plasma Zn concentrations were greater for ZINC vs. CON and GLY vs. ZS (P ≤ 0.01); tissue Zn concentrations were unaffected (P ≥ 0.27). Liver Cu concentrations were lesser for ZINC vs. CON (P = 0.03). Longissimus dorsi Mn concentrations were greater for ZINC vs. CON (P = 0.05) and tended to be lesser for GLY vs. ZS (P = 0.09). Digestibility of DM, organic matter (OM), and NDF was lesser for ZINC vs. CON (P ≤ 0.05); acid detergent fiber digestibility tended to be greater for GLY vs. ZS (P = 0.06). Nitrogen retention (g/d) tended to be greater for GLY vs. ZS (P = 0.10), and N apparent absorption was lesser for ZINC vs. CON (P = 0.02). Zinc intake, fecal output, retention, and apparent absorption were greater for ZINC vs. CON (P ≤ 0.01). Apparent absorption of Zn was −5.1%, 12.8%, and 15.0% for CON, ZS, and GLY, respectively. Nitrogen and Zn retention and apparent absorption were not correlated for CON (P ≥ 0.14) but were positively correlated for ZINC (retention: P = 0.02, r = 0.52; apparent absorption: P < 0.01, r = 0.73). Intestinal expression of Zn transporter ZIP4 was lesser for ZINC vs. CON (P = 0.02). Liver expression of metallothionein-1 (MT1) tended to be greater for GLY vs. ZS (P = 0.07). Although Zn apparent absorption did not differ between sources (P = 0.71), differences in post-absorptive metabolism may be responsible for greater plasma Zn concentrations and liver MT1 expression for GLY-supplemented lambs, suggesting improved bioavailability of GLY relative to ZS.