bluuebunny/crossref_metadata_2025 huggingface dataset:
Management of Load-Balancing Data Stream in Interposer-Based Network-on-Chip Using Specific Virtual Channels
Authors: Soleymani Mona , Reshadi Midia , Khademzadeh Ahmad
Published: August 2020

The interaction between cores and memory blocks, in multiprocessor chips and smart systems, has always been a concern as it affects network latency, memory capacity, and power consumption. A new 2.5-dimensional architecture has been introduced in which the communication between the processing elements and the memory blocks is provided through a layer called the interposer. If the core wants to connect to another, it uses the top layer, and if it wants to interact with the memory blocks, it uses the interposer layer. In a case that coherence traffic at the processing layer increases to the extent that congestion occurs, a part of this traffic may be transferred to the interposer network under a mechanism called load balancing. When coherence traffic is moved to the interposer layer, as an alternative way, this may interfere with memory traffic. This paper introduces a mechanism in which the aforementioned interference may be avoided by defining two different virtual channels and using multiple links which specifically determines which memory block is going to be accessed. Our method is based on the destination address to recognize which channel and link should be selected while using the interposer layer. The simulation results show that the proposed mechanism has improved by 32% and 14% latency compared to the traditional load-balancing and unbalanced mechanisms, respectively.
Study on Waste Type Identification Method Based on Bird Flock Neural Network
Authors: Li Shifeng , Chen Liyu
Published: August 2020

According to the waste type identification requirement in waste classification, a waste type identification method based on a bird flock neural network (BFNN) was proposed. The problem of obtaining the feature dataset of waste images was considered, and color histogram and texture feature extraction techniques were used. The local optimum problem of a typical backpropagation neural network (BPNN) was considered, and a bird flock optimization (BFO) algorithm was proposed. The accuracy problem of the typical BPNN was considered, and a new online weight adjustment method of neurons was proposed. The number of hidden layer neurons (nodes) of the typical BPNN was considered, and an online adjustment method was proposed. The experimental results show that the recyclables (paper, plastic, glass, and cloth) and nonrecyclables can effectively be identified by the waste type identification method based on the BFNN, and the recognition accuracy is 81% which meets actual needs.
Effect of Rejuvenated Binder on the Pavement Rutting Performance at Different Aging Levels
Authors: Ali Hesham A. , Haddadi Farshad , Mohammadafzali Mojtaba , Sholar Greg , Moseley Howard
Published: August 2020

The use of Reclaimed Asphalt Pavement (RAP) in virgin mixtures is an effective means of promoting sustainability. The objective of this research was to investigate the effect of rejuvenated binder on the rutting performance and moisture susceptibility of high RAP content mixtures. All mixtures were tested at three aging levels, one short‐term aging level, and two long‐term aging levels. The performance of the specimens and the level of diffusion were measured and compared, and the correlation between binder test and rutting test results was evaluated. Based on the results, the degree of diffusion is not a determining factor in the performance of the mixture. However, the amount of the rejuvenator and Superpave performance grading (PG) of the rejuvenated binder is the most effective parameter. Results indicate that the short‐term aged recycled mix exhibits a better rutting resistance than the virgin mix when its HTPG is 6°C higher than the virgin mix.
Implementing canny edge detection algorithm for noisy image
Authors: Akbari Sekehravani Ehsan , Babulak Eduard , Masoodi Mehdi
Published: August 2020

Edge detection is a significant stage in different image processing operations like pattern recognition, feature extraction, and computer vision. Although the Canny edge detection algorithm exhibits high precision is computationally more complex contrasted to other edge detection methods. Due to the traditional Canny algorithm uses the Gaussian filter, which gives the edge detail represents blurry also its effect in filtering salt-and-pepper noise is not good. In order to resolve this problem, we utilized the median filter to maintain the details of the image and eliminate the noise. This paper presents implementing and enhance the accuracy of Canny edge detection for noisy images. Results present that this proposed method can definitely overcome noise disorders, preserve the edge useful data, and likewise enhance the edge detection precision.
Optimization of distribution network configuration with multi objective function based on improved cuckoo search algorithm
Authors: Nguyen Thuan Thanh
Published: August 2020

This paper proposes an improved cuckoo search (ICSA) for solving the distribution network reconfiguration (NR) problem with multi-objective function. The membership functions are considered consisting of minimizing of power loss, load balancing among branches and among the feeders, node voltage deviation and switching operation numbers. ICSA is developed from the original CSA with adding the local search mechanism for exploiting around the current best solution. The effectiveness of the ICSA has validated on the 70-node and the 83-node practical systems. The obtained results have been compared to those from runner root algorithm (RRA) and other methods in the literature. The obtained results demonstrate that ICSA has high ability for searching the optimal solution with higher successful rate and better quality of obtained solution as well as smaller iterations compared to RRA and other methods. Therefore, ICSA is a reliable method for the multi-objective NR problems.
English poems categorization using text mining and rough set theory
Authors: Ali Alsaidi Saif , Sadeq Ahmed T. , Abdullah Hasanen S.
Published: August 2020

In recent years, Text Mining wasan important topic because of the growth of digital text data from many sources such as government document, Email, Social Media, Website, etc. The English poemsare one of the text data to categorization English Poems will use Text categorization, Text categorization is a method in which classify documents into one or more categories that were predefined the category based on the text content in a document .In this paper we will solve the problem of how to categorize the English poem into one of the English Poems categorizations by using text mining technique and Machine learning algorithm, Our data set consist of seven categorizations for poems the data set is divided into two-part training (learning)and testing data. In the proposed model we apply the text preprocessing for the documents file to reduce the number of feature and reduce dimensionality the preprocessing process converts the text poem to features and remove the irrelevant feature by using text mining process (tokenize,remove stop word and stemming), to reduce the feature vector of the remaining feature we usetwo methods for feature selection and use Rough set theory as machine learning algorithm to perform the categorization, and we get 88% success classification of the proposed model.
Generation of infectious disease alerts through the use of geolocation
Authors: Sarasa Cabezuelo Antonio
Published: August 2020

In recent years, there have been several cases of global epidemics such as influenza B or Ebola. In these cases, several factors are key to limit the effects of the epidemic and avoid contagion. Between of them is the speed of knowing which persons are infected, which persons has been in contact with any infected person or know what the focus of the epidemic. In general, obtaining this information requires a process of research among the first affected that can be slow and complicated. This article describes a tool that aims to generate alerts when there are data about an epidemic, and notify all persons who could be exposed to contagion and prevent new infections occurs.
New feature selection based on kernel
Authors: Rustam Zuherman , Hartini Sri
Published: August 2020

Feature selection is an essential issue in machine learning. It discards the unnecessary or redundant features in the dataset. This paper introduced the new feature selection based on kernel function using 16 the real-world datasets from UCI data repository, and k-means clustering was utilized as the classifier using radial basis function (RBF) and polynomial kernel function. After sorting the features using the new feature selection, 75 percent of it was examined and evaluated using 10-fold cross-validation, then the accuracy, F1-Score, and running time were compared. From the experiments, it was concluded that the performance of the new feature selection based on RBF kernel function varied according to the value of the kernel parameter, opposite with the polynomial kernel function. Moreover, the new feature selection based on RBF has a faster running time compared to the polynomial kernel function. Besides, the proposed method has higher accuracy and F1-Score until 40 percent difference in several datasets compared to the commonly used feature selection techniques such as Fisher score, Chi-Square test, and Laplacian score. Therefore, this method can be considered to use for feature selection
Extended systematic clustering: Microdata protection by distributing semsitive values
Authors: Widodo Widodo , Wibowo Wahyu Catur , Budiardjo Eko K. , Achsan Harry T. Y.
Published: August 2020

Anonymity data for multiple sensitive attributes in microdata publishing is a growing field at present. This field has several models for anonymizing such as k-anonymity and l-diversity. Generalization and suppression became a common technique in anonymize data. But, the real problem in multiple sensitive attributes is sensitive value distribution. If sensitive values do not distribute evenly to each quasi identifier group, it is potentially revealed to sensitive value holder. This research investigated on how the high-sensitive values are distributed evenly into each group. We proposed a novel method/algorithm for distributing high-sensitive values when it forms groups. This method distributes high-sensitive values evenly and varies high-sensitive values in a group. We called our method as extended systematic clustering since it is an extension of systematic clustering method. Diversity metrics was used for evaluating our method. Experiment result showed our method outperformed systematic clustering with average diversity value 0.9719 while systematic clustering 0.3316.
Compensation of time-varying clock-offset in a LBL navigation
Authors: M. Simamora Yohannes S. , Tjokronegoro Harijono A. , Leksono Edi , Brodjonegoro Irsan S.
Published: August 2020

This paper presents compensation of the clock-offset in a long baseline (LBL)navigation. It departs from the existing literature mainly in dealing with a time-varyingclock-offset, i.e. the clock-rate drifts over the time. Specifically, the clock-offsetdynamics are introduced to the ToFs as an autoregressive filter.Subsequently,interactions among the now biased ToFs and the kinematics of an autonomousunderwater vehicle (AUV)–the navigation subject–are represented in a state-spaceform. Implementing the so-called graphic approach, minimum sensor requirementfor this system’s observability is then explicated. Finally, a standard discrete Kalmanfilter is deployed as the state estimator. By simulation, it is demonstrated that theestimator manages to compensate the offset and to provide localization with less than1 m accuracy
The enhancement of logging system accuracy for infrastructure as a service cloud
Authors: Wiriya Surapong , Wongthai Winai , Phoka Thanathorn
Published: August 2020

We introduce the novel technical results of the enhanced logging system for customer virtual machines (VMs) in an Infrastructure as a Service (IaaS) cloud. The main contribution is that the enhanced system can work with a better system's accuracy and speed, with the simplicity of the design and implementation. We measure the accuracy of the unenhanced logging system, then find a quick solution to enhance the system based on the results of the measurement. To measure and enhance the unenhanced system, we increase the main memory and CPU cores of the VMs then collect the accuracy results from each increment configuration. We analyze the results and propose to use the taskset tool to enhance the accuracy of the system. Found three main findings include: firstly, the accuracy of the enhanced system is about 20% on maximum better than the unenhanced one;  the enhanced system accuracy becomes 100%; lastly, the enhanced system can detect a file with the smaller file size as almost 12% smaller. The findings can be a basis to design the logging systems in an IaaS cloud, to decrease hardware and energy investment. To the best of our knowledge, the contribution and findings are not in the literature.
Framework for developing algorithmic fairness
Authors: Kristiadi Dedy Prasetya , Sunarya Po Abas , Ismanto Melvin , Dylan Joshua , Santoso Ignasius Raffael , Spits Warnars Harco Leslie Hendric
Published: August 2020

In a world where the algorithm can control the lives of society, it is not surprising that specific complications in determining the fairness in the algorithmic decision will arise at some point. Machine learning has been the de facto tool to forecast a problem that humans cannot reliably predict without injecting some amount of subjectivity in it (i.e., eliminating the “irrational” nature of humans). In this paper, we proposed a framework for defining a fair algorithm metric by compiling information and propositions from various papers into a single summarized list of fairness requirements (guideline alike). The researcher can then adopt it as a foundation or reference to aid them in developing their interpretation of algorithmic fairness. Therefore, future work for this domain would have a more straightforward development process. We also found while structuring this framework that to develop a concept of fairness that everyone can accept, it would require collaboration with other domain expertise (e.g., social science, law, etc.) to avoid any misinformation or naivety that might occur from that particular subject. That is because this field of algorithmic fairness is far broader than one would think initially; various problems from the multiple points of view could come by unnoticed to the novice’s eye. In the real world, using active discriminator attributes such as religion, race, nation, tribe, religion, and gender become the problems, but in the algorithm, it becomes the fairness reason.
Twitter sentiment analysis of the relocation of Indonesia's capital city
Authors: Sutoyo Edi , Almaarif Ahmad
Published: August 2020

Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media.
Comparison of routing protocol performance on multimedia services on software defined network
Authors: Monita Vivi , Irawati Indrarini Dyah , Tulloh Rohmat
Published: August 2020

Software defined network (SDN) is a new paradigm in network engineering, where control plane and data plane are separated. Data plane is carried out on each node, while a control plane is centrally located. In conventional networks, the planes are implemented in the firmware of the router. In this paper, we implemented multimedia services on SDN using exterior and interior routing protocols, such as open shortest path first (OSPF), border gateway protocol (BGP) and routing information protocol (RIP), measured the quality of service (QoS), and analyze the measurement results. The test results showed that the greater the background traffic provided, the smaller the throughput. Traffic on the network will be congested, so the available bandwidth is also increasingly used up so that the number of bits sent every second decrease. Mean opinion score (MOS) test results are 4 showing good categories based on ITU-T standards.
Simulation and experimental study on PID control of a quadrotor MAV with perturbation
Authors: Noordin A. , Basri M. A. M. , Mohamed Z.
Published: August 2020

This paper presents a proportional-integral-derivative (PID) flight controller for a quadrotor micro air vehicle (MAV). The MAV (Parrot Mambo minidrones) is small, therefore, a slight perturbation will affect its performance. Hence, for the actuated dynamics, roll (ϕ), pitch (θ), yaw (ψ), and z stabilization, a PID control scheme is proposed. Furthermore, the same controller technique is also applied for under-actuated dynamics x and y position control. The newtonian model is simulated using simulink with a normal Gaussian noise of force as external disturbances. using simulink support package for Parrot Minidrones by MATLAB and based on the simulation parameter, the algorithm is deployed using Bluetooth® Low energy connection via personal area network (PAN). A slight force by hand is applied as perturbation during hovering to investigation system performances. Finally, the simulation and experimental on this commercial MAV, Parrot Mambo minidrones shows good performance of the flight controller scheme in the presence of external disturbances.
Improved support vector machine using optimization techniques for an aerobic granular sludge
Authors: Ahmad Yasmin Nur Sakinah , Wahab Norhaliza Abdul , Anuar Aznah Nor
Published: August 2020

Aerobic granular sludge (AGS) is one of the treatment methods often used in wastewater systems. The dynamic behavior of AGS is complex and hard to predict especially when it comes to a limited data set. Theoretically, support vector machine (SVM) is a good prediction tool in handling limited data set. In this paper, an improved SVM using optimization approaches for better predictions is proposed. Two different types of optimization are built which are particle swarm optimization (PSO) and genetic algorithm (GA). The prediction of the models using SVM-PSO, SVM-GA and SVM-Grid Search are developed and compared prior to several feature analysis for verification purposes. The experimental data under hot temperature of 50˚C obtained from sequencing batch reactor is used. From simulation results, the proposed SVM with optimizations improve the prediction of chemical oxygen demand compared to the conventional grid search method and hence provide better prediction of effluent quality using AGS wastewater treatment systems.
A circular monopole patch antenna loaded with inverted L-shaped stub for GPS application
Authors: A. Sowmya G. , Kamath Akshtha , Keshava Anala , Kumar Om Prakash , Vincent Shweta , Ali Tanweer
Published: August 2020

This article introduces a planar monopole patch antenna for global positioning system (GPS) application. Our design has a circular patch with a stub of inverted L-shape which is used as the radiating portion and partial ground. Our proposed design of the stub allows the antenna to operate at a frequency of 1.5 GHz. The circular monopole is fed by an offset feeding to have an impedance match of 50 ohms. The compact antenna has been designed and simulated on RT Duroid 6006 material with relative permittivity ( ) of 6.15 and 0.0019 as the loss tangent. Our antenna has a dimension of 65 × 55 × 2.54 mm3 . A gain of 1.23 dB is observed at the resonant frequency of 1.5 GHz and the antenna exhibits dipole like radiation pattern both in E and H-plane. The antenna has better impedance matching, good gain and steady characteristics of radiation pattern across the operating bandwidth. High frequency structure simulator (HFSS) v.13.0 is used to carry out all the simulations.
Advantages and recent advances of smart energy grid
Authors: Taha Mohammed Qasim
Published: August 2020

Smart grid is widely recognized technology used to improve the stability and losses of the electric power system. It is encouraging reliability, efficiency, and effective control of the supply of electrical energy. However, it is a hot topic for recent publications and still has a limited understanding among researchers. This review work is to provide insight and support to the beginner researchers since this topic needs a multidisciplinary background knowledge. The conventional electric transmission system and distribution networks struggle to provide resilient performance and reliable service and real-time data. Also, smart grid id a promising network maneuver to stabilize the system once any disturbances break out by using the distributed renewable energy generators, while the conventional networks lack for flexibility to integrate with renewable energy generators or microgrids. This comprehensive work is conducted to map previous controbution in a coherent manar, including the specifications, features, and fundamentals that are presented to benefit the interested readers interested in smart grid development.
Polarization insensitive switchable metamaterial absorber/reflector for X-band applications
Authors: Mustapha M. G. , Rahim M. K. A. , Murad N. A. , Ayop O. , Tuntrakool S. , Baba M. A. , Iliyasu A. Y. , Jalil Mohd Ezwan
Published: August 2020

A unit cell of squared shaped polarization-insensitive switchable metamaterial absorber/reflector is presented. The structure operates at 10.20 GHz under both absorber mode and reflector mode configurations. Copper wire bridging the gaps to form a circular shape structure were used as switches for operation mode selections. The structure was designed on an FR4 substrate, and the incidental wave angles were varied from 0 to 50 degrees. The structure demonstrated almost 100% absorption at resonance, 3.314 GHz percentage bandwidth at 80% as an absorber. On the other hand, as reflector, it demonstrated almost a 90% reflection and a usable bandwidth of 3.327 GHz.
Effects of bending on a flexible metamaterial absorber
Authors: Isa Siti Nurzulaiha , Ayop Osman , Mohd Supa’at Abu Sahmah , A. Rahim Mohammad Kamal , Murad Noor Asniza , Zubir Farid , Majid Huda A.
Published: August 2020

This paper presents a study of bending a metamaterial based absorber. The study of bending is important for textile material since it can be easily crumpled. The basic absorber that is simulated for the study is an annulled circle as the top patch, and metal ground plane that sandwich a textile-based substrate. The center frequency for the absorber is 10.525 GHz. The type of bending is divided into two parts, which is convex bending and concave bending. Through series of simulations, the effects of the bending on the absorptivity and the shifting of the resonant frequency is observed. Also, the study on the change of incident and polarization angle is also included to support the basis of flexible metamaterial absorber affected by the bending.
Bangla handwritten character recognition using MobileNet V1 architecture
Authors: Ghosh Tapotosh , Abedin Md. Min-Ha-Zul , Chowdhury Shayer Mahmud , Tasnim Zarin , Karim Tajbia , Reza S. M. Salim , Saika Sabrina , Yousuf Mohammad Abu
Published: August 2020

Handwritten character recognition is a very tough task in case of complex shaped alphabet set like Bangla script. As optical character recognition (OCR) has a huge application in mobile devices, model needs to be suitable for mobile applications. Many researches have been performed in this arena but none of them achieved satisfactory accuracy or could not detect more than 200 characters. MobileNet is a state of art (convolutional neural network) CNN architecture which is designed for mobile devices as it requires less computing power. In this paper, we used MobileNet for handwritten character recognition. It has achieved 96.46% accuracy in recognizing 231 classes (171 compound, 50 basic and 10 numerals), 96.17% accuracy in 171 compound character classes, 98.37% accuracy in 50 basic character classes and 99.56% accuracy in 10 numeral character classes.
Circuit-based method for extracting the resistive leakage current of metal oxide surge arrester
Authors: Khavari Amir Hesam , Munir Abdullah , Malek Zulkurnain Abdul-
Published: August 2020

Resistive leakage current based condition assessment of metal oxide surge arrester (MOSA) is one of the most extensively employed technique to monitor its degradation. An extraction method is customarily required to extract the resistive component from the total leakage current. The existing methods to extract the resistive current are complex and less accurate. Therefore, this paper describes a simple and accurate circuit-based method to extract the resistive current using equivalent model and measured leakage current of the arrester. The accuracy of the proposed method is validated through experimental results on ABB’s 120 kV surge arrester, EMTP and QuickField software simulations. The performance of the method is also analyzed and verified experimentally on 72, 180 and 240 kV rated ABB’s surge arresters. The obtained results of resistive leakage current have shown the maximum error of 0.001%. Simple and easier computational steps with higher accuracy are the key benefits of the proposed technique.
Internet of things-based telemonitoring rehabilitation system for knee injuries
Authors: M. Al-Omri Muheeb Musaed , Mohammed Alduais Nayef Abdulwahab , Adon Mohamad Nazib , H. Y. Saad Abdul-Malik , Abdul-Qawy Antar Shaddad H. , Sutikno Tole
Published: August 2020

Rehabilitation engineering, as one of the active research areas in biomedical science, needs further investigations and improvements. The process of rehabilitation, whether after a stroke, ligament, or accident-related injuries, is commonly based on clinical assessment tools, which can be executed, either by self-reported (home-based) treatment or through observer-rated therapy. However, people with reduced mobility (e.g., stroke, surgical, and ligament patients) can benefit from rehabilitation programs only if effective and appropriate assistive tools are provided. In this paper, a new internet of things (IoT)-based telemonitoring system is introduced for knee injuries’ rehabilitation (Knee-Rehab). The proposed system is a real-time rehabilitation and monitoring framework designed to be a portable, home-based, and online-based instrument comprised of bio-mechanical, bio-instrumentation and IoT-based elements. The system helps patients to rest at home after surgeries or physical treatment, do their rehab-exercises, and receive suggestions form their advisors, which gain the ability to monitor the situation over the exercising time and propose necessary medication/activities to be followed by the patients accordingly, based on their current status. The experimental measurements show the high accuracy achieved by the developed system in terms of monitored knee joint angle, where the maximum error is 3.5° compared to manual goniometer measurements.
Integrating Scrum development process with UX design flow
Authors: Khaled Al Ghanmi Nora , Shahida Mohd Jamail Nor
Published: August 2020

Nowadays, Agile software development practices are being widely adapted all over the world. Scrum is one of the most known Agile models, it satisfies the business needs and put the main focus on the product. One common challenge for the development of customer-facing products is having a good user experience. This paper presents integrating Scrum development process with user experience design flow. In this study, papers relating to the topic of user experience (UX) process integration with Agile development process, how to measure it and how to improve it, from the year 2010 onwards are reviewed. This is to identify how organizations can integrate UX design flow and Scrum development and get the benefits of both. The conducted review identifies a number of limitations in the existing integrations efforts. A proposed process model to resolve these limitations is presented. Along with our experience in implementing it on an ongoing software development project. The results of applying this process, its impact on the project outcomes quality and the employees’ satisfaction with the process are discussed. The goal of this study is to aid organizations in integrating UX design into their development process.
Development of intelligent road maintenance system mobile apps for a highway
Authors: Mohd Jamail Nurul Husna , A. G. Abdul Halim , Mohd Jamail Nor Shahida
Published: August 2020

Roads are one of the main assets of a country where the network infrastructure offers economic and social benefits. It is understood that road maintenance would improve economic development and social benefits, and the poorly maintained road network would destruct mobility, raise accident rates, intensify isolation, poverty and vehicle operating costs. Unidentified road environment covers many aspects and based on that matter, it is important to improve the quality of inspection reports, whereby the efficiencies by mobile application shall be utilized. Developed system mobile apps have been found to be effective, versatile to use, as they can be used anywhere and at any time. The goal was therefore to recognize the types of defects, issues and deficiencies in current road maintenance practices and to develop a computerized highway maintenance program. The scope of the research will be limited to a case study at PLUS highway, connecting the north and south highway of peninsular Malaysia. The findings showed that road maintenance work is not adequately and effectively designed because it requires more time and includes a lot of paperwork. Due to this matter, the proposed mobile application would provide an invaluable added benefit to intelligent system of PLUS highway
Tap changer optimisation using embedded differential evolutionary programming technique for loss control in power system
Authors: Faris Ahmad , Musirin Ismail , Jelani Shahrizal , Ismail Saiful Amri , Mansor Mohd Helmi , Kumar A. V. Senthil
Published: August 2020

Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and system loss increment. A new optimization technique termed as embedded differential evolutionary programming (EDEP) is proposed, which integrates the traditional differential evolution (DE) and evolutionary programming (EP). Consequently, EDEP was for solving optimizations problem in power system through the tap changer optimizations scheme. Results obtained from this study are significantly superior compared to the traditional EP with implementation on the IEEE 30-bus reliability test system (RTS) for the loss minimization scheme.
High performance of excitation system for synchronous generator based on modeling analysis
Authors: Ahmed Yasir Abdulhafedh , Al-Mashhadany Yousif I. M. , Nayyef Mustafa Ahmed
Published: August 2020

Mathematical description of electromechanical systems operation is powerful parameter to get high performance with practical implement of the systems. This paper describes a mathematical presentation for the behavior excitation system of synchronous generator based on the optimal values of the parameters. The study of the mathematical modeling for dynamics of excitation system required the knowledge for the effect of each parameter to get the typical values provided by the manufacturer implementing. The simulation of the final model which obtained was conducted on Matlab version 2019b. The final results of simulation for the mathematical model are satisfactory, and it proves the ability of independence this model as practical implement.
Impact of Bleeding and Myocardial Infarction on Mortality in All-Comer Patients Undergoing Percutaneous Coronary Intervention
Authors: Hara Hironori , Takahashi Kuniaki , Kogame Norihiro , Tomaniak Mariusz , Kerkmeijer Laura S.M. , Ono Masafumi , Kawashima Hideyuki , Wang Rutao , Gao Chao , Wykrzykowska Joanna J. , de Winter Robbert J. , Neumann Franz-Josef , Plante Sylvain , Lemos Neto Pedro Alves , Garg Scot , Jüni Peter , Vranckx Pascal , Windecker Stephan , Valgimigli Marco , Hamm Christian , Steg Philippe Gabriel , Onuma Yoshinobu , Serruys Patrick W.
Published: August 2020

Background:

Bleeding and myocardial infarction (MI) after percutaneous coronary intervention are independent risk factors for mortality. This study aimed to investigate the association of all-cause mortality after percutaneous coronary intervention with site-reported bleeding and MI, when considered as individual, repeated, or combined events.

Methods:

We used the data from the GLOBAL LEADERS trial (GLOBAL LEADERS: A Clinical Study Comparing Two Forms of Anti-Platelet Therapy After Stent Implantation), an all-comers trial of 15 968 patients undergoing percutaneous coronary intervention. Bleeding was defined as Bleeding Academic Research Consortium (BARC) 2, 3, or 5, whereas MI included periprocedural and spontaneous MIs according to the Third Universal Definition.

Results:

At 2-year follow-up, 1061 and 498 patients (6.64% and 3.12%) experienced bleeding and MI, respectively. Patients with a bleeding event had a 10.8% mortality (hazard ratio [HR], 5.97 [95% CI, 4.76–7.49];
P
<0.001), and the mortality of patients with an MI was 10.4% (HR, 5.06 [95% CI, 3.72–6.90];
P
<0.001), whereas the overall mortality was 2.99%. Albeit reduced over time, MI and even minor BARC 2 bleeding significantly influenced mortality beyond 1 year after adverse events (HR of MI, 2.32 [95% CI, 1.18–4.55];
P
=0.014, and HR of BARC 2 bleeding, 1.79 [95% CI, 1.02–3.15];
P
=0.044). The mortality rates in patients with repetitive bleeding, repetitive MI, and both bleeding and MI were 16.1%, 19.2%, and 19.0%, and their HRs for 2-year mortality were 8.58 (95% CI, 5.63–13.09;
P
<0.001), 5.57 (95% CI, 2.53–12.25;
P
<0.001), and 6.60 (95% CI, 3.44–12.65;
P
<0.001), respectively. De-escalation of antiplatelet therapy at the time of BARC 3 bleeding was associated with a lower subsequent bleeding or MI rate, compared with continuation of antiplatelet therapy (HR, 0.32 [95% CI, 0.11–0.92];
P
=0.034).

Conclusions:

The fatal impact of bleeding and MI persisted beyond one year. Additional bleeding or MIs resulted in a poorer prognosis. De-escalation of antiplatelet therapy at the time of BARC 3 bleeding could have a major safety merit. These results emphasize the importance of considering the net clinical benefit including ischemic and bleeding events.

Registration:

URL:
https://www.clinicaltrials.gov
. Unique identifier: NCT01813435.
Scene Four
Authors: Dinesh Nandita
Published: August 2020

This scene presents a glimpse into the lives of Kashmir’s artists.
Scene Six
Authors: Dinesh Nandita
Published: August 2020

This scene spotlights incarcerated rebels, the women in their lives, and the various factors have led to their current condition.
Scene Fourteen
Authors: Dinesh Nandita
Published: August 2020

This scene asks the audience to consider how soldiers cope with boredom in times of war.
Scene Sixteen
Authors: Dinesh Nandita
Published: August 2020

This scene draws texts from some of Kashmir’s most revered female artists, to pay homage to the region’s women.
Scene Seventeen
Authors: Dinesh Nandita
Published: August 2020

A satirical creation, this scene asks the audience to consider what might happen if Who wants to be a Millionaire were to create an episode about Kashmir.
Scene Nineteen
Authors: Dinesh Nandita
Published: August 2020

This scene presents multiple voices surrounding the return of Kashmiri Pandits to the region.
Scene Twenty-One
Authors: Dinesh Nandita
Published: August 2020

What about the disappeared, this scene asks. How do we mourn those whose fate remains unknown?
Improvements in Supercooled Liquid Water Simulations of Low-Level Mixed-Phase Clouds over the Southern Ocean Using a Single-Column Model
Authors: Seiki Tatsuya , Roh Woosub
Published: August 2020

A high-resolution global atmospheric model, the nonhydrostatic icosahedral atmospheric model (NICAM), exhibited underestimation biases in low-level mixed-phase clouds in the midlatitudes and polar regions. The ice-cloud microphysics used in a single-moment bulk cloud microphysics scheme (NSW6) was evaluated and improved using a single-column model by reference to a double-moment bulk cloud microphysics scheme (NDW6). Budget analysis indicated that excessive action of the Bergeron–Findeisen and riming processes crucially reduced supercooled liquid water. In addition, the rapid production of rain directly reduced cloud water and indirectly reduced cloud water through the production of snow and graupel by riming. These biases in growth rates were found to originate from the number concentration diagnosis used in NSW6. The diagnosis based on the midlatitude cloud systems assumption was completely different from the one for low-level mixed-phase clouds. To alleviate underestimation biases, rain production, heterogeneous ice nucleation, vapor deposition by snow and graupel, and riming processes were revised. The sequential revisions of cloud microphysics alleviated the underestimation biases step by step without parameter tuning. The lifetime of cloud layers simulated by NSW6 was reasonably prolonged.
The Benefits and Challenges of Globalisation for the Development of Higher Education Teaching and Research: A Case Study of an Emerging University in East Africa
Authors: Tan Rachel Sing-Ee , Harland Tony , Daniel Ben
Published: August 2020

Globalisation presents the opportunity for universities to have a world-wide presence but newer institutions in developing countries have difficulty in embarking on such a journey. This paper presents a case study of an emerging university in East Africa and explores the challenges of high ambition while responding to globalisation. Interviews with senior administrators and academics showed that much time and effort was spent responding to funding issues and aid conditions, attaining international standards in research and teaching, and managing challenges of digital technologies against a background of limited infrastructure. In response to globalisation, we argue that emerging universities of this type should consider re-directing limited resources to focus on long-term projects for growing human capital through professional development while developing basic infrastructure needs.
Finite-time dissipative control for networked control systems with hybrid-triggered scheme
Authors: Zhang Qian , Yan Huaicheng , Chen Shiming , Zhan Xisheng , Jiang Xiaowei
Published: August 2020

This paper is concerned with the problem of finite-time dissipative control for networked control systems by hybrid triggered scheme. In order to save network resources, a hybrid triggered scheme is proposed, which consists of time-triggered scheme and event-triggered scheme simultaneously. Firstly, sufficient conditions are derived to guarantee that the closed-loop system is finite-time bounded (FTBD) and [Formula: see text] dissipative. Secondly, the corresponding controller design approach is presented based on the derived conditions. Finally, a numerical example is presented to show the effectiveness of the proposed approach.
Artificial Intelligence-Based Quantification of Epithelial Proliferation in Mammary Glands of Rats and Oviducts of Göttingen Minipigs
Authors: Hvid Henning , Skydsgaard Mikala , Jensen Nikolai K. , Viuff Birgitte M. , Jensen Henrik E. , Oleksiewicz Martin B. , Kvist Peter H.
Published: August 2020

Quantitative assessment of proliferation can be an important endpoint in toxicologic pathology. Traditionally, cell proliferation is quantified by labor-intensive manual counting of positive and negative cells after immunohistochemical staining for proliferation markers (eg, Ki67, bromo-2′-deoxyuridine, or proliferating cell nuclear antigen). Currently, there is a lot of interest in replacing manual evaluation of histology end points with image analysis tools based on artificial intelligence. The aim of the present study was to explore if a commercially available image analysis software can be used to quantify epithelial proliferative activity in rat mammary gland and minipig oviduct. First, algorithms based on artificial intelligence were trained to detect epithelium in each tissue. Areas of BrdU- or Ki67-positive nuclei and negative nuclei were subsequently quantified with threshold analysis. Artificial intelligence-based and manually counted labelling indices were strongly correlated and equally well detected the estrous cycle influence on proliferation in mammary gland and oviduct epithelium, as well as the dramatically increased proliferation in rat mammary glands after treatment with estradiol and progesterone. In conclusion, quantification of epithelial proliferation in two reproductive tissues can be achieved in a reliable fashion using image analysis software based on artificial intelligence, thus avoiding time- and labor-intensive manual counting, requiring trained operators.
Multispectral Imaging for Automated Tissue Identification of Normal Human Surgical Specimens
Authors: Shenson Jared A. , Liu George S. , Farrell Joyce , Blevins Nikolas H.
Published: August 2020

Objective

Safe surgery requires the accurate discrimination of tissue intraoperatively. We assess the feasibility of using multispectral imaging and deep learning to enhance surgical vision by automated identification of normal human head and neck tissues.

Study Design

Construction and feasibility testing of novel multispectral imaging system for surgery.

Setting

Academic university hospital.

Subjects and Methods

Multispectral images of fresh‐preserved human cadaveric tissues were captured with our adapted digital operating microscope. Eleven tissue types were sampled, each sequentially exposed to 6 lighting conditions. Two convolutional neural network machine learning models were developed to classify tissues based on multispectral and white‐light color images (ARRInet‐M and ARRInet‐W, respectively). Blinded otolaryngology residents were asked to identify tissue specimens from white‐light color images, and their performance was compared with that of the ARRInet models.

Results

A novel multispectral imaging system was developed with minimal adaptation to an existing digital operating microscope. With 81.8% accuracy in tissue identification of full‐size images, the multispectral ARRInet‐M classifier outperformed the white‐light‐only ARRInet‐W model (45.5%) and surgical residents (69.7%). Challenges with discrimination occurred with parotid vs fat and blood vessels vs nerve.

Conclusions

A deep learning model using multispectral imaging outperformed a similar model and surgical residents using traditional white‐light imaging at the task of classifying normal human head and neck tissue ex vivo. These results suggest that multispectral imaging can enhance surgical vision and augment surgeons’ ability to identify tissues during a procedure.
Evidence‐Based Medicine in Otolaryngology, Part XI: Modeling and Analysis to Support Decisions
Authors: Caulley Lisa , Hunink Myriam G. , Randolph Gregory W. , Shin Jennifer J.
Published: August 2020

Objective

To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data‐driven choices.

Data Sources

Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions.

Review Methods

Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence‐Based Medicine in Otolaryngology series thus provides a step‐by‐step introduction to decision models, their typical framework, and favored approaches to inform data‐driven practice for patient‐level decisions, as well as comparative assessments of proposed health interventions for larger populations.

Conclusions

Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care.

Implications for Practice

Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence‐based approach to clinical management and policy.
Efficacy of Adenoidectomy for the Treatment of Mild Sleep Apnea in Children
Authors: Tipold Austin , Vazifedan Turaj , Baldassari Cristina M.
Published: August 2020

Objectives

(1) To assess outcomes in children undergoing adenoidectomy for the treatment of mild obstructive sleep apnea (OSA). (2) To identify clinical factors that predict which children will have persistent obstruction following adenoidectomy.

Study Design

Case series with chart review over a 10‐year period.

Setting

Tertiary children’s hospital.

Subjects and Methods

Children between 2 and 17 years old undergoing adenoidectomy for treatment of mild OSA (obstructive apnea‐hypopnea index [AHI] between 1 and 5 on polysomnogram) were included. The need for additional medical or surgical intervention following adenoidectomy was recorded. When available, postoperative polysomnogram data were reviewed.

Results

In total, 134 children with a mean age of 5.4 years were included. Fifty‐three percent (n = 71) were female and 57% (n = 76) were black. The mean (SD) baseline AHI was 2.2 (1.09). Caregivers reported a moderate impact of sleep disturbance on quality of life with a mean (SD) preoperative total OSA–18 score of 64.1 (19.28). Postadenoidectomy outcomes were reported for 105 patients (78%) with a mean follow‐up time of 6 months. Sixty‐nine percent (n = 72) of children had resolution of obstructive symptoms. While 31% (n = 33) of children required additional intervention following adenoidectomy, only 6.8% (n = 9) underwent a subsequent tonsillectomy. Demographic factors such as age and baseline AHI did not predict which children required additional treatment following adenoidectomy.

Conclusion

Adenoidectomy may be an effective treatment for mild OSA. A randomized trial comparing outcomes for adenoidectomy and adenotonsillectomy is needed to determine the ideal surgical treatment for nonsevere OSA in children.