bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance comparison between fixed tilt angle and solar tracking systems at Basra governorate: A case study
Authors: R. Alnaab Ismaeil , M. Taha Harwan , A. Abdulwahab Zainab , Salah Al-Radhi Mohammed
Published: April 2022

Theoretical calculations and online system simulations using PVWatts and global solar atlas simulators, were conducted in this study to find the difference in solar power production between fixed solar power systems and tracking systems at Basra Governorate. The research included the analysis of geographical location and weather conditions and their effect on output power. The reliant power resource types and power generation of the southern region of Iraq as well as load demands were demonstrated and discussed in this research. Furthermore, the sun path, solar angles and solar radiations were considered in this study, in addition to the mathematical calculations of optimum tilt angles. The methodology used in this study was based upon theoretical and online measures of real-time weather factors, solar angles, solar radiations and model properties of the examined system. The results and factors of different systems including: peak sun hours per day, dc to ac derate factors, tilt angles, solar radiations and power production were compared to multiple similar research elements that were accomplished around the same region and some other countries. The study concluded that solar tracking system absorbs more radiations and produces an annual production of 15–30% higher than fixed tilt angle system.
Advanced control of a permanent magnet synchronous generator for a wind turbine
Authors: Belkacem Abdelkader , Boudjema Zinelaabidine , Bachir Ghalem , Taleb Rachid
Published: April 2022

This article presents an improved vector control scheme based on super twisting continuous sliding mode for a permanent magnet synchronous generator integrated in a dual roror wind turbine system. To augment the energy effectiveness of wind systems, several research has recently been realized by different researchers and in various technologies fields. The field of machine control occupied a large part of this research as the objective was always to find the most optimal control solution. Two main objectives are targeted in this work. The first goal is to develop the vector control performance of the permanent magnet synchronous generator by using second order continuous sliding mode controller, which is known for their robustness and ability to reduce chattering phenomenon. The second objective of this work is to use a dual rotor wind turbine in order to increase the energy efficiency of the wind power system used. The obtained simulation results showed the efficacy of the techniques used.
Adaptive backstepping control of linear induction motors using artificial neural network for load estimation
Authors: Mahmoudi Omar , Boucheta Abdelkrim
Published: April 2022

Linear induction motors (LIMs) make performing a direct linear motion possible without any mechanical rotary to linear motion transforming parts. Obtaining a precise mathematical model of such type of motors presents a difficulty due to time varying parameters and external load disturbance. This paper proposes an adaptive backstepping controller structure based on lyapunov stability for controlling a LIM position. Which can guarantee the annulment of position tracking error, despite of parameter uncertainties. Parameter update laws are extracted to estimate mover mass, friction coefficient and load force disturbance, which are assumed to be constant parameters; as a result, compensating their undesirable effect on control design. Then, load disturbance estimate is replaced with an artificial neural network (ANN) to reduce the estimation error. The numerical validation has shown better performance compared to the conventional backstepping controller, and proved the robustness of the proposed adaptive controller design against parameter changes.
A new properties of fuzzy b-metric spaces
Authors: Mohammed Ali Mayada N. , Sabri Raghad I. , Sadiq Fatema Ahmad
Published: April 2022

Metric spaces are specific types of topological spaces with pleasing “geometric” characteristics and they have a number of appealing properties and are commonly used in both pure and applied sciences. In this work, the structure of cartesian product space in the setting of a fuzzy b-metric space (Fb-M space) framework is introduced, which is an extension allows to create the large-scale structure for the space of the type fuzzy b-metric. The possibility of transferring some of the results and important features related to Fb-M space to this suggested space are discussed and demonstrated. The Cartesian product of two Fb-M spaces is proved as Fb-M space, this allows, to investigate and prove the topological expectation on the fuzzy product b-metric space. Furthermore, certain specific fuzzy b-metrics on F2, and fuzzy Euclidean plane are obtained in this way.
Performance analysis of different intonation models in Kannada speech synthesis
Authors: Chakrasali Sadashiva Veerappa , Indira Krishnappa , Narasimhaiah Sunitha Yariyur , Chandraiah Shadaksharaiah
Published: April 2022

Text to speech (TTS) is a system that generates artificial speech from text input. The prosodic models used improve the quality of the synthesized speech especially naturalness and intelligibility. The prosody involves intonation, intonation refers to the variations in the pitch frequency (F0) with respect to time in an utterance. This work mainly concentrates on building feedback neural network model to predict F0 contour in the utterances using Fujisaki intonation model parameters as the input features to the network since the Fujisaki intonation model is data driven and not a rule based one. In this work we have built 4-layer feedback neural network in the festival framework. Finally, the synthetically generated Kannada speech using the neural network model, is compared for its performance with the classification and regression tree (CART) model and Tilt model. Database of simple declarative Kannada sentences created by Carnegie Mellon University have been deployed in this work. From the study it is very clear that F0 contours can be accurately predicted using CART and neural network models, whereas naturalness and intelligibility is high in CART model rather than neural network model.
Uplink millimeter-wave multi-cell multi-user massive multi-input multi-output systems
Authors: Kodimyala Srinivas , Tadisetty Srinivasulu
Published: April 2022

In this paper, we delve into the maximized spectral efficiency (SE) of millimeter-wave (mmWave) multicell multiuser massive MIMO Systems for uplink transmission with low-resolution phase shifters (LRPSs). Millimeter-wave massive multiple-input multiple-output (mMIMO) is an important technology for upcoming cellular networks which will provide higher bandwidth and throughput than current wireless systems and networks. LRPSs are commonly used to minimize power consumption, maximize spectral efficiency and diminish the complexity of hybrid precoder and combiner. In this paper, we consider a hybrid analog-digital precoder and combiner design with LRPSs for mmWave multi-cell multiuser mMIMO systems for uplink transmission to spectral efficiency in terms of iterations. The proposed technique outperforms when compared to traditional optimization approaches concerning spectral efficiency and bit error rate (BER). We show through simulation results that our designs with LRPSs outperform standard iteration procedures.
Simulating the Covid-19 epidemic event and its prevention measures using python programming
Authors: Hamid Mustofa Abi , Aditama Dimas , Permata Endi , Kholifah Nur , Nurtanto Muhammad , Abdul Majid Nuur Wachid
Published: April 2022

A simulation is needed to observe and indicate how much preventive measures influence the pandemic flow, controlling and stopping it. This study succeeded in making a stochastic susceptible infected recovered deceased (SIRD) simulation using Python programming language to determine the effectiveness of prevention methods such as masks policy, social distancing, vaccination, quarantine, and lockdown. Every preventive measure is modeled based on an equivalent actual event and every essential aspect that affects the course of the pandemic. A person is represented as a circle moving freely in two-dimensional space, and disease spreads through person-to-person contact. This simulator then tested using parameters to simulate COVID-19 and found significant results between communities that implement preventive measures and those that do not. We found that within 106 days, 284 people were infected, but when five preventive methods are applied for a total of 33 days, only 31 people were infected. Adequate to simulate epidemic events and their prevention measures, this simulator can also be used as a learning tool with factors in epidemic events such as population density, mobility, infection rate, disease mortality, and every effect of each preventive measure. Users can change and influence the simulation course using interactive and straightforward software tools.
Power loss minimization with simultaneous location and sizing of distribution generation units using artificial algae algorithm
Authors: Chauhan Vineeta S. , Chakravorty Jaydeep
Published: April 2022

Power loss is one of the important pointers used to measure the performance of distributions networks. Many optimization algorithms have been proposed to solve various optimal power flow problems in Electrical Engineering. In this paper, a novel technique, artificial algae algorithm is developed to robustly detect the optimal location and size of distributed generation (DG) units for minimization of total power losses without violating the equality and inequality constraints. The main objective of optimal power flow (OPF) is to maximize or minimize the objective function using various constraint so that steady-state operation point is achieved. The concept of optimal power flow in power system helps to minimize real power loss. In the proposed approach, various control variables like generator bus, voltage magnitudes, and transformer tap settings are considered. The proposed algorithm is simulated in MATLAB and effectiveness is carried on IEEE 33 bus radial distribution system and satisfactory results are achieved when compared with other optimization techniques. A notable improvement in reduction of active power losses with 3 DG operating at different power factors are 65.5%, 42.4%, and 77.8% respectively, were achieved in comparison to the system without DGs and as compared with other research papers.
Investigation of pattern division multiple access technique in wireless communication networks
Authors: Yonis Aws Zuheer , Mohammed Khalid Khalil
Published: April 2022

Recently, pattern division multiple access (PDMA) is a non-orthogonal multiple access system that is now being developed in next-generation telecoms to address the requirement for mass connectivity. The core premise of non-orthogonal multiple access is to simultaneously serve multiple users with varying power levels across the same spectrum resources such as time, frequency, code, as well as space with minimal inter-user interference. A simulation analysis of significant technology enhancements focusing on PDMA aims to describe the benefits of the two plans now being examined by the third-generation partnership project for 5G technologies, namely filtered orthogonal frequency division multiplexing (F-OFDM) and windowed orthogonal frequency division multiplexing (W-OFDM), and to compare them to alternative modulation processes such as 16, 32, and 128 modulations. The research results explained the PDMA is less bit error rate used in multiple access technologies compare with W-OFDM and F-OFDM.
Design a monitoring system for COVID-19 patients
Authors: Hendi Hayder Ibrahim , Mshali Haider Hassan
Published: April 2022

This paper considers to developing application software that can assist COVID-19 patients in-home quarantine to know their situations and call the emergence center when the patient needs it. It includes a smart band as well as an application on the smartphone, the smart band can determine blood oxygen levels, the temperature of the patient, environmental temperature and humidity, also daily activities that affect the decision to go to the hospital or stay at home. The core of the proposed project is using ontology and semantics web to process the data that coming from sensors (physiology and environment), and the information of patients stored in the database on the mobile application. The response depends on the dataset of affect sensors parameters and type of activity the patient at the time. There are three types of response to proposed program is (normal, alert, and emergency).
Improving the sub-image classification of invasive ductal carcinoma in histology images
Authors: Kawattikul Khanabhorn , Sermsai Kodchanipa , Chomphuwiset Phatthanaphong
Published: April 2022

Whole slide image (WSI) processing is a common technique used in the analysis process performed by pathologists. Identifying precise and accurate regions of cancerous in the tissue is an important process in the disease diagnosis modality. This work proposes an automated technique for identifying invasive ductal carcinoma (IDC) in histology images using. An image is divided into small non-overlapped patches (or image windows). Then, the task is to classify the image patches into different classes, i.e., i) IDC and ii) non-IDC. We employ a two-stage classification-based to classify the patches, as to identify IDC regions in the tissue. In the first stage (patch-level classification), image patch classification is carried out using a conventional handcrafted feature and deep-learning technique are explored. The second stage (post-processing) undergoes a refinement process, which considers the spatial relationships between the neighboring patches. This stage aims to amend some of miss-classified patches. Markov random field (MRF) is implemented in this stage to examine the relationships of the patches and their neighborhoods. The experiments are conducted on public dataset. The experimental results show the post-processing can improve the performance of the classification in the first stage using the handcrafted-based technique and deep learning.
Prediction of of heart diseases utilising support vector machine and artificial neural network
Authors: Faieq Alaa Khaleel , Mijwil Maad M.
Published: April 2022

The heart, like a pump, is an organ about the size of a fist, mainly composed of muscle and connective tissue that functions to distribute blood to tissues. The heart is located under the rib cage, above the diaphragm between the lungs, slightly closer to the left. Sometimes a small, unexpected problem with the veins or the valves that supply the heart affects a person's life and can lead to death. Early diagnosis is essential to predict diseases that affect the human heart and lead people to live another period of life. In this context, the authors introduce two methods for early diagnosis of heart disease, the support vector machine and artificial neural network. The medical data is taken from the University of California Irvine (UCI) Machine Learning Repository database, and it contains reports of 170 people. The investigation results confirm that the optimal execution is the support vector machine technique. It gives high-accuracy prediction results. As for the performance of the forward propagation artificial neural networks (ANN) technique is acceptable.
Automatic deception detection system based on hybrid feature extraction techniques
Authors: Abd Shaimaa Hameed , Hashim Ivan A. , Jalal Ali Sadeq Abdulhadi
Published: April 2022

Human face is considered as a rich source of non-verbal features. These features have proven their efficiency, so they are used by the deception detection system (DDS) to distinguish liar from innocent subjects. The suggested DDS utilized three kinds of features, these are facial expressions, head movements and eye gaze. Facial expressions are simply encoded and represented in the form of action units (AUs) based on facial action coding system (FACS). Head movements are represented based on both transitions and rotation. For eye gaze features, the eye gaze directional angle in both x-axis and y-axis are extracted. The collected database used to prove validity and robustness of the suggested system contains videos for 102 subjects from both genders with age range 18-55 years. The detection accuracy of the suggested DDS based on applying the logistic regression classifier is equal to 88.0631%. The proposed system has proven its robustness and the achievement of the highest detection accuracy when compared with previously designed systems.
Light weight serverless computing at fog nodes for internet of things systems
Authors: Elkholy Mohamed , Marzok Marwa A.
Published: April 2022

Internet of things (IoT) systems collect large size of data from huge numbers of sensors. A wide rage of IoT systems relies on cloud resources to process and analyze the collected data. However, passing large amount of data to the cloud affects the overall performance and cannot support real-time requirements. Serverless computing is a promising technique that allows developer to write an application code, in any programming language, and specify an event to start its execution. Thus, IoT system can get a good benefit of serverless environment. The proposed work introduces a framework to allow Serverless computing to take place on the Fog nodes near the data collectors. The proposed framework is implemented as an extension to a Kubernetes cluster that manages a set of Docker containers at the fog layer. A prototype of the proposed solution was implemented using Node.Js for coding and YAML files to transfer data. The proposed framework was evaluated against traditional cloud Serverless execution. The experimental results proved the significant enhancement of the framework by dcreasing the respond time especially for data intensive IoT applications.
Sentiment analysis using global vector and long short-term memory
Authors: Kusum Kusum , Panda Supriya P.
Published: April 2022

Tweet sentiment analysis is a Deep Learning study that is beneficial for automatically determining public opinion on a certain topic. Using the Long Short-Term Memory (LSTM) algorithm, this paper aims to proposes a Twitter analysis technique that divides Tweets into two categories (positive and negative). The Global Vector (GloVe) word embedding score is used to rate many selected words as network input. GloVe converts words into vectors by building a corpus matrix. The GloVe outperforms its prior model, owing to its smaller vector and corpora sizes. GloVe has a higher accuracy than the model word embedding word2vec, Continuous Bag of Word(CBoW), and word2vec Skip-gram. The preprocessed term variation was conducted to test the performance of sentiment classification. The test results show that this proposed method has succeeded in classifying with the best results with an accuracy of 95.61%.
The development of smart flowerpot based on internet of things and mobile and web application technology
Authors: Janpla Satien , Jewpanich Chaiwat , Tachpetpaiboon Nisanart , Prongsanthia Waraporn , Jewpanich Butsirin
Published: April 2022

Cultivation of ornamental plants in the office is popular among office workers and the general public because it can create a good environment in the area, but the plant growers must pay attention to watering the plants, because it may cause the plant to die. With the advancement of internet of things (IoT) technology, used to control devices wirelessly, this research developed the smart flowerpot system that works through mobile and web applications, using a microcontroller to control the system and connect to users via mobile and web application that can monitor the system, and control the operation both directly and automatically. When soil moisture is reduced to a predetermined value, the system will order the plants to be watered automatically, and when the water level is almost completely reduced, the ultrasonic sensor will send a notification to the mobile application to let the user know, after testing the system, it was found that the smart plant pot can work efficiently and can automatically water the plants.
A hybrid big data movies recommendation model based k-nearest neighbors and matrix factorization
Authors: Ez-zahout Abderrahmane , Gueddah Hicham , Nasry Abir , Madani Rabie , Omary Fouzia
Published: April 2022

On the subject of broadcasting the information, finding someone’s favorite book or movie in a sea of data containing books and movies has become a crucial issue. In an era when there are so many genres and types of movies and books, the customer may find it difficult to choose which to discover in the first place. Thus, personalized recommendation systems play an important role because of the value that is attributed to movies and books nowadays, and considering that there are so many to choose from that the user may not be able to have a specific target. In this context, our proposed work, design and implement a prototype of movie recommendation system while taking into consideration the real requirement for the search of movies and books. The research of movie recommendation system by using the k-nearest neighbors approach and collaborative filtering algorithm are adopted to extract the criteria for a good use case on recommender systems. At last, the results are as what was expected as they showed that the system has a good recommendation effect.
Classification based topic extraction using domain-specific vocabulary: a supervised approach
Authors: Kalra Vandana , Kashyap Indu , Kaur Harmeet
Published: April 2022

Recently, a probabilistic topic modelling approach, latent dirichlet allocation (LDA), has been extensively applied in the arena of document classification. However, classical LDA is an unsupervised algorithm implemented using a fixed number of topics without prior domain knowledge and generates different outcomes with the change in the order of documents. This article presents a comprehensive framework to evade the order effect and unsupervised probabilistic nature. First, the framework creates the vocabulary specific to the category using a weight-dependent model that extracts distinctive features suitable for supervised classification. Then, it transforms a classified cluster of documents from the domain corpus to the relevant topic making it more robust to noise. The framework was tested on a comprehensive collection of benchmark news datasets that vary in sample size, class characteristics, and classification tasks. In contrast to the conventional classification methods, the proposed framework achieved 95.56% and 95.23% accuracy when applied on two datasets, indicating that the proposed algorithm has a better classification capability. Furthermore, the topics extracted from the classified clusters are highly relevant to domain categories.
Path planning for assisting blind people in purposeful navigation
Authors: Díaz-Toro Andrés , Mosquera-Ortega Paula , Herrera-Silva Gustavo , Campaña-Bastidas Sixto
Published: April 2022

Blind people present dificulties for reaching objects of interest in the daily life. In this sense, the integration of a path planning module for assisting blind people in purposeful navigation is noteworthy. In this work, we present an algorithm that leverages the high capability of an embedded computer with graphics processing unit (GPU) NVIDIA Jetson TX2 for computing optimal paths to objects of interest. The algorithm computes the optimal path to the objective, considering changes in the environment and changes in the position of the user. The algorithm is efficient for computing new paths when the environment changes by reusing parts of previous computations. In order to compare the performance, the algorithms were implemented and evaluated in MATLAB, C++ and CUDA, for different size of the grid and percentage of unknown obstacles. We found that the implementation on GPU has a speed up of 20 times W.R.T the implementation in C++ and more than 400 times W.R.T the implementation in MATLAB. These results boost us to integrate this module to our main system based on a stereo camera and a haptic belt and so provide to the user assistance in purposeful navigation at real time.
Analysing most efficient deep learning model to detect COVID-19 from computer tomography images
Authors: Shamrat F.M. Javed Mehedi , Chakraborty Sovon , Ahammad Rasel , Shitab Tanzil Mahbub , Kazi Md.Aslam , Hossain Alamin , Mahmud Imran
Published: April 2022

COVID-19 illness has a detrimental impact on the respiratory system, and the severity of the infection may be determined utilizing a selected imaging technique. Chest computer tomography (CT) imaging is a reliable diagnostic technique for finding COVID-19 early and slowing its progression. Recent research shows that deep learning algorithms, particularly convolutional neural network (CNN), may accurately diagnose COVID-19 using lung CT scan images. But in an emergency, detection accuracy simply is not enough. Determinants of data loss and classification completion time play a critical element. This study addresses the issue by finding the most efficient CNN model with the least data loss and classification time. Eight deep learning models, including Max Pooling 2D, Average Pooling 2D, VGG19, VGG16, MobileNetV2, InceptionV3, AlexNet, NFNet using a dataset of 16000 CT scans image data of COVID-19 and non-COVID-19 are compared in the study. Using the confusion matrix, the performance of the models is compared and together with the data loss and completion time. It is observed from the research that MobileNetV2 provides the highest accurate result of 99.12% with the least data loss of 0.0504% in the lowest classification completion time of 16.5secs per epoch. Thus, employing MobileNetV2 gives the best and the quickest result in an emergency.
Detection and extraction of digital footprints from the iDrive cloud storage using web browser forensics analysis
Authors: Adesina Adesoji , Adebiyi Ayodele , Ayo Charles
Published: April 2022

STorage as a service (STaaS) allows its subscribers the ability to access their stored data with the use of internet enabled digital devices at anywhere, anyplace and anytime. The easy accessibility of cloud storage with digital devices is one of the major benefits of cloud computing but this benefit can also be exploited by cybercriminals to perform various forms of malicious usages. During forensic investigation, forensic examiners are expected to provided evidence in relation to the malicious usages but the physical inaccessibility to the digital artifacts on the cloud servers, the difficulty in retrieving evidential artifacts from various cloud storage services and the difficulty in obtaining forensic logs from the concerned cloud service providers among other factors make it difficult to perform forensic investigations. This paper provided step by step experimental guidelines to extract digital artifacts from Google Chrome and Internet Explorer from Windows 10 personal computer using iDrive cloud storage as a case study. The study used Nirsoft forensic tool to locate the relevant forensic artifacts and an integrated conceptual digital forensic framework was adopted to carry out the investigation. This study increases the knowledge of client forensics using web browser analysis during cloud storage forensic investigation.
Real-time twitter data analytics of mental illness in COVID-19: sentiment analysis using deep neural network
Authors: Sugumaran Poonkuzhali , Uma Anu Barathi Bhagavathi Kannu
Published: April 2022

The World Health Organization (WHO) states that the COVID-19 epidemic is being treated as a pandemic, with thousands of individuals infected and dead worldwide. School and college students are suffering from their online classes without any physical activities. Working men and women are also suffering from their working situations, as lots of people have lost their jobs and unemployment rates have become high due to the pandemic, and people are also losing physical contact with other family members, friends, and colleagues. The main objective of the proposed model is to monitor and analyse the real-time Twitter data-related tweets, such as coronavirus mental illness that are commonly used while referencing the pandemic. We have compared three deep learning approaches to sentiment analysis and found them to be useful. The first deep learning technique is to use a basic recurrent neural network (RNN), and the second is to use a deep learning RRN with long short-term memory (LSTM), followed by a gated recurrent unit (GRU). The experiment results indicate that the recurrent neural network built using GRU has the maximum accuracy of 99.47% for positive, negative, and neutral words and statements in Twitter data.
An analysis on micronutrient deficiency in plant leaf and soil using digital image processing
Authors: Reddy Anthay Swetha , Chokkalingam Arun , Jeyashanker Komathi B. , Natarajan Bharathiraja
Published: April 2022

The plant requires thirteen different nutrients. The two main types of nutrients are micronutrients and macronutrients. Diseases develop due to deficiency of vital nutrients, resulting in colored spots on the leaves. Plant development is affected by toxicity or lack of one or more of these nutrients, resulting in plant death. As a result, a continuous monitoring system is necessary to know the nutritional status of the plants to enhance production efficiency and output. Optical image recognition-based medical technology can identify indicators of inaccuracy faster than the human eye. Consequently, farmers are prepared to take prompt and effective remedial action. This article investigates the nutrient deficits in plants using image processing techniques.
Hardware implementation of Sobel edge detection system for blood cells images-based field programmable gate array
Authors: Younis Ahmed Khazal , Younis Basma MohammedKamal , Jarjees Mohammed Sabah
Published: April 2022

The microscopic-blood image has been used to diagnose various diseases according to the morphological specifications of red and white blood cells. However, the manual analysis and procedures are not accurate due to the human error. Therefore, several studies conducted to find new techniques to perform this analysis using computer algorithms. The complexity of these algorithms led to thinking in simpler ways or to the hardware solutions. On the other hand, edge detection is a mathematical procedure that play an essential role in the field of medical image processing. It is considered as one of the foundations' processes for other procedures, such as the segmentation and the classification of the image. The Sobel filter is one of the conventional methods that is used to perform the edge detection process. It is based on finding the local contrast for the level of intensity of the image. This paper presents a proposed and a new method for detecting the edges of cells in the microscopic blood images using Sobel filter and its hardware implementation on the field programmable gate array (FPGA) chip. Three different techniques are proposed: MATLAB, OpenCV standard code, and FPGA customize code which give the best visual results, minimum timing results than the others.
Investigate the optimal power system by using hybrid optimization of multiple energy resources software
Authors: Hasan Ghanim Thiab , Mutlaq Ali Hlal , Salih Mohammad Omar
Published: April 2022

Increasing the effects of global pollution and the availability of renewable energy sources has push many countries to use reasonable energy sources such as wind and solar energy. This paper presents a case study of evaluating a hybrid renewable energy system by using a hybrid optimization of multiple energy resources (HOMER) software program based on the entered data available from the net for the considered location. The hybrid system consisting of a wind turbine, a photovoltaic system, a battery and a diesel generator. The simulation results are presented in a graphical curves n HOMER software. The obtained results indicate that by using the HOMER simulation program, the optimal design of the hybrid electrical power system for the considered location can be achieved which can help the designer to decide the types and number of the competent required for conducting the intending hybrid electrical power system which results in optimum output power in addition to reducing the overall operating costs.
Periprocedural Pericardial Effusion Complicating Transcatheter Left Atrial Appendage Occlusion: A Report From the NCDR LAAO Registry
Authors: Price Matthew J. , Valderrábano Miguel , Zimmerman Sarah , Friedman Daniel J. , Kar Saibal , Curtis Jeptha P. , Masoudi Frederick A. , Freeman James V.
Published: April 2022

Background:

Pericardial effusion (PE) is a potential complication of transcatheter left atrial appendage occlusion. The objective of this study was to investigate the incidence, associated characteristics, and outcomes of PE following left atrial appendage occlusion.

Methods:

Patients in the NCDR LAAO Registry who underwent a Watchman procedure between January 1, 2016 and December 31, 2019 were included. The primary outcome was in-hospital PE requiring intervention (percutaneous drainage or surgery). Odds ratios (ORs) were calculated for adverse event rates associated with PE.

Results:

The study population consisted of 65 355 patients. The mean patient age was 76.2±8.1 years, and the mean CHA
2
DS
2
-VASc score was 4.6±1.5. PE occurred in 881 patients (1.35%). Clinical variables independently associated with PE included older age, female sex, left ventricular function, paroxysmal atrial fibrillation, prior bleeding, lower serum albumin, and preprocedural dual antiplatelet therapy; procedural variables included number of delivery sheaths used, sinus rhythm during the procedure, and moderate sedation rather than general anesthesia. PE was associated with increased risk of in-hospital stroke (OR, 6.58 [95% CI, 3.32–13.06];
P
<0.0001), death (OR, 56.88 [95% CI, 39.79–81.32];
P
<0.0001), and the composite of death, stroke, or systemic embolism (OR, 28.64 [95% CI, 21.24–38.61];
P
<0.0001). PE during the index hospitalization was associated with increased risk of death (OR, 3.52 [95% CI, 2.23–5.54];
P
<0.0001) and the composite of death, stroke, or systemic embolism (OR, 3.42 [95% CI, 2.31–5.07];
P
<0.0001) between discharge and 45-day follow-up.

Conclusions:

In-hospital PE during transcatheter left atrial appendage occlusion is infrequent but associated with a substantially higher risk of adverse events, including in-hospital and early postdischarge mortality. Strategies to minimize PE are critical to improve the risk-benefit ratio for this therapy.
Quality of Life After Fractional Flow Reserve–Guided PCI Compared With Coronary Bypass Surgery
Authors: Fearon William F. , Zimmermann Frederik M. , Ding Victoria Y. , Zelis Jo M. , Piroth Zsolt , Davidavicius Giedrius , Mansour Samer , Kharbanda Rajesh , Östlund-Papadogeorgos Nikolaos , Oldroyd Keith G. , Wendler Olaf , Reardon Michael J. , Woo Y. Joseph , Yeung Alan C. , Pijls Nico H.J. , De Bruyne Bernard , Desai Manisha , Hlatky Mark A. ,   on behalf of the FAME 3 Investigators
Published: April 2022

Background:

Previous studies have shown that quality of life improves after coronary revascularization more so after coronary artery bypass grafting (CABG) than after percutaneous coronary intervention (PCI). This study aimed to evaluate the effect of fractional flow reserve guidance and current generation, zotarolimus drug-eluting stents on quality of life after PCI compared with CABG.

Methods:

The FAME 3 trial (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation) is a multicenter, international trial including 1500 patients with 3-vessel coronary artery disease who were randomly assigned to either CABG or fractional flow reserve–guided PCI. Quality of life was measured using the European Quality of Life–5 Dimensions (EQ-5D) questionnaire at baseline and 1 and 12 months. The Canadian Cardiovascular Class angina grade and working status were assessed at the same time points and at 6 months. The primary objective was to compare EQ-5D summary index at 12 months. Secondary end points included angina grade and work status.

Results:

The EQ-5D summary index at 12 months did not differ between the PCI and CABG groups (difference, 0.001 [95% CI, –0.016 to 0.017];
P
=0.946). The trajectory of EQ-5D during the 12 months differed (
P
<0.001) between PCI and CABG: at 1 month, EQ-5D was 0.063 (95% CI, 0.047 to 0.079) higher in the PCI group. A similar trajectory was found for the EQ (EuroQol) visual analogue scale. The proportion of patients with Canadian Cardiovascular Class 2 or greater angina at 12 months was 6.2% versus 3.1% (odds ratio, 2.5 [95% CI, 0.96–6.8]), respectively, in the PCI group compared with the CABG group. A greater percentage of younger patients (<65 years old) were working at 12 months in the PCI group compared with the CABG group (68% versus 57%; odds ratio, 3.9 [95% CI, 1.7–8.8]).

Conclusions:

In the FAME 3 trial, quality of life after fractional flow reserve–guided PCI with current generation drug-eluting stents compared with CABG was similar at 1 year. The rate of significant angina was low in both groups and not significantly different. The trajectory of improvement in quality of life was significantly better after PCI, as was working status in those <65 years old.

Registration:

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT02100722.
Sacred or Profane? The Compassion Relief Movement’s Transnationalism in Taiwan, Japan, Malaysia, and the United States
Authors: Huang C. Julia
Published: April 2022

The Buddhist Compassion Relief Foundation (Tzu-Chi or Ciji) is primarily a lay Buddhist movement that focuses on relieving human suffering through secular action. Inspired and led by a Buddhist nun, Compassion Relief is at present the largest formal association in Taiwan, with increasing overseas expansion: in the last decade, Compassion Relief has persistently delivered relief goods to different contents, and overseas Chinese, especially emigrants from Taiwan, have formed branches in 35 countries. Overseas devotees carry out Compassion Relief missions by localising Compassion Relief ’s Buddhist charitable practice in their host societies and by forging and sustaining ties with the headquarters in Taiwan, especially through various forms of so-called ‘homecomings’. To what extent can Compassion Relief ’s overseas expansion be termed a manifestation of Buddhist universalism? To what extent can it be termed a religious-based Taiwan-centred Chinese transnationalism? Based on my field research in Taiwan and among Compassion Relief branches in the United States, Japan and Malaysia, this paper is a preliminary description of the structure of Compassion Relief transnationalism. It will show that this peculiar form of religious transnationalism is global in scope and cultural in form; it is sacred in motif and profane in terms if its practice.
‘Love Transcends Borders’ or ‘Blood is Thicker Than Water’? The Charity Work of the Compassion Relief Foundation in the People’s Republic of China
Authors: Laliberté André
Published: April 2022

This paper looks at the Compassion Relief Foundation, a charity organisation originating from Taiwan, and tries to understand how it has managed to provide relief to victims of natural disasters for more than ten years in nineteen provinces, autonomous regions, and special municipalities of the People’s Republic of China, despite the vicissitudes of cross-Straits relations and amidst recent bouts of religious persecution. The essay starts with an introduction to Compassion Relief: it presents its history as a charity organization, and then situates its activities in the PRC within the context of tense relations between China and Taiwan, as well as economic reforms in the PRC. The paper then questions three hypotheses advanced to explain the acceptance of Compassion Relief ’s activities by the Chinese government. It discusses the view that such approval results from political calculations directed at Taiwanese; the argument that it heralds change in the Chinese state’s approach towards the social role of religion; and the claim that it signals the emergence of an autonomous civil society free of government interference. The essay expresses reservations about these views and concludes that the activities of Compassion Relief in the PRC rather suggests the existence of a symbiotic relationship between society and state officials at the local level.
Moving Targets: Chinese Christian Proselytising Among Transnational Migrants from the People’s Republic of China
Authors: Nyíri Pál
Published: April 2022

This paper addresses the question of proselytism as an active practice of shaping other people’s identities. Specifically, it looks at the identity discourse promoted by ethnic Chinese evangelicals and compares it to that of Jehovah’s Witnesses. Both Christian churches proselytise among contemporary transnational migrants from the People’s Republic of China (PRC) in Europe, each making use of a sophisticated organisation adapted to the transnational practices of these migrants. Yet while evangelicals promote a transnational ethnic identity quite compatible with the secular discourse of the global Chinese migrant, Jehovah’s Witnesses promote an anti-secular, non-ethnic cosmopolitanism. As a result, global Chinese evangelical ties more easily become part of global migratory and business networks.

The paper is based on fieldwork among Chinese evangelical organisations and Jehovah’s Witnesses in Italy, Hungary, Russia, and the United Kingdom, supplemented with interviews in the United States (the headquarters of many Chinese evangelical organisations) and a survey of printed and online resources used in proselytising.
‘In My Heart I Opposed Opium’: Opium and the Politics of the Wang Jingwei Government, 1940–45
Authors: Martin Brian G.
Published: April 2022

The Wang Jingwei government has been reviled as the Chinese collaborationist regime par excellence, and one of the major indictments against it was its involvement with the alleged Japanese ‘narcotisation policy’. The politics of collaboration, however, were complex, and are not fully captured by a one-dimensional portrayal of the leading collaborators as ‘national traitors’. The Wang Jingwei government was, indeed, complicit in facilitating the Japanese-sponsored opium monopoly during its early years, although it played only a marginal role in running this monopoly. At the same time, as this article seeks to demonstrate, the regime did attempt to continue implementing the pre-war Nationalist government’s opium suppression programme. Its motives were mixed: it wanted to bolster its legitimacy by portraying itself as the successor regime to the pre-war Nationalist government, and, also like that government, it sought to bolster its parlous finances by recourse to an opium tax. Political developments in Japan in 1943 enabled the Wang Jingwei government to gain control of the opium monopoly, and from 1944 until its demise it made a genuine attempt to implement a policy of opium suppression. This policy achieved some success. The government, however, never resolved the ambiguity between the political aims and the financial needs that drove its policy; nor did it effectively overcome the demoralisation produced by years of open trafficking; and it was never able to curb the Japanese military’s narcotic operations.
The Effect of Topical Tranexamic Acid in Endoscopic Sinus Surgery: A Triple Blind Randomized Clinical Trial
Authors: Achour Imen , Ben Rhaiem Zied , Thabet Wadii , Jdidi Jihene , Mnejja Malek , Hammami Bouthaina , Chakroun Amine , Charfeddine Ilhem
Published: April 2022

Objective:

Our aim is to evaluate the effect of topical tranexamic acid (TA) on bleeding and surgical quality field in the functional endoscopic sinus surgery (FESS).

Methods:

A total of 74 patients who underwent FESS due to chronic rhinosinusitis were included. The patients were randomized into 2 groups. TA group (n = 37) received a topical cotton pledget soaked with TA and placebo (PL) group (n = 37) received a pledget soaked with saline solution.

Results:

A significant effect was noted for the TA group versus the PL group in the grade 1 of the Boezaart scale at 35 minutes (4 for TA group and no case for PL group). This effect was absent for higher grades. We did not notice a significant effect between the 2 groups at 5 minutes. Blood loss was 359 ml in the TA group versus 441 ml in the PL group. No significant change was observed between the 2 groups concerning the blood parameters. No side effects were reported.

Conclusion:

Despite its safety when administrated locally and its low cost, TA provides limited effect on quality of surgical field after 35 minutes of the start of FESS in the patients with chronic rhinosinusitis. This effect was absent at the start of the intervention and when analyzing the blood loss and hematologic parameters.
Identifying Posterior Fossa Abnormality in Pediatric Aerodigestive Patients, a Case Series
Authors: Adigwu Yvonne , Osterbauer Beth , Chowdhuri Sampreeti , Shabbir Moizza , Ward Sally Davidson , Bhardwaj Vrinda , Hochstim Christian , Bansal Manvi
Published: April 2022

Objectives:

Multidisciplinary clinics like Aerodigestive programs focus on issues associated with airway, pulmonary, and gastrointestinal issues. Rarely, significant neurological issues like posterior fossa abnormality are identified as the primary etiology. We describe 3 such patients and compare their clinical presentation to the other patients seen in Aerodigestive clinic.

Methods:

A retrospective chart review was conducted to review the 3 posterior fossa patients and the remainder of children that were referred to the Aerodigestive Clinic at Children’s Hospital Los Angeles from June 2016 to August 2018. Clinical characteristics including triple endoscopies and sleep studies were recorded.

Results:

Of the 110 patients included for review, 3 patients (3%) had an underlying posterior fossa abnormality; all of whom had symptoms of sleep disordered breathing along with dysphagia compared with 30% incidence of this symptom profile in the remaining Aerodigestive population.

Conclusion:

Presence of sleep disordered breathing and dysphagia, with underlying vomiting history, warrants considering evaluation for posterior fossa abnormalities in addition to traditional workup for aerodigestive disorders. Due to the rarity of this presentation and small sample size, future studies with multicenter collaboration may help better describe identifiers to delineate this population with similar aerodigestive symptoms and clarify diagnostic algorithms.
Who’s Afraid of Conglomerate Mergers?
Authors: Witt Anne C.
Published: April 2022

Conglomerate merger control went out of fashion in the United States and the European Union several decades ago. Both jurisdictions embraced the premise that nonhorizontal mergers should normally be considered benign because exclusionary theories of harm are economically implausible, and nonhorizontal mergers are almost always certain to result in significant efficiency effects that the merged entity can be expected to pass on to consumers. Conglomerate effects analysis subsequently all but disappeared from the enforcement practice. However, the emergence of a handful of powerful digital platforms with vast global ecosystems of interconnected services is currently causing competition agencies a great deal of concern. Their growth has not been entirely internal. Collectively, Alphabet, Meta, Apple, Amazon, and Apple have acquired over eight hundred companies. Many of their targets were innovative start-ups operating in complementary markets. This contribution compares and critically assesses how this development has affected the U.S., EU, and U.K. competition agencies’ approach to conglomerate merger control. It finds that, as a reaction to the advent of Big Tech, conglomerate effects analysis has made a significant comeback in EU merger control. While the U.S. and U.K. authorities have not yet intervened against any conglomerate acquisitions in practice, evidence suggests that they are also more open to nonhorizontal theories of harm again.