bluuebunny/crossref_metadata_2025 huggingface dataset:
A novel frequency reconfigurable antenna for smart grid applications in TV white space band
Authors: Kumar Sanjeev , Rayavarapu Neela , Vummadisetty Naidu Praveen
Published: October 2022

This paper presents the design and analysis of a frequency reconfigurable, aperture coupled rectangular patch antenna for use in smart grid applications in TV white space bands. The proposed antenna model has been realized on multi-substrate layers of Polylactic acid (PLA) material (εr=2.65, tanδ=0.003) with a ground plane sandwiched in between them. An aperture has been made in the ground plane for coupling energy to the patch. The overall system dimensions are 270×270 mm. The feature of frequency reconfigurability has been achieved by incorporating a switch and varying the reactance of the feed line on the bottom substrate. A rectangular slot on the long feed line improves impedance matching. The ON and OFF states of the switch provide two operating frequency bands namely 630.13 to 636.7 MHz and 619.16 to 625.3 MHz respectively. The proposed aperture coupled reconfigurable system operates with a maximum gain of 6.4 dB and average efficiency of 78.5% in both bands. The measured results are satisfactory and the proposed antenna will be suitable for operation in the smart grid environment.
An improved 2×2 array antenna using both-sided microwave integrated circuit technology for circular polarization
Authors: Chowdhury Piyas , Chakma Nishako , Murshed Abu Hena , Hossain Md. Azad , Hssain Quazi Delwar
Published: October 2022

A circularly polarized microstrip patch array antenna using both-sided microwave integrated circuit (MIC) technology with a triple feed network has been proposed in this article. The antenna elements, feed structure and both-sided MIC technology are used and arranged in such a way to obtain circular polarization alongside high gain without using an external matching circuit. The 50 Ω microstrip line is used to energize the antenna where the antenna’s total feed network is made up of both series and parallel combinations of microstrip and slot line. The antenna was realized using Teflon glass fiber substrate (εr)=2.15 with a thickness of 0.8 mm. The antenna has some splendid parameters including S11 of less than -35 dB, a gain of 12 dBi with an omnidirectional pattern and an axial ratio of 0.7 dB at the operating frequency. The antenna possesses a bandwidth of 430 MHz (4.22%) after operating at X-band in the frequency spectrum. The antenna’s simulated parameters were investigated with the help of advanced design system (ADS) simulation software in microwave momentum mode.
Efficient systematic turbo polar decoding based on optimized scaling factor and early termination mechanism
Authors: Hamad Ahmed A. , Taih Gatte Mohammed , Abdul-Rahaim Laith Ali
Published: October 2022

In this paper, an efficient early termination (ET) mechanism for systematic turbo-polar code (STPC) based on optimal estimation of scaling factor (SF) is proposed. The gradient of the regression line which best fits the distance between a priori and extrinsic information is used to estimate the SF. The multiplication of the extrinsic information by the proposed SF presents effectiveness in resolving the correlation issue between intrinsic and extrinsic reliability information traded between the two typical parallel concatenated soft-cancellation (SCAN) decoders. It is shown that the SF has improved the conventional STPC by about 0.3 dB with an interleaver length of 64 bits, and about 1 dB over the systematic polar code (SPC) at a bit error rate (BER) of . A new scheme is proposed as a stopping criterion, which is mainly based on the estimated value of SF at the second component decoder and the decoded frozen bits for each decoding iteration. It is shown that the proposed ET results in halving the average number of iterations (ANI) without adding considerable complexity. Moreover, the modified codes present comparable results in terms of BER to the codes that utilize fix number of iterations.
One datum and many values for sustainable Industry 4.0: a prognostic and health management use case
Authors: Sabbatini Luisiana , Belli Alberto , Palma Lorenzo , Pierleoni Paola
Published: October 2022

Industrial context of today, driven by the Industry 4.0 paradigm, is overwhelmed by data. Decreasing cost of innovative technologies, and recent market dynamics have pushed and pulled respectively for those architectures and practices in which data are the masters. While advancing, we have to take care of waste, even though intangibility of data makes them hardly connected to waste. In this paper we are going to reflect on data intensive context of today, focusing on the industrial sector. A smart approach for fully exploiting data collecting infrastructures is proposed, and its declination in a prognostic and health management (PHM) use case set inside an automatic painting system is presented. The contributions of this papers are mainly two: first of all, the general conceptual take-away of "data re-use" is presented and discussed. Moreover, a PHM solution for painting system's number plates, based on optical character recognition (OCR), is proposed and tested as a proof-of-concept for the "data re-use" concept. Summarizing, the already-in-use data sharing principle for achieving transparency and integration inside Industry 4.0, is presented as complementary with the proposed "data re-use", in order to develop a really sustainable shift toward the future.
Quadratic exponential random early detection: a new enhanced random early detection-oriented congestion control algorithm for routers
Authors: Hassan Samuel Oluwatosin , Rufai Adewole Usman , Nwaocha Vivian Ogochukwu , Ogunlere Samson Ojo , Adegbenjo Aderonke Adelola , Agbaje Michael Olugbenga , Enem Theophilus Aniemeka
Published: October 2022

Network congestion is still a problem on the internet. The random early detection (RED) algorithm being the most notable and widely implemented congestion algorithm in routers faces the problems of queue instability and large delay arising from the presence of an ineffectual singular linear packet dropping function. This research article presents a refinement to RED, named quadratic exponential random early detection (QERED) algorithm, which exploits the advantages of two drop functions, namely quadratic and exponential in order to enhance the performance of RED algorithm. ns-3 simulation studies using various traffic load conditions to assess and benchmark the effectiveness of QERED with two improved variants of RED affirmed that QERED offers a better performance in terms of average queue size and delay metrics at various network scenarios. Fortunately, to replace/upgrade the implementation for RED algorithm with QERED’s in routers will require minimal effort due to the fact that nothing more besides the packet dropping probability profile got to be adjusted.
Mobile network connectivity analysis for device to device communication in 5G network
Authors: Laguidi Ahmed , Hachad Tarik , Hachad Lamiae
Published: October 2022

Since long term evolved release 14 (LTE R14), the device to device (D2D) communications have become a promising technology for in-band or out-band mobile communication networks. In addition, D2D communications constitute an essential component of the fifth-generation mobile network (5G). For example, to improve capability communication, reduce the power dissipation, reduce latency within the networks and implement new applications and services. However, reducing the congestion in D2D communications and improving the mobile network connectivity are the essential problems to propose these new applications or services. This paper presents new solutions to reduce the congestion of devices around a base station and improve the performance of the D2D network; in terms of the number of connected devices or user equipment (UE). The simulation results show that our proposed solution can improve the network capacity by doubling the number of connected devices (or UE) and reducing the congestion. For this reason, our proposition makes it possible to reduce the financial cost by reducing the cost of deploying equipment. For example, instead of using two base stations, we can use only one station to connect the same number of devices.
Design of efficient reversible floating-point arithmetic unit on field programmable gate array platform and its performance analysis
Authors: Sanjeevaiah Girija , Bhandari Gajanan Sangeetha
Published: October 2022

The reversible logic gates are used to improve the power dissipation in modern computer applications. The floating-point numbers with reversible features are added advantage to performing complex algorithms with high-performance computations. This manuscript implements an efficient reversible floating-point arithmetic (RFPA) unit, and its performance metrics are realized in detail. The RFP adder/subtractor (A/S), RFP multiplier, and RFP divider units are designed as a part of the RFP arithmetic unit. The RFPA unit is designed by considering basic reversible gates. The mantissa part of the RFP multiplier is created using a 24x24 Wallace tree multiplier. In contrast, the reciprocal unit of the RFP divider is designed using Newton Raphson’s method. The RFPA unit and its submodules are executed in parallel by utilizing one clock cycle individually. The RFPA unit and its submodules are synthesized separately on the Vivado IDE environment and obtained the implementation results on Artix-7 field programmable gate array (FPGA). The RFPA unit utilizes only 18.44% slice look-up tables (LUTs) by consuming the 0.891 W total power on Artix-7 FPGA. The RFPA unit sub-models are compared with existing approaches with better performance metrics and chip resource utilization improvements.
Blockchain technology and internet of things: review, challenge and security concern
Authors: Mahmood Mahmood Subhy , Al Dabagh Najla Badie
Published: October 2022

Blockchain (BC) has received high attention from many researchers recently because it has decentralization, trusted auditability, and transparency as its main properties. BC has contributed fundamentally to the development of applications like cryptocurrencies, health care, the internet of things (IoT), and so on. The IoT is envisioned to include billions of pervasive and mission-critical sensors and actuators connected to the internet. This network of smart devices is expected to generate and have access to vast amounts of information, creating unique opportunities for new applications, but significant security and privacy issues emerge concurrently because it does not contain robust security systems. BC provides many services like privacy, security, and provenance to the systems that depends on. This research includes analyzing and a comprehensive review of BC technologies. Moreover, the proposed solutions in academia with the methodologies that used to integrate blockchain with IoT are presented. Also, the types of attacks on blockchain are collected and classified. Furthermore, the main contributions and challenges that are included in the literature are explored, then the relevant recommendations for solving the explored challenges are proposed. In conclusion, the integration of BC with IoT could produce promising results in enhancing the security and privacy of IoT environment.
MapReduce-iterative support vector machine classifier: novel fraud detection systems in healthcare insurance industry
Authors: Mary Arockiam Jenita , Seraphim Pushpanathan Angelin Claret
Published: October 2022

Fraud in healthcare insurance claims is one of the significant research challenges that affect the growth of the healthcare services. The healthcare frauds are happening through subscribers, companies and the providers. The development of a decision support is to automate the claim data from service provider and to offset the patient’s challenges. In this paper, a novel hybridized big data and statistical machine learning technique, named MapReduce based iterative support vector machine (MR-ISVM) that provide a set of sophisticated steps for the automatic detection of fraudulent claims in the health insurance databases. The experimental results have proven that the MR-ISVM classifier outperforms better in classification and detection than other support vector machine (SVM) kernel classifiers. From the results, a positive impact seen in declining the computational time on processing the healthcare insurance claims without compromising the classification accuracy is achieved. The proposed MR-ISVM classifier achieves 87.73% accuracy than the linear (75.3%) and radial basis function (79.98%).
Linear regression models with autoregressive integrated moving average errors for measurements from real time kinematics-global navigation satellite system during dynamic test
Authors: Ng Kok Mun , Nahar Ravenny Sandin , IbneReaz Mamun
Published: October 2022

The autoregressive integrated moving average (ARIMA) method has been used to model global navigation satellite systems (GNSS) measurement errors. Most ARIMA error models describe time series data of static GNSS receivers. Its application for modeling of GNSS under dynamic tests is not evident. In this paper, we aim to describe real time kinematic-GNSS (RTK-GNSS) errors during dynamic tests using linear regression with ARIMA errors to establish a proof of concept via simulation that measurement errors along a trajectory logged by the RTK-GNSS can be “filtered”, which will result in improved positioning accuracy. Three sets of trajectory data of an RTK-GNSS logged in a multipath location were collected. Preliminary analysis on the data reveals the inability of the RTK-GNSS to achieve fixed integer solution most of the time, along with the presence of correlated noise in the error residuals. The best linear regression models with ARIMA errors for each data set were identified using the Akaike information criterion (AIC). The models were implemented via simulations to predict improved coordinate points. Evaluation on model residuals using autocorrelation, partial correlation, scatter plot, quantile-quantile (QQ) plot and histogram indicated that the models fitted the data well. Mean absolute errors were improved by up to 57.35% using the developed models.
A hybrid modified lightweight algorithm for achieving data integrity and confidentiality
Authors: Muhalhal Lamia A. , Alshawi Imad S.
Published: October 2022

Encryption algorithms aim to make data secure enough to be decrypted by an attacker. This paper combines the Speck and the Salsa20 to make it difficult for an attacker to exploit any weaknesses in these two algorithms and create a new lightweight hybrid algorithm called Speck-Salsa20 algorithm for data integrity and confidentiality (SSDIC). SSDIC uses less energy and has an efficient throughput. It works well in both hardware and software and can handle a variety of explicit plaintext and key sizes. SSDIC solves the difficulties of the Speck algorithm. The sequence generated by Speck is not random and fails to meet an acceptable success rate when tested in statistical tests. It is processed by generating a random key using the Salsa20 algorithm. Salsa20 is a high-speed secure algorithm that is faster than advanced encryption standard (AES) and can be used on devices with low resources. It uses a 256-bit key hash function. The recovery of the right half of the original key of the Speck algorithm is also handled by modifying the Speck round function and the key schedule. Simulation results show, according to a National Institute of Standards and Technology (NIST) test, the performance achieved by the SSDIC is increased by nearly 66% more than that achieved from the Speck in terms of data integrity and confidentiality.
Performance analysis of a residential photovoltaic string under partial shading
Authors: Yu Byunggyu , Jung Youngseok
Published: October 2022

As one of the most prominent renewable energy resources, Photovoltaic (PV) generation has been growing dramatically over the last years and its applications are classified into residential, commercial, and power plant class, based on its power capacity. Especially, a typical residential PV system has been configured with serially connected PV modules. For serially connected PV modules, shading is a critical factor to reduce the whole string PV output power. This paper presents study on performance analysis of a residential PV string under partial shading. This analysis contains how many maximum power points (MPPs) occur and how much power would be lost while a PV string is under partial shading with different shading positions and different shading intensities. PSIM simulation tool is used to verify the performance analysis. As a result, the number of MPPs is directly related with the number of shading intensities, regardless of the shading position. In this paper, it was verified that 8 maximum power points were generated for 8 solar intensities regardless of the location of the shadows under extreme conditions. These results can be utilized for fault diagnosis in the PV string.
Software quality model based on development team characteristics
Authors: Asfa Hamidreza , Javdani Gandomani Taghi
Published: October 2022

Many factors have a significant impact on producing high-quality software products. Development team members are among the most important factors. Paying attention to the quality from this perspective will be a good innovation in the software development industry. Given that team members play a very important role in software products, this study tries to focus specifically on team characteristics in software product quality and provide a qualitative model based on this. The required data were collected through observations and interviews with project managers and development team members in several companies under study. Then, data were analyzed through hierarchical analysis. According to the results, the use of this model led to the improvement of the software development process so that the team members were satisfied with it. Also, time management was improved, and the customer expressed his satisfaction with the use of this model. Finally, data analysis showed that this model may lead to faster product delivery.
Dynamic congestion management system for cloud service broker
Authors: Alwada’n Tariq , Al-Tamimi Abdel-Karim , Mohammad Adel Hamdan , Salem Maher , Muhammad Yar
Published: October 2022

The cloud computing model offers a shared pool of resources and services with diverse models presented to the clients through the internet by an on-demand scalable and dynamic pay-per-use model. The developers have identified the need for an automated system (cloud service broker (CSB)) that can contribute to exploiting the cloud capability, enhancing its functionality, and improving its performance. This research presents a dynamic congestion management (DCM) system which can manage the massive amount of cloud requests while considering the required quality for the clients’ requirements as regulated by the service-level policy. In addition, this research introduces a forwarding policy that can be utilized to choose high-priority calls coming from the cloud service requesters and passes them by the broker to the suitable cloud resources. The policy has made use of one of the mechanisms that are used by Cisco to assist the administration of the congestion that might take place at the broker side. Furthermore, the DCM system is used to help in provisioning and monitoring the works of the cloud providers through the job operation. The proposed DCM system was implemented and evaluated by using the CloudSim tool.
A doctor recommender system based on collaborative and content filtering
Authors: Shambour Qusai Y. , Al-Zyoud Mahran M. , Hussein Abdelrahman H. , Kharma Qasem M.
Published: October 2022

The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity.
Classification of heterogeneous Malayalam documents based on structural features using deep learning models
Authors: Balakrishnan Jayakumari Bipin Nair , Thomas Kavana Amel
Published: October 2022

The proposed work gives a comparative study on performance of various pretrained deep learning models for classifying Malayalam documents such as agreement documents, notebook images, and palm leaves. The documents are classified based on their visual and structural features. The dataset was manually collected from different sources. The method of research proceeds with preprocessing, feature extraction, and classification. The proposed work deals with three fine-tuned deep learning models such as visual geometry group-16 (VGG-16), convolutional neural network (CNN) and AlexNet. The models attained high accuracies of 99.7%, 96%, and 95%, respectively. Among the three models, the fine-tuned VGG-16 model was found to perform better attaining a very high accuracy on the dataset. As a future work, methods to classify the documents based on content as well as spectral features can be developed.
Power flow analysis of DC distribution system in a ship with non-electric propulsion
Authors: Kurniawan Akhmad Reinaldy , Kurniawan Adi , Sarwito Sardono , Gumilang Ahlur Roi Novanto , Budianto Firman
Published: October 2022

Direct current (DC) shipboard power distribution system offers higher power efficiency and voltage stability compared to the alternating current (AC) systems due to lower impedance. The implementation of DC distribution system in all-electric ship seems to be worthy since the reduction of power loss and voltage drop could overcome the drawback of DC system. However, the effectiveness of DC distribution system in ship with non-electric propulsion has not been investigated yet. Unlike in an all-electric ship, electric power flow in the distribution system of a ship with mechanic propulsion is considerably lower. The study aims to provide numerical analysis of power loss and voltage drop reduction on DC distribution system that applied to a ship with mechanic propulsion. The power flow analysis is performed on a tanker ship. Contrary to the hypothesis, the results show that the DC power distribution increase the power losses about 15% compared to AC system due to the addition of rectifier and inverter. However, the voltage drops are decreased in DC distribution system. Further investigation in the other aspects should be performed before concludes whether DC distribution system is worthy to be used in the aforementioned ship.
Internet of things applications using Raspberry-Pi: a survey
Authors: M. Hosny Khalid , Magdi Amal , Salah Ahmad , El-Komy Osama , Lashin Nabil A.
Published: October 2022

The internet of things (IoT) is the communication of everything with anything else, with the primary goal of data transfer over a network. Raspberry Pi, a low-cost computer device with minimal energy consumption is employed in IoT applications designed to accomplish many of the same tasks as a normal desktop computer. Raspberry Pi is a quad-core computer with parallel processing capabilities that may be used to speed up computations and processes. The Raspberry Pi is an extremely useful and promising technology that offers portability, parallelism, low cost, and low power consumption, making it ideal for IoT applications. In this article, the authors provide an overview of IoT and Raspberry Pi and research on IoT applications using Raspberry Pi in various fields, including transportation, agriculture, and medicine. This article will outline the details of several research publications on Raspberry Pi-based IoT applications.
Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps
Authors: Dhinakaran Priya , Gowdra Shivanandappa Mamatha , Devnath Srijan
Published: October 2022

The detection of keratoconus has been a difficult and arduous process over the years for ophthalmologists who have devised traditional approaches of diagnosis including the slit-lamp examination and observation of thinning of the corneal. The main contribution of this paper is using deep learning models namely Resnet50 and EfficientNet to not just detect whether an eye has been infected with keratoconus or not but also accurately detect the stages of infection namely mild, moderate, and advanced. The dataset used consists of corneal topographic maps and pentacam images. Individually the models achieved 97% and 94% accuracy on the dataset. We have also employed class activated maps (CAM) to observe and help visualize which areas of the images are utilized when making classifications for the different stages of keratoconus. Using deep learning models to predict the detection and severity of the infection can drastically speed up and provide accurate results at the same time.
An FPGA application of home security code using verilog
Authors: Aminuddin Zaim Zakwan , Hamzah Irni Hamiza Binti , Samat Ahmad Asri Abd , Idris Mohaiyedin , Rahim Alhan Farhanah Abd , Soh Zainal Hisham Che
Published: October 2022

Traditional entrance keys performed a number of drawbacks, including the ease with which they can be stolen, duplicated, or misplaced, allowing unauthorized people to gain access to cash, valuables, and other important documents. As known, most digital electronic entrance locks use application specific integrated circuits (ASICs) as based, which have the disadvantage of being unable to be reconfigured, as opposed to field programmable gate arrays (FPGA). This project proposed a home security code lock using verilog hardware descriptive language (HDL) with two unlocking modes, using a button or a keypad which can be changed using a switch. The 7-segment on the Altera DE2-115 trainer board is used to display the passcode press by the keypad. The result of simulation on the keypad using finite state machine (FSM) technique was fulfill the theoretical concept in which it will go to the next state each time the correct input or passcode was entered. When the wrong input or passcode was entered, it will be entered to reset mode. As the conclusion, a fully comply output according to the theoretical FSM concept is fully achieved in this project.
Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network
Authors: Mispan Mohd Syafiq , Jidin Aiman Zakwan , Nasir Haslinah Mohd , Brahin Noor Mohd Ariff , Nawi Illani Mohd
Published: October 2022

A hardware fingerprinting primitive known as physical unclonable function (PUF) has a huge potential for secret-key cryptography and identification/authentication applications. The hardware fingerprint is manifested by the random and unique binary strings extracted from the integrated circuit (IC) which exist due to inherent process variations during its fabrication. PUF technology has a huge potential to be used for device identification and authentication in resource-constrained internet of things (IoT) applications such as wireless sensor networks (WSN). A secret computational model of PUF is suggested to be stored in the verifier’s database as an alternative to challenge and response pairs (CRPs) to reduce area consumption. Therefore, in this paper, the design steps to build a PUF model in NodeMCU ESP8266 using an artificial neural network (ANN) are presented. Arbiter-PUF is used in our study and NodeMCU ESP8266 is chosen because it is suitable to be used as a sensor node or sink in WSN applications. ANN with a resilient back-propagation training algorithm is used as it can model the non-linearity with high accuracy. The results show that ANN can model the arbiter-PUF with approximately 99.5% prediction accuracy and the PUF model only consumes 309,889 bytes of memory space.
Performing the classification of pulsation cardiac beats automatically by using CNN with various dimensions of kernels
Authors: Alagarsamy Manjunathan , Vedam Joseph Michael Jerard , Shanmugam Nithyadevi , Eswaran Paramasivam Muthan , Sankaraiyer Gomathy , Suriyan Kannadhasan
Published: October 2022

The clinical indication of arrhythmia identifies specific aberrant circumstances in heart pumping that may be detected using electrical impulses during conduction or by allowing a little amount of current to travel through the electrodes, disrupting the cardiac muscle's resistance. The electrocardiogram (ECG) is one of the most important instruments for detecting cardiac arrhythmia since it is the most least intrusive and effective procedure. Physically or visually inspecting the heart is time-consuming and difficult, hence the development of computer aided diagnosis (CAD) is being developed to aid clinical decision-making. In this suggested research, a convolutional neural network (CNN)-based approach is used to automate the heartbeat classification process in order to identify cardiac arrhythmia. The improved enhancement of CNN structure has been implemented in this suggested research. The feature maps are then subjected to the max pooling process. Finally, feature maps are generated by concatenating kernels of different sizes and delivering them as an input to the fully linked layers. The MIT BIH arrhythmia database is used to implement this approach, and the total average accuracy is 99.21%. The proof of the suggested study's efficiency and efficacy in identifying cardiac arrhythmia has also been done via an experimental comparison
Managing health supply chain using blockchain technology: state of art challenges and solution
Authors: Sabri Yassine , Harchi Said , El Kamoun Najib
Published: October 2022

The COVID-19 flare-up in late 2019 caused a worldwide medical crisis. In a littlemore than a quarter of a year, the quantity of COVID new cases has risen to in excessof 1,000,000 around the world.The fast transmission of the infection prompts newcases to be accounted for all around the world constantly. At the same time, mortalityrate and contaminations keep on rising rapidly. Therefore, the COVID-19 pandemichas led to the implementation of lockdowns and social distancing rules that negativelyimpact global economies. It has upturned many challenges which are mentioned inthis paper,we proposed a solution to these major problems, blockchain technology canplay a vital role in closing maximum escape clauses in the present system to improvevisibility and traceability and to provide a clear far vision for the stakeholders presentin pharmaceutical industry.
Monitoring and control of single-phase electrical systems using IoT based microcontrollers
Authors: Reddy Puthireddy Umapathi , Naresh Kelothu
Published: October 2022

The internet of things (IoT) has a significant impact on increasing the efficiency and quality of electricity in large power systems. Smart voltage and current monitoring systems (SVCMS) have been developed using a novel method. Using an Arduino Uno microcontroller and a Wi-Fi module, the integrated SVCMS design measures voltage and current sensor findings and sends the processed data to an Android phone. In order to communicate, the global systems for mobile communication (GSM) module is required. When anything goes wrong, an Android app may be used to notify users by text message or email and help them take the right action. It is possible to remotely monitor and control the operation of a gadget using logical programming techniques in Arduino which results easy load control.
Smart vehicle management by using sensors and an IoT based black box
Authors: Rahman Mohammad Minhazur , Kabir A. Z. M. Tahmidul , Mizan Al Mamun , Alvi Kazi Mushfiqur Rahman , Nabil Nazmus Sakib , Ahmad Istiak
Published: October 2022

As the number of transports on the road increases every day, so does the number of accidents. Reckless driving and consuming alcohol are two of the leading causes of accidents. Apart from these issues, the safety of humans and vehicles is also critical. A thorough investigation is required to minimize the accident rate and improve human safety, particularly if an incident occurs. The purpose of this study is to develop a few sensor-based black box system that will help us reduce traffic accidents by continuously providing precise guidance to the driver. At the same time, the evidence will be uploaded to its server for further evaluation. This system also includes a way of detecting drowsiness in the driver. Finally, using global positioning system (GPS) and global system for mobile communications (GSM), the relevant authorities will get information on the vehicle's condition and whereabouts. For security purposes, a panic button is introduced here to get emergency help from the security personnel by detecting the victim’s area.
Large-Scale Algorithmic Search Identifies Stiff and Sloppy Dimensions in Synaptic Architectures Consistent With Murine Neocortical Wiring
Authors: Jabri Tarek , MacLean Jason N.
Published: October 2022

Complex systems can be defined by “sloppy” dimensions, meaning that their behavior is unmodified by large changes to specific parameter combinations, and “stiff” dimensions, whose change results in considerable behavioral modification. In the neocortex, sloppiness in synaptic architectures would be crucial to allow for the maintenance of asynchronous irregular spiking dynamics with low firing rates despite a diversity of inputs, states, and short- and long-term plasticity. Using simulations on neural networks with first-order spiking statistics matched to firing in murine visual cortex while varying connectivity parameters, we determined the stiff and sloppy parameters of synaptic architectures across three classes of input (brief, continuous, and cyclical). Algorithmically generated connectivity parameter values drawn from a large portion of the parameter space reveal that specific combinations of excitatory and inhibitory connectivity are stiff and that all other architectural details are sloppy. Stiff dimensions are consistent across input classes with self-sustaining synaptic architectures following brief input occupying a smaller subspace as compared to the other input classes. Experimentally estimated connectivity probabilities from mouse visual cortex are consistent with the connectivity correlations found and fall in the same region of the parameter space as architectures identified algorithmically. This suggests that simple statistical descriptions of spiking dynamics are a sufficient and parsimonious description of neocortical activity when examining structure-function relationships at the mesoscopic scale. Additionally, coarse graining cell types does not prevent the generation of accurate, informative, and interpretable models underlying simple spiking activity. This unbiased investigation provides further evidence of the importance of the interrelationship of excitatory and inhibitory connectivity to establish and maintain stable spiking dynamical regimes in the neocortex.
Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes
Authors: Pedigo Benjamin D. , Winding Michael , Priebe Carey E. , Vogelstein Joshua T.
Published: October 2022

Graph matching algorithms attempt to find the best correspondence between the nodes of two networks. These techniques have been used to match individual neurons in nanoscale connectomes—in particular, to find pairings of neurons across hemispheres. However, since graph matching techniques deal with two isolated networks, they have only utilized the ipsilateral (same hemisphere) subgraphs when performing the matching. Here, we present a modification to a state-of-the-art graph matching algorithm that allows it to solve what we call the bisected graph matching problem. This modification allows us to leverage the connections between the brain hemispheres when predicting neuron pairs. Via simulations and experiments on real connectome datasets, we show that this approach improves matching accuracy when sufficient edge correlation is present between the contralateral (between hemisphere) subgraphs. We also show how matching accuracy can be further improved by combining our approach with previously proposed extensions to graph matching, which utilize edge types and previously known neuron pairings. We expect that our proposed method will improve future endeavors to accurately match neurons across hemispheres in connectomes, and be useful in other applications where the bisected graph matching problem arises.
Three species of Amanita section Lepidella (Amanitaceae, Agaricales) from northern Thailand
Authors: LIU YUAN S. , KUMLA JATURONG , SUWANNARACH NAKARIN , SYSOUPHANTHONG PHONGEUN , LUMYONG SAISAMORN
Published: October 2022

The genus Amanita, comprising three subgenera and eleven sections, is one of the most species diverse genera within the class Agaricomycetes. To date, 54 species have been validly reported from Thailand. During surveys of macro-fungi in northern Thailand in 2019 and 2020, three Amanita species were collected and identified as the members of Amanita sect. Lepidella based on morphological and molecular data. Among the three species, two species were identified as A. aureofloccosa and A. manicata and A. aureofloccosa is reported as a new record in Thailand. Interestingly, the third species was distinct from all other known species within sect. Lepidella. We describe the third species as a new species, namely A. albicarnosa. All these three taxa are documented with morphological characteristics, a phylogenetic tree, line drawings, color photographs, and comparisons with morphologically similar taxa. A distributional map of the sampling sites and a key to the Thai species of Amanita sect. Lepidella are also provided.
Herminium gongshanense (Orchidaceae), a new species from China
Authors: YE CHAO , PANDEY TIRTHA RAJ , JIN XIAO-HUA
Published: October 2022

A new species of Herminium (Orchidaceae) from China, H. gongshanense, is described and illustrated based on morphological characters and molecular systematics. Herminium gongshanense is rather isolated in Herminium and morphological similar to H. singulum but differs from it by having peduncle without bract-like leaflet, lip 3-lobed, lateral-lobes enclosing column, staminodes filamentous and half of column. Molecular phylogenetic results based on one nuclear marker (nrITS) and two plastid markers (matK and rbcL) indicated that H. gongshanense is sister to the remaining Herminium.
Protest During a Pandemic: How Covid-19 Affected Social Movements in the United States
Authors: Rohlinger Deana A. , Meyer David S.
Published: October 2022

This paper explores how a global health crisis affects the causes and consequences of social movements. Drawing on media coverage, press releases, emails, and other available primary data sources, we examine how the pandemic changed the opportunities and conditions for activists on the right and left and those they challenge. We begin by considering the nature of the COVID-19 pandemic and the concomitant government response, which alters the structure of political opportunities activists face. We then look at the development of a range of protest campaigns that have emerged in response, assessing changes in opportunities for activists to reach and mobilize target constituencies, the construction of grievances, nature of alliances, as well as innovation in tactics and organization. Finally, we consider the potential outcomes of these protests during the pandemic and extending afterward.
Procainamide, N-Acetylprocainamide, Antinuclear Antibody and Systemic Lupus Erythomatosus
Authors: Reidenberg Marcus M. , Drayer Dennis E.
Published: October 2022

Long-term therapy with procainamide (PA) leads to the systemic lupus erythematosus syndrome (SLE) in about 30% of patients and 80% develop antinuclear antibodies. Acetylation of procainamide results in the formation of N-acetylprocainamide (NAPA) the propensity of which to induce SLE and to increase antinuclear antibodies is negligible while its antiarrhythmic properties remain. Slow acetylators of PA have a greater tendency to induce SLE consistent with the observation that it is the level of PA that is responsible for the observed immunologically-mediated side effects of the compound. This is also suggested by the remission of PA-induced SLE when PA is replaced with NAPA for the control of cardiac arrhythmias. Thus, NAPA, compared to the parent compound, has little tendency to induce SLE.
Acceptance of using technologies in telesupervision during the pandemic: An international study
Authors: Mo Kitty Yuen-han , O’Donoghue Kieran
Published: October 2022

This study utilizes the Technology Acceptance Model by exploring the relationship between user acceptance attitude and actual usage behaviors of technological tools in telesupervision among supervisors in international societies. Specifically, the age of supervisors is examined to see whether it mediates the relationship between acceptance attitude and usage behavior. Survey data were collected from 194 supervisors in international societies using online Survey Monkey. The results indicated a significant relationship between user acceptance attitude and actual usage behaviors of technological tools in telesupervision. Implications of these findings for supervision training and further telesupervision development are discussed.
TRAF3IP2–IL-17 Axis Strengthens the Gingival Defense against Pathogens
Authors: Zhang J. , Sun L. , Withanage M.H.H. , Ganesan S.M. , Williamson M.A. , Marchesan J.T. , Jiao Y. , Teles F.R. , Yu N. , Liu Y. , Wu D. , Moss K.L. , Mangalam A.K. , Zeng E. , Lei Y.L. , Zhang S.
Published: October 2022

Recent genome-wide association studies have suggested novel risk loci associated with periodontitis, which is initiated by dysbiosis in subgingival plaque and leads to destruction of teeth-supporting structures. One such genetic locus was the tumor necrosis factor receptor–associated factor 3 interacting protein 2 ( TRAF3IP2), a gene encoding the gate-keeping interleukin (IL)–17 receptor adaptor. In this study, we first determined that carriers of the lead exonic variant rs13190932 within the TRAF3IP2 locus combined with a high plaque microbial burden was associated with more severe periodontitis than noncarriers. We then demonstrated that TRAF3IP2 is essential in the IL-17–mediated CCL2 and IL-8 chemokine production in primary gingival epithelial cells. Further analysis suggested that rs13190932 may serve a surrogate variant for a genuine loss-of-function variant rs33980500 within the same gene. Traf3ip2 null mice ( Traf3ip2–/–) were more susceptible than wild-type (WT) mice to the Porphyromonas gingivalis–induced periodontal alveolar bone loss. Such bone loss was associated with a delayed P. gingivalis clearance and an attenuated neutrophil recruitment in the gingiva of Traf3ip2–/– mice. Transcriptomic data showed decreased expression of antimicrobial genes, including Lcn2, S100a8, and Defb1, in the Traf3ip2–/– mouse gingiva in comparison to WT mice prior to or upon P. gingivalis oral challenge. Further 16S ribosomal RNA sequencing analysis identified a distinct microbial community in the Traf3ip2–/– mouse oral plaque, which was featured by a reduced microbial diversity and an overabundance of Streptococcus genus bacteria. More P. gingivalis was observed in the Traf3ip2–/– mouse gingiva than WT control animals in a ligature-promoted P. gingivalis invasion model. In agreement, neutrophil depletion resulted in more local gingival tissue invasion by P. gingivalis. Thus, we identified a homeostatic IL-17-TRAF3IP2-neutrophil axis underpinning host defense against a keystone periodontal pathogen.
A Daily Diary Investigation of the Fear of Missing Out and Diminishing Daily Emotional Well-Being: The Moderating Role of Cognitive Reappraisal
Authors: Hartanto Andree , Wong Joax , Lua Verity Y. Q. , Tng Germaine Y. Q. , Kasturiratna K. T. A. Sandeeshwara , Majeed Nadyanna M.
Published: October 2022

With modern societies becoming ever-increasingly interconnected due to technology and media, we have gained unprecedented access and exposure to other people’s lives. This has resulted in a greater desire to constantly be socially connected with the activities of others, or the fear of missing out (FoMO). While much of the present available research has established the association between FoMO and diminished emotional well-being, little has been done to identify protective factors that can help one cope with the negative psychological consequences of FoMO. Utilizing data from a 7-day diary study of a large sample of young adults ( N = 261), the current study aimed to examine the moderating role of cognitive reappraisal in attenuating diminished emotional well-being associated with FoMO. Multilevel modeling showed that cognitive reappraisal attenuated the day-to-day within-person associations between daily FoMO and indicators of daily emotional well-being such as negative affectivity, anxiety, and depressive symptoms.
How Do Aging Self-Stereotypes Relate to Social Isolation in Older Adults? The Intervening Roles of Sense of Coherence and Cellphone Use
Authors: Lin Yao , Zhang Baoshan , Ma Yuting
Published: October 2022

The aim of this study was to investigate the relationships among aging self-stereotype, sense of coherence (SOC), and social isolation, as well as further explore the moderating role of cellphone use in older adults. The 817 Chinese older adults were assessed with regard to aging self-stereotypes, social isolation, SOC and cellphone use. Results from mediation analyses revealed that the negative effect of aging self-stereotype on social isolation was mediated by SOC. Moderated mediation analyses further indicated that the path from SOC to social isolation was stronger and the path from aging self-stereotype to social isolation was weaker for older adults with a higher level of cellphone use. These findings indicated that negative stereotypes were related to weaker SOC and are associated with more severe social isolation. Frequent use of cellphones can alleviate the negative effect of aging self-stereotype on social isolation and enhance the positive effect of SOC on social isolation.
Exorcisme catholique en France contemporaine, entre thérapeutique et régulation institutionnelle
Authors: Giménez Béliveau Verónica
Published: October 2022

This article proposes to study the contemporary implementation of Catholic exorcism rituals in France. It is a growing demand that challenges the Catholic Church and expresses discomforts in contemporary society. Based on a study conducted in France between 2016 and 2020, the research is spread over various dioceses and works with diverse actors (priests, exorcists, ritual requesters, lay collaborators) belonging to plural groups. Using qualitative strategies – mainly ethnography, active interviews and document analysis – this article analyses the population requesting exorcism, and attempts to understand the environments in which the practices are anchored and the work of the exorcists. This article shows the dialogue established between the practice of exorcism and medical interpretations, and the deployment of a new listening and regulation of the Church towards an increased demand which is related to indeterminate misfortunes linked to the field of health and healing.