bluuebunny/crossref_metadata_2025 huggingface dataset:
MOCAB/HEFT algorithm of multi radio wireless communication improved achievement assessment
Authors: Nasser Thaar Habeb , Hamza Ekhlas Kadhum , Hasan Ahmed Mudheher
Published: November 2022

Network-wide conveying is vital in remote associations, and the great part of these broadcasts are built on single-channel single-radio (SC-SR) network frameworks. The problem of the current work is divided into two parts. The first part shows that increasing broadcast and redundancy lead to an increase in time consumption. The second problem is solving complexity problems when tasks are scheduled in a heterogeneous manner in a computing system, where the processors in the network may not be identical and take different time periods to carry out the same task. The goals of this work are to reduce the total cost of network-wide broadcasting to minimize the search space and to solve the complexity problem when tasks are scheduled in a heterogeneous way in the computing system. The MOCAB algorithm is used to select the best transmission path over the network in the first stage. Then, the tasks will be scheduled using the heterogeneous earliest finish time (HEFT) algorithm to extract the values of actual finish time (AFT), earliest start time (EST), and earliest finish time (EFT). The performance of the MOCAB algorithm was evaluated with that of the HEFT algorithm in terms of the delivery ratio of packets delivered. The results showed that the MOCAB algorithm outperformed the HEFT.
Stereo matching algorithm for autonomous vehicle navigation using integrated matching cost and non-local aggregation
Authors: Zahari Madiha , Hamzah Rostam Affendi , Manap Nurulfajar Abd , Herman Adi Irwan
Published: November 2022

Stereo matching algorithm plays an important role in an autonomous vehicle navigation system to ensure accurate three-dimensional (3D) information is provided. The disparity map produced by the stereo matching algorithm directly impacts the quality of the 3D information provided to the navigation system. However, the accuracy of the matching algorithm is a challenging part to be solved since it is directly affected by the surrounding environment such as different brightness, less texture surface, and different image pair exposure. In this paper, a new framework of stereo matching algorithm that used the integration of census transform (CT) and sum of absolute difference (SAD) at the matching cost computation step, non-local cost aggregation at the second step, winner take all strategy at the third step, and a median filter at the final step to minimize disparity map error. The results show that the accuracy of the disparity map is improved using the proposed methods after some parameter adjustment. Based on the standard Middlebury and KITTI benchmarking dataset, it shows that the proposed framework produced accurate results compared with other established methods.
Automatic keyphrases extraction: an overview of deep learning approaches
Authors: Ajallouda Lahbib , Fagroud Fatima Zahra , Zellou Ahmed , Benlahmar El habib
Published: November 2022

Automatic keyphrases extraction (AKE) is a principal task in natural language processing (NLP). Several techniques have been exploited to improve the process of extracting keyphrases from documents. Deep learning (DL) algorithms are the latest techniques used in prediction and extraction of keyphrases. DL is one of the most complex types of machine learning, relying on the use of artificial neural networks to make the machine follow the same decision-making path as the human brain. In this paper, we present a review of deep learning-based methods for AKE from documents, to highlight their contribution to improving keyphrase extraction performance. This review will also provide researchers with a collection of data and information on the mechanisms of deep learning algorithms in the AKE domain. This will allow them to solve problems encountered by AKE approaches and propose new methods for improving key-extraction performance.
Finite element method based design and performance analysis of universal motor for agro applications
Authors: Sharma Sudhir Kumar , Manna Manpreet Singh
Published: November 2022

A universal motor is one that is capable of operating on either AC or DC power supply. The commutator is a component of the motor that has a significant impact on how efficiently the motor operates. It is essential to conduct an analysis of the pole structure of the universal motor in order to investigate the many aspects. The parametric study of a universal motor with a rating of 1 horsepower and 15,000 revolutions per minute that was designed with various combinations of brush angle and pole embrace factor for use in agricultural applications. The purpose of this study is to improve the effectiveness of the motor while preserving the ideal tolerance range for the model's other parameters as much as possible. The approach now allows for a greater degree of personalization for each distinct combination of factors. With the assistance of the finite element method (FEM), the transient solution is carried out so that the performance of the motor can be evaluated more accurately. The model that has been designed provides major design improvements, one of which is an improved average torque.
Automatic spelling error detection and correction for Tigrigna information retrieval: a hybrid approach
Authors: Desta Solomon Gebremariam , Lehal Gurpreet Singh
Published: November 2022

This paper proposes a hybrid approach to design and implement query spelling error detection and correction (SEDC) for Tigrigna information retrieval (IR). Our approach, which is the main contribution to this work, is fast and robust to achieve better performance and also helps the users to easily insert their corrected queries to retrieve relevant information from the IR. This is achieved by combining the normalized measure of bigram overlap using the Jaccard coefficient (J.C) technique, a dynamic programming algorithm for edit distance, and probability of occurrence, which were used to make suggestions for the misspelt words. Our approach was evaluated on the SEDC subtasks separately. It achieved an F-measure of 98.85% on the spelling error detection subtask and an accuracy of 95.36% on the spelling error correction subtask. Thus, a comparison was conducted between our approach and the existing Tigrigna spell checker. It is found that our approach outperformed the existing spell checker and shows a 5.36% improvement in accuracy. This is by far the most promising result with regard to correcting the misspelt users’ queries and improving the overall performance of the IR.
Signal multiple encodings by using autoencoder deep learning
Authors: Anaz Ammar Sameer , Al-Ridha Moatasem Yaseen , Al-Nima Raid Rafi Omar
Published: November 2022

Encryption is a substantial phase in information security. It permits only approved persons to get private information. This study suggests a signal multi-encryptions system (SMES) technique for coding and decoding signals created by a deep autoencoder network (DAN). The DAN of four layers is employed for a coding package of signals multiple times before decoding or restructuring the original signals again. The suggested SMES offers a high level of security as it can produce and exploit multiple encryptions for signals. Many statistical calculations are applied to measure the reliability of the system. The outcomes are promising where noteworthy encryptions-decryptions are obtained.
Multimodal music emotion recognition in Indonesian songs based on CNN-LSTM, XLNet transformers
Authors: Sams Andrew Steven , Zahra Amalia
Published: November 2022

Music carries emotional information and allows the listener to feel the emotions contained in the music. This study proposes a multimodal music emotion recognition (MER) system using Indonesian song and lyrics data. In the proposed multimodal system, the audio data will use the mel spectrogram feature, and the lyrics feature will be extracted by going through the tokenizing process from XLNet. Convolutional long short term memory network (CNN-LSTM) performs the audio classification task, while XLNet transformers performs the lyrics classification task. The outputs of the two classification tasks are probability weight and actual prediction with the value of positive, neutral, and negative emotions, which are then combined using the stacking ensemble method. The combined output will be trained into an artificial neural network (ANN) model to get the best probability weight output. The multimodal system achieves the best performance with an accuracy of 80.56%. The results showed that the multimodal method of recognizing musical emotions gave better performance than the single modal method. In addition, hyperparameter tuning can affect the performance of multimodal systems.
Design and analysis several band antenna for wireless communication
Authors: Nghaimesh ِAbeer Khalid , Jassim Ali Khalid , Abid Ali Waleed Khalid
Published: November 2022

This article describes the construction of a dual-band planar monopole antenna. A microstrip patch antenna with a feedline impedance of 50 ohm and a patch composed of G-shaped and inverted L-shaped strips is used to make the suggested antenna ultra wideband for frequencies ranging from 3.1 to 10.6 GHz. In order to design the antennas, we need to know the dimensions 40x40x1.6 mm3 and the thickness of the ground plane (0.035 mm) (5.2 GHz). There is a method of altering the present distribution by introducing slots. the proposed worldwide interoperability for microwave access (WiMAX) and wireless local area network (WLAN) bands, with a peak gain of 5.2% and an omnidirectional radiation pattern, suitable for ultra wide band (UWB) were shown to be viable.
Design security architecture for unmanned aerial vehicles by 5G cloud network based implementation of SDN with NFV and AI
Authors: Lehmoud Ahssan Ahmed Mohammed , Obeis Nawfal Turki , Mutar Ahmed Fakhir
Published: November 2022

A recent progression of unmanned aerial vehicles (UAV) augmentation its employments for different applications. It’s also vulnerable to being, stolen, lost, stray, or destroyed at status of a security infringements for the UAV network. The proposed strategy is defending against of different attacks through using artificial intelligence by implements five steps: RGSK, GCSCS, SEDC, HSSC, and FVNF. UAV authentication is happened in the first step through the Curve448. We performance deep reinforcement learning to run with GCS for packet assignment as it implemented for switch current state identification before updating. In our work we ability to alleviate for attack of flow table overloading by assigned of packets as an under loaded or idle switches. Then, selected the least loaded switch by applied 5 tuples. Hence, we divided SDN to SEDCs and HSSC forms. First in the SEDC we using Shannon entropy to achieve classified of input packet in to regular and suspicious packets. Last will forwarded regular packets to cloud layer. By growing multiple self-organizing maps for maintained in NFV that used to classify suspicious packets as classes normal or malicious packet. The proposed performance work evaluates using NS3.26 show up the better strategy to secure UAV for different attacks.
Optical network on chip: design of wavelength routed optical ring architecture
Authors: Kavitha Thandapani , Maheswaran Gopalswamy , Maheswaran Joly , Pappa Chandramohan K.
Published: November 2022

Network on chip (NoC) technology has now achieved a mature stage of development as a result of their use as a key component in many successful commercial devices. As multiprocessors continue to scale, these ship based electronic networks are more challenging to meet their power budget communication requirements. Innovative technology is emerging with the aim of offering shorter latencies and greater bandwidth with lower power consumption. Ring topology provides superior results among the all wavelength routed topologies in the chip optical network. In this paper, we proposed an optical ring network-on-chip (ORNoC) architecture which is contention free. Communication matrix is used to assign a single waveguide/wavelength pair to implement simultaneous communications. The design constraints for the proposed architecture will be wavelength reused on a single waveguide for multiple communications. We imply automatic wavelength/waveguide assignment for effective design and will prove that the proposed architecture can connect more number of nodes and less wavelengths per waveguide.
Stock market analysis with the usage of machine learning and deep learning algorithms
Authors: Sarma Seethiraju L. V. V. D. , Sekhar Dorai Venkata , Murali Gudipatu
Published: November 2022

In this work we are focusing on listing out various works in the understanding of various parameters and context to get the overview of stock market analysis in the context of machine learning (ML) and deep learning (DL) models. The work focusses on the stock market analysis along with methodologies and algorithms used to understand the trends and the corresponding results as part of those studies. The importance of this work is to summarize and analyse the parameters which are highly influenced the understandingof the stock market trends. The outcome of the work is understanding the important factors which directly and indirectly influences the stock value raise and drop. The work highlights the methodologies and the algorithms used to stock market data analysis and efficient and effective recommendation of stable stocks to the customers. Further we are listing out the research gaps and future enhancements of the studies which are left over in the earlier works. The work pops up the limitations of some of the works in the existing works along with significance of hyper parameter techniques to clearly identify the features through which we can get more possibilities of better analysis of the data.
Synthesis of sliding mode control for flexible-joint manipulators based on serial invariant manifolds
Authors: Thang Le Tran , Son Tran Van , Khoa Truong Dang , Chiem Nguyen Xuan
Published: November 2022

This paper focuses on synthesizing sliding mode control (SMC) for flexible-joint manipulators (FJM) based on serial invariant manifolds in order to increase the control quality for the system. SMC based on the serial invariant manifolds is proposed. The control law is found based on synergetic control theory (SCT) and analytical design of aggregated regulators (ADAR) method. In order to improve the control quality due to the effect of the stiffness value between two links in the system, a mechanism for constructing manifolds is built. The time response of the outer loop manifolds close to the actuator will be larger in the next round. The control quality of the system can be pre-evaluated through the parameters of the designed manifolds. Global stability is demonstrated by using the Lyapunov function in the design process. Finally, the effectiveness of the proposed controller based on SCT is demonstrated by numerical simulation results and compared with the traditional SMC.
Exploiting artificial intelligence for combating COVID-19: a review and appraisal
Authors: Sharma Richa , Pandey Himanshu , Agarwal Ambuj Kumar
Published: November 2022

Machine learning algorithms immediately became critical in the battle against the COVID-19 outbreak. Diagnoses, medicine research, an illness spread predictions, and population surveillance all required the use of artificial intelligence (AI) methods as the epidemic grew in scope. To combat COVID-19, screening procedures that are both effective and rapid are required. At COVID-19, AI developers took a chance to show how AI can benefit all mankind. It was only after the employment of AI in the battle against COVID-19. AI's various and diverse applications in the epidemic are documented in this study. It is the purpose of this study to help shape the future development and usage of these technologies, whether in the present or future health crises.
CON_PCA method to solve the human monochromacy color blindness
Authors: Ahmed Basaeir Y. , Ali Huda A.
Published: November 2022

Color blindness is an inherited genetic ocular disorder. Acquired or congenital color blindness can become a major cause of problems in everyday life tasks. Contributing to solving the trouble of color blindness is important. Because the problem of color blindness leads to the non-discrimination of data seen by the infected person. Data visualization can contribute to solving the hassle of color blindness. This paper proposed a new technique using data visualization ideas that may contribute to easy data recognition. Our focus has been on monochromacy color blindness. The results are based on the famous Ishihara data sets measuring color blindness. The efficiency of the results was mathematically proven using equations and images via a website to simulate color blindness.
An experimental study of tomato viral leaf diseases detection using machine learning classification techniques
Authors: Sagar Sanjeela , Singh Jaswinder
Published: November 2022

Agriculture is the backbone of India and more than 50% of the population is dependent on it. With the increasing demand for food with the increase in population, it is the need of time that crops should be prevented against diseases. More than 1K acres of land with tomato diseases got affected in Pune only during this pandemic (2021). It could have been prevented by correct identification of the disease and then by corrective measures. This paper presents the experimental and comparative study of tomato leaf disease classification using various traditional machine learning algorithms like random forest (RF), support vector machines (SVM), naïve bayes (NB), and deep learning convolutional neural network (CNN) algorithm. In this study, it is perceived that CNN with a pre-trained Inception v3 model was able to detect and classify better than traditional methods with more than 95% accuracy.
Cluster-based segmentation for tobacco plant detection and classification
Authors: Thimmegowda Thirthe Gowda Mallinathapura , Jayaramaiah Chandrika
Published: November 2022

Tobacco is one of the major economical crops in the agriculture sector. It is essential to detect tobacco plants using unmanned aerial vehicle (UAV) images for improved crop yield and plays an important role in the early treatment of tobacco plants. The proposed research work is carried out in three phases: In the first phase, we collect images from UAV’s and apply the French Commision Internationale de l'eclairage (CIE) L*a*b colour space model as pre-processing operations and segmentation. And then two prominent motion descriptors namely histogram of flow (HOF) and motion boundary histogram (MBH) are combined with the optimal histogram of oriented gradients (HOG) descriptor for exploring optimal motion trajectory and spatial measurements. And finally, the spatial variations with respect to the scale and illumination changes are incorporated using the optimal HOG descriptor. Here both dense motion patterns and HOG are refined using hierarchical feature selection using principal component analysis (PCA). The proposed model is trained and evaluated on different tobacco UAV image datasets and done a comparative analysis of different machine learning (ML) algorithms. The proposed model achieves good performance with 95% accuracy and 92% of sensitivity.
The effectiveness of big data classification control based on principal component analysis
Authors: Mohammed Mostafa Abdulghafoor , Akawee Mostafa Mahmood , Saleh Ziyad Hussien , Hasan Raed Abdulkareem , Ali Ahmed Hussein , Sutikno Tole
Published: November 2022

Large-scale datasets are becoming more common, yet they can be challenging to understand and interpret. When dealing with big datasets, principal component analysis (PCA) is used to minimize the dimensionality of the data while maintaining interpretability and avoiding information loss. It accomplishes this by producing new uncorrelated variables that gradually reduce the variance of the system. In the field of data analysis, PCA is a multivariate statistical technique commonly used to obtain rules explaining the separation of groups in a given situation. Classes are predicted using a classification algorithm, a supervised learning technique that indicates which type of data points will be presented. Creating a classification model using classification algorithms is required before any successful classification can be achieved. It is possible to predict the future using a variety of categorized strategies. It is necessary to reduce the dimensionality of data sets using the PCA approach. This article will begin by introducing the basic ideas of PCA and discussing what it can and cannot do. It will then describe some variants of PCA and their application and then shows how PCA improves the performance using a series of experiments.
The software requirements process for designing a microcontroller-based voice-controlled system
Authors: Hussien Nadia Mahmood , Mohialden Yasmin Makki , Akawee Mustafa Mahmood , Mohammed Mostafa Abdulghafoor
Published: November 2022

Smartphones of today are capable of controlling motors, music systems, and lighting. This project's objective is to construct a robot car for the elderly and disabled that is based on the Arduino platform. Voice instructions can be used to wirelessly control the robotic car that the user is riding. The robot is able to move to the left and right, as well as forward and backward, and it can also stop. The voice-controlled robot vehicle built using Arduino and operated by an HC-05 module is connected to Bluetooth. The exact spoken commands are sent to the robot through the phone via an application that runs on android. The Arduino, which is in charge of controlling the robotic automobile, gets commands through a Bluetooth transceiver module, which then relays them to the Arduino. The hardware consists of an android phone, an android-powered motor drive, an Arduino, and Bluetooth. This system was developed with the help of Arduino C and the android-meets-robot framework. The primary goals of this piece of writing are to gain an understanding of how to create the criteria for a voice-controlled system that is based on Arduino.
A novel approach for new architecture for green data centre
Authors: Al-Fatlawi Ahmed Abdulhassan , Al-Barazanchi Israa
Published: November 2022

The massive energy usage of data centers may be traced in part to the growing number of data centers and workstations because of economies of scale for cloud computing. It does, though, indicate wasteful power usage. Consequently, researching ways to improve the energy efficiency of datacenter equipment is now a crucial aspect in reducing datacenter power consumption. In this study, we describe methodologies and algorithms for flexible, energy-efficient, and effective load balancing in data centers, resulting in lower energy consumption by systems. Because of difficulties related with energy consumption, such as capital expenditure, operational costs, and environmental effect, renewable energy is becoming an increasing major consideration in data centers. With the rise in environmental consequences around the world, data centers must consider energy efficiency as one of the most critical factors. Cloud computing techniques have made a huge impact all over the world. Green DC is a concept that has been tested. We suggested an algorithm in this paper based on certain current algorithm limitations that will help to reduce environmental effect. We investigated the efficiency of our program further and used a load balancing method to improve its performance.
Automated water quality monitoring and regression-based forecasting system for aquaculture
Authors: Wei Toh Yin , Tindik Emmanuel Steward , Fui Ching Fui , Haviluddin Haviluddin , Hijazi Mohd Hanafi Ahmad
Published: November 2022

Water quality in fish tanks is essential to reduce fish mortality. Many factors affect the water quality, such as pH, dissolved oxygen, and temperature in fish tanks. Existing work has presented water quality monitoring systems for aquaculture, which are useful for automatic monitoring and notify any incidence of decline in water quality. It enables the fish farms to make interventions to reduce fish mortality. However, advanced monitoring through forecasting is necessary to ensure consistent optimum water quality. This paper presents a web-based water quality monitoring and forecasting system for aquaculture. First, a water quality forecasting model based on the long short-term memory is designed and developed. The model is evaluated and fine-tuned using the existing public dataset. Second, the prototype of the water quality monitoring and forecasting system is developed. An Arduino and Raspberry Pi based water quality data acquisition tool is built. A web-based application is then developed to present the monitoring data and forecasting. A notification module is included to send an alert message to the fish farmers when necessary. The system is tested and evaluated at the fish hatchery in Universiti Malaysia Sabah. The findings show that the proposed system provides better water quality management for fish farms.
A proposed software for controlling operating system-dependent functionality
Authors: Abdulameer Alazawi Sundos , Mahmood Hussien Nadia , Makki Mohialden Yasmin , Mohammed Mostafa Abdulghafoor
Published: November 2022

When the operating system environment temperature rises above safety, the CPU may become unresponsive or even malfunction. To address this problem, to achieve this goal a two-part system was designed. The first, consists of a controlled sensor that constantly monitors the room environment temperature and alerts the user if it rises above acceptable levels for computer use. The second part adopts a Python that uses the OS module, which provides a portable interface for OS-dependent tasks and shuts down the device to prevent it from behaving unexpectedly. A series of experiments at different temperatures demonstrated the ability of the device to alert the user.
A parametric study on strawberry radiated shaped monopole antenna for ultrawide-band applications
Authors: Al-Gburi Ahmed Jamal Abdullah , Zakaria Zahriladha , Ibrahim Imran Mohd , Akbar Muhammad Firdaus , Al-Obaidi Aymen Dheyaa Khaleel , Khabba Asma
Published: November 2022

This article gives a parametric study on strawberry-shaped monopole antennas for ultra-wideband (UWB) systems. The antenna design consisted of three different parametric design steps to structure the strawberry radiated monopole antenna. The scheduled strawberry monopole antenna was simulated on an FR4 substrate in a low profile for UWB applications. The total physical dimension is 26 mm×26 mm×1.6 mm, corresponding to the centre frequency of 7.5 GHz. The strawberry antenna is fed via a coplanar waveguide (CPW) to attain the best impedance matching for UWB systems. The presented monopole antenna has an impedance UWB bandwidth of 11.0 GHz from 2.6 GHz up to 13.6 GHz at −10 dB return loss. The simulated UWB strawberry monopole antenna displays an omnidirectional radiation behaviour with a simulated gain of 7.3 dB at 13.6 GHz, a directivity of 7.5 dBi at 13.6 GHz and favourable radiation efficiency of 97%. The proposed monopole UWB strawberry antenna has the technological possibility to be used for UWB applications.
Exercise for the Prevention of Anthracycline-Induced Functional Disability and Cardiac Dysfunction: The BREXIT Study
Authors: Foulkes Stephen J. , Howden Erin J. , Haykowsky Mark J. , Antill Yoland , Salim Agus , Nightingale Sophie S. , Loi Sherene , Claus Piet , Janssens Kristel , Mitchell Amy M. , Wright Leah , Costello Ben T. , Lindqvist Anniina , Burnham Lauren , Wallace Imogen , Daly Robin M. , Fraser Steve F. , La Gerche André
Published: November 2022

Background:

Breast cancer survivors treated with anthracycline-based chemotherapy (AC) have increased risk of functional limitation and cardiac dysfunction. We conducted a 12-month randomized controlled trial in 104 patients with early-stage breast cancer scheduled for AC to determine whether 12 months of exercise training (ExT) could attenuate functional disability (primary end point), improve cardiorespiratory fitness (VO
2
peak), and prevent cardiac dysfunction.

Methods:

Women 40 to 75 years of age with stage I to III breast cancer scheduled for AC were randomized to 3 to 4 days per week aerobic and resistance ExT for 12 months (n=52) or usual care (UC; n=52). Functional measures were performed at baseline, at 4 weeks after AC (4 months), and at 12 months, comprising: (1) cardiopulmonary exercise testing to quantify VO
2
peak and functional disability (VO
2
peak ≤18.0 mL·kg
−1
·min
−1
); (2) cardiac reserve (response from rest to peak exercise), quantified with exercise cardiac magnetic resonance measures to determine changes in left and right ventricular ejection fraction, cardiac output, and stroke volume; (3) standard-of-care echocardiography-derived resting left ventricular ejection fraction and global longitudinal strain; and (4) biochemistry (troponin and BNP [B-type natriuretic peptide]).

Results:

Among 104 participants randomized, greater study attrition was observed among UC participants (
P
=0.031), with 93 women assessed at 4 months (ExT, n=49; UC, n=44) and 87 women assessed at 12 months (ExT, n=49; UC, n=38). ExT attenuated functional disability at 4 months (odds ratio, 0.32 [95% CI, 0.11–0.94];
P
=0.03) but not at 12 months (odds ratio, 0.27 [95% CI, 0.06–1.12];
P
=0.07). In a per-protocol analysis, functional disability was prevented entirely at 12 months among participants adherent to ExT (ExT, 0% versus UC, 20%;
P
=0.005). Compared with UC at 12 months, ExT was associated with a net 3.5-mL·kg
−1
·min
−1
improvement in VO
2
peak that coincided with greater cardiac output, stroke volume, and left and right ventricular ejection fraction reserve (
P
<0.001 for all). There was no effect of ExT on resting measures of left ventricular function. Postchemotherapy troponin increased less in ExT than in UC (8-fold versus 16-fold increase;
P
=0.002). There were no changes in BNP in either group.

Conclusions:

In women with early-stage breast cancer undergoing AC, 12 months of ExT did not attenuate functional disability, but provided large, clinically meaningful benefits on VO
2
peak and cardiac reserve.

Registration:

URL:
https://www.anzctr.org.au/
; Unique identifier: ACTRN12617001408370.
Misery Perfusion and Tau Deposition in Atherosclerotic Major Cerebral Artery Disease: A
18
F-Florzolotau Positron Emission Tomography Study
Authors: Yamauchi Hiroshi , Kagawa Shinya , Kusano Kuninori , Ito Miki , Okuyama Chio
Published: November 2022

Background:

Studies using animal models have shown that cerebral hypoperfusion causes hyperphosphorylation of tau protein, leading to neuronal damage. However, the relationship between hypoperfusion and tau deposition in humans is unclear. Hence, we aimed to determine whether cerebral hypoperfusion leading to decreased blood flow relative to metabolic demand [increased oxygen extraction fraction (OEF), misery perfusion] is associated with increased tau deposition in patients with atherosclerotic internal carotid artery or middle cerebral artery disease.

Methods:

We prospectively evaluated the distribution of tau aggregate deposition using positron emission tomography and
18
F-florzolotau (PMPBB3 [1-fluoro-3-((2-((1E,3E)-4-(6-(methylamino)pyridine-3-yl)buta-1,3-dien-1-yl)benzo[d]thiazol-6-yl)oxy)propan-2-ol)]) in 8 patients with atherosclerotic disease of the internal carotid artery or middle cerebral artery. The standardized uptake value ratio of
18
F-florzolotau at 100 to 110 minutes after injection was calculated using the cerebellar cortex as a reference region and was correlated with OEF obtained from
15
O-gas positron emission tomography in the middle cerebral artery distributions.

Results:

Significant decreases in cerebral blood flow and cerebral metabolic rate of oxygen and increases in OEF were found in the hemisphere ipsilateral to the arterial lesion.
18
F-florzolotau standardized uptake value ratio in this region was also greater than that in the contralateral hemisphere. In the ipsilateral hemisphere,
18
F-florzolotau standardized uptake value ratio positively correlated with OEF values.

Conclusions:

This pilot study with a small sample size suggests that increases in OEF–misery perfusion–may be associated with increased tau aggregates deposition in atherosclerotic internal carotid artery or middle cerebral artery disease.
Odara, a new genus to Edessinae (Hemiptera, Pentatomidae) with Eleven New Species
Authors: Campos Breno B. , Fernandes José A. M.
Published: November 2022

The genus Odara gen. n. is here described and composed exclusively of new species: Odara ori sp. n., Odara amazonica sp. n., Odara caribensis sp. n., Odara dracula sp. n., Odara maculatus sp. n., Odara osaensis sp. n., Odara peruensis sp. n., Odara rondoniensis sp. n., Odara tambopatensis sp. n., Odara umbabarauma sp. n. and Odara variegata sp. n. The new genus is characterized by the dorsal coloration green, humeral angles tooth-like generally with black apices, corium dark brown to black with yellow veins, parameres spearhead-shaped, and the sclerotized swelling of the ductus receptaculi in the female internal genitalia. The species are distributed from Central America to the Atlantic coast of South America, from Costa Rica to Rio de Janeiro [Brazil]. Diagnosis, keys to the species, and distributional maps are provided. Photographs of dorsal and ventral view, pygophore, female genital plates, male and female internal genitalia are also provided.
The Shaping of the ‘Political’: A Gendered Intellectual History of Ideas in Modern India (1880s–1940s)
Authors: Jha Priyanka
Published: November 2022

The world has often borne witness to the great Indian traditions – from the ancient scriptures to Gandhian moral and ethical philosophy – that have played a pivotal role in the global ‘political’ tradition of thinking. For centuries, Indian women have explicated, contributed to, and influenced this tradition. This strand has been a significant interlocutor in debates on concerns and values central to the human condition. However, given that women are the gendered subaltern, the centrality and attention that this line of thinking should have received or evoked as a ‘political tradition’ has been either scant or relegated to the margins in relation to other ideational trajectories. Women’s political thought has been meticulously and systematically relegated to the margins and reduced to a mere call for ‘social reform’. Subsequently, very few Indian women have been engaged as political thinkers or philosophers in India up to the present. However, this deficit is not peculiar to post-colonial societies like India but has global resonance and follows a universal trajectory. Thus, this paper locates the gendered political intellectual tradition of modern India by engaging with women thinkers’ works and their political ideas to subvert the biased, male-centric canon.
Two new Brazilian species in the Coelocephalapion nodicorne species-group (Brentidae, Apioninae, Apionini, Oxystomatina) associated with Euphorbiaceae
Authors: DE SOUSA WESLEY OLIVEIRA , ANDERSON ROBERT S.
Published: November 2022

Two new Brazilian species of the genus Coelocephalapion Wagner, 1914 placed in the C. nodicorne species-group (Brentidae: Apioninae) reared from Euphorbiaceae are described and illustrated: Coelocephalapion paleariae sp. n. (Type locality: Botucatu, São Paulo state; reared from seeds Croton glandulosus L.) and Coelocephalapion geraldinhoi sp. n. (Type locality: Dores do Indaiá, Minas Gerais state; reared from leaf galls on Croton antisyphiliticus Mart.). Aside from species descriptions, we also provide a key to species of the C. nodicorne species-group.
Affordable Care Act Medicaid Expansion is Associated With Increased Utilization of Minimally Invasive Lung Resection for Early Stage Lung Cancer
Authors: Dingillo Gianna , Alvarado Christine E. , Rice Jonathan D. , Sinopoli Jillian , Badrinathan Avanti , Linden Philip A. , Towe Christopher W.
Published: November 2022

Objective

Minimally invasive lung resection (MILR) is underutilized in the United States. Under the Affordable Care Act (ACA), 39 states adopted Medicaid expansion, while 12 did not. Although Medicaid expansion has been associated with improved access to cancer care, its effect on utilization of MILR is unclear. We hypothesize that MILR would increase in Medicaid expansion states.

Methods

The National Cancer Database was queried for adult patients from 2010 to 2018 with cT1/2N0M0 non-small cell lung cancer who received surgical resection by wedge, segmentectomy, or lobectomy. Patients were grouped by whether they received care in a state without Medicaid expansion vs expansion in January 2014. The outcome of interest was MILR (defined as video-assisted or robotic-assisted thoracoscopy) relative to open. Multivariable difference in differences (DID) cross-sectional analysis was used to estimate the average treatment effect (ATE) of Medicaid expansion.

Results

There were 41,439 patients who met inclusion criteria: 20,446 (49.3%) in expansion states and 20,993 (50.7%) in non-expansion states. Multivariable DID analysis showed that Medicaid expansion was associated with an increase in Medicaid insurance type with an ATE of 7.4% (95% CI 7.1-7.7%, P = .002). Medicaid expansion was also associated with increased MILR utilization in unadjusted analysis (10,278/20,446 (50.3%) vs 9,953/20,993 (47.4%), p < .001) and in multivariable DID analysis (ATE 0.6%, 95% CI 0.3-0.8%, P = .008).

Conclusions

Although Medicaid expansion was associated with increased utilization of MILR for early stage lung cancer, the treatment effect was modest. This suggests that barriers in access to MILR are larger than simply access to care.
Extending the Marijuana Gateway Hypothesis Beyond Drug Use to Violent Offending: Examining Dual Systems Imbalance as a Mediator
Authors: Wojciechowski Thomas W.
Published: November 2022

Prior research has posited that marijuana may act as a gateway drug that increases risk for progression to other forms of drug use. However, many cognitive mechanisms posited to underpin this relationship are general risk factors associated with other behaviors also, like violence. This study investigated whether marijuana use initiation during adolescence acts as a gateway to violent offending operating through imbalance in dual systems model construct development. The Pathways to Desistance data were analyzed. Generalized structural equation modeling was used to test for direct and indirect effects of interest. Results indicated that lifetime marijuana use prior to baseline predicted increased wave three violent offending risk. This relationship was significantly mediated by dual systems imbalance as predicted. Implications are discussed.
Economic slowdowns and international conflict
Authors: Jung Sung Chul
Published: November 2022

This article examines whether and how economic slowdowns lead to military conflict. Although a weak economy (as reflected, for example, by high inflation and unemployment rates) has often been regarded as a cause of interstate conflict, this study proposes that worse economy, that is, a negative trend of growth rates, tends to make political leaders face domestic challenge from dissatisfied public and look for diversionary targets. In addition, it hypothesizes that economically troubled states will target their junior trade partners, which are less likely to retaliate harshly. The results, based on an analysis of directed dyads from 1950 to 2010, show that a state is more likely to initiate military conflict when economic growth slows. When the initiator is an autocracy and the target is a democracy, this aggression is likely directed toward a vulnerable trade partner. However, a potential initiator’s political unrest does not increase the tendency to target a junior economic partner. These findings suggest that an autocracy’s slowing economy can threaten a politically dissimilar and economically dependent state, and that political and economic unrest have different effects on diversionary conflict as these factors interact with the domestic and international environments. Given our globalized economy and the current erosion of democracy worldwide, we could experience more frequent conflicts between economically troubled autocracies and their junior trade partners.
The Good Death Among Black, Indigenous, and/or People of Color: Which Aspects of a Good Death Are Most Important?
Authors: Suntai Zainab , Laha-Walsh Kirsten , Albright David L.
Published: November 2022

A good death is one where a patient’s preferences and wishes are fully respected. The purpose of this study was to determine which aspects of a good death are most important to Black/Indigenous, and/or people of color (BIPOC). Participants were recruited from Amazon Mechanical Turk, and then directed to complete a quantitative survey indicating the importance of each item on the Good Death Inventory. A logistic regression model was used to identify any differences in the importance of each item based on the racial background of the respondent. After accounting for other independent variables, the results showed that BIPOC were more likely to indicate that factors such as life completion, receiving all treatment possible, and religious/spiritual support were important aspects of a good death. Results indicate the need for culturally tailored tools that account for cultural differences in what constitutes a good death.
Sex Does Not Sell: The Effect of Sexual Content on Advertisement Effectiveness and Interference with Memory for Program Information
Authors: Ciuvat Roberta-Maria , Furnham Adrian , McClelland Alastair
Published: November 2022

Does increasing the sexual content of advertisements lead, though memory processes, to greater sales? By employing a between-participants design, we aimed to explore how sexual advertising affects explicit and implicit memory, and whether it impairs memory for information preceding the commercials (retroactive interference) or following the commercials (proactive interference). We randomly assigned 182 young participants in the UK to one of two groups who watched the same TV program containing an advertisement break during which either sexual or nonsexual advertisements were shown, while brands were held constant across conditions. Participants were then tested on their explicit and implicit memory for both the advertising content and program information. Results revealed that implicit memory was better for nonsexual than for sexual advertisements. Unexpectedly, there was no group difference in participants’ explicit memory for the advertisements. Further, sexual advertising resulted in retroactive interference with program information, whereas proactive memory for program information was not impaired. We acknowledge various study limitations and discuss proposals for future research.
The Relationship Between Trait Competitiveness and Competitive Attitudes: The Moderating Effect of Core Self-Evaluation
Authors: Swab R. Gabrielle , Galvan John M. , Sherlock Chelsea
Published: November 2022

This article explores the multifaceted nature of the individual motivations behind engaging in competition. In doing so, we investigate the theoretical and empirical links between general trait competitiveness and the three competitive attitudes: personal development, hypercompetition, and competition avoidant, as moderated by core self-evaluation. Findings indicate that it is not merely the level of general trait competitiveness that influences an individuals attitude towards competing, but heightened levels of core self-evaluation decrease the neurotic and unhealthy competitive attitudes of hypercompetitive and competition avoidant individuals. We discuss the implications for these relationships.
It’s More Than Skin-Deep: Gendered Racial Microaggressions, Skin Tone Satisfaction, and Traumatic Stress Symptoms Among Black Women
Authors: Sissoko D. R. Gina , Lewis Jioni A. , Nadal Kevin L.
Published: November 2022

This study examined the moderating effects of skin tone and skin tone satisfaction on the relations between gendered racial microaggressions and traumatic stress symptoms among self-identified Black women between 18 and 50 years old ( N = 237). It was hypothesized that skin tone and skin tone satisfaction would moderate the relation between gendered racial microaggressions and traumatic stress, such that having a darker skin tone and lower levels of skin tone satisfaction would exacerbate the association between gendered racial microaggressions and traumatic stress symptoms. Results from two hierarchical multiple linear regression analyses indicated skin tone satisfaction significantly moderated the association between gendered racial microaggressions and traumatic stress symptoms, whereas skin tone did not. Findings suggest that skin tone satisfaction may play a bigger role in buffering or exacerbating the link between gendered racial microaggressions and mental health among Black women than skin tone alone. The results can inform future research and clinical practice on the important role of skin tone satisfaction in the link between gendered racial microaggressions and traumatic stress among Black women.
Columbia Teachers College and Educational Psychology in Brazil: Circulation and Appropriation
Authors: Rocha Ana Cristina Santos Matos , Warde Mirian J.
Published: November 2022

Background/Context:

In Brazil, the 1920s and 1930s were characterized by initiatives intended to reform public school systems at the state level. As in the case of both educators whose work we analyze in this article, Brazilian educators traveled abroad searching for experiences that could help them think about the modernization of education in their own country. Some, like Isaías Alves and Noemy Rudolfer, advocated for using psychological tests in schools as part of the change. They translated and adjusted the foreign tests, which were given to primary school students during the 1930s.

Focus of Study:

Our work deals with the experiences of two Brazilians who visited Teachers College (TC), Columbia University, in New York, in 1930: Isaías Alves and Noemy da Silveira Rudolfer. Both coordinated services of applied psychology when they returned to Brazil: Alves in Rio de Janeiro and Rudolfer in São Paulo.

Research Design:

Our work develops a historical analysis of the use of psychological tests in Brazil through the experiences of these two educators.

Conclusions/Recommendations:

Using Alves and Rudolfer as a reference this article intends to demonstrate the relationship that Brazilian educators developed with the American education theories. Along this process, TC was a significant space of mediation between the Brazilian educators and the American professors and authors. Although TC played a substantial role in Rudolfer’s and Alves’s work, both took different approaches to local conditions. In doing so, they produced another knowledge, showing some of the possibilities open to Brazilian educators when they looked at American authors discussed by TC professors.