bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of an Optimization Model for Water Supply Chain Using Storage Reservoir Operation for Efficient Irrigation System
Authors: Alfaisal Faisal M. , Alam Shamshad , Alharbi Raied Saad , Kaur Kiranjeet , Khan Mohammad Amir , Athar Mohammad Faraz , Rahin Saima Ahmed
Published: January 2023

The model was created to assist in the appropriate allocation of water to produce crops to optimize net profit through monthly reservoir operation. The model maximizes net crop revenue and determines the type and size of the cultivated crop for each zone, taking into account monthly reservoir water availability. The following factors constrain the optimization model: (1) monthly reservoir water availability; (2) monthly water demand and irrigated farmland for crops; (3) limited crop areas in each zone; (4) projected final storage; (5) proportional sharing rule (PSR) for each zone. The linear programming (LP) algorithm is used to formulate the model, which is then solved using the general algebraic modeling system (GAMS). The model is applied to Hali Dam and validated using two criteria: (1) baseline scenarios (non-PSR) and (2) PSR scenarios in which all zones must have the same amount of water. The results demonstrate that the PSR scenarios give all of these zones identical rights for water delivery, with a total net profit reduction of around 2.6 percent at the planned final storage of 100 Hm3. As a result, the current model can be utilised to optimize dam water consumption in the future. The methodology is applied to a reservoir of Hali Dam in Saudi Arabia to demonstrate the model’s practical application.
Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment
Authors: Shan Youcheng
Published: January 2023

Aiming at the serious colloquialism of social network texts and the sparse semantic features, this article proposes a CNN-BiGRU-based sentiment analysis method for social network texts in the big data environment. First, the dependency syntax tree is introduced to represent the dependency relationship between words to construct the word vector to represent the text. Then, sentiment features with different granularity are extracted by multiple convolution kernels of different sizes in a convolution neural network (CNN). These sentiment features are input into bidirectional gated recurrent unit (BiGRU) network for analysis to obtain deeper sentiment features. Finally, a certain number of neurons are discarded by the Dropout method, and sentiment types are classified by the Sigmoid activation function. The Weibo_senti_100k Weibo data set is used to demonstrate the proposed method. The results show that if the Dropout value is set to 0.25 and the Adam optimizer is selected, the analysis performance is the best. The accuracy, precision, recall, and AUC are about 94.09%, 95.13%, 92.87%, and 0.953, respectively, which has certain application value.
CD73-Dependent Adenosine Signaling through Adora2b Drives Immunosuppression in Ductal Pancreatic Cancer
Authors: Faraoni Erika Y. , Singh Kanchan , Chandra Vidhi , Le Roux Olivereen , Dai Yulin , Sahin Ismet , O'Brien Baylee J. , Strickland Lincoln N. , Li Le , Vucic Emily , Warner Amanda N. , Pruski Melissa , Clark Trent , Van Buren George , Thosani Nirav C. , Bynon John S. , Wray Curtis J. , Bar-Sagi Dafna , Poulsen Kyle L. , Vornik Lana A. , Savage Michelle I. , Sei Shizuko , Mohammed Altaf , Zhao Zhongming , Brown Powel H. , Mills Tingting , Eltzschig Holger K. , McAllister Florencia , Bailey-Lundberg Jennifer M.
Published: January 2023

The microenvironment that surrounds pancreatic ductal adenocarcinoma (PDAC) is profoundly desmoplastic and immunosuppressive. Understanding triggers of immunosuppression during the process of pancreatic tumorigenesis would aid in establishing targets for effective prevention and therapy. Here, we interrogated differential molecular mechanisms dependent on cell of origin and subtype that promote immunosuppression during PDAC initiation and in established tumors. Transcriptomic analysis of cell-of-origin–dependent epithelial gene signatures revealed that Nt5e/CD73, a cell-surface enzyme required for extracellular adenosine generation, is one of the top 10% of genes overexpressed in murine tumors arising from the ductal pancreatic epithelium as opposed to those rising from acinar cells. These findings were confirmed by IHC and high-performance liquid chromatography. Analysis in human PDAC subtypes indicated that high Nt5e in murine ductal PDAC models overlaps with high NT5E in human PDAC squamous and basal subtypes, considered to have the highest immunosuppression and worst prognosis. Multiplex immunofluorescent analysis showed that activated CD8+ T cells in the PDAC tumor microenvironment express high levels of CD73, indicating an opportunity for immunotherapeutic targeting. Delivery of CD73 small-molecule inhibitors through various delivery routes reduced tumor development and growth in genetically engineered and syngeneic mouse models. In addition, the adenosine receptor Adora2b was a determinant of adenosine-mediated immunosuppression in PDAC. These findings highlight a molecular trigger of the immunosuppressive PDAC microenvironment elevated in the ductal cell of origin, linking biology with subtype classification, critical components for PDAC immunoprevention and personalized approaches for immunotherapeutic intervention.

Significance:

Ductal-derived pancreatic tumors have elevated epithelial and CD8+GZM+ T-cell CD73 expression that confers sensitivity to small-molecule inhibition of CD73 or Adora2b to promote CD8+ T-cell–mediated tumor regression.

See related commentary by DelGiorno, p. 977
EPOC: Evaluar la relación entre el grado de disnea y el estado nutricional de los exacerbados
Authors: Fong-Ponce Briseyra Guadalupe
Published: January 2023

Introduction: Malnutrition is underdiagnosed in chronic obstructive pulmonary disease. Objectives: This study aimed to evaluate the nutritional status of COPD patients and the link between dyspnea and nutritional status. Methods: This longitudinal observational study included patients hospitalized with exacerbated COPD. Nutritional status was assessed using Nutrition Risk Screening 2002, anthropometric, and biochemical assessments, in the first 48 hours of hospitalization. Results: Thirty patients were evaluated. According to the Nutrition Risk Screening 2002, half of the patients were at increased risk of malnutrition. 36.7% were classified as malnourished if we only considered the body mass index. From the evaluation of the tricipital skin fold, 69.0% were classified as malnourished, with 48.3% having severe malnutrition. According to the serum albumin level, 29.6% had malnutrition criteria. A significant association between dyspnea and increasing age (p = 0.037) was found. There was a strong association between the fold classification and the degrees of severity of dyspnea (Fisher exact test: 13.60, p = 0.001, V Cramer = 0.826). Most patients were malnourished and had higher grades of dyspnea. Tricipital skinfold reflects subcutaneous adipose tissue; this anthropometric measurement seems to be a good method to classify the nutritional status of COPD patients. It classified the biggest portion of patients as malnourished. Conclusion: The number of patients classified as malnourished changed with the method under analysis. The tricipital skin fold parameter was strongly associated with the dyspnea score. Most patients had adipose tissue and muscular mass depletion.
Effect of Youthful Blood Environment and Its Key Stem Cell Factor on Renal Interstitial Fibrosis in Elderly Mice
Authors: Huang Qi , Liu Dong , Cui Shaoyuan , Li Ping , Yin Zhong , Li Diangeng , Cao Dan , Zhang Yinping , Cai Guangyan , Chen Xiangmei , Sun Xuefeng
Published: January 2023

Introduction: Youthful blood environment was shown to decelerate the aging process of the kidney and to attenuate senile renal fibrosis in a young-old parabiotic animal model; in addition, we identified a stem cell factor (SCF) that is closely linked with the process. This research was to investigate the effect of youthful blood environment on senile renal interstitial fibrosis and the role of SCF. Methods: We bred SCF receptor c-Kit gene loss-of-function Wps/Wps mice and established a combination mice model that was subjected to unilateral ureteral obstructive (UUO) and parabiotic surgeries. Parabiotic mice were divided into isochronic parabiotic (young-young [Y-IP] and old-old [O-IP]) and heterochronic parabiotic (young-old [HP]) groups. UUO surgery was performed in one of the parabiotic pairs in the IP group (Y-IPuuo and O-IPuuo) and in the elderly mice in the HP group (O-HPuuo). In order to study the role of SCF/c-kit on renal interstitial fibrosis, UUO surgery was performed in wildtype (WT) and Wps/Wps mice. Results: Fourteen days after UUO surgery, the kidney interstitial fibrosis area, kidney function, and the expressions of SCF/c-Kit, pNF-κB, and fibrosis-related proteins in the O-HPuuo group were significantly lower than those in the Ouuo and O-IPuuo groups. Compared with WT UUO mice, the expressions of pNF-κB and fibrosis-related proteins and the kidney function were all significantly decreased in Wps/Wps UUO mice. Conclusion: Youthful blood environment downregulated the expressions of SCF/c-Kit in elderly UUO mice and ameliorated UUO-induced kidney fibrosis and function loss.
Study of the performance of fault-tolerant multi-level inverter included in shunt active power filter
Authors: Benaouda Omar Fethi , Mezaache Mohamed , Djeghloud Hind , Bendiabdellah Azzedine
Published: January 2023

Nowadays, the large number of shunt active power filters (SAPF) is installed in many grid networks to eliminate the source currents harmonics and enhance power quality. These filters are installed in different places according to the filtration requirements. The connection between SAPF and grid network has a negative effect during the open-circuit fault of the insulated gate bipolar transistor (IGBT) switch of the SAPF. This paper proposes the application of the new diagnostic method based on the trigonometric circle and mean value variations techniques to the early detection and precise location of the open-circuit fault of the IGBT switches, and the inclusion of the modified reconfigurable inverter topology to allow the perfect continuity of the filter currents, and improve the diagnostic of the open-circuit fault. A single-sided amplitude spectrum technique (SSAS) is applied on the source currents to get the THDi% value. The obtained simulation results prove, the great success of the proposed diagnostic method, the ability of the modified reconfigurable inverter to be adapted to the grid network, the short response time between the diagnosis and the reconfiguration process is about 7 ms which is very sufficient to guarantee the rapid continuity of the shunt active power filter.
Power quality optimization using a novel backstepping control of a three-phase grid-connected photovoltaic systems
Authors: Naddami Salwa , Ababssi Najib
Published: January 2023

A novel nonlinear backstepping controller based on direct current (DC) link voltage control is proposed in three-phase grid-connected solar photovoltaic (PV) systems to control the active and reactive power flow between the PV system and the grid with improved power quality in terms of pure sinusoidal current injection with lower total harmonic distortion (THD), as well as to ensure unity power factor, or to compensate for reactive power required by the load, i.e., the electrical grid. The output power of the PV array is supplied to the grid through a boost converter with maximum power point tracking (MPPT) control and an inverter. Simulation results of the proposed controller show good robustness under nominal conditions, parameter variations, and load disturbances, which presents the main advantage of this controller as compared to an existing controller. The performance of this work was evaluated using a MATLAB/Simulink environment.
Convolutional neural network based key generation for security of data through encryption with advanced encryption standard
Authors: Negabi Ismail , Ait El Asri Smail , El Adib Samir , Raissouni Naoufal
Published: January 2023

Machine learning techniques, especially deep learning, are playing an increasingly important role in our lives. Deep learning uses different models to extract information from the data. They have already had a huge impact in areas such as health (i.e., cancer diagnosis), self-driving cars, speech recognition, and data encryption. Recently, deep learning models, including convolutional neural networks (CNN), have been proven to be more effective in the security field. Moreover, the National Institute of Standards and Technology (NIST) recommends the advanced encryption standard (AES) algorithm as the most often utilized encryption method in several security applications. In this paper, a crypt-intelligent system (CIS) capable of securing data is proposed. It is based on the combination of the performance of CNN with the AES, by substituting the key expansion unit of AES with a CNN architecture that performs the key generation. Our CIS is described using very high-speed integrated circuit (VHSIC) hardware description language (VHDL), simulated by ModelSim, synthesized, and implemented with Xilinx ISE 14.7. Finally, the Airtex-7 series XC7A100T device has achieved an encryption throughput of 965.88 Mbps. In addition, the CIS offers a high degree of flexibility and is supported by reconfigurability, based on the experimental results, if sufficient resources are available, the architecture can provide performance that can satisfy cryptographic applications.
Ultra-wideband CMOS power amplifier for wireless body area network applications: a review
Authors: El-Feky Nagham Gamal , Ellaithy Dina Mohamed , Fedawy Mostafa Hassan
Published: January 2023

A survey on ultra-wideband complementary metal-oxide semiconductor (CMOS) power amplifiers for wireless body area network (WBAN) applications is presented in this paper. Formidable growth in the CMOS integrated circuits technology enhances the development in biomedical manufacture. WBAN is a promising mechanism that collects essential data from wearable sensors connected to the network and transmitted it wirelessly to a central patient monitoring station. The ultra-wideband (UWB) technology exploits the frequency band from 3.1 to 10.6 GHz and provides no interference to other communication systems, low power consumption, low-radiated power, and high data rate. These features permit it to be compatible with medical applications. The demand target is to have one transceiver integrated circuit (IC) for WBAN applications, consequently, UWB is utilized to decrease the hardware complexity. The power amplifier (PA) is the common electronic device that employing in the UWB transmitter to boost the input power to the desired output power and then feed it to the antenna of the transmitter. The advance in the design and implementation of ultra-wideband CMOS power amplifiers enhances the performance of the UWB-transceivers for WBAN applications. A review of recently published CMOS PA designs is reported in this paper with comparison tables listing wideband power amplifiers' performance.
Experimental study of compressor electric current detection for a split-type air conditioner affects energy savings
Authors: Saengchandr Banjerd , Sukontanakarn Viroch , Waiyagan Kriangkrai
Published: January 2023

The paper presents an experimental study that aims to measure the compressor electric current of a split-type air conditioner for analyzing the various abnormal condition of the R-32 refrigerant pressure, especially for detecting compressor electric current while occurring dirt in the evaporator coil and condenser coil. The method was to install sensor devices to measure the temperature and humidity of inlet air and outlet air, and the velocity of the air outlet of the evaporator unit. In condenser unit was to measure the electric current compressor and electric power input. All data from sensors send to the Arduino board and using Parallax Data Acquisition (PLX-DAQ) Excel Macro for the record. The results show physical behavior and the changing of compressor electric current according to the abnormal condition of the refrigerant system, blocking of condenser and evaporator coil.
Waist-to-height ratio assessment device
Authors: Abana Ertie , Accad Mycah , Pagauisan Marvin James , Taguiam Patrick , Ferrer Mary Ronalie
Published: January 2023

Many diseases are associated with excess abdominal fat like cardiovascular diseases. Monitoring and controlling abdominal fat led to one of the many factors that can change the status of a person’s health. Awareness of the waist-to-height ratio (WHtR) can be a guide to adjusting to a person’s lifestyle and maintaining a normal WHtR value. This study developed the WHtR assessment device that automatically calculates the WHtR value, displays the health status, and suggests the ideal waist circumference. The device is composed of a microcontroller that interconnects the other components of the device. A touchscreen liquid crystal display component was used as an input and output unit at the same time. The several testing that was conducted revealed accurate WHtR value calculation. The device is effective in assessing the health status of all age groups. The ideal waist circumference from the device was compared to manual computation and found that the success rate is one hundred percent (100%).
Sensor and internet of things based integrated inundation mitigation for smart city
Authors: Al Kindhi Berlian , Lasminto Umboro , Triana Masca Indra , Damarnegara Satria , Anavatti Sreenatha G.
Published: January 2023

Flooding is a natural phenomenon that often occurs in tropical countries. Drainage design is one of the efforts to prevent floods, however, when the rainfall is high, there are still several inundation points that occur. This requires comprehensive handling to reduce the impact of these inundations, to get an adaptive solution, the use of internet of things based (IoT) tools is one of the alternatives proposed. This study proposes an IoT-based flood inundation monitoring system, which includes a water level reader, a web-based inundation monitoring system, a flood inundation area and depth reporting system as evaluation materials for the government city. The sensor module that we propose is a series of sensors in a hollow cylinder design to reduce water ripples. The server application is displayed in the form of an interactive area mapping which is divided into 4 layers for 4 different analyzes so that central officers can quickly coordinate with field officers to carry out mitigation actions in the affected area. The module requires a low cost and easy installation process compared to a liquid sensor, besides that the display in the form of a web makes it easier for officers to access monitoring applications anywhere compared to geographic information system based (GIS) applications. This research has been carried out and tested in one of the major cities in Indonesia.
Segmentation of optic disc in retinal images for glaucoma diagnosis by saliency level set with enhanced active contour model
Authors: Naz Sobia , Radhakrishna Rao Kabbinale Ananda
Published: January 2023

Glaucoma is an ophthalmic disease which is among the chief causes of visual impairment across the globe. The clarity of the optic disc (OD) is crucial for recognizing glaucoma. Since existing methods are unable to successfully integrate multi-view information derived from shape and appearance to precisely explain OD for segmentation, this paper proposes a saliency-based level set with an enhanced active contour method (SL-EACM), a modified locally statistical active contour model, and entropy-based optical disc localization. The significant contributions are that i) the SL-EACM is introduced to address the often noticed problem of intensity inhomogeneity brought on by defects in imaging equipment or fluctuations in lighting; ii) to prevent the integrity of the OD structures from being compromised by pathological alterations and artery blockage, local image probability data is included from a multi-dimensional feature space around the region of interest in the model; and iii) the model incorporates prior shape information into the technique, for enhancing the accuracy in identifying the OD structures from surrounding regions. Public databases such as CHASE_DB, DRIONS-DB, and Drishti-GS are used to evaluate the proposed model. The findings from numerous trials demonstrate that the proposed model outperforms state-of-the-art approaches in terms of qualitative and quantitative outcomes.
A genetic algorithm coupled with tree-based pruning for mining closed association rules
Authors: Suresh Ponmudiyan Poovan Jashma , Acharya Udupi Dinesh , Subba Reddy Nandanvana Veerappareddy
Published: January 2023

Due to the voluminous amount of itemsets that are generated, the association rules extracted from these itemsets contain redundancy, and designing an effective approach to address this issue is of paramount importance. Although multiple algorithms were proposed in recent years for mining closed association rules most of them underperform in terms of run time or memory. Another issue that remains challenging is the nature of the dataset. While some of the existing algorithms perform well on dense datasets others perform well on sparse datasets. This paper aims to handle these drawbacks by using a genetic algorithm for mining closed association rules. Recent studies have shown that genetic algorithms perform better than conventional algorithms due to their bitwise operations of crossover and mutation. Bitwise operations are predominantly faster than conventional approaches and bits consume lesser memory thereby improving the overall performance of the algorithm. To address the redundancy in the mined association rules a tree-based pruning algorithm has been designed here. This works on the principle of minimal antecedent and maximal consequent. Experiments have shown that the proposed approach works well on both dense and sparse datasets while surpassing existing techniques with regard to run time and memory.
Fisher exact Boschloo and polynomial vector learning for malware detection
Authors: Kudrekar Sheelavathy Veerabhadrappa , Vinayaka Murthy Udaya Rani
Published: January 2023

Computer technology shows swift progress that has infiltrated people’s lives with the candidness and pliability of computers to work ease shows security breaches. Thus, malware detection methods perform modifications in running the malware based on behavioral and content factors. The factors are taken into consideration compromises of convergence rate and speed. This research paper proposed a method called fisher exact Boschloo and polynomial vector learning (FEB-PVL) to perform both content and behavioral-based malware detection with early convergence to speed up the process. First, the input dataset is provided as input then fisher exact Boschloo’s test Bernoulli feature extraction model is applied to obtain independent observations of two binary variables. Next, the extracted network features form input to polynomial regression support vector learning to different malware classes from benign classes. The proposed method validates the results with respect to the malware and the benign files. The present research aimed to develop the behaviors to detect the accuracy process of the features that have minimum time speeds the overall performances. The proposed FEB-PVL increases the true positive rate and reduces the false positive rate and hence increasing the precision rate using FEB-PVL by 7% compared to existing approaches.
A comparative analysis between two heuristic algorithms for the graph vertex coloring problem
Authors: Kralev Velin , Kraleva Radoslava
Published: January 2023

This study focuses on two heuristic algorithms for the graph vertex coloring problem: the sequential (greedy) coloring algorithm (SCA) and the Welsh–Powell algorithm (WPA). The code of the algorithms is presented and discussed. The methodology and conditions of the experiments are presented. The execution time of the algorithms was calculated as the average of four different starts of the algorithms for all analyzed graphs, taking into consideration the multitasking mode of the operating system. In the graphs with less than 600 vertices, in 90% of cases, both algorithms generated the same solutions. In only 10% of cases, the WPA algorithm generates better solutions. However, in the graphs with more than 1,000 vertices, in 35% of cases, the WPA algorithm generates better solutions. The results show that the difference in the execution time of the algorithms for all graphs is acceptable, but the quality of the solutions generated by the WPA algorithm in more than 20% of cases is better compared to the SC algorithm. The results also show that the quality of the solutions is not related to the number of iterations performed by the algorithms.
Horizontal trajectory based mobile multi-sink routing in underwater sensor networks
Authors: Patil Vijayalaxmi R , Kanavalli Anita
Published: January 2023

Scientific, commercial, exploration, and monitoring applications of underwater sensor networks have drawn the attention of researchers toward the investigation of routing protocols that are robust, scalable, and energy efficient. This has brought significant research in network layer routing protocols. Irrespective of the field of application it is desirable to increase network lifetime by reducing energy consumed by sensor nodes in the network or by balancing energy in the entire network. Energy balancing refers to the uniform distribution of the network’s residual energy such that all nodes remain alive for a long time. It requires uniform energy consumption by each sensor node in the network instead of the same node being involved in every transmission. In this paper, we discuss two routing methods for three-dimensional environments in which the water region under monitor is divided into subregions of equal height and each subregion has a sink. Nodes in the subregion send data to the sink designated for that subregion. The first method called static multi-sink routing uses static sinks and the second method called horizontal trajectory-based mobile multi-sink routing (HT-MMR) uses mobile sinks with a horizontal trajectory. Simulation results show that the proposed HT-MMR reduces average energy consumption and average energy tax by 16.69% and 16.44% respectively. HT-MMR is energy efficient as it enhances network lifetime by 11.11%.
Survey on data aggregation based security attacks in wireless sensor network
Authors: Tabassum Nikhath , D. Devanagavi Geetha , C. Biradar Rajashekhar , Ravindra Chaya
Published: January 2023

Wireless sensor network (WSN) has applications in military, health care, environmental monitoring, infrastructure, industrial and commercial applications. The WSN is expected to maintain data integrity in all its network operations. However, due to the nature of wireless connectivity, WSN is prone to various attacks that alter or steal the data exchanged between the nodes. These attacks can disrupt the network processes and also the accuracy of its results. In this survey paper, we have reviewed various attacks available in the literature till date. We have also listed existing methods that focus on data aggregation based security mechanisms in WSN to counter the attacks. We have classified and compared these methods owing to their encryption techniques. This paper intends to support researchers to understand the basic attacks prevalent in WSN and schemes to counter such attacks.
Deep learning in phishing mitigation: a uniform resource locator-based predictive model
Authors: Salah Hamzah , Zuhair Hiba
Published: January 2023

To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optimized eventually. However, the optimized methods produce gratuitous performance overhead due to the limited exploration of advanced phishing cues. Thus, a phishing uniform resource locator-based predictive model is enhanced by this work to defeat this deficiency using deep learning algorithms. This model’s architecture encompasses pre-processing of the effective feature space that is made up of 60 mutual uniform resource locator (URL) phishing features, and a dual deep learning-based model of convolution neural network with bi-directional long short-term memory (CNN-BiLSTM). The proposed predictive model is trained and tested on a dataset of 14,000 phish URLs and 28,074 legitimate URLs. Experimentally, the performance outputs are remarked with a 0.01% false positive rate (FPR) and 99.27% testing accuracy.
Health Electroencephalogram epileptic classification based on Hilbert probability similarity
Authors: Al-Hamzawi Abdulkareem , Al-Shammary Dhiah , Hammadi Alaa Hussein
Published: January 2023

This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. Hilbert similarity probability-based measure is exploited to measure the similarity between signals. The proposed system consisted of models based on Hilbert probability similarity (HPS) to predict the state for the specific EEG signal. Particle swarm optimization (PSO) has been employed for feature selection and extraction. Furthermore, the used dataset in this study is Bonn University's publicly available EEG dataset. Several metrics are calculated to assess the performance of the suggested systems such as accuracy, precision, recall, and F1-score. The experimental results show that the suggested model is an effective tool for classifying EEG signals, with an accuracy of up to 100% for two-class status.
Multivariate sample similarity measure for feature selection with a resemblance model
Authors: Assegie Tsehay Admassu , Salau Ayodeji Olalekan , Omeje Crescent Onyebuchi , Braide Sepiribo Lucky
Published: January 2023

Feature selection improves the classification performance of machine learning models. It also identifies the important features and eliminates those with little significance. Furthermore, feature selection reduces the dimensionality of training and testing data points. This study proposes a feature selection method that uses a multivariate sample similarity measure. The method selects features with significant contributions using a machine-learning model. The multivariate sample similarity measure is evaluated using the University of California, Irvine heart disease dataset and compared with existing feature selection methods. The multivariate sample similarity measure is evaluated with metrics such as minimum subset selected, accuracy, F1-score, and area under the curve (AUC). The results show that the proposed method is able to diagnose chest pain, thallium scan, and major vessels scanned using X-rays with a high capability to distinguish between healthy and heart disease patients with a 99.6% accuracy.
Multi-label learning by extended multi-tier stacked ensemble method with label correlated feature subset augmentation
Authors: Hemavati Hemavati , Devi Visweswariah Susheela , Aparna Ramalingappa
Published: January 2023

Classification is one of the basic and most important operations that can be used in data science and machine learning applications. Multi-label classification is an extension of the multi-class problem where a set of class labels are associated with a particular instance at a time. In a multiclass problem, a single class label is associated with an instance at a time. However, there are many different stacked ensemble methods that have been proposed and because of the complexity associated with the multi-label problems, there is still a lot of scope for improving the prediction accuracy. In this paper, we are proposing the novel extended multi-tier stacked ensemble (EMSTE) method with label correlation
by feature subset selection technique and then augmenting those feature subsets while constructing the intermediate dataset for improving the prediction accuracy in the generalization phase of the stacking. The performance effect of the proposed method has been compared with existing methods and showed that our proposed method outperforms the other methods.
A novel hybrid deep learning model for price prediction
Authors: Abdullah Walid , Salah Ahmed
Published: January 2023

Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models’ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNN-LSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.
Ultrasound renal stone diagnosis based on convolutional neural network and VGG16 features
Authors: Alkurdy Noor Hamzah , Aljobouri Hadeel K. , Wadi Zainab Kassim
Published: January 2023

This paper deals with the classification of the kidneys for renal stones on ultrasound images. Convolutional neural network (CNN) and pre-trained CNN (VGG16) models are used to extract features from ultrasound images. Extreme gradient boosting (XGBoost) classifiers and random forests are used for classification. The features extracted from CNN and VGG16 are used to compare the performance of XGBoost and random forest. An image with normal and renal stones was classified. This work uses 630 real ultrasound images from Al-Diwaniyah General Teaching Hospital (a lithotripsy center) in Iraq. Classifier performance is evaluated using its accuracy, recall, and F1 score. With an accuracy of 99.47%, CNN-XGBoost is the most accurate model.
Application and growth of long-range communications technology in vehicular communications
Authors: Abdul Razak Siti Fatimah , Yogarayan Sumendra , Kamis Noor Hisham , Abdullah Mohd Fikri Azli , Yusof Ibrahim
Published: January 2023

Long range communications technology (LoRa) has been widely used in a variety of applications and researched in different domains to exploit its full potential. Its openness makes it ideal for a variety of internet of things (IoT) installations which further allows opportunities for viable solutions in vehicular communications. Hence, a bibliometric analysis was performed to distinguish the application and growth of the technology specifically in vehicular communications. The scoping review processes from Arksey and O’Malley was applied to guide the review process. The selected scholarly works adhered to the PRISMA-Sc framework where 385 articles from two main electronic databases, i.e., Scopus and Science Direct which discussed LoRa in vehicular communications contexts were assessed. This study aims to: i) examine how LoRa’s research has grown from year 2010 to 2021 among the scholars; ii) determine key areas discussed in LoRa’s vehicular communications research. Findings from 70 studies in the final analysis indicated that LoRa has been widely studied based on application, theoretical or protocol and performance. However, it has not been widely explored in vehicular context. Hence, our findings support the global research community in this context.
Design of sliding mode controller for chaotic Josephson-junction
Authors: Harb Bassam A. , Harb Ahmad M.
Published: January 2023

It is known that a shunted nonlinear resistive-capacitive-inductance Josephson-junction (RCLSJ) model has a chaotic attractor. This attractor is created as a result of Hopf bifurcation that occurs when a certain direct current (DC) applied to one of the junction terminals. This chaotic attractor prevents the system from reaching the phase-locked state and hence degrade the performance of the junction. This paper aims at controlling and taming this chaotic attractor induced in this model and pulling the system to the phase-locked state. To achieve this task, a sliding mode controller is proposed. The design procedures involve two steps. In the first one, we construct a suitable sliding surface so that the dynamic of the system follows the sliding manifolds in order to meet design specifications. Secondly, a control law is created to force the chaotic attractor to slide on the sliding surface and hence stabilizes system trajectory. The RCLSJ model under consideration is simulated with and without the designed controller. Results demonstrate the validity of the designed controller in taming the induced chaos and stabilizing the system under investigation.
Growth Differentiation Factor‐15 Predicts Mortality and Heart Failure Exacerbation But Not Ventricular Arrhythmias in Patients With Cardiomyopathy
Authors: Binder M. Scott , Yanek Lisa R. , Yang Wanjun , Butcher Barbara , Norgard Sanaz , Marine Joseph E. , Kolandaivelu Aravindan , Chrispin Jonathan , Fedarko Neal S. , Calkins Hugh , O'Rourke Brian , Wu Katherine C. , Tomaselli Gordon F. , Barth Andreas S.
Published: January 2023

Background

Heart failure (HF) has been increasing in prevalence, and a need exists for biomarkers with improved predictive and prognostic ability. GDF‐15 (growth differentiation factor‐15) is a novel biomarker associated with HF mortality, but no serial studies of GDF‐15 have been conducted. This study aimed to investigate the association between GDF‐15 levels over time and the occurrence of ventricular arrhythmias, HF hospitalizations, and all‐cause mortality.

Methods and Results

We used a retrospective case–control design to analyze 148 patients with ischemic and nonischemic cardiomyopathies and primary prevention implantable cardioverter‐defibrillator (ICD) from the PROSe‐ICD (Prospective Observational Study of the ICD in Sudden Cardiac Death Prevention) cohort. Patients had blood drawn every 6 months and after each appropriate ICD therapy and were followed for a median follow‐up of 4.6 years, between 2005 to 2019. We compared serum GDF‐15 levels within ±90 days of an event among those with a ventricular tachycardia/fibrillation event requiring ICD therapies and those hospitalized for decompensated HF. A comparator/control group comprised patients with GDF‐15 levels available during 2‐year follow‐up periods without events. Median follow‐up was 4.6 years in the 148 patients studied (mean age 58±12, 27% women). The HF cohort had greater median GDF‐15 values within 90 days (1797 pg/mL) and 30 days (2039 pg/mL) compared with the control group (1062 pg/mL, both
P
<0.0001). No difference was found between the ventricular tachycardia/fibrillation subgroup within 90 days (1173 pg/mL,
P
=0.60) or 30 days (1173 pg/mL,
P
=0.78) and the control group. GDF‐15 was also significantly predictive of mortality (hazard ratio, 3.17 [95% CI, 2.33–4.30]).

Conclusions

GDF‐15 levels are associated with HF hospitalization and mortality but not ventricular arrhythmic events.
Periodontal Disease Associated With Interstitial Myocardial Fibrosis: The Multiethnic Study of Atherosclerosis
Authors: Doughan Maria , Chehab Omar , de Vasconcellos Henrique Doria , Zeitoun Ralph , Varadarajan Vinithra , Doughan Bassel , Wu Colin O. , Blaha Michael J , Bluemke David A. , Lima Joao A. C.
Published: January 2023

Background

Periodontitis is a chronic inflammatory disease common among adults. It has been suggested that periodontal disease (PD) may be a contributing risk factor for cardiovascular disease; however, pathways underlying such a relationship require further investigation.

Methods and Results

A total of 665 men (mean age 68±9 years) and 611 women (mean age 67±9 years) enrolled in the MESA (Multiethnic Study of Atherosclerosis) underwent PD assessment using a 2‐item questionnaire at baseline (2000–2002) and had cardiovascular magnetic resonance 10 years later. PD was defined when participants reported either a history of periodontitis or gum disease or lost teeth caused by periodontitis or gum disease. Multivariable linear regression models were constructed to assess the associations of baseline self‐reported PD with cardiovascular magnetic resonance–obtained measures of interstitial myocardial fibrosis (IMF), including extracellular volume and native T1 time. Men with a self‐reported history of PD had greater extracellular volume percent (ß=0.6%±0.2,
P
=0.01). This association was independent of age, left ventricular mass, traditional cardiovascular risk factors, and history of myocardial infarction. In a subsequent model, substituting myocardial infarction for coronary artery calcium score, the association of PD with IMF remained significant (ß=0.6%±0.3,
P
=0.03). In women, a self‐reported history of PD was not linked to higher IMF. Importantly, a self‐reported history of PD was not found to be associated with myocardial scar independent of sex (odds ratio, 1.01 [95% CI, 0.62–1.65];
P
=0.9).

Conclusions

In a community‐based setting, men but not women with a self‐reported PD history at baseline were found to be associated with increased measures of IMF. These findings support a plausible link between PD, a proinflammatory condition, and subclinical IMF.
The Cult of the Persuasive: Why U.S. Security Assistance Fails
Authors: Metz Rachel Tecott
Published: January 2023

Security assistance is a pillar of U.S. foreign policy and a ubiquitous feature of international relations. The record, however, is mixed at best. Security assistance is hard because recipient leaders are often motivated to implement policies that keep their militaries weak. The central challenge of security assistance, then, is influence. How does the United States aim to influence recipient leaders to improve their militaries, and what drives its approach? Influence in security assistance can be understood as an escalation ladder with four rungs: teaching, persuasion, conditionality, and direct command. Washington increasingly delegates security assistance to the Department of Defense, and the latter to the U.S. Army. U.S. Army advisers tend to rely exclusively on teaching and persuasion, even when recipient leaders routinely ignore their advice. The U.S. Army's preference for persuasion and aversion to conditionality in security assistance can be traced to its bureaucratic interests and to the ideology that it has developed—the cult of the persuasive—to advance those interests. A case study examines the bureaucratic drivers of the U.S. Army's persistent reliance on persuasion to influence Iraqi leaders to reform and strengthen the Iraqi Army. Qualitative analysis leverages over one hundred original interviews, as well as oral histories and recently declassified U.S. Central Command documents. The findings illustrate how the interests and ideologies of the military services tasked with implementing U.S. foreign policy can instead undermine it.
Push and Pull on the Periphery: Inadvertent Expansion in World Politics
Authors: Anderson Nicholas D.
Published: January 2023

Why do great powers engage in territorial expansion? Much of the existing literature views expansion as a largely intentional activity directed by the leaders of powerful states. Yet nearly 25 percent of important historical instances of great power expansion are initiated by actors on the periphery of the state or empire without authorization from their superiors at the center. Periphery-driven “inadvertent expansion” is most likely to occur when leaders in the capital have limited control over their agents on the periphery. Through their actions, peripheral agents effectively constrain leaders from withdrawing from these newly captured territories because of sunk costs, domestic political pressure, and national honor. When leaders in the capital expect geopolitical consequences from regional or other great powers, such as economic sanctions, militarized crises, or war, they are far less likely to authorize the territorial claims. A mixed-methods research strategy combines new quantitative data on great power territorial expansion with three qualitative case studies of successful (and failed) inadvertent expansion by Russia, Japan, and France. Inadvertent expansion has not completely gone away, particularly among smaller states, where government authority can be weak, control over states’ apparatuses can be loose, and civil-military relations can be challenging.
The Role of Entertainment Media in Promoting Culture: The Case of Japanese Cartoons and Superhero tv Series in 80s-90s Thai Society
Authors: Lopattananont Thanayod
Published: January 2023

This research delved into the specificity of Japanese cartoons and superhero tv series during the 80s-90s based on two questions; how their content familiarized the Thai audience with the culture of Japan and what impact they have delivered. Content analysis, audience analysis, and document research were used as the research methodology. The discovery suggested that Japanese cartoons and superhero tv series were culturally powerful. Their presentation of the local culture was comprehensible, repetitive, and joyous, causing the audience to be connected to the culture of Japan with ease. Each cartoon and superhero tv series also possessed high entertainment quality that drew mass attention from the Thai audience as well as enlarging the consumption scale. This came to light as a result of some creative techniques employed by their creators and, therefore, engendered some significant impacts as explained in the theory of soft power.
Global Soil Moisture–Climate Interactions during the Peak Growing Season
Authors: Feng Yao , Wang Hong , Liu Wenbin , Sun Fubao
Published: January 2023

Soil moisture (SM) during the vegetation growing season largely affects plant transpiration and photosynthesis, and further alters the land energy and water balance through its impact on the energy partition into latent and sensible heat fluxes. To highlight the impact of strong vegetation activity, we investigate global SM–climate interactions over the peak growing season (PGS) during 1982–2015 based on multisource datasets. Results suggest widespread positive SM–precipitation (P), SM–evapotranspiration (ET), and negative SM–temperature (T) interactions with non-negligible negative SM–P, SM–ET, and positive SM–T interactions over PGS. Relative to the influence of individual climate factors on SM, the compounding effect of climate factors strengthens SM–climate interactions. Simultaneously, variations of SM are dominated by precipitation from 50°N toward the south, by evapotranspiration from 50°N toward the north, and by temperature over the Sahara, western and central Asia, and the Tibetan Plateau. Importantly, the higher probability of concurrent SM wetness and climate extremes indicates the instant response of SM wetness to extreme climate. In contrast, the resistance of vegetation partially contributes to a consequent slower response of SM dryness to extreme climate. We highlight the significance of the compounding effects of climate factors in understanding SM–climate interaction in the context of strong vegetation activity, and the response of SM wetness and dryness to climate extremes.