bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated Interictal Epileptiform Discharge Detection from Scalp EEG Using Scalable Time-series Classification Approaches
Authors: Nhu D. , Janmohamed M. , Shakhatreh L. , Gonen O. , Perucca P. , Gilligan A. , Kwan P. , O’Brien T. J. , Tan C. W. , Kuhlmann L.
Published: September 2022

Deep learning for automated interictal epileptiform discharge (IED) detection has been topical with many published papers in recent years. All existing works viewed EEG signals as time-series and developed specific models for IED classification; however, general time-series classification (TSC) methods were not considered. Moreover, none of these methods were evaluated on any public datasets, making direct comparisons challenging. This paper explored two state-of-the-art convolutional-based TSC algorithms, InceptionTime and Minirocket, on IED detection. We fine-tuned and cross-evaluated them on a public (Temple University Events — TUEV) and two private datasets and provided ready metrics for benchmarking future work. We observed that the optimal parameters correlated with the clinical duration of an IED and achieved the best area under precision-recall curve (AUPRC) of 0.98 and F1 of 0.80 on the private datasets, respectively. The AUPRC and F1 on the TUEV dataset were 0.99 and 0.97, respectively. While algorithms trained on the private sets maintained their performance when tested on the TUEV data, those trained on TUEV could not generalize well to the private data. These results emerge from differences in the class distributions across datasets and indicate a need for public datasets with a better diversity of IED waveforms, background activities and artifacts to facilitate standardization and benchmarking of algorithms.
Global transmission of COVID-19 — A gravity model approach
Authors: Woo Hyungsoo , Kwon Okyu , Yang Jae-Suk
Published: September 2022

This paper aims to describe the spatiotemporal transmission of COVID-19 and examine how various factors influence the global spread of COVID-19 using a modified gravity model. Log-linearizing the model, we run a negative binomial regression with observational data from 22 January 2020 to 31 December 2020. In the first model, population size and GDP per capita are positively related to the sum of newly confirmed COVID-19 cases within a 10-day window; the values for both variables are statistically significant throughout the study period. However, the significance of geographic distance varies. When a single geographic source exits in the early stage, the value is statistically significant. In the intermediate stage, when disease transmission is explosive between countries, the distance loses its statistical significance due to the emergence of multiple geographic transmission sources. In the containment stage, when the spread of disease is more likely to occur within a country, distance becomes statistically significant. According to the second model, the government’s internal movement control and nonpharmaceutical intervention policy, percentage of the population over 70 years old, and population-weighted density are statistically significant and are positively related to the incidence of COVID-19. By contrast, average monthly temperature, international travel restriction policies, and political regimes are statistically significant and negatively associated with the dependent variable.
A large diffusion and small amplification dynamics for density classification on graphs
Authors: Leal Laura , Montealegre Pedro , Osses Axel , Rapaport Ivan
Published: September 2022

The density classification problem on graphs consists in finding a local dynamics such that, given a graph and an initial configuration of 0’s and 1’s assigned to the nodes of the graph, the dynamics converge to the fixed point configuration of all 1’s if the fraction of 1’s is greater than the critical density (typically 1/2) and, otherwise, it converges to the all 0’s fixed point configuration. To solve this problem, we follow the idea proposed in [R. Briceño, P. M. de Espanés, A. Osses and I. Rapaport, Physica D 261, 70 (2013)], where the authors designed a cellular automaton inspired by two mechanisms: diffusion and amplification. We apply this approach to different well-known graph classes: complete, regular, star, Erdös–Rényi and Barabási–Albert graphs.
Belief Propagation for Unbalanced Assignment Problem
Authors: Wang Yajing , Liang Dongyue , Yang Weihua
Published: September 2022

The unbalanced assignment problem (UAP) aims to distribute a set of jobs to some workers. The cost of the jobs is different when they are distributed to different workers. The goal is: (1) minimizing the total cost of arranging jobs to workers; (2) making the distribution of jobs as even as possible among all the workers. We transform the UAP into a min-cost network flow problem with squared terms, and apply the belief propagation (BP) algorithm to deal with it. We prove that, when the min-cost network flow problem has a unique optimal solution, the BP algorithm converges to the optimal solution within [Formula: see text] iterations, where [Formula: see text] represents the number of vertices of the flow network, [Formula: see text] is the difference between value of the optimal solution and the second optimal solution and [Formula: see text] is the maximum value of the terms of the objective function. Next, we prove that BP converges to the optimal solution in [Formula: see text] operations, where [Formula: see text] represents the number of edges and [Formula: see text] is the tight upper bound of the slope of the terms of the objective function.
A Unified Approach to Single-Machine Scheduling with Position-Based Processing Times, Machine Availability, and Job Rejection
Authors: Ji Min , Qi Kaili , Cheng T. C. E. , Jiang Yiwei
Published: September 2022

We present a unified approach to single-machine scheduling with position-based processing times, an availability constraint, and job rejection. The approach uses two general position-based processing time functions to model both the learning and aging effects in scheduling. In addition, taking machine availability and job rejection into consideration, the models are more realistic, which seek to minimize the sum of the makespan of the accepted jobs and the total penalty of the rejected jobs. We present fully polynomial-time approximation schemes to address the two NP-hard problems.
Anisotropic expansion, dissipative hydrodynamics from kinetic theory
Authors: Pruseth Priyanka Priyadarshini , Mahapatra Swapna
Published: September 2022

In this paper, we consider Kasner space-time describing anisotropic three-dimensional expansion of the fluid and obtain the dissipative evolution equations for shear stress tensor and energy density from kinetic theory. For this, we use the iterative solution of relativistic Boltzmann equation with relaxation time approximation. We show that our results for second- and third-order evolution equations reduce to those of one-dimensional expansion case under suitable conditions for the anisotropic parameters in Kasner space-time.
New window on matter-antimatter differences from BESIII
Authors: Zhou Xiaorong , Olsen Stephen Lars
Published: September 2022

Recent measurements of [Formula: see text]-related comparisons of the weak-interaction parameters that describe the decays of the [Formula: see text] and the [Formula: see text] doubly strange hyperon and antihyperon are reviewed and their significance is discussed.
The cosmological behavior and geometrical diagnostic for new Tsallis agegraphic dark energy with sign-changeable interaction
Authors: Xu Y. D.
Published: September 2022

We explore the cosmic behavior of the sign-changeable interacting new Tsallis agegraphic dark energy (NTADE) model in a spatially flat universe. The deceleration and the equation of state (EoS) parameters of the model are calculated. We also investigate the model in the universe with the spatial curvature. The spatial curvature may affect the time of onset of the acceleration. Furthermore, we demonstrate two geometric diagnostics analyses including the statefinder [Formula: see text] and [Formula: see text] to the discriminant the model from the standard LCDM model. The trajectories of evolution for [Formula: see text] and [Formula: see text] diagnostic planes are shown for different Tsallis parameter [Formula: see text] and interaction parameter [Formula: see text].
Gas kinetics of galactic disks explains rotation curves of S-type galaxies without a need for dark matter
Authors: Lipovka Anton A.
Published: September 2022

In this paper, I show that the generally accepted methods of classical mechanics are not applicable for calculating the outer parts of the rotation curves (RCs) of galaxies, where the influence of collisions on gas dynamics becomes dominant. In addition, the hydrodynamic approach cannot be used for this purpose due to an extreme rarefaction of the gas. I develop a new approach for describing the gas dynamics in outer regions of galactic disks, where the gas dynamics is determined mainly by collisions. Equations (free from restrictions imposed on hydrodynamics) are obtained that describe the dynamics of rarefied gas. The resulting equation [Formula: see text] (14) relates two quantities: the tangential velocity of the gas as a function of the distance from the center of a galaxy (RC) and the radial distribution of the gas density.

It is shown that if the physical properties of the rarefied gas are properly taken into account, then dark matter (DM) is not required, and the “nonphysical” (non-Keplerian) RCs of the outer parts of the galactic disks are tailwinds that can be described within the framework of conventional gas kinetics. To illustrate the correctness of the obtained model, two galaxies with flat RCs (NGC7331 and NGC3198) are considered. From the observed RCs, using Eq. ( 14 ), the radial densities of the gas are calculated. An excellent agreement was obtained between the calculated gas densities and their observed values, which is a serious argument in favor of the developed model. Thus, the nonphysical RCs of spiral galaxies represent the tailwinds of gas, the dynamics of which is naturally described by the kinetic equation without involving the concept of DM.

Total masses of two galaxies NGC7331 and NGC3198 have been calculated. Implications for cosmology are discussed.
Finite temperature contributions to cosmological constant
Authors: Park I. Y.
Published: September 2022

We reexamine the cosmological constant (CC) problem in a finite temperature setup and propose an intriguing possibility of carrying out perturbative analysis by employing a renormalization scheme in which the renormalized Higgs mass (or resummed mass, to be more precise) is taken to be on the order of the cosmic microwave background (CMB) temperature. Our proposal hinges on the fact that although the physical value of the CC does not depend on one’s renormalization scheme, whether or not a fine tuning is involved does. The CC problem is avoided in the sense that the renormalization process no longer requires finetuning. This is achieved essentially by renormalization scheme-independence of a physical quantity, which in turn is assured by bare perturbation theory. The proposal shifts the CC problem to a peculiarity of the consequent perturbation series for the Higgs mass (and other massive sectors of the Standard Model); the peculiarity is interpreted as an indicator of new physics after the expected mathematical structure of the series is scrutinized. Finite-temperature-induced complexification of the effective potential is observed and its interpretation is given. A consistency check in the cosmology context is suggested.
Risk Assessment and Prediction of Underground Utility Tunnels Based on Bayesian Network: A Case Study in Beijing, China
Authors: Chen Yongjun , Li Xiaojian , Wang Wenjuan , Wu Guangye , Wang Lulin
Published: September 2022

The underground utility tunnels accommodate various types of urban lifelines, which are of great significance for improving the living standards of the citizens. With the rapid development of underground utility tunnels, the large-scale underground utility tunnel systems are gradually becoming the operational lifeblood of China’s large cities. Currently, most of the underground utility tunnels’ risks are estimated and analyzed from a static perspective, and the analysis results are one-sided. This study proposes a dynamic risk evaluation framework. A risk assessment and sensitivity analysis framework based on Bayesian network is established in this study. Combined with the groundwater and electric tunnel risk accident case study, the operation and maintenance data of Beijing Future Science and Technology City from 2010 to 2018 are collected for learning to obtain the conditional probability of the Bayesian network node by using the K2 algorithm. The overall evolution process from the beginning to the end of groundwater tunnel accidents is clearly described and displayed. Through sensitivity analysis and critical path analysis, the critical points of an accident and the probabilities of risk occurrence are identified and predicted. This proposed framework could facilitate the underground utility tunnel management for controlling risk resources, mitigating risk damage and reducing risk losses.
A Radiation-Hardened Delay-Locked Loop Applied for Multiple Frequency Ranges
Authors: Chen Yushi , Zhuang Yiqi
Published: September 2022

A radiation-hardened delay-locked loop (DLL) applied for multiple frequency ranges is proposed in this paper. Novel and effective radiation-hardened techniques are applied in charge pump (CP) and voltage-controlled delay line (VCDL) to avoid inverted lock error and missing pulses error caused by single-event transient (SET). In order to ensure a wide operation frequency range, a frequency detection module is designed to detect the frequency of input signals and adjust the current source array of VCDL so as to change its delay time. Besides, an accelerate module is designed to reduce the lock time of DLL by providing an extra discharge current. According to the simulation results, the proposed DLL shows good performance when ion strikes occur and can work properly in a wide operation frequency range from 3.7[Formula: see text]GHz to 7.4[Formula: see text]GHz. Also, the proposed accelerate module helps to reduce lock time by 22.2%.
High-Speed and Energy-Efficient Sense Amplifier
Authors: Upadhyaya Yogendra Kumar , Hasan Mohd
Published: September 2022

Sense amplifier is a key element of memory and a CMOS-based spin hybrid circuit. Magnetic tunnel junction (MTJ) is a key spintronic device used in spin-based memory and hybrid circuits. This paper presents a design of a voltage-controlled magnetic anisotropy MTJ-based sense amplifier (VCMA-SA) that offers better reliability, power efficiency and high-speed performance. MTJ switching between different resistive states in this paper takes place through the faster and energy-efficient voltage-controlled magnetic anisotropy (VCMA) scheme rather than the traditional spin-transfer torque (STT)-based magnetization switching. This paper demonstrates that SA based on VCMA-MTJ switching is faster, reliable and more energy-efficient compared with iPMA-based STT switching.
An On-Board Task Scheduling Method Based on Evolutionary Optimization Algorithm
Authors: Dan Feng , Bo Liu , Jian Gong
Published: September 2022

In order to meet the requirements of task scheduling in on-board distributed computing environment, an evolutionary optimization scheduling method for on-board tasks was proposed based on the on-board dynamic heterogeneous computing resource model and task model. In this method, task scheduling priority coding was used to adapt to dynamic changes of computing resources. Heuristic critical path for comprehensive index balance was adopted to implement the evaluation, so as to achieve a balance between task makespan, power consumption and reliability. Multi-group neighborhood search strategy was applied to avoid the algorithm falling into local optimization and simplify the complexity of scheduling algorithm. Fault-tolerant strategy of convergence monitoring and stop loss reconfiguration were designed for scheduling unit fault tolerance. Scheduling strategy based on active and backup redundant subtasks was used for computing unit fault tolerance. The simulation results showed the efficiency of this method to deal with the scenario of on-board distributed computing resources and implement the optimal scheduling of on-board tasks.
Deep Neural Network-based Approach to Identifying Road Lanes
Authors: Abadleh Ahmad , Qatawneh Marwa
Published: September 2022

In this paper, we present a new method for Lane Detection (LD) to reduce the impact of some issues associated with Autonomous Driving Cars (ADCs). It relies on the fact that self-driving will be the getaway of the transportation future. The ADC technology has expanded as it does not require human assistance. For ACD to be fully utilized, Machine Learning (ML) tools, such as Deep Neural Networks (DNNs), are required. However, ACD technology uses tools like a camera, GPS, Ultrasonic Sensor (US), and others, which work together to produce accurate ACDs. While adopting ACD, several issues need to be addressed such as carves, blurry, unclean, shadow, sunlight, pedestrians, and weather conditions. The paper proposes a new method to detect when a portion of a road line is missing due to weather conditions or old marking, protecting pedestrians and cars. For lane detection, the DNN model and Hough transform are used. CULane datasets including training and testing samples were used in the experiments. The results show high accuracy levels of 92%, indicating the ability to detect road lanes.
Entropy Weighted and Kernalized Power K-Means Clustering Based Lesion Segmentation and Optimized Deep Learning for Diabetic Retinopathy Detection
Authors: Elwin J. Granty Regina , Kumar K. Suresh , Ananth J. P. , Kumar R. Ramesh
Published: September 2022

Diabetic retinopathy (DR), a common complication of diabetes, is one of the leading causes of visual loss in a growing population. Thus, it is essential to identify DR at an early stage in order to minimize the problem of vision loss. As a result, the Henry Gas SailFish Optimizer (HGSO) algorithm and a successful lesion segmentation method based on Entropy Weighted and Kernalized Power K-Means Clustering (EWKPC) are developed for the DR detection. However, the SailFish Optimizer (SFO) and Henry Gas Solubility Optimization (HGSO) were combined to form the novel approach known as HGSO. Here, noise from the image is removed during pre-processing using the Region of Interest (ROI) approach and median filtering. Another use of the suggested EWKPC approach is lesion segmentation. Based on the outcomes of the data augmentation phase, Deep Convolutional Neural Network (DCNN)-based DR detection is carried out using the created HGSO algorithm. Along with effective performance, the created HGSO-based DCNN also displayed higher accuracy (0.910), specificity (0.929), and sensitivity (0.909).
Micro-network Based Convolutional Neural Network with Integration of Multilayer Feature Fusion Strategy for Human Activity Recognition
Authors: Kushwaha Arati , Khare Manish , Khare Ashish
Published: September 2022

Convolutional neural networks (CNN) have shown remarkable performance in enormous computer vision applications over the years, and many works have been done for human activity recognition (HAR) using CNN. However, most of deep learning-based architectures require large training data and plenty of computational resources. Therefore, we proposed a simple and efficient deep learning model for human activity recognition, which works on complex visual data and require less computational resources. In the proposed work, we designed a novel CNN architecture by stacking the repeated components called micro-networks to incorporate multiscale processing in the network layers. We have used a feature fusion strategy to pass the previous layer’s abstract complementary information to the next adjacent layer, believing that each layer encapsulates specific feature maps. Therefore, the hidden complementary information can potentially enhance the feature discrimination capacity of the network and help in learning the network. The proposed architecture is fully trained from scratch with stochastic gradient descent (SGD) optimizer at 0.05 initial learning rate and a softmax classifier is used for activity recognition. The merit of the proposed method over standard deep learning models is its computational efficiency in terms of learnable parameters and computational resources. The proposed model gives good performance on small as well as large size datasets. For authentication of the proposed method several extensive experiments are conducted on publically available datasets, namely UCF-101, HMDB-51, YouTube, and IXMAS datasets. The results have shown the outperformance of the proposed method over the existing state-of-the-art methods.
Dual Stream Conditional Generative Adversarial Network Fusion for Video Abnormal Behavior Detection
Authors: Zhao Mengyao , Hu Zhengping , Li Shufang , Sun Zhe
Published: September 2022

Deep learning has been successfully applied to video anomaly detection. However, the way that deep network learn spatio-temporal features autonomously will ignore the specificity of different pattern features. Therefore, this paper focuses on how to efficiently learn deep appearance feature, introduces the idea of learning appearance information by predicting future frame, and proposes dual stream conditional generative adversarial network fusion for video abnormal behavior detection. The video frame and its corresponding optical flow image are transferred to the conditional generative adversarial network to learn the motion feature representation. In addition, inputting the video frame and its corresponding future frame to the network to generate the appearance representation complementary to motion feature. The model is only trained with normal events, therefore it is not able to generate abnormal events accurately. During the test, for the foreground moving targets, the images generated by the model are compared with the ground truth to obtain a two-stream anomaly probability distribution model based on the mean square error used to achieve the purpose of region anomaly detection. Experiments on the public datasets show that the proposed method can effectively detect and locate abnormal behaviors in the video.
Predicting Integer Overflow Errors via Supervised Learning
Authors: Luo Yu , Xu Weifeng , Xu Dianxiang
Published: September 2022

An integer overflow error occurs when an integer operation in computer software evaluates a value out of the integer range. It can lead to a fatal system failure. The existing approaches to detecting integer overflow errors rely on data/control-flow analysis of the code or execution of the code with test cases. This paper presents a supervised learning approach to predicting whether each method in a given Java program has an integer overflow error by treating the source code as text. Built upon real-world programs, our Java dataset covers all integer data types and operations in Java, the methods for preventing integer overflow errors, and adversarial samples. We have evaluated six classification models, BERT, DistilBERT, codeBERT, Code2Vec, fastText, and NBSVM. They represent different text embedding techniques for dealing with source code. The experiment results show that BERT and its variants have outperformed other models. We have applied the resultant BERT model to eleven real-world projects, including JDK13.0 and ten top-ranked GitHub projects, and revealed 181 integer overflow errors. In addition, we have evaluated the classification models with a C/C++ dataset. The result is similar to that of the Java dataset.
Lower bounds for the warping degree of a knot projection
Authors: Ohya Atsushi , Shimizu Ayaka
Published: September 2022

The warping degree of an oriented knot diagram is the minimal number of crossings which we meet as an under-crossing first when we travel along the diagram from a fixed point. The warping degree of a knot projection is the minimal value of the warping degree for all oriented alternating diagrams obtained from the knot projection. In this paper, we consider the maximal number of regions which share no crossings for a knot projection with a fixed crossing, and give lower bounds for the warping degree.
Deformation-dependent mass Bohr Hamiltonian with Anharmonic potential for triaxial nuclei
Authors: Mounouna Kamargue P. , Zarma A. , Ema’a Ema’a J. M. , Yamino S. Doka , Ben Bolie G. H.
Published: September 2022

In this work, we study the collective Bohr Hamiltonian of triaxial nuclei within deformation-dependent mass formalism (DDM) using the new Anharmonic potential. The Schrödinger equation with Bohr Hamiltonian is separated into two parts and the [Formula: see text]-part is solved by mean of Nikiforov–Uvarov method to obtain the energy spectra. The [Formula: see text]-part is defined by a ring shape potential and solved with the same method. The energies and the [Formula: see text] transition rates for nuclide [Formula: see text]Pt are calculated and compared with the experimental data and with some others models in the same formalism.
Electromagnetic moments and magnetic form factors of the ground states of odd-A nuclei in the vicinity of Z = 20 closure
Authors: Radhi R. A. , Alzubadi Ali A.
Published: September 2022

Magnetic dipole and electric quadrupole moments are calculated for odd-[Formula: see text] nuclei in the vicinity of [Formula: see text] closure. Also, elastic magnetic electron scattering form factors are calculated for some of these nuclei, for which there are available experimental data. Excitation out of major shell space is taken into account through a microscopic theory which allows particle–hole excitation from the core and model-space orbits to all higher orbits with [Formula: see text] excitations, with [Formula: see text] as the number of shells that gives convergence to core-polarization matrix elements. Effective charges are obtained for each isotope. Core polarization (CP) is essential for obtaining a reasonable description of the electric quadrupole moments, but has no effect on the magnetic dipole moments but squeezes the magnetic form factors, and describes the data very well. For nuclei where there are data for large momentum transfer values, Skyrme–Hartree–Fock (SHF) method is used to generate from it a one-body potential in Hartree–Fock theory to calculate the single-particle matrix elements. Those with high momentum transfer data which give evidence of the structure of the different multipoles, are more well described using SHF formalism than the harmonic oscillator (HO) single-particle matrix elements.
Stock Liquidity Risk and Cash Preservation
Authors: Yeh Shih-Kuo , Yang Wan-Ru , Chen Ren-Raw , Yeh Chung-Ying
Published: September 2022

This paper investigates the relationship among liquidity risk, cash-holdings, financial constraints, and capital-raising costs. Our results show that cash preservation has different impacts on the liquidity risk explained by different aspects. The liquidity risk is negatively related with cash holdings. More financial constraints increase the liquidity risk. The firms with low capital-raising costs hold more cash because of liquidity needs.
Analytical View of Pricing Weather and Freight Derivatives: 1950–2020
Authors: Satya Sekhar G. V.
Published: September 2022

A weather derivative is financial instrument that companies or individuals use to hedge against the risk of weather-related losses. Freight derivatives value is derived from the future levels of freight rates, like a dry bulk-a category of cargo stowed in bulk, consisting of grain, cotton, coal, etc., carrying rates, and oil tanker rates.

Numerous empirical studies exist on weather and freight derivatives and their pricing models such as Indifference Pricing Approach, Arbitrage Pricing Model, Financial Pricing model, Benchmark Pricing Approach, Fair Pricing Approach, Actuarial Pricing, Consumer Based Pricing Method, and Index Modeling. This paper aims to address issues relating to the functioning of weather and freight derivatives.

This paper also focuses on the studies published on weather and freight derivatives’ pricing models during the last seven decades (i.e., 1950–2020).

Objectives:

(1) To understand the vital issues relating to models of weather and freight derivatives. (2) To compare pricing models of weather and freight derivatives during the last seven decades (i.e., 1950–2020).
Social Activism and Firm Valuation: An Examination of ‘Taking a Knee’ Protests and National Football League Sponsors
Authors: Sampath Vijay , O’Connor Arthur J. , Topaloglu Omer , Legister Calvester
Published: September 2022

We build upon social movement and investor attention theories to investigate the effect of “taking a knee” protests on the abnormal stock returns of NFL sponsoring companies. The study is conducted in two phases. Employing event study methodology in the first phase, we measured the abnormal returns and trading volumes of the companies during a four-year period from 2016 to 2020. While the results for abnormal returns are economically small, we found that there is 13.42% increase in trading volume in [0,[Formula: see text]5], 61.28% in [0,[Formula: see text]15], and 160.01% in [0,[Formula: see text]30] event windows. There was also information leakage prior to the events as indicated by the 36.08 increase in trading volume in the [[Formula: see text]30, [Formula: see text]2] event window. We ran multiple regression models in the second phase to further examine the degree to which the unique circumstances surrounding the protests explain variations in abnormal returns. Companies that had higher brand values and the negative emotional tone of the comments made by the President of the United States about the protests were positively associated with abnormal returns. The results are also robust to methodological changes using abnormal returns adjusted for trading volume. Overall, the findings suggest that social activism by athletes is likely to affect investor attention and sentiment, thereby garnering a spillover effect on sponsoring companies’ stock prices and trading volumes.
Revenue Classification Shifting: Does CEO Gender Matter?
Authors: Ouyang Wenjing , Ngo Thanh , Wang Hongxia
Published: September 2022

Instead of managing bottom-line earnings, firms may use revenue classification shifting to inflate core earnings due to its less risky, less costly, but viable features. Female CEOs, being perceived more risk-averse and more ethical, may engage in either more or less revenue classification shifting than male CEOs. Using a sample of 36,427 US firm-year observations from 1993 to 2019, we find that female CEOs tend to engage in less revenue classification shifting than male CEOs when the monitoring environment is looser but increase revenue classification shifting when the monitoring environment becomes more stringent. Further analyses indicate that female CEOs may substitute revenue classification shifting for more costly earnings management tools under stricter monitoring. The results are robust to endogeneity tests, sample selection bias, and additional control variables, providing new evidence that female CEOs are more risk-averse.
EARLY KERATOCONUS DISEASE DETECTION USING ORBSCAN II CORNEAL TOPOGRAPHY
Authors: EL AMINE LAZOUNI MOHAMMED , MESSADI MAHAMED , FEROUI AMEL , MAHMOUDI SAïD
Published: September 2022

Keratoconus is an eye disease causing progressive corneal thinning. At an early stage (fruste keratoconus), the symptoms can overlap with those of other eye disorders, making the diagnosis difficult. This led us to propose a new image processing pipeline to automatically calculate the main descriptors used by Ophthalmologists to detect keratoconus, and then classify these data in order to propose an intelligent system able to help specialists in the early recognition of this pathology. To accomplish this, we elaborated a new benchmark database from a corneal topographic Orbscan II device. For keratoconus classification, five different machine learning methods are tested on our new locally collected database, which are: K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Decision Trees (DT), and two neural networks classifiers (the Radial Basis Function (RBF) and the Multi-Layer Perceptron (MLP)). Experimental results indicate that all classifiers achieved good precision when using all descriptors (the numerical parameters given by the ORBSCAN II topographer and the descriptors obtained after an image processing of the topography map). Furthermore, the SVM outperformed the other classifiers with an accuracy rate of 95.04%. These results were confirmed and validated by a group of experts in ophthalmology and prove the efficiency of our system and the coherence of our new database.
THERMODYNAMIC PERFORMANCE ANALYSIS OF HUMAN CARDIOVASCULAR SYSTEM FOR NORMAL AND CARDIOMEGALY CONDITIONS
Authors: DUTTA ABHIJIT , CHATTOPADHYAY HIMADRI , ÇATAK JALE
Published: September 2022

This study aims to determine the thermodynamic performance of the cardiovascular system for healthy and cardiomegaly conditions where work done by the heart and lung is considered as input expenses. Analysis based on the second law of thermodynamics was performed under three distinct physiological conditions: resting, moderate, and excessive activity levels. From this analysis, it is noted that, exergy transfer rate increases with the increase in the physiological activity level. The exergetic efficiency of the cardiovascular system is about 55% at resting and changes by about 4% with physical activity level. The human cardiovascular system efficiency is increased with an increase in breathing air inlet temperature and relative humidity irrespective of physiological conditions. The work also quantifies the efficiency of the heart and lung as individual systems. For a fixed breathing air inlet temperature and relative humidity, the cardiovascular system performance increases with a decrease in physical activity levels.
PHYSIOLOGICAL PARAMETERS AND SEVERITY OF CORONAVIRUS INFECTION: CASE STUDY
Authors: HADI AMEER M. , AL-MAWLA YASI HAIDER , AL-IMARI MUSTAFA JAWAD , ABBOOD SARAH KAMIL , ALSAFFAR MARWA FADHIL
Published: September 2022

Preface: Coronavirus has several mechanisms that facilitate its entry into the target cell such as cholesterol by acting concurrently with angiotensin-converting enzyme (ACE2) at entry site, where the virus uses it to increase its entry and spread. We conducted a practical research for measuring Cholesterol, Triglyceride (TG) and Lactate dehydrogenase concentrations in COVID-19 patients. Objectives: The aim of this research is to see the correlation between Cholesterol, TG and Lactate dehydrogenase and the severity of coronavirus infection. Methods: This study is made at Al-Zahra Teaching Hospital during the period from March 2021 to July 2021 and it was performed on two groups: patients group that involved 30 samples (15 male and 15 female), age ranging from 18 to 84 years old and another group consisting of 30 individuals who were not infected with SARS-CoV-2. Results: Our findings show that the high value for markers under this study associated with coronavirus was detected in lactate dehydrogenase ([Formula: see text] IU/l), ([Formula: see text] Mg/dl) for TG and ([Formula: see text] Mg/dl) for cholesterol. High concentration of cholesterol is shown in the age category between 54 and 64 years is represented as (487 Mg/dl and 502 Mg/dl), TG at ages 42, 54 and 64 years is represented as 497, 509 and 521 Mg/dl, Lactate dehydrogenase at ages 80–84 years that represented is as 739, 809 and 812 IU/l. According to physiological parameters which were elevated, a high concentration was observed in almost both sexes, as males were more affected than females when determining cholesterol at (500 Mg/dl), while females had more than males with respect to lactate dehydrogenase and TGs at (520 Mg/dl and 800 IU/l), respectively. In the correlation test, we found the highest significant differences, according to the correlation test, between cholesterol and lactate dehydrogenase at (1) cholesterol and TG at (0.000), TG and lactate dehydrogenase at (0.739). Conclusions: We conclude that COVID-19 patients suffer from increased markers under the studies on cholesterol, TG and lactate dehydrogenase.
CASCADES IN WORLD INPUT–OUTPUT NETWORK: ILLUSION OF STABILITY
Authors: LEONIDOV ANDREY , RADIONOV STANISLAV , VASILYEVA EKATERINA
Published: September 2022

In this study, we address an issue of stability of global production network by constructing a scheme combining classical Leontieff input–output analysis and modeling of contagion in financial networks. We propose a model of disruptive cascades in global economy with nonlinear effects similar to those in the financial contagion model proposed by Elliott, Golub and Jackson (2014). We apply the model to the analysis of stability of the global input–output sectoral network using the data from the World Input-Output Database (WIOD). We show that this sectoral network appears to be very stable with respect to sectoral shocks. However, by introducing synthetic substructure of economic sectors in the WIOD data we show that this stability is to a significant extent an artifact of working with aggregated data. Namely, we show that the impact of economic shocks may be much more pronounced if an underlying disaggregated network is sufficiently sparse.
GCMCDTI: Graph convolutional autoencoder framework for predicting drug–target interactions based on matrix completion
Authors: Li Jing , Zhang Chen , Li Zhengwei , Nie Ru , Han Pengyong , Yang Wenjia , Liao Hongmei
Published: September 2022

Identification of potential drug–target interactions (DTIs) plays a pivotal role in the development of drug and target discovery in the public healthcare sector. However, biological experiments for predicting interactions between drugs and targets are still expensive, complicated, and time-consuming. Thus, computational methods are widely applied for aiding drug–target interaction prediction. In this paper, we propose a novel model, named GCMCDTI, for DTIs prediction which adopts a graph convolutional network based on matrix completion. We regard the association prediction between drugs and targets as link prediction and treat the process as matrix completion, and then a graph convolutional auto-encoder framework is employed to construct the drug and target embeddings. Then, a bilinear decoder is applied to reconstruct the DTI matrix. We conduct our experiments on four benchmark datasets consisting of enzymes, G protein-coupled receptors (GPCRs), ion channels, and nuclear receptors. The five-fold cross-validation results achieve the high average AUC values of 95.78%, 95.31%, 93.90%, and 91.77%, respectively. To further evaluate our method, we compare our proposed method with other state-of-the-art approaches. The comparison results illustrate that our proposed method obtains improvement in performance on DTI prediction. The proposed method will be a good choice in the field of DTI prediction.
Improvement of Linear Tetrahedral Element Performance by Using Substructuring Method
Authors: Waluyo Sugeng , Eriawan Andi , Fadhil Muhammad
Published: September 2022

We apply here a class of substructuring method to improve the performance of linear tetrahedral element used in finite element analysis (FEA). The method is novel and relied on the construction of mesh inside a tetrahedron volume which behaves as an assembly of substructures. The corresponding stiffness matrix of the mesh is assembled using a static condensation procedure which is used further to obtain strain energy from a set of particular displacement vectors. This energy is the key to obtain a so-called energy ratio that will modify the stiffness matrix of a linear tetrahedral element. In the numerical tests, we show that the method can improve the performance of the tetrahedral element to approximate displacement and stress fields from the analytical solutions for cantilever and stress concentration problems, respectively.
Managing Ideation and Concept Integration in the Product Innovation Work Process for Non-Assembled Products
Authors: Lager Thomas , Simms Christopher Don , Frishammar Johan
Published: September 2022

In an exploratory inquiry, involving informants in 19 global manufacturing companies in six sectors of the process industries, ideation, and concept integration during the pre-development of non-assembled products was studied. New and previously deployed constructs and concepts related to innovation in a process-industrial context have initially been developed, refined, and empirically tested. The findings demonstrate the importance of an early integration of constructs and concepts for raw material innovation, innovation-related process technology, and product innovation, as a prerequisite for successful ideation of new or improved products in the process industries. Companies in different sectors of the process industries can implement and use the novel “integrated framework” for contextualization and conceptualization of new product ideas in their development or reconfiguration of an enhanced work process for non-assembled products.
On Hosoya polynomial and subsequent indices of pent–heptagonal carbon nanosheets
Authors: Sheikh Umber , Rashid Sidra , Ozel Cenap , Pincak Richard
Published: September 2022

The pent–heptagonal carbon nanosheets (CNSs) admit significant industrial applications. We shall compute the distance-based Hosoya polynomial of CNSs including [Formula: see text] and [Formula: see text]. The first-, second- and third-order derivatives of Hosoya polynomial, respectively, lead to the Wiener, hyper-Wiener and Tratch–Stankevitch–Zefirov (TSZ) indices which are the distance-based topological indices. These indices have many physical applications and can be correlated to physio-chemical properties of chemical structures. Results exhibit that despite the fact that both sheets have the same chemical formula, they have different Hosoya polynomials and related distance-based topological indices, resulting in distinct quantitative structure–property relation (QSPR) and quantitative structure–activity relation (QSAR) due to configuration differences.
On the Coronavirus Disease Death Rate Modeling Utilizing Generalized Exponential Kumaraswamy
Authors: Barati Ramin , Rashidi Sara Fanati
Published: September 2022

This paper aims to model COVID-19 in different countries, including Iran, Canada, Italy, and Mexico. A novel five-parameter lifetime distribution termed the Odd generalized exponential Kumaraswamy-inverse exponential distribution (OEKIE) is presented by combining the Kumaraswamy-inverse exponential distribution with the odd generalized exponential generator. The theoretical features of the new distribution, as well as its reliability functions, moments, and order statistics are investigated. The novel distribution has numerous advantages, since its density has a variety of symmetric and asymmetric forms. Furthermore, The graphs of the hazard rate function exhibit various asymmetrical shapes such as decreasing, increasing, upside-down bathtub shapes, and inverted J-shapes making OEKIE suitable for modeling hazards behaviors more likely to be observed in practical settings like human mortality, and biological applications. The OEKIE’s parameters are estimated using the maximum likelihood approach. The effectiveness of OEKIE is demonstrated through both numerical study and applications to four COVID-19 mortality rate data sets. The OEKIE provides best fits to COVID-19 data compared to other extended forms of the Kumaraswamy and inverse exponential distributions which may attract wider applications in different fields.
On disjoint maximum and maximal independent sets in graphs and inverse independence number
Authors: Kaci Fatma
Published: September 2022

In this paper, we give a class of graphs that do not admit disjoint maximum and maximal independent (MMI) sets. The concept of inverse independence was introduced by Bhat and Bhat in [Inverse independence number of a graph, Int. J. Comput. Appl. 42(5) (2012) 9–13]. Let [Formula: see text] be a [Formula: see text]-set in [Formula: see text]. An independent set [Formula: see text] is called an inverse independent set with respect to [Formula: see text]. The inverse independence number [Formula: see text] is the size of the largest inverse independent set in [Formula: see text]. Bhat and Bhat gave few bounds on the independence number of a graph, we continue the study by giving some new bounds and exact value for particular classes of graphs: spider tree, the rooted product and Cartesian product of two particular graphs.
Sharp bounds for spectral radius of Dα-matrix of graphs
Authors: Ganie Hilal A.
Published: September 2022

For a simple connected graph [Formula: see text], the generalized distance matrix [Formula: see text] is defined as [Formula: see text], where [Formula: see text] and [Formula: see text] are, respectively, the diagonal matrix of vertex transmission degrees and the distance matrix of [Formula: see text]. In this paper, we will study the spectral radius of the generalized distance matrix [Formula: see text] of a connected graph [Formula: see text]. We obtain some upper and lower bounds for the generalized distance spectral radius in terms of various parameters, like the vertex transmission degrees, the average transmission 2-degrees, etc., associated with the structure of the graph. We characterize the extremal graphs attaining these bounds. Further, we show that our bounds improve some previously known bounds existing in the literature.
On regularity and intra regularity of the components of a Morita context of monoids
Authors: Chakraborty Kaushik , Sardar Sujit Kumar , Shum K. P.
Published: September 2022

The notions of regular, intra regular and duo bi-acts of a Morita context of monoids have been introduced. Some characterizations of regular and intra regular bi-acts have been obtained in terms of b-subacts. Also connections among regularity of acts and [Formula: see text]-regularity of power semimodules, intra regularity of acts and intra [Formula: see text]-regularity of power semimodules have been observed using power functor. Then the study of the interplay among various components of a Morita context of monoids in terms of regularity, intra regularity and duoness have been accomplished. It has been deduced that regularity, intra regularity and being duo are Morita invariant properties. Finally regular (intra regular, duo) Morita context has been defined and it has been proved that for a duo Morita context of monoids regular and intra regular Morita context coincide.
Magnetic Properties of a Cylindrical Nanotube with Spin-1/2 Core and Spin-3/2 Shell
Authors: Hachem N. , El Bouziani M.
Published: September 2022

The magnetic properties and phase diagrams of a ferrimagnetic cylindrical Ising nanotube with spin-1/2 core and spin-3/2 shell are studied using mean-field approximation and Monte Carlo simulation. The effects of shell and core-shell interface exchange couplings and crystal field on the magnetic properties of the system are examined. For some physical parameters, the critical and compensation behaviors are highlighted and first-order phase transitions are observed in the ferrimagnetic ordered region at low temperatures. A comparison between the two methods was performed.