bluuebunny/crossref_metadata_2025 huggingface dataset:
Semi-Quantum Identification without Information Leakage
Authors: Yang Chun-Wei , Wang Hung-Wen , Lin Jason , Tsai Chia-Wei
Published: January 2023

In 2019, Zhou et al. proposed semi-quantum identification (also known as semi-quantum authentication, SQA), which proceeds under a measure-resend and measurement-free environment. However, Zhou et al.’s SQA protocol suffers from severe information leakages. An eavesdropper can obtain an intact authentication key without being detected under this environment. In particular, Zhou et al.’s measure-resend SQA protocol is vulnerable to double CNOT attacks, while the measurement-free SQA protocol is vulnerable to man-in-the-middle attacks. Hence, this study reveals the severe security issues of Zhou et al.’s SQA protocol and proposes an improved protocol with guaranteed security. The proposed measure-resend SQA protocol is immune to double CNOT attacks. Since the photons sent back and forth are identical, Eve cannot obtain any information by cross-comparing these photons. In the proposed measurement-free SQA protocol, the eavesdropper cannot obtain the order of the transmitted photons because it was previously a pre-shared key to decide the order of the photons. Hence, the proposed measurement-free SQA protocol can withstand man-in-the-middle attacks.
Risk-Taking, Financial Knowledge, and Risky Investment Intention: Expanding Theory of Planned Behavior Using a Moderating-Mediating Model
Authors: Sobaih Abu Elnasr E. , Elshaer Ibrahim A.
Published: January 2023

This research examines the impact of financial knowledge on risky investment intention via the lens of the theory of planned behavior (TPB). The research developed a comprehensive model to test the mediation effect of the three TPB antecedents on the link between financial knowledge and risky investment intention. The research investigates the moderating effect of risk-taking on the link between three TPB constructs and risky investment intention. For these purposes, we used a pre-tested survey, was directed to senior university students in public universities in Saudi Arabia. The findings of SmartPLS showed a significant positive influence of financial knowledge on attitudes towards risky investment, subjective norms (SNs), and perceived behavioral control (PBC). Both SNs and PBC have a significant positive influence on risky investment intention. Nonetheless, the personal attitude of students failed to have a significant direct or mediating influence on risky investment intention. Additionally, risk-taking did not have a moderating effect on the link between personal attitude and risky investment intention. Students belong to a risk-adverse culture, which could justify the insignificant impact of their personal attitudes on risky investment intention. On the other side, SNs and PBC have a mediating effect on the link between financial knowledge and risky investment intention. Risk-taking has a moderating effect on the link between SNs, PBC, and risky investment intention. The research extends the use of TPB by validating its assumptions about driving the investment intention of university graduates.
Emotional Speaker Verification Using Novel Modified Capsule Neural Network
Authors: Nassif Ali Bou , Shahin Ismail , Nemmour Nawel , Hindawi Noor , Elnagar Ashraf
Published: January 2023

Capsule Neural Network (CapsNet) models are regarded as efficient substitutes for convolutional neural networks (CNN) due to their powerful hierarchical representation capability. Nevertheless, CNN endure their inability of recording spatial information in spectrograms. The main constraint of CapsNet is related to the compression method which can be implemented in CNN models but cannot be directly employed in CapsNet. As a result, we propose a novel architecture based on dual-channel long short-term memory compressed CapsNet (DC-LSTM–COMP CapsNet) for speaker verification in emotional as well as stressful talking environments. The proposed approach is perceived as a modified Capsule network that attempts to overcome the limitations that exist within the original CapsNet, as well as in CNN while enhancing the verification performance. The proposed architecture is assessed on four distinct databases. The experimental analysis reveals that the average speaker verification performance is improved in comparison with CNN, the original CapsNet, as well as the conventional classifiers. The proposed algorithm notably achieves the best verification accuracy across the four speech databases. For example, using the Emirati dataset, the average percentage equal error rates (EERs) obtained is 10.50%, based on the proposed architecture which outperforms other deep and classical models.
The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data
Authors: Mastor Adam Braima S. , Alghamdi Abdulaziz S. , Ngesa Oscar , Mung’atu Joseph , Chesneau Christophe , Afify Ahmed Z.
Published: January 2023

A fully parametric accelerated failure time (AFT) model with a flexible, novel modified exponential Weibull baseline distribution called the extended exponential Weibull accelerated failure time (ExEW-AFT) model is proposed. The model is presented using the multi-parameter survival regression model, where more than one distributional parameter is linked to the covariates. The model formulation, probabilistic functions, and some of its sub-models were derived. The parameters of the introduced model are estimated using the maximum likelihood approach. An extensive simulation study is used to assess the estimates’ performance using different scenarios based on the baseline hazard shape. The proposed model is applied to a real-life right-censored COVID-19 data set from Sudan to illustrate the practical applicability of the proposed AFT model.
The Hamilton–Jacobi–Bellman Equation for Differential Games with Composite Distribution of Random Time Horizon
Authors: Balas Tatyana , Tur Anna
Published: January 2023

A differential game with random duration is considered. The terminal time of the game is a random variable settled using a composite distribution function. Such a scenario occurs when the operating mode of the system changes over time at the appropriate switching points. On each interval between switchings, the distribution of the terminal time is characterized by its own distribution function. A method for solving such games using dynamic programming is proposed. An example of a non-renewable resource extraction model is given, where a solution of the problem of maximizing the total payoff in closed-loop strategies is found. An analytical view of the optimal control of each player and the optimal trajectory depending on the parameters of the described model is obtained.
Optimal Sizing of a Photovoltaic Pumping System Integrated with Water Storage Tank Considering Cost/Reliability Assessment Using Enhanced Artificial Rabbits Optimization: A Case Study
Authors: Mazloumi Abdolhamid , Poolad Alireza , Mokhtari Mohammad Sadegh , Altman Morteza Babaee , Abdelaziz Almoataz Y. , Elsisi Mahmoud
Published: January 2023

In this paper, optimal sizing of a photovoltaic (PV) pumping system with a water storage tank (WST) is developed to meet the water demand to minimize the life cycle cost (LCC) and satisfy the probability of interrupted water (pIW) constraint considering real region data. The component sizing, including the PV resources and the WST, is determined optimally based on LCC and pIW using a new meta-heuristic method named enhanced artificial rabbits optimization (EARO) via a nonlinear inertia weight reduction strategy to overcome the premature convergence of its conventional algorithm. The WST is sized optimally regarding the lack of irradiation and inaccessibility of the pumping system so that it is able to improve the water supply reliability. The LCC for water extraction heights of 5 and 10 m is obtained at 0.2955 M$ and 0.2993 M$, respectively, and the pIW in these two scenarios is calculated as zero, which means the complete and reliable supply of the water demand of the customers using the proposed methodology based on the EARO. Also, the results demonstrated the superior performance of EARO in comparison with artificial rabbits optimization (ARO) and particle swarm optimization (PSO); these methods have supplied customers’ water demands with higher costs and lower reliability than the proposed EARO method. Also, during the sensitivity analysis, the results showed that changes in the irradiance and height of the water extraction have a considerable effect on the cost and ability to meet customer demand.
Stroke Localization Using Multiple Ridge Regression Predictors Based on Electromagnetic Signals
Authors: Gao Shang , Zhu Guohun , Bialkowski Alina , Zhou Xujuan
Published: January 2023

Localizing stroke may be critical for elucidating underlying pathophysiology. This study proposes a ridge regression–meanshift (RRMS) framework using electromagnetic signals obtained from 16 antennas placed around the anthropomorphic head phantom. A total of 608 intracranial haemorrhage (ICH) and ischemic (IS) signals are collected and evaluated for RRMS, where each type of signal contains two different diameters of stroke phantoms. Subsequently, multiple ridge regression predictors then give the target distances from the antennas and mean shift is used to cluster the predicted stroke location based on these distances. The test results show that the training time and economic cost are significantly reduced as the average prediction time only takes 0.61 s to achieve an accurate result (average position error = 0.74 cm) using a conventional laptop. It has great potential to be used as an auxiliary standard medical method, or rapid diagnosis of stroke patients in underdeveloped areas, due to its rapidity, good deployability, and low hardware cost.
A Generalized Linear Transformation and Its Effects on Logistic Regression
Authors: Zeng Guoping , Tao Sha
Published: January 2023

Linear transformations such as min–max normalization and z-score standardization are commonly used in logistic regression for the purpose of scaling. However, the work in the literature on linear transformations in logistic regression has two major limitations. First, most work focuses on improving the fit of the regression model. Second, the effects of transformations are rarely discussed. In this paper, we first generalized a linear transformation for a single variable to multiple variables by matrix multiplication. We then studied various effects of a generalized linear transformation in logistic regression. We showed that an invertible generalized linear transformation has no effects on predictions, multicollinearity, pseudo-complete separation and complete separation. We also showed that multiple linear transformations do not have effects on the variance inflation factor (VIF). Numeric examples with a real data were presented to validate our results. Our results of no effects justify the rationality of linear transformations in logistic regression.
Novel Formulas of Schröder Polynomials and Their Related Numbers
Authors: Abd-Elhameed Waleed Mohamed , Amin Amr Kamel
Published: January 2023

This paper explores the Schröder polynomials, a class of polynomials that produce the famous Schröder numbers when x=1. The three-term recurrence relation and the inversion formula of these polynomials are a couple of the fundamental Schröder polynomial characteristics that are given. The derivatives of the moments of Schröder polynomials are given. From this formula, the moments of these polynomials and also their high-order derivatives are deduced as two significant special cases. The derivatives of Schröder polynomials are further expressed in new forms using other polynomials. Connection formulas between Schröder polynomials and a few other polynomials are provided as a direct result of these formulas. Furthermore, new expressions that link some celebrated numbers with Schröder numbers are also given. The formula for the repeated integrals of these polynomials is derived in terms of Schröder polynomials. Furthermore, some linearization formulas involving Schröder polynomials are established.
A Survey on Isogeometric Collocation Methods with Applications
Authors: Ren Jingwen , Lin Hongwei
Published: January 2023

Isogeometric analysis (IGA) is an effective numerical method for connecting computer-aided design and engineering, which has been widely applied in various aspects of computational mechanics. IGA involves Galerkin and collocation formulations. Exploiting the same high-order non-uniform rational B-spline (NURBS) bases that span the physical domain and the solution space leads to increased accuracy and fast computation. Although IGA Galerkin provides optimal convergence, IGA collocation performs better in terms of the ratio of accuracy to computational time. Without numerical integration, by working directly with the strong form of the partial differential equation over the physical domain defined by NURBS geometry, the derivatives of the NURBS-expressed numerical solution at some chosen collocation points can be calculated. In this study, we survey the methodological framework and the research prospects of IGA. The collocation schemes in the IGA collocation method that affect the convergence performance are addressed in this paper. Recent studies and application developments are reviewed as well.
A Comparative Numerical Study of Heat and Mass Transfer Individualities in Casson Stagnation Point Fluid Flow Past a Flat and Cylindrical Surfaces
Authors: Rehman Khalil Ur , Shatanawi Wasfi , Yaseen Saba
Published: January 2023

There is a consensus among researchers that the simultaneous involvement of heat and mass transfer in fluid flow owns numerous daily life applications like energy systems, automobiles, cooling of electronic devices, power generation by the stream, electric power, and diagnosing and characterizing diseases, to mention just a few. Owing to such motivation, we considered both heat and mass transfer aspects in non-Newtonian fluid flow regimes. The Casson fluid is considered as a non-Newtonian fluid. For better novelty the flow is considered at both flat and cylindrical surfaces along with stagnation point, magnetic field, mixed convection, heat generation, viscous dissipation, thermal radiations, and temperature-dependent thermal conductivity. The ultimate differential equations are nonlinear, and hence difficult to solve analytically. Therefore, a numerical scheme, namely the shooting method with the Runge–Kutta algorithm, is adopted to report an acceptable solution for flow field description. The outcomes are shared comparatively for flat and cylindrical surfaces. We have seen that compared to a flat surface, the cylindrical surface has a larger Nusselt number magnitude.
Numerical Simulation for a High-Dimensional Chaotic Lorenz System Based on Gegenbauer Wavelet Polynomials
Authors: Alqhtani Manal , Khader Mohamed M. , Saad Khaled Mohammed
Published: January 2023

We provide an effective simulation to investigate the solution behavior of nine-dimensional chaos for the fractional (Caputo-sense) Lorenz system using a new approximate technique of the spectral collocation method (SCM) depending on the properties of Gegenbauer wavelet polynomials (GWPs). This technique reduces the given problem to a non-linear system of algebraic equations. We satisfy the accuracy and efficiency of the proposed method by computing the residual error function. The numerical solutions obtained are compared with the results obtained by implementing the Runge–Kutta method of order four. The results show that the given procedure is an easily applied and efficient tool to simulate this model.
ResInformer: Residual Transformer-Based Artificial Time-Series Forecasting Model for PM2.5 Concentration in Three Major Chinese Cities
Authors: Al-qaness Mohammed A. A. , Dahou Abdelghani , Ewees Ahmed A. , Abualigah Laith , Huai Jianzhu , Abd Elaziz Mohamed , Helmi Ahmed M.
Published: January 2023

Many Chinese cities have severe air pollution due to the rapid development of the Chinese economy, urbanization, and industrialization. Particulate matter (PM2.5) is a significant component of air pollutants. It is related to cardiopulmonary and other systemic diseases because of its ability to penetrate the human respiratory system. Forecasting air PM2.5 is a critical task that helps governments and local authorities to make necessary plans and actions. Thus, in the current study, we develop a new deep learning approach to forecast the concentration of PM2.5 in three major cities in China, Beijing, Shijiazhuang, and Wuhan. The developed model is based on the Informer architecture, where the attention distillation block is improved with a residual block-inspired structure from efficient networks, and we named the model ResInformer. We use air quality index datasets that cover 98 months collected from 1 January 2014 to 17 February 2022 to train and test the model. We also test the proposed model for 20 months. The evaluation outcomes show that the ResInformer and ResInformerStack perform better than the original model and yield better forecasting results. This study’s methodology is easily adapted for similar efforts of fast computational modeling.
A New Fuzzy Reinforcement Learning Method for Effective Chemotherapy
Authors: Alsaadi Fawaz E. , Yasami Amirreza , Volos Christos , Bekiros Stelios , Jahanshahi Hadi
Published: January 2023

A key challenge for drug dosing schedules is the ability to learn an optimal control policy even when there is a paucity of accurate information about the systems. Artificial intelligence has great potential for shaping a smart control policy for the dosage of drugs for any treatment. Motivated by this issue, in the present research paper a Caputo–Fabrizio fractional-order model of cancer chemotherapy treatment was elaborated and analyzed. A fix-point theorem and an iterative method were implemented to prove the existence and uniqueness of the solutions of the proposed model. Afterward, in order to control cancer through chemotherapy treatment, a fuzzy-reinforcement learning-based control method that uses the State-Action-Reward-State-Action (SARSA) algorithm was proposed. Finally, so as to assess the performance of the proposed control method, the simulations were conducted for young and elderly patients and for ten simulated patients with different parameters. Then, the results of the proposed control method were compared with Watkins’s Q-learning control method for cancer chemotherapy drug dosing. The results of the simulations demonstrate the superiority of the proposed control method in terms of mean squared error, mean variance of the error, and the mean squared of the control action—in other words, in terms of the eradication of tumor cells, keeping normal cells, and the amount of usage of the drug during chemotherapy treatment.
Neural Teleportation
Authors: Armenta Marco , Judge Thierry , Painchaud Nathan , Skandarani Youssef , Lemaire Carl , Gibeau Sanchez Gabriel , Spino Philippe , Jodoin Pierre-Marc
Published: January 2023

In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks. Neural teleportation teleports a network to a new position in the weight space and preserves its function. This phenomenon comes directly from the definitions of representation theory applied to neural networks and it turns out to be a very simple operation that has remarkable properties. We shed light on the surprising and counter-intuitive consequences neural teleportation has on the loss landscape. In particular, we show that teleportation can be used to explore loss level curves, that it changes the local loss landscape, sharpens global minima and boosts back-propagated gradients at any moment during the learning process.
Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection
Authors: Zhang Baisong , Hou Sujuan , Karim Awudu , Wang Jing , Jia Weikuan , Zheng Yuanjie
Published: January 2023

Logo detection is a technology that identifies logos in images and returns their locations. With logo detection technology, brands can check how often their logos are displayed on social media platforms and elsewhere online and how they appear. It has received a lot of attention for its wide applications across different sectors, such as brand identity protection, product brand management, and logo duration monitoring. Particularly, logo detection technology can offer various benefits for companies to help brands measure their logo coverage, track their brand perception, secure their brand value, increase the effectiveness of their marketing campaigns and build brand awareness more effectively. However, compared with the general object detection, logo detection is more challenging due to the existence of both small logo objects and large aspect ratio logo objects. In this paper, we propose a novel approach, named Discriminative Semantic Feature Pyramid Network with Guided Anchoring (DSFP-GA), which can address these challenges via aggregating the semantic information and generating different aspect ratio anchor boxes. More specifically, our approach mainly consists of two components, namely Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA). The former is proposed to fuse semantic features into low-level feature maps to obtain discriminative representation of small logo objects, while the latter is further integrated into DSFP to generate large aspect ratio anchor boxes for detecting large aspect ratio logo objects. Extensive experimental results on four benchmarks demonstrate the effectiveness of the proposed DSFP-GA. Moreover, we further conduct visual analysis and ablation studies to illustrate the strength of the proposed DSFP-GA when detecting both small logo objects and large aspect logo objects.
Asymptotic Analysis for One-Stage Stochastic Linear Complementarity Problems and Applications
Authors: Lin Shuang , Zhang Jie , Qiu Chen
Published: January 2023

One-stage stochastic linear complementarity problem (SLCP) is a special case of a multi-stage stochastic linear complementarity problem, which has important applications in economic engineering and operations management. In this paper, we establish asymptotic analysis results of a sample-average approximation (SAA) estimator for the SLCP. The asymptotic normality analysis results for the stochastic-constrained optimization problem are extended to the SLCP model and then the conditions, which ensure the convergence in distribution of the sample-average approximation estimator for the SLCP to multivariate normal with zero mean vector and a covariance matrix, are obtained. The results obtained are finally applied for estimating the confidence region of a solution for the SLCP.
Numerical Study of Thermo-Electric Conversion for TEG Mounted Wavy Walled Triangular Vented Cavity Considering Nanofluid with Different-Shaped Nanoparticles
Authors: Selimefendigil Fatih , Omri Mohamed , Aich Walid , Besbes Hatem , Ben Khedher Nidhal , Alshammari Badr M. , Kolsi Lioua
Published: January 2023

The effects of the combined utilization of wavy wall and different nanoparticle shapes in heat transfer fluid for a thermoelectric generator (TEG) mounted vented cavity are numerically analyzed. A triangular wave form of the cavity is used, while spherical and cylindrical-shaped alumina nanoparticles are used in water up to a loading amount of 0.03 as solid volume fraction. The impacts of wave amplitude on flow and output power features are significant compared to those of the wave number. The increment in the generated power is in the range of 74.48–92.4% when the wave amplitude is varied. The nanoparticle shape and loading amount are effective in the rise of the TEG power, while by using cylindrical-shaped nanoparticles, higher powers are produced as compared to spherical ones. The rise in the TEG power by the highest loading amount is achieved as 50.7% with cylindrical-shaped particles, while it is only 4% with spherical-shaped ones. Up to a 194% rise of TEG power is attained by using the triangular wavy form of the wall and including cylindrical-shaped nanoparticles as compared to a flat-walled cavity using only pure fluid.
Increased Thromboxane A2 Levels in Pulmonary Artery Smooth Muscle Cells Isolated from Patients with Chronic Obstructive Pulmonary Disease
Authors: Alqarni Abdullah A.
Published: January 2023

Introduction: Pulmonary hypertension due to chronic obstructive pulmonary disease (COPD) is classified as Group 3 pulmonary hypertension, with no current proven targeted therapies. It has been shown that cigarette smoke, the main risk factor for COPD, can increase thromboxane A2 production in healthy human pulmonary artery smooth muscle cells and pulmonary artery endothelial cells, and that blocking the effect of increased thromboxane A2 using daltroban, a thromboxane A2 receptor antagonist, can inhibit cigarette smoke-induced pulmonary artery cell proliferation. However, it is largely unknown whether thromboxane A2 is increased in smokers with COPD. Therefore, the aim of this study was to assess the level of thromboxane A2 production in patients with COPD who smoke. Methods: Pulmonary artery smooth muscle cells from three smokers with COPD and three healthy donors were cultured in cell culture medium. The culture medium was collected and the thromboxane B2 (a stable metabolite of thromboxane A2) released in the culture medium was quantified using an ELISA kit. The data were normalised with the total protein concentration and then expressed in pg/mg protein. Demographic data were collected and baseline pulmonary function tests of patients with COPD were conducted. Results: The mean age of patients with COPD was 69 ± 7 years. All patients were smokers and had a mean smoking history of 39.66 ± 9.50 packs per year. The mean forced expiratory volume in one second, that is, FEV1%, and the ratio of forced vital capacity (FVC) to FEV1% of COPD patients were 63.33 ± 19.60% and 52.66 ± 14.64%, respectively. The results revealed that thromboxane A2 production was significantly increased in pulmonary artery smooth muscle cells from smokers with COPD (434.56 ± 82.88 pg/mg protein) compared with the thromboxane A2 levels in pulmonary artery smooth muscle cells from healthy donors (160 ± 59.3 pg/mg protein). Conclusions: This is the first report of increased thromboxane A2 production in pulmonary artery smooth muscle cells from smokers with COPD. This observation strongly suggests that thromboxane A2 can be used as a novel therapeutic target for the treatment of patients with COPD-associated pulmonary hypertension.
Cocaine Abuse as an Immunological Trigger in a Case Diagnosed with Eales Disease
Authors: Iannetti Ludovico , Scarinci Fabio , Alisi Ludovico , Beccia Alessandro , Cacciamani Andrea , Saturno Maria Carmela , Gharbiya Magda
Published: January 2023

Background: Eales disease is a clinical syndrome affecting the mid-peripheral retina with an idiopathic occlusive vasculitis and possible subsequent retinal neovascularization. The disease can develop into visually threatening complications. Case Presentation: We report the case of a 40-year-old Caucasian male with a history of cocaine abuse who presented with blurred vision in the left eye (LE). Fundus examination showed vitreous hemorrhages, peripheral sheathing of venous blood vessels, areas of retinal neovascularization in the LE, and peripheral occlusive phlebitis in the right eye. The full serologic panel was negative except for the heterozygous mutation of factor V Leiden. Clinical and biochemical parameters suggested a diagnosis of Eales disease. Therapy with dexamethasone, 1 mg per kg per day, tapered down slowly over 4 months, and peripheral laser photocoagulation allowed a regression of clinical signs and symptoms. Conclusion: This case shows an uncommon presentation of Eales disease associated with cocaine abuse. Both cocaine abuse and a thrombophilic pattern, as cofactors, might have sensitized the retinal microcirculation on the pathogenetic route to this retinal pathology. Furthermore, in view of this hypothesis, a thorough ocular and general medical history investigating drug abuse and coagulation disorders is recommended for ophthalmologists in such cases.
Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions
Authors: Martins Miguel , Mascarenhas Miguel , Afonso João , Ribeiro Tiago , Cardoso Pedro , Mendes Francisco , Cardoso Hélder , Andrade Patrícia , Ferreira João , Macedo Guilherme
Published: January 2023

Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn’s disease. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. Materials and Methods: A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision–recall curve (AUC-PR). Results: Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm’s accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. Conclusion: To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation.
Phosphorus Magnetic Resonance Spectroscopy (31P MRS) and Cardiovascular Disease: The Importance of Energy
Authors: Tsampasian Vasiliki , Cameron Donnie , Sobhan Rashed , Bazoukis George , Vassiliou Vassilios S.
Published: January 2023

Background and Objectives: The heart is the organ with the highest metabolic demand in the body, and it relies on high ATP turnover and efficient energy substrate utilisation in order to function normally. The derangement of myocardial energetics may lead to abnormalities in cardiac metabolism, which herald the symptoms of heart failure (HF). In addition, phosphorus magnetic resonance spectroscopy (31P MRS) is the only available non-invasive method that allows clinicians and researchers to evaluate the myocardial metabolic state in vivo. This review summarises the importance of myocardial energetics and provides a systematic review of all the available research studies utilising 31P MRS to evaluate patients with a range of cardiac pathologies. Materials and Methods: We have performed a systematic review of all available studies that used 31P MRS for the investigation of myocardial energetics in cardiovascular disease. Results: A systematic search of the Medline database, the Cochrane library, and Web of Science yielded 1092 results, out of which 62 studies were included in the systematic review. The 31P MRS has been used in numerous studies and has demonstrated that impaired myocardial energetics is often the beginning of pathological processes in several cardiac pathologies. Conclusions: The 31P MRS has become a valuable tool in the understanding of myocardial metabolic changes and their impact on the diagnosis, risk stratification, and prognosis of patients with cardiovascular diseases.
The Association between Burnout, Social Support, and Psychological Capital among Primary Care Providers in Togo: A Cross-Sectional Study
Authors: Bafei Solim Essomandan Clémence , Chen Jiaping , Qian Yinan , Yuan Lei , Zhou Yimin , Sambou Muhammed Lamin , Walker Anita Nyarkoa , Li Wei , Liu Sijun
Published: January 2023

Background and Objectives: Job burnout is prevalent among primary care providers (PCPs) in different countries, and the factors that can alleviate burnout in these countries have been explored. However, no study has addressed the prevalence and the correlates of job burnout among Togolese PCPs. Therefore, we aimed to examine the prevalence of burnout and its association with social support and psychological capital among PCPs in Togo. Material and Methods: We conducted a cross-sectional study in Togo from 5 to 17 November 2020 among 279 PCPs of 28 peripheral care units (PCUs). Participants completed the Maslach Burnout Inventory, Job Content Questionnaire, and Psychological Capital Questionnaire. Data were analyzed using the Mann–Whitney U test, Kruskal–Wallis H test, Pearson correlation analysis, and multiple linear regression. Results: We received 279 responses, out of which 37.28% experienced a high level of emotional exhaustion (EE), 13.62% had a high level of depersonalization (DP), and 19.71% experienced low levels of personal accomplishment (PA). EE had a significant negative correlation with the supervisor’s support. In contrast, self-efficacy, hope, optimism, and resilience had a significant negative correlation with DP and a significant positive correlation with PA. Furthermore, supervisors’ support significantly predicted lower levels of EE. Optimism significantly predicted lower levels of DP and higher levels of PA. Conclusions: Burnout is common among Togolese PCPs, and self-efficacy, optimism, and supervisors’ support significantly contribute to low levels of job burnout among Togolese PCPs. This study provided insight into intervention programs to prevent burnout among PCPs in Togo.
Relation of Maternal Pre-Pregnancy Factors and Childhood Asthma: A Cross-Sectional Survey in Pre-School Children Aged 2–5 Years Old
Authors: Papandreou Dimitrios , Pavlidou Eleni , Tyrovolas Stefanos , Mantzorou Maria , Andreou Eleni , Psara Evmorfia , Antasouras Georgios , Vasios Georgios K. , Poulios Efthymios , Giaginis Constantinos
Published: January 2023

Background and Objectives: Asthma constitutes a constant, prolonged, inflammation-related pulmonary disorder in childhood with serious public health concerns. Several maternal risk factors can enhance the prevalence of its development in this stage of life; however, the currently available data remain contradictory and/or inconsistent. We aim to evaluate the potential impacts of mothers’ sociodemographic, anthropometric and prenatal and perinatal factors on the prevalence of developing asthma in pre-school children. Materials and Methods: This is a retrospective cross-sectional survey, which includes 5133 women and their matched pre-school children. Childhood asthma was diagnosed using validated questionnaires. Statistical analysis was accomplished to evaluate whether maternal sociodemographic, anthropometric and prenatal and perinatal factors can increase the probability of childhood asthma in pre-school age. Results: A prevalence of 4.5% of childhood asthma was recorded in pre-school age. Maternal age and pre-pregnancy overweight and obesity, caesarean section, gestational diabetes and hypertension and not breastfeeding were associated with childhood asthma after adjustment for multiple confounding factors. Conclusion: Our research showed that several maternal factors increase the prevalence of childhood asthma in pre-school age. Suitable and effective health policies and strategies should be taken into account to confront the predominant maternal factors that increase its prevalence in pre-school age.
Coping with Symptoms of Mental Health Disorders among University Students during the COVID-19 Pandemic in Relation to Their Lifestyle Habits
Authors: Jovanović Tamara , Višnjić Aleksandar
Published: January 2023

Background and Objectives: The time of the pandemic brought great difficulties, both in state and interstate systems, industry, trade, and with individuals themselves. In addition, numerous studies have shown a drastic increase in mental disorders in people around the world. Therefore, the basic idea of our study was to investigate these disorders in university students in relation to their different lifestyles. Materials and Methods: The cross-sectional study was carried out at the University of Niš (Serbia) from December 2021 to February 2022. All of the participants were assessed by using appropriate questionnaires. The study included 1400 randomly selected students (692 females and 708 males). The statistical analysis of the data included the application of multiple regression analyses and correlation tests. Results: Statistical analysis indicates that extremely severe levels of depression symptoms were reported by 232 students (16.6%). Severe and extremely severe anxiety symptoms were reported by 480 students (34.3%). Multiple linear regression analysis found that for the increased depressive symptoms, the “most deserving” parameters were related to the consumption of alcoholic beverages and psychoactive substances (β = 0.10, and 0.11, respectively), compared to the period before the COVID-19 pandemic. For anxiety symptoms, the main role was played by alcohol consumption (β = 0.11) but also by the use of social networks as an adequate substitute for deprived content during the pandemic (β = 0.13). Alcohol consumption was the most “responsible” for elevated stress levels compared to the period before the pandemic (β = 0.19). Conclusions: Due to the COVID-19 pandemic, symptoms of depression, anxiety, and stress were drastically increased in the university students. There was significantly more frequent consumption of alcoholic beverages and psychoactive substances among them. That is why social support from a close environment is the most important strategy in coping with mental health issues during emergency situations.
International Consensus on Definition of Mild-to-Moderate Ulcerative Colitis Disease Activity in Adult Patients
Authors: Caron Bénédicte , Jairath Vipul , D’Amico Ferdinando , Al Awadhi Sameer , Dignass Axel , Hart Ailsa L. , Kobayashi Taku , Kotze Paulo Gustavo , Magro Fernando , Siegmund Britta , Paridaens Kristine , Danese Silvio , Peyrin-Biroulet Laurent
Published: January 2023

Background and Objectives: At present, there is no consensus definition of mild-to-moderate disease activity in patients with ulcerative colitis. The objective of the present study was to establish a reliable definition of mild-to-moderate disease activity in adult patients with ulcerative colitis. Materials and Methods: Twelve physicians from around the world participated in a virtual consensus meeting on 26 September 2022. All the physicians had expertise in the diagnosis and treatment of inflammatory bowel disease. After a systematic review of the literature and expert opinion, a modified version of the RAND/University of California, Los Angeles appropriateness method was applied. A total of 49 statements were identified and then anonymously rated (on a 9-point scale) as being appropriate (scores of 7 to 9), uncertain (4 to 6) or inappropriate (1 to 3). The survey results were reviewed and amended before a second round of voting. Results: Symptom and endoscopic-based measurements are of prime importance for assessing mild-to-moderate ulcerative colitis activity in clinical trials. The experts considered that clinical activity should be assessed in terms of stool frequency, rectal bleeding and fecal urgency, whereas endoscopic activity should be evaluated with regard to the vascular pattern, bleeding, erosions and ulcers. Fecal calprotectin was considered to be a suitable disease activity marker in mild-to-moderate ulcerative colitis. Lastly, mild-to-moderate ulcerative colitis should not have more than a small impact on the patient’s daily activities. Conclusions: The present recommendations constitute a standardized framework for defining mild-to-moderate disease activity in clinical trials in the field of ulcerative colitis.
Melatonin as a Repurposed Drug for Melanoma Treatment
Authors: Pathipaka Rachana , Thyagarajan Anita , Sahu Ravi P.
Published: January 2023

Melanoma is the most aggressive type of skin cancer, with a greater risk of metastasis and a higher prevalence and mortality rate. This cancer type has been demonstrated to develop resistance to the known treatment options such as conventional therapeutic agents and targeted therapy that are currently being used as the standard of care. Drug repurposing has been explored as a potential alternative treatment strategy against disease pathophysiologies, including melanoma. To that end, multiple studies have suggested that melatonin produced by the pineal gland possesses anti-proliferative and oncostatic effects in experimental melanoma models. The anticarcinogenic activity of melatonin is attributed to its ability to target a variety of oncogenic signaling pathways, including the MAPK pathways which are involved in regulating the behavior of cancer cells, including cell survival and proliferation. Additionally, preclinical studies have demonstrated that melatonin in combination with chemotherapeutic agents exerts synergistic effects against melanoma. The goal of this review is to highlight the mechanistic insights of melatonin as a monotherapy or combinational therapy for melanoma treatment.
Effect of Heat Treatments on Microstructure and Mechanical Properties of Low-Cost Ti-6Al-4V Alloy Produced by Thermomechanical Powder Consolidation Route
Authors: Singh Ajit Pal , Torrens Rob , Gabbitas Brian , Sivaswamy Giribaskar
Published: January 2023

This paper investigates the level of properties enhancement achievable by heat-treating Ti-6Al-4V alloy produced from a blended powder mixture using a thermomechanical powder consolidation route involving warm uniaxial pressing and vacuum sintering followed by extrusion at super transus temperature (1150 °C). The as-extruded material with a higher oxygen content of 0.55 wt.% was subjected to two different sub-transus annealing treatments: HT-A: 955 °C/1 h-furnace cooling and HT-B: 925 °C/4 h-cooling @ 50 °C/h to 760 °C-furnace cooling. Room temperature Charpy v-notch impact toughness tests and tensile tests were performed to ascertain the effect of microstructural changes during post-extrusion annealing treatments. After impact tests, analysis of microstructures and fracture surfaces of samples was carried out using optical and scanning electron microscopy. The as-extruded material displayed mean impact toughness of 4 J along with a yield strength of 956 MPa, an ultimate tensile strength of 1150 MPa, and an elongation to fracture of 2.4%. The annealing treatments gave a noticeable enhancement in the impact toughness (average values 5–6 J obtained) while maintaining a yield strength and ultimate tensile strength level of about 992 MPa and 1164–1181 MPa, respectively. Additionally, the level of change in ductility was limited for each sub-transus annealing treatment, and HT-A has given only a 30% increase compared to as-extruded material.
Quercetin, a Plant Flavonol Attenuates Diabetic Complications, Renal Tissue Damage, Renal Oxidative Stress and Inflammation in Streptozotocin-Induced Diabetic Rats
Authors: Rahmani Arshad Husain , Alsahli Mohammed A. , Khan Amjad Ali , Almatroodi Saleh A.
Published: January 2023

Diabetes mellitus is a metabolic syndrome characterized by increased glucose levels, oxidative stress, hyperlipidemia, and frequently decreased insulin levels. The current research was carried out for eight consecutive weeks to evaluate the possible reno-protective effects of quercetin (50 mg/kg b.w.) on streptozotocin (STZ) (55 mg/kg b.w.) induced diabetes rat models. Various physiological, biochemical, and histopathological parameters were determined in control, diabetic control, and quercetin-treated diabetic rats. The current findings demonstrated that diabetes control rats showed significantly decreased body weights (198 ± 10 vs. 214 ± 13 g) and insulin levels (0.28 ± 0.04 vs. 1.15 ± 0.05 ng/mL) in comparison to normal control. Besides this, the other parameters showed increased values, such as fasting blood glucose, triglyceride (TG), and total cholesterol levels (99 ± 5 vs. 230 ± 7 mg/dL, 122.9 ± 8.7 vs. 230.7 ± 7.2 mg/dL, 97.34 ± 5.7 vs. 146.3 ± 8 mg/dL) (p < 0.05). In addition, the urea and creatinine levels (39.9 ± 1.8 mg/dL and 102.7 ± 7.8 μmol/L) were also high in diabetes control rats. After 8 weeks of quercetin treatment in STZ-treated animals, body weight, insulin, and fasting blood sugar levels were significantly restored (p < 0.05). The inflammatory markers (TNF-α, IL-6, and IL-1β) were significantly increased (52.64 ± 2, 95.64 ± 3, 23.3 ± 1.2 pg/mL) and antioxidant enzymes levels (SOD, GST, CAT, and GSH) were significantly decreased (40.3 ± 3 U/mg, 81.9 ± 10 mU/mg, 14.2 ± 2 U/mg, 19.9 ± 2 μmol/g) in diabetic rats. All the parameters in diabetic animals treated with quercetin were restored towards their normal values. Histopathological findings revealed that the quercetin-treated group showed kidney architecture maintenance, reduction of fibrosis, and decreased expression of COX-2 protein. These results determined that quercetin has reno-protective effects, and conclude that quercetin possesses a strong antidiabetic potential and might act as a therapeutic agent in the prevention or delay of diabetes-associated kidney dysfunction.
Efficient Performance of the Methane-Carbon Dioxide Reform Process in a Fluidized Bed Reactor
Authors: Pacífico José A. , Lima Filho Nelson M. , de Abreu Cesar A. Moraes
Published: January 2023

The reforming of methane with CO2 was carried out efficiently in a fluidized bed reactor at 973 K under atmospheric pressure, taking advantage of the nickel catalyst efficiency achieved with a bed of particulate fines. The fluidization operation was characterized by determining a minimum velocity of 3.11 × 10−3 ms−1 and higher velocities. The reactor worked with surface speeds of up to 1.84 × 10−2 ms−1, providing conversions from 45% to 51% and a syngas yield of 97%. The control base of the operation focused on the use of CO2 was established through the reaction steps assumed for the process, including methane cracking, reverse Boudouard reaction, and RWGS (reverse reaction of water gas-shift). The reactor designed to operate in two zones was able to simultaneously process surface reactions and catalyst regeneration using feed with 50% excess CO2 in relation to methane. Predictions indicating the production of syngas of different compositions quantified with the H2/CO ratio from 2.30 to 0.91 decreasing with space-time were validated with the results available for process design.