bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning-Based Detection for Unauthorized Access to IoT Devices
Authors: Aljabri Malak , Alahmadi Amal A. , Mohammad Rami Mustafa A. , Alhaidari Fahd , Aboulnour Menna , Alomari Dorieh M. , Mirza Samiha
Published: March 2023

The Internet of Things (IoT) has become widely adopted in businesses, organizations, and daily lives. They are usually characterized by transferring and processing sensitive data. Attackers have exploited this prospect of IoT devices to compromise user data’s integrity and confidentiality. Considering the dynamic nature of the attacks, artificial intelligence (AI)-based techniques incorporating machine learning (ML) are promising techniques for identifying such attacks. However, the dataset being utilized features engineering techniques, and the kind of classifiers play significant roles in how accurate AI-based predictions are. Therefore, for the IoT environment, there is a need to contribute more to this context by evaluating different AI-based techniques on datasets that effectively capture the environment’s properties. In this paper, we evaluated various ML models with the consideration of both binary and multiclass classification models validated on a new dedicated IoT dataset. Moreover, we investigated the impact of different features engineering techniques including correlation analysis and information gain. The experimental work conducted on bagging, k-nearest neighbor (KNN), J48, random forest (RF), logistic regression (LR), and multi-layer perceptron (MLP) models revealed that RF achieved the highest performance across all experiment sets, with a receiver operating characteristic (ROC) of 99.9%.
Lava Flow Hazard and Its Implication in Geopark Development for the Active Harrat Khaybar Intracontinental Monogenetic Volcanic Field, Saudi Arabia
Authors: Németh Károly , Moufti Mohammed Rashad
Published: March 2023

Harrat Khaybar is an active monogenetic volcanic field in western Saudi Arabia that hosts spectacular monogenetic volcanoes and a Holocene volcanic cone with extensive lava fields. The volcanic region is a subject of intensive land use development, especially along tourism ventures, where the volcanic features are the key elements to utilize for increasing visitation rates to the region. The youngest eruption is suspected to be Holocene and occurred fewer than 5000 years ago based on the cross-cutting relationship between the youngest lava flows and archaeological sites. Lava flows are typical, from pāhoehoe to ‘a‘ā types with great diversity of transitional textural forms. Here, we recorded typical transitional lava flow surface textures from the youngest flows identified by digital-elevation-model-based terrain analysis, satellite imagery, and direct field observations. We performed lava flow simulations using the Q-LavHA plug-in within the QGIS environment. Lava flow simulations yielded satisfactory results if we applied eruptions along fissures, long simulation distances, and ~5 m lava flow thickness. In these simulations, the upper flow regimes were reconstructed well, but long individual lava flows were not possible to simulate, suggesting that morphological steps likely promoted lava ponding, inflation, and sudden deflation by releasing melts further along shallow syneruptive valley networks.
Development of a Methodology and Model for Land Administration Data Dissemination Processes
Authors: Križanović Josip , Roić Miodrag
Published: March 2023

Land administration (LA) is concerned with processes. Simply put, LA cannot be understood, built, or improved unless the processes associated with it are understood. When it comes to the processes involved in LA, two general processes can be identified, namely registration and dissemination. Nowadays, processes are implemented electronically; however, paper-based thinking is still present, and the performance of processes is impeded by siloed data management. These issues could be addressed through the employment of standards such as the Land Administration Domain Model. Processes are not yet part of the standard, but their inclusion in future extensions is planned. Moreover, a literature analysis indicated that there is no standardized methodology or model available for describing LA processes. Consequently, we modeled one part of land administration processes, namely data dissemination, by developing a methodology and model to describe it. The methodology was developed with the goal of providing guidelines for other researchers when modeling use cases of data dissemination processes by enabling comparisons of models, searching for best practices, and developing standardized process models. Additionally, the methodology was tested on a conceptual use case to prove its viability. Furthermore, a model based on conceptual modeling and an activity-centric process modeling approach was developed and linked to existing classes of the Land Administration Domain Model. The organizational and technological challenges which might arise when improving data dissemination processes are discussed, and possible outcomes of the developed methodology and model are provided.
A Sustainable Two-Echelon Logistics Model with Shipment Consolidation
Authors: Mostafa Noha A. , Eldebaiky Omar
Published: March 2023

Background: Shipment consolidation is a concept in logistics management in which two or more shipments are transported by using the same vehicle with the aim of using less resources. Methods: The objective of this manuscript is to study shipment consolidation and assess its impact on cost environment, to achieve this, a mathematical model was developed to optimize shipment consolidation while reducing the emissions and minimizing the costs. Results: A case study from major dairy products manufacturers in Egypt was used to validate the model and evaluate the outcomes. A comparison was made between two transportation models, with and without consolidation. Results show that shipment consolidation reduced the total costs by 40% in addition to consuming less fuel, and consequently producing less emissions. Conclusions: These findings emphasize the importance of shipment consolidation and how it can be used to achieve more sustainability in logistics management.
Analysis of Nonlinear Time-Domain Lubrication Characteristics of the Hydrodynamic Journal Bearing System
Authors: Lin Changgang , Jian Fan , Sun Shili , Sima Can , Qi Libo , Zou Mingsong
Published: March 2023

The nonlinear time-domain lubrication characteristics of the hydrodynamic journal bearing system are studied in this paper. The motion equation of the hydrodynamic journal bearing system is established based on the balance of the relationship among the water film force, journal inertia force, and external load. The water film pressure distribution of the sliding bearing is calculated by the finite difference method. Firstly, the variation law of the water film pressure distribution with time under the external periodic load is calculated considering the inertial force of the journal. The influence of the initial eccentricity on the orbit of the journal center is studied. Secondly, the maximum water film pressure, the orbit of the journal center, eccentricity, water film pressure, and the minimum water film thickness of the bearing under the action of circumferential and unidirectional periodic external loads are calculated, and the effects of inertial force and rotational speed on the dynamic characteristics of the bearing are analyzed. Finally, the water film dynamic characteristics under low speed and heavy load are studied. The result shows that the pressure of the dimensionless water film caused by inertial force is reduced by 7 to 10 percent at the rotational speed between 200 r/min and 800 r/min, which means that the influence of inertia force cannot be ignored.
Study on the Effect of Oil Supply on the Sound Field Characteristics of Full Ceramic Ball Bearings under Oil Lubrication
Authors: Sun Jian , Fang Xin , Yao Jinmei , Guan Renyun , Zhang Zhe , Zhang Guangxiang
Published: March 2023

To study the full sound field distribution characteristics of full ceramic ball bearings, reduce the radiation noise of the bearings, and improve their service performance. In this paper, the sound field distribution characteristics of 6206 silicon nitride ceramic deep groove ball bearings are studied under different oil supplies. A mathematical model of the sound field distribution of full ceramic ball bearings under oil lubrication is established, and the validity of the model is verified by experimental data. The bearing cavity simulation model of the full ceramic ball bearing is established, and the influence of oil supply on the operation characteristics of the full ceramic ball bearing is studied. Through theoretical and experimental research, the circular distribution law of the noise signal of ceramic ball bearings under different oil supplies is revealed. It is found that there is an optimal fuel supply when the speed and load are constant. Under optimal oil supply lubrication conditions, the full ceramic ball bearing has the minimum radiation noise, and the bearing exhibits optimal lubrication state, vibration and temperature rise characteristics. The new contribution of this paper: with the increase in oil supply, the sound pressure level of radiation noise of full ceramic ball bearings decreases and then increases. The research results reveal the radiation noise mechanism of full ceramic ball bearings, which is of great significance for enriching its theory and method.
Viscosity Variations and Tribological Performances of Oleylamine-Modified Fe3O4 Nanoparticles as Mineral Oil Additives
Authors: Wang Xiaoyu , Liu Huanchen , Zhao Qilong , Wang Xiaobo , Lou Wenjing
Published: March 2023

In order to improve the flowabilities and anti-friction and anti-wear properties of lubricants, the viscosity variations and tribological performances of oleylamine-modified Fe3O4 nanoparticles as mineral oil additives were systematically investigated via rotational parallel plate rheometer, ball–disc reciprocating tribometer, non-contact three-dimensional surface profiler, scanning electron microscope, energy dispersive X-ray spectrometer and X-ray photoelectron spectroscopy. Spherical monodisperse Fe3O4 nanoparticles were synthesized and dispersed into mineral oils to obtain lubricants with mass fractions of 1%, 3%, 5%, 8%, 10% and 20%, respectively. These lubricants have excellent stabilities within 12 months. Interestingly, the dynamic viscosity and kinematic viscosity of the lubricants first decrease and then increase with the increase in Fe3O4 content, and the lubricants’ viscosity is at a minimum when the mass concentration is 5%. The tensile curves also show that with the mass fraction increase, the lubricants’ tackiness and adhesion have the same change law, and both reach the lowest point when the mass concentration is 5%. Meanwhile, Fe3O4 nanoparticles can improve the tribological properties of the base oils. It is worth noting that the maximum reduction in the wear volume at 25 °C is up to 93.8% compared with base oils when the additive concentration of the Fe3O4 nanoparticles is 5 wt%.
Thrust Vector Controller Comparison for a Finless Rocket
Authors: Sopegno Laura , Livreri Patrizia , Stefanovic Margareta , Valavanis Kimon P.
Published: March 2023

The paper focuses on comparing applicability, tuning, and performance of different controllers implemented and tested on a finless rocket during its boost phase. The objective was to evaluate the advantages and disadvantages of each controller, such that the most appropriate one would then be developed and implemented in real-time in the finless rocket. The compared controllers were Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and Proportional Integral Derivative (PID). To control the attitude of the rocket, emphasis is given to the Thrust Vector Control (TVC) component (sub-system) through the gimballing of the rocket engine. The launcher is commanded through the control input thrust gimbal angle δ, while the output parameter is expressed in terms of the pitch angle θ. After deriving a linearized state–space model, rocket stability is addressed before controller implementation and testing. The comparative study showed that both LQR and LQG track pitch angle changes rapidly, thus providing efficient closed-loop dynamic tracking. Tuning of the LQR controller, through the Q and R weighting matrices, illustrates how variations directly affect performance of the closed-loop system by varying the values of the feedback gain (K). The LQG controller provides a more realistic profile because, in general, not all variables are measurable and available for feedback. However, disturbances affecting the system are better handled and reduced with the PID controller, thus overcoming steady-state errors due to aerodynamic and model uncertainty. Overall controller performance is evaluated in terms of overshoot, settling and rise time, and steady-state error.
Wear Parameter Diagnostics of Industrial Milling Machine with Support Vector Regression
Authors: Mäkiaho Teemu , Vainio Henri , Koskinen Kari T.
Published: March 2023

Modern industrial machine applications often contain data collection functions through automation systems or external sensors. Yet, while the different data collection mechanisms might be effortless to construct, it is advised to have a well-balanced consideration of the possible data inputs based on the machine characteristics, usage, and operational environment. Prior consideration of the collected data parameters reduces the risk of excessive data, yet another challenge remains to distinguish meaningful features significant for the purpose. This research illustrates a peripheral milling machine data collection and data pre-processing approach to diagnose significant machine parameters relevant to milling blade wear. The experiences gained from this research encourage conducting pre-categorisation of data significant for the purpose, those being manual setup data, programmable logic controller (PLC) automation system data, calculated parameters, and measured parameters under this study. Further, the results from the raw data pre-processing phase performed with Pearson Correlation Coefficient and permutation feature importance methods indicate that the most dominant correlation to recognised wear characteristics in the case machine context is perceived with vibration excitation monitoring. The root mean square (RMS) vibration signal is further predicted by using the support vector regression (SVR) algorithm to test the SVR’s overall suitability for the asset’s health index (HI) approximation. It was found that the SVR algorithm has sufficient data parameter behaviour forecast capabilities to be used in the peripheral milling machine prognostic process and its development. The SVR with Gaussian radial basis function (RBF) kernel receives the highest scoring metrics; therefore, outperforming the linear and polynomial kernels compared as part of the study.
Effects of Tool Edge Geometry and Cutting Conditions on the Performance Indicators in Dry Turning AISI 1045 Steel
Authors: Abbas Adel T. , El Rayes Magdy M. , Al-Abduljabbar Abdulhamid A. , Ragab Adham E. , Benyahia Faycal , Elkaseer Ahmed
Published: March 2023

This article presents an experimental investigation and statistical analysis of the effects of cutting conditions on the machining performance of AISI 1045 steel using a wiper-shaped insert. Experimental findings are used to compare the machining performance obtained using wiper inserts with those obtained using conventional round-nose inserts as recently reported in the literature. In addition, the effects of process conditions, namely cutting speed, feed rate, and depth of cut, are analyzed in order to obtain optimum conditions for both types of inserts. The goal is to achieve the optimal machining outcomes: minimum surface roughness, resultant cutting force, and cutting temperature, but maximum material removal rate. A full factorial design was followed to conduct the experimental trials, while ANOVA was utilized to estimate the effect of each factor on the process responses. A desirability function optimization tool was used to optimize the studied responses. The results reveal that the optimum material removal rate for wiper-shaped inserts is 67% more than that of conventional inserts, while maintaining a 0.7 µm surface roughness value. The superior results obtained with wiper-shaped inserts allow wiper tools to use higher feed rates, resulting in larger material removal rates while obtaining the same surface quality.
Exploration of Flow Instability Characteristics in a Two-Stage Axial-Flow Compressor via Numerical Simulation Method
Authors: Wang Tong , Dou Xiangyuan , Liu Yongwen
Published: March 2023

A four-blade-passage numerical model was developed for a two-stage axial-flow compressor with an inlet guide vane (IGV) for the purpose of studying the steady and dynamic characteristics of the compressor approaching its stability limit. The flow-field information indicated that the tip-leakage flow decreased more rapidly from the blade’s leading edge to the trailing edge, with a decreasing flow rate. The leakage flow was verified to be driven via the blade load over the whole operating range. Further research on the blade load was carried out. The magnitude of the highest blade load in the leading-edge portion of the first-stage rotor determined the lowest flow rate with steady-simulation analysis. The circumferential grooves on the rotor improved the rotor’s stable range via reducing the blade load. Unsteady-simulation results showed that the extreme blade load appeared first at the front of the first stage, with a decreasing flow rate. The second stage played a positive compensative role through releasing some of the load from the first stage. It can be generalized that the lowest flow rate at a specific speed is determined via not only any single stage but also other stages in a multistage axial-flow compressor.
Comparative Study of Path Tracking Controllers on Low Friction Roads for Autonomous Vehicles
Authors: Lee Jaepoong , Yim Seongjin
Published: March 2023

This paper presents a comparison among path tracking controllers on low-friction roads for autonomous vehicles. There are two goals in this paper. The first is to check the performance of path tracking controllers on low-friction roads, and the second is to check the effectiveness of four-wheel steering (4WS) for path tracking. To fully investigate the performance of path-tracking controllers on low-friction roads in this paper, the pure pursuit method, Stanley method, PID control, linear quadratic regulator, sliding mode control and model predictive control are designed and compared in terms of some measures. Front and four-wheel steering are adopted as actuators for path tracking. To utilize 4WS in the pure pursuit method, Stanley method and PID control, a yaw rate tracking control is adopted. With the designed path tracking controllers, a simulation is conducted on vehicle simulation software. From the simulation results, it is shown that most path tracking controllers are effective for path tracking on low-friction roads if finely tuned, and that 4WS is not recommended for path tracking on low-friction roads.
Heart Failure Detection Using Instance Quantum Circuit Approach and Traditional Predictive Analysis
Authors: Alsubai Shtwai , Alqahtani Abdullah , Binbusayyis Adel , Sha Mohemmed , Gumaei Abdu , Wang Shuihua
Published: March 2023

The earlier prediction of heart diseases and appropriate treatment are important for preventing cardiac failure complications and reducing the mortality rate. The traditional prediction and classification approaches have resulted in a minimum rate of prediction accuracy and hence to overcome the pitfalls in existing systems, the present research is aimed to perform the prediction of heart diseases with quantum learning. When quantum learning is employed in ML (Machine Learning) and DL (Deep Learning) algorithms, complex data can be performed efficiently with less time and a higher accuracy rate. Moreover, the proposed ML and DL algorithms possess the ability to adapt to predictions with alterations in the dataset integrated with quantum computing that provides robustness in the earlier detection of chronic diseases. The Cleveland heart disease dataset is being pre-processed for the checking of missing values to avoid incorrect predictions and also for improvising the rate of accuracy. Further, SVM (Support Vector Machine), DT (Decision Tree) and RF (Random Forest) are used to perform classification. Finally, disease prediction is performed with the proposed instance-based quantum ML and DL method in which the number of qubits is computed with respect to features and optimized with instance-based learning. Additionally, a comparative assessment is provided for quantifying the differences between the standard classification algorithms with quantum-based learning in order to determine the significance of quantum-based detection in heart failure. From the results, the accuracy of the proposed system using instance-based quantum DL and instance-based quantum ML is found to be 98% and 83.6% respectively.
Exploring the Impact of Nanomaterials on Heat- and Mass-Transfer Properties of Carreau-Yasuda Fluid with Gyrotactic Bioconvection Peristaltic Phenomena
Authors: Alahmadi Hani , Alkinidri Mohammed Omar
Published: March 2023

This research aims to investigate the impact of nanomaterials on the heat and mass transfer properties of fluids, with a particular focus on exploring the bioconvection phenomena. To achieve this, the study considers Carreau-Yasuda (CY) fluid, which is known for its shear thickening and thinning nature. The effects of a porous medium, radiation, and viscous dissipation are also considered to analyze heat-transfer rates. Velocity and thermal slip constraints are applied to the wall, while zero-mass flux conditions explain the concentration behavior of nanomaterials at the wall. The governing equations and conditions are simplified using a lubrication approach, and a numerical approach is used to solve the final equations with the help of constraints. The velocity, temperature, and concentration of nanomaterials and gyrotactic microorganisms are analyzed through graphs. The study finds that increasing the thermophoresis parameter leads to an increase in the concentration of nanomaterials. However, the opposite trend is noticed for the concentration of motile microorganisms. The results suggest that the addition of nanomaterials to fluids can significantly impact heat- and mass-transfer properties, and may have implications for biological processes.
Restricted Distance-Type Gaussian Estimators Based on Density Power Divergence and Their Applications in Hypothesis Testing
Authors: Felipe Ángel , Jaenada María , Miranda Pedro , Pardo Leandro
Published: March 2023

In this paper, we introduce the restricted minimum density power divergence Gaussian estimator (MDPDGE) and study its main asymptotic properties. In addition, we examine it robustness through its influence function analysis. Restricted estimators are required in many practical situations, such as testing composite null hypotheses, and we provide in this case constrained estimators to inherent restrictions of the underlying distribution. Furthermore, we derive robust Rao-type test statistics based on the MDPDGE for testing a simple null hypothesis, and we deduce explicit expressions for some main important distributions. Finally, we empirically evaluate the efficiency and robustness of the method through a simulation study.
Degree of the Product of Two Algebraic Numbers One of Which Is of Prime Degree
Authors: Virbalas Paulius
Published: March 2023

Let α and β be two algebraic numbers such that deg(α)=m and deg(β)=p, where p is a prime number not dividing m. This research is focused on the following two objectives: to discover new conditions under which deg(αβ)=mp; to determine the complete list of values deg(αβ) can take. With respect to the first question, we find that if the minimal polynomial of β over Q is neither xp+c nor x2+cx+c2, then necessarily deg(αβ)=mp and αβ is a primitive element of Q(α,β). This supplements some earlier results by Weintraub. With respect to the second question, we determine that if p>2 and p−1 divides m, then for every divisor k of p−1, there exist α and β such that deg(αβ)=mp/k.
Weighted Hardy–Rellich Inequality for Dunkl Operators
Authors: Lyu Jielin , Jin Yongyang , Shen Shoufeng , Tang Li
Published: March 2023

In this paper, we proved a weighted Hardy–Rellich inequality for Dunkl operators based on the spherical h-harmonic decomposition theory of Dunkl operators. Moreover, we obtained the explicit constant of the inequalities, which is optimal in some cases. Our results extend some known inequalities.
X-STATIS: A Multivariate Approach to Characterize the Evolution of E-Participation, from a Global Perspective
Authors: Rodríguez-Martínez Carmen C. , Cubilla-Montilla Mitzi , Vicente-Galindo Purificación , Galindo-Villardón Purificación
Published: March 2023

This paper aims to categorize countries by their e-participation index, according to political, capacity, and governmental environment factors; examine how they are projected based on these factors; and analyze whether this projection corresponds to the current state of e-participation development. It is the first study to provide an overview of the e-participation level using multivariate analysis techniques for three-way data analysis, specifically, the X-STATIS methodology and cluster analysis. These techniques enable the simultaneous representation of countries, factors, conditions, trajectories, and groupings, taking into account national conditions in the evolution of e-participation from 2008 to 2016. The results show that when the conditions of each country interact with the level of e-participation development, and depending on the economic development, 7% of countries are lagging behind in e-participation evolution, given their institutional and political capacity. This delay is particularly relevant in countries that enjoy a higher level of socioeconomic status. Meanwhile, 38% are above the level they would correspond to.
Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control
Authors: Pan Qi , Zhou Yanli , Fei Juntao
Published: March 2023

This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller.
How Does the Competitiveness Index Promote Foreign Direct Investment at the Provincial Level in Vietnam? An Integrated Grey Delphi–DEA Model Approach
Authors: Nguyen Phi-Hung , Nguyen Thi-Ly , Le Hong-Quan , Pham Thuy-Quynh , Nguyen Hoang-Anh , Pham Chi-Vinh
Published: March 2023

Foreign direct investment (FDI) is an important factor in building a strong economy for a country, particularly in developing and emerging markets. Both domestic enterprises and policy makers have been motivated to attract FDI for the benefits of FDI, such as technological transfers, spillover benefits, and rising competition. There is a need for a functional model to assess how the competitive index affects FDI attractiveness. Therefore, in this study, the authors use an integrated model of Grey Delphi, the Data Envelopment Analysis Super Slack-Based Measure Model (DEA–Super SBM), and the Malmquist Model (DEA–Malmquist) to evaluate the FDI attractiveness of Vietnamese provinces from 2017 to 2021. Firstly, ten critical dimensions of the provincial competitive index (PCI) affecting the number of FDI by cases and amount of FDI capital were validated via the Grey Delphi method. Secondly, the Super-SBM model is applied to assess the FDI efficiency of 63 provinces in Vietnam from 2017 to 2021. Then, the DEA–Malmquist model is employed to analyze the total change in the productivity of 63 provinces’ FDI performance in Vietnam. The findings of this study revealed that the efficiency of FDI in Vietnam’s provinces is relatively low, and there is a significant variation in the attractiveness of FDI among the provinces. This study can provide valuable insights for policy makers and other stakeholders in developing effective strategies to attract FDI and foster economic development.
On the Devylder–Goovaerts Conjecture in Ruin Theory
Authors: Loisel Stéphane , Minier Charles
Published: March 2023

The Devylder–Goovaerts conjecture is probably the oldest conjecture in actuarial mathematics and has received a lot of attention in recent years. It claims that ruin with equalized claim amounts is always less likely than in the classical model. Investigating the validity of this conjecture is important both from a theoretical aspect and a practical point of view, as it suggests that one always underestimates the risk of insolvency by replacing claim amounts with the average claim amount a posteriori. We first state a simplified version of the conjecture in the discrete-time risk model when one equalizes aggregate claim amounts and prove that it holds. We then use properties of the Pareto distribution in risk theory and other ideas to target candidate counterexamples and provide several counterexamples to the original Devylder–Goovaerts conjecture.
Exact Permutation and Bootstrap Distribution of Generalized Pairwise Comparisons Statistics
Authors: Anderson William N. , Verbeeck Johan
Published: March 2023

To analyze multivariate outcomes in clinical trials, several authors have suggested generalizations of the univariate Mann–Whitney test. As the Mann–Whitney statistic compares the subjects’ outcome pairwise, the multivariate generalizations are known as generalized pairwise comparisons (GPC) statistics. For GPC statistics such as the net treatment benefit, the win ratio, and the win odds, asymptotic based or re-sampling tests have been suggested in the literature. However, asymptotic methods require a sufficiently high sample size to be accurate, and re-sampling methods come with a high computational burden. We use graph theory notation to obtain closed-form formulas for the expectation and the variance of the permutation and bootstrap sampling distribution of the GPC statistics, which can be utilized to develop fast and accurate inferential tests for each of the GPC statistics. A simple example and a simulation study demonstrate the accuracy of the exact permutation and bootstrap methods, even in very small samples. As the time complexity is O(N2), where N is the total number of patients, the exact methods are fast. In situations where asymptotic methods have been used to obtain these variance matrices, the new methods will be more accurate and equally fast. In situations where bootstrap has been used, the new methods will be both more accurate and much faster.
A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition
Authors: Alharbi Hadeel , Alshammari Obaid , Jerbi Houssem , Simos Theodore E. , Katsikis Vasilios N. , Mourtas Spyridon D. , Sahas Romanos D.
Published: March 2023

Employee attrition, defined as the voluntary resignation of a subset of a company’s workforce, represents a direct threat to the financial health and overall prosperity of a firm. From lost reputation and sales to the undermining of the company’s long-term strategy and corporate secrets, the effects of employee attrition are multidimensional and, in the absence of thorough planning, may endanger the very existence of the firm. It is thus impeccable in today’s competitive environment that a company acquires tools that enable timely prediction of employee attrition and thus leave room either for retention campaigns or for the formulation of strategical maneuvers that will allow the firm to undergo their replacement process with its economic activity left unscathed. To this end, a weights and structure determination (WASD) neural network utilizing Fresnel cosine integrals in the determination of its activation functions, termed FCI-WASD, is developed through a process of three discrete stages. Those consist of populating the hidden layer with a sufficient number of neurons, fine-tuning the obtained structure through a neuron trimming process, and finally, storing the necessary portions of the network that will allow for its successful future recreation and application. Upon testing the FCI-WASD on two publicly available employee attrition datasets and comparing its performance to that of five popular and well-established classifiers, the vast majority of them coming from MATLAB’s classification learner app, the FCI-WASD demonstrated superior performance with the overall results suggesting that it is a competitive as well as reliable model that may be used with confidence in the task of employee attrition classification.
Semi-Synthesis and Biological Evaluation of 25(R)-26-Acetoxy-3β,5α-Dihydroxycholest-6-One
Authors: Maimaitiming Mireguli , Lv Ling , Zhang Xuetao , Xia Shuli , Li Xin , Wang Pingyuan , Liu Zhiqing , Wang Chang-Yun
Published: March 2023

Previously, we identified a series of steroids (1–6) that showed potent anti-virus activities against respiratory syncytial virus (RSV), with IC50 values ranging from 3.23 to 0.19 µM. In this work, we first semi-synthesized and characterized the single isomer of 5, 25(R)-26-acetoxy-3β,5α-dihydroxycholest-6-one, named as (25R)-5, in seven steps from a commercially available compound diosgenin (7), with a total yield of 2.8%. Unfortunately, compound (25R)-5 and the intermediates only showed slight inhibitions against RSV replication at the concentration of 10 µM, but they possessed potent cytotoxicity activities against human bladder cancer 5637 (HTB-9) and hepatic cancer HepG2, with IC50 values ranging from 3.0 to 15.5 µM without any impression of normal liver cell proliferation at 20 µM. Among them, the target compound (25R)-5 possessed cytotoxicity activities against 5637 (HTB-9) and HepG2 with IC50 values of 4.8 µM and 15.5 µM, respectively. Further studies indicated that compound (25R)-5 inhibited cancer cell proliferation through inducing early and late-stage apoptosis. Collectively, we have semi-synthesized, characterized and biologically evaluated the 25R-isomer of compound 5; the biological results suggested that compound (25R)-5 could be a good lead for further anti-cancer studies, especially for anti-human liver cancer.
Venous Thrombosis Due to Duplication of the Inferior Vena Cava with Decreased Protein C Activity: A Case Report
Authors: Okada Yukiya , Kenzaka Tsuneaki
Published: March 2023

Introduction: Duplication of the inferior vena cava (IVC) is a congenital venous malformation that occurs in 0.2%–3% of the population as a result of persistent left and right supracardinal veins. The IVC duplication is prone to deep vein thrombosis due to endothelial dysfunction and associated venous stasis. This is a rare case of recurrent venous thrombosis due to IVC duplication and decreased protein C activity. Case: A 57-year-old male presented with swelling of the left lower limb that gradually developed over a one-week period preceding his visit. He reported a history of superior mesenteric vein thrombosis, approximately three years ago, for which he received anticoagulation therapy for three months. Thoracoabdominal contrast-enhanced computed tomography (CT) revealed thrombi in the locations of the bilateral main pulmonary arteries, IVC duplication, left common iliac vein, left IVC, and left renal vein. Blood work confirmed protein C activity of 21% (baseline 64%–146%), that could have contributed to the recurrent IVC thrombosis and formation of pulmonary artery thrombus. Subsequently, the patient was hospitalized and started on anticoagulation therapy. The swelling in the left lower extremity gradually improved, and the patient was instructed to continue anticoagulation therapy permanently. Conclusion: When investigating venous thrombosis of unknown or recurrent origin, it is necessary to include venous malformations and abnormal activity of blood coagulation factors in differential diagnosis.
New Insights into the Communications of the Facial Vein with the Dural Venous Sinuses
Authors: Siwetz Martin , Widni-Pajank Hannes , Hammer Niels , Pilsl Ulrike , Bruneder Simon , Wree Andreas , Antipova Veronica
Published: March 2023

Background and Objectives: Anastomoses of the extracranial and intracranial venous system have been described in the literature. The presence of such anastomoses may facilitate a possible spread of infection into the dural venous sinuses. However, the frequency and relevance of such anastomoses is highly debated. The aim of this study was to quantify frequencies of anastomoses between the facial vein and the dural venous sinuses. Materials and Methods: In 32 sides of 16 specimens, latex was injected into the facial vein. Dissection was carried out to follow and described these anastomoses, yielding the presence of latex in the intracranial venous system. Results: In 97% of cases, a dispersal of latex into the cavernous sinus as well as anastomoses was observed. A further dispersal of latex into other dural venous sinuses was found at rates ranging between 34% (transverse sinus)—88% (superior petrosal sinus), respectively. Conclusions: The presence of anastomoses between the extracranial and intracranial venous system in a majority of cases needs to be considered when dealing with pathologies as well as procedures in the facial region.
Mass Spectrometry Contribution to Pediatric Cancers Research
Authors: Agostini Marco , Traldi Pietro , Hamdan Mahmoud
Published: March 2023

For over four decades, mass spectrometry-based methods have provided a wealth of information relevant to various challenges in the field of cancers research. These challenges included identification and validation of novel biomarkers for various diseases, in particular for various forms of cancer. These biomarkers serve various objectives including monitoring patient response to the various forms of therapy, differentiating subgroups of the same type of cancer, and providing proteomic data to complement datasets generated by genomic, epigenetic, and transcriptomic methods. The same proteomic data can be used to provide prognostic information and could guide scientists and medics to new and innovative targeted therapies The past decade has seen a rapid emergence of epigenetics as a major contributor to carcinogenesis. This development has given a fresh momentum to MS-based proteomics, which demonstrated to be an unrivalled tool for the analyses of protein post-translational modifications associated with chromatin modifications. In particular, high-resolution mass spectrometry has been recently used for systematic quantification of chromatin modifications. Data generated by this approach are central in the search for new therapies for various forms of cancer and will help in attempts to decipher antitumor drug resistance. To appreciate the contribution of mass spectrometry-based proteomics to biomarkers discovery and to our understanding of mechanisms behind the initiation and progression of various forms of cancer, a number of recent investigations are discussed. These investigations also include results provided by two-dimensional gel electrophoresis combined with mass spectrometry.
Multimodal Remote Home Monitoring of Lung Transplant Recipients during COVID-19 Vaccinations: Usability Pilot Study of the COVIDA Desk Incorporating Wearable Devices
Authors: Schuurmans Macé M. , Muszynski Michal , Li Xiang , Marcinkevičs Ričards , Zimmerli Lukas , Monserrat Lopez Diego , Michel Bruno , Weiss Jonas , Hage René , Roeder Maurice , Vogt Julia E. , Brunschwiler Thomas
Published: March 2023

Background and Objectives: Remote patient monitoring (RPM) of vital signs and symptoms for lung transplant recipients (LTRs) has become increasingly relevant in many situations. Nevertheless, RPM research integrating multisensory home monitoring in LTRs is scarce. We developed a novel multisensory home monitoring device and tested it in the context of COVID-19 vaccinations. We hypothesize that multisensory RPM and smartphone-based questionnaire feedback on signs and symptoms will be well accepted among LTRs. To assess the usability and acceptability of a remote monitoring system consisting of wearable devices, including home spirometry and a smartphone-based questionnaire application for symptom and vital sign monitoring using wearable devices, during the first and second SARS-CoV-2 vaccination. Materials and Methods: Observational usability pilot study for six weeks of home monitoring with the COVIDA Desk for LTRs. During the first week after the vaccination, intensive monitoring was performed by recording data on physical activity, spirometry, temperature, pulse oximetry and self-reported symptoms, signs and additional measurements. During the subsequent days, the number of monitoring assessments was reduced. LTRs reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. Results: Ten LTRs planning to receive the first COVID-19 vaccinations were recruited. For the intensive monitoring study phase, LTRs recorded symptoms, signs and additional measurements. The most frequent adverse events reported were local pain, fatigue, sleep disturbance and headache. The duration of these symptoms was 5–8 days post-vaccination. Adherence to the main monitoring devices was high. LTRs rated usability as high. The majority were willing to continue monitoring. Conclusions: The COVIDA Desk showed favorable technical performance and was well accepted by the LTRs during the vaccination phase of the pandemic. The feasibility of the RPM system deployment was proven by the rapid recruitment uptake, technical performance (i.e., low number of errors), favorable user experience questionnaires and detailed individual user feedback.
Potential Protective Role of Pregnancy and Breastfeeding in Delaying Onset Symptoms Related to Multiple Sclerosis
Authors: Logoteta Alessandra , Piccioni Maria Grazia , Nistri Riccardo , De Giglio Laura , Bruno Valentina , La Torre Giuseppe , Ianni Stefano , Fabrizi Luana , Muzii Ludovico , Pozzilli Carlo , Ruggieri Serena
Published: March 2023

The impact of pregnancy and breastfeeding on the development and outcomes of Multiple sclerosis (MS) has been debated for decades. Since several factors can influence the evolution of the disease, the protective role of multiparity and breastfeeding remains uncertain, as well the role of hormone replacement therapy in the perimenopausal period. We report two cases of relatively late-onset MS in two parous women, who developed their first neurological symptoms after six and nine pregnancies, respectively. Both women breastfed each of their children for 3 to 12 months. One of them underwent surgical menopause and received hormone replacement therapy for 7 years before MS onset. We performed a systematic literature review to highlight the characteristics shared by women who develop the disease in similar conditions, after unique hormonal imbalances, and to collect promising evidence on this controversial issue. Several studies suggest that the beneficial effects of pregnancy and breastfeeding on MS onset and disability accumulation may only be realized when several pregnancies occur. However, these data on pregnancy and breastfeeding and their long-term benefits on MS outcomes suffer from the possibility of reverse causality, as women with milder impairment might choose to become pregnant more readily than those with a higher level of disability. Thus, the hypothesis that multiparity might have a protective role on MS outcomes needs to be tested in larger prospective cohort studies of neo-diagnosed women, evaluating both clinical and radiological features at presentation.
Validating Sustainable Career Indicators: A Case Study in a European Energy Company
Authors: Curado Carla , Gonçalves Tiago , Ribeiro Cláudia
Published: March 2023

The literature on careers is rapidly evolving, presenting relevant academic developments. Considering the volatility of the environment and the workforce and the search for sustainability, a new research avenue concerning sustainable careers is emerging. Sustainable careers are regarded as a complex mental schema represented by experiences and continuity patterns grounded on individual subjective evaluations, such as happiness, health and productivity. According to conceptual models, these are fundamental individual indicators that allow the attainment of a sustainable career. By following this theoretical proposal, the work tests the conceptual model using proxies for its indicators (job satisfaction, well-being and organizational citizenship behavior). We validate the use of these proxies by performing association, variance, and cluster analysis on data coming from a survey conducted on employees of a European energy company. The results corroborate our hypotheses and support the choice of the selected proxies as adequate operationalization of the indicators. This study contributes to theory and practice alike by validating measures to represent each indicator and their association with sustainable careers. The study contributes to the development of research on sustainable careers by providing a set of measures that can be used to profit from an existing theoretical model and operationalize it in future studies exploring its contribution to several other variables. There are managerial implications that arise from our results and may help human resources managers contribute to the sustainable careers of their employees. We acknowledge the study’s limitations at the end of the paper and offer future directions for research.
Utilization of Low Molecular Weight Carbon Sources by Fungi and Saprolegniales: Implications for Their Ecology and Taxonomy
Authors: Masigol Hossein , Grossart Hans-Peter , Taheri Seyedeh Roksana , Mostowfizadeh-Ghalamfarsa Reza , Pourmoghaddam Mohammad Javad , Bouket Ali Chenari , Khodaparast Seyed Akbar
Published: March 2023

Contributions of fungal and oomycete communities to freshwater carbon cycling have received increasing attention in the past years. It has been shown that fungi and oomycetes constitute key players in the organic matter cycling of freshwater ecosystems. Therefore, studying their interactions with dissolved organic matter is crucial for understanding the aquatic carbon cycle. Therefore, we studied the consumption rates of various carbon sources using 17 fungal and 8 oomycete strains recovered from various freshwater ecosystems using EcoPlate™ and FF MicroPlate™ approaches. Furthermore, phylogenetic relationships between strains were determined via single and multigene phylogenetic analyses of the internal transcribed spacer regions. Our results indicated that the studied fungal and oomycete strains could be distinguished based on their carbon utilization patterns, as indicated by their phylogenetic distance. Thereby, some carbon sources had a higher discriminative strength to categorize the studied strains and thus were applied in a polyphasic approach. We concluded that studying the catabolic potential enables a better understanding of taxonomic relationships and ecological roles of fungal vs. oomycete strains.