bluuebunny/crossref_metadata_2025 huggingface dataset:
Softening/Hardening Damage Model and Numerical Implementation of Seabed Silt-Steel Interface in Yellow River Underwater Delta
Authors: Yu Peng , Liu Honghua , Geng Lin , Wang Shuai , Yu Yang , Zhu Chenghao , Yang Qi , Liu Hongjun , Guan Yong
Published: July 2023

The interaction between soil and structure is a research hotspot in ocean engineering, and the shear performance of interfaces is an essential factor affecting the bearing capacity of offshore structures. Taking the Yellow River Underwater Delta as the research area, the Softening/Hardening damage model of the silt–steel interface and the determination method of model parameters are proposed based on the statistical damage theory. Through the interface monotonic shear test under the conditions of different normal stress, roughness and water content, the shear mechanical properties and volumetric deformation laws on the silt–steel interface are analyzed, and the damage model parameters are obtained. Finally, a FRIC subroutine for the damage model was developed based on ABAQUS. The research results indicate the following: (1) The interface between silt and steel exhibits two characteristics, softening/hardening and shear shrinkage/expansion, under different conditions. Roughness significantly impacts interfacial cohesion, while water content mainly affects the internal friction angle. (2) The softening model based on the classic rock damage model can better simulate the stress–strain relationship of the silt–steel interface under high normal stress and low water content. In contrast, the hardening model based on the classic hyperbola model can better simulate the stress–strain relationship under low normal stress and high water content. The calculated results of the softening/hardening model agree with the experimental results, and the model has 7 parameters. (3) The developed FRIC subroutine can effectively simulate the nonlinear mechanical behavior of the interface between silt and steel. The research results provide a reference for exploring the stability analysis of offshore structures considering interface weakening effects.
Role of the Wall Layer in 3D-Printed Composites under the Salt Spray Condition
Authors: Kim Do-Hyeon , Seo Hyoung-Seock
Published: July 2023

While the mechanical strength of 3D-printed composites is an area of active research, few studies have considered their application to the marine industry. In particular, the role of wall layers is an issue because of their lack of the contribution to the mechanical strength although they help prevent water penetration. In this study, experiments were performed to investigate the effects of salt spray exposure on the mechanical strength of continuous fiber 3D-printed composites with and without the wall layer. Specimens were printed using continuous fiber filaments in the same direction as the loading direction with and without a wall layer. The period of salt spray exposure was set to 15 and 30 days, and the saltwater absorption rate was calculated for each specimen. Tensile tests were performed to determine the effect of the exposure period on the tensile modulus and strength. The results showed that the tensile strength decreased with an increasing exposure period and that the presence of the wall layer reduced the rate of decrease in the mechanical strength. The results confirmed that a wall layer prevents the penetration of saltwater, which may facilitate the potential application of 3D-printed composites in the marine industry.
Electrostatic Atomized Water Particles Induce Disease Resistance in Muskmelon (Cucumis melo L.) against Postharvest Fruit Rot Caused by Fusarium incarnatum
Authors: Kaewsuksaeng Samak , Wonglom Prisana , Sunpapao Anurag
Published: July 2023

The postharvest quality of muskmelon can be affected by fruit rot caused by the fungus Fusarium incarnatum, resulting in loss of quality. The utilization of electrostatic atomized water particles (EAWPs) in agriculture applications has been shown to induce disease resistance in plants. Therefore, in this study, we determined the effect of electrostatic atomized water particles (EAWPs) on the disease resistance of muskmelon fruits against postharvest fruit rot caused by F. incarnatum. EAWPs were applied to muskmelon fruits for 0, 30, 60, and 90 min. EAWP-treated muskmelon fruits were inoculated with F. incarnatum, and disease progress was measured. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) of the chitinase (CmCHI) and β-1,3-glucanase (CmGLU) genes of Cucumis melo (muskmelon) was performed for EAWP-treated and -untreated muskmelon fruits. The activities of cell-wall-degrading enzymes (CWDEs), chitinase, and β-1,3-glucanase were also assayed in EAWP-treated and -untreated muskmelon fruits. The results showed that disease progress was limited by EAWP treatment for 30 min prior to pathogen inoculation. Muskmelon fruits treated with EAWPs for 30 min showed an upregulation of CWDE genes, CmCHI and CmGLU, as observed by qRT-PCR, leading to high chitinase and β-1,3-glucanase activities, as observed through enzyme assays. The results of SEM microscopy revealed that the effect of the crude enzymes of EAWP-treated muskmelon caused morphological changes in F. incarnatum mycelia. Furthermore, treatment with EAWPs preserved postharvest quality in muskmelon, including with regard to texture stiffness and total chlorophyll contents, compared to untreated muskmelon. These results demonstrate that the pretreatment of muskmelon with EAWPs suppresses the development of F. incarnatum in the early stage of infection by regulating gene expression of CWDEs and elevating the activities of CWDEs, while also maintaining postharvest muskmelon quality.
Metabolic Changes and Antioxidant Response in Ustilago maydis Grown in Acetate
Authors: Romero-Aguilar Lucero , Hernández-Morfín Katia Daniela , Guerra-Sánchez Guadalupe , Pardo Juan Pablo
Published: July 2023

Ustilago maydis is an important model to study intermediary and mitochondrial metabolism, among other processes. U. maydis can grow, at very different rates, on glucose, lactate, glycerol, and ethanol as carbon sources. Under nitrogen starvation and glucose as the only carbon source, this fungus synthesizes and accumulates neutral lipids in the form of lipid droplets (LD). In this work, we studied the accumulation of triacylglycerols in cells cultured in a medium containing acetate, a direct precursor of the acetyl-CoA required for the synthesis of fatty acids. The metabolic adaptation of cells to acetate was studied by measuring the activities of key enzymes involved in glycolysis, gluconeogenesis, and the pentose phosphate pathways. Since growth on acetate induces oxidative stress, the activities of some antioxidant enzymes were also assayed. The results show that cells grown in acetate plus nitrate did not increase the amount of LD, but increased the activities of glutathione reductase, glutathione peroxidase, catalase, and superoxide dismutase, suggesting a higher production of reactive oxygen species in cells growing in acetate. The phosphofructokinase-1 (PFK1) was the enzyme with the lowest specific activity in the glycolytic pathway, suggesting that PFK1 controls the flux of glycolysis. As expected, the activity of the phosphoenolpyruvate carboxykinase, a gluconeogenic enzyme, was present only in the acetate condition. In summary, in the presence of acetate as the only carbon source, U. maydis synthesized fatty acids, which were directed into the production of phospholipids and neutral lipids for biomass generation, but without any excessive accumulation of LD.
Particle MCMC in Forecasting Frailty-Correlated Default Models with Expert Opinion
Authors: Nguyen Ha
Published: July 2023

Predicting corporate default risk has long been a crucial topic in the finance field, as bankruptcies impose enormous costs on market participants as well as the economy as a whole. This paper aims to forecast frailty-correlated default models with subjective judgements on a sample of U.S. public non-financial firms spanning January 1980–June 2019. We consider a reduced-form model and adopt a Bayesian approach coupled with the Particle Markov Chain Monte Carlo (Particle MCMC) algorithm to scrutinize this problem. The findings show that the 1-year prediction for frailty-correlated default models with different prior distributions is relatively good, whereas the prediction accuracy ratios for frailty-correlated default models with non-informative and subjective prior distributions over various prediction horizons are not significantly different.
A GIS-Based Index of Physical Susceptibility to Flooding as a Tool for Flood Risk Management
Authors: Miranda Francis , Franco Anna Beatriz , Rezende Osvaldo , da Costa Bruno B. F. , Najjar Mohammad , Haddad Assed N. , Miguez Marcelo
Published: July 2023

The identification and classification of flood-prone areas comprise a fundamental step in the Flood Risk Management approach, providing subsidies for land use planning, floodproofing policies, the design of mitigation measures and early warning systems. To address this issue, a frequently used preliminary tool is the flood susceptibility mapping of a region using a range of widely available data. Therefore, the present study introduces an index-based approach able to qualitatively assess flood-prone areas, named Physical Susceptibility to Floods Index (PhySFI), based on a multi-criteria decision-making method and developed in a GIS environment. The methodology presupposes a critical discussion of variables commonly used in other flood indexes, intending to simplify the proposed representation, and emphasizes the role of the user/modeler. PhySFI is composed of just four indicators, based on physical parameters of the assessed environment. This index was developed and first applied in the city of Rio de Janeiro, as part of the Rio de Janeiro Climate Change Adaptation Plan. The validation process was based on a comparative analysis with flood extent and height simulated by the hydrodynamic modeling of four watersheds within the study area, with different urbanization processes for each one. The results indicate that the index is a powerful preliminary tool to assess flood-prone areas in coastal cities.
An Impact Assessment of GHG Taxation on Emilia-Romagna Dairy Farms through an Agent-Based Model Based on PMP
Authors: Baldi Lisa , Arfini Filippo , Calzolai Sara , Donati Michele
Published: July 2023

The aim of this work is to assess the structural, production, environmental, and economic impact of an increasing tax on climate change gas emissions related to milk production under the current CAP payment system. The analysis is performed using an Agent-Based Model (ABM) based on Positive Mathematical Programming (PMP). The integration between ABM and PMP makes it possible to simulate farmers’ strategies considering the interaction between them, the territorial specificity, and the heterogeneity of farms in the presence of little information on production costs. It also makes it possible to add a social and cultural perspective to the economic factors. The model is calibrated using FADN data for the Emilia-Romagna region (Italy) from the year 2020. The results show that farmers belonging to different age groups make decisions based on economic profitability, but also on their social and cultural background. To maximise their utility functions, farmers can opt for more efficient agricultural management practices that may result in the exchange of production factors, especially land. The overall impact penalises less efficient farms and agricultural production with higher negative externalities.
Investigation and Comparison of Spatial–Temporal Characteristics of Farmland Fragmentation in the Beijing–Tianjin–Hebei Region, China, and Bavaria, Germany
Authors: Niu Lu , Xu Mengyao , Vries Walter Timo de
Published: July 2023

Farmland fragmentation has emerged as the primary manifestation of global land use changes during the last century. Following the economic reform and opening up in China from the 1980s, the Beijing–Tianjin–Hebei (BTH) region has witnessed continuous farmland fragmentation. Understanding the spatial–temporal dynamics of farmland fragmentation is crucial for formulating sustainable land use management strategies. However, the specific causes and locations of farmland fragmentation remain unclear, as do potential significant differences or similarities across different countries. Given this quandary, this study empirically analyzes the spatial–temporal characteristics of farmland fragmentation in two different contexts: the BTH region in China and Bavaria in Germany. The study utilizes multiple theoretical models for temporal and spatial farmland fragmentation, applying the comprehensive index method, landscape pattern analysis, and the magic cube model. The results indicate that the farmland fragmentation index (FFI) value in BTH and Bavaria first increased or remained stable, but afterwards, both decreased and increased again. Moreover, the spatial analysis demonstrated high significance values for the FFI in the northern and western BTH region and in northern and southern Bavaria. There are, furthermore, significant differences in the FFI in different macro landforms. The FFI in the mountain regions is significantly higher than that of the plains. Finally, the results also demonstrate that a decreasing FFI relates to the overall low values within an FFI region. The theoretical framework in this study appears to align with empirical results, and thus provides a reference for future policy measures to protect farmland.
Do Land Use and Land Cover Scenarios Support More Integrated Land Use Management?
Authors: Rigo Roberta , Houet Thomas
Published: July 2023

In agricultural landscape management, the conventional top-down approaches that primarily focus on market-led responses struggle to preserve the landscape elements essential for environmental sustainability. To address this deficiency, land use and land cover change (LUCC) scenarios promote an integrated understanding of landscape dynamics and highlight the inconsistency between the compartmentalisation of the public sector (“siloisation”) and the necessity for management that reflects the interdependencies of socio-ecological systems. This study investigates the extent to which the creation and dissemination of LUCC scenarios lead to modifications in the values, attitudes, and behaviours of local actors engaged in land management, giving particular emphasis to the role of these scenarios in encouraging integrated management. To accomplish this objective, we interviewed local actors who actively participated in the co-construction of the scenario narratives or learned about the scenarios during dissemination workshops. We then analysed the data via a thematic and lexicometric analysis. The findings highlighted the dual function of these scenarios as a catalyst for pre-existing political will to promote integrated management and as a tool for raising awareness about major environmental challenges. At the group level, the outcomes encompassed aspects such as basing political decisions on the results of scenarios and fostering collaboration between institutions. These outcomes were observed among the actors involved in co-constructing scenarios or those with pre-existing motivations to pursue integrated management initiatives. Additional personal outcomes included an increased awareness of environmental challenges and the consolidation of non-formalised knowledge. We argue that combining co-construction and dissemination enhances the outcomes of scenarios considerably.
Sustainability Assessments of Peri-Urban Areas: An Evaluation Model for the Territorialization of the Sustainable Development Goals
Authors: De Toro Pasquale , Formato Enrico , Fierro Nicola
Published: July 2023

This research tests a sustainability assessment based on the 2030 Agenda’s Sustainable Development Goals (SDGs) through a process of their territorialization and implementation. This process enables the development of a spatial decision support system (SDSS) that can be integrated with strategic environmental assessments in urban planning. The assessment takes place on the transversality of the sustainability concept, considering the three dimensions (environmental, social, and economic) in a single assessment through the spatial sustainability assessment model (SSAM) by integrating geographic information systems (GIS) and multicriteria analyses. Economic development, social equity, and ecological integrity represent the three common visions for rethinking peri-urban edges. The choice of key indicators is due to the possibilities for action of urban plans and the vision of SDG 11a, which aims to support ‘positive economic, social, and environmental links among urban, peri-urban and rural areas by strengthening national and regional development planning’. In addition, they were selected to be representative of sustainable planning processes in the peri-urban area. In recognizing the limits of urban expansion processes, in the peri-urban area, it is necessary to promote a different growth based on agri-environmental values, the production of biodiversity reserves and corridors, new models of inhabiting open space, and the consolidation of civic and collective uses. The paper tests the assessment methodology in two urban plans of the Metropolitan City of Naples that address the development of the peri-urban area with different strategies. This provides insight into how to support decision-making processes so that economic development, social equity, and ecological integrity represent three common and integrated visions to enable development that is consistent with SDGs. The results show that it is possible to identify trade-offs among the three dimensions. In fact, where there are environmental subtractions necessary to accommodate peri-urban land-relation functions, these are offset by the social values of collective use and by the values of the current economy that aim to redistribute present resources.
Spanish in the Southeast: What a Swarm of Variables Can Tell Us about a Newly Forming Bilingual Community
Authors: Michnowicz Jim , Ronquest Rebecca , Chetty Sarah , Green Georgia , Oliver Stephanie
Published: July 2023

The southeastern United States has experienced rapid growth in the Hispanic population in recent decades, giving rise to a newly forming bilingual community. The present study builds on previous work by the authors via expansion of a “variable swarm”: the analysis of multiple linguistic variables simultaneously for the same set of speakers, with the goal of understanding patterns of accommodation and change within the community. The initial study included four linguistic variables (prosodic rhythm, bilingual discourse markers, the realization of /bdg/ and vowel space), and the present study adds an additional four variables (bilingual filled pauses, subject pronoun realization, code switching, and the labiodental realization of orthographic ) for 23 speakers of Mexican and Central American origin across two sociolinguistic generations (G1 vs. G2). Results for individual speakers show a pattern of adoption of some features by speakers of both generations (such as English-influenced prosodic rhythm and phonological filled pauses), while other, possibly more salient forms directly integrated from English (English discourse markers and code switching) exhibit later, highly variable rates of adoption, suggesting that speakers may consciously manipulate these variables as part of a process of active identity construction. Likewise, G1 speakers show fewer correlations among linguistic variables than G2 speakers, and patterns reveal that some bilingual forms are incorporated in tandem due to shared phonological traits or discourse functions. The innovative swarm analysis further contributes to the advancement of techniques employed in sociolinguistic research by serving as a bridge between traditional first- and second-wave studies that focus on a single variable, and third-wave studies that focus more on variation at the individual level.
Lexical Borrowings from Spanish into Wayuunaiki: Contact, Classification, and Motivations
Authors: De La Rosa Yacomelo Johan , Ramírez González Rudecindo , Viloria Rodríguez Leonel , Valdez Jimenez Wendy , Guerra-Lyons Jesús
Published: July 2023

This study identifies and analyzes lexical borrowings from Spanish into Wayuunaiki (an Arawak language spoken in Colombia and Venezuela). The analysis, based on bibliographic documentation and fieldwork, focuses on the borrowability and semantic domains of Spanish loanwords and the factors motivating Spanish loanword adoption into Wayuunaiki. The results show that, despite the typological distance, Wayuunaiki is prone to adopting lexical items from Spanish, as evident in the moderate number of lexical borrowings identified. A key motivating factor for Spanish loanword incorporation is Wayuunaiki speakers’ need for lexical items to refer to new concepts adopted from the dominant Spanish-speaking culture. This phenomenon is partly due to the contact dynamics between Wayuunaiki and Spanish. Besides expanding on the description of the Wayuunaiki language and its contact with Spanish, this study contributes to identifying factors and motivations favoring the adoption of loanwords between typologically distant languages.
Cardiovascular Effects of Weight Loss in Obese Patients with Diabetes: Is Bariatric Surgery the Additional Arrow in the Quiver?
Authors: Bottino Roberta , Carbone Andreina , Formisano Tiziana , D’Elia Saverio , Orlandi Massimiliano , Sperlongano Simona , Molinari Daniele , Castaldo Pasquale , Palladino Alberto , Barbareschi Consiglia , Tolone Salvatore , Docimo Ludovico , Cimmino Giovanni
Published: July 2023

Obesity is an increasingly widespread disease worldwide because of lifestyle changes. It is associated with an increased risk of cardiovascular disease, primarily type 2 diabetes mellitus, with an increase in major cardiovascular adverse events. Bariatric surgery has been shown to be able to reduce the incidence of obesity-related cardiovascular disease and thus overall mortality. This result has been shown to be the result of hormonal and metabolic effects induced by post-surgical anatomical changes, with important effects on multiple hormonal and molecular axes that make this treatment more effective than conservative therapy in determining a marked improvement in the patient’s cardiovascular risk profile. This review, therefore, aimed to examine the surgical techniques currently available and how these might be responsible not only for weight loss but also for metabolic improvement and cardiovascular benefits in patients undergoing such procedures.
Prevalence of Long COVID Symptoms Related to SARS-CoV-2 Strains
Authors: Aloè Teresita , Novelli Federica , Puppo Gianfranco , Pinelli Valentina , Barisione Emanuela , Trucco Elisa , Costanzo Roberta , Covesnon Maria Grazia , Grillo Federica , Zoccali Patrizia , Milanese Manlio , Maniscalco Sara , Tagliabue Elena , Piroddi Ines Maria Grazia , Venturi Simonetta , Serra Maria , Scordamaglia Francesca , Ferrari Marta , Serafini Antonella
Published: July 2023

Background: Few studies have assessed the differences of patterns of Long COVID (L-COVID) with regards to the pathogenetic SARS-CoV-2 strains. Objectives: To investigate the relationship between demographic and clinical characteristics of acute phase of infection and the persistence of L-COVID symptoms and clinical presentation across different SARS-CoV-2 strains. Methods: In this observational-multicenter study we recorded all demographic and clinical characteristics, severity of infection, presence/persistence of symptoms of fatigue, dyspnoea and altered quality of life (QoL) at baseline and after 6 months, in a sample of Italian patients from Liguria between March 2020 and March 2022. Results: 308 patients (mean age 63.2 years; 55.5% men) with previous COVID were enrolled. Obese patients were 21.2% with a significant difference in obesity prevalence across the second and third wave (p = 0.012). Treatment strategies differed between waves (p < 0.001): more patients required invasive mechanical ventilation in the first wave, more patients were treated with high-flow nasal cannula/non-invasive ventilation in the in the second and more patients were treated with oxygen-therapy in the fourth wave. At baseline, a high proportion of patients were symptomatic (dyspnoea and fatigue), with impairment in some QoL indicators. A higher prevalence of patients with pain, were seen in the first wave compared to later infections (p = 0.01). At follow-up, we observed improvement of dyspnoea, fatigue and some dimensions of QoL scale evaluation such as mobility, usual activities, pain evaluations; instead there was no improvement in remaining QoL scale indicators (usual care and anxiety-depression). Conclusions: There were no significant differences in the prevalence of the most frequent L-COVID symptoms, except for QoL pain domain that was especially associated with classical variant. Our results show substantial impact on social and professional life and usual care activities. These findings highlight the importance of multidisciplinary post COVID follow-up care including mental health support and rehabilitation program.
Analysis of Dynamic Mesh Stiffness and Dynamic Response of Helical Gear Based on Sparse Polynomial Chaos Expansion
Authors: Tian Hongxu , Huang Wenkang , Liu Zimeng , Ma Hui
Published: July 2023

This paper presents an efficient method for obtaining the dynamic mesh stiffness and dynamic response of a helical gear pair. Unlike the traditional dynamic model that utilizes a time-dependent sequence, the mesh stiffness using the presented method is updated according to the gear displacement vector at each sub-step of the numerical calculation. Three-dimensional loaded tooth contact analysis (3D LTCA) is used to determine the mesh stiffness, and a surrogate model based on sparse polynomial chaos expansion (SPCE) is proposed to improve the computational efficiency, which is achieved by reducing the number of coefficients in the polynomial chaos expansion (PCE) model though a quantum genetic algorithm. During the calculation, the gear displacement vector at each sub-step is converted into the changes in center distance, misalignment angle, and mesh force, which are then introduced into the SPCE model to update the mesh stiffness for subsequent calculations. The results suggest that the SPCE model exhibits high accuracy and can significantly improve the computational efficiency of the PCE model, making it suitable for dynamic calculations. Upon updating the mesh stiffness during the dynamic calculation, the mesh stiffness declines, the dynamic transmission error (DTE) increases, and the frequency components of the responses change significantly.
Component Criticality Analysis for Improved Ship Machinery Reliability
Authors: Daya Abdullahi , Lazakis Iraklis
Published: July 2023

Redundancy in ship systems is provided to ensure operational resilience through equipment backups, which ensure system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems; hence, it is provided with a good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired blackouts is common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensuring a more robust maintenance and support platform for efficient ship operations. In this regard, a hybrid reliability and fault detection analysis using DFTA and ANN was conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of four marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate an overall low system reliability of less than 70 percent within the first 24 of the 78 operational months. Component criticality-using reliability importance measures obtained through DFTA was used to identify all components with more than a 40 percent contribution to subsystem failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Network classification. The ANN has identified a failure pattern threshold at about 200 kva, which can be attributed to overheating, hence establishing a link between component failure and generator performance.
Observer-Based Control of a Microrobot Navigating within a 3D Blood Vessel along a Trajectory Delivered by a Joystick Device
Authors: Larbi Meziane , Guechi El-Hadi , Maidi Ahmed , Belharet Karim
Published: July 2023

In this paper, an observer-based state feedback control strategy for trajectory tracking of a magnetic microrobot navigating within a 3D blood vessel is proposed. The desired trajectory to be followed by the microrobot is generated by an operator using a joystick device. To deal with the significant effect of both external disturbances and parametric uncertainties, often encountered in biological environments, a state feedback stabilization, that enforces the output tracking despite any environmental disturbances, is developed. Then, for the purpose of implementation, a state observer is developed to recover the whole state from the measured position of the microrobot. The state feedback and observer gains are determined separately by solving a set of linear matrix inequalities derived in the framework of Lyapunov stability theory. Simulation runs are performed to demonstrate the performance of the proposed control strategy.
Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System
Authors: Ivančan Jelena , Lisjak Dragutin , Pavletić Duško , Kolar Davor
Published: July 2023

The reliable operation of a process plant is critical to the safety, performance, and profitability of a business. Failure Mode and Effects Analysis (FMEA) is a process of reviewing systems, subsystems, and equipment that identify potential failure modes, their root causes, and consequences. FMEA is also a risk assessment tool that has been used successfully in a wide range of process industries as an integral part of reliability-centered maintenance, safety management, and continuous improvement. The method has indeed been criticized, especially in the area of system assessment, but engineers still predominantly use traditional, unmodified FMEA best practices. In this study, a new conceptual model is proposed to improve the traditional technique and make FMEA a more autonomous, data-driven, and accurate method. The conceptual model of improved FMEA uses ANFIS and FIS models in one automated process that aims to solve the defect handling process from failure detection to quantification of risk level and prioritization of dedicated mitigation action.
Predictive Speed Control of a DC Universal Motor Applied to Monitor Electric Vehicle Batteries
Authors: Buenestado Pablo , Gibergans-Báguena José , Acho Leonardo , Pujol-Vázquez Gisela
Published: July 2023

This article deals with the design of a simple predictive control algorithm applied to a bidirectional DC-DC power converter for the angular speed control of a DC motor. We used the dynamics of a DC motor but mathematically reduced them to arrive at a simple model that is ideal for our purpose, not only to meet the control objective but also to generate reliable data for further analysis. This predictive control approach is based on the discrete time mathematical model of a DC motor. A huge capacitor to emulate an electric vehicle battery was then successfully connected to our experimental platform. Due to the robustness of the proposed control algorithm, the same predictive control scheme provided sufficient information to monitor the battery’s state. On this basis, and due to the system’s efficiency, it was possible to configure a fault detection scheme in our electric car battery emulator using only classical statistical tools. A PIC18F252 microcontroller was used in our experimental platform to implement our predictive control algorithm. It was then appropriately coupled to the power electronics required by the DC-DC converter to drive the DC motor. Our experimental results proved the excellent performance of the control method and also of the health monitoring system. On the other hand, the main difficulty in achieving our main goal was the realization of discrete control, which had to be as simple as possible while maintaining the control objective and while also being capable of generating reliable data for the health monitoring stage. Thus, the primary contribution of this work was the development of the predictive control of the speed of a universal motor, followed by the modification of the experimental design to simulate an electric car battery and the introduction of a novel statistical method for fault detection.
Families of Extended Exponentiated Generalized Distributions and Applications of Medical Data Using Burr III Extended Exponentiated Weibull Distribution
Authors: Hussain Sajid , Ul Hassan Mahmood , Rashid Muhammad Sajid , Ahmed Rashid
Published: July 2023

In this article, four new families named as Weibull extended exponentiated-X (WEE-X), Lomax extended exponentiated-X (LEE-X), Logistic extended exponentiated-X (LGCEE-X), and Burr III extended exponentiated-X (BIIIEE-X) with their quantile functions are proposed. The expressions for distribution function and density function of BIIIEE-X family are written in terms of linear combinations of the exponentiated densities based to parent model. New models, i.e., Weibul extended exponentiated Weibull (WEEW), Lomax extended exponentiated Weibull (LEEW), Logistic extended exponentiated Weibull (LGCEEW), and Burr III extended exponentiated-Weibull (BIIIEEW) distributions are derived, were plotted for functions of probability density and hazard rate at different levels of parameters. Some mathematical properties of the BIIIEEW model are disclosed. The maximum likelihood method for the BIIIEEW model are described. Numerical applications of the BIIIEEW model to disease of cancer datasets are provided.
A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems
Authors: Chalabi Nour Elhouda , Attia Abdelouahab , Alnowibet Khalid Abdulaziz , Zawbaa Hossam M. , Masri Hatem , Mohamed Ali Wagdy
Published: July 2023

Metaheuristics in recent years has proven its effectiveness; however, robust algorithms that can solve real-world problems are always needed. In this paper, we suggest the first extended version of the recently introduced gaining–sharing knowledge optimization (GSK) algorithm, named multiobjective gaining–sharing knowledge optimization (MOGSK), to deal with multiobjective optimization problems (MOPs). MOGSK employs an external archive population to store the nondominated solutions generated thus far, with the aim of guiding the solutions during the exploration process. Furthermore, fast nondominated sorting with crowding distance was incorporated to sustain the diversity of the solutions and ensure the convergence towards the Pareto optimal set, while the ϵ-dominance relation was used to update the archive population solutions. ϵ-dominance helps provide a good boost to diversity, coverage, and convergence overall. The validation of the proposed MOGSK was conducted using five biobjective (ZDT) and seven three-objective test functions (DTLZ) problems, along with the recently introduced CEC 2021, with fifty-five test problems in total, including power electronics, process design and synthesis, mechanical design, chemical engineering, and power system optimization. The proposed MOGSK was compared with seven existing optimization algorithms, including MOEAD, eMOEA, MOPSO, NSGAII, SPEA2, KnEA, and GrEA. The experimental findings show the good behavior of our proposed MOGSK against the comparative algorithms in particular real-world optimization problems.
Heavy-Tailed Probability Distributions: Some Examples of Their Appearance
Authors: Klebanov Lev B. , Kuvaeva-Gudoshnikova Yulia V. , Rachev Svetlozar T.
Published: July 2023

We provide two examples of the appearance of heavy-tailed distributions in social sciences applications. Among these distributions are the laws of Pareto and Lotka and some new ones. The examples are illustrated through the construction of suitable toy models.
Polynomial Recurrence for SDEs with a Gradient-Type Drift, Revisited
Authors: Veretennikov Alexander
Published: July 2023

In this paper, polynomial recurrence bounds for a class of stochastic differential equations with a rotational symmetric gradient type drift and an additive Wiener process are established, as well as certain a priori moment inequalities for solutions. The key feature of this paper is that the approach does not use Lyapunov functions because it is not clear how to construct them. The method based on Dynkin’s (nonrandom) chain of equations is applied instead. Another key feature is that the asymptotic conditions on the potential near infinity are assumed as inequalities—which allows for more flexibility compared to a single limit at infinity, making it less restrictive.
Characterizations of the Frame Bundle Admitting Metallic Structures on Almost Quadratic ϕ-Manifolds
Authors: Khan Mohammad Nazrul Islam , De Uday Chand , Alam Teg
Published: July 2023

In this work, we have characterized the frame bundle FM admitting metallic structures on almost quadratic ϕ-manifolds ϕ2=pϕ+qI−qη⊗ζ, where p is an arbitrary constant and q is a nonzero constant. The complete lifts of an almost quadratic ϕ-structure to the metallic structure on FM are constructed. We also prove the existence of a metallic structure on FM with the aid of the J˜ tensor field, which we define. Results for the 2-Form and its derivative are then obtained. Additionally, we derive the expressions of the Nijenhuis tensor of a tensor field J˜ on FM. Finally, we construct an example of it to finish.
A Long-Term Prediction Method of Computer Parameter Degradation Based on Curriculum Learning and Transfer Learning
Authors: Mao Yuanhong , Ma Zhong , Liu Xi , He Pengchao , Chai Bo
Published: July 2023

The long-term prediction of the degradation of key computer parameters improves maintenance performance. Traditional prediction methods may suffer from cumulative errors in iterative prediction, which affect the model’s long-term prediction accuracy. Our network adopts curriculum learning and transfer learning methods, which can effectively solve this problem. The training network uses a dual-branch Siamese network. One branch intermixes the predicted and annotated data as input and uses curriculum learning to train. The other branch uses the original annotated data for training. To further align the hidden distributions of the two branches, the transfer learning method calculates the covariance matrices of the time series of the two branches by correlation alignment loss. A single branch is used in the test for prediction without increasing the inference computation. Compared with the current mainstream networks, our method can effectively improve the accuracy of long-term prediction with the improvements above.
How Does Green Store Brand Introduction Influence the Effects of Government Subsidy on Supply Chain Performance?
Authors: Zhong Junyi , Huo Jiazhen
Published: July 2023

With the rising awareness of environmental protection and concern for sustainable development, green products have been highly favored by consumers, enterprises, and the government. As a matter of fact, not only do manufacturers produce green products, but retailers would also like to introduce their green store brands. However, the costly green investment hinders the improvement of the products’ green degree. Therefore, the government may provide financial support to motivate enterprises to increase their products’ green degree. This study investigates how the presence of green store brands and government subsidies affect green supply chain performance. Four models are discussed using the Stackelberg game theoretic approach, and then, the optimal solutions in different cases are compared. The results show that (1) regardless of the government subsidy, the green store brand introduction always reduces the manufacturer’s profit and improves the retailer’s profit and environmental benefit; (2) In most cases, the implementation of a government subsidy can effectively improve the products’ green degree and benefit the supply chain members. However, it is surprising to find that the government subsidy may be detrimental to the manufacturer once the green store brand is introduced; (3) Interestingly, the introduction of green store brand may have an expansion effect, a shrinkage effect or even an inverse effect on the effects of government subsidies on supply chain performance, and these effects become more significant with the increasing green preference of consumers, product substitute, and subsidy rate. The new findings also provide some implications for supply chain members and the government in green supply chain management (GSCM) and green innovation.
Solving Inverse Problem of Distributed-Order Time-Fractional Diffusion Equations Using Boundary Observations and L2 Regularization
Authors: Yuan Lele , Liang Kewei , Wang Huidi
Published: July 2023

This article investigates the inverse problem of estimating the weight function using boundary observations in a distributed-order time-fractional diffusion equation. We propose a method based on L2 regularization to convert the inverse problem into a regularized minimization problem, and we solve it using the conjugate gradient algorithm. The minimization functional only needs the weight to have L2 regularity. We prove the weak closedness of the inverse operator, which ensures the existence, stability, and convergence of the regularized solution for the weight in L2(0,1). We propose a weak source condition for the weight in C[0,1] and, based on this, we prove the convergence rate for the regularized solution. In the conjugate gradient algorithm, we derive the gradient of the objective functional through the adjoint technique. The effectiveness of the proposed method and the convergence rate are demonstrated by two numerical examples in two dimensions.
Using an Interval Type-2 Fuzzy AROMAN Decision-Making Method to Improve the Sustainability of the Postal Network in Rural Areas
Authors: Nikolić Ivana , Milutinović Jelena , Božanić Darko , Dobrodolac Momčilo
Published: July 2023

One of the crucial pillars of each state’s development strategy relates to service provision in rural areas. An adequate scope of these services is a prerequisite for uniform regional progress. Postal operators play a key role in supporting these development policies, by providing postal, financial and transportation services to each citizen in a state, regardless of place of residence. The postal network represents one of the biggest logistics networks worldwide. However, since it is not financially justified to provide services to all citizens, even to those that live in the most remote areas, the question of how to optimize the postal network is always topical. This problem is very complex because the postal units’ existence in rural areas cannot be considered just from an economic standpoint; many other criteria should be considered. The model proposed in this paper can be considered a decision-making tool designed to support policymakers in planning the postal network. First, we identify the criteria that should be considered in decision-making by an extensive literature review. We then apply the FUCOM method to determine the importance of individual criteria. Finally, we propose an Interval Type-2 Fuzzy AROMAN approach to determine which postal unit should be reorganized.
Eigenvalue Problem for a Reduced Dynamo Model in Thick Astrophysical Discs
Authors: Mikhailov Evgeny , Pashentseva Maria
Published: July 2023

Magnetic fields of different astrophysical objects are generated by the dynamo mechanism. Dynamo is based on the alpha-effect and differential rotation, which are described using a system of parabolic equations. Their solution is an important problem in magnetohydrodynamics and mathematical physics. They can be solved assuming exponential growth of the solution, which leads to an eigenvalue problem for a differential operator connected with spatial coordinates. Here, we describe a system of equations connected with the generation of magnetic field in discs, which are associated with galaxies and binary systems. For an ideal case of an infinitely thin disc, the eigenvalue problem can be precisely solved. If we take into account the finite thickness of the disc, the problem becomes more difficult. The solution can be found using asymptotical methods based on perturbations of the eigenvalues. Here, we present two different models which describe field evolution for different cases. For the first, we find eigenvalues taking into account linear and quadratic terms for the perturbations in the eigenvalue problem. For the second, we find eigenvalues using only linear terms; this is quite sufficient. Results were verified through numerical modeling, and basic computational tests show proper correspondence between different methods.
A Series Expansion of a Logarithmic Expression and a Decreasing Property of the Ratio of Two Logarithmic Expressions Containing Sine
Authors: Liu Xin-Le , Long Hai-Xia , Qi Feng
Published: July 2023

In the paper, by virtue of a derivative formula for the ratio of two differentiable functions and with the help of a monotonicity rule, the authors expand a logarithmic expression involving the sine function into the Maclaurin power series in terms of specific determinants and prove a decreasing property of the ratio of two logarithmic expressions containing the sine function. These results are interesting purely in pure mathematics.
Epidemic Spreading on Weighted Co-Evolving Multiplex Networks
Authors: Song Bo , Wu Huiming , Song Yurong , Wang Xu , Jiang Guoping
Published: July 2023

The individual behaviors driven by information diffusion show an undeniable impact on the process of epidemic spreading and have been continuously evolving with the dynamic processes. In this paper, a novel weighted co-evolving multiplex network model is proposed to describe the interaction between information diffusion in online social networks and epidemic spreading in adaptive physical contact networks. Considering the difference in the connections between individuals, the heterogeneous rewiring rate, which is proportional to the strength of the connection, is introduced in our model. The simulation results show that the maximum infection scale decreases as the information acceptance probability grows, and the final infection decreases as the rewiring behaviors increase. Interestingly, an infection peak appears in our model due to the interaction between information diffusion and epidemic spread.
Exact Solutions of Reaction–Diffusion PDEs with Anisotropic Time Delay
Authors: Polyanin Andrei D. , Sorokin Vsevolod G.
Published: July 2023

This study is devoted to reaction–diffusion equations with spatially anisotropic time delay. Reaction–diffusion PDEs with either constant or variable transfer coefficients are considered. Nonlinear equations of a fairly general form containing one, two, or more arbitrary functions and free parameters are analyzed. For the first time, reductions and exact solutions for such complex delay PDEs are constructed. Additive, multiplicative, generalized, and functional separable solutions and some other exact solutions are presented. In addition to reaction–diffusion equations, wave-type PDEs with spatially anisotropic time delay are considered. Overall, more than twenty new exact solutions to reaction–diffusion and wave-type equations with anisotropic time delay are found. The described nonlinear delay PDEs and their solutions can be used to formulate test problems applicable to the verification of approximate analytical and numerical methods for solving complex PDEs with variable delay.