Multi-criteria Decision-making Algorithm Based on Linear Diophantine Fuzzy Aggregation Operators

Due to lack of relevant information in many real-life circumstances, it is not acceptable for experts to exactly quantify their judgments with a crisp number. We investigate the aggregation approaches for "lin-ear Diophantine fuzzy numbers" (LDFNs) using Frank operations. We first extend the Frank t-conorm and t-norm (FTcTn) to linear Diophan-tine fuzzy environments and offer numerous new LDFN operations, we develop several new linear Diophantine fuzzy aggregation opera-tors (AOs), including the "linear Diophantine fuzzy Frank weighted averaging (LDFFWA) operator and the linear Diophantine fuzzy Frank weighted geometric (LDFFWG) operator". We also exhibit several properties of these AOs and investigate their relationships. The FTcTn has two major benefits. They work similarly to algebraic, Hamacher and Einstein t-conorms and t-norms (TcTn). Second, they include a feature that leads to a more adaptive and accurate aggregation pro-cess, making them more successful than other broad TcTn techniques. We also employ these operators to provide a strategy for dealing with "multi-criteria decision-making" (MCDM) with LDFNs. Furthermore, using the suggested AOs, a quantitative test case of the new carnivo-rous (NCOVID19) issue is presented as an implementation for expedi-ent decision-making. The goal of this numerical model is to illustrate the usefulness and feasibility of the AOs provided.

FINANCIAL SECURITIES FOR TOUR OPERATORS IN CASE OF INSOLVENCY RESULTING FROM THE DIRECTIVE 2015/2302 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL WHICH ARE IN FORCE IN POLAND

The insolvency of travel agencies is dealt with in a special way by the EU legislator. European Union law introduces legal solutions for the benefit of consumers insofar as the relevant services are not performed by organizers as a consequence of its insolvency. The current 2015/2302 Directive provides much more comprehensive protection than 90/314/EWG Directive for travelers in the event of insolvency of a tour operator. However, in the past, in the practical functioning of travel agencies, it has repeatedly turned out that the Polish legislation has not been able to guarantee full protection provided for in EU law. This situation has changed. In Poland, since August 1, 2018 the system of security and financial guarantees in the event of insolvency of organizers and traders facilitating linked travel arrangements consists of two pillars. If Pillar I funds are exhausted, the costs of actions taken by the Marshal of the Province related to the repatriation of the customers of an insolvent tour operator will be covered from Pillar II, which is created from contributions to the Tourist Guarantee Fund. Due to the COVID pandemic, another form of security was introduced in Poland from January 1,2021 - Tourist Assistance Fund. The fund is designed to support tourism entrepreneurs in the event of extraordinary circumstances. The aim of the paper is to present the legal regulations in force in Poland in the field of financial security of tour operators in the event of their insolvency and to analyze whether these solutions sufficiently protect the interests of travelers. Conclusions included in the paper justify the statement that the extension of the security system by Pillar II make the full protection possible. The two-pillar solution should be sufficient in case of insolvency of a travel agency and that it fully implements the EU recommendations.

Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data

Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.

The elephant in the Zoom: the role of virtual safaris during the COVID-19 pandemic for conservation resilience

The economic turmoil and restrictions on human movement precipitated by the COVID-19 pandemic severely impacted conservation efforts. Many conservation actors rapidly implemented various adaptive measures in response to the cessation of the nature-based tourism industry, the primary revenue source for much of conservation in sub-Saharan Africa. This timely preliminary study examined the innovative use of virtual safaris, a form of virtual nature-based tourism, as an adaptive response to the crisis. Eight in-depth semi-structured interviews and two written responses from a range of ‘conservation operators' provided insight into motivations, benefits, and challenges associated with using virtual safaris. This novel study found three mechanisms through which virtual safaris helped to alleviate the effects of COVID-19 with the potential to develop conservation resilience: 1) as a stopgap measure, 2) for revenue diversification, and 3) as a means of scaling ecosystem services. Virtual safaris provided a critical lifeline for conservation operators, created a new tool to connect with distant audiences, and strengthened relationships with donors. However, this research highlighted a need to re-evaluate the role of sustainable tourism within conservation, with transformative changes essential to enhance future conservation resilience.

The SEIR Covid-19 model described by fractional-order difference equations: analysis and application with real data in Brazil

Several efforts have been recently devoted to the studies on epidemic mathematical models based on fractional-order operators, by virtue of their capability to take into account memory effects and nonlocal features. The aim of this paper is to make a contribution to the topic by introducing a novel Covid-19 model described by non-integer-order difference equations. By conducting a stability analysis, the paper shows that the conceived system has two fixed points at most, i.e. a disease-free fixed point and an endemic fixed point. In particular, a theorem is proved, which assures the global stability of the disease-free fixed point, indicating that the pandemic will disappear when a simple condition on the system parameters is satisfied. Finally, simulation results are carried out with the aim to highlight the capability of the conceived approach.

The Pandemic Shut-In: Reactivating Wells Shut-In in Spring/Summer 2020 – The Impact on 10-Year Forecast and EUR Outlook: A Duvernay Oil Case Study

The COVID-19 pandemic forced Canadian oil and gas operators to cut crude oil production by almost 1 MMb/d in the first half of 2020 due to low oil prices driven by reduced demand. This study explores the forecast and EUR performance of unconventional horizontal oil wells producing from the Duvernay Formation in central Alberta that were shut-in versus those that continued to produce uninterrupted throughout the reduced production period. How were forecasted production and EURs impacted? Did the manner in which the wells were completed play a role? This paper investigates these questions and more in a regional case study of 95 unconventional Duvernay oil wells using public data and a fully automated, physio-statistical, predictive analytical production forecasting tool. The bases of the performance comparison were the results of a 10-year forecast and EUR outlook for the wells evaluated in January, 2020 before the production slow down, and then re-evaluated in January, 2021, 12 months later, after the wells that were shut-in were back on production. In general, wells that continued producing uninterrupted throughout the study period exhibited significantly improved forecast and EUR performance over wells that were shut-in. Analyzing the performance of the largest field (Cygnet with 32 wells), with respect to lateral length, the results pointed to shorter wells that were shut-in exhibiting the poorest performance, where the wells' EUR performance degraded by 7% on average. The proppant intensity study for the same wells told a similar story, with shut-in wells with smaller fracs exhibiting negligible EUR improvement (0.4%) compared to the other categories of wells, with respect to frac size and shut-in status. A proximity study investigated two pads, one with only shut-in wells and the other with only non-shut-in wells, with the results pointing to competitive drainage between individual wells despite the overall performance of a given pad being neutral. Copyright 2022, Unconventional Resources Technology Conference (URTeC)

TOPSIS approach for MCGDM based on intuitionistic fuzzy rough Dombi aggregation operations

Atanassov presented the dominant notion of intuitionistic fuzzy sets which brought revolution in different fields of science since their inception. The operations of t-norm and t-conorm introduced by Dombi were known as Dombi operations and Dombi operational parameter possesses natural flexibility with the resilience of variability. The advantage of Dombi operational parameter is very important to express the experts' attitude in decision-making. This study aims to propose intuitionistic fuzzy rough TOPSIS method based on Dombi operations. For this, first we propose some new operational laws based on Dombi operations to aggregate averaging and geometric aggregation operators under the hybrid study of intuitionistic fuzzy sets and rough sets. On the proposed concept, we present intuitionistic fuzzy rough Dombi weighted averaging, intuitionistic fuzzy rough Dombi ordered weighted averaging, and intuitionistic fuzzy rough Dombi hybrid averaging operators. Moreover, on the developed concept, we present intuitionistic fuzzy rough Dombi weighted geometric, intuitionistic fuzzy rough Dombi ordered weighted geometric, and intuitionistic fuzzy rough Dombi hybrid geometric operators. The basic related properties of the developed operators are presented in detailed. Then the algorithm for MCGDM based on TOPSIS method for intuitionistic fuzzy rough Dombi averaging and geometric operators is presented. By applying accumulated geometric operator, the intuitionistic fuzzy rough numbers are converted into the intuitionistic fuzzy numbers. The massive outbreak of the pandemic COVID-19 promoted the challenging scenario for the world organizations including scientists, laboratories, and researchers to conduct special clinical treatment strategies to prevent the people from COVID-19 pandemic. Additionally, an illustrative example is proposed to solve MCGDM problem to diagnose the most severe patient of COVID-19 by applying TOPSIS. Finally, a comparative analysis of the developed model is presented with some existing methods which show the applicability and superiority of the developed model.

An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation: IMS

PurposeThis work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.Design/methodology/approachThe proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.FindingsThe proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.Practical implicationsThanks to the abnormal risk panel;human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.Originality/valueThe monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products' waste avoidance.

Performance of health plan operators: before and during the pandemic

Objective: To analyze the economic-financial and operational performance of Health Plan Operators (HPO), before (3Q2018-4Q2019) and during the pandemic (1Q2020-2Q2021) under the theory of institutional isomorphism. Method: This is descriptive documentary research that uses statistical techniques to compare medians (Wilcoxon and Kruskal-Wallis) and correlation (Spearman) between performance and socioeconomic indicators, considering the five Health Plan Operators (HPO) with the largest number of beneficiaries. Results: A statistically significant reduction was observed, after the beginning of the pandemic, for the indicators of profitability, expenses, cost variation, and financial result. On the other hand, there was a growth in current liquidity and an increase in the mean receipt timeframe of payments (CRPM) and event payment (PMPE). The performance comparison shows that in the pandemic, profitability and PMPE were no longer significantly different. As for the association between the variables, the unemployment rate shows the highest number of relationships with the performance indices. Theoretical/methodological contributions: Highlights the performance of the five largest OPS, considering the Sars-Cov-2 pandemic period, considering that these companies can be directly affected by the potential increase in demand for health care by their beneficiaries. By the theoretical prism of institutional isomorphism, these companies are references for the sector.

Post-Pandemic Sector-Based Investment Model Using Generalized Liouville–Caputo Type

In this article, Euler's technique was employed to solve the novel post-pandemic sector-based investment mathematical model. The solution was established within the framework of the new generalized Caputo-type fractional derivative for the system under consideration that serves as an example of the investment model. The mathematical investment model consists of a system of four fractional-order nonlinear differential equations of the generalized Liouville–Caputo type. Moreover, the existence and uniqueness of solutions for the above fractional order model under pandemic situations were investigated using the well-known Schauder and Banach fixed-point theorem technique. The stability analysis in the context of Ulam—Hyers and generalized Ulam—Hyers criteria was also discussed. Using the investment model under consideration, a new analysis was conducted. Figures that depict the behavior of the classes of the projected model were used to discuss the obtained results. The demonstrated results of the employed technique are extremely emphatic and simple to apply to the system of non-linear equations. When a generalized Liouville–Caputo fractional derivative parameter (ρ) is changed, the results are asymmetric. The current work can attest to the novel generalized Caputo-type fractional operator's suitability for use in mathematical epidemiology and real-world problems towards the future pandemic circumstances.

Multi-Criteria Decision-Making with Novel Pythagorean Fuzzy Aggregation Operators

Unpredictability and fuzziness coexist in decision-making analysis due to the complexity of the decision-making environment. "Pythagorean fuzzy numbers” (PFNs) outperform "intuitionistic fuzzy numbers” (IFNs) when dealing with unclear data. The "Pythagorean fuzzy set” (PFS) is a useful tool because it removes the restriction that the sum of membership degrees be less than or equal to one by substituting the square sum for the sum of membership degrees. This study proposes two aggregating operators (AOs). The recommended operators outperform the already specified PFN operators. The proposed operator is utilised in the multicriteria decision-making process to identify the best candidate for instruction (MCDM).

A study of quadratic Diophantine fuzzy sets with structural properties and their application in face mask detection during COVID-19

During the COVID-19 pandemic, identifying face masks with artificial intelligence was a crucial challenge for decision support systems. To address this challenge, we propose a quadratic Diophantine fuzzy decision-making model to rank artificial intelligence techniques for detecting masks, aiming to prevent the global spread of the disease. Our paper introduces the innovative concept of quadratic Diophantine fuzzy sets (QDFSs), which are advanced tools for modeling the uncertainty inherent in a given phenomenon. We investigate the structural properties of QDFSs and demonstrate that they generalize various fuzzy sets. In addition, we introduce essential algebraic operations, set-theoretical operations, and aggregation operators. Finally, we present a numerical case study that applies our proposed algorithms to select a unique face mask detection method and evaluate the effectiveness of our techniques. Our findings demonstrate the viability of our mask identification methodology during the COVID-19 outbreak. © 2023 the Author(s), licensee AIMS Press.

Numerical Simulation for a Hybrid Variable-Order Multi-Vaccination COVID-19 Mathematical Model

In this paper, a hybrid variable-order mathematical model for multi-vaccination COVID-19 is analyzed. The hybrid variable-order derivative is defined as a linear combination of the variable-order integral of Riemann–Liouville and the variable-order Caputo derivative. A symmetry parameter σ is presented in order to be consistent with the physical model problem. The existence, uniqueness, boundedness and positivity of the proposed model are given. Moreover, the stability of the proposed model is discussed. The theta finite difference method with the discretization of the hybrid variable-order operator is developed for solving numerically the model problem. This method can be explicit or fully implicit with a large stability region depending on values of the factor Θ. The convergence and stability analysis of the proposed method are proved. Moreover, the fourth order generalized Runge–Kutta method is also used to study the proposed model. Comparative studies and numerical examples are presented. We found that the proposed model is also more general than the model in the previous study;the results obtained by the proposed method are more stable than previous research in this area.

Fractional Optimal Control Model of SARS-CoV-2 (COVID-19) Disease in Ghana

Research focus on optimal control problems brought on by fractional differential equations has been extensively applied in practice. However, because they are still open ended and challenging, a number of problems with fractional mathematical modeling and problems with optimal control require additional study. Using fractional-order derivatives defined in the Atangana–Baleanu–Caputo sense, we alter the integer-order model that has been proposed in the literature. We prove the solution's existence, uniqueness, equilibrium points, fundamental reproduction number, and local stability of the equilibrium points. The operator's numerical approach was put into practice to obtain a numerical simulation to back up the analytical conclusions. Fractional optimum controls were incorporated into the model to identify the most efficient intervention strategies for controlling the disease. Utilizing actual data from Ghana for the months of March 2020 to March 2021, the model is validated. The simulation's results show that the fractional operator significantly affected each compartment and that the incidence rate of the population rose when v≥0.6. The examination of the most effective control technique discovered that social exclusion and vaccination were both very effective methods for halting the development of the illness.

Impact of COVID-19 pandemic on financial performance and financial failure risk in travel agency and tour operators activities sector

In this study, the effect of the Covid-19 pandemic on the financial performance and financial failure risk of the travel agency and tour operator activities sector in Turkey was examined. For this purpose, the financial performance of the sector and the risk of financial failure were analyzed using data from the real sector statistics of the Central Bank of the Republic of Turkey on the subs-sector of Travel Agencies and Tour Operators Activities for the years 2018, 2019 and 2020. While examining the financial performance of the sector by calculating liquidity ratios, asset utilization ratios, financial structure ratios and profitability ratios;financial failure risk was analyzed using Altman Z Score, Altman Z' Score, Fulmer H Score, Springate Score and Ohlson Score models. In the study, it was revealed that during the Covid-19 pandemic, the travel agency and tour operator activities sector experienced cash shortages, credit debts increased and, accordingly, its financial costs increased and losses were experienced. At the same time, it has been determined that the sector is exposed to the risk of financial failure during the Covid-19 pandemic.

Predictive model to determine the growth of mobile money transactions in Zambia using data mining techniques

Mobile money has been known to be a successful venture around the world especially so, for African countries due to the many limitations that traditional banks have like operations, expensive transaction costs and cumbersome process to open account to mention but a few. The presence of mobile money has not only allowed the unbanked population to have accounts but has also alleviated poverty for many rural communities. Zambia has seen an increase of mobile money accounts and COVID-19 has exacerbated this increase. Therefore, this paper sought to determine data mining algorithm best predicts mobile money transaction growth. This paper was quantitative in nature and used aggregated monthly mobile money data (from Zambian mobile network operators) from 2013 to 2020 as its sample which was collected from Bank of Zambia and Zambia Information Communications and Technology Authority. The paper further used WEKA data mining tool for data analysis following the Cross-Industrial Standard Process for data mining guidelines. The performance from best to least is K-nearest neighbour, random forest, support vector machines, multilayer perceptron and linear regression. The predictions from data mining techniques can be deployed to predict growth of mobile money and hence be used in financial inclusion policy formulation and other strategies that can further improve service delivery by mobile network operators.

Fractional-type integral operators and their applications to trend estimation of COVID-19

In this paper, we construct a novel family of fractional-type integral operators of a function (Formula presented.) by replacing sample values (Formula presented.) with the fractional mean values of that function. We give some explicit formulas for higher order moments of the proposed operators and investigate some approximation properties. We also define the fractional variants of Mirakyan–Favard–Szász and Baskakov-type operators and calculate the higher order moments of these operators. We give an explicit formula for fractional derivatives of proposed operators with the help of the Caputo-type fractional derivative Furthermore, several graphical and numerical results are presented in detail to demonstrate the accuracy, applicability, and validity of the proposed operators. Finally, an illustrative real-world example associated with the recent trend of Covid-19 has been investigated to demonstrate the modeling capabilities of fractional-type integral operators. © 2023 John Wiley & Sons, Ltd.

Generalized plithogenic whole hypersoft set, PFHSS-Matrix, operators and applications as COVID-19 data structures

This article is a preliminary draft for initiating and commencing a new pioneer dimension of expression. To deal with higher-dimensional data or information flowing in this modern era of information technology and artificial intelligence, some innovative super algebraic structures are essential to be formulated. In this paper, we have introduced such matrices that have multiple layers and clusters of layers to portray multi-dimensional data or massively dispersed information of the plithogenic universe made up of numerous subjects their attributes, and sub-attributes. For grasping that field of parallel information, events, and realities flowing from the micro to the macro level of universes, we have constructed hypersoft and hyper-super-soft matrices in a Plithogenic Fuzzy environment. These Matrices classify the non-physical attributes by accumulating the physical subjects and further sort the physical subjects by accumulating their non-physical attributes. We presented them as Plithogenic Attributive Subjectively Whole Hyper-Super-Soft-Matrix (PASWHSS-Matrix) and Plithogenic Subjective Attributively Whole-Hyper-Super-Soft-Matrix (PSAWHSS-Matrix). Several types of views and level-layers of these matrices are described. In addition, some local aggregation operators for Plithogenic Fuzzy Hypersoft Set (PPFHS-Set) are developed. Finally, few applications of these matrices and operators are used as numerical examples of COVID-19 data structures. [ABSTRACT FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Maritime container terminal infrastructure, network corporatization, and global terminal operators: Implications for international business policy

Maritime shipping lines and global terminal operators have benefited from economies of scale to expand geographically and functionally their infrastructure, leading to a corporatized network. Terminal operators are key asset managers seeking value creation by expanding the global maritime container terminal infrastructure network. While corporatization has systematically ensured that terminal capacity was created to accommodate the rise in global trade volumes, the network hit its boundaries when confronted with COVID-19 induced global supply chain disruptions. This paper provides a better understanding of the importance of infrastructure and observed corporatization as a framework for explaining economic processes, notably when transport infrastructures are extensive and capital-intensive. The structure of the global container shipping network is analyzed to unveil the realities of liner service networks operated by shipping lines, and the market structure and consolidation in container shipping and terminal operations. The discussion on the corporatization of the global maritime infrastructure network for container handling is embedded in international business literature. This study also extracts the main implications of the current structure and governance of the global maritime infrastructure network for international business policy, with a particular focus on the current market structure and network resilience.

Exploring the impact of COVID-19 on the travel intermediaries in India: mapping trends and strategies

The COVID-19 outbreak has challenged the global tourism sector, and it has posed a significant threat to the existence of tourism business across the globe, especially in emerging economies. This study examines the effect of the COVID-19 pandemic on travel intermediary businesses in India and their strategies to overcome the present predicament. The study draws on the interviews with 15 executives of selected travel intermediary firms located around the country. The findings emphasise the economic downturn, customer retention policies, resilience strategies and current shifts in the industry. The outcomes also identify the recent tendency towards the traditional travel booking/holiday-booking trend towards traditional intermediaries. This research contributes to a better understanding and implementation of business continuity methods in tourist studies, and its results have practical implications for administrators and governments. Copyright © 2022 Inderscience Enterprises Ltd.