The dataset's analysis is based on the period between 2007 and 2020. The study's progression is governed by a three-part methodological framework. We commence by considering the network of scientific organizations, establishing a connection between two institutions that participate in the same funded research project. This endeavor leads to the construction of intricate, yearly networks. Four nodal centrality measures are computed by us, each with details that are both relevant and informative. read more We proceed by applying a rank-size procedure to each network and each centrality measure, analyzing four meaningful parametric curve categories to fit the ranked data sets. By the end of this step, the best-fitting curve and calibrated parameters are derived. We employ a clustering procedure, built upon the best-fit curves of ranked data, as our third step to distinguish the recurring patterns and discrepancies in the yearly activities of research and scientific institutions. The combined use of the three methodological approaches offers a transparent perspective on recent European research activities.

Having relied on offshore outsourcing for many years, companies are now re-plotting their global production strategy across diverse locations. Following the protracted supply chain disruptions caused by the COVID-19 pandemic over the last several years, numerous multinational companies are now actively considering bringing their operations back to their domestic locations (reshoring). Concurrently, the U.S. government is putting forward tax penalties as a method to encourage corporations to relocate production to domestic facilities. This research explores the modifications to offshoring and reshoring production strategies by global supply chains, comparing two scenarios: (1) current corporate tax regimes; (2) proposed tax penalty regimes. We investigate cost variations, tax frameworks, market entry limitations, and production uncertainties to determine the factors influencing multinational companies' decisions to reshore manufacturing. The proposed tax penalty strongly suggests a higher likelihood of multinational companies transferring production from their primary foreign country to alternative locations with lower production costs. Based on our analytical findings and numerical simulations, reshoring is a rare event, appearing only in situations where foreign production costs are equivalent to or very close to those of the domestic country. Along with considering potential national tax reforms, we delve into the influence of the G7's proposed global minimum tax rate on companies' decisions regarding relocating operations domestically or abroad.

Based on the conventional credit risk structured model's projections, risky asset values tend to follow a pattern of geometric Brownian motion. Conversely, the value of risky assets continues to be non-continuous and dynamic, fluctuating in response to prevailing conditions. It is not possible to precisely assess the true Knight Uncertainty risks in financial marketplaces via a single probability measure. Within this backdrop, the current research work examines a structural credit risk model applicable to the Levy market, focusing on Knight uncertainty. Employing the Levy-Laplace exponent, this study developed a dynamic pricing model, yielding price intervals for default probability, stock value, and enterprise bond value. The study aimed to formulate clear, explicit solutions to the three previously-discussed value processes, predicated on the assumption of a log-normal jump process. A numerical analysis was undertaken at the study's conclusion to evaluate the critical role of Knight Uncertainty in determining default probability and firm stock valuation.

Humanitarian operations have yet to embrace delivery drones as a systematic method, but these drones hold promise for significantly boosting the efficiency and efficacy of future delivery systems. In light of this, we analyze the impact of factors related to the implementation of delivery drones in humanitarian logistics operations by service providers. A model illustrating potential obstacles to adoption and development is formulated based on the Technology Acceptance Model, considering security, perceived usefulness, ease of use, and attitude as influential factors impacting the intention to utilize the technology. The validation of the model was undertaken using empirical data compiled from 103 respondents of the 10 top logistics companies located in China, between May and August 2016. A survey aimed to explore the reasons behind the adoption or non-adoption of delivery drones. The adoption rate of drone delivery within the logistics sector is directly correlated to the user-friendliness and the proactive security measures taken to protect the drone, the package, and the recipient. This initial investigation into drone usage for humanitarian logistics, the first of its type, considers operational, supply chain, and behavioral elements.

The widespread nature of COVID-19 has brought numerous challenges and predicaments to healthcare systems globally. Because of the large influx of patients and the constrained resources available within the healthcare system, a variety of difficulties in hospitalizing patients have been observed. Insufficient medical provision, resulting from these limitations, might lead to a heightened number of COVID-19 fatalities. They can also contribute to increasing the risk of infection within the broader community. A two-phased design for a hospital supply chain, encompassing existing and temporary facilities, forms the basis of this investigation. The focus encompasses efficient distribution of medications and medical supplies, and the management of hospital waste. Due to the unpredictable volume of future patients, the initial phase involves employing trained artificial neural networks to predict patient numbers in subsequent periods, thereby producing various possible scenarios based on historical data. Employing the K-Means clustering algorithm results in a reduction of these scenarios. In the second phase, a two-stage stochastic programming model, accounting for multiple objectives and time periods, is developed. This model uses the scenarios from the preceding phase, reflecting uncertainty and disruptions in facilities. The proposed model seeks to accomplish the maximization of the minimum allocation-to-demand ratio, the minimization of aggregate disease transmission risk, and the minimization of the total time taken for transportation. Additionally, a practical case study is scrutinized in Tehran, the capital of Iran. Temporary facility locations, as shown by the results, concentrated in areas with high population density and a scarcity of nearby services. Of the temporary facilities available, temporary hospitals can absorb a maximum of 26% of the total demand, which exerts significant pressure on the existing hospital infrastructure, potentially resulting in their decommissioning. The findings further suggested that temporary facilities allow for the preservation of an ideal allocation-to-demand ratio, even during disruptions. First, our analysis examines (1) the mistakes in demand forecasting and the generated scenarios, (2) the effect of demand parameters on the allocation-to-demand ratio, total time, and total risk, (3) how strategies utilizing temporary hospitals deal with unexpected demand changes, (4) the impact of facility disruptions on the network of the supply chain.

In an e-marketplace, we analyze the pricing and quality strategies of two competing firms, taking into account customer reviews. Through the development of two-phase game-theoretic models and the examination of resulting equilibria, we evaluate the best course of action among diverse product strategies: static strategies, price adjustments, quality level modifications, and dynamic adjustments to both price and quality. sandwich immunoassay Our findings highlight the effect of online customer reviews, prompting companies to improve product quality and offer lower prices in the early stages, but then to decrease quality and charge higher prices in later phases. Moreover, firms should contemplate optimal product strategies, conditional on the influence of customers' personalized appraisals of product quality, as communicated through disclosed product information, on the overall perceived product value and consumer ambiguity about the product's suitability. Our comparative study suggests that the dual-element dynamic strategy has a greater potential for surpassing other strategies financially. Moreover, our models explore how the best quality and pricing choices alter when rival companies possess different starting online customer reviews. Further examination suggests that a dynamic pricing strategy may produce superior financial results relative to a dynamic quality strategy, which contradicts the findings of the basic analysis. multidrug-resistant infection The dual-element dynamic strategy, the dynamic quality strategy, the integrated approach of dual-element dynamic strategy and dynamic pricing, and finally, the dynamic pricing strategy, should be sequentially implemented by firms, given the amplified role of customer assessments of product quality in determining overall perceived utility and the increased weight given by later customers to their own assessments.

The cross-efficiency method (CEM), a technique drawing on data envelopment analysis, empowers policymakers with a strong tool for evaluating the efficiency of decision-making units. However, the traditional CEM presents two significant shortcomings. The model's failure to acknowledge the individual preferences of decision-makers (DMs) prevents it from portraying the importance of self-evaluation in contrast to evaluations performed by peers. In the second place, the evaluation process overlooks the vital role played by the anti-efficient frontier. This study's goal is to incorporate prospect theory into the double-frontier CEM, thus tackling the current inadequacies and taking into account the varying inclinations of decision-makers toward gains and losses.