To mitigate these shortcomings, this paper employs an aggregation method grounded in prospect theory and consensus degree (APC) to capture the subjective preferences of decision-makers. The second issue is resolved by the inclusion of APC in the optimistic and pessimistic CEM algorithms. The double-frontier CEM, aggregated using APC (DAPC), is formed by the amalgamation of two different perspectives at the end of the process. A real-world application of DAPC evaluates the performance of 17 Iranian airlines, using three input variables and four output measures. narrative medicine The findings show that the DMs' preferences affect both viewpoints in a significant manner. The ranking results of more than half the airlines exhibit a substantial divergence, based on the two points of view. Through its handling of these divergences, DAPC, as the findings confirm, produces more complete ranking results by integrating and evaluating both subjective viewpoints simultaneously. The research also demonstrates the level to which each airline's DAPC effectiveness is influenced by each opinion. The performance of IRA is most affected by an optimistic perspective (8092%), whereas the performance of IRZ is primarily determined by a pessimistic point of view (7345%). KIS, the most efficient airline, is followed closely by PYA. Unlike other airlines, IRA has the lowest efficiency rating, followed by IRC in terms of performance.

This research project investigates a supply chain, a collaboration between a manufacturer and a retailer. A nationally recognized brand (NB) product is manufactured, while the retailer sells both the NB product and their own premium store brand (PSB) item. Innovation in product quality allows the manufacturer to effectively compete with the retailer over time. NB product loyalty is anticipated to benefit from both advertising and improved quality over time. We present four scenarios, namely: (1) Decentralization (D), (2) Centralization (C), (3) Coordination through a revenue-sharing contract (RSH), and (4) Coordination through a two-part tariff contract (TPT). Based on a numerical example, parametric analyses are conducted on a newly developed Stackelberg differential game model, generating actionable managerial insights. Retailers benefit financially from the co-sale of PSB and NB products, according to our research.
Additional materials for the online document are presented at the cited website: 101007/s10479-023-05372-9.
At 101007/s10479-023-05372-9, supplemental content accompanies the online version of the publication.

Forecasting carbon prices with accuracy enables more effective allocation of carbon emissions, thereby maintaining a sustainable balance between economic progress and the possible repercussions of climate change. We propose, in this paper, a new two-stage forecasting framework for prices across international carbon markets, built upon decomposition and re-estimation methods. In the EU, the Emissions Trading System (ETS), and China's five principal pilot programs, constitute our study focus for the period between May 2014 and January 2022. Employing Singular Spectrum Analysis (SSA), the raw carbon price data is initially segmented into various sub-elements, which are then synthesized into trend and periodic factors. After the subsequences have been decomposed, a subsequent application of six machine learning and deep learning methods allows the data to be assembled and consequently enables the prediction of the final carbon prices. For forecasting carbon prices, specifically within the European ETS and comparable systems in China, Support Vector Regression (SSA-SVR) and Least Squares Support Vector Regression (SSA-LSSVR) demonstrate superior performance compared to other machine learning models. An intriguing outcome of our experiments is that sophisticated prediction models for carbon prices exhibit less than optimal performance. Although the COVID-19 pandemic and macroeconomic elements, as well as the cost of other forms of energy, have been considered, our framework continues to yield effective results.

A university's instructional program is fundamentally defined by its course timetables. Timetable quality, though subjectively assessed by students and lecturers based on personal preferences, is also evaluated by collective standards, including balanced workloads and the prevention of excessive idle time. Individual student preferences and the incorporation of online courses are significant factors that contribute to a crucial challenge and opportunity in the design of curriculum-based timetables, especially as these options are necessary for educational flexibility as seen during pandemic periods. Lectures and tutorials, when structured in a large/small format, can be further optimized in terms of both overall scheduling and individual student assignments to tutorial groups. In this paper, we detail a multi-level approach to university timetabling. At the strategic level, a lecture and tutorial plan is established for a collection of study programs; operationally, individual timetables are constructed for each student, integrating the lecture schedule with a selection of tutorials from the tutorial plan, prioritizing individual student choices. To enhance lecture, tutorial, and individual schedules, we employ a matheuristic incorporating a genetic algorithm within a mathematical programming-based planning framework, thereby optimizing the overall university program's timetable to achieve balanced performance criteria. Given that assessing the fitness function necessitates the complete execution of the planning procedure, we offer a surrogate representation, an artificial neural network metamodel. The computational outcomes demonstrate the procedure's aptitude for producing high-quality schedules.

From the perspective of the Atangana-Baleanu fractional model, incorporating acquired immunity, the transmission of COVID-19 is investigated. Harmonic incidence mean-type procedures are intended for complete elimination of exposed and infected populations in a finite timeframe. The next-generation matrix serves as the foundation for determining the reproduction number. The Castillo-Chavez approach facilitates the achievement of a globally disease-free equilibrium point. The global stability of the endemic equilibrium is demonstrable through the use of the additive compound matrix. Leveraging Pontryagin's maximum principle, we introduce three control parameters to formulate the optimal control strategies. Analytical solutions for fractional-order derivatives can be obtained using the Laplace transform. An enhanced understanding of transmission dynamics resulted from the examination of graphical outcomes.

The paper constructs a nonlocal dispersal epidemic model incorporating air pollution to reflect the wide-reaching impact of pollutant dispersal and human migration, where the transmission rate depends directly on pollutant concentration levels. In this research, the global existence and uniqueness of positive solutions are verified, and the basic reproduction number, R0, is defined. Global dynamics of the uniformly persistent disease, R01, are simultaneously investigated. A numerical method has been devised to approximate the value of R0. Verification of theoretical conclusions is achieved through the use of illustrative examples, highlighting how dispersal rate affects the basic reproduction number, R0.

Employing both field and lab data, we establish a link between leader charisma and actions taken to mitigate the spread of COVID-19. A deep neural network algorithm was utilized to code a panel of U.S. governor speeches, identifying charisma signals. Abiraterone supplier The model utilizes citizen smartphone data to illuminate variations in stay-at-home behavior, highlighting a powerful effect of charisma signaling on increased stay-at-home behavior, unaffected by state-level citizen political affiliations or governor's party allegiance. The results were notably influenced by Republican governors with a particularly high charisma rating, demonstrating a greater effect in comparison to the results obtained with Democratic governors under equivalent circumstances. Analysis of governor speeches suggests that a one standard deviation improvement in charismatic communication could potentially have saved 5,350 lives from February 28, 2020, through May 14, 2020. The implications of these results are that political leaders should contemplate augmenting policy responses to pandemics or similar public health crises with supplementary soft-power mechanisms, including the teachable quality of charisma, especially for populations requiring a persuasive approach.

Vaccination's ability to provide protection against SARS-CoV-2 infection differs based on the vaccine's type, the timeframe following vaccination or infection, and the specific variation of the SARS-CoV-2 virus. Our prospective observational study investigated the immunogenicity of a booster vaccination with AZD1222, administered after two initial doses of CoronaVac, contrasting this with a group of individuals who acquired SARS-CoV-2 infection after having received two CoronaVac doses. Cerebrospinal fluid biomarkers Immunity against both wild-type and the Omicron variant (BA.1) at the 3- and 6-month mark post-infection or booster was assessed via a surrogate virus neutralization test (sVNT). Forty-one of the 89 participants comprised the infection group, while 48 were in the booster group. Three months post-infection or post-booster vaccination, the median sVNT (interquartile range) against the wild-type virus was 9787% (9757%-9793%) and 9765% (9538%-9800%), respectively, while the corresponding sVNT against Omicron was 188% (0%-4710%) and 2446 (1169-3547%), respectively (p-values of 0.066 and 0.072, respectively). At a six-month follow-up, the median sVNT against wild-type was 9768% (9586%-9792%) in the infection group, exceeding the 947% (9538%-9800%) in the booster group (p=0.003). Results from the three-month evaluation indicate no appreciable difference in immune responses towards wild-type and Omicron between the two groups studied. The infection group, however, demonstrated improved immunity at the six-month mark in contrast to the booster group.