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This paper aims to prepare a new donor–π–acceptor (D–π–A) and acceptor–π– D–π–A (A–π–D–π–A) phenothiazine (PTZ) in conjugation with vinyl isophorone (PTZ-1 and PTZ-2) were designed and their molecular shape, electrical structures and characteristics have been explored using the density functional theory (DFT). The results satisfactorily explain that the higher conjugative effect resulted in a smaller high occupied molecular orbital–lowest unoccupied molecular orbital gap (Eg). Both compounds show intramolecular charge transfer (ICT) transitions in the ultraviolet (UV)–visible range, with a bathochromic shift and higher absorption oscillator strength, as determined by DFT calculations.

The produced PTZ-1 and PTZ-2 sensors were characterized using various spectroscopic methods, including Fourier-transform infrared spectroscopy and nuclear magnetic resonance spectroscopy (¹H/¹³CNMR). UV–visible absorbance spectra of the generated D–π–A PTZ-1 and A–π–D–π–A PTZ-2 dyes were explored in different solvents of changeable polarities to illustrate positive solvatochromism correlated to intramolecular charge transfer.

The emission spectra of PTZ-1 and PTZ-2 showed strong solvent-dependent band intensity and wavelength. Stokes shifts were monitored to increase with the increase of the solvent polarity up to 4122 cm⁻¹ for the most polar solvent. Linear energy-solvation relationship was applied to inspect solvent-dependent Stokes shifting. Quantum yield (ф) of PTZ-1 and PTZ-2 was also explored. The maximum UV–visible absorbance wavelengths were detected at 417 and 419 nm, whereas the fluorescence intensity was monitored at 586 and 588 nm.

The PTZ-1 and PTZ-2 dyes leading to colorimetric and emission spectral changes together with a color shift from yellow to red.

First-generation college students in the United States are a unique demographic that is often characterized by the institutions that serve them with a risk-laden and deficit-based model. However, our analysis of the transcripts of open-ended, semi-structured interviews with 22 “first-gen” respondents suggests they are actively deft, agentic, self-determining parties to processes of identity construction that are both externally imposed and potentially stigmatizing, as well as exemplars of survivance and determination. We deploy a grounded theory approach to an open-coding process, modeled after the extended case method, while viewing our data through a novel synthesis of the dual theoretical lenses of structural and radical/structural symbolic interactionism and intersectional/standpoint feminist traditions, in order to reveal the complex, unfolding, active strategies students used to make sense of their obstacles, successes, co-created identities, and distinctive institutional encounters. We find that contrary to the dictates of prevailing paradigms, identity-building among first-gens is an incremental and bidirectional process through which students actively perceive and engage existing power structures to persist and even thrive amid incredibly trying, challenging, distressing, and even traumatic circumstances. Our findings suggest that successful institutional interventional strategies designed to serve this functionally unique student population (and particularly those tailored to the COVID-moment) would do well to listen deeply to their voices, consider the secondary consequences of “protectionary” policies as potentially more harmful than helpful, and fundamentally, to reexamine the presumption that such students present just institutional risk and vulnerability, but also present a valuable addition to university environments, due to the unique perspective and broader scale of vision their experiences afford them.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”

Our result of this paper aims to indicate that the beta pricing formula could be applied in a long-term model setting as well.

In this paper, we show that the capital asset pricing model can be derived from a three-period general equilibrium model.

We show that our extended model yields a Pareto efficient outcome.

The capital asset pricing model (CAPM) model can be used for pricing long-lived assets.

Long-term modelling and sustainability can be modelled in our setting.

Our results were only known for two periods. The extension to 3 periods opens up a large scope of applicational possibilities in asset pricing, behavioural analysis and long-term efficiency.

We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

The design goal for the tracking interval of high-speed railway trains in China is 3 min, but it is difficult to achieve, and it is widely believed that it is mainly limited by the tracking interval of train arrivals. If the train arrival tracking interval can be compressed, it will be beneficial for China's high-speed railway to achieve a 3-min train tracking interval. The goal of this article is to study how to compress the train arrival tracking interval.

By simulating the process of dense train groups arriving at the station and stopping, the headway between train arrivals at the station was calculated, and the pattern of train arrival headway was obtained, changing the traditional understanding that the train arrival headway is considered the main factor limiting the headway of trains.

When the running speed of trains is high, the headway between trains is short, the length of the station approach throat area is considerable and frequent train arrivals at the station, the arrival headway for the first group or several groups of trains will exceed the headway, but the subsequent sets of trains will have a headway equal to the arrival headway. This convergence characteristic is obtained by appropriately increasing the running time.

According to this pattern, there is no need to overly emphasize the impact of train arrival headway on the headway. This plays an important role in compressing train headway and improving high-speed railway capacity.

Racial stigma and racial criminalization have been centralizing pillars of the construction of Blackness in the United States. Taking such systemic injustice and racism as a given, then question then becomes how these macro-level arrangements are reflected in micro-level processes. This work uses radical interactionism and stigma theory to explore the potential implications for racialized identity construction and the development of “criminalized subjectivity” among Black undergraduate students at a predominately white university in the Midwest. I use semistructured interviews to explore the implications of racial stigma and criminalization on micro-level identity construction and how understandings of these issues can change across space and over the course of one's life. Findings demonstrate that Black university students are keenly aware of this particular stigma and its consequences in increasingly complex ways from the time they are school-aged children. They were aware of this stigma as a social fact but did not internalize it as a true reflection of themselves; said internalization was thwarted through strong self-concept and racial socialization. This increasingly complex awareness is also informed by an intersectional lens for some interviewees. I argue not only that the concept of stigma must be explicitly placed within these larger systems but also that understanding and identity-building are both rooted in ever-evolving processes of interaction and meaning-making. This research contributes to scholarship that applies a critical lens to Goffmanian stigma rooted in Black sociology and criminology and from the perspectives of the stigmatized themselves.

The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours threshold is met. After receiving maintenance service, the model ignores previous flying hours and the aircraft can keep on flying until the threshold value is reached again. Moreover, the model considers aircraft age and efficiency to assign them to flights.

The aircraft maintenance routing problem (AMRP), as one of the most important problems in the aviation industry, determines the optimal route for each aircraft along with meeting maintenance requirements. This paper presents a bi-objective mixed-integer programming model for AMRP in which several criteria such as aircraft efficiency and ferrying flights are considered.

As the solution approaches, epsilon-constraint method and a non-dominated sorting genetic algorithm (NSGA-II), including a new initializing algorithm, are used. To verify the efficiency of NSGA-II, 31 test problems in different scales are solved using NSGA-II and GAMS. The results show that the optimality gap in NSGA-II is less than 0.06%. Finally, the model was solved based on real data of American Eagle Airlines extracted from Kaggle datasets.

The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.