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The purpose of this study is to analyze the first major federal education policy, the Elementary and Secondary Education Act of 1965, and the most recent federal policy, the Every Student Succeeds Act of 2015, through a Black critical theory (BlackCrit) lens to understand better how these educational policies have served as antiblack projects. Furthermore, this study locates examples of educational Freedom Dreams in the past and present to imagine new possibilities in Black education.

By analyzing education policy documents and history through BlackCrit methods, the authors expose how education policy is inherently an antiblack project. Freedom Dreams catalyze possibilities for future education.

The data confirms that while these policies purport equity and accountability in education, they, in practice, exacerbate antiblackness through inequitably mandated standardized testing, distributed funding and policed schooling.

This paper applies BlackCrit analysis of education policy to reimagine Black educational possibilities.

This research aims to test the simultaneous effects of the perceived threat of COVID-19, e-health literacy, e-health access barrier, loyalty toward healthy foods in general, loyalty toward functional foods, the affordability of healthy foods in general and the affordability of functional foods on health-related quality of life (HrQoL) during the COVID-19 pandemic.

A survey with 400 respondents in Banten, Indonesia, was performed. The data were analyzed using multiple regression analysis.

The results of the research showed that HrQoL during the COVID-19 pandemic was positively affected by e-health literacy and the affordability of healthy foods in general while negatively affected by the e-health access barrier, the perceived threat of COVID-19 and loyalty toward healthy foods in general. Furthermore, HrQoL was not influenced by loyalty toward and affordability of functional foods.

This research was conducted in Banten. Due to the operational limitations during the COVID-19 pandemic, this research used a purposive sampling technique. Therefore, the next research should retest the model in different contexts and locations.

To improve HrQoL during the COVID-19 pandemic, citizens need to be educated on finding and utilizing credible online health information during the COVID-19 pandemic. Governments and health service providers should also strive to offer ease of access to credible online health information. Furthermore, the affordability of healthy foods, in general, should be managed well.

A few studies on HrQoL during COVID-19 pandemic were performed. However, there is a lack of paper that examines the role of food customer loyalty and affordability in a model of HrQoL during the COVID-19 pandemic. To the best of the authors’ knowledge, this paper is the first that involved and tested the role of food customer loyalty and affordability in a model of HrQoL during the COVID-19 pandemic.

In recent years, using aberration-corrected transmission electron microscopy, the authors have achieved precisely detecting the structural evolution of passive film as well as its interface zone at atomic scale. The purpose of this paper aims to make a brief review to show the authors’ new understanding and perspective on the issue of critical factors determining stability of passive film of Fe-Cr alloy.

The introduction of single crystal enabled the authors to obtain a distinct metal/passive film interface and better characterize the structure of the interface region. The authors use aberration-corrected TEM to conduct cross-sectional observation and directly capture the details across the entire film at a high spatial and energy resolution.

Apart from the passive film itself, the interface zone, including metal/film (Me/F) interface and the adjacent metal side, is also the site which is attacked. Accordingly, the nature of the interface zone, such as microstructure, composition and atomic configuration, is one of the critical factors determining the stability of passive film.

Deciphering the critical factors determining the stability of passive film is of great significance and has been a fundamental issue in corrosion science. Great attention has been paid to the nature of the passive film itself. In contrast, the possible role of the interface between the passive film and the metal is rarely taken into account. Based on the advanced analytical tool with high spatial resolution, the authors have specified the significant role of interface structures on the macro-scale stability of passive film.

This paper provides a structural model to value startup companies and determine the optimal level of research and development (R&D) spending by these companies.

This paper describes a new variant of float-the-money options, which can act as a financial instrument for financing R&D expenses for a specific time horizon or development stage, allowing the investor to share in the startup's value appreciation over that duration. Another innovation of this paper is that it develops a structural model for evaluating optimal level of R&D spending over a given time horizon. The paper deploys the Gompertz-Cox model for the R&D project outcomes, which facilitates investigation of how increased level of R&D input can enhance the company's value growth.

The author first introduces a time-varying drift term into standard Black-Scholes model to account for the varying growth rates of the startup at different stages, and the author interprets venture capital's investment in the startup as a “float-the-money” option. The author then incorporates the probabilities of startup failures at multiple stages into their financial valuation. The author gets a closed-form pricing formula for the contingent option of value appreciation. Finally, the author utilizes Cox proportional hazards model to analyze the optimal level of R&D input that maximizes the return on investment.

The integrated contingent claims model links the change in the financial valuation of startups with the incremental R&D spending. The Gompertz-Cox contingency model for R&D success rate is used to quantify the optimal level of R&D input. This model assumption may be simplistic, but nevertheless illustrative.

Once supplemented with actual transaction data, the model can serve as a reference benchmark valuation of new project deals and previously invested projects seeking exit.

The integrated structural model can potentially have much wider applications beyond valuation of startup companies. For instance, in valuing a company's risk management, the level of R&D spending in the model can be replaced by the company's budget for risk management. As another promising application, in evaluating a country's economic growth rate in the face of rising climate risks, the level of R&D spending in this paper can be replaced by a country's investment in addressing climate risks.

This paper is the first to develop an integrated valuation model for startups by combining the real-world R&D project contingencies with risk-neutral valuation of the potential payoffs.

In vehicular ad hoc networks (VANETs), the information transmitted is broadcast in a free access environment. Therefore, VANETs are vulnerable against attacks that can directly perturb the performance of the networks and then provoke big fall of capability. Black hole attack is an example such attack, where the attacker node pretends that having the shortest path to the destination node and then drops the packets. This paper aims to present a new method to detect the black hole attack in real-time in a VANET network.

This method is based on capability indicators that are widely used in industrial production processes. If the different capability indicators are greater than 1.33 and the stability ratio (S_r) is greater than 75%, the network is stable and the vehicles are communicating in an environment without the black hole attack. When the malicious nodes representing the black hole attacks are activated one by one, the fall of capability becomes more visible and the network is unstable, out of control and unmanaged, due to the presence of the attacks. The simulations were conducted using NS-3 for the network simulation and simulation of urban mobility for generating the mobility model.

The proposed mechanism does not impose significant overheads or extensive modifications in the standard Institute of Electrical and Electronics Engineers 802.11p or in the routing protocols. In addition, it can be implemented at any receiving node which allows identifying malicious nodes in real-time. The simulation results demonstrated the effectiveness of proposed scheme to detect the impact of the attack very early, especially with the use of the short-term capability indicators (Cp, Cpk and Cpm) of each performance metrics (throughput and packet loss ratio), which are more efficient at detecting quickly and very early the small deviations over a very short time. This study also calculated another indicator of network stability which is S_r, which allows to make a final decision if the network is under control and that the vehicles are communicating in an environment without the black hole attack.

According to the best of the authors’ knowledge, the method, using capability indicators for detecting the black hole attack in VANETs, has not been presented previously in the literature.

The purpose of this study is to propose a methodological approach for modeling catastrophic consequences caused by black swan events, based on complexity science, and framed on Feyerabend’s anarchistic theory of knowledge. An empirical application is presented to illustrate the proposed approach.

Thom’s nonlinear differential equations of morphogenesis are used to develop a theoretical model of the impact of catastrophes on international business (IB). The model is then estimated using real-world data on the performance of multinational airlines during the SARS-CoV-2 (COVID-19) pandemic.

The catastrophe model exhibits a remarkable capability to simultaneously capture complex linear and nonlinear relationships. Through empirical estimations and simulations, this approach enables the analysis of IB phenomena under normal conditions, as well as during black swan events.

To the best of the author’s knowledge, this study is the first attempt to estimate the impact of black swan events in IB using a catastrophe model grounded in complexity theory. The proposed model successfully integrates the abrupt and profound effects of catastrophes on multinational corporations, offering a critical perspective on the theoretical and practical use of complexity science in IB.

Financial mathematics is one of the most rapidly evolving fields in today’s banking and cooperative industries. In the current study, a new fractional differentiation operator with a nonsingular kernel based on the Robotnov fractional exponential function (RFEF) is considered for the Black–Scholes model, which is the most important model in finance. For simulations, homotopy perturbation and the Laplace transform are used and the obtained solutions are expressed in terms of the generalized Mittag-Leffler function (MLF).

The homotopy perturbation method (HPM) with the help of the Laplace transform is presented here to check the behaviours of the solutions of the Black–Scholes model. HPM is well known for its accuracy and simplicity.

In this attempt, the exact solutions to a famous financial market problem, namely, the BS option pricing model, are obtained using homotopy perturbation and the LT method, where the fractional derivative is taken in a new YAC sense. We obtained solutions for each financial market problem in terms of the generalized Mittag-Leffler function.

The Black–Scholes model is presented using a new kind of operator, the Yang-Abdel-Aty-Cattani (YAC) operator. That is a new concept. The revised model is solved using a well-known semi-analytic technique, the homotopy perturbation method (HPM), with the help of the Laplace transform. Also, the obtained solutions are compared with the exact solutions to prove the effectiveness of the proposed work. The different characteristics of the solutions are investigated for different values of fractional-order derivatives.

Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch.

In this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”.

The results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented.

Grey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking.

Compared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.

In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.

The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.

In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.