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This paper presents the Edge Load Management and Optimization through Pseudoflow Prediction (ELMOPP) algorithm, which aims to solve problems detailed in previous algorithms; through machine learning with nested long short-term memory (NLSTM) modules and graph theory, the algorithm attempts to predict the near future using past data and traffic patterns to inform its real-time decisions and better mitigate traffic by predicting future traffic flow based on past flow and using those predictions to both maximize present traffic flow and decrease future traffic congestion.

ELMOPP was tested against the ITLC and OAF traffic management algorithms using a simulation modeled after the one presented in the ITLC paper, a single-intersection simulation.

The collected data supports the conclusion that ELMOPP statistically significantly outperforms both algorithms in throughput rate, a measure of how many vehicles are able to exit inroads every second.

Furthermore, while ITLC and OAF require the use of GPS transponders and GPS, speed sensors and radio, respectively, ELMOPP only uses traffic light camera footage, something that is almost always readily available in contrast to GPS and speed sensors.

The process of knowledge creation is recognized as an essential process for organizational learning and innovation. Creating knowledge to solve the problems and complexities of today's world is like opening a black box. Hence, the higher education system and universities are exploring ways to overcome the complexities and cope with global changes. In this regard, interdisciplinary collaborations and activities are crucial in creating knowledge and innovation to counter these changes. This study aimed to know the experiences of Shiraz university interdisciplinary researchers in the field of humanities and also design and explain the conceptual model of knowledge creation in interdisciplinary research teams in the field of humanities.

In this qualitative research, grounded theory was implemented based on Strauss and Corbin's systematic approach. The sampling method was purposeful, and the participants included sixteen faculty members of shiraz university who had at least one experience of performing an interdisciplinary activity in one of the humanities fields. The first participant was selected as a pilot, and the rest were selected by snowball sampling. Semi-structured interviews were also used to collect data and continued until theoretical saturation was attained. After collecting the available information and interviewing the people, the data were organized and analyzed in three stages, open coding, axial coding, and selective coding, using the proposed framework of Strauss and Corbin. Finally, the researcher reached a final and meaningful categorization.

In this research, the results were presented as a paradigm model of knowledge creation in the interdisciplinary research teams in the field of humanities. The paradigm model of the study consists of causal factors (internal and external factors), main categories (specialized competencies, scientific discourse, understanding of knowledge domains), strategies (structuring and synchronizing), context (individual and organizational), interfering factors (leadership, industry, and society), and consequences (individual and group achievement).

The present study aimed to explore the experiences of researchers in the interdisciplinary humanities research teams on knowledge creation in qualitative research. The study used Strauss and Corbin's systematic approach to recognize the causal factors of knowledge creation and the contexts. Discovering the main category of knowledge creation in interdisciplinary research teams, the authors analyze the strategies and consequences of knowledge creation.

The construction industry contributes to economic development by providing physical equipment and infrastructures. However, it also generates some undesirable outputs such as waste and environmental pollution, especially in developing countries. Due to the importance of the green supply chain management (GSCM) philosophy, for solving these problems, the current study aims to evaluate the drivers of GSCM adoption in the construction industry of Iran.

This research uses a descriptive and practical methodology. The participated experts in the study include senior managers of the construction department in Rasht municipality who had relevant academic education and suitable experiences in urban and industrial construction. The experts took part in both qualitative and quantitative phases of the research, namely verification of the drivers extracted from literature and ranking them in ascending order. In the quantitative phase, Step-Wise Weight Assessment Ratio Analysis (SWARA) as a new multi-criterion decision-making (MCDM) method is used to evaluate the drivers of GSCM adoption using MATLAB software.

The results show that environmental management systems, green product design and innovational capability with weights of 0.347, 0.218 and 0.143 are the most significant sub-drivers, respectively. The less important factor is an investment in environmental technology.

This study evaluated the motivational factors of GSCM in the construction industry. The findings help governments, companies and green supply chain (GSC) managers to improve their knowledge about GSCM and make the best decisions to decrease environmental pollution.

Many healthcare institutions, such as hospitals, have recently implemented quality improvement initiatives such as Lean Six Sigma (LSS). However, only a few have sustained the initiatives and remained successful. One of the main reasons for the failure of LSS implementation is that managers tend to view LSS as individual projects. Managers lack a Change Management (CM) focus during the implementation. The primary purpose of this study is to document the implementation of LSS through a CM approach to improve sustainability.

Define-Measure-Analyse-Improve-Control (DMAIC) and the Awareness-Desire-Knowledge-Ability-Reinforcement (ADKAR), a popular CM approach, are combined to propose a new framework. The usefulness of the proposed framework is demonstrated using a case study in a multispeciality hospital located in southern India.

The study found that several factors are responsible for the high Length of Stay (LOS) for patients in the Emergency Department (ED). By implementing this proposed model to implement LSS and taking corrective actions, the average LOS was reduced from 267 to 158 min (a 40% reduction approximately).

The complete step-by-step approach is explained, and the LOS was considerably reduced during the pilot project. The findings will provide valuable insights for healthcare practitioners to understand the steps involved in the combined DMAIC-ADKAR model. The findings would also give healthcare practitioners the confidence to identify suitable tools and implement LSS in organisations where the practitioners work.

According to the authors' knowledge, this is the first study that synergises two models (DMAIC and ADKAR) into a single framework to implement in a hospital.

This study introduces an ecological framework for disabilities meant to provide a new model of viewing and learning about disabilities and special education. This model projects a multi-systemic view of factors that influence a person's life, where people with disabilities are active actors in the development of the world. The increased awareness about interconnectedness, globalization, inter- and trans-disciplinarity, influences on human experience, greening, sustainability, inequality, inequity and lack of opportunities is shifting how people think about potential and growth.

The methodological approach is qualitative, interpretive research.

In disability studies, the Ecological Model of Disabilities helps reframe this uniqueness as part of the spectrum of human experiences. In special education, the Ecoducation Model helps reframe the learning experience.

This research is conceptual, but it is also all-inclusive, rendering itself to a wide application in educational settings.

The Ecoducation Model for Special Education is specific to the education of children and adults with disabilities, and it is directly compatible with the broader Ecological Model of Disabilities. These ecological models can be applied to all levels of the ecological system, and to different ecodemes of population. Nevertheless, the ecological models need to be locally implemented, with general principles tailored to national traditions, laws and resources.

Advocating for the pursuit of individual well-being within the larger society, both models call for practical changes in a multitude of areas, including legislation and policy, training of professional personnel, sufficient financial input in programs designed for the care of children and adults with disabilities, change in societal mentalities to fight discrimination, disempowerment and isolation. Because the scope of ecological frameworks is incommensurate, being both interdisciplinary and transdisciplinary, further research possibilities are countless. The ecological perspective opens the fields of disability studies and special education to new theoretical and empirical possibilities.

Two epistemological models are described as new frameworks in disability studies: the Ecological Model of Disabilities and the Ecoducation Model for Special Education. Both are original models that look into the education and inclusion of the person with disabilities.

The paper aims to provide an efficient dispatching schedule for ready-mix concrete (RMC) trucks and create a balance between batch plants and construction sites.

The paper focused on developing a new metaheuristic swarm intelligence algorithm using Java code. The paper used statistical criterion: mean, standard deviation, running time to verify the effectiveness of the proposed optimization method and compared its derivatives with other algorithms, such as genetic algorithm (GA), Tabu search (TS), bee colony optimization (BCO), ant lion optimizer (ALO), grey wolf optimizer (GWO), dragonfly algorithm (DA) and particle swarm optimization (PSO).

The paper proved that integrating GWO and DA yields better results than independent algorithms and some selected algorithms in the literature. It also suggests that multi-independent batch plants could effectively cooperate in a system to deliver RMC to various construction sites.

The paper provides a compelling new hybrid swarm intelligence algorithm and a model allowing multi-independent batch plants to work in a system to deliver RMC. It fulfills an identified need to study how batch plant managers can expand their dispatching network, increase their competitiveness and improve their supply chain operations.

This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading?

Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted.

Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information.

To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.

This study examines the dynamics of the comovement and causal relationship between conventional (Bitcoin, Ethereum and Binance coin) and Islamic (OneGram, X8X token and HelloGold) cryptocurrencies.

This study uses wavelet coherence approach to examine the time-varying lead-lag relationship between conventional and Islamic cryptocurrencies. Furthermore, the authors use BEKK-GARCH model to estimate the optimal weights, hedge ratio and hedging effectiveness in pre-COVID-19 and during the COVID-19 period.

The authors find no significant comovement in pre-COVID-19. However, the authors find significant positive comovement in conventional and Islamic cryptocurrencies at the beginning of the pandemic, and in most cases, conventional cryptocurrencies are leading. X8X and HelloGold have no/weak correlation with conventional cryptocurrencies, implying that investors can diversify the risk by making an Islamic and conventional cryptocurrencies portfolio. The authors also calculate the optimal weights, hedge ratio and hedging effectiveness using the BEKK-GARCH model. Based on the optimal weights, for the portfolios of conventional–Islamic cryptocurrencies, investors are suggested to increase their investment in Islamic cryptocurrencies during the COVID-19 than normal period. The results of hedge ratios show that hedging costs are higher during COVID-19 than before.

The findings of the paper offer several practical policy implications for investors, portfolio manager, Shariah advisors and policymakers pertaining to asset allocation, risk management, forecasting and diversification. Specifically, investors can maximize the risk adjusted returns of their conventional cryptocurrencies portfolio by adding some portions of Islamic cryptocurrencies. Considering the comovement is time-varying, investors/manager should adjust their investment strategies frequently. For the entrepreneurs in crypto-industry, it is advised to introduce new Islamic cryptocurrencies, as it has a huge growth potential because of their distinct features and performance.

This is the first study that explores the linkages between conventional and Islamic cryptocurrencies, therefore this study extends the literature of Islamic finance, stablecoins and cryptocurrencies in pre-COVID-19 and during COVID-19 period. The study results provide insights to conventional crypto investor on how to manage their portfolio during normal and turbulent period.

This study investigates information flow of market constituents and global indices at multi-frequencies.

The study’s findings were obtained using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (I-CEEMDAN)-based cluster analysis executed for Rényi effective transfer entropy (RETE).

The authors find that significant negative information flows among sustainability equities (SEs) and conventional equities (CEs) at most multi-frequencies, which exacerbates diversification benefits. The information flows are mostly bi-directional, highlighting the importance of stock markets' constituents and their global indices in portfolio construction.

The authors advocate that both SE and CE markets are mostly heterogeneous, revealing some levels of markets inefficiencies.

The empirical literature on CEs is replete with several dynamics, revealing their returns behaviour for diversification purposes, leaving very little to know about the returns behaviour of SE. Wherein, an avalanche of several initiatives on Corporate Social Responsibility (CSR) enjoin firms to operate socially responsible, but investors need to have a clear reason to remain sustainable into the foreseeable future period. Accordingly, the humble desire of investors is the formation of a well-diversified portfolio and would highly demand stocks to the extent that they form a reliable portfolio, especially, amid SEs and/or CEs.

本研究擬審查多頻率的及為市場成份的信息流和全球指數。

研究人員使用基於改良完全集合經驗模態分解自適應噪聲(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)的聚類分析法,取得Rényi有效轉移熵,藉此得到研究結果。

我們發現、於大部份多頻率,在持續性股票和傳統股票間有顯著的負信息流動,這會增加多樣化的益處。這些信息流大部份是雙向的,這強調了股票市場成份及其全球指數在構建投資組合上的重要性。

我們認為持續性股票市場和傳統股票市場大多為異質市場,這顯示了市場的低效率,而且這低效率的程度頗大。

關於傳統股票的實證性文獻裡是充滿了變革動力的,這顯示了它們以多樣化為目的的回報行為。這使我們對關於持續性股票的回報行為、認識變得實在太少了。於此,大量的企業社會責任的新措施不斷提醒各公司、要本著企業社會責任的理念去營運;但投資者需清晰明白他們為何需在可見的將來保持可持續性。因此,他們卑微的願望是一個較好的多樣化投資組合得以形成,故此他們高度要求股票要有組成可靠投資組合的性質和能力,特別是在持續性股票和/或傳統股票當中。

Human group behavior is the driving force behind many complex social and economic phenomena. Few studies have integrated multi-dimensional travel patterns and city interest points to construct urban security risk indicators. This paper combines traffic data and urban alarm data to analyze the safe travel characteristics of the urban population. The research results are helpful to explore the diversity of human group behavior, grasp the temporal and spatial laws and reveal regional security risks. It provides a reference for optimizing resource deployment and group intelligence analysis in emergency management.

Based on the dynamics index of group behavior, this paper mines the data of large shared bikes and ride-hailing in a big city of China. We integrate the urban interest points and travel dynamic characteristics, construct the urban traffic safety index based on alarm behavior and further calculate the urban safety index.

This study found significant differences in the travel power index among ride-sharing users. There is a positive correlation between user shared bike trips and the power-law bimodal phenomenon in the logarithmic coordinate system. It is closely related to the urban public security index.

Based on group-shared dynamic index integrated alarm, we innovatively constructed an urban public safety index and analyzed the correlation of travel alarm behavior. The research results fully reveal the internal mechanism of the group behavior safety index and provide a valuable supplement for the police intelligence analysis.