PurposeSmart contracts are self-executing computer programmes that have the potential to be used in several applications instead of traditional written contracts. With the recent rise of smart ...systems (e.g. Internet of things) and digital platforms (e.g. blockchain), smart contracts are gaining high interest in both business and academia. In this work, a framework for smart contracts was proposed with using reputation as the system currency, and conducts currency mining through fulfilling the physical commitments that are agreed upon.Design/methodology/approachA game theory model is developed to represent the proposed system, and then a system dynamics simulator is used to check the response of the blockchain with different sizes.FindingsThe numerical results showed that the proposed system could identify the takeover attacks and protect the blockchain from being controlled by an outsider. Another important finding is that careful setting of the maximum currency amount can improve the scalability of the blockchain and prevent the currency inflation.Research limitations/implicationsThis work is proposed as a conceptual framework for supply chain 4.0. Future work will be dedicated to implement and experiment the proposed framework for other characteristics that may be encountered in the context of supply chain 4.0, such as different suppliers' tiers, different customer typologies and smart logistics applications, which may reveal other challenges and provide additional interesting insights.Practical implicationsBy using the proposed framework, smart contracts and blockchains can be implemented to handle many issues in the context of operations and supply chain 4.0, especially in times of turbulence such as the COVID-19 global pandemic crisis.Originality/valueThis work emphasizes that smart contracts are not too smart to be applied in the context of supply chain 4.0. The proposed framework of smart contracts is expected to serve supply chain 4.0 by automating the knowledge work and enabling scenario planning through the game theory model. It will also improve online transparency and order processing in real-time through secured multitier connectivity. This can be applied in global supply chain functions backed with digitization, notably during the time of the pandemic, in which e-commerce and online shopping have changed the rules of the game.
The maintenance of human health and the development of disease are both significantly influenced by the gut microbiota. The development of omics technologies improves the understanding of the gut ...microbial ecosystem. Metagenomics has emphasized the diversity of the gut microbiome; however, it does not provide reliable insight on the dark matter of microorganisms or the minor populations. As a result of the rebirth of cultural techniques in microbiology; the field of "culturomics" is created to cultivate the unidentified bacteria that reside inside the human gut. In the 21st century's discipline of clinical microbiology; microbial culturomics becomes a promising strategy that may be used to cultivate hundreds of novel microorganisms linked to human; thus, opening new insights on the host-microbial relationships. Novel taxa and species will be detected by optimizing the culture conditions; followed by quick identification using mass spectrometry or molecular next generation sequencing. Culturomics of the human gut microbiota can be used as a bactriotherapy for the inflammatory bowel diseases and the respiratory illnesses like COVID-19, and as an immunomodulatory agent for cancer therapy. Furthermore, culturomics is a big store for discovering new antibacterial agents and resistance genes. The aim of this review was to highlight the background; methodologies, and future use of culturomics to study the human gut microbiota.
The emergence of new digital industrial technology, known as Industry 4.0, has a positive impact on the performance of the supply chain. Warehouses are a basic part of the supply chain; they are used ...to store products and manage the inventory level. A sound warehouse management system can lead to cost reduction and also can improve customer satisfaction. Traditional warehouse management models have become less efficient and unsuitable for today’s increasing market requirements. For the past decades, information and communication technology has been used for warehouse management. This paper presents a new approach for warehouse management by utilizing one of the main pillars of Industry 4.0, the Internet of Things. This new technology enables the connection of several objects through collecting real-time data and sharing them; the resulting information can then be used to support automated decision-making. The architecture of this application is illustrated and its potential benefits are overviewed. A framework is proposed to implement this approach in warehousing management, which can help in providing real-time visibility of everything in the warehouse, increasing speed and efficiency, and preventing inventory shortage and counterfeiting. This proposal gives an effective roadmap for enterprises to improve their warehouses by using the Internet of Things.
Background
Fossil fuel utilization is the biggest contributor to the emissions of greenhouse gases which are the main reason for global warming. Solar energy photovoltaic (PV) technology is one of ...the most rapidly rising technologies and is a sturdy candidate to replace fossil fuels due to its versatility. Egypt receives high solar intensity which makes it a perfect place for utilizing this technology. However, for the past years, the focus in Egypt was on using solar energy for residential applications, henceforth a research gap was identified in studying the feasibility of using solar energy for industrial applications in Egypt. To ensure the sustainability of this application, this feasibility study addresses technical, economic, environmental, and social aspects.
Results
A case study is investigated for utilizing solar PV panels for energy generation in Egypt at an industrial site. A food factory was studied under three scenarios. Scenario 1 is the baseline case for the other scenarios with fixed tilted PV panels and no storage, Scenario 2 is the same as Scenario 1 with difference in is the model of the PV panels with no tracking or storage system. Scenario 3 has a vertical axis tracking system. Software was used to simulate the performance of the three scenarios for 25 years. Results have shown that Scenario 1 and Scenario 2 had close values of the annual energy production. However, Scenario 3 produces 2047 MWh annually which is considerably higher. Finally, a sensitivity analysis is carried out to test the effect of some economic parameters on the financial feasibility.
Conclusions
All the three scenarios are found to be feasible. Scenario 1 has the shortest discounted payback period with a net present value of 414,110.12 USD, a nominal levelized cost of energy of 0.022 USD/kWh, and avoided CO
2
emissions of 14,898.993 tons. Although Scenario 3 has higher costs, it has higher energy production and better impact on the environment with 18,891.435 tons of avoided CO
2
emissions. The paper concluded that a generalization could be done about using solar PV systems in Egypt for energy generation to be sustainable and feasible technically, economically, and environmentally.
Manufacturing and production processes have become more complicated and usually consist of multiple stages to meet customers' requirements. This poses big challenges for quality monitoring due to the ...vast amount of data and the interactive effects of many factors on the final product quality. This research introduces a smart real-time quality monitoring and inspection framework capable of predicting and determining the quality deviations for complex and multistage manufacturing systems as early as possible; introduces a hybrid quality inspection approach based on both predictive models and physical inspection in order to enhance the quality monitoring process, save resources, reduce inspection time and costs. Several supervised and unsupervised machine learning techniques such as support vector machine, random forest, artificial neural network, principal component analysis were used to build the quality monitoring model with considering the cumulative effects of different manufacturing stages and the unbalance and dynamic nature of the manufacturing processes. A complex semiconductor manufacturing dataset was used to verify and assess the performance of the proposed framework. The results prove the ability of the suggested framework to enhance the quality monitoring process in multistage manufacturing systems and the ability of the hybrid quality inspection approach to reduce the inspection volume and cost.
The application of big data in the energy sector is considered as one of the main elements of Energy Internet. Crucial and promising challenges exist especially with the integration of renewable ...energy sources and smart grids. The ability to collect data and to properly use it for better decision-making is a key feature; in this work, the benefits and challenges of implementing big data analytics for renewable energy power stations are addressed. A framework was developed for the potential implementation of big data analytics for smart grids and renewable energy power utilities. A five-step approach is proposed for predicting the smart grid stability by using five different machine learning methods. Data from a decentralized smart grid data system consisting of 60,000 instances and 12 attributes was used to predict the stability of the system through three different machine learning methods. The results of fitting the penalized linear regression model show an accuracy of 96% for the model implemented using 70% of the data as a training set. Using the random forest tree model has shown 84% accuracy, and the decision tree model has shown 78% accuracy. Both the convolutional neural network model and the gradient boosted decision tree model yielded 87% for the classification model.
The main limitation of this work is that the amount of data available in the data setdataset is considered relatively small for big data analytics; however the cloud computing and real-time event analysis provided was suitable for big data analytics framework. Future research should include bigger datasets with variety of renewable energy sources and demand across more countries.
•Energy internet technologies and applications in smart grids.•Review of works that used big data analytics in energy management.•Framework of Big data in smart grids and renewable energy.•Data from a decentralized smart grid consisting of 60,000 instances was used.•Machine learning application to predict grid’s stability with accuracy up to 96%.
The COVID-19 pandemic and the Russian–Ukrainian war have significantly impacted global supply chains, including the food supply chain, in numerous countries. As one of the leading wheat importers, ...Egypt has been adversely affected by the simultaneous occurrence of these two events. Baladi bread is an integral part of the daily diet in Egypt, so any disruption affecting its availability can have a severe impact on the country’s food security. This study aims to simulate the causes and effects of potential disruptions that could occur, such as increased transportation time, unavailability of sourcing, and surge in demand due to lockdowns and panic buying. The East Cairo region was chosen as a case study to model the Baladi bread supply chain. A discrete-event simulation model was developed using anyLogistix software (version 2.15.1) for this study. Five key performance indicators were selected to evaluate, analyze, and compare the outcomes of each scenario in terms of the performance and operation of the food supply chain: service level by product, lead time, demand backlog, average daily available inventory in the mills, and on-hand inventory of wheat in the silos. The results indicate that the supply chain has been significantly impacted by the disruptions caused by these two events, leading to decreased availability of Baladi bread, unmet demand, extended lead times, and high backlogs. By utilizing the research findings, proactive strategies can be developed to minimize the impact of such disruptions in the future and maximize food security and supply chain resilience.
Introduction
The aim of this study was to investigate the characteristics and outcome of systemic lupus erythematosus (SLE) among elderly-onset patients.
Methods
This study included 575 SLE patients ...managed at Cairo, Alexandria, and Helwan universities from August 2014 to 2018: of whom 49 (8.5%), 420 (73%), and 106 (18.4%) were elderly- (> 50 years), adult- (17–50 years), and juvenile- (≤ 16 years) onset patients, respectively. Cumulative characteristics were recorded. Disease activity at the last visit was investigated through the Systemic Lupus Erythematosus Disease Activity Index-2K (SLEDAI-2K), whereby lupus low disease activity (LLDA) was defined as a SLEDAI-2K score ≤ 4. The disease outcome was assessed through investigating disease damage (Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI)) and the prevalence of mortality. Quantitative and categorical data were compared using Kruskal–Wallis and Mann–Whitney tests, and chi-square (
χ
2
) test, respectively.
Results
Late-onset SLE (LSLE) patients demonstrated the lowest prevalence of constitutional and mucocutaneous manifestations (
p
< 0.001), serositis (
p
= 0.006), nephritis (
p
< 0.001), neuropsychiatric involvement (
p
< 0.001), and hypocomplementinemia (
p
< 0.001), but showed the highest prevalence of comorbidities and multimorbidity (comorbidities ≥ 2) (
p
< 0.001), and positive anti-ds DNA antibodies (
p
< 0.001). Elderly-onset patients demonstrated the lowest SLEDAI-2K and SDI scores, achieved LLDA the most (
p
< 0.001), and developed any damage (SDI ≥ 1) the least (
p
< 0.001). The prevalence of mortality was comparable across the three age groups (
p
= 0.6).
Conclusions
Late-onset SLE patients (8.5%) showed the lowest prevalence of major organ involvement and the highest prevalence of comorbidities, and demonstrated more favorable disease activity and damage indices.
Key Points
• The disease characteristics and outcome among LSLE patients are characterized by being controversial, with studies from the Middle East being limited. Our cohort constituted of 8.5% elderly-onset SLE patients—who were characterized by the lowest prevalence of major organ involvement and the lowest activity and damage indices—making the disease pattern more favorable in this age group, despite being characterized by the highest prevalence of comorbidities.
The research objective of this paper is to develop a storytelling‐based knowledge‐sharing application that enables users to co‐create their own stories for both individuals and groups. To address ...this, a design science research methodology was applied for elucidating users' requirements. As empirical evidence, a case study was conducted on the children's book industry to synthesize a knowledge‐sharing design application named “StoryWeb”. Usability tests were conducted to reconfigure users' feedback and suggestions after two StoryWeb prototypes were developed. This study makes three main contributions. First, it empirically tests individual's or group's creativity and co‐creation by a view of knowledge sharing. Second, it methodologically applies a design thinking approach into a knowledge‐sharing study. Third, it also practically suggests feasible guidelines for the creativity and innovation research community on which features of storytelling‐based applications can be configured.
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