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Numerous consensus mechanisms have been suggested to cater to the specific characteristics of fog computing. To comprehensively understand their unique features, performance, and applications in fog computing, it is crucial to conduct a systematic analysis of these mechanisms. For this study, 79 relevant articles were carefully selected based on predefined criteria. Among these articles, 35 employed work-proof-based consensus mechanisms, 24 utilized voting-based mechanisms, and 22 adopted capability-based mechanisms. Among the 26 identified consensus mechanisms, proof of work remains the most prevalent one. It is important to note that the scope of this paper is limited to the research available in the predominant databases at the time of writing. Future research may expand to include additional databases and more recent literature in this domain.

Software architecture forms the cornerstone for achieving and ensuring various software quality attributes. It encompasses the collected requirements of the product, serving as a blueprint that delineates quality features for all project stakeholders, along with methods for measurement and control. Despite the significant increase in IoT-based agricultural systems, there is a dearth of studies on the quality elements of their software architecture. To address this need, this study offers an overview of components and services tailored to address specific quality attributes pertinent to agriculture systems. It identifies, investigates, and presents quality attributes influencing the design of software architecture for IoT-based agriculture systems. This paper identified and discussed several quality attributes, including performance, scalability, flexibility, interoperability, productivity, extensibility, and security, and mapped them to corresponding components of the IoT-based agriculture software architecture. Also, several issues were identified and discussed for the software architecture quality of IoT-based agriculture systems, such as real-time processing and interoperability due to the various devices and protocols utilized in these systems. The findings of this study offer valuable insights for developing, executing, and refining IoT-based agricultural systems to fulfill the changing requirements of the agriculture industry.

The Fog computing concept has been introduced to aid in the data processing of Internet of things applications using Cloud computing. Due to the profitable benefits of this combination, several papers have lately been published proposing the deployment of Blockchain alongside Fog computing in a variety of fields. A comprehensive evaluation and synthesis of the literature on Blockchain-Fog computing integration applications that have emerged in recent years is required. Although there have been several articles on the integration of Blockchain with Fog computing, the applications connected with this combination are still fragmented and require further exploration. Hence, in this paper, the applications of Blockchain-Fog computing integration are identified using a systematic literature review technique and tailored search criteria generated from the study objectives. This article found and evaluated 144 relevant papers. The findings of this article can be used as a resource for future Fog computing research and designs.

Recognizing aircraft automatically by using satellite images has different applications in both the civil and military sectors. However, due to the complexity and variety of the foreground and background of the analyzed images, it remains challenging to obtain a suitable representation of aircraft for identification. Many studies and solutions have been presented in the literature, but only a few studies have suggested handling the issue using semantic image segmentation techniques due to the lack of publicly labeled datasets. With the advancement of CNNs, researchers have presented some CNN architectures, such as U-Net, which has the ability to obtain very good performance using a small training dataset. The U-Net architecture has received much attention for segmenting 2D and 3D biomedical images and has been recognized to be highly successful for pixel-wise satellite image classification. In this paper, we propose a binary image segmentation model to recognize aircraft by exploiting and adopting the U-Net architecture for remote sensing satellite images. The proposed model does not require a significant amount of labeled data and alleviates the need for manual aircraft feature extraction. The public dense labeling remote sensing dataset is used to perform the experiments and measure the robustness and performance of the proposed model. The mean IoU and pixel accuracy are adopted as metrics to assess the obtained results. The results in the testing dataset indicate that the proposed model can achieve a 95.08% mean IoU and a pixel accuracy of 98.24%.

Document image retrieval (DIR) systems simplify access to digital data within printed documents by capturing images. These systems act as bridges between print and digital realms, with demand in organizations handling both formats. In education, students use DIR to access online materials, clarify topics, and find solutions in printed textbooks by photographing content with their phones. DIR excels in handling complex figures and formulas. We propose using ensembles of DIR systems instead of single-feature models to enhance DIR’s efficacy. We introduce “Vote-Based DIR” and “The Strong Decision-Based DIR”. These ensembles combine various techniques, like optical code reading, spatial analysis, and image features, improving document retrieval. Our study, using a dataset of university exam preparation materials, shows that ensemble DIR systems outperform individual ones, promising better accuracy and efficiency in digitizing printed content, which is especially beneficial in education.

Abstract The fog computing concept was proposed to help cloud computing for the data processing of Internet of Things (IoT) applications. However, fog computing faces several challenges such as security, privacy, and storage. One way to address these challenges is to integrate blockchain with fog computing. There are several applications of blockchain-fog computing integration that have been proposed, recently, due to their lucrative benefits such as enhancing security and privacy. There is a need to systematically review and synthesize the literature on this topic of blockchain-fog computing integration. The purposes of integrating blockchain and fog computing were determined using a systematic literature review approach and tailored search criteria established from the research questions. In this research, 181 relevant papers were found and reviewed. The results showed that the authors proposed the combination of blockchain and fog computing for several purposes such as security, privacy, access control, and trust management. A lack of standards and laws may make it difficult for blockchain and fog computing to be integrated in the future, particularly in light of newly developed technologies like quantum computing and artificial intelligence. The findings of this paper serve as a resource for researchers and practitioners of blockchain-fog computing integration for future research and designs.

Identifying the appropriate stakeholder is an integral part of the requirements engineering process and the primary factor influencing system success. The E-learning system is a significant platform with a variety of stakeholders that must be identified. After the launch of the E-learning system, issues such as identifying stakeholders arise and threaten the system's continuity. Moreover, the use of non-scientific and incompatible methods for identifying stakeholders, as well as the limitations of some of these methods, are among other issues that are highlighted in this research. This study investigates the issue of Stakeholder Identification (SI) in the E-learning system and identifies the most influential factors influencing this significant issue. By conducting a comprehensive literature review and employing qualitative data collection and analysis, these factors are addressed. There were 35 survey questionnaires were distributed to experts, analysts, and developers in the field of the E-learning system in Malaysia in 2022 to collect the data. The results reported in this paper demonstrated that there are difficulties in defining the actual stakeholders, as well as a lack of knowledge in employing the appropriate techniques to identify them, which were then analysed and compared to generate the list of factors. The factors identified in this paper help industries understand how critical it is to identify the actual stakeholders of an E-learning system at an early stage. Therefore, industries should be able to develop stable and reliable E-learning systems. This research provides several contributions, including crucial theoretical and practical insights into how E-learning systems are used and adopted. Contributes to the current literature by outlining the key obstacles caused by SI concerns. Identify the most significant components of SI issues, i.e., information quality, time and cost, system utility, and technology. Finally, this paper provides important recommendations for academics, developers, and regulators, allowing them to better understand the critical factors of effective E-learning system implementation.

A new method was developed to 3D print high-thermal-conductivity, pitch-based continuous carbon fiber composites. This process uses a dual extruder to selectively print polylactic acid (PLA) or PLA-coated pitch carbon fibers. The effective thermal conductivity of unidirectionally 3D-printed specimens was characterized experimentally for samples fabricated from fibers with different conductivities and at different volume fractions: Three grades of pitch carbon fibers were used with conductivities ranging from 140 to 800 W/mK. Carbon fiber volume fractions ranging from 6.25% to 11.7% were investigated. A maximum thermal effective thermal conductivity of 37.1 W/mK was measured for a 9.5% volume fraction of the K13D2U pitch carbon fiber which is significantly higher than that of any 3D-printed carbon-based composites measured previously. However, the measured effective thermal conductivities were significantly lower than those predicted by the parallel model. Breakage of the fibers post-printing was confirmed using micro-computed tomography and suggests that higher effective thermal conductivities can be obtained by better mitigating fiber breakage during the printing process. Future directions for this technology are discussed.

The growth in textual data associated with the increased usage of online services and the simplicity of having access to these data has resulted in a rise in the number of text classification research papers. Text classification has a significant influence on several domains such as news categorization, the detection of spam content, and sentiment analysis. The classification of Turkish text is the focus of this work since only a few studies have been conducted in this context. We utilize data obtained from customers’ inquiries that come to an institution to evaluate the proposed techniques. Classes are assigned to such inquiries specified in the institution’s internal procedures. The Support Vector Machine, Naïve Bayes, Long Term-Short Memory, Random Forest, and Logistic Regression algorithms were used to classify the data. The performance of the various techniques was then analyzed after and before data preparation, and the results were compared. The Long Term-Short Memory technique demonstrated superior effectiveness in terms of accuracy, achieving an 84% accuracy rate, surpassing the best accuracy record of traditional techniques, which was 78% accuracy for the Support Vector Machine technique. The techniques performed better once the number of categories in the dataset was reduced. Moreover, the findings show that data preparation and coherence between the classes’ number and the number of training sets are significant variables influencing the techniques’ performance. The findings of this study and the text classification technique utilized may be applied to data in dialects other than Turkish.

Geographically distributed agile development may experience a high failure rate due to communication issues, which has a negative influence on project performance. One suggested solution in the literature is to enhance both communication and project performance by implementing agile enterprise architecture. However, the empirical evidence that supports this claim is still scarce. To address this gap, this study empirically explores the role of agile enterprise architecture as an artifact in distributed agile development. The findings of an in‐depth qualitative case study from a dispersed agile development organization that involves teams distributed over three locations are used in this work. Over 2 months, data was gathered by interviewing 12 key members of the team and watching three Sprint sessions of agile software development. Text analysis qualitative approach was used to analyze the data. The findings imply that agile enterprise architecture has a positive effect on distributed agile software development communication, quality, and functionality. Agile enterprise architecture may also support on‐time completion, but a trade‐off with on‐budget may be necessary. These findings provide valuable insights, frameworks, and best practices that support organizations in achieving greater agility, collaboration, and success in their distributed software development initiatives. As this is one of the first studies to look at the influence of agile enterprise architecture on distributed agile software development communication and performance, further research is needed to confirm and expand on the conclusions of this study. [ABSTRACT FROM AUTHOR]

3D printing, especially fused filament fabrication, presents a potentially attractive manufacturing technique for thermal applications such as polymer heat exchangers due to the ability to create complex internal geometries which can be used to enhance convective heat transfer. Recently, commercial and modified open-source printers have utilized continuous fibers such as carbon fiber to create continuous fiber reinforced polymer composites (FRPCs) which enhance the mechanical properties of the printed products. This continuous filler network can also serve to improve thermal conductivity. In this study, the effective thermal conductivity of 3D-printed FRPCs is characterized using a steady-state, modified, guarded hot plate apparatus. The effect of the fiber direction and volume fraction on the effective thermal conductivity of the 3D-printed composites was characterized experimentally and modeled analytically using series and parallel models. Thermal conductivities of up to 2.97 W/mK were measured for samples in which the fibers were aligned with the direction of heat flow. Measured values were in good agreement with analytical model predictions. The importance of fiber conductivity on overall performance of the FRPCs was further demonstrated using a handlaid-up, pitch-based carbon fiber sample which exhibited an effective thermal conductivity of 23.6 W/mK.

Real-world data obtained from integrating heterogeneous data sources are often multi-valued, uncertain, imprecise, error-prone, outdated, and have different degrees of accuracy and correctness. It is critical to resolve data uncertainty and conflicts to present quality data that reflect actual world values. This task is called data fusion. In this paper, we deal with the problem of data fusion based on probabilistic entity linkage and uncertainty management in conflict data. Data fusion has been widely explored in the research community. However, concerns such as explicit uncertainty management and on-demand data fusion, which can cope with dynamic data sources, have not been studied well. This paper proposes a new probabilistic data fusion modeling approach that attempts to find true data values under conditions of uncertain or conflicted multi-valued attributes. These attributes are generated from the probabilistic linkage and merging alternatives of multi-corresponding entities. Consequently, the paper identifies and formulates several data fusion cases and sample spaces that require further conditional computation using our computational fusion method. The identification is established to fit with a real-world data fusion problem. In the real world, there is always the possibility of heterogeneous data sources, the integration of probabilistic entities, single or multiple truth values for certain attributes, and different combinations of attribute values as alternatives for each generated entity. We validate our probabilistic data fusion approach through mathematical representation based on three data sources with different reliability scores. The validity of the approach was assessed via implementation into our probabilistic integration system to show how it can manage and resolve different cases of data conflicts and inconsistencies. The outcome showed improved accuracy in identifying true values due to the association of constructive evidence.