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The authors offer observation on utilizing tables, figures, charts and graphs to help understand the research presented in a simple manner but also engage and sustain the reader's interest. Topics discussed include benefits provided by the use of tables/figures/charts/graphs, general methodology of design and submission, and copyright issues of using material from government publications/public domain.

We study consensus in a protocol capturing in a simplified manner the major features of the majority of Proof of Stake blockchains. A committee is formed; one member proposes a block; and the others can check its validity and vote for it. Blocks with a majority of votes are produced. When an invalid block is produced, the stakes of the members who voted for it are "slashed." Profit-maximizing members interact with adversaries seeking to disrupt consensus. When slashing is limited, free-riding and moral-hazard lead to invalid blocks in equilibrium. We propose a protocol modification producing only valid blocks in equilibrium. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Developing accurate and computationally efficient algorithms for system performance metrics calculation is critical to implementing effective control and optimization in manufacturing system operations. In this paper, we propose a recursive decomposition/aggregation-based method for calculating the performance metrics of assembly/disassembly systems with multiple merge/split operations and sub-assemblies. It is assumed that the machines follow the exponential reliability model and the buffers are of finite capacity. To achieve this, we first consider assembly systems with multiple component lines merging at a single assembly operation. By decomposing the system into a set of virtual serial lines, we derive an analytical procedure to approximate the starvation and blockage probabilities of the merge operation, which are used to recursively update the parameters of the virtual serial lines. Then, the performance metrics of the original assembly system are approximated based on the corresponding machines and buffers in these virtual serial lines. Next, we extend the algorithm to assembly/disassembly systems with multiple merge/split operations and sub-assemblies. This is accomplished by identifying the so-called assembly/disassembly units formed based on the virtual serial lines and applying the calculations derived earlier recursively. Simulation experiments are carried out to justify the convergence, computational efficiency, and approximation accuracy of the proposed algorithms. An industrial case study is presented to demonstrate the theoretical methods in practical applications. [ABSTRACT FROM AUTHOR]

Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images. [ABSTRACT FROM AUTHOR]

When wireless sensor networks are used in smart homes, the connection state will be unstable due to signal masking attenuation. This will cause low packet rate, high time delay and high cost in the network. In this paper, a network routing algorithm for wireless sensing based on connection conditions is designed. Secondly, the expected number of sends is proposed to evaluate the stability of links. Based on this, the following network signal delivery situation is forecasted in real time and quickly. According to the estimated expected number of transmissions, the path is dynamically corrected to effectively avoid attenuation in the channel and achieve optimal system performance. Experimental results show that the method proposed in this paper can improve the efficiency of message sending and reduce the routing cost under the condition of masking effect. [ABSTRACT FROM AUTHOR]

Purpose: On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data. Design/methodology/approach: First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption. Findings: Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability. Originality/value: This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability. Highlights: The stability of electricity consumption is important to the stable operation of the grid. An improved FP-growth algorithm is employed to explore the influencing factors. The improved algorithm enables the mining of rules containing specific attribute labels. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In present days, Automation of everything has become essential. Internet of things (IoT) play an important role among all medical advances of IT. In this paper, feasible solutions are discussed to compare and design better healthcare systems. A thorough investigation and survey of suitable approaches were done to select IoT based systems in hospitals consisting of various high precision sensors. OBJECTIVES: The challenge healthcare system face is to manage the real time patient’s data with high accuracy. Second challenge is at fog devices level to manage the load distribution to all sensors with limited availability of bandwidth. METHODS: This paper summarizes the selection criterions of suitable load balancing algorithms to reduce energy consumption and computational cost of fog devices and increase the network usage that are supposed to be used in IoT based healthcare systems. According to the survey BNBKnapack algorithm has been selected as best suitable approach to analyze the overall performance of fog devices and results are also verify the same. RESULTS: Comparative analysis of Overall performance of fog devices has been proposed with using SJF algorithm and Adaptive BNBKnapsack algorithm. It has been observed by analysing system performance, which is found as best among other load balancing algorithm Adaptive BNBKnapsack is successfully reduce the energy consumption by (99.29%), computational cost by (98.34%) and increase the network usage by (99.95%) of system CONCLUSION: It has been observed by analysing system performance, Adaptive BNBKnapsack Load balancing is successfully able to reduce the computational cost and energy consumption also increase the network usage of the fog network. The performance of the system is found best among other load balancing algorithm. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To ensure the long-term stable and uninterrupted service of satellite navigation systems, the robustness and reliability of time–frequency systems are crucial. Integrity monitoring is an effective method to enhance the robustness and reliability of time–frequency systems. Time–frequency signals are fundamental for integrity monitoring, with their time differences and frequency biases serving as essential indicators. These indicators are influenced by the inherent characteristics of the time–frequency signals, as well as the links and equipment they traverse. Meanwhile, existing research primarily focuses on only monitoring the integrity of the time–frequency signals' output by the atomic clock group, neglecting the integrity monitoring of the time–frequency signals generated and distributed by the time–frequency signal generation and distribution subsystem. This paper introduces a time–frequency signal integrity monitoring algorithm based on the temperature compensation frequency bias combination model. By analyzing the characteristics of time difference measurements, constructing the temperature compensation frequency bias combination model, and extracting and monitoring noise and frequency bias features from the time difference measurements, the algorithm achieves comprehensive time–frequency signal integrity monitoring. Experimental results demonstrate that the algorithm can effectively detect, identify, and alert users to time–frequency signal faults. Additionally, the model and the integrity monitoring parameters developed in this paper exhibit high adaptability, making them directly applicable to the integrity monitoring of time–frequency signals across various links. Compared with traditional monitoring algorithms, the algorithm proposed in this paper greatly improves the effectiveness, adaptability, and real-time performance of time–frequency signal integrity monitoring. [ABSTRACT FROM AUTHOR]

In the quality inspection process of high-voltage cables, several commonly used indicators include cable length, insulation thickness, and the number of conductors within the core. Among these factors, the count of conductors holds particular significance as a key determinant of cable quality. Machine vision technology has found extensive application in automatically detecting the number of conductors in cross-sectional images of high-voltage cables. However, the presence of scratch-type defects in cut high-voltage cable cross-sections can significantly compromise the precision of conductor count detection. To address this problem, this paper introduces a novel improved total variation (TV) algorithm, marking the first-ever application of the TV algorithm in this domain. Considering the staircase effect, the direct use of the TV algorithm is prone to cause serious loss of image edge information. The proposed algorithm firstly introduces multimodal features to effectively mitigate the staircase effect. While eliminating scratch-type defects, the algorithm endeavors to preserve the original image's edge information, consequently yielding a noteworthy enhancement in detection accuracy. Furthermore, a dataset was curated, comprising images of cross-sections of high-voltage cables of varying sizes, each displaying an assortment of scratch-type defects. Experimental findings conclusively demonstrate the algorithm's exceptional efficiency in eradicating diverse scratch-type defects within high-voltage cable cross-sections. The average scratch elimination rate surpasses 90%, with an impressive 96.15% achieved on cable sample 4. A series of conducted ablation experiments in this paper substantiate a significant enhancement in cable image quality. Notably, the Edge Preservation Index (EPI) exhibits an improvement of approximately 20%, resulting in a substantial boost to conductor count detection accuracy, thus effectively enhancing the quality of high-voltage cable production. [ABSTRACT FROM AUTHOR]