Existing schemes that combine provable data possession (PDP) and proof of ownership (PoW) for data integrity and efficient deduplication face several practical shortcomings. First, integrity tag generation relies on data block indices or user keys, causing redundant tags for duplicate data and increasing storage overhead. Second, dynamic operations like data insertion and deletion require linear scanning, resulting in a high computational complexity of$O(N)$. Furthermore, in multi-cloud backup environments, existing mechanisms struggle to ensure consistency between data ciphertexts and their tags. If an audit fails, locating damaged replicas requires repetitive verification by the third-party auditor (TPA), leading to high inefficiency. To overcome these issues, this paper proposes a multi-cloud data auditing mechanism (MDAM), which is based on an authenticated data structure called variable merkle hash tree (VMHT). MDAM utilizes message-derived RSA tags and secret-sharing-based proxy re-signing to achieve efficient deduplication and secure user-tag association. By integrating updatable block-level message-locked encryption (UMLE) technology with a variable branching structure, it supports dynamic updates with an$O(\log N)$complexity, saving storage and improving update efficiency. Additionally, MDAM employs a dynamic Bloom filter for efficient fault localization. Experimental results demonstrate that MDAM surpasses existing schemes in computational overhead, update efficiency, and fault localization performance.

Encrypted deduplication is attractive because it can provide high storage efficiency while protecting data privacy. Most existing schemes achieve encrypted deduplication against brute-force attacks (BFAs) based on server-aided encryption. Unfortunately, the centralized key server in server-aided encryption can potentially become a single point of failure. To this end, distributed server-aided encryption is presented, which splits a system-level master key into multiple shares and distributes them across several key servers. However, it is hard to improve security and scalability with this method simultaneously.This paper presents a secure and scalable encrypted deduplication scheme ScalaDep. ScalaDep achieves a new design paradigm centered on location-hiding secret sharing of data keys. As the number of deployed key servers increases, the attack cost of adversaries increases while the number of requests handled by each key server decreases, enhancing both scalability and security. Furthermore, we propose a two-phase duplicate detection method for our paradigm, which utilizes short hashes and key identifiers to achieve secure duplicate detection against BFAs. Additionally, based on the allreduce algorithm, ScalaDep enables all key servers to collaboratively record the number of client requests and resist online BFAs by enforcing rate limiting. Security analysis and performance evaluation demonstrate the security and efficiency of ScalaDep.

As an effective alternative to the power cables and overhead lines in the electrical transmission networks, the gas-insulated metal enclosed transmission line (GIL) has been receiving ever-increasing usage worldwide. The GIL reliability, as one of the important characteristics of GIL equipment, is greatly determined by the processing technologies used in manufacturing, transportation, and installation stages, and failures caused by poor processing technologies can cause great damage to GIL equipment, which can further result in great losses to the electrified wire netting. Thus, it is necessary to study and accurately predict the reliability of GIL equipment to discover potential failure modes and take precautions before GIL equipment goes into operation. In this paper, a new reliability prediction model called the multilayer directed and weighted network (M-LDAWN) reliability prediction model is proposed to analyze and predict the GIL equipment reliability by concerning failure propagation and practical processing technologies. In the reliability prediction process, the reliability minimum cut sets triggered by substandard processing technologies are searched by the M-LDAWN model, and the increased failure rate of GIL equipment under each reliability minimum cut set is calculated considering the amplification and concatenation effects of failure propagation. Using the proposed reliability prediction model, the effects of substandard processing technologies on the GIL equipment reliability can be quantified, and the reliability fluctuation caused by them can be calculated. The effectiveness of the proposed framework is demonstrated on a practical GIL reliability prediction example.

Failure mode and effects analysis (FMEA) is a widely used reliability analysis tool for identifying and eliminating known or potential failures in system, design, and process. In traditional FMEA, failure modes are evaluated by FMEA team members with respect to three risk factors: severity (S), occurrence (O), and detectability (D), and ranked via their risk priority number (RPN), which is obtained by multiplying the crisp values of S, O, and D. However, traditional RPN has been considerably criticized due to the following shortcomings: not considering the different weights of risk factors; the identical value of RPN for different combinations of S, O, and D; the diversity and uncertainty of evaluation information given by FMEA team members and without considering the dependence among different failure modes. Although significant efforts have been made in FMEA literatures to overcome these shortcomings, there are still some deficiencies. In this paper, a new risk priority model is presented for FMEA by using the house of reliability (HoR)-based rough VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) approach. In the proposed model, the HoR is introduced to identify the dependence among different failure modes and the link between failure modes and the risk factors of O, D and the subcriteria of S. Rough number is introduced to manipulate the subjectivity and vagueness in decision making and VIKOR approach is used to determine the risk priority order of failure modes in a comprehensive way. Finally, an illustrative case in transmission system of a vertical machining center has demonstrated the effectiveness and practicality of the proposed model.

The principles of the immune system and Monoclonal were introduced briefly. Focused on the text expressed by the vector space model which was processed by semantic computation, an adaptive polyclonal clustering algorithm was proposed. Firstly, the calculation method was defined for the affinity between antibody and antigens and the affinity of antibodies, the genetic operation factors were designed, replacement, inverse, colonel, crossover, mutation, death, concatenate and clustering included, secondly, the process was given, and lastly, the clustering processes and analysis were done based on the text sets in a corpora. The experiments verifies that the algorithm proposed above can get the rational clustering number and have a better correct identification rate and recall rate.