In Norway, the current regulation for permanent plugging and abandonment of offshore wells is prescriptive, where the requirements for the number and size of plugs do not consider the different types of wells. One then disregards the fact that the wells differ with respect to, for example, flow potential. A differentiation between the wells could allow for cost-saving benefits from the least critical wells, which environment. In this regard, a special challenge lies in how much weight should be given to uncertainties and, in particular, the cautionary principle. In this paper, we look more closely into this issue. We conclude that it is not appropriate to use a static approach to manage plug and abandonment operations. As different ways of plugging and abandonment may be appropriate depending on the well situation, the approach should allow different weights to be given to uncertainties and the cautionary principle. Wells with limited flow potential, for example, should not give as much weight to the cautionary principle as wells with high flow potential. We argue for the use of a dynamic approach to manage plug and abandonment operations, ranging from one extreme, where decisions are made with strong reference to expected cost for some plug and abandonment wells, to another, where the cautionary principle is adopted instead for other wells, with a strong reference to leakage consequences (such as environmental aspects).

The frequent utilization of websites has captivated numerous researchers, who have sought to upgrade the performance of websites through behavior analysis. Weblog feature concerning web mining is employed in this paper to construct a web page recommendation model. The feed forward counter model (FFC) is presented to effectively determine association rules with a single data iteration technique. Hence, when the recommended model is executed, the time of execution is diminished. The particle swarm optimization (PSO) algorithm is introduced in the work to pick relevant pages from a given user path as the recommended pages. The association rule aids in the work as the fitness value (FV). The actual dataset is acquired from the project tunnel website. The improvement of numerous evaluation parameters, like the precision, coverage, and m-metric, is achieved using the feed forward association rule with PSO for the next page recommendation system.

This paper presents a novel architecture named as Concurrent Cellular Automation Logic Block Observer (CCALBO) BIST structure. This technique is a combined result from the designs of CBILBO (Concurrent Built In Logic Block Observer) and CALBO (Cellular Automation Logic Block Observer). Architecture of designing the CCALBO cell and its fault masking probability with inclusion in a combination and sequential CUT has been considered. Vedic Multiplier and Multiply & Accumulate Unit (MAC) are used as circuit under test (CUT). CCALBO-based BIST is compared with most competitive technique CBILBO-based BIST. Major parameters such as power and area have been considered. Compared to CBILBO technique, CCALBO-based BIST has presented significant dynamic power reduction using Vedic multiplier and MAC unit as circuit under test (CUT). It has been observed that area also gets reduced in the novel CCALBO design, because parallel in parallel out (PIPO) registers are used to generate scan chains in test logic. CCALBO-based BIST has been found to achieve fault coverage of 100% in lesser duration as compared to CBILBO. Overall, CCALBO is better over CBILBO in the major aspects of area and low power dissipation and more reliable with respect to testing.

Presently, mankind is in the era of rapid advancement and technological change. The product lifecycle has been shortening dramatically and the manufacturing industry is moving from mass customization of products to high personalization of mass products. This era is widely known as fourth Industrial revolution or Industry 4.0. It seems that sooner or later every society has to adopt and implement it. In this paper, an attempt has been made to identify, categorize, and prioritize the barriers in-front of present manufacturing industry in implementation of widely acclaimed Industry 4.0 practices. The ranking of the challenges/ barriers in adoption of this highly sophisticated manufacturing are identified and ranked categorically as well as across the categories with the help of a survey based empirical study. Such obtained ranking of barriers are optimized through Best-Worst Method. The results provide the insights of barriers, their prominence and priorities of our industries, mainly Indian, in adoption of this era of digital, intelligent and connected manufacturing.

Simultaneous quality reliability analysis can detect the weak links in production process as early as possible, which can significantly improve product reliability. Aiming at the reliability in product quality, a model based on rough set and Bayesian network (RS-BN) is proposed in this paper. Simplify expert knowledge and reduce product quality factors using rough set theory, and the minimal product quality rules can be obtained. Then the Bayesian network is constructed and trained by the minimum rules. Based on the minimal rules, the complexity of Bayesian network structure and the difficulties of product reliability analysis are largely decreased. To verify the performance of the proposed RS-BN model, a competition dataset is utilized and four evaluation indicators are investigated, i.e., accuracy, F1-score, recall, and precision. Experimental results indicated that the proposed model is superior to the other three comparative models.

For the temperature distribution problem of multilayer thermal protective clothing, pde tool kit optimization and one-dimensional Matlab unidirectional search algorithm were used to establish a non-steady static human body-clothing-environment heat exchange pool and two-dimensional heat conduction model. In addition, Matlab, Lingo, and Excel were applied to solve a series of relevant problems. For the two-dimensional heat transfer problem, model optimization was conducted, and the values of time and space intervals were taken rationally to subdivide the grid and accelerate the rate of model algorithm.

Similar remote sensing image classification is often affected by complex backgrounds, illumination changes and noise interference. To improve the classification accuracy of similar remote sensing scenes, a novel image algorithm is proposed based on the fusion of global and local texture features. Specifically, the proposed algorithm is composed of three steps. Firstly, adaptive threshold local ternary pattern (ATLTP) and Tamura texture features of remote sensing images are extracted separately. Secondly, both texture features are fused together in a cascade manner to mitigate the interference of complex backgrounds in remote sensing images. Finally, on the basis of the fused texture features, the remote sensing images are classified by Support Vector Machine (SVM). To evaluate the performance of the proposed algorithm, extensive experiments are conducted on two standard remote sensing datasets of UC Merced Land-Use and NWPU-RESISC45. The effectiveness of the proposed algorithm is validated by the average classification accuracy improvement of 8.94%.

With the widespread application of intelligent software, more rigorous requirements are placed on the security and reliability of intelligent software programs. The major challenge is that the traditional test approaches cannot be easily adapted to the testing of intelligent software since the test oracle is not available and testing intelligent software needs to focus on training sets. The classifier is a typical intelligent software which has an uncertain output. As a result, the accuracy rate cannot be used to judge whether the classifier is defective. Therefore, this paper proposes an approach for testing classifiers based on compositional metamorphic relations. Initially, it was recommended to construct composite metamorphic relations to generate derivative test cases from the original test cases; followed by training classifier to predict the classification of the test data set; then checks their consistency between the original test cases and the derivative test cases against compositional metamorphic relations, and the detected violations are reported as bugs. The experiment is carried on the ID3 decision tree and compares the mutant detection capability with the on one-dimensional metamorphic testing. From the results received from the experiment conducted in this research shows that the proposed approach improves 16.7% of mutant kill rates.

With the continuous expansion of the computer system application field, the complexity of software system is also improving. Software defect detection has gradually become an important research direction in the field of software engineering. At present, the mixed statistics and machine learning methods have been proved to be able to implement software defect detection models well. However, the evaluation index of the detection model is diverse and it is difficult to determine which model evaluation indicators are in line with the actual expectations. Aiming at this kind of problem, a software defect detection model evaluation method based on fuzzy multi-objective attribute decision making is proposed. First, extract the characteristics of software modules, use McCabe and Halstead software modules to measure attributes. Then select five common classification algorithms to establish software defect detection models, and obtain seven evaluation index values of each model. Further, based on fuzzy multi-objective attribute decision making method with fuzzy analytic hierarchy process (FAHP) to compare multiple objectives, and obtain the results of index determination. Finally, the fuzzy evaluation algorithm is used to convert the evaluation index of qualitative evaluation into quantitative evaluation to obtain the final decision evaluation value. The experimental results show the effectiveness and practicality of the method.

In order to determine the turning point between the periods of early failure and random failure, a Weibull Two-fold Piecewise Model is proposed. Then, the procedures of determining the turning point of the bathtub curve of special vehicles are specified in this method. Finally, the turning point of the bathtub curve of a special vehicle is determined based on this paper's method. And the rationality of this method is verified. All in all, the result, derived from this method, is helpful to determine the period of the delivery test.

The reliability of CNC gear hobbing machine is the basic factor for ensuring the quality, which is the most important factor that constitutes the market competitiveness of products. Therefore, a hybrid model of dual Weibull distribution was proposed to analyze the malfunction characteristics of CNC gear hobbing machine in this paper. By using the actual experimental data from working-sites, the parameters for hybrid model of dual Weibull distribution were estimated through Weibull Probability Paper and mathematic software. Comparisons were carried out between single Weibull distribution with dual parameters and the proposed method. The results show that the proposed method provide higher agreement with experimental data plots.

The popularity of commercial virtual reality (VR) devices provides novel and immersive training approaches for disaster evacuation safety education. Disaster-scene simulation provides users with highly immersive experiences and behavioral analyses. The ability to capture user performance during VR training is essential to enhancing survivability and reducing injuries and deaths. In this paper, a VR-based fire evacuation training approach is proposed. An optimal escape-route mathematical model is developed based on factors of temporal endurance under high temperatures and harmful gases. Human behavior at the scene of the disaster is analyzed to evaluate escape abilities. Experimental results show that our method is effective, offering a new method of disaster safety training that overcomes the limitations of traditional approaches (e.g., poor reality, limited interactions, and lack of user study).

Mashups, which can produce enriched results using fast integrating application programming interfaces (API) and data sources, play a pivot role in building web-based and mobile applications. However, the rapidly increasing number of APIs makes it difficult to choose suitable APIs for a mashup, especially when the historical relationships between APIs and mashups are very sparse. Many researchers have sought to develop algorithms to recommend APIs to mashups. Some hybrid approaches integrate model-based collaborative filtering and auxiliary information and have achieved good prediction performance. However, there are few models that analyze the contextual information of services' descriptions. In addition, many approaches explore the context information of APIs, but few of them consider the context information of mashups. This paper presents a new model named CDHMF for recommender systems, which uses the Word2Vec technique to integrate the contextual information into a probabilistic factorization matrix (PMF). We analyze descriptions not only for APIs but also for mashups. Our experiments are performed with datasets from ProgrammableWeb. The results show that CDHMF significantly outperforms some state-of-the-art recommender systems in mashup service applications.

The effective and accurate area measurement of body surface injury regions is important for the appraisal work of forensic medicine of public security. Considering that most of the area measurement methods used in current forensic appraisals still have strong dependence on man-made manipulation, a fuzzy c-means (FCM)-based area measurement system for body surface injury regions is designed in this paper. Aiming at the problems of poor clustering effects and poor real-time performance of FCM in the area measurement process, a design method based on super-pixel segmentation and multi-subgroup parallel artificial fish swarm is proposed in the designed system, and on this basis, the overall process framework and system workflow are designed and given. Finally, the simulation results show that the designed system can effectively improve the segmentation quality, segmentation speed, and area measurement precision of the injury region, thus providing better effectiveness and accuracy for the appraisal work of forensic medicine.

Distributed generation changes distribution network topology and power flow direction, creating active distribution networks and making traditional fault location algorithms inapplicable. This paper proposes a fault section location method for active distribution networks based on the wolf pack and differential evolution algorithms, introducing the differential evolution algorithm to the wolf pack algorithm to enrich the population diversity and enhance the global optimization performance. It constructs an evaluation function and a switching function model of active distribution networks and tests the algorithm validity using a benchmark function. The algorithm simulates the fault section location of a 33-node distribution network with DG under different conditions and compares it with other three algorithms. The simulation results show that the algorithm can accurately locate fault sections and has good fault tolerance.

In modern society, people carry mobile devices everywhere. However, these mobile devices often stay in an idle status, which leads to wasted resources. Thus, many researchers have sought methods to place tasks on idle mobile devices to avoid resource waste. In this paper, we propose three cooperative algorithms. The first algorithm is employed to find credible participants, which is the foundation of the latter two algorithms. The second and third algorithms are task replica assignment algorithms based on credible participants in mobile self-organized crowdsensing, and they are used in offline and online situations, respectively. The latter two algorithms adopt the greedy strategy and include constraints in assignment strategy for replicas. The experiments show that the proposed algorithms dramatically increase the probability of finding accurate results, increasing from slightly more than 0.6 for the original algorithms to nearly 0.98, even though the proposed algorithms have slightly longer average execution times.