Evaluation model for multi-process capabilities of stranded wire

Stranded wire is the most important component of familiar mechanical equipment such as elevators, cable cars, and cranes. The quality of these products that are used on a daily basis are mainly affected by the tensile strength of stranded wire. In order to attain the purpose of economical design and a long life span of stranded wire, a less relaxation property of strand type is suitable for manufactured tools. Thus, the manufacturing industries of stranded wire need to reach the goals of high tensile strength and low relaxation. To ensure the required quality of stranded wire, the strand pull test and the long period relaxation test are two important quality assurance tests. There are three specific items of the tensile strength test that belong to the larger-the-better quality type. The quality type of the smaller-the-better is for the long period relaxation test. However, many existing methods are able to measure process capability for the product with a single quality characteristic although it cannot be applied to most products with multiple properties. Thus, the indices of C_pu and C_pl, for the larger-the-better and the smaller-the-better quality type respectively proposed by Kane [5], are quoted and combined to propose a new index to evaluate the quality of multiple characteristics of stranded wire in this article. The principle of statistics is then used to derive the one-to-one mathematical relationship of this new index and ratio of satisfactory production process. Finally, the procedure and criteria to evaluate the quality of stranded wire is proposed. This integrated multi-quality property capability analysis model can be used to evaluate the multi-process capabilities and provide continuous improvements on the manufacturing process of stranded wire.     

Optimisation of maintenance strategy for rural road network using genetic algorithm

The objective of this paper is to develop an optimised maintenance strategy for the rural road network of Kerala state. This is accomplished with the development of a bi-objective deterministic optimisation model which simultaneously satisfies the objectives of both minimisation of total maintenance cost and maximisation of performance of the road network. The model is capable of planning the maintenance activities over a multi-year planning period. The performance of the road network is accounted using the composite index, namely Pavement Condition Index. The constraint-based genetic algorithm was used as the optimisation tool since it very well takes care of the combinatorial nature of the network-level pavement maintenance programming. The applicability of the model is illustrated using a case study for the rural road network of Kerala state in India. The effort made through this research work to develop a suitable Pavement Maintenance and Management System for rural road network can lead to the proper maintenance and upkeep of the rural roads, thereby triggering a positive impact on the Indian economy.     

The Dirty Details of Gopher Maintenance

Abstract

This paper describes the details of maintaining an Internet information service, focusing on the experience of Documents Center staff in the University of Michigan Library with the “Department of Commerce Economic Data (UMich)” gopher area. Described are the evolution of the service, the logistics of obtaining and posting the files, developing staff skills and knowledge, file selection, and provision of enhanced access and assistance. Long term considerations are discussed. Internet information services differ from more traditional library services and materials maintenance because they require special skills, equipment, and constant reassessment. Despite this, the benefits of providing such services are significant for the academic library's clientele, its staff, and the Internet community overall.     

Indices for the evaluation of wildfire spread simulations using contemporaneous predictions and observations of burnt area

Methods to objectively evaluate performance are critical for model development. In contrast to recent advances in wildfire simulation, there has been limited attention to evaluating fire model performance. Information to validate fire models is typically limited, commonly to a few perimeter observations at a small number of points in time. We review metrics for comparing two burnt areas at a point in time: observed and predicted. These are compared in an idealised landscape and with a case study evaluating the performance of simulations of an Australian wildfire. We assessed: Shape Deviation Index (SDI), Jaccard's coefficient, F1, Sørensen's Similarity and Area Difference Index (ADI). For decomposing fit into error components (overprediction and underprediction) we assessed the partial indices of SDI and ADI, Precision and Recall. The various metrics were evaluated for their ability to represent error and their suitability for use in model improvement frameworks.     

An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images

Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help of an MRI scanner. With the slice images obtained using an MRI scanner, certain image processing techniques are utilized to have a clear anatomy of brain tissues. One such image processing technique is hybrid self-organizing map (SOM) with fuzzy K means (FKM) algorithm, which offers successful identification of tumor and good segmentation of tissue regions present inside the tissues of brain. The proposed algorithm is efficient in terms of Jaccard Index, Dice Overlap Index (DOI), sensitivity, specificity, peak signal to noise ratio (PSNR), mean square error (MSE), computational time and memory requirement. The algorithm proposed through this paper has better data handling capacities and it also performs efficient processing upon the input magnetic resonance (MR) brain images. Automatic detection of tumor region in MR (magnetic resonance) brain images has a high impact in helping the radio surgeons assess the size of the tumor present inside the tissues of brain and it also supports in identifying the exact topographical location of tumor region. The proposed hybrid SOM-FKM algorithm assists the radio surgeon by providing an automated tissue segmentation and tumor identification, thus enhancing radio therapeutic procedures. The efficiency of the proposed technique is verified using the clinical images obtained from four patients, along with the images taken from Harvard Brain Repository.     

Optimization of a spectrally tunable LED daylight simulator

Daylight simulators (i.e., D50, D55, D65, and D75 simulators) play an important role for industrial applications, such as surface color industry, recipe formulation, and graphic arts industry. Conventional light sources, including xenon lamps, tungsten‐halogen lamps, and fluorescent lamps, have been used as daylight simulators. In this article, a genetic algorithm is employed to investigate optimized channels for each daylight simulator. It is found that at least six LED channels are required to produce high quality daylight simulators in terms of the CIE Metamerism Indices. Furthermore, it is possible to create a spectrally tunable daylight simulator using six LED channels with wavelengths of 357, 420, 474, 533, 595, and 660 nm. This wavelength combination with appropriate intensity combinations can produce D50, D55, D65, and D75 simulators with good performance, in terms of the CIE Metamerism Indices. The color appearance of various color samples or objects under such a daylight simulator is found to be very similar to those under a corresponding daylight illuminant. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 419–423, 2017     

Reliability of self-administered Work Ability Index questionnaire among Korean workers

Although the Work Ability Index (WAI) has been used in many countries, its reliability is yet to be validated in Korea. In our study, test–retest results of WAI total score, WAI category and seven subscales were compared. The correlation coefficients of WAI total score and subscales 1 and 2 between test and retest were 0.70, 0.80 and 0.63, respectively. The κ values on WAI category, subscales 4, 5, 6 and 7 were 0.52, 0.32, 0.31, 0.48 and 0.85, respectively. The results of our reliability test show that WAI scores of female, younger and private company workers were found to be higher than those of male, older and public company workers, respectively. We conclude that overall test–retest reliability of WAI in Korea is acceptable. Another notable observation from our study is that work ability dimension (subscales 1, 2 and 7) had a higher reliability, whereas health dimension (subscales 3–6) had a lower reliability.     

SBH: Super byte-aligned hybrid bitmap compression

Bitmap indexes are commonly used in data warehousing applications such as on-line analytic processing (OLAP). Storing the bitmaps in compressed form has been shown to be effective not only for low cardinality attributes, as conventional wisdom would suggest, but also for high cardinality attributes. Compressed bitmap indexes, such as Byte-aligned Bitmap Compression (BBC), Word-Aligned Hybrid (WAH) and several of their variants have been shown to be efficient in terms of both time and space, compared to traditional database indexes. In this paper, we propose a new technique for compressed bitmap indexing, called Super Byte-aligned Hybrid (SBH) bitmap compression, which improves upon the current state-of-the-art compression schemes. In our empirical evaluation, the query processing time of SBH was about five times faster than that of WAH, while the size of its compressed bitmap indexes was retained nearly close to that of BBC.     

Forecasting conditional climate-change using a hybrid approach

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.     

Carbon intensity of electricity in ASEAN: Drivers,performance and outlook

The Association of Southeast Asian Nations (ASEAN), with its ten member countries, has a total population exceeding 600 million. Its energy-related CO₂ emissions have been growing and in 2013 amounted to 3.6% of total global emissions. About 40% of ASEAN's energy-related CO₂ emissions are currently attributable to electricity production. In view of this high share, we study the CO₂ emissions of ASEAN's electricity production sector with a focus on the aggregate emission intensity (ACI) given by the level of CO₂ emissions for each unit of electricity produced. Drivers of ACI are analysed for individual countries and spatial analysis is conducted by comparing factors contributing to differences between the ACIs of individual countries and that of the ASEAN average. Arising from these analyses and in light of the current developments, it is concluded that drastic actions need to be taken both at the national and regional levels in order to reduce growth in the region's electricity-related CO₂ emissions. Two key policy issues, namely overcoming national circumstances to improve electricity generation mix and improving power generation efficiency, are further discussed.