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This perspectives article addresses risk in cyber defense and identifies opportunities to incorporate risk analysis principles into the cybersecurity field. The Science of Security (SoS) initiative at the National Security Agency seeks to further and promote interdisciplinary research in cybersecurity. SoS organizes its research into the Five Hard Problems (5HP): (1) scalability and composability; (2) policy-governed secure collaboration; (3) security-metrics-driven evaluation, design, development, and deployment; (4) resilient architectures; and (5) understanding and accounting for human behavior. However, a vast majority of the research sponsored by SoS does not consider risk and when it does so, only implicitly. Therefore, we identify opportunities for risk analysis in each hard problem and propose approaches to address these objectives. Such collaborations between risk and cybersecurity researchers will enable growth and insight in both fields, as risk analysts may apply existing methodology in a new realm, while the cybersecurity community benefits from accepted practices for describing, quantifying, working with, and mitigating risk.

The realm of cybersecurity is perhaps one of the most quickly evolving areas within today’s research space. New and emerging technologies, as well as the growth of cybersecurity environment...

Process capability indices provide a measure of the output of an in-control process that conforms to a set of specification limits. These measures, which assume that process output is approximately normally distributed, are intended for measuring process capability for manufacturing systems. When the performance of a system results in a product that fails to fall within a given specification range, however, the product is typically scrapped or reworked, and the actual distribution that the customer perceives after inspection is truncated. In this paper, the concept of a truncated measure for three types of quality characteristics is introduced as the key to linking customer perception to process capability. Subsequently, a set of customer-perceived process capability indices is presented as an extension of traditional manufacturer-based counterparts. Finally, data transformation-based process capability indices are also discussed. A comparative study and numerical example reveal considerable differences among the traditional and proposed process capability indices. It is believed that the proposed process capability index for various quality characteristics may more aptly lead to process improvement by facilitating a better understanding of the integrated effects found in engineering design problems. Copyright © 2015 John Wiley & Sons, Ltd.

Robust design (RD) methods, which are based upon the concept of building quality into products or processes, are increasingly popular in the science and engineering research communities. One particular area of RD research that has not received considerable attention is in working with multiple time series responses, observed frequently within the field of pharmaceutical science. In order to determine the optimal pharmaceutical formulation, or input factor settings, suitable robust experimental design and analysis methods must be performed. To achieve this objective, the primary aim of this paper is to propose a new methodology that specifically addresses the multi-response time series problem for a pharmaceutical formulation process. First, an experimental format and framework for testing drug release kinetics is proposed by implementing a mixture experimental design and time series response modeling. Second, an alternative robust design model is developed to identify the optimal pharmaceutical formulation, based upon the time series target profiles for drug release kinetics. Finally, a case study associated with a drug development process is performed to validate the proposed model. The results of this case study indicate that the optimal drug release kinetics is significantly similar to the target profile.

Engineers and scientists often identify robust parameter design as one of the most important process and quality improvement methods. Focused on statistical modeling and numerical optimization strategies, most researchers typically assume a process with reasonably small variability. Realistically, however, industrial processes often exhibit larger variability, particularly in mass production lines. In such cases, many of the modeling assumptions behind the robust parameter design models available in the literature do not hold. Accordingly, the results and recommendations provided to decision makers could generate suboptimal modifications to processes and products. As manufacturers seek improved methods for ensuring quality in resource-constrained environments, experimenters should examine trade-offs to achieve the levels of precision that best support their decision making. In contrast to previous research, this paper proposes a trade-off analysis between the cost of replication and the desired precision of generated solutions. We consider several techniques in the early stages of experimental design, using Monte Carlo simulation as a tool, for revealing potential options to the decision maker. This is perhaps the first study to show the avenue which may lead to more effective robust parameter design models with the optimal combination of cost constraints and desired precision of solutions.

For the manufacturing community, determining the optimal process mean can often lead to a significant reduction in waste and increased opportunity for monetary gain. Given the process specification limits and associated rework or rejection costs, the traditional method for identifying the optimal process mean involves assuming values for each of the process distribution parameters prior to implementing an optimization scheme. In contrast, this article proposes integrating response surface methods into the framework of the problem, thus removing the need to make assumptions on the parameters. Furthermore, whereas researchers have studied models to investigate this research problem for a single quality characteristic and multiple nominal-the-best type characteristics, this article specifically examines the mixed multiple quality characteristic problem. A non-linear programming routine with economic considerations is established to facilitate the identification of the optimal process mean vector. An analysis...

PurposeThe selection of the optimal process target for a manufacturing process is critically important as it directly affects the defect rate, rejection and rework costs, and the loss to customers. A recent review of process target literature suggests that future work should incorporate models using multiple quality characteristics. Thus, the purpose of this paper is to create a more flexible and realistic approach to solving the multi‐response process target problem.Design/methodology/approachA design of experiments methodology is proposed to provide estimates of process parameters and a nonlinear constrained optimization scheme is employed to identify optimal settings.FindingsThe approximation of cost savings undoubtedly has a higher degree of accuracy than in the case where the engineer assumes values for the process parameters. Furthermore, greater flexibility is obtained in finding solutions that support both the manufacturer and the customer.Research limitations/implicationsThis methodology relies on controlled experimentation and the replication of observations made on multiple nominal‐the‐best quality characteristics. Future research may include examining the effects of using smaller‐the‐better or larger‐the‐better type characteristics.Originality/valueUnlike traditional attempts at solving the process target problem where the process mean, variance, and covariance between characteristics are assumed known in advance, this paper uses an approach that removes these assumptions, thereby providing a more practical depiction of the overall system. Furthermore, this methodology broadens the scope of process target problem research by seeking the simultaneous optimization of process parameters and considering a loss in quality attributed to deviation from a target value.

The contemporary industrial environment continues to rely on the identification of the optimal process target as a means to minimise the product defect rate and ultimately reduce manufacturing costs. Within the context of the optimal process target problem, this paper will offer three distinct contributions. First, a review of literature associated with the process target problem indicates that most research work assumes a known process distribution mean and variance prior to the identification of optimal settings. In contrast, this paper will incorporate the use of response surface designs into solving the process target problem, thus removing the need to make assumptions regarding the process parameters. Second, most research regarding the development of response surface designs either assumes that the same number of observations are made on a quality characteristic of interest, or model error always exhibits a uniform pattern of constant variance. This paper, however, will incorporate alternative model...

Within an industrial manufacturing environment, Process Capability Indices (PCIs) enable engineers to assess the process performance and ultimately improve the product quality. Despite the fact that most industrial products manufactured today possess multiple quality characteristics, the vast majority of the literature within this area primarily focuses on univariate measures to assess process capability. One particular univariate index, Cpm, is widely used to account for deviations between the location of the process mean and the target value of a process. While some researchers have sought to develop multivariate analogues of Cpm, modeling the loss in quality associated with multiple quality characteristics continues to remain a challenge. This paper proposes a multivariate PCI that more appropriately estimates quality loss, while offering greater flexibility in conforming to various industrial applications, and maintaining a more realistic approach to assessing process capability. Copyright © 2010 John Wiley & Sons, Ltd.

The selection of the optimal process target is critically important as it directly affects the defect rate, material cost, scrap and rework costs, and the loss to customers. Within the context of the optimal process target problem, a new model is proposed in this paper and two distinct contributions to the related topic are offered. First, while most research work assumes a given process distribution with a known variance, this paper integrates response surface designs into solving the optimal process target problem, thus removing the need to make assumptions regarding process parameters. Second, typical response surface designs consider second-order fitted functions; however, this paper considers a procedure to include higher-order polynomial terms that will result in higher prediction capabilities, thereby giving a more accurate representation of the true process. A constrained nonlinear optimization scheme is used to facilitate the development of this methodology and a numerical example is provided for illustration.

Innovative engineering techniques are often sought within the manufacturing environment to improve product quality and promote more cost-effective strategies. Robust design methods are frequently used to serve this purpose, with the objective of minimizing the variability inherent within a particular process or system. A review of the literature suggests that most robust design research involves the study of static quality characteristics, given a pre-defined specification interval or region and target value. In addition to proposing a methodology for working with dynamic quality characteristics where the specifications and target value may change over time, this paper offers two other distinct contributions. First, those researchers who have examined dynamic systems traditionally consider the effects of a signal factor on a response variable on the identification of optimal factor settings. In contrast, this paper will consider the effects of a quality characteristic changing over time, thus removing the need to confine the problem to signal–response systems. Furthermore, most researchers consider the optimization of the process mean according to the costs of non-conforming to an established specification interval or region. This paper, however, utilizes a methodology involving the simultaneous optimization of the process mean and variance while expanding the problem to consider a loss in quality attributed to deviation from a target value over time. Copyright © 2010 John Wiley & Sons, Ltd.

One of the most popular quality engineering tools is the process capability index (PCI), which relates the allowable spread of a process defined by engineering specifications to the natural spread of a process. The traditional PCI does not account for the loss in quality when product characteristics fail to achieve their process target value. This research, in contrast, proposes indices, namely posterior PCIs, that consider the underlying result of observations after inspection. Utilising a truncated distribution as the basis for measurement along with a target-based quality loss function for capability analyses, several posterior indices are developed in this paper. A simulation technique is implemented to compare the proposed posterior PCIs with traditional measures. Finally, the confidence interval approximations for the posterior PCIs are derived. Our results suggest using the proposed posterior indices for capability analyses when industrial processes require that non-conforming products be scrapped prior to shipping to the customer.

Within the last 20 years, the opportunities for manufacturing at the nano-level have risen substantially. Despite this growth, the industry continues to face the challenge of obtaining a significant experimental yield that is worthy of the cost to produce at the atomic level. Today, most researchers utilise response surface methodology to evaluate and improve existing techniques in nanoparticle production. While these methods are appropriate for identifying optimal factor settings in the traditional manufacturing process, they require some refinement under significantly elevated degrees of quality. This paper investigates the use of higher-order response surface designs in the improvement of nanomanufacturing processes.

In a manufacturing environment where concerns for product quality or excessive cost are often present, process optimisation is typically an engineering objective. One method that supports the reduction of nonconformance costs and loss in product quality is the identification of the optimal process mean. Given the specification limits and associated costs for a process, the traditional method uses assumed values for the process parameters to estimate the ideal location of the mean. In contrast, this paper proposes using a methodology that removes the need to make assumptions on the process parameters. Furthermore, while most research examines the role of the nominal-the-best characteristic in the design of the optimal process mean, this paper specifically looks at smaller-the-better and larger-the-better quality characteristics. The skew normal distribution, which is relatively new to engineering applications, is considered in modelling these characteristics. A non-linear programming routine with economic considerations is used to facilitate this study.

This paper investigates the use of higher-order response surface designs for solving multi-response optimisation problems using the desirability function approach. A methodology is proposed that incorporates screening procedures and evaluation measures to facilitate the selection of an appropriate model. Several case studies are examined to illustrate the breadth and scope of the problem as it relates to identifying the optimal factor settings for a process. A comparison of the results using the proposed methodology and those using the traditional desirability function approach is also shown to suggest the need for greater precision and accuracy.

For any given manufacturing system, the initial setting of the process target is critical in preventing excessive product rejection and rework costs. Often referred to as the ‘process target problem’, the traditional approach relies first on the assumption of certain values for the process mean and variance, prior to identifying the optimal setting. This paper, in contrast, proposes integrating estimated response surface functions developed for the mean and variance based upon observations made on a given process, thus removing any assumption on the parameters. In addition, this paper considers non-standard experimental regions, where constraints may exist on the factor space or restrictions are implemented on the number of experimental runs conducted. In doing so, greater flexibility is obtained in finding solutions to process target problems and the scope of the research field is broadened. Non-linear programming methods are used to facilitate this approach, and numerical examples are provided to illust...

Since the integration of response surface methods into process robustness studies, many researchers have suggested numerous approaches to further enhance product development. Generally, these robust design methods seek the factor settings that minimise variability and the deviation of the mean from the desired target value. In the absence of a uniform approach to modelling process variability, researchers have typically chosen the standard deviation, variance, or logarithm of the standard deviation. Each measure, however, can produce a different set of optimal factor settings, thus complicating comparison studies. The purpose of this paper is to examine the effects of variability measure selection on solutions and suggest a uniform approach.

When the objective of the manufacturer is to eliminate waste or create a more efficient process, innovative techniques are often sought for process improvement. The statistical tools used by Six Sigma practitioners are specifically designed for this purpose; they are frequently used to address problems across a wide number of production systems. Each manufacturing application, however, brings a unique array of potential causes and effects that must be considered to properly model process behaviour. This paper specifically examines a shimming process for a mechanically-driven screw press, whereby compressor airfoils (blades) are produced for a gas turbine. To enhance product quality and identify improvements in process performance, the five-phase approach to problem solving used in Six Sigma projects is applied. As a result of the changes implemented in this case study, the product defect level is reduced by as much as 94% and the Six Sigma rating is increased from 0.868 to 3.207.

According to Stout,1a neurilemoma (schwannoma) is a solitary, encapsulated, benign tumor arising from the cells of the neurilemma, or sheath of Schwann. This relatively rare neoplasm may occur along the course of a peripheral, cranial, or sympathetic nerve. Although many of these growths are situated about the head and neck, they are exceedingly rare in the larynx. As far as we can determine, there have been no previous reports of a neurilemoma arising from the epiglottis. The following report concerns a patient from whom we removed a neurilemoma in this portion of the larynx.

The weight of evidence found in the literature and the results of this study lead us to believe that the effect of pregnancy upon the hearing loss due to clinical otosclerosis is significant and real, although variable and largely unpredictable. This effect has often been exaggerated, however. Hearing difficulties during pregnancy have been overlooked too frequently and often have been studied inadequately. Prompt detection and otologic evaluation will allow a complete audiometric follow-up of the earlier pregnancies in otosclerotic women. Patients afflicted with the more advanced stages of otosclerosis must be aided by the otologist in the choice between an operation and a hearing aid. The results in hearing restoration after the one-stage fenestration operation and the variety of procedures performed on the stapes (particularly stapedoplasty techniques) have afforded extraordinarily good results up to now. If the patient is not amenable to surgical treatment, she may benefit greatly from the use of a suitable hearing aid and from lip reading. Thus, with a properly selected device and instruction in the early adjustment period, many persons who have severe loss of hearing are able to lead useful, happy, and efficient lives. In light of past studies, the facts summarized on the foregoing pages, and the possibilities in otosurgery and aural rehabilitation, we find that much reassurance can be offered to otosclerotic women who are anticipating future gestations. Certainly one would find it most difficult to justify a therapeutic abortion or sterilization in cases of otosclerosis complicated by pregnancy.

From the Sections of Otolaryngology and Rbinology, Surgical Pathology and Plastic Surger,y, Mayo Clinic. microscopic examinations of a11 avaiIabIe tissues in these tumors were studied. V ERRucous squamous carcinoma is a morphologic type of epidermoid carcinoma with distinctive cIinica1 and pathoIogic features [I]. In 1941, FriedelI and Rosenthal [2] reported on eight patients with verrucous lesions on the bucca1 mucosa and Iower gingiva, in which tobacco chewing was thought to be an important causative factor. Ackerman [?I first described the typica microscopic findings for this Iesion of the ora cavity in 1948, aIso reIating its unique clinical course in a study of thirty-one patients. In addition, some diff&lty may he encountered in the proper pathoIogic diagnosis of this tumor unless one is aware of its typicalIy we11 differentiated composition and distinctive pattern of growth. Due to the sIow growing, yet invasive, character of this variety of ora carcinoma, a much Ionger foIIow-up period wouId appear essentia1 for a better understanding of its cIinica1 course and to determine, if possibIe, the most effective treatment. Therefore, this report concerns the study of the cIinica1 and pathologic features, treatment, and prognosis of such oral cavity Iesions encountered at the Mayo CIinic. CLINICAL DATA