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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...

The practice of threat modeling to identify and mitigate, or eliminate, vulnerabilities, has a long history in military operations research. More recently, these practices are applied to the cyber domain to give a defender an increased advantage. Most cybersecurity research, however, is performed in the computer science and network infrastructure fields where the focus is on theory and infrastructure management. Consequently, the work is lacking traditional operations research perspectives in risk analysis, assessment, and decision support that may lead to a greater overall defensive posture. This chapter outlines several operations research techniques used in modeling cybersecurity threats and proposes a value model framework for security metrics and best practices that is supported by data and interviews with subject matter experts. The value model, which includes a framework that can be customized for any organization, is illustrated using notional data. Finally, through a survey of cyber professionals with both military and corporate industry experience, an inventory of valued attributes for a secure cyber system is provided, along with potential differences in values based on an organization’s history of attacks and/or breaches. This model, grounded in the principles of military operations research, will enable organizations to assess the performance of their respective cyber systems, manage risk, and continuously improve their processes.

In order to overcome the negative effects of a vaccination against coccidiosis, the use of feed additives can be beneficial. In this trial, the effect of a butyrate (B) and/or a tannin extract (T) on male broiler production performances was assessed while mixing a coccidiostatic in the feed, vaccinating the broilers or by using none of these two strategies (negative control=C). In total, eight treatments with eight replicates (=pens) of 30 male broilers each, were tested: C, C+vaccination, C+coccidiostatic (diclazuril), C+B, C+vaccination+B, C+coccidiostatic+B, C+vaccination+T, C+vaccination+B+T. The broilers were not inoculated with Eimeria spp. during the trial. A three-phase feeding scheme was used with a starter (0-11d), grower (11-28d) and finisher (28-39d) mash diet. At the end of each period, body weight (BW) and feed intake (FI) were measured per pen, and daily weight gain (DWG) and feed conversion ratio (FCR) were calculated. For statistical analysis, all parameters were analysed by a General Linear Model with treatment as fixed factor and block as a random factor. To have an idea of the differences between the treatments, partial post hoc tests (Tukey) were performed while defining treatment as a combination of two factors (protection and additive). When only B was added, only a significant effect in the finisher period was found for the DWG and FCR, with the vaccinated group having the best production results. When a vaccination was given, in general, the addition of B, T or B+T (depending on the parameter) had a positive influence on the production results, especially in the finisher period. Key Words

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...

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.

Head and neck (HN) cancer is mainly a cancer of the elderly; however, the implementation of comprehensive geriatric assessment (CGA) to quantify functional age in these patients has not yet been studied. We evaluated the diagnostic performance of screening tools [Vulnerable Elders Survey-13 (VES-13), G8 and the Combined Screening Tool 'VES-13 + (17-G8)' or CST], the feasibility of serial CGA, and correlations with health-related quality of life evolution [HRQOL; European Organisation for Research and Treatment of Cancer Quality of Life Questionnaires (EORTC QLQ)-C30 and -HN35] during therapy in hundred patients, aged ≥65 years, with primary HN cancer undergoing curative radio(chemo)therapy. Respectively 36.8%, 69.0%, 62.1% and 71.3% were defined vulnerable according to VES-13, G8, CST and CGA at week 0, mostly due to presence of severe grade co-morbidities, difficulties in community functioning and nutritional problems. At week 4, significantly more patients were identified vulnerable due to nutritional, functional and emotional deterioration. The CST did not achieve the predefined proportion necessary for validation. Vulnerable patients reported lower function and higher symptom HRQOL scores as compared with fit patients. A comparable deterioration in HRQOL was observed in both groups through therapy. In conclusion, G8 remains the screening tool of choice. Serial CGA identifies the evolution of multidimensional health problems and HRQOL conditions during therapy with potential to guide individualised supportive care.

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.

It is often stated that stuttering is a common speech disorder in individuals with Tourette syndrome (TS). It has also been suggested, however, that the fluency failures observed in people with TS do not completely conform to the classic pattern of stuttering. The present paper describes the results of an analysis of the speech patterns of three individuals with TS. A picture emerged that bears some resemblance to stuttering, cluttering, and palilalia but that is also different from each of these disfluency types.

Human activities have severely deteriorated the Flemish river systems, and many functions such as drinking water supply, fishing, ... are threatened. Because their restoration entails drastic social (e.g. change in habits with regard to water use and discharge, urban planning) and economical (e.g. investment in nature restoration, wastewater treatment system installation) consequences, the decisions should be taken with enough forethought. Ecosystem models can act as interesting tools to support decision-making in river restoration management. In particular models that can predict the habitat requirements of organisms are of considerable importance to ensure that the planned actions have the desired effects on the aquatic ecosystems. In preliminary studies, Artificial Neural Network (ANN) models were tested and optimized to obtain the best model configuration for the prediction of the habitat suitability for Gammarus pulex based on the abiotic characteristics of their aquatic environment in the Zwalm river basin (Flanders, Belgium). Although, these ANN models are in general quite robust with a rather high predictive reliability, the model performance has to be increased with regard to simulations for river restoration management. In particular, spatial-temporal expert-rules have to be included. Migration kinetics (downstream drift and upstream migration) of the organism and migration barriers along the river (weirs, impounded river sections, ...) can deliver important additional information on the effectiveness of the restoration plans, and also on the timing of the expected effects. This paper presents an overview and quantification of the factors affecting the upstream and downstream movements of Gammarus pulex. During further research, ANN models will be used to predict the habitat suitability for Gammarus pulex after several restoration options. The migration models, implemented in a Geographical Information System (GIS), are applied to calculate the migration time to the restored parts of the river. In this way, decision makers have an idea whether and when a selected restoration option has the desired effect.

When the manufacturing objective is process or product improvement, quality practitioners will frequently resort to one or more approaches within the broader class of response surface methodology. Several techniques, such as the dual response, robust parameter design, and desirability function approach, may be effective tools to solve the multi-response optimisation problem. All of these techniques are designed to identify the factor settings that lead to an optimal solution in terms of the mean or variance among characteristics. The skewness in the distribution of observations for one or more characteristics, however, is not considered. The techniques also traditionally rely on the fit of second-order response surface designs in estimating each response, which may be unreliable in some cases. In contrast, this paper offers an approach to solving complex multi-response optimisation problems that considers both the error associated with process skewness and the accuracy of a response surface.

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...

The tenuous economic conditions of the past several years continue to threaten the economic existence of many companies, forcing them to re-examine ways for reducing costs without surrendering quality. A common technique for achieving high quality at minimal cost focuses on identifying the ideal process mean setting among various quality characteristics. In the design of this approach, referred to as the ‘optimal process mean problem’, comparatively little research has addressed the realities of mixed multiple quality characteristics and the dynamic nature of process variability. To address this gap, this paper examines situations involving mixed multiple quality characteristics, proposing a methodology to accurately predict the location of the optimal process mean vector as it shifts in response to changing process variability. This knowledge of a feasible region for the optimal vector can help to achieve robustness in cost by providing a mechanism for maintaining the most profitable process target settings.

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.

The primary goal of robust design is to determine the optimum operating conditions which minimise the variability of system performance and the deviation of the performance from its ideal target value. Response surface methodology (RSM), which is a key statistical method in modelling robust design, explores the functional relationship between several explanatory factors and a response variable. Our literature study indicates that three different estimators for variability measure, such as standard deviation, variance and the logarithmic transformation of standard deviation, are typically used; however, depending on which estimator is selected, different sets of optimum operating conditions are obtained which can further complicate comparison studies and data analysis. This question has not been addressed adequately in the robust design research community. This paper investigates how the selection of the three different estimators of variability affects optimal operating conditions in the context of response surface designs. The nominal-the-best quality characteristic is used to facilitate this experiment and numerical examples are provided to illustrate the findings.