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Background Patient and public involvement (PPI) is a cornerstone in enhancing healthcare research and delivery, including clinical guideline development. Health outcomes concern changes in the health status of an individual or population that are attributable to an intervention. Discussion of relevant health outcomes impacts the resulting clinical guidelines for practice. This study explores how the input of PPI contributors at the National Institute of Health and Care Excellence (NICE) is integrated into guideline development, particularly in relation to health outcome selection. Methods The study used an ethnographic methodological approach. Data comprised: observations of committee meetings, scoping workshops and training sessions, and in-depth interviews with PPI contributors, health professionals and chairs from clinical guideline development committees. Data were analysed thematically. Results PPI contributors’ input in the guideline development process was often of limited scope, particularly in selecting health outcomes. Key constraints on their input included: the technical content and language of guidelines, assumed differences in the health-related priorities between PPI contributors and health professionals, and the linear timeline of the guideline development process. However, PPI contributors can influence clinical guideline development including the selection of relevant health outcomes. This was achieved through several factors and highlights the important role of the committee chair, the importance of training and support for all committee members, the use of plain language and the opportunity for all committee members to engage. Conclusions Lay member input during the outcome selection phase of clinical guideline development is achievable, but there are challenges to overcome. Study findings identify ways that future guideline developers can support meaningful lay involvement in guideline development and health outcome selection.

Among software testing techniques for the detection of software defects, state-transition-based testing which utilizes state diagrams is pretty well known and is being used widely. Such software testing techniques that are based on specifications of a software and perform various tests by utilizing a number of state diagrams are quite well known. According to different testing environment including individuals, applied methods, and the size of a perceived system, the resulting diagrams might highly show the discrepancy regarding some perspectives such as correctness, the quality of representation, and so on. This significantly affects the understanding of a system such that the system under consideration might be interpreted in different ways. The highly complex state diagrams can have a negative effect on the productive and positive utilization of state diagrams in the software system development including the analysis or the state-transition-based test process. Many state diagram-based process conventional approaches for automatic software analysis do not take the complexity problem into deep consideration so much. Moreover, the state diagrams generated by the methods that place a strong emphasis on complexity can generate inappropriate test cases according to the possible misled guidelines or the incorrect objectives. In this paper, we propose methods for resolution of the complexity problem in a state diagram without modification of the states or the transition relationships. In our proposed methods, we provide a representation model in which a software system can be interpreted in the context of a state diagram and an automatic generation method for the proposed representation. We also illustrate a case study for justification of plausibility of the proposed methods.

Recognizing the importance of sustainability, manufacturers are pursuing the well-being of society by addressing all three dimensions of sustainability: environmental, economic, and societal aspects. Sustainability metrics can measure the progress toward sustainability. However, the existing metrics that assess manufacturing systems’ sustainability patterns suffer from repetitive or subjective measurements, inefficiency in information dissemination, and partial consideration of sustainability. This research created an improved sustainability assessment framework. A comprehensive set of sustainability indicators covering various patterns of manufacturing systems are developed. The distance-to-target methodology is adopted and tailored to aggregate and compute the values of all sustainability indicators. The evaluation results from example cases indicate that the distance-to-target-based sustainability assessment framework is unbiased and reproducible with transparent computation processes and efficient interpretation of the results.

CyberManufacturing system (CMS) is a vision for future manufacturing systems. The concept delineates a vision of advanced manufacturing system integrated with technologies such as Internet of Things, Cloud Computing, Sensors Network and Machine Learning. As a result, cyber-attacks such as Stuxnet attack will increase along with growing simultaneous connectivity. Now, cyber-physical attacks are new and unique risks to CMSs and modern cyber security countermeasure is not enough. To learn this new vulnerability, the cyber-physical attacks is defined via a taxonomy under the vision of CMS. Machine learning on physical data is studied for detecting cyber-physical attacks. Two examples were developed with simulation and experiments: 3D printing malicious attack and CNC milling machine malicious attack. By implementing machine learning methods in physical data, the anomaly detection algorithm reached 96.1% accuracy in detecting cyber-physical attacks in 3D printing process; random forest algorithm reached on average 91.1% accuracy in detecting cyber-physical attacks in CNC milling process.

Requirement-based testing (RBT) is widely known for the efficient testing in the limited resources. However RBT is difficult to generate automatic test cases; thus it needs complex methods. This paper suggests our automatic test case generation for all coverage (statement, condition, decision, condition/decision, modified condition/decision, and multiple condition coverage) based on the model-based testing. To do this, we extend the original control flow graph with multiple conditions for all condition related coverage, which is called multiple conditions control flow graph, and adapt a model transformation using metamodel mechanism for test case generation. As a result, our proposed method successfully applies to the prior test requirement.

Confronted with growing sustainability awareness, mounting environmental pressure, manufacturers are seriously striving to address sustainability-related issues without sacrificing customers’ needs and market competitiveness. A new manufacturing system called cybermanufacturing system (CMS) has great potential in addressing sustainability issues by handling manufacturing tasks differently from and better than traditional manufacturing systems. CMS is a vision for future manufacturing where physical components are fully integrated and seamlessly networked with computational processes, forming an on-demand, intelligent, and communicative manufacturing resource and capability repository with optimal, sustainability-oriented manufacturing solutions. The recent developments in the Internet of things, cloud computing, fog computing, service-oriented technologies, etc., all contributed to the development of CMS. In this new manufacturing paradigm, every manufacturing resource or capability is digitalized, registered, and networked to each other directly or through the Internet, thus enabling intelligent behaviors of manufacturing components and systems such as self-awareness, self-prediction, self-optimization, and self-configuration, among others. In this research, a comprehensive definition of CMS has been developed, a suggested architecture of CMS has been constructed, and important functions of CMS have been identified. Simulation models have been developed and used to investigate the sustainability benefits of CMS. The simulation results show promising sustainability benefits of CMS over traditional manufacturing systems.

Software vulnerability is the attack surface. Therefore, vulnerabilities innate in software should be detected for software security assurance. Vulnerability detection method can be divided into static vulnerability detection and dynamic vulnerability detection. Static vulnerability detection is more commonly used for vulnerability detection. This method has many benefits, but it also creates false positives. Therefore, this paper proposes a method to combine static and dynamic detection to reduce false positives created from static vulnerability detection. The proposed method verifies the vulnerability by implanting a fault, based on the information received from static code analysis.

This paper examines various methods encompassing the authentication of users in accessing Electronic Medical Records (EMRs). From a methodological perspective, multiple authentication methods have been researched from both a desktop and mobile accessibility perspective. Each method is investigated at a high level, along with comparative analyses, as well as real world examples. The projected outcome of this examination is a better understanding of the sophistication required in protecting the vital privacy constraints of an individual's Protected Health Information (PHI). In understanding the implications of protecting healthcare data in today's technological world, the scope of this paper is to grasp an overview of confidentiality as it pertains to information security. In addressing this topic, a high level overview of the three goals of information security are examined; in particular, the goal of confidentiality is the primary focus. Expanding upon the goal of confidentiality, healthcare accessibility legal aspects are considered, with a focus upon the Health Insurance Portability and Accountability Act of 1996 (HIPAA). With the primary focus of this examination being access to EMRs, the paper will consider two types of accessibility of concern: access from a physician, or group of physicians; and access from an individual patient.

Today’s hypercompetitive worldwide market, demanding customers, and technological advances force corporations who develop new products to look into all the possible areas of improvement throughout the entire product life cycle management process. One of the research areas that have been overlooked in the past is engineering change management (ECM). This paper presents a simulation model for investigating the mutual impacts of ECM process and new product development (NPD) process on each other. The discrete event simulation model investigates how different NPD and ECM process characteristics are interrelated, and how these interactions eventually affect the lead time, cost, and quality of an NPD project. This model has the capacity to account for three different levels of uncertainties, namely activity uncertainty, design solution uncertainty, and environment uncertainty, and their effects on the probabilities of NPD rework. Decisions to be made by considering EC impacts are drawn from an enterprise level system perspective.

Engineering changes to products aim to correct design faults, exploit new market opportunities, improve overall product performance, or reduce production costs. These changes are thus considered as opportunities to stay competitive in the market place and companies strive to implement them in their products at the lowest possible costs. Such engineering changes in a collaborative environment require the original equipment manufacturer to work together with suppliers for designing, manufacturing, and marketing a product profitably. Simulation of an engineering change management process provides an understanding of important factors affecting the engineering change management process and helps in identifying any critical components of the entire process. In this research, system dynamics models have been developed to study the complex interrelationships among various members in a collaborative supply chain to achieve effective and efficient engineering change management processes. To address the complexity of the models, several standard templates have been developed and used.

Hospitals and other health-care providers today are being pressed more than ever to use technologies for reducing medical errors. Particularly, medication errors are likely to increase fast as Americans age. This paper intends to provide a starting point for understanding information technologies and database systems supporting such technologies as Computerized Physician Order Entry (CPOE), Automated Dispensing System (ADS), and Bar Coding System designed to reduce medication errors in hospitals. Although vendors provide the necessary communication software and applications, actions involving governments, technology vendors, pharmaceutical companies, and clinical researchers are needed to put to actual use the applications with a massive potential to significantly reduce medication-related errors.

Most of the part family formation methods are concerned with “how to form” the families as opposed to “how to identify” the families. However, a more appropriate approach would be to identify “naturally occurring” families since these methods are based on the production flow analysis, which uses already implemented routing data. This paper presents a new approach using the memory association of neural networks to identify naturally existing families. The developed system, Feature-Based Memory Association Network (FBMAN), operates by the exhaustive association approach which deals with the difficult problem of exceptional parts. Comparison with the results generated by other methods proves the effectiveness of FBMAN.

Concurrent formation of part families and machine cells can be achieved by using various algorithms based on the part-machine matrix. Although, part features form the basis for the formation of the part-machine matrix, part features are not directly considered in the process of forming part families and machine cells. The goal of this paper is to understand the characteristics of machine cells in terms of part features. The relationship between part features and machine cells is captured as a decision tree. The decision tree also becomes a tool for post-clustering analysis including evaluation of different clustering methods and for the classification of ambiguous parts. The simplicity of the decision tree, the number of parts classified by each leaf of the tree, and the effect of pruning on the tree are used as metrics to evaluate the clustering methods.

Prostate cancer metastasizes to bone in the majority of patients with advanced disease leading to painfully debilitating fractures, spinal compression and rapid decline. In addition, prostate cancer bone metastases often become resistant to standard therapies including androgen deprivation, radiation and chemotherapy. There are currently few models to elucidate mechanisms of interaction between the bone microenvironment and prostate cancer. It is, thus, essential to develop new patient-derived, orthotopic models. Here we report the development and characterization of PCSD1 (Prostate Cancer San Diego 1), a novel patient-derived intra-femoral xenograft model of prostate bone metastatic cancer that recapitulates mixed osteolytic and osteoblastic lesions. Methods A femoral bone metastasis of prostate cancer was removed during hemiarthroplasty and transplanted into Rag2 -/- ;γ c -/- mice either intra-femorally or sub-cutaneously. Xenograft tumors that developed were analyzed for prostate cancer biomarker expression using RT-PCR and immunohistochemistry. Osteoblastic, osteolytic and mixed lesion formation was measured using micro-computed tomography (microCT). Results PCSD1 cells isolated directly from the patient formed tumors in all mice that were transplanted intra-femorally or sub-cutaneously into Rag2 -/- ;γ c -/- mice. Xenograft tumors expressed human prostate specific antigen (PSA) in RT-PCR and immunohistochemical analyses. PCSD1 tumors also expressed AR, NKX3.1, Keratins 8 and 18, and AMACR. Histologic and microCT analyses revealed that intra-femoral PCSD1 xenograft tumors formed mixed osteolytic and osteoblastic lesions. PCSD1 tumors have been serially passaged in mice as xenografts intra-femorally or sub-cutaneously as well as grown in culture. Conclusions PCSD1 xenografts tumors were characterized as advanced, luminal epithelial prostate cancer from a bone metastasis using RT-PCR and immunohistochemical biomarker analyses. PCSD1 intra-femoral xenografts formed mixed osteoblastic/osteolytic lesions that closely resembled the bone lesions in the patient. PCSD1 is a new primary prostate cancer bone metastasis-derived xenograft model to study metastatic disease in the bone and to develop novel therapies for inhibiting prostate cancer growth in the bone-niche.

Initial part family formation and subsequent part classification are two important problems to be addressed in applying the group technology principle. Although these two problems are closely related, they have been treated separately. As an aggregate problem, the automatic creation of new part families during the classification process, is investigated. A two-layer neural network using the adaptive resonance theory is adopted. The capability of this neural network model of dealing with the stability-plasticity dilemma is utilised in classifying the parts into families and creating new families if necessary. A heuristic algorithm using the neural network is described, with illustrative examples.

The part family/machine group identification (or formation) is the crux of implementing Group Technology, and a well-studied subject. However, most of the approaches have neglected the original family concept proposed by Burbidge, that there are already ‘naturally occurring’ families existing. A desirable approach should be that of identifying these families rather than forcing to form the families. This paper describes a neurocomputing model which is inspired by the way the biological neuronal systems reach intelligent decisions. A comprehensive survey of previous approaches is presented. The simulation results from an example are provided to show how the model is used to identify the part families and the machine groups. The advantages of the neurocomputing model and future directions are discussed.

This paper presents a new approach to part classification in group technology. It advocates the introduction of a feature-based solid-modelling scheme for part representation which, in turn, helps in identifying features of interest. The extracted features of the part are then used to determine the part family to which the part belongs. A parallel distributed processing (PDP) model has been utilised in developing a learning module for the part-classification problem. The proposed model has been implemented in the Unix environment of a Sun work-station. The usefulness of the proposed model has been validated with an example of 16 parts in three part families.

A new HB (Harmonic Balance)/AFT (Alternating Frequency Time) method is further developed to obtain synchronous and subsynchronous whirling response of nonlinear MDOF rotor systems. Using the HBM, the nonlinear differential equations of a rotor system can be transformed to algebraic equations with unknown harmonic coefficients. A technique is applied to reduce the algebraic equations to only those of the nonlinear coordinates. Stability analysis of the periodic solutions is performed via perturbation of the solutions. To further reduce the computational time for the stability analysis, the reduced system parameters (mass, damping, and stiffness) are calculated in terms of the already known harmonic coefficients. For illustration, a simple MDOF rotor system with a piecewise-linear bearing clearance is used to demonstrate the accuracy of the calculated steady-state solutions and their bifurcation boundaries. Employing ideas from modern dynamics theory, the example MDOF nonlinear rotor system is shown to exhibit subsynchronous, quasi-periodic and chaotic whirling motions.

This paper presents a new approach to forming group-technology part families for cellular manufacturing. The approach is based on neural-network technology, which mimics the way biological brain neurons perform to generate intelligent decisions. A procedure of forming part families using parallel and simple artificial neurons is described with examples. The implications and advantages of using neural networks in group technology are discussed.

A HB (Harmonic Balance)/AFT (Alternating Frequency/Time) technique is developed to obtain synchronous and subsynchronous whirling motions of a horizontal Jeffcott rotor with bearing clearances. The method utilizes an explicit Jacobian form for the iterative process which guarantees convergence at all parameter values. The method is shown to constitute a robust and accurate numerical scheme for the analysis of two dimensional nonlinear rotor problems. The stability analysis of the steady-state motions is obtained using perturbed equations about the periodic motions. The Floquet multipliers of the associated Monodromy matrix are determined using a new discrete HB/AFT method. Flip bifurcation boundaries were obtained which facilitated detection of possible rotor chaotic (irregular) motion as parameters of the system are changed. Quasi-periodic motion is also shown to occur as a result of a secondary Hopf bifurcation due to increase of the destabilizing cross-coupling stiffness coefficients in the rotor model.