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Chinese medicines (CM) are gaining more attentions from all over the world. However, there are a large body of questions to be answered because of the chemical complexity of CM and intricate molecular reactions within human body. In recent years, gut microbiota and metabolomics have emerged as two cynosures in deciphering the mechanisms of how our body is functioning. Since gut microbiota and host is a closely interrelated system, paying attention only to gut microbiota or metabolites may omit the interplays among CM, gut microbiota, and hosts. To systemically study these interplays, a network understanding of CM components, gut microbiota, metabolites of gut microbiota, metabolites in human body is necessary. Although there are some obstacles impeding the application of this integrative approach, the potential areas for implementation of the integrative approach is vast. These areas include, but not limited to, elucidating the mechanisms of CM at system level, screening bioactive compounds in CM, and guiding quality control of CM.

The desire for energy-dense and fast-charged battery technology in consumer electronics, electric vehicle, grid, and aviation applications is pushing the envelope from materials to cell and pack designs. However, some approaches could inherently decrease safety of the battery—thus requiring the development of advanced management and diagnostics. Safety of lithium-ion batteries (LiBs), particularly in multicell configurations, is highly variable and could evolve with use. Existing works primarily focus on cell life and safety diagnostics without considering module and pack-level uncertainties and sometimes imply that cell-level electrochemical diagnostics would work in modules or packs, a naive oversimplification. Using example case studies, we highlight the potential and challenges associated with extending single-cell diagnostics to multiple cells, note the existing gaps, and motivate the research, development, and support communities to devote efforts to fill the gap by developing diagnostics at these levels for current- and future-generation LiBs.

Development of the Warrior Injury Assessment Manikin (WIAMan) capability has included the creation of injury assessment reference curves (IARCs) specific to under-body blast (UBB) loading mechanisms and injuries. The WIAMan IARCs were created from high-rate vertical loading tests of component post-mortem human surrogates (PMHS) and analogous components of the WIAMan anthropomorphic test device (ATD). Validation of the WIAMan IARCs is required prior to the WIAMan ATD being utilized for injury assessment in live-fire vehicle test events. A portion of the validation process involves evaluating the ability of the IARCs to predict injury at the system level (whole body). This study evaluates a methodology to assess the performance of the WIAMan IARCs using match-paired tests of whole body PMHS and the WIAMan ATD. The methodology includes a qualitative analysis designed to identify false-positive and false-negative ATD predictions, as well as a quantitative analysis that utilizes area under the receiver-operating characteristic curve (AROC) and Brier score indices to grade IARC performance. Three WIAMan IARCs were used to exemplify the proposed methodology and results are provided. Attributes of the false-prediction, AROC, and Brier score portions of the methodology are presented, with results indicating the new methodology is thorough and robust in evaluation of IARCs.

The increasing cyber connectivity of vehicles and between vehicles and infrastructure will drastically reshape mobility in the coming decades. Although the advent of connected mobility is expected to benefit travelers and society by smoothing traffic, improving rider convenience, and reducing accidents, the augmented cyber components in connected and autonomous vehicles and related infrastructure also give rise to cyber attacks on the transportation system. Yet, little attention has been paid to transportation cyber resilience. This paper thus proposes an investigation on this topic with a comprehensive literature review. Cyber components and plausible autonomous mobility systems operation scenarios are discussed, before identifying possible cyber attacks to autonomous mobility systems at the vehicle and system levels. The discussion then moves to existing practices to enhance cybersecurity, and several strategies are investigated toward enhancing autonomous mobility system cyber resilience. At the vehicle level, creating layers and separation to reduce cyber component connectivity and deploying an independent procedure for data collection and processing are important in vehicle design and manufacturing. At the system level, recommended strategies include keeping redundancy in transportation capacity, maintaining a separate road network, and deploying different sub-autonomous mobility systems.

Real-time hybrid simulation (RTHS) plays an essential role in understanding the time-dependent behavior of structures when subject to extreme hazard loadings in civil engineering research. During RTHS, physical experiment on critical substructure component(s) and numerical simulation of the remaining structure are seamlessly integrated to obtain the whole structural response at the system level. To leverage testing equipment among several laboratories for complex structure systems, distributed real-time hybrid simulation (dRTHS) was proposed and has been successfully applied in seismic experimentation of building structures. In addition to actuator delay experienced in a typical RTHS experiment, network delay due to data transmission between controllers of loading equipment located at geographically distributed laboratories also exists in dRTHS. To compensate the total large and varying time delay, four delay compensation methods were implemented in a recently developed dRTHS testing platform and their performances were evaluated through a series of virtual and physcial dRTHS experiments. The development of the dRTHS testing platform and the four delay compensation methods are introduced first. Then testing results demonstrating the effectivness of these methods are presented together with the reliability of dRTHS experimental method in earthquake engineering and its potential applicability in other engineering fields.

To ease the configuration and maintenance of complex cellular networks, the self-organizing network (SON) is introduced. SON contains three major sub-functional groups: self-configuration, self-optimization, and self-healing. Among these, fault diagnosis in self-healing is crucial, and it is usually considered as a classification problem which is commonly addressed by supervised machine learning methods. However, the barrier to these methods is the difficulty in obtaining sufficient network fault data with label (fault cause). To achieve an effective classifier and dramatically reduce the number of labeled instances needed, we propose an active learning based fault diagnosis scheme, which can select unlabeled instances for labeling actively. According to the selection criteria, there are several query strategies. In this paper, we apply uncertainty sampling as the query strategy due to its low computational cost and high efficiency. Besides, we implement random sampling as a contrast which is a nonactive learning method. To verify the effectiveness of the proposed scheme, we construct a long term evolution (LTE) system level simulator by Network Simulator 3. Then several fault scenarios are simulated, and the records of key performance indicators with fault causes are collected. Extensive experiments demonstrate that the proposed scheme is effective in reducing the number of labeled instances needed, and it is also valid in the class-imbalanced data. Specifically, to achieve a classifier with an accuracy of 99%, the active learning based method only needs 74 labeled instances but the nonactive learning method needs 1354 ones.

Artificial muscles are capable of generating actuation in microsystems with outstanding compliance. Recent years have witnessed a growing academic interest in artificial muscles and their application in many areas, such as soft robotics and biomedical devices. This paper aims to provide a comparative review of recent advances in artificial muscle based on various operating mechanisms. The advantages and limitations of each operating mechanism are analyzed and compared. According to the unique application requirements and electrical and mechanical properties of the muscle types, we suggest suitable artificial muscle mechanisms for specific microsystem applications. Finally, we discuss potential strategies for energy delivery, conversion, and storage to promote the energy autonomy of microrobotic systems at a system level.

For cell culture scaffold innovation, 3DPVS, namely 3D printed vibratory scaffold, was indicated as a future novel product,and it currently stands at conceptual development stage. One essential part for 3DPVS design is innovation, and TRIZ (algorithm of inventive problem solving) was studied as promising method for generating novel conceptual solutions. This study targets designing and solving 3DPVS problems using TRIZ in the new biodimension. We aim to utilize TRIZ to conduct a multi-layer problem-solving process, which is to address design concerns of 3DPVS, especially at super-system to system level. In this connection, TRIZ is used to address basic constraints and contradictions inside regarding trinity of 3D printing,3D scaffold and bio-based vibratory functionality. In the study, five basic conceptual solutions for potential 3DPVS, namely magnetic, electric, mechanical, light and thermal based, have been generated. A brief evaluation has also been conducted,where magnetic-based 3DPVS shows the relatively highest applicability as potential 3DPVS. Compared with traditional experimental-oriented processes for biodesign, the approach of utilizing TRIZ can be inspiring and reinvigorating, which prepares a ground for future 3DPVS design to address detailed sub-system concerns. This study might, to some extent, fill a gap in scaffold design and TRIZ literature and hopefully provide a comprehensive perspective of a timely topic. Refereed/Peer-reviewed

This paper presents a copula technique to develop time-variant seismic fragility curves for corroded bridges at the system level and considers the realistic time-varying dependence among component seismic demands. Based on material deterioration mechanisms and incremental dynamic analysis, the time-evolving seismic demands of components were obtained in the form of marginal probability distributions. The time-varying dependences among bridge components were then captured with the best fitting copula function, which was selected from the commonly used copula classes by the empirical distribution based analysis method. The system time-variant fragility curves at different damage states were developed and the effects of time-varying dependences among components on the bridge system fragility were investigated. The results indicate the time-varying dependence among components significantly affects the time-variant fragility of the bridge system. The copula technique captures the nonlinear dependence among component seismic demands accurately and easily by separating the marginal distributions and the dependence among them.

The complex behaviour of a network emerges as a product of all the interactions between its components as a single entity. The aim of system level investigations has been to identify emergent properties of a network. Identifying crucial components which are responsible for maintaining integrity of networks is essential, to understand or control them. This study presents a method to rank the participation of nodes and edges in a network using perturbation analysis to identify crucial players that contribute to the integrity of the network. The spectra of a network capture maximal features with minimal loss of information. Unlike earlier methods which evaluate perturbation in a network, based on the change in centralities or paths, the present method uses a network comparison score (Network Similarity Score) which quantifies changes at edge level to global entity level using graph spectral properties. The method is evaluated on realistic complex networks of protein structures of Muscarinic acetylcholine receptors. The important amino acid residues (nodes) and their interactions (edges) derived from the study have been correlated with experimental findings. The potential of perturbation score as a predictive tool for any real-world network is also discussed.

Curcumin is an important bioactive component of turmeric and also one of the important natural products, which has been investigated extensively. The precise mode of action of curcumin and its impact on system level protein networks are still not well studied. To identify the curcumin governed regulatory action on protein interaction network (PIN), an interectome was created based on 788 key proteins, extracted from PubMed literatures, and constructed by using STRING and Cytoscape programs. The PIN rewired by curcumin was a scale-free, extremely linked biological system. MCODE plug-in was used for sub-modulization analysis, wherein we identified 25 modules; ClueGo plug-in was used for the pathway’s enrichment analysis, wherein 37 enriched signalling pathways were obtained. Most of them were associated with human diseases groups, particularly carcinogenesis, inflammation, and infectious diseases. Finally, the analysis of topological characteristic like bottleneck, degree, GO term/pathways analysis, bio-kinetics simulation, molecular docking, and dynamics studies were performed for the selection of key regulatory proteins of curcumin-rewired PIN. The current findings deduce a precise molecular mechanism that curcumin might exert in the system. This comprehensive in-silico study will help to understand how curcumin induces its anti-cancerous, anti-inflammatory, and anti-microbial effects in the human body.

Background Aging is a fundamental biological process, where key bio-markers interact with each other and synergistically regulate the aging process. Thus aging dysfunction will induce many disorders. Finding aging markers and re-constructing networks based on multi-omics data (i.e. methylation, transcriptional and so on) are informative to study the aging process. However, optimizing the model to predict aging have not been performed systemically, although it is critical to identify potential molecular mechanism of aging related diseases. Methods This paper aims to model the aging self-organization system using a series of supervised learning methods, and study complex molecular mechanisms of aging at system level: i.e. optimizing the aging network; summarizing interactions between aging markers; accumulating patterns of aging markers within module; finding order-parameters in the aging self-organization system. Results In this work, the normal aging process is modeled based on multi-omics profiles across different tissues. In addition, the computational pipeline aims to model aging self-organizing systems and study the relationship between aging and related diseases (i.e. cancers), thus provide useful indicators of aging related diseases and could help to improve prediction abilities of diagnostics. Conclusions The aging process could be studied thoroughly by modelling the self-organization system, where key functions and the crosstalk between aging and cancers were identified.

This paper presents an extensive overview of the challenges that arise when testing large IoT applications at the system level. In order do that we start from analyzing behavior of local entities such as IoT devices or people interacting with the IoT system. The interactions of these local entities eventually leads to an emergent behavior. Both the emergent behavior and the local behavior need to be taken into account when testing IoT systems. Therefore, we present a novel hybrid simulation based testing approach that is able to effectively facilitate interactions of these local entities. Furthermore, we introduce various solutions to the challenges that arise when implementing this hybrid methodology. These challenges are mainly related to the IoT development pipeline, synchronization between real-life and simulation environment and the scalability constraints of modern simulation techniques.

The declination of the dependability requirements of the complex systems is one of the most critical processes of systems conception as well as systems engineering. We consider that these dependability requirements must be globally considered at the complete system level if they are to be respected before being allocated on the components of the system. Our approach is based on a three-dimensional decomposition model of the system whose purpose is to describe the way in which the system operates in order to deduce its dysfunction that serves as a basic support for the declination of the dependability requirements. The proposed method is applied to a nitric acid cooling system before being introduced into a chemical reactor.

The literature on self-driving cars and ethics continues to grow. Yet much of it focuses on ethical complexities emerging from an individual vehicle. That is an important but insufficient step towards determining how the technology will impact human lives and society more generally. What must complement ongoing discussions is a broader, system level of analysis that engages with the interactions and effects that these cars will have on one another and on the socio-technical systems in which they are embedded. To bring the conversation of self-driving cars to the system level, we make use of two traffic scenarios which highlight some of the complexities that designers, policymakers, and others should consider related to the technology. We then describe three approaches that could be used to address such complexities and their associated shortcomings. We conclude by bringing attention to the "Moral Responsibility for Computing Artifacts: The Rules", a framework that can provide insight into how to approach ethical issues related to self-driving cars.

In today’s competitive environment of Industry 4.0, cyber-physical systems (CPS) of various advanced manufacturing paradigms have brought new challenges to maintenance managements. Efficient prognostics and health management (PHM) policies, which can integrate both individual machine deteriorations and different manufacturing paradigms, are urgently needed. Newly proposed PHM methodologies are systematically reviewed in this paper: as the decision basis, an operating load based forecasting algorithm is proposed for machine health prognosis; at the machine level, a dynamic multi-attribute maintenance model is studied for diverse machines in CPS; at the system level, novel opportunistic maintenance policies are developed for complex flow-line production, mass customization and reconfigurable manufacturing systems, respectively. This framework of PHM methodologies has been validated in industrial implementations.

To avoid unresponsiveness, Android developers utilize asynchronous programming to schedule long-running tasks in the background. In this work, we conduct a systematic study on IntentService, one of the async constructs provided by Android using static program analysis, and find that in Android 6, 974 intents can be sent by third-party applications without protection. Based on this observation, we develop a tool, ATUIN, to demonstrate the feasibility of attacking a CPU automatically by exploiting the intents that can be handled by an Android system. Furthermore, by investigating the unprotected intents, we discover tens of critical vulnerabilities that have not been reported before, including Wi-Fi DoS, telephone signal blocking, SIM card removal, homescreen hiding, and NFC state cheating. Our study sheds light on research into protecting asynchronous programming from being exploited by hackers.

This paper focuses on the feasibility problem of the final solution obtained by the relaxation collaborative optimization method. First, a mixed relaxation (MR) method is developed to enhance the possible premature convergence and non-solution problems of the variable relaxation method. Then the feasibility problem of the final solution is analyzed, that is the optimal solution obtained from system level may not satisfy the constraints of original optimization problem due to the inconsistency between system level and subsystem level for shared variables. A method of modified collaborative optimization (MCO) is put forward to solve this problem. In this method, the impact of the inconsistency is considered and supplemented in the subsystem-level constraints in order to approximate the original constraints as much as possible. At last, based on the MR method, the feasibility problem of the final solution is illustrated by three examples, and the effectiveness and accuracy of MCO are demonstrated by the optimization results.

We present a novel approach for efficient optimization of systems consisting of expensive to simulate components and relatively inexpensive system-level simulations. We consider the types of problem in which the components of the system problem are independent in the sense that they do not exchange coupling variables, however, design variables can be shared across components. Component metamodels are constructed using Kriging. The metamodels are adaptively sampled based on a system level infill sampling criterion using Efficient Global Optimization. The effectiveness of the technique is demonstrated by applying it on numerical examples and an engineering case study. Results show steady and fast converge to the global deterministic optimum of the problems.

Innovation from the component to the system level is necessary to realize the full potential of the next generation of wireless communication.

This paper is focused on developing experimentally validated models of piping components as well as a complete two story piping system. The damage in a piping system is characterized in terms of “First Leakage” based on the observations made in laboratory experiments. Monotonic and cyclic testing for pipe components of various sizes shows that the limit-state for leakage is governed predominantly by flexural deformations at the threaded and groove fit T-joints. As a first step in the validation process, the nonlinear models for the flexural behavior of T-joints are reconciled with the experimental results. Next, the validated component models are incorporated in the piping model for a two story sprinkler system. The system level piping model is then integrated with ceiling systems that interact with sprinkler heads. The complex piping ceiling system model is validated by comparison with the corresponding experimental data.

This paper develops and tests a dynamic model of hospital focus. It does so by tracing the performance trajectories of specialist and general hospitals to identify whether a performance gap exists and whether it widens or shrinks over time. Our longitudinal analyses of all hospital organizations within the English National Health Service (NHS) reveal not only a notable performance gap between specialist and general hospitals in particular with regards to patient satisfaction that widens over time, but also the emergence of a gap especially with regards to hospital staff job satisfaction. These findings reflect the considerable potential of specialization as a means to enhance hospital effectiveness. However, they also alert health policy makers to the threat of a widening performance gap between specialist and general hospitals with potential negative repercussions at the patient and health system level.

In order to estimate the reliability of wind turbine gearbox based on the system level, a generalized stress-strength model is introduced. Considering that the system works properly under a variety of random stresses which affect every component, the total stress on the system is given by a known linear combination of the stresses of all components. Then the strength of the system can be viewed as a linear combination of the strengths of relative components. In this model, stress and strength are independent of each other. Reliability of the system is the probability that strength exceeds stress. Finally, the reliability of wind turbine gearbox is estimated by the multivariable reliability calculation method. The corresponding result is compared with the results of reliability in the extreme cases (completely dependent and completely independent) by the traditional evaluation method.

There is wide geographic variation in bariatric surgery rates, although higher regional rates of obesity are not correlated with higher rates of surgery. In this study, four system-level factors were explored as contributors to this geographic variation. Geographic utilization rates of bariatric surgery showed no correlation to the number of bariatric surgeons, number of accredited centers, and the percentage of patients with a recent primary care visit. The total number of surgical discharges was weakly correlated with bariatric surgery rates (r = 0.26, p =

In this paper, we present a system level approach to smart wheelchair system (SWS) navigation in urban environments. The proposed SWS ecosystem has two primary components: a mapping service which generates large-scale landmark maps, and the SWS vehicle itself, which is a client of the mapping service. The SWS prototype integrates 3D LIDAR/imaging systems which provide robust perception in unstructured, outdoor environments. It also leverages these same sensors for map-based localization. In demonstrating the efficacy of the approach, the SWS navigated autonomously over a distance of more than 12 km in a representative urban environment without once losing localization, and without the use of GPS.

Despite the widespread use of traditional Chinese medicine (TCM) in clinical settings, proving its effectiveness via scientific trials is still a challenge. TCM views the human body as a complex dynamical system, and focuses on the balance of the human body, both internally and with its external environment. Such fundamental concepts require investigations using system-level quantification approaches, which are beyond conventional reductionism. Only methods that quantify dynamical complexity can bring new insights into the evaluation of TCM. In a previous article, we briefly introduced the potential value of Multiscale Entropy (MSE) analysis in TCM. This article aims to explain the existing challenges in TCM quantification, to introduce the consistency of dynamical complexity theories and TCM theories, and to inspire future system-level research on health and disease.

BACKGROUND: The gap between treatment guidelines and clinical practice in prediabetes management has been identified in previous studies. The knowledge related to addressing lifestyle change during office visits in clinical practice to manage prediabetes is limited. OBJECTIVE: To describe patterns of lifestyle management addressed during office-based visits involving patients with prediabetes and identify factors associated with addressing lifestyle management during physician office visits in the USA. DESIGN: Cross-sectional study PARTICIPANTS: US National Ambulatory Medical Care Survey (NAMCS) data from 2013 to 2015 were combined to identify office-based visits involving patients with prediabetes. MAIN MEASURES: The major outcome is lifestyle management including diet/nutrition, exercise, and/or weight reduction. Patient and physician characteristics were collected for analysis. The prevalence and patterns of addressing lifestyle management during visits were estimated and described. Multivariate logistic regression model identified significant factors associated with lifestyle management. The patient visit weight was applied to all analyses to achieve nationally representative estimates. KEY RESULTS: Among 4039 office-based visits involving patients with prediabetes between 2013 and 2015, 22.8% indicated lifestyle management was addressed during the visits. Diet/nutrition, exercise, and weight reduction accounted for 86.1%, 62.6%, and 34.1% of the visits with lifestyle management addressed, respectively. Lifestyle management was more likely to be addressed during the visits involving patients with hyperlipidemia (OR = 1.74, 95% CI 1.24–2.46) and obesity (OR = 4.03, 95% CI 2.91–5.56), seeing primary physicians (vs. other specialties, OR = 1.46, 95% CI 1.03–2.08), and living in the southern region (vs. northeast, OR = 1.96, 95% CI 1.20–3.19). CONCLUSIONS: The prevalence of addressing lifestyle management during office visits involving patients with prediabetes remained low in the USA. Patients’ clinical characteristics, geographic region, and physician’s specialty were associated with addressing lifestyle management during the visits. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-018-4724-z) contains supplementary material, which is available to authorized users.

Fault diagnosis is important to the design and maintenance of large multiprocessor systems. PMC model is the most well known and widely studied model in the system level diagnosis of multiprocessor systems. Under the PMC model, a diagnosis algorithm based on some graph-coloring techniques has been proposed in this paper. Given a syndrome $$\sigma $$ź, the first part of the algorithm can locate all the definitely faulty vertices. Then in the second part of the algorithm a diagnosis graph corresponding to the syndrome can be constructed and the suspicious faulty sets can be determined by finding the maximal independent sets of the diagnosis graph. A weight is assigned to each suspicious faulty vertex set which can measure its occurring probability. The algorithm is shown to be correct, not based on any conjecture and can be applied to the fault identification for any multiprocessor system.

Idiopathic Pulmonary Fibrosis (IPF) is an incurable progressive fibrotic disease of the lungs. We currently lack a systematic understanding of IPF biology and a systems approach may offer new therapeutic insights. Here, for the first time, a large volume of high throughput genomics data has been unified to derive the most common molecular signatures of IPF. A set of 39 differentially expressed genes (DEGs) was found critical to distinguish IPF. Using high confidence evidences and experimental data, system level networks for IPF were reconstructed, involving 737 DEGs found common across at least two independent studies. This all provided one of the most comprehensive molecular system views for IPF underlining the regulatory and molecular consequences associated. 56 pathways crosstalks were identified which included critical pathways with specified directionality. The associated steps gained and lost due to crosstalk during IPF were also identified. A serially connected system of five crucial genes was found, potentially controlled by nine miRNAs and eight transcription factors exclusively in IPF when compared to NSIP and Sarcoidosis. Findings from this study have been implemented into a comprehensive molecular and systems database on IPF to facilitate devising diagnostic and therapeutic solutions for this deadly disease.

Traditional Chinese medicine (TCM) has been gradually accepted by the world. Despite its widespread use in clinical settings, a major challenge in TCM is to study it scientifically. This difficulty arises from the fact that TCM views human body as a complex dynamical system, and focuses on the balance of the human body, both internally and with its external environment. As a result, conventional tools that are based on reductionist approach are not adequate. Methods that can quantify the dynamics of complex integrative systems may bring new insights and utilities about the clinical practice and evaluation of efficacy of TCM. The dynamical complexity theory recently proposed and its computational algorithm, Multiscale Entropy (MSE) analysis, are consistent with TCM concepts. This new system level analysis has been successfully applied to many health and disease related topics in medicine. We believe that there could be many promising applications of this dynamical complexity concept in TCM. In this article, we propose some promising applications and research areas that TCM practitioners and researchers can pursue.

The term systems pharmacology describes a field of study that uses computational and experimental approaches to broaden the view of drug actions rooted in molecular interactions and advance the process of drug discovery. The aim of this work is to stick out the role that the systems pharmacology plays across the multi-target drug discovery from natural products for cardiovascular diseases (CVDs). Firstly, based on network pharmacology methods, we reconstructed the drug-target and target-target networks to determine the putative protein target set of multi-target drugs for CVDs treatment. Secondly, we reintegrated a compound dataset of natural products and then obtained a multi-target compounds subset by virtual-screening process. Thirdly, a drug-likeness evaluation was applied to find the ADME-favorable compounds in this subset. Finally, we conducted in vitro experiments to evaluate the reliability of the selected chemicals and targets. We found that four of the five randomly selected natural molecules can effectively act on the target set for CVDs, indicating the reasonability of our systems-based method. This strategy may serve as a new model for multi-target drug discovery of complex diseases.

In this paper, we analyze the physical layer abstraction for bit interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) system from a parallel bit channel perspective. By combining the exponential effective SNR (signal-to-noise ratio) mapping (EESM) with the maximum a posteriori (MAP) algorithm, a bit LLR (log-likelihood ratio) wise EESM (BL-EESM) method is proposed. This method can abstract the link performance with high accuracy, especially for the case when channel estimation is imperfect. Afterward, the BL-EESM method is simplified by utilizing the non-linear quantization idea, which can reduce the times of exponential operation by two orders of magnitude at wide system bandwidth, yet shows little loss in accuracy. Our proposal can be applied to both system level simulations to save the time consumption and to practical terminals to facilitate the adaptive modulation and coding (AMC) procedure, bringing about throughput improvement at low hardware cost.

This paper deals with redundancy allocation problem in interval environment that maximizes the overall system reliability subject to the given resource constraints and also minimizes the overall system cost subject to the given resources including an additional constraint on system reliability. Here, the reliability of each component is assumed as interval valued and the cost coefficients as well as the amount of resources are imprecise and interval valued. These types of problems have been formulated as an interval valued nonlinear integer programming problem. In this paper, we have formulated two types of redundancy, viz. component level redundancy known as low-level redundancy and the system level redundancy known as high-level redundancy. These problems have been transformed as an unconstrained optimization problem using penalty function technique and solved using genetic algorithm. Finally, two numerical examples (one for low-level redundancy and another for high-level redundancy) have been solved and the computational results have been compared.

This paper presents a novel fully testable and diagnosable structure for phase-frequency detectors. All procedures of converting the conventional PFD to the fully testable one are reported step by step. Also, the probability-based testability of proposed architecture is calculated. In addition, area considerations related to new fully testable PFD are introduced completely. At last, the proposed structure is designed in both system level and circuit level. The results of fully testable PFD in 0.13-μm CMOS technology are shown. Simulation results confirm the theoretical analysis.

This project describes application of an evidenced-based, trauma-informed treatment framework, Attachment, Regulation and Competency (ARC), with complexly traumatized youth in residential treatment. The processes of implementing the ARC model into clinical and milieu programming at two residential treatment programs are described. Particular attention is paid to system-level processes and strategies for embedding ARC in a sustainable manner. Pilot data demonstrated a significant relation between use of ARC and reductions in PTSD symptoms, externalizing and internalizing behaviors, and the frequency of restraints used across programs. Preliminary findings contribute to an emerging empirical basis for the ARC model and are supportive of its clinical utility as a practice in the residential context. Next steps include: a) expanding the study findings by conducting controlled efficacy research, b) examining system level variables as mediators of change, and c) describing the full operation stage of implementation of the ARC framework.

This study examined the effect of using the Toyota Production System (TPS) to change intake procedures on treatment timeliness within a semi-rural community mental health clinic. One hundred randomly selected cases opened the year before the change and one hundred randomly selected cases opened the year after the change were reviewed. An analysis of covariance (ANCOVA) demonstrated that changing intake procedures significantly decreased the number of days consumers waited for appointments (F(1,160)=4.9; p=.03) from an average of 11 days to 8 days. The pattern of difference on treatment timeliness was significantly different between adult and child programs (F(1,160)=4.2; p=.04), with children waiting an average of 4 days longer than adults for appointments. Findings suggest that small system level changes may elicit important changes and that TPS offers a valuable model to improve processes within community mental health settings. Results also indicate that different factors drive adult and children’s treatment timeliness.

Optical imaging has a long history in physiology and in neurophysiology in particular. Over the past 15 years or so, new methodologies have emerged that combine genetic engineering with light-based imaging methods. This merger has resulted in a tool box of genetically encoded optical indicators that enable nondestructive and long-lasting monitoring of neuronal activities at the cellular, circuit, and system level. This review describes the historical roots and fundamental concepts underlying these new approaches, evaluates current progress in this field, and concludes with a forward-looking perspective on current work and potential future developments in this field.

Next-generation sequencing (NGS) technologies generate higher resolution and less noise data that can allow the assembly of bacterial genome sequences. It also enables the characterization and quantification of transcriptomes, and the genome-wide profiling of DNA-protein interactions. With decreasing cost of NGS, such revolutionary advances in technology has become a powerful tool for studying bacterial genome complexity, which in turn will be used to design synthetic genome. This review describes the NGS approaches, the challenges associated with their application and the advances made so far in characterizing bacterial genomes, transcriptomes, and interactomes. We anticipate these high-throughput data to be a resourceful and broadly used for elucidating bacterial cells at the system level and furthermore, for the synthesis of intelligent biological systems for biotechnological purposes.

Of all the requirements for e-voting systems, robustness is the one that has received the least attention. This paper is concerned with addressing this issue. It is argued that a two-level consideration of robustness can facilitate the design of e-voting systems and enhance their resilience. An approach is proposed which requires, as a first step, an explicit awareness of robustness at protocol level and robustness at system level. The second step involves the identification of appropriate technologies and their integration into an architecture where the two forms of robustness are addressed. The approach is illustrated by the design and implementation of a service-oriented architecture for robust e-voting, based on the FOO92 protocol. The service-oriented architecture provided the framework for the integration of selected technologies such as blind signatures, encryption and onion routing. In addition to the just-in-time composition of the e-voting system, it supports the distribution of tasks and state. The system conforms to most e-voting requirements.

Understanding the measured performance of parallel applications in real systems is difficult--with the aim to utilize the resources available, optimizations deployed in hardware and software layers build up to complex systems. However, in order to identify bottlenecks the performance must be assessed. This paper introduces PIOsimHD, an event-driven simulator for MPI-IO applications and the underlying (heterogeneous) cluster computers. With the help of the simulator runs of MPI-IO applications can be conducted in-silico; this includes detailed simulation of collective communication patterns as well as simulation of parallel I/O. The simulation estimates upper bounds for expected performance and helps assessing observed performance. Together with HDTrace, an environment which allows tracing the behavior of MPI programs and internals of MPI and PVFS, PIOsimHD enables us to localize inefficiencies, to conduct research on optimizations for communication algorithms, and to evaluate arbitrary and future systems. In this paper the simulator is introduced and an excerpt of the conducted validation is presented, which demonstrates the accuracy of the models for our cluster.

This paper presents an approach for the structural modeling and analysis of a typical gas turbine system. This approach has been applied to the systems and subsystems, which are integral parts of a typical gas turbine system. Since a gas turbine system performance is measured in terms of fluid flow energy transformations across its various assemblies and subassemblies, the performance of such subsystems affects the overall performance of the gas turbine system. An attempt has been made to correlate the associativity of such subsystems contributing to overall gas turbine system functional evaluation using graph theoretic approach. The characteristic equations at the system level as well as subsystem level have been developed on the basis of associativity of various factors affecting their performance. A permanent function has been proposed for the functional model of a gas turbine system, which further leads to selection, identification and optimal evaluation of gas turbine systems.

Knowledge translation is emerging as an increasingly important area of research and practice aiming to remedy the disconnection between evidence-based clinical care and real-life practice. The discipline is informed by a large body of literature and practical tools, which can inform providers and leaders in health care. Front-line clinicians should be aware of the principles of knowledge translation and the evidence that defines successful and suboptimal strategies for introducing evidence-based care at the system level.

Several necessary system and organizational support conditions for wraparound have been identified (Walker et al. 2003). Yet, the relationship between these necessary system level conditions and wraparound fidelity has only recently begun to be examined. Similarly, few studies have included a measure of wraparound fidelity when examining the relationship between wraparound implementation and youth outcomes. The statewide implementation of a wraparound demonstration grant offers the opportunity to explore these relationships and to identify factors that predict improvement in functioning for youth receiving wraparound. Findings suggest that significant relationships exist between (1) the stage of development of necessary support conditions for wraparound and wraparound fidelity and (2) wraparound fidelity and improvement in youth outcomes. Specific elements of wraparound (i.e., outcomes based and community based) and baseline needs and strengths (e.g., high levels of anxiety and conduct issues, poor functioning at home and in school, judgment, and risks) were found to predict a reduction in youth needs. Other unexpected relationships between youth outcomes and the cultural competence element of wraparound and being multi-racial were also discovered. These findings reinforce the importance of supporting high fidelity wraparound for youth and their families in a recovery focused behavioral health system.

In order to understand the role of crossover in differential evolution, theoretical analysis and comparative study of crossover in differential evolution are presented in this paper. Two new crossover methods, namely consecutive binomial crossover and non-consecutive exponential crossover, are designed. The probability distribution and expectation of crossover length for binomial and exponential crossover used in this paper are derived. Various differential evolution algorithms with different crossover methods including mutation-only differential evolution are comprehensively compared at system level instead of parameter level. Based on the theoretical analysis and simulation results, the effect of crossover on the reliability and efficiency of differential evolution algorithms is discussed. Some insights are revealed.

System level barriers have been associated with inadequate follow-up of abnormal cervical cytology.The aim of this study was to develop and evaluate an electronic tracking system to improve follow-up of abnormal Pap tests.We implemented an electronic medical record (EMR)-based Pap test tracking system at two clinical practices at an inner-city academic health center. The system generated a provider-specific monthly report of all abnormal Pap results, and provided a patient-specific Pap tracking table embedded in the EMR for each subject.We compared abnormal Pap test follow-up rates for the 24 months pre-intervention with rates 12 months following its implementation (post-intervention). The evaluation followed all subjects for 12 months from the date of their abnormal Pap test, looking for diagnostic resolution.Subjects were young women (mean age = 30.5) of primarily white (42%) and African American (37%) descent, who spoke English (88%). Forty-eight percent were insured through publicly subsidized insurance. Controlling for type of abnormality and practice location, the adjusted mean time to resolution decreased significantly from 108 days (confidence interval, CI 105-112 days) in the pre-intervention period to 86 days (CI 81-91 days).Our study cannot demonstrate that with follow up, we directly avoided cases of invasive cervical cancer. However, we show that in an at-risk urban population, an automated, EMR-based tracking system reduced the time to resolution, and increased the number of women who achieved diagnostic resolution.

This paper discusses the evaluation of automated metrics developed for the purpose of evaluating machine translation (MT) technology. A general discussion of the usefulness of automated metrics is offered. The NIST MetricsMATR evaluation of MT metrology is described, including its objectives, protocols, participants, and test data. The methodology employed to evaluate the submitted metrics is reviewed. A summary is provided for the general classes of evaluated metrics. Overall results of this evaluation are presented, primarily by means of correlation statistics, showing the degree of agreement between the automated metric scores and the scores of human judgments. Metrics are analyzed at the sentence, document, and system level with results conditioned by various properties of the test data. This paper concludes with some perspective on the improvements that should be incorporated into future evaluations of metrics for MT evaluation.

In the present paper, we consider a (n − k + 1)-out-of-n system with identical components where it is assumed that the lifetimes of the components are independent and have a common distribution function F. We assume that the system fails at time t or sometime before t, t > 0. Under these conditions, we are interested in the study of the mean time elapsed since the failure of the components. We call this as the mean past lifetime (MPL) of the components at the system level. Several properties of the MPL are studied. It is proved that the relation between the proposed MPL and the underlying distribution is one-to-one. We have shown that when the components of the system have decreasing reversed hazard then the MPL of the system is increasing with respect to time. Some examples are also provided.

This study assessed the degree to which services in south–central Ontario, Canada, were coordinated to meet the supportive care needs of palliative cancer patients and their families. Programs within the region that were identified as providing supportive care to palliative cancer patients and their families were eligible to participate in the study. Program administrators participated in a semi-structured interview and direct-care providers completed a survey instrument. Administrators from 37 (97%) of 38 eligible programs and 109 direct-care providers representing 26 (70%) programs participated in the study. Most administrator and direct-care respondents felt that existing services in the community were responsive to palliative care patients’ individual needs. However, at a system level, most respondents in both groups felt that required services were not available and that resources were inadequate. The most frequently reported unmet supportive care need identified by both respondent groups was psychological/social support. Most administrator (69%) and direct-care (64%) respondents felt that palliative care services were not available when needed. The majority of administrator and direct-care respondents were satisfied with the exchange of patient information within and between programs, although direct-care staff identified a deficit in information transferred on palliative care patients’ social/psychological status. The study demonstrated the value of a theory-based approach to evaluate the coordination of palliative cancer care services. The findings revealed that service programs faced significant challenges in their efforts to provide coordinated care.

The recent developments in CMOS technology are about opening a new exciting door to affordable 60-GHz radio applications. One promising application area for 60-GHz radio is home networks in which there is an increasing demand for multi-gigabit wireless networking. The research efforts so far have generally focused on utilizing the 60-GHz band for point-to-point communication by addressing its physical aspects like propagation characteristics and channel models. The issues of 60-GHz networking at the system level have not been adequately considered. In this paper, the challenges and the research issues of 60-GHz networking are reviewed from the system-level perspective. We present them in the context of wireless local area networks for future home networks with discussions on some possible solutions for the introduced challenges.

This paper discusses the utility of scaling laws to materials informatics and presents the algorithm Scaling LAW (SLAW), useful to obtain scaling laws from statistical data. These laws can be used to extrapolate known materials property data to untested materials by using other more readily available information. This technique is independent of a characteristic length or time scale, so it is useful for a broad diversity of problems. In some cases, SLAW can reproduce the mathematical expression that would have been obtained through an analytical treatment of the problem. This technique was originally designed for mining statistical data in materials processing and materials behavior at a system level, and it shows promise for the study of the relationship between structure and properties in materials.