High-level Guidance Research Articles

Four Social Brain Regions, Their Dysfunctions, and Sequelae, Extensively Explain Autism Spectrum Disorder Symptomatology.

Autism spectrum disorder (ASD) is a challenging neurodevelopmental disorder with symptoms in social, language, sensory, motor, cognitive, emotional, repetitive behavior, and self-sufficient living domains. The important research question examined is the elucidation of the pathogenic neurocircuitry that underlies ASD symptomatology in all its richness and heterogeneity. The presented model builds on earlier social brain research, and hypothesizes that four social brain regions largely drive ASD symptomatology: amygdala, orbitofrontal cortex (OFC), temporoparietal cortex (TPC), and insula. The amygdala’s contributions to ASD largely derive from its major involvement in fine-grained intangible knowledge representations and high-level guidance of gaze. In addition, disrupted brain regions can drive disturbance of strongly interconnected brain regions to produce further symptoms. These and related effects are proposed to underlie abnormalities of the visual cortex, inferior frontal gyrus (IFG), caudate nucleus, and hippocampus as well as associated symptoms. The model is supported by neuroimaging, neuropsychological, neuroanatomical, cellular, physiological, and behavioral evidence. Collectively, the model proposes a novel, parsimonious, and empirically testable account of the pathogenic neurocircuitry of ASD, an extensive account of its symptomatology, a novel physiological biomarker with potential for earlier diagnosis, and novel experiments to further elucidate the mechanisms of brain abnormalities and symptomatology in ASD.

A REVIEW PAPER ON ATTRIBUTE-BASED ENCRYPTION FOR MESSAGE PRIVACY IN CLOUD

The notion of attribute-based encryption (ABE)was proposed as an economical alternative to public-keyinfrastructures. It is the set of descriptive attributes, used as anidentity to generate a secret key, as well as serving as theaccess structure that performs access control. ABE is also auseful building block in various cryptographic primitives suchas searchable encryption. . It successfully integratesEncryption and Access Control and is ideal for sharing secretsamong groups, especially in a Cloud environment. Mostdeveloped ABE schemes support key-policy or ciphertextpolicyaccess control in addition to other features such asdecentralized authority, efficient revocation and keydelegation. This paper surveys mainstream papers, analyzesmain features for desired ABE systems, and classifies theminto different categories. With this high-level guidance, futureresearchers can treat these features as individual modules andselect related ones to build their ABE systems on demand.For ABE, it is not realistic to trust a single authority tomonitor all attributes and hence distributing control over manyattribute-authorities is desirable.A multi-authority ABE scheme can be realized with a trustedcentral authority (CA) which issues part of the decryption keyaccording to a user's global identifier (GID). However, this CAmay have the power to decrypt every cipher text, and the useof a consistent GID allowed the attribute-authorities tocollectively build user's attributes. Decentralized ABE schemecan eliminate the burden of heavy communication andcollaborative computation. It is observed that privacypreservingdecentralized key-policy ABE scheme has claimedto achieve better privacy for users and is provably secure inthe standard model

Minimum control effort–based path planning and nonlinear guidance for autonomous mobile robots

This article puts forth a framework using model-based techniques for path planning and guidance for an autonomous mobile robot in a constrained environment. The path plan is synthesized using a numerical navigation function algorithm that will form its potential contour levels based on the “minimum control effort.” Then, an improved nonlinear model predictive control approach is employed to generate high-level guidance commands for the mobile robot to track a trajectory fitted along the planned path leading to the goal. A backstepping-like nonlinear guidance law is also implemented for comparison with the NMPC formulation. Finally, the performance of the resulting framework using both nonlinear guidance techniques is verified in simulation where the environment is constrained by the presence of static obstacles.

Closed-Loop Policies for Operational Tests of Safety-Critical Systems

Manufacturers of safety-critical systems must make the case that their product is sufficiently safe for public deployment. Much of this case often relies upon critical event outcomes from real-world testing, requiring manufacturers to be strategic about how they allocate testing resources in order to maximize their chances of demonstrating system safety. This paper frames the partially observable and the belief-dependent problem of test scheduling as a Markov decision process, which can be solved efficiently to yield closed-loop manufacturer testing policies. By solving for policies over a wide range of problem formulations, we are able to provide high-level guidance for manufacturers and regulators on issues relating to the testing of safety-critical systems. This guidance spans an array of topics, including circumstances under which manufacturers should continue testing despite observed incidents, when manufacturers should test aggressively, and when regulators should increase or reduce the real-world testing requirements for safety-critical systems.

Research and Global Health Emergencies: On the Essential Role of Best Practice.

This article addresses an important, overlooked regulatory challenge during global health emergencies (GHEs). It provides novel insights into how, and why, best practice can support decision makers in interpreting and implementing key guidance on conducting research during GHEs. The ability to conduct research before, during and after such events is crucial. The recent West-African Ebola outbreaks and the Zika virus have highlighted considerable room for improvement in meeting the imperative to research and rapidly develop effective therapies. A means of effectively capturing these experiences and folding them into future decision-making is lacking; the need for effective practical translational measures remains. The challenge for the research community lies in extracting meaningful action-guiding content from pre-existing guidelines—which draw upon practical examples of guidelines ‘in action’—that assist in determining how to act in a particular (future) situation. Insights are provided into the role of best practice as a means to do so; such examples can provide invaluable support to decision makers in interpreting high-level guidance; overarching guidelines retain their necessary level of generality and flexibility, whilst corresponding best practice examples—which incorporate important lessons learned—illustrate how such guidelines can be interpreted at a practical level.

Integrating quantitative defense-in-depth metrics into new reactor designs

Risk-informed, performance-based (RIPB) methods have progressed to the point where high-level guidance can be used to augment traditional, deterministic, nuclear safety design practices in areas important to nuclear reactor safety. This paper describes an approach for augmenting the traditional defense-in-depth (DID) qualitative approach with quantitative risk information from a plant-specific probabilistic risk assessment (PRA) in a way that is structured, can be applied on a consistent basis, and allows for clear acceptance criteria. Adding performance-based targets that should be achieved is expected to result in safer and more economical plant designs. Evaluations of DID can be conducted throughout the design process as well as in support of design certification and operating license applications to identify where defense protections could be enhanced or relaxed. Consistent with the United States Nuclear Regulatory Commission's policy statement encouraging greater use of PRA to improve safety decision making and regulatory efficiency, this scenario-based DID method can be used to evaluate changes and overall plant design as part of the normal design control process. Although the RIPB method presented in this paper was developed for application to advanced passive light water reactor designs, the metrics could be tailored to other reactor designs. This risk-informed approach to DID helps to ensure that public and worker risk insights are integrated into the design process holistically.

Rethinking sustainability strategies

PurposeThe purpose of this paper is to provide both theorists and practitioners with a conceptual framework that links sustainability strategies more closely with Porter’s generic strategies. The intent of this approach is to establish sustainability, fundamentally, as a strategic process. The proposed models set a strategic context to tie sustainability, to mediating variables, such as innovation and technology, while also linking them to generic strategies (low cost leader, differentiation, and focus) and firm financial performance in a causal chain. The proposed model gives rise to conclusions about the effectiveness of sustainability strategies that are consistent with emerging research about the role of radical innovation in sustainability.Design/methodology/approachThe paper proposes two conceptual frameworks designed to link sustainability with business strategy. These models are rooted in evolving understandings of business strategy arising from Porter’s original explanations of generic strategies and sources of competitive advantage. The first model is a causal model that links drivers, such as type of competitive strategy and mode of innovation, to competitive outcomes and firm financial performance. The second model describes how different modes of technology development, in sustainability initiatives, cause changes in firm competitive and financial outcomes.FindingsThe conclusions arising from the model-based insights suggest that conventional continuous and incremental improvement sustainability practices hold the potential to pose strategic risks to some firms – depending on their core business strategy. By contrast, the model provides a logical, yet, less known, rationale that suggests radical innovation in sustainability practices may pose fewer strategic risks. It may also offer relatively more competitive and financial advantages than well-established programs relying on incremental innovation.Research limitations/implicationsAlthough the proposed conceptual frameworks are rooted in strategic management theories, the proposed models and expected outcomes have not yet been empirically tested or validated. However, initially, these models appear to have more face validity in explaining breakthrough sustainability success stories, such as Nike, than do competing explanations. Most importantly, the counter-intuitive finding that radical innovation is likely to be more effective in driving both sustainability and financial outcomes is a topic for future investigation.Practical implicationsThe proposed models and accompanying rationale have direct implications for practitioners. They provide practitioners with a road map to logically and deductively frame sustainability strategies based on their current business strategy. Practitioners are often hindered by the lack of high-level guidance for making the transition from operationally focused sustainability tactics to strategies than are congruent with current business strategies. The current paradigm of using incremental sustainability strategies on an ad hoc basis does not always provide neutral outcomes regarding financial effects and competitive advantage – they may yield negative effects.Social implicationsThe importance of sustainability strategies and management practices cannot be overstated. On a global scale, evidence indicates that most corporate sustainability programs are ineffective at slowing the rate of global forces offsetting sustainability. The proposed models and strategic management approach are intended to dramatically increase the effectiveness of sustainability improvement by closely aligning them with corporate strategies. Historically, companies have struggled to make the leap from randomly using eco-efficiency tools to making sustainability a key component of their business strategy.Originality/valueThis paper integrates a number of diverse lines of inquiry from the strategic management literature into a counter-intuitive approach for integrating sustainability into a firm’s core business strategy. The proposed conceptual frameworks can be used, prospectively, to design new sustainability strategies, or it can be used, analytically (retrospectively), to understand reasons for failure or under-performance in sustainability initiatives.

Enterprise Thinking for Self-aware Systems

The paper aims to provide high-level guidance for architects of cyber-physical enterprises. We propose that interactions within such systems should be largely self-determined, based on system self-awareness and dynamic re-configuration, with the architecture evolving based on a set of foundational principles, rather than being pre-defined by an external designer. We investigate the suitability of typical development life cycles and identify architectural challenges in the context of dynamic cyber-physical systems that utilize the power of the Internet of Things. Desired systemic attributes are defined, which are necessary for making suitable core architectural choices. The application of the findings is exemplified through a case study, a synthesis of issues, and implications for further research.

Interface model enabling decomposition method for architecture definition of mechatronic systems

Solving a complex problem often requires a way to break it down into smaller, interconnected and manageable sub-problems, and then to join them together. The concept of breaking down a problem into smaller pieces is generally referred to as decomposition. The design of mechatronic systems is an example of such complex problems, as it is based on the integration of several disciplines, such as mechanical, electrical and software engineering. Decomposition is thus a common technique to help designers to obtain solutions for the design of mechatronic systems during the systems engineering process. However, an effective decomposition method which can fully solve the design problems of mechatronic systems has not yet been proposed in systems engineering.The goal of the paper is to formalise this decomposition method based on an interface model. This method is applicable to the architecture definition in the context of the design of mechatronic systems during their conceptual design phase. The proposed decomposition method provides designers with high-level guidance to help them to achieve the appropriate hierarchies and granularities for the architecture of mechatronic systems. The proposed decomposition method is applied and demonstrated using the systems engineering practices of a 3D measurement system.

Development of default uncertainties for the value/benefit attributes in the regulatory analysis technical evaluation handbook

NUREG/BR-0184, Regulatory Analysis Technical Evaluation (RATE) Handbook, was produced in 1997 as an update to the original NUREG/CR-3568, A Handbook for Value-Impact Assessment (1983). Both documents, especially the later RATE Handbook, have been used extensively by the USNRC and its contractors not only for regulatory analyses to support backfit considerations but also for similar applications, such as Severe Accident Management Alternative (SAMA) analyses as part of license renewals. While both provided high-level guidance on the performance of uncertainty analyses for the various value/benefit attributes, detailed quantification was not of prime interest at the times of the Handbooks’ development, defaulting only to best estimates with low and high bounds on these attributes. As the USNRC examines the possibility of updating the RATE Handbook, renewed interest in a more quantitative approach to uncertainty analyses for the attributes has surfaced. As the result of an effort to enhance the RATE Handbook to permit at least default uncertainty analyses for the value/benefit attributes, it has proven feasible to assign default uncertainties in terms of 95th %ile upper bounds (and absolute lower bounds) on the five dominant value/benefit attributes, and their sum, when performing a regulatory analysis via the RATE Handbook. Appropriate default lower bounds of zero (no value/benefit) and an upper bound (95th %ile) that is four times higher than the mean (for individual value/benefit attributes) or three times higher (for their summation) can be recommended. Distributions in the form of histograms on the summed value/benefit attributes are also provided which could be combined, after appropriate scaling and most likely via simulation, with their counterpart(s) from the impact/cost analysis to yield a final distribution on the net overall value/impact (benefit/cost).

Research for an Information Guide System of Irregular Space Based on User Route

Background This research aims to build a spatial and wayfinding information guide-line properly based on the visitor routein an irregular architectural space, Dongdaemun Design Plaza (DDP). It is essential for irregular architectural spaces to design user routes for seamless wayfinding, a clear understanding of congested points, providing suitable information and the suggestion of information signage with high visibility and easy user comprehension throughout the space. Methods The space syntax program was utilized in order to find congested areas in DDP. The wayfinding route plan was set for each user-type based on the result of the space syntax measurements. The user test was also executed by placing the prototype of signage within a hierarchy of spatial information in the DDP access area. Lastly, the overall direction and the proper wayfinding scheme were suggested with the result of this study. Results Providing high-level guidance for each major facility and suitable spatial information after segmenting access points within an irregular architectural space is crucial for easy wayfinding. In addition, offering proper and hierarchical information according to specific spatial conditions such as congested areas with static and dynamic sign media without interruption help create identity for a spatial structure to maneuver the user’s way from the start or crossroads to the destination. By installing spatial information signage, fluent user-circulation and avoidance of user wayfinding congestion were apparent. This suggests each type and amount of information focused on user-route and functional class and placement of sign media are critical in wayfinding for irregular spaces. Conclusions This study suggests a systematic process to provide guidance of spatial information in irregular architectural spaces through the research of optimal wayfinding by identifying and measuring the problems of user-centered spatial information in irregular architectural spaces.

Detection guided deconvolutional network for hierarchical feature learning

Deep learning models have gained significant interest as a way of building hierarchical image representation. However, current models still perform far behind human vision system because of the lack of selective property, the lack of high-level guidance for learning and the weakness to learn from few examples. To address these problems, we propose a detection-guided hierarchical learning algorithm for image representation. First, we train a multi-layer deconvolutional network in an unsupervised bottom-up scheme. During the training process, we use each raw image as an input, and decompose an image using multiple alternating layers of non-negative convolutional sparse coding and max-pooling. Inspired from the observation that the filters in top layer can be selectively activated by different high-level structures of images, i.e., one or partial filters should correspond to a particular object class, we update the filters in network by minimizing the reconstruction errors of the corresponding feature maps with respect to certain object detection maps obtained by a set of pre-trained detectors. With the fine-tuned network, we can extract the features of given images in a purely unsupervised way with no need of detectors. We evaluate the proposed feature representation on the task of object recognition, for which an SVM classifier with spatial pyramid matching kernel is used. Experiments on the datasets of PASCAL VOC 2007, Caltech-101 and Caltech-256 demonstrate that our approach outperforms some recent hierarchical feature descriptors as well as classical hand-crafted features.

Prospects, challenges and latest developments in designing a scalable big data stream computing system

In the big data era, big data stream computing, as a computing paradigm, is gaining traction for real-time and online data computing applications, and is specially designed to solve the dilemma of real-time data stream computing by processing data online within real-time constraints. It is used to compute large amounts of data in the form of continuous data streams. Each computation is represented by a data stream graph, usually a directed graph. In this paper, the computing paradigm of big data stream computation in data stream graph is given, and some application scenarios and big data stream characteristics are presented. A series of challenges in designing a scalable big data stream computing system in big data environments are summarised by referring to some research results of big data stream computing system, which include stateless system architecture, elastically adaptive scheduling strategy and fine-grained fault tolerance strategy. All these challenges will greatly help us to understand big data steam computing, and provide a high-level guidance in designing an excellent and useful big data stream computing system.

Marketing Doctrine: A Principles-Based Approach to Guiding Marketing Decision Making in Firms

The authors introduce and examine a new marketing concept that a small set of leading firms has begun to adopt: marketing doctrine. Marketing doctrine refers to a firm's unique principles, distilled from its experiences, which provide firm-wide guidance on market-facing choices. As such, marketing doctrine provides a firm-wide common approach to decision making. Importantly, marketing doctrine helps a firm address the classic consistency–flexibility conundrum by providing high-level guidance to all decision makers in the firm (thus ensuring consistency) but not specifying execution details (thus allowing for local flexibility). Across three samples, the authors explore the concept using a discovery-oriented, theories-in-use approach with 35 executives from several industries. This article makes four contributions. First, it offers a parsimonious definition of the marketing doctrine construct and contrasts it with related constructs. Second, it offers insight into how firms can develop marketing doctrine. Third, it develops a conceptual model that identifies the antecedents and consequences of marketing doctrine use. Finally, the authors explore the moderating effects of three unpredictable environments (competitive intensity, market turbulence, and structural flux) on the marketing doctrine use–performance relationship.

Experimental verification of nonlinear model predictive tracking control for six-wheeled unmanned ground vehicles

This paper presents a nonlinear model predictive tracking control scheme for a six-wheeled nonholonomic unmanned ground vehicles (UGVs). It is employed as a high-level guidance control with kinematic approximation for UGV motion. A nonlinear model predictive control algorithm solves trajectory planning and optimal control problems by sequentially solving an online numerical optimization problem. The optimal control inputs for the UGV are obtained with a gradient descent optimization algorithm considering constraints of UGV motion as well as its input constraints. The characteristics of the proposed controller in terms of tracking performance and collision avoidance were investigated. The real-time performance of the proposed numerical optimization algorithm was verified with an experimental six-wheeled UGV platform in indoor and outdoor environments.

Comparative study between Cluster, Grid, Utility, Cloud and Autonomic computing

Autonomic computing was an idea that came to existence because of the great need to its basic concepts, which it's to have available resources, applications that can manage themselves in accordance with high level guidance from humans. The idea of Autonomic computing is not new, but it developed Cloud computing through the years to reach the current situation (Autonomic computing) In this paper there will be an overview for other types of computing beside Autonomic computing to highlight the differences between them, the characteristics and the advantages and disadvantages for each. That comparative will lead to the importance of Autonomic computing and its wide open future

Automatic Crosswind Flight of Tethered Wings for Airborne Wind Energy: Modeling, Control Design, and Experimental Results

An approach to control tethered wings for airborne wind energy is proposed. A fixed length of the lines is considered, and the aim of the control system is to obtain figure-eight crosswind trajectories. The proposed technique is based on the notion of the wing's "velocity angle" and, in contrast with most existing approaches, it does not require a measurement of the wind speed or of the effective wind at the wing's location. Moreover, the proposed approach features few parameters, whose effects on the system's behavior are very intuitive, hence simplifying tuning procedures. A simplified model of the steering dynamics of the wing is derived from first-principle laws, compared with experimental data and used for the control design. The control algorithm is divided into a low-level loop for the velocity angle and a high-level guidance strategy to achieve the desired flight patterns. The robustness of the inner loop is verified analytically, and the overall control system is tested experimentally on a small-scale prototype, with varying wind conditions and using different wings.

MaRDI-Gross – Data Management and Preservation Planning for Large Projects

The MaRDI-Gross project aims to provide high-level guidance for the strategic and engineering development of Data Management and Preservation plans for Big Science data. The main outcome will be a document, the nominal audience for which is therefore rather narrow. However, the intention is that the document be of use to other planners and data architects who wish to implement good practice in this area.The goal of the document is not to provide mechanically applicable recipes, but to allow the user to develop and lead a high-level plan which is appropriate to their organisation. Throughout, the document is informed where appropriate by the OAIS reference model, which generally provides a useful guide to HEP data management planning.

FAST POWER LINE DETECTION AND LOCALIZATION USING STEERABLE FILTER FOR ACTIVE UAV GUIDANCE

Abstract. In this paper we present a fast power line detection and localisation algorithm as well as propose a high-level guidance architecture for active vision-based Unmanned Aerial Vehicle (UAV) guidance. The detection stage is based on steerable filters for edge ridge detection, followed by a line fitting algorithm to refine candidate power lines in images. The guidance architecture assumes an UAV with an onboard Gimbal camera. We first control the position of the Gimbal such that the power line is in the field of view of the camera. Then its pose is used to generate the appropriate control commands such that the aircraft moves and flies above the lines. We present initial experimental results for the detection stage which shows that the proposed algorithm outperforms two state-of-the-art line detection algorithms for power line detection from aerial imagery.

A Robust Fuzzy Autonomous Underwater Vehicle (AUV) Docking Approach for Unknown Current Disturbances

In this paper, a robust autonomous underwater vehicle (AUV) docking approach that can handle unknown water currents is presented. Several assumptions and design considerations are made in our work. First, there is no onboard velocity sensor, and thus water currents will need to be estimated and compensated online. Second, the vehicle uses its ultrashort baseline (USBL) system to home itself toward an acoustic source situated at a rigidly bottom-mounted underwater docking station. Third, the dock is oriented at a preknown heading, and its absolute position is a priori known. Our docking approach incorporates a Tagaki-Sugeno-Kang (TSK) fuzzy inference system (FIS) that assists the vehicle with high level guidance maneuvers in the form of fuzzified commanded heading and speed vector fields. A current compensator is designed and applied to the fuzzy docking guidance to allow the vehicle to maintain its course in the presence of current disturbance. An extended Kalman Filter (EKF) is formulated to estimate the current and vehicle states. Extensive Monte Carlo simulations were performed to evaluate the current estimator and vehicle docking performance under various docking conditions. The simulation results demonstrated the inherent robustness of this designed fuzzy docking approach against unknown current disturbances, without any real-time velocity measurements.