Hierarchical Chain Research Articles

EVOLvINC: EValuating knOwLedge INtegration Capacity in multistakeholder governance

Research and policy processes in many fields, such as sustainability and health, are increasingly relying on transdisciplinary cooperation among a multitude of governmental, nongovernmental, and private actors from local to global levels. In the absence of hierarchical chains of command, multistakeholder governance may accommodate conflicting or diverse interests and facilitate collective action, but its effectiveness depends on its capacity to integrate systems, transformation, and target knowledge. Approaches to foster such governance are nascent and quickly evolving, and methodological standards to facilitate comparison and learning from best practice are needed. However, there is currently no evaluation approach that (i) comprehensively assesses the capacity for knowledge integration in multistakeholder governance, (ii) draws on the best available knowledge that is being developed in various fields, and (iii) combines a systematic and transferable methodological design with pragmatic feasibility. We brought together 20 experts from institutions in nine countries, all working on evaluation approaches for collaborative science–policy initiatives. In a synthesis process that included a 2-day workshop and follow-up work among a core group of participants, we developed a tool for evaluating knowledge integration capacity in multistakeholder governance (EVOLvINC). Its 23 indicators incorporate previously defined criteria and components of transdisciplinary evaluations into a single, comprehensive framework that operationalizes the capacity for integrating systems, target, and transformation knowledge during an initiative’s (a) design and planning processes at the policy formulation stage, (b) organization and working processes at the implementation stage, and (c) sharing and learning processes at the evaluation stage of the policy cycle. EVOLvINC is (i) implemented through a questionnaire, (ii) builds on established indicators where possible, (iii) offers a consistent and transparent semiquantitative scoring and aggregation algorithm, and (iv) uses spider diagrams for visualizing results. The tool builds on experience and expertise from both the northern and southern hemispheres and was empirically validated with seven science–policy initiatives in six African and Asian countries. As a generalized framework, EVOLvINC thus enables a structured reflection on the capacity of multistakeholder governance processes to foster knowledge integration. Its emphasis on dialog and exploration allows adaptation to contextual specificities, highlights relative strengths and weaknesses, and suggests avenues for shaping multistakeholder governance toward mutual learning, capacity building, and strengthened networks. The validation suggests that the adaptive capacity of multistakeholder governance could be best enhanced by considering systems characteristics at the policy formulation stage and fostering adaptive and generic learning at the evaluation stage of the policy cycle.

Hierarchical decision-making produces persistent differences in learning performance

Human organizations are commonly characterized by a hierarchical chain of command that facilitates division of labor and integration of effort. Higher-level employees set the strategic frame that constrains lower-level employees who carry out the detailed operations serving to implement the strategy. Typically, strategy and operational decisions are carried out by different individuals that act over different timescales and rely on different kinds of information. We hypothesize that when such decision processes are hierarchically distributed among different individuals, they produce highly heterogeneous and strongly path-dependent joint learning dynamics. To investigate this, we design laboratory experiments of human dyads facing repeated joint tasks, in which one individual is assigned the role of carrying out strategy decisions and the other operational ones. The experimental behavior generates a puzzling bimodal performance distribution–some pairs learn, some fail to learn after a few periods. We also develop a computational model that mirrors the experimental settings and predicts the heterogeneity of performance by human dyads. Comparison of experimental and simulation data suggests that self-reinforcing dynamics arising from initial choices are sufficient to explain the performance heterogeneity observed experimentally.

Forget-me-some: General versus special purpose models in a hierarchical probabilistic task.

Humans build models of their environments and act according to what they have learnt. In simple experimental environments, such model-based behaviour is often well accounted for as if subjects are ideal Bayesian observers. However, more complex probabilistic tasks require more sophisticated forms of inference that are sufficiently computationally and statistically taxing as to demand approximation. Here, we study properties of two approximation schemes in the context of a serial reaction time task in which stimuli were generated from a hierarchical Markov chain. One, pre-existing, scheme was a generically powerful variational method for hierarchical inference which has recently become popular as an account of psychological and neural data across a wide swathe of probabilistic tasks. A second, novel, scheme was more specifically tailored to the task at hand. We show that the latter model fit significantly better than the former. This suggests that our subjects were sensitive to many of the particular constraints of a complex behavioural task. Further, the tailored model provided a different perspective on the effects of cholinergic manipulations in the task. Neither model fit the behaviour on more complex contingencies that competently. These results illustrate the benefits and challenges that come with the general and special purpose modelling approaches and raise important questions of how they can advance our current understanding of learning mechanisms in the brain.

A Robust and Self-Adaptive Clocking Technique for SFQ Circuits

Living on the verge of the IoT era, the entire world is excited about the potential of mining the monumental amounts of data that would become available in the near future. However, this increasing abundance of data requires supercomputers faster and more powerful than were ever built without the need to build a nuclear plant next to each one! Single flux quantum (SFQ) technology has the potential to meet those booming demands for lower power consumption and higher operation speeds in the electronics industry and future exascale supercomputing systems. Nevertheless, the promised benefits of three orders of magnitude lower power at an order of magnitude higher performance have yet to be attained. In particular, variability and scalability have been long-term obstacles for the technology to advance, compete, and replace the well-founded Silicon CMOS. Notwithstanding that SFQ fabrication has quite improved over the past two decades, its level of variability has to be taken into design consideration. Moreover, the absence of an established tools flow hinders the scalability and the automatibility, which are among the trumps of CMOS technology in digital VLSI. Specifically, as a consequence, ultra-high-speed clocking of large-scale SFQ circuits in the presence of large levels of variability represents a tough obstacle for the technology to advance. In this paper, we propose an innovative self-adaptive clocking technique, which is designed to be resilient in such uncertain environments. Whereas the traditional balanced tree zero-skew clocking is not reliable for large-scale SFQ designs, our proposed hierarchical chains of homogeneous clover-leaves clocking inherits its robustness from spatially correlated cell delays and from the timing robustness of the SFQ traditional counter-flow clocking. Our simulations on ISCAS’85 benchmark circuits show that our robust clocking costs can be on average within 34% as fast as ideal zero-skew jitter-free clock trees with an average area overhead of 58%. Moreover, lower area overhead is also possible at the cost of performance, demonstrating the possible trade-off between performance and area. We assert that these overheads are acceptable given the benefits of more reliable functionality, as quantified by yield, as well as improved scalability. In particular, previous efforts reported that a similar, but more limited, clocking structure leads to up to 93% higher yield than zero-skew clock trees.

Mono-bit millimeter-wave channel estimation: Bayesian and adaptive quantization frameworks

To achieve a more reasonable cost, employment of a mono-bit quantization structure is essential in wide bandwidth millimeter-wave communications (mmW). However, in mmW channel estimation, one of the critical challenges of mono-bit quantization in each branch of the antenna is the loss of data amplitude. The latest researches offer several approaches to estimate the channel in mono-bit single and multiple antenna structures. Such methods not consider the recovery of channel amplitude and consequently are limited to finding direction. Moreover, they do not provide sparsity into account. Thus, two new schemes are introduced based on a non-adaptive Bayesian algorithm and adaptive sigma–delta modulation (SDM) to tackle these issues. In both methods, we exploit the sparsity of mmWC channel in the angle domain. To extract the sparse-based Laplacian density in case that the sparsity level is unknown, we propose a Bayesian algorithm which employs a hierarchical chain composed of Gaussian and exponential prior. Furthermore, in case that the sparsity level of an application is known or can be estimated, the adaptive mono-bit quantization schemes is proposed which retrieves the amplitude of the sparse channel by the aid of SDM and CS tools. Simulation results show that the adaptive mono-bit estimator outperforms the non-adaptive Bayesian approach comparing the mean square error as an index. Also, the proposed adaptive method, especially when a sufficient number of one-bit measurements are available, can asymptotically converge into optimum full-bit observations with lower computational complexity.

A projection-based reformulation and decomposition algorithm for global optimization of a class of mixed integer bilevel linear programs

We propose an extended variant of the reformulation and decomposition algorithm for solving a special class of mixed-integer bilevel linear programs (MIBLPs) where continuous and integer variables are involved in both upper- and lower-level problems. In particular, we consider MIBLPs with upper-level constraints that involve lower-level variables. We assume that the inducible region is nonempty and all variables are bounded. By using the reformulation and decomposition scheme, an MIBLP is first converted into its equivalent single-level formulation, then computed by a column-and-constraint generation based decomposition algorithm. The solution procedure is enhanced by a projection strategy that does not require the relatively complete response property. To ensure its performance, we prove that our new method converges to the global optimal solution in a finite number of iterations. A large-scale computational study on random instances and instances of hierarchical supply chain planning are presented to demonstrate the effectiveness of the algorithm.

Innovation in mining value chains: New evidence from Latin America

The paper investigates new opportunities for innovation and linkages associated to mining activities in Brazil, Chile and Peru. Three types of opportunities were researched: demand side, supply side and local specificities. The last source of opportunities is key for natural resource related activities. The evidence shows that an increasing demand is introducing important incentives for innovation and local suppliers. Nevertheless, a hierarchical value chain, dominated by few large firms, and poor linkages is blocking the diffusion of innovations and hindering suppliers’ development. The emergence of a group of highly innovative suppliers, which were identified in the three countries, is explained mostly by new technological and knowledge opportunities, which are not exploited by large incumbents and open spaces for new entrants. Local specificities are also key in the explanation of local suppliers. It remains a challenge however, how these, most of which were created to satisfy local needs, will move from local to global.

Analisis de la entomofauna benéfica en cultivos de maíz transgénico y convencional, Córdoba-Colombia

La adopción de los cultivos genéticamente modificados (OGM) ha llevado a la necesidad de evaluar los impactos sobre los organismos no objetivos. Para ello se estudio el impacto del cultivo transgénico maíz (Zea mays) sobre la entomofauna benéfica. Se colectaron artrópodos en ocho lotes de maíz convencional y transgénico mediante las técnicas de Berlesse, Pitfall, Jama y Trampas amarillas. El análisis de diversidad se realizó utilizando el método propuesto por Jost (2006) a través de tres valores q = 0, 1 y 2 y se construyeron curvas de rango-abundancia. Para la formación de los grupos funcionales, se realizó un análisis de conglomerados empleando el método de encadenamiento jerárquico de Ward. Se colectaron 6,932 individuos entre cultivos convencionales y transgénico, registrando 102 taxones discriminados en 6 especies, 42 géneros y 54 familias. El cultivo transgénico registró la mayor abundancia con 51,60% y el convencional un 48,39%. La curva de rango-abundancia no indica diferencias entre los cultivo para las localidades de Cereté y San Pelayo. El dendrograma arrojo ocho (8) grupos funcionales. Al analizar la diversidad alfa verdadera 0D, se obtuvo en ambas localidades los ocho (8) grupos funcionales; la diversidad verdadera en el cultivo convencional fue 1D = 6,76 y el transgénico 1D = 6,83. Para el orden 2D fue 6,17 en convencional y 6,21 en transgénico; se puede concluir que en estos resultados no se observaron diferencias significativas con relación a los tres perfiles de diversidad entre los cultivos convencional y transgénico.

Masses and Mixing of Neutral Leptons in a Grand Unified E6 Model with Intermediate Pati-Salam Symmetry

A brief review of the assignment of elementary fermions and bosons to irreducible multiplets in grand unified $E_6$ models is followed by a discussion of different, hierarchical symmetry breaking chains from $E_6$ down to $SU(3)_C \times U(1)_{EM}$. We concentrate here on a model with an intermediate Pati-Salam symmetry for which $(B-L)$ is conserved. In particular, the mass/mixing matrix of electrically neutral fermions (i.e.neutrinos) that would be derived from Yukawa couplings is constructed. The pattern of neutrino masses and some bounds on mixing parameters are discussed.

Entropy-Based Algorithm for Supply-Chain Complexity Assessment

This paper considers a graph model of hierarchical supply chains. The goal is to measure the complexity of links between different components of the chain, for instance, between the principal equipment manufacturer (a root node) and its suppliers (preceding supply nodes). The information entropy is used to serve as a measure of knowledge about the complexity of shortages and pitfalls in relationship between the supply chain components under uncertainty. The concept of conditional (relative) entropy is introduced which is a generalization of the conventional (non-relative) entropy. An entropy-based algorithm providing efficient assessment of the supply chain complexity as a function of the SC size is developed.

Stiff and hierarchical chain nature of atactic and stereoregular poly(substituted methylene)s

AbstractIn this study, we estimated the chain nature of atactic and stereoregular poly(substituted methylene)s containing a phenylmethoxy side chain at each main‐chain backbone carbon atom using synchrotron X‐ray scattering to understand the topological and conformational properties of these rigid polymers. The atactic poly(substituted methylene) showed a linear chain nature. Contrastingly, the stereoregular poly(substituted methylene) exhibited a random coil structure. © 2018 Society of Chemical Industry

Controllable supramolecular chain aggregation through nano-steric hindrance functionalization for multi-color larger-area electroluminescence

Suppression of hierarchical chain aggregationvianano-steric hindrance functionalization is an effective strategy to restrain long-wavelength emission and improving the device performance.

Radiative Magnetic Reconnection Near Accreting Black Holes

Abstract A radiative mechanism is proposed for magnetic flares near luminous accreting black holes. It is based on recent first-principle simulations of magnetic reconnection, which show a hierarchical chain of fast-moving plasmoids. The reconnection occurs in a compact region (comparable to the black hole radius), and the chain experiences fast Compton cooling accompanied by electron–positron pair creation. The distribution of plasmoid speeds is shaped by radiative losses, and the self-regulated chain radiates its energy in hard X-rays. The mechanism is illustrated by Monte-Carlo simulations of the transfer of seed soft photons through the reconnection layer. The emerging radiation spectrum has a cutoff near 100 keV similar to the hard-state spectra of X-ray binaries and AGN. We discuss how the chain cooling differs from previous phenomenological emission models, and suggest that it can explain the hard X-ray activity of accreting black holes from first principles. Particles accelerated at the X-points of the chain produce an additional high-energy component, explaining the “hybrid Comptonization” observed in Cyg X-1.

Governance, value chain positioning and firms' heterogeneous performance: The case of Tuscany

The recent trade literature has shown how incomplete contracts can shape firms' boundary and the decision of whether to outsource or integrate vertically. Related evidence and conceptualizations from the business literature show that buyer-supplier relations in global value chains can take several governance structures, depending on the degree of vertical coordination and power relations between firms. Building upon these two non-competing strands of the literature, we construct a taxonomy of firms that considers their positioning (upstream or downstream), their belonging to domestic or global value chains and the type of relations they entertain with other firms. We apply our taxonomy to the 2011 census of firms operating in Tuscany. We first describe regional characteristics and then study how positioning and governance affect firms' decisions and performance. Our results show that firms in hierarchical value chains are more productive than those in market chains; that firms involved in global value chains outperform the one outside or in domestic value chains, and, finally, that firms which are both suppliers and buyers in a value chain have the highest productivity premium.

Communication Networks and Performance of Four New Delhi City Parks

Urban green spaces are very important for human wellbeing and environmental sustainability. The efficiency of managing urban green spaces often depends on communication and integration of information. We performed a social network analysis survey for assessing the communication structure among the staff in four New Delhi city parks. We were interested in the relationship between three topological properties of the social networks (centre of gravity, MaxS, compactness) and three network-independent attributes of their performance (average satisfaction, visitor’s median expected improvements, number of species identified). The presence of a dynamic leader, improved communication and flow of information down the hierarchical chain and lastly, maximization of interaction and strengthening of relationships of the co-workers are three network properties that emerge as very important for a well-performing park. Through this study, we demonstrate the ability of social network analysis to provide simple, yet powerful, insights that can assist in improving the management of urban green spaces.

Complexity in the governance of tourism networks: Balancing between external pressure and internal expectations

Abstract Tourism destinations are complex systems of interrelated entities without a hierarchical chain of command. In order to enhance the ability of destinations to create a high quality, authentic tourism experience, a management perspective based on networks, personal relationships, trust, and reciprocity is suggested. However, while theoretically this ‘network approach’ represents a promising way of making the tourism industry more innovative and competitive, few convincing empirical examples have so far been published on how tourism networks can be successfully managed. This paper focuses on the gap between theory and practice. After summarizing the literature on network management in tourism, in-depth interviews with 12 network managers in Flanders (Belgium) give insights on how networks are currently managed, as well as how and why network managers choose their priorities. The main conclusion is that while all interviewed network managers claimed that they had adopted a ‘network approach’, most of them still spent most of their time and energy on traditional power-related tasks, such as the top-down provision of information, lobbying, and representing the network towards external stakeholders. Trust-related activities, such as actively pursuing member collaboration, member engagement, and facilitating proximity and partnerships with and between (potential) network members were only applied by a small minority. Network managers who actively strengthened the network structure through trust-related management activities indicated more often that their networks were resilient to external pressure. This highlights the importance for network managers of making pragmatic trade-offs between various management approaches. This might need further professionalization of the sector, including targeted training and (postgraduate) education.

Temporally Dependent Rate-Distortion Optimization for Low-Delay Hierarchical Video Coding.

Low-delay hierarchical coding structure (LD-HCS), as one of the most important components in the latest High Efficiency Video Coding (HEVC) standard, greatly improves coding performance. It groups consecutive P/B frames into different layers and encodes them with different quantization parameters (QPs) and reference mechanisms in such a way that temporal dependency among frames can be exploited. However, due to varying characteristics of video contents, temporal dependency among coding units differs significantly from each other in the same or different layers, while a fixed LD-HCS scheme cannot take full advantage of the dependency, leading to a substantial loss in coding performance. This paper addresses the temporally dependent rate distortion optimization (RDO) problem by attempting to exploit varying temporal dependency of different units. First, the temporal relationship of different frames under the LD-HCS is examined, and hierarchical temporal propagation chains are constructed to represent the temporal dependency among coding units in different frames. Then, a hierarchical temporally dependent RDO scheme is developed specifically for the LD-HCS based on a source distortion propagation model. Experimental results show that our proposed scheme can achieve 2.5% and 2.3% BD-rate gain in average compared with the HEVC codec under the same configuration of P and B frames, respectively, with a negligible increase in encoding time. Furthermore, coupled with QP adaption, our proposed method can achieve higher coding gains, e.g., with multi-QP optimization, about 5.4% and 5.0% BD-rate saving in average over the HEVC codec under the same setting of P and B frames, respectively.

Hierarchical Value Chains Encompassing Freight Transportation and Logistics Sectors in the United States

This research reveals value chain (VC) clusters in which freight transportation modes (i.e., truck, rail, air, water, and pipelines), freight supporting activities (i.e., couriers and messengers and warehousing and storage), and other industry sectors have strong economic inter-dependencies. The most recent input–output data in the United States were used to construct the 2014 direct requirements matrix and generate a network of economic interdependencies between industry sectors. Community detection, a complex network analysis method, was used to reveal the Corresponding VCs in the economic network. A novel procedure uncovered hierarchical VC communities (VCCs) for each freight transportation and logistics sector. Thus, seven distinctive VCCs with unique underlying activities were found. In general, freight industries are hierarchically clustered to VCCs related to raw materials; services; metals; agriculture; textile, apparel, and paper; wood products; and others. Results are important for the areas of transportation economics, regional economic development, competitiveness, and multimodal freight transportation.

Increasing cognitive load attenuates right arm swing in healthy human walking.

Human arm swing looks and feels highly automated, yet it is increasingly apparent that higher centres, including the cortex, are involved in many aspects of locomotor control. The addition of a cognitive task increases arm swing asymmetry during walking, but the characteristics and mechanism of this asymmetry are unclear. We hypothesized that this effect is lateralized and a Stroop word-colour naming task—primarily involving left hemisphere structures—would reduce right arm swing only. We recorded gait in 83 healthy subjects aged 18–80 walking normally on a treadmill and while performing a congruent and incongruent Stroop task. The primary measure of arm swing asymmetry—an index based on both three-dimensional wrist trajectories in which positive values indicate proportionally smaller movements on the right—increased significantly under dual-task conditions in those aged 40–59 and further still in the over-60s, driven by reduced right arm flexion. Right arm swing attenuation appears to be the norm in humans performing a locomotor-cognitive dual-task, confirming a prominent role of the brain in locomotor behaviour. Women under 60 are surprisingly resistant to this effect, revealing unexpected gender differences atop the hierarchical chain of locomotor control.

Multiple dimensions of power influencing knowledge integration in supply chains

This study investigates how multiple dimensions of power each facilitate knowledge integration within innovation projects in supply chains and their interrelationships. Adopting a process perspective of knowledge, we offer an alternative to much of the existing debate, which has focussed on the possession of resources. We collected data from four case‐study Original Equipment Manufacturers and six associated suppliers and analysed these using Template Analysis and cross‐case analysis. Our findings reveal how the power of the system, operationalized through relative performance measures, performance measurement mechanisms and the individuals in‐charge of them, provides a facilitative context within which other dimensions of power operate. Here, the power of resources (expert and legitimate power), processes (associated with raising issues, cross‐functional teams, early supplier involvement and reviews) and meaning (creating legitimacy through reviews) interact to support knowledge integration within innovation projects in supply chains. This, we argue, emphasises the plurality of power dimensions deployed and importance of their interrelationships in facilitating knowledge integration within hierarchical supply chain networks.