Search

The Volterra/Wiener neural network (VWNN) has been shown to be an effective tool for on-line estimation of non-linear restoring forces and responses. However, the power of the VWNN for on-line identification has not been fully harnessed due to the high sensitivity of its parameters. This study adopts a probabilistic approach in examining the effects of the VWNN's parameters on the robustness and stability of its estimation capabilities. Large ensembles of simulations were conducted in which random (earthquake-like) ground motions were used to excite representative non-linear structures, and on-line estimation of their acceleration responses was performed. The nonlinearity in the system was introduced via hysteretic restoring forces, and a variety of cases were tested, including softening and hardening. The results showed that each design parameter within the VWNN was linked to a certain type of performance sensitivity. The adaptive gain that controls the change in the weights of the VWNN was also directly linked to the stability of the estimates, as small increases in the gain led to the estimates diverging. Within the neural network, the weight within the transfer function was found to directly correlate with accuracy. The optimum set of parameters for a given excitation often produced unstable solutions for other excitations, but by understanding the relationships between the parameters and their sensitivities, a set of parameters could be carefully chosen to consistently produce accurate and stable on-line estimates for all simulations. The knowledge gained from the relationships between VWNN parameters also allowed for informed decisions on parameter sets for simulations involving different classes of nonlinearities. Offering users a starting point provides a necessary and helpful feature so often missing from other non-linear identification schemes that deal with non-parametric identification of complex nonlinear systems. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies substitution between empty containers of different types in an attempt to reduce the cost of empty container interchange. Since finding an optimal solution requires substantial computational efforts, a heuristic method is developed which yields an integer solution relatively fast. The developed methodology divides the problem into dependent and independent parts and applies a branch-and-bound procedure to the dependent part. Available data from the combined Los Angeles and Long Beach ports are used to evaluate the proposed technique. Computational tests are used to demonstrate the efficiency of the developed method in terms of computational time and solution quality. [Copyright &y& Elsevier]

E-mobility is a key element in the future energy systems. The capabilities of EVs are many and vary since they can provide valuable system flexibility services, including management of congestion in transmission grids. According to the literature, leaving the charging process uncontrolled could hinder some of the present challenges in the power system. The development of a suitable charging management system is required to address different stakeholders’ needs in the electro-mobility value chain. This paper focuses on the design of such a system, the TwinEV module, that offers high-value services to electric vehicles (EV) users. This module is based on a Smart Charging Tool (SCT), aiming to deliver a more user-central and cooperative approach to the EV charging processes. The methodology of the SCT tool, as well as the supportive optimization algorithm, are explained thoroughly. The architecture and the web applications of TwinEV module are analyzed. Finally, the deployment and testing results are presented.

The development of follicular helper CD4 T (TFH) cells is a dynamic process resulting in a heterogenous pool of TFH subsets. However, the cellular and molecular determinants of this heterogeneity and the possible mechanistic links between them is not clear. We found that human TFH differentiation is associated with significant changes in phenotypic, chemokine, functional, metabolic and transcriptional profile. Furthermore, this differentiation was associated with distinct positioning to follicular proliferating B cells. Single-cell T cell receptor (TCR) clonotype analysis indicated the transitioning toward PD-1hiCD57hi phenotype. Furthermore, the differentiation of TFH cells was associated with significant reduction in TCR level and drastic changes in immunological synapse formation. TFH synapse lacks a tight cSMAC (central supra molecular activation Cluster) but displays the TCR in peripheral microclusters, which are potentially advantageous in the ability of germinal center (GC) B cells to receive necessary help. Our data reveal significant aspects of human TFH heterogeneity and suggest that the PD-1hiCD57hi TFH cells, in particular, are endowed with distinctive programming and spatial positioning for optimal GC B cell help. [ABSTRACT FROM AUTHOR]

Summary: Generation of antigen‐specific humoral responses following vaccination or infection requires the maturation and function of highly specialized immune cells in secondary lymphoid organs (SLO), such as lymph nodes or tonsils. Factors that orchestrate the dynamics of these cells are still poorly understood. Currently, experimental approaches that enable a detailed description of the function of the immune system in SLO have been mainly developed and optimized in animal models. Conversely, methodological approaches in humans are mainly based on the use of blood‐associated material because of the challenging access to tissues. Indeed, only few studies in humans were able to provide a discrete description of the complex network of cytokines, chemokines and lymphocytes acting in tissues after antigenic challenge. Furthermore, even fewer data are currently available on the interaction occurring within the complex micro‐architecture of the SLO. This information is crucial in order to design particular vaccination strategies, especially for patients affected by chronic and immune compromising medical conditions who are under‐vaccinated or who respond poorly to immunizations. Analysis of immune cells in different human tissues by high‐throughput technologies, able to obtain data ranging from gene signature to protein expression and cell phenotypes, is needed to dissect the peculiarity of each immune cell in a definite human tissue. The main aim of this review is to provide an in‐depth description of the current available methodologies, proven evidence and future perspectives in the analysis of immune mechanisms following immunization or infections in SLO. [ABSTRACT FROM AUTHOR]

Occupant behavior and business processes in a building environment constitute an inseparable set of important factors that drives energy consumption. Existing methodologies for building energy management lag behind in addressing these core parameters by focusing explicitly on the building’s structural components. Additional layers of information regarding indoor and outdoor environmental conditions and occupant behavior patterns, mostly driven by everyday business processes (schedules, loads, and specific business activities related to occupancy patterns and building operations), are necessary for the effective and efficient modeling of building energy performance in order to establish a holistic energy efficiency management framework. The aim of this paper was to develop a context-driven framework in which multiple levels of information regarding occupant behavior patterns resulting from everyday business processes were incorporated for efficient energy management in buildings. A preliminary framework evaluation was performed in a multifaceted university building involving a number of spaces, employees, business processes, and data from sensors and metering devices. The results derived by linking operational aspects and environmental conditions (temperature, humidity, and luminance) to occupant behavior underlying business processes and organizational structures indicated the potential energy savings: a max of 7.08% for Heating, ventilation, and air conditioning (HVAC), 19.46% for lighting and a maximum of 6.34% saving related to office appliances.

Inelasticity of demand along with the distributed energy sources and energy market democratization pose significant challenges which have considerable negative impacts on overall grid balance. The need for increased capacity and flexibility in the era of energy market digitalization has introduced new requirements in the energy supply network which could not be satisfied without continuous and costly local power network upgrades. Additionally, with the emergence of Smart Homes (SHs) and Home Energy Management (HEM) systems for monitoring and operating household appliances, opportunities have arisen for automated Demand Response (DR). DR is exploited for the modification of the consumer energy demand, in response to the specific conditions within the electricity system (e.g., peak period network congestion). In order to optimally integrate DR in the broader Smart Grid (SG) system, modelling of the system parameters and safety analysis is required. In this paper, the implementation of STPA (System-Theoretic Process Analysis) structured method, as a relatively new hazard analysis technique for complex systems is presented and the feasibility of STPA implementation for loss prevention on a Demand Response system for home energy management, and within the complex SG context, is examined. The applied method delivers a mechanism useful in understanding where gaps in current operational risk structures may exist. The STPA findings in terms of loss scenarios can be used to generate a variety of safeguards to ensure secure operational control and in implementing targeted strategies through standard approaches of risk assessment.

Container movement by trucks with time constraints at origins and destinations is modeled as an asymmetric “multi-Traveling Salesmen Problem with Time Windows” (m-TSPTW) with social constraints. A two-phase exact algorithm based on dynamic programming (DP) is proposed that finds the best routes for a fleet of trucks. Since the m-TSPTW problem is NP-hard, the computational time for optimally solving large size problems becomes prohibitive. For large size problems, we develop a hybrid methodology consisting of DP in conjunction with genetic algorithms. The developed algorithms are compared with an insertion heuristic method. Computational results demonstrate the efficiency of the developed algorithms. [Copyright &y& Elsevier]