Configuration Model Research Articles

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

Abstract New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge Machine Learning (EdgeML) addresses this challenge by integrating
Machine Learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and
storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a Field Programmable Gate Array (FPGA). Our demonstration focuses on an angular array of electron spectrometers,
referred to as the ’CookieBox,’ developed for the Linac Coherent Light Source II (LCLS-II) project. The EdgeML system captures 51.2 Gbps from a 6.4 GS/s analog to digital converter and is designed to integrate data pre-processing and ML inside an
FPGA. Our implementation achieves an inference latency of 0.2 μs for the ML model, and a total latency of 0.4 μs for the complete EdgeML system, which includes preprocessing, data transmission, digitization, and ML inference. The modular design of
the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

A bi-objective optimization framework for configuration of battery energy storage system considering energy loss and economy

To address a bi-objective optimization configuration problem of battery energy storage system (BESS) in distributed energy system (DES) considering energy loss and economy, a perturbation and observation approach (P&O) is proposed in this article. First, in a DES, the configuration model of BESS is established. Then, a novel way is designed that transforming a bi-objective optimization problem into a single objective optimization problem with variable conditions. And the P&O process of the proposed method is presented. Finally, in a simulation case, compared with a single optimization objective of energy loss or economy, the P&O method improves 5.71-fold or 2.94-fold in each direction, respectively and effectively balances the contradiction between them. In addition, with the efficiency and electricity cost of BESS increasing, the rated capacity of BESS changes by approximately 10%. And the location is a key factor affecting the configuration scheme of BESS in DES.

Optimizing job satisfaction: a configurational framework for integrating HRD, HR, and employee attributes

Purpose Addressing a significant gap in current understanding, this study explores how workforce development, workplace practices, and employee characteristics interact to influence job satisfaction. We introduce a configurational framework to bolster talent development and enhance job satisfaction in organizations. Design/methodology/approach Through conceptual analysis, we identify and propose strategic talent management configurations that include two-way and three-way interactions between HRD initiatives, HR practices, and employee attributes. We demonstrate the potential efficacy of these tailored configurations with real-world examples, such as aligning developmental support with employee learning styles and job design. Findings A configurational model is proposed that offers a practical framework for leveraging the interplay between HRD initiatives (e.g., training and mentoring), HR practices (e.g., compensation and performance management), and employee attributes (e.g., personality and cognition) to create a tailored, supportive, and engaging work environment. Originality/value The findings provide a novel and actionable framework for workforce development. This configurational model emphasizes the strategic alignment of HRD initiatives, HR practices, and employee attributes, equipping practitioners with a valuable tool to enhance talent management, optimize job satisfaction, improve resource allocation, and ultimately boost organizational performance.

Multi-objective optimal configuration of CCHP system containing hybrid electric-hydrogen energy storage system

In order to cope with the increasing energy demand and achieve the “double carbon “goal of China’s 14th Five-Year Plan,” combined with hydrogen energy storage technology, it has the characteristics of zero pollution, high efficiency and rich source. In the context of reducing energy consumption and the vigorous development of hydrogen energy storage technology, a multi-objective optimization configuration model with economy, energy consumption index and carbon emission index is proposed, which takes into account the working characteristics of the hydrogen energy storage system, and the exothermic heat release from the electrolysis tanks and fuel cells when they are working to provide the loads with an additional heat source of the Combined Cooling, Heating and Power (CCHP) system, to reduce energy consumption and carbon emission. Finally, taking a region as an example, a multi-objective optimization algorithm based on decomposition is used to solve the model, so as to obtain a series of alternatives with better optimization effect. At the same time, the two-way projection method based on interval intuitionistic fuzzy information is used to make decisions, and the scheme that optimizes the economy, energy consumption index and carbon emission index is obtained, which verifies the feasibility of the system proposed in this paper.

Mechanical properties of diamond-type triply periodic minimal surface structures fabricated by photo-curing 3D printing

Triply periodic minimal surface (TPMS) structures have attracted significant attention owing to their smooth surface configuration and parametric modeling properties. In this study, photo-curing 3D printing was employed to generate diamond-type TPMS structures, and micro-CT scanning revealed the presence of internal defects within the 3D printed TPMS structures. Two key design variables were explored: volume fraction and unit cell size. Quasi-static compression experiments were conducted to delve into the compression properties and energy absorption capabilities of the 3D printed TPMS structures. The findings reveal that increasing the volume fraction significantly enhances the compressive modulus, ultimate strength, and energy absorption capacity of TPMS structures. Additionally, increasing the cell size improves compression properties and energy absorption per unit volume. To predict the coupling effect of volume fraction and unit cell size on the compression performance of TPMS structures, a bivariate quadratic regression model was established. In addition, TPMS structures were subjected to load-unload cyclic experiments, shedding light on the evolution patterns of residual strain and hysteresis energy during cyclic loading. It provides insights into the design of reusable and fatigue-resistant diamond-type TPMS structures for various engineering applications.

Dynamic Fleet Configuration Model for Optimizing Earthmoving Operations Using Mixed Integer Linear Programming

Dynamic Fleet Configuration Model for Optimizing Earthmoving Operations Using Mixed Integer Linear Programming

WHY SELFIE? GENDER INVARIANCE IN MOTIVES FOR TAKING AND POSTING SELFIES

The study of motivation to engage in media-related behavioral addictions requires more research attention. Studies had identified generic motives across different types of media-related behavioral addictions, while other studies have revealed unique motives that are only applicable in specific media-related behavioral addictions. The current study aims to evaluate the psychometric properties of the Motives for Taking and Posting Selfies Scale (MTPSS). A total of 207 university students in Malaysia were recruited to participate in this study, with 98 males (47.3%) and 109 females (52.7%), aged between 19 and 24 years old (M = 20.92, SD = 1.23). The results support the original four-factor structure of the MTPSS, namely appraisal-seeking self-presentation, entertainment, status-updating self-presentation, and documentation. In terms of reliability, the MTPSS demonstrated good internal consistency. The gender invariance of MTPSS was tested in configural, metric, scalar, and residual invariance models, supporting the notion that males and females responded similarly to the items of the MTPSS. Overall, the psychometric evidence suggests that the MTPSS is a valid and reliable tool for identifying motives of taking and posting selfies. With evidence of reliability, validity, and gender invariance, the MTPSS is found to be effective in understanding the motivational drivers of selfie-taking and posting behaviors.

Optimal Configuration Model for Large Capacity Synchronous Condenser Considering Transient Voltage Stability in Multiple UHV DC Receiving End Grids

In a multi-fed DC environment, the UHV DC recipient grid faces significant challenges related to DC phase shift failure and voltage instability due to the high AC/DC coupling strength and low system inertia level. While the new large-capacity synchronous condensers (SCs) can provide effective transient reactive power support, the associated investment and operation costs are high. Therefore, it is valuable to investigate the optimization of SC configuration at key nodes in the recipient grid in a scientific and rational manner. This study begins by qualitatively and quantitatively analyzing the dynamic characteristics of DC reactive power and induction motors under AC faults. The sub-transient and transient reactive power output model is established to describe the SC output characteristics, elucidating the coupling relationship between the SC’s reactive power output and the DC reactive power demand at different time scales. Subsequently, a critical stabilized voltage index for dynamic loads is defined, and the SC’s reactive power compensation target is quantitatively calculated across different time scales, revealing the impact of transient changes in DC reactive power on the transient voltage stability of the multi-fed DC environment with dynamic load integration. Finally, an optimal configuration model for the large-capacity SC is proposed under the critical stability constraint of dynamic loads to maximize the SC’s reactive power support capability at the lowest economic cost. The proposed model is validated in a multi-fed DC area, demonstrating that the optimal configuration scheme effectively addresses issues related to DC phase shift failures and voltage instability resulting from AC bus voltage drops.

Unveiling the complexity of civil service effectiveness index: An asymmetric and ANN modeling

Unraveling the factors influencing civil service effectiveness becomes imperative in an era marked by escalating demands for efficient governance. This study attempts to meet this necessity by delving into the complex dynamics among core executive functions, mission support facilities, service delivery functions, and attributes, aiming to elucidate their collective impact on civil service effectiveness. Utilizing a unique methodological blend of Fuzzy-set Qualitative Comparative Analysis (fsQCA) and Artificial Neural Networks (ANN), it delves into the relationships among core executive functions, mission support facilities, service delivery functions, and attributes within the International Civil Service Effectiveness (InCiSE) Index framework. The research uses a configurational model that optimizes CSE and assesses the relative importance of various components. The study reveals significant correlations among the variables. It indicates that all CSE indicators influence but are not equally important in triggering the effectiveness of civil service administration. Key configurations, such as integrating strategic governance with mission support functions and high-level strategy with operational execution, are critical for enhancing civil service effectiveness. It underscores the importance of prioritizing core executive functions and attributes to improve civil service administration. Theoretically, the study enriches contingency theory and contributes to the civil service and administration literature by integrating a configurational approach with machine learning insights. Practically, it provides actionable insights for governance improvement, promoting the application of innovative methodologies in public service to enhance organizational environment and civil service capacities. Original in its approach, this study fills a gap in the literature by applying a hybrid fsQCA-ANN model to explore the configurational and construct ranks influencing civil service effectiveness, offering an inclusive analysis that triangulates qualitative and quantitative data. The findings indicate that civil service effectiveness is highly complex because of its process, involvement of diverse backgrounds of civil servants, critical understanding about the key pillars of good governance and service delivery mechanism. Thus, the findings advance academic understanding and provide practical strategies for policymakers and practitioners to foster better governance through targeted interventions that enhance transparency, accountability, and responsiveness in civil service organizations.

Sampling random graphs with specified degree sequences

The configuration model is a standard tool for uniformly generating random graphs with a specified degree sequence, and is often used as a null model to evaluate how much of an observed network’s structure can be explained by its degree structure alone. A Markov chain Monte Carlo (MCMC) algorithm, based on a degree-preserving double-edge swap, provides an asymptotic solution to sample from the configuration model. However, accurately and efficiently detecting when this Markov chain is sufficiently close to its stationary distribution remains an unsolved problem. Here, we provide a solution to sample from the configuration model using this standard MCMC algorithm. We develop an algorithm, based on the assortativity of the sampled graphs, for estimating the gap between effectively independent MCMC states, and a computationally efficient gap-estimation heuristic derived from analyzing a corpus of 509 empirical networks. We provide a convergence detection method based on the Dickey-Fuller Generalized Least Squares test, which we show is more accurate and efficient than three alternative Markov chain convergence tests. Supplementary materials for the proposed methods can be found here.

Soft Matter Electrolytes: Mechanism of Ionic Conduction Compared to Liquid or Solid Electrolytes

Soft matter electrolytes could solve the safety problem of widely used liquid electrolytes in Li-ion batteries which are burnable upon heating. Simultaneously, they could solve the problem of poor contact between electrodes and solid electrolytes. However, the ionic conductivity of soft matter electrolytes is relatively low when mechanical properties are relatively good. In the present review, mechanisms of ionic conduction in soft matter electrolytes are discussed in order to achieve higher ionic conductivity with sufficient mechanical properties where soft matter electrolytes are defined as polymer electrolytes and polymeric or inorganic gel electrolytes. They could also be defined by Young’s modulus from about Pa to Pa. Many soft matter electrolytes exhibit VFT (Vogel–Fulcher–Tammann) type temperature dependence of ionic conductivity. VFT behavior is explained by the free volume model or the configurational entropy model, which is discussed in detail. Mostly, the amorphous phase of polymer is a better ionic conductor compared to the crystalline phase. There are, however, some experimental and theoretical reports that the crystalline phase is a better ionic conductor. Some methods to increase the ionic conductivity of polymer electrolytes are discussed, such as cavitation under tensile deformation and the microporous structure of polymer electrolytes, which could be explained by the conduction mechanism of soft matter electrolytes.

Dynamic Random Intersection Graph: Dynamic Local Convergence and Giant Structure

ABSTRACTRandom intersection graphs containing an underlying community structure are a popular choice for modeling real‐world networks. Given the group memberships, the classical random intersection graph is obtained by connecting individuals when they share at least one group. We extend this approach and make the communities dynamic by letting them alternate between an active and inactive phase. We analyse the new model, delivering results on degree distribution, local convergence, largest connected component, and maximum group size, paying particular attention to the dynamic description of these properties. We also describe the connection between our model and the bipartite configuration model, which is of independent interest.

Two-Tier Configuration Model for the Optimization of Enterprise Costs and User Satisfaction for Rural Microgrids

Two-Tier Configuration Model for the Optimization of Enterprise Costs and User Satisfaction for Rural Microgrids

Do human resource systems indeed have "system" effects? The dual internal fit model of a high-performance work system.

The configurational or "internal fit" perspective proposes that human resource (HR) systems are most effective when individual practices are configured such that they fit together and are mutually reinforcing. The Ability-Motivation-Opportunity (AMO) model has emerged as a predominant way to select and configure HR practices based on whether they attempt to enhance employee ability, motivation, or opportunities. Despite the widespread use of the configurational perspective and AMO model in building a high-performance work system (HPWS), researchers have not clearly articulated how HR practices across the AMO domains should be configured to maximize internal fit across the system. Moreover, research has overlooked the hierarchical nature of an HPWS, such that HR practices are nested within a particular AMO domain, and the AMO domains, in turn, are nested within the HPWS. To address these gaps, we develop and test a dual internal fit model that specifies synergistic interactions within and among AMO domains. Analyses of six-wave panel data from 640 firms reveal that internal fit effects of HR practices simultaneously exist within (i.e., HR practice-level interactions) and among AMO domains (i.e., AMO domain-level interactions) to predict workforce productivity and ultimately firm profitability. Moreover, the two sets of interactions predict outcomes beyond the additive effects of the HR practices on which prior research has typically focused. These findings show that HR practices can be configured to have "system" effects. They also highlight the value of the dual internal fit model to understand the performance benefits of optimally configured HR systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evaluation of Tidal Asymmetry and Its Effect on Tidal Energy Resources in the Great Island Region of the Gulf of California

Hydrokinetic tidal energy is one of the few marine renewable energy resources with sufficiently mature technology for commercial exploitation. However, several parameters affect its exploitability, such as the minimum speed threshold, ambient turbulence levels, or tidal asymmetry, to name but a few. These parameters are particularly important in regions with lower mean speeds than those in first-generation sites, such as the North Sea. The Gulf of California is one of those regions. In this paper, a Delft3D Flexible Mesh Suite (Delft3D FM) model in barotropic configuration is set up over the Gulf of California using a flexible mesh with resolution varying from O (500 m) in the deep regions to O (10 m) in the coastal regions. A simulation is run over the year of 2020, with a tidal forcing of 75 components. The model is validated at four tidal gauge locations and four Acoustic Doppler Current profiler (ADCP) locations. The speed, U, and tidal power density (TPD) indicators used for the validation were the annual means, the annual means for speeds above the 0.5 m s−1 threshold, the annual means of the spring tide maxima, and the annual maxima. The contour maps of the annual means, that is, the annual means for speeds above the 0.5 m s−1 threshold, allow us to identify tidal energy hot spots throughout the Gulf of California, particularly in the Great Island region (GIR). In this region, these hot spots have higher U and TPD values, in agreement with previous studies. The patterns of circulation around Tiburón Island and San Esteban Island on the East, and Ángel de la Guarda Island and San Lorenzo Island on the West, the four islands in the region with the highest tidal energy potential, are also discussed while recognizing that Tiburón Channel, between Tiburón Island and San Esteban Island, has proved to be the best siting location, based on the technical results obtained so far. The hot spots sites are further characterized by computing the tidal asymmetry in these small regions, showing the locations of the sites with smallest asymmetry, which would be the best for tidal energy exploitation. The hot spots around San Esteban Island are particularly important because they have the largest TPD in the GIR, with the model predicting a TPD on the order of 500–1000 W m−2. Here, complementary field measurements obtained with two ADCPs, close to San Esteban Island, one at 15 m depth, SEs (shallow region), and the other at 60 m depth, SEd (deep region), produced TPDs of 1200 W m−2 and 400 W m−2, respectively. The analysis of the vertical profiles and the tidal asymmetry over the vertical shows the importance of developing 3D models in future investigations.

A Configurable Software Model of a Self-Adaptive Robotic System

Self-adaptation, meant to increase reliability, is a crucial feature of cyber-physical systems operating in uncertain physical environments. Ensuring safety properties of self-adaptive systems is of utter importance, especially when operating in remote environments where communication with a human operator is limited, like under water or in space. This paper presents a software model that allows the analysis of one such self-adaptive system, a configurable underwater robot used for pipeline inspection, by means of the probabilistic model checker ProFeat. Furthermore, it shows that the configurable software model is easily extensible to further, possibly more complex use cases and analyses.

Net-zero carbon emission oriented Bi-level optimal capacity planning of integrated energy system considering carbon capture and hydrogen facilities

The integrated energy system (IES) is considered an efficient energy provision paradigm to improve the utilization efficiency of renewable generation (RG), reduce energy cost and improve energy supply reliability. However, the multi-dimensional uncertainties and the hydrogen utilization have posed enormous challenges to the optimal planning of the IES. This paper proposes a net-zero carbon emission oriented bi-level optimal planning model for the IES with full consideration of the multiple operational uncertainties in the IES (RG, multiple demands). The upper level is an optimal capacity configuration model, and the economic costs, energy selling profits, the penalty for power abandonment and energy supply shortage are settled as the objectives. The lower level is an optimal energy dispatch model with the objective function of minimizing the total operating cost of the IES on typical days. An integrated energy system framework consisting of multiple energy flows is formulated in the presence of carbon capture system (CCS) and hydrogen-to-power (H2P) facilities. To fully characterize the uncertainties of RG and multiple demands, a data-driven scenario generation method is adopted to expand the operational scenarios, in which probability distribution assumptions and prior knowledge are not necessary. Finally, the effectiveness of the proposed solution is extensively demonstrated through numerical simulation, and a sensitivity analysis is carried out to assess the impact of changes in electricity and gas prices on the IES planning results.

Multi-energy synergistic planning of distributed energy supply system: Wind-solar-hydrogen coupling energy supply

This study is to improve the efficiency of energy utilization with the continuous growth of global energy demand and the increasingly severe environmental problem. A hydrogen-containing distributed energy supply system is proposed, which includes a hydrogen energy subsystem. The main driver of the system is renewable energy, while hydrogen energy is used as a carrier to assist the flow of energy. This study proposes an improved following electric load (FEL) strategy based on a comprehensive consideration of energy interactions within the system, and designs an optimal configuration model aimed at improving its economic, environmental and energy benefits. In addition, an improved rime algorithm (IRIME) is proposed, whose convergence performance and solution stability are greatly improved. A multi-objective optimization algorithm with better convergence performance, multi-objective improved rime algorithm (MO-IRIME), is proposed for solving the optimal configuration model to obtain the optimal capacity of the equipment inside hydrogen-containing distributed energy supply system. The total annual cost is reduced by 44.80%, the primary energy consumption by 68.47% and the pollutant gas emissions by 75.79% compared to the reference system. In addition, the way of interaction and coordination of multiple energy sources in the daily cycle is analyzed.

Day-Ahead Nonlinear Optimization Scheduling for Industrial Park Energy Systems with Hybrid Energy Storage

Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks, while lowering the industrial parks’ carbon emissions and accommodating diverse load demands from users. However, most optimization research on hybrid energy storage has adopted rule-based passive-control principles, failing to fully leverage the advantages of active energy storage. To address this gap in the literature, this study develops a detailed model for an industrial park energy system with hybrid energy storage (IPES-HES), taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks. An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day. An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm II (NSGA-II). A method using the improved NSGA-II is developed for day-ahead nonlinear scheduling, based on configuration optimization. The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD (5.5%) and 1600.3 kW (43.7%), respectively, compared with an operation strategy based on proportional electricity storage on a typical summer day. Overall, the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.

Handrim Reaction Force and Moment Assessment Using a Minimal IMU Configuration and Non-Linear Modeling Approach during Manual Wheelchair Propulsion.

Manual wheelchair propulsion represents a repetitive and constraining task, which leads mainly to the development of joint injury in spinal cord-injured people. One of the main reasons is the load sustained by the shoulder joint during the propulsion cycle. Moreover, the load at the shoulder joint is highly correlated with the force and moment acting at the handrim level. The main objective of this study is related to the estimation of handrim reactions forces and moments during wheelchair propulsion using only a single inertial measurement unit per hand. Two approaches are proposed here: Firstly, a method of identification of a non-linear transfer function based on the Hammerstein-Wiener (HW) modeling approach was used. The latter represents a typical multi-input single output in a system engineering modeling approach. Secondly, a specific variant of recurrent neural network called BiLSTM is proposed to predict the time-series data of force and moments at the handrim level. Eleven subjects participated in this study in a linear propulsion protocol, while the forces and moments were measured by a dynamic platform. The two input signals were the linear acceleration as well the angular velocity of the wrist joint. The horizontal, vertical and sagittal moments were estimated by the two approaches. The mean average error (MAE) shows a value of 6.10 N and 4.30 N for the horizontal force for BiLSTM and HW, respectively. The results for the vertical direction show a MAE of 5.91 N and 7.59 N for BiLSTM and HW, respectively. Finally, the MAE for the sagittal moment varies from 0.96 Nm (BiLSTM) to 1.09 Nm for the HW model. The approaches seem similar with respect to the MAE and can be considered accurate knowing that the order of magnitude of the uncertainties of the dynamic platform was reported to be 2.2 N for the horizontal and vertical forces and 2.24 Nm for the sagittal moments. However, it should be noted that HW necessitates the knowledge of the average force and patterns of each subject, whereas the BiLSTM method do not involve the average patterns, which shows its superiority for time-series data prediction. The results provided in this study show the possibility of measuring dynamic forces acting at the handrim level during wheelchair manual propulsion in ecological environments.