Development Of Large-scale Systems Research Articles

Addressing Constraint Coupling and Autonomous Decision-Making Challenges: An Analysis of Large-Scale UAV Trajectory-Planning Techniques

With the increase in UAV scale and mission diversity, trajectory planning systems faces more and more complex constraints, which are often conflicting and strongly coupled, placing higher demands on the real-time and response capabilities of the system. At the same time, conflicts and strong coupling pose challenges the autonomous decision-making capability of the system, affecting the accuracy and efficiency of the planning system in complex environments. However, recent research advances addressing these issues have not been fully summarized. An in-depth exploration of constraint handling techniques and autonomous decision-making issues will be of great significance to the development of large-scale UAV systems. Therefore, this paper aims to provide a comprehensive overview of this topic. Firstly, the functions and application scenarios of large-scale UAV trajectory planning are introduced and classified in detail according to the planning method, realization function and the presence or absence of constraints. Then, the constraint handling techniques are described in detail, focusing on the priority ranking of constraints and the principles of their fusion and transformation methods. Then, the importance of autonomous decision-making in large-scale UAV trajectory planning is described in depth, and related dynamic adjustment algorithms are introduced. Finally, the future research directions and challenges of large-scale UAV trajectory planning are outlooked, providing directions and references for future research in the fields of UAV clustering and UAV cooperative flight.

Stabilizing zinc anodes for dual-membrane Zn-Ce redox flow battery via constructing zincophilic protective layer

The development of large-scale grid storage systems has increased interest in redox flow batteries due to their flexible design characteristics, which can help overcome the intermittency of renewable energy. Zn-Ce redox flow battery is considered as one of the most promising redox flow batteries due to its high voltage and low cost. However, using Zn metal anodes in this context remains problematic due to issues with corrosion and dendrite growth, as well as electrolyte incompatibility, which limits their cycling performance and practical application. Herein, an indium-based protective layer is constructed on the Zn anode cooperating with a dual-membrane cell configuration to solve these problems. Due to its unique physical and chemical properties, the zincophilic indium protective layer can effectively prevent electrolyte corrosion as well as regulate the behavior of Zn plating and stripping. Moreover, a dual-membrane cell configuration is used to mitigate electrolyte incompatibility since it uses specified ions as charge carriers and blocks the infamous H+ poisoning on the zinc side. The cell exhibits a high discharge voltage plateau of 2.2 V at 20 mA cm−2 and maintains a high average energy efficiency of 84.39 % over 80 cycles. This work presents a practical method to enhance the cycling performance of the Zn-Ce battery by incorporating an In-based protective layer with a dual-membrane cell configuration, resulting in unparalleled efficiency and stability.

Surface S doping induced ladder-regulation of lattice oxygen on the vertical FeCoOOH for water oxidation

Reasonable design and manipulation of appropriately activated lattice oxygen on the catalytic surface is key to efficient oxygen evolution reaction (OER). Utilizing anionic regulation mechanism, partial and rapid sulfur (S) substitution for oxygen can provide optimal surface reconstruction of CoFe oxo/(hydroxo)ide materials during the OER process. S-doping can modulate the local electronic structure of CoFe oxo/(hydroxo)ide compounds, thereby reducing the oxygen vacancies formation energy. This is the first gradient to form more oxygen vacancies, thus activating lattice oxygen. In addition, the hybridization between Co/Fe 3d and O 2p is enhanced by the trace S doping on the surface of CoFe oxo/(hydroxo)ides, suggesting a faster rate of electronic transfer. Therefore, the electrochemical measurements show that the obtained electrocatalyst demonstrates an overpotential of 361.1 mV at 100 mA cm−2 and long-term stability. The characterizations after stability tests confirm that surface S was rapidly leached to form a second gradient high concentration oxygen vacancies. Moreover, the formation of uniformly dispersed vertical nanoplate arrays of FeCoOOH exposes a wealth of active sites. This work provides a promising ladder regulation strategy of lattice oxygen for the design of pre-catalysts for the development of large-scale water decomposition systems.

Using boundary objects and methodological island (BOMI) modeling in large-scale agile systems development

AbstractLarge-scale systems development commonly faces the challenge of managing relevant knowledge between different organizational groups, particularly in increasingly agile contexts. Here, there is a conflict between coordination and group autonomy, and it is challenging to determine what necessary coordination information must be shared by what teams or groups, and what can be left to local team management. We introduce a way to manage this complexity using a modeling framework based on two core concepts: methodological islands (i.e., groups using different development methods than the surrounding organization) and boundary objects (i.e., artifacts that create a common understanding across team borders). However, we found that companies often lack a systematic way of assessing coordination issues and the use of boundary objects between methodological islands. As part of an iterative design science study, we have addressed this gap by producing a modeling framework (BOMI: Boundary Objects and Methodological Islands) to better capture and analyze coordination and knowledge management in practice. This framework includes a metamodel, as well as a list of bad smells over this metamodel that can be leveraged to detect inter-team coordination issues. The framework also includes a methodology to suggest concrete modeling steps and broader guidelines to help apply the approach successfully in practice. We have developed Eclipse-based tool support for the BOMI method, allowing for both graphical and textual model creation, and including an implementation of views over BOMI instance models in order to manage model complexity. We have evaluated these artifacts iteratively together with five large-scale companies developing complex systems. In this work, we describe the BOMI framework and its iterative evaluation in several real cases, reporting on lessons learned and identifying future work. We have produced a matured and stable modeling framework which facilitates understanding and reflection over complex organizational configurations, communication, governance, and coordination of knowledge artifacts in large-scale agile system development.

Hard carbon as anode material for metal-ion batteries

The development of large-scale energy storage systems based on the mature technology of lithium-ion batteries is hampered by the high cost of lithium. Therefore, analogous technologies based on other alkali metals (sodium and potassium) are being developed. Among various types of negative electrode (anode) materials for such batteries, carbon materials are most promising; in particular, hard carbon is of most interest. The present review addresses the current state of research on the structure, composition and properties of this type of material and gives analysis of methods of its preparation and investigation. Description of the microstructure of hard carbon is a highly ambiguous and challenging problem; therefore, the review pays special attention to various microstructural models. In addition, the methods of synthesis are systematized and the results of studies of the physicochemical properties of hard carbon are analyzed. The correlations between the preparation method, characteristics and electrochemical properties in metal-ion batteries are identified. A large array of results of electrochemical studies is considered, the views on the mechanisms of electrochemical interactions of Na<sup>+</sup> and K<sup>+</sup> cations with hard carbon are systematized, and the currently existing contradictions in various models of the interaction mechanisms are shown.<br> Bibliography — 247 references.

Research on the loss characteristics of high-voltage cascaded energy storage systems based on IGCTs

High-voltage cascaded energy storage systems have become a major technical direction for the development of large-scale energy storage systems due to the advantages of large unit capacity, high overall efficiency, satisfactory economy, reliable safety, and easy access to grid dispatching. The loss characteristics analysis is the design basis of the water-cooling system of a high-voltage cascaded energy storage system, and its accurate calculation can determine the system’s safe and reliable operation of the system. This paper first introduces the four-quadrant operation principles of a cascaded H-bridge energy storage system, and analyzes the calculation method of the loss of the Integrated Gate-Commutated Thyristor based power module; On this basis, it studies the loss characteristics of the cascaded energy storage system and analyzes the influence of different modulation strategies and third harmonic injection on the loss characteristics of the energy storage system; Finally, this paper has completed the loss test for the engineering prototype module and compared the test results with theoretical calculation results to verify the accuracy of the loss calculation method of the high-voltage cascaded energy storage system.

Assessment of the onshore storage capacity of hydrogen in Argentina’s natural gas fields

Increasing the proportion of electricity from renewable energy sources is critical for the reduction of CO 2 emissions required to meet the commitments of the Paris Agreement. However, intermittency in energy generation due to the daily and seasonal variability of wind and solar radiation is a major challenge. Addressing this issue requires the development of large-scale energy storage systems. Low-carbon hydrogen production and storage in geological reservoirs offers a potential seasonal energy storage solution. Here, we provide the first assessment of the underground storage capacity for hydrogen in Argentina and identify sites where future clusters of low-carbon hydrogen production could be co-located with suitable geological storage sites. We outline a production-based methodology that couples historical hydrocarbon production with remaining reserves to provide a reliable estimation of the amount of hydrogen that can be stored in each reservoir. Assuming a cushion gas requirement of 50%, our results show that a combined storage capacity of 2860 TWh of hydrogen exists. This far exceeds the requirements to meet the seasonal electricity demand for residential consumption in the country. The results of our sensitivity analysis show that the two most sensitive variables are temperature and compressibility, suggesting that high-pressure shallow reservoirs are most suitable for underground hydrogen storage. We additionally propose locations for two H 2 production and storage hubs that would permit the integration of storage sites with renewable developments and natural gas reserves that could be combined with carbon capture and storage to produce low-carbon hydrogen. Thematic collection: This article is part of the Hydrogen as a future energy source collection available at: https://www.lyellcollection.org/topic/collections/hydrogen

Generating simplified ammonia reaction model using genetic algorithm and its integration into numerical combustion simulation of 1 MW test facility

A small reaction model for ammonia is necessary for the design and development of large-scale ammonia combustion systems using computational fluid dynamics (CFD) simulation. The present study generated a simplified reaction model for ammonia using a genetic algorithm (14 species and 45 reactions). Optimization was applied to reproduce the combustion properties of ignition delay times, mole fractions of ammonia and nitric oxide at the exit of a single perfectly stirred reactor (PSR), those of a two-staged PSR, and laminar flame speed. The generated simplified reaction model for ammonia was applied to combustion CFD simulation for a 1 MW boiler test facility. Results of CFD simulation reproduced the characteristics of two-staged combustion well. Nitrous oxide emissions predicted by the simulation agreed well with experimental results.

High-speed silicon-integrated photonic radio frequency switch based on optical switching.

Photonic radio frequency (RF) switches are promising to replace conventional electronic RF switches in modern RF communication systems owing to their high switching speed and immunity to electromagnetic interference. However, existing photonic RF switches are generally based on frequency or polarization filtering. Thus, they require more light sources and filters to increase the number of switching channels, consequently limiting scalability. We propose a silicon-integrated photonic RF switch based on optical switching. RF signals are first modulated into the optical domain and switched through phase control of the phase shifters in the optical switch. Switching is not related to the frequency or polarization of the optical carriers, thus reducing the number of light sources required. Experimental results demonstrate 10-GHz switching of two RF signals with frequencies of 20 GHz and 30 GHz. The proposed photonic RF switch can be further expanded to form a large switch matrix, possibly contributing to the development of large-scale RF communication systems.

CONCEPTS OF SUSTAINABLE ORGANIZATIONAL DEVELOPMENT OF LARGE-SCALE ECONOMIC PRODUCTION SYSTEMS

The formed system of conceptual provisions for ensuring the sustainability of organizational development of large-scale economic and production systems is indicative of the implementation of the author's system of hypotheses regarding: the need to use the integration capabilities of individual business entities to gain advantages in competition and to support the sustainability of coordinated development; supporting the sustainability of large-scale economic and production systems by optimizing the components of the integration base, correctly forming the architecture of large-scale economic and production systems and choosing the most appropriate organizational form; the possibility of modeling the development of large-scale economic and production systems through the correlation of its architectural representation with the stages of the life cycle; the use of the competencies of business entities as a basis for integration interaction within the framework of a certain economic and production system; subordination of the efficiency of functioning and development of large-scale economic and production systems is determined by the degree of disclosure of the potential of integration interaction of input to large-scale economic and production participants. The proof of these hypotheses made it possible to emphasize that ensuring the sustainability of the organizational development of large-scale economic and production systems is achieved due to the optimization of the structure and interaction parameters of strategic business units (participants) of the large-scale economic and production system, which increases the relevance of developments in the field of institutional regulation of the life activities of large-scale economic and production systems . Such formation of large-scale systems creates additional competitive advantages through, for example, the concentration of resources on the most effective business practices or through the creation of a more effective system of calculations. At the same time, the implementation of integration processes or an increase in the scope of activities always causes the manifestation of certain factors-threats and difficulties, the solution of which requires an appropriate economic basis. So, first of all, the increase in the scale of enterprise activity leads to the complication of the organizational structure and the appearance of numerous defects in ensuring the operation of management mechanisms. Secondly, the occurrence of any type of enterprise merger increases the probability of negative manifestation of corporate conflicts. Thirdly, attention should be paid to the threat of dispersal of property relations as a result of integration-disintegration processes. Finally, the variety of possible organizational forms of interaction between enterprises requires a certain theoretical unification.

Comparative life cycle greenhouse gas emissions assessment of battery energy storage technologies for grid applications

With an ever-increasing penetration of renewable energy sources into the power grid, the development and commercialization of large-scale energy storage systems (ESSs) have been enforced. It is imperative to evaluate the environmental sustainability of ESSs in grid applications to achieve sustainable development goals. In the present work, a cradle-to-grave life cycle analysis model, which incorporates the manufacturing, usage, and recycling processes, was developed for prominent electrochemical energy storage technologies, including lithium iron phosphate batteries (LIPBs), nickel cobalt manganese oxide batteries (NCMBs), and vanadium redox flow batteries (VRFBs). A case study was conducted based on per MWh of energy stored. The greenhouse gas (GHG) emissions of LIPBs, NCMBs, and VRFBs under the Chinese electrical grid peak-shaving scenario were determined to be 323, 263, and 425 kg CO2-eq/MWh, respectively. The key components contributing to the GHG emissions were identified. The GHG emissions of different batteries in renewable energy sources (photovoltaic and wind) were evaluated. Moreover, the GHG emissions under the future electricity mixes were predicted according to the carbon peaking and carbon neutrality goals. The GHG emissions of LIPBs, NCMBs, and VRFBs under the Announced Pledges Scenario could be reduced by approximately 23–27% in 2030 and 66–75% in 2050. Moreover, sensitivity analysis was performed, indicating that the GHG emissions were directly linked with the round-trip efficiency. The results could promote the environment, policy, and business model optimization efforts for large-scale energy storage in low-carbon power systems.

Towards a high-density photonic tensor core enabled by intensity-modulated microrings and photonic wire bonding

We propose a photonic processing unit for high-density analog computation using intensity-modulation-based microring modulators (IM-MRMs). The output signal at the fixed resonance wavelength is directly intensity modulated by changing the extinction ratio (ER) of the IM-MRM . Thanks to the intensity-modulated approach, the proposed photonic processing unit is less sensitive to the inter-channel crosstalk. Simulation results reveal that the proposed design offers a maximum of 17-fold increase in wavelength channel density compared to its wavelength-modulated counterpart. Therefore, a photonic tensor core of size 512 times 512 can be realized by current foundry lines. A convolutional neural network (CNN) simulator with 6-bit precision is built for handwritten digit recognition task using the proposed modulator. Simulation results show an overall accuracy of 96.76%, when the wavelength channel spacing suffers a 3-dB power penalty. To experimentally validate the system, 1000 dot product operations are carried out with a 4-bit signed system on a co-packaged photonic chip, where optical and electrical I/Os are realized using photonic and electrical wire bonding techniques. Study of the measurement results show a mean squared error (MSE) of 3.09times 10^{-3} for dot product calculations. The proposed IM-MRM, therefore, renders the crosstalk issue tractable and provides a solution for the development of large-scale optical information processing systems with multiple wavelengths.

ChaordicLedger: Knowledge Transfer for Industry

In the development of large-scale, integrated systems, the consistent clarity and distribution of knowledge and relevant status is crucial to success. There are many existing methods and commercial tools for providing context and traceability from requirements and specifications to a system's artifacts, but these tools are often vendor-locked and require access to cloud-based services and necessitate high licensing costs. Further, modern large-scale systems development involves multiple business partners, each of which needs to ensure their teams have granular, role-based access to all relevant information without impediment; centralized warehousing and gatekeeping should not be handled by a single entity if the information is to remain readily accessible. Instead, a permissioned, distributed knowledgebase that avoids vendor lock-in enables a consistent, real-time view of information that provides equivalent context for developers and other stakeholders. ChaordicLedger, a free and open source (FOSS) project joins the transparency and smart contract aspects of Distributed Ledger Technology with the storage capabilities of a Distributed File System to fulfill this industrial application while allowing for industry-specific customizations.

Task-based co-activation patterns reliably predict resting state canonical network engagement during development

Neurodevelopmental research has traditionally focused on development of individual structures, yet multiple lines of evidence indicate parallel development of large-scale systems, including canonical neural networks (i.e., default mode, frontoparietal). However, the relationship between region- vs. network-level development remains poorly understood. The current study tests the ability of a recently developed multi-task coactivation matrix approach to predict canonical resting state network engagement at baseline and at two-year follow-up in a large and cohort of young adolescents. Pre-processed tabulated neuroimaging data were obtained from the Adolescent Brain and Cognitive Development (ABCD) study, assessing youth at baseline (N = 6073, age = 10.0 ± 0.6 years, 3056 female) and at two-year follow-up (N = 3539, age = 11.9 ± 0.6 years, 1726 female). Individual multi-task co-activation matrices were constructed from the beta weights of task contrasts from the stop signal task, the monetary incentive delay task, and emotional N-back task. Activation-based predictive modeling, a cross-validated machine learning approach, was adopted to predict resting-state canonical network engagement from multi-task co-activation matrices at baseline. Note that the tabulated data used different parcellations of the task fMRI data (“ASEG” and Desikan) and the resting-state fMRI data (Gordon). Despite this, the model successfully predicted connectivity within the default mode network (DMN, rho = 0.179 ± 0.002, p < 0.001) across participants and identified a subset of co-activations within parietal and occipital macroscale brain regions as key contributors to model performance, suggesting an underlying common brain functional architecture across cognitive domains. Notably, predictive features for resting-state connectivity within the DMN identified at baseline also predicted DMN connectivity at two-year follow-up (rho = 0.258). These results indicate that multi-task co-activation matrices are functionally meaningful and can be used to predict resting-state connectivity. Interestingly, given that predictive features within the co-activation matrices identified at baseline can be extended to predictions at a future time point, our results suggest that task-based neural features and models are valid predictors of resting state network level connectivity across the course of development. Future work is encouraged to verify these findings with more consistent parcellations between task-based and resting-state fMRI, and with longer developmental trajectories.

Probing non-Markovian quantum dynamics with data-driven analysis: Beyond “black-box” machine-learning models

A precise understanding of the influence of a quantum system's environment on its dynamics, which is at the heart of the theory of open quantum systems, is crucial for further progress in the development of controllable large-scale quantum systems. However, existing approaches to account for complex system-environment interaction in the presence of memory effects are either based on heuristic and oversimplified principles or give rise to computational difficulties. In practice, one can leverage on available experimental data and replace first-principles simulations with a data-driven analysis that is often much simpler. Inspired by recent advances in data analysis and machine learning, we propose a data-driven approach to the analysis of the non-Markovian dynamics of open quantum systems. Our method allows, on the one hand, capturing the most important characteristics of open quantum systems such as the effective dimension of the environment and the spectrum of the joint system-environment quantum dynamics, and, on the other hand, reconstructing a predictive model of non-Markovian quantum dynamics, and denoising the measured quantum trajectories. We demonstrate the performance of the proposed approach with various models of open quantum systems, including a qubit coupled with a finite environment, a spin-boson model, and the damped Jaynes-Cummings model.

Synthetic-Gas Production through Chemical Looping Process with Concentrating Solar Dish: Temperature-Distribution Evaluation

The energy crisis and the adaptation of the global energy structure promote the development of renewable energies, in particular solar energy, also for syngas production. In this work, attention was focused on solar devices, necessary to provide high-temperature heat for the reduction reaction of metal oxides involved in the chemical looping driven by solar energy. Thermochemical processes for synthetic-gas production and CO2 sequestration were investigated using a concentrating solar thermal system. This paper proposes a useful forecasting model of the receiver temperature to make a realistic estimate of the system’s producibility for the different periods of the year. The model proposed was validated in the winter season, and the predicted temperature varied below 5% considering the real experimental data (442–472 °C). The validated model was used to evaluate the temperature receiver in spring and in summer, when the thermal level is reliable for thermochemical processes. From the spring season until the completion of the summer season, optimum conditions inside the receiver were reached (above 1000 °C). These preliminary findings could be used for the development of large-scale production systems.

Relationship between Interpersonal Skills in Project Success

The most asset to any project, regardless of the industry is the project manager. Research has shown whether the project fails or succeeds depends on the project manager, and the skills they bring to the table. The research problem in this study is the lack of research regarding the relationship between the interpersonal skills of the project manager and the success and / or failure of large-scale IT systems development projects. The existing literature indicated a strong positive relationship between the interpersonal skills and leadership traits of senior managers who manage with a passive leadership style and the primary IT systems development project success factor of cost. There was also a strong positive relationship between the interpersonal skills and leadership traits of senior managers who manage with leadership outcomes and the primary IT systems development project success factor of scheduling.

Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device

We develop a model of Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti memristive devices and demonstrate, both experimentally and numerically, an inverted spike-rate-dependent plasticity effect. The effect consists of the reduction of the learning rate with an increase in the frequency of spikes generated by the phase-locked loop neuron. The memristor model uses two internal state variables representing the number of complete filaments and the concentration of the charged traps. While the former state variable defines the device resistance and is associated with the distribution of oxygen vacancies, the latter affects the internal electric field and modulates the migration of vacancies. Several neural circuit configurations that include pairs and populations of memristively coupled neurons are analyzed numerically. The results of this study may contribute to the development of large-scale self-organized artificial cognitive systems based on neural synchrony.

Foundations and Applications of Information Systems Dynamics

Although numerous advances have been made in information technology in the past decades, there is still a lack of progress in information systems dynamics (ISD), owing to the lack of a mathematical foundation needed to describe information and the lack of an analytical framework to evaluate information systems. The value of ISD lies in its ability to guide the design, development, application, and evaluation of large-scale information system-of-systems (SoSs), just as mechanical dynamics theories guide mechanical systems engineering. This paper reports on a breakthrough in these fundamental challenges by proposing a framework for information space, improving a mathematical theory for information measurement, and proposing a dynamic configuration model for information systems. In this way, it establishes a basic theoretical framework for ISD. The proposed theoretical methodologies have been successfully applied and verified in the Smart Court SoSs Engineering Project of China and have achieved significant improvements in the quality and efficiency of Chinese court informatization. The proposed ISD provides an innovative paradigm for the analysis, design, development, and evaluation of large-scale complex information systems, such as electronic government and smart cities.

Progress on Fe-Based Polyanionic Oxide Cathodes Materials toward Grid-Scale Energy Storage for Sodium-Ion Batteries.

The development of large-scale energy storage systems (EESs) is pivotal for applying intermittent renewable energy sources such as solar energy and wind energy. Lithium-ion batteries with LiFePO4 cathode have been explored in the integrated wind and solar power EESs, due to their long cycle life, safety, and low cost of Fe. Considering the penurious reserve and regional distribution of lithium resources, the Fe-based sodium-ion battery cathodes with earth-abundant elements, environmental friendliness, and safety appear to be the better substitutes in impending grid-scale energy storage. Compared to the transition metal oxide and Prussian blue analogs, the Fe-based polyanionic oxide cathodes possess high thermal stability, ultra-long cycle life, and adjustable voltage, which is more commercially viable in the future. This review summarizes the research progress of single Fe-based polyanionic and mixed polyanionic oxide cathodes for the potential sodium-ion batteries EESs candidates. In detail, the synthesized method, crystal structure, electrochemical properties, bottlenecks, and optimization method of Fe-based polyanionic oxide cathodes are discussed systematically. The insights presented in this review may serve as a guideline for designing and optimizing Fe-based polyanionic oxide cathodes for coming commercial sodium-ion batteries EESs.