Process Configuration Research Articles

Theoretical study of Jahn Teller effect on ion diffusion in trigonal prismatic 1H-MnO2

The trigonal prismatic MnO6 is a basic structural unit of Mn-based compound which has been seldom explored in theoretical and experimental studies. In this paper, the diffusion potential energy surfaces of Li+, Na+, K+, Mg2+, Al3+, and NH4+ ion in trigonal prismatic 1H-MnO2 are presented, which give an intuitionistic picture of the Jahn-Teller effect on the diffusion process. Based on the nudged elastic band method, it is found that the diffusion of ions in trigonal prismatic MnO2 is accompanied by consecutive structural distortion. The low diffusion barriers indicate trigonal prismatic MnO2 is a fast ion conductor. Band structure calculations show the semimetal characteristic of the intercalated compound, which is fit for high electrochemical performance. The details of the diffusion process are also highlighted. Our theoretical study provides an in-depth understanding and research on the fundamentals of the diffusion process in the special configuration in MnO2 and helps to develop novel high-performance electrodes.

Exploration of environmentally friendly processes for converting CO2 into propanol through direct hydrogenation

This study firstly explores six process configurations for the conversion of CO2 to propanol via direct hydrogenation. The variations in the proposed configurations lie in the technologies used for off-gas treatment (such as pressure swing adsorption, oxyfuel combustion, autothermal reforming, and chemical absorption) and the intensification of separation (including the incorporation of the hydration reaction of ethylene oxide) within the process. Energy efficiency analysis, techno-economic analysis (in minimum required selling price, MRSP), and life cycle assessment (on global warming potential, GWP) were conducted to evaluate all proposed schemes. Overall, this study suggests that enhancing the selectivity towards propanol and implementing a suitable off-gas treatment strategy are crucial for this process. Based on the findings, we recommend Scheme 4, which involves auto-thermal reforming for off-gas treatment, as the optimal configuration. It leads to an energy efficiency of 45.33 %. Despite the higher MRSP (3.12 USD/kg when using grey H2, 7.45 USD/kg when using green H2, commercial process: 1.4 to 1.6 USD/kg), it significantly reduces GWP (3.19 kg-CO2-eq/kg when using grey H2, 1.59 kg-CO2-eq/kg when using green H2) created from the conventional process (6.77 kg-CO2-eq/kg). Given appropriate economic incentives, the proposed process could serve as a more environmentally friendly option for propanol production.

Word frequency statistics based on MapReduce on serverless platforms

This paper investigates the application of serverless computing in conjunction with the MapReduce framework, particularly in machine learning (ML) tasks. The MapReduce programming model has been widely used to process large-scale datasets by simplifying parallel and distributed data processing. This study explores how the combination of these two technologies can provide more efficient and cost-effective ML solutions. Through a detailed analysis of serverless environments and the MapReduce framework, this paper shows how the combination can advance the fields of cloud computing and machine learning. The experimental part includes the implementation of Map-Reduce model on a serverless platform, exploring the impact of different parameter settings on performance and improving efficiency by optimizing the data processing flow. In addition, the paper analyzes the use of memory and CPU resources and derives the relationship between dataset size, memory consumption and processor configuration and execution time. Through these experiments and analyses, this paper provides an empirical basis and theoretical support for the optimization of cloud computing frameworks.

Sliding and Adaptive Windows to Improve Change Mining in Process Variability

A configurable process Change Mining approach can detect changes from a collection of event logs and provide details on the unexpected behavior of all process variants of a configurable process. The strength of Change Mining lies in its ability to serve both conformance checking and enhancement purposes; users can simultaneously detect changes and ensure process conformance using a single, integrated framework. In prior research, a configurable process Change Mining algorithm has been introduced. Combined with our proposed preprocessing and change log generation methods, this algorithm forms a complete framework for detecting and recording changes in a collection of event logs. Testing the framework on synthetic data revealed limitations in detecting changes in different types of variable fragments. Consequently, it is recommended that the preprocessing approach be enhanced by applying a filtering algorithm based on sliding and adaptive windows. Our improved approach has been tested on various types of variable fragments to demonstrate its efficacy in enhancing Change Mining performance.

Face-specific identification impairments following sight-providing treatment may be alleviated by an initial period of low visual acuity

Identifying faces requires configural processing of visual information. We previously proposed that the poor visual acuity experienced by newborns in their first year of life lays the groundwork for such configural processing by forcing integration over larger spatial fields. This hypothesis predicts that children treated for congenital cataracts late in life will exhibit persistent impairments in face- but not object-identification, because they begin their visual journey with higher than newborn acuity. This would not be the case for patients whose pretreatment condition has allowed for initial low acuity vision, like that of a newborn. Here, we test this prediction by assessing the development of facial identification skill in three groups: patients treated for congenital cataracts whose pretreatment visual acuity was worse than that of a newborn, patients whose pretreatment acuity was better than that of a newborn, and age-matched controls. We find that while both patient groups show significant gains in object-identification, the emergence of face identification is determined by pretreatment acuity: patients with pre-operative acuity worse than a newborn did not show any improvements on face-identification tasks despite years of visual experience, whereas those with pretreatment acuity comparable to a newborn improved on both the object- and face-identification tasks. These findings not only answer our research question but also provide new insights into the role of early visual acuity in facial identification development. We discuss these results in the context of both typical and atypical visual development.

Highly Efficient Hybrid Reconfigurable Intelligent Surface Approach for Power Loss Reduction and Coverage Area Enhancement in 6G Networks

This paper introduces a novel efficient hybrid reconfigurable intelligent surface (RIS) approach designed to significantly reduce power loss and enhance coverage area in 6G networks. The core innovation of this approach lies in an advanced iterative algorithm introduced as the Hybrid reconfigurable intelligent surface decision-making algorithm (HRIS-DMA) that integrates precise user location data into the RIS configuration process. By dynamically adjusting RIS elements to reflect and direct signals based on real-time user positions, this method minimises signal attenuation and optimises signal propagation. The mechanism driving the performance gains includes precise beamforming and intelligent reflection, continuously refined through iterative updates. This technique ensures robust signal strength and expanded coverage, addressing the challenges of dense and diverse deployment scenarios in 6G networks. The proposed scheme’s application in 6G networks demonstrates substantial improvements in signal quality and network reliability, paving the way for enhanced user experiences and efficient communication infrastructures. This novel approach was tested using MATLAB R2023a , and its performance was evaluated using three downlink scenarios: zero to few, few to moderate, and moderate to many obstacles. The three scenarios show higher coverages than conventional simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and base station (BS) handover. Based on the evaluation metrics, the analysis results of the novel HRIS-DMA show 70% less signal power loss, 0.17 μs less system delay, 25 dB and 12 dB channel gain compared with the conventional STAR-RIS and BS handover, respectively, and 95% improvement in the overall system’s efficiency compared to STAR-RIS and 13% compared to BS-BS handover.

Energy consumption optimization of CO2 capture and compression in natural gas combined cycle power plant through configuration modification and process integration

Currently, natural gas combined cycle (NGCC) power plants account for a quarter of global electricity power supply and lead to greenhouse gas emissions. Carbon capture and storage (CCS) is one of the most effective technologies to reduce carbon emissions in the short term. However, the monoethanolamine (MEA)-based CO2 absorption and compression process is energy-intensive, which significantly reduces the power generation efficiency of NGCC. In addition, the waste hot and cold energy from NGCC and liquefied natural gas (LNG) regasification process, which are generally wasted, can be recovered for the CCS process. Therefore, this study aims to reduce the energy consumption of the carbon capture process and recover waste LNG cold energy and hot energy in the NGCC through configuration modification and process integration. The results show that the energy consumption of CO2 regeneration in the proposed CO2 capture process configuration is reduced by 18.03 %, and the net power efficiency of the NGCC plant increases from 48.88 % to 50.10 %. Furthermore, a cascade two-stage organic Rankine cycle is integrated into the system for waste heat recovery, which can generate 5.04 MW electric power and reduce the efficiency penalty of the plant from 13.72 % to 10.29 %. By contrast, the alternative application of LNG cold energy to the CO2 compression process reduces compression power by 3.95 MW with a lower footprint and utility requirement while the efficiency penalty is decreased by 3.15 %.

Plasmon-induced electron transportation in heterostructure N-rGO/NiFe-LDH@Au with enhanced photoelectrochemical water oxidation

Facile design and fabrication of an ideal heterointerface model is considered as a significant advancement towards photoelectrochemical (PEC) water splitting efficiency. Here, we detail the incorporation of plasmonic Au nanoparticles into N-doped reduced graphene oxide/NiFe-layered double hydroxide (N-rGO/NiFe-LDH) interface by cost-effective coprecipitation-photoreduction method and photoelectrode assemblage via drop casted technique for enhanced PEC water oxidation. Au nanoparticles efficiently harvest the light energy upon plasmonic excitation and transfer the energy towards contacted NiFe-LDH and N-rGO, thereby promoting PEC performances. In comparison to N-rGO/NiFe-LDH, the as-obtained N-rGO/NiFe-LDH@Au composite exhibits 1.46 fold times enhancement in photocurrent density (5.7 mA/cm2 at 1.23 V vs RHE) and considerable cathodic shift of 20 mV at onset potential of 0.18 V for PEC water oxidation. Additionally, N-rGO/NiFe-LDH@Au exhibits incident photon to current conversion efficiency (IPCE) of 10 % and better photostability of more than 6 h for boosted water splitting. The incorporation of Au nanoparticles significantly augmented the photoactivity as apparent from the photocurrent density assessment. The improved PEC performance of N-rGO/NiFe-LDH@Au photoelectrode is accredited to the surface plasmon resonance (SPR) induced electron transfer process and heterostructure configuration holding numerous defect sites for smooth charge transportation. This fabrication approach is quite a simplistic method to enlarge the optical behavior and effective performance of the PEC system and also provide guidance to assemble other sophisticated materials suitable for the energy conversion process.

Computer‐Aided Design of Large‐Scale Nanomaterials Synthesis Processes: A Detailed Review

AbstractEfforts from the scientific community and the private sector are required to lower the costs of large‐scale production of nanomaterials (NMs) to enhance their commercialization. In this work, the computer‐aided process design of large‐scale NM synthesis procedures is comprehensively reviewed. Moreover, a generalized process flow diagram for all large‐scale production processes was constructed by surveying numerous scalable experimental procedures reported in the literature. Previous studies reporting simulation cases of large‐scale production processes of NMs and nanocomposites (NCs) are also reviewed based on the type of material produced, e.g., oxides, sulfides, carbonaceous materials, organic materials, metals, and other types of NMs. Finally, technical insights from classical chemical engineering specializations, such as altering process configurations, optimizing process variables, integrating chemical processes, utilizing renewable energy sources, conducting computational calculations, employing machine learning techniques, and studying the process's environmental impact, are reviewed for large‐scale NMs and NCs synthesis.

A holistic platform for accelerating sorbent-based carbon capture

Reducing carbon dioxide (CO2) emissions urgently requires the large-scale deployment of carbon-capture technologies. These technologies must separate CO2 from various sources and deliver it to different sinks1,2. The quest for optimal solutions for specific source–sink pairs is a complex, multi-objective challenge involving multiple stakeholders and depends on social, economic and regional contexts. Currently, research follows a sequential approach: chemists focus on materials design3 and engineers on optimizing processes4,5, which are then operated at a scale that impacts the economy and the environment. Assessing these impacts, such as the greenhouse gas emissions over the plant’s lifetime, is typically one of the final steps6. Here we introduce the PrISMa (Process-Informed design of tailor-made Sorbent Materials) platform, which integrates materials, process design, techno-economics and life-cycle assessment. We compare more than 60 case studies capturing CO2 from various sources in 5 global regions using different technologies. The platform simultaneously informs various stakeholders about the cost-effectiveness of technologies, process configurations and locations, reveals the molecular characteristics of the top-performing sorbents, and provides insights on environmental impacts, co-benefits and trade-offs. By uniting stakeholders at an early research stage, PrISMa accelerates carbon-capture technology development during this critical period as we aim for a net-zero world.

Design and implementation of real-time log analysis based on ELK

With the rapid development of the IT industry, the integrated solution of real-time log analysis has become a member of the new generation of IT operation and maintenance structure. The traditional manual log analysis system is no longer suitable for the rapid development of the operation and maintenance system. The log analysis software commonly used in enterprises includes Elasticsearch, Logstash, and Kibana systems. The advantage is that it is free and open source. "Design and implementation of real-time log analysis based on ELK" was born for system maintenance and developers to understand server software and hardware information through logs, and check the cumbersome operations in the configuration process. Because frequent analysis of the log can understand the load of the server, performance security, so as to take timely measures to correct errors. If you manage dozens or hundreds of servers, there is no centralized log management system to collect and summarize the logs on all servers, which causes great difficulties for operation and maintenance managers. "Design and implementation of real-time log analysis based on ELK" uses Elasticsearch, Logstash, and Kibana to achieve unified collection and classification of server logs, which can solve the defects of traditional log analysis platforms, improve the efficiency of server application positioning problems, and help enterprises solve data center problems running problems of related applications.

Analysis of Software Tools for Automation of Configuration and Management Functions in It Infrastructures

The work by the authors, using a systematic approach, analyzes a group of software tools that are functionally oriented towards the automated implementation of configuration and management processes in IT infrastructures. The research profile focuses on a methodology known in the professional environment as “Infrastructure as Code” (IaC) and is one of the foundational methodologies implemented in a systemic combination within the DevOps methodology. This methodology is actively used in processes of dynamic formation, deployment, and maintenance of corporate IT infrastructures in many modern successful high-tech companies to achieve the best business performance, efficiency, guaranteed success, and security. The article discusses two basic approaches to building software tools that implement the IaC methodology, namely the declarative and imperative approaches. The main emphasis is placed on the formation of a set of advantages and disadvantages inherent in software tools such as Terraform, ARM, Ansible, and CloudFormation. The focus of researchers on these four software tools is explained by their leading positions in a fairly extensive lineup of possible alternative software products that allow for a comprehensive implementation of the IaC methodology in the context of full and functional systemic deployment of the DevOps methodology in specific implementations of corporate IT infrastructures. The authors' generalized conclusion of original scientific research is that there is currently no single clearly distinguished universal software tool among others that fully satisfies the entire spectrum of requirements and needs. Potential users in this context are communities of DevOps professionals and clients – owners and managers of modern dynamic high- tech and successful companies, firms, and businesses that rely on modern information systems and technologies.

LEVERAGING MISSION SOLUTION CONFIGURATION THROUGH MBSE AND TRADESPACE EXPLORATION

AbstractThales is a worldwide leader in innovative radar and mission solution systems used by naval ships. As the demand for personalized products increased through time, Thales shifted from a project‐oriented to a product‐oriented approach. This shift aims to capitalize on variants, minimize customization, and streamline operations. In this context, Thales established a mission solution configuration process (SCP) to facilitate the selection of product variants to compose a system during the bidding phase. However, the current SCP has limitations, constraining exploration and integration with engineering processes and system data. Consequently, the proposed systems sometimes fall short of the most optimal solution the client could get. Therefore, the objective of this work is to develop and validate an improved mission solution configuration process to streamline the creation and selection of product variants at Thales, particularly during the bidding phase, to better meet client needs and operational requirements. This method integrates Model‐Based Systems Engineering (MBSE) and Tradespace Exploration (TSE), utilizing ARCADIA as the methodology and Capella as the tool. A descriptive model is generated for analytical purposes within TSE, employing Multi‐Attribute‐Utility‐Theory (MAUT) and Pareto‐Optimization for evaluating and selecting optimal mission solution variants. Validation was conducted through a Coast Guard mission case study involving 125 solution variants, revealing Pareto‐optimal solutions balancing performance and cost. This method enhances the current configuration process by aligning client and operational needs with Thales's sales and product teams, ensuring accurate interpretation of requirements and minimizing information inconsistencies. The case study results identify technological gaps in variant designs, guiding research and development efforts towards subsystems or components with significant impact on system performance.

Reduced Reliance on Configural Processing for Infant Faces: Evidence From the Thatcher Illusion [Conference Presentation Abstract

Reduced Reliance on Configural Processing for Infant Faces: Evidence From the Thatcher Illusion [Conference Presentation Abstract

Understanding the energy and emission implications of new technologies in a kraft mill: Insights from a CADSIM Plus simulation model

Kraft mills play a vital role in energy transition because they have significant potential to reduce their own energy utilization and produce energy/products to decarbonize other sectors. Through biomass combustion and potential biogenic carbon emissions capture, these mills can contribute to offsetting emissions from other sectors. This research investigates the departmental and cross-departmental implications of technology upgrades on energy, steam, emissions, water, and chemicals using a CADSIM Plus simulation model. The model provides a comprehensive analysis of mass and energy balances, offering valuable insights into the benefits and limitations of each technology. The model facilitates scenario analysis and comparisons of process configurations, enabling data-driven decision-making for sustainable and competitive operations. Six high-impact technologies, including additional evaporator effects, weak black liquor membrane concentration, belt displacement washer for brownstock washing, oxygen delignification, and improvements to the pulp machine shoe press and vacuum pumps, are evaluated. Individual technologies resulted in energy savings of 1.2% to 5.4%, biomass consumption reductions of 8.6% to 31.6%, and total emissions reductions of 1.6% to 5.9%. Strategic decision-making must consider existing mill limitations, future technology implementation, and potential production increases. Future research will explore product diversification, biorefineries, and pathways to achieve carbon-negative operations, aiming to reduce emissions and secure a competitive future for kraft mills.

Techno-economic study of different process configurations of CO2 capture by MEA absorption and utilization of an MCDM method for their ranking

Carbon dioxide emissions from fossil fuel-fired power stations are attracting growing attention as a potential approach for reducing greenhouse gas emissions. Post-combustion capture of CO2 with monoethanolamine (MEA) is one of the most successful and mature technologies for mitigating carbon dioxide emissions. The most crucial problem of this technique is the high heat requirement of the reboiler for solvent regeneration. In this study, seven different configurations for carbon capture, namely, standard absorption, vapor recompression, vapor recompression process with split-flow, absorption column with intercooling, rich-split, combined, and rich-split with vapor recompression, are studied. The simulation results showed that the reboiler energy consumption intensity of alternative configurations lowered by 15.2 %, 18.4 %, 2.9 %, 7.4 %, 20.3 %, and 23.2 %, respectively, with regard to the standard absorption configuration. Seven configurations were ranked based on the total investment cost, steam consumption, power consumption, and water consumption. Combined and rich-split were the best, and vapor recompression and Rich-split with vapor recompression were the worst.

Optimized Fabrication Process and PD Characteristics of MVDC Multilayer Insulation Cable Systems for Next Generation Wide-Body All-Electric Aircraft

For wide-body all-electric aircraft (AEA), a high-power-delivery, low-system-mass electric power system (EPS) necessitates advanced cable technologies. Increasing voltage levels enhances power density yet poses challenges in aircraft cable design, including managing arc-related risks, partial discharges (PDs), and thermal management. Developing multilayer multifunctional electrical insulation (MMEI) systems for aircraft applications is a feasible option to tackle these challenges and reduce the size and mass of cable systems. This approach involves selecting layers of different materials to address specific challenges. Our prior research concentrated on the modeling and simulation-based design of MMEI systems for MVDC power cables. Experimental tests are essential for determining the behavior of PDs under varying pressure conditions. Also, the dielectric strength and time to failure of the designs need to be assessed. In this work, the fabrication process of a down-selected MMEI flat configuration is discussed and analyzed. This paper analyzes the fabrication process of power cables employing MMEI configurations and evaluates the PD characteristics of down-selected ARC-SC-T-MMEI cable samples. This study presents a detailed analysis of the characteristics of PD under atmospheric and low-pressure conditions, which will provide essential insights into the design of MVDC cables for future AEA applications.

Evaluasi Risiko Celah Keamanan Aplikasi E-Office menggunakan Metode OWASP

Based on data from Badan Siber dan Sandi Negara (BSSN) in 2022, it was reported that a total of 1,950 security vulnerabilities were found in 457 electronic systems across various applications widely used by the public. The purpose of this research is to evaluate the risk of existing security vulnerabilities in the E-Office application and determine the level and impact that these vulnerabilities can cause. This research focuses on information system security, specifically evaluating the risk of security vulnerabilities in the E-Office application of the Ogan Ilir Regency. The research was conducted using the Open Web Application Security Project (OWASP) method with a risk rating assessment. The research process began with a literature review to gather data and information sources, determine the scope and research objectives, test, identify security vulnerabilities, analyze security vulnerabilities, and the results of the analysis. The research subject is the E-Office application of Ogan Ilir Regency, with the object of the research being the security vulnerabilities in that application. OWASPZap was used as a tool to obtain data on security vulnerabilities, and using OWASPZap, 38 security vulnerabilities were found, with 18 of them meeting the criteria of the OWASP Top 10. Our findings indicate that the security vulnerabilities in the E-Office application of Ogan Ilir Regency include vulnerabilities in authentication levels, access control, configuration, and data validation processes.

Comparative Configurational Process Analysis: A New Set-Theoretic Technique for Longitudinal Case Analysis

In the past 20 years, researchers have significantly advanced various management fields by examining organizational phenomena through a configurational lens, including competitive strategies, corporate governance mechanisms, and innovation systems. Qualitative comparative analysis (QCA) has emerged as a primary method for empirically investigating organizational configurations. However, QCA has traditionally struggled to capture the temporal aspects of configurational phenomena. In this paper, we present configurational comparative process analysis (C2PA), which merges QCA with sequence analysis. We introduce the concept of configurational themes—recognizable temporal patterns of recurring combinations of explanatory conditions—to identify and track the temporal dynamics among these phenomena. We also outline configurational matching—a method for empirically identifying these themes by distinguishing theme-defining from theme-supporting conditions. C2PA allows researchers to explore the temporal dynamics of configurational phenomena, such as their stability, emergence, and decline at critical junctures. We illustrate the application of C2PA through a study of shareholder value orientation and discuss its potential for addressing key questions in management research.

Sex differences in social brain neural responses in autism: temporal profiles of configural face-processing within data-driven time windows

Face-processing timing differences may underlie visual social attention differences between autistic and non-autistic people, and males and females. This study investigates the timing of the effects of neurotype and sex on face-processing, and their dependence on age. We analysed EEG data during upright and inverted photographs of faces from 492 participants from the Longitudinal European Autism Project (141 neurotypical males, 76 neurotypical females, 202 autistic males, 73 autistic females; age 6–30 years). We detected timings of sex/diagnosis effects on event-related potential amplitudes at the posterior–temporal channel P8 with Bootstrapped Cluster-based Permutation Analysis and conducted Growth Curve Analysis (GCA) to investigate the timecourse and dependence on age of neural signals. The periods of influence of neurotype and sex overlapped but differed in onset (respectively, 260 and 310 ms post-stimulus), with sex effects lasting longer. GCA revealed a smaller and later amplitude peak in autistic female children compared to non-autistic female children; this difference decreased in adolescence and was not significant in adulthood. No age-dependent neurotype difference was significant in males. These findings indicate that sex and neurotype influence longer latency face processing and implicates cognitive rather than perceptual processing. Sex may have more overarching effects than neurotype on configural face processing.