Operational Framework Research Articles

Operational resilience implementation: Challenges and opportunities for UK building societies

Operational resilience is the preparedness and existence of resilient IT systems, processes and resources to provide services continuously during a disruption. This study examines the operational resilience implementation challenges and opportunities for UK building societies and wider financial institutions. The key challenges are resource constraints, understanding regulatory requirements, supplier concerns, domain knowledge, legacy systems and manual-driven processes. Some challenges are being addressed with awareness and training, improved governance and risk management, recruitment and industry support networks. The opportunities include realising the actual state of IT systems, adopting cloud solutions, resources and capabilities, as well as improved domain knowledge and a better understanding of services and markets. Regulators need to be more explicit about their expectations and requirements, while the industry needs to collaborate on setting up common frames of reference for operational resilience frameworks. Lastly, the study provides a holistic view of operational resilience, including its implementation and management challenges, control measures and emerging opportunities.

Optimal Operation Framework of the Hydrogen-electric Integrated Energy System by Considering the Power to Hydrogen Efficiency Factor

Optimal Operation Framework of the Hydrogen-electric Integrated Energy System by Considering the Power to Hydrogen Efficiency Factor

Two-stage robust optimization for optimal operation of hybrid hydrogen–electricity storage system considering ladder-type carbon trading

The prominent problems of renewable energy curtailment and its uncertainty have become a hot topic. To the end, with consideration of environmental friendliness, energy utilization efficiency and operation cost, this paper proposes a hybrid hydrogen–electricity storage system (HHES) operation framework comprising assorted types of coupling devices and carbon capture system (CCS) under ladder-type carbon trading (LCT) mechanism. By considering energy balance constraints, equipments’ operation constraints and erratic rates of RESs and multiple loads, a scheduling model with source-load uncertainties is developed with the goal of minimizing the system’s operation cost and carbon emission. Then, a two-stage tri-level robust model is built to find the optimal scheduling plan under multiple uncertainties given the various sources and loads. The first level is to make an optimal dispatch plan and the second level is to find the worst scenario under the given uncertainty set. The third level aims at enhancing renewable energy accommodation rate and reducing load shedding rate in the worst case. Since the model is formed as a tri-level mixed integer optimization problem, the Column and Constraint Generation (CCG) method is adopted to achieve the optimal result. Finally, numerical analyses are performed. The results show that the proposed approach realizes a flexible dispatch of multiple energy and a high utilization level of multiple storages. Furthermore, this paper studies the influence of uncertainty and uncertainty budget parameters with the comparison of the deterministic model and robust models with different uncertainties. The results show that the proposed HHES has strong robustness and can provide a good reference for energy dispatch.

Optimal operation of multi-energy carriers considering energy hubs in unbalanced distribution networks under uncertainty

This article presents a two-stage stochastic programming model to address the dispatching scheduling problem in an energy hub, considering an unbalanced active low-voltage (LV) network. A three-phase version of the second-order cone relaxation of DistFlow AC optimal power flow (AC-OPF) is employed to incorporate unbalanced network constraints, while the objective minimizes the Local Energy Community (LEC) operational cost. The model results have been validated using OpenDSS, encompassing energy losses, voltage levels, and active/reactive power. Likewise, a comparative analysis between the three-phase model and the traditional single-phase model, using a modified version of the IEEE European LV Test Feeder as a case study, reveals interesting differences, such that the single-phase model underestimates voltage limits during photovoltaic (PV) system operation and overestimates energy purchased from the main grid, compared with the three-phase model. Similarly, the comparison results reveal that discrepancies between the single and three-phase models intensify as the power injected from PV systems rises. This notably impacts the total energy purchased from the grid, battery operation, and the satisfaction of thermal consumption through electricity. Finally, while the three-phase model offers valuable insights into security levels for voltage and grid energy purchase, its longer computational time makes it more suitable for strategic use rather than daily operational frameworks.

Strategic orientation, green supply chain management practices, and organizational performance: Basis for sustainable campus operation framework

Strategic orientation, green supply chain management practices, and organizational performance: Basis for sustainable campus operation framework

The application of novel shear deformation theory and nonlocal elasticity theory to study the mechanical response of composite nanoplates

The use of composite structures, which have many layers of materials, has become more prevalent in the field of engineering. One of the advantages of this approach is its ability to use the inherent strengths of the constituent materials, resulting in a substantial increase in their load-bearing capability. Hence, this research represents the pioneering investigation into the static bending and free vibration characteristics of composite nanoplates including several layers of materials, whereby the material layers are interconnected via intricate profiles characterized by square wave and sine waveforms. The purpose of this endeavor is to fully capitalize on the benefits of attending courses in order to enhance practical working efficiency. This study also incorporates the use of two innovative third-order shear deformation theories. Simultaneously, considering the negligible size impact facilitated by the nonlocal theory, the mathematical formulations and equilibrium equations are derived using the Hamilton principle. The issue has been addressed using a four-node element with six degrees of freedom per node. One novel aspect of this study is its consideration of the impact of initial shape imperfections in various manifestations. Additionally, the elastic foundation incorporates characteristics that exhibit spatial variation. This statement provides a somewhat more accurate depiction of the behavior shown by actual structures. The numerical findings have been meticulously computed and thoroughly examined. Notably, it is possible to determine the optimal number of wavelengths in the profile to enhance the load-bearing capability of the structure. The findings derived from this study have significant value in informing the design of operational frameworks in practical settings.

A legal and regulatory compliance framework for maritime operations in Nigerian oil companies

The maritime sector plays a critical role in Nigeria's oil industry, serving as the primary conduit for offshore oil exploration, production, and transportation. As the country remains heavily reliant on its oil revenues, ensuring that maritime operations comply with legal and regulatory standards is crucial for operational safety, environmental sustainability, and economic stability. This review outlines a comprehensive legal and regulatory compliance framework for maritime operations in Nigerian oil companies, focusing on the roles of key regulatory bodies such as the Department of Petroleum Resources (DPR), the Nigerian Maritime Administration and Safety Agency (NIMASA), and the National Environmental Standards and Regulations Enforcement Agency (NESREA). It explores major laws such as the Coastal and Inland Shipping (Cabotage) Act, Petroleum Industry Act (PIA), and Environmental Impact Assessment (EIA) Act, highlighting their implications for offshore drilling, oil transportation, and environmental protection. Additionally, the review discusses the compliance challenges Nigerian oil companies face, including environmental risks, maritime security threats, jurisdictional conflicts, and the technical demands of adhering to safety standards. To address these challenges, the study emphasizes the need for regular audits, technology integration, capacity building, and collaborative engagements between oil companies and regulatory authorities. Through case studies on oil spill response and the enforcement of cabotage laws, the review underscores the importance of robust regulatory enforcement to ensure safety, mitigate environmental damage, and enhance operational efficiency. This framework is essential for the future growth of Nigeria’s oil sector, balancing economic gains with legal compliance and sustainability in maritime operations.

Assessment of Power Equipment Operational Safety in the Sustainable Management of Residual Lifespan

Introduction. Ensuring the safe operation of energy facilities is a critical issue at all life cycle stages. It is associated with the adoption of management decisions when rescheduling and assessing the remaining resources, which are made based on a comprehensive assessment of the technical condition of the equipment, taking into account the economic efficiency and environmental requirements for further operation, which is the basis for technical and economic assessments. Aim and tasks. This study aims to develop a technical and economic assessment of the safe operation of power equipment to ensure sustainable management of the remaining service lifespan. Results. An algorithm for assessing pipeline system elements by the coolant environment is proposed, which allows for the pipeline load and changes in erosion and corrosion caused by coolant movement. It was proposed that the equipment be systematised by safety class and coolant. When conducting a technical and economic assessment, these parameters and the costs of extending the service life and upgrading the existing equipment were considered in the standard calculations of a certain type of equipment. For this purpose, mathematical models for calculating the residual life for each type, obtained and confirmed based on empirical studies, are proposed. Research findings into ensuring the reliability and safety of operation with various heat carriers have shown that it is necessary to consider low-cycle and high-cycle loads and erosion processes. It was found that the erosive wear rate is 0.3-0.4 mm/year for bending sections of the main circulation pipeline, where maximum loads occur relative to low-cycle and vibration loads, and the stress is 212.5 MPa when assessing the residual life of 7 years. Conclusions. The cost-effectiveness of reassigning the service life is reasonable, as it does not require additional investment in upgrading or replacing equipment subject to annual technical diagnostics. The systematisation of pipelines, improved codifiers, lifespan assessment models, and algorithms for evaluating the remaining life under various loadings enhance the regulatory framework for safe nuclear power plant operations and support technical and economic evaluations for extending their lifespan.

The assembly and dynamics of ecological communities in an ever‐changing world

AbstractAlternative perspectives on the maintenance of biodiversity and the assembly of ecological communities suggest that both processes cannot be investigated simultaneously. In this concept and synthesis, we challenge this view by presenting major theoretical advances in structural stability and permanence theory. These advances, which provide complementary views, allow studying the short‐ and long‐term dynamics of ecological communities as changes in species richness, composition, and abundance. Here, the global attractor, technically named informational structure (IS), is the central element to construct from information of species' intrinsic growth rates and their strength and sign of interactions. The global attractor has four main properties: (1) It contains all the limits of what is feasible and unfeasible of the dynamical behavior of an ecological system, therefore, (2) it provides a thorough characterization of all combinations of species' richness and composition in which species can coexist (i.e., feasible and stable equilibrium), (3) as well as all connections (paths) of assembly between coexisting communities. Importantly, (4) such topology of coexisting communities and their connections changes when environmental (abiotic and biotic) variation affects the ability of species to grow and interact with others. Overall, these four properties allow switching from a traditional evaluation of species coexistence at equilibrium to a much more realistic nonequilibrium perspective where changes in the structure of the global attractor underlie the transient ecological dynamics. Several fields in ecology can benefit from the study of an IS. For instance, it can serve to evaluate community responses after the end of a perturbation, to design restoration trajectories, to study the consequences of biological invasions on the persistence of native species within communities, or to assess ecosystem health status. We illustrate this latter possibility with empirical observations of 7 years in Mediterranean annual grasslands. We document that extremely wet or dry years generate ISs supporting few coexisting communities and few assembly paths. The remaining communities distinguish winners from losers of ongoing climate change and indicate the limits to future community assembly opportunities. A fully tractable operational framework is readily available to understand and predict the assembly and dynamics of ecological communities in an ever‐changing world.

Establishing a Marine Protected Area network using a Marine Spatial Planning approach: A reflection on practical challenges and opportunities for social–ecological integration

AbstractIntegrative social–ecological approaches are crucial for addressing sustainability challenges in coastal and marine systems. Among these, Marine Spatial Planning (MSP) emerges as a pivotal approach for integrated management. Often, the establishment of Marine Protected Areas (MPAs) and the application of MSP occur in parallel. Given the potential synergies, there is a need to better understand and address barriers to the adoption of MSP approaches for integrative conservation mechanisms. Using São Tomé and Príncipe as a case study, we illustrate how MSP was employed as an operational framework for establishing an MPA network. Drawing on the experiences of people directly involved in this co‐design process, we reflect on the main challenges and opportunities in achieving social–ecological integration, and highlight recommendations for conservation practitioners and planners. Applying MSP was perceived to contribute substantially to multiple project goals, with some (e.g., incorporating perspectives and needs of vulnerable groups) more challenging to achieve. While MSP enhanced conceptual, disciplinary, methodological and functional integration, practical challenges in implementation hindered the extent to which each of these was achieved. Given international commitments to Blue Growth, high fisheries dependence and current patterns of change, developing effective integrative MSP approaches is essential for social–ecological resilience.

Proposed Strategy for Maintaining A Small-Family-Business Enterprise in a Competitive Property Market

This study addresses the growing challenges faced by small family businesses in the competitive property market by proposing strategic interventions specifically designed to sustain and enhance the viability of Allure Realty Group, a family-run enterprise established in 2012 in Jabodetabek. With increasing complexity in the property sector due to urbanization, shifting consumer demands, and stringent regulatory environments, the research aims to develop a robust strategic framework. The methodology involves a combination of qualitative and quantitative approaches, including surveys and interviews, to identify key problems and collect data. Analytical tools such as the Marketing Mix 7P, VRIO Framework, and STP Analysis for internal factors, and PESTLE Analysis, Porter’s Five Forces, Competitor and Customer Analysis for external factors are used to conduct SWOT and TOWS analyses for strategic development. The study hypothesizes that targeted strategies will significantly improve the business's resilience and competitiveness in the market. Key objectives include identifying challenges faced by Allure Realty Group, developing strategies to address these issues, and implementing actions to improve operational efficiency, customer satisfaction, and market expansion. The expected outcomes are enhanced operational frameworks, increased customer satisfaction, and a larger market share, contributing to the business's long-term sustainability. This research contributes to business management theory by providing insights into strategies for small family businesses in rapidly evolving markets. The findings aim to bridge traditional business values with modern management practices, offering valuable guidance for both academic researchers and business practitioners in navigating the complexities of the property industry.

A Systematic Review of Speech Fluency Measurement in L2 Monologues and Dialogues

This study synthesises measures of L2 speech fluency in both monologues and dialogues. About monologue fluency measurement, we provide a synthesis of measurement based on Skehan's (2003) three dimensions (speed, breakdown, and repair) and discrepancies among scholars. As for dialogue fluency, the limited number of studies precludes a conclusion of well-established measurement. Furthermore, the reasons for the paucity of studies in this area are elucidated. The study revealed inconsistencies in the standardisation and methodological definitions employed by scholars in monologue fluency research, resulting in a lack of uniformity in the data collected. Concerning dialogues, there is a notable absence of theoretical and operational frameworks.

Graph pooling in graph neural networks: methods and their applications in omics studies

Graph neural networks (GNNs) process the graph-structured data using neural networks and have proven successful in various graph processing tasks. Currently, graph pooling operators have emerged as crucial components that bridge the gap between node representation learning and diverse graph-level tasks by transforming node representations into graph representations. Given the rapid growth and widespread adoption of graph pooling, this review aims to summarize the existing graph pooling operators for GNNs and their representative applications in omics. Specifically, we first present a comprehensive taxonomy of existing graph pooling algorithms, expanding the categorization for both global and hierarchical pooling operators, and for the first time reviewing the inverse operation of graph pooling, named unpooling. Next, we describe the general evaluation framework for graph pooling operators, encompassing three fundamental aspects: experimental setup, ablation analysis, and model interpretation. We also discuss open issues that significantly influence the design of graph pooling operators, including complexity, connectivity, adaptability, additional loss, and attention mechanisms. Finally, we summarize bioinformatics applications of graph pooling operators in omics, including graphs of gene interaction, medical images, and protein structures for drug discovery and disease diagnosis. Furthermore, we showcase the impact of graph pooling operators on research in specific real-world domains, with a focus on prediction performance and model interpretability. This review provides methodological insights in machine learning based graph modeling and related omics research, as well as an ongoing resource by gathering related papers and code in a dedicated GitHub repository (https://github.com/Hou-WJ/Graph-Pooling-Operators-and-Bioinformatics-Applications).

Implementation of Extreme Learning Machine Method with Particle Swarm Optimization to Classify of Chronic Kidney Disease

Kidney Disease (CKD) appears as a pathological condition due to infection of the kidneys and blockages due to the formation of kidney stones. In the Indonesian context, kidney disease is the second most common disease after heart disease based on BPJS Health data. Notably, in this scenario, medical practitioners and individuals with specialized knowledge in the field are still faced with challenges in effectively classifying CKD cases, thereby making them vulnerable to erroneous diagnostic conclusions. The main objective underlying this particular research effort revolves around increasing the level of accuracy that characterizes the CKD classification process by orchestrating the incorporation of Particle Swarm Optimization (PSO) techniques into the operational framework of Extreme Learning Machines (ELM) with the aim of ensuring optimal results. Configuration of input weights and critical biases to achieve superior diagnostic results. The results obtained from the investigation process include many numerical parameters including but not limited to determining the ideal number of hidden nodes set at 11, population size 80, identification of the most preferred number of iterations denoted by the Best value of 20, aggregate inertia weight assessed at 0.5, along with the constants 1 (c1) and 2 (c2) each registering a value of 1, culminating in the achievement of an accuracy metric pegged at an impressive level of 98.50%. Consequently, the implications obtained from this empirical investigation strengthen the assertion that the use of PSO optimization strategies within the operational framework of ELM has the potential to yield major advances in the classification evaluation domain related to CKD diagnosis.

Blockchain Based Analytical Bug Tracking in Software Engineering

This article examines a blockchain-based bug tracking system in detail, focusing on its four main components: the Blockchain Platform, the Consensus Mechanism (also known as Proof of Work, PoW), the Data Structure, and Smart Contracts. To improve the efficiency, security, and transparency of bug tracking in software development, we use mathematical models to dissect the operational framework and functionalities that underpin this cutting-edge system. The Blockchain Platform is the foundational layer. It stores data in multiple locations, cannot be changed, and provides a secure environment for fixing software bugs. We use mathematical abstractions to demonstrate how the blockchain is put together, emphasizing the importance of cryptographic hashes in maintaining data integrity and connecting blocks. People pay close attention to the Consensus Mechanism, particularly Proof of Work (PoW), because it is so important for verifying transactions and maintaining the blockchain's integrity. Using a mathematical model, we demonstrate how Proof of Work (PoW) prevents bad behavior while keeping the network safe and democratic by requiring work to be done on a computer to add blocks. The Data Structure module is examined using a model that depicts how bug reports are stored and found on the blockchain. This model demonstrates the efficiency of storing bug reports as transactions, which allows for quick access and unchangeable record-keeping. People associate smart contracts with self-running programs that manage permissions, automate workflows, and provide incentives for reporting bugs. Mathematical models are used to demonstrate how these contracts' conditions and actions are documented, emphasizing their importance in automating tasks and improving system reliability. This article uses mathematical modeling to provide a clear and structured understanding of a blockchain-based bug tracking system and demonstrate how it could change the way bugs are tracked by leveraging blockchain technology's strengths.

Research on the optimal scheduling strategy of the integrated energy system of electricity to hydrogen under the stepped carbon trading mechanism

Under the guidance of energy-saving and emission reduction goals, a low-carbon economic operation method for integrated energy systems (IES) has been proposed. This strategy aims to enhance energy utilization efficiency, bolster equipment operational flexibility, and significantly cut down on carbon emissions from the IES. Firstly, a thorough exploration of the two-stage operational framework of Power-to-Gas (P2G) technology is conducted. Electrolyzers, methane reactors, and hydrogen fuel cells (HFCs) are introduced as replacements for traditional P2G equipment, with the objective of harnessing the multiple benefits of hydrogen energy. Secondly, a cogeneration and HFC operational strategy with adjustable heat-to-electricity ratio is introduced to further enhance the IES’s low-carbon and economic performance. Finally, a step-by-step carbon trading mechanism is introduced to effectively steer the IES towards carbon emission control.

The Prokaryotic Roots of Eukaryotic Immune Systems.

Over the past two decades, studies have revealed profound evolutionary connections between prokaryotic and eukaryotic immune systems, challenging the notion of their unrelatedness. Immune systems across the tree of life share an operational framework, shaping their biochemical logic and evolutionary trajectories. The diversification of immune genes in the prokaryotic superkingdoms, followed by lateral transfer to eukaryotes, was central to the emergence of innate immunity in the latter. These include protein domains related to nucleotide second messenger-dependent systems, NAD+/nucleotide degradation, and P-loop NTPase domains of the STAND and GTPase clades playing pivotal roles in eukaryotic immunity and inflammation. Moreover, several domains orchestrating programmed cell death, ultimately of prokaryotic provenance, suggest an intimate link between immunity and the emergence of multicellularity in eukaryotes such as animals. While eukaryotes directly adopted some proteins from bacterial immune systems, they repurposed others for new immune functions from bacterial interorganismal conflict systems. These emerging immune components hold substantial biotechnological potential.

Utilizing serverless framework for dynamic visualization and operations in geospatial applications

ABSTRACT While substantial efforts have been invested in the development of Discrete Global Grid Systems (DGGS) spatial operations and their potential applications in the geospatial domain, it has become evident that there is a demand for an efficient and scalable system to handle the visualization of large-scale DGGS data. This study demonstrated the potential of DGGS in conjunction with the serverless framework for dynamic visualization at various resolutions, which is based on data storage and effective querying using PostgreSQL integrated into Amazon Aurora Serverless. The use of Amazon Web Services (AWS) Lambda for on-the-fly generation of hexagon geometries significantly reduced the storage requirements and improved the speed of the visualization process. In addition, we implemented on-the-fly spatial operations including point binning, thresholding, aggregation, and neighborhood operations in the DGGS, highlighting the capabilities of DGGS in vector and raster processing. The proposed system has shown promising results in terms of efficiency, scalability, and adaptability, making it a viable solution for large-scale geospatial data processing and visualization. Case studies using flood risk data and terrain data further illustrate the system’s practical applicability in on-the-fly spatial operations and rapid visualization.

Measuring Data Quality from Building Registers: A Case Study in Italy

Geographic data quality is a complex issue requiring continuous operational improvements. Considering this to be one of the most topical research and technical issues in official statistics and environmental monitoring, this study re-connects the operational dimension of ‘geographic data quality’ with the broader issue of monitoring the quality of official statistics. By estimating the accuracy of public (spatially explicit) data, this study illustrates an operational framework with an exploratory exercise in estimating the geographic data quality characteristic of a specific information source within the official statistical system (i.e., building registry) in a given European country, namely, Italy. The results of this exercise provide a paradigmatic example of profound innovation in the activities of statistical services in Europe and specifically the Italian National Statistical Institute (Istat), transitioning from independent (and poorly connected) field surveys to an integrated system of registries. Since several studies are based on spatially explicit survey units, it is essential to estimate the quality of geographical data, especially those derived from information sources where space is topical information, such as (local, regional, or national) building registers. Thanks to the results of an empirical exercise applied to Italian building registers, the present article will discuss the issue of data accuracy, considered the main issue related to monitoring geographic data quality, from an official statistics’ perspective. Statistical indicators will be proposed for the assessment of systematic and random errors of spatially explicit measures, possibly enabling a quali-quantitative improvement in the semantic content of building registers that address the inherent requirements of official statistics. Such indicators have some positive implications for the entire system of official statistics in Italy and, for generalization, within the European Statistical System.

Overview of Embedded Rust Operating Systems and Frameworks.

Embedded Operating Systems (OSs) are often developed in the C programming language. Developers justify this choice by the performance that can be achieved, the low memory footprint, and the ease of mapping hardware to software, as well as the strong adoption by industry of this programming language. The downside is that C is prone to security vulnerabilities unknowingly introduced by the software developer. Examples of such vulnerabilities are use-after-free, and buffer overflows. Like C, Rust is a compiled programming language that guarantees memory safety at compile time by adhering to a set of rules. There already exist a few OSs and frameworks that are entirely written in Rust, targeting sensor nodes. In this work, we give an overview of these OSs and frameworks and compare them on the basis of the features they provide, such as application isolation, scheduling, inter-process communication, and networking. Furthermore, we compare the OSs on the basis of the performance they provide, such as cycles and memory usage.