Operational Benefits Research Articles

Artificial Intelligence Management System of Floating Nuclear Reactors for Implementing Remote Distributed Energy Environments

This paper explores future energy systems primarily powered by innovative floating reactors. These reactors exhibit high potential, particularly for supplying power to remote areas beyond the reach of traditional power grids. In this study, a novel artificial intelligence (AI) system is introduced to optimize the management of floating reactors in remote, distributed environments, where the reactors must move between different sites and energize their local grids. This research marks the first application of AI in providing management decisions for those reactor types in such distributed assembled environments. Specifically, an intelligent management system has been developed to coordinate the reactor’s operations across various sites. The proposed system leverages fuzzy logic tools, which enable it to account for multiple uncertain factors when making management decisions. The system’s performance is tested through simulated scenarios and benchmarked against a round-robin fixed time method. The results demonstrate the system’s efficiency in delivering both economic and operational benefits.

Carbon emissions reduction in shipping based on four-party evolutionary game

In order to realize a win-win situation between economic development and environmental benefits, this paper constructs a four-party evolutionary game model including the government, two homogeneous ports and shipping companies based on evolutionary game theory. By calculating the payoff matrices of the four parties and replicating the dynamic equations, according to the Jacobi matrix, we study and discuss the possible stabilization points of the model under five different scenarios. The game is simulated using MATLAB and the relevant parameters are selected for sensitivity analysis. The results show that the environmental benefits are maximized when the government does not implement the policy and the port and shipping companies use the shore electricty system (i.e., the stability point E12 (0,1,1,1)). Meanwhile, by analyzing the port size sensitivity, when t=1.116, the large-scale port evolution tends to 0, while the small-scale port fluctuates up and down, which leads to the conclusion that the small-scale ports have more potential to implement shore electricty and are able to gain benefits faster. This study provides theoretical support for the implementation of shore electricty systems, while pointing out the key role of the government in promoting the development of shore electricty. It provides a reference for effectively promoting the use of shore electricty in the context of carbon emission reduction, which is especially important for the implementation of shore electricty in small-scale ports and helps to maximize the environmental benefits of port operations.

EVLearn: extending the cityLearn framework with electric vehicle simulation

Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (V1G) emerge as a potential solution to the Electric Vehicles’ (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced perspective, the adoption of these strategies is still hindered by an array of operational problems. Key among these is the lack of a simulation platform that allows to validate and refine V2G and V1G strategies. Including the development, training, and testing in the context of Energy Communities (ECs) incorporating multiple flexible energy assets. Addressing this gap, first we introduce the EVLearn, an open-source extension for the existing CityLearn simulation framework. EVLearn provides both V2G and V1G energy management simulation capabilities into the study of broader energy management strategies of CityLearn by modeling EVs, their charging infrastructure and associated energy flexibility dynamics. Results validated the extension of CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario.

Harnessing AI for enhanced evidence-based laboratory medicine (EBLM).

The integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personalized patient care. AI technologies(machine learning, natural language processing and computer vision) advance evidence-based laboratory medicine (EBLM) by automating and optimizing critical processes(formulating clinical questions, conducting literature searches, appraising evidence, and developing clinical guidelines). These reduce the time for systematic reviews, ensuring consistency in appraisal, and enabling real-time updates to guidelines. AI supports personalized medicine by analyzing large datasets, genetic information and electronic health records (EHRs), to tailor diagnostic and treatment plans to patient profiles. Predictive analytics enhance outcomes by leveraging historical data and ongoing monitoring to predict responses and optimize care pathways. Despite the transformative potential, there are challenges. The accuracy, transparency, and explainability of AI algorithms is critical for gaining trust and ensuring ethical deployment. Integration into existing clinical workflows requires collaboration between AI developers and users to ensure seamless user-friendly adoption. Ethical considerations, such as privacy,data security, and algorithmic bias, must also be addressed to mitigate risks and ensure equitable healthcare delivery. Regulatory frameworks, eg. The EU AI Regulation, emphasize transparency, data governance, and human oversight, particularly for high-risk AI systems. The economic and operational benefits are cost savings, improved diagnostic precision, and enhanced patient outcomes. Future trends (federated learning and self-supervised learning), will enhance the scalability and applicability of AI in EBLM, paving the way for a new era of precision medicine. AI in EBLM has the potential to transform healthcare delivery, improve patient outcomes, and advance personalized/precision medicine.

Enhancing overall performances of large constructed wetlands using an integrated approach of numerical simulation and response surface method--Taking Bagong hybrid CWs as an example.

Constructed wetlands (CWs) with low carbon properties represented an effective approach for treating low-polluted water and improving water quality. Here, a research scheme was proposed to achieve maximum operation benefits of the large-scale CWs through parameter identification, operation simulation, evaluation, and analysis of the water quality process. Based on the two-dimensional water hydrodynamic model coupling with the Eco-Lab water quality module (with nutrients), simulation for Bagong hybrid CWs was successfully conducted. Variables were estimated using response surface design (RSD), and the values of model parameters were calibrated in accordance with central combination design (CCD) experiments. Ten key parameters in the water quality process affecting the overall pollutant removal of CWs were identified, and the water quality model of the CWs could be replicated well with the actual situation within the 95% confidence interval. The water purification effect of the CWs under five different influent flow conditions was simulated, and the maximum hydraulic loading (0.036m3·(m-2·h-1)) of the CWs was determined with the effluent water quality to meet the design requirements. The integrated method could effectively enhance the efficiency of parameter calibration and significantly reduce the simulation workload. The results of pollutants migration and transformation under different influent conditions could provide a universally applicable and valid method in engineering applications, and process a promising application prospect.

Enhancing Roadway Efficiency with GPS Based Toll System

Abstract: The traditional systems for toll collection are plagued with various problems, including traffic buildup at toll booths, long waiting time on users, and administrative costs. This research proposes a GPS-Based Toll Collection System that would help to solve these problems by using better location-based technologies for the automatic tolling of vehicles. This system consists of a combination of a GPS receiver, a microprocessor, and a battery inside the vehicle. It can track the vehicle's course in real-time and the position where the vehicle enters or exits the toll road zone is automatically detected and the toll amount calculated per distance traveled using pre-defined rates or dynamic pricing models. The user-friendly web interface allows vehicle owners to register their vehicles, maintain a digital wallet, and view transaction histories. The deducting toll is automatic without the need for any toll booth physical interaction, efficiently accelerating the toll collection process, thereby causing less traffic congestion, more throughput, and substantial operational cost reduction in staff and manual handling. Apart from better user convenience and operational benefits, the automated, highly accurate, and transparent toll collection system assures equal opportunities for all persons entering the national highways by way of toll. This paper discusses the architecture of this system, major components, critical challenges faced during its implementation, and its potential impact on the transport sector. Comparison of the proposed GPS-based system with traditional tolling methods reveals advantages such as lower maintenance costs, improved scalability, and real-time data processing. It also examines security protocols employed to ensure data privacy and fraud prevention. Lastly, the study concludes with an account of the proposed improvements and the possibility of the wide-scale deployment of the system in furtherance of smarter and more sustainable transportation infrastructure development

The convergence of big data and SRE in modern agriculture: Transforming farming through technology

This article examines the transformative integration of Big Data analytics and Site Reliability Engineering (SRE) principles in modern agriculture. The article explores how precision agriculture leverages advanced technologies including IoT sensors, drone surveillance, and satellite imagery, to revolutionize traditional farming practices. It analyzes the implementation of sophisticated data collection infrastructures, Big Data systems, and SRE practices in agricultural settings. The article investigates the operational benefits and sustainability implications of these technological integrations, highlighting improvements in resource optimization, yield enhancement, and environmental conservation. Furthermore, it discusses future perspectives in agricultural technology, including edge computing applications, AI-driven autonomous systems, blockchain integration, and climate change adaptation strategies.

Subsurface hazardous environments and hydrological threats in underground mines: an OCTEM detection case study

ABSTRACT The extraction of mineral resources has progressively transitioned from shallow and easily accessible deposits to deeper and more challenging ores, thereby increasing the complexity and concealment of mining environments. The Opposing-Coils Transient Electromagnetic Method (OCTEM), an enhanced version of the Transient Electromagnetic Method (TEM), offers notable advantages, including rapid measurement times, strong penetration capabilities, high efficiency, and minimal influence from surface terrain. This study used Tongkeng Mine, situated in the karst topography of Southwest China, as a case study to evaluate the effectiveness of OCTEM in detecting concealed disaster-prone zones and hydrogeological characteristics within mines. The results indicated the following: (1) Periodic underground monitoring of caving zones and goafs using OCTEM could facilitate the safe extraction of minerals. (2) OCTEM proved highly effective in detecting surface subsidence in previously repaired collapse areas. (3) OCTEM demonstrated promising potential for preliminary screening and investigation of hidden karst caves and goafs resulting from unregulated mining activities in karst regions, thereby reducing reliance on traditional, experience-based drilling methods and lowering exploration costs. Overall, this study effectively contributed to reducing safety maintenance costs and enhancing the economic benefits of mining operations.

Digital entrepreneurial ecosystem: the role of the sharing economy in driving innovation

ABSTRACT This study examines the role of the sharing economy within digital entrepreneurial ecosystems (DEEs) and its influence on fostering opportunities for digital entrepreneurs. By exploring the complex interactions among businesses, technologies, and markets, the paper highlights how DEEs facilitate the adoption of sharing economy-related digital business practices. Using a qualitative exploratory approach, we conducted open-ended interviews with 88 business managers actively engaged in the sharing economy across various sectors. Thematic analysis was employed to uncover the key challenges and opportunities businesses face in integrating digital transformation processes (DTP) into their strategies. The research introduces key frameworks, including Sharing Economy Digital Infrastructure Governance (SEDIG), Sharing Economy Digital User Citizenship (SEDUC), and Sharing Economy Digital Marketplace (SEDM), to categorize ecosystem players and analyse their collaborative efforts in supporting digital entrepreneurship. Findings emphasize the need for greater consistency in SEDIG approaches across regulatory bodies, as regional disparities in governance create uncertainty for entrepreneurs. The study also underscores the long-term benefits of responsible operations and technological investments, despite initial costs, and calls for policy interventions to address ongoing entrepreneurial ecosystem challenges.

Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance

This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, and sustainability in predictive manufacturing, which led to the development of this model. Using the PICO methodology (Population, Intervention, Comparison, Outcome), the study evaluated the implementation of these technologies in Alpha Company, comparing results before and after the model’s adoption. The intervention integrated RPA and ML to improve failure prediction accuracy and optimize maintenance operations. Results showed a 100% increase in mean time between failures (MTBF), a 67% reduction in mean time to repair (MTTR), a 37.5% decrease in maintenance costs, and a 71.4% reduction in unplanned downtime costs. Challenges such as initial implementation costs and the need for continuous training were also noted. Future research could explore integrating big data and AI to further improve prediction accuracy. This model demonstrates that integrating RPA and ML leads to operational improvements, cost reductions, and environmental benefits, contributing to the sustainability of industrial operations.

A movement-based delay model for signalised alternative intersections

Analytical models play a crucial role in evaluating Alternative Intersections and Interchanges (AIIs) and developing their signal plans. A key application is comparing O-D movement delays to measure the operational benefits of AIIs over conventional intersections. However, existing models often fail to account for time-varying arrival patterns and the segregation of O-D movements within the same lane group. This paper addresses these gaps by proposing new appropriate delay models that incorporate the Queue Accumulation Polygon (QAP) concept for uniform delay calculations. The QAP enables realistic modelling of arrival patterns and segregates delays by queues formed from different movements. Other delay terms are also developed to reflect AII operating conditions. Validation using various AIIs and traffic scenarios shows the proposed models align with field observations and movement-specific microsimulation results, outperforming widely used U.S. analytical models. These improvements enhance the accuracy of delay estimation and support better AII design and analysis.

The role of technology in the skills and creativity of chefs

This research note addresses a gap in current research on the role of technology in restaurants, which often focuses on operational benefits and customer experience rather than the experiential impact on chefs. Based on an exploratory qualitative research design using semi-structured interviews with chefs, the study explores how chefs perceive the influence of integrating technological tools in their future practices and the impact on their skills and creativity. The findings provide insights into the facilitative and destructive roles of technology. The paper contributes to the understanding of how and why technology enhances and inhibits human skills and creativity, thus guiding businesses in hospitality about the adoption of technology in restaurants.

The use of facial recognition for targeting under international law

Abstract In the quest for “identity dominance” over the enemy, armed forces are increasingly leveraging biometrics for a variety of purposes. This paper focuses on the combat employment of one of them – facial recognition, which, unlike other biometrics, does not appear to have been widely utilized for targeting purposes yet. With the purchasing patterns of advanced militaries suggesting that such a development is around the corner, this paper assesses the compliance with international humanitarian law (IHL) of the use of facial recognition technologies for targeting purposes. It peruses the applicable legal framework to demonstrate that IHL is neutral towards the use of new technologies and that the right to privacy under international human rights law does not preclude the use of biometrics in hostilities. The analysis zooms in on two specific use cases in which facial recognition is likely to be employed on the battlefield, namely (1) targeted killings against combatants and (2) targeted killings against civilians directly participating in hostilities. The paper closes with an acknowledgment that while facial recognition does have obvious operational benefits, it also has the potential to exacerbate targeting practices that stretch the limits of IHL.

The impact of operational excellence methodologies on sustainable performance in the service sector: a systematic literature review

In today's competitive marketplace, organizations adopt Lean, Six Sigma, and Lean Six Sigma to enhance efficiency, reduce waste, improve quality, and increasingly promote sustainability. This article systematically reviews the impact of these methodologies on sustainable performance in the service sector, which contributes 51.6% of GDP across 168 countries. Analyzing 99 articles from 2013-2023, the study reveals limited research linking Lean Six Sigma to sustainable performance in services. Findings show a predominant focus on operational benefits (79%), with minimal emphasis on financial (15%), social (3%), and environmental (2.66%) measures. Our findings highlight the need for research addressing the comprehensive impact of these methodologies on all dimensions of sustainable performance.

Applying neural networks combined with Monte Carlo simulation in dam operations to obtain operational, economic and environmental gains

The waste of potable water is a problem that affects a population’s supply and the environment, raising the need for studies focusing on the adoption of efficient actions and modern technological resources, such as artificial intelligence (AI), for sustainable water management. However, in the literature there are few studies on the operational, economic and environmental benefits of using AI in dam management. In addition, no study has been found on this topic addressing the Cantareira system, located in the metropolitan region of São Paulo, Brazil, which is one of the largest water supply systems in the world. This work presents an approach combining an artificial neural network and the Monte Carlo simulation method for floodgate control in the Cantareira system. Furthermore, parameters are explored that make the simulations of water collection and distribution more realistic. The results (root mean squared error (RMSE) = 0.076 and R2 = 0.963) confirm the viability of using the proposed approach to minimize water waste and flood risks, as well as to increase efficiency in water resource management. Furthermore, this study advances the state of the art by presenting a set of operational, economic and environmental benefits directly associated with the adoption of AI in floodgate management.

PLAIN PAVEMENT; AN ECONOMIC ALTERNATIVE

The paper "Plain Pavement; An Economical Alternative" by Ralph C. Blum and Clinton E. Solberg examines the economic pressures faced by state transportation departments and the innovative measures implemented to reduce costs in highway construction. Focusing on the Wisconsin Department of Transportation (WDOT), the study details the transition from steel-reinforced concrete pavement to non-reinforced plain concrete pavement (PCP) in a 20-mile segment of Interstate Highway 43. This shift resulted in a significant cost reduction of $1.6 million. The authors discuss the historical context and economic analysis that influenced the decision, emphasizing the need for cost efficiency due to the declining purchasing power of state dollars and increasing construction material costs. The paper explores the construction process, the performance and maintenance of plain versus reinforced pavements, and the long-term economic and operational benefits. It concludes that the use of PCP not only offered immediate financial relief but also maintained quality and durability, making it a viable economic alternative for future highway projects. The findings suggest that PCP could potentially offer a sustainable model for managing limited resources while addressing the need for infrastructure development. (Abstract generated by AI tool ChatGPT 4)

Game optimization for photovoltaic microgrid group and the shared energy storage operator considering energy storage frequency modulation–cost loss and source–load uncertainty

The high uncertainty of power generation in photovoltaic microgrids and the high cost of energy storage allocation limit the development of photovoltaic microgrids. Therefore, this study proposes a trading strategy mechanism for multiple photovoltaic microgrids (PMs) and shared energy storage operator (SESO) based on the Stackelberg game. The trading mechanism fully considers the loss cost of shared energy storage operation, the benefits of participating in the frequency regulation auxiliary service market, and the demand response characteristics of photovoltaic microgrids. Then, a source–load uncertainty risk model is constructed based on the information intermittent decision theory, which is improved by the confidence interval fuzzy set. Finally, a distributed combinatorial algorithm using mixed integer linear programming with an improved multiverse algorithm is used for the solution. The results show that introducing SESO effectively reduces all the operating costs of PMs, and the daily operational benefits of SESO reach 8774.56 yuan. Second, the frequency modulation (FM) performance score of the energy storage device is improved by 14.45%. The proposed game optimization model not only achieves a win-win situation for multiple players but also broadens the physical function of shared energy storage participating in FM service and realizes the risk preference regulation of operating players.

Application of Rapid Filters for Cleaning Natural Water in the Conditions of the Kyrgyz Republic

This study evaluates the implementation and effectiveness of the rapid filtration method for enhancing natural water treatment in the Kyrgyz Republic, where outdated water treatment systems reduce both efficiency and reliability. The study examines the effectiveness of rapid filters in improving contaminant removal to meet modern water quality standards. Key aspects such as filtration efficiency, operational benefits, and coagulant incorporation, are assessed by comparing rapid filters with traditional slow sand filtration methods. The study employs empirical analysis, evaluations of existing systems, and case studies of filter-loading materials. Findings indicate that rapid filters significantly enhance water treatment efficiency, particularly when using advanced filter media that increase surface area. The integration of rapid filtration into water treatment facilities in Kyrgyzstan can provide significant environmental and economic benefits. This method offers a strategic framework for modernizing water treatment plants to improve public health and environmental sustainability.

Performance analysis of working with relational and non-relational databases in Java applications

The article presents a performance analysis of connections to both relational and non-relational databases, critical components of the functionality of modern web applications. The study is concerned with evaluating the advantages of manually integrated database drivers compared to the comprehensive Spring Data module. In addition, the impact of the Spring Framework on the performance of drivers responsible for database connections was investigated. Based on the results obtained, there are performance benefits for CRUD operations when adding drivers manually and using the Spring Framework for JDBC and MongoDB drivers.

Magnetic Molecularly Imprinted Polymers with Hydrophilic Shells for the Selective Enrichment and Detection of Rosmarinic Acid in Aqueous Extraction.

Rosmarinic acid (RA) is a natural active compound widely found in many plants belonging to the family of Lamiaceae, Boraginaceae, and so on, which has various important bioactivities, including being anti-oxidative, anti-inflammatory, antiviral, etc. Herein, novel hydrophilic magnetic molecularly imprinted polymers (HMMIPs) with a regular core-shell structure were successfully developed using RA as a template molecule, acrylamide (AM) as a functional monomer, N-N 'methylenebisacrylamide (MBA) as a cross-linking agent, and water as the porogen. After a series of characterization and adsorption performance analyses, it was found that HMMIPs are hydrophilic with an adsorption capacity of 8.012 ± 0.54 mg/g, an imprinting factor of 3.64, and a selectivity coefficient of 2.63~2.91. Furthermore, the HMMIPs can be rapidly separated from other components under the influence of external magnetic fields. The HMMIPs were employed for the determination of RA present in the Perilla frutescens and Rosmarinus officinalis aqueous extract with recoveries of 88.2~107.3%. These results indicated that HMMIPs of RA have the benefits of straightforward operation, rapid adsorption, and high selectivity, rendering it an appropriate way for the expedient and selective isolation of RA in an intricate matrix.