Operational Benefits Research Articles

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.

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.

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)

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.

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.

Resilient gas dependency-based planning of electricity distribution systems considering energy storage systems.

This article presents a planning framework to improve the weather-related resilience of natural gas-dependent electricity distribution systems. The problem is formulated as a two-stage stochastic mixed integer linear programing model. In the first stage, the measures for distribution line hardening, gas-fired distributed generation (DG) placement, electrical energy storage resource allocation, and tie-switch placement are determined. The second stage minimizes the electricity distribution system load shedding in realized hurricanes to achieve a compromise between operational benefits and investment costs when the dependence of electricity distribution system on the natural gas exists. The proposed stochastic model considers random failures of electricity distribution system lines and random errors in forecasting the load demand. The Monte Carlo simulation is employed to generate multiple scenarios for defining the uncertainties of electricity distribution system. For the sake of simplicity, weather-related outages of natural gas pipelines are considered deterministic. The nonlinear natural gas constraints are linearized and incorporated into the stochastic optimization model. The proposed framework was successfully implemented on an interconnected energy system composed of a 33-bus electricity distribution system and a 14-node natural gas distribution network. Numerical results of the defined case studies and a devised comparative study validate the effectiveness of the proposed framework as well.

Machine-Learning-Driven Optimization of Cold Spray Process Parameters: Robust Inverse Analysis for Higher Deposition Efficiency

Cold spray technology has become essential for industries requiring efficient material deposition, yet achieving optimal deposition efficiency (DE) presents challenges due to complex interactions among process parameters. This study developed a two-stage machine learning (ML) framework incorporating Bayesian optimization to address these challenges. In the first stage, a classification model predicted the occurrence of deposition, while the second stage used a regression model to forecast DE values given deposition presence. The approach was validated on Aluminum 6061 data, demonstrating its capability to accurately predict DE and identify optimal process parameters for target efficiencies. Model interpretability was enhanced with SHAP analysis, which identified gas temperature and gas type as primary factors affecting DE. Scenario-based inverse analysis further validated the framework by comparing model-predicted parameters to literature data, revealing high accuracy in replicating real-world conditions. Notably, substituting hydrogen as the gas carrier reduced the required gas temperature and pressure for high DE values, suggesting economic and operational benefits over helium and nitrogen. This study demonstrates the effectiveness of AI-driven solutions in optimizing cold spray processes, contributing to more efficient and practical approaches in material deposition.

Menggerakan Wakaf Kompos; Aksi Lokal Penguatan Peran Ekologis Masjid untuk Mitigasi Perubahan Iklim

The objective of this community service project is to explore and strengthen the role of mosques as ecological agents in mitigating climate change through the innovative “Compost Waqf” initiative. Conducted at Nurul Amin Mosque in Metro City, this study employed a Community-Based Research (CBR) and Participatory Rural Appraisal (PRA) approach, involving 120 participants, including mosque administrators and congregants. This program integrates Islamic teachings with practical solutions for managing organic waste, enhancing environmental literacy, and fostering climate action awareness among mosque attendees. By focusing on waste segregation and composting, the initiative significantly reduced organic waste, minimized greenhouse gas emissions, and provided sustainable economic benefits for mosque operations and community empowerment. This study demonstrates how integrating Islamic environmental values with sustainable practices transforms mosques into centres of ecological education and local climate action. The Compost Waqf initiative bridges spiritual responsibility with environmental stewardship, providing a scalable and replicable model for mosques to address global climate challenges while fostering collective awareness and behavioural change within communities. The research highlights the strategic role of mosques in promoting ecological resilience, positioning them as key actors in climate change mitigation efforts.

Optimal Control Scheme of Electric Vehicle Charging Using Combined Model of XGBoost and Cumulative Prospect Theory

In this paper, we propose the XPaC (XGBoost Prediction and Cumulative Prospect Theory (CPT)) model to minimize the operational losses of the power grid, taking into account both the prediction of electric vehicle (EV) charging demand and the associated uncertainties, such as when customers will charge, how much electric energy they will need, and for how long. Given that power utilities supply electricity with limited resources, it is crucial to efficiently control EV charging peaks or predict charging demand during specific periods to maintain stable grid operations. While the total amount of EV charging is a key factor, when and where the charging occurs can be even more critical for the effective management of the grid. Although numerous studies have focused on individually predicting EV charging patterns or demand and evaluating the effectiveness of EV charging control, comprehensive assessments of the actual operational benefits and losses resulting from charging control based on predicted charging behavior remain limited. In this study, we firstly compare the performance of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and decision tree-based XGBoost regression models in predicting hourly charging probabilities and the need for grid demand control. Using the predicted results, we applied the CPT algorithm to analyze the optimal operational scenarios and assess the expected profit and loss for the power grid. Since the charging control optimizer with XPaC incorporates real-world operational data and uses actual records for analysis, it is expected to provide a robust solution for managing the demand arising from the rapid growth of electric vehicles, while operating within the constraints of limited energy resources.

Unified Detection and Feature Extraction of Ships in Satellite Images

The increasing importance of maritime surveillance, particularly in monitoring dark ships, highlights the need for advanced detection models that go beyond simple ship localisation. Current approaches largely focus on either detection or feature extraction, leaving a gap in unified methods capable of providing detailed ship characteristics. This study addresses this gap by developing a unified model for ship detection and characterisation from Synthetic Aperture Radar images, estimating features such as true length, true breadth, and heading. The model is designed to detect ships of varying sizes while simultaneously estimating their characteristics, and experimental results show a high detection accuracy, with a recall of 87.7% and an F1-score of 93.5%. The model also effectively estimates ship dimensions, with mean errors of 1.4 ± 16.2 m for length and 1.5 ± 4.5 m for breadth. Estimating the heading proved challenging for smaller ships, but was accurate for larger ships. A total of 50% of the heading estimates were within 15 degrees of error. This unified approach offers practical benefits for maritime operations. It is especially useful in situations where both ship detection and detailed information are needed, such as predicting future ship positions or identifying ships.

Analysis of ship electrical load management to reduce exhaust gas emissions in landing platform dock (LPD) vessel class

This paper investigates the impact of electrical load management on reducing exhaust gas emissions in Landing Platform Dock (LPD) class vessels. The study focuses on the implementation of Integrated Fully Electric Propulsion (IFEP) systems and Variable Speed Drives (VSD) to enhance energy efficiency and minimize environmental impact. The conventional Diesel Engine Propulsion system in LPD vessels often results in high fuel consumption and significant emissions, particularly under non-optimal operating conditions. By transitioning to an IFEP system and optimizing electrical load management using VSD, the study demonstrates a substantial reduction in carbon dioxide (CO₂), nitrogen oxides (NOₓ), and sulfur oxides (SOₓ) emissions. The results indicate a potential emission reduction of up to 40%, alongside a 20% improvement in overall energy efficiency. Additionally, the operational benefits of these technologies include increased flexibility, reduced maintenance costs, and extended equipment lifespan. The findings underscore the importance of adopting advanced propulsion and load management technologies in naval vessels to meet stricter environmental regulations and support sustainable maritime operations. This study provides a framework for future research and practical applications in expanding these technologies across different ship types and operational scenarios.

A Review of Benefits and Risks of Outsourcing Building Operation and Maintenance

Over the past few years, there has been a growing trend and high demand for outsourcing as a procurement strategy in the field of building operations and maintenance. Successful outsourcing can bring numerous advantages to organisations, encompassing a wide range of benefits. However, if outsourcing fails, there are substantial risks that organisations may face. The objective of this paper is to analyse the current literature concerning the advantages and disadvantages of outsourcing building operations and maintenance. Additionally, it aims to compile the findings and identify any potential research gaps in the existing body of knowledge. A comprehensive search through a systematic review of the literature was conducted across numerous journals. Key terms derived from the initial literature review are used first to categorise whether the studies address outsourcing benefits, risks, motivations, or issues. The benefits and risks of outsourcing building operations and maintenance construct were then identified using content analysis methodology and an inductive coding technique. Through a methodological and well-organised approach, this paper offers an extensive collection of previously recognised benefits and risks of outsourcing building operations and maintenance. The findings of this paper indicate several expected benefits of outsourcing building operation and maintenance, including cost savings, a focus on core functions, quality improvement, reduced capital expenditure, increased economic efficiency, and greater flexibility. This paper also classifies the risk of outsourcing building operations and maintenance into five risk factors: client risk, vendor risk, contract risk, communication or relationship risk, and general risk. The result reveals that a well-executed outsourcing strategy assists organisations in effectively managing their building operations and maintenance tasks.

Big Data Transformation Across Industries - Adoption Drivers and Key Strategies

Big Data has transformed industries worldwide by empowering organizations to utilize massive data volumes for actionable insights that drive strategic and operational benefits. This whitepaper investigates Big Data adoption trends across a range of industries, examining crucial drivers, organizational and cultural considerations, and strategic methods to implement successful implementation. With an emphasis on early adopters, emerging industries, and diverse use cases, this whitepaper offers a practical roadmap to guide organizations considering Big Data transformation.

Energy-efficient joint performance optimization of cloud data centre users/operator using memetic algorithm

ABSTRACT The use of cloud computing as a cost-effective and flexible method has currently drawn the attention of cloud service providers. When offering various cloud services to users, a certain level of Service Level Agreement (SLA) must be guaranteed by the cloud operator, based on the service received at the user level. Considering the non-linear dependency of network users’ Quality of Experience (QoE) on cloud resources (RAM, disk memory, network bandwidth, and CPU core usage) as well as the non-linear and time-varying dependency of cloud operator efficiency on the level of resources consumed by users, joint optimization of user experience and cloud operator efficiency poses significant computational challenges. This paper focuses on the green optimization of both user experience and the benefits of cloud data centre operators, employing a metaheuristic algorithm, which essentially combines a genetic algorithm with an innovative approach. In this approach, the efficiency of the cloud data centre operator, energy consumption of data centres, and user experience quality are jointly optimized. Simulation results demonstrate the improvement of the combined user/operator utility function compared to traditional methods, considering energy consumption constraints.

Leveraging Jasper Reports for Dynamic Financial Reporting in Modern Tolling Systems: A Technical Implementation Guide

This article presents a comprehensive technical implementation guide for leveraging Jasper Reports in modern tolling systems to enhance dynamic financial reporting capabilities. The article explores the evolution of electronic toll collection infrastructures and their integration with advanced reporting systems. It examines the core components of dynamic reporting architecture, including data flow pipelines, real-time parameter handling, and system integration points. The article delves into report customization and format optimization techniques, highlighting the importance of stakeholder-specific templates and dynamic parameter implementation. The article also investigates financial analysis capabilities through AI-driven smart information systems and big data technologies, focusing on daily ledger management and revenue tracking mechanisms. Advanced analytics features, including ranking functions and hierarchical data visualization, are analyzed for their impact on operational efficiency. The implementation of automated report scheduling and distribution frameworks is discussed, along with their security features and reliability metrics. The article concludes by examining the operational benefits achieved through these implementations, including improved decision support, stakeholder communication, and cost reduction through automation.

Tail-End Evaluation Based on the Cost-Effectiveness of Bridge Life Cycles

The number of bridges in China is increasing year by year. The contradiction between the declining performance of bridge structures and the increasing traffic volume in China is becoming increasingly prominent, causing a concentrated upsurge in operation and maintenance needs during the service life of the bridges. In accordance with the goal and vision of the comprehensive construction of the Bridge Structural Health Monitoring (BSHM) system, the study of bridge cost-effectiveness now focuses on the installation, operation, and maintenance costs of the BSHM system, the renovation costs of bridge structure repair and maintenance, and the benefits of the BSHM system in assisting in expansion of the service life of bridges. In this study, a new cost-effectiveness framework is proposed that could be used for the operation and maintenance period. The cost-effectiveness framework is constructed from five perspectives: installation, operation, and maintenance costs of the BSHM system; repair and renovation costs during the operation and maintenance period; demolition costs; operation benefits during the operation and maintenance period; and the recovery benefits. The difference between costs and benefits is calculated to evaluate the cost-effectiveness, the service life, and the structural reliability of bridges, in order to provide auxiliary decision making for bridges. A bridge in China is used as an example; the difference between the reliability and operation of the bridge and the maintenance benefits and costs corresponding to different operating times is calculated, and maintenance decision analysis is performed. This can greatly extend the service time of the bridge without major repairs when the bridge reliability meets the requirements. Therefore, the BSHM operation and maintenance cost-effectiveness framework reduces resource waste, reduces operation and maintenance costs, and maximizes resource utilization and benefits, while ensuring the safety of the bridge structure and extending the service life of the bridge.