Operational Costs Research Articles

A hybrid Improved Salp Swarm Algorithm and Harris Hawk Optimizer for energy planning in microgrids with minimum operating cost

ABSTRACT Achieving optimal energy planning in Microgrids (MGs) is pivotal for addressing complex challenges associated with cost-effective and reliable energy supplies. This paper proposes a novel hybrid metaheuristic algorithm for optimal energy planning in microgrids using an Improved Salp Swarm Algorithm with Harris Hawk Foraging (ISSAHF). This technique is based on an improved multi-leader Salp Swarm Algorithm with an elite leader following strategy combined with Harris Hawks foraging. A simulation study is conducted on a low-voltage microgrid in off-grid and grid-connected modes. The optimization algorithm resulted in a daily average cost of 28.3370€ in off-grid mode compared to 19.2676€ in grid-connected one. Furthermore, the statistical study shows that the proposed algorithm outperforms well-established metaheuristic techniques regarding search capability and robustness. It yields mean optimal cost of 623.5248€ in off-grid and 404.7475€ for the grid-connected one, compared to other optimization techniques that vary from 667.2141€ to 959.5747€ in off-grid mode, and from 424.5841€ to 813.932€ in grid-connected mode. For robustness, the proposed technique performs well with a standard deviation of 20.765€ compared to the best (17.024€) and the worst (47.2423€) cases in off-grid mode, while in grid-connected mode, it is 28.8771€ compared to the best (21.6316€) and the worst (45.3774€) values.

Automating Infrastructure Management: Benefits and Challenges of Ansible and Terraform Implementation Across Sectors

This article examines the implementation and impact of Infrastructure as Code (IaC) practices using Ansible and Terraform across various industries. Through a mixed-methods approach combining case studies, surveys, and quantitative analysis, we investigate how these tools enable more efficient, scalable, and repeatable infrastructure deployments. Our findings reveal significant benefits, including reduced operational costs (average 30% reduction), improved resource utilization (up to 40% increase), and enhanced disaster recovery capabilities (50% faster recovery times). However, challenges such as skills gaps, security concerns, and legacy system integration persist. The article provides insights into industry-specific applications, highlighting how finance, healthcare, and telecommunications sectors leverage these tools to meet unique demands. We present best practices for successful implementation, emphasizing continuous monitoring, data governance, and cross-functional collaboration. This article contributes to the growing body of literature on IaC and offers practical recommendations for organizations seeking to optimize their infrastructure management strategies in an increasingly digital landscape.

Dynamic adaptation in power transmission: integrating robust optimization with online learning for renewable uncertainties

IntroductionThe increasing integration of renewable energy sources, such as wind and solar, into power grids introduces significant challenges due to their inherent variability and unpredictability. Traditional fossil-fuel-based power systems are ill-equipped to maintain stability and cost-effectiveness in this evolving energy landscape.MethodsThis study presents a novel framework that integrates robust optimization with online learning to dynamically manage uncertainties in renewable energy generation. Robust optimization ensures system resilience under worst-case scenarios, while the online learning component continuously updates operational strategies based on real-time data. The framework was tested using an IEEE 30-bus test system under varying levels of renewable energy integration.ResultsSimulation results show that the proposed framework reduces operational costs by up to 12% and enhances system reliability by 1.4% as renewable energy integration increases from 10% to 50%. Additionally, the need for reserve power is significantly reduced, particularly under conditions of high variability in renewable energy outputs.DiscussionThe integration of robust optimization with online learning provides a dynamic and adaptive solution for the sustainable management of power transmission systems. This approach not only improves economic and environmental outcomes but also enhances grid stability, making it a promising strategy for addressing the challenges posed by the increasing reliance on renewable energy.

Sustainable Water Conservation Techniques for an Office Building

The research paper titled "Sustainable Water Conservation Techniques for an Office Building" explores methods to reduce water consumption and promote sustainable practices in office buildings. The study focuses on the rising global water crisis due to population growth and increasing urbanization, advocating for the reuse of rainwater and wastewater as alternatives to conventional water sources. Key objectives include assessing current water distribution systems, comparing existing water management techniques, and proposing sustainable water-saving measures. By integrating solutions such as water-efficient fixtures, greywater recycling, and rainwater harvesting, the study aims to significantly reduce water demand and operational costs, contributing to long-term sustainability​.

Provider costs of treating opioid dependence with extended-release buprenorphine in Australia.

The costs of providing medication-assisted treatment for opioid dependence can determine its scale of provision. To provide estimates of the costs of extended-release buprenorphine (BUP-XR), we performed a bottom-up costing analysis of provider operational treatment costs. Data were collected in a single-arm open label trial of BUP-XR injections conducted in specialist public drug treatment services and primary care private practices in three Australian states (the CoLAB study). The unit costs of resources used for each activity were combined with quantities used at each participating facility to arrive at the average annual cost per client. One hundred participants across the six health facility sites received monthly subcutaneous BUP-XR injections administered by a health-care practitioner. The average cost of providing 1 year of treatment per participant was $6656 ($6026-$8326). Screening cost (initial assessment and medical history) was $282 while monthly follow-up appointments cost $531 per client. The main cost driver was the monthly treatment costs accounting for 79% of the average annual client cost, with medication costs comprising 95% of this cost. With medication costs making up the largest proportion of treatment costs, treatment using BUP-XR has the potential to free up other health system resources, for example, staff time. The costs reported in this study can be used in an economic evaluation to estimate the net benefit or cost-effectiveness of BUP-XR especially when compared to other opioid agonist treatments.

Integrating Kubernetes Autoscaling for Cost Efficiency in Cloud Services

Kubernetes Autoscaling Mechanism for Integration into Cloud Services to Achieve Cost Efficiency Organizations have turned towards containerized applications and microservices architecture. Optimizing and using resources appropriately as per the expected operational cost becomes the need of the hour. There are several autoscaling mechanisms within Kubernetes, that include Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler, working towards cost optimization. We study predictive scaling algorithms, multi-dimensional autoscaling strategies, and machine learning-based approaches for resource allocation. Among the new challenges of implementing the solution are the methodologies followed in evaluating the research, which also involves complex advanced optimization techniques: from integrating serverless, towards multicloud autoscaling. Our findings will give an understanding of the status quo of Kubernetes autoscaling towards cost efficiency and recommendations for future research and industrial implementation.

Labour rights protection and export expansion: Evidence from SA8000 certification

Protecting labour rights is an important approach to shaping the new advantages of trade competition and achieving high-quality development of China’s export trade. This paper considers SA8000 certification as the criterion for labour rights protection and manually collects information from firms certified by the National Certification and Accreditation Administration of China. Next, we investigate the effects of SA8000 certification on firm exports. The results show that SA8000 certification significantly expands the export scale while improving export quality. The mechanism analysis shows that the improvement effects are achieved in three ways: reducing operating costs, improving social reputation and increasing labour efficiency. Moreover, the positive effects of SA8000 certification are more profound for resource-intensive firms, non-state-owned firms and large firms. Our study provides an important reference for safeguarding labour rights and reconstructing new competitive advantages in exports.

Scattered-fields-induced tunable narrow resonance in low-index dielectric meta-lattices for transflective display

The development of a transflective display is important for reducing the energy consumption of an outdoor display-device. Currently, it is desirable to have a transflector which can be switched between its reflective and transparent states. The present work provides a novel yet simple route for the development of such a transflector based on one-dimensional (1D) meta-lattice systems. The system studied is shown capable of exciting tunable narrow resonance in the reflectance spectra through the generation of the scattered-field-induced polarizing-field in the system. Surprisingly, while most work on dielectric metasurface relies on high-index material, here it is found that the phenomenon can be achieved with common low-index material. Hence, it is a promising alternative for the development of future display technology with more economical operational cost.

Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework

Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity. One of the neuroimaging modalities that produces large amounts of time-series data is Electroencephalography (EEG). It captures the neural dynamics in a task or resting brain state with high temporal resolution. EEG electrodes placed on the scalp acquire electrical activity from the brain. These electrical potentials attenuate as they cross multiple layers of brain tissue and fluid yielding relatively weaker signals than noise-low signal-to-noise ratio. EEG signals are further distorted by internal physiological artifacts, such as eye movements (EOG) or heartbeat (ECG), and external noise, such as line noise (50 Hz). EOG artifacts, due to their proximity to the frontal brain regions, are particularly challenging to eliminate. Therefore, a widely used EOG rejection method, independent component analysis (ICA), demands manual inspection of the marked EOG components before they are rejected from the EEG data. We underscore the inaccuracy of automatized ICA rejection and provide an auxiliary algorithm-Second Layer Inspection for EOG (SLOG) in the clinical environment. SLOG based on spatial and temporal patterns of eye movements, re-examines the already marked EOG artifacts and confirms no EEG-related activity is mistakenly eliminated in this artifact rejection step. SLOG achieved a 99% precision rate on the simulated dataset while 85% precision on the real EEG dataset. One of the primary considerations for cloud-based applications is operational costs, including computing power. Algorithms like SLOG allow us to maintain data fidelity and precision without overloading the cloud platforms and maxing out our budgets.

A review of vehicle speed measurement methods for the statistical pass-by noise testing

The Statistical Pass-By method (SPB, ISO11819-1) is used in environmental impact studies to evaluate, classify, and compare different road surfaces according to their influence on traffic noise. It relates the noise level to the type of vehicles and their speed. There are different methods to measure vehicle speed and the cost plays a significant role in their practicality and widespread adoption. Radar, Lidar, and Photocells-based method systems typically involve higher initial costs due to the sophisticated technology employed, but they offer real-time and accurate data, justifying their investment. On the other hand, Video camera systems may have lower initial costs, but their accuracy can be influenced by environmental conditions and calibration requirements. This paper will assess the cost-effectiveness of these technologies, considering not only the initial setup expenses but also maintenance and operational costs over time. Cost-benefit analyses are crucial in determining the most economical yet reliable solution for specific budget constraints. As the research in this field progresses, a key focus lies in optimizing the balance between cost and performance to make these systems more accessible and practical also for a wide range of applications, ensuring that advancements benefit both traffic management authorities and users alike.

Evaluation of the Management Policy of BOS Funds in Elementary Schools

General background however, the effective management of these funds remains a significant challenge, particularly in certain regions. Specific background this study focuses on evaluating the management policy of BOS funds at an elementary school, highlighting the need for systematic assessment criteria that align with established standards. Knowledge gap despite the existence of frameworks for evaluating fund management, there is limited research on their practical application in specific educational contexts. The aim is to analyze the BOS fund management by employing evaluation indicators based on Wirawan's framework, which includes input, process, output, and impact assessments. The results reveal that while the school’s facilities and infrastructure are generally in good condition, delays in fund distribution have adversely affected maintenance efforts. Key factors influencing successful fund management include meticulous planning, effective coordination, and accurate reporting. Furthermore, the study finds that BOS funds significantly reduce operational costs, benefiting both educators and students. Novelty this research contributes novel insights into the evaluation process of educational funding, illustrating how structured assessments can improve management practices. The implications of this study suggest that better fund management can lead to enhanced educational outcomes and resource allocation, ultimately fostering an environment conducive to learning. Highlights: Evaluation of BOS fund management enhances accountability and resource allocation. Delays in fund distribution impact maintenance of school facilities. Structured assessments improve educational outcomes and support effective learning environments. Keywords: BOS , Educational Management, Evaluation, Operational Costs, Infrastructure

Approach to Validate ISPM-15 Compliance for Commercial Treatment Certification of Dielectric Standard Heating of Bulk Solid Wood Packing Materials using Radio Frequency

Abstract To substantially reduce the risk of alien invasive species moving to new geographic areas, phytosanitary treatment of wood packaging materials (WPM) in compliance with the International Standard of Phytosanitary Measures No. 15 (ISPM-15) is required by trading partners. Approved treatments include conventional heating, methyl bromide and sulfuryl fluoride fumigation, and dielectric heating (DH). The DH standard was officially adopted in 2013 but has not been practiced commercially due primarily to insufficient operational validation at commercial scale. In 2022, we converted our 50-kW radio frequency (RF) unit with a 1,200-board foot capacity from an oscillator electromagnetic field power generator to a solid-state power supply, which allows for selective power input adjustments during treatment; we also switched from a five-plate to a three-plate winged electrode system to improve heating uniformity. Each loading cycle can treat sufficient material to build ∼94 standard Grocery Manufacturers Association pallets. Our research team characterized the dielectric heating pattern and options for monitoring wood temperatures over a wide-ranging test matrix of WPM that varied by wood species, dimension, moisture content, and loading configuration. We found that this upgraded RF system markedly reduced treatment times and improved heating uniformity, allowing us to develop methods that can be used to verify compliance with ISPM-15 for improved technology transfer to industry. We also discuss the operational cost of RF treatment and make general cost comparisons to conventional heat treatment for WPM.

Digital Engineering in Photonics: Optimizing Laser Processing

This article explores the transformative impact of digital engineering on photonic technologies, emphasizing advancements in laser processing through digital models, artificial intelligence (AI), and freeform optics. It presents a comprehensive review of how these technologies enhance efficiency, precision, and control in manufacturing processes. Digital models are pivotal for predicting and optimizing thermal effects in laser processing, thereby reducing material deformation and defects. The integration of AI further refines these models, improving productivity and quality in applications such as micromachining and cladding. Additionally, the combination of AI with freeform optics advances laser technology by enabling real-time adjustments and customizable beam profiles, which enhance processing versatility and reduce material damage. The use of digital twins is also examined as a key development in laser-based manufacturing, offering significant improvements in process optimization, defect reduction, and system efficiency. By incorporating real-time monitoring, machine learning, and physics-based modeling, digital twins facilitate precise simulations and predictions, leading to more effective and reliable manufacturing practices. Overall, the integration of digital twins, AI, and freeform optics into laser processing marks a significant progression in manufacturing technology. These advancements collectively enhance precision, efficiency, and adaptability, resulting in improved product quality and reduced operational costs. The continued evolution of these technologies is expected to drive further advancements in manufacturing practices, offering more robust solutions for complex production environments.

Unlocking the secrets of Neapolitan pizza: A concise review of wood-fired, electric, and gas pizza ovens.

This review explores the Neapolitan pizza baking process in a traditional wood-fired oven, employing visual color analysis and IR thermal scanning to detail heat exchange mechanisms. During cooking, the oven floor temperature in the pizza area decreased proportionally to the pizza's mass, whereas the free area maintained a constant temperature of 439±3°C. An IR thermal camera indicated that the oven dome temperature reached approximately 480°C with a weak flame and 500°C with a strong flame. The pizza's bottom achieved a maximum temperature of 100±9°C, facilitated by the Pizzaiolo's skill in lifting and rotating the pizza for uniform cooking. Top temperatures varied: up to 180°C for white pizza and 84°C and 67°C for tomato and Margherita pizzas, respectively. IRIS electronic eye analysis revealed more browning and blackening on the pizza's top compared to its bottom, with peaks of about 26% and 8% for white pizza, respectively. Rapid baking is pivotal in Neapolitan pizza-making, necessitating precise heat and mass transfer management to influence sensory attributes. European Commission Regulation No. 97/2010 mandates wood-fired ovens for true Neapolitan pizza, but environmental concerns prompted the Associazione Verace Pizza Napoletana to certify gas or electric ovens when wood-fired ovens are impractical. Operating costs vary: liquefied petroleum gas ovens are the costliest, with costs ranging from €5.38 to €6.19/h, whereas natural gas and electric ovens have operating costs between €2.70 and €4.10/h. At €0.15/kg, firewood is the most economical, supporting traditionalist views. However, natural gas and electric ovens present competitive costs under stringent antipollution laws, making them viable alternatives.

Less is more: Optimisation of variable catalyst loading in CO[formula omitted] electroreduction

The electrochemical conversion of CO2 is a promising method of carbon-neutral chemical production. However, commercial realisation in aqueous electrolytes is challenging, due to competition with the hydrogen evolution reaction (HER), and the propensity for CO2 to participate in the carbonate equilibrium reactions. These two phenomena are linked through OH− ions, as both the by-product of the catalytic reactions and the culprit behind the parasitic carbonate reactions. By reducing the local catalyst loading where the CO2 concentration is low, the HER is decreased more than the reactions that are dependent on CO2 as a reactant. Therefore, it is possible to improve reaction selectivity and reactant utilisation while reducing the capital cost of catalyst. We demonstrate this theory through an analytical solution of a 1D flow electrolyser model. We extend this to a comprehensive model of a contemporary gas-diffusion electrode (GDE) setup. We find that the operation costs are dominated by the electrolyser power consumption and, to a lesser degree, the cost of CO2 and its recovery at the anode. We numerically obtain the catalyst loading profiles that maximise operating profit. The optimisation process reveals that profits are maximised for high gas flow rates, and consequently, low single pass conversions, where the CO2 concentration is as high as possible. However, when lower gas flow rates are used for practical reasons, variable catalyst loadings are shown to lead to significant operational improvements, especially in the production of higher C products that require a greater number of electrons transferred. The model is made freely available in MATLAB and its use is encouraged in determining the applicability of variable catalyst loading to future experimental setups.

Energy Efficiency of Glasshouses and Plant Factories for Sustainable Urban Farming in the Desert Southwest of the United States of America

The extreme heat and water scarcity of the desert southwest in the United States of America present significant challenges for growing food crops. However, controlled-environment agriculture offers a promising solution for plant production in these harsh conditions. Glasshouses and plant factories represent advanced but energy-intensive production methods among controlled-environment agriculture techniques. This review aims to comprehensively assess how controlled-environment agriculture can thrive and be sustained in the desert southwest by evaluating the energy efficiency of controlled glasshouses and building-integrated plant factories. The analysis focuses on the efficiency of these systems’ energy and water consumption, mainly using artificial lighting, heating, cooling, ventilation, and water management through various hydroponic techniques. Approximately 50% of operational energy costs in controlled glasshouses are dedicated to cooling, whereas 25–30% of energy expenses in building-integrated plant factories are allocated to artificial lighting. Building-integrated plant factories with aeroponic systems have demonstrated superior water use and energy efficiency compared to controlled glasshouses in desert environments. Integrating photovoltaic solar energy and glass rooftops in building-integrated plant factories can significantly reduce energy costs for urban farming in the desert southwest.

Fine-tuning ocean energy storages for reservoir-integrated wave energy converters with dynamic activation of mechanical energy storage features

The proportion of renewable energy in the power supply has experienced substantial growth; however, its intermittent nature and inherent uncertainty present notable challenges in its integration. This study aims to explore the significance of energy flexibility in energy management by focusing on a hybrid renewable wave-wind zero-energy system implemented in a coastal building. Specifically, the study investigates various sources of flexibility within the system and introduces a novel energy storage mechanism utilising the in-built reservoir of the wave energy converter. The research provides new insights into the flexibility of control strategies from the generation and storage sides. The cost-responsive controls involved on/off-peak periods and monthly variations. Several attempts have been made to develop cost-responsive controls that depend upon the electricity tariffs; these include energy shift and peak shaving. The lower discharging limits and trigger lines were compared in the simulation environment to improve the cost-saving, energy-matching, and peak-shaving performances. In this study, the first group scheduled discharging; however, only limited improvements (0.5%) were achieved. Consequently, the second group shifted to varying the minimum fraction state of the reservoir under a lower discharging limit, which reduced operational cost by 4.5%. Using different discharging lines by considering the current month and the period, the third group achieved the expected peak shaving and correspondingly reduced the operational cost to 90.3 %. The final group showed that an additional battery could improve the peak-shaving and cost-saving performance but not the net present value, owing to high investment costs. Furthermore, annual savings increased for different battery capacities from 4.5 % to 6.8 % by conducting the energy shift. Peak shaving control with additional battery reduced maximum demand and substantially reduced costs from 6.4 % to 10.8 %. The study recommends focusing on peak shaving with extra batteries for further cost savings during the on-peak period. This research brings novelty by integrating flexibility control for both generation- and storage-sides in ocean renewable energy systems. It proposes using a wave energy converter as a mechanical energy storage reservoir, reducing costs and ensuring adequate capacity. The study emphasises dynamic storage control, adjusting in real time based on conditions and energy demands.

Approximation Scheme for the Single-Client Capacitated Facility Location Problem With Operational Cost Budget Constraint

Approximation Scheme for the Single-Client Capacitated Facility Location Problem With Operational Cost Budget Constraint

Analisis Penerapan Akuntansi Lingkungan Terhadap Pengendalian Biaya Operasional Pengelolaan Limbah pada Rumah Sakit Karya Husada

This research aims to find out how environmental accounting is applied to control operational costs for waste management at Karya Husada Hospital. This research was conducted using qualitative methods. Data sources use primary data and secondary data. The data analysis technique used is descriptive qualitative using the Miles and Huberman model. The results of this research show that Karya Husada Hospital has managed its waste well, this is proven by the absence of problems or complaints from the surrounding community regarding waste at the hospital. Karya Husada Hospital has implemented environmental accounting and carried out the process of Recognizing, Measuring, Recording, Presenting, and also Disclosure of environmental related costs. Karya Husada Hospital does not have a specific environmental cost report. Controlling operational costs for waste management at Karya Husada Hospital has not been effective, where there has been a deviation from realized costs exceeding previously budgeted costs. This deviation occurred because the amount of medical and domestic waste increased along with the increase in patient and family visits at Karya Husada Hospital.

Optimizing reservoir characterization: insights from integrated data analysis

The Onshore Hydrocarbon prospectivity of the Niger Delta X-field is examined through the integration of 3D seismic and recorded information from well log data. Probable reservoirs of hydrocarbon-bearing were delineated to tackle the non-uniqueness in the identification of hydrocarbon quantity of concern. Three significant faulting patterns or systems were delineated and their architectural attributes depict a typical Niger Delta embedded anticlinal structural pattern. The study field exhibited both synthetic and antithetic structures, hence only fault on delineated reservoirs was used for structural modeling. All well locations were sited within the fault-supported synthetic and anticlinal structures and the static characteristics within the well coordinates were analyzed through petrophysical assessment. Depicted reservoir sands show low gamma ray readings, low volume of shale, considerate hydrocarbon saturation and low water saturation. The established petrophysical models showed a better net-to-gross (NTG) ratio, for the entire delineated reservoir units. This has contributed to the assessment of hydrocarbon-bearing reservoir units before employing the strategy for field development scenario, in order to eliminate dry hole drilling campaign. These will contribute to the reduction of operational costs, having delineated the accurate geometry and petrophysical model of hydrocarbon reservoir units.