Variable Demand Research Articles

The Impact of Service Demand Variability on Data Center Performance

The Impact of Service Demand Variability on Data Center Performance

OptiPower AI: A deep reinforcement learning framework for intelligent cluster energy management and V2X optimization in industrial applications

Peer-to-peer (P2P) energy trading has emerged as a practical solution for efficient energy management, particularly with the rising affordability of renewable energy and electric vehicles (EVs). This paper introduces the Optimal Power Artificial Intelligence (OptiPower AI) algorithm, a significant advancement in Intelligent Cluster Energy Management (ICEM). OptiPower AI combines Double Deep Q-Network (DDQN)-based Deep Reinforcement Learning (DRL), Vehicle-to-Everything (V2X) technology, and P2P energy trading to optimize energy distribution among clusters of prosumers and consumers. The system efficiently manages Renewable Energy Sources (RES) and EVs, achieving a 19.18% reduction in energy costs and a 50.02% decrease in average energy prices across V2X and P2P scenarios.OptiPower AI uses DRL to dynamically allocate energy and implement real-time pricing, enhancing energy efficiency and user satisfaction. Simulations based on meteorological data from Tunisia validate the system’s ability to improve thermal comfort, increase energy savings, and lower costs. The model’s parameters enable accurate forecasting and allocation, showcasing OptiPower AI’s reliability in variable demand conditions. This work advances the application of DRL in decentralized, sustainable P2P energy management systems for industrial clusters, addressing critical challenges in energy distribution, efficiency, and cost reduction.

Optimal control approach for solving a supply chain problem under variable demand and emissions tax regulation with an unknown production rate

Optimal control approach for solving a supply chain problem under variable demand and emissions tax regulation with an unknown production rate

Power price stability and the insurance value of renewable technologies

Abstract To understand if renewables stabilize or destabilize electricity prices, we simulate European power markets as projected by the National Energy and Climate Plans for 2030 but replicating the historical variability in electricity demand, the prices of fossil fuels and weather. We propose a β-sensitivity metric, defined as the projected increase in the average annual price of electricity when the price of natural gas increases by 1 euro. We show that annual power prices spikes would be more moderate because the β-sensitivity would fall from 1.4 euros to 1 euro. Deployment of solar photovoltaic and wind technologies exceeding 30% of the 2030 target would lower it further, below 0.5 euros. Our framework shows that this stabilization of prices would produce social welfare gains, that is, we find an insurance value of renewables. Because market mechanisms do not internalize this value, we argue that it should be explicitly considered in energy policy decisions.

Modelling and Evaluation of Work Zone Queues: Istanbul Case Study

Road/lane closures due to maintenance works adversely affect the traffic conditions, causing delays in the various locations of the whole network. In Istanbul, Metropolitan Municipality charge the companies for the planned closure of the highways for a given time interval using simple spreadsheet calculations. These calculations include assumed capacity and speed values for each type of highway. In this paper, it is aimed to establish a methodology for the work zone evaluation that considers seasonality in demand and spatiotemporal variability in the flow and capacity. This goal will be achieved by observing and assessing the characteristics of İstanbul roadways and work zone areas. After the assessment, a traffic microsimulation software, namely PARAMICS, was used to simulate various road and work zone combinations to produce traffic data, including queue length, waiting times, vehicle speeds, flow and density of work zone section. Various machine-learning models were developed and their performance on test data will be analyzed. The selected model was used as a sub-model to estimate the expected delays, speeds and accelerations on different road types, different demands and lane closure combinations. A second sub-model used the delay, speed and acceleration values to estimate the total excess user cost due to road closure with determined delay-cost parameters.

Optimizing health system volume flexibility: key determinants and strategies during the COVID-19 crisis.

The need to match highly variable demand for healthcare services with existing capacity has been a significant health system challenge experienced by the COVID-19 pandemic. Despite mounting research on various COVID-19 responses in the recent literature, there has not been a structured review examining key determinants of health system volume flexibility. The focus of the present research is to review recent literature based on a conceptual framework developed based on the volume flexibility literature on COVID-19. The review also provides a meaningful way to guide practice and future research in the area. A literature review was performed guided by the theoretical frameworks developed in the previous volume of flexibility literature. Selected research papers were reviewed and analyzed to identify key determinants of health system volume flexibility. Seven determinants of health system volume flexibility under three domains (demand management, capacity management, and performance) included demand management strategies, integration, capacity management strategies, workforce management, technology, quality of disease outcomes, and health system knowledge. The research provides both health systems researchers as well as practitioners with a foundational conceptual framework and knowledge related to health system volume flexibility. In addition, this research identifies future research areas to expand knowledge relevant to health system volume flexibility.

Power Demand Patterns of Public Electric Vehicle Charging: A 2030 Forecast Based on Real-Life Data

As the adoption of electric vehicles accelerates, understanding the impact of public charging on the power grid is crucial. However, today, a notable gap exists in the literature regarding approaches capable of accurately estimating the expected influence of e-mobility power demand on electrical grids, especially at medium and low voltage levels. To fill this gap, in this study, a procedure is proposed to estimate the power demand patterns of public car parks in a 2030 scenario. To this end, data collected from real-life car parks in Italy are used in Monte Carlo simulations, where probabilistic daily power demand curves are created with different maximum charging powers (from 7.4 kW to ultra-fast charging). The results highlight high variability in the power demand depending on the location and type of car park. City center car parks exhibit peak demand during morning hours, linked to commercial activities, while car parks near railway stations and hospitals show demand patterns aligned with transportation and healthcare needs. Business area car parks, in contrast, have a more pronounced demand during work hours on weekdays, with much lower activity during weekends. This study also demonstrates that, in some situations, ultra-fast charging can increase peak power demand from the grid by up to 210%. Given their contribution to the existing literature, the power demand patterns from this research constitute a valuable starting point for future studies aimed at quantitatively assessing the impact of e-mobility on the power system. In addition, they can effectively support decision-makers in optimally designing the e-mobility recharge infrastructure.

Investigating the Relationship between Job Demand and Job Control and the Mediating Role of Desire to Have Children among Employees of the Health Network in Saman City in 2023

Introduction: Work is an aspect of life where job demand and job control are important factors in providing services. And having children can affect the aforementioned variables, so the purpose of this research is to investigate the relationship between job demand and job control with the mediating role of desire for having children among employees of health network in Saman city in 2023 methods: This was a descriptive-analytical study. The statistical population was 250 people from the personnel of the mentioned network who were selected through convenience sampling method. In order to collect data, questionnaires of job demand (Karasek), job control (Brief) and desire to have children were used. Results: The average age of the employees was 37/82 ± 8/79, of whom 42/8% were between 31 and 40 years old, and the average employment history of the employees was 13/03 ± 7/85. 68/8% of the participants were female, 58% had contractual or official employment status. The mean variable of job demand was 26/27 ± 5/699. The mean of occupational control variable was 88/64 ± 12/64. The mean variable of desire to have children was 28/95±5/30. A strong, inverse and significant relationship and a weak, inverse and significant relationship were observed between the job demand variable and job control and also between the job demand variable and the desire to have children. Also, no significant relationship was observed between the job control variable and the desire to have children. Conclusion: By increasing the job demand variable to a large amount, job control should be reduced, and by increasing the job demand variable to a small amount, the desire to have children should be reduced. Also, by increasing or decreasing the variable of job control, there are no changes in the variable of willingness to have children, so the above results can be used to improve the quality of services and improve the health of society.

Data-Driven Order Consolidation with Vehicle Routing Optimization

This study compares time-based and quantity-based consolidation strategies within the Vehicle Routing Problem (VRP) framework to optimize supplier profitability and logistical efficiency. The time-based model consolidates deliveries at fixed intervals, offering predictable routes, reduced customer wait times, and cost efficiency in stable markets. Conversely, the quantity-based model dynamically adjusts delivery volumes to meet fluctuating demand, providing flexibility in dynamic environments but potentially increasing long-term costs due to logistical complexity. Using a mixed-integer linear programming (MILP) model, sensitivity analyses, and scenario-based experiments, the study demonstrates that the time-based model excels in stable conditions, while the quantity-based model performs better in highly variable demand scenarios. These findings provide actionable insights for selecting consolidation strategies that optimize delivery operations and enhance supply chain performance based on market dynamics.

Selecting CMIP6 models to assess the impact of climate change on energy need for heating and cooling in Europe

The CMIP project has reached the end of its sixth phase, with a large set of simulations that represent a very large volume of data. The exploitation of all the simulations in impact studies would be extremely costly in computing time and storage. Moreover, these models suffer from several sources of uncertainties (coarse spatial resolution, physical parameterizations, ...) which produce large systematic biases.This study details an approach developed for the selection of a subset of simulations from the CMIP6 multi-model ensemble to assess the impact of climate change on the energy demand in Europe. At a climatic time scale, besides socio-economic factors, energy demand variability and evolution depend mainly on air temperature. The developed process of selection combines two main criteria applied to the daily mean, maximum, and minimum air temperature from all CMIP6 simulations where these variables are available. The first one consists of choosing a subset of models that represents the whole range of possible temperature changes in the future compared to the historical climate. The second criterion considers the skills of the CMIP6 historical simulations over Europe with respect to ERA5 climatology in order to discard the climate simulations with the poorest representation of the present climate. The methodology of selection allows the maximization of the diversity of climate projections among the best-performing models.

Variables in the projection of physician demand and supply in primary care

Primary care services improve healthcare outcomes and limit unnecessary specialty care. Thus, it is essential to monitor primary care physician demand and supply projections to suggest evidence-based healthcare reforms and promote better healthcare delivery. This study evaluates 28 demand variables, 50 supply variables, and 26 additional variables associated with the demand and supply projections of physicians by reviewing scenarios from other countries, including Taiwan, Singapore, Japan, and the United States of America. The results indicate that Korea uses less diverse demand and supply indicators and suggest the need to implement variables used in the other four countries to improve projection modeling.

Irregular sleep/wake patterns in student-athletes exposed to early morning training

ABSTRACT This study aimed to examine the sleep parameters and sleep/wake regularity of a cohort of student-athletes who start training between 06:30 and 07:00. Twenty-one male Rugby Union players, aged 21 ± 2 years and competing at a national level, were assessed using actigraphy over two weeks, and the Athlete Sleep Screening Questionnaire (ASSQ). Sleep/wake regularity was calculated using the Sleep Regularity Index (SRI). Wilcoxon signed-rank tests showed that nocturnal sleep preceding morning training had a significantly shorter sleep duration (1.8 hr, r = .67), and advanced sleep onset (0.9 hr, r = .50) and sleep offset times (3.2 hr, r = .85) compared to nocturnal sleep preceding free days. The variability of training demands resulted in an inconsistent sleep pattern between consecutive days, resulting in a median SRI score of 67.0 (interquartile range: 17.0). Pearson correlations revealed that lower SRI was significantly associated with a higher daily sleep duration including naps (r = -.62), delayed sleep onset (r = -.50) and sleep offset (r = -.60), and a later chronotype assessed using the ASSQ (r = .52). These findings indicate that early morning training is a factor contributing to irregular sleep/wake patterns in student-athletes, and where feasible should be scheduled at an alternative time.

A surge in female condom distribution during the COVID-19 pandemic in Gauteng province, South Africa

BackgroundFemale Condoms are 90–95% effective against HIV transmission when correctly and consistently used and are also cost-effective. In general, condoms prevent sexually transmitted infections (STIs) and unwanted pregnancies. Although the COVID-19 pandemic had the potential to undermine routine healthcare services delivery and utilisation, there is limited evidence about the pandemic’s effect on Female Condom uptake in Gauteng, one of the hardest-hit provinces in South Africa. This study aimed to compare female condom distribution in Gauteng Province, South Africa, before and during COVID-19, to inform decision-making for the attainment of universal access to sexual and reproductive health (SRH) by 2023 as per the sustainable development goal 3.7 and also, to empower women over their SRH during future pandemics.MethodsThis secondary data anslysis examined the percentage change in routine female condom distribution in Gauteng province and its five districts as an indirect effect (lockdown) of the COVID-19 pandemic, by comparing the aggregated District Health Information System (DHIS) data collated from primary health care facilities within the ditrict, before COVID-19 (2018–2019) and during COVID-19 (2020). Analysis in MS Excel 2016 illustrates the changes in the patterns and the trend in female condom distribution over the study period.ResultsIn 2020, during the COVID-19 pandemic, Gauteng province experienced a 43.7% increase in female condom distribution compared to the pre-COVID-19 period of 2019. The highest female condom distribution during the pandemic was observed in the Ekurhuleni Metropolitan (150.0%), followed by the Sedibeng (92.8%) and the Johannesburg Metropolitan (67.9%) districts respectively. However, the Tshwane Metropolitan (-8.5%) and the West Rand (-16.6%) districts experienced a decline in female condom distribution during COVID-19.ConclusionGauteng province witnessed a substantial surge in female condom distribution during COVID-19 in 2020, with a disproportionate district variation in demand. Female condom distribution is crucial, necessitating its enhancement and the continuum of distribution and stakeholders stockpiling at all times with particular attention to a potential increase in demand during outbreaks with lockdowns. Undisrupted access to female condoms will enable a consistent and correct use and empower women against HIV, STIs and unintended pregnancies, as a strive towards universal access to SRH.

Adaptive Energy Management Strategy for Sustainable xEV Charging Stations in Hybrid Microgrid Architecture

Integrating electric vehicles (EVs) into power grids presents critical energy management challenges, especially in microgrid systems powered by renewable energy sources. This study introduces a novel energy management strategy for EV charging stations utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. The system dynamically optimizes the coordination of renewable energy sources solar photovoltaic (PV) panels and wind turbines energy storage, and EV chargers. By leveraging real-time data and predictive algorithms, the ANFIS controller adapts to fluctuations in energy supply and demand, ensuring optimal performance. The innovation of this work lies in combining fuzzy logic with neural network-based learning to enhance decision-making under uncertain and variable renewable energy conditions. The proposed approach employs a robust design methodology, integrating neural network training with fuzzy logic system development, to create an adaptive and intelligent control system. Simulation results using MATLAB/Simulink demonstrate a 92% increase in energy efficiency and an 89% enhancement in load-handling capacity compared to conventional methods. The system effectively manages renewable energy variability, battery state-of-charge, and load demand, maintaining stable electrical characteristics even under dynamic wind and solar conditions. This work underscores the importance of advanced AI-driven control strategies in enabling sustainable EV charging infrastructure within microgrid environments.

An Inventory Model of a Decaying Item with Fuzzy Lifetime and Decay Rate under Dynamic Trade Credit and Variable Demand

An inventory model of a decaying item is proposed considering dynamic credit period to the consumers, fuzzy life time of the units, time-dependent fuzzy decaying rate, and freshness dependent dynamic imprecise demand. Decaying rate of units depends on its lifetime and is considered as a dynamic monotonic increasing function of the same. To encourage the consumers, the retailer provides a credit on a part of the purchase and due to the dynamic decreasing nature of the freshness of units, the credit interval is considered as dynamic increasing. The unit cost, consumer credit period, and credit amount have impact on demand. As demand is fuzzy, some units may left at any cycle ending and are put up for sell in lot in a lump sum price. The model is formulated using fuzzy mathematical tools to decide the optimal cycle length, unit selling price, and order quantity to optimize the annual fuzzy profit from a cycle. A modified form of particle swarm optimization is applied to get the best decision, in which, using fuzzy credibility measure the objectives corresponding to two feasible solutions are compared to select the better one. Proper fuzzy simulation approaches are followed to determine the required credibility measures.

Synchronization in Public Transportation: A Review of Challenges and Techniques

Performing synchronization in public transport is one of the most challenging tasks that transport managers perform when organizing the processes of passenger servicing. Many variables characterizing existing public transport lines should be considered in the final timetable; in addition, the complexity of the transportation system, the variability in transport demand, and the stochasticity of the servicing process both in time and space have a significant influence on the result of synchronization. The synchronization problem in real-world applications does not have an exact solution, so in practice, a variety of techniques were developed to achieve a rational solution in a reasonable time. In our paper, we classify existing approaches to solving the problem of public transport synchronization, describe the essence of the most promising methods, and study their popularity based on the most recent scientific publications. As the result of our research, we show the most promising directions for the future development of synchronization methods and their application in public transportation.

Bulk System Reliability Assessment Incorporating Nodal Correlations in Supply–Demand Variabilities and Uncertainties Created with Net-Zero Emission Targets

Bulk System Reliability Assessment Incorporating Nodal Correlations in Supply–Demand Variabilities and Uncertainties Created with Net-Zero Emission Targets

Impaired oxidative phosphorylation drives primary tumor escape and metastasis.

Metastasis causes most cancer deaths and reflects transitions from primary tumor escape to seeding and growth at metastatic sites. Epithelial-to-mesenchymal transition (EMT) is important early in metastasis to enable cancer cells to detach from neighboring cells, become migratory, and escape the primary tumor. While different phases of metastasis expose cells to variable nutrient environments and demands, the metabolic requirements and plasticity of each step are uncertain. Here we show that EMT and primary tumor escape are stimulated by disrupted oxidative metabolism. Using Renal Cell Carcinoma (RCC) patient samples, we identified the mitochondrial electron transport inhibitor NDUFA4L2 as upregulated in cells undergoing EMT. Deletion of NDUFA4L2 enhanced oxidative metabolism and prevented EMT and metastasis while NDUFA4L2 overexpression enhanced these processes. Mechanistically, NDUFA4L2 suppressed oxidative phosphorylation and caused citric acid cycle intermediates to accumulate, which modified chromatin accessibility of EMT-related loci to drive primary tumor escape. The effect of impaired mitochondrial metabolism to drive EMT appeared general, as renal cell carcinoma patient tumors driven by fumarate hydratase mutations with disrupted oxidative phosphorylation were highly metastatic and also had robust EMT. These findings highlight the importance of dynamic shifts in metabolism for cell migration and metastasis, with mitochondrial impairment driving early phases of this process. Understanding mitochondrial dynamics may have important implications in both basic and translational efforts to prevent cancer deaths.

Data-driven time-varying discrete grey model for demand forecasting

Demand forecasting is critical for strategic decision-making in the business sector. However, estimating demand variations remains challenging due to their time-varying tendencies. To address this, we propose a novel universal approach called the time-varying discrete grey model, along with a data-driven model structure identification algorithm. Specifically, we introduce a universal model form that incorporates polynomial time-varying parameters to unify existing discrete grey models. We then discuss significant properties, such as unbiasedness and affine transformation. Furthermore, we develop a data-driven algorithm that adaptively identifies the optimal model structure via subset selection strategies. To validate the effectiveness of our approach, we conduct Monte Carlo simulations to assess its robustness against noise and evaluate its performance in multi-step-ahead forecasting. Finally, through extensive experiments on real-world datasets from USA State Retail Sales, we demonstrate the superiority of our model over benchmark approaches, including statistical models and artificial intelligence methods. Our proposed approach offers a competitive alternative for demand forecasting in the business sector.

Targeting Metabolic and Epigenetic Vulnerabilities in Glioblastoma with SN-38 and Rabusertib Combination Therapy.

Glioblastoma (GBM), the most prevalent primary malignant brain tumor, remains challenging to treat due to extensive inter- and intra-tumor heterogeneity. This variability demands combination treatments to improve therapeutic outcomes. A significant obstacle in treating GBM is the expression of O6-methylguanine-DNA methyltransferase, a DNA repair enzyme that reduces the efficacy of the standard alkylating agent, temozolomide, in about 50% of patients. This underscores the need for novel, more targeted therapies. Our study investigates the metabolic-epigenetic impact of combining SN-38, a novel topoisomerase inhibitor inducing DNA double-strand breaks, with rabusertib, a checkpoint kinase 1 inhibitor. We identified this synergistic combination through high-throughput drug screening across a panel of GBM cell lines using a cancer drug library combined with SN-38. A secondary metabolic screening with the PEDS algorithm demonstrated a synergistic modulation of purine, one-carbon, and redox metabolism. Furthermore, the combined treatment led to the significant depletion of epigenetically relevant metabolites such as 5-methyl-cytosine, acetyl-lysine, and trimethyl-lysine. Reduced intermediates of the glutathione cycle indicated increased cellular stress following combinatorial treatment. Overall, the combination of SN-38 and rabusertib synergistically disrupts metabolites associated with epigenetic adaptations, leading to cytotoxicity independent of O6-methylguanine-DNA methyltransferase status, thereby underpinning this combination as a promising candidate for combinatorial therapy in GBM.