Real-time Demand Research Articles

Dynamic Lock-And-Release Mechanism Enables Reduced ΔG at Low Temperatures for High-Performance Polyanionic Cathode in Sodium-Ion Batteries.

Low-temperature synthesis of polyanionic cathodes for sodium-ion batteries is highly desirable but often plagued by prolonged reaction times and suboptimal crystallinity. To address these challenges, a novel self-adaptive coordination field regulation (SACFR) strategy based on a dynamic lock-and-release (DLR) mechanism is introduced. Specifically, urea is used as a DLR carrier during synthesis, which dynamically "locks" and "releases" vanadium ions for controlled release, simultaneously "locking" H+ ions to enhance phosphate group release, thereby creating a self-adaptive coordination field that can intelligently respond to real-time demands of the reaction system. This dynamic coordination behavior contributes to both an improvement in reaction kinetics and a significant reduction in Gibbs free energy change (ΔG). As a result, the kinetic efficiency and thermodynamic spontaneity of the reaction are greatly enhanced, enabling the efficient synthesis of high-crystalline Na3V2O2(PO4)2F (NVOPF) at 90 °C within just 3 hours. The as-prepared NVOPF cathode exhibits exceptional rate performance and ultra-stable cycling stability across a broad temperature range. Furthermore, the successful kilogram-scale synthesis underscores the practical potential of the innovative strategy. This work pioneers the regulation of coordination field chemistry for polyanionic cathode synthesis, providing transformative insights into material design.

Joint Optimization of Cost and Scheduling for Urban Air Mobility Operation Based on Safety Concerns and Time-Varying Demand

As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, eVTOL performance, and passenger maximum waiting time as constraints. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm was utilized to obtain the optimal solution. The optimal solution obtained in this study can serve 138,610,575 passengers during eVTOLs’ entire lifecycle (15 years) with a total cost of CNY 368.57 hundred million, with the cost of CNY 265.9 per passenger. Although it is higher than the driving cost, it saves 1–1.5 h and thus has high cost effectiveness during rush hours.

Hybrid intelligent algorithm aided energy consumption optimization in smart grid systems with edge computing

The rapid proliferation of smart grid systems necessitates efficient management of energy resources, particularly in the context of mobile edge computing (MEC) networks. This paper presents a novel approach to optimize the energy consumption in smart grid systems with the integration of edge computing, employing a hybrid intelligent algorithm (HIA) empowered by particle swarm optimization (PSO). The primary objective is to enhance the sustainability and operational efficiency of smart grid infrastructures by minimizing the energy consumption in the MEC networks. The proposed HIA utilizes PSO to dynamically allocate computational tasks and manage resources among edge devices based on real-time demand fluctuations. This adaptive approach aims to achieve the optimal load balancing and energy efficiency across the smart grid ecosystem. By leveraging the PSO’s ability to iteratively refine solutions and adapt to changing environmental conditions, the algorithm optimizes the energy consumption while maintaining requisite service levels and reliability. Simulation experiments and case studies validate the effectiveness of the proposed PSO-based HIA in reducing the energy consumption without compromising system other performances. The results demonstrate substantial improvements in the energy efficiency, illustrating the feasibility and benefits of employing intelligent algorithms tailored for edge computing environments within smart grid systems. This research contributes to advancing sustainable smart grid technologies by introducing a robust framework for energy optimization through hybrid intelligent algorithms.

Optimization of Microgrid fed from Hybrid Energy Sources using the Swarm Intelligence

To combat the widening gap between demand and supply, it is crucial to include renewableenergy sources as a Distributed Generation newline (DG). Despite their usefulness in reducing the gap, REsources place a heavy pressure on generation scheduling, particularly wind power. Utilities are compelledto use wind curtailment due to fluctuations in wind energy output, which reduces the incentive to switch torenewable sources of power. Demand Side Management (DSM) is one approach to this problem, since itadjusts demand in real time to match generation while the grid is operating. Conventional DemandResponse (DR) systems were created to minimise consumption during peak hours by arranging userloads in exchange for incentives. The Smart Grid (SG) concept provides a high-tech means ofcommunication for keeping tabs on and managing the power grid. This motivated the creation of asophisticated DR technique known as Demand Dispatch (DD), in which consumption is timesynchronizedwith electricity production. In order to deal with the fluctuations in wind power production,this thesis proposes a DD method. The entities involved in DD and the way in which data is sharedbetween them are laid out in an operational framework. In order to find and evaluate all of the studiesthat have been published on a certain topic, researchers often undertake systematic literature reviews.

A dynamic yard space reservation algorithm based on reward-penalty mechanism

Increasing throughput at container terminals leads to higher yard occupancy rates, reduced yard service capacity, and inefficient use of space. These issues result in longer waiting times for trucks, underutilized yard slots, and longer distances between berth positions and the yard, particularly under high occupancy conditions. Traditional methods for allocating yard space fail to adequately consider dynamic container arrival patterns, conflict between container operations, and efficient use of yard resources. This paper introduces an innovative approach to improve yard efficiency by dividing yard container retrieval into time intervals and dynamically reserving yard slots within each interval. This method aims to minimize operational conflicts, reduce distances between blocks and berths, and avoid excessive reservations. We propose a dynamic reservation algorithm based on a reward-penalty mechanism to handle the problem's complexity and real-time demands. Furthermore, a time-series reward-penalty mechanism, which considers past reservation feedback and future reservation potential, ensures the concentration and continuity of container groups in the yard. Our evaluation metrics and multiple experimental scenarios demonstrate that this method significantly enhances yard efficiency and overall port productivity compared to static and other dynamic algorithms.

Mathematical optimization strategy for online scheduling of complex manufacturing systems based on thermal energy optimization and fuzzy mathematical model

With the rapid development of the manufacturing industry and the advancement of the intelligent process, the scheduling problem of complex manufacturing systems becomes more and more complicated, especially in the aspect of energy management and optimization. This study aims to propose an online scheduling mathematical optimization strategy based on thermal energy optimization and fuzzy mathematical model, so as to improve the scheduling efficiency and resource allocation rationality of complex manufacturing systems under different production conditions. A scheduling model considering thermal energy utilization rate, production priority and real-time demand is established by using fuzzy mathematics. The model deals with the uncertain factors by fuzzy logic and solves them by genetic algorithm to realize the dynamic adjustment and optimal scheduling of production resources. Through the experimental verification of a complex manufacturing system, the improvement of scheduling efficiency not only reduces the operating cost, but also improves the flexibility and response speed of the system. The fuzzy mathematical model based on thermal energy optimization provides an effective on-line scheduling strategy for complex manufacturing systems, which can realize efficient scheduling and energy management in dynamic environment.

MLino bench: A comprehensive benchmarking tool for evaluating ML models on edge devices

In today’s rapidly evolving technological landscape, Machine Learning (ML) has become an integral part of our daily lives, ranging from recommendation systems to advanced medical diagnostics and autonomous vehicles. As ML continues to advance, its applications extend beyond conventional boundaries. With the continuous refinement of the models and frameworks, the possibilities for leveraging these technologies into devices that traditionally lacked any form of computational autonomy are ever-expanding. This shift towards embedding ML capabilities directly into edge devices brings new challenges due to the stringent limitations these devices have in terms of memory, power consumption, and cost. The ML models implemented on such devices must find an equilibrium between memory footprint and performance, attaining a classification time that fulfills real-time demands and maintains a similar level of accuracy as the desktop version. Without automated assistance in managing these considerations, the complexity of evaluating multiple models can lead to suboptimal decisions.In this paper, we introduce MLino Bench, an open-source benchmarking tool tailored for assessing lightweight ML models on edge devices with limited resources and capabilities. The tool accommodates various models, frameworks, and platforms, presenting a meticulous design that enables a comprehensive evaluation directly on the target device. It encompasses crucial metrics such as on-target accuracy, classification time, and model size, providing a versatile framework that assists practitioners in decision-making when deploying models to such devices.The tool employs a fully streamlined benchmark flow involving training the ML model in a high-level interpreted language, porting, compiling, flashing, and finally benchmarking on the actual target. Our experimental evaluation of the tool highlights its flexibility in assessing multiple ML models across different model hyperparameters, frameworks, datasets, and embedded platforms. Furthermore, a distinctive advantage compared to state-of-the-art ML benchmarking tools is the inclusion of classical ML models, including Random Forests, Decision Trees, Support Vector Machines, Naive Bayes, and more. This sets our tool apart from others that predominantly emphasize only neural network models. Due to this inclusive approach, our tool facilitates the evaluation of ML models across a broad spectrum of devices, ranging from resource-constrained edge devices to those with medium and advanced computational capabilities.

Optimizing real-time demand response in smart homes through fuzzy-based energy management and control system

Optimizing real-time demand response in smart homes through fuzzy-based energy management and control system

An incentive strategy for the retention of impatient passengers in ride-sourcing markets

A prominent problem plagued the ride-sourcing service providers is the instantaneous real-time demand–supply imbalance. This entails a terrible user experience and causes adverse sustainable results such as customer churn for the platform, which will damage both drivers’ income and the platform revenue. To reduce passenger cancellation caused by extended wait times, we propose an incentive-based queue management strategy —a discount method— to enhance the waiting experience and encourage passengers to stay in a post-peak period . We formulate the waiting process for a ride-sourcing service as an M/M/c+M queue system with impatient passengers, and model passengers’ reneging behaviors by characterizing the impact of discounted trip fare and updated queue information on their travel utilities. Based on such a behavior model, we can analyze the effect of the discount strategy on the queueing process, which allows us to maximize the platform’s profit growth by tailoring the discount strategy. Our result shows that the discount strategy can effectively increase the platform’s profit. Under the specific condition of the discount strategy, the earlier the strategy is implemented in the post-peak period, the higher platform profit can be achieved. Moreover, we explore the impact of queueing system parameters on the performance of the discount strategy and gain some interesting insights.

Control system for automatic adjustment of departure intervals of public transport vehicles

In numerous urban areas, fine-tuning bus schedules to align with fluctuating passenger numbers poses a sizable challenge, compounded by diverse factors such as weather and unexpected events. This paper conducts a comprehensive examination of existing optimization methods aimed at enhancing the departure intervals of public transport systems, particularly focusing on the Bidirectional Coordination Optimization Algorithm (BCOA) and the genetic algorithm. Through systematic analysis, these methodologies are meticulously explored for their effectiveness in addressing the scheduling challenges faced by urban transit systems. Additionally, it delves into specific instances where the Proportional-Integral-Derivative (PID) control mechanism has been employed to adeptly adjust the timing of bus departures based on real-time demand. The PID system's proven reliability and efficacy spotlight its potential as a transformative tool for advancing public transit systems. By leveraging such technology, there's tangible optimism for achieving a more responsive, efficient, and passenger-friendly public transportation network in the years to come.

Optimizing water quality in urban distribution networks: Leveraging digital twin technology for real-time demand management

Optimizing water quality in urban distribution networks: Leveraging digital twin technology for real-time demand management

The Future of DevOps Compute: A Survey of Innovative Strategies for Efficient Resource Utilization

This paper investigates innovative strategies employed by DevOps teams to optimize compute resource utilization within their environments. High compute resource utilization is critical for efficient application development and deployment in DevOps workflows. Traditional virtual machines (VMs) often lead to resource waste due to overprovisioning and static allocation. This research analyzes the growing adoption of containers for achieving higher density and finer-grained resource control. Additionally, the impact of autoscaling and serverless functions on dynamically adjusting compute resources based on real-time demand is examined. Through a comprehensive survey, the paper identifies key trends, challenges, and best practices associated with optimizing compute utilization in DevOps. The findings aim to guide DevOps teams towards efficient and cost-effective compute resource management strategies within the ever- evolving landscape of application development. Keywords—DevOps, Compute Resource Utilization, Containers, Virtual Machines, Autoscaling, Serverless Functions, Resource Management, Application Development, Cost Optimization, Dynamic Resource Allocation

LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation.

Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems for colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of clinical applications due to their substantial parameter count and computational load, especially those based on Transformer architectures. To tackle these challenges, a novel lightweight long-range context fusion network, named LightCF-Net, is proposed in this paper. This network attempts to model long-range spatial dependencies while maintaining real-time performance, to better distinguish polyps from background noise and thus improve segmentation accuracy. A novel Fusion Attention Encoder (FAEncoder) is designed in the proposed network, which integrates Large Kernel Attention (LKA) and channel attention mechanisms to extract deep representational features of polyps and unearth long-range dependencies. Furthermore, a newly designed Visual Attention Mamba module (VAM) is added to the skip connections, modeling long-range context dependencies in the encoder-extracted features and reducing background noise interference through the attention mechanism. Finally, a Pyramid Split Attention module (PSA) is used in the bottleneck layer to extract richer multi-scale contextual features. The proposed method was thoroughly evaluated on four renowned polyp segmentation datasets: Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, and ETIS. Experimental findings demonstrate that the proposed method delivers higher segmentation accuracy in less time, consistently outperforming the most advanced lightweight polyp segmentation networks.

Prediction-Driven Surge Planning with Application to Emergency Department Nurse Staffing

Determining emergency department (ED) nurse staffing decisions to balance quality of service and staffing costs can be extremely challenging, especially when there is a high level of uncertainty in patient demand. Increasing data availability and continuing advancements in predictive analytics provide an opportunity to mitigate demand uncertainty by using demand forecasts. In this work, we study a two-stage prediction-driven staffing framework where the prediction models are integrated with the base (made weeks in advance) and surge (made nearly real-time) nurse staffing decisions in the ED. We quantify the benefit of having the ability to use the more expensive surge staffing and identify the importance of balancing demand uncertainty versus system stochasticity. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Last, we develop a unified framework that combines parameter estimation, real-time demand forecasts, and nurse staffing in the ED. High-fidelity simulation experiments for the ED demonstrate that the proposed framework has the potential to reduce annual staffing costs by 10%–16% ($2 M–$3 M) while guaranteeing timely access to care. This paper was accepted by David Simchi-Levi, healthcare management. Funding: J. Dong was partially supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant CMMI-1944209]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.02781 .

A novel adjusted real-time decision-making for dynamic distribution in the grocery supply chain

PurposeThe current research has developed a novel method to update the decisions regarding real-time data, named the dynamic adjusted real-time decision-making (DARDEM), for updating the decisions of a grocery supply chain that avoids both frequent modifications of decisions and apathy. The DARDEM method is an integration of unsupervised machine learning and mathematical modeling. This study aims to propose a dynamic proposed a dynamic distribution structure and developed a bi-objective mixed-integer linear program to make distribution decisions along with supplier selection in the supply chain.Design/methodology/approachThe constantly changing environment of the grocery supply chains shows the necessity for dynamic distribution systems. In addition, new disruptive technologies of Industry 4.0, such as the Internet of Things, provide real-time data availability. Under such conditions, updating decisions has a crucial impact on the continued success of the supply chains. Optimization models have traditionally relied on estimated average input parameters, making it challenging to incorporate real-time data into their framework.FindingsThe proposed dynamic distribution and DARDEM method are studied in an e-grocery supply chain to minimize the total cost and complexity of the supply chain simultaneously. The proposed dynamic structure outperforms traditional distribution structures in a grocery supply chain, particularly when there is higher demand dispersion. The study showed that the DARDEM solution, the online solution, achieved an average difference of 1.54% compared to the offline solution, the optimal solution obtained in the presence of complete information. Moreover, the proposed method reduced the number of changes in downstream and upstream decisions by 30.32% and 40%, respectively, compared to the shortsighted approach.Originality/valueIntroducing a dynamic distribution structure in the supply chain that can effectively manage the challenges posed by real-time demand data, providing a balance between distribution stability and flexibility. The research develops a bi-objective mixed-integer linear program to make distribution decisions and supplier selections in the supply chain simultaneously. This model helps minimize the total cost and complexity of the e-grocery supply chain, providing valuable insights into decision-making processes. Developing a novel method to determine the status of the supply chain and online decision-making in the supply chain based on real-time data, enhancing the adaptability of the system to changing conditions. Implementing and analyzing the proposed MILP model and the developed real-time decision-making method in a case study in a grocery supply chain.

Flexible head-following motion planning for scalable and bendable continuum robots

Continuum robots, which are characterized by high length-to-diameter ratios and flexible structures, show great potential for various applications in confined and irregular environments. Due to the combination of motion modes, the existence of multiple solutions, and the presence of complex obstacle constraints, motion planning for these robots is highly challenging. To tackle the challenges of online and flexible operation for continuum robots, we propose a flexible head-following motion planning method that is suitable for scalable and bendable continuum robots. Firstly, we establish a piecewise constant curvature (PCC) kinematic model for scalable and bendable continuum robots. The article proposes an adaptive auxiliary points model and a method for updating key nodes in head-following motion to enhance the precise tracking capability for paths with different curvatures. Additionally, the article integrates the strategy for adjusting the posture of local joints of the robot into the head-following motion planning method, which is beneficial for achieving safe obstacle avoidance in local areas. The article concludes by presenting the results of multiple sets of motion simulation experiments and prototype experiments. The study demonstrates that the algorithm presented in this paper effectively navigates and adjusts posture to avoid obstacles, meeting the real-time demands of online operations. The average time for a single-step solution is 4.41×10−5 s, and the average tracking accuracy for circular paths is 7.8928 mm.

Biological plausible algorithm for seizure detection: Toward AI-enabled electroceuticals at the edge

Nearly 1% of people worldwide suffer from epilepsy. Electroencephalogram (EEG)-based diagnostics and monitoring tools, such as scalp EEG, subscalp EEG, stereo EEG, or sub/epi-dural EEG recordings [also known as electrocorticography (ECoG)], are widely used in different settings as the gold standard techniques to perform seizure identification, localization, and more primarily in epilepsy or suspected epilepsy in patients. Techniques such as subscalp EEG and ECoG offer long-term brain interaction, potentially replacing traditional electroceuticals with smart closed-loop therapies. However, these systems require continuous on-device training due to real-time demands and high power consumption. Inspired by the brain architecture, biologically plausible algorithms, such as some neuromorphic computing, show promise in addressing these challenges. In our research, we utilized liquid time-constant spiking neural networks with forward propagation through time to detect seizures in scalp-EEG. We trained and validated our model on the Temple University Hospital dataset and tested its generalization on out-of-sample data from the Royal Prince Alfred Hospital (RPAH) and EPILEPSIAE datasets. Our model achieved high area under the receiver operating characteristic curve (AUROC) scores of 0.83 in both datasets. We assessed the robustness by decreasing the memory size by 90% and obtained an overall AUROC of 0.82 in the RPAH dataset and 0.83 in the EPILEPSIAE dataset. Our model showed outstanding results of 3.1 μJ power consumption per inference and a 20% firing rate during training. This allows for incorporating bio-inspired efficient algorithms for on-device training, tackling challenges such as memory, power consumption, and efficiency.

Design and Implementation of an Iot-Based Safety and Energy Efficient System

The paper provides a safe and power-efficient Internet of Things (IoT)-based solution. With the growing concerns about environmental sustainability and the demand for improved safety measures in today’s society, it has become critical to focus on building systems that decrease energy consumption and assure the safety of humans and the environment. The proposed system employs cutting-edge technologies and novel approaches that will result in optimal energy usage with minimal or no human involvement thereby assuring the safety of people. The proposed system leverages on existing IoT technologies and is comprised of smart sensors, energy-efficient gadgets, and a centralized control unit. Therefore, it is able to demonstrate cutting edge power control strategies including power gating and dynamic central power regulation which control the usage of gadgets to operate at optimal power levels vis-a-vis the real-time demand to achieve energy efficiency. The system is also able to collect data on occupancy, lighting, temperature, and power consumption trends which can assist in making data-driven choices that improve the usage of energy. The proposed system provides large energy savings without sacrificing user comfort by dynamically altering lighting, and cooling systems based on occupants and ambient factors. Multiple simulations and real-world trials in homes and institutions’ buildings were used to assess the efficacy of the suggested system. The findings revealed substantial reductions in energy consumption without jeopardizing safety.

Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing

Edge computing brings computation and storage resources to the edge of the mobile network to solve the problems of low latency and high real-time demand. However, edge computing is more vulnerable to malicious attacks due to its open and dynamic environments. In this article, we investigate security defense strategies in edge computing systems, focusing on scenarios with one attacker and multiple defenders to determine optimal defense strategies with minimal resource allocation. Firstly, we formulate the interactions between the defenders and the attackers as the mean-field Stackelberg game model, where the state and the objective functions of the defenders are coupled through the mean-field term, and are strongly influenced by the strategy of the attacker. Then, we analyze the local optimal strategies of the defenders given an arbitrary strategy of the attackers. We demonstrate the Nash equilibrium and the mean-field equilibrium for both the defenders and the attackers. Finally, simulation analysis will illustrate the dynamic evolution of the defense strategy of the defenders and the trajectory of the attackers based on the proposed Stackelberg game model.

5G and edge: A reinforcement learning approach for Virtual Network Embedding with cost optimization and improved acceptance rate

5G technologies are fueling a revolution across numerous industries, including manufacturing, healthcare, and entertainment, by enabling the development and deployment of novel applications at the network’s edge. To meet the demanding service level agreements of these industries, a dynamic and adaptable infrastructure strategy that combines cloud and edge computing models is needed. This hybrid approach offers the benefits of both centralized cloud processing and decentralized edge computing, optimized for responsiveness and efficiency. A key element for success is an orchestration mechanism that dynamically allocates resources to ensure the infrastructure can adapt to fluctuating demands in real time, optimizing resource utilization and meeting SLA requirements. Among these mechanisms, virtual network embedding (VNE) and dynamic resource management (DRM) have emerged as tools for defining where and how edge technology should be used. However, current VNE approaches struggle to adapt to real-time fluctuations in demand across geographically distributed edge resources. This work introduces a novel resource allocation algorithm, the VNE-CRS, which uses an Artificial Intelligence technique called Reinforcement Learning to orchestrate resources across multiple domains. This approach benefits from the strength of Reinforcement Learning: its ability to consider the entire problem from beginning to end while incorporating various aspects of 5G Quality of Service Indicators for optimal decision-making. Experiments were conducted to evaluate the performance of VNE-CRS against state-of-the-art algorithms for multi-domain edge environments. Results have shown that employing Reinforcement Learning techniques for VNE resource allocation yields performance gains of 12.32 percentage points in comparison with the GRC algorithm and 28.80 percentage points in comparison with the base edge environment, presenting an acceptability rate closer to the Public Cloud environment with all benefits of edge environment. In conclusion, VNE-CRS offers an efficient solution for resource allocation in 5G environments, achieving superior performance and transforming the VNE architecture into a comprehensive orchestration system that optimizes infrastructure utilization for strategic long-term benefits.