Real-time Coordination Research Articles

Cross-Border Interaction between Kazakhstan and Uzbekistan in Logistics: Conceptual Approaches

The article examines cross-border cooperation between Kazakhstan and Uzbekistan, focusing on developing logistics in border regions. Central Asian regions face significant barriers, such as logistical inefficiencies, legal and customs discrepancies, and weak coordination, which hinder their ability to meet the growing demand for freight transportation. The article aims to propose conceptual approaches for harmonizing regulations, simplifying customs procedures, and removing obstacles to the movement of goods and services across border regions. The research methodology includes case studies of transport corridors and content analysis of data from international organizations and national statistical agencies. The analysis of the Trans-Caspian Route and the Southern Corridor highlights challenges such as high transport costs, lack of institutional support, geopolitical influences, and investment risks. Special attention is given to crossborder industrial logistics zones (ILZs), which could serve as strategic tools for reducing transaction costs and expediting cargo processing. These zones should be integrated with international transport networks and automated customs systems. The research results show that sustainable development of crossborder cooperation requires not only infrastructure investments but also institutional reforms, including the establishment of joint regulatory bodies and harmonization of legislation to eliminate administrative barriers. The article provides recommendations for simplifying customs procedures, establishing industriallogistics centers, developing transport corridors, and forming a concept of cross-border interaction. Future research directions include assessing the economic impact of the proposed ILZs and exploring digital platforms for real-time coordination of freight transportation.

Real-Time Coordination of Multiple Robotic Arms With Reactive Trajectory Modulation

Real-Time Coordination of Multiple Robotic Arms With Reactive Trajectory Modulation

Real-Time Coordination and Communication Process for the Issuance of Emergency Ambulance Alerts

The lack of attention to emergency alerts in road accidents in a timely manner may be due to the lack of management of resources or the lack of communication between hospital and patrol departments. The purpose of this research is to reduce the management time to provide attention to requests on highways. An alert and resource management process is proposed. This process will allow sending the optimized information in the shortest possible time. The experimentation was worked with a set of test instances and simulated in the laboratory using mechatronic devices to show the communication, coordination and attention in real time. The results obtained were acceptable, it was possible to identify the alert to be included in the algorithm which must comply with the restrictions and subsequently calculate the optimal route to be followed by the mechatronic device to reach the request node for the attention of the distress alert.

Real-time coordination of integrated transmission and distribution systems: Flexibility modeling and distributed NMPC scheduling

This paper proposes a real-time distributed operational architecture to coordinate integrated transmission and distribution systems (ITD). At the distribution system level, the distribution system operator (DSO) calculates the aggregated flexibility of all controllable devices by power-energy envelopes and provides them to the transmission system operator (TSO). At the transmission system level, a distributed nonlinear model predictive control (nmpc) approach is proposed to coordinate the economic dispatch of multiple TSOs, considering the aggregated flexibility of all distribution systems. The subproblems of the proposed approach are associated with different TSOs and individual time periods. In addition, the aggregated flexibility of controllable devices in distribution networks is encapsulated, re-calculated, and communicated through the power-energy envelopes, facilitating a reduction in computational complexity and eliminating redundant information exchanges between TSOs and DSOs, thereby enhancing privacy and security. The framework’s effectiveness and applicability in real-world scenarios are validated through simulated operational scenarios on a summer day in Germany, highlighting its robustness in the face of significant prediction mismatches due to severe weather conditions.

Distributed Computing Optimization in Unmanned Aerial Vehicle Swarm Cooperative Networks

The Unmanned Aerial Vehicle (UAV) swarm cooperative networks across diverse domains present significant challenges in management and control to maximize collaborative efficiency, particularly in applications for large-scale and dynamic environments where real-time coordination is essential. This challenge has led to the development of a UAV swarm cooperative network flow prediction model, facilitating the strategic anticipation of network dynamics. A novel algorithm for distributed, finite-time cooperative control is introduced, grounded in this model. This algorithm optimizes the operational efficiency and control precision of UAV systems, while simultaneously augmenting the collaborative efficiency and stability of the UAV swarms. It marks a significant advancement in distributed computing strategies, offering a viable solution for the real-time coordination challenges in extensive UAV swarm cooperative networks. The proposed approach leverages multi-agent technology, integrating spatial-temporal constraints, location limitations, and path selection in cooperative behavior for dynamic and effective network management. This study not only enhances the understanding of UAV swarm dynamics but also contributes to the practical application of UAV swarms in areas requiring precise coordination and robust network stability.

Rescue Relief Agency Management For Disaster Recovery

In the aftermath of a natural or man-made disaster, the swift and coordinated deployment of rescue agencies is critical to minimizing casualties and maximizing the effectiveness of relief efforts. However, the current landscape of disaster response is often hampered by the absence of a centralized platform for communication and collaboration among various rescue agencies. This lack of a unified system often leads to duplication of efforts, inefficient resource allocation, and delays in providing aid to those in dire need. To address these challenges and enhance the efficiency of disaster response, we propose the development of a comprehensive mobile application that would enable rescue agencies to seamlessly register their information, share real-time location updates, and coordinate their operations effectively. This proposed mobile application would serve as a central hub for rescue agencies to register their information, including their location, contact details, areas of expertise, and available resources. This information could be entered manually or automated using GPS or other location tracking technologies. Once the database is populated, the application would provide a user-friendly interface that allows users to visualize the locations of registered rescue agencies on a map. Additionally, users could filter the results based on specific criteria, such as the type of disaster, the resources available, or the time since the last reported activity. Beyond displaying the locations of rescue agencies, the application would also facilitate communication and collaboration among these organizations. Rescue agencies could send alerts or requests for assistance directly through the application, enabling them to coordinate their efforts and optimize resource utilization. Moreover, the application could facilitate the sharing of critical resources such as medical equipment, transportation, and communication infrastructure, ensuring that these resources are deployed where they are most needed. Overall, the proposed mobile application has the potential to revolutionize disaster response by providing a centralized platform for communication, collaboration, and resource management among rescue agencies. By enabling real-time coordination and efficient resource allocation, this application could significantly enhance the effectiveness of disaster relief efforts, saving lives and minimizing the impact of natural and man-made disasters..

Leveraging AI in Disaster Management: Enhancing Response and Recovery for Natural and Man-Made Disasters

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the paradigm of disaster management through enhanced prediction accuracies and optimization of response strategies to natural and artificial disasters. This article discusses the use of AI-powered systems in predicting disasters related to hurricanes, earthquakes, and floods, thereby enabling proactive measures to reduce damage. The article also demonstrates how AI optimizes the relief effort in terms of resource allocation, real-time coordination, and post-disaster communication. AI lessens casualties, accelerates recovery, and improves decision-making through the analysis of vast data and predictive models during an emergency. The article also looks into the challenges of data quality, ethical issues, and integration with existing disaster management frameworks while emphasizing its potential to be transformative in saving lives and reducing the impacts of disasters.

Dynamic scheduling of decentralized high-end equipment R&D projects via deep reinforcement learning

Distributed research and development (R&D) plays a pivotal role in high-end equipment manufacturing enterprises, significantly impacting interdisciplinary innovation and expediting development cycles. In the uncertain environment characterized by random arrivals of new projects, achieving dynamic scheduling and real-time coordination for decentralized R&D projects is a challenging problem. This paper studies a dynamic decentralized resource-constrained multi-project scheduling problem with product transfers (DDRCMPSP-PT). The objective is to minimize the development cycle after initial local schedules are generated to minimize the individual project makespan. To tackle the problem, we develop a decentralized multi-agent system using the dynamic coordination mechanism for resource assignment (DMAS/DCMRA). An up-to-date deep reinforcement learning (DRL) algorithm, dueling double deep Q-learning (D3QN) with prioritized replay, is employed to select the optimal strategy to resolve the global resource conflicts adaptively. Two priority rules are presented based on the structural properties and make up the action space of the DRL agent together with ten high-quality priority rules. Extensive simulation experiment results based on real enterprise cases reveal that the DMAS/DCMRA outperforms all traditional priority rule-based methods. The proposed priority rules also show competitive performance compared to other priority rules, which validates the applicability and superiority of the proposed method. This research offers a new agile project management technique for organizations running multiple R&D projects and managing crucial bottleneck resources.

Repetition for real-time coordination of action: Lexical and non-lexical vocalizations in collaborative time management

Repetition has often been argued to be a semiotic device that iconically signifies ‘more content’, such as intensity and plurality. However, through multimodal interaction analysis of materials in English, Estonian, and Swedish, this paper demonstrates how self-repetition is used to coordinate actions across participants and temporally organize the ongoing activity. The data are taken from infant mealtimes, pilates classes, dance training, boardgames, rock climbing, and opera rehearsals. Repetition of both lexical and non-lexical tokens can prolong, postpone, and generally organize segments of action as well as co-create rhythms and moves in a moment-by-moment reflexive relationship with other (non-vocalizing) participants. A crucial feature of repetitions is that they can be flexibly extended to fit the other’s public performance, its launching, continuation, and projectable completion. We argue that the iconicity of repetition emerges through its indexical relationship to other bodies, as a real-time jointly achieved phenomenon.

Nitrogen sensing and regulatory networks: it's about time and space.

A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its "hit-and-run" mode of target gene regulation, and temporal transcriptional cascade identified by "network walking." Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions.

Software Defined Layer 4.5 Customization for Agile Network Operation

Protocol customizations primarily come in two forms: those driven by public extensions to open standard protocols; and dialecting and performance tuning driven by an enterprise network’s private security and performance needs. Current deployment of protocol customizations is mostly ad hoc, through manual configuration or script programs that are highly specialized to each customization. This method lacks the agility necessary to support the relatively high tempo of private customizations. Also, it is common for today’s protocol customization efforts to experience middlebox interference. We propose a systematic framework of network-wide orchestration and continuous management of protocol customization to enable agile operation for enterprise and data-center networks. By introducing a logically centralized orchestrator along with a Layer 4.5 fine-grained device customization solution, our framework will allow operators to configure, deploy, and monitor customized flows from a single vantage point, providing timely detection of rogue devices as well as real-time coordination of middlebox traversal. Results from prototyping and experimentation confirm utility of our framework while incurring modest processing overhead, at the levels of 3% and 0.5% for sample customized flows and non-customized flows, respectively. Furthermore, we present two major system refinements: (i) generalizing the design of receiving modules to support customization of encrypted flows, and (ii) adding logic system wide to support seamless rotation of customization modules for live flows. Finally, we discuss specific agile network operation use cases enabled by our solution and outline future work.

A Unified Framework for Integrated Energy Systems Optimization With Day-Ahead and Real-Time Coordination

A Unified Framework for Integrated Energy Systems Optimization With Day-Ahead and Real-Time Coordination

AI-Based Self-Learning Robotic Arm Using Microcontroller

Robotic arms are essential components of day-to-day life likely manufacturing and logistics operations to provide more speed, more flexible decision-making and reduce the possibility of errors. The objective of this project is to make a robotic arm with machine learning features to automate the assembly by object and colour detection using openCV, and TensorFlow library. The basic arm is made of light material to provide maximum mobility which is controlled by an ATmega328P microcontroller with the aid of an Arduino development environment for hassle-free interfacing sensors. A webcam is used for sensing the surroundings. Feed from waveform is processed by openCV and TensorFlow library Using a Python environment the real-time coordinates data and colour information will be fed to the microcontroller unit that controls the arm servos accordingly to grip the appropriate object and place them in the predetermined location. The arm is also capable of learning trajectory which is taught to it using human hands. The use of easily available components reduces the cost but the same time leaves much room for future improvement. There are two approaches to machine learning used in robotic systems, the first one is a conventional approach to machine learning which is a complicated, expansive asset that is not suitable for several types of automation industries. A self-learning approach, called a behaviour-based robotic arm with a machine learning feature to automate the assembly by the object and colour detection using openCV, a python environment to be built to address the limitations of conventional robotics methodologies. This approach aims to enhance the capability of robotic platforms to offer more flexible decision-making, cost-effectiveness, reduced human error, and higher precision in processes.

Tiny-YOLO distance measurement and object detection coordination system for the BarelangFC robot

<span lang="EN-US">A humanoid robot called BarelangFC was designed to take part in the Kontes Robot Indonesia (KRI) competition, in the robot coordination division. In this division, each robot is expected to recognize its opponents and to pass the ball towards a team member to establish coordination between the robots. In order to achieve this team coordination, a fast and accurate system is needed to detect and estimate the other robot’s position in real time. Moreover, each robot has to estimate its team members’ locations based on its camera reading, so that the ball can be passed without error. This research proposes a Tiny-YOLO deep learning method to detect the location of a team member robot and presents a real-time coordination system using a ZED camera. To establish the coordinate system, the distance between the robots was estimated using a trigonometric equation to ensure that the robot was able to pass the ball towards another robot. To verify our method, real-time experiments was carried out using an NVDIA Jetson NX Xavier, and the results showed that the robot could estimate the distance correctly before passing the ball toward another robot.</span>

Sodium homeostasis and signalling: The core and the hub of astrocyte function

Neuronal activity and neurochemical stimulation trigger spatio-temporal changes in the cytoplasmic concentration of Na+ ions in astrocytes. These changes constitute the substrate for Na+ signalling and are fundamental for astrocytic excitability. Astrocytic Na+ signals are generated by Na+ influx through neurotransmitter transporters, with primary contribution of glutamate transporters, and through cationic channels; whereas recovery from Na+ transients is mediated mainly by the plasmalemmal Na+/K+ ATPase. Astrocytic Na+ signals regulate the activity of plasmalemmal transporters critical for homeostatic function of astrocytes, thus providing real-time coordination between neuronal activity and astrocytic support.

A Two-Stage Real-Time Optimization Model of Arterial Signal Coordination Based on Reverse Causal-Effect Modeling Approach

The arterial signal coordination is an effective method to improve traffic operational efficiency and reduce vehicle delay. In this paper, a two-stage arterial signal coordination model under dynamic traffic demands is established, and the signal timing and offset are adjusted according to the dynamic traffic demands. The objective is to minimize the expected intersection delay and the overflow of the coordinated direction. In the first stage, a calculation model for intersection signal timing based on phase clearing reliability is proposed by the reverse causal-effect modeling approach, which can calculate the signal timing of each intersection in real time. In the second stage, an offset calculation model is established to achieve the goal of minimizing delay in the coordinated direction, which can calculate the offset of trunk coordination in real time. The concept of phase clearance reliability is introduced in the model, which can dynamically adjust the balance between the coordinated phase and the non-coordinated phase, thus taking the overall control efficiency of intersections into account. We then develop an algorithm to solve the problem and then apply the model and the solution algorithm to an arterial road with three intersections to investigate and compare its performance with the Allsop’s method and the Webster’s method. A comparison between the proposed coordinated two-stage logic and a coordinated actuated logic is also conducted in the case study to show the advantages and disadvantages.

The Pap Hub: A Novel Centralized System for Cervical Cancer Screening and Prevention

Summary Complex guidelines for managing the results of cervical cancer screening create specific challenges in ensuring appropriate follow-up for patients. The authors describe the development and implementation of a centralized Pap Hub system for the real-time coordination of up-to-date cervical cancer screening and follow-up at HealthPartners in Minnesota. Preliminary review of 8-year trends in screening rates under the Pap Hub shows an overall improvement in screening rates that exceeded the national average. The review also demonstrated that despite an overall improvement in screening rates in the population, there exist and remain disparities between white patients and patients of color. Qualitative review of the end-user experience with the Pap Hub showed a high degree of satisfaction for both patients and providers in coordination of cervical cancer screening and follow-up.

Real-time coordination of multiple shuttle-conveyor-belts for inventory control of multi-quality stockpiles

A hybrid model predictive control formulation with continuous and discrete variables that models multiple crushed-ore stockpiles per conveyor-belt is proposed for better conveying and stockpiling performance. The aim is to minimize the inventory level or holdup squared deviations from targets by varying the fill-time, idle-time, or run-length of the shuttle-conveyor-belts’ tripper-cars over multiple stockpiles’ positions (the discrete actuation or manipulated variable). In the problem, amounts of crushed-ore dropped by gravity from conveyor-belts fill up the upper-level of the stockpiles considering quality balances to specify blended ore by the mixtures of the material dropped by synchronized shuttle-conveyor-belts’ tripper-cars. The problem is solved as a mixed-integer quadratic programming (MIQP) that uses amount-of-quality balances as a linear (LP) approximation, tightening the MIQP-solutions to schedules of simultaneous blending of different quality shuttle-conveyor-belts’ raw materials. Results of the initialization and steady-state stages of the inventory-control and logistics-quality optimization highlight the modeling and solution strategies.

An IoT-based Framework of Vehicle Accident Detection for Smart City

Smart sensors have revolutionized the world with their capability of capturing physical parameters. The sensors provide vital data to the monitoring station in the applications of environment, disaster management, transportation, etc. A framework is built up with an Internet of Things-based approach that detects vehicle accident with the sensors and the information is stored and monitored on the Thingspeak cloud. The alert is raised whenever an accident occurs to the vehicle with the help of an accelerometer, ultrasonic sensor, and GPS. The message of the real-time coordinates is transmitted with the communication module. The monitoring station takes the necessary action by recognizing the accident occurred and providing the emergency services to the hotspot immediately. The integrated approach will be beneficial as it will detect any mishap with the vehicle and facilitate the emergency help to the passengers in the vehicle. The proposed framework will be beneficial for saving the human life in any smart city.

Validation of real-time inside-out tracking and depth realization technologies for augmented reality-based neuronavigation.

Concomitant with the significant advances in computing technology, the utilization of augmented reality-based navigation in clinical applications is being actively researched. In this light, we developed novel object tracking and depth realization technologies to apply augmented reality-based neuronavigation to brain surgery. We developed real-time inside-out tracking based on visual inertial odometry and a visual inertial simultaneous localization and mapping algorithm. The cube quick response marker and depth data obtained from light detection and ranging sensors are used for continuous tracking. For depth realization, order-independent transparency, clipping, and annotation and measurement functions were developed. In this study, the augmented reality model of a brain tumor patient was applied to its life-size three-dimensional (3D) printed model. Using real-time inside-out tracking, we confirmed that the augmented reality model remained consistent with the 3D printed patient model without flutter, regardless of the movement of the visualization device. The coordination accuracy during real-time inside-out tracking was also validated. The average movement error of the X and Y axes was 0.34 ± 0.21 and 0.04 ± 0.08mm, respectively. Further, the application of order-independent transparency with multilayer alpha blending and filtered alpha compositing improved the perception of overlapping internal brain structures. Clipping, and annotation and measurement functions were also developed to aid depth perception and worked perfectly during real-time coordination. We named this system METAMEDIP navigation. The results validate the efficacy of the real-time inside-out tracking and depth realization technology. With these novel technologies developed for continuous tracking and depth perception in augmented reality environments, we are able to overcome the critical obstacles in the development of clinically applicable augmented reality neuronavigation.