Minimum Response Time Research Articles

Multiobjective location-routing problem of relief commodities with reliability.

The applicability and efficiency of location-routing problems for relief commodities motivate several researchers to develop optimization models and algorithms with regards to real-world cases. This study by presenting a multiobjective mixed integer mathematical model for the location-routing of the medical relief problem at the time of a disaster, proposes a new extension to this applicable model with reliability considerations. The proposed model focuses on the location of temporary relief centers and delivers the pharmaceutical commodities to centers at the shortest possible time with reliability assurance. The model includes three simultaneous objectives of minimizing response time, minimizing operational costs, and maximizing the reliability of the transportation network. As far as we know, this study firstly optimizes these three objectives simultaneously. Another novelty is to add the uncertainty of the problem. In this regard, the inherent uncertainty is formulated by a scenario-based approach. Considering the multiobjectiveness of the proposed model, the Epsilon constraint method as a solution algorithm has been used to solve the model. Results are tested for numerical examples via different scenarios. The results represent the excellent performance of the model to minimize the costs and to increase the reliability for the proposed location-routing problem of relief commodities.

Predicting emergency medical service call demand: A modern spatiotemporal machine learning approach

The primary goal of emergency medical service (EMS) agencies is to effectively allocate the ambulances and personnel required to provide sufficient geographic coverage of a service area while minimizing response times to high-priority call requests. Given that the demand for ambulances is known to fluctuate spatially and temporally based on the time of day and day of the week, EMS practitioners depend on call volume forecasts to develop staffing and dynamic redeployment plans. In this study, a series of daily, hourly, and spatially distributed hourly call volume predictions are generated using a multi-layer perceptron (MLP) artificial neural network model following feature selection using an ensemble-based decision tree model. For spatially distributed predictions, K-Means clustering is applied to produce heterogeneous spatial clusters based on call location and associated call volume densities. The predictive performance of the MLP model is benchmarked against both a selection of traditional time-series forecasting techniques and a common industry method. Results show that MLP models outperform time-series and industry forecasting methods, specifically at finer levels of spatial granularity where the need for more accurate call volumes forecasts is more essential.

Protection Coordination of Bidirectional Overcurrent Relays Using Developed Particle Swarm Optimization Approach Considering Distribution Generation Penetration and Fault Current Limiter Placement

This article is treated with the protection coordination of bidirectional overcurrent relays (OCRs) in distribution power systems to obtain the best performance at a minimum total time dial setting (TDS). High penetration of distribution generation (DG) into distribution systems may significantly affect the protective system; since they increase the short-circuit level by injecting an extra share of fault current, it results in changes in the power/current flow direction. Accordingly, the fault current limiter (FCL) devices should be utilized to mitigate the short-circuit current. FCLs and DGs both will disturb the protection settings of protective equipment. The characteristics and the set point of inverse time relays depend on the fault clearing time (FCT) and the pick-up current. Therefore, an optimization approach is required to minimize the FCTs and TDSs of main relays and backup ones to protect all equipment. Moreover, this strategy selects the optimal placement of the FCL cooperatively according to the cost minimization. The optimization approach is implemented based on the particle swarm optimization (PSO) method within an extra parameter to improve the convergence speed and find the global solution, especially at initial iterations. The proposed optimization approach named developed PSO (DPSO) is tested on the IEEE 14-bus system and compared with some existing methods. Finally, the analysis and evaluations present the effectiveness of the best current transformer (CT) settings with the minimum response time.

Storage method for customer preference information in e-commerce platform based on similarity matching algorithm

In order to overcome the problems of small IOPs parameters, long response time and inaccuracy of customer access key information similarity in existing e-commerce platform information storage, this paper proposes a new method of customer preference information storage in e-commerce platform. In this method, crawler algorithm is used to collect customer access information, and key customer access information is extracted by setting indicators. LibSVM classifier is used to classify customer access key information, similarity matching algorithm is used to calculate the similarity of customer access key information, and collaborative filtering recommendation algorithm is used to recommend relevant information for customers, so as to realise the storage of customer preference information in e-commerce platform under the background of artificial intelligence. Experiments show that the proposed storage method greatly improves the IOPs parameters, and the minimum storage response time is 21 ms, which fully shows that the proposed method has better storage performance.

Storage method for customer preference information in e-commerce platform based on similarity matching algorithm

In order to overcome the problems of small IOPs parameters, long response time and inaccuracy of customer access key information similarity in existing e-commerce platform information storage, this paper proposes a new method of customer preference information storage in e-commerce platform. In this method, crawler algorithm is used to collect customer access information, and key customer access information is extracted by setting indicators. LibSVM classifier is used to classify customer access key information, similarity matching algorithm is used to calculate the similarity of customer access key information, and collaborative filtering recommendation algorithm is used to recommend relevant information for customers, so as to realise the storage of customer preference information in e-commerce platform under the background of artificial intelligence. Experiments show that the proposed storage method greatly improves the IOPs parameters, and the minimum storage response time is 21 ms, which fully shows that the proposed method has better storage performance.

Zinc Oxide Thin-Film Transistor with Catalytic Electrodes for Hydrogen Sensing at Room Temperature

Palladium-Titanium (Pd-Ti)/n-ZnO Schottky contact based thin-film transistor (TFT) was fabricated on a thermally oxidized n-Si substrate with a compatible fabrication process and the hydrogen-sensing response was evaluated under room temperature (RT) without the presence of any heating element which reduces design complexity as well as power consumption significantly. Structural, mechanical, chemical and optical properties of the RF (Radio Frequency) sputtered ZnO thin film with 150 nm thickness as a gas sensing element have been investigated. Electrical characteristics and concentration dependent (500 ppm to 4500 ppm) RT sensor response (in the context of increasing drain current as the Schottky barrier height decreases) of the fabricated device with the maximum sensitivity of 70.8% (at 4500 ppm H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) including a minimum response time of 20 s (at 4500 ppm H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) and recovery time of 40 s (at 500 ppm H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) have been demonstrated. Sufficiently impressive performance of the sensor has been observed with respect to repeatability, selectivity, time-depending stability and humidity analysis at RT.

Portability and Acceleration of Deep Learning Inferences to Detect Rapid Earthquake Damage From VHR Remote Sensing Images Using Intel OpenVINO Toolkit

Accurate and effective rapid detection in remote sensing images plays an extremely important role in natural disasters, landslides, flooding problems, and military defense. Specially in earthquake damage detection, time critical tasks, such as performing the damage assessment or providing immediate delivery of relief assistance, require responses for swift decisions. To minimize response time, this article proposes the portability and acceleration of the inferences process on a deep convolution neural network model to detect rapid earthquake damage on very high resolution (VHR) remote sensing data using the Intel OpenVINO toolkit. This model, based on a previous work, has been optimized for high performance computing techniques to minimize processing times. Along with the immediate responses, the correctness of the result is also very crucial parameter for such type of applications. For analysis and performance, we use the GeoEye-1 VHR disaster images of the Haiti earthquake occurred in year 2010. Experimental results show that the optimized model provides good accuracy for damage detection with significant execution speed on CPU+GPU using mixed precision technique compared to other previous work. Moreover, OpenVINO toolkit presents satisfactory performance in the inference stage compared to other toolkits, such as TensorFlow serving, using asynchronous inference mode executed on an Intel Xeon processor and Intel Movidius NCS as coprocessor.

Learning-Based Computation Offloading Approaches in UAVs-Assisted Edge Computing

Technological evolutions in unmanned aerial vehicle (UAV) industry have granted UAVs more computing and storage resources, leading to the vision of UAVs-assisted edge computing, in which the computing missions can be offloaded from a cellular network to a UAV cloudlet. In this paper, we propose a UAVs-assisted computation offloading paradigm, where a group of UAVs fly around, while providing value-added edge computing services. The complex computing missions are decomposed as some typical task-flows with inter-dependencies. By taking into consideration the inter-dependencies of the tasks, dynamic network states, and energy constraints of the UAVs, we formulate the average mission response time minimization problem and then model it as a Markov decision process. Specifically, each time a mission arrives or a task execution finishes, we should decide the target helper for the next task execution and the fraction of the bandwidth allocated to the communication. To separate the evaluation of the integrated decision, we propose multi-agent reinforcement learning (MARL) algorithms, where the target helper and the bandwidth allocation are determined by two agents. We design respective advantage evaluation functions for the agents to solve the multi-agent credit assignment challenge, and further extend the on-policy algorithm to off-policy. Simulation results show that the proposed MARL-based approaches have desirable convergence property, and can adapt to the dynamic environment. The proposed approaches can significantly reduce the average mission response time compared with other benchmark approaches.

Impact of timing in post-warning prepositioning decisions on performance measures of disaster management: A real-life application

This research analyzes the impact of timing of post-warning and pre-disaster stock prepositioning decisions in disasters with an advance warning, such as hurricanes. From the warning time to the landfall time, disaster planners receive updated information regarding the hurricane's trajectory and the potential target region iteratively. The planners should decide when the best trigger time (TT) is to start the preparedness activities (prepositioning stocks of emergency goods). The quality of a given TT is evaluated concerning two performance measures: (i) the total logistics cost in the preparedness and response phases; and (ii) the minimum response time (RT) needed to transfer goods from the response facilities to affected areas in the response phase.A stochastic optimization model is designed to determine the best TT, preparedness decisions, and response operations. The computational complexity of the model is reduced using a graph-theoretic conversion. We test the converted model on experimental data based on major hurricanes from 2001 to 2017 on the United States southeastern coast. Sensitivity analysis of the model shows that delaying TT makes a non-linear reduction in the total logistics cost and non-linear increment in the shortest possible RT. The optimal TT makes the best compromise between the total logistics cost and the speed of the response operations, according to the preference of disaster planners.

Optimal dispatching method based on actual ramp rates of power generation units for minimising load demand response time

This study proposes a method to determine optimal load demand changes of power generation units, in order to complete a total load demand change for power grids in the minimum response time. The main feature is to exploit the information of actual ramp rates, which are the maximum changing speeds of generated powers in practice from power generation units. The proposed method is composed of two steps. First, actual ramp rates are estimated from historical data based on the piece-wise linear representations of generated powers. Second, optimal values of load demand changes are determined based on the solutions to an optimisation problem minimising the response time. A main challenge is on the uncertainties of estimated actual ramp rates and their effects on the response time. This challenge is resolved by exploiting Bayesian estimators to yield posterior probability distributions of the estimated actual ramp rates, from which the optimal response time and its confidence interval are obtained. Numerical examples are provided to support the proposed method and compare with existing methods.

Artificial Intelligent Load Balance Agent on Network Traffic Across Multiple Heterogeneous Distributed Computing Systems

Distributed computing is a very common term in the field of computer science and also a very general computing paradigm which is being used by almost all the scientific and industrial computing applications. All the information technology applications have based upon the use of the distributed computing. It came on demand day by day with the evolution high speed internet. Distributed services are generally deployed on different servers somewhere at a particular geographical region and its consumers are mostly spread over the world wide. Most commercial application now a days providing its platform to access multiple different distributed computing service to provide a set of different consumer services to their customers like an example of ecommerce giant vendors Amazon and PayTM are providing many consumer services like Shopping, Bill payment, Purchasing travel ticket and Top-up mobile balance etc. on a single platform. Since the needs of customers increases day by day and they want as much as services on a single platform, so there comes up of accessing many distributed services on a single platform over some network. As soon as this kind of computer applications started to come into the commercial market, the number of its users started to increase exponentially over the time. By the time it became very difficult to handle and respond to its various users at the same. There is a high risk for the organizations of losing their customers, if they are unable to provide services with a minimal possible response time. If an application has comparatively less features but its response is fast, then it became a widely used product in the market. Hence almost all the organization invests a separate large amount of money annually on the maintenance of its computer applications to boost its performance and response. Even though there are still many challenges in this field, since organization has a limited computing and networking resources to provide their services to its users which are in millions in number. Hence, it has been always a major area of research for many engineers and scientists from all over the world to implement and deliver an algorithm that would be able to solve all these types of problem and provide the services up to the customer satisfaction. This research paper provides the systematic literaraute review on different and best of the till known load balancing system which is artificial intelligence enough to determine the resource capacity of the host server to allow only permitted number of jobs at the same time which falls under resource constraint of the server.

Maintenance Decision-Making of an Urban Rail Transit System in a Regionalized Network-Wide Perspective

The networked operation of Urban Rail Transit (URT) brings the new challenge of network-wide maintenance. This research focuses on the URT Network-Wide Maintenance Decision-Making Problem (URT-NMDP), including regionalized maintenance network design and maintenance resource allocation. In this work, we proposed a bi-objective integer programming model that integrates the characteristics of set coverage and P-median models, resulting in the regionalized maintenance network design model. Some critical factors are considered in the model, such as the importance of node, the maximum failure response time, and maintenance guarantee rules. We designed a NSGA-II based algorithm to solve the model. Moreover, due to the uncertainty of failures in the URT network, we developed the method of allocating maintenance resources based on Monte Carlo simulation to strengthen the reliability of the regionalized maintenance network. With the model and algorithm presented in this work, we obtained Pareto optimal solutions of URT-NMDP, i.e., URT network-wide maintenance planning schemes, which include the number and location of maintenance points, the allocation of demand points, and the amount of maintenance units. Finally, a real-world case is studied to evaluate the operating performance of these schemes for verifying the method in our paper. The results of the case study demonstrate that the reasonable and tested-in-practice maximum failure response time is the precondition for the efficient URT maintenance network. The maintenance scheme considered the weighted importance of node shows the optimal performance, with the shortest overall maintenance path and the minimum average failure response time and investment cost on maintenance resources.

Ultracompact all-optical full adders using an interference effect based on 2D photonic crystal nanoring resonators

Given the special place of hybrid logic circuits such as all-optical full adders in next-generation digital systems, a new kind of these structures using two-dimensional (2D) photonic crystals (2D-PC) is designed and simulated herein. The proposed structure is made of a hexagonal nanoring resonator (NRR), a coupling rod, and several waveguides. In this all-optical full adder, the mechanism of the interference effect in the PCs is used to simplify and minimize the structure. To make the structure flexible, the radius of the dielectric rod in the whole structure and the NRR are considered based on a lattice constant of 0.2a and 0.04a, respectively. The structure is operated at a wavelength of 1550 nm, considering the value of the power entering the waveguides and that exiting the Carry and Sum ports. To analyze the all-optical full adder, the plane-wave expansion method and finite-difference time-domain method are applied respectively to calculate the bandgap diagram and obtain the transmission and propagation of the optical field. In the proposed structure, the contrast ratio at the Carry and is been investigated in a unique and novel way, yielding values of 10.68 and 9.03 dB, respectively. In addition, the maximum and minimum response time for the Carry and Sum are obtained as 1.6 and 0.75 ps, respectively. The total footprint of the structure is about 183 µm2. Due to its ultracompact size, low power consumption, fast response time, and simple structure, this all-optical full adder is suitable for use in low-power optical integrated circuits.

Effect of molecular weight on electro – optic switching times of liquid crystal polymer doped with cobalt oxide nanoparticles

Nonlinearity liquid crystal cell which doped with Ferroelectric nanoparticles characters studied in this paper according to their eclectic absorption. Polysiloxin series with side chain liquid polymers used with side chain polymers and molecular weight ran This study consternate on polymers electro.optic characteristic with various molecular weights by using the mesogenic units and a stable polysiloxane.Suspension sensitivity or Electro-visual response to the mark of the used electric field, a real character to electric liquid crystals. The addition of Co3O4, nanoparticles to polymer rise dielectric anisotropy and minimize response times. It observed that the voltages rises with molecular weight rising, and when intensity value rise we gain voltage operating.Spectrometer used infrared measurements to locate the orientational order parameters, for liquid crystals with a side series. The orientational order parameters rely on temperature with various molecular weights and the threshold voltage.

A hybrid optimization method for reallocation of mobile resources

In this paper, we propose a hybrid optimization method to compute a reallocation of ambulances to obtain improved response times. As we want to minimize response times by changing ambulances allocations, we develop a hybrid algorithm based on a genetic algorithm, with randomized ambulances configurations as population individuals. Also, we embed into the genetic algorithm discrete event simulations to model the reporting, assignment, travel, and attendance processes. We later find that the algorithm optimizes the response times for simulated events, even though these times don’t yet compare to response times found in real data. So, we need to evaluate any improvement in real response times. As a study case, we use data from 2014 to 2017 provided by the health authority of Bogotá, Colombia, that contains real values of emergency medical incidents, and the quantity and type of ambulances that attended such incidents.

Hierarchical siting of macro fire station and micro fire station

With the rapid increase of city building density, public emergency service system for providing fire services faces increasing challenge in reducing the loss of lives and property, especially for the reduction of massive casualties in fire accidents. For obtaining a higher benefit from public service facilities, GIS-based techniques such as location optimization are commonly used. However, as a special facility, fire emergency facilities are quite particular in siting and providing services, and they have their unique demands including specific response time, benefit maximization, workload balancing and cost minimization; traditional optimization methods for fire facility siting are difficult to account for all of these objectives. Furthermore, the public emergency services agencies in China are implementing a plan to establish a hierarchical fire service system by siting fire stations with different capacities, and under this context, the general covering models with the same level of facilities are limited in their effectiveness. Therefore, this paper proposes a hierarchical covering model which takes into account the different characteristics of different levels of fire facilities (i.e. macro fire station and micro fire station). The case study of Nanjing city proves that our model is effective in practical applications of emergency services optimization.

Healable, highly sensitive LPG sensor based on Ni0.4Zn0.6Fe2O4 nanohybrid grown by autocombustion process

In this study, dual-layered self-healable LPG sensor which can successfully re-establish both of its structural and sensing properties after an external deformation occurred, has been reported. Here Ni0.4Zn0.6Fe2O4 (NZF) works as a highly sensitive LPG sensor which can detect LPG well below the lowest explosion limit (LEL) efficiently. The self-healing phenomenon has been achieved by urea-formaldehyde (U-F) microcapsules encapsulating NZF and linseed oil. The dual-layered self-healable sensing film is prepared by forming a lower layer using urea-formaldehyde (U-F) microcapsules capturing linseed oil and NZF, serving as healing layer, and an upper layer using NZF aiding sensing phenomenon. LPG sensing parameters like sensitivity, sensor response, recovery time and response time were investigated in case of both uncracked sample and healed sample to get a clear idea of how much sensing properties were recovered after healing took place. The minimum response and recovery times of the uncracked film corresponding to 0.5 vol.% LPG concentrations were found to be 28.08 s and 52.46 s respectively, whereas, the same after healing were as 34.39 s and 45.17 s respectively. After healing, ∼ 86.4 % sensitivity was restored. This is the first dual-layered microcapsule-based LPG sensor, which has healing property.

Artificial intelligence in trauma systems

Local trauma care and regional trauma systems are data-rich environments that are amenable to machine learning, artificial intelligence, and big-data analysis mechanisms to improve timely access to care, to measure outcomes, and to improve quality of care. Pilot work has been done to demonstrate that these methods are useful to predict patient flow at individual centers, so that staffing models can be adapted to match workflow. Artificial intelligence has also been proven useful in the development of regional trauma systems as a tool to determine the optimal location of a new trauma center based on trauma-patient geospatial injury data and to minimize response times across the trauma network. Although the utility of artificial intelligence is apparent and proven in small pilot studies, its operationalization across the broader trauma system and trauma surgery space has been slow because of cost, stakeholder buy-in, and lack of expertise or knowledge of its utility. Nevertheless, as new trauma centers or systems are developed, or existing centers are retooled, machine learning and sophisticated analytics are likely to be important components to help facilitate decision-making in a wide range of areas, from determining bedside nursing and provider ratios to determining where to locate new trauma centers or emergency medical services teams.

Offloading Decision in Edge Computing for Continuous Applications Under Uncertainty

Edge computing (EC) is an emerging paradigm to push sufficient computation resources towards the network edge, improving application performance significantly by offloading applications to the edge computing node. We investigate continuous application offloading decision in EC, for which it is uncertain how users operate continuous applications and how long continuous applications last before completion. That means some characteristics of continuous applications, e.g., the number of user operations, the uploading and downloading data size for offloading computation of each user operation, and the number of central processing unit (CPU) cycles required to execute computation of each user operation, are unknown when making offloading decision. In this scenario, an energy consumption constrained average response time minimization problem among multiple users for continuous applications under uncertainty is formulated. To tackle this problem, we propose the Response Time-Improved Offloading algorithm with Energy Constraint (RTIOEC) to make offloading decision with fewer characteristics of applications. The evaluation results show that the RTIOEC algorithm achieves comparatively short average response time of continuous applications while satisfying the energy consumption constraint with a predefined upper bound of violation probability. Our results demonstrate the practicality of the RTIOEC algorithm in offloading decision in EC for continuous applications under uncertainty.

A Method to Improve Distribution System Reliability Using Available Mobile Generators

This article presents a new method for the minimization of reliability costs of distribution systems (DSs) through switch reallocation, using genetic algorithms to find the best solutions for the switch placement problem, when a mobile emergency generator (MEG) is available for use. MEGs can be used as versatile reliability enhancers in a DS because differently from fixed distributed generation, they have mobility and can be easily connected in different parts of a feeder during different faults. An MEG prepositioning strategy is also presented, which aims to obtain the near optimal initial position for the MEG in the network, focusing to minimize response time. It can be checked if the improvement on reliability indices can compensate for the costs associated with the MEG. Simulations on a real feeder are done, and the results show significant improvements on reliability costs.