Location decision of low-altitude service station for transfer flight based on modified immune algorithm

Research on multiobjective optimization (MO) becomes one of the hot points of intelligent computation. Compared with evolutionary algorithm, the artificial immune system used for solving MO problems (MOPs) has shown many good performances in improving the convergence speed and maintaining the diversity of the antibody population. However, the simple clonal selection computation has some difficulties in handling some more complex MOPs. In this paper, the simple clonal selection strategy is improved and a novel immune clonal algorithm (NICA) is proposed. The improvements in NICA are mainly focus on four aspects. 1) Antibodies in the antibody population are divided into dominated ones and nondominated ones, which is suitable for the characteristic of one multiobjective optimization problem has a series Pareto-optimal solutions. 2) The entire cloning is adopted instead of different antibodies having different clonal rate. 3) The clonal selection is based on the Pareto-dominance and one antibody is selected or not depending on whether it is a nondominated one, which is different from the traditional clonal selection manner. 4) The antibody population updating operation after the clonal selection is adopted, which makes antibody population under a certain size and guarantees the convergence of the algorithm. The influences of the main parameters are analyzed empirically. Compared with the existed algorithms, simulation results on MOPs and constrained MOPs show that NICA in most problems is able to And much better spread of solutions and better convergence near the true Pareto-optimal front.

This study introduces a distributed e-learning system on power line network. E-learning system, in general, consists of learning management system (LMS) for managing users and courses, learning content management system (LCMS) for offering educational contents to learners and course authoring for course and content design. Today, there is a great deal of requests, issued simultaneously by worldwide users from any place at any time, for e-learning systems. Because of enormous amount of requests and simultaneous connections of users, server's performance and response time may degrade. Power line communication has high influence and is high speed medium that can be utilized as platform for e-learning. On the other hand, it is possible to implement distributed e-learning system on a power line network to distribute load of requests on multiple servers and improve their performances. In this paper, we propose software architecture and network design for distributed e-learning system on power line network.

Towards cost efficient mobile service and information management in ubiquitous environment with cloud resource scheduling

Optimization of the wire bonding process of an integrated circuit (IC) is a multi-objective optimization problem (MOOP). In this research, an integrated multi-objective immune algorithm (MOIA) that combines an artificial immune algorithm (IA) with an artificial neural network (ANN) and a generalized Pareto-based scale-independent fitness function (GPSIFF) is developed to find the optimal process parameters for the first bond of an IC wire bonding. The back-propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and the multi-responses, and is applied to generate the multiple response values for each antibody generated by the IA. The GPSIFF is then used to evaluate the affinity for each antibody and to find the non-dominated solutions. The “Error Ratio” is then applied to measure the convergence of the integrated approach. The “Spread Metric” is used to measure the diversity of the proposed approach. Implementation results show that the integrated MOIA approach does generate the Pareto-optimal solutions for the decision maker, and the Pareto-optimal solutions have good convergence and diversity performance.

A novel immune algorithm is suggested for finding Pareto-optimal solutions to multiobjective optimization problems based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. In the proposed algorithm, a randomly weighted sum of multiple objectives is used as a fitness function, and a local search algorithm is incorporated to facilitate the exploitation of the search space. Specifically, a new truncation algorithm with similar individuals (TASI) is proposed to preserve the diversity of the population. Also, a new selection operator is presented to create the new population based on TASI. Simulation results on seven standard problems (ZDT2, ZDT6, DEB, VNT, BNH, OSY and KIT) show that the proposed algorithm is able to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the vector immune algorithm and the elitist non-dominated sorting genetic system.

Background and Purpose: There are no contemporary national-level data on Emergency Medical Services (EMS) response times for suspected stroke in the United States (US). Because effective stroke treatment is time-dependent, we characterized response times for suspected stroke, and examined whether they met guideline recommendations. Methods: Using the National EMS Information System dataset, we included 911 calls for patients ≥ 18 years with an EMS provider impression of stroke. We examined variation in the total EMS response time by dispatch notification of stroke, age, sex, race, region, time of day, day of the week, as well as the proportion of EMS responses that met guideline recommended response times. Total EMS response time included call center dispatch time (receipt of call by dispatch to EMS being notified), EMS dispatch time (dispatch informing EMS to EMS starts moving), time to scene (EMS starts moving to EMS arrival on scene), time on scene (EMS arrival on scene to EMS leaving scene), and transport time (EMS leaving scene to reaching treatment facility). Results: We identified 184,179 events with primary impressions of stroke (mean age 70.4 ± 16.4 years, 55% male). Median total EMS response time was 36 (IQR 28.7–48.0) minutes. Longer response times were observed for patients aged 65–74 years, of white race, females, and from non-urban areas. Dispatch identification of stroke versus “other” was associated with marginally faster response times (36.0 versus 36.7 minutes, p < 0.01). When compared to recommended guidelines, 78% of EMS responses met dispatch delay of <1 minute, 72% met time to scene of <8 minutes, and 46% met on-scene time of <15 minutes. Conclusions: In the United States, time from receipt of 9-1-1 calls to treatment center arrival takes a median of 36 minutes for stroke patients, an improvement upon previously published times. The fact that 22%–46% of EMS responses did not meet stroke guidelines highlights an opportunity for improvement. Future studies should examine EMS diagnostic accuracy nationally or regionally using outcomes based approaches, as accurate recognition of prehospital strokes is vital in order to improve response times, adhere to guidelines, and ultimately provide timely and effective stroke treatment.

Objective To investigate the epidemiological features of out-of-hospital cardiac arrest (OHCA) in Zhejiang and to analysis factors associated with outcomes for providing evidence on improving the success rate of cardiopulmonary resuscitation (CPR). Methods Clinical data of 493 patients with OHCA collected from the emergency department (ED) of Zhejiang Provincial People's Hospital, Ningbo Emergency Medical Service Center and Shaoxing Emergency Medical Service Center from January 2012 to August 2016 were analyzed retrospectively. All the data were recorded following the Utstein style included causes of arrest, location of arrest, first arrest rhythm, witnessed by bystanders, bystander CPR, pre-hospital defibrillation, pre-hospital intubation, pre-hospital epinephrine administration, emergency medical services (EMS) response time, return of spontaneous circulation (ROSC) at scene/enroute, ROSC at ED, admission to hospital, survival to hospital discharge and neurological outcomes at discharge. Factors associated with 30 days survival rate of patients with OHCA were analyzed. Results 493 patients were enrolled, of whom 342 were male and 151 were female. The average age was (58.8±21.4) years. The causes of arrests consisted of cardiac etiology (219 cases), trauma (155 cases), respiratory disease (22 cases), drowning (19 cases), electrocution (8 cases) and others (70 cases). Most of the events occurred at home (65.1%) and public places (22.7%). 55.2% patients were witnessed by bystanders while bystander CPR was performed in only 2.6% cases. Asystole was the predominant rhythm (78.7%) observed by the ambulance crew at the arrest site while only 5.5% first arrest rhythms were ventricular fibrillation/ventricular tachycardia (VF/VT). Only 6.9% patients underwent pre-hospital defibrillation. Pre-hospital intubations were attempted in 16.4% patients. Epinephrine was administered in 56.4% patients at scene or in ambulances. EMS response time was (13.6±8.0) minutes. 4.5% patients had ROSC at scene/enroute and 7.7% had ROSC at ED. Only 9.7% patients were admitted to hospital and 1.2% discharged alive. 0.8% patients were still in hospital on 30th day. The 30-day survival rate was 2.0% (10/493) and only 0.8% patients had neurologically favorable survivals [with the cerebral performance category (CPC) score of 1 or 2]. Witnessed by bystanders (3.31% vs. 0.45%), VF/VT as the first arrest rhythm (7.41% vs. 1.72%), bystander CPR (15.38% vs. 1.67%), pre-hospital defibrillation (8.82% vs. 1.53%) and EMS response time < 10 minutes (3.57% vs. 0.74%) could improve 30-day survival rate of OHCA significantly (all P < 0.05). Conclusion Resuscitation survival of OHCA in Zhejiang province was unsatisfactory. Improvements are required in series aspects of OHCA survival chain. Key words: Out-of-hospital cardiac arrest; Cardiopulmonary resuscitation; Epidemiology; Outcome; Emergency medical services response time

In this paper, we develop a multi-objective model to optimally control the lead time of a manufacturing system, using goal attainment method. The manufacturing system is modelled as a new queueing system based on the failure rate of products, in which the products may have quality problems and need to be repaired in each service station. It is assumed that the product order arrives according to a Poisson process. In each service station, there is only one server (machines) with exponentially distributed processing time, in which the service rate (capacity) is controllable. The maintenance time of manufacturing and assembly operations is exponentially distributed. By using the total probability decomposition and Markov process method, the Queue Length Distribution, the recursion expression of the equilibrium distribution and the mean waiting time are obtained. Then, we develop a multi-objective optimal control problem, in which the average lead time, the maintenance costs and the operating costs of the system per period are minimized using goal attainment method.

Mobile edge computing aims at improving application response time and energy efficiency by deploying data processing at the edge of the network. Due to the proliferation of Internet of Things and interactive applications, the ever-increasing demand for low latency calls for novel approaches to further pushing the envelope of mobile edge computing beyond existing task offloading and distributed processing mechanisms. In this paper, we identify a new tradeoff between Quality-of-Result (QoR) and service response time in mobile edge computing. Our key idea is motivated by the observation that a growing set of edge applications involving media processing, machine learning, and data mining can tolerate some level of quality loss in the computed result. By relaxing the need for highest QoR, significant improvement in service response time can be achieved. Toward this end, we present a novel optimization framework, MobiQoR, which minimizes service response time and app energy consumption by jointly optimizing the QoR of all edge nodes and the offloading strategy. The proposed MobiQoR is prototyped using Parse, an open source mobile back-end tool, on Android smartphones. Using representative applications including face recognition and movie recommendation, our evaluation with real-world datasets shows that MobiQoR reduces response time and energy consumption by up to 77% (in face recognition) and 189.3% (in movie recommendation) over existing strategies under the same level of QoR relaxation.

In this paper a binary integer linear programming model is proposed for scheduling tour of service personnel in aftersale service process. The model aims to minimize the service personnel’s overall travel time of visiting all customers with additional side conditions that customers should be served by first come first serve (FCFS) priority basis within specified service response time. These side conditions are important strategies in present era of time-based competition. The objective and side conditions are considered for both profitability and effectiveness. Indeed, for given service response time, optimal tour plan would minimize the quantity of service personnel. This decreases the hiring cost of service personnel and thus increases the profitability of after-sale service process. On the other hand, for given quantity of service personnel, optimal tour plan would reduce the service response time. Responsive after-sale service on FCFS priority basis will increase the customer satisfaction, which in turn leads to more repeat orders and hence improves the profitability. The proposed model can capture all realistic constraints of service management process, such as time windows, skill requirement, balanced workload distribution, etc. A numerical illustration is included to demonstrate the effectiveness of the proposed model in conducting economic analysis of an after-sale service process.

Financial technology (Fintech) has emerged as a transformative force in the global financial landscape, offering innovative solutions to longstanding challenges. This study delves into the Egyptian context to assess the profound impact of fintech on financial inclusion—a crucial factor for economic development and social equity. Through empirical analysis, this research endeavors to unveil the multifaceted ways in which fintech is reshaping the financial landscape in Egypt, particularly regarding expanding access to financial services for underserved and unbanked populations. The data was collected from financial statements of banks over the period of 2018 – 2022, with keywords identified in annual reports to create dummy variables for analysis and analyzing it by STATA through logistic regression to determine whether there is a significant impact or not. The results reveal that the research supports the hypothesis that fintech has a significant effect on financial inclusion, as well as on reliability, availability, accessibility, and efficiency of electronic government services. Thus, this study contributes valuable insights for the Egyptian government to develop electronic payment systems and regulations that maximize the availability, accessibility, efficiency, and responsiveness of electronic government services, ultimately improving the lives of the nation’s residents. Keywords: Fintech, E-payment bills, Modes of payment, E-government system, Financial Inclusion

High-quality transit service is a vital aspect of any modern city. When unexpected interruptions to the transit service occur, they reduce the quality of service provided to the public. One of the main strategies that is employed to deal with rail service interruptions is “bus bridging,” whereby buses from scheduled services are deployed to offer shuttle services. Very few efforts are found in the literature that have investigated this policy’s effectiveness. Therefore, this study aims at exploring the different aspects and impacts of retracting buses from scheduled services in response to subway and streetcar service interruptions in Toronto. It explores the size of the deployment, as well as the system response and recovery times using detailed subway and streetcar shuttle service reports collected in 2015. The paper shows remarkable fluctuations not only in the utilized number of shuttle service buses over time, but also on the service response and recovery times.

Objective: While emergency medical service (EMS) response times (ERT) remain a leading measure of system performance in many developed countries, relatively few studies have explored the factors associated with meeting benchmark performance for potentially time critical incidents. The purpose of this study was to identify system-level and patient-level factors associated with ERT, which are readily available at the time of ambulance dispatch. Methods: Between July 2009 and June 2014, we included data from 1,000,458 EMS responses to time critical “lights and sirens” incidents in Melbourne, Australia. The primary outcome measure was ERT, defined as the time from emergency call to the arrival of the first EMS team on scene. Quantile regression models were used to identify system-level and patient-level factors associated with 10-percentile intervals of ERT. Results: The median ERT was 10.6 minutes (IQR: 8.1–14.0), increasing from 9.6 minutes (IQR: 7.6–12.5) in 2009/10 to 11.0 minutes (IQR: 8.4–14.7) in 2013/14 (p < 0.001). System-level factors independently associated with the 90th percentile ERT were distance to scene, activation time, turnout time, case upgrade, hour of day, day of week, workload in the previous hour, ambulance skill set, priority zero case (e.g., suspected cardiac or respiratory arrest), and average hospital delay time in the previous hour. Patient-level factors such as age, gender, chief medical complaint, and severity of complaint were also significantly associated with ERT. Conclusions: System-level and patient-level factors available at the time of ambulance dispatch are useful predictors of ERT performance, which could be used to improve the timeliness of EMS response.

The integration of software defined networking (SDN) and network function virtualization (NFV) is a promising approach to support flexible service deployment and provisioning. In particular, NFV enables a network service to be represented as one network service function chain, which is composed of several virtual network functions in a specific order. However, with regard to service provisioning in multidomain SDN networks, virtual network functions are deployed in several different SDN domains due to limited resource capacity of each SDN domain. It results in the network service deployment complexity increase and causes additional operating cost. On the other hand, most current network service deployment proposals are limited to static user request scenarios. How to effectively deploy network service across multiple SDN domains to satisfy dynamic service demands of end users is a challenging problem. To address the aforementioned challenge, in this paper, we study the problem of dynamic network service deployment across multiple SDN domains. Specifically, we first design a novel service deployment framework to support dynamic deployment and optimization of virtual network function in multidomain SDN networks. Then, we formulate the problem as a multiobjective optimization problem with the target of jointly minimizing resource consumption cost and operating cost. Furthermore, we transform the multiobjective optimization problem into a single objective optimization model and present an efficient heuristic service deployment approach to solve it by leveraging Dijkstra algorithm. The simulation results demonstrate that the proposed service deployment method is efficient and outperforms comparison methods in terms of service rejection ratio, resource cost, and operating cost.

Recently, multiobjective immune algorithms (MOIAs) become popular, which are designed for multiobjective optimization problems (MOPs). However, most existing MOIAs put more attention on maintaining diversity as the used clonal selection strategy will allocate more cloning for the sparse areas, which may hamper the convergence to speed to the optimal Pareto front, especially for some complicated MOPs. To alleviate the phenomenon mentioned above, we propose a dynamic mechanism into traditional MOIAs in this paper, aiming to balance convergence and diversity, called BCD-MOIA. First, MOP will be decomposed into several single subproblems by decomposition method, and then these subproblems will be optimized simultaneously. Second, we propose a novel measure metric instead of the crowding distance to assign the clone number for each solution, which includes two main parts. The first part focuses on the diversity performance, i.e., the perpendicular distance between solution and its associated weight vectors. The second part uses the aggregated function values quantified by the decomposition method, which is more efficient for accelerating the convergence speed and maintaining diversity as well. Moreover, a dynamic mechanism is performed during the whole evolutionary process, focusing on diversity and convergence at different stages. By this way, our proposed algorithm can tradeoff the performance on convergence and diversity dynamically. The effectiveness of our proposed algorithm BCD-MOIA is validated by comparing with three competitive MOIAs and three multi-objective evolutionary algorithms for tackling two sets of complicated problems.