Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yan Pan ◽  
Shining Li ◽  
Qianwu Chen ◽  
Nan Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Load Capacity ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Promising Alternative ◽  
Logistics Industry ◽  
Last Mile ◽  
Delivery Scheduling ◽  
Schedule Approach

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

scholarly journals Prenatal Nutritional Strategies to Reduce the Risk of Preterm Birth

2021 ◽  
pp. 1-9
Author(s):  
Karen Patricia Best ◽  
Judith Gomersall ◽  
Maria Makrides
Keyword(s):  
Preterm Birth ◽  
Large Scale ◽  
Cochrane Review ◽  
Cost Effective ◽  
Omega 3 ◽  
Promising Alternative ◽  
Nutritional Interventions ◽  
Dietary Nutrients ◽  
Preterm Babies ◽  
Nutritional Strategies

Worldwide, around 15 million preterm babies are born annually, and despite intensive research, the specific mechanisms triggering preterm birth (PTB) remain unclear. Cost-effective primary prevention strategies to reduce PTB are required, and nutritional interventions offer a promising alternative. Nutrients contribute to a variety of mechanisms that are potentially important to preterm delivery, such as infection, inflammation, oxidative stress, and muscle contractility. Several observational studies have explored the association between dietary nutrients and/or dietary patterns and PTB, often with contrasting results. Randomized trial evidence on the effects of supplementation with zinc, multiple micronutrients (iron and folic acid), and vitamin D is promising; however, results are inconsistent, and many studies are not adequately powered for outcomes of PTB. Large-scale clinical trials with PTB as the primary outcome are needed before any firm conclusions can be drawn for these nutrients. The strongest evidence to date for a nutritional solution exists for omega-3 long-chain polyunsaturated fatty acids (LCPUFAs), key nutrients in fish. In 2018, a Cochrane Review (including 70 studies) showed that prenatal supplementation with omega-3 LCPUFAs reduced the risk of PTB and early PTB (EPTB) compared with no omega-3 supplementation. However, the largest trial of omega-3 supplementation in pregnancy, the Omega-3 to Reduce the Incidence of Prematurity (ORIP) trial (<i>n</i> = 5,544), showed no reduction in EPTB and a reduction in PTB only in a prespecified analysis of singleton pregnancies. Exploratory analyses from the ORIP trial found that women with low baseline total omega-3 status were at higher risk of EPTB, and that this risk was substantially reduced with omega-3 supplementation. In contrast, women with replete or high baseline total omega-3 status were already at low risk of EPTB and additional omega-3 supplementation increased the risk of EPTB compared to control. These findings suggest that determining an individual woman’s PUFA status may be the most precise way to inform recommendations to reduce her risk of PTB.

scholarly journals An A pproach to Smart Parking A lgorithm using a nt Colony Optimization and Decision Tree Algorithm

2021 ◽  
Vol 10 (12) ◽  
pp. 58-63
Author(s):  
Ankita Yadav ◽  
◽  
Mohammad Arif ◽  
Keyword(s):  
Decision Tree ◽  
Traffic Congestion ◽  
Large Scale ◽  
Cost Effective ◽  
Road Accidents ◽  
The Past ◽  
Parking Management ◽  
Smart Parking ◽  
Pros And Cons ◽  
Result Analysis

This research is conducted in order to deal with the main problem of traffic congestion and road accidents that is basically caused because of the improper parking management. . Hence, it is important that cities have a well-managed parking system. In the past various researches has been done to design a suitable smart paring algorithm. However, each research had their own pros and cons. Our research leads to a smart algorithm that is secure and is convenient enough to develop a system that can be manage the available slots and can notify the users about the available parking slot beforehand to the client. The result analysis clearly shows that the algorithm proposed and designed is more accurate than other algorithms used in the past. The proposed algorithm is designed using ACO, decision tree, and GPS mapping. The idea of working on this research was to provide a solution that is cost effective, helps people on large scale and maintains the laws and order.

scholarly journals Community detection in large scale congested urban road networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260201
Author(s):  
Seyed Arman Haghbayan ◽  
Nikolas Geroliminis ◽  
Meisam Akbarzadeh
Keyword(s):  
San Francisco ◽  
Community Detection ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Large Scale ◽  
Cost Effective ◽  
Computational Time ◽  
Management Tools ◽  
Traffic Condition ◽  
Urban Networks

Traffic congestion in large urban networks may take different shapes and propagates non-uniformly variations from day to day. Given the fact that congestion on a road segment is spatially correlated to adjacent roads and propagates spatiotemporally with finite speed, it is essential to describe the main pockets of congestion in a city with a small number of clusters. For example, the perimeter control with macroscopic fundamental diagrams is one of the effective traffic management tools. Perimeter control adjusts the inflow to pre-specified regions of a city through signal timing on the border of a region in order to optimize the traffic condition within the region. The precision of macroscopic fundamental diagrams depends on the homogeneity of traffic condition on road segments of the region. Hence, previous studies have defined the boundaries of the region under perimeter control subjected to the regional homogeneity. In this study, a cost-effective method is proposed for the mentioned problem that simultaneously considers homogeneity, contiguity and compactness of clusters and has a shorter computational time. Since it is necessary to control the cost and complexity of perimeter control in terms of the number of traffic signals, sparse parts of the network could be potential candidates for boundaries. Therefore, a community detection method (Infomap) is initially adopted and then those clusters are improved by refining the communities in relation to roads with the highest heterogeneity. The proposed method is applied to Shenzhen, China and San Francisco, USA and the outcomes are compared to previous studies. The results of comparison reveal that the proposed method is as effective as the best previous methods in detecting homogenous communities, but it outperforms them in contiguity. It is worth noting that this is the first method that guarantees the connectedness of clusters, which is a prerequisite of perimeter control.

scholarly journals Roadmap for advanced aqueous batteries: From design of materials to applications

2020 ◽  
Vol 6 (21) ◽  
pp. eaba4098 ◽  
Author(s):  
Dongliang Chao ◽  
Wanhai Zhou ◽  
Fangxi Xie ◽  
Chao Ye ◽  
Huan Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Cost Effective ◽  
Material Design ◽  
Advanced Materials ◽  
Promising Alternative ◽  
Practical Applications ◽  
Limited Output ◽  
Key Issues ◽  
Fundamental Research ◽  
Aqueous Batteries

Safety concerns about organic media-based batteries are the key public arguments against their widespread usage. Aqueous batteries (ABs), based on water which is environmentally benign, provide a promising alternative for safe, cost-effective, and scalable energy storage, with high power density and tolerance against mishandling. Research interests and achievements in ABs have surged globally in the past 5 years. However, their large-scale application is plagued by the limited output voltage and inadequate energy density. We present the challenges in AB fundamental research, focusing on the design of advanced materials and practical applications of whole devices. Potential interactions of the challenges in different AB systems are established. A critical appraisal of recent advances in ABs is presented for addressing the key issues, with special emphasis on the connection between advanced materials and emerging electrochemistry. Last, we provide a roadmap starting with material design and ending with the commercialization of next-generation reliable ABs.

scholarly journals Improving Last-Mile Service Delivery Using Phone-Based Monitoring

2021 ◽  
Vol 13 (2) ◽  
pp. 52-82
Author(s):  
Karthik Muralidharan ◽  
Paul Niehaus ◽  
Sandip Sukhtankar ◽  
Jeffrey Weaver
Keyword(s):  
Service Delivery ◽  
Public Service ◽  
Program Implementation ◽  
Large Scale ◽  
Effective Means ◽  
Cost Effective ◽  
Percent Reduction ◽  
Last Mile ◽  
Scale Experiment ◽  
The Impact

Improving “ last-mile” public service delivery is a recurring challenge in developing countries. Could the widespread adoption of mobile phones provide a scalable, cost-effective means for improvement? We use a large-scale experiment to evaluate the impact of phone-based monitoring on a program that transferred nearly a billion dollars to 5.7 million Indian farmers. In randomly selected jurisdictions, officials were informed that program implementation would be measured via calls with beneficiaries. This led to a 7.8 percent reduction in the number of farmers who did not receive their transfers. The program was highly cost-effective, costing 3.6 cents for each additional dollar delivered. (JEL O13, O33, Q12, Q18)

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals Multi-Country Scale Assessment of Available Energy Recovery Potential Using Micro-Hydropower in Drinking, Pressurised Irrigation and Wastewater Networks, Covering Part of the EU

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 899
Author(s):  
Djordje Mitrovic ◽  
Miguel Crespo Chacón ◽  
Aida Mérida García ◽  
Jorge García Morillo ◽  
Juan Antonio Rodríguez Diaz ◽  
...  
Keyword(s):  
Drinking Water ◽  
Northern Ireland ◽  
Energy Recovery ◽  
Large Scale ◽  
Significant Proportion ◽  
Value Added ◽  
Cost Effective ◽  
Net Energy ◽  
Water Networks ◽  
Scale Assessment

Studies have shown micro-hydropower (MHP) opportunities for energy recovery and CO2 reductions in the water sector. This paper conducts a large-scale assessment of this potential using a dataset amassed across six EU countries (Ireland, Northern Ireland, Scotland, Wales, Spain, and Portugal) for the drinking water, irrigation, and wastewater sectors. Extrapolating the collected data, the total annual MHP potential was estimated between 482.3 and 821.6 GWh, depending on the assumptions, divided among Ireland (15.5–32.2 GWh), Scotland (17.8–139.7 GWh), Northern Ireland (5.9–8.2 GWh), Wales (10.2–8.1 GWh), Spain (375.3–539.9 GWh), and Portugal (57.6–93.5 GWh) and distributed across the drinking water (43–67%), irrigation (51–30%), and wastewater (6–3%) sectors. The findings demonstrated reductions in energy consumption in water networks between 1.7 and 13.0%. Forty-five percent of the energy estimated from the analysed sites was associated with just 3% of their number, having a power output capacity >15 kW. This demonstrated that a significant proportion of energy could be exploited at a small number of sites, with a valuable contribution to net energy efficiency gains and CO2 emission reductions. This also demonstrates cost-effective, value-added, multi-country benefits to policy makers, establishing the case to incentivise MHP in water networks to help achieve the desired CO2 emissions reductions targets.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Anti-meningococcal B vaccination in Italian adolescents: a cost-effective health opportunity

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D Panatto ◽  
P Landa ◽  
D Amicizia ◽  
P L Lai ◽  
E Lecini ◽  
...  
Keyword(s):  
Large Scale ◽  
Public Health Problem ◽  
Vaccination Strategy ◽  
Cost Effective ◽  
Dose Schedule ◽  
Developed Countries ◽  
Invasive Disease ◽  
International Setting ◽  
The Impact

Abstract Background Invasive disease due to Neisseria meningitidis (Nm) is a serious public health problem even in developed countries, owing to its high lethality rate (8-15%) and the invalidating sequelae suffered by many (up to 60%) survivors. As the microorganism is transmitted via the airborne route, the only available weapon in the fight against Nm invasive disease is vaccination. Our aim was to carry out an HTA to evaluate the costs and benefits of anti-meningococcal B (MenB) vaccination with Trumenba® in adolescents in Italy, while also considering the impact of this new vaccination strategy on organizational and ethics aspects. Methods A lifetime Markov model was developed. MenB vaccination with the two-dose schedule of Trumenba® in adolescents was compared with 'non-vaccination'. Two perspectives were considered: the National Health Service (NHS) and society. Three disease phases were defined: acute, post-acute and long-term. Epidemiological, economic and health utilities data were taken from Italian and international literature. The analysis was conducted by means of Microsoft Excel 2010®. Results Our study indicated that vaccinating adolescents (11th year of life) with Trumenba® was cost-effective with an ICER = € 7,912/QALY from the NHS perspective and € 7,758/QALY from the perspective of society. Vaccinating adolescents reduces the number of cases of disease due to meningococcus B in one of the periods of highest incidence of the disease, resulting in significant economic and health savings. Conclusions This is the first study to evaluate the overall impact of free MenB vaccination in adolescents both in Italy and in the international setting. Although cases of invasive disease due to meningococcus B are few, if the overall impact of the disease is adequately considered, it becomes clear that including anti-meningococcal B vaccination into the immunization program for adolescents is strongly recommended from the health and economic standpoints. Key messages Free, large-scale MenB vaccination is key to strengthening the global fight against invasive meningococcal disease. Anti-meningococcal B vaccination in adolescents is a cost-effective health opportunity.

scholarly journals Environmental Engineering Techniques to Restore Degraded Posidonia oceanica Meadows

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 661
Author(s):  
Luigi Piazzi ◽  
Stefano Acunto ◽  
Francesca Frau ◽  
Fabrizio Atzori ◽  
Maria Francesca Cinti ◽  
...  
Keyword(s):  
Large Scale ◽  
Natural Habitat ◽  
Cost Benefit ◽  
Cost Effective ◽  
Posidonia Oceanica ◽  
Environmental Engineering ◽  
Bottom Surface ◽  
Sessile Invertebrates ◽  
Set Up ◽  
Short Time

Seagrass planting techniques have shown to be an effective tool for restoring degraded meadows and ecosystem function. In the Mediterranean Sea, most restoration efforts have been addressed to the endemic seagrass Posidonia oceanica, but cost-benefit analyses have shown unpromising results. This study aimed at evaluating the effectiveness of environmental engineering techniques generally employed in terrestrial systems to restore the P. oceanica meadows: two different restoration efforts were considered, either exploring non-degradable mats or, for the first time, degradable mats. Both of them provided encouraging results, as the loss of transplanting plots was null or very low and the survival of cuttings stabilized to about 50%. Data collected are to be considered positive as the survived cuttings are enough to allow the future spread of the patches. The utilized techniques provided a cost-effective restoration tool likely affordable for large-scale projects, as the methods allowed to set up a wide bottom surface to restore in a relatively short time without any particular expensive device. Moreover, the mats, comparing with other anchoring methods, enhanced the colonization of other organisms such as macroalgae and sessile invertebrates, contributing to generate a natural habitat.

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