Fast Route Research Articles

Boundary effects on topological characteristics of urban road networks

Urban road networks (URNs), as simplified views and important components of cities, have different structures, resulting in varying levels of transport efficiency, accessibility, resilience, and many socio-economic indicators. Thus, topological characteristics of URNs have received great attention in the literature, while existing studies have used various boundaries to extract URNs for analysis. This naturally leads to the question of whether topological patterns concluded using small-size boundaries keep consistent with those uncovered using commonly adopted administrative boundaries or daily travel range-based boundaries. This paper conducts a large-scale empirical analysis to reveal the boundary effects on 22 topological metrics of URNs across 363 cities in mainland China. Statistical results show that boundaries have negligible effects on the average node degree, edge density, orientation entropy of road segments, and the eccentricity for the shortest or fastest routes, while other metrics including the clustering coefficient, proportion of high-level road segments, and average edge length together with route-related metrics such as average angular deviation show significant differences between road networks extracted using different boundaries. In addition, the high-centrality components identified using varied boundaries show significant differences in terms of their locations, with only 21%–28% of high-centrality nodes overlapping between the road networks extracted using administrative and daily travel range-based boundaries. These findings provide useful insights to assist urban planning and better predict the influence of a road network structure on the movement of people and the flow of socio-economic activities, particularly in the context of rapid urbanization and the ever-increasing sprawl of road networks.

A Novel Hybrid Energy Efficient Model using Clustering in Wireless Sensor Networks

An innovative hybrid approach is presented in this paper aimed at rapid predicting the optimum routing path selection in WSNs using the Lagrangian method and Clustering. The main motive of this work proposed is to maximize the clustering productivity, reduce the congestion in routing system and optimize the energy range in the network. By combining the route prediction through clustering and energy flow estimates in the routing protocol design-based algorithm, this will combine both the objectives of rapid route prediction and energy flow estimation. A Distributional Heterogeneous Pathfinder (DHP) based routing algorithm was used in this optimal clustering system to predict the best routing paths in the network and to optimize network energy consumption. In general, sensor communication in the Internet of Things takes place with tremendous amounts of data transmission and other interactions. The results drawn from simulation shows the higher efficiency and fast route detection with better energy transmission of data through the network.

High-performance Ag2Se film by a template method for flexible thermoelectric generator

Herein, we developed a facile method to prepare a high-performance Ag2Se thermoelectric (TE) film. First, Ag2Se powders were synthesized by the reaction between Ag nanowires (NWs) with Se precursor at room temperature (RT), then vacuum filtered on a nylon membrane, and finally hot pressed. The film exhibits a high power factor of 1636.9 μW m−1 K−2 at RT via optimizing the Ag/Se ratio. The film exhibits good flexibility because of the nylon substrate and the nylon well combined with the Ag2Se film. Moreover, a flexible TE generator (f-TEG) consisting of six optimized film legs was assembled, and it generates a maximum output power of ∼3.42 μW (corresponding power density of ∼13.5 W m−2) under a temperature gradient of 31.4 K. This work paves a simple, fast, and feasible route to prepare high-performance flexible Ag2Se film.

Ancient mitochondrial genome diversity in South America: Contributions from Quebrada del Toro, Northwestern Argentina.

The objective of this study was to enhance our understanding of the population history in South America, specifically Northwestern Argentina, by analyzing complete ancient mitogenomes of individuals from the Ojo de Agua archeological site (970 BP) in Quebrada del Toro (Salta, Argentina). We analyzed teeth from four individuals from the site Ojo de Agua (970 ± 60 BP), located in Quebrada del Toro (Andean region of Northwestern Argentina). DNA extracts were converted to double-stranded DNA libraries and indexed using unique dual-indexing primer combinations. DNA libraries were then enriched for the complete mitochondrial genome, pooled at equimolar concentrations, and sequenced on an Illumina® MiSeq™ platform. Reads from high quality libraries were trimmed, merged, and then mapped to the revised Cambridge Reference Sequence. The aDNA damage patterns were assessed and contamination estimated. Finally, variants were called, filtered, and the consensus mitogenome was constructed and used for haplogroup assignment. We also compiled available mitogenome sequences from ancient and present-day populations from the Southcentral Andes and other surrounding regions in Argentina. Maximum Likelihood and Bayesian phylogenetic reconstructions were obtained using the generated dataset. We successfully obtained the complete mitogenome sequence from one individual with an average depth coverage of 102X. We discovered a novel haplotype that was assigned to haplogroup D1. Phylogenetic reconstructions suggests that this haplotype falls within the sister branches of the D1j lineage, forming a well-supported clade. The estimate TMRCA of this clade that includes D1j and its sister branches ranged between 12,535 and 18,669 ya. The sequence analyzed in this study represents the first ancient mitogenome from within the valley region in Northwestern Argentina. We found that a representative of a lineage highly associated with D1j was already present approximately 1000 BP in the region. Our results agree with the proposed origin of D1j in other regions north of Patagonia and independent of the Pacific coast fast migratory route, contrary to what was originally hypothesized. This study highlights the lack of information regarding pre-Hispanic genetic diversity and contributes to the knowledge about the peopling process in South America.

ANALISIS KEPUASAN DAN KEPENTINGAN FASILITAS DAN LAYANAN ANGKUTAN BERBASIS ONLINE DI KOTA PADANG

As a student city, the city of Padang is a residence for students from various regions. Limited public transportation in the city of Padang, students prefer to use online-based transportation for their mobility. This study aims to identify personal characteristics and travel experiences, satisfaction levels, and consumer interests of online-based transportation as a mode of transportation in the city of Padang. Research respondents are students and college students who study in the city of Padang. The online-based transportation used is Go-Jek. Data was collected through online questionnaire interviews. Data processing was carried out by descriptive statistical methods that described the characteristics of respondents, and statistical analysis of cross-tabulation was also carried out to find the level of influence between personal characteristics and travel experiences with online transportation. Analysis of the level of satisfaction and interest of online-based transportation consumers based on the Importance Performance Analysis (IPA) method. The average value of the level of satisfaction and consumer interest in aspects of service, reliability, comfort, security, and tariffs are 2.75 and 2.94, respectively. There are two attributes of facilities and services that are considered necessary. However, the level of service is not satisfactory, namely the availability of comfortable security devices for passengers and the availability of the fastest route.

Simulating macroscopic high-order harmonic generation driven by structured laser beams using artificial intelligence

Artificial intelligence, and in particular deep learning, is becoming a powerful tool to access complex simulations in intense ultrafast laser science. One of the most challenging tasks to model strong-field physics, and in particular, high-order harmonic generation (HHG), is to accurately describe the microscopic quantum picture—that takes place at the sub-nanometer/attosecond spatiotemporal scales—together with the macroscopic one—at the millimeter/femtosecond scales—to reproduce experimental conditions. The exact description would require to couple the laser-driven wavepacket dynamics given by the three-dimensional time-dependent Schrödinger equation (3D-TDSE) with the Maxwell equations, to account for propagation. However, such simulations are beyond the state-of-the-art computational capabilities, and approximations are required. Here we introduce the use of artificial intelligence to compute macroscopic HHG simulations where the single-atom wavepacket dynamics are described by the 3D-TDSE. We use neural networks to infer the 3D-TDSE microscopic HHG response, which is coupled with the exact solution of the integral Maxwell equations to account for harmonic phase-matching. This method is especially suited to compute macroscopic HHG driven by structured laser beams carrying orbital angular momentum within minutes or even seconds. Our work introduces an alternative and fast route to accurately compute extreme-ultraviolet/x-ray attosecond pulse generation.

Modelos de cálculo para a produtividade total dos fatores

A produtividade mede o nível de eficiência que uma economia apresenta em produzir bens e serviços. Assim, aumentar a produtividade é a maneira mais rápida de se atingir crescimento econômico e bem-estar social. Este estudo busca estimar e comprar quatro modelos diferentes para o cálculo da Produtividade Total dos Fatores (Total Factor Productivity - TFP). Os modelos escolhidos foram: Olley Pakes, 1996 - OP; Levinsohn Petrin, 2003 - LP; Wooldridge, 2009 - Wool; e, Ackerberg, Caves e Frazer, 2015 - ACF. Os resultados sugerem que o modelo ACF (2015) é um aprimoramento dos modelos OP e LP, além de apresentar resultados com significância estatística. O modelo Wool (2009) também é um aprimoramento e, novamente, apresenta resultados similares. Como o modelo ACF apresenta maior dispersão, o modelo Wool apresenta-se como a melhor escolha.

On the dynamics of a dual space–time fractional nonlinear Schrödinger model in optical fibers

The nonlinear Schrödinger equation (NLSE) is a significant nonlinear complex model known as the most important struture to represent light pulse propagation in optical fibers, which plays a key role in demonstrating the proliferation specifically short pulse in optical fibers and have large appliances in telecommunication system and ultra fast signal routing in nonlinear optics. In this study, we look into explicit solutions of a coupled fractional NLSE for a dual space–time, which reflect numerous explicit travelling wave solutions, as well as their physical interpretation. We use the fractional complex transform to obtain multiple exact answers using the unified technique and the extended simple equation method. These exact answers include trigonometric function solutions as well as dark, bright, singular, periodic, and optical soliton solutions. For a set of appropriate parameters, Mathematica is used to represent the dynamics of various wave structures as 3D, 2D, and contour visualizations. These findings show that the proposed methodologies are simple, prolific, and capable of studying the nature of numerous physical models of complex phenomena in modern science.

Survivability Mapping Strategy for Virtual Wireless Sensor Networks for Link Failures in the Internet of Things

In the case of virtual wireless sensor networks, a link-failure-oriented survivable virtual sensor network mapping algorithm (F-SVNE) is proposed to address the issue of link failure in the underlying wireless sensor network. First, the algorithm utilizes the fast routing strategy and creates a backup route set based on a multi-path selection algorithm to reduce the delay in fault recovery caused by path selection. Second, the survivable virtual sensor network mapping algorithm is adopted based on the routing set. Finally, in the mapping stage, the efficiency and reliability of the algorithm are comprehensively considered, and the path with the largest survival probability is selected from the backup route set of the faulty link to remap the virtual link affected by the link failure. Empirical results demonstrate that the F-SVNE algorithm can efficaciously lessen the failure recovery delay, and improve the long-term average revenue–cost ratio and average failure recovery rate.

Development of the Gadingsari Village Tourism Geographic Information System Application

Gadingsari Village is a city located in Jogjakarta, has cultural diversity as an asset of regional wealth, and must be developed as optimally as possible. Many tourists visit tourist attractions in Gadingsari Village, but there are difficulties in choosing the tourist attractions visited due to limited information about tourist attractions in Gadingsari Village. The other problems are congestion problems, cost problems, time problems,s, and labor problems. The purpose of this study is to create a geographic information system for tourism in Gadingsari village. The system is built using PHP and MySQL programming languages as database management as well as the application of the Dijkstra algorithm used to find the shortest path based on the smallest weight from one point of departure to another. The tests carried out in this study used the User Acceptance Test, aiming to determine the user's understanding of the application created. The resulting geographic information system consists of several features, namely the information feature of tourist places, and the feature of finding the fastest route to the tourist spot. In the fastest route search feature, the Dijkstra algorithm works optimally so that it can quickly find the fastest route to tourist places in Gadingsari Village.

Implementasi Algoritma A* (A-Star) dan Greedy Dalam Penentuan Routing Pada Wide Area Network (WAN)

The continuous development of the internet has led to changes in routing complexity. In the context of a wide area network (WAN), network flow optimization becomes a major issue in the selection of the fastest route, which involves routing protocols. Algorithms in routing protocols are designed to determine the shortest path. Therefore, studies are needed regarding how to build a routing algorithm that can be used to get the shortest path in each process. This study aims to reduce network workload and compare the computation involved in determining the shortest path using the Greedy algorithm and the A* algorithm. The simulation results show that the A* algorithm is superior to greedy. The measurement results show that the final cost of the Greedy algorithm is 44 and the A* algorithm is 38. Taking into account the heuristic value for each node, the A* algorithm is superior to the Greedy algorithm in determining the shortest path.

Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks.

Fast convergence routing is a critical issue for Low Earth Orbit (LEO) constellation networks because these networks have dynamic topology changes, and transmission requirements can vary over time. However, most of the previous research has focused on the Open Shortest Path First (OSPF) routing algorithm, which is not well-suited to handle the frequent changes in the link state of the LEO satellite network. In this regard, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, where the satellite can quickly obtain the network link status and adjust its routing strategy accordingly. In FRL-SR, each satellite node is considered an agent, and the agent selects the appropriate port for packet forwarding based on its routing policy. Whenever the satellite network state changes, the agent sends "hello" packets to the neighboring nodes to update their routing policy. Compared to traditional reinforcement learning algorithms, FRL-SR can perceive network information faster and converge faster. Additionally, FRL-SR can mask the dynamics of the satellite network topology and adaptively adjust the forwarding strategy based on the link state. The experimental results demonstrate that the proposed FRL-SR algorithm outperforms the Dijkstra algorithm in the performance of average delay, packet arriving ratio, and network load balance.

Carbon-Doped Nickle Via a Fast Decarbonization Route for Enhanced Hydrogen Oxidation Reaction in Alkaline Media.

Nickel (Ni) based materials with non-metal heteroatom doping are competitive substitutes for platinum group catalyst toward alkaline hydrogen oxidation reaction (HOR). However, the incorporation of non-metal atom into the lattice of conventional fcc phase Ni can easily trigger a structural phase transformation, forming hcp phase nonmetallic intermetallic compounds. Such tangle phenomenon makes it difficult to uncover the relationship between HOR catalytic activity and doping effect on fcc phase Ni. Herein, taking trace carbon doped Ni (C-Ni) nanoparticles as an example, a new nonmetal doped Ni nanoparticles synthesized by a simple fast decarbonization route using Ni3 C as precursor is presented, which provides an ideal platform to study the structure-activity relationship between alkaline HOR performance and non-metal doping effect toward fcc phase Ni. The obtained C-Ni exhibits an enhanced alkaline HOR catalytic activity compared with pure Ni, approaching to commercial Pt/C. X-ray absorption spectroscopy confirms that the trace carbon doping can modulate the electronic structure of conventional fcc phase nickel. Besides, theoretical calculations suggest that the introducing of C atoms can effectively regulate the d-band center of Ni atoms, resulting in the optimized hydrogen absorption, thereby improving the HOR activity.

Search mechanism for data contents based on bloom filter and tree hybrid structure in system wide information management

AbstractThe system wide information management (SWIM) system infrastructure layer uses information‐centric networking (ICN) to implement the cache routing and sharing of air traffic information data. The SWIM network searches and forwards routes on the basis of content names. The variable length, hierarchical name structure, and routing updates caused by the frequent publication and deletion of content make the implementation of fast name routing lookup algorithms a significant but arduous task. To address this problem, this study designed a name matching mechanism on the basis of the hybrid structure of the Bloom filter and the Tree. Compared with the traditional Bloom filter search scheme, this search mechanism reduces the quantity of Bloom filter insertion entries and reduces the possibility of hash collisions. Compared with the traditional Tree search scheme, the proposed mechanism can effectively solve the problem of numerous memory accesses caused by extremely long name prefixes, improving the query efficiency. In addition, the proposed composite structure is compared with several methods, such as DIPIT and NPT, by analyzing the effects of layering, global delay and search speed. The experimental results show that the proposed structure has a favourable performance in terms of storage overhead and search speed.

Road network distances and detours in Europe: Radial profiles and city size effects

The form and the size of cities influence their social, economic and environmental outcomes. The form of a city is itself influenced by the shape of its road network, but this relationship and how it is affected by city size are unclear. We analyse how road distances to the main centre vary across 300 European cities and how radial physical detours (i.e. the distance on the road network compared to the Euclidean distance) are affected by city size and extent. We use landuse and population data to sample potential residences and compute the fastest routes to the main centre. We find a linear relationship between road and Euclidean distances, and for the first time document an average radial physical detour of 1.343 across Europe. We then rescale distance bands so to make cities of different population size comparable and show the effect of different urban delineations. We find that physical detour ratios increase when core cities only are considered without suburbs. At the urban region scale, radial physical detours increase with city size, especially when other significant geographical factors (latitude, longitude, elevation change and proximity to coast) are controlled for. When the central part of cities only is considered, larger cities have smaller radial physical detours.

From Metasurface to Low-RCS Array Antenna: A Fast and Efficient Route to Design Stealthy Array Antennas

In the conventional routes to obtain stealth array antennas, various techniques are used to reduce the radar cross sections (RCS) of existing antennas based on experience-relying trial-and-error and time-consuming optimizations. To address the issue, we propose a reverse thinking, i.e., low scattering performance precedes radiation performance, and present a universal and definite route to achieve low-RCS array antennas directly from metasurfaces rather than from antennas. A specific design method is carefully elaborated to illustrate this route. Firstly, a low RCS metasurface is designed. Then an interdigital arrangement of the metasurface and proper feeding techniques are exploited to excite parts of the metasurface cells to produce efficient radiations. These partially excited cells play twofold roles as the radiating structures for array antenna and as part of scattering structures for low-RCS property. As examples, one- and two-dimensional metasurface-based array antennas are elaborately designed and fabricated, in which good radiation performance and broadband low RCSs are achieved simultaneously. Both numerical simulations and experimental results verify the effectiveness of the proposed method. It suggests that the proposed route is more likely to speed up the design process and yield an integration structure and controllable performance.

Harris Hawks optimization for ambulance vehicle routing in smart cities

The ambulance routing problem is one of the capacitated ambulance routing problem forms. It deals with injuries and their requests for saving. Therefore, the main aim of the ambulance routing problem is to determine the minimum (i.e., optimum) required distances of between: 1) accident places and the ambulance station; 2) the location of the nearest hospital and the accident places. Although of the efforts proposed in the literature, determining the optimum route is crucial. Therefore, this article seeks to tackle ambulance vehicle routing in smart cities using Harris Hawks Optimization (HHO) algorithm. It attempts to take the victims as quickly as possible and confidently. Several engineering optimization problems confirm that HHO outperforms many well-known Swarm intelligence approaches. In our system, let’s use the node approach to produce a city map. Initially, the control station receives accident site information and sends it to the hospital and the ambulance. The HHO vehicle routing algorithm receives data from the driver; the data includes the location of the accident and the node position of the ambulance vehicle. Then, the driver’s shortest route to the accident scene by the HHO. The locations of the accident and hospital are updated by the driver once the car reaches the accident site. The fastest route (which results in the least travel time) to the hospital is then determined. The HHO can provide offline information for a potential combination of the coordinates of destination and source. Extensive simulation experiments demonstrated that the HHO can provide optimal solutions. Furthermore, performance evaluation experiments demonstrated the superiority of the HHO algorithm over its counterparts (SAODV, TVR, and TBM methods). Furthermore, for ten malicious nodes, the PDF of the algorithm was 0.91, which is higher than the counterparts

Gamma radiation induced synthesis of double network hydrophilic cryogels at low pH loaded with AuNPs for fast and efficient degradation of Congo red

γ−radiation is a clean, fast and efficient synthetic route for polymeric materials. it is very powerful tool to generate free radicals during polymerization without any need of initiator and accelerator in both hydrophilic and hydrophobic media. Here in this work, for the first time, we fabricated acrylamide-methyl-propane sulfonic acid based-single and double network hydrophilic cryogels via γ−radiation at low pH. pH highly effects the synthesis of acrylamide-methyl-propane sulfonic acid based cryogels using redox initiation method. Along with pH, the effect of radiation time and radiation dose rate on cryo-polymerization was also studied. Gold hybrid cryogels (single and double network) were fabricated via self-reduction method at ambient conditions. Fourier transform infrared spectroscopy was implemented for chemical structure analysis of the synthesized material. The surface morphology of single and double network cryogels was observed via scanning electron microscopy. For the hybrid cryogels materials analysis x-ray diffraction, thermal gravimetric analysis, energy dispersive x-ray analysis, x-ray photoelectron spectroscopy and transmission electron microscopy and was used to confirm the presence of nanoparticles in the polymer network. The fabricated hybrid cryogels (0.15 g) were used for the degradation of Congo red (50 mL) as model dye in the presence of NaBH4 (0.02 g) with efficient and fast degradation activity. The apparent rate constant value (Kapp) for single and double network hybrid cryogels was 0.3652 min−1 and 0.2268 min−1, respectively. The activation energy, enthalpy and entropy was calculated (34.44 kJ.mol−1, –30.85 kJ.mol−1 and –146.30 J·mol−1k−1, respectively) using Arrhenius and Eyring equations for the fabricated catalyst. The catalyst was tasted in the tap water system and its efficiency was satisfied in real world too. The fabricated double wall hybrid catalyst will have a significant importance in the environmental field.

Protocol optimization and assessment of genotypic response for inbred line development through doubled haploid production in maize

BackgroundDoubled haploid technology offers the fastest route of inbred line development by rapidly fixing the desirable combinations in a single year. However, the differential response of haploid induction to genetic background of maternal lines accompanied with low induction rate and high mortality rate due to artificial chromosomal doubling of haploid seedlings creates hindrance in doubled haploid production on a commercial scale under tropical conditions. To speed up the hybrid breeding programme in sub-tropical maize, efforts are reported here to optimize the protocol for efficient production of fixed lines using haploid inducers. The second-generation haploid inducers i.e. CIM2GTAILs obtained from CIMMYT, Mexico were used for haploid induction in 13 F1s of diverse backgrounds. For standardization of chromosomal doubling protocol, various concentrations of colchicine and two seedling growth stages were used to determine the extent of chromosomal doubling and survival rate of doubled haploid plants.ResultsA high mean haploid induction rate is obtained from CIM2GTAIL P2 (10%) as compared to CIM2GTAIL P1 (7.46%). Out of four treatments, CIMMYT reported protocol of chromosome doubling in tropical maize comprising combination of 0.07% colchicine and 0.1% DMSO at V2 stage is highly effective for acquiring doubled haploid plants in sub-tropical adapted maize with high survival rate of 52.7%. However, increasing the colchicine concentration from 0.07 to 0.1% led to high mortality rate.ConclusionAccording to the findings, the haploid induction rate, survival rate and overall success rate varied depending upon the genotype of the inducer and the source population along with the concentrations of chemical used. The optimized protocol developed using CIMMYT haploid inducer CIM2GTAIL P2 for efficient doubled haploid production will not only fasten the breeding programme but will also reduce the production cost of doubled haploid with great efficiency in sub-tropical maize.

Semiconducting cobalt oxide nanocatalyst obtained through an eco-friendly thermal decomposition

The combination of an intense absorption of visible light and p-type semiconducting nature makes spinel cobalt oxide (Co3O4) a very attractive material for various optoelectronic applications. However, the traditional methods for its synthesis have been either time- and energy-consuming or relying on toxic chemicals. To solve this issue, a simple, facile, and eco-friendly method of synthesis was successfully developed to obtain spinel Co3O4 nanoparticles. The novel method for obtaining pure and monophasic Co3O4 reported here is based on the thermal decomposition of hexaaquacobalt(II) D-camphor10-sulfonate at 900 °C. This fast solid-state synthesis route overcomes the disadvantages of many combustion methods, most critically by avoiding the use of toxic organic solvents. The synthesized material was subjected to a detailed characterization to assess its potential for use as a nanocatalyst. The band gap measurements indicated the presence of two band gaps, one at 2.10 eV and another at 1.22 eV, confirming the purity and semiconducting properties of the sample. The electrochemical studies demonstrated a significant enhancement in the electron transfer kinetics with the addition of the synthesized Co3O4 to the carbon-paste electrode, leading to an enhanced electrocatalytic performance. These prominent functional properties, suitable for a wide range of technological applications, pave way for the implementation of the reported method for the synthesis of Co3O4 on a larger industrial scale.