Fast Route Research Articles

Unraveling the adsorption mechanism of methylene blue onto selective pH precipitated Kraft lignins: Kinetic, equilibrium and thermodynamic aspects

Lignin has been used on its crude or modified forms for adsorption purposes. This work evaluated the influence of selective pH precipitation of Kraft lignins (KLs) on their adsorptive performance for removing methylene blue (MB). The alkaline and acid KLs (KL A and KLB, respectively) were characterized by FTIR, 31P NMR, GPC and pHPZC analyses. The effects of biosorbent and adsorbate concentrations, pH, ionic strength, contact time and temperature on the MB adsorption were evaluated. The equilibrium, kinetic and thermodynamic parameters were calculated by Langmuir and Freundlich isotherms, pseudo-first and second order and Van't Hoff and Gibbs models, respectively. KL A and KL B presented peculiar structural features, mainly hydroxyls concentration and Mw values, which have influenced on the removal efficiency of MB and the adsorptive capacities of KL A (>80 %; ≥80 mg g−1) and KL B (>90 %; ≥20 mg g−1), respectively. The equilibrium, kinetic and thermodynamic parameters have shown that MB adsorption presented different mechanisms for each KL, but it only has driven by chemisorption for KL B. Therefore, KL A and KL B can be considered as potential novel biosorbents obtained through a clean, fast and simple route for textile wastewater treatment.

Algoritma Dijkstra dan Bellman-Ford dalam Sistem Pemetaan Barbershop di Kota Lhokseumawe

A Barbershop service provider is a company that provides hair care to the community. Many people are currently doing business in this field, and many business actors are opening Barbershops in a variety of locations, ranging from campuses to office districts to densely populated towns. In Lhokseumawe City, there are 12 Barbershops. The application's benefit is that it can identify the shortest path from the user's location to the selected Barbershop, as well as the Barbershop's location and a brief description of the Barbershops in Lhokseumawe City. Only the system's defined nodes can be used to find the fastest route to the Barbershop. Dijkstra's method was chosen because it works against all current alternative functions and provides the shortest path from all nodes, ensuring that the shortest path is produced optimally. Because the Bellman-Ford algorithm is a variant of the BFS (best-first-search) algorithm, which is also employed in the search for the closest distance when the search for the closest distance has a negative weight, it was chosen. The same results were obtained in picking the route based on the results of the route selection test. However, when the two techniques are compared in terms of program execution time, Dijkstra's algorithm is faster than the Bellman-Ford algorithm.

Determining the Optimal Rice Distribution Route in Medan City Using Dijkstra's Algorithm

Distribution is an activity carried out to spread the product throughout the market so that consumers can buy it. Distribution can also affect the price of goods. Therefore, the distribution must be effective. The purpose of this study is to determine the shortest route that is effective in distribution so that product prices are not high and the company does not suffer losses. The data analysis method used is Dijkstra's Algorithm obtained with data on the distance between the starting point and the destination point through predetermined points. The calculation results show that the shortest and fastest routes to be taken based on the dijkstra algorithm are: A-20-19-F-26-C-D-11-12-B-E with a distance of 40.71 km. There are several road routes that can be chosen to be used but the shortest route that can be taken is 40.71 km. Medan Rice Distributors are expected to choose the shortest transportation route so that rice distribution can be carried out quickly and optimally.

Hybrid long short-term memory deep learning model and Dijkstra’s Algorithm for fastest travel route recommendation considering eco-routing factors

ABSTRACT The exponential growth of vehicles has led to increased traffic congestion and pollutants emission in cities. Intelligent transportation systems (ITS) shall be built by integrating Artificial Intelligence(AI). Planning optimal route with minimum travel time is a great challenge for the traffic management system. A hybrid model integrating long-short-term memory(LSTM) deep-learning model and Dijkstra’s algorithm for recommending the fastest route is proposed in this work. The proposed hybrid approach reduces the travel time to a greater extent. Vehicle speed greatly influences eco-driving factors such as fuel consumption and exhaust emission. Thus, for each road links, the fuel consumption and emission of pollutants such as carbon monoxide(CO), nitrogen oxides(NOx), and hydrocarbon(HC) are estimated for different type of vehicles. The results showed the proposed approach decreases fuel consumption, CO emission, NOx emission, and HC emission up to 54.5%, 61.5%, 64.2%, and 81.9%, respectively, compared to the traditional shortest path.

Halide-Free Continuous Synthesis of Hydrophobic Ionic Liquids.

Herein, we present a novel approach for the halide-free, continuous-flow preparation of hydrophobic ionic liquids (ILs) relying on the bis(trifluoromethanesulfonyl)imide (bistriflimide, NTf2–) anion. The simple yet fast two-step synthetic route, which involves the formation of different alkyl bistriflimides (R4NTf2), followed by the quaternization with an amine nucleophile, led to the desired ILs in high yields and excellent purities without any byproduct formation. The variable alkyl chain (R4) length and the broad range of the applicable nucleophiles (R1R2R3N) offer considerable flexibility to the synthetic protocol. The quaternization can be performed under solvent-free conditions; moreover, the homogeneous nature of these reactions allows the application of modern continuous-flow technologies. Given these advantages, the methodology can afford not just a fast and efficient alternative for the conventional synthesis of such compounds with reduced waste water production but their negligible halide content might provide a significantly broader application range of the IL products, especially for the field of materials science.

A fuzzy-based fast routing algorithm with guaranteed latency-throughput over software defined networks

With the development of online communications over the Internet, the network is required to guarantee the Quality of Service (QoS) requirements for different traffic flows. High quality and fast communication can be provided by Software-Defined Networks (SDN). SDN is a promising architecture for efficient management of network resources by separating control logic from data planes. In order to ensure the QoS requirements of traffic flows in SDN, this paper proposes a Fuzzy-based fast and efficient Routing algorithm with guaranteed Latency-Throughput (FRLR). The FRLR develops the dynamic routing problem in SDN using a traffic flow classification mechanism based on a fuzzy system. The purpose of the fuzzy system is to efficiently distribute resource allocation to improve the handling of future requests. In addition, the FRLR classifies traffic flows based on latency to reduce computational complexity. Besides, FRLR can effectively direct traffic flows and reduce network energy consumption by identifying critical links and selecting appropriate paths. Simulations on standard topologies with different scenarios confirm the better performance of FRLR by guaranteeing QoS requirements. The FRLR classifies traffic flows efficiently and offers significant improvements in the scalability and throughput of the network. Also, FRLR has provided superior results compared to some state-of-the-art algorithms such as MDA, MDWCRA, FARP, ECMP, and QI-RM. Experimental results show that FRLR works on average between 7% and 24% better than other algorithms.

A Fast Route Conflict Detection Method based on Space Box

Routes Conflict detection is of great importance to air traffic control. Due to previous route conflict detection algorithm has a large number of computations, high time complexity and low reliability. This paper proposes a fast route conflict detection method based on the space box. Firstly, the space to be detected is divided into several space boxes, and the space box numbers of the track in the airspace are calculated according to the track coordinates. Secondly, in the case that multiple batches of tracks exist in the same space box, the location of the tracks is predicted according to the current track parameters. If the distance between the tracks gradually decreases, it is judged that the tracks have conflict risk and an alarm is required. The simulation results show that the algorithm has low time complexity, and high efficiency, and is suitable for parallel processing and large-scale route conflict detection, which has high application value.

Facile Synthesis of TiO2 Nanoparticles and Their Reduced Graphene Oxides (RGO) Based Nanocomposites as Electrodes for Dye Sensitized Solar Cells (DSSCs) with Enhanced Efficiency

Recently, Dye-sensitized solar cells (DSSCs) have very attracted a huge deal of industries and academics owing to its number of properties including cost-effectiveness, and practically high power energy conversion efficiency. However, there is always a need to develop new research technologies to increase solar cells efficiency based on oxide heterojunctions. In this work, a fast, environment friendly and economic route were used to prepare TiO2, and their nanocomposites containing reduced graphene oxide (RGO), and carbon nanotubes (CNTs) for the fabrication of DSSCs device onto ITO substrates. The prepared nanostructures were well characterized by X-Ray diffraction (XRD), FESEM, TEM, and Raman measurements. XRD analysis confirmed that TiO2 nanoparticles were crystalline with tetragonal phase. Morphological studies performed by FESEM and TEM images showed that the TiO2 possessed spherical morphology which were uniform and densely covered on the surface of the RGO nanosheets. The DSSCs were fabricated using prepared TiO2 nanoparticles and nanocomposites with RGO and CNTs as working electrode. The results revealed that the DSSCs fabricated using nanocomposites showed enhanced performance as compared with the pure samples without RGO. In particular, the TiO2/CNTs (0.5 wt%) nanocomposites electrode exhibited highest power conversion efficiency (PCE) of DSSCs with a maximum value of 0.679% compared to 0.269% of DSSC with pure TiO2, and 0.472% of DSSC with TiO2/RGO. This method would provide a pathway to produce a low-cost technological process to develop high efficiency DSSCs.

Thermoelectric properties of Sm-doped BiCuSeO oxyselenides fabricated by two-step reactive sintering

Among layered oxygen-containing compounds, BiCuSeO is one of the most promising candidates for thermoelectric applications due to its intrinsically low thermal conductivity and good thermal stability. However, the rather poor electrical conductivity of pristine BiCuSeO hinders its potential. Further enhancement of the thermoelectric performance by single doping at Bi site is limited mainly due to dramatic decrease of carrier mobility. Thus, new strategies, such as dual doping or doping with variable-valence elements seem to be promising. Along with that, the development of a fast and scalable synthesis route is essential for the industrial-scale fabrication of thermoelectric materials. Hence, in this paper, Bi 1– x Sm x CuSeO samples (0 ≤ x ≤ 0.08) have been synthesized with a simple and scalable reactive sintering process. For comparison, Bi 1– x Sm x CuSeO oxyselenides were also obtained by the conventional solid-state route. Our results highlight that, Sm for Bi substitution increases the electrical conductivity by 1.5–2 times and decreases the Seebeck coefficient by ~1.4 times at 873 K for both series. Overall, considering the increase of lattice thermal conductivity upon doping and not optimized power factor, the figure of merit z T is reducing upon doping. • Sm-doped BiCuSeO oxyselenides fabricated by reactive sintering. • Sm doping leads to an increase of σ from 39 Ω –1 cm –1 to ~55 Ω –1 cm –1 at 873 K. • Quality factor is reduced upon Sm doping due to increase in κ and decrease of μ w .

Ring Cross-over Based GA for DFMB Chip Design and Medical Image Compression

: Currently, the clinical data stored in the cloud is easily accessible, and the patient’s data can be shared among treatment centers. In such a case, to handle additional data, the cloud data must be of a lesser scale. A process of compression was introduced to minimize the data with no losing data in order to achieve this size reduction. This paper conducts the experiment in two approaches: fast routing operations and compression from the chip in the DMFB approach. To apply this process of compression, the collected data from the chip was transformed into an image, and then compression of the image was performed utilizing a genetic algorithm (GA) based on a ring crossover. Consequently, the biochip of the 8x8 array is integrated into the power and area with the ring cross-module for an effective energy consumption operation. The technique of the process is utilized by the Microfluidic (MF) feature to handle and maintain the droplets. Also, the optimization process is performed by combining related pin actuation segments in parallel and the control pin to prevent pin-actuation conflicts. Through the optimization process, it synchronizes the length. This proposed approach decreases the consumption of the power and area. The outcome of the simulation indicates an increase in dynamic power, static power, and delay. Image compression is performed with the aid of this algorithm. In addition, for better outcomes, this GA compression application was contrasted with wavelet compressions.

Hierarchical Heterostructure Engineering of Layered Double Hydroxides on Nickel Sulfides Heteronanowire Arrays as Efficient Cathode for Alkaline Aqueous Zinc Batteries.

Aqueous alkaline rechargeable nickel-zinc (Ni-Zn) batteries possess great potential for large-scale energy storage systems because of their high output voltage, cheap cost, and intrinsic safety. However, the practical applicability of Ni-Zn batteries has been limited by traditional Ni-based cathodes with low capacity and poor cycle stability. Rational design of electrode structure and composition is highly desired but still significantly challenging. Herein, uniform self-supported hierarchical heterostructure composites interacting NiCo-layered double hydroxide with 1D nickel sulfides heteronanowire rooted on Ni foam (NF\Ni3 S2 /NiS@NiCo-LDH) are successfully developed by a hydrothermal sulfurization-electrodeposition process. The self-supported 3D hierarchical heterostructured composites nanoarray provides abundant reactive sites, rapid ion diffusion channels, and fast electron transfer routes, as well as strong structural stability. More significantly, the strong interfacial charge transfer between Ni3 S2 /NiS heteronanowire and NiCo-LDH effectively modifies the electronic structure of the composites and thereby improving the reaction kinetics. Consequently, the NF\Ni3 S2 /NiS@NiCo-LDH electrode presents a superior capacity of 434.5 mAh g-1 (1.73 mAh cm-2 ) at 3mA cm-2 . In addition, the fabricated NF\Ni3 S2 /NiS@NiCo-LDH//Zn battery can offer a maximal energy density and power density as large as 556.3Wh kg-1 and 26.3kW kg-1 , respectively, as well as an exceptional cycling performance.

The Backhaul Profit Maximization Problem: Optimization Models and Solution Procedures

We present a compact mixed integer program (MIP) for the backhaul profit maximization problem in which a freight carrier seeks to generate profit from an empty delivery vehicle’s backhaul trip from its last scheduled delivery to its depot by allowing it to deviate from the least expensive (or fastest) route to accept pickup-and-delivery requests between various points on the route as allowed by its capacity and required return time. The MIP is inspired by a novel representation of multicommodity flow that significantly reduces the size of the constraint matrix compared with a formulation based on the classical node-arc representation. This, in turn, leads to faster solution times when using a state-of-the-art MIP solver. In an empirical study of both formulations, problem instances with 10 potential pickup/drop-off locations and up to 72 pickup-and-delivery requests were solved an average 1.44 times faster in real time with our formulation, whereas instances with 20 locations and up to 332 pickup-and-delivery requests were solved an average of 11.88 times faster. The largest instances in the comparative study had 60 locations and up to 3,267 pickup-and-delivery requests; these instances required an average of more than 54 hours of real time to solve with the node-arc–based formulation but were solved in an average of under two hours of real time using our compact formulation. We also present a heuristic algorithm based on our compact formulation that finds near optimal solutions to each of the 60-location instances within 22 minutes of real time and near optimal solutions to instances with up to 80 locations within four and a half hours of real time.

Optimal route to quantum chaos in the Bose–Hubbard model

The dependence of the chaotic phase of the Bose–Hubbard Hamiltonian on particle number N, system size L and particle density is investigated in terms of spectral and eigenstate features. We analyse the development of the chaotic phase as the limit of infinite Hilbert space dimension is approached along different directions, and show that the fastest route to chaos is the path at fixed density n ≲ 1. The limit N → ∞ at constant L leads to a slower convergence of the chaotic phase towards the random matrix theory benchmarks. In this case, from the distribution of the eigenstate generalized fractal dimensions, the chaotic phase becomes more distinguishable from random matrix theory for larger N, in a similar way as along trajectories at fixed density.

Intelligent modelling for the elimination of lanthanides (La3+, Ce3+, Nd3+ and Eu3+) from aqueous solution by magnetic CoFe2O4 and CoFe2O4-GO spinel ferrite nanocomposites

In this research, a novel CoFe2O4-GO (Graphen Oxide) resulting from the combination of high applicable magnetic and organic base materials and synthesized with a simple and fast co-precipitation route was synthesized for the REEs (Rare Earth Elements) extraction. This adsorbent could remove the La3+, Ce3+, Nd3+ and Eu3+ by maximum adsorption capacity of 625, 626, 714.2, 1111.2 mg/g at optimized pH = 6, respectively. A data-driven model was obtained using Group Method of Data Handling (GMDH)-based Neural Network to estimate the adsorption capacity of these LREEs as a function of time, pH, temperature, adsorbent ζ (zeta)- potential, initial concentration of lanthanides ions, and ε which is defined by the physico-chemical properties of lanthanides. The results clearly indicated that the model estimate the experimental values with good deviation (mostly less than 10%) and it can be used for the prediction of the results from other similar researches with less than 25% deviation. The results of sensitivity analysis indicated that the adsorption capacity is more sensitive to pH of the solution, temperature, and ε. Finally, the desorption studies showed an excellent removal efficiency (97%) at least for three adsorption-desorption cycles. These results claimed that the CoFe2O4-GO is a highly efficient adsorbent for the REEs extraction.

Spatial Configuration of Logistics Firms Relative to Cape Town International Airport, South Africa

Background: A multi-faceted analysis of the positioning of logistics facilities relative to airports is a topical aspect of the interconnections between transport infrastructure and the spatial configuration of economic activities. Regardless of the wide range of studies that exist on the topic, little is known about the logistics catchment of airports. Towards extending the literature, the objective of the paper is to analyse the spatial configuration of logistics firms relative to Cape Town International Airport (CTIA) in South Africa. Methods: The inclination of logistics facilities to locate in industrial areas is widely acknowledged in the literature. As such, the data on the main industrial and economic nodes in the City of Cape Town metropolitan area were obtained from the City of Cape Town municipality and used to ascertain the extent of the industrial areas across the metropolitan area. The resultant geographic information system (GIS) shapefile of the industrial and economic nodes was used to obtain the logistics firms’ data from AfriGIS. Following the cleaning of the data from AfriGIS, ArcGIS 10.8 and QGIS 3.16 were used for spatial analysis (buffer, kernel density and network analysis) to establish the spatial configuration of the logistics firms relative to CTIA. Results: The results showed that of the 985 logistics firms in the metropolitan area, 937 were located within 20 km of CTIA. Furthermore, the kernel density results showed that high density values were found in the environs of the Cape Town central business district (CBD), Port of Cape Town and Milnerton. The results of the network analysis indicated that the furthest firm was 50 km and 64 min away on the shortest route to CTIA, while it was 55 km and 49 min away on the fastest route. Conclusions: Relative to the literature, the results show that CTIA has the potential to facilitate the emergence of a logistics hub at the city or metropolitan scale.

PITS: An Intelligent Transportation System in pandemic times

The control of the pandemic caused by SARS-CoV-2 is a challenge for governments all around the globe. To manage this situation, countries have adopted a bundle of measures, including restrictions to population mobility. As a consequence, drivers face with the problem of obtaining fast routes to reach their destinations. In this context, some recent works combine Intelligent Transportation Systems (ITS) with big data processing technologies taking the traffic information into account. However, there are no proposals able to gather the COVID-19 health information, assist in the decision-making process, and compute fast routes in an all-in-one solution. In this paper, we propose a Pandemic Intelligent Transportation System (PITS) based on Complex Event Processing (CEP), Fuzzy Logic (FL) and Colored Petri Nets (CPN). CEP is used to process the COVID-19 health indicators and FL to provide recommendations about city areas that should not be crossed. CPNs are then used to create map models of health areas with the mobility restriction information and obtain fast routes for drivers to reach their destinations. The application of PITS to Madrid region (Spain) demonstrates that this system provides support for authorities in the decision-making process about mobility restrictions and obtain fast routes for drivers. PITS is a versatile proposal which can easily be adapted to other scenarios in order to tackle different emergency situations.

(Invited) Rare-Earth-Based Nanoparticles As Multimodal Bioprobes

The remarkable optomagnetic properties of the rare-earths (RE) make RE-based materials ideal for biomedical applications, including diagnostic (e.g., imaging, nanothermometry) and therapeutic (e.g., drug delivery, photodynamic therapy) approaches. This is due the unique electronic properties of the f-elements allowing for upconversion and near-infrared emission under near-infrared excitation as well as high magnetic moments.Yet, challenges remain; low emission intensity and efficiency of small nanoparticles (NPs), and reliable, fast synthesis routes. As material chemists, we tackle these challenges with new designs of RE-NPs by chemically controlled synthesis, application-oriented surface chemistry, and understanding of structure-property-relationships. Sodium rare-earth fluorides (NaREF4) are our favorite materials, and we developed a fast and reliable microwave-assisted synthesis approach allowing crystalline phase and size control in the sub 15nm realm. Such control is crucial for the understanding of fundamental structure-property relationships and to optimize their optical and magnetic properties, when aiming for the design of next-generation optical probes or contrast agents for magnetic resonance imaging. For instance, NaGdF4 NPs are gaining interest as alternative MRI contrast agent, while co-doping with RE3+ ions renders them excellent candidates for photoluminescent optical probes. The hexagonal crystalline phase of NaGdF4 is known as the more efficient host material for upconversion emission, yet interestingly, it was found that its cubic counterpart shows superior performance as MRI contrast agent. Having a fast and reliable synthesis route towards NaREF4 NPs on hand, we now explore various nanoparticle architectures and compositions with the goal to optimize their optomagnetic properties, ultimately resulting in the design of biocompatible multimodal bioprobes.This presentation will shed light on recent results and remaining challenges in the field of RE-based nanostructures with respect to their microwave-assisted synthesis as well as structural and optomagnetic properties, seeking biomedical application, while also touching on hyperspectral imaging as an emerging analytical tool offering spatio-spectral information about RE-based materials.

First Human In Vivo Neuroelectrophysiology Recordings of Uncrossed Dentatothalamocortical White-Matter Connections: On the Fast Tract.

We aim to demonstrate intraoperative recording of cerebellar to cortical pathways that have not been previously recorded in humans, though imaged. We report 2 cases with intraoperative neurophysiologic mapping of cerebellocortical tracts. Direct electrical stimulation of subcortical cerebellum along with recordings of cortical evoked potential and motor muscle recordings was performed during surgery. MR tractography data from healthy participants were used to further illustrate the pathways. Neurophysiologic recordings showed large waveforms of evoked potentials in bilateral electrodes over premotor/motor cortices on stimulation of the dentate nucleus. EMG recordings showed responses in face and neck muscles on stimulation of the dentate nucleus at the motor threshold. We thus demonstrated first-in-human in vivo neurophysiologic evidence of cerebellum to cortex responses through an uncrossed dentatothalamocortical tract to the motor/premotor cortices. This technique provides a methodology for the direct mapping of the cerebellum and cerebello-cerebral connections. We hypothesize a direct structural connection from the dentate nucleus to the premotor and motor cortices, as well as to ipsilateral hemibody muscles, acting as a fast route of cerebellar output and back up for immediate motor responses. This will further help explain the modulatory effects of the cerebellum on motor, language, and cognitive functions.

A Fast Routing Capsule Network With Improved Dense Blocks

Routing algorithm in most existing Capsule Networks (CapsNets) is always a challenging problem due to its heavy computational burden. To address this issue, we propose a “fast routing” algorithm, where the high-level capsules are activated by the statistical characteristic of votes from low-level capsule vectors. In this way, the votes and their distribution are both considered, and iterations in the traditional “dynamic routing” algorithm are eliminated. The comparison experiment on MNIST dataset reveals that CapsNet with fast routing promotes time efficiency of CapsNet with dynamic routing (DR) by 71.2%, as well as improves classification accuracy by 6%. Moreover, improved dense blocks (IDB) are developed to make a powerful feature extraction, where layer position is utilized to calculate its filters to encourage feature reuse, while reducing redundant features. Finally, the proposed CapsNet with “fast routing” and IDB, namely fast routing CapsNet (FR-CapsNet), outperforms state-of-the-art capsule models in multiple benchmark datasets.

Thermoelectric properties of a high entropy half-Heusler alloy processed by a fast powder metallurgy route

A half-Heusler (HH) type high entropy alloy (HEA) Ti2NiCoSnSb has been synthesized by a fast powder metallurgy route for the first time. Mechanical alloying (MA) by wet milling produced a powder with a minor fraction of the HH phase. The dry milling route resulted in the desired single-phase HH material. Consolidation of the nanocrystalline mechanically alloyed (MA) powder by spark plasma sintering (SPS) resulted in a majority HH phase. Interestingly, the nanocrystalline alloy exhibited simultaneous enhancement in the Seebeck coefficient and electrical conductivity, with a maximum ZT of 0.13 at 973 K observed for the dry milled alloy. The band structure obtained by density functional theory (DFT) was in good agreement with the ultraviolet-visible-near infrared (UV-Vis-NIR) absorption spectroscopy results. The DFT calculations and microstructural analysis suggest that phase separation strongly influenced the thermoelectric properties. The band structure calculations provided a good rationale for the phase evolution and thermoelectric properties.