Reliable Routing Research Articles

Epoxidation of Chalcone Derivatives under Visible-Light Irradiation: An Organophotoredox Catalytic Approach

AbstractEpoxidation of α,β-unsaturated ketones under visible-light irradiation constitutes a significant chemical transformation with potential applications in the synthesis of epoxypropane derivatives. An organophotoredox catalytic system is herein reported to facilitate efficient aerobic epoxidation. This protocol enables the conversion of α,β-unsaturated ketones into their corresponding epoxy products with moderate to high yields under benign reaction conditions. The methodology demonstrates broad functional-group compatibility, providing a reliable and direct route to a variety of functionalized epoxypropane compounds. Additionally, the absence of heavy metals in this strategy renders it particularly suitable for the pharmaceutical industry, offering a new avenue for the synthesis of epichlorohydrin drugs.

DAR-DRL: A dynamic adaptive routing method based on deep reinforcement learning

Mobile-centric wireless networks offer users a diverse range of services and experiences. However, existing intelligent routing methods often struggle to make suitable routing decisions during dynamic network changes, significantly limiting transmission performance. This paper proposes a dynamic adaptive routing method based on Deep Reinforcement Learning (DAR-DRL) to effectively address these challenges. First, to accurately model network state information in complex and dynamically changing routing tasks, we introduce a link-aware graph learning model (LA-GNN) that efficiently senses network information of varying structures through a hierarchical aggregated message-passing neural network. Second, to ensure routing reliability in dynamic environments, we design a hop-by-hop routing strategy featuring a large acceptance domain and a reliability guarantee reward function. This mechanism adaptively avoids routing holes and loops across various network scenarios while enhancing the robustness of routing under dynamic conditions. Experimental results demonstrate that the proposed DAR-DRL method achieves the network routing task with shorter end-to-end delays, lower packet loss rates, and higher throughput compared to existing mainstream methods across common dynamic network scenarios, including cases with dynamic traffic variations, random link failures (small topology changes), and significant topology alterations.

Service Quality and Passenger Satisfaction on the Coastal Routes of Southwest Crete

The aim of this paper is to validate the correctness of the defining variables (categories) used by Sitzimis (2024a) to measure the quality of services provided on the coastal lines of southwest Crete. In this context it tries to determine both how much they affect the overall passenger satisfaction and which one is the most important. This research is both novel and distinct and could hopefully be expanded to include comparable coastal routes around the world and other means of transport. It is also expected to benefit shipowners, passengers, and policymakers by maintaining the level of services provided at a satisfactory level. Methodologically, we have used confirmatory factor analysis to determine the real factors affecting the quality of coastal services in southwest Crete. A non-parametric correlation test has been applied between the degree of satisfaction and the above factors. It has shown that there is a moderate positive correlation. We have tested through multiple regression both the extent to which these factors have an impact on passengers’ satisfaction and their relative significance. The resulting evaluation factors are "comfort, safety, land and on-board services", "pricing policy" and "route reliability". They have predicted 63.5% of passengers’ satisfaction variance and the first factor is the most significant. Their improvement is expected to stimulate the users’ satisfaction. Ιt appears that the five categories used by Sitzimis, and their hierarchical classification, have not been fully confirmed by the factor analysis.

Versatile synthesis of 2-functionalized dihydrothiazolo[2,3-b]quinazolines through regioselective electrophilic intramolecular heterocyclization of 3-alkenyl-2-thioxoquinazolin-4-ones

Electrophilic intramolecular heterocyclizations of 3-alkenyl-2-thioxoquinazolin-4-ones, induced by halogens, NBS, sulfuryl chloride, protic acids, selenium, or tellurium halides, proceed with high regioselectivity. This process leads to the formation of 2-functionalized dihydrothiazolo[2,3-b]quinazolines, showcasing the versatility and reliability of this synthetic route across various electrophilic reagents.

A Kelp Inspired High‐Power Density Triboelectric Nanogenerator with Stacking Structure for Multiple Directional Ocean Wave Energy Harvesting

AbstractOcean wave energy is one of the most promising green energies in the wild. However, it is still challenging to effectively collect wave energy due to its randomness and irregularity. In this work, a kelp inspired high‐power density triboelectric nanogenerator (K‐TENG) is presented for harvesting wave energy with characteristics in multiple directions. The proposed K‐TENG consists of a series of stacked leaf‐like units. The influence of configuration parameters, including pellet diameters, pellet numbers, unit sizes, oscillation frequency, swing amplitude, and wave directions on output performances of leaf‐like units, are extensively investigated. Experimental data indicates that a single leaf‐like unit can achieve a maximum output voltage of 623.14 V as well as a maximum current of 1.48 µA and realize energy harvesting from different wave directions. A K‐TENG composed of 15 leaf‐like units demonstrates a high‐power density of 18.77 W m−3 at a wave frequency of 2.5 Hz, which successfully powers a digital watch and 414 light‐emitting diodes (LEDs). This work is hoped to provide a simple and reliable route to effectively harvest ocean wave energy.

The Effect of Railway Projects Increasing Safety on the Frequency of Occurrences

This contribution is focused on the presentation of individual research results aimed at evaluating the socio-economic impacts associated with increasing the safety and reliability of the railway transport route. The goal of the research is to propose and subsequently verify an original approach for assessing the impact of the implementation of investment projects, including measures aimed at increasing the safety and reliability of railway transport routes, on the resulting number of occurrences that happen on the railway in the Czech Republic. The proposed procedure is based on several key documents. In addition to already existing national methodologies, these are mainly the database of occurrences managed by the Railway Administration of the Czech Republic, including approximately 1000 occurrences for each year of the evaluated period (2009–2018), and information on 33 projects on the railway transport route, where the effects of their implementation on the overall frequency of occurrences are examined events in the subject location. The output of the research is a methodical approach for assessing the impact of the implementation of projects aimed at increasing the safety and reliability of the railway transport route. We perceive the impact on the occurrences from the point of view of the frequency of their occurrence and from the point of view of the socio-economic impacts that are achieved as a result of the implementation of the projects. From the point of view of the frequency of occurrence, a reduction in its value of 4.63% was found. As part of the research, the impact on the occurrence of extraordinary events is also assessed in the context of the scope of the railway transport route, both with regard to the length of the reconstructed track and also with regard to traffic performance.

Carbothermal Reduction-Assisted Synthesis of a Carbon-Supported Highly Dispersed PtSn Nanoalloy for the Oxygen Reduction Reaction.

Exploring high-performance and low-platinum-based electrocatalysts to accelerate the oxygen reduction reaction (ORR) at the air cathode of zinc-air batteries remains crucial. Herein, by combining electroless deposition and carbothermal reduction, a nitrogen-doped carbon-supported highly dispersed PtSn alloy nanocatalyst (PtSn/NC) was prepared for a high-efficiency ORR process. Electrochemical measurements show that PtSn/NC exhibits excellent electrocatalytic ORR activity with a half-wave potential of 0.850 V versus reversible hydrogen electrode (RHE), which is higher than that of commercial Pt/C (0.815 V). The PtSn/NC-based (20 μgPt cm-2) rechargeable Zn-air battery exhibited astonishing performance with a maximum power density of up to 150.1 mW cm-2, as well as excellent rate performance and charge/discharge stability. Physical characterization reveals that carbothermal reduction could compel the transformation of Sn oxide into metallic Sn, which then alloys with the deposited Pt atoms to form the PtSn nanoalloy, in which electrons are transferred from Sn atoms to neighboring Pt atoms, thereby improving the ability of Pt-based active sites to catalyze the ORR process in PtSn/NC by optimizing the unoccupied d-band of Pt atoms. This work provides a reliable and innovative route for the rational design of highly dispersed Pt-based alloy ORR electrocatalysts.

Estimation of travel time reduction for ice-breaking ships when considering ice information data

Abstract In light of the recent increase in polar shipping and potential future increase with continued climate change reliable routing in ice-covered waters becomes increasingly important for environmental, economic and safety concerns. Dependable route suggestions have the potential to reduce travel times through polar waters significantly. We apply the Anytime Repairing A* pathfinding algorithm to classified Copernicus Sentinel 1 radar images to estimate how much travel times can be reduced. For multiple example scenarios, it is quantified how much the travel time is reduced if a ship follows these suggestions compared to navigating without any ice information available exterior to the visual range (VR). It was found that having ice information available is most beneficial in complex ice situations, where it can reduce travel time by up to 34% for a VR of 2 km.

A multi-stage game framework for new route promotion: Behavioral strategy and dynamic evolution of shippers, carriers, and governments

New freight routes are being planned and emerging globally, but often rely on government subsidies to enhance their competitiveness during the initial promotional phase. This study proposes a multi-stage game framework to explore the influence mechanism of behavioral strategies among three stakeholders: shipper, carrier, and government. Based on the assessment of shippers’ route choice preferences, we examine the dynamic evolution of carriers’ and governments’ behavioral strategies in the practical context of bounded rationality and dynamic learning. We theoretically derive the evolutionary paths and stable outcomes of dynamic systems across eight scenarios encompassing all the possible practical values to be taken, which provide broader and more valuable decision support for new route promotion. Finally, the feasibility and validity of the proposed model are verified by the actual case of the New International Land-Sea Trade Corridor. The findings indicate that government subsidies are not always effective and provide a precise subsidy range that can incentivize carriers to switch to the new route. Moreover, the study clarifies the impact of key factors such as subsidy, tax, route reliability, transportation time, and time value of cargo on the promotion of new routes through numerical simulation, and provides feasible management recommendations for government and route operations.

A Reliable Multipath Intercluster Routing Protocol Based on Link Stability

A Reliable Multipath Intercluster Routing Protocol Based on Link Stability

Metal oxide particle electrodes for degradation of high concentration phenol wastewater via electrocatalytic advanced oxidation

High-concentration phenol wastewater is pollutant of concern that pose significant risks to human health and the environment. Three-dimensional electrocatalytic oxidation is one of the most promising wastewater treatment technologies because of its high treatment efficiency, low energy consumption and low secondary pollution. Lower-cost and higher-performance particles still faces great challenges. In this work, metal oxide particle electrodes were prepared using granular activated carbon (GAC) as a substrate to study the degradation of phenol by three-dimensional electrocatalytic oxidation. GAC particle electrodes loaded with different monometallic oxides (Mn, Fe, Co, Ce) and bimetallic oxides (Fe and Ce) were prepared by the impregnation method. The effectiveness of the particle electrodes in degrading phenol was greatly improved after active components loading. Among all monometallic oxide particle electrodes, the concentration degradation efficiency was in the order of Ce/GAC > Co/GAC >Mn/GAC > Fe/GAC, and the COD degradation efficiency was Ce/GAC > Fe/GAC > Co/GAC >Mn/GAC. After optimizing the loading metal type and loading amount, it was found that the 1.1% Fe-2.7% Ce/GAC particle electrode perform the best, with a phenol degradation efficiency of 95.48%, a COD degradation rate of 94.35%, an energy consumption of 0.75 kWh/kg COD. This lower-cost and higher-performance particle highlights a reliable route for solving the problem of particle electrode materials limiting the efficient treatment of phenol-containing wastewater.

SROR: A Secure and Reliable Opportunistic Routing for VANETs

In Vehicular Ad Hoc Networks (VANETs), high mobility of vehicles issues a huge challenge to the reliability and security of transmitting packets. Therefore, a Secure and Reliable Opportunistic Routing (SROR) is proposed in this paper. During construction of Candidate Forwarding Nodes (CFNs) set, the relative velocity, connectivity probability, and packet forwarding ratio are taken into consideration. The aim of SROR is to maximally improve the packet delivery ratio as well as reduce the end-to-end delay. The selection of a relay node from CFNs is formalized as a Markov Decision Process (MDP) optimization. The SROR algorithm extracts useful knowledge from historical behavior of nodes by interacting with the environment. This useful knowledge are utilized to select the relay node as well as to prevent the malicious nodes from forwarding packets. In addition, the influence of different learning rate and exploratory factor policy on rewards of agents are analyzed. The experimental results show that the performance of SROR outperforms the benchmarks in terms of the packet delivery ratio, end-to-end delay, and attack success ratio. As vehicle density ranges from 10 to 50 and percentage of malicious vehicles is fixed at 10%, the average of packet delivery ratio, end-to-end delay, and attack success ratio are 0.82, 0.26s, and 0.37, respectively, outperforming benchmark protocols.

ENIAO: Energy Aware Faulty Node Re-Placement Integrated With Duty Cycling and Improved Harmony Search Based Clustering through Adaptive Fish School Search Routing for WSN Optimization

In wireless sensor networks (WSNs), sensor nodes are constrained by resource constraints. The limited energy supply and susceptibility to failure greatly affect WSN lifespan, hindering long-term deployment. ENIAO is an Integrated cross-layer Optimized Routing Approach for WSNs that is fault-tolerant and energy-efficient. A bio-inspired clustering architecture and adaptive duty cycling are incorporated into ENIAO's routing optimization. In a clustering protocol, the network is partitioned, and paths are dynamically optimized within and between clusters. By optimizing active/sleep schedules, duty cycling optimizes energy efficiency. A variety of network conditions have been simulated to assess ENIAO's performance. Regarding fault tolerance and energy consumption, ENIAO significantly prolongs the network lifetime. As compared to benchmark protocols, it achieves higher throughput. As a result of the cross-layer design, ENIAO is automatically adapted to optimize energy usage and routing reliability. In the long run, large-scale IoT deployments are possible with ENIAO due to the integrated approach.

Solving the Robust Shortest Path Problem with Multimodal Transportation

This paper explores the challenges of finding robust shortest paths in multimodal transportation networks. With the increasing complexity and uncertainties in modern transportation systems, developing efficient and reliable routing strategies that can adapt to various disruptions and modal changes is essential. By incorporating practical constraints in parameter uncertainty, this paper establishes a robust shortest path mixed-integer programming model based on a multimodal transportation network under transportation time uncertainty. To solve robust shortest path problems with multimodal transportation, we propose a modified Dijkstra algorithm that integrates parameter uncertainty with multimodal transportation. The effectiveness of the proposed multimodal transportation shortest path algorithm is verified using empirical experiments on test sets of different scales and a comparison of the runtime using a commercial solver. The experimental results on the multimodal transportation networks demonstrate the effectiveness of our approach in providing robust and efficient routing solutions. The results demonstrate that the proposed method can generate optimal solutions to the robust shortest path problem in multimodal transportation under time uncertainty and has practical significance.

Optimization of reliable vehicle routing problem for medical waste collection with time windows in stochastic transportation networks

The collection and transportation of medical waste (MW) is critical because of environmental and public safety implications. However, the variability in travel times of waste transport vehicles, influenced by various factors, complicates this process. This article proposes a multi-objective optimization model to enhance travel time reliability (TTR) while minimizing vehicle transportation costs and penalties for time window violations. Given the NP-hard nature of the problem, a genetic algorithm enhanced by an improved harmony search genetic algorithm (IHSGA) is utilized. The model and algorithm are evaluated in N City, China. The results highlight the significance of TTR in optimizing MW collection operations. Sensitivity analysis of key parameters validates the model’s robustness. Furthermore, comparative analysis demonstrates the superiority of IHSGA over traditional methods. These findings underscore the importance of incorporating additional costs associated with travel time variability in decision making, and advocate for strategies to mitigate such uncertainties.

Scalable transfer printing approach to heterogeneous integration of InP lasers on silicon-on-insulator waveguide platform

InP-based edge-emitting O-band lasers are integrated onto silicon photonics circuit employing micro-transfer printing technology. Blocks of unpatterned InP gain material of typical size 1000 × 60 μ m2 are first transferred onto 400 nm thick silicon rib waveguides with the fabrication steps performed on the target wafer to realize the final lasers. As a result, the InP ridge waveguides are aligned with lithographic accuracy to the underlying Si waveguides resulting in an approach free from any misalignment stemming from the transfer printing process. The fabricated Distributed Bragg Reflector laser shows lasing around 100 mA current injection with minimum 1 mW of output power coupled to a single mode fiber. This integration method paves a reliable route toward scaling-up the integration of active devices such as lasers, modulators, and detectors on 300-mm diameter silicon wafers, which requires high-uniformity across the wafer.

A design kinetic and hydrodynamic study of a coupled dual fluidized bed MTO reactor-regenerator system

The methanol-to-olefin (MTO) process, employing a dual-fluidized bed system, has introduced a new and reliable route for producing light olefins. This study intends to employ a comprehensive approach to simultaneously design and evaluate the reactor and regenerator performance. Namely, an inclusive design for a pilot-scale MTO system, encompassing the reactor, regenerator, interconnected catalyst transfer pipelines, and other critical operational aspects, is presented. The computational fluid dynamics (CFD) method using the Eulerian-Eulerian model was used to evaluate hydrodynamics, while the coupled reactor-regenerator kinetic model was developed by combining reaction kinetics with the two-phase model. The models were validated against experimental data and demonstrated a promising agreement. More importantly, an innovative procedure was devised to apply these modelling techniques to design this dual-fluidized bed system—the final design for the 10 kg catalyst inventory in the reactor at the pilot scale. The system achieves methanol conversion of 98.87 %, with ethylene and propylene product mass fractions of 45.51 % and 37.79 %, respectively.

Centimeter-Scale Assembly of Fractal Organic Crystals with Crisscross Structures for Flexible Photosynapses.

Fractal assembly technology enables scalable construction of organic crystal patterns for emerging nanoelectronics and optoelectronics. Here, a polymer-templating assembly strategy is presented for centimeter-scale patterned growth of fractal organic crystals (FOCs). These structures are formed by drop-coating perylene solution directly onto a gelatin-modified surface, resulting in the formation of crisscross fractal patterns. By adjusting the tilt angle of the template, the morphology of FOCs can be effectively controlled, with the diameter distribution of each level branch ranging from hundreds to ten micrometers. The planar FOC device exhibits flexible photoreception and photosynaptic capabilities, with a high specific detectivity of 1.35×109 Jones and paired-pulse facilitation (PPF) index of 104%, withstanding a 0.5cm bending radius during bending test. These findings present a reliable route for large-scale assembly of flexible organic crystalline materials toward neuromorphic electronics.

Connectivity reliability evaluation and most reliable shipping route choice in a seaborne crude oil network

The connectivity reliability of strait and canal nodes in seaborne crude oil networks is uncertain because of various risk factors. Existing studies have mainly focused on road networks and often ignored real-world factors by assuming fixed and identical values for the connectivity reliability of each node, leading to inaccurate estimations. Few studies have considered node reliability when identifying the most reliable routes for oil shipments in response to various external and changing risks. To address these limitations, we first establish new connectivity reliability evaluation methods for both nodes and networks. Then, we develop the α-most reliable shipping route and the very most reliable shipping route models using uncertain programming to dynamically identify the most reliable routes for crude oil, ensuring timely and safe transportation. We apply these models to China's seaborne network of imported crude oil. The results show a network connectivity reliability of 0.6228, which is impacted by unreliable origin–destination pairs in the Middle East. The risk values of the most reliable oil shipping routes vary regionally, with higher values in Africa and the Middle East than in Asia and Latin America. As node risk increases, regional disparities also increase. These findings will aid in the development of energy transportation and import strategies to enhance transportation reliability.

Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids.

Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols.