Quickest Path Research Articles

A Monte Carlo-based algorithm for the quickest path flow network reliability problem

A Monte Carlo-based algorithm for the quickest path flow network reliability problem

Connected or not? The value of air connectivity for regional development in Europe

ABSTRACT This paper studies the connectivity of European airports and their impact on regional development, measured by employment. It uses flight schedule data for 207 NUTS-2 regions from 2012 to 2019 in a network analysis. Statistical analysis shows that the degree grew steadily, while the shortest and quickest path connectivity decreased significantly in 2019. Moreover, the panel data analyses indicate that degree consistently positively impacts employment, whereas the significance of the shortest and quickest path’s impact is less consistent, indicating that focusing on increasing a region’s number of distinct edges might be most effective in stimulating regional development.

Combined effects of rainfall‐runoff events and antecedent soil moisture on runoff generation processes in an upland forested headwater area

AbstractIn this study, we investigate the combined effect of different rainfall‐runoff event types and antecedent soil moisture (ASM) on runoff processes in the headwater elementary discharge area of a small forested upland catchment. The study focuses on (i) the relationship between soil moisture thresholds and runoff generation; (ii) the combined effect of ASM and tree vicinity and (iii) the relationship between different rainfall‐runoff event types and different types of runoff (baseflow and stormflow). The results suggest that ASM has a strong impact on local runoff generation processes. Soil water content (35%–36%) threshold exceedance was related to stormflow runoff generation caused by the activation of quick preferential flow paths in the soil during storm events, especially in the upper and the deepest soil layers. At the same time, unexpected non‐linear increases in baseflow runoff ratios were documented during dry, precipitation‐free, periods and when the 31%–34% soil moisture threshold was exceeded, presumably due to the hydrological connection of farther slope areas during these conditions. Multiple stormflow periods, which exhibited the lowest runoff coefficient, were the most significant events in terms of water retention and soil water recharge due to increased vertical hydrological connectivity enabling more rapid transport to deeper soil layers. However, this rainfall type occurred least often over the study period. The important role of forest stands (individual trees) in creating spatial patterns of soil moisture and preferential infiltration paths to deeper soil layers was also confirmed. These results contribute towards a better conceptualisation of hydrological behaviour in elementary headwater discharge areas and highlight the potential dangers associated with expected increases in extreme weather events.

Shortest or locally quickest? A prediction-based approach for evacuation choice simulation between multiple staircases

Staircase choice is one of the most critical factors leading to the difference in pedestrian flow and evacuation routes in buildings with multiple staircases. Neither the shortest path to the building exit nor the locally quickest path to the nearest staircase can represent the natural mode of evacuation path choices for an authentic evacuation simulation. Thus, a prediction-based approach is established to predict and simulate evacuation choices, which helps to address three key issues: (1) extracting evacuation data through a controlled experiment; (2) establishing a Logit model for staircase choice prediction based on experimental data; (3) developing a prediction-based cellular automaton model. The proposed approach has achieved the coupling between choice prediction and evacuation simulation. A comparison with Pathfinder software is conducted to reveal the superiority of the prediction-based CA model for simulating staircase choice.

Carbon nanotube incorporated quaternized lignin-based loose nanofiltration membrane toward dye/salt separation

Lignin is a promising raw material for membrane fabrication due to its abundant oxygen-containing functional groups, branched molecular structure, and high abundance in the botanic field. In this report, polydopamine (PDA) coated carbon nanotubes (PCNTs) are synthesized. Then, a PCNT-incorporated lignin-based loose nanofiltration membrane is fabricated using quaternized lignin (QL) as a raw material, triphosgene (TP) as a crosslinker, and PCNTs as nanoadditive by interfacial polymerization. The results show that the PCNTs uniformly distribute in the QL-based separation layer providing quick diffusion paths for water molecules and salt ions. The coated PDA layer improves the chemical compatibility between the PCNTs and the separation layer. The incorporated PCNTs optimize the membrane microstructure and elevate the pure water permeance to 45.5 LMH bar−1. The membrane exhibits high rejections to various dyes (> 90%) and high salt permeation (> 90%), demonstrating an excellent dye/salt separation performance. Moreover, it has satisfactory capabilities for resisting dye adsorption and fouling, and its separation capability also has good tolerances to the acid, alkali, and active chlorine treatments. The quaternary ammonium ions and hydrophilic surface endow the membrane with favorable bactericidal property (99.3%) and bacteria-adhesion resistance. Besides, TP is a better crosslinker than trimesoyl chloride for optimizing the membrane microstructure and crosslinking degree in terms of dye/salt separation.

Eggshell as a biotemplate for the synthesis of layered porous carbon by co-pyrolysis with eucalyptus leaves

Based on its substantial specific surface area (SSA), quick ion transport path, and excellent pore structure, biomass carbon is a common ingredient in energy storage materials. In this study, hierarchical porous carbon was fabricated by the hard template method, using eucalyptus leaves (EL) as raw material, eggshells (ES) obtained from biomass waste act as a green biotemplating agent, and through co-pyrolysis technology to acquire high-value biomass carbon materials. A large SSA with a hierarchical porous structure displayed a honeycomb appearance obtained by adding eggshells in appropriate proportions. The ELES0.25 electrodes demonstrated a specific capacitance (Cs) of 310 F g−1 (0.5 A g−1) and 232 F g−1 (20 A g−1) in a three-electrode system, which displayed a capacitance retention of 74.8% with an outstanding electrochemical stability. The assembled ELES0.25//ELES0.25 symmetric supercapacitor displays a desirable energy density of 7.35 Wh kg−1 at a power density of 65 W kg−1 and preserves a capacitance retention of 97.38% after a charge/discharge cycle for 10,000 times. This research on ES acts as the biological template for assisted pyrolysis synthesis of porous carbon and proposes a novel approach for the clean and effective use of biomass waste.

Using a Node–Child Matrix to Address the Quickest Path Problem in Multistate Flow Networks under Transmission Cost Constraints

The quickest path problem in multistate flow networks, which is also known as the quickest path reliability problem (QPRP), aims at calculating the probability of successfully sending a minimum of d flow units/data/commodity from a source node to a destination node via one minimal path (MP) within a specified time frame of T units. Several exact and approximative algorithms have been proposed in the literature to address this problem. Most of the exact algorithms in the literature need prior knowledge of all of the network’s minimal paths (MPs), which is considered a weak point. In addition to the time, the budget is always limited in real-world systems, making it an essential consideration in the analysis of systems’ performance. Hence, this study considers the QPRP under cost constraints and provides an efficient approach based on a node–child matrix to address the problem without knowing the MPs. We show the correctness of the algorithm, compute the complexity results, illustrate it through a benchmark example, and describe our extensive experimental results on one thousand randomly generated test problems and well-established benchmarks to showcase its practical superiority over the available algorithms in the literature.

Cloud‐fog assisted human tracing and disaster evacuation framework

AbstractMany kinds of natural hazards and man‐made calamities have occurred in recent years, each of which has left a huge number of victims and has been challenging to deal with. The solution is an effective evacuation planning system, which is gaining the interest of governments, academic institutions, and industries all across the world. This study focuses on quick and organized evacuation from densely populated areas during disasters to decrease damage successfully. This research proposes a fog‐cloud‐assisted effective disaster evacuation framework. The data accumulation layer uses IoT, mobile networks, and GPS technologies to collect environmental and location‐based data to track the inhabitants. The fog layer is utilized for (i) disaster event detection and (ii) human tracing and crowd density analysis. A cloud layer makes it easier for an evacuation algorithm to produce an evacuation map that will point evacuees to the exit while computing a quick and safe path utilizing environmental and inhabitant information gathered from the fog. The findings reveal that the performance of Model A surpasses the split delivery vehicle routing problem model with the minimum demand of vehicles and with improvement of 38.1% average. Also, A* takes less time (18.3 s) than other state‐of‐the‐art techniques to find the best optimal route for evacuation. Implementation and performance analysis demonstrate the efficiency and effectiveness of the proposed framework.

Penerapan Algoritma Dijkstra dalam Pemetaan UMKM Berbasis Android

This study uses the Java programming language and the Android platform to map Micro, Small, and Medium-Sized Enterprises (MSMEs) in the Deli Serdang area using the Dijkstra algorithm. The purpose of this MSME mapping is to give consumers utilizing a mobile-based Geographic Information System (GIS) information about the closest location of MSMEs. A software development methodology combining quantitative and qualitative methodologies is used in this study. Data about the location of MSME's was gathered via surveys and conversations with company owners. Using SketchUp software, this data was then processed and examined in order to create a visual representation of the locations of MSMEs in the Deli Serdang region. Additionally, Java was used in the development of Android-based mobile applications to provide an interactive and user-friendly display. The location of MSMEs is listed on this application, along with suggestions for the quickest path from the user's current location to the MSMEs they wish to visit. According to the study findings, the Dijkstra algorithm has been successful in recommending the shortest route and fastest travel time to MSMEs, enabling users of this application to locate MSME sites with accuracy and speed. Additionally, users may view comprehensive details about each MSME, including a description of the company, its hours of operation, and contact information. It is anticipated that this application will improve the area of Deli Serdang's information accessibility for tourists and prospective MSME customers. Aside from that, it is envisaged that it will boost MSMEs' competitiveness and promote them while supporting regional economic expansion.

Utilizing Dynamic Programming for Optional Shortest Route Determination

Fuel prices have seen a significant increase. This encourages individuals to save costs on their fuel expenditure by finding the shortest route to their destination. The shorter the route, the less fuel will be released. This study answers the challenge of determining the shortest route between BTN Lembah Furia Sentani in Jayapura Regency to the Department of Mathematics of Cenderawasih University in Papua Province. Many roads can be passed from BTN Lembah Furia Sentani in Jayapura Regency, to the Department of Mathematics of Cenderawasih University in Papua Province. As is well known, that the streets in Jayapura City have many alternative roads with different characters. The existing road characteristics include density, physical condition of the road and the size of the road width. With the existing road character, it can be assumed that each road has an average travel length. This average value is used as the cost of the road. To solve this problem, a dynamic programming approach is used to identify optimal routes while minimizing costs. The dynamic program utilizes analyzed and calculated data to yield the best outcomes. The results indicate that the quickest path from BTN Lembah Furia Sentani to the Mathematics Department at Cenderawasih University is via Komba Netar Bridge, GKI Petrus Waena Church, covering a distance of 25.2 kilometers

Dynamic Three-Dimensional Lift Planning for Intelligent Boom Cranes

Manually planning a crane's lifting path is complicated and laborious since it typically depends on the planner's experience. We examined dynamic three-dimensional lift planning, including problem formulation for multiobjective planning and problem-solving with an improved A* algorithm. First, we quantified various feasible movements by introducing energy and time consumption models to determine the most efficient and quickest path. Second, we examined a new method for determining the visible lifting path in Configuration space (C-space) and derived an obstacle enveloping method for collision detection in C-space. Our method is both safe and feasible. Finally, we proposed an improved A* algorithm for lift planning considering multiple objectives and featuring better computational efficiency. The experimental and simulation results demonstrate that the proposed lift planning method can comprehensively consider multiple objectives, thus providing a high-quality path for actual lifting. Furthermore, compared with the ordinary A* algorithm, the improved A* algorithm had better computational efficiency and consumed less energy and time. To our best knowledge, this is the first novel algorithm for planning crane lifts considering energy consumption and visibility in C-space.

Accelerated transcranial magnetic stimulation for major depressive disorder: A quick path to relief?

Transcranial magnetic stimulation (TMS) is a safe, tolerable, and evidence-based intervention for major depressive disorder (MDD). However, even after decades of research, nearly half of the patients with MDD fail to respond to conventional TMS, with responding slowly and requiring daily attendance at the treatment site for 4-6 weeks. To intensify antidepressant efficacy and shorten treatment duration, accelerated TMS protocols, which involve multiple sessions per day over a few days, have been proposed and evaluated for safety and viability. We reviewed and summarized the current knowledge in accelerated TMS, including stimulation parameters, antidepressant efficacy, anti-suicidal efficacy, safety, and adverse effects. Limitations and suggestions for future directions are also addressed, along with a brief discussion on the application of accelerated TMS during the COVID-19 pandemic. This article is categorized under: Neuroscience > Clinical Neuroscience.

Swarm Intelligence-Inspired Meta-Heuristics Hybrid Optimization for Multi-Constraint Routing in Vehicular Adhoc Networks

In recent years, the popularity of vehicle adhoc networks (VANET) in wireless intelligent transportation systems has significantly increased. It is difficult to determine the quickest path between the source and the target in a VANET traffic system. Longer routes feature increased network overhead, more expensive connections, more path failures, and worse routing efficiency. Identifying the shortest route in optimization, often known as the Travelling Salesman Problem (TSP) and traffic congestion, is a well-known combinatorial optimization challenge with various practical applications. To improve routing efficiency, the HACOSMO (Hybrid-Ant Colony Optimization with Spider Monkey Optimization) system’s recommended meta-heuristic approach finds the shortest path using distance and traffic based principles. The simulation (NS-2) outcomes focused on the efficiency of the proposed method beats the other existing methods like ACO, GRACO, OACO, and IDBACOR in terms of overhead, throughput, latency, packet failure, and message transmission ratio. According to HACOSMO, routing overhead is 8% to 11% under IDBACOR, 10% to 14% under GRACO,19% to 31% under OACO, and 24% to 37% under ACO for a variety of vehicle counts and speed ranges. When compared to IDBACOR, the proposed method enhances throughput by 3% to 7%, 6% to 8% when compared to GRACO, 11% to 15% when compared to OACO, and 16% to 25% when compared to ACO.

How do journals publishing palliative and end‐of‐life care research report ethical approval and informed consent?

AbstractThis study explores how papers published in international journals in palliative and end‐of‐life care report ethical approval and informed consent. A literature search following PRISMA guidelines was conducted in PubMed, the Web of Science Core Collection, Scopus, the ProQuest Social Science Premium Collection, PsycINFO, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL). A total of 169 empirical studies from 101 journals were deductively coded and analysed. The results showed that 5% of publications provided no information on ethical approval, 12% reported minimal information, 56% reported rudimentary information, and 27% reported comprehensive details. We also found that 13% did not report any information on informed consent, 17% reported minimal information, 50% reported rudimentary information, and 19% reported comprehensive details. The prevalence of missing and incomplete ethical statements and inadequate reporting of informed consent processes in recent publications raises concerns and highlights the need for improvement. We suggest that journals advocate high reporting standards and potentially reject papers that do not meet ethical requirements, as this is the quickest path to improvement.

Time-Efficient Identification Procedure for Neurological Complications of Rescue Patients in an Emergency Scenario Using Hardware-Accelerated Artificial Intelligence Models

During an emergency rescue operation, rescuers have to deal with many different health complications like cardiovascular, respiratory, neurological, psychiatric, etc. The identification process of the common health complications in rescue events is not very difficult or time-consuming because the health vital symptoms or primary observations are enough to identify, but it is quite difficult with some complications related to neurology e.g., schizophrenia, epilepsy with non-motor seizures, or retrograde amnesia because they cannot be identified with the trend of health vital data. The symptoms have a wide spectrum and are often non-distinguishable from other types of complications. Further, waiting for results from medical tests like MRI and ECG is time-consuming and not suitable for emergency cases where a quick treatment path is an obvious necessity after the diagnosis. In this paper, we present a novel solution for overcoming these challenges by employing artificial intelligence (AI) models in the diagnostic procedure of neurological complications in rescue situations. The novelty lies in the procedure of generating input features from raw rescue data used in AI models, as the data are not like traditional clinical data collected from hospital repositories. Rather, the data were gathered directly from more than 200,000 rescue cases and required natural language processing techniques to extract meaningful information. A step-by-step analysis of developing multiple AI models that can facilitate the fast identification of neurological complications, in general, is presented in this paper. Advanced data analytics are used to analyze the complete record of 273,183 rescue events in a duration of almost 10 years, including rescuers’ analysis of the complications and their diagnostic methods. To develop the detection model, seven different machine learning algorithms-Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbor (KNN), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were used. Observing the model’s performance, we conclude that the neural network and extreme gradient boosting show the best performance in terms of selected evaluation criteria. To utilize this result in practical scenarios, the paper also depicts the possibility of embedding such machine learning models in hardware like FPGA. The goal is to achieve fast detection results, which is a primary requirement in any rescue mission. An inference time analysis of the selected ML models and VTA AI accelerator of Apache-TVM machine learning compiler used for the FPGA is also presented in this research.

An efficient factoring algorithm for the quickest path multi-state flow network reliability problem

The exact evaluation of the quickest path multi-state flow network reliability parameter belongs to the family of NP-hard problems. All methods known up to date for its resolution consist of two steps. The first step generates all relevant paths with respect to the reliability under consideration. The second one, having exponential time-complexity with respect to the number of relevant paths, exploits them to compute the reliability as the probability of the union of events not mutually exclusive. As a consequence, published methods are impractical for configurations with more than thirty relevant paths. In this paper, we present a new method, called the factoring method, which finds a relevant path, and uses one of its link and the total expectation theorem for transforming the given problem on two similar ones but related to smaller multi-state flow networks. This transformation, with polynomial execution-time complexity, is then recursively applied until termination conditions are met. By illustrations on versions of NSFNet, ARPANET and complete topologies, we highlight that the proposed method allows to compute quickly the reliability of multi-state flow networks with hundreds or even thousands of relevant paths and outperforms significantly the method based on the Depth-First-Search procedure (DFS-based method), which in previous literature was established as the most efficient one.

International orienteering experts’ consensus on the definition, development, cause, impact and methods to reduce mental fatigue in orienteering: A Delphi study

ABSTRACT Orienteering is an outdoor activity wherein participants use a map and compass to locate control points and choose the quickest path to the next control point in a natural environment. Attentional focus, rapid decision-making, and high aerobic fitness may influence orienteering performance. Therefore, this research aimed to seek international orienteering expert consensus regarding the definition, development, causes, influences and methods to reduce mental fatigue (MF) in orienteering based on practical experience. Following ethical approval, a three-round Delphi survey was conducted online with twenty-four orienteering coaches and athletes (or former athletes) from 10 different countries with international orienteering competition experience. The threshold of consensus was ≥ 70% agreement among respondents. The experts agreed that MF exists in daily life and orienteering with a substantial negative effect on their conscious decision-making performance and psychological responses. The experts disagreed that the form of MF that athletes experienced in orienteering training are similar to the competition. However, there was no agreement that MF would impact endurance and high-speed running performance during orienteering. This research refines the definition of MF and summarises the distinctions in what causes MF in orienteering training and competition, implying that MF should be addressed separately.

Numerical investigation of thermal failure of a fuel subassembly foot wall during molten material relocation under various flow blockage scenarios in sodium-cooled fast reactor

The innovative Fuel Assembly Inner DUct Structure (FAIDUS) design inherently offers a quick relocation path for the molten core material to descend downwards via the inner duct and arrive at the foot of the Subassembly (SA) following the local core meltdown during Total coolant Flow Blockage (TFB) accident. With its high thermal energy and continual heat generation, the amassed molten mass targets the surrounding foot structure, causing the foot wall to fail. The thermal damage progression and discrete failure of the foot wall govern the later phase of the melt relocation that signals the accident state. To understand the thermal damage progression of SA foot, transient numerical simulations are performed using a commercial computational fluid dynamics solver, ANSYS Fluent. An enthalpy-porosity approach based solidification/melting model is employed to predict the wall failure mechanism and associated event sequences. The computational model is validated against the wall failure test results available in the literature. Subsequently, the foot wall failure analysis is undertaken using a 2-D axisymmetric physical model for various plausible blockage occasions that initiate the TFB accident near the SA foot. The numerical solution endeavors to explicate the transient behavior of molten pool, convection-diffusion controlled phase interface development, thermal transport mechanism, liquid front propagation across the foot cross-section, and associated thermal hydraulics. The present findings indicate that the standard SA foot design perpetuates the accident scenario by releasing the molten mass through the ruptured wall segment after failure. To address this, an improved slotted discriminator-based foot design is proposed in the present study, and the effectiveness of the modified foot design in realizing a positive melt relocation to the core catcher is examined. The simulation results suggest that the modified foot design permits the intended melt release to the core catcher, confirming the Controlled Material Relocation (CMR) strategy and restraining the accident propagation to neighboring SA.

Optimal flow and capacity allocation in multiple joint quickest paths of directed networks

Shared vertices or edges in joint paths bring difficulties to flow routing and scheduling with delay requirements in networks with consideration of both edge lengths and capacities since flow along the different paths will encounter each other in capacitated edges with time dislocation. For an amount of flow, the quickest path problem (QPP) presents a good link for path lengths and capacities with the transmission time of the flow. Extended from the QPP, we propose an edge-path form traffic model for an amount of flow through multiple joint paths with different lengths in one-source one-sink directed capacitated networks. Then, an optimization model for minimum transmission time within feasible traffic constrained by edge capacities is constructed. We then derived the vertex–edge form of the optimization model from the edge-path form. The proposed optimization models in both forms are proved to be linear fractional programming problems, which can be solved in polynomial time. A routing algorithm based on the solution of the vertex–edge form optimization is developed combined with the DFS-based route-searching method. The proposed model and algorithm could be applicable in the real-time operation and management of practical network systems.

Evidence for divergence in phenology over morphology in response to limiting similarity in montane communities of Rhododendron

Abstract The coexistence of closely related species is key to understanding the nature of biodiversity hotspots where in situ diversification has yielded rich communities of close relatives. Limiting similarity predicts that co‐occurring species are differentiated in their niches; identifying the axes of differentiation in sympatric close relatives can thus help reveal the eco‐evolutionary dynamics of community assembly. For flowering plants, these axes may be temporal (related to reproductive phenology) or morphological (related to functional traits). We collected fine‐scale data on abundance, morphology and phenology over a flowering season for 34 species of Rhododendron (Ericaceae) spanning a 2700 m elevation gradient in a nature reserve in the eastern Hengduan Mountains, China. We used null models to test for patterns of clustering versus overdispersion in species' abundances, phylogenetic relatedness and functional traits across sites, and applied joint distribution models to examine the correlates of pairwise associations. We found that species tended to be spatially aggregated, indicating that communities are not strongly structured by competitive exclusion. At higher elevation sites, species tended to be vegetatively more similar (clustered) and closely related. Environmental variables, including climate and topography, were strong predictors of species' ranges. No evidence of niche differentiation was detected along spatial or morphological (functional) axes, but along the temporal axis, the phenology of co‐occurring species showed significant divergence, and was less phylogenetically conserved compared to morphological traits. Synthesis. Local communities of Rhododendron in its centre of diversity are structured by environmental filtering and the effects of limiting similarity. Evidence for the latter is apparent in the pervasive phenological divergence of co‐occurring species, likely driven by reproductive interference from shared pollinators. The evolutionary lability of flowering time appears to render it the quickest path to coexistence for recently diverged species that experience secondary contact in this biodiversity hotspot.