Optimal Route Research Articles

Uncrewed Aerial Vehicle Routing Problem for Integrated Crewed and Uncrewed Aircraft Operations

Last-mile delivery systems are one of the numerous industrial applications of uncrewed aircraft systems (UAS). This study analyzes UAS-based package delivery systems in the context of the integrated operations of crewed and uncrewed aircraft. It introduces a multi-trip, time-dependent vehicle routing problem aimed at optimizing UAS delivery routes within integrated operations. By conducting numerical simulations of helicopters (crewed aircraft) and UAS missions sharing the same airspace, the optimal UAS routes for resolving potential conflicts in the shared airspace can be effectively determined without any adverse impact on helicopter operations. Knowing the helicopter mission times in advance enables the efficient planning of UAS missions. The results of this study provide valuable insights regarding the implementation of integrated crewed and uncrewed aircraft operations.

A two-stage approach for secure node localizationand optimal route selection for enhanced performance in wireless sensor networks

A two-stage approach for secure node localizationand optimal route selection for enhanced performance in wireless sensor networks

Determining Tourist Destination Priorities Using Website-Based Particle Swarm Optimization Methods (Case Study : North Padang Lawas Regency)

. Optimization of tourist routes is very important to minimize travel time and reduce travel costs. This study focuses on optimizing tourist routes in North Padang Lawas Regency using Multi Attribute Utility Theory (MAUT), and Particle Swarm Optimization (PSO) in the context of the Traveling Salesman Problem (TSP). This study discusses problems such as the lack of priority of tourist destinations and the need for shorter travel times. The research process includes problem identification, literature review, data collection through field observations and interviews, and data processing with MAUT to prioritize destinations. The identified priority destinations are Hotel Sapadia Gunung Tua, Barumun Nagari, Batik Sekar Najogi, Durian and Manggis Agrotourism, Rumah Makan Holat Alhamdulillah, Waterboom Gunung Tua, and Candi Bahal I, II, III. Furthermore, PSO is applied to determine the optimal travel route. PSO finds a route with a total travel time of 351 minutes, although the three-day travel time is extended to 375 minutes.

A hierarchical deep reinforcement learning method for solving urban route planning problems under large-scale customers and real-time traffic conditions

As urbanization and economic growth advance, large-scale customers and real-time traffic conditions have become crucial factors in urban route planning. Deep reinforcement learning is considered the most effective method for solving urban route planning problems involving large-scale customers and real-time traffic conditions. Due to memory usage limitations, existing deep reinforcement learning methods cannot identify candidate customers or determine optimal travel routes in large-scale and real-time environments. To tackle these problems, this study introduces a hierarchical deep reinforcement learning method utilizing an improved transformer model (HDRLITF) based on the divide-and-conquer concept. Graph attention networks and gate mechanisms are integrated into the transformer model to capture dynamic features and improve the model’s performance. A two-stage training method, based on the actor-critic algorithm, is proposed to determine the optimal policy function. To evaluate the HDRLITF method, experiments were conducted using datasets from the cities of Shenzhen and Chengdu in China. The experimental results suggest that the HDRLITF method can effectively interact with real-time traffic environments and obtain high-quality solutions compared to other deep reinforcement learning methods. The robustness and reliability of HDRLITF were further validated across multiple traffic scenarios and indicators.

Activity Assessment of Factor Replacement Therapies to Guide Infusion Rate Protocols in Patients with Bleeding Disorders

Abstract Introduction The development of institutional protocols guiding administration of clotting factor replacements in patients with bleeding disorders requires an understanding of product stability over infusion duration. Inclusion of new factor products on formulary at our institution raised consideration for optimal administration routes, including whether a bolus intravenous push versus infusion over 4-12h provided acceptable factor activities. Here we assessed the activity of two clotting factor replacement therapies over a 24h period to guide clinical care protocols. Method This study was performed in the Special Coagulation Lab as a collaboration between pharmacy, hematology, and laboratory medicine. Two vials each of Novoeight, a recombinant antihemophilic factor used in patients with factor VIII deficiency, and Vonvendi, a recombinant von Willebrand Factor (VWF) replacement approved for prophylaxis in adults with Type 3 von Willebrand Disease, were reconstituted to clinically relevant potencies (13 IU/ml, Novoeight; 27 IU/ml, Vonvendi). Products were spiked separately into factor VIII-deficient plasma (Novoeight) or assay diluent (Vonvendi), given inability to obtain VWF-deficient plasma, and tested for activity at various timepoints. Novoeight samples were assessed by traditional one-stage FVIII assays, while Vonvendi samples were assessed for VWF antigen, ristocetin cofactor activity (RCA), and glycoprotein Ib binding activity (GP1bM). Results Novoeight samples maintained similar FVIII activities at all timepoints tested, with differences falling within the coefficient of variation of the assay. Specifically, the two samples had FVIII levels of 108% and 95% at baseline and levels of 98% and 91%, respectively, at 12h, which was the timepoint of greatest interest given plans to allow 12h infusion rates. For Vonvendi samples, measurement of VWF antigen remained fairly stable throughout the 24h period. However, VWF activity decreased in both RCA and GP1bM assays over time, including by 4h. RCA values were 193% and 190% at baseline, 113% and 95% at 4h, and 63% and 63% at 12h. GP1bM was 140% and 133% at baseline, 92% and 91% at 4h, and 56% and 70% at 12h. Together, these results supported implementation of protocols allowing for extended (up to 12h) infusion of Novoeight and short (3-5min) bolus infusion of Vonvendi. Conclusion Our study demonstrated maintained Novoeight activity over the study period, confirming infusion rates of 12h should be efficacious. Conversely, Vonvendi yielded significantly decreased activity levels in as little as 4h, indicating the need for faster infusion rates. Based on our results, current practices at our institution were adapted to include extended infusion for Novoeight only. Drug activity studies may not directly reflect patient outcomes, and additional studies of factor activities following product infusion into patients and clinical hemostasis correlates are necessary. Nevertheless, the clinical lab should continue to play a primary role in testing, monitoring, and developing patient care protocols for therapeutic drugs through multidisciplinary collaboration.

Ginsenosides as Promising Therapeutic Agents for Glioma: Mechanisms of Action and Future Perspectives

Abstract: Brain cancers, particularly gliomas, are a significant cause of mortality worldwide. Gliomas are primary tumors of the central nervous system (CNS) and are characterized by diverse clinical and biological features. Despite advancements in clinical approaches and surgical techniques, the treatment of high-grade gliomas still poses multiple challenges. This article focuses on a key active substance found in Panax ginseng called Ginsenosides. Ginsenosides belong to a specific class of triterpenoid saponins and have demonstrated various therapeutic effects, including neuroprotective, anticancer, anti-inflammatory, and neuroprotective functions. These compounds have shown potential in the treatment of gliomas and other cancers. Several pathways associated with ginsenosides, such as Rg3, Rh2, Rd, and Rb1, have been extensively studied, and these compounds have been proposed as potential targets in glioma treatment. The precise mechanisms of action of ginsenosides in gliomas are still being investigated, but their ability to modulate various signalling pathways and exert multiple therapeutic effects makes them promising candidates for further research and development. Clinical trials and additional studies are necessary to validate their therapeutic benefits and determine the optimal dosage, administration route, and potential combination with other treatment modalities. In summary, ginsenosides, the active compounds found in Panax ginseng, exhibit various therapeutic effects, including potential anti-cancer properties in gliomas. Their ability to modulate multiple pathways makes them promising targets for further research in the field of glioma treatment. However, more studies are required to establish their effectiveness and safety in clinical settings.

Mechanism Study on Innovative Influences of AI and Blockchain on Supply Chain Logistics: Case Study of JD and Alibaba

This study explores the innovative impact mechanisms of artificial intelligence (AI) and blockchain in supply chain and logistics through the case study of JD and Alibaba. The successful cases of JD and Alibaba in supply chain management show that by integrating AI and blockchain technologies, companies can automate, smarten and make supply chain logistics more efficient, thereby reducing costs, improving efficiency and enhancing overall competitiveness. Applying AI in supply chain logistics, such as demand forecasting, inventory optimization and transport route planning, can provide accurate data analysis and decision support. It can help reduce inventory costs, shorten transportation time and optimize resource allocation in the supply chain. However, AI and blockchain technologies still face several challenges, such as data privacy and security issues, technology costs and standardization. Future research and practice will continue to work on solving these challenges, further optimizing and improving the application of AI and blockchain in supply chain logistics, and promoting innovation and development in supply chain logistics.

A Butterfly Algorithm That Combines Chaos Mapping and Fused Particle Swarm Optimization for UAV Path Planning

Effective path planning is essential for autonomous drone flight to enhance task efficiency. Many researchers have applied swarm intelligence algorithms to drone path planning. For instance, the traditional Butterfly Optimization Algorithm (BOA) has been used for this purpose. However, traditional BOA faces challenges such as slow convergence and susceptibility to being trapped in local optima. An Improved Butterfly Optimization Algorithm (IBOA) has been developed to identify optimal routes to address these limitations. Initially, ICMIC mapping is utilized to establish the butterfly community, enhancing the initial population’s diversity and preventing premature algorithm convergence. Following this, a population reset strategy is introduced, replacing weaker individuals over a specified number of iterations while maintaining a constant population size. This strategy enhances the algorithm’s ability to avoid local optima and increases its robustness. Additionally, characteristics of the Particle Swarm Optimization (PSO) algorithm are integrated to enhance the butterfly’s location update mechanism, accelerating the algorithm’s convergence rate. To evaluate the performance of the IBOA algorithm, this study designed a CEC2020 function test experiment and compared it with several swarm intelligence algorithms. The results showed that IBOA achieved the best performance in 70% of the function tests, outperforming 75% of the other algorithms. In the path planning experiments within a simulated environment, IBOA quickly converged to the optimal path, and the paths it planned were the shortest and safest compared to those generated by other algorithms.

WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks

Wireless Sensor Networks (WSNs) are vital for collecting data from remote environments. Nevertheless, the limited energy resources of sensor nodes render energy-efficient routing a critical concern for the successful operation of WSNs. To address these concerns, clustering, and routing are essential tasks in WSNs; clustering aims to organize sensor nodes into groups or clusters to minimize energy usage and prolong the network's lifespan. On the other hand, routing involves determining the optimum paths for transmitting data from the source nodes to the destination nodes. Nonetheless, it has been established that the current energy-efficient routing problem is an NP-hard, requiring a trade-off between energy and overall network performance. In this paper, we proposed a Hybrid Whale-Ant Optimization Algorithm (WAOA) for energy-efficient routing in WSNs. The proposed WAOA utilizes the Whale Optimization Algorithm (WOA) to find the suitable cluster head in the predefined search space, while the Ant Colony Optimization (ACO) searches the optimal route from the source cluster sensors to the cluster head within its predefined space. Linear programming construction is employed to formulate optimization problems for cluster head selection and search for the optimal route. The performance analysis demonstrates that the proposed WAOA performs better than MOORP, MMABC, and AZEBR by 5.78 %,16.11 %, and 18.52 %, respectively, in terms of network lifetime.

Comparative Analysis of the MDORA and AODV Routing Protocols for Vehicles Operating at Constant and Variable Speeds

The primary focuses of investigation have been and continue to be strategies for traffic management at intersections and measures to enhance road safety. An essential remedy for this issue is a Vehicular Ad-hoc Network (VANET). At VANET, a variety of protocols are used to route communications between vehicles. At this research, we utilize the Multipath Dynamic Optimized Routing Algorithm (MDORA), which is an acronym for position-based maximum distance on-demand routing. By choosing the vehicle closest to the destination vehicle, this protocol has the advantage of choosing the next best jumping path, which lowers the number of hops. In two scenarios—vehicle travel at a dynamic speed and constant speed—this paper compares the MDORA and AODV protocols. three standards: Factors including the time spent on communication, the pace at which packets are delivered, and the time it takes for information to travel from one end to another, were used to assess how well the two protocols performed. Finally, a comparison of the number of missed packets between the MDORA and AODV protocols was done for moving vehicles at constant and dynamic speeds, The MDORA protocol can identify the optimal route even when cars are moving at varying speeds, resulting in minimal packet loss.

Optimized administration of human embryonic stem cell-derived immunity-and-matrix regulatory cells for mouse lung injury and fibrosis

BackgroundLung injury and pulmonary fibrosis (PF), frequently arising as sequelae of severe and acute lung disease, currently face a dearth of effective therapeutic potions. Mesenchymal stem cells (MSCs) with immunomodulatory and tissue repair functions have immense potential to treat lung injury and PF. However, the optimal route of administration, timing, and frequency of dosing remain elusive. Human embryonic stem cell-derived immunity-and-matrix-regulatory cells (IMRCs) have shown therapeutic potential for lung injury and PF.MethodsTo ascertain the optimal therapeutic regimen for IMRCs in PF, we conducted an experimental study. Utilizing a mouse model of PF induced by bleomycin (BLM), IMRCs were administered via either a single or double intravenous (IV) or intratracheal (IT) injection on the first and seventh days post-BLM induction.ResultsOur findings revealed that IV infusion of IMRCs surpassed IT infusion in enhancing survival rates, facilitating body weight recovery, and optimizing Ashcroft and Szapiel scores among the model mice. Notably, IV administration exhibited a more profound ability to mitigate lung inflammation and fibrosis. Moreover, earlier and more frequent administrations of IMRCs were found to be advantageous in enhancing their therapeutic effects. Specifically, early administration with two IV infusions significantly improved body weight, lung organ coefficient, pulmonary ventilation and diffusion functions, and PF. This was accompanied by an increase in alveolar type I and II epithelial cells and a suppression of macrophage infiltration via CD24.ConclusionCollectively, these results suggested that IMRCs infusion ameliorated lung injury by promoting lung regeneration and inhibiting macrophage infiltration in a route, time, and frequency-dependent manner.

Pharmacokinetic and pharmacodynamic evaluation of various vasopressin doses and routes of administration in a neonatal piglet model

Epinephrine is the only recommended vasopressor during neonatal cardiopulmonary resuscitation. However, there are concerns about the potential adverse effects of epinephrine, which might hamper efficacy during cardiopulmonary resuscitation. An alternative might be vasopressin, which has a preferable adverse effect profile, however, its optimal dose and route of administration is unknown. We aimed to compare the pharmacodynamics and pharmacokinetics of various vasopressin doses administered via intravenous (IV), intraosseous (IO), endotracheal (ETT), and intranasal (IN) routes in healthy neonatal piglets. Forty-four post-transitional piglets (1–3 days of age) were anesthetized, intubated via a tracheostomy, and randomized to receive vasopressin via intravenous (control), IO, ETT, or IN route. Heart rate (HR), arterial blood pressure, carotid blood flow, and cardiac function (e.g., stroke volume, ejection fraction) were continuously recorded throughout the experiment. Blood was collected prior to drug administration and throughout the observation period for pharmacodynamics and pharmacokinetic analysis. Significant changes in hemodynamic parameters were observed following IO administration of vasopressin while pharmacokinetic parameters were not different between IV and IO vasopressin. Administration of vasopressin via ETT or IN did not change hemodynamic parameters and had significantly lower maximum plasma concentrations and systemic absorption compared to piglets administered IV vasopressin (p < 0.05). The IV and IO routes appear the most effective for vasopressin administration in neonatal piglets, while the ETT and IN routes appear unsuitable for vasopressin administration.

Interdisciplinary aspect in the organization of research and project activities of students of a technical university: on the example of the "Graph theory" discipline

The article considers the possibilities of using educational and professional humanitarian-oriented research tasks in the educational process of the university as a means of forming and developing research skills of students of technical specialties. The process of solving educational and professional tasks is considered by the example of constructing an optimal route using graph theory methods.

Applying the Simulated Annealing Algorithm to the Set Orienteering Problem with Mandatory Visits

This study addresses the set orienteering problem with mandatory visits (SOPMV), a variant of the team orienteering problem (SOP). In SOPMV, certain critical sets must be visited. The study began by formulating the mathematical model for SOPMV. To tackle the challenge of obtaining a feasible route within time constraints using the original MILP approach, a two-stage mixed-integer linear programming (MILP) model is proposed. Subsequently, a simulated annealing (SA) algorithm and a dynamic programming method were employed to identify the optimal route. The proposed SA algorithm was used to solve the SOP and was compared to other algorithms, demonstrating its effectiveness. The SA was then applied to solve the SOPMV problem. The results indicate that the solutions obtained using SA are superior and more efficient compared to those derived from the original MILP and the two-stage MILP. Additionally, the results reveal that the solution quality deteriorates as the ratio of the set of mandatory visits increases or the maximum allowable travel time decreases. This study represents the first attempt to integrate mandatory visits into SOP, thereby establishing a new research direction in this area. The potential impact of this research is significant, as it introduces new possibilities for addressing complex combinatorial optimization problems.

An agent-based framework for resilience analysis of service networks

Service networks made up of nodes and links inevitably suffer from performance degradation due to the negative effect of natural disasters and intentional attacks. Resilience is defined as the capability of recovering from disruptive events. Resilience analysis is of vital importance to evaluate network performance during the whole operation process. Given the dynamic characteristic of service networks, it is difficult to reflect the actual performance through static models. Moreover, resilience assessment should proceed with multiple metrics because network resilience is a multifaceted capability. To this end, an agent-based framework for resilience analysis is developed in this paper. Nodes are regarded as agents with independent decision-making to better respond to disruptions. The multi-agent negotiation mechanism is introduced to satisfy service requirements using deep Q-learning. In addition, network resilience is comprehensively assessed in terms of reliability, supportability and maintainability. A case study of Iridium system is conducted to illustrate the applicability of the agent-based framework. The results show that the developed framework can select the optimal route for task assignment and quantify resilience in the dynamic environment.

Iron Treatment in Patients with Iron Deficiency Before and After Metabolic and Bariatric Surgery: A Narrative Review.

Iron is an essential nutrient in living organisms with multiple vital functions. Iron deficiency (ID) can cause long term health consequences beyond iron deficiency anemia (IDA). The high prevalence of ID and its long-term effects in patients with obesity and after metabolic and bariatric surgery (MBS) is recognized. Nevertheless, there is limited knowledge of the optimal route or dose for treatment of patients with obesity and post-MBS, and an evidence-based universal guideline for prevention and treatment of ID in short- and long-term post-MBS (PMBS) is not yet available. ID in the general population is currently treated with oral or parenteral iron, where oral iron treatment is considered the preferred option with parenteral iron as a second-line treatment in case there is intolerance or lack of response to oral iron. In patients with obesity with chronic low-grade inflammation and PMBS patients with altered gut anatomy and function, there are also alterations in the bioavailability and higher risks of side effects of available oral irons. The conclusions of current studies exploring effective treatment of iron deficiency in this population have been inconsistent and further well-planned randomized and prospective studies are needed. This is a narrative review of the literature on the available treatment options and strategies for treatment of ID in PMBS patients to recognize the knowledge gaps and provides topics of future research.

Logistics Performance Index-driven in operational planning for logistics companies: A smart transportation approach

The responsibility of Logistics Companies (LCs) extends beyond simply handling logistical processes and choosing transportation routes; they focus on being competitive and efficient in their operations. In multiple origins and destinations networks, LCs attempt to select the most optimal routes from each point. To address this challenge and reduce the time needed to identify potential routes, a novel method has been developed in this research for LCs. In operational planning, additional factors need to be considered to increase efficiency, customer satisfaction, and competitiveness. To confront this issue, the study proposes a novel model and contributions, inspired by the LPI, to apply the transformative impact of smart ports, the integration of smart containers, the choices of clearing and forwarding agents and port operators, and time uncertainty in the multi-objective framework. Specifically, the mathematical model's objectives include cost, time, customer satisfaction, and environmental impact in the periodic multimodal network. An innovative customer satisfaction function is introduced by integrating the selection of smart containers and smart ports, emphasizing their impact on enhancing customer satisfaction, while addressing time uncertainty through a chance-constrained approach and coefficients of variation. The model is solved using goal programming, and the results show that smart ports and smart containers can majorly affect transportation time and customer satisfaction. Furthermore, comparing the results to other studies demonstrates its superiority in decision-making for LCs, particularly by including time uncertainty and the role of clearing and forwarding agents and port operators. Therefore, the model holds practical significance in lowering costs, enhancing customer satisfaction, and facilitating smart international logistics. This research offers insights that are not only useful for the LCs but also for other stakeholders in the transport industry.

IoT-Enabled Street Dustbin Monitoring System Based on Machine Learning

Abstract In this article, a novel method is proposed by using the power of the Internet of Things (IoT) and Machine Learning (ML) to monitor and manage street dustbins effectively. The proposed innovative solution aimed at optimizing waste management processes and improper disposing of the waste in street dustbins. The system involves the installation of sensors in dustbins, which collect real-time data about the fill level of the bins. The capability of the proposed model allows for dynamic adjustment of waste collection schedules, reducing unnecessary trips and thus saving time, energy, and resources. In addition to that, the model is also trained in various waste disposal scenario such as improper disposal, massive garbage dumps, and e-waste. The proposed model is able to predict and report the trained conditions with the 100% accuracy. The system is implemented using Raspberry Pi 4, a single-board computer with the necessary hardware interface. A suitable web interface has been developed to oversee the status of dustbins and provide optimum travel route for collection of waste from the street dustbins. The proposed system not only enhances operational efficiency but also contributes to environmental sustainability.

Optimal resilience-based restoration plan for disrupted interdependent infrastructure networks under uncertainty

The functioning of vital services in a society depends heavily on the proper and continuous operation of critical infrastructure networks, including water, transportation, power, natural gas, and telecommunication. Such networks are essential for maintaining the economy, security, and overall quality of life. However, since these critical networks are interdependent; hence, they are susceptible to various types of disruptions caused by natural disasters, failures, or malicious activities, leading to varying degrees of performance impacts that could directly affect people's daily lives. Thus, a disruption to one network can quickly spread to others, resulting in significant impacts on daily life. Therefore, restoring such disrupted networks in a timely manner is crucial to ensure a prompt recovery and minimize the impact on daily life. In this work, we consider the problem of restoring a set of interdependent infrastructure networks following a disruptive event. We study two essential factors that can accelerate the recovery process: (i) the restoration facilities location, which are considered as dispatching points for restoration crews, and (ii) the routes of the restoration crews within the interdependent networks. Accordingly, we formulate two optimization models using mixed integer programming. The first model, restoration facility location model, finds the optimal location for restoration facilities within interdependent infrastructure networks considering uncertainty in disruptions. The second model, restoration crew routing model, determine the optimal routes for restoration crews within the interdependent infrastructure networks considering: (i) travelled distance, (ii) restoration time required for disrupted network components, and (iii) different importance of network components. The two optimization models are solved through generated interdependent power and water networks to demonstrate their effectiveness.

Relationship between optic nerve length and interoptic angle in the prediction of optic chiasm location

Introduction and objectivesThe sellar region is an area in the base of the skull that is among the most common sites for tumors of the central nervous system. Surgical interventions are currently performed via different routes. While the optic chiasm occupies its expected position in 70% of the population, it can deviate from this position. In such cases, surgery involving this region becomes more difficult as the known surgical routes are narrowed. Advance awareness of these variations can help surgeons to identify the optimal route for safe surgical intervention in the sellar region. By performing simple measurements of both the lengths of the optic nerves and the angle between them, a surgeon can predict the location of the chiasm. Materials and methodsTwenty specimens collected from autopsies performed at Bursa Forensic Medicine İnstitute were examined to determine the optic chiasm types and the relationships between the surrounding subchiasmal structures. ResultsAmong the 20 specimens, we found two prefixed (10%), 10 normo-fixed (50%), and eight postfixed chiasms (40%). The mean interoptic angle was 81.03 (±17.41)⁰. Prefixed chiasms had angles in the range 115.36°–124.76 ° (mean 120.06 [±6.65]⁰), normo-fixed chiasm angles were between 83.11° and 97.53 ° (mean 86.07 [±6.73]⁰), and postfixed chiasms ranged between 53.01 ° and 78.71 ° (mean 69.20 [±9.13]⁰). The length of the right optic nerve ranged between 6.95 and 13.83 mm (mean 10.25 [±1.81] mm), and the length of the left between 7.25 and 12.51 mm (mean 10.40 [±1.47] mm). Obtuse angles indicated that the chiasm was prefixed, and acute angles were indicative of a postfixed chiasm. There was a strong negative correlation between optic nerve lengths and the interoptic angle; thus, as the length of the nerves increases, the interoptic angle becomes more acute. ConclusionsWe have proposed a simple measurement of the optic nerve lengths and the angle between them to predict the relative location of the OC, which can be done easily on MRI.