Optimal Planning Research Articles

Optimal path planning for unmanned aerial vehicles in power line inspection in rechargeable scenario

To address the issue that a single charge of UAVs (Unmanned Aerial Vehicles) do not satisfy task requirements, this paper proposes a solution involving the deployment of charging stations in the mission area. An energy-efficient path planning method is designed for UAVs with charging stations, which simultaneously determines the deployment locations and quantities of charging stations. The method formulates the energy optimization path planning problem of UAVs as an integer linear programming and an algorithm based on greedy strategy is designed for solving the model. A numerical example including simulations across various scenarios demonstrate that the proposed method can search a more energy-efficient paths compared to single-agent multi-sortie schemes with a greedy strategy. This research contributes to the development of intelligent and autonomous UAV systems for power line inspection, enhancing grid reliability and safety.

UAV 2D Path Planning Considering Ground Suitability for Flying Safety in Digital Twin

Abstract. The utilization of various UAVs such as facility inspection and delivery is increasing. Ensuring safety in carrying out various UAV missions is an important issue related to human and property damage. Safe operation of UAVs flying autonomously along pre-located routes requires safe route planning to prevent collisions between aircraft in consideration of ground and air obstacles. Pass flight is also essential to minimize damage in the event of a crash. This paper deals with the optimal UAV 2D routing plan considering ground compatibility for flight safety. High ground compatibility here means that the damage caused by the crash is less than when it is not. In this paper, we first defined dynamic and static obstacles. Next, we derived ground compatibility considering dynamic and static obstacles. We used estimation algorithms and land use data to calculate ground compatibility values for flight safety, and through experiments, we calculated an optimal 2DUAV route plan for flight safety.

Mechanical properties of pediatric low-grade gliomas in children with and without neurofibromatosis type 1.

Prognoses for pediatric brain tumors are suboptimal, as even in low-grade tumors, management techniques can lead to damage in the developing brain. Therefore, advanced neuroimaging methods are critical for developing optimal management plans and improving patient care. Magnetic resonance elastography (MRE) has allowed for the characterization of adult gliomas by their mechanical properties, which are uniquely sensitive to the complex interplay of cellularity, vasculature, and interstitium. However, pediatric tumors differ in behavior and cytoarchitecture, and their mechanical properties have never been assessed. Here, we conduct the first study of pediatric brain tumor mechanical properties by using MRE to measure tissue stiffness and damping ratio in low grade gliomas (LGGs). We additionally measure the mechanical properties of non-neoplastic focal abnormal signal intensities (FASIs) in children with neurofibromatosis type 1 (NF1). 23 patients age 4-17 years who had MR imaging results consistent with a primary LGG or with NF1 were included in this study. We found that pediatric gliomas are on an average 10.9% softer (p = 0.010) with a 17.3% lower (p = 0.009) viscosity than reference tissue. Softness of tumors appeared consistent across tumor subtypes and unrelated to tumor size or contrast-enhancement. In NF1 we found that, unlike gliomas, FASIs are stiffer, though not significantly, than reference tissue by an average of 10.4% and have a 16.7% lower damping ratio. Measuring tumor mechanical properties patterning and heterogeneity has potential to aid in prediction of biological behavior and inform management strategies for pediatric patients.

Optimization of route planning for the mobile robot using a hybrid Neuro-IWO technique

Optimization of route planning for the mobile robot using a hybrid Neuro-IWO technique

Optimal planning of SOP in distribution network considering 5G BS collaboration

AbstractThe flexibility of soft open point (SOP) in spatial power regulation enhances the distribution network's (DN) integration of large‐scale renewable energy sources. However, the high cost of SOP and its limited capability for temporal power regulation impede its widespread adoption. Given the rapid expansion of 5G base stations (BSs), utilizing their energy storage to participate in DN planning and operation optimization provides a promising solution. Therefore, this paper proposes an optimal planning method of SOP in DN, considering collaborations with 5G BSs. The objective is to enhance DN’s power regulation in both temporal and spatial dimensions, while minimizing the investment cost of SOP and fully utilizing the unused capacity in base station energy storage (BSES). Firstly, the flexible regulation models of SOP and 5G BS are established, with the real‐time dispatchability of BSES formulated. Then, a bi‐level optimization model is proposed, where the planning layer aims to minimize the total cost, while the operational layer aims to decrease the average voltage deviation. Additionally, an improved Shapley value method based on interactive power is developed for benefit allocation, which enhances the engagement of 5G BSs to participate in DN regulation. The effectiveness of proposed method is validated by simulation results.

Joint optimization of part quality inspection planning, buffer allocation, and preventive maintenance in a serial manufacturing

Joint optimization of part quality inspection planning, buffer allocation, and preventive maintenance in a serial manufacturing

Overall plan optimization of BWB UAV based on comprehensive evaluation

Based on the shape and performance characteristics of the wing-body fusion layout UAV, a multidisciplinary comprehensive analysis model, comprehensive evaluation model and optimization model suitable for the overall scheme of the UAV in the conceptual design stage are established, and the corresponding tools are developed. The geometric, weight, aerodynamic and stealth characteristics of the overall scheme of the wing-body fusion layout UAV are analyzed by combining numerical analysis and engineering methods. The improved TOPSIS method is used to establish a comprehensive evaluation model from the perspective of the loading capacity, economy and adaptability of the UAV. The rationality of the analysis model is verified by taking multiple UAV schemes as examples. Using the parallel subset simulation optimization algorithm, the traditional multi-objective optimization is completed with the maximum cruise lift-to-drag ratio and the minimum forward RCS as the goal, and the scheme optimization based on comprehensive evaluation is completed with the strongest competitiveness as the goal. Based on all the schemes generated by the multi-objective optimization process, the parameter correlation analysis is completed, and the influence of the span, sweep angle and twist angle on the aerodynamic and stealth characteristics is verified. The RCS of the final multi-objective optimization scheme is reduced 41.6%, and the cruise lift-to-drag ratio is improved 3.5% ; the competitiveness score of the comprehensive evaluation optimization scheme is improved 44.0%.

Implementation and validation of a very-high-energy electron model in the matRad treatment planning system.

While electron beams of up to 20 MeV are commonly used in radiotherapy, the use of very-high-energy electrons (VHEEs) in the range of 100-200 MeV is now becoming a realistic option thanks to the recent advancements in accelerator technology. Indeed, VHEE offers several clinically attractive features and can be delivered using various conformation methods (including scanning, collimation, and focussing) at ultra-high dose rates. To date, there is a lack of research tools for fast simulation of treatment plans using VHEE beams. This work aims to implement and validate a simple and fast dose calculation algorithm based on the Fermi-Eyges theory of multiple Coulomb scattering for VHEE radiation therapy, with energies up to 200 MeV. A treatment planning system (TPS) toolkit with VHEE modality would indeed allow for further preclinical investigations, including treatment plan optimization and evaluation, and thus contribute to the gradual introduction of VHEE radiotherapy in clinical practice. A VHEE pencil beam scanning double Gaussian model was introduced into the open-source TPS matRad environment along with new functions and options dedicated to VHEE dose calculations. Various geometries and field configurations were then calculated in matRad (up to 200 MeV and 15×15 cm2, with complex bone or lung heterogeneities) and the results were compared to Monte Carlo simulations in the TOPAS/Geant4 toolkit. Two types of beam model (divergent or focused) were also tested. Examples of clinical treatment plans were computed, and the results were compared between the two codes. VHEE modality was fully implemented in matRad with GUI capabilities while preserving all original TPS features. New relevant options such as the importation of specific spot-lists or adjustment of the lateral dose calculation cutoff to optimize the calculation speed were validated. Single spot and square field dose distributions were validated in water alone as well as in clinically relevant inhomogeneities. Dose maps from the VHEE model in matRad were in good agreement with TOPAS (2D gamma index [2%/1mm] with passing rates superior to 90%, <6% mean dose differences), except for large interface heterogeneities. This work describes the implementation of a simple but efficient VHEE simulation model in matRad. A few configurations were studied in order to validate the model against accurate Monte Carlo simulations, demonstrating its usefulness for carrying out preliminary studies involving VHEE radiotherapy.

Prediction of feed force with machine learning algorithms in boring of AISI P20 plastic mold steel

Feed force plays a critical role in machining performance, including efficiency, hole quality, energy consumption, and tool life. While feed force is typically measured using dynamometers, these devices are often impractical in real-world experiments due to their complexity and cost. In particular, boring operations, which are essential for enlarging holes within tight tolerances, depend on precise feed force control to maintain high-quality hole outcomes. Achieving this precision requires reliable pre-process feed force estimation. However, traditional analytical methods for calculating feed force in boring operations often fall short, largely due to factors such as in-hole dynamics, the inclination of the rake surface, and the slenderness of the boring bar. This study aims to apply single and hybrid machine learning methods to accurately model and predict feed force in the boring process. Unlike previous research, which focused on feature selection, we propose the use of all possible combinations of input feature subsets to determine the optimal input features. The results indicate that feed force can be predicted effectively using these optimized feature combinations. Such accurate feed force prediction is crucial for effective process optimization, material selection, and process planning.

Refined Identification of Urban Functional Zones Integrating Multisource Data Features: A Case Study of Lanzhou, China

Identifying urban functional zones is one of the important foundational activities for urban renewal and the development of high-quality urban areas. Efficient and accurate identification methods for urban functional zones are significant for smart city planning and industrial layout optimization. However, existing studies have not adequately considered the impact of the interactions between human activities and geographical space provision on the delineation of urban functional zones. Therefore, from the perspective of integrating the spatiotemporal characteristics of human activities with the distribution of urban functional facilities, by incorporating mobile signaling, POI (point of interest), and building outline data, we propose a multifactorial weighted kernel density model that integrates ‘human activity–land feature area–public awareness’ to delineate urban functional zones quantitatively. The results show that the urban functional zones in the central city area of Lanzhou are primarily characterized by dominant single functional zones nested within mixed functional zones, forming a spatial pattern of ‘single–mixed’ synergistic development. Mixed function zones are widely distributed in the center of Lanzhou City. However, the area accounted for a relatively small proportion, the overall degree of functional mixing is not high, and the inter-district differences are obvious. The confusion matrix showed 85% accuracy and a Kappa coefficient of 0.83.

Replacing manual planning with automatic iterative planning for locally advanced rectal cancer VMAT treatment.

To develop and implement a fully automatic iterative planning (AIP) system in the clinical practice, generating volumetric-modulated arc therapy plans combined with simultaneous integrated boost technique VMAT (SIB-VMAT) for locally advanced rectal cancer (LARC) patients. The designed AIP system aimed to automate the entire planning process through a web-based service, includingauxiliary structuregeneration,plan creation,field configuration,plan optimization,dose calculation, andplan assessment. The system was implemented based on theEclipse scriptingapplication programming interface and an efficientiterative optimization algorithm was proposed to reduce the required iterations in the optimization process. To verify the performance of the implemented AIP system, we retrospectively selected a total of 106 patients and performed dosimetric comparisons between the automatic plans (APs) and the manual plans (MPs), in terms of dose-volume histogram (DVH) metrics, homogeneity index (HI), and conformity index (CI) for different volumes of interest. The AIP system has successfully created 106 APs within clinically acceptable timeframes. The average planning time per case was 36.8±6.5 min, with an average iteration number of 6.8(±1.1) in plan optimization. Compared to MPs, APs exhibited better performance in the planning target volume conformity and hotspot control ( ). The organs at risk (OARs) sparing was significantly improved in APs, with mean dose reductions in the femoral heads, the bone marrow, and the SmallBowel-Avoid of 0.53Gy, 1.18Gy, and 1.00Gy, respectively ( ). Slight improvement was also observed in the urinary bladder and the small bowel . Additionally, quality variation between plans from different planners was observed in DVH metrics while the APs represented better plan quality consistency. An AIP system has been implemented and integrated into the clinical treatment planning workflow. The AIP-generated SIB-VMAT plans for LARC have demonstrated superior plan quality and consistency compared with the manual counterparts. In the meantime, the planning time has been reduced by the AIP approach. Based on the reported results, the implemented AIP framework has been proven to improve plan quality and planning efficiency, liberating planners from the laborious parameter-tuning in the optimization phase.

Responses of a short column-supported highrise tower to adjacent deep excavations in water-rich sandy strata and dynamic optimization of protection plans

Responses of a short column-supported highrise tower to adjacent deep excavations in water-rich sandy strata and dynamic optimization of protection plans

Seeing the Big‐ to Fine‐Grained Picture: Exploring the Baseline Status of Mammal Occupancy Across Myanmar Using Scale‐Optimised Modelling

ABSTRACTAimMyanmar, an Indo‐Burmese biodiversity hotspot, lacks baseline data on species occurrence and distribution. This hinders biodiversity monitoring and optimisation of conservation and development plans. We aim to document baseline mammal occupancy, interactions with environmental factors and scale‐dependent responses.LocationHkakaborazi National Park, Htamanthi Wildlife Sanctuary, Alaungdaw Kathapa National Park, Rakhine Yoma Elephant Range, Say Taung and Myinmoletkhat Key Biodiversity Areas distributed across Myanmar.MethodsCamera trap data throughout Myanmar were used to analyse species occupancy. We conducted a multiscale hierarchical spatial modelling process, using local and pooled data across Myanmar. We also optimised spatial scale across five scales and six predictors, using univariate occupancy models. We then selected scale‐optimised variables for multivariate modelling, repeating this process for each species across local, regional and national datasets.ResultsThe study identified 47 terrestrial species and observed strong scale‐dependent nonstationarity in occupancy estimates. Relationships with environmental variables differed among species and were highly scale dependent. Importantly, occupancy estimates produced by pooling data across sites were greatly different from any of the estimates for the individual sites, suggesting that high heterogeneity in occurrence and abundance across sites among species requires local or nested occupancy estimates to account for spatial heterogeneity and variation.Main ConclusionsFuture conservation efforts should focus on Northern Myanmar if range‐restricted and rare species are to be protected, while focus should still be given to common species which serve as potential indicators of overall community structure. The nonstationarity of occupancy results from different datasets underscores the potential for misleading interpretations from aggregated data in nonstationary ecological systems. Metareplicated analyses of local, geographically and ecologically proximal regional datasets provide an important view of spatial variation in occupancy patterns guiding conservation design and improving understanding of the drivers of biodiversity patterns and change across large regions, such as Southeast Asia.

Performance Evaluation for the Expansion of Multi-Level Rail Transit Network in Xi’an Metropolitan Area: Empirical Analysis on Accessibility and Resilience

As the main form of new urbanization, the coordinated development of cities in metropolitan areas requires reliable and efficient rail transit skeleton support. However, in the rapid development of metropolitan areas, the layout and analysis of multi-level rail transit systems have a certain lag. Taking the Xi’an metropolitan area as an example, this study analyzes the comprehensive accessibility and resilience of the multi-level rail transit network, and proposes an expansion plan accordingly. The traffic analysis zone (TAZ) is divided by towns and streets, and the relationship between points of interest (POIs) and the regional average level is analyzed using DEA. The improved weighted average travel time model is built with the analysis results as regional weights; a site selection model based on multiple construction influencing factors is proposed, and four expansion plans, namely, economic optimal, environmental optimal, transport optimal, and integrated optimal, are designed. The peak passenger flow scenario and the “failure–reparation” scenario during the entire operation period are designed to analyze the resilience of four plans, and the resilience is quantified by the elasticity curve of the maximum connected subgraph ratio (MCSR) changing over time. The research results show that the transport optimal plan has the best comprehensive accessibility and resilience, reducing travel costs in Houzhenzi Town, which has the worst accessibility, by 34%. The expansion model and evaluation method in this study can provide an empirical example for the development of other metropolitan areas and provide a reasonable benchmark and guidance for the development of multi-level rail transit networks in future urban areas.

Multiphysics Field Coupled to a Numerical Simulation Study on Heavy Oil Reservoir Development via Electromagnetic Heating in a SAGD-like Process

Conventional thermal recovery methods for heavy oil suffer from significant issues such as high water consumption, excessive greenhouse gas emissions, and substantial heat losses. In contrast, electromagnetic heating, as a waterless method for heavy oil recovery, offers numerous advantages, including high thermal energy utilization, reduced carbon emissions, and volumetric heating of the reservoir, making it a focus of recent research in heavy oil thermal recovery technologies. This paper presents a numerical simulation study of electromagnetic heating for heavy oil recovery, using a heavy oil block in the Bohai Bay oilfield in China as a case study. Firstly, a multiphysics field coupled to a mathematical model was established, considering the impact of the temperature on the heavy oil viscosity, the threshold pressure gradient of non-Darcy flow, and the dielectric properties of the reservoir, along with heat dissipation from overlying and undercover sandstone and gravitational effects on fluid flow. Secondly, a numerical simulation method for the coupled multiphysics fields was developed, and the convergence and stability of the numerical simulation method were tested. Finally, a sensitivity analysis based on the numerical simulation results identified the factors affecting heavy oil production. It was found that electromagnetic heating significantly enhances heavy oil production, and the threshold pressure gradient greatly influences the prediction of heavy oil production. Moreover, heat dissipation from the overlying and undercover sandstone severely reduces cumulative oil production. When the production well is located below the electromagnetic heating antenna, larger well spacing results in higher cumulative heavy oil production. Higher heavy oil production is achieved when the antenna is positioned at the center of the reservoir for the studied cases. Power has a big effect on increasing heavy oil production, but its influence diminishes as power increases. There exists an optimal range of electromagnetic frequencies for maximum cumulative production, and higher water saturation leads to poorer electromagnetic heating efficiency. This study provides a theoretical foundation and technical support for the numerical simulation technology and development plan optimization of heavy oil reservoirs subjected to electromagnetic heating.

Efficiency of dynamic traffic signal control in work zones with shuttle operation: theory and practice

Portable traffic signals with fixed-time signal plans are a common type of traffic control at work zones with shuttle traffic. The most-used alternatives are flagging and intelligent transport systems with traffic-actuated signals. These can provide more efficient traffic control, but existing policies often do little to encourage their practical application. This paper provides a clear and accessible overview of shuttle operations and a comparison of the main signal control types while addressing some knowledge gaps, such as whole-day operation efficiency. The relations between different variables of the signal plan and traffic flow are derived to build a theoretical framework and models for both deterministic and random arrivals to estimate delays and to find the optimal signal plan setting for a wide range of circumstances. The hypothetical scenarios are supported by a case study. Traffic and signal control data from several construction phases of a work zone with shuttle operation were gathered, processed, analysed, and used to compare different control scenarios. The results provide solid evidence for the efficiency of the dynamic systems. The magnitude of the difference is heavily affected by circumstances. The efficiency of the green signal almost doubled with the dynamic control. The case study also revealed a severe impact of road conditions (milling) on the work zone capacity. Several standards and policies are proposed based on the findings to encourage wider and more efficient adoption of traffic-actuated signals in work zones.

Hemorrhagic intramedullary spinal cord metastasis from renal cell carcinoma: a rare case 15 years after cured renal cell carcinoma. Illustrative case.

Renal cell carcinoma (RCC), the most common kidney cancer, often metastasizes to bones, lungs, liver, and the central nervous system. Intramedullary spinal metastasis from RCC is rare but can cause significant neurological deficits, necessitating prompt diagnosis and treatment through surgical intervention, radiotherapy, and immunotherapy. An 86-year-old man presented with progressive right lower-limb weakness and reduced sensation over 3 weeks. His medical history included a right nephrectomy for RCC 15 years earlier and L4-S1 spondylosis. Imaging identified a bleeding lesion in the conus medullaris at T11-12 and an incidental left kidney mass. Urgent surgical exploration led to a T12 laminectomy and en bloc removal of the lesion, which was confirmed as RCC metastasis. Postoperatively, the patient received focused radiotherapy and immunotherapy, showing significant motor and sensory improvement before dying 3 months later. This case underscores the importance of comprehensive diagnostic imaging for the accurate identification and characterization of spinal lesions. An interdisciplinary approach involving neurosurgeons, oncologists, radiologists, and pathologists is crucial for optimal treatment planning. Urgent surgical intervention can effectively address acute neurological deficits caused by intramedullary lesions. Additionally, adhering to postoperative care instructions, such as deep venous thrombosis prophylaxis, is vital to prevent fatal complications. https://thejns.org/doi/10.3171/CASE24349.

Optimal Academic Plan Derived from Articulation Agreements: A Preliminary Experiment on Human-Generated and (Hypothetical) Algorithm-Generated Academic Plans

ABSTRACT Our preliminary experiment examined a potential pain point with ASSIST, California’s database of articulation agreements. That pain point is cross-referencing multiple articulation agreements to manually develop an optimal academic plan. Optimal is defined as the minimal set of community college courses that satisfy all transfer requirements for the multiple universities a student is preparing to apply to. Accordingly, we designed a low-fidelity prototype that lists the minimal set of courses a hypothetical optimization algorithm would output based on selected articulation agreements. Twenty-four students were tasked with creating an optimal academic plan using either ASSIST (which requires manual optimization) or the optimization prototype (which already provides the minimal set of classes). Prototype users had less optimality mistakes, were faster, and provided higher usability ratings compared to ASSIST users. Going forward, future research needs to move beyond our proof of value of a hypothetical optimization algorithm and toward actually implementing an algorithm.

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.

Application of artificial intelligence in construction

This paper mainly introduces the application and assistance of artificial intelligence in architecture and construction cost, as well as the future prospects and development trends. The study adopts a quantitative research design of survey research and data collection method of literature analysis. In addition, it can further explore the innovative applications of artificial intelligence in architectural design, such as spatial planning optimization based on big data analysis and simulation experience through virtual reality technology. At the same time, it can also deeply explore the role of artificial intelligence in the automated management of construction processes, material selection optimization, etc., and give detailed explanations with specific cases. In addition, when discussing the future prospects and development trends, it can consider the prediction of the impact of the continuous evolution of artificial intelligence technology on the construction industry, such as the wider application of machine learning algorithms in project management, the gradual maturity of intelligent sensor systems in building equipment monitoring. At the same time, it can also analyze the profound impact of artificial intelligence on the future development of the construction industry from economic, social and environmental angles, and put forward corresponding solutions.