Optimal Planning Research Articles

The Role of AI in Improving Surgical Outcomes

Artificial intelligence (AI) is transforming surgical procedures by improving precision, efficiency, and patient outcomes. This study investigates the role of artificial intelligence in the optimization of robotic-assisted surgeries, preoperative planning, and intraoperative decision-making. With the rise of machine learning algorithms trained on surgical data, AI can detect issues, increase safety, and supplement surgical teams’ capabilities. This review focuses on existing surgical practice challenges, the incorporation of AI into the operating theater, and the potential for AI-driven technologies to reduce human errors. The ethical concerns and future developments required for widespread AI usage in surgery are also addressed. Keywords: Artificial intelligence, surgical robots, machine learning, patient safety, preoperative planning.

Multi-objective optimization of distributed photovoltaics on building surfaces from visual impact

ABSTRACT Deployment of distributed building photovoltaic (PV) applications creates a complicated decision-making process to balance conflicting objectives with various constraints. As an important social component, visual impact is introduced into a multi-objective optimization framework together with performance-cost and spatial compactness as three objectives for the planning of distributed PV systems. The proposed framework is applied to a study case, located in Xi’an city, China, to present the main technical process for PV deployments on 4618 building surfaces. Considering the spatial heterogeneity of solar irradiation and visibility, optimal plans were achieved and representative solutions were discussed and visualized in a 3D environment. Moreover, the effectiveness of the proposed approach was verified in comparison with two heuristic planning methods. The multi-objective optimization framework provides an energy planning approach for distributed building PV deployments, enabling planners to make informed decisions that balance competing priorities among performance-cost, visual impact, and compactness.

Comparative efficacy and safety of ciprofol, propofol, and propofol and etomidate mixture, and ciprofol and etomidate mixture in patients undergoing painless gastroscopy: A randomized, double-blind controlled clinical trial.

To compare the efficacy and safety of ciprofol, propofol, propofol and etomidate mixture or ciprofol and etomidate mixture in patients undergoing painless gastroscopic anesthesia, and to explore the optimal plan to relieve the patient's discomfort. A total of 120 patients scheduled for painless gastroscopy were randomly assigned to 4 groups: propofol (Group P), ciprofol (Group C), propofol-etomidate mixture (Group P-E), and ciprofol-etomidate mixture (Group C-E). The success rate of gastroscopy examination, patient satisfaction, incidence of injection pain, hemodynamic parameters, induction time, procedure time, the consumption of drugs, awakening time, and incidence of adverse events were evaluated. All patients in the study successfully completed the gastroscopy. The satisfaction of patients in Group C-E was significantly higher than that in Group P (P < .05), but there was no statistical significance in the patient satisfaction among the other groups. Compared with Group P, the incidence of injection pain in Groups C and C-E significantly decreased (P < .05). There were no significant differences in the SBP, diastolic blood pressure, HR, and SpO2 among the 4 groups (P > .05). The awakening time of Group C was significantly longer than that of Groups P and P-E (P < .05), but there was no statistically significant difference in the awakening time of other groups. Ciprofol demonstrated efficacy in inducing sedation or anesthesia during painless gastroscopy that was similar to propofol, while exhibiting a comparable safety profile. Moreover, the combination of propofol and etomidate, as well as the combination of ciprofol and etomidate, were both shown to be equally safe and effective for this clinical application. These findings suggest that ciprofol can be considered as a safe and effective alternative for painless gastroscopy, and the ciprofol-etomidate mixture may be a better choice.

Comprehensive and practical optimal delivery planning system for replacing liquefied petroleum gas cylinders

Comprehensive and practical optimal delivery planning system for replacing liquefied petroleum gas cylinders

Efficient allocation of energy storage and renewable energy system for performance and reliability improvement of distribution system

This paper aims to investigate the impact of optimal planning of renewable energy systems and independent battery energy storage systems (BESS) on the performance and reliability of distribution systems (DS). A unique energy management scheme (EMS) controls the charging and discharging schedules of the BESS. The transit search optimization (TSO) algorithm determines the optimal allocation of wind turbine (WT) and solar photovoltaic (SPV) generator units, along with BESS units. The optimization focuses on improving the DS reliability in terms of energy supply, increasing the feeder security margin, and reducing apparent power loss. Multiple case studies are designed to study the efficacy of the problem outcomes and are validated using Bus 4 of the Roy Bollington Test System (RBTS). To obtain results that are close to actual operating conditions, the modelling process takes into account the time-varying load profile and the dynamic power outputs from the SPV and WT units. The findings show that the suggested method works well by enhancing the feeder security margin by a maximum of 11.25%, reducing expected energy not supplied (EENS) by 4.3%, and improving the power loss profile by 11.5%. In addition, the feeder upgrade deferral (FUD) years also show a maximum improvement of 20.8%.

Dose tracking assessment for magnetic resonance guided adaptive radiotherapy of rectal cancers

BackgroundMagnetic resonance-guided adaptive radiotherapy (MRgART) at MR-Linac allows for plan optimisation on the MR-based synthetic CT (sCT) images, adjusting the target and organs at risk according to the patient’s daily anatomy. Conversely, conventional linac image-guided radiotherapy (IGRT) involves rigid realignment of regions of interest to the daily anatomy, followed by the delivery of the reference computed tomography (CT) plan. This study aims to evaluate the effectiveness of MRgART versus IGRT for rectal cancer patients undergoing short-course radiotherapy, while also assessing the dose accumulation process to support the findings and determine its usefulness in enhancing treatment accuracy.MethodsNineteen rectal cancer patients treated with a 1.5 Tesla MR-Linac with a prescription dose of 25 Gy (5 Gy x 5) and undergoing daily adapted radiotherapy by plan optimization based on online MR-based sCT images, were included in this retrospective study. For each adapted plan (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{T}\ ext{P}}_{\ ext{a}\ ext{d}\ ext{a}\ ext{p}}$$\\end{document}), a second plan (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{T}\ ext{P}}_{\ ext{I}\ ext{G}\ ext{R}\ ext{T}}$$\\end{document}) was generated by recalculating the reference CT plan on the daily MR-based sCT images after rigid registration with the reference CT images to simulate the IGRT workflow. Dosimetry of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{T}\ ext{P}}_{\ ext{a}\ ext{d}\ ext{a}\ ext{p}}$$\\end{document} and\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\:{\ ext{T}\ ext{P}}_{\ ext{I}\ ext{G}\ ext{R}\ ext{T}}$$\\end{document}was compared for each fraction. Cumulative doses on the first and last fractions were evaluated for both workflows. The dosimetry per single fraction and the cumulative doses were compared using dose-volume histogram parameters.ResultsNinety-five fractions delivered with MRgART were compared to corresponding simulated IGRT fractions. All MRgART fractions fulfilled the target clinical requirements. IGRT treatments did not meet the expected target coverage for 63 out of 94 fractions (67.0%), with 13 fractions showing a V95 median point percentage decrease of 2.78% (range, 1.65-4.16%), and 55 fractions exceeding the V107% threshold with a median value of 15.4 cc (range, 6.0-43.8 cc). For the bladder, the median \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{D}}_{15\ ext{c}\ ext{c}}$$\\end{document} values were 18.18 Gy for the adaptive fractions and 19.60 Gy for the IGRT fractions. Similarly the median \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{D}}_{5\ ext{c}\ ext{c}}$$\\end{document} values for the small bowel were 23.40 Gy and 25.69 Gy, respectively. No statistically significant differences were observed in the doses accumulated on the first or last fraction for the adaptive workflow, with results consistent with the single adaptive fractions. In contrast, accumulated doses in the IGRT workflow showed significant variations mitigating the high dose constraint, nevertheless, more than half of the patients still did not meet clinical requirements.ConclusionsMRgART for short-course rectal cancer treatments ensures that the dose delivered matches each fraction of the planned dose and the results are confirmed by the dose accumulation process, which therefore seems redundant. In contrast, IGRT may lead to target dose discrepancies and non-compliance with organs at risk constraints and dose accumulation can still highlight notable dosimetric differences.

Voyage scheduling and energy management co-optimization in hydrogen-powered ships

The maritime sector is striving to lessen its dependence on fossil fuels, motivated by both economic factors related to fuel efficiency and the policies established by the International Maritime Organization (IMO). Consequently, there has been a growing focus on developing and deploying all-electric ships (AES) as a more sustainable alternative to conventional fossil fuel-powered vessels. AES presents several advantages over conventional ships, including zero emissions, enhanced energy efficiency, and greater sustainability. However, the implementation of AES also brings new challenges, such as the necessity to manage a hybrid microgrid and optimize voyage scheduling. Hybrid fuel cell (FC)-battery energy storage (BES) technology can significantly contribute to addressing these requirements. To tackle these challenges, this article introduces a comprehensive framework for the optimal planning of hybrid microgrids and voyage scheduling in AES. The framework incorporates multi-objective optimization techniques based on genetic algorithms to achieve a balance between cost and voyage time. The proposed framework is applied to a case study of a ship operating on a fixed route between two ports. The simulation results indicated that the total cost per voyage, associated with the objective function of cost minimization and voyage time reduction, has decreased by 12% and increased by 40%, respectively, when compared to the regular ship speed profile. Additionally, the travel time has been reduced by half an hour and 2 h, respectively, in comparison to the regular ship speed profile. To verify the results, the optimization problem for cost and voyage scheduling is also solved by Mayfly Algorithm (MA) separately. Thus, it can be illustrated the effectiveness of the framework in enhancing energy efficiency and fuel economy while preserving the ship's performance.

Optimal planning of wind and solar complementary AC/DC microgrids under distributed power capacity constraints

Abstract The conventional AC/DC microgrid wind-solar complementary optimization planning method mainly uses the CvaR (conditional value at risk) risk value stochastic model to calculate the randomness of output electricity price, which is vulnerable to changes in load expectations, resulting in the per unit value of photovoltaic load output not meeting the actual demand of the microgrid. Therefore, under the constraints of distributed generation capacity, an optimal planning method of wind-solar complementation for AC/DC microgrids is designed. That is to say, considering the distributed generation capacity constraints, the optimal planning model of wind-solar complementation of AC and DC microgrids is constructed, and the bidirectional adjustment equation of the optimal planning of wind-solar complementation of AC and DC microgrids is generated, thus realizing the optimal planning of wind-solar complementation of AC and DC microgrids. The experimental results show that the PV output per unit value is higher and the load output per unit value is lower in the design of the wind-solar complementary optimization planning method of AC/DC microgrid considering distributed generation capacity constraints, which meets the requirements of efficient operation of the microgrid and has certain economic value.

Accuracy of Tooth Segmentation in the Digital Kesling Setup of Two Different Software Programs: A Retrospective Study.

Introduction Precise virtual setup creation and orthodontic appliance fabrication depend on accurate teeth segmentation from intraoral scans. This accuracy is also fundamental for successful orthodontic treatment, as it ensures correct diagnosis and optimal treatment planning. A number of software packages that facilitate the building of virtual setups have been made available in recent years. The performance of these software packages on automatic tooth segmentation has not been widely studied. Hence, the aim of this study was to evaluate the accuracy of automated teeth segmentation in the digital Keslingsetup of Ortho Studio (Maestro 3D Dental Studio, Bordeaux, France) and OrthoAnalyzer (3Shape, Copenhagen, Denmark) software systems. Materials and methods All the scans were taken from the same intraoral scanner (Runyes 3D intraoral scanner; Runyes Medical, Ningbo, China). The scans were stored and imported as stereolithography (STL) files into the Maestro Ortho Studio and 3Shape OrthoAnalyzer software systems. Subsequently, the digital photos underwent alignment in both software applications, an essential stage in each respective workflow prior to any further processing. The digitized images were automatically segmented in Maestro and 3Shape software by a single researcher. For each software interface, the accuracy of teeth segmentation was assessed. An independent t-test, with a significance level set at p < 0.05, was used to evaluate the statistical significance between the two software segmentations. Results The total number of teeth segmented by both software programs utilizing the 12 intraoral scans was 336 for both groups. Successful identification of the tooth segments was 98.21% (n = 330) for 3Shape software and 98.8% (n = 332) for Maestro software. There was no significant difference in the accuracy of determining the tooth segmentations between anterior and posterior teeth, respectively, between both groups, with a p-value of 0.523. Conclusion There were no statistically significant differences between the two software programs, and both demonstrated high success rates for auto-tooth segmentation. Although both programs had excellent success rates, Maestro 3D performed more accurately than 3Shape OrthoAnalyzer.

Clinical and therapeutic implications of cavernous sinus invasion in pituitary adenomas.

Invasion of the cavernous sinus by pituitary adenomas impedes complete surgical resection, compromises biochemical remission, and increases the risk of further tumor recurrence. Accurate preoperative MRI-based diagnosis or intraoperative direct inspection of cavernous sinus invasion are essential for optimal surgical planning and for tailoring postoperative therapeutic strategies, depending on whether a total resection has been achieved, or tumoral tissue has been left in surgically inaccessible locations. The molecular mechanisms underlying the invasive behavior of pituitary adenomas remain poorly understood, hindering the development of targeted therapies. Some studies have identified genes overexpressed in pituitary adenomas invading the cavernous sinus, offering insights into the acquisition of invasive behavior. Their main limitation however lies in comparing purely intrasellar specimens obtained from invasive and non-invasive adenomas. Further, precise anatomical knowledge of the medial wall of the cavernous sinus is crucial for grasping the mechanisms of invasion. Recently, alongside the standard intrasellar surgery, extended endoscopic intracavernous surgical procedures with systematic selective resection of the medial wall of the cavernous sinus have shown promising results for invasive secreting pituitary adenomas. The first- and second-generation somatostatin agonist ligands and cabergoline are used with variable efficacy to control secretory activity and/or growth of intracavernous remnants. Tumor regrowth usually requires surgical reintervention, sometimes combined with radiotherapy or radiosurgery which is applied despite their benign nature. Unraveling the molecular pathways driving invasive behavior of pituitary adenomas and their tropism to the cavernous sinuses is the key for developing efficient innovative treatment modalities that could reduce the need for repeated surgery or radiotherapy.

Atopic Dermatitis (Eczema) Guidelines 2023: Highlights

Atopic Dermatitis (AD) is one of the most common chronic inflammatory skin disorders with a complex pathogenesis. AD is characterized by eczematous skin lesions, pruritus, and recurrent skin infections; with negative impact on patients (and caregivers) quality of life.The American Academy of Allergy, Asthma and Immunology (AAAAI)/American College of Allergy, Asthma and Immunology (ACAAI) Joint Task Force (JTF) Atopic Dermatitis Guideline Panel recently released updated AD guidelines. This guideline focuses on addressing clinical questions using trustworthy guideline development standards, including mitigating the potential influence of financial and non-financial conflicts of interest, and GRADE methodology. A multidisciplinary panel used systematic reviews and meta-analyses to inform specific recommendations addressing optimal use of (1) topical treatments, (2) dilute bleach bath, (3) dietary avoidance/elimination, (4) allergen immunotherapy and (5) systemic treatments. The comprehensive recommendations, emphasizing the third principle of evidence-based medicine - that evidence alone is never enough; that patient values and preferences must be carefully considered when determining optimal treatments for patients and populations - provide a framework to support clinicians in selecting an optimal treatment plan for each patient.This review provides an overview of the guideline and discusses how those recommendations relate to current practice.

Evolutionary Optimization for Unmanned Underwater Vehicle Navigation

Unmanned Underwater Vehicles (UUVs) play a vital role in various underwater exploration and surveillance tasks. However, the effective navigation of UUVs in complex underwater environments poses significant challenges due to factors such as limited communication, dynamic currents, and other difficulties. Evolutionary optimization techniques have developed as promising tools for enhancing UUV navigation abilities. This review provides a brief overview of the application of evolutionary optimization algorithms, including genetic algorithms, evolutionary approaches, and particle swarm optimization, in the context of UUV navigation. The fundamental principles of these algorithms and their applications in path planning, path optimization, localization, obstacle avoidance, and mission planning for UUVs are discussed in brief. Through an analysis of existing literature and case studies, the use of evolutionary optimization in improving the navigation efficiency, accuracy, and robustness of UUVs is highlighted. Additionally, current challenges were identified, and future research directions to advance the integration of evolutionary optimization techniques in UUV navigation systems are also discussed. Overall, this aims to provide insights into the potential of evolutionary optimization for addressing the navigation challenges faced by unmanned underwater vehicles and promoting advancements in underwater exploration and surveillance technologies.

272. HOSPITAL VARIATION IN PALLIATIVE TREATMENT FOR ESOPHAGEAL AND GASTROESOPHAGEAL JUNCTION CANCER: A NATIONWIDE POPULATION-BASED STUDY IN THE NETHERLANDS

Abstract Background Palliative care for patients with esophageal (EC) includes a large variety of treatments and data on variation of palliative care across centers are unknown. This study aims to evaluate treatment variation at the hospital level for EC and GC patients in the Netherlands. Methods A nationwide retrospective cohort study was conducted using population-based data from the Netherlands Cancer Registry. Patients diagnosed with inoperable advanced (cT4B) or synchronous metastatic (cM1) EC between 2015-2022 were included. Probability of receiving potentially life-prolonging treatment (palliative resection, chemoradiotherapy or systemic treatment) according to hospital of diagnosis was analyzed using multilevel logistic regression analysis. Hospitals were categorized in tertiles according to this probability (high, medium, low) and overall survival (OS) was assessed using cox regression analysis. Results 8,580 EC patients were included. Potentially life-prolonging treatment ranged from 23.5%-74.0% across hospitals of diagnosis (p&amp;lt;0.0001). After adjustment for patient and tumor characteristics, the probability of receiving these treatments was associated with hospital of diagnosis (p&amp;lt;0.0001). 13 of 79 hospitals demonstrated a significantly lower probability of giving potentially life-prolonging treatment. Patients diagnosed in hospitals with low probability of potentially life-prolonging treatment with had significantly worse OS than medium- (HR 0.85 (0.80-0.90) p&amp;lt;0.0001) or high-probability hospitals (HR 0.77 (0.73-0.82) p&amp;lt;0.0001). Conclusion This study shows substantial variation in palliative treatment for EC and GC care across Dutch hospitals, emphasizing the challenge of defining optimal treatment plans. Hospital of diagnosis was significantly associated with the probability of receiving potentially life-prolonging treatment and also with overall survival. These results suggest that appropriate selection of patients for treatment with potentially life-prolonging treatment leads to improved OS.

Optimization of personnel evacuation routes for sudden nuclear accidents based on hybrid genetic-grey wolf optimizer algorithm

To address the emergency situation resulting from a sudden nuclear accident, it is imperative to develop an optimal emergency response plan that ensures the prompt and safe evacuation of affected individuals from the hazardous area.The paper proposed the hybrid genetic-grey wolf optimizer algorithm (GA-GWO).This algorithm integrated radiation environment and road network to plan evacuation paths that minimizes the radiation dose. This algorithm enhanced the convergence and search capabilities of the genetic algorithm (GA) by incorporating wolf pack characteristics and internal competitive behavior. This improvement allowed for more efficient exploration of potential evacuation routes. The feasibility of GA-GWO was validated through a visualization simulation utilizing Web Geographic Information Systems (WebGIS). The findings indicated that GA-GWO generated evacuation paths that minimize radiation dose, providing safe routes for the affected population. This approach demonstrates significant potential for application in emergency response planning for sudden nuclear accidents, ensuring both prompt and safe evacuations.

Joint Trajectory Planning and Transmit Resource Optimization for Multi-Target Tracking in Multi-UAV-Enabled MIMO Radar System

Joint Trajectory Planning and Transmit Resource Optimization for Multi-Target Tracking in Multi-UAV-Enabled MIMO Radar System

Increased Risk of 10-Year Revision Following Total Hip Arthroplasty for Femoral Neck Fracture

BackgroundAs the incidence of femoral neck fractures (FNFs) increases with the aging population, understanding its impact on surgical outcomes is important to improving implant survival and patient satisfaction. Despite increasing use of total hip arthroplasty (THA) as management for FNF, few studies have examined long-term implant survivability. Thus, this study sought to determine the 10-years cumulative incidence of revision and indications for revision in patients undergoing THA for FNF in comparison to osteoarthritis. MethodsPatients who underwent primary THA for FNF or osteoarthritis were identified using a national administrative claims database and propensity-score matched in a 1:2 ratio based on age, gender, the Charlson Comorbidity Index (CCI), smoking, obesity, and diabetes mellitus. Kaplan-Meier and Cox proportional hazards analyses were used to observe the cumulative incidence and risk of all-cause revision, periprosthetic joint infection (PJI), dislocation, mechanical loosening, and periprosthetic fracture (PPF) within 10 years of primary THA. In total, 19,735 patients who underwent THA for FNF and 39,383 patients who underwent THA for osteoarthritis were included. ResultsThe 10-years cumulative incidences of all-cause revision (7.1 versus 4.9%), PJI (5.0 versus 3.3%), dislocation (6.8 versus 3.8%), mechanical loosening (3.1 versus 1.9%), and PPF (7.8 versus 4.0%) were significantly higher for those who underwent THA for FNF versus osteoarthritis. Femoral neck fractures were associated with higher risks of revision (hazard ratio [HR]: 1.6), PJI (HR: 1.7), dislocation (HR: 2.0), mechanical loosening (HR: 1.6), and PPF (HR: 2.2) (P < 0.001 for all). ConclusionsDespite the advantages of THA, femoral neck fractures remain a major risk factor for long-term complications. Tailored preoperative planning, surgical techniques, and postoperative bone health optimization in these patients may help minimize poor outcomes.

Different Patterns of Autoantibody Secretion by Peripheral Blood Mononuclear Cells in Autoimmune Nodopathies.

Autoimmune nodopathies with antibodies against the paranodal proteins show a distinct phenotype of a severe sensorimotor neuropathy. In some patients, complete remission can be achieved after treatment with rituximab whereas others show a chronic course. For optimal planning of treatment, predicting the course of disease and therapeutic response is crucial. We stimulated peripheral blood mononuclear cells in vitro to find out whether secretion of specific autoantibodies may be a predictor of the course of disease and response to rituximab. Three patterns could be identified: In most patients with anti-Neurofascin-155-, anti-Contactin-1-, and anti-Caspr1-IgG4 autoantibodies, in vitro production of autoantibodies was detected, indicating autoantigen-specific memory B cells and short-lived plasma cells/plasmablasts as the major source of autoantibodies. These patients generally showed a good response to rituximab. In a subgroup of patients with anti-Neurofascin-155-IgG4 autoantibodies and insufficient response to rituximab, no in vitro autoantibody production was found despite high serum titers, indicating autoantibody secretion by long-lived plasma cells outside the peripheral blood. In the patients with anti-pan-Neurofascin autoantibodies-all with a monophasic course of disease-no in vitro autoantibody production could be measured, suggesting a lack of autoantigen-specific memory B cells. In some of them, autoantibody production by unstimulated cells was detectable, presumably corresponding to high amounts of autoantigen-specific plasmablasts-well in line with a severe but monophasic course of disease. Our data suggest that different B-cell responses may occur in autoimmune nodopathies and may serve as markers of courses of disease and response to rituximab.

Failure rate analysis and maintenance plan optimization method for civil aircraft parts based on data fusion

In the face of data scarcity in the optimization of maintenance strategies for civil aircraft, traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design. This study addresses this issue by presenting a novel combined data fusion algorithm, which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information. Through a comprehensive analysis of two different maintenance projects, this study illustrates the application process of the algorithm. Building upon the analysis results, this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization. The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20% in terms of inspection efficiency ratio. This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs. The findings of this study open up novel perspectives for airlines grappling with data scarcity, offer fresh strategies for the optimization of aviation maintenance practices, and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis.

Optimal planning for integrated electricity and heat systems using CNN-BiLSTM-Attention network forecasts

This paper investigates optimal planning of integrated electricity and heat systems (IEHS) based on convolutional neural network-bidirectional long short term memory with attention mechanism (CNN-BiLSTM-Attention) network forecasts. Firstly, CNN and BiLSTM are employed to extract the spatio-temporal features of IEHS data, and the attention mechanism automatically assigns corresponding weights to BiLSTM to distinguish the importance of different time load sequences, then a combined CNN-BiLSTM-Attention network is constructed, which allows for a more accurate load forecasting. Furthermore, we establish optimal planning strategy to improve efficient operation and energy efficiency of the IEHS, in which the upper-level planning model with the optimization objective of minimizing comprehensive operation costs is formulated, the lower-level efficiency model aiming to balance revenue and cost is considered, then genetic algorithm and iterative solution strategy of Cplex solver are applied to optimize the bi-level objective functions. Simulation results show that the proposed CNN-BiLSTM-Attention model obtained smaller RMSE, MAE and MAPE compared to several other forecasting models. In addition, the optimization method could obtain global optimal solution of the bi-level objective functions, which improves the energy utilization efficiency of the IEHS.

Data-driven robust optimisation of hydrogen infrastructure planning under demand uncertainty using a hybrid decomposition method

In the race towards “Net-zero”, hydrogen has emerged as one of the key alternatives to carbon-based fossil fuels for a sustainable decarbonisation. This work studies the spatially explicit multi-period hydrogen infrastructure planning under demand uncertainty that contributes to the heat decarbonisation in Great Britain. Demand uncertainty surrounding future hydrogen supply chains poses challenges to cost optimisation and system security, so uncertainty-resilient policies are required to ensure robust operations. In this work, we employ data-driven robust optimisation to develop a framework for uncertainty-aware representative days explicitly characterised by polyhedral uncertainty sets. The proposed framework is applied on a multi-period mixed-integer linear model with dual temporal resolution which aims to determine the optimal yearly investment decisions and hourly operational decisions for the hydrogen infrastructure planning under demand uncertainty. To efficiently solve the large-scale two-stage adaptive robust optimisation problem, a hybrid decomposition algorithm is developed based on a two-step hierarchical procedure and the column-and-constraint generation method, which can significantly reduce the computational complexity. The optimisation results highlight how uncertainty can result in the total cost increase, and verify the advantages on controlling solution conservatism in the adaptive robust optimisation compared to the static robust optimisation.