Optimal Strategy Research Articles

A review of the current treatment methods for retroperitoneal fibrosis with obstructive uropathy

AbstractIntroduction and aimsRetroperitoneal fibrosis (RPF) is a fibroinflammatory disease in which patients may suffer obstructive uropathy (OU). The optimum treatment strategy for RPF with secondary OU is currently unclear, and the aim of this literature review is to assess the methods used to treat this patient cohort.MethodsMedline, Embase, Cinahl, the Cochrane Library and PubMed were systematically searched to find studies assessing treatment outcomes in this patient cohort. After reviewing the studies' titles, abstracts and full texts, 12 were found that matched our search aims. Data from these publications were analysed and reported.ResultsThe demographic and symptomatic features of patients across the 12 studies were representative of the general RPF population. No randomised control trials (RCTs) were found, and just one study formally compared outcomes between patients who underwent different treatment strategies. Many of the studies concluded that using medical and surgical methods in combination led to positive outcomes; whereas, others found positive outcomes following a variety of regimens. Many studies also highlighted, however, that significant minorities required further treatment after initial therapy. Conclusions regarding optimum treatment methods were limited as most publications did not formally compare outcomes following different strategies and had an observational study design.ConclusionAlthough positive outcomes were commonly seen following medical, surgical and a combination of treatments, the literature currently lacks research formally comparing outcomes after assigning specific treatment protocols to groups of RPF patients. More research is therefore required to determine how to best manage RPF leading to secondary OU.

Substratifying the risk of covert malignancy in significant rectal polyps: Outcomes from a specialist multidisciplinary team (MDT).

A treatment strategy for patients with a significant polyp or early colon cancer (SPECC) of the rectum presents a challenge due to the significant rate of covert malignancy and lack of standardized assessment. For this reason, NICE recommends multidisciplinary meetings to improve outcomes. The primary aim of the present study was to report the performance of our specialist early rectal cancer (SERC) multidisciplinary team (MDT) in correctly substratifying the risk of cancer and to discuss the limitations of staging investigations in those patients with "poor outcomes". This was a retrospective review of patients referred to our SERC MDT from 2014 to 2019. Lesions were assigned by the MDT to three pre-resection categories (low, intermediate, high) according to the risk of covert malignancy. Resection method and final histology were compared to the pre-resection categories. Of 350 SPECC lesions, 174 were assessed as low-risk, 108 intermediate-risk and 68 high-risk. The cancer incidence was 4.8%, 8.3% and 53%, respectively (15.5% overall). Eight lesions were categorized as low-risk but following piecemeal resection were found to be malignant. Five lesions, three of which were categorized as high-risk, were ultimately benign following conventional surgery. One pT1sm1 cancer, removed by anterior resection, may have been treated by local excision. A total of 83% of malignant polyps were triaged to an en bloc resection technique and surgical resection avoided for nearly all benign lesions. However, 12 patients from this cohort were deemed to have a poor outcome because of miscategorization. Further comparative research is needed to establish the optimum strategy for rectal SPECC lesion assessment. There is currently no consensus for staging significant polyps of the rectum. This paper reports the effectiveness of a specialist early rectal cancer MDT to correctly risk-stratify significant rectal polyps. It underscores the importance of accurate categorization for treatment decision-making, while acknowledging the limitations of current staging modalities.

Distributed Optimal Solutions for Multiagent Pursuit-Evasion Games for Capture and Formation Control

In this paper, distributed optimal solutions are designed for networked multiagent pursuit-evasion (MPE) games for capture and formation control. In the games, the pursuers aim to minimize the distance from their target evaders while the evaders attempt to maximize it, and at the same time, all players desire to maintain cohesion with their teammates. The goals of agents are obviously reflected in the obtained optimal control strategies which consist of an attracting term and/or a repelling term. Nash equilibrium is obtained by means of optimal strategies using the solutions of the HJI equations. Furthermore, three scenarios are considered in the MPE game: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple-pursuer-multiple-evader, where sufficient conditions are given for pursuers in achieving exponential capture or formation control with ultimate zero or bounded errors. It is shown that the conditions depend on the structure of the communication graph, the parameters in the controllers, and the expected formation configurations. Finally, both simulations and real flight experiments successfully demonstrate the effectiveness of the proposed strategies.

A Condition Knowledge Representation and Feedback Learning Framework for Dynamic Optimization of Integrated Energy Systems.

An optimal energy scheduling strategy for integrated energy systems (IESs) can effectively improve the energy utilization efficiency and reduce carbon emissions. Due to the large-scale state space of IES caused by uncertain factors, it would be beneficial for the model training process to formulate a reasonable state-space representation. Thus, a condition knowledge representation and feedback learning framework based on contrastive reinforcement learning is designed in this study. Considering that different state conditions would bring inconsistent daily economic costs, a dynamic optimization model based on deterministic deep policy gradient is established, so that the condition samples can be partitioned according to the preoptimized daily costs. In order to represent the overall conditions on a daily basis and constrain the uncertain states in the IES environment, the state-space representation is constructed by a contrastive network considering the time dependence of variables. A Monte-Carlo policy gradient-based learning architecture is further proposed to optimize the condition partition and improve the policy learning performance. To verify the effectiveness of the proposed method, typical load operation scenarios of an IES are used in our simulations. The human experience strategies and state-of-the-art approaches are selected for comparisons. The results validate the advantages of the proposed approach in terms of cost effectiveness and ability to adapt in uncertain environments.

Optimal Traffic Control for a Tandem Intersection With Improved Lane Assignments at Presignals

This article focuses on the optimal traffic control strategy for a tandem intersection with pre­signals. First, the impact of a lane assignment scheme at presignals on the operational performance of the tandem intersection is analyzed. An improved lane assignment method is thus proposed to avoid setting the lanes with the same traffic flow direction adjacent to each other as far as possible. Second, vehicle delay models behind the presignals and in the sorting area are established, respectively. Third, considering green times of presignal phases and sorting area lengths on all approaches as variables, an optimization model is proposed to minimize the average vehicle delay. Finally, a case study is conducted on a congested intersection in Changchun, China, to validate the proposed method. The effects of the proposed improved lane assignment scheme on the tandem intersection are analyzed compared with the traditional lane assignment scheme. The results show that the former can decrease average vehicle delay by 22.9% and increase intersection capacity by 18.6%.

Long-term clinical outcomes of hypofractionated stereotactic radiotherapy using the CyberKnife robotic radiosurgery system for jugular foramen schwannomas.

Jugular foramen schwannomas (JFSs) are rarely seen, benign tumors with slow growth. Today, management options for JFSs include observation, surgery, and radiation. However, the optimal treatment strategy remains controversial. Stereotactic radiosurgery serves as a minimally invasive alternative or adjuvant therapeutic regimen of microsurgery. Gamma Knife radiosurgery is suitable for patients with JFS who have small- and medium-sized tumors and normal cranial nerve (CN) function. Hypofractionated stereotactic radiotherapy (HSRT) offers a potential radiobiological advantage and may result in better preservation of normal structures compared to single-fraction stereotactic radiosurgery. The aim of the article was to review the clinical and radiographic outcomes of patients with JFS who were treated using HSRT. The authors retrospectively analyzed 74 patients with JFS who received HSRT between January 2009 and January 2020 in the authors' center. Among them, 53 patients were newly diagnosed with JFS, 19 patients had a previous history of microsurgical resection, and the other 2 patients underwent CyberKnife because of tumor recurrence after Gamma Knife radiosurgery. A total of 73 patients had preexisting CN symptoms and signs. The median tumor volume was 14.8 cm3 (range 0.5-41.2 cm3), and most of them (70.3%) were ≥ 10 cm3. The radiation dose regimen was prescribed depending on the tumor size, and more fractions were used in larger tumors. The median margin doses prescribed were 18.2 Gy/2 fractions, 21.0 Gy/3 fractions, and 21.6 Gy/4 fractions. The median follow-up was 103 months (range 18-158 months). After treatment, 42 (56.8%) patients had tumor regression, 27 (36.5%) patients had stable tumors, and 5 (6.8%) experienced tumor progression. Among them, MRI revealed that 1 patient had a complete response. Three patients received surgery at a median of 25 months because of tumor progression. One patient underwent ventriculoperitoneal shunt insertion for hydrocephalus that developed after HSRT independent of tumor progression. The 5-year progression-free survival rate was 93.2%. Preexisting cranial neuropathies improved in 46 patients, remained stable in 14, and worsened in 14. HSRT proved to be a safe and effective primary or adjuvant treatment strategy for JFSs, although 14 patients (18.9%) experienced some degree of delayed symptomatic deterioration posttreatment. This therapeutic option was demonstrated to provide both excellent tumor control and improvement in CN function.

Simultaneously Calibration of Multi Hand–Eye Robot System Based on Graph

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. Systems with multi-eyes (cameras) and multi-hands (robots) are particularly sensitive to calibration errors. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the pose between each unit, but the serialized incremental calibration strategy cannot avoid the error accumulation problem in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization with different sensor's accuracy considered. As a general calibration method, it can be applied to different multi-robot systems. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method.

Comparison of multi-criteria decision-making methods for selection of optimum passive design strategy

In the pursuit of achieving high-performance building design, the selection of the most suitable passive design strategies often involves the use of multi-criteria decision-making (MCDM) methods to address multiple conflicting criteria simultaneously. However, identifying the appropriate MCDM method for a specific building design context poses a challenge, as methods commonly effective in other contexts may not yield equivalent results. This study evaluates five MCDM methods (AHP, COPRAS, TOPSIS, VIKOR, and WSM) to understand their sensitivity in recommending the best solution. The considered criteria are energy demand, thermal comfort and daylight availability. The sensitivity analysis involves the impact of the variability of assigned weights on the rank shifting given by the considered MCDM method and the sensitivity of each criterion to weights variability. The findings reveal that implementing a fair-weight allocation leads to similar top 5 solutions among all MCDM methods. However, when a negative shift is applied to each criterion weight, AHP demonstrates greater robustness to weight variability compared to the other methods evaluated, while VIKOR is the most sensitive to weight variation.

Effects of Early Versus Delayed Feeding in Patients With Acute Pancreatitis: A Systematic Review and Meta-analysis.

The aim of this study was to summarize the optimal strategy for early feeding in patients with acute pancreatitis. The search was undertaken in electronic databases, which compared early with delayed feeding in acute pancreatitis. The primary outcome was the length of hospital stay (LOHS). The second outcomes were intolerance of refeeding, mortality, and total cost of each patient. This meta-analysis followed the "Preferred Reporting Items for Systematic Reviews and Meta-analyses" guideline. Research is registered by PROSPERO, CRD42020192133. A total of 20 trials involving 2168 patients were included, randomly assigned to the early feeding group (N = 1033) and delayed feeding group (N = 1135). The LOHS was significantly lower in the early feeding group than the delayed feeding group (mean difference: -2.35, 95% CI: -2.89 to -1.80; P < 0.0001), no matter the mild or severe subgroup ( Pint = 0.69). The secondary outcome of feeding intolerance and mortality were no significant difference (risk ratio: 0.96, 0.40 to 2.16, P = 0.87 and 0.91, 0.57 to 1.46, P = 0.69; respectively). Moreover, the hospitalization cost was significantly less in the early feeding group, resulting in an average savings of 50%. In patients with severe pancreatitis, early feeding after 24 hours may be beneficial ( Pint = 0.001). Early oral feeding can significantly reduce the LOHS and hospitalization costs in patients with acute pancreatitis without increasing feeding intolerance or mortality. In patients with severe pancreatitis, early feeding after 24 hours may be beneficial.

Integrated Hybrid Engine Cycle Design and Power Management Optimization

Abstract A novel integrated gas turbine cycle design and power management optimization methodology for parallel hybrid electric propulsion architectures is presented in this paper. The gas turbine multipoint cycle design method is extended to turboprop and turbofan architectures, and several trade studies are performed initially at the cycle level. It is shown that the maximum degree of electrification is limited by the surge margin levels of the booster in the turbofan configuration. An aircraft mission-level assessment is then performed using the integrated optimization method initially for an A320 Neo style aircraft case. The results indicate that the optimal cycle redesigned hybrid electric propulsion system (HEPS) favors takeoff and climb power on-takes while optimal retrofit HEPS favor cruise power on-takes. It is shown that for current battery energy density (250 Wh/Kg), there is no fuel burn benefit. Furthermore, even for optimistic energy density values (750 Wh/kg) the maximum fuel burn benefit for a 500 nm mission is 5.5% and 4% for redesigned and retrofit HEPS, respectively. The power management strategies for HEPS configurations also differ based on gas turbine technology with improvement in gas turbine technology showing greater scope for electrification. The method is then extended to ATR 72 style aircraft case, showing greater fuel burn benefits across the flight mission envelope. The power management strategies also change depending on the objective function, and optimum strategies are reported for direct operating cost or fuel burn. The retrofit case studies show a benefit in direct operating cost compared to redesigned case studies for ATR 72. Finally, a novel multimission approach is shown to highlight the overall fuel burn and direct operating cost benefit across the aircraft mission cluster.

Performance evaluation and optimization of the cascade refrigeration system based on the digital twin model

Cascade refrigeration systems (CRSs) have become the priority choice in low-temperature applications, causing severe energy waste due to the lack of practical control strategies. Currently, refrigeration simulations are mainly based on the simplified thermodynamic or Artificial Intelligence (AI) model. However, the former is insufficiently accurate, while the latter is of unclear thermodynamic significance. By connecting the physical and digital space, the digital twin technology provides exciting opportunities for combining thermodynamic and AI methods. In this paper, an integrated digital twin model for the CRS is innovatively proposed based on the modified semi-empirical model of the twin-screw refrigeration compressor. As a major update, the identification and update method of characteristic parameters is developed based on real-time operating data to ensure performance consistency between digital and physical twins. By comparing with the simplified model, results show that the digital twin model can improve prediction accuracy by 7.42%–23.62%. Founded on the proposed model, the thermodynamic performance of the CRS is evaluated. To facilitate on-site applications, the definition of flat intermediate temperature is given, which is fitted to a control correlation with several easily observed variables as the inputs. Taking a food factory as a case study, simulation results reveal that the optimum intermediate temperature should be significantly lower than the original, and the coefficient of performance (COP) has an average improvement potential of 9.1%. By deploying the optimum strategy under the on-site condition, a superior energy-saving rate of up to 13.1% is attained according to test reports.

Abstract LB444: Identification of a novel and selective Transglutaminase 2 (TGM2) inhibitors modulate tumor microenvironment in Glioblastoma

Abstract Background: TGM (Transglutaminase) family enzymes are Ca2+ dependent and engaged in specific posttranslational modifications by cross-linking extracellular matrix (ECM) proteins. ECM protein complex is more stable and resistant to enzymatic degradation. These enzymes modify proteins by cross-linking epsilon-(gamma-glutamyl), lysine, or (gamma-glutamyl) polyamine bonds. TGM2 (Transglutaminase 2) is a ubiquitous enzyme, and its expression is identified in the cytoplasm, plasma membrane, and the nucleus of various cells. The membrane-bound form of TGM2 binds GTP and functions as a G protein. TGM2 regulates cell survival, proliferation, migration, and modulation through ECM organization. We explored the role of TGM2 in Glioblastoma cells and its role in modulating the tumor microenvironment, cell proliferation, and survival. In addition to TGM2's role in GBM tumors, its expression has been linked to resistance to certain therapies in glioblastoma was investigated. Targeted modulation of macrophages with TGM2 inhibitors impacts the immune response against GBM, potentially enhancing the efficacy. Understanding the intricate interplay between TGM2, and GBM-associated macrophages is a key component of the tumor microenvironment (TME) and crucial for the development of targeted TGM2 inhibitors. Materials and Methods: Human Transglutaminase 2 (hTGM2) activity of inhibitors was assayed using the fluorescent transamination assay incorporating dansylcadaverine into glutamine-donor substrate N, N-dimethyl casein, and DCC. Cellular efficacies of selected TGM2 inhibitors evaluated in U138 and U87 glioblastoma cells in CellTiter-Glo Luminescent cell viability assay. Results: We have identified a series of novel small molecule TGM2 inhibitors based on our initial lead fragment hits with an IC50s 6-29 uM from our in-house MolecuLern fragment library. Fragments2Lead (F2L) optimizations strategies, synthesis, and SAR studies provided a lead SLX-9029 and SLX-9031 inhibitors with IC50s of 1.2, 1.5 uM in inhibiting TGM2 activity in our established cell-free fluorescent transamination assay. The initial TMG2 inhibitor leads further screening for TGM2-expressed U138 and U87 glioblastoma cells demonstrating promising cellular efficacies as singles agents. Additional GBM cell profiling, combination studies along with safety secondary pharmacology, cellular toxicities, and PK results will be discussed. Conclusion: In summary, we identified promising lead TGM2 inhibitors that show activity in both cell-free and cell-based TGM2-expressed GMB cells. The TGM family members, selectivity, additional GBM cellular profiling, ADME, cellular toxicity, and in vivo PK studies are underway, and these results will be presented. Citation Format: Hariprasad Vankayalapati, Chenyu Lin, Zhaoang li, Kyle Medley, David J. Bearss. Identification of a novel and selective Transglutaminase 2 (TGM2) inhibitors modulate tumor microenvironment in Glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB444.

Surgical management of lower limb radiculopathy following acute singe-level osteoporotic vertebral fracture of lower lumbar spine in geriatric patient: a retrospective study

BackgroundRadiculopathy of the lower limb after acute osteoporotic vertebral fractures (OVFs) in the lower lumbar spine is uncommon in geriatric patients. Moreover, surgical intervention is generally recommended in patients who are irresponsive to conservative treatment. Determining an optimum surgical strategy is challenging considering the poor general condition of this population. Thus, herein, we established an algorithm for surgically managing this clinical scenario, hoping to provide a reference for making a surgical decision.MethodsWe retrospectively studied patients who suffered from new-onset radiculopathy of the lower limb after acute single-level OVFs in the lower lumbar spine and eventually underwent surgical intervention at our department. Information on the demographics, bone quality, AO spine classification of the vertebral fracture, pre-existing degenerative changes, including foraminal stenosis and lumbar disc herniation, and surgical intervention type was collected. Additionally, clinical outcomes, including preoperative and postoperative visual analog scale (VAS) scores for back and leg pain, Oswestry disability index (ODI), and MacNab criterion for response to surgery, were evaluated.ResultsFrom September 2019 to December 2021, a total of 22 patients with a mean age of 68.59 ± 9.74 years were analyzed. The most involved vertebra was L5 (54.5%), followed by L4 (27.3%) and L3 (18.2%). Among the 22 patients, 15 (68.2%) were diagnosed with the A1 type fracture of AO classification, and among them, 11 (73.3%) were characterized by the collapse of the inferior end plate (IEP). Three patients (13.6%) suffered from A2-type fractures, whereas four patients (18.2%) suffered from A3-type fractures. Pre-existing degenerative changes were observed in 12 patients (54.5%) of the patients. A total of 16 patients (72.7%) were treated by percutaneous kyphoplasty (PKP). Additionally, three patients underwent posterior instrumentation and fusion, two patients underwent a secondary endoscopic foraminoplasty, and one patient underwent a secondary radiofrequency ablation. The mean follow-up period was 17.42 ± 9.62 months. The mean VAS scores for leg and back pain and ODI decreased significantly after the surgery (P < 0.05). The total satisfaction rate at the last follow-up was 90.9% per the Macnab criterion.ConclusionPatients with OVFs in the IEP are predisposed to suffer from radiculopathy of the lower limb. PKP alone or in combination with other minimally invasive surgical strategies is safe and effective in treating stable fractures. Additionally, aggressive surgical intervention should be considered in patients with unstable fractures or severe foraminal encroachment.

Gaussian Process Latent Variable Model-Based Multi-Output Modeling of Incomplete Data

The rapid development of sensor technologies allows the acquisition of high dimensional sensing data. Multi-output modeling techniques have been developed to leverage the data for decision making. However, the data often contain segments of missing values, which cause great information loss and thus affect the modeling performance. This study explores the missing pattern and the correlation structure of missing segments and maximally exploits useful information in the data to improve multi-output modeling accuracy. Specifically, a new multi-output modeling method is developed based on Gaussian Process Latent Variable Model (GPLVM). A decision score is developed to seek an optimal modeling strategy and then a tailored Expectation-Maximization (EM) algorithm based on GPLVM is designed to estimate the missing segments while optimizing model parameters. The proposed method demonstrates superior performance in both a simulation study and a case study, which makes it a powerful tool to enable process automation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In real-life applications, missing values are constantly present in multi-output sensing data, which greatly affects data-driven decision-making. Modeling of such data becomes more difficult when consecutive observations within or across different outputs are missing. Existing methods often discard the missing values and extract information only from the available observations. However, the pattern of missing values may contain important messages that can potentially boost the modeling performance. This research develops a new framework based on GPLVM to model multi-output data with segmented missing patterns. A tailored EM algorithm is developed to iteratively impute the missing values and optimize model parameters. In addition, a decision score that quantifies both the missing pattern and correlation is designed to determine an optimal modeling strategy. The proposed method can benefit many applications across different industries that require modeling of multi-output incomplete data, especially when the data have many segments of missing observations.

Adaptive Virotherapy Strategy for Organism With Constrained Input Using Medicine Dosage Regulation Mechanism.

In this article, the constrained adaptive control strategy based on virotherapy is investigated for organism using the medicine dosage regulation mechanism (MDRM). First, the tumor-virus-immune interaction dynamics is established to model the relations among the tumor cells (TCs), virus particles, and the immune response. The adaptive dynamic programming (ADP) method is extended to approximately obtain the optimal strategy for the interaction system to reduce the populations of TCs. Due to the consideration of asymmetric control constraints, the nonquadratic functions are proposed to formulate the value function such that the corresponding Hamilton-Jacobi-Bellman equation (HJBE) is derived which can be deemed as the cornerstone of ADP algorithms. Then, the ADP method of a single-critic network architecture which integrates MDRM is proposed to obtain the approximate solutions of HJBE and eventually derive the optimal strategy. The design of MDRM makes it possible for the dosage of the agentia containing oncolytic virus particles to be regulated timely and necessarily. Furthermore, the uniform ultimate boundedness of the system states and critic weight estimation errors is validated by Lyapunov stability analysis. Finally, simulation results are given to show the effectiveness of the derived therapeutic strategy.

705 Urgent or Emergent Endovascular Intervention for Neurological Deficits Post-carotid Endarterectomy: A Single-institutional Case Series and Systematic Review of the Literature

INTRODUCTION: Historically the management of acute neurological deficit following carotid endarterectomy (CEA) has been operative re-exploration; however, given the proliferation of endovascular techniques, and utilization of CT angiography, endovascular intervention is an alternative to redo-CEA. METHODS: All patients undergoing CEA in the Department of Neurosurgery at Rhode Island Hospital (Providence, RI) were reviewed. A systematic literature review was conducted in PubMed according to PRISMA guidelines to identify papers relevant to endovascular procedures addressing acute neurological deficits following CEA, which yielded 2499 unique articles. Review by 2 investigators revealed 12 relevant articles. RESULTS: Four patients from our institutional cohort met inclusion criteria. Among these, stenting was technically successful in 3 patients, 2 of which achieved near-full recovery. One patient underwent successful carotid stenting and ipsilateral M2 thrombectomy for tandem occlusion. Stent placement was unsuccessful in the remaining patient due to mechanical constriction. Articles obtained through the literature review revealed 57 further relevant cases. Of those with sufficiently complete data, 30/39 (76.9%) were male with an age range of 43 - 78 years old. Twenty-eight of 32 (87.5%) patients underwent CEA for symptomatic carotid stenosis. Post-CEA angiographic evaluation was most commonly conducted for routine completion imaging (21/57, 36.8%), non-specific stroke symptoms (15/57, 26.3%), and extremity weakness (7/57, 12.3%). Thrombotic stenosis (23/43, 53.4%) and arterial dissection (15/43, 34.9%) were the most common angiography findings. Two (3.5%) stent procedures were unsuccessful, and in the immediate postoperative period 8 patients (14.0%) had significant neurological deficits and only 3 deaths (5.3%) were reported. CONCLUSIONS: Endovascular intervention may be considered in situations where patients have neurological deficit after CEA. Direct comparison of outcomes in patients who have endovascular intervention vs. redo CEA may help identify the optimal management strategy.

Optimal Dual-Side Asymmetric Modulation Strategy for Dual Active Bridge Converter to Improve Efficiency Over a Wide Voltage Range

Optimal Dual-Side Asymmetric Modulation Strategy for Dual Active Bridge Converter to Improve Efficiency Over a Wide Voltage Range

A New Manipulating Method of Metal Micro-Components Based on Electrochemistry

This article presents a new manipulating method of metal micro-components based on electrochemistry aiming at the problems of inaccurate release and easy damage to manipulated objects by conventional micro gripping tools and vacuum adsorption tools. This method uses a small-sized micropipette to manipulate large-sized metal micro-components. The reliable picking up is achieved by forming a strong chemical bond between the small-sized pipette and the manipulation object. The accurate and non-destructive release is achieved by converting the chemical bonding force into a more easily overcome capillary force through electrolysis. The electrochemical and the force models of the metal microcomponents based on electrochemistry are simulated by COMSOL and Abaqus, to obtain optimal electrochemical parameters and manipulation strategies. The experiments of picking up and releasing were performed to demonstrate the performance of the proposed method. The results show that this method can enable reliable and accurate manipulation of large-size objects with small-size tools and have a high success rate and efficiency.

Deep Meta Q-Learning Based Multi-Task Offloading in Edge-Cloud Systems

Resource-constrained edge devices can not efficiently handle the explosive growth of mobile data and the increasing computational demand of modern-day user applications. Task offloading allows the migration of complex tasks from user devices to the remote edge-cloud servers thereby reducing their computational burden and energy consumption while also improving the efficiency of task processing. However, obtaining the optimal offloading strategy in a multi-task offloading decision-making process is an NP-hard problem. Existing Deep learning techniques with slow learning rates and weak adaptability are not suitable for dynamic multi-user scenarios. In this article, we propose a novel deep meta-reinforcement learning-based approach to the multi-task offloading problem using a combination of first-order meta-learning and deep Q-learning methods. We establish the meta-generalization bounds for the proposed algorithm and demonstrate that it can reduce the time and energy consumption of IoT applications by up to 15%. Through rigorous simulations, we show that our method achieves near-optimal offloading solutions while also being able to adapt to dynamic edge-cloud environments.

Energy-Aware Positioning Service Provisioning for Cloud-Edge-Vehicle Collaborative Network Based on DRL and Service Function Chain

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.