Allocation Method Research Articles

Research on motion control strategy of athlete muscle training based on blockchain and visual image analysis

Muscle training is an important part of athletes’ physical training. Its goal is to keep athletes in high intensity and improve sports performance in physical training. With the continuous improvement of the level of competitive sports, there are still many problems in the training of competitive athletes. The research on the control strategies of some technical movements in the process of athletes’ muscle development would help to improve the efficiency and effect of athletes’ training. By analyzing and quant description of muscle movement control process based on blockchain and visual image processing technology, the muscle movement control rule is discussed and then a complete movement database is constructed. At the same time, combined with the sports experiment method and cognitive behavioral theory, this paper analyzed the exercise load, exercise time allocation, muscle training time allocation methods and the corresponding muscle training strategy selection methods under different muscle states, providing theoretical basis for athletes to conduct muscle strength training scientifically and efficiently. This paper first analyzed the importance of athletes’ muscle training action control. After that, the research was mainly carried out from the following two aspects. The first is to analyze the motion control strategy of athlete muscle training based on blockchain and visual image, and summarize its characteristics. The second is to use electronic imaging technology to analyze the data related to posture control in athletes’ muscle training, and explore the characteristics and changes of muscle posture. At last, this paper put forward relevant algorithms about visual images and conducted experimental research on athletes’ physical indicators and performance under different muscle training according to the research in this paper. It was concluded that the score ratio of athletes’ performance after muscle training action control was 7.39% higher than that of general muscle training. Therefore, it is very important to strengthen the research of movement technology in muscle training and analyze the influence of some training control strategies on the development of athletes’ muscles in training.

Joint Antenna Selection and Power Allocation Method Based on Quantum Energy Valley Optimization Algorithm for Massive MIMO IoT Systems

Joint Antenna Selection and Power Allocation Method Based on Quantum Energy Valley Optimization Algorithm for Massive MIMO IoT Systems

The Sharing Energy Storage Mechanism for Demand Side Energy Communities

Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side.

Factors associated with non-publication of clinical trials in cardiovascular medicine: a cross-sectional analysis

Abstract Background/introduction Cardiovascular medicine is the fourth most-funded area of clinical research as it received an estimate for 7% of the total research funding. Nearly 8% of all clinical trials are cardiovascular-related (1,2). Non-publication of the registered clinical trials wastes money and time, encourages dissemination bias, and creates a body of possibly inaccurate literature, all of which obstruct scientific advancement. (3) Purpose To study the factors associated with the non-publication of cardiovascular clinical trials between January 2000 and June 2022. Methods We performed a cross-sectional analysis using ClinicalTrials.gov to determine all completed, discontinued, published, and unpublished cardiovascular medicine clinical trials between January 2000 and June 2022. For each trial, data on the trial phase, funding source, intervention type, enrolment size, trial completion, and publication status were extracted and subjected to logistic regression to determine clinical trial publication predictor factors. Results Among 3946 trials in cardiovascular medicine registered in ClinicalTrials.gov, there were 3215 completed trials included in the analysis. Of these, 34.2% (n=1100) were unpublished. On multivariate logistic regression, triple-blinded and quadruple-blinded trials were less likely to be unpublished compared to open-label trials (odds ratio [OR] 0.622; 95% confidence interval [CI] 0.45-0.85; p=0.003) and (OR 0.54; CI 0.42-0.69; p<0.001), respectively. Enrolment of 100 participants or more was associated with a decreased likelihood of non-publication (OR 0.46; CI 0.39-0.55; p<0.001). In addition, randomised trials were less likely to be unpublished than non-randomised trials (OR 0.67; CI 0.50-0.88; p=0.004). There was no significant effect of the intervention type, phase of the trial, trial participants' age and gender, or the trial funding resource on the publication status of cardiovascular medicine clinical trials. Conclusions It is observed that a considerable number of clinical trials in cardiovascular medicine remain unpublished. This issue raises some concerns regarding the risks and discomfort participants may encounter during the trial, while no results are added to the literature. Blinding of the trials, enrolment size, and allocation methods are the main predictors of trial publication.Forest plot of logistic regression

The Trustworthiness Metric Model of Interface Based on Defects

The interface is a crucial element in component-based software, enabling the linkage of distinct components to facilitate interaction. Defects within the interface can significantly impact the overall trustworthiness of the system. Therefore, it is essential to assess the interface trustworthiness based on a defect-centric approach. This paper introduces a novel model for evaluating interface trustworthiness, anchored in defect analysis. First, the defect types are formalized based on interface specifications. Then, the comprehensive weight allocation method is established to characterize the importance degree of each interface defect type by combining the G1 and CRITIC methods. Subsequently, the attributes of the interface are evaluated by defect value analysis, and the trustworthiness measurement model of the interface is proposed based on these attributes. Furthermore, to evaluate the trustworthiness of the whole system, the trustworthiness measure models under different combination structure of components are established. Finally, the model’s’ applicability is demonstrated through an illustrative example. This trustworthiness evaluation from the interface view can guide interface designers to obtain high-quality interfaces and improve the trustworthiness of the entire software.

Comparing the effects of oral sucrose and kangaroo mother care on selected physiological variables and pain resulting from venipuncture in premature newborns admitted to neonatal intensive care units

BackgroundPremature newborns admitted to neonatal intensive care units (NICUs) undergo numerous painful interventions during care and treatment. The purpose of this study was to compare the effects of using sucrose and kangaroo mother care by on selected physiological variables and pain resulting from venipuncture in premature infants admitted to NICUs affiliated with Shiraz University of Medical Sciences.MethodsThis clinical trial included premature infants admitted to 2 NICUs. The sample size consisted of 66 neonates, with 22 newborns in each group. Randomization was performed using the block allocation method. Data collection involved a demographic questionnaire, the neonatal infant pain scale, and a pulse oximetry device. Friedman, Kruskal–Wallis, and Dunn’s post hoc tests employed for data analysis, with a significance level of P < 0.05.ResultsThe use of oral sucrose and kangaroo care demonstrated significant differences in breathing rate, heart rate, and average arterial blood oxygen saturation during and after venipuncture (P < 0.05). Oral sucrose was found to be significantly more effective than kangaroo care. Neonates under kangaroo care exhibited more regular heart rates compared to the other group (P < 0.05). The utilization of oral sucrose and kangaroo care had varying effects on the average pain score resulting from venipuncture in premature neonates (P < 0.05).ConclusionsBoth methods proved effective in reducing pain and improving physiological variables. However, due to the superior effectiveness of sucrose administration, it is recommended as a cost-effective and easily implementable method in NICUs.Trial registration: Iranian Registry of Clinical Trials IRCT20191215045749N1. (29/03/2020).

UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison

This paper introduces UniSchedApi, an API-based solution that revolutionizes optimized university resource scheduling. The primary focus of the research is the detailed evaluation of two automatic resource allocation methods: Tabu Search (TS) and Genetic Algorithm (GA). The paper thoroughly explores how these methods address challenges associated with resource allocation in university environments, considering critical factors such as teacher availability, student time constraints, classroom features (including computers, projectors, TV's, specialized laboratories, specialized equipment, etc.), among others. The evaluation is carried out meticulously, measuring the performance and memory resource usage of both algorithms, considering the comparison with the manual scheduling. The results reveal that the TS algorithm excels in terms of temporal efficiency and computational resource usage. Based on these findings, UniSchedApi implements GA and TS but uses TS as the default algorithm, ensuring more efficient and optimized management of academic resources. This research not only presents a practical solution with UniSchedApi but also provides a deep understanding of the methods for evaluating and selecting algorithms to address specific challenges in university resource allocation. These results lay the groundwork for future improvements in academic resource management.

Iterative Function Allocation of Single Pilot Operations Based on Biclustering Method

The successful transition from a two-pilot to a single-pilot mode cannot solely rely on simply augmenting advanced automation in the cockpit. The degree of support provided by functions at different levels of automation varies, and introducing new functions simultaneously brings new demands for the single pilot, leading to imbalanced workload. To address these shortcomings and enhance the rationality of function allocation under workload constraints, this paper introduces an iterative function allocation method. Initially, the functions are categorized and classified into different levels of automation to obtain the initial function allocation scheme. Subsequently, a novel bicluster mining algorithm called K-Miner is proposed to identify elements requiring iterative design. As a case study, the multitask surface scenario in SPO is considered. By applying the proposed iterative design method, the task performance of the single pilot is improved, the workload of the single pilot is effectively reduced, and situational awareness is improved to a certain extent.

A Novel Clinching Auction Mechanism for Edge Computing Resource Allocation With Budget Limits

ABSTRACTAn auction mechanism is an effective resource allocation method that can increase the revenue of resource providers in the field of edge computing. Existing auction mechanism designs mostly aim to maximize social welfare when allocating resources, but these schemes lead to low revenue. In contrast, clinching auctions have achieved good results in spectrum allocation and advertising due to their high revenue. Therefore, a clinching auction mechanism is a promising tool for allocating edge computing resources. However, clinching auctions have the drawback that they can only allocate homogeneous finitely divisible goods, meaning that they cannot be directly applied for resource allocation in edge computing. This article presents two new auction mechanisms that improve on the clinching auction. Specifically, based on the principle of increasing global prices and local competition, two mechanisms are designed, one from the perspective of resource providers (MDCAM‐ECS) and the other from the perspective of users (MDCAM‐User), to solve the problem of edge computing resource allocation and pricing with deployment constraints and user budget constraints. The mechanisms proposed in this article have the properties of individual rationality, truthfulness, and computational efficiency. In the experiments, in terms of social welfare and revenue, our algorithms can achieve a 20% improvement over existing algorithms, such as fixed‐price, Vickery–Clarke–Groves (VCG), and monotonic critical‐price mechanisms. Additionally, in most experiments, our algorithm can ensure resource utilization greater than 80%.

Effect of different doses of esketamine on the median effective concentration of propofol for inhibiting body movement during hysteroscopy

The objective of this study is to investigate the effects of various doses of esketamine on the median effective concentration (EC50) of propofol required for inhibiting body movement during hysteroscopy. Additionally, this research aims to explore the pharmacodynamic interactions between esketamine and propofol. Prospective, double-blind, up-down sequential allocation study. Operating room, post-anesthesia care unit (PACU), and general ward. A total of 90 patients were allocated into three groups in a randomized, double-blinded manner as follows: 0.1 mg/kg esketamine combined with propofol intravenous injection (EP0.1) group, 0.2 mg/kg esketamine combined with propofol intravenous injection (EP0.2) group, 0.3 mg/kg esketamine combined with propofol of intravenous injection (EP0.3) group. For the initial patient in each group, the starting effector target concentration of propofol was set at 4 µg/ml. Each patient received an initial intravenous injection of 0.04 mg/kg midazolam, followed by the administration of the appropriate dose of esketamine. Ten seconds after the esketamine injection, propofol was administered intravenously to achieve the target concentration. In accordance with the sequential method principle, the concentration of propofol for the subsequent patient was adjusted based on the response of the previous patient. Effective inhibition of body movement was defined as the absence of any involuntary body movements throughout the entire surgical process. If the previous patient exhibited body movements, the propofol concentration for the next patient was increased by 0.5 µg/ml; conversely, if no movements were observed, it was decreased by 0.5 µg/ml. The up-down sequential allocation method and probit regression were used to calculate the EC50 of propofol. Hospital Anxiety and Depression Scale-Anxiety (HADS-A) and Depression (HADS-D) score, adverse events, hemodynamic changes, demographic data and clinical characteristics. The EC50 of propofol was 3.849 μg/ml (95% CI: 3.419–4.281) in the EP0.1 group, 3.641 μg/ml (95% CI: 2.807−4.200) in the EP0.2 group, and 3.417 μg/ml (95% CI: 2.845–3.852) in the EP0.3 group. These findings suggest that esketamine can dose-dependently reduce the EC50 of propofol. Esketamine can dose-dependently reduce the EC50 of propofol in hysteroscopy, while concurrently lowering patients’ HADS-A and HADS-D scores 24 h post-operation. It is concluded that the optimal dose of esketamine, when combined with propofol for hysteroscopy, is 0.3 mg/kg.

Adjustable viewpoint allocation for enhancing the viewing angle and reducing the crosstalk in super multi-view display.

In this Letter, we propose an adjustable viewpoint allocation method with forward and backward ray tracing to enhance the viewing angle and reduce the crosstalk in super multi-view (SMV) display. The synthetic image (SI) is initially calculated by backward ray tracing according to the viewing distance and the viewpoint interval. Forward ray tracing is then performed on the result of the viewpoint allocation to correct the deviation between the actual and the ideal viewpoint positions. Optical experiments are performed to verify the effectiveness of the proposed method. The results show that the proposed method effectively suppresses the crosstalk between viewpoints and eliminates artifacts in adjacent viewing areas within the ideal viewing angle, thereby enhancing the actual viewing angle. Meanwhile, due to the ability of reallocating information on the SI, the proposed method can achieve varying information density from different views. It is expected that our proposed method could be widely used in light field display to realize high quality display in the future.

Application of pole allocation to optimize passive viscous dampers represented by the Maxwell model

The Maxwell model is used to understand the realistic effectiveness of vibration reduction in the building structures. The model consists of a dashpot and spring in series. The dashpot and spring represent a viscous damper and joint between the damper and the structural frame, respectively. However, few studies have investigated the optimal damper capacity and the corresponding spring stiffness in the practical parameter ranges. Additionally, the optimal damper derived using the fixed-point theory has no relationships with that derived via pole allocation. Therefore, in this study, to examine the effects of the joint spring, the pole allocation method was used to design a structural system wherein the Maxwell model was incorporated into a single-degree-of-freedom damped model. We introduced a closed-form expression, which explicitly described the relationships between the target structural damping ratio, damper capacity, and joint spring. The Maxwell model constrained the control effectiveness using damper parameters and simultaneously suggested an optimal and realistic joint spring for the damper. Pole allocation was also applied to a multi-degree-of-freedom (M-DOF) damped structural system employing multiple Maxwell models. The newly proposed optimized joint spring could easily control the additional damping effect on the M-DOF system. The Maxwell model can be optimized almost manually. The scheme is more useful in the preliminary design stage than the previous numerical optimizations. This study extended the mathematical equation governing the building vibrations to the Maxwell model. The extended equation served as a unified description for considering structural passive control.

Resource Use Efficiency and Constraints Faced by Potato Growers in Kannauj District of Uttar Pradesh, India

In the present paper, an attempt has been made to examine Potato cultivation in different categories of farmers in Kannauj district of Uttar Pradesh. Primary data was collect through personal interview by pre-structured and pre-tested schedule; the data was collect for the agricultural year 2022-23. A sample size of 100 farmers, marginal (61) Small (29) Medium (10) were interviewed form 5 village of Kannauj block in Kannauj district select the farmers by using the proportionate allocation method, with the data analysis, the result found that average holding size 0.84, 1.84 and 3.91 ha. in respect of marginal small and medium farm, respectively. In the case of potato, returns to scale of marginal, small and medium size group of farms were 0.8989, 0.8525 and 0.8280, respectively. Returns to scale in all three categories of the farms were found less than unity. It indicates the production of potato is characterized by decreasing returns to scale on each farm situation. Another factor of production i.e. manure and fertilizer was found significantly associated with dependent variable at 1% level probability in all farm situation. The problem related with hired human labour and technical knowledge was notice at I and II rank by the sample farmers (i.e. Unavailability of machines and tractor and Lack of improved varieties).

Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy

As the global focus on environmental conservation and energy stability intensifies, enhancing energy efficiency and mitigating pollution emissions have emerged as pivotal issues that cannot be overlooked. In order to make a multi-energy-coupled integrated energy system (IES) that can meet the demand of load diversity under low-carbon economic operation, an optimal capacity allocation model of an electricity–heat–hydrogen multi-energy-coupled IES is proposed, with the objectives of minimizing operating costs and pollutant emissions and minimizing peak-to-valley loads on the grid side. Different Energy management strategies with different storage priorities are proposed, and the proposed NSNGO algorithm is used to solve the above model. The results show that the total profit after optimization is 5.91% higher on average compared to the comparison type, and the pollutant emission scalar function is reduced by 980.64 (g), which is 7.48% lower. The peak–valley difference of the regional power system before optimization is 0.5952, and the peak–valley difference of the regional power system after optimization is 0.4142, which is reduced by 30.40%, and the proposed capacity allocation method can realize the economic operation of the multi-energy-coupled integrated energy system.

Dynamic Flexible Allocation of Slots in Container Line Transport

Due to the imbalance between supply and demand, liner container transportation often faces the problem of low slot utilization, which will occur in the shipping process, such as dry container demand exceeding the available dry slots and reefer slots not being fully utilized. This makes it important and challenging to maintain a balance between the actual demand and the limited number of slots allocated for liner container transport. Therefore, this study proposes a flexible allocation method: expanding the types of containers that can be loaded in the same slot. This method is suitable for handling each dynamic arrival container booking request by shipping enterprises, making decisions to accept or reject, and flexibly allocating shipping slots. In order to maximize the total revenue generated by accepting container booking requests during the entire booking acceptance cycle, we establish a dynamic programming model for the flexible allocation of slots. For model solving, we use the Q-learning reinforcement learning algorithm. Compared with traditional heuristic algorithms, this algorithm can improve solving efficiency and facilitate decision-making at the operational level of shipping enterprises. In terms of model performance, examples of different scales are used for comparison and training; the results are compared with the model without flexible allocation, and it is proved that the model proposed in this paper can obtain higher returns than the model without flexible allocation. The results show that the model and Q-learning algorithm can help enterprises solve the problem of the flexible allocation of shipping slots, and thus, this research has practical significance.

Estimation of Urban Tree Chlorophyll Content and Leaf Area Index Using Sentinel-2 Images and 3D Radiative Transfer Model Inversion

Urban trees play an important role in mitigating effects of climate change and provide essential ecosystem services. However, the urban environment can stress trees, requiring the use of effective monitoring methods to assess their health and functionality. The objective of this study, which focused on four deciduous tree species in Rennes, France, was to evaluate the ability of hybrid inversion models to estimate leaf chlorophyll content (LCC), leaf area index (LAI), and canopy chlorophyll content (CCC) of urban trees using eight Sentinel-2 (S2) images acquired in 2021. Simulations were performed using the 3D radiative transfer model DART, and the hybrid inversion models were developed using machine-learning regression algorithms (random forest (RF) and gaussian process regression). Model performance was assessed using in situ measurements, and relations between satellite data and in situ measurements were investigated using spatial allocation (SA) methods at the pixel and tree scales. The influence of including environment features (EFs) as model inputs was also assessed. The results indicated that random forest models that included EFs and used the pixel-scale SA method were the most accurate with R2 values of 0.33, 0.29, and 0.46 for LCC, LAI, and CCC, respectively, with notable variability among species.

A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services

IMT-2020 (International Mobile Telecommunications-2020) is the prevailing mobile communication technology at the moment, significantly affecting societal progress. Nevertheless, the roll-out of the IMT-2020 system has introduced numerous interferences to existing services. The coexistence with fixed satellite services has become a topical issue currently under consideration. This paper discusses the compatibility and interference issues between IMT-2020 and the 14 GHz FSS (fixed-satellite service) uplink, as well as the spectrum access issue solved by artificial intelligence methods. The study shows that the interference from IMT-2020 macro-base stations to FSS space stations exceeds the ITU standard by approximately 10 dB. To control the interference, a partition-based power control algorithm is proposed, which divides ground base stations into multiple areas and virtualizes each area’s base stations into a single large base station then applies power control to maximize the total transmission power of the base stations within the area. Furthermore, three intra-partition power control algorithms are introduced: average power allocation, power allocation based on channel gain, andna power allocation method based on the maximum intra-partition sum rate. Additionally, under the assumption that dynamic satellite nodes are available in the system for ground user access, a spectrum access algorithm utilizing deep reinforcement learning is designed. Simulation results confirm the effectiveness of the proposed scheme, which can reduce the interference from the IMT-2020 system to the FSS service below the threshold, ensuring harmonious coexistence of the two services.

Efficient and Lightweight Model-Predictive IoT Energy Management

Multi-sensor IoT devices often rely on renewable energy and batteries to support diverse field deployments. A device’s sensors use significant energy, so careful energy management is needed to maximize its operating time while meeting application requirements. Model Predictive Control (MPC), a successful energy management technique for other application domains, requires significant computational resources usually not available in typical IoT sensing devices. We develop and evaluate a low-complexity approximation to MPC for IoT device operations powered by renewable (solar) energy, where the approximation is guided by the charging characteristics of real batteries. The complex MPC optimization problem is solved by decomposing it into a time-dependent energy allocation problem, and a task-dependent sensor scheduling problem, each of which is solvable with cubic time complexity. We utilize a novel combination of an incremental max-min fair allocation method and a recursive dynamic programming-like procedure. This low-complexity predictive optimization step is integrated with a very simple parabola-based adaptive solar prediction to provide a full system solution, termed Predictive EneRgy Management for IoT (PERMIT) , that can be implemented in lightweight IoT devices. PERMIT is evaluated through experiments on a solar-powered Raspberry Pi device with multiple sensors. We also complement our evaluations with simulations based on real device data traces. Results show that PERMIT’s low-complexity algorithm approximates the exact MPC solution very closely. PERMIT also performs significantly better than Signpost, a comparable IoT energy management solution.

Contribution-Based Resource Allocation for Effective Federated Learning in UAV-Assisted Edge Networks.

This paper considers UAVs as edge computing nodes and investigates a novel network resource allocation method for federated learning within a three-layer wireless network architecture containing cloud, edges (UAVs), and clients. To address the issue of fair bandwidth resource allocation among clients participating in federated learning, a contribution calculation strategy based on the Shapley value (SV) used as the weight for model aggregation is proposed. On this basis, a client selection and wireless resource allocation method based on model contribution is further designed. By reducing the training and aggregation frequency of the low-contribution clients during the asynchronous aggregation phase, the limited bandwidth resources are allocated to high-contribution clients, thus improving the convergence speed and accuracy of the global model. Simulation experiments demonstrate that the proposed method can significantly reduce the system delay and total energy consumption with gains between 15% and 50% while also improving the final accuracy of the global model by 0.3% and 2% on both short-term and long-term perspectives, respectively.

Context-aware coupler reconfiguration for tunable coupler-based superconducting quantum computers

Abstract Crosstalk, caused by unwanted interactions from the surrounding environment, remains a fundamental challenge in existing superconducting quantum computers (SQCs). We propose a method for qubit placement, connectivity, and logical qubit allocation on tunable-coupler SQCs to eliminate unnecessary qubit connections and optimize resources while reducing crosstalk errors. Existing mitigation methods carry trade-offs, like increasing qubit connectivity or software-based gate scheduling. Our method, the Context-Aware COupler REconfiguration (CA-CORE) compilation method, aligns with application-specific design principles. It optimizes the qubit connections for improved SQC performance, leveraging tunable couplers. Through contextual analysis of qubit correlations, we configure an efficient coupling map considering SQC constraints. We then apply the SWAP-based Bidirectional Heuristic Search (SABRE) qubit mapping method and crosstalk-adaptive scheduling to further optimize the quantum circuit. Our architecture reduces depth by an average of 18% and 27%, and by up to 50% and 60%, compared to lattice and heavy-hex architectures, respectively. With crosstalk optimization through adaptive scheduling, we achieve performance improvements of 35%, 20%, and 160% on fully-enabled grid, lattice, and heavy-hex topologies, respectively.