Weightings Research Articles

Crosscap contribution to late-time two-point correlators

We show that in Jackiw-Teitelboim (JT) gravity, late-time two-point functions can get a leading non-decaying contribution from a spacetime with the topology of a Möbius strip (a disk with one crosscap). There is an interesting interplay between this contribution and the standard “plateau”. The two can add together or cancel, depending on topological weighting factors. We match this behavior to Random Matrix Theory (RMT) and the N mod 8 periodicity of Sachdev-Kitaev-Ye (SYK) results.

Development of simultaneous quantitative analytical method for three active components of Korean mint (Agastache rugosa (Fisch. & C.A.Mey.) Kuntze) extract in human plasma using ultra-high-performance liquid chromatography–tandem mass spectrometry

Agastache rugosa contains phenolic compounds and flavonoids, and has been extensively used as a traditional herbal medicine. The major components in Agastache rugosa extract (ARE) are rosmarinic acid, tilianin, and acacetin, for which several analytical techniques have been reported. However, these substances have yet to be simultaneously quantified in human plasma. In this study, we aimed to simultaneously determine the three active components of ARE in human plasma by developing a reliable quantitative analytical method using ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS). Chromatographic separation of the plasma samples was achieved using an ACQUITY UPLC® BEH C18 column with a gradient mobile phase of water and acetonitrile containing 0.1 % formic acid. Mass spectrometric detection was performed using a triple quadrupole tandem mass spectrometer in negative electrospray ionization (ESI−) and multiple reaction monitoring (MRM) modes. The developed quantitative method was validated for the three active components. All three analytes exhibited a linear response over the ranges of 0.5–50 ng/mL for rosmarinic acid, 0.1–20 ng/mL for acacetin, and 0.5–20 ng/mL for tilianin with a weighting factor of 1/x (where x is the concentration). At three quality control (QC) concentration levels (low, medium, and high), including the lower limit of quantitation (LLOQ), acceptable accuracy (±15 %) was achieved in the intra- and interday validations. The concentration of rosmarinic acid was highest in plasma. Tilianin and acacetin appeared and were eliminated earlier in the plasma than rosmarinic acid. This study provides a successfully validated method that can be used in further clinical applications of Agastache rugosa extracts.

Connected vehicles’ dynamic route planning based on reinforcement learning

Real-time route planning is a challenging work for the Internet of Vehicles (IoV) systems. Some people have used swarm intelligence algorithms to schedule routes for connected vehicles. These algorithms are always sensitive to the setting of initial parameters. These days, reinforcement learning algorithms to some extent overcome this drawback. However, previous research based on reinforcement learning has not taken into account the real-time variation in the number of vehicles on each lane. In this work, a new reinforcement learning algorithm, named dynamical-weighted Q-learning (DWQL), is proposed. This approach plans the routes for connected vehicles by introducing dynamic weighting factors into Q-learning algorithm. By employing dynamic weighting factors, the algorithm selects the optimal route for connected vehicles based on their current positions as well as congestion status of the accessible roads. The experimental results show that compared to existing algorithms, the DWQL algorithm proposed in this paper can effectively reduce the duration, time loss, waiting time, economic cost, and improve the traffic efficiency of the IoV system.

Breast cancer diagnosis through knowledge distillation of Swin transformer-based teacher–student models

Breast cancer is a significant global health concern, emphasizing the crucial need for a timely and accurate diagnosis to enhance survival rates. Traditional diagnostic methods rely on pathologists analyzing whole-slide images (WSIs) to identify and diagnose malignancies. However, this task is complex, demanding specialized expertise and imposing a substantial workload on pathologists. Additionally, existing deep learning models, commonly employed for classifying histopathology images, often need enhancements to ensure their suitability for real-time deployment on WSI, especially when trained for small regions of interest (ROIs). This article introduces two Swin transformer-based architectures: the teacher model, characterized by its moderate size, and the lightweight student model. Both models are trained using a publicly available dataset of breast cancer histopathology images, focusing on ROIs with varying magnification factors. Transfer learning is applied to train the teacher model, and knowledge distillation (KD) transfers its capabilities to the student model. To enhance validation accuracy and minimize the total loss in KD, we employ the state–action–reward–state–action (SARSA) reinforcement learning algorithm. The algorithm dynamically computes temperature and a weighting factor throughout the KD process to achieve high accuracy within a considerably shorter training timeframe. Additionally, the student model is deployed to analyze malignancies in WSI. Despite the student model being only one-third the size and flops of the teacher model, it achieves an impressive accuracy of 98.71%, slightly below the teacher’s accuracy of 98.91%. Experimental results demonstrate that the student model can process WSIs at a throughput of 1.67 samples s−1 with an accuracy of 82%. The proposed student model, trained using KD and the SARSA algorithm, exhibits promising breast cancer classification and WSI analysis performance. These findings indicate its potential for assisting pathologists in diagnosing breast cancer accurately and effectively.

Epileptic Seizure Detection in EEG Signal Using Optimized Convolutional Neural Network with Selected Feature Set

This article implements novel epileptic seizure detection by EEG signal that includes various phases: Feature extraction, Feature selection, and Classification. Originally, the input EEG signal is subjected to the preprocessing phase, and from the preprocessed signal, certain features are extracted in which the features like TDFs, FDFs, and TFDFs are extracted. Here, the Time Domain Features (TDFs) include Non-linear energy (NE), Permutation Entropy (PE), and Weighted Permutation Entropy (WPE); the Frequency Domain Features (FDFs) consists of Intensity Weighted Mean Frequency (IWMF); and the Time-Frequency Domain Features (TFDFs) consists of the “Weighted Multi-scale Rényi Permutation Entropy (WMRPE)”. Moreover, the extracted features are subjected to the feature selection phase. An improved chi-square model is proposed for selecting the appropriate features. Then, the chosen features are subjected to the classification process. Here, the classification is performed using an Optimized Convolutional Neural Network (CNN). To make the detection more precise, the CNN weights are tuned optimally through a Weighting Factor Based Shark Smell Optimization (IWFSSO) model. The MCC of the implemented CNN+IWFSSO method achieved a better value for a learning percentage 90%; nevertheless, the compared existing models accomplish a smaller value for dataset 2.

3D Kriging interpolation for traffic noise visualization: designing noise observation points and valuation of spatial interpolation accuracy

Identifying the risk of traffic noise is vital in minimizing traffic noise pollution in urban areas. As noise travels in every direction, 3D visualization of traffic noise is essential, which involves visualising traffic noise along the facades of buildings. A standard traffic noise model is necessary to calculate traffic noise levels, as several factors affect traffic noise. Moreover, designing noise observation points in 3D and spatial interpolation play significant roles in 3D noise visualisation. Therefore, this study demonstrates the results by elaborating on the spatial interpolation and designing noise observation points. A noise observation point consists of four parameters in 3D space. Generally, Inverse Distance Weighted (IDW), Triangular Irregular Network (TIN), and Kriging do not support the interpolation of four parameters in 3D. However, 3D Kriging in Empirical Bayesian Kriging provides significant opportunities to interpolate noise levels in 3D. However, the elements of the function of spatial interpolations are vital for accuracy. The 3D Kriging uses different variograms according to semivariance. This variogram directly impacts the weighting factor of 3D Kriging. Therefore, this study develops a comparison to identify the impact of different variograms on the accuracy of 3D Kriging interpolation on traffic noise.

Dose-rate dependence and IMRT QA suitability of EBT3 radiochromic films for pulse reduced dose-rate radiotherapy (PRDR) dosimetry.

Pulsed reduced dose rate (PRDR) is an emerging radiotherapy technique for recurrent diseases. It is pertinent that the linac beam characteristics are evaluated for PRDR dose rates and a suitable dosimeter is employed for IMRT QA. This study sought to investigate the pulse characteristics of a 6 MV photon beam during PRDR irradiations on a commercial linac. The feasibility of using EBT3 radiochromic film for use in IMRT QA was also investigated by comparing its response to a commercial diode array phantom. A plastic scintillator detector was employed to measure the photon pulse characteristics across nominal repetition rates (NRRs) in the 5-600 MU/min range. Film was irradiated with dose rates in the 0.033-4Gy/min range to study the dose rate dependence. Five clinical PRDR treatment plans were selected for IMRT QA with the Delta4 phantom and EBT3 film sheets. The planned and measured dose were compared using gamma analysis with a criterion of 3%/3mm. EBT3 film QA was performed using a cumulative technique and a weighting factor technique. Negligible differences were observed in the pulse width and height data between the investigated NRRs. The pulse width was measured to be 3.15±0.01 and the PRF was calculated to be 3-357Hz for the 5-600 MU/min NRRs. The EBT3 film was found to be dose rate independent within 3%. The gamma pass rates (GPRs) were above 99% and 90% for the Delta4 phantom and the EBT3 film using the cumulative QA method, respectively. GPRs as low as 80% were noted for the weighting factor EBT3 QA method. Altering the NRRs changes the mean dose rate while the instantaneous dose rate remains constant. The EBT3 film was found to be suitable for PRDR dosimetry and IMRT QA with minimal dose rate dependence.

Weighting factors design in model predictive direct torque control based on cascaded neural network

AbstractAn innovative control strategy is introduced in this paper for model predictive direct torque control (MPDTC) of a permanent magnet synchronous motor (PMSM) inverter, which is based on a cascaded neural network for the automatic selection and online tuning of weighting factors. Specifically, the lower network in the proposed two‐stage cascaded network is trained by utilizing the weighting factors and the corresponding converter parameters under a specific working condition to establish data substitution models under that working condition and determine the fitness function for obtaining the optimal weighting factors. The upper network of the cascaded network is trained using various working conditions and their corresponding optimal weighting factors calculated by the lower network, with the goal of achieving real‐time tuning of the weighting factors under variable working conditions. By employing a data‐driven method to design the weighting factors at each working condition offline, this method reduces online tuning to a single calculation in the upper network and minimizes the online calculation burden. This approach enables the weighting factors to be adjusted in real‐time according to changes in the working conditions, leading to enhanced accuracy of weighting factor optimization. Experimental results demonstrate that the proposed strategy improves the overall performance of the inverters.

Fuzzy Gain Scheduling of the Fractional-Order PID Controller for a Continuous Stirred-Tank Reactor Process

In most chemical industries, continuous stirred-tank reactors (CSTRs) are essential for the production of chemicals. The CSTR process is highly nonlinear and has both stable and unstable equilibrium points. Control system engineers face a difficult issue when designing a single controller for temperature control in both stable and unstable areas. Due to these problems, implementing the conventional proportional-integral-derivative (PID) controller may lead to instability in controlled variables. Therefore, in this paper, the entire operating region is divided into three regions. The fractional-order PID controller as a local controller is designed in each operating region to control the temperature, and its parameters are tuned with a genetic algorithm by minimizing the integral of absolute error and control input change with a weighting factor. Then, a fuzzy gain scheduling scheme based on the Tagaki-Sugno fuzzy model is used to properly interpolate the outputs of the local controllers. A set of simulations are carried out to demonstrate the effectiveness of the proposed controller, and its performance is compared with that of an adaptive nonlinear controller and fuzzy gain scheduling of the PID controller.

A multi-sensor data fusion algorithm based on consistency preprocessing and adaptive weighting

ABSTRACT In the data collection of a multi-sensor system, there are problems with large errors, conflicts, and redundancy. To solve the above problem, a multi-sensor data fusion algorithm based on anomaly data preprocessing and adaptive weighted estimation is proposed. To improve the reliability of the algorithm, first, for a single sensor measurement signal sequence, a consistency preprocessing using the off-centre distance method is performed, and the weighting factor of each measurement data is calculated. Then, the measurement signal sequence is weighted and fused; Secondly, in response to the uneven distribution of measurement errors among multiple sensors in different directions, an adaptive weighted data fusion method based on the principle of optimal weight allocation is proposed. The proposed method was compared with the adaptive weighting method and arithmetic mean method. The simulation results showed that the total mean square error of the data fusion results obtained using the proposed algorithm is smaller. The proposed algorithm can effectively improve the accuracy of data measurement, reduce redundancy, and improve the stability of data measurement.

Improved multi-order Vold-Kalman filter for order tracking analysis using split cosine and sine terms

Order tracking analysis is an effective tool for the analysis of non-stationary vibration and acoustical signals for the purposes of machinery fault diagnosis and operational modal analysis. The second-generation Vold-Kalman filter is widely used for order tracking analysis; however, the formulation of the data equation is based on incomplete complex kernels, which introduce an imaginary part error in the cost function. In this work, the data equation of the second generation Vold-Kalman filter is re-formulated using separated cosine and sine kernels that necessarily provide a real-valued sum. Doing so will minimize the error in the data equation and offer better optimization scheme to reduce the error in the structural equation and; hence, provide smooth envelopes. Unlike Vold-Kalman method which requires high filter weighting factors to suppress envelope artifacts, the proposed method can achieve high accuracy even when using small weighting factor as deduced from the simulation and experimental investigations. The proposed method can be used to measure the envelopes of close and crossing orders, as well as orders experiencing resonance conditions or orders with high slew rate due to lower filter decay time.

Bio-inspired altitude changing extension to the 3DVFH* local obstacle avoidance algorithm

Obstacle avoidance is critical for unmanned aerial vehicles (UAVs) operating autonomously. Obstacle avoidance algorithms either rely on global environment data or local sensor data. Local path planners react to unforeseen objects and plan purely on local sensor information. Similarly, animals need to find feasible paths based on local information about their surroundings. Therefore, their behavior is a valuable source of inspiration for path planning. Bumblebees tend to fly vertically over far-away obstacles and horizontally around close ones, implying two zones for different flight strategies depending on the distance to obstacles. This work enhances the local path planner 3DVFH* with this bio-inspired strategy. The algorithm alters the goal-driven function of the 3DVFH* to climb-preferring if obstacles are far away. Prior experiments with bumblebees led to two definitions of flight zone limits depending on the distance to obstacles, leading to two algorithm variants. Both variants reduce the probability of not reaching the goal of a 3DVFH* implementation in Matlab/Simulink. The best variant, 3DVFH*b-b, reduces this probability from 70.7 to 18.6% in city-like worlds using a strong vertical evasion strategy. Energy consumption is higher, and flight paths are longer compared to the algorithm version with pronounced horizontal evasion tendency. A parameter study analyzes the effect of different weighting factors in the cost function. The best parameter combination shows a failure probability of 6.9% in city-like worlds and reduces energy consumption by 28%. Our findings demonstrate the potential of bio-inspired approaches for improving the performance of local path planning algorithms for UAV.

Analytical reconstructions of full-scan multiple source-translation computed tomography under large field of views.

This paper is to investigate the high-quality analytical reconstructions of multiple source-translation computed tomography (mSTCT) under an extended field of view (FOV). Under the larger FOVs, the previously proposed backprojection filtration (BPF) algorithms for mSTCT, including D-BPF and S-BPF (their differences are different derivate directions along the detector and source, respectively), make some errors and artifacts in the reconstructed images due to a backprojection weighting factor and the half-scan mode, which deviates from the intention of mSTCT imaging. In this paper, to achieve reconstruction with as little error as possible under the extremely extended FOV, we combine the full-scan mSTCT (F-mSTCT) geometry with the previous BPF algorithms to study the performance and derive a suitable redundancy-weighted function for F-mSTCT. The experimental results indicate FS-BPF can get high-quality, stable images under the extremely extended FOV of imaging a large object, though it requires more projections than FD-BPF. Finally, for different practical requirements in extending FOV imaging, we give suggestions on algorithm selection.

Synthesis and validation algorithm followed by a weighting procedure to create a detailed anthropometric dataset for the German working-age population

For the German working-age population no publicly available and detailed anthropometric raw dataset exists, although several studies have collected anthropometric datasets. Unfortunately, the publication of raw data may be restricted by data usage regulations. This study presents a synthesis and validation algorithm to create a virtual copy of an already existing dataset. A detailed anthropometric dataset from a regional epidemiological public-health study in Germany was used for the synthesis and validation algorithm. Results revealed only minor deviations within the validation process. Compared to the original dataset, the virtual dataset was statistically almost identical. In a next step, the virtual dataset was weighted to approximate nationally representative values. In summary, the computed unweighted and weighted virtual data can be published without restrictions and used for ergonomic designing. Furthermore, the synthesis and validation algorithm is suitable for the generation of virtual copies and can be applied to other detailed anthropometric datasets.

Statistical inference for time-to-event data in non-randomized cohorts with selective attrition.

In multi-season clinical trials with a randomize-once strategy, patients enrolled from previous seasons who stay alive and remain in the study will be treated according to the initial randomization in subsequent seasons. To address the potentially selective attrition from earlier seasons for the non-randomized cohorts, we develop an inverse probability of treatment weighting method using season-specific propensity scores to produce unbiased estimates of survival functions or hazard ratios. Bootstrap variance estimators are used to account for the randomness in the estimated weights and the potential correlations in repeated events within each patient from season to season. Simulation studies show that the weighting procedure and bootstrap variance estimator provide unbiased estimates and valid inferences in Kaplan-Meier estimates and Cox proportional hazard models. Finally, data from the INVESTED trial are analyzed to illustrate the proposed method.

Predicting the Risk of Verticillium Wilt in Olive Orchards Using Fuzzy Logic

Developing models to understand disease dynamics and predict the risk of disease outbreaks to facilitate decision making is an integral component of plant disease management. However, these models have not yet been developed for one of the most damaging diseases in Mediterranean olive-growing areas (verticillium wilt (VW), caused by the fungus Verticillium dahliae Kleb.), although there are parameters (e.g., level of V. dahliae inoculum in the soil, level of susceptibility of the olive cultivar, isothermality, coefficient of variation of seasonal precipitation, etc.) that have previously been correlated with the severity of the disease. Using the data from previous VW studies conducted in the Guadalquivir Valley of Andalusia (one of the most damaged areas worldwide), in this work, a set of fuzzy logic (FL) models is developed with the aforementioned disease and climatic parameters, and the results are compared with machine learning (ML) models, of known effectiveness, to predict the risk levels of VW appearance in an olive orchard. Under these conditions, both groups of models were less effective than those previously studied with simpler models or models used under controlled conditions. However, the accuracy achieved with the most efficient FL model (60%; classification system based on fuzzy rules using the Ishibuchi method with a weighting factor) was somewhat greater than the efficiency achieved with the most efficient ML model (59.0%; decision tree classifier), in addition to being more appropriate (from a practical point of view) for the incorporation into a decision support system by allowing the risk of appearance of each observation to be known by providing rules for each of the combinations of the different parameters with similar precision. Therefore, in this study, we propose the FL methodology as suitable to act as an expert system for the future creation of a decision support system for VW in olives.

IMBoost: A New Weighting Factor for Boosting to Improve the Classification Performance of Imbalanced Data

Imbalanced datasets pose significant challenges in the field of machine learning, as they consist of samples where one class (majority) dominates over the other class (minority). Although AdaBoost is a popular ensemble method known for its good performance in addressing various problems, it fails when dealing with imbalanced data sets due to its bias towards the majority class samples. In this study, we propose a novel weighting factor to enhance the performance of AdaBoost (called IMBoost). Our approach involves computing weights for both minority and majority class samples based on the performance of classifier on each class individually. Subsequently, we resample the data sets according to these new weights. To evaluate the effectiveness of our method, we compare it with six well-known ensemble methods on 30 imbalanced data sets and 4 synthetic data sets using ROC, precision-eecall AUC, and G-mean metrics. The results demonstrate the superiority of IMBoost. To further analyze the performance, we employ statistical tests, which confirm the excellence of our method.

Enhanced mind-matter interactions following rTMS induced frontal lobe inhibition

A major barrier to acceptance of psi is that effects are small and hard to replicate. To address this issue, we developed a novel neurobiological model to study this controversial phenomenon based upon the concept that the brain may act as a psi-inhibitory filter. Our previous research in individuals with frontal lobe damage suggests that this filter includes the left medial middle frontal region. We report our findings in healthy participants with rTMS induced reversible brain lesions. In support of our a priori hypothesis, we found a significant psi effect following rTMS inhibition of the left medial middle frontal lobe. This significant effect was found using a post hoc weighting procedure aligned with our overarching hypothesis. This suggests that the brain may inhibit psi and that individuals with neurological or reversible rTMS induced frontal lesions may comprise an enriched sample for detection and replication of this controversial phenomenon. Our findings are potentially transformative for the way we view interactions between the brain and seemingly random events.

Cluster Head Selection in Device to Device (D2D) Communication Based on Weighted Network Performance Factor

In this study, an unsupervised machine learning algorithm, Self Organizing Map was utilized to cluster D2D User Equipment using their network values as inputs. A weighting factor referred to as Hardware Sensing Factor (HSF) was formulated to take into account the device’s channel quality and the status of its underlying hardware circuitry. The values of the HSF were used as inputs to cluster the devices and to select cluster head for each cluster. The performance of SOM when HSF was input was compared with the performance when RSSI, RSRP or RSRQ was used as input. The comparison showed that the use of HSF as input to SOM cluster algorithm gave better cluster performance than the use of respective network values such as RSSI, RSRP or RSRQ. In addition, the use of HSF as input data to both SOM and K-Means algorithms showed that SOM cluster formation has better performance than K-Means algorithm.

Low‐computation‐burden model predictive current control for the grid‐tied quasi‐Z‐source inverter

AbstractThe appealing design known as the quasi‐Z‐source inverter (qZSI) can function in both buck and boost modes. In the meantime, model predictive control (MPC) for power transformers has attracted a lot of interest. In order to improve performance and solve the shortcomings of the current MPC, this paper suggests a low‐computation‐burden model predictive current control (LCB‐MPCC) for the grid‐tied qZSI. Two active vectors, one zero vector, and one shoot‐through (ST) vector are used in the production of the output voltage. To get around the weighting factor, a modified sliding‐mode control technique is suggested for instantly controlling the inductor current and capacitor voltage to their regulating spots. The duty cycle of the ST vector is acquired by the dead‐beat control to restrict the inductor current and significantly reduce the inductor current ripple. Additionally, an effective and quick optimal sector selection strategy is provided that uses a look‐up table to pick the optimal sector rather than a number of intricate calculations. Therefore, the proposed LCB‐MPCC greatly reduces the computational burden. To demonstrate the efficacy and benefits of the suggested technique, simulation and experimental results are presented.