Effectiveness Of Method Research Articles

Lithium-ion battery state of health estimation using a hybrid model with electrochemical impedance spectroscopy

The State of Health (SOH) of lithium-ion batteries is crucial for maintaining their safety and reliability. Electrochemical impedance spectroscopy (EIS) provides extensive information about the battery state, but the data across high-, medium-, and low-frequency ranges are not independent. Practical challenges in obtaining full-frequency EIS data include long measurement times, low accuracy due to noise, and high costs. This paper proposes an SOH estimation method based on offline EIS and domain-adversarial neural network (DaNN) transfer. Initially, we use a feature search strategy on experimental EIS data to identify the optimal impedance feature subset through the root mean square error (RMSE) and comprehensive indicators. These features are then input into the DaNN for SOH estimation using transfer algorithms. Finally, based on feature correlation analysis, weights are allocated using a Gaussian process regression model for a secondary prediction, optimizing the SOH results. Experiments on laboratory calendar aging and publicly available cycling aging datasets demonstrate the method's effectiveness, achieving an RMSE of <1 %. The proposed method significantly improves SOH estimation accuracy and efficiency, addressing the limitations of traditional methods and reducing dependency on extensive measurement data. This advancement enhances the safety, reliability, and cost-effectiveness of battery applications.

P-glycoprotein-mediated herb-drug interaction evaluation between Tenacissoside G and paclitaxel.

P-glycoprotein (P-gp)-mediated herb-drug interactions (HDIs) may impact drug efficacy and safety. Tenacissoside G (Tsd-G), a major active component of Marsdenia tenacissima, exhibits anticancer activity. To analyze the effect of Tsd-G on the pharmacokinetics of paclitaxel (PTX), researchers selected 30 Sprague-Dawley (SD) rats, randomized into a solvent control group, a verapamil positive control group, and 20, 40, and 60 mg/kg Tsd-G groups. After seven consecutive days of intraperitoneal injection of verapamil or Tsd-G, a single dose of 6 mg/kg PTX was injected intravenously. Plasma samples were collected at different time points, and proteins were precipitated using a methanol-acetonitrile solution. An ultrahigh-performance liquid chromatography-tandem mass spectrometry method was developed, with docetaxel as an internal standard, and quantified using positive ion multiple reaction monitoring (MRM) mode. This analytical method's specificity, accuracy, precision, recovery, matrix effect, and sample stability meet the requirements for biological sample determination. After Tsd-G administration in rats, the mean residence time of PTX was significantly prolonged. And Tsd-G can stably bind to P-gp by forming hydrogen bonds and inhibiting the expression of P-gp in rat liver. Although the metabolites of PTX were not detected in this study, the above results still indicate the existence of HDIs between Tsd-G and PTX, and P-gp may be the main target to mediate HDIs.

Teaching ethics and ethical leadership with the Harvard Everest simulation

PurposeThis is a step-by-step guide for teaching Ethics and Ethical Leadership utilizing the Harvard Everest Leadership and Teams Simulation, V.3. The suggested approach is focused on facilitating a discussion and coaching students to recognize and solve problems by applying moral theory explicitly and deliberately. Applying this approach can also help them develop a habit of analyzing one’s own and others' behavior using ethical lenses and principles of authentic leadership. It offers practical debrief steps and specific discussion questions that can be used as a standalone resource or alongside the Harvard teaching note accompanying the simulation.Design/methodology/approachThis innovative approach enables teaching and coaching students on the ethical aspects of their leadership and decision-making based on this hands-on experience with the Harvard Everest Leadership and Teams Simulation. This approach enriches the original HBR teaching suggestions by enabling students to recognize moral dilemmas, confront typical rationalizations and practice ethical actions and decision-making in real time.FindingsIn-class discussion and student reflection assignments provide evidence of the method's effectiveness in translating values into impactful insights and enhanced likelihood of ethical behaviors in real-life scenarios. Students’ end-of-class feedback and course evaluations often cite the benefits of using the Everest simulation as a backdrop for raising self-awareness and practicing ethical decision-making.Practical implicationsThe approach discussed in the paper can serve as a flexible framework for analyzing and debriefing the HBR Everest simulation and other simulations, “survival scenario” exercises and activities designed to teach and facilitate practicing ethical leadership, authentic leadership and ethical decision making. It is adaptable and can be effectively applied across various disciplines centered around ethical leadership, teamwork, communication and decision-making in higher education and business.Originality/valueHarvard Everest Leadership and Teams Simulation is among the most popular ones in business education and is used by teachers worldwide (Roberto &amp; Edmondson, 2017). While a comprehensive teaching note on communication and group dynamics is available through Harvard Business Publishing, it still needs to address the ethical issues students face during the simulation. This paper provides a roadmap for instructors who want to improve the student experience with ethical decision-making and ethical leadership.

Numerical Analysis and Health Implications of Electric Fields from High Voltage Overhead Transmission Lines for Safety and Design Optimization

This paper calculates and analyses electric fields generated by high-voltage overhead power transmission lines. These fields have significant implications for safety and design considerations. The study utilises a numerical technique to determine the electric field distribution in regions containing multiple charges. Based on this method, a computer program is developed to calculate the electric field profile at ground level and two meters above ground level for both 400 kV overhead power lines. The results demonstrate the method's effectiveness in accurately computing the electric field intensity for various line configurations, making it applicable to any power line design. The paper also highlights the potential health effects associated with exposure to these electric fields, including leukaemia, breast cancer, cognitive impairments, and reproductive consequences. The proposed method can also be applied to other transmission lines, providing valuable insights into health considerations. The accuracy of the computation methods is validated using the finite element analysis method. This research contributes to the understanding and assessing ground-level electric fields, offering industry professionals and researchers valuable insights into the safety and design of power transmission systems.

An Object-Centric Hierarchical Pose Estimation Method Using Semantic High-Definition Maps for General Autonomous Driving.

To achieve Level 4 and above autonomous driving, a robust and stable autonomous driving system is essential to adapt to various environmental changes. This paper aims to perform vehicle pose estimation, a crucial element in forming autonomous driving systems, more universally and robustly. The prevalent method for vehicle pose estimation in autonomous driving systems relies on Real-Time Kinematic (RTK) sensor data, ensuring accurate location acquisition. However, due to the characteristics of RTK sensors, precise positioning is challenging or impossible in indoor spaces or areas with signal interference, leading to inaccurate pose estimation and hindering autonomous driving in such scenarios. This paper proposes a method to overcome these challenges by leveraging objects registered in a high-precision map. The proposed approach involves creating a semantic high-definition (HD) map with added objects, forming object-centric features, recognizing locations using these features, and accurately estimating the vehicle's pose from the recognized location. This proposed method enhances the precision of vehicle pose estimation in environments where acquiring RTK sensor data is challenging, enabling more robust and stable autonomous driving. The paper demonstrates the proposed method's effectiveness through simulation and real-world experiments, showcasing its capability for more precise pose estimation.

Retina Blood Vessels Segmentation and Classification with the Multi-featured Approach.

Segmenting retinal blood vessels poses a significant challenge due to the irregularities inherent in small vessels. The complexity arises from the intricate task of effectively merging features at multiple levels, coupled with potential spatial information loss during successive down-sampling steps. This particularly affects the identification of small and faintly contrasting vessels. To address these challenges, we present a model tailored for automated arterial and venous (A/V) classification, complementing blood vessel segmentation. This paper presents an advanced methodology for segmenting and classifying retinal vessels using a series of sophisticated pre-processing and feature extraction techniques. The ensemble filter approach, incorporating Bilateral and Laplacian edge detectors, enhances image contrast and preserves edges. The proposed algorithm further refines the image by generating an orientation map. During the vessel extraction step, a complete convolution network processes the input image to create a detailed vessel map, enhanced by attention operations that improve modeling perception and resilience. The encoder extracts semantic features, while the Attention Module refines blood vessel depiction, resulting in highly accurate segmentation outcomes. The model was verified using the STARE dataset, which includes 400 images; the DRIVE dataset with 40 images; the HRF dataset with 45 images; and the INSPIRE-AVR dataset containing 40 images. The proposed model demonstrated superior performance across all datasets, achieving an accuracy of 97.5% on the DRIVE dataset, 99.25% on the STARE dataset, 98.33% on the INSPIREAVR dataset, and 98.67% on the HRF dataset. These results highlight the method's effectiveness in accurately segmenting and classifying retinal vessels.

Molecular dissection of the parental contribution in Paeonia Itoh hybrids.

Hybrid breeding between herbaceous peonies (the maternal parent) and tree peonies (the paternal parent) results in Paeonia Itoh hybrids (Itoh peonies), a triploid species that combines advantageous traits from both parental species, thus offering great economic value. However, the exact genetic contribution of the two parents is unclear. In this study, we introduce a straightforward approach utilizing heterozygous single-nucleotide polymorphisms (SNPs) and Sanger sequencing of targeted gene fragments to trace the original bases back to their parents in Itoh peonies. Our results indicate that in triploid Itoh peonies, only one set of genes is derived from herbaceous peonies, and two sets of genes are derived from the tree peonies. Notably, the presence of three distinct bases of heterozygous SNPs across multiple Itoh cultivars suggests that the gametes from the paternal parents carry two sets of heterozygous homologous chromosomes, which could be due to Meiosis I failure during gamete formation. To validate our method's effectiveness in parentage determination, we analyze two Itoh hybrids and their parents, confirming its practical utility. This research presents a method to reveal the parental genetic contribution in Itoh peonies, which could enhance the efficiency and precision of hybrid breeding programs of triploids in Paeonia and other plant species.

A stochastic iterative peer-to-peer energy market clearing in smart energy communities considering participation priorities of prosumers

The penetration of distributed energy resources (DERs) has changed the role of a consumer to a prosumer, i.e., producer and consumer. This new role provides the opportunity for peer-to-peer(P2P) energy trading. In this paper, a three-stage iterative framework is proposed to clear the price and quantity of trading in P2P markets while addressing price and DER uncertainties by the Monte Carlo simulation (MCS) method. Initially, bids and offers of customers are determined by implementing an advanced satisfaction-based home energy management system (HEMS) at each home. Subsequently, the market operator prioritizes bids and offers according to the amount of customers’ participation in the market. Finally, the P2P market is cleared by application of the alternating direction method of multipliers (ADMM), and the market clearing prices (MCPs) are determined. MCPs are used as a parameter to repeat the three stages, and the procedure is redone until the stopping rule is met. The proposed method's effectiveness has been investigated in communities with 8, 50, and 100 prosumers. Results indicate a 69.51 % cost reduction in a smart energy community with 50 homes through P2P energy market participation. The proposed market clearing method is compared with the common mid-market rate (MMR) and Stackelberg game methods and demonstrates over 25 % reduction in community costs.

A novel deep learning approach for optimizing preparation and usage of nonsteroidal anti-inflammatory drugs (NSAIDs) in spine care

Nonsteroidal anti-inflammatory drugs (NSAIDs) are essential for the management of pain and inflammation brought by illnesses of the spine. NSAIDs are essential in the treatment of inflammation and pain in a variety of spinal disorders. In order to relieve pain for patients and enhance their quality of life, NSAIDs should be used effectively. The effectiveness and safety of NSAIDs for spinal pain were assessed using a systematic review and meta-analysis. We gathered the patient data. The insufficient data is cleaned using tokenization and the data extraction employs Term Frequency-Inverse Document Frequency (TF-IDF). A Monarch Butterfly Optimization Dynamic Generative Adversarial Networks (MBO+DGAN) for the preparation and use of NSAIDs in spine care. Our studies show the possibility of the MBO+DGAN strategy to enhance the administration of NSAIDs in spine care, leading to improved clinical results, patient experiences and possibly lower healthcare costs. The MBO+DGAN assesses a method's effectiveness by evaluating its performance in terms of Accuracy (95%), Sensitivity (96%), Precision (97%) and Specificity (94%). The opportunity to use MBO+DGAN to improve how NSAIDs are created and utilized in the complex world of spine care. This technique offers an effective approach to enhance patient care and outcomes in the difficult field of spine healthcare by enabling customized treatment plans.

A type of multigrid method for semilinear elliptic problems based on symmetric interior penalty discontinuous Galerkin method

AbstractThis article introduces a new kind of multigrid approach for semilinear elliptic problems, which is based on the symmetric interior penalty discontinuous Galerkin (SIPDG) method. We first give an optimal error estimate of the SIPDG method for the problem. Then, we design a type of multigrid method, which is called the multilevel correction method, and derive a priori error estimates. The primary idea of this method is to take the solution of the semilinear problem and utilize it to establish a sequence of solutions for associated linear boundary value problem on discontinuous finite element spaces and a newly defined low dimensional augmented subspace. Lastly, numerical experiments are offered to confirm the suggested method's precision and effectiveness.

Enhanced control of double integrating plus time delay systems using a two‐degree‐of‐freedom Smith predictor

AbstractThis article presents a novel control approach using a two‐degree‐of‐freedom (2‐DOF) Smith predictor structure for double integrating plus time delay plants. The PD and PID controllers, integral components of the Smith predictor scheme, serve distinct roles in the control process. The PD controller is responsible for set‐point tracking, while both PD and PID controllers contribute to the rejection of load disturbances. Each controller is designed using the pole placement technique, with an appropriate tuning parameter established to balance performance and robustness effectively. A low‐pass filter is incorporated to improve the response of the set‐point signal by attenuating high‐frequency noise. The proposed method's effectiveness is compared with Skogestad internal model control, a well‐known PID tuning method, to benchmark performance improvements and demonstrate the advantages of the 2‐DOF Smith predictor approach. Simulation and comparison analysis demonstrates that the proposed controller setting is suitable for industrial applications due to its quick set‐point tracking, effective load disturbance rejection, and smooth control signal. The study highlights the practical benefits of the 2‐DOF Smith predictor in enhancing industrial control system performance and suggests directions for future research, including experimental validation and robustness analysis under varying system parameters.

Attenuation Tomography Using Low-Frequency Ultrasound for Thorax Imaging: Feasibility Study.

Low-frequency ultrasound can permeate human thorax and can be applied in functional imaging of the respiratory system. In this study, we investigate the transmission of low-frequency ultrasound through the human thorax and propose a waveform matching method to track the changes in the transmission signal during subject's respiration. The method's effectiveness is validated through experiments involving ten human subjects. Furthermore, the experimental findings indicate that the traveltime of the first-arrival signal remains consistent throughout the respiratory cycle. Leveraging this observation, we introduce an algorithm for ultrasound thorax attenuation factor differential imaging. By computing the paths and energy variation of the first-arrival signal from the received waveform, the algorithm reconstructs the distribution of attenuation factor differences between two different thorax states, providing insights into the functional status of the respiratory system. Numerical experiments, using both normal thorax and defective thorax models, confirm the algorithm's feasibility and its robustness against noise, variations in transducer position and orientation. These results highlight the potential of low-frequency ultrasound for bedside, continuous monitoring of human respiratory system through functional imaging.

Mixtures of logistic normal multinomial regression models for microbiome data

In the realm of bioinformatics, we frequently encounter discrete data, particularly microbiome taxa count data obtained through 16S rRNA sequencing. These microbiome datasets are commonly characterized by their high dimensionality and the ability to provide insights solely into relative abundance, necessitating their classification as compositional data. Analyzing such data presents challenges due to their confinement within a simplex. Additionally, microbiome taxa counts are subject to influence by various biological and environmental factors like age, gender, and diet. Thus, we have developed a novel approach involving regression-based mixtures of logistic normal multinomial models for clustering microbiome data. These models effectively categorize samples into more homogeneous subpopulations, enabling the exploration of relationships between bacterial abundance and biological or environmental covariates within each identified group. To enhance the accuracy and efficiency of parameter estimation, we employ a robust framework based on variational Gaussian approximations (VGA). Our proposed method's effectiveness is demonstrated through its application to simulated and real datasets.

A simple and quick run time RP-HPLC method for simultaneously analysis of pyrazosulfuron ethyl and pretilachlor content on GR formulation

Aim of the study was to develop and validate simple and quick run time reverse phase HPLC method for the simultaneous determination of pyrazosulfuron ethyl and pretilachlor in the pesticide formulation of GR. The method was executed toward using the Agilent Eclipse XDB C-18 column (150 x 4.6 mm; 3.5 μm), alongside the mobile phase comprising of 0.1 % (ortho phosphoric acid) with water: acetonitrile (30:70) at a column rate of flow 1.2 mL per minute and determination has been carried out in 220 nm. The retention time for Pyrazosulfuron Ethyl as well as Pretilachlor have been determined at 1.99 as well as at 4.58 minute, correspondingly with runtime 7 min. The method's effectiveness has been validated using SANCO recommendations for LOD, robustness, accuracy, precision, linearity, selectivity and specificity. There are several analytical methods reported for individual content estimation, but single method is not available to determine simultaneous content. The developed method has been utilized to estimate pyrazosulfuron ethyl and pretilachlor content in industries.

Cladding modes’ interference in MM-tapered SM-MM fiber devices: Experimental demonstration of a high-sensitivity temperature sensor

Here, we report our experimental demonstration of a high-performance temperature sensor fabricated by cascading dissimilar multimode fibers and tapered single mode fiber coated with an index-matching gel of high thermo-optic coefficient. The output power is recorded under varying temperature conditions of the coated material. Besides wavelength-shifting based multimode interferometer sensor, here a unique methodology is adopted where an intensity based fiber temperature sensor is realized. Our optimized design yields a temperature measurement sensitivity of 8.1253 µW/°C, accuracy of ± 0.9 0C, response time of 392 ms and resolution of 0.1 0C within the range of 29 °C to 64 °C. Analytical mode overlap and Ansys Lumerical simulation validate experimental results, affirming the method's effectiveness.

Axial compressive behavior of short columns of BFRP strip-PVC tube composite reinforced fiber recycled concrete

In this paper, axial compression tests were performed on recycled fiber concrete short columns reinforced by BFRP strips and PVC tube. The main variables investigated were the type of reinforcement (BFRP strip reinforcement or BFRP strip-PVC tube composite reinforcement) and the volumetric configuration rate of BFRP (One or three different number of strips layers and 25 mm/37.5 mm/50 mm with different strip spacing). A set of specimens was selected for each experimental group and simulated through ABAQUS to be used to assess the accuracy of the test. The investigation results show that BFRP strip-PVC tube composite reinforcement not only improves the ultimate bearing capacity of the specimen, but also improves the damage morphology of the fiber-recycled concrete short columns. The reinforcement method's effect on the specimen's stiffness is more significant during the nonlinear strengthening phase of the stress-strain curve. The specimen’s peak stress and ultimate strain of the specimens increased significantly with higher volumetric configuration rate of BFRP. Based on the analysis of the experimental results, the stress-strain calculation model with a softened section is also of better applicability. The study’ results provide a theoretical basis and reference for the future application of fiber recycled concrete.

A novel battery SOC estimation method based on random search optimized LSTM neural network

Battery state of charge (SOC) estimation is crucial for assessing electric vehicle safety and evaluating the remaining driving range. Owing to the complexity, variability of operating conditions, and the highly nonlinear internal mechanisms of batteries, accurate SOC estimation remains a focal point of current research. Therefore, this paper proposes a random search optimization-based Long Short-Term Memory (RS-LSTM) neural network for precise SOC estimation. The paper firstly uses the CALCE dataset, extracting discharge capacity and discharge energy as critical features from six battery parameters by employing the random forest algorithm. The Look-back, Epoch, Batch size, and Learning rate parameters in the LSTM neural network optimized by random search algorithm. The study result reveals optimal settings (Look back: 45, Epoch: 177, Batch size: 64, Learning rate: 0.0026) achieving superior estimation accuracy, evidenced by mean average error（MAE） and root mean square error（RMSE）of 0.221 % and 0.262 %, respectively. Furthermore, the method's superiority, effectiveness, robustness, and applicability were verified by conducting tests across various estimation methods, various SOC estimation intervals, various temperature conditions, the addition of Gaussian noise, and tests on experimental and real-world vehicle data. The research process demonstrates that the proposed method has superior precision and indicates promising potential for future applications.

The Implementation of the Brain Rhythm Method in Enhancing the Quality of Quran Memorization

It is imperative to raise the standard of Quran memorization, particularly in madrasah diniyah where many teachers require capacity building. The purpose of this study is to assess the brain rhythm method's effectiveness, a cognitive learning approach that synchronizes Quranic recitation with specific brainwave patterns to optimize memorization and retention. The study employs a quasi-experimental method with a sample of 72 sixth-grade students from 8 madrasah diniyah in Bandung Regency. Quran memorization tests were used for data collection, assessing accuracy, fluency, and retention over different time intervals. An independent sample t-test study revealed a statistically significant increase in the group utilizing the brain rhythm method's Quran memory quality over the control group, with a significance level of 0.010 (p 0.05). These findings suggest that brain rhythm techniques can enhance learning outcomes in Quran memorization. Practically, Islamic educational institutions could consider incorporating these methods to enhance teaching effectiveness. It would be beneficial to investigate this method's long-term effects and suitability for use in various educational contexts in future research. Additionally, investigating the underlying mechanisms of how brain rhythm synchronization aids memorization could provide deeper insights and potentially lead to further advancements in teaching strategies. This approach may inspire the development of new educational techniques that leverage cognitive science to improve religious education. Moreover, examining the scalability of this method and its integration with modern educational technologies could further enhance its impact. Thus, the brain rhythm method holds significant potential for advancing the field of Islamic education.

PiRNA-disease association prediction based on multi-channel graph variational autoencoder.

Piwi-interacting RNA (piRNA) is a type of non-coding small RNA that is highly expressed in mammalian testis. PiRNA has been implicated in various human diseases, but the experimental validation of piRNA-disease associations is costly and time-consuming. In this article, a novel computational method for predicting piRNA-disease associations using a multi-channel graph variational autoencoder (MC-GVAE) is proposed. This method integrates four types of similarity networks for piRNAs and diseases, which are derived from piRNA sequences, disease semantics, piRNA Gaussian Interaction Profile (GIP) kernel, and disease GIP kernel, respectively. These networks are modeled by a graph VAE framework, which can learn low-dimensional and informative feature representations for piRNAs and diseases. Then, a multi-channel method is used to fuse the feature representations from different networks. Finally, a three-layer neural network classifier is applied to predict the potential associations between piRNAs and diseases. The method was evaluated on a benchmark dataset containing 5,002 experimentally validated associations with 4,350 piRNAs and 21 diseases, constructed from the piRDisease v1.0 database. It achieved state-of-the-art performance, with an average AUC value of 0.9310 and an AUPR value of 0.9247 under five-fold cross-validation. This demonstrates the method's effectiveness and superiority in piRNA-disease association prediction.

Audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional network in strong noise environments

Abstract Fault diagnosis of belt conveyors is crucial for coal mine production, but audio-based fault diagnosis in underground coal mines remains challenging due to the strong noise environment. To address this problem, a method for audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional networks in a strong noise environment is proposed. Firstly, the improved beluga whale optimization is designed by introducing the non-linear balance factor and non-linear probability and combining them with the proposed cyclical shock factor to optimize the variational modal decomposition parameters to achieve noise reduction and signal reconstruction. Secondly, an improved adaptive noise reduction convolutional network is developed using an adaptive threshold activation function and an improved loss function to enhance noise robustness and fault diagnosis accuracy. Finally, the proposed method's effectiveness is evaluated in low and strong noise environments, with experimental results demonstrating superior fault diagnosis performance. In low noise environments, the fault diagnosis accuracy is 98.61%, and in strong noise environments, it is 98.96%, outperforming existing fault diagnosis methods.