Weightings Research Articles

Breast Cancer Survivors' Perceptions of Mastectomy Reconstruction: A Comparative Analysis of Medical Tattooing Impact on Aesthetics.

Studies indicate that physical appearance changes affect a significant proportion of breast cancer survivors, often leading to post-surgical distress. Both reconstructive surgery and medical tattoos are associated with patient-reported satisfaction, yet further research is necessary to understand their combined impact on aesthetic outcomes from the patient perspective. This study examined how breast cancer survivors evaluated the cosmetic and decision satisfaction of other patients who made various cosmetic intervention choices post-mastectomy. Breast cancer survivor support networks (n = 180) across 20 states agreed to share information about the online survey to their members. Participants (n = 251) rated the cosmetic outcome and decision satisfaction of images depicting other patients who had received mastectomy with three types of intervention: reconstruction alone, medical tattooing alone (flat closure), and reconstruction and medical tattooing combined. A factorial MANOVA produced significant within-subjects effects indicating participants consistently rated images of patients with a combination of reconstruction and medical tattooing highest in cosmetic quality, lowest in scar visibility, and highest in treatment decision satisfaction. The findings suggest that medical tattooing serves as a valuable complementary cosmetic intervention, with many survivors expressing interest and recognizing potential benefits once informed about the option. However, the study's limitations, such as the need for a more diverse range of patient images for evaluation, should be acknowledged. Future research should address these gaps by incorporating photographs of patients across varying demographics, including age, body weight, disease severity, and surgical procedures, to minimize bias. Despite these limitations, this study highlights the significant role that medical tattooing can play in enhancing aesthetic outcomes after surgery, particularly from a patient-centered perspective. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors https://link.springer.com/journal/00266 .

Enhancing image reconstruction in photoacoustic imaging using spatial coherence mean-to-standard-deviation factor beamforming

In photoacoustic imaging (PAI), a delay-and-sum (DAS) beamforming reconstruction algorithm is widely used due to its ease of implementation and fast execution. However, it is plagued by issues such as high sidelobe artifacts and low contrast, that significantly hinder the ability to differentiate various structures in the reconstructed images. In this study, we propose an adaptive weighting factor called spatial coherence mean-to-standard deviation factor (scMSF) in DAS, which is extended into the spatial frequency domain. By combining scMSF with a minimum variance (MV) algorithm, the clutter level is reduced, thereby enhancing the image contrast. Quantitative results obtained from the phantom experiment demonstrate that our proposed method improves contrast ratio (CR) by 30.15 dB and signal-to-noise ratio (SNR) by 8.62 dB compared to DAS while also improving full-width at half maxima (FWHM) by 56%. From the in-vivo experiments, the scMSF-based reconstruction image exhibits a higher generalized contrast-to-noise ratio (gCNR), indicating improved target detectability with a 25.6% enhancement over DAS and a 22.5% improvement over MV.

Використання теорії невизначеності при дослідженні показників просторового сприйняття в єдиноборствах

Purpose: to study the indicators of spatial perception of martial artists using the provisions of uncertainty theories. Material and Methods. Research methods: analysis and synthesis of scientific and methodological literature, psychophysiological measurements, mathematical and statistical methods using licensed programs RStudio and Numbers. The study was conducted in several stages. At the first stage, taking into account the peculiarities of martial artists' sports activities, a mobile application «Spatial Perception» was developed to assess their spatial perception. At the second stage, the use of the application in the field was tested and the capabilities of the mobile application were expanded in terms of obtaining detailed characteristics of measurements using the provisions of the theory of uncertainty. The study involved junior taekwondo athletes aged 14-17 years (n=25, qualification: 1st category and candidates for master of sports of Ukraine). Results: according to the measurements of the studied martial artists, it can be noted that the highest frequency of reaction time indicators lies within 583 ms (n=291). At the second stage of the test exercise, where the performer needs to react to non-moving 3D figures, there is an increase in the interquartile range by 22 ms and a violation of the symmetry of the data. It was at this stage that the largest value of the standard deviation was noted (SD=172,0 ms). This indicates a high variability of reaction time (V=22,7 %). Also, we can see an increase in reaction time at the fourth stage compared to the result of the third stage by 11,5 ms and an increase in the percentage of errors by 1,4 % (at this stage, the highest percentage of errors was recorded at 8,2 %). Based on the use of membership functions, fuzzy sets were created, which made it possible to evaluate each indicator on a scale from 0 to 1. The analysis of the test exercise performance by martial artists who have the category of KMS (candidate for master of sports) shows that the overall score for the entire test is «Good», the weighting factor is 0,74, which is closer to the «Excellent» score. It should be noted that the highest weighting coefficient was observed in the second stage of the test task. The high weighting factor (0,79) at this stage reflects the ability to quickly recognize 3D shapes in space based on the analysis of individual parts of the shapes. We can also note the low percentage of errors (3 %) at the fourth, most difficult stage. This may indicate the ability to maintain attention on the object and resistance to disturbing visual stimuli. According to the results of the martial artists who have the 1st category, it was noted that they performed the test exercise with a score of «Good» and have a total weighting factor of 0,34. The score at each stage of the test is «Good», but the values of the weighting coefficients are closer to the «Satisfactory» score. The values of the weighting coefficients demonstrate that the level of spatial perception of this group of athletes is below average. It is also worth noting the increase in the percentage of errors, especially at the fourth stage (11,7 %), which indicates a negative impact on the reaction time of additional visual stimuli. The biggest difference between weight coefficients of the studied groups was noted at the second stage and made 49 %, the smallest at the fourth stage – 27 %. Conclusions. The use of fuzzy logic methods is a promising area of activity and finds its application in sports activities. The levels of manifestation of spatial perception by weight coefficients have been determined. Thus, a group of sportsmen with a category of CMS received an overall mark for the whole test «Good»with a weight coefficient of 0,74, and martial artists of the 1st category received an overall mark for the whole test «Good» with a weight coefficient of 0,34. Comparison of results of the estimation of reaction time of martial artists with a category of CMS with sportsmen of the 1st category testifies that there are statistically significant differences between indicators (p<0,05). The use of the theory of uncertainty in the assessment of spatial perception makes it possible to create flexible assessment systems and allows combining different indicators into one complex result.

Improved Osprey Optimization Algorithm with Multi-Strategy Fusion

The osprey optimization algorithm (OOA) is an effective metaheuristic algorithm. Although the OOA has the characteristics of strong optimality-seeking ability and fast convergence speed, it also has the disadvantages of imbalance between global exploration and local exploitation ability, easily falling into local optima in the later stage, and reduced population diversity and convergence speed. Therefore, this paper proposes an improved osprey optimization algorithm (IOOA) with multi-strategy fusion. First, Fuch chaotic mapping is used to initialize the ospreys’ population and increase the population diversity. Then, an adaptive weighting factor is introduced in the exploration phase of the algorithm to help the algorithm improve the convergence accuracy. The Cauchy variation strategy is integrated in the algorithm’s exploitation stage to enhance the diversity of the ospreys’ population and avoid falling into local optima. Finally, a Warner mechanism for the sparrow search algorithm is introduced to coordinate the algorithm’s local optimization and global search capabilities. The IOOA with various optimization algorithms is tested in a simulation for 10 benchmark test functions and 15 CEC2017 test functions, and non-parametric tests are performed on the IOOA. Experimental results show that the IOOA achieves improved accuracy and stability. The application of the IOOA to the three-bar truss engineering design problem further verifies its superiority in dealing with practical optimization problems.

A Semantic and Optimized Focused Crawler Based on Semantic Graph and Genetic Algorithm

A focused crawler automatically retrieves, organizes, and extracts specific topic-related information from the internet for analysis and application. Currently, most focused crawlers assess the relevance of web pages to a given topic through methods such as keyword matching, semantic analysis, and link structures. However, these existing focused crawlers suffer from issues such as misleading directions and reduced accuracy due to the lack of semantic analysis of topic terms, as well as biased computation of topic relevance caused by the absence of effective weighting factors. To solve the above-mentioned problems, this study proposes a semantic and optimized focused crawler based on Semantic Graph and Genetic Algorithm. The proposed crawler eliminates ambiguous terms by constructing a semantic graph, optimizes the weighting factors of topic relevance with asymmetry by using a genetic algorithm, and combines both above two points to predict the priority of each unvisited hyperlink. The experiment results indicate that the proposed SG-GA Crawler improves the evaluation indicators compared with the other three focused crawlers, including VSM Crawler, SSRM Crawler, and SG Crawler. More specifically, the percentage improvement achieved by the proposed method exceeds 19%, 19%, and 13% in terms of three evaluation indicators, including the number of relevant web pages, acquisition rate, and average relevance, respectively. In conclusion, the proposed focused crawler can grab more quantity and higher quality topic-related web pages from the Internet.

A continuous-discrete adaptive observer with a diagonal gain matrix and sigma-modification: applications in chaos synchronization and dynamical state estimation

In this paper, we propose an improved continuous-discrete observer with enhanced convergence properties and reduced sensitivity to persistent excitations. Our approach introduces several key innovations, including a customised diagonal gain matrix which improves the convergence speed, a sigma-modification technique that relaxes the persistent excitation conditions, and a weighting factor tailored to optimise the estimation of unknown parameters. Through comprehensive numerical experiments, we illustrate the effectiveness of our observer in two distinct scenarios. Firstly, we demonstrate its application in synchronising a 4-component chaotic dynamical system. Secondly, we showcase its utility in tracking a reduced 2-component chemical engineering process. Our simulation results reveal that the proposed observer outperforms existing methods by achieving quicker convergence and higher estimation accuracy. This study contributes to the advancement of adaptive observer design, offering valuable insights for its application across diverse fields.

The Detailed Analysis of Polish Patients with Non-Small Cell Lung Cancer Through Insights from Molecular Testing (POL-MOL Study)

Molecular testing is recommended in patients with metastatic non-small cell lung cancer (NSCLC), but the extent of its use in Poland is unknown. The aim of the POL-MOL study was to investigate the frequency of using molecular testing in Polish patients with NSCLC. The invited Polish oncologists completed two questionnaires, and data for 1001 patients undergoing systemic treatment for NSCLC were collected. The use of molecular tests for the following genetic mutations was recorded: EGFR (del19, sub21), EGFR (other than del19/sub21), EGFR T790M, ALK (expression and rearrangement), RET, NTRK, ROS1, BRAF, HER2, and MET, as well as for immunochemical assessment of programmed cell death ligand 1 (PD-L1). Thanks to the weighting procedure, the results are representative of the population of Polish patients treated for NSCLC. Molecular tests were applied in 78% of patients with NSCL, 70% of patients with NSCLC not otherwise specified, and in 12% of patients with squamous cell carcinoma of the lung. The frequency of application increased with disease stage in all groups. In patients with squamous cell carcinoma, approximately 30% of tests for EGFR, ALK, and RET mutations were positive, which confirms the importance of testing at least a preselected subgroup of patients.

Design of a Robust Controller for Induction Motor Drive Systems Based on Extendable Fuzzy Theory

In this paper, an extendable fuzzy robust speed controller suitable for induction motor drive systems was proposed. Firstly, the two-degrees-of-freedom (2DOF) robust control technology with feedforward control and disturbance elimination method was adopted. Upon parameter variation and load disturbance, the motor drive system could utilize a robust controller to generate compensation signals and reduce the impact on the controlling performance of the motor drive system. The magnitude of the compensation signal was adjusted via the weighting factor. However, should a fixed weighting factor be adopted, system instability might be generated easily when time delay and saturation of control force occur. Based on the above, the smart method of extendable fuzzy theory (EFT) was adopted in this paper to adjust adequate weighting factors, where the controlling performance of the induction motor drive system could be improved accordingly. Lastly, the simulation software Matlab/Simulink (R2023b version) was applied to simulate the utilization of the controlling method proposed for the induction motor drive system. The simulation results proved that the extendable fuzzy robust speed controller proposed provided better speed tracking and load regulation-controlling performance than the conventional robust controller.

Inversion Analysis for Thermal Parameters of Mass Concrete Based on the Sparrow Search Algorithm Improved by Mixed Strategies

In the traditional mass concrete temperature field calculation, the accuracy of the thermal parameters is extremely important. However, the actual thermal parameters of mass concrete may have some errors with the laboratory-measured values or specification values due to the site ambient temperature, concrete surface insulation measures, cooling water flow, etc. Therefore, it can be combined with the measured temperature of the field temperature sensors using the sparrow search algorithm (SSA) for the inverse analysis of thermal parameters. Firstly, to address the problem that SSA has low convergence accuracy and easily falls into local optimum, a mixed strategy was adopted to improve the algorithm, including Logistic Chaos mapping initialization of the population, the introduction of adaptive weighting factors, and the use of the Cauchy mutation strategy. Then, the performance test was carried out to compare the performance of the algorithm with three different intelligent algorithms and reflect the superiority of the SSA that was improved by mixed strategies (SSAIMSs). Finally, the proposed method was applied to the thermal parameter inversion of a mass concrete pile cap. The inversion results demonstrated that SSAIMSs can improve the accuracy and speed of thermal parameter inversion, and the calculated results of the thermal parameters and temperatures obtained using the SSAIMSs matched well with the measured results in the field, which can meet the accuracy requirements of the actual engineering.

Two-Stage Multiple-Vector Model Predictive Control for Multiple-Phase Electric-Drive-Reconstructed Power Management for Solar-Powered Vehicles

Electric-drive-reconstructed onboard chargers (EDROCs), also known as electric-drive-reconstructed power management systems, are a promising alternative to conventional onboard chargers due to their characteristics of low cost and high power density. The model predictive control offers a fast dynamic response, simple implementation, and the ability to control multiple targets simultaneously. In this paper, a two-stage multi-vector model predictive current control (MPCC) of a six-phase EDROC for solar-powered electric vehicles (EVs) is proposed. Firstly, the topology for the EDROC incorporating a six-phase symmetrical permanent magnet synchronous machine (PMSM) is introduced, and the operation principles of the DC charge mode, the drive mode, and, especially, the in-motion charge mode are analyzed in detail. After that, a two-stage multi-vector MPCC method is proposed by using the multi-vector MPC technique and designing a two-stage MPC structure to eliminate the regulation of the weighting factor of the MPC. Finally, the effectiveness of the proposed method is verified on a self-designed 2 kW EDROC platform.

Enhancing convergence speed with feature enforcing physics-informed neural networks using boundary conditions as prior knowledge

This research introduces an accelerated training approach for Vanilla Physics-Informed Neural Networks (PINNs) that addresses three factors affecting the loss function: the initial weight state of the neural network, the ratio of domain to boundary points, and the loss weighting factor. The proposed method involves two phases. In the initial phase, a unique loss function is created using a subset of boundary conditions and partial differential equation terms. Furthermore, we introduce preprocessing procedures that aim to decrease the variance during initialization and choose domain points according to the initial weight state of various neural networks. The second phase resembles Vanilla-PINN training, but a portion of the random weights are substituted with weights from the first phase. This implies that the neural network’s structure is designed to prioritize the boundary conditions, subsequently affecting the overall convergence. The study evaluates the method using three benchmarks: two-dimensional flow over a cylinder, an inverse problem of inlet velocity determination, and the Burger equation. Incorporating weights generated in the first training phase neutralizes imbalance effects. Notably, the proposed approach outperforms Vanilla-PINN in terms of speed, convergence likelihood and eliminates the need for hyperparameter tuning to balance the loss function.

Beyond the Agatston calcium score: role of calcium density and other calcified plaque markers for cardiovascular disease prediction.

To review the current evidence and highlight future strategies regarding consideration of coronary artery calcium (CAC) density in cardiovascular disease (CVD) risk stratification. Expressed as the product of plaque area and a peak calcium density weighting factor, the Agatston method is the gold-standard for measuring CAC on noncontrast cardiac computed tomography. Over the last decade, observational data have suggested that calcium density is inversely associated with CVD events and confers additional prognostic information independent of traditional risk factors and Agatston CAC scores. Specific density measures have been assessed including peak calcium density, mean CAC density, and CAC area-density discordance. Beyond calcium density, the number of affected arteries and regional distribution of CAC which may be correlated with CAC density have also improved the predictive utility of the Agatston score. Calcium density is inversely associated with CVD risk after considering plaque area and/or volume. Calcium markers including density, vessel involvement, and regional distribution confer additional prognostic information for the prediction of incident CVD among those with prevalent subclinical atherosclerosis. A future area of study includes calcium radiomics ('calcium-omics') and whether the artificial intelligence-derived automated measurement of calcium markers beyond the Agatston score may be of value in CVD risk stratification among individuals with early to advanced subclinical atherosclerosis.

A review on model predictive control strategies for AC motor drives

AbstractIn the past decade, model predictive control (MPC) has been widely used in AC motor drives. Although a series of improved MPC strategies have been proposed, researchers are still seeking more effective solutions for different application backgrounds and application requirements to achieve the goals such as algorithm simplification, weighting factor design, parameter robustness enhancement, current/torque ripple suppression, and so on. In this paper, aiming at the above problems, the MPC strategy and its optimisation technology for AC motor drive system are comprehensively reviewed. Firstly, this paper introduces the classical MPC technology with the permanent magnet synchronous motor drive system fed by two‐level voltage source inverter as the control object. Secondly, this paper introduces and discusses the existing MPC optimisation methods from the aspects of current/torque ripple suppression, algorithm simplification, weight factor design and parameter robustness enhancement. Finally, based on the current industrial development requirements for AC motor drive systems and the research status of MPC optimisation methods in the existing literature, the current challenges and future trends of MPC technology are discussed.

Development and Experimental Study of Supercritical Flow Payload for Extravehicular Mounting on TZ-6.

This paper provides a detailed description of the development and experimental results of the supercritical flow experiment payload carried on the TZ-6 cargo spacecraft, as well as a systematic verification of the out-of-cabin deployment experiment. The technical and engineering indicators of the payload deployment experiment are analyzed, and the functional modules of the payload are shown. The paper provides a detailed description of the design, installation location, size, weight, temperature, illumination, pressure, radiation, control, command reception, telemetry data, downlink data, and experimental procedures for the out-of-cabin payload in the extreme conditions of space. The paper presents the annular liquid surface state and temperature oscillation signals obtained from the space experiment and conducts ground matching experiments to verify the results, providing scientific references for the design and condition setting of space experiments and comparisons for the experimental results to obtain the flow field structure under supercritical conditions. The paper provides a specific summary and discussion of the space fluid science experiment project, providing useful references for future long-term in-orbit scientific research using cargo spacecraft.

Social Space Ratio: Calculating the Rate of Public Space Activities That Enhance Social Interaction on a Pedestrian Street in Karlstad, Sweden

William H. Whyte took on the challenge of assessing the amount of public space in a city based on its carrying capacity, pointing out that popular public spaces offer more room for social activities. However, the absence of qualitative characteristics makes this assessment even more challenging to implement. This study aims to find a method to gauge the carrying capacity of urban public spaces by calculating the social space ratio for pedestrian-only streets in Karlstad, Sweden, and quantifying this relationship. The social space ratio represents the proportion of public spaces that foster social interaction throughout their entire area. The method began by selecting the most relevant conceptual framework for social public spaces and then sought theory-based characteristics to assign to seven social activities on Karlstad’s pedestrian-only streets. The authors performed a comprehensive search of the literature utilizing the PRISMA approach, gathering information from credible references, placemaking toolkits, transportation toolkits, and academic sources. This was performed to determine the weighting factors and effective social areas by evaluating these activities in terms of nine categories of the chosen framework: accessibility, traffic, social infrastructure, security, places to meet, senses and experience, architecture and aesthetics, development and maintenance, and control and programming. We devised a method to calculate the carrying capacity and social space ratio of Karlstad’s pedestrian-only streets, resulting in a ratio of 0.38. The research led to the development of eight quality-control tools to analyze the seven social activities in public places. This innovative approach helps researchers and municipal planners evaluate the benefits and drawbacks of these spaces, contributing significantly to Swedish urban planning and enabling future studies to create a social area factor.

B-293 Enzyme linked immunosorbent assay development and full validation for the quantification of the antibody conjugated fraction of an antiFAP ADC in human serum

Abstract Background Cancer remains a global public health issue. Despite recent progress in early diagnosis and treatment strategies, development of anti-tumor therapies with reduced systemic toxicity remains a challenge. Antibody-drug conjugates (ADCs), a new, highly-potent drug class are synthesized by linking a small molecule cytotoxic drug to a tumor specific antibody, are an emerging treatment option for many cancers. Despite advances in ADCs, study of the pharmacokinetics (PK) is challenging. A complete PK profile requires at least three analytical methods to determine: conjugated antibody, total antibody, and free drug by Ligand Binding Assays (LBA) and liquid chromatography/tandem mass spectrometry (LC-MS/MS) respectively. Objective To develop and validate an ELISA method to quantify the conjugated antibody fraction of an anti-fibroblast activation protein (FAP) ADC against solid tumors in human serum. Methods Conjugated antibody assay: This indirect sandwich ELISA utilizes human FAP as capture antigen (Abcam) with a proprietary rabbit polyclonal anti-drug IgG as detection antibody. The signal is amplified by the use of a second antibody conjugated to Horse Radish Peroxidase (Abcam). Absorbance is measured at 450nm. Immunoassay method validation was done in compliance with the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) M10 which establishes the limit for precision in CV% and accuracy in Relative Error (RE%) at ± 20% each (± 25% for LLOQ and ULOQ). Additionally, the total error (TE%) for accuracy and precision batches, should be ≤ 30% (≤ 40% at LLOQ and ULOQ). Results The weighted calibration curves were linear over a concentration range 0.5 to 100 µg/mL. A four-parameter regression model with a 1/Y2 weighting factor was adopted to best fit the OD450 values and concentrations. The intra- and inter-assay accuracy and precision for RE% and CV% was <12%, with a TE% < 23% for all concentrations tested. The quantification limit of 0.5 µg/mL complies with ICH criteria with a RE%, CV% and TE% of -10%, 8,5% and 15% respectively. Selectivity and specificity test fall within the criteria indicating that no individual matrix or co-medication affects the samples. The results from the dilution linearity obtained were an RE% of 11, -16 and -6 for 1:50, 1:100 and 1:200 respectively. Furthermore, not hook effect was observed as the dilution QC results gave above the quantification limit. Finally, the analyte was found to be stable at -20° and -80°for 148 days, at room temperature for 24hr and after 5 freeze/thaw cycles with a maximum RE and CV of 12 and 14% respectively in all the stability samples. Conclusions We have developed and optimized an ELISA method for measuring the antibody conjugated fraction of the ADC in serum. This method complies with the criteria of the ICH50 for selectivity, specificity, accuracy and precision, dilution linearity and hook effect in a concentration range between 0.5 - 100 µg/mL. Further, serum specimens are stable for 24h at room temperature, 5 freeze/thaw cycles and 148 days at -20 and -80 °C.

A Simple Online Tuning Flux Weighting Factor in Finite Control Set – Predictive Torque Control for Induction Motor Drive

The Finite Control Set-Predictive Torque Control (FSC-PTC) is a practical and intuitive method to control an Induction Motor (IM). However, the challenge of this technique is the difficulty of tuning the weighting factor so that it performs optimally at all speeds. Hence, this paper proposes a new method for improving the optimization technique by automatically tuning the weighting factor using a flux controller. The flux controller is represented as a DC gain and designed to be proportional to the absolute value of the predicted stator flux error. This method eliminates the need for complex computation to select the suitable weighting factor while also efficiently regulating flux and torque. The proposed method is experimentally validated on a DS1104 controller board and compared to a constant flux weighting factor method that was tested under speed conditions. The results demonstrate that the new method can further decrease the torque ripple compared to the constant weighting factor while maintaining the flux ripple and harmonic distortion limits.

An Evidential Multi-Target Domain Adaptation Method Based on Weighted Fusion for Cross-Domain Pattern Classification.

For cross-domain pattern classification, the supervised information (i.e., labeled patterns) in the source domain is often employed to help classify the unlabeled target domain patterns. In practice, multiple target domains are usually available. The unlabeled patterns (in different target domains) which have high-confidence predictions, can also provide some pseudo-supervised information for the downstream classification task. The performance in each target domain would be further improved if the pseudo-supervised information in different target domains can be effectively used. To this end, we propose an evidential multi-target domain adaptation (EMDA) method to take full advantage of the useful information in the single-source and multiple target domains. In EMDA, we first align distributions of the source and target domains by reducing maximum mean discrepancy (MMD) and covariance difference across domains. After that, we use the classifier learned by the labeled source domain data to classify query patterns in the target domains. The query patterns with high-confidence predictions are then selected to train a new classifier for yielding an extra piece of soft classification results of query patterns. The two pieces of soft classification results are then combined by evidence theory. In practice, their reliabilities/weights are usually diverse, and an equal treatment of them often yields the unreliable combination result. Thus, we propose to use the distribution discrepancy across domains to estimate their weighting factors, and discount them before fusing. The evidential combination of the two pieces of discounted soft classification results is employed to make the final class decision. The effectiveness of EMDA was verified by comparing with many advanced domain adaptation methods on several cross-domain pattern classification benchmark datasets.

An improved finite set model predictive control of SRM drive based on a voltage vectors strategy for low torque ripple

The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However, the existing Model Predictive Control (MPC) using multiple control objectives and maximum sectors in the switching table results in high torque ripples due to the improper sector partition, Voltage Vectors (VVs) and weight factor (K1) selection. This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. The proposed VVs are derived using sector partition based on the rotor position. The control is designed as a single cost function with the weighting factor contributing to smooth torque by selecting optimal control signals. Simulation studies and experiments with a four-phase 8/6 SRM drive verifies the enhanced FS-MPC for real-time implementation. The dynamic speed and ripple values of SRM Drives are measured using a mixed signal oscilloscope and the sensor probes. The laboratory outcomes calculate the analytical equations to validate the findings. The calculated value of torque ripple is 9 % through this FS-MPC. The study reveals that the proposed method is well suited for torque ripple reduction than flux ripples.

Utilizing polydispersity in three-dimensional random fibrous based sound absorbing materials

The distribution of fiber diameters plays a crucial role in the transport and sound absorbing properties of a three-dimensional random fibrous (3D-RF) medium. Conventionally, volume-weighted averaging of fiber diameters has been utilized as an appropriate microstructural descriptor to predict the static viscous permeability of 3D-RF media. However, the long wavelength acoustical properties of a 3D-RF medium are also sensitive to the smallest fibers, this is particularly true in the high-frequency regime. In our recent research, we demonstrated that an inverse volume-weighted averaging of fiber diameters can effectively serve as a complementary microstructural descriptor to capture the high-frequency behavior of polydisperse fibrous media. In the present work, we reexamine the identification of two representative volume elements (RVEs) which relies on the reconstruction of 3D-RF microstructures having volume-weighted and inverse-volume weighted averaged fiber diameters, respectively in the low-frequency and high frequency regimes. We investigate the implication of such a weighting procedure on the transport and sound absorbing properties of polydisperse fibrous media, highlighting their potential advantages. Furthermore, we discuss the challenges associated with this research field. Finally, we provide a brief perspective of the future directions and opportunities for advancing this area of study.