Accurate and robust state of charge (SOC) estimation of lithium-ion batteries is very important to prolong battery life and prevent catastrophic failures. However, the accuracy of SOC estimation is ...seriously affected by unknown noise, uncertain interference and temperature. In this article, a novel model fusion method is presented to achieve precise SOC estimation in the case of non-Gaussian noise and outliers. A state-space model of battery system is first developed to conduct SOC estimate. Then, a novel robust kernel Takagi-Sugeno fuzzy method to minimize the mean and variance of model error is developed to characterize the electrical performance of battery. This modeling strategy uses local nonlinear modeling mechanism, which the nonlinear relationship between data can be well represented, thus it can obtain a superior modeling performance. Finally, a multi-innovation unscented Kalman filter (UKF) algorithm considering the historical state information is designed to incorporate with the robust fuzzy model to filter out the noise in the observation and update the SOC estimation. Additional stability analysis shows the convergence of the proposed multi-innovation UKF algorithm. Experiments and verifications show that the presented estimation architecture is effective and has better modeling ability compared with several common methods.
Computer-aided diagnosis (CAD) systems of breast cancer histopathological images automated classification can help reduce the manual observation workload of pathologists. In the classification of ...breast cancer histopathology images, due to the small number and high-resolution of the training samples, the patch-based image classification methods have become very necessary. However, adopting a patches-based classification method is very challenging, since the patch-level datasets extracted from whole slide images (WSIs) contain many mislabeled patches. Existing patch-based classification methods have paid little attention to addressing the mislabeled patches for improving the performance of classification. To solve this problem, we propose a novel approach, named DenseNet121-AnoGAN, for classifying breast histopathological images into benign and malignant classes. The proposed approach consists of two major parts: using an unsupervised anomaly detection with generative adversarial networks (AnoGAN) to screen mislabeled patches and using densely connected convolutional network (DenseNet) to extract multi-layered features of the discriminative patches. The performance of the proposed approach is evaluated on the publicly available BreaKHis dataset using 5-fold cross validation. The proposed DenseNet121-AnoGAN can be better suited to coarse-grained high-resolution images and achieved satisfactory classification performance in 40X and 100X images. The best accuracy of 99.13% and the best F1score of 99.38% have been obtained at the image level for the 40X magnification factor. We have also investigated the performance of AnoGAN on the other classification networks, including AlexNet, VGG16, VGG19, and ResNet50. Our experiments show that whether it is at the patient-level accuracy or at the image-level accuracy, the classification networks with AnoGAN have provided better performance than the classification networks without AnoGAN.
Increasing number of adults are willing to seek orthodontic treatment, but treatment duration for them is commonly longer. Although there have been studies on molecular biological changes during ...tooth movement, few have focused on microstructural changes in alveolar bone.
This study aims to compare the microstructural changes in alveolar bone during orthodontic tooth movement in adolescent and adult rats.
25 6-week-old and 25 8-month-old male Sprague-Dawley (SD) rats were used to build orthodontic tooth-movement models. On Days 0, 1, 3, 7 and 14, the rats were sacrificed. Microcomputed tomography was used to evaluate tooth movement, alveolar crest height loss and microstructural parameters of alveolar bone (bone volume fraction, trabecular thickness, trabecular separation and trabecular number).
Tooth movement in the adult group was slower than in the adolescent group. Alveolar bone crest height in adults was lower than it was in adolescents on Day 0. Under orthodontic force, the alveolar crest in both groups decreased and the degree of decrease are higher at early stage in adolescents. The microstructural parameters indicated that the alveolar bone was originally denser in the adult rats. With orthodontic force, it tended to be looser.
Under orthodontic force, changes in alveolar bone differ between adolescent and adult rats. Tooth movements in adults are slower, and the decrease in alveolar bone density are more severe.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The multidrug resistance (MDR) is a widely observed phenotype that contributed to the major obstacle of impairing the outcome of cancer chemotherapy. With the aim to reverse MDR in the breast cancer ...cell line, the autophagy-related 7 (ATG7) small interfering RNA (siRNA) capable of downregulating the cellular autophagy level was loaded into a cationic nanostructured lipid carrier (NLC) with doxorubicin (Dox) to build a platform (NLC/D-R) for effective chemotherapy of breast cancer. Our results revealed that NLC/D-R was well-dispersed nanoparticles with satisfy protection to siRNA. In addition, NLC/D-R also exerted a sufficient drug release of both cargos under an acidic environment with high stability and biocompatibility at the physiological environment. Furthermore, NLC/D-R showed a preferable transfection profile to PEI 25k. The downregulated autophagy level in NLCF-7/Adr cells resulted in reverse of MDR and accumulated Dox retention in cells. The in vitro cytotoxicity using both cells on flat surfaces and multicellular tumor spheroid (NLCTS) model confirmed that NLC/D-R showed much elevated anticancer performance than NLC/Dox or NLC/siRNA, which suggested the synergistic effect between anti-autophagy and chemotherapy.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Cancer continues to pose a severe threat to global health, making pursuing effective treatments more critical than ever. Traditional therapies, although pivotal in managing cancer, encounter ...considerable challenges, including drug resistance, poor drug solubility, and difficulties targeting tumors, specifically limiting their overall efficacy. Nanomedicine's application in cancer therapy signals a new epoch, distinguished by the improvement of the specificity, efficacy, and tolerability of cancer treatments. This review explores the mechanisms and advantages of nanoparticle-mediated drug delivery, highlighting passive and active targeting strategies. Furthermore, it explores the transformative potential of nanomedicine in tumor therapeutics, delving into its applications across various treatment modalities, including surgery, chemotherapy, immunotherapy, radiotherapy, photodynamic and photothermal therapy, gene therapy, as well as tumor diagnosis and imaging. Meanwhile, the outlook of nanomedicine in tumor therapeutics is discussed, emphasizing the need for addressing toxicity concerns, improving drug delivery strategies, enhancing carrier stability and controlled release, simplifying nano-design, and exploring novel manufacturing technologies. Overall, integrating nanomedicine in cancer treatment holds immense potential for revolutionizing cancer therapeutics and improving patient outcomes.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Access to mental health treatment across Canada remains a challenge, with many reporting unmet care needs. National and provincial e-Mental health (eMH) programs have been developed over the past ...decade across Canada, with many more emerging during COVID-19 in an attempt to reduce barriers related to geography, isolation, transportation, physical disability, and availability. The aim of this study was to identify factors associated with the utilization of eMH services across Canada during the COVID-19 pandemic using Andersen and Newman’s framework of health service utilization. This study used data gathered from the 2021 Canadian Digital Health Survey, a cross-sectional, web-based survey of 12,052 Canadians aged 16 years and older with internet access. Bivariate associations between the use of eMH services and health service utilization factors (predisposing, enabling, illness level) of survey respondents were assessed using χ sup.2 tests for categorical variables and t tests for the continuous variable. Logistic regression was used to predict the probability of using eMH services given the respondents’ predisposing, enabling, and illness-level factors while adjusting for respondents’ age and gender. The proportion of eMH service users among survey respondents was small (883/12,052, 7.33%). Results from the logistic regression suggest that users of eMH services were likely to be those with regular family physician access (odds ratio OR 1.57, P=.02), living in nonrural communities (OR 1.08, P<.001), having undergraduate (OR 1.40, P=.001) or postgraduate (OR 1.48, P=.003) education, and being eHealth literate (OR 1.05, P<.001). Those with lower eMH usage were less likely to speak English at home (OR 0.06, P<.001). Our study provides empirical evidence on the impact of individual health utilization factors on the use of eMH among Canadians during the COVID-19 pandemic. Given the opportunities and promise of eMH services in increasing access to care, future digital interventions should both tailor themselves toward users of these services and consider awareness campaigns to reach nonusers. Future research should also focus on understanding the reasons behind the use and nonuse of eMH services.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Many distributed parameter systems (DPSs) have strongly nonlinear spatiotemporal dynamics, unknown parameters and complex boundary conditions, which make it difficult to obtain accurate prediction ...and control in actual practice. In this paper, a data-driven spatiotemporal model predictive control (MPC) strategy is proposed for nonlinear DPSs. It first develops a low-order nonlinear spatiotemporal model by using kernel principal component analysis to reconstruct the nonlinear spatial dynamics, so that the spatial nonlinearity is better reserved in contrast with the traditional data-driven DPS modeling methods. On this basis, a spatiotemporal MPC is proposed for nonlinear DPSs. In this control strategy, a new objective function is constructed with consideration of errors on not only time but also space, which overcomes the shortcoming of the traditional MPC due to the ignorance of nonlinear spatial dynamics. The stability and effectiveness of the proposed spatiotemporal control strategy are demonstrated by mathematical stability and comparative case studies.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The application of a bidirectional laser requires the laser intensity in both directions to be balanced. However, the CW and CCW light intensities in current bidirectional erbium-doped fiber laser ...experiments differ due to the gain competition effect. There is no report on equalizing the intensity in the CW and CCW directions. This paper proposes a bidirectional non-reciprocal optical attenuator using the Faraday optical rotation effect. Continuous attenuation adjustment is realized by changing the angle between the polarizer's transmission axis and the linear polarized light. In this study, we analyzed the influence of different parameters on the device's performance, built a non-reciprocal attenuator, and tested the bidirectional attenuation curve, which was consistent with the simulation results. The device was integrated into a bidirectional fiber laser, and the light intensity in both directions was balanced through non-reciprocal adjustment. Combined with closed-loop control, the average intensity difference fluctuation between the two directions was controlled at 0.28% relative to the average power, realizing stable long-term bidirectional fiber laser intensity equalization.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Past studies have proposed solutions that analyze Stack Overflow content to help users find desired information or aid various downstream software engineering tasks. A common step performed by those ...solutions is to extract suitable representations of posts; typically, in the form of meaningful vectors. These vectors are then used for different tasks, for example, tag recommendation, relatedness prediction, post classification, and API recommendation. Intuitively, the quality of the vector representations of posts determines the effectiveness of the solutions in performing the respective tasks. In this work, to aid existing studies that analyze Stack Overflow posts, we propose a specialized deep learning architecture Post2Vec which extracts distributed representations of Stack Overflow posts. Post2Vec is aware of different types of content present in Stack Overflow posts, i.e., title, description, and code snippets, and integrates them seamlessly to learn post representations. Tags provided by Stack Overflow users that serve as a common vocabulary that captures the semantics of posts are used to guide Post2Vec in its task. To evaluate the quality of Post2Vec's deep learning architecture, we first investigate its end-to-end effectiveness in tag recommendation task. The results are compared to those of state-of-the-art tag recommendation approaches that also employ deep neural networks. We observe that Post2Vec achieves 15-25 percent improvement in terms of F1-score@5 at a lower computational cost. Moreover, to evaluate the value of representations learned by Post2Vec, we use them for three other tasks, i.e., relatedness prediction, post classification, and API recommendation. We demonstrate that the representations can be used to boost the effectiveness of state-of-the-art solutions for the three tasks by substantial margins (by 10, 7, and 10 percent in terms of F1-score, F1-score, and correctness, respectively). We release our replication package at https://github.com/maxxbw/Post2Vec .
Atrial fibrillation (AF) is the most common arrhythmia, which requires long-term diagnosis and treatment in a daily routine. Recently, ballistocardiogram (BCG), an unobstructive cardiac function ...monitoring method, is widely studied for AF detection. Usually, the AF classification performance depends on the characterization property of extracted features. Therefore, we propose a novel nonlinear persistent homology feature, combining theory from nonlinear dynamics and persistent homology based topological methods, which is aimed at providing supplementary to existing features and further improving AF diagnosis performance. In this research, one-dimension BCG series were reconstructed to high-dimension phase space point clouds firstly, which contain more abundant rhythm information. Then topological distributions of the point clouds were represented as persistent homology barcodes using topological data analysis. Finally, the statistics of the barcodes were considered as 9 persistent homology features to quantify the barcodes. To validate the proposed AF detection method, we collected 4000 AF and non-AF segments of BCG from 73 subjects, and 6 machine learning classifiers were performed. By combining the 9 persistent homology features with 17 previously proposed features, our features brought a 6.17% increment in accuracy compared with the 17 features solely (<inline-formula> <tex-math notation="LaTeX">{p} < 0.001 </tex-math></inline-formula>), reaching 94.50%. Using feature selection technique, 12 effective features were reserved and achieves 93.50% classification accuracy. It follows that the proposed features contribute to improving AF detection performance for a larger amount of BCG data, which contains versatile pathological information and individual differences.
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