This paper proposes a novel approach to health monitoring and multifault detection in permanent magnet synchronous machines using direct flux measurement with search coils. Unlike other ...spectrum-based fault detection schemes, only the fundamental frequency component of the measured voltage is utilized for fault detection. Therefore, the performance of the proposed scheme is not affected by nonstationary speed or harmonics introduced by the power supply. In addition, location of interturn short circuits and direction of static eccentricity can be detected, which have never been done by any other scheme. In spite of the invasive nature of the technique, it is very suitable for mission-critical applications and emerging applications such as off-shore wind turbines, hybrid vehicle technology, and military applications, where early detection of faults is of paramount importance. 2-D simulations using finite element analysis have been presented to validate the proposed method under different operating conditions. Experimental introduction of stator interturn short circuit, demagnetization, and static eccentricity has been discussed, and the proposed scheme is experimentally implemented to examine its effectiveness.
Chronic kidney disease is considered an inflammatory state and a high fiber intake is associated with decreased inflammation in the general population. Here, we determined whether fiber intake is ...associated with decreased inflammation and mortality in chronic kidney disease, and whether kidney disease modifies the associations of fiber intake with inflammation and mortality. To do this, we analyzed data from 14,543 participants in the National Health and Nutrition Examination Survey III. The prevalence of chronic kidney disease (estimated glomerular filtration rate less than 60ml/min per 1.73m2) was 5.8%. For each 10-g/day increase in total fiber intake, the odds of elevated serum C-reactive protein levels were decreased by 11% and 38% in those without and with kidney disease, respectively. Dietary total fiber intake was not significantly associated with mortality in those without but was inversely related to mortality in those with kidney disease. The relationship of total fiber with inflammation and mortality differed significantly in those with and without kidney disease. Thus, high dietary total fiber intake is associated with lower risk of inflammation and mortality in kidney disease and these associations are stronger in magnitude in those with kidney disease. Interventional trials are needed to establish the effects of fiber intake on inflammation and mortality in kidney disease.
Insider threats remain a serious anxiety for organizations, government agencies, and businesses. Normally, the most hazardous cyber attacks are formed by trusted insiders and not by malicious ...outsiders. The malicious behaviors resulting from unplanned or planned mishandling of resources, data, networks, and systems of an organization constitute an insider threat. The unsupervised behavioral anomaly detection methods are mostly developed by the traditional machine learning methods for identifying unusual or anomalous variations in user behavior. The insider threat mainly originates from an individual inside the organization who is a current or former employee who has access to sensitive information about the organization. For achieving an improvement over traditional methods, the Stacked Convolutional Neural Network- Attentional Bi-directional Gated Recurrent Unit model is proposed in this paper to detect insider threats. The CNN-Attentional BiGRU model utilizes the user activity logs and user information for time-series classification. Using the log files, the temporal data representations, and weekly and daily numerical features from various sub-models of CNN are learned by the stacked generalization. Based on the chosen feature vectors, a model is trained on the CERT insider threat dataset. The stacked CNN is combined with the Attentional BiGRU model to incorporate more complex features of the user activity logs and user data during each convolution operation without raising network parameters. Thus the classification performance is improved with less complexity. The non-linear time control, chaos-based strategy, update rules, and opposite-based learning strategies are evaluated for generating the Modified-Equilibrium Optimization. The simulation outputs obtained by the model are 92.52% accuracy, 98% Precision, 95% Recall, and 96% F1-score. Thus, the proposed model has reached higher detection performance.
A large number of association rules often minimizes the reliability of data mining results; hence, a dimensionality reduction technique is crucial for data analysis. When analyzing massive datasets, ...existing models take more time to scan the entire database because they discover unnecessary items and transactions that are not necessary for data analysis. For this purpose, the Fuzzy Rough Set-based Horse Herd Optimization (FRS-HHO) algorithm is proposed to be integrated with the Map Reduce algorithm to minimize query retrieval time and improve performance. The HHO algorithm minimizes the number of unnecessary items and transactions with minimal support value from the dataset to maximize fitness based on multiple objectives such as support, confidence, interestingness, and lift to evaluate the quality of association rules. The feature value of each item in the population is obtained by a Map Reduce-based fitness function to generate optimal frequent itemsets with minimum time. The Horse Herd Optimization (HHO) is employed to solve the high-dimensional optimization problems. The proposed FRS-HHO approach takes less time to execute for dimensions and has a space complexity of 38% for a total of 10 k transactions. Also, the FRS-HHO approach offers a speedup rate of 17% and a 12% decrease in input–output communication cost when compared to other approaches. The proposed FRS-HHO model enhances performance in terms of execution time, space complexity, and speed.
Numerous public networks, namely Instagram, YouTube, Facebook, Twitter, etc., share their own feelings and idea as videotapes, posts, and pictures. In future research, adapting to such data and ...mining valuable information from it will be an undeniably troublesome errand. This paper proposes a novel audio–video–textual-based multimodal sentiment analysis approach. The proposed approach investigates the sentiments that are collected from the web recordings that utilize audio, video, and textual modalities for further extraction. A feature-level fusion technique is employed in fusing the extracted features from different modalities. Therefore, the extracted features are optimally chosen by using a novel oppositional grass bee optimization (OGBEE) algorithm to obtain the best optimal feature set. Here, 12 benchmark functions are developed to validate the numerical efficiency and the effectiveness of a novel OGBEE algorithm for various aspects. Moreover, our proposed approach utilizes multilayer perceptron-based neural network (MLP-NN) for sentiment classification. The experimental analysis reveals that the proposed approach provides better classification accuracy of about 95.2% with less computational time.
Nowadays, the concept of data mining is employed widely and created a great deal of attention due to its fast arrival. Numerous approaches to frequent itemsets and association rule mining (ARM) are ...exemplified in recent years, but still, the performances based on scalability and processing time are considered as a major drawback that results in obtaining the solutions with very poor quality. To overcome such shortcomings, this article proposes three significant phases, namely, the data pre‐processing phase, data pre‐processing, frequent itemset mining, and ARM. In data pre‐processing phase, the collected twitter datasets are pre‐processed to eliminate redundant data and convert them into an appropriate format for further mining. In the frequent itemset mining phase, an Apriori algorithm is employed for the exact mining of frequent itemsets. The ARM phase utilizes the fuzzy manta ray foraging (FMRF) optimization algorithm that involves the generation of association rules from the huge itemsets thereby achieving minimum confidence and minimum support value. Here, the recent tweets regarding Covid‐19, trump2020, joebiden, draintheswamp, and Godzilla are the datasets collected from the Twitter web link. The experimental analysis and the comparative performances are performed for various simulation measures and the results reveal that the proposed approach provides effective performances when compared with various other existing approaches.
A discussion of the use of carbonic anhydrase in investigating protein-ligand binding is presented. The properties of carbonic anhydrase make it ideal as a model for biophysics and biomedical ...chemical research.
Next-generation antiandrogen therapies, such as enzalutamide and abiraterone, have had a profound impact on the management of metastatic castration-resistant prostate cancer (mCRPC). However, mCRPC ...patients invariably develop resistance to these agents. Here, a series of clonal cell lines were developed from enzalutamide-resistant prostate tumor xenografts to study the molecular mechanism of resistance and test their oncogenic potential under various treatment conditions. Androgen receptor (AR) signaling was maintained in these cell lines, which acquired potential resistance mechanisms, including expression of AR-variant 7 (AR-v7) and glucocorticoid receptor. BET bromodomain inhibitors were shown previously to attenuate AR signaling in mCRPC; here, we demonstrate the efficacy of bromodomain and extraterminal (BET) inhibitors in enzalutamide-resistant prostate cancer models. AR antagonists, enzalutamide, and ARN509 exhibit enhanced prostate tumor growth inhibition when combined with BET inhibitors, JQ1 and OTX015, respectively. Taken together, these data provide a compelling preclinical rationale to combine BET inhibitors with AR antagonists to subvert resistance mechanisms.
Therapeutic combinations of BET inhibitors and AR antagonists may enhance the clinical efficacy in the treatment of mCRPC.
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Secure routing and communication with confidentiality based on encryption of texts in multiple natural languages are challenging issues in wireless sensor networks which are widely used in recent ...applications. The existing works on Elliptic Curve Cryptography based secured routing algorithms are focused only on the encryption and decryption of single language text encrypted over a Prime finite field. In this article, a new algorithm called Multi‐Language ECC encrypted Secure Routing algorithm with trust management is proposed, in order to ensure confidentiality and integrity which focuses on the encryption of plain text using Riemann's zeta function and Elliptic Curve Cryptography for improving the key strength which is applied for encryption over a range of multi languages namely Tamil, English, Hindi French and German which are supported by Unicode and routing the text security. From the experiments conducted using the proposed multi‐lingual encryption algorithm with network routing, we prove that the suggested method provides greater security than the current secure routing algorithms due to the use of Zeta function and Gamma function with ECC key and trust management. but also boasts reduced complexity compared to other existing multi‐lingual encryption algorithms.
Resistance to androgen deprivation therapies and increased androgen receptor (AR) activity are major drivers of castration-resistant prostate cancer (CRPC). Although prior work has focused on ...targeting AR directly, co-activators of AR signaling, which may represent new therapeutic targets, are relatively underexplored. Here we demonstrate that the mixed-lineage leukemia protein (MLL) complex, a well-known driver of MLL fusion-positive leukemia, acts as a co-activator of AR signaling. AR directly interacts with the MLL complex via the menin-MLL subunit. Menin expression is higher in CRPC than in both hormone-naive prostate cancer and benign prostate tissue, and high menin expression correlates with poor overall survival of individuals diagnosed with prostate cancer. Treatment with a small-molecule inhibitor of menin-MLL interaction blocks AR signaling and inhibits the growth of castration-resistant tumors in vivo in mice. Taken together, this work identifies the MLL complex as a crucial co-activator of AR and a potential therapeutic target in advanced prostate cancer.
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