Tag: nevin manimala

Sensors (Basel). 2023 Sep 4;23(17):7646. doi: 10.3390/s23177646.

ABSTRACT

In low-voltage distribution systems, the load types are complex, so traditional detection methods cannot effectively identify series arc faults. To address this problem, this paper proposes an arc fault detection method based on multimodal feature fusion. Firstly, the different mode features of the current signal are extracted by mathematical statistics, Fourier transform, wavelet packet transform, and continuous wavelet transform. The different modal features include one-dimensional features, such as time-domain features, frequency-domain features, and wavelet packet energy features, and two-dimensional features of time-spectrum images. Secondly, the extracted features are preprocessed and prioritized for importance based on different machine learning algorithms to improve the feature data quality. The features of higher importance are input into an arc fault detection model. Finally, an arc fault detection model is constructed based on a one-dimensional convolutional network and a deep residual shrinkage network to achieve high accuracy. The proposed detection method has higher detection accuracy and better performance compared with the arc fault detection method based on single-mode features.

PMID:37688107 | DOI:10.3390/s23177646

Sensors (Basel). 2023 Sep 3;23(17):7639. doi: 10.3390/s23177639.

ABSTRACT

Currently, strawberry harvesting relies heavily on human labour and subjective assessments of ripeness, resulting in inconsistent post-harvest quality. Therefore, the aim of this work is to automate this process and provide a more accurate and efficient way of assessing ripeness. We explored a unique combination of YOLOv7 object detection and augmented reality technology to detect and visualise the ripeness of strawberries. Our results showed that the proposed YOLOv7 object detection model, which employed transfer learning, fine-tuning and multi-scale training, accurately identified the level of ripeness of each strawberry with an mAP of 0.89 and an F1 score of 0.92. The tiny models have an average detection time of 18 ms per frame at a resolution of 1280 × 720 using a high-performance computer, thereby enabling real-time detection in the field. Our findings distinctly establish the superior performance of YOLOv7 when compared to other cutting-edge methodologies. We also suggest using Microsoft HoloLens 2 to overlay predicted ripeness labels onto each strawberry in the real world, providing a visual representation of the ripeness level. Despite some challenges, this work highlights the potential of augmented reality to assist farmers in harvesting support, which could have significant implications for current agricultural practices.

PMID:37688097 | DOI:10.3390/s23177639

Sensors (Basel). 2023 Aug 30;23(17):7536. doi: 10.3390/s23177536.

ABSTRACT

According to the survey statistics, most traffic accidents are caused by the driver’s behavior and status irregularities. Because there is no multi-level dangerous state grading system at home and abroad, this paper proposes a complex state grading system for real-time detection and dynamic tracking of the driver’s state. The system uses OpenMV as the acquisition camera combined with the cradle head tracking system to collect the driver’s current driving image in real-time dynamically, combines the YOLOX algorithm with the OpenPose algorithm to judge the driver’s dangerous driving behavior by detecting unsafe objects in the cab and the driver’s posture, and combines the improved Retinaface face detection algorithm with the Dlib feature-point algorithm to discriminate the fatigue driving state of the driver. The experimental results show that the accuracy of the three driver danger levels (R1, R2, and R3) obtained by the proposed system reaches 95.8%, 94.5%, and 96.3%, respectively. The experimental results of this system have a specific practical significance in driver-distracted driving warnings.

PMID:37687991 | DOI:10.3390/s23177536

Sensors (Basel). 2023 Aug 29;23(17):7512. doi: 10.3390/s23177512.

ABSTRACT

As a key 5G technology, massive multiple-input multiple-output (MIMO) can effectively improve system capacity and reduce latency. This paper proposes a user scheduling and spectrum allocation method based on combinatorial multi-armed bandit (CMAB) for a massive MIMO system. Compared with traditional methods, the proposed CMAB-based method can avoid channel estimation for all users, significantly reduce pilot overhead, and improve spectral efficiency. Specifically, the proposed method is a two-stage method; in the first stage, we transform the user scheduling problem into a CMAB problem, with each user being referred to as a base arm and the energy of the channel being considered a reward. A linear upper confidence bound (UCB) arm selection algorithm is proposed. It is proved that the proposed user scheduling algorithm experiences logarithmic regret over time. In the second stage, by grouping the statistical channel state information (CSI), such that the statistical CSI of the users in the angular domain in different groups is approximately orthogonal, we are able to select one user in each group and allocate a subcarrier to the selected users, so that the channels of users on each subcarrier are approximately orthogonal, which can reduce the inter-user interference and improve the spectral efficiency. The simulation results validate that the proposed method has a high spectral efficiency.

PMID:37687968 | DOI:10.3390/s23177512

Sensors (Basel). 2023 Aug 28;23(17):7483. doi: 10.3390/s23177483.

ABSTRACT

The utilization of multibeam sonar systems has significantly facilitated the acquisition of underwater bathymetric data. However, efficiently processing vast amounts of multibeam point cloud data remains a challenge, particularly in terms of rejecting massive outliers. This paper proposes a novel solution by implementing a cone model filtering method for multibeam bathymetric point cloud data filtering. Initially, statistical analysis is employed to remove large-scale outliers from the raw point cloud data in order to enhance its resistance to variance for subsequent processing. Subsequently, virtual grids and voxel down-sampling are introduced to determine the angles and vertices of the model within each grid. Finally, the point cloud data was inverted, and the custom parameters were redefined to facilitate bi-directional data filtering. Experimental results demonstrate that compared to the commonly used filtering method the proposed method in this paper effectively removes outliers while minimizing excessive filtering, with minimal differences in standard deviations from human-computer interactive filtering. Furthermore, it yields a 3.57% improvement in accuracy compared to the Combined Uncertainty and Bathymetry Estimator method. These findings suggest that the newly proposed method is comparatively more effective and stable, exhibiting great potential for mitigating excessive filtering in areas with complex terrain.

PMID:37687939 | DOI:10.3390/s23177483

Med Image Anal. 2023 Sep 1;90:102941. doi: 10.1016/j.media.2023.102941. Online ahead of print.

ABSTRACT

Although many deep learning models-based medical applications are performance-driven, i.e., accuracy-oriented, their explainability is more critical. This is especially the case with neuroimaging, where we are often interested in identifying biomarkers underlying brain development or disorders. Herein we propose an explainable deep learning approach by elucidating the information transmission mechanism between two layers of a deep network with a joint feature selection strategy that considers several shallow-layer explainable machine learning models and sparse learning of the deep network. At the end, we apply and validate the proposed approach to the analysis of dynamic brain functional connectivity (FC) from fMRI in a brain development study. Our approach can identify the differences within and between functional brain networks over age during development. The results indicate that the brain network transits from undifferentiated structures to more specialized and organized ones, and the information processing ability becomes more efficient as age increases. In addition, we detect two developmental patterns in the brain network: the FCs in regions related to visual and sound processing and mental regulation become weakened, while those between regions corresponding to emotional processing and cognitive activities are enhanced.

PMID:37683445 | DOI:10.1016/j.media.2023.102941

Mar Pollut Bull. 2023 Sep 6;195:115459. doi: 10.1016/j.marpolbul.2023.115459. Online ahead of print.

ABSTRACT

Oil spill is the main cause of marine pollution in the waterbodies with rich oil resources. In this study, we developed and compared the performance of variable-complexity machine-learning models to detect oil spill origin, extent, and movement over large scales. To this end, we trained Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN) models by using the statistical, geometrical, and textural features of Sentinel-1 SAR data. Our results in the Persian Gulf showed that CNN is superior to RF and SVM classifiers in oil spill detection, as evidenced by the testing accuracy of 95.8 %, 86.0 %, and 78.9 %, respectively. The results suggested utilizing both ascending and descending orbit pass directions to track the movement of oil spill and the underlying transport rate. The proposed methodology enables the detection of probable leaking tankers and platforms, which aids in identifying other sources of oil pollution than tankers and platforms.

PMID:37683396 | DOI:10.1016/j.marpolbul.2023.115459

J Environ Manage. 2023 Sep 6;345:118872. doi: 10.1016/j.jenvman.2023.118872. Online ahead of print.

ABSTRACT

The health of agroecosystems is subsiding unremittingly, and the over-use of chemical fertilizers is one of the key reasons. It is hypothesized that integrating biochar, a carbon (C)-rich product, would be an effective approach to reducing the uses of synthetic fertilizers and securing crop productivity through improving soil properties and nutrient cycling. The bamboo biochar at different quantities (4-12 Mg ha^-1) and combinations with chemical fertilizers were tested in stevia (Stevia rebaudiana) farming in silty clay acidic soil. The integration of biochar at 8 Mg ha^-1 with 100% nitrogen (N), phosphorus (P), and potassium (K) produced statistically (p ≤ 0.05) higher leaf area index, dry leaf yield, and steviol glycosides yield by about 18.0-33.0, 25.8-44.9, and 20.5-59.4%, respectively, compared with the 100% NPK via improving soil physicochemical properties. Soil bulk density was reduced by 5-8% with biochar at ≥ 8 Mg ha^-1, indicating the soil porosity was increased by altering the soil macrostructure. The soil pH was significantly (p ≤ 0.05) augmented with the addition of biochar alone or in the combination of N because of the alkaline nature of the used biochar (pH = 9.65). Furthermore, integrating biochar at 8 Mg ha^-1 with 100% NPK increased 22.7% soil organic C compared with the sole 100% NPK. The priming effect of applied N activates soil microorganisms to mineralize the stable C. Our results satisfy the hypothesis that adding bamboo biochar would be a novel strategy for sustaining productivity by altering soil physicochemical properties.

PMID:37683384 | DOI:10.1016/j.jenvman.2023.118872

J Geriatr Oncol. 2023 Sep 6;14(8):101621. doi: 10.1016/j.jgo.2023.101621. Online ahead of print.

ABSTRACT

INTRODUCTION: Prostate cancer (PC) is the second commonest malignancy and fifth leading cause of cancer death in men worldwide. Older men are more likely to develop PC but are underrepresented in pivotal clinical trials, leading to challenges in treatment selection in the real-world setting. We aimed to examine treatment patterns and outcomes in older Australians with metastatic castration-resistant prostate cancer (mCRPC).

MATERIALS AND METHODS: We identified 753 men with mCRPC within the electronic CRPC Australian Database (ePAD). Clinical data were analysed retrospectively to assess outcomes including time to treatment failure (TTF), overall survival (OS), PSA doubling time (PSADT), PSA₅₀ response rate, and pre-defined adverse events of special interest (AESIs). Descriptive statistics were used to report baseline characteristics, stratified by age groups (<75y, 75-85y and >85y). Groups were compared using Kruskal-Wallis and Chi-square analyses. Time-to-event analyses were performed using Kaplan-Meier methods and compared through log-rank tests. Cox proportional hazards univariate and multivariate analyses were performed to evaluate the influence of variables on OS.

RESULTS: Fifty-seven percent of men were aged <75y, 31% 75-85y, and 12% >85y. Patients ≥75y more frequently received only one line of systemic therapy (40% of <75y vs 66% 75-85y vs 68% >85y; P < 0.01). With increasing age, patients were more likely to receive androgen receptor signalling inhibitors (ARSIs) as initial therapy (42% of <75y vs 70% of 75-85y vs 84% of >85y; p < 0.01). PSA₅₀ response rates or TTF did not significantly differ between age groups for chemotherapy or ARSIs. Patients >85y receiving enzalutamide had poorer OS but this was not an independent prognostic variable on multivariate analysis (hazard ratio [HR] 0.93(0.09-9.35); p = 0.95). PSADT >3 months was an independent positive prognostic factor for patients receiving any systemic therapy. Older patients who received docetaxel were more likely to experience AESIs (18% in <75y vs 37% 75-85y vs 33% >85y, p = 0.038) and to stop treatment as a result (21% in <75y vs 39% in 75-85y; p = 0.011).

DISCUSSION: In our mCRPC cohort, older men received fewer lines of systemic therapy and were more likely to cease docetaxel due to adverse events. However, treatment outcomes were similar in most subgroups, highlighting the importance of individualised assessment regardless of age.

PMID:37683368 | DOI:10.1016/j.jgo.2023.101621

Eur J Med Chem. 2023 Sep 4;260:115791. doi: 10.1016/j.ejmech.2023.115791. Online ahead of print.

ABSTRACT

Natural products have been utilized for medicinal purposes for millennia, endowing them with a rich source of chemical scaffolds and pharmacological leads for drug discovery. Among the vast array of natural products, flavonoids represent a prominent class, renowned for their diverse biological activities and promising therapeutic advantages. Notably, their anti-inflammatory properties have positioned them as promising lead compounds for developing novel drugs combating various inflammatory diseases. This review presents a comprehensive overview of flavonoids, highlighting their manifold anti-inflammatory activities and elucidating the underlying pathways in mediating inflammation. Furthermore, this review encompasses systematical classification of flavonoids, related anti-inflammatory targets, involved in vitro and in vivo test models, and detailed statistical analysis. We hope this review will provide researchers engaged in active natural products and anti-inflammatory drug discovery with practical information and potential leads.

PMID:37683361 | DOI:10.1016/j.ejmech.2023.115791