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Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.

Spectral Doppler and plethysmographic waveforms vary significantly in the presence of circulatory assist devices. Understanding the effect these devices have on the waveforms and flow characteristics is necessary for the appropriate interpretation of duplex ultrasound and other noninvasive vascular studies. We review the different arterial waveforms that can be seen with circulatory assist devices.

Bariatric surgery has emerged as a therapy for obesity and the associated comorbidities. Obesity has been shown to be a risk factor for atrial fibrillation as well as venous thromboembolism, both of which are conditions that warrant anticoagulation. There is significant underrepresentation of the morbidly obese population in prospective trials that evaluated direct oral anticoagulants and vitamin K antagonists in atrial fibrillation and venous thromboembolism. We aim to review all the available data that assessed these oral anticoagulants in the morbidly obese population (body mass index >40 kg/m2 and weight >120 kg) and in the post-bariatric surgery population. Our findings suggest that direct oral anticoagulants may be safe and effective for anticoagulation in morbidly obese patients for both atrial fibrillation and venous thromboembolism. However, warfarin is the preferred agent in the post-bariatric surgery population, given the limited number of studies on direct oral anticoagulants in this population. Further adequately powered randomized control trials are needed to confirm the safety and efficacy of these oral anticoagulants in the morbidly obese and post-bariatric surgery population.

Diabetic retinopathy (DR) is one of the most common causes of vision loss or blindness globally. Early detection of retinal eye lesions like hard exudates, soft exudates, microaneurysms, and hemorrhages is crucial to detect DR in a human eye. Therefore, accurate segmentation of lesions from eye fundus images is essential to develop efficient automated DR detection systems. This paper presented a novel hard and soft exudates lesions segmentation method called Efficient Dual-Decoder Boosted Network (EDBNet). EDBNet is composed of the following main components: 1) pre-trained ImageNet ResNet50 encoder with Atrous Spatial Pyramid Pooling (ASPP), 2) UNet decoder block with Gated Skip Connections mechanism to enhance capture more details of fundus images, 3) dual-decoder boosted to improve the performance segmentation of retinal lesion in the eye fundus images, and fusion outputs of the dual-decoder boosted to generate enhanced exudates segmentation. The effectiveness of the proposed framework is assessed on the IDRiD publicly dataset in terms of accuracy, Area Under Precision-Recall (AUPR), IOU, and Dice metrics. EDBNet obtains 99.8, 74.4, 78.0, and 87.6% of soft exudates, respectively. For hard exudates, EDBNet achieves 99.5, 85.3, 80.3, and 89.1%, respectively. The experimental results also demonstrate that EDBNet outperforms many state-of-the-art methods.

Cancer is generally defined as the uncontrollable increase of number of cells in the body. These cells might be formed anywhere in the body and spread to other parts of the body. Although the mortality rate of cancer is high, it is possible to decrease cancer cases by up to 30% to 50% through taking a healthy lifestyle and avoiding unhealthy habits. Imaging is one of the powerful technologies used for detecting and treating cancer at its early stages. Nowadays, scientists admit that medical images hold more information than their diagnosis, which is called a radiomics approach. Radiomics demonstrate that images comprise numerous quantitative features that are useful in predicting, detecting, and treating cancers in a personalized manner. While radiomics can extract numerous features, not all of them are useful. It should not be neglected that the outcome of data analysis is highly dependent on the selected features. There are different ways of finding the most reliable features. One possible way is to select all extracted features, analyze them, and find the most reproducible and reliable ones. Different statistical analysis metrics could analyze the features. To discover and introduce the most accurate metrics, in this paper, different statistical metrics used for measuring the stability and reproducibility of the features are investigated.

Most crack image datasets are developed for crack segmentation or detection. They cannot be used to train a deep learning model to detect and segment cracks simultaneously. Most of existing datasets do not include a very accurate annotation. Besides, some crack images cannot be used to train deep learning models because of their inferior quality. In this paper, we propose a promising curated crack image dataset that allows the development of crack segmentation, detection, and classification on the same set of images simultaneously. There is no dataset for road crack that involves detection and segmentation tasks to the best of our knowledge. The current version of the curated database consists of 506 images derived from the RDD2020 dataset taken from multi-countries (Japan, Czech, and India). We use the curated dataset to build different deep learning-based crack detection and segmentation methods. Our experiments demonstrate that the proposed dataset yields promising results for crack detection and segmentation.

Breast cancer must be detected early to reduce the mortality rate. Ultrasound images can make it easier for the clinician to diagnose cases of dense breasts. This study presents a deep vision transformer-based approach for predicting breast cancer malignancy scores from ultrasound images. In particular, various state-of-the-art deep vision transformers such as BEiT, CaiT, Swin, XCiT, and Vis-Former are adapted and trained to extract robust radiomics to classify breast tumors in ultrasound images as benign or malignant. The best-performing model is used to predict the malignancy score of each input ultrasound image. Experimental results revealed that the proposed approach achieves promising results for the detection of malignant tumors of the breast on ultrasound images.

Image quality assessment (IQA) algorithms are critical for determining the quality of high-resolution photographs. This work proposes a hybrid NR IQA approach that uses deep transfer learning to enhance classic NR IQA with deep learning characteristics. Firstly, we simulate a pseudo reference image (PRI) from the input image. Then, we used a pre-trained inception-v3 deep feature extractor to generate the feature maps from the input distorted image and PRI. The distance between the feature maps of the input distorted image and PRI are measured using the local structural similarity (LSS) method. A nonlinear mapping function is used to calculate the final quality scores. When compared to previous work, the proposed method has a promising performance.

Breast cancer is the most frequently diagnosed cancer in women globally. Early and accurate detection and classification of breast tumors are critical in improving treatment strategies and increasing the patient survival rate. Digital breast tomosynthesis (DBT) is an advanced form of mammography that aids better in the early detection and diagnosis of breast disease. This paper proposes a breast tumor classification method based on analyzing and evaluating the performance of various of the most innovative deep learning classification models in cooperation with a support vector machine (SVM) classifier for a DBT dataset. Specifically, we study the ability to use transfer learning from non-medical images to classify tumors in unseen DBT medical images. In addition, we utilize the fine-tuning technique to improve classification accuracy.

Automated cell nuclei delineation in whole-slide imaging (WSI) is a fundamental step for many tasks like cancer cell recognition, cancer grading, and cancer subtype classification. Although numerous computational methods have been proposed for segmenting nuclei in WSI images based on image processing and deep learning, existing approaches face major challenges such as color variation due to the use of different stains, the various structures of cell nuclei, and the overlapping and clumped cell nuclei. To circumvent these challenges in this article, we propose an efficient and accurate cell nuclei segmentation method based on deep learning, in which a set of accurate individual cell nuclei segmentation models are developed to predict rough segmentation masks, and then a learnable aggregation network (LANet) is used to predict the final nuclei masks. Besides, we develop cell nuclei segmentation software (with a graphical user interface—GUI) that includes the proposed method and other deep-learning-based cell nuclei segmentation methods. A challenging WSI dataset collected from different centers and organs is used to demonstrate the efficiency of our method. The experimental results reveal that our method obtains a competitive performance compared to the existing approaches in terms of the aggregated Jaccard index (AJI=89.25%) and F1-score (F1=73.02%). The developed nuclei segmentation software can be downloaded from https://github.com/loaysh2010/Cell-Nuclei-Segmentation-GUI-Application.

The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. The comparison reveals the high reliability of the proposed forecasting model with different sites and solar profiles.

Chewing lice comprise a large group of ectoparasites that colonize and adversely affect several domestic and wild birds including pigeons. In Saudi Arabia, there is a lack of studies describing such ectoparasites and their infestation rates. Through this work, a new record, Columbicola, tschulyschman Eichler (C. tschulyschman Eichler) was collected from domestic pigeons (Columba livia domestica, Linnaeus). The collected C. tschulyschman Eichler was morphologically identified based on specific taxonomic keys. Mitochondrial (COI) and nuclear (EF-1α) gene fragments were used for molecular identification and phylogenetic reconstruction. In this study, the C. tschulyschman Eichler accounted for around 69.40%. To our knowledge, this is the first report of C. tschulyschman Eichler in Riyadh, Saudi Arabia. To improve the tree topology and differentiate between genera, further studies should utilize the 16s rRNA.

Wireless community networks (WCNs) are large, heterogeneous, dynamic, and decentralized networks. Such complex characteristics raise different challenges, such as the effect of wireless communications on the performance of networks and routing protocols. The prediction approaches of link quality (LQ) can improve the performance of routing algorithms of WCNs while avoiding weak links. The prediction of LQ in WCNs can be a complex task because of the fluctuated nature of LQ measurements due to the dynamic wireless environment. In this paper, a deep learning based approach is proposed to accurately predict LQ in WCNs. Specifically, we propose the use of two variants of deep recurrent neural network (RNN): long short-term memory recurrent neural networks (LSTM-RNN) and gated recurrent unit (GRU). The positive feature of the proposed variants is that they can handle the fluctuating nature of LQ due to their ability to learn and exploit the context in LQ time-series. The experimental results on data collected from a real-world WCN show that the proposed LSTM-RNN and GRU models accurately predict LQ in WCNs compared to related methods. The proposed approach could be a helpful tool for accurately predicting LQ, thereby improving the performance of routing protocols of WCNs.

This paper deals with the simultaneous tuning of proportional integral controller of static synchronous compensator (STATCOM) associated with lead-lag controller (LL) in order to improve the stabil...

The intermittent photovoltaic (PV) units significantly affect the performance of distribution systems, and they often cause several operational problems, most importantly, voltage rise/drop. At high PV penetration, excessive tap movements of transformers and high curtailed PV power are expected to completely solve the voltage violation problem. In this paper, we propose an optimal voltage control method for distribution systems considering the number of tap movements of transformers and the active power curtailment of PV units. The objective function of the proposed method comprises: 1) voltage drop violation, 2) voltage rise violation, 3) tap movement rate (TMR) of transformers, and 4) curtailed power of PV (CPPV). A multiobjective grey wolf optimizer integrated with a Lévy mutation operator (GWO-Lévy) is formulated to accurately solve the voltage control problem. A 24-h simulation is performed on the 119-bus distribution system with PV and different types of loads. The performance of GWO-Lévy is compared with three other optimizers, finding that it achieves the best performance. The simulation results demonstrate the efficacy of the proposed method for solving the voltage violation problem with PV while simultaneously optimizing TMR and CPPV.

Clot in transit (CIT) is a rare condition in which a venous thromboembolism becomes lodged in the right heart. It is seen in up to 18% of patients with massive pulmonary embolism, and if left untreated, mortality rates are between 80% and 100%. The identification and management of CIT are crucial. However, there are no current guidelines for the treatment of CIT. We present the case of a 44-year-old woman who was found to have CIT that was ultimately treated with medical management.Clot in transit (CIT) is a dangerous entity that must be promptly managed.Risk factors for CIT include a history of heart failure, a pre-existing central venous catheter and recent hospitalization.New interventions are emerging for the treatment of CIT.

Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods.

Beekeeping is essential for the global food supply, yet honeybee health and hive numbers are increasingly threatened by habitat alteration, climate change, agrochemical overuse, pathogens, diseases, and insect pests. However, pests and diseases that have unknown spatial distribution and influences are blamed for diminishing honeybee colonies over the world. The greater wax moth (GWM)

Introduction: Socioeconomic factors including neighborhoods with limited resources have a negative impact on cardiovascular outcomes in peripheral arterial disease (PAD) patients. An area deprivation index (ADI) quantifies a region’s socioeconomic conditions, which have been linked to health outcomes. Hypothesis: The patients with PAD living in disadvantaged neighborhoods as identified with higher ADI, will have higher co-morbidities and lower access to treatment, resulting in higher incidence of ST-elevation myocardial infarction (STEMI). Methods: Using electronic medical records, we identified patients with PAD (n=6331), using ICD 10 code of I 70 and I 73, with encounter from January 1, 2019 to June 2, 2021. Patients were stratified into four different groups by ADI. Baseline characteristics, comorbidities and medications were recorded for all patients. Using Wilcoxon Rank Sum Test and Pearson Chi Square tests, descriptive statistics are presented for baseline characteristics and comorbidities in table 1. Results: Patients with PAD with the highest ADI (n=1582), were younger (64 ±12), more likely to be African American (59.5%) and current smokers (41.4%) (p Conclusions: PAD patients living in neighborhoods with higher ADI have higher co-morbidities as compared to those in areas with lower ADI. The incidence of STEMI is low in all groups independent of ADI

Introduction: Digoxin was one of the first agents used in the management of heart failure with reduced ejection fraction (HFrEF). Concerns over its safety, efficacy and the introduction of guideline-directed medical therapy (GDMT) has relegated it to a secondary role. The effects of digoxin are still under debate, and its use in patients on GDMT remains unclear. Hypothesis: Patients with HFrEF on digoxin can tolerate higher doses of beta blocker and angiotensin converting enzyme inhibitors (ACEI), angiotensin receptor blocker (ARB) or angiotensin receptor-neprilysin inhibitor (ARNI). Methods: A retrospective chart review was performed on 233 patients with HFrEF managed at a tertiary care center in Cleveland, Ohio. A bivariate analysis was performed to compare patients on digoxin with patients not on digoxin in terms of ability to progress the dosing of beta blocker and ACEI/ARB/ARNI. Results: Cases and controls were similar in age, gender, height, weight, ethnicity, and no significant differences were noted when comparing baseline comorbidities and biochemical testing (Table 1). There was no significant difference between the two groups in terms of patients receiving higher doses of beta-blockers (p=0.34), ACEI (p=0.33), ARB (p=0.57), or ARNI (0.65). Stacked column charts showing the differences in dosing between the groups for all four categories are displayed in Figure 1. Conclusions: There was no significant difference between the doses of beta blocker, ACEI, ARB or ARNI among HFrEF patients on digoxin compared to those that were not. From these results, we conclude that digoxin use may not significantly affect the ability to increase the doses of GDMT in patients with HFrEF.

The optic disc (OD) is the point where the retinal vessels begin. OD carries essential information linked to Diabetic Retinopathy and glaucoma that may cause vision loss. Therefore, accurate segmentation of the optic disc from eye fundus images is essential to develop efficient automated DR and glaucoma detection systems. This paper presents a deep learning-based system for OD segmentation based on an ensemble of efficient semantic segmentation models for medical image segmentation. The aggregation of the different DL models was performed with the ordered weighted averaging (OWA) operators. We proposed the use of a dynamically generated set of weights that can give a different contribution to the models according to their performance during the segmentation of OD in the eye fundus images. The effectiveness of the proposed system was assessed on a fundus image dataset collected from the Hospital Sant Joan de Reus. We obtained Jaccard, Dice, Precision, and Recall scores of 95.40, 95.10, 96.70, and 93.90%, respectively.

Prostate cancer is the most common malignant male tumor. Magnetic Resonance Imaging (MRI) plays a crucial role in the detection, diagnosis, and treatment of prostate cancer diseases. Computer-aided diagnosis systems can help doctors to analyze MRI images and detect prostate cancer earlier. One of the key stages of prostate cancer CAD systems is the automatic delineation of the prostate. Deep learning has recently demonstrated promising segmentation results with medical images. The purpose of this paper is to compare the state-of-the-art of deep learning-based approaches for prostate delineation in MRI images and discussing their limitations and strengths. Besides, we introduce a promising perspective for prostate tumor classification in MRI images. This perspective includes the use of the best segmentation model to detect the prostate tumors in MRI images. Then, we will employ the segmented images to extract the radiomics features that will be used to discriminate benign or malignant prostate tumors.

EfficientNet is a recent Deep Convolutional Neural Network (DCNN) architecture intended to be proportionally extendible in depth, width and resolution. Through its variants, it can achieve state of the art accuracy on the ImageNet classification task as well as on other classical challenges. Although its name refers to its efficiency with respect to the ratio between outcome (accuracy) and needed resources (number of parameters, flops), we are studying a method to reduce the original number of trainable parameters by more than 84% while keeping a very similar degree of accuracy. Our proposal is to improve the pointwise (1x1) convolutions, whose number of parameters rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Basically, our tweak consists in grouping filters into parallel branches, where each branch processes a fraction of the input channels. However, by doing so, the learning capability of the DCNN is degraded. To avoid this effect, we suggest interleaving the output of filters from different branches at intermediate layers of consecutive pointwise convolutions. Our experiments with the CIFAR-10 dataset show that our optimized EfficientNet has similar learning capacity to the original layout when training from scratch.

Digital breast tomosynthesis (DBT) is one of the powerful breast cancer screening technologies. DBT can improve the ability of radiologists to detect breast cancer, especially in the case of dense breasts, where it beats mammography. Although many automated methods were proposed to detect breast lesions in mammographic images, very few methods were proposed for DBT due to the unavailability of enough annotated DBT images for training object detectors. In this paper, we present fully automated deep-learning breast lesion detection methods. Specifically, we study the effectiveness of two data augmentation techniques (channel replication and channel-concatenation) with five state-of-the-art deep learning detection models. Our preliminary results on a challenging publically available DBT dataset showed that the channel-concatenation data augmentation technique can significantly improve the breast lesion detection results for deep learning-based breast lesion detectors.

Lung cancer is a dangerous non-communicable disease attacking both women and men and every year it causes thousands of deaths worldwide. Accurate lung nodule segmentation in computed tomography (CT) images can help detect lung cancer early. Since there are different locations and indistinguishable shapes of lung nodules in CT images, the accuracy of the existing automated lung nodule segmentation methods still needs further enhancements. In an attempt towards overcoming the above-mentioned challenges, this paper presents WEU-Net; an end-to-end encoder-decoder deep learning approach to accurately segment lung nodules in CT images. Specifically, we use a U-Net network as a baseline and propose a weight excitation (WE) mechanism to encourage the deep learning network to learn lung nodule-relevant contextual features during the training stage. WEU-Net was trained and validated on a publicly available CT images dataset called LIDC-IDRI. The experimental results demonstrated that WEU-Net achieved a Dice score of 82.83% and a Jaccard similarity coefficient of 70.55%.

The Food and Agriculture Organization (FAO) estimated that plant diseases cost the world economy $220 billion in 2019. In this paper, we propose a lightweight Deep Convolutional Neural Network (DCNN) for automatic and reliable plant leaf diseases classification. The proposed method starts by converting input images of plant leaves from RGB to CIE LAB coordinates. Then, L and AB channels go into separate branches along with the first three layers of a modified Inception V3 architecture. This approach saves from 1/3 to 1/2 of the parameters in the separated branches. It also provides better classification reliability when perturbing the original RGB images with several types of noise (salt and pepper, blurring, motion blurring and occlusions). These types of noise simulate common image variability found in the natural environment. We hypothesize that the filters in the AB branch provide better resistance to these types of variability due to their relatively low frequency in the image-space domain.

Quality of retinal image is vital for screening of ailments pertaining to eye such as glaucoma, diabetic retinopathy (DR) and age related macular degeneration. Therefore, assessing quality of retinal image prior to any kind of diagnosis has assumed significance in Computer Aided Desgin (CAD) applications. The rationale behind this is that reliability of retinal image is to be guaranteed to have dependable diagnosis. In this paper, we propose a novel retinal fundus image quality assessment (RIQA) method based on autoencoder network to assess retinal images if the image is acceptable for screening or not. The autoencoder network architecture is well suited to precisely to properly represent the key features of the image quality, especially when the network can correctly reconstruct the input image. The proposed model consists of encoder and decoder successive networks. The encoder will be used for representing the features of the input image. In turn , the decoder will be used for reconstruct the input image. The features get from encoder network will then be fed to a classifier in order to classify the quality of retinal image to two classes: gradable or ungradable. The experimental results revealed more useful assessment and the proposed deep model provides a superior performance for RIQA. Thus, our model can serve real-world Clinical Decision Support Systems in the healthcare domain.

Blind or non-referential image quality assessment (NR-IQA) indicates the problem of evaluating the visual quality of an image without any reference, Therefore, the need to develop a new measure that does not depend on the reference pristine image. This paper presents a NR-IQA method based on restoration scheme and a structural similarity index measure (SSIM). Specifically, we use blind restoration schemes for blurred images by reblurring the blurred image and then we use it as a reference image. Finally, we use the SSIM as a full reference metric. The experiments performed on standard test images as well as medical images. The results demonstrated that our results using a structural similarity index measure are better than other methods such as spectral kurtosis-based method.

This Letter proposes a promising deep learning-based method for crack segmentation based on gated skip connection. The proposed gated skip connection enables the decoder layers to promote crack-aware feature representations from the encoder layers by applying high weights on the crack-relevant features that come from the encoder layers and lower weights for irrelevant features. Unlike the related methods, the authors do not apply any pre-processing or refinement steps to improve the crack segmentation results. The proposed method beats the state-of-the-art methods with an open benchmark database (IoU of 87.5).

This paper proposes a new method for diagnosis Broken Rotor Bar (BRB) faults in three phase squirrel-cage induction motors. The proposed method is based on the stator current signature analysis usi...

Saudi folklore, with all its elements, is a rich material of great importance in the formation of cultural, artistic and socialcharacteristics in the Arabian Peninsula, and because it possesses expressive elements of a special character, the diversity of thatexpression in the Saudi cultural heritage. The design elements in folk art and in all types of plastic art are the vocabulary of theform language used by the popular artist, the modern artist, the potter, the designer, and even all plastic artists, and they arecalled the elements of design or formation in relation to their capabilities in taking any flexible body and their ability to merge,compose and unite each other to form a form Totally for the popular artwork, but for the plastic artwork in general. Whateverthose elements - the perception of the artist in general and the popular artist in particular is well-aware that helps him in theplanning process and makes his work easy of course, as well as helps him in evaluating his design and development, and thushelps him in activating his popular culture among all members of society through their taste of his artistic works in the field of art.Plastic and folk art with its various branches, including porcelain. Point, line and area are among the elements that the popularartist employed in his artwork.

The present work aims to optimize the mechanical brush finishing of Metal Inert Gas (MIG) welded joints on AISI316L thin steel sheet. The innovative methodology is based on the experimental design methodology of the Taguchi design (L9) and the analysis of variance (ANOVA) with objective function of microhardness in brushed layers. A “Signal to Noise” approach is adopted with the objective of “Large is Better”. The speed of rotation of the brush, the speed of advance of the sample, the number of brushing passes, and the depression ratio of the brush fibers are the four three-level factors of the L9 design. The study of the effects of these factors showed the major influence of the depression and the number of passes on the magnitude of hardening of the brushed layers in the various zones of the weld. Minor interactions, found in the regression model, are noted between the different factors. Measurements of microhardness in the depths of each zone reveal distinct cure rates from one zone to another. The melted zone of the weld undergone the minimum hardening, unlike the heat affected zone (HAZ), whose microhardness contribution reaches 30% compared with the non-brushed welded sample. It is revealed that the main results lie in the microhardness contribution, of the order of 30% at the joint, while keeping the same level of magnitude of the mechanical strength using the optimal parameters.

The penetration of photovoltaic (PV) has obviously been increased in distribution systems throughout the world. To sufficiently assess the energy losses with PV, comprehensive simulations with high time-resolution data are required. These simulations have a heavy computational burden, which makes it difficult to analyze distribution systems and evaluate PV impacts with fine resolutions. To cope with this issue, most related works down-sample, cluster, or quantize the full data to reduce the computational time on the expense of the accuracy. In this paper, we propose a fast yet accurate energy-loss assessment approach in distribution systems using machine learning. The unique feature of the proposed approach is that it uses all data to estimate losses, which yields accurate results close to the exact solutions in a very short time. The simulation results demonstrate that the proposed approach extremely reduces the computational time of energy-loss estimation with high accuracy rates. The speedup of the proposed approach with respect to power flow simulations for a yearlong at a 30-s time resolution is 28 691 (99.9965 $\%$ reduction in computational time). The effectiveness of the proposed approach is also illustrated by applying it to optimize the PV size for minimizing energy losses.