Wind Power Integration with Smart Grid and Storage System: Prospects and Limitations

Wind power generation is playing a pivotal role in adopting renewable energy sources in many countries. Over the past decades, we have seen steady growth in wind power generation throughout the world. This article aims to summarize the operation, conversion and integration of the wind power with conventional grid and local microgrids so that it can be a one-stop reference for early career researchers. The study is carried out primarily based on the horizontal axis wind turbine and the vertical axis wind turbine. Afterward, the types and methods of storing this electric power generated are discussed elaborately. On top of that, this paper summarizes the ways of connecting the wind farms with conventional grid and microgrid to portray a clear picture of existing technologies. Section-wise, the prospects and limitations are discussed and opportunities for future technologies are highlighted. It is envisaged that, this paper will help researchers and engineering professionals to grasp the fundamental concepts related to wind power generation concisely and effectively

Performance analysis of photovoltaic passive heat storage system with microencapsulated paraffin wax for thermoelectric generation

The depletion of non-renewable energy sources and negative effects towards the environment push research towards the widespread adoption of renewable energy sources such as solar energy. The main drawback of solar panels is that temperatures above 27°C will result in an efficiency drop of 0.1-0.5%/°C. In previous studies, usage of photovoltaic thermal (PVT) systems was mainly for the purpose of heating water, warming buildings, and drying crops. This research will focus on the usage of a standalone PVT and thermoelectric generator (TEG) system whereby it uses heat extracted from the PVT system for thermoelectric generation. A passive standalone PVT-TEG system design with microencapsulated paraffin wax as a phase change material (PCM) as a heat storage medium was created. The heat stored in the PCM is used as a heat source for thermoelectric generation. To extract the heat from the PV panel, an aluminum heatsink underneath the PV panel is used as a heat absorber to passively extract heat without external power sources. This setup reduces the surface temperature by 22.7°C. Transient thermal analysis and thermoelectric simulation of the system was conducted through Computational Fluid Dynamics (CFD) using ANSYS 2019 software. The error recorded between the experimental and simulation results was 4.2%. This proposed system panel successfully increased the electrical efficiency of the PV panel by approximately 12.8%, where the overall electrical power produced shows a significant increase from 7.7W to 17.7W

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities

Composition and Diversity of Tree Species with DBH of 5 cm and above at Pulau Banding, Gerik, Perak, Malaysia

A total of 92 individual trees with DBH of 5 cm and above comprising 21 families, 28 genera and 35 species were measured, identified and recorded. This study aimed to enumerate the tree species composition and estimate the diversity index of trees with DBH of 5 cm and above at Pulau Banding, Perak. An ecological plot size of 70 m x 70 m or 0.49 ha was established and divided into three subplots. The data collection was collected to determine the number of species, number of individuals and DBH within the plots. The Shannon Diversity Index was estimated at H’ = 2.98 (H’max = 3.53) while the Simpson’s Index (D) was 0.10 and Species Evenness (E) was 0.85. Murraya paniculata (Rutaceae) was the most important species with an IV i (Important Value Index) of 24.7%, while Dipterocarpaceae was the dominant family for the study area with an IV i of 45.36%. The total aboveground biomass of all trees with a DBH of 5 cm and above in a 0.49 ha plot in Pulau Banding was estimated at 66.2 t/ha. Hence this study is providing preliminary data on tree species composition at Pulau Banding, Perak for conserving the remaining valued timber trees that are still in the regeneration phase

The prevalence of pulmonary hypertension after successful tuberculosis treatment in a community sample of adult patients

There are an estimated 155 million survivors of tuberculosis (TB). Clinical experience suggests that post-pulmonary tuberculosis lung disease (PTLD) is an important cause of group 3 pulmonary hypertension (PH). However, TB is not listed as a cause of PH in most guidelines. A cross-sectional, community-based study was conducted in non-health care seeking adults who had successfully completed TB treatment. Subjects underwent questionnaires, spirometry, a six-minute walk distance test (6MWD) and transthoracic echocardiography (TTE). Screen probable PH was defined on TTE as an estimated pulmonary artery peak systolic pressure (PASP) of ≥40mmHg. One hundred adults (71 males) were enrolled, with a mean age of 42 years (SD 13.8 years) and a median of one TB episode (IQR: 1 -2). Co-morbidities included hypertension (21%), diabetes (16%), HIV (10%) and asthma/COPD (5%). Only 25% had no residual symptoms after TB. Probable PH was found in 9%, while 7% had borderline raised PASP values (PASP 35-40mmHg). An association was found between PH and the number of previous TB episodes, with each additional episode of TB increasing the odds of PH-post-TB 2.13-fold (CI 1.17-3.88; P=0.013). All of those found to have PH were smokers or ex-smokers yielding an unadjusted odds ratio for PH-post-TB of 3.67 (95%CI 0.77-17.46). There was no statistical difference in spirometry or 6MWD, between those with and without PH. Neither symptoms nor co-morbidities demonstrated significant association with PH. PH after TB was a common finding in this community-based population. Further research is needed to confirm and determine the significance of these findings

Association of depression with newly diagnosed type 2 diabetes among adults aged between 25 to 60 years in Karachi, Pakistan

<p>Abstract</p> <p>Background</p> <p>The combination of depression with type 2 diabetes is a public health problem. If diabetes is managed in its initial phase, the morbidity and mortality due to this combination may be prevented at an early stage. Therefore, we aimed to determine the association of depression with newly diagnosed type 2 diabetes among adults aged between 25 to 60 years in Karachi, Pakistan.</p> <p>Methods</p> <p>From July 2006 to September 2007, a matched case control study (n = 592) was conducted in Civil Hospital, Karachi. Incident cases of type 2 diabetes (n = 296) diagnosed within one month were recruited from diabetic Out Patient Department (OPD) of Civil Hospital, Karachi. They were matched on age and sex with controls (n = 296), who were attendants sitting in the medical out patient department of the same hospital, recruited on the basis of absence of classical symptoms of polyuria and polydispia along with random blood glucose level of <200 mg/dl measured by a glucometer. Depression was identified by the Siddiqui Shah Depression Scale. Conditional logistic regression was applied to examine the association of depression and other independent variables with newly diagnosed type 2 diabetes at 95% C.I. and P < 0.05.</p> <p>Results</p> <p>The study comprised of 592 subjects with 432(73%) males and 160(27%) females. Depression was significantly associated with newly diagnosed type 2 diabetes having mild level (mOR: 3.86; 95%CI: 2.22,6.71) and moderate to severe level (mOR: 3.41; 95%CI: 2.07,5.61). History of (h/o) gestational diabetes (mOR: 2.83; 95%CI: 1.05,7.64), family h/o diabetes (mOR: 1.59; 95%CI: 1.04,2.43), nuclear family (mOR: 1.75; 95%CI: 1.14,2.69), BMI (mOR: 1.62; 95%CI: 1.01,2.60 for obese and mOR: 2.12; 95%CI: 1.19,3.79 for overweight vs healthy to underweight) were also significantly associated with outcome, adjusting for age, sex, marital status, h/o smoking and h/o high BP.</p> <p>Conclusions</p> <p>Diabetics should be screened simultaneously for depression and concomitant preventive strategies for gestational diabetes, nuclear family and high BMI should also be used to prevent mortality/morbidity among patients between 25 to 60 years of age.</p

Distribution and Diversity of Family Rubiaceae in Pulau Banding, Gerik, Perak

The distribution and diversity of the Rubiaceae species were investigated along one trail at Pulau Banding, Gerik, Perak. This study aims to identify and estimate the diversity of the Rubiaceae species. The diversity and distribution of the Rubiaceae species were calculated using Shannon-Wiener’s Diversity Index, Simpson's Diversity Index and relative abundance index. A total of 139 individuals from nine species were recorded from Pulau Banding, Perak which are Mitracarpus hirtus, Gardenia carinata, Aidia densiflora, Hypobathrum hirtum, Coffea arabica, Psychotria marginata, Lasianthus constrictus, Porterandia anisophyllea and Ixora finlaysoniana. The values of the Shannon-Wiener’s Diversity Index (H′) and Simpson's Diversity Index (D) are 1.55 and 3.27, respectively. This indicates that M. hirtus is the most dominant species, encompassing 49% of all recorded Rubiaceae. Since it has many benefits such as food, timber, medicine, and the diversity of conservation value, this is vital as baseline data for researchers to propose solutions to the stakeholders and conservation sustainability of the Rubiaceae family as plant resources in Pulau Banding, Perak

A real time method for distinguishing COVID-19 utilizing 2D-CNN and transfer learning

Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models

Plant Disease Classifier: Detection of Dual-Crop Diseases using Lightweight 2D CNN Architecture

Tomatoes are the most widely grown crop in the world, and they may be found in a variety of forms in every kitchen, regardless of cuisine. It is, after potato and sweet potato, the most widely farmed crop on the planet. Cotton is another essential cash crop because most farmers grow it in huge quantities. However, many diseases reduce the quality and quantity of tomato and cotton crops, resulting in a significant loss in production and productivity. It is critical to detect these disorders at an early stage of diagnosis. The purpose of this work is to categorize 14 classes for both cotton and tomato crops, with 12 diseased classes and two healthy classes using a deep learning-based lightweight 2D CNN architecture and to implement the model in an android application named “Plant Disease Classifier” for smartphone-assisted plant disease diagnosis system, the results of the experiments reveal that the proposed model outperforms the pre-trained models VGG16, VGG19 and InceptionV3 despite having fewer parameters. With slightly larger parameters than MobileNet and MobileNetV2,proposed model also attains considerably larger accuracy than these models. The classification accuracy varies between 57% and 92% for these models, and the proposed model’s average accuracy is 97.36%. Also, the precision, recall, F1-score of the proposed model is 97 % and Area Under Curve (AUC) score of the model is 99.9% which is an indicator of the very good performance of the model. Class activation maps were shown using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to visually explain the disease detected by the proposed model, and a heatmap was produced to indicate the responsible region for classification. The app works very impressively and classified the correct disease in a shorter period of time of about 4.84 ms due to the lightweight nature of the model

Recommendations for the transition of patients with ADHD from child to adult healthcare services:a consensus statement from the UK adult ADHD network

The aim of this consensus statement was to discuss transition of patients with ADHD from child to adult healthcare services, and formulate recommendations to facilitate successful transition. An expert workshop was convened in June 2012 by the UK Adult ADHD Network (UKAAN), attended by a multidisciplinary team of mental health professionals, allied professionals and patients. It was concluded that transitions must be planned through joint meetings involving referring/receiving services, patients and their families. Negotiation may be required to balance parental desire for continued involvement in their child’s care, and the child’s growing autonomy. Clear transition protocols can maintain standards of care, detailing relevant timeframes, responsibilities of agencies and preparing contingencies. Transition should be viewed as a process not an event, and should normally occur by the age of 18, however flexibility is required to accommodate individual needs. Transition is often poorly experienced, and adherence to clear recommendations is necessary to ensure effective transition and prevent drop-out from services