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Load shedding is an operating condition in which the electrical grid is temporarily disconnected from the load. The objective is to minimize the gap between available generation capacity and load demand while maintaining an equitable supply for all consumers. Load shedding is a prominent problem for many developing countries. To address this issue, this paper explores the potential of a hybrid energy system (HES) to provide uninterrupted power supply at the distribution feeder despite load shedding from electrical grid. The proposed HES in this work combines photovoltaic (PV) array, battery storage system (BSS) and diesel generator (DG). The HES is equipped with energy management scheme (EMS) that ensures continuous power supply, improves energy efficiency, and minimizes the electricity cost. To accomplish these tasks, the EMS operates the system in one of three modes: grid mode, renewable energy source mode and the diesel generator mode. Besides, the proposed methodology allows injecting surplus PV energy into the grid, thus maximizing PV utilization and improving power system's reliability. The results of this study will assist policymakers to determine the prospect of renewable based hybrid system to supply sustainable power and eliminate the energy problems in the power deficit countries. [ABSTRACT FROM AUTHOR]

We adopted a commonly used epidemiologic model known as Susceptible-Infectious-Recovered (SIR) by adding a compartment vaccination to locate the COVID-19 hotspot in Bangladesh. Using data from the Directorate General of Health Services (DGHS), we utilized the Susceptible-Vaccinated-Infectious-Recovered (SVIR) model to assess vaccine efficacy. Vaccination against COVID-19 has begun in industrialized nations and will be completed by early December 2020. Before 2023, mass vaccination for impoverished or low-income nations would be extremely difficult. Bangladesh, a lower-middle-income country, started a statewide COVID-19 immunization effort in early February 2021. This study aimed to recognize the effectiveness of vaccination on the infected population in Bangladesh. Additionally, the study aimed to create public awareness of the importance of vaccination during a pandemic. [ABSTRACT FROM AUTHOR]

The renewed interest for power generation using renewables due to global trends provides an opportunity to rethink the approach to address the old yet existing load shedding problem. In the literature, limited studies are available that address the load shedding problem using a hybrid renewable energy system. This paper aims to fill this gap by proposing a techno-economic optimisation of a hybrid renewable energy system to mitigate the effect of load shedding at the distribution level. The proposed system in this work is configured using a photovoltaic array, wind turbines, an energy storage unit (of batteries), and a diesel generator system. The proposed system is equipped with a rule-based energy management scheme to ensure efficient utilisation and scheduling of the sources. The sizes of the photovoltaic array, wind turbine unit, and the batteries are optimised via the grasshopper optimisation algorithm based on the multi-criterion decision that includes loss of power supply probability, levelised cost of electricity, and payback period. The results for the actual case study in Quetta, Pakistan, show that the optimum sizes of the photovoltaic array, wind turbines, and the batteries are 35.75 kW, 10 kW, and 28.8 kWh, respectively. The sizes are based on the minimum values of levelised cost of electricity (6.64 cents/kWh), loss of power supply probability (0.0092), and payback period (7.4 years). These results are compared with conventional methods (generators, uninterruptible power supply, and a combined system of generator and uninterruptible power supply system) commonly used to deal with the load shedding problem. The results show that the renewable based hybrid system is a reliable and cost-effective option to address grid intermittency problem. [ABSTRACT FROM AUTHOR]

Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application of hybrid energy system (HES) that comprises of photovoltaic (PV) array, battery storage system (BSS) and stand-by diesel generator (DGen) to mitigate the problem of load shedding. The main work involves techno-economic modelling to optimize the size of HES such that the levelized cost of electricity (LCOE) is minimized. The particle swarm optimization (PSO) algorithm is used to determine the optimum size of the components (PV, BSS). Simulations are performed in MATLAB using real dataset of irradiance, temperature and load shedding schedule of the small residential community situated in the city of Quetta, Pakistan. The LCOE for the HES system under study is 8.32 cents/kWh—which is lower than the conventional load shedding solution, namely the uninterruptable power supply (UPS) (13.06 cents/kWh) and diesel and generator system (29.19 cents/kWh). In fact, the LCOE of the HRES is lower than the grid electricity price of Pakistan (9.3 cents/kWh). Besides that, the HES alleviates the grid burden by 47.9% and 13.1% compared to the solution using the UPS and generator, respectively. The outcomes of the study suggests that HES is able to improve reliability and availability of electric power for regions that is affected by the load shedding issue. [ABSTRACT FROM AUTHOR]

This study investigates the potential application of Stateflow (SF) to design an energy management strategy (EMS) for a renewable-based hybrid energy system (HES). The SF is an extended finite state machine; it provides a platform to design, model, and execute complex event-driven systems using an interactive graphical environment. The HES comprises photovoltaics (PV), energy storage units (ESU) and a diesel generator (Gen), integrated with the power grid that experiences a regular load shedding condition (scheduled power outages). The EMS optimizes the energy production and utilization during both modes of HES operation, i.e., grid-connected mode and the islanded mode. For islanded operation mode, a resilient power delivery is ensured when the system is subjected to intermittent renewable supply and grid vulnerability. The contributions of this paper are twofold: first is to propose an integrated framework of HES to address the problem of load shedding, and second is to design and implement a resilient EMS in the SF environment. The validation of the proposed EMS demonstrates its feasibility to serve the load for various operating scenarios. The latter include operations under seasonal variation, abnormal weather conditions, and different load shedding patterns. The simulation results reveal that the proposed EMS not only ensures uninterrupted power supply during load shedding but also reduces grid burden by maximizing the use of PV energy. In addition, the SF-based adopted methodology is envisaged to be a useful alternative to the popular design method using the conventional software tools, particularly for event-driven systems. [ABSTRACT FROM AUTHOR]

Coding polymorphisms in several DNA repair genes have been reported to affect the DNA repair capacity and are associated with genetic susceptibility to many human cancers, including gastric cancer. An understanding of these DNA repair gene polymorphisms might assess not only the risk of humans exposed to environmental carcinogens but also their responses to different therapeutical approaches, which target the DNA repair pathway. In the present study, polymorphic variants of two DNA repair genes, XRCC1 Arg399Gln and XPD Lys751Gln, were chosen to be studied in association with gastric cancer susceptibility in the Kashmiri population. A total of 180 confirmed cases of gastric cancer (GC) and 200 hospital-based controls from Government Shri Maharaja Hari Singh Hospital, Srinagar, were included in the study. The genotyping for XRCC1 and XPD genes was carried out by polymerase chain reaction-restriction fragment length polymorphism. We found that tobacco smoking is strongly associated with GC risk (OR = 25.65; 95% CI: 5.49–119.7). However, we did not find any association of polymorphism of XRCC1 Arg399Gln (OR = 1.56; 95% CI: 0.32–7.82) and XPD Lys751Gln (OR = 0.46; CI: 0.10–2.19) with GC risk in the study population. The combination of genotypes and gender stratification of XRCC1 and XPD genotypic frequency did not change the results. Consumption of large volumes of salt tea was also not associated with gastric cancer risk. Polymorphic variants of XRCC1 Arg399Gln and XPD Lys751Gln are not associated with the risk of gastric cancer in the Kashmiri population. However, replicative studies with larger sample size are needed to substantiate the findings. [ABSTRACT FROM AUTHOR]

Breast carcinogenesis is a multistep process, involving both genetic and epigenetic modification process of genes, involved in diverse pathways ranging from DNA repair to metabolic processes. This study was undertaken to assess the role of promoter methylation of GSTP1 gene, a member of glutathione-S-transferase family of enzymes, in relation to its expression, polymorphism, and clinicopathological parameters. Tissue samples were taken from breast cancer patients and paired with their normal adjacent tissues. A total of 51 subjects were studied, in which the frequency of promoter methylation in cancerous tissue was 37.25% as against 11% in the normal tissues (p ≤ 0.001). The hypermethylated status of the gene was significantly associated with the loss of the protein expression (r = -0.449, p = 0.001, odds ratio = 7.42, 95% confidence interval = 2.05-26.92). Furthermore, when compared with the clinical parameters, the significant association was found between the promoter hypermethylation and lymph node metastasis (p ≤ 0.001), tumor stage (p = 0.039), tumor grade (p = 0.028), estrogen receptor status (p = 0.018), and progesterone receptor status (p = 0.046). Our study is the first of its kind in Kashmiri population, which indicates that GSTP1 shows aberrant methylation pattern in the breast cancer with the consequent loss in the protein expression. Furthermore, it also shows that the gene polymorphism (Ile105Val) at codon 105 is not related to the promoter methylation and two are the independent events in breast cancer development. [ABSTRACT FROM AUTHOR]

Background: Methylation at C-5 (5-mdC) of CpG base pairs, the most abundant epigenetic modification of DNA, is catalyzed by 3 essential DNA methyltransferases (Dnmt1, Dnmt3a and Dnmt3b). Aberrations in DNA methylation and Dnmts are linked to different diseases including cancer. However, their role in alcoholic liver disease (ALD) has not been elucidated. Methodology/Principal Findings: Dnmtl wild type (Dnmt1+/+) and hypomorphic (Dnmt1N/+) male mice that express reduced level of Dnmt1 were fed Lieber-DeCarli liquid diet containing ethanol for 6 weeks. Control mice were pair-fed calorie-matched alcohol-free liquid diet, and Dnmtase activity, 5-mdC content, gene expression profile and liver histopathology were evaluated. Ethanol feeding caused pronounced decrease in hepatic Dnmtase activity in Dnmt1+/+ mice due to decrease in Dnmt1 and Dnmt3b protein levels and upregulation of miR-148 and miR-152 that target both Dnmt1 and Dnmt3b. Microarray and qPCR analysis showed that the genes involved in lipid, xenobiotic and glutathione metabolism, mitochondrial function and cell proliferation were dysregulated in the wild type mice fed alcohol. Surprisingly, Dnmt1N/+ mice were less susceptible to alcoholic steatosis compared to Dnmt1+/+ mice. Expression of several key genes involved in alcohol (Aldh3b1), lipid (Ppara, Lepr, Vldlr, Agpat9) and xenobiotic (Cyp39a1) metabolism, and oxidative stress (Mr-1, Fmo3) were significantly (P<0.05) altered in Dnmt1N/+ mice relative to the wild type mice fed alcohol diet. However, CpG islands encompassing the promoter regions of Agpat9, Lepr, Mr1 and Ppara were methylation-free in both genotypes irrespective of the diet, suggesting that promoter methylation does not regulate their expression. Similarly, 5-mdC content of the liver genome, as measured by LC-MS/MS analysis, was not affected by alcohol diet in the wild type or hypomorphic mice. Conclusions/Significance: Although feeding alcohol diet reduced Dnmtase activity, the loss of one copy of Dnmt1 protected mice from alcoholic hepatosteatosis by dysregulating genes involved in lipid metabolism and oxidative stress. [ABSTRACT FROM AUTHOR]

Machine learning (ML) offers new technologies in the precision agriculture domain with its intelligent algorithms and strong computation. Oil palm is one of the rich crops that is also emerging with modern technologies to meet global sustainability standards. This article presents a comprehensive review of research dedicated to the application of ML in the oil palm agricultural industry over the last decade (2011–2020). A systematic review was structured to answer seven predefined research questions by analysing 61 papers after applying exclusion criteria. The works analysed were categorized into two main groups: (1) regression analysis used to predict fruit yield, harvest time, oil yield, and seasonal impacts and (2) classification techniques to classify trees, fruit, disease levels, canopy, and land. Based on defined research questions, investigation of the reviewed literature included yearly distribution and geographical distribution of articles, highly adopted algorithms, input data, used features, and model performance evaluation criteria. Detailed quantitative–qualitative investigations have revealed that ML is still underutilised for predictive analysis of oil palm. However, smart systems integrated with machine vision and artificial intelligence are evolving to reform oil palm agri-business. This article offers an opportunity to understand the significance of ML in the oil palm agricultural industry and provides a roadmap for future research in this domain. [ABSTRACT FROM AUTHOR]

Simple Summary: As pancreatic cancer is a disease with a high fatality rate, a better understanding of how it develops and the identification of new potential targets for its treatment are greatly needed. In this current study, we showed that the expression of amyloid precursor-like protein 2 (APLP2) in pancreatic cancer epithelial cells is higher than in precursor lesion epithelial cells, thus indicating that APLP2 increases during human pancreatic cancer development. We also generated a new mouse model that demonstrated the deletion of APLP2 expression specifically within the pancreas prolongs survival and decreases metastasis for mice with pancreatic cancer. Taken together, these findings open a new avenue toward comprehending and treating pancreatic cancer. In the United States, pancreatic cancer is a major cause of cancer-related deaths. Although substantial efforts have been made to understand pancreatic cancer biology and improve therapeutic efficacy, patients still face a bleak chance of survival. A greater understanding of pancreatic cancer development and the identification of novel treatment targets are desperately needed. Our analysis of gene expression data from patient samples showed an increase in amyloid precursor-like protein 2 (APLP2) expression within primary tumor epithelium relative to pancreatic intraepithelial neoplasia (PanIN) epithelial cells. Augmented expression of APLP2 in primary tumors compared to adjacent stroma was also observed. Genetically engineered mouse models of spontaneous pancreatic ductal adenocarcinoma were used to investigate APLP2′s role in cancer development. We found that APLP2 expression intensifies significantly during pancreatic cancer initiation and progression in the LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx-1-Cre (KPC) mouse model, as shown by immunohistochemistry analysis. In studies utilizing pancreas-specific heterozygous and homozygous knockout of APLP2 in the KPC mouse model background, we observed significantly prolonged survival and reduced metastatic progression of pancreatic cancer. These results demonstrate the importance of APLP2 in pancreatic cancer initiation and metastasis and indicate that APLP2 should be considered a potential therapeutic target for this disease. [ABSTRACT FROM AUTHOR]

The article analysis the giant tabular iceberg A68a which broke up in the ocean and postulated various methods which triggered the ocean-current shear. It explores the interactions between icebergs and other components of the climate system and which help in improving the fidelity of global climate simulations.