Economic Feasibility of Stand-Alone Wind Energy Hybrid with Bioenergy from Anaerobic Digestion for Electrification of Remote Area of Pakistan

Hybrid Renewable Energy systems (HRES) are gaining importance throughout the world because of the finite sources of oil and gas reservoirs. These have the great ability in the production of electrical energy and cleaning the environment. It is difficult to get grid electricity in the remote areas where no infrastructure exists. The utilization of renewable sources is the ultimate solution for the generation of electricity. In this paper, the economic modeling of Hybrid system consisting of Wind/biomass is explored for the remote area ‘Jangiah’ of Balochistan province, Pakistan. Anaerobic Digestion of biomass is used to get biogas. This source is used to complement the uncertainties in the wind production. Homer is used to simulate the hybrid model. Economic analysis is performed to get the net present value (NPV) and cost of energy. It is observed that wind/biomass alone is capable to meet the demand of community which consumes 60 kW peak daily along with the storage backup. This system is the most economical with COE equal to 0.118 US$/kWh following the hybrid biomass/wind/diesel system with COE 0.202 US$/kWh. The sensitivity analysis is carried out and shows that the proposed system is sensitive to the prices of fossil fuel and project lifespan. The net present value increases as the lifetime of the project increases from 15 years to 30 years. It can also be concluded that if the price of the diesel drops below 0.8 US$/liter, the traditional system using fossil fuels will become the most suitable system for the generation of electricity in remote areas

The modelling of wind farm layout optimization for the reduction of wake losses

The objective of the present research is to find out the optimized dimensions of the wind farm area and turbines layout to reduce the overall cost per unit power. The velocity deficits caused by the wakes of each turbine were calculated by using Jensen's wake model. The optimal positions of wind turbine placement are evaluated by using genetic algorithm, while sustaining the obligatory space between adjacent turbines for operation safety. The research on the wind farm area dimensions and fully utilization of upstream wind velocity is currently lacking in literature. The logical application of area dimensions and genetic algorithm improved the overall efficiency of the wind farm. It is concluded that proposed duel level optimization method outperforms the existing ones. The total wind farm area (2km × 2km) was divided into 100 identical cells, with each cell having dimensions 200m × 200m . The performance of the proposed method is compared with the results from previous studies. The simulation results showed that power output of the wind farm was increased by using same area with different dimensions. It was observed that by using the same number of wind turbines, the total efficiency of wind farm was increased by 7 %

Acquisition of abstract words for cognitive robots

Abstract word learning and comprehension is a very crucial and important issue because of its application and problematic nature. This problem does not just belong to the cognitive robotics field, as it also has significance in neuroscience and cognitive science. There are many issues like symbol grounding problem and sensory motor processing within grounded cognition framework and conceptual knowledge representation methods that have to be addressed and solved for the acquisition of abstract words in cognitive robots. This paper explains these concepts and matters, and also elucidates how these are linked to this problem. In this paper, first symbol grounding problem is discussed, and after that an overview of grounded cognition be given along with detail of methods/ideas that suggest how abstract word representation could use sensory motor system. Finally, the computation methods used for the representation of conceptual knowledge are discussed. Two cognitive robotics models based on Neural network and Semantic network that ground abstract words are presented and compared via simulation experiment to find out the pros and cons of computation methods for this problem. The aim of this paper is to explore the building blocks of cognitive robotics model at theoretical and experimental level, for grounding of abstract words

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform radiometric calibration. A kernel-driven semiempirical BRDF correction method based on the Ross Thick-Li Sparse (RTLS) model was used to normalize the data for both changes in solar elevation and sensor view angle differences attributed to pixel location within the field of view. Following rigorous radiometric and BRDF corrections, novel rule-based methods were developed to conduct automatic soil removal; and a newly proposed approach was used for image quality assessment; additionally, shadow masking and plot-level feature extraction were carried out. Our results show that the automated calibration, processing, storage, and analysis pipeline developed in this work can effectively handle massive amounts of hyperspectral data and address the urgent challenges related to the production of sustainable bioenergy and food crops, targeting methods to accelerate plant breeding for improving yield and biomass traits

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

The global retinoblastoma outcome study : a prospective, cluster-based analysis of 4064 patients from 149 countries

DATA SHARING : The study data will become available online once all analyses are complete.BACKGROUND : Retinoblastoma is the most common intraocular cancer worldwide. There is some evidence to suggest that major differences exist in treatment outcomes for children with retinoblastoma from different regions, but these differences have not been assessed on a global scale. We aimed to report 3-year outcomes for children with retinoblastoma globally and to investigate factors associated with survival. METHODS : We did a prospective cluster-based analysis of treatment-naive patients with retinoblastoma who were diagnosed between Jan 1, 2017, and Dec 31, 2017, then treated and followed up for 3 years. Patients were recruited from 260 specialised treatment centres worldwide. Data were obtained from participating centres on primary and additional treatments, duration of follow-up, metastasis, eye globe salvage, and survival outcome. We analysed time to death and time to enucleation with Cox regression models. FINDINGS : The cohort included 4064 children from 149 countries. The median age at diagnosis was 23·2 months (IQR 11·0–36·5). Extraocular tumour spread (cT4 of the cTNMH classification) at diagnosis was reported in five (0·8%) of 636 children from high-income countries, 55 (5·4%) of 1027 children from upper-middle-income countries, 342 (19·7%) of 1738 children from lower-middle-income countries, and 196 (42·9%) of 457 children from low-income countries. Enucleation surgery was available for all children and intravenous chemotherapy was available for 4014 (98·8%) of 4064 children. The 3-year survival rate was 99·5% (95% CI 98·8–100·0) for children from high-income countries, 91·2% (89·5–93·0) for children from upper-middle-income countries, 80·3% (78·3–82·3) for children from lower-middle-income countries, and 57·3% (52·1-63·0) for children from low-income countries. On analysis, independent factors for worse survival were residence in low-income countries compared to high-income countries (hazard ratio 16·67; 95% CI 4·76–50·00), cT4 advanced tumour compared to cT1 (8·98; 4·44–18·18), and older age at diagnosis in children up to 3 years (1·38 per year; 1·23–1·56). For children aged 3–7 years, the mortality risk decreased slightly (p=0·0104 for the change in slope). INTERPRETATION : This study, estimated to include approximately half of all new retinoblastoma cases worldwide in 2017, shows profound inequity in survival of children depending on the national income level of their country of residence. In high-income countries, death from retinoblastoma is rare, whereas in low-income countries estimated 3-year survival is just over 50%. Although essential treatments are available in nearly all countries, early diagnosis and treatment in low-income countries are key to improving survival outcomes.The Queen Elizabeth Diamond Jubilee Trust and the Wellcome Trust.https://www.thelancet.com/journals/langlo/homeam2023Paediatrics and Child Healt

Raw genotyped total called structural variant (SV)

Genomic structural mutations especially deletion are an important source of variation in many species and can play key roles in phenotypic diversification and evolution. Previous work in many plant species, including some crops, has identified multiple instances of structural variations (SVs) occurring in or near genes related to stress response and disease resistance, suggesting a possible role for SVs in local adaptation. Sorghum (Sorghum bicolor (L.) Moench) is one of the most widely grown cereal crops in the world, and over the course of its history it has been adapted to an array of different climates as well as bred for multiple purposes, resulting in a striking phenotypic diversity within the existing germplasm. In this study, we identified genome-wide deletions in the Biomass Association Panel (BAP), a collection of 347 diverse sorghum genotypes collected from multiple countries and continents. Using Illumina-based, short-read whole genome resequencing data from every genotype, we found a total of 22,359 deletions after filtering. The size of deletions ranged from 51 to 89,716 bp with a median size of 956 bp. The global site frequency spectrum of the deletions fit a model of neutral evolution, suggesting that the majority of deletions were not under any types of selection. Clustering results based on SNPs separated the deletions of the genotypes into eight clusters which largely corresponded with geographic origins. Even though most deletions appeared to be neutral, a handful of cluster-specific deletions were found in genes related to biotic (plant defense and bacterial resistance) and abiotic stress (drought and temperature) responses, supporting the possibility that at least some deletions contribute to local adaptation in sorghum.,The VCF file was the product of LUMPY pipeline integrating with genotyping and CNV detecting step to generate a merge SV,The pipeline for calling SVs in the BAP was adopted from the svtools pipeline (Larson et al. 2019). Briefly, de-multiplexed sequences reads in FASTQ format for each individual were aligned to version 3.0.1 of the BTx623 reference genome (as downloaded from Phytozome v12.1.6: https://phytozome.jgi.doe.gov/pz/portal.html) using the program speedseq (Chiang et al. 2015). Structural variations were identified in each individual aligned BAM file using LUMPY (Layer et al. 2014) with default parameters. The resulting 347 structural variation files were then sorted and merged with svtools (Larson et al. 2019). A full tutorial of this process has been delineated by the authors of svtools, and can be found at https://github.com/hall-lab/svtools/blob/master/Tutorial.md. The merged vcf was then used to calculate a genotype for each individual at the variant positions resulting in a fully genotyped vcf file of each individual. CNVnator(Abyzov et al. 2011) was run within svtools in order to annotate the called variants based on copy number. Subsequently, svtools merged the genotyped and CNV-annotated vcf files to remove any redundant variants that were called by both programs