Recognition of unseen object class by a human being is always based on the relationship between seen and unseen classes, given that human has some background knowledge of the unseen object class. ...Zero-shot learning is a learning paradigm that tries to develop a recognition model to recognize mutually exclusive training and testing classes. A zero-shot learning model trained on labeled data can also recognize unseen classes when sufficient information about the relationship between seen and unseen classes is given. Semantic space contains semantic information about seen and unseen classes. It is an important part of zero-shot learning and acts as a bridge between seen and unseen classes. In this article, we provide a compact and comprehensive survey on zero-shot learning. First, we explain the different ways to construct semantic space along with its pros and cons. Next, we present a categorization of zero-shot learning methods from the semantic space construction point of view. Furthermore, this paper also presents performance evaluation measures with a relevant and influential zero-shot learning database.
Congestive heart failure (CHF) is one of the primary sources of mortality and morbidity among the global population. Over 26 million individuals globally are affected by heart disease, and its ...prevalence is rising by 2% yearly. With advances in healthcare technologies, if we predict CHF in the early stages, one of the leading global mortality factors can be reduced. Therefore, the main objective of this study is to use machine learning applications to enhance the diagnosis of CHF and to reduce the cost of diagnosis by employing minimum features to forecast the possibility of a CHF occurring. We employ a deep neural network (DNN) classifier for CHF classification and compare the performance of DNN with various machine learning classifiers. In this research, we use a very challenging dataset, called the Cardiovascular Health Study (CHS) dataset, and a unique pre-processing technique by integrating C4.5 and K-nearest neighbor (KNN). While the C4.5 technique is used to find significant features and remove the outlier data from the dataset, the KNN algorithm is employed for missing data imputation. For classification, we compare six state-of-the-art machine learning (ML) algorithms (KNN, logistic regression (LR), naive Bayes (NB), random forest (RF), support vector machine (SVM), and decision tree (DT)) with DNN. To evaluate the performance, we use seven statistical measurements (i.e., accuracy, specificity, sensitivity, F1-score, precision, Matthew's correlation coefficient, and false positive rate). Overall, our results reflect our proposed integrated approach, which outperformed other machine learning algorithms in terms of CHF prediction, reducing patient expenses by reducing the number of medical tests. The proposed model obtained 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision.
Drought is a leading abiotic constraints for onion production globally. Breeding by using unique genetic resources for drought tolerance is a vital mitigation strategy. With a total of 100 onion ...genotypes were screened for drought tolerance using multivariate analysis. The experiment was conducted in a controlled rainout shelter for 2 years 2017-2018 and 2018-2019 in a randomized block design with three replications and two treatments (control and drought stress). The plant was exposed to drought stress during the bulb development stage (i.e., 50-75 days after transplanting). The genotypes were screened on the basis of the drought tolerance efficiency (DTE), percent bulb yield reduction, and results of multivariate analysis viz. hierarchical cluster analysis by Ward's method, discriminate analysis and principal component analysis. The analysis of variance indicated significant differences among the tested genotypes and treatments for all the parameters studied, viz. phenotypic, physiological, biochemical, and yield attributes. Bulb yield was strongly positively correlated with membrane stability index (MSI), relative water content (RWC), total chlorophyll content, antioxidant enzyme activity, and leaf area under drought stress. The genotypes were categorized into five groups namely, highly tolerant, tolerant, intermediate, sensitive, and highly sensitive based on genetic distance. Under drought conditions, clusters II and IV contained highly tolerant and highly sensitive genotypes, respectively. Tolerant genotypes, viz. Acc. 1656, Acc. 1658, W-009, and W-085, had higher DTE (>90%), fewer yield losses (<20%), and performed superiorly for different traits under drought stress. Acc. 1627 and Acc. 1639 were found to be highly drought-sensitive genotypes, with more than 70% yield loss. In biplot, the tolerant genotypes (Acc. 1656, Acc. 1658, W-085, W-009, W-397, W-396, W-414, and W-448) were positively associated with bulb yield, DTE, RWC, MSI, leaf area, and antioxidant enzyme activity under drought stress. The study thus identified tolerant genotypes with favorable adaptive traits that may be useful in onion breeding program for drought tolerance.
Patients treated with cranial radiation therapy (RT) are at risk for sensorineural hearing loss (SNHL). Although SNHL is often characterized as a delayed consequence of anticancer therapy, ...longitudinal reports of SNHL in childhood cancer survivors treated with contemporary RT are limited. We report the incidence, onset, severity, and long-term trajectory of SNHL among children receiving RT. Potential risk factors for SNHL were also identified.
Serial audiologic testing was conducted on 235 pediatric patients who were treated with conformal or intensity-modulated RT as part of an institutional phase II trial for localized primary brain tumors, including craniopharyngioma, ependymoma, and juvenile pilocytic astrocytoma. All but one patient had measurable cochlear radiation dose (CRD) greater than 0 Gy. The median follow-up from RT initiation to latest audiogram was 9 years with a median of 11 post-RT audiograms per patient. Audiograms were classified by the Chang Ototoxicity Grading Scale. Progression was defined by an increase in Chang grade from SNHL onset to the most recent evaluation.
At last evaluation, SNHL was prevalent in 14% of patients: 2.1% had mild and 11.9% had significant SNHL requiring hearing aids. Median time from RT to SNHL onset was 3.6 years (range, 0.4 to 13.2 years). Among 29 patients with follow-up evaluations after SNHL onset, 65.5% experienced continued decline in hearing sensitivity in either ear and 34.5% had no change. Younger age at RT initiation (hazard ratio HR, 2.32; 95% CI, 1.21 to 4.46), higher CRD (HR, 1.07; 95% CI, 1.03 to 1.11), and cerebrospinal fluid shunting (HR, 2.02; 95% CI, 1.07 to 3.78) were associated with SNHL.
SNHL is a late effect of RT that likely worsens over time. Long-term audiologic follow-up for a minimum of 10 years post-RT is recommended.
Central Public Works Department has constructed a net zero energy building for the Ministry of Environment, Forest and Climate Change, first of its kind in India, generating onsite solar power as per ...its total requirement. To make net zero energy building, the demand was optimised by using energy-efficient materials and equipments and then providing rooftop solar power panels on courtyard, rooftop and extended cantilever mild steel structures. To make the building energy-efficient, various green building features were adopted in this centrally air-conditioned building including geothermal heat exchange system. The building has ground plus seven-storey reinforced concrete framed structure with three basements for car parking and installation of essential services. The building was planned and executed as five star green building conforming to green rating for Integrated Habitat Assessment (GRIHA) council of India. Green building features included architectural, civil, electrical and mechanical, landscape and horticulture criteria. The paper discusses the measures adopted to get five star green rating and details of onsite net zero energy building.
Introduction
Waterlogging is a major stress that severely affects onion cultivation worldwide, and developing stress-tolerant varieties could be a valuable measure for overcoming its adverse effects. ...Gathering information regarding the molecular mechanisms and gene expression patterns of waterlogging-tolerant and sensitive genotypes is an effective method for improving stress tolerance in onions. To date, the waterlogging tolerance-governing molecular mechanism in onions is unknown.
Methods
This study identified the differentially expressed genes (DEGs) through transcriptome analysis in leaf tissue of two onion genotypes (Acc. 1666; tolerant and W-344; sensitive) presenting contrasting responses to waterlogging stress.
Results
Differential gene expression analysis revealed that in Acc. 1666, 1629 and 3271 genes were upregulated and downregulated, respectively. In W-344, 2134 and 1909 genes were upregulated and downregulated, respectively, under waterlogging stress. The proteins coded by these DEGs regulate several key biological processes to overcome waterlogging stress such as phytohormone production, antioxidant enzymes, programmed cell death, and energy production. The clusters of orthologous group pathway analysis revealed that DEGs contributed to the post-translational modification, energy production, and carbohydrate metabolism-related pathways under waterlogging stress. The enzyme assay demonstrated higher activity of antioxidant enzymes in Acc. 1666 than in W-344. The differential expression of waterlogging tolerance related genes, such as those related to antioxidant enzymes, phytohormone biosynthesis, carbohydrate metabolism, and transcriptional factors, suggested that significant fine reprogramming of gene expression occurs in response to waterlogging stress in onion. A few genes such as ADH, PDC, PEP carboxylase, WRKY22, and Respiratory burst oxidase D were exclusively upregulated in Acc. 1666.
Discussion
The molecular information about DEGs identified in the present study would be valuable for improving stress tolerance and for developing waterlogging tolerant onion varieties.
Abstract
Due to the complexity of high temperature and cutting tool wear, most machined components are still facing problems in terms of harder functional fillers that reinforce aluminium matrix ...composites. Conversely, abrasive water jet machining (AWJM) incredibly useful for the cutting of anisotropic and non-homogeneous metal matrix composites. In this research article, silicon carbide (SiC) particulates were utilized as reinforcement in the AA6026 matrix material (AA6026/SiC) and machined using AWJM under different process parameters namely SiC loading, traverse speed and stand-off distance. Two different compositions of SiC (4, and 8 wt%) were considered to fabricate AA6026 composites using the stir casting. In addition, outputs have been examined, e.g., surface roughness, material removal rate, and kerf angle. An optical microscope, scanning electron microscope, Brinell hardness tester and universal testing machine have been used to characterize the matrix material AA6026 and its composites. Microstructural analysis revealed that the inclusion of SiC particulates in AA6026 affects the very fine grain size of the composite. Furthermore, the 8 wt% composite exhibits the evolution of the Al-Si eutectic phase during solidification. Processing of these composites was performed using the L
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orthogonal geometry, successfully improving the parameters of the abrasive water jet process. The output response shows that reducing the SiC load improves the surface roughness under the key parameters of traverse speed and stand-off distance. However, increasing the SiC loading increases the material removal rate and kerf angle under the key parameters, namely traverse speed, and stand-off distance.
Zero-shot learning (ZSL) is a learning paradigm that tries to develop a recognition model to recognize mutually exclusive training and testing classes. To recognize mutually exclusive classes, some ...kind of correlation between training and testing classes are required. This paper proposed an inductive solution of the ZSL problem in two stages: (1) a supervised multiclass classifier is trained on the training set and further asked to classify the testing images to its nearest training class. (2) A mapping function, which maps training class to testing class is used to obtain the final class for each testing image. The correlation between seen classes and unseen classes are obtained using the mapping function. We have proposed a graphical mapping function based on a fully connected bipartite graph for mapping between training and testing classes. Each edge of the bipartite graph is assigned a weight calculated by exploiting the semantic space. The proposed model is evaluated over the three well-known ZSL datasets: AWA2, CUB and aPY and obtained 66.59%, 48.95%, and 32.91% mean accuracy respectively. The obtained f1 score of the proposed method is 0.675, 0.565 and 0.492 on AWA2, CUB and aPY dataset respectively.
Target tracking in wireless sensor networks (WSNs) is one of the highly researched applications. Work to be done in this area typically requires systematized groups of sensor nodes which monitors the ...target and delivers dimensions of a target’s position change or precise distance dimensions from the nodes to the target, and predicting those change in movement of the target too. These deliverables are sent to the centralized entity for the further processing. In the case of sensor faults and impulsive environments, these are, hard to achieve precisely in real practice. WSN having the constraints of limited sensing range, it is of immense significance to design mechanism which provides coordination amongst nodes for unfailing tracking and with a high probability too, at least the target can always be detected and tracked, while the entirety network energy expenditure can be reduced for longer network lifetime. Due to unpredicted nature of the target, design of target tracking mechanism demands prediction algorithm to be implemented for the prediction of target trajectory. Design also demands network to be optimized in terms of energy expenditure to cope with early draining of node’s battery which are very small in size and with low capacity, which in turns helps to increase the life time of the network. To overcome the said issues, proposed work uses target state dynamics to predict target trajectory and implementation of Particle Swarm Optimization for the network optimization to save on overall network energy expense and hence to increase network life time.
Corrosion behavior of cross (cold- and cryo-rolled) Mg–10Li–1.5Ca in 0.6 M NaCl was investigated. Cross-rolling was carried out to reduce the thickness to 50% at room temperature. Scanning electron ...microscopy study was conducted to understand structural transformation. The potentiodynamic polarization test was performed in 0.6 M NaCl solution to investigate the corrosion mechanism. The results show that cryo-rolled Mg alloy exhibits superior corrosion resistance than the cold-rolled and as-received alloy due to the extreme grain refinement of α-Mg phase and fine homogeneous distribution of intermetallic (Mg
2
Ca) compounds along the phase boundaries. The different corrosion products such as magnesium hydroxide and lithium hydroxide are formed over the surface. The uniform dense corrosion product Mg(OH)
2
passivates corrosion of Mg–Li–Ca alloy, thereby improving the corrosion resistance of the alloy.
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