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The literature describes the belowground and aboveground phenology of potato to be linearly related. Bud formation is synchronous with tuber initiation and flowering with tuber filling. Many agronomic and breeding studies on potato use non‐destructive aboveground phenology to assess belowground development. No information is currently available on the influence of water deficit on the synchrony of above‐ and belowground development in potato. Five contrasting potato genotypes were subjected to four irrigation treatments on two different soil types. The irrigation treatments were as follows: fully watered, early drought, intermediate drought, and late drought. In 5‐day intervals after withholding water, detailed belowground and aboveground development was recorded. Results showed that the synchrony between aboveground and belowground development is strongly influenced by both water deficit and development stage at drought initiation. Under early drought, the aboveground development was hastened and belowground development was delayed. The opposite was found in later development stages. The earlier the drought was initiated, the longer the tuber filling phase was, while the bulking phase was shortened. We concluded that under terminal drought conditions aboveground development and belowground development need to be evaluated separately and cannot follow the standard evaluation system that uses aboveground phenology as a proxy for tuber formation belowground development rates. [ABSTRACT FROM AUTHOR]

Potato is a drought susceptible crop and even short drought spells reduce tuber yields notably. In an earlier study we developed a stress severity index (SSI) based on the development stage of a genotype at the onset of drought and the soil water deficit based on soil water tension. Here, we test the suitability of the SSI combined with remotely sensed data as a screening tool to select drought‐tolerant potato genotypes. Normalized difference vegetation index (NDVI) and the photochemical reflectance index (PRI) were obtained from reflectance measurements and thermography. Temperature data from the thermography allow using the difference between leaf and air temperature (∆T) to estimate the transpirational cooling of the leaves. Via cluster analysis including SSI, tuber yield reduction under drought, NDVI, PRI and thermography, three groups were distinguished: 1. SSI < 1000 with fast decreasing NDVI, PRI and ∆T, 2. SSI 1000–2000 with almost constant NDVI and ∆T and 3. SSI > 2000 described by small changes of NDVI, PRI and temperature deficit. For SSI < 1000, ∆T, PRI and NDVI showed to be good indicators of genotypic performance under drought. Potential strategies for drought resistance in potato detectable through remote sensing are discussed. [ABSTRACT FROM AUTHOR]

Sweetpotato, Ipomoea batatas (L.), a key food security crop, is negatively impacted by heat, drought, and salinity stress. The orange‐fleshed sweetpotato cultivar "Beauregard" was exposed to heat, salt, and drought treatments for 24 and 48 h to identify genes responding to each stress condition in leaves. Analysis revealed both common (35 up regulated, 259 down regulated genes in the three stress conditions) and unique sets of up regulated (1337 genes by drought, 516 genes by heat, and 97 genes by salt stress) and down regulated (2445 genes by drought, 678 genes by heat, and 204 genes by salt stress) differentially expressed genes (DEGs) suggesting common, yet stress‐specific transcriptional responses to these three abiotic stressors. Gene Ontology analysis of down regulated DEGs common to both heat and salt stress revealed enrichment of terms associated with "cell population proliferation" suggestive of an impact on the cell cycle by the two stress conditions. To identify shared and unique gene co‐expression networks under multiple abiotic stress conditions, weighted gene co‐expression network analysis was performed using gene expression profiles from heat, salt, and drought stress treated 'Beauregard' leaves yielding 18 co‐expression modules. One module was enriched for "response to water deprivation," "response to abscisic acid," and "nitrate transport" indicating synergetic crosstalk between nitrogen, water, and phytohormones with genes encoding osmotin, cell expansion, and cell wall modification proteins present as key hub genes in this drought‐associated module. This research lays the groundwork for exploring to a further degree, mechanisms for abiotic stress tolerance in sweetpotato. [ABSTRACT FROM AUTHOR]

Microchannel heat exchangers are heat exchangers with a tube diameter of less than 1 mm. Conventional cooling approaches such as the forced‐air cooling technique fail in high technological compact systems because of the small‐sized surfaces of chips and circuits. In comparison, microchannel heat exchangers are being extensively utilized in compact‐sized devices where a high heat‐transfer medium is required. Moreover, consumers' continued desire for compact products has prompted researchers to study microchannel heat exchangers for their ability to boost the rate of heat transfer that ensures the safety of compact designs. This study presents the evaluation of performance parameters and the manufacturing aspects of microchannel heat exchangers. This study also examines how microchannel heat exchangers are affected by several parameters, including the type of working fluid used, Brownian motion, geometry of the channel, Reynolds number, Nusselt number, Knudsen number, wall resistance of the channel, the effect of gravity, and inlet and outlet arrangement for fluid. Investigating the various geometries for the microchannel indicates that the least pressure drop occurs in square shape cross‐section channels while the highest pressure drop occurs in channels with triangular cross‐sections. Moreover, it has been observed that, with the addition of nanoparticles to the working fluid, the thermal properties of the exchangers as well as the pressure drop increases while at the same time it reduces the boundary layer thickness. In addition, the Reynolds number affects the performance irrespective of the channel geometry. When the fluid is added with nanoparticles, like, Al2O3 and copper oxide (CuO) with different volumetric fractions (φ) of 0%, 0.5%, 1%, 1.5%, and 2%, the performance of the microchannel rises with rising the Reynolds number but conversely when the fluid is used in pure form the performance decreases with the rising value of Reynolds number. In addition, it has been observed that the overall improvement is obtained at φ = 2% and Re = 100 for CuO–water nanofluid. Apart from this, the least heat transfer is recorded at φ = 0.5% and Re = 1.00 for both nanofluids. Moreover, this study concludes that the Nusselt number is independent of the Reynolds number in the regime of laminar flow. It is further evident that as the nanoparticle size reduces, the Nusselt number rises when all the remaining conditions are the same. Apart from this, investigating the inlet/outlet arrangement through finite volume method for D‐, I‐, N‐, S‐, U‐, and V‐type arrangements, it is evident that the V‐type sink has overall greater performance. In terms of gravity, it is observed that it has no effect on the performance of microchannel heat exchangers. This study further illustrates the effects of the fabrication method on the performance of the microchannel heat exchangers. [ABSTRACT FROM AUTHOR]

Venturia inaequalis, the causal fungal pathogen of apple scab, has evolved with its Malus hosts during apple domestication. This co‐evolution has resulted in a genetic structure in V. inaequalis populations from Europe and central Asia based on host species and geographic isolation. However, it is not yet clear if geographic isolation has led to population differentiation in North American isolates. We resequenced the genomes of 54 V. inaequalis isolates from North America and analysed them with publicly available genome sequences of 90 European and central Asian isolates for variant discovery and population structure. A total of 204,566 high‐quality single‐nucleotide polymorphisms (SNPs) were identified and used to assess population genetic structure and genomic diversity across 123 Venturia spp. isolates from around the world. Population genetic analysis identified four clusters based on Malus and non‐Malus hosts and Venturia species; this differentiation was supported by genetic diversity parameters including FST, Nei's π and Tajima's D. Genetic structure analysis did not reveal a distinct subpopulation of North American V. inaequalis isolates within the global isolates. Additionally, structure was not observed in V. inaequalis isolates collected on M. × domestica between North America and Europe. These observations indicate that geographical isolation has not contributed to the population differentiation between North America and Europe/central Asia. These results will enhance our understanding of the evolution of V. inaequalis, the emergence of virulent isolates and their implications for managing apple scab effectively. [ABSTRACT FROM AUTHOR]

Tourism, energy consumption, economic growth, and financial development (FD) have serious consequences on environmental quality, which have gained attention of researchers. This research study attempts to highlight the impact of these variables on carbon dioxide (CO2) emissions in the Asian top‐four tourism countries, namely, Thailand, Singapore, Malaysia and Hong Kong. To achieve these objectives, the study applied Fully Modified Ordinary Least Squares (FMOLS) econometric technique to estimate the long‐run relationship, using annual data. Moreover, the study also applied the Dumitrescu–Hurlin panel causality test to identify the direction of the causality. The findings of the study show that energy consumption and trade openness have a negative impact on CO2 emissions. Moreover, tourism and FDI affect carbon emissions positively. Moreover, the study also validated the N‐shaped environmental Kuznets curve in these countries, indicating that economic growth positively influences carbon emissions in the early stages. However, in the middle stage, economic growth positively affects CO2 emissions, while in the later stage, it affects positively. Further, a unidirectional causality has been found from trade, economic growth and tourist arrivals to FD. Furthermore, based on empirical findings, the study suggests various policy implications for the government of the top‐four Asian tourist countries. [ABSTRACT FROM AUTHOR]

In the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion‐based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre‐trained CNN model named EfficientNetB0 is fine‐tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework. [ABSTRACT FROM AUTHOR]

Summary: In this article, the problem of angular velocity trajectory tracking for converter‐fed DC motor system with mismatched disturbances/uncertainties is investigated using a continuous nonsingular terminal sliding mode control. The fourth order dynamic model of the system is quite higher than the second order of the traditional one, which makes the control design more challenging in terms of availability of various signals and tuning of the overall control parameters. Also, the actuator dynamics of the converter not only increase the system order but also generate mismatched disturbances/uncertainties, which is difficult to counteract through baseline sliding mode controller. The disturbance estimation information obtained from the finite time disturbance observer is incorporated into the design of nonsingular terminal sliding manifold. Then, a nonsingular terminal sliding mode controller is designed to achieve finite time tracking performance despite matched and mismatched disturbances/uncertainties. In addition, it retains the fine properties of nominal performance recovery as well as chattering alleviation. Finally, the superiority of the proposed method is shown both through numerical simulation and experimental results. [ABSTRACT FROM AUTHOR]

Summary: We present the first worldwide study on the apple (Malus × domestica) fruit microbiome that examines questions regarding the composition and the assembly of microbial communities on and in apple fruit. Results revealed that the composition and structure of the fungal and bacterial communities associated with apple fruit vary and are highly dependent on geographical location. The study also confirmed that the spatial variation in the fungal and bacterial composition of different fruit tissues exists at a global level. Fungal diversity varied significantly in fruit harvested in different geographical locations and suggests a potential link between location and the type and rate of postharvest diseases that develop in each country. The global core microbiome of apple fruit was represented by several beneficial microbial taxa and accounted for a large fraction of the fruit microbial community. The study provides foundational information about the apple fruit microbiome that can be utilized for the development of novel approaches for the management of fruit quality and safety, as well as for reducing losses due to the establishment and proliferation of postharvest pathogens. It also lays the groundwork for studying the complex microbial interactions that occur on apple fruit surfaces. [ABSTRACT FROM AUTHOR]

Blur detection (BD) is an important and challenging task in digital imaging and computer vision applications. Accurate segmentation of homogenous smooth and blur regions, low-contrast focal regions, missing patches, and background clutter, without having any prior information about the blur, are the fundamental challenges of BD. Previous work on BD has emphasized much effort on designing local sharpness metric maps from the images. However, the smooth/blurred regions having the same patterns as sharp regions make them problematic. This paper presents a robust novel method to extract the local metric map for blurred and nonblurred regions based on multisequential deviated patterns (MSDPs). Unlike the preceding, MSDP extracts the local sharpness metric map on the images at multiple scales using different adaptive thresholds to overcome the problems of smooth/blur regions and missing patches. By using the integral values of the image along with image masking and Otsu thresholding, highly accurate segmented regions of the images are acquired. We argue/hypothesize that the local sharpness map extraction by using direct integral information of the image is highly affected by the threshold selected for distinction between the regions, whereas MSDP feature extraction overcomes the limitations substantially by using automatic threshold computation over multiple scales of the images. Moreover, the proposed method extracts the relatively accurate sharp regions from the high-dense blur and noisy images. Experiments are conducted on two commonly used SHI and DUT datasets for blur and sharp region classifications. The results indicate the effectiveness of the proposed method in terms of sharp segmented regions. Experimental results of qualitative and quantitative comparisons of the proposed method with ten comparative methods demonstrate the superiority of our method. Moreover, the proposed method is also computationally efficient over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Degummed, refined, winterized (at −60°C), and non‐winterized soybean, sunflower, cottonseed, and safflower oil samples were used for the production of conjugated linoleic acid (CLA) by urea treatment. Fatty acids' composition and CLA isomers of winterized and non‐winterized versions of oil were analyzed on GC‐MS. Winterization improved the concentration of linoleic acids (LAs) in all vegetable oils. Safflower oil exhibited the highest concentration of LAs at 84.19% followed by sunflower oil (68.54%), cottonseed oil (63.74%), and soybean oil (59.88%), respectively. In safflower oil derived CLAs, concentrations of ∆9c,11t‐18:2, ∆10t,12c‐18:2, ∆9c,11c‐18:2, ∆10c,12t‐18:2, ∆8,9,11,10,12c‐c18:2, and ∆8,9,11,10,12t‐t18:2 were calculated 47.68%, 39.95%, 5.42%, 1.89%, 3.06%, 0.55%, and 1.45%, while in winterized safflower oil derived CLAs, these concentrations were 55.67%, 41.92%, 0.19%, 0.78%, 0.65%, 0.47%, and 0.32%, respectively, with significant difference in induction period and peroxide value. Yield of CLA from winterize soybean, sunflower cottonseed, and safflower oil was 22.58%, 24.95%, 23.16%, and 36.68%. Practical applications: Conjugated linoleic acids (CLAs) are naturally present in milk, dairy products, and beef. However, the amount of CLAs in dairy products and beef accounts limited moreover, the yield of CLA supplement produced from safflower oil is about 20%. To make the industrial processes economical, and sustainable fortification strategies, yield of CLA supplement should be increased. In the current study, winterized version of sunflower, soybean, cottonseed, and safflower oils were used to produce CLA supplement. Using winterized version, the yield of CLA supplement from safflower oil was increased to 36.68%. This work is highly suitable for the food industries to produce functional foods containing CLA. [ABSTRACT FROM AUTHOR]

This study investigates the problem of designing a finite‐time functional interval observer (FTFIO) for linear systems with uncertainties. A novel structure is proposed to design an FTFIO for uncertain systems. Based on the proposed structure, the sufficient conditions for the existence of such a finite‐time observer are established as a set of coefficient matrix equations with a constraint matrix. Meanwhile, using the obtained existence conditions, the parametric expression of an FTFIO is developed by adopting the solutions to a type of first‐order Sylvester equations, which provides extra degrees of freedom. Finally, the FTFIO design method is efficiently applied to the estimation problem in two examples, including the simple numerical system and chaotic system. [ABSTRACT FROM AUTHOR]

Premise: Apple orchards in the United States are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs and increased environmental and health impacts. Methods and Results: We have manually captured 3651 high‐quality, real‐life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset of images, expert‐annotated to create a pilot data set for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for the Plant Pathology Challenge as part of the Fine‐Grained Visual Categorization (FGVC) workshop at the 2020 Computer Vision and Pattern Recognition conference (CVPR 2020). Participants were asked to use the image data set to train a machine learning model to classify disease categories and develop an algorithm for disease severity quantification. The top three area under the ROC curve (AUC) values submitted to the private leaderboard were 0.98445, 0.98182, and 0.98089. We also trained an off‐the‐shelf convolutional neural network on this data for disease classification and achieved 97% accuracy on a held‐out test set. Discussion: This data set will contribute toward development and deployment of machine learning–based automated plant disease classification algorithms to ultimately realize fast and accurate disease detection. We will continue to add images to the pilot data set for a larger, more comprehensive expert‐annotated data set for future Kaggle competitions and to explore more advanced methods for disease classification and quantification. [ABSTRACT FROM AUTHOR]

Interval observer design and related techniques have been researched and applied in many engineering fields and continue to be an active research area in the estimation and control society for the last two decades. An Interval observer is a special class of observers that generates a bounded interval vector for the real state vector in a guaranteed way under the assumption that the uncertainties are unknown but bounded. Some of the basic concepts and the main developments in the designs and applications of interval observer for continuous‐time, discrete‐time (linear and non‐linear), fuzzy and switched systems are reviewed in this work. It also provides a brief discussion of the main approaches in this area with clear descriptions of their structures and future directions. [ABSTRACT FROM AUTHOR]

Summary: Pear (Pyrus; 2n = 34), the third most important temperate fruit crop, has great nutritional and economic value. Despite the availability of many genomic resources in pear, it is challenging to genotype novel germplasm resources and breeding progeny in a timely and cost‐effective manner. Genotyping arrays can provide fast, efficient and high‐throughput genetic characterization of diverse germplasm, genetic mapping and breeding populations. We present here 200K AXIOM® PyrSNP, a large‐scale single nucleotide polymorphism (SNP) genotyping array to facilitate genotyping of Pyrus species. A diverse panel of 113 re‐sequenced pear genotypes was used to discover SNPs to promote increased adoption of the array. A set of 188 diverse accessions and an F1 population of 98 individuals from 'Cuiguan' × 'Starkrimson' was genotyped with the array to assess its effectiveness. A large majority of SNPs (166 335 or 83%) are of high quality. The high density and uniform distribution of the array SNPs facilitated prediction of centromeric regions on 17 pear chromosomes, and significantly improved the genome assembly from 75.5% to 81.4% based on genetic mapping. Identification of a gene associated with flowering time and candidate genes linked to size of fruit core via genome wide association studies showed the usefulness of the array in pear genetic research. The newly developed high‐density SNP array presents an important tool for rapid and high‐throughput genotyping in pear for genetic map construction, QTL identification and genomic selection. [ABSTRACT FROM AUTHOR]

The current study examined the impact of perceived calling on nurses' organizational commitment, organizational citizenship behavior, workplace deviant behavior, and turnover intentions. Moreover, the mediating role of career commitment and the moderating role of living a calling are also investigated. This paper posits a mediated moderation model wherein calling and living a calling interact to predict organizational commitment, organizational citizenship behavior, workplace deviant behavior, and turnover intentions with career commitment mediating the effect of the interaction on the three outcome variables. Data were collected from 294 registered nurses who had worked for a minimum of 6 months in the public or private health care system. Correlation and regression analyses were used to test the proposed hypotheses. Living a calling moderated the effect of calling on career commitment, such that these relations were more robust for nurses with a stronger sense of living a calling. Furthermore, career commitment fully mediated the effect of calling on organizational commitment, organizational citizenship behavior, workplace deviant behavior, and turnover intentions. The indirect effects of career commitment were only significant for nurses with high levels of living a calling. These results imply that possessing a sense of calling brings greater career commitment, which evidently leads to stronger organizational commitment and organizational citizenship behaviors, and lower workplace deviant behavior and turnover intentions. More specifically, nurse's sense of calling could lead to voluntary acts of helping colleagues and participation in organizational activities due to features associated with having a sense of calling, including pro‐social intention and other‐oriented thoughts. Calling might also decrease turnover intentions and deviant behaviors among nurses. Research about the dynamics of calling in nursing and medical professionals is limited. Recognizing the need, the present research has widened the scope of calling research by investigating the relationships between calling and various work‐related variables within Asian nursing context. [ABSTRACT FROM AUTHOR]