360 research outputs found
Hyperspectral image dataset for salt stress phenotyping of wheat
No full textThe dataset contains hyperspectral images of four wheat lines, each with a control and a salt (NaCl) treatment. Images were captured by a hyperspectral camera (PIKA II, Resonon) under natural light condition one day after salt application when there were no visual symptoms in wheat plants. The camera recorded the spectral response of both control and salt tanks of each line over 240 spectral channels in visible and near infrared region (400 nm to 900 nm) with about 2.1 nm spectral resolution, 640 spatial channels in the cross-track direction, and about 1 mm spatial resolution. Raw images were converted to radiance (Wm−2sr−1nm−1) using a vendor-provided calibration file, and then converted to reflectance (%) using a Spectralon panel. In total 25 spectral bands were disregarded due to high noise. Subsequent to noisy band removal, vegetation pixels were segmented from background using spectral vegetation indices and morphological operation. Although the goal of this study was plant phenotyping to rank salt tolerance of wheat lines, this dataset can be used for other research purposes, such as developing classification algorithms to discriminate healthy and stressed plants and developing methods for spectral feature selection to reduce the dimension of hyperspectral images.United States Department of Agriculture-Agricultural Research Service
the National Science Foundation (IOS 1025881 and IOS 1361554)
Minnesota Agricultural Experiment StationMoghimi, Ali; Yang, Ce. (2018). Hyperspectral image dataset for salt stress phenotyping of wheat. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/D69Q3K
UAV-based hyperspectral dataset for high-throughput yield phenotyping in wheat
No full textThe dataset was collected by a hyperspectral camera (PIKA II, Resonon, Inc.) mounted on an unmanned aerial vehicle (UAV, DJI Matrice 600 Pro) from three experimental yield trial fields (C3, C4, and C9) during two consecutive growing seasons 2017 (C3 and C9) and 2018 (C4). The aerial hyperspectral images were captured within two weeks prior to harvest over 240 spectral channels in visible and near infrared region (400 nm to 900 nm) with about 2.1 nm spectral resolution and about 2 cm spatial resolution. Subsequent to radiometric calibration and noisy band removal, plots were cropped from the hyperspectral images and saved as 3D matrices with Matlab (MAT files) and Python (NPY files) format. The dataset entails hyperspectral cubes of 1021 wheat plots and the grain yield of plots harvested by a combine. The corresponding ground truth data (yield) for each hyperspectral cube representing a plot can be found based on the field (e.g., C3, C4, and C9) and plot ID.Moghimi, Ali; Yang, Ce; Anderson, James A.. (2020). UAV-based hyperspectral dataset for high-throughput yield phenotyping in wheat. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/0ch0-vb18
sj-pdf-2-acr-10.1177_0284185116663045 - Supplemental material for Ultrasonography of inferior vena cava to determine central venous pressure: a meta-analysis and meta-regression
No full textSupplemental material, sj-pdf-2-acr-10.1177_0284185116663045 for Ultrasonography of inferior vena cava to determine central venous pressure: a meta-analysis and meta-regression by Mostafa Alavi-Moghaddam, Ali Kabir, Majid Shojaee, Mohammad Manouchehrifar and Mehrdad Moghimi in Acta Radiologica</p
sj-pdf-1-acr-10.1177_0284185116663045 - Supplemental material for Ultrasonography of inferior vena cava to determine central venous pressure: a meta-analysis and meta-regression
No full textSupplemental material, sj-pdf-1-acr-10.1177_0284185116663045 for Ultrasonography of inferior vena cava to determine central venous pressure: a meta-analysis and meta-regression by Mostafa Alavi-Moghaddam, Ali Kabir, Majid Shojaee, Mohammad Manouchehrifar and Mehrdad Moghimi in Acta Radiologica</p
Principles and fundamentals of Islamic management
No full text"The traditional approach to business and public management assumes that management decisions and outcomes will remain the same, irrespective of the environment in which they are applied. However, the value systems operating within a society can also influence the principles that govern modern management within organizations. Principles and Fundamentals of Islamic Management examines the concept of business and public management from the viewpoint of Islam, with close reference to the Quran and other illuminating Islamic sources. Seyed Mohammad Moghimi provides key insights from an Islamic perspective across a comprehensive range of management topics, including planning, decision making and policy making, organizing, human resources management, directing and organizational control. The book concludes by analyzing the role of a company director within an Islamic context. Through this in-depth exploration of Islamic management principles and fundamentals, the author creates a modern and practical framework suitable for use by international business managers. Providing a much-needed insight into the practicalities of management operations in an Islamic context, this book is essential reading for researchers, managers, and for students of Islamic management at both undergraduate and graduate levels"--Back cove
SPIRITUALITY IN THE WORK PLACE AND ITS IMPACTS ON THE EFFICIENCY OF MANAGEMENT
No full textIn the modern word, successful organizations have undertaken new values and approaches, and due to these values, they have achieved more morality and success. People are also deeply interested in embracing morality, not only in their personal lives, but also in their career and social life. When the society is packed with technology, communication, complication and instability, people show a tendency toward morality to fill the vacuity appeared in their lives, not only within their personal lives, but also within their career life where they spend a part of their time. Encouraging morality in work has some advantages for organizations. Morality at work results in creativity, honesty and trust, self-success, organization, commitment, and better performance of the organization. When someone feels committed to the organization s/he works for is loyal to moral and human values and respects its employees, s/he feels a kind of adaptation with the values of the organization and works for those values. The more a person is committed to morality, the more his/her creativity, mental and spiritual justice, moral and social justice, and managerial and ruling justice will be. People who have values based on theism, believe in the divine origin of the human being and in the afterlife and consider themselves as responsible and answerable before God, their existence society, and the world. This paper, in addition to giving a definition of morality, has studied morality at work from the viewpoint of different theorists, and the essence of morality from the viewpoint of religion, naturalism and existentialism, and its correlation with important managerial and organizational variablesSpirituality, Justice, Naturalism, Religious Viewpoint, Existentialism
Integrating Hyperspectral Imaging and Artificial Intelligence to Develop Automated Frameworks for High-throughput Phenotyping in Wheat
No full textUniversity of Minnesota Ph.D. dissertation. February 2019. Major: Bioproducts/Biosystems Science Engineering and Management. Advisors: Ce Yang, Peter Marchetto. 1 computer file (PDF); xx, 173 pages.The present dissertation was motivated by the need to apply innovative technologies, automation, and artificial intelligence to agriculture in order to promote crop production while protecting our environment. The main objective of this dissertation was to develop sensor-based, automated frameworks for high-throughput phenotyping of wheat to identify advanced wheat varieties based on three desired traits, including yield potential, tolerance to salt stress (an abiotic stress), and resistance to Fusarium head blight disease (a biotic stress). We leveraged the advantages of hyperspectral imaging, a sophisticated sensing technology, and artificial intelligence including machine learning and deep learning algorithms. Through integrating imaging and high-resolution spectroscopy, hyperspectral imaging provides valuable insights into the internal activity of plants, leaf tissue structure, and physiological changes of plants in response to their environment. Alternatively, advanced machine learning and deep learning algorithms are uniquely suited to extract meaningful features and recognize latent patterns associated with the desired phenotyping trait, and ultimately make accurate inferences and prediction. In the first study (Chapter 2), we focused on salt stress phenotyping of wheat in a hydroponic system. A novel method was proposed for hyperspectral image analysis to assess the salt tolerance of four wheat varieties in a quantitative, interpretable, and don-invasive manner. The results of this study demonstrated the feasibility of quantitative ranking of salt tolerance in wheat varieties only one day after applying the salt treatment. In the second study (Chapter 3), we developed an ensemble feature selection pipeline by integrating six supervised feature selection techniques to identify the most informative spectral bands from high-dimensional hyperspectral images captured for plant phenotyping applications. First, the spectral features were ranked based on their ability to discriminate salt-stressed wheat plants from healthy plants at the earliest stages of stress. The proposed method could drastically reduce the dimension of hyperspectral images from 215 to 15 while improving the accuracy of classifying healthy and stressed vegetation pixels by 8.5%. Second, a clustering algorithm was proposed to form six broad spectral bands around the most prominent spectral features to aid in development of a multispectral camera. In the third study (Chapter 4), we aimed to develop a phenotyping framework for Fusarium head blight (FHB), a devastating disease attacking small grain crops. The most informative spectral bands were identified to detect FHB-infected spikes. The results of this study revealed that a set of two broad spectral bands (766 nm and 696 nm) returns a classification accuracy of 99% in detecting FHB-infected spikes. In the fourth study (Chapter 5), we developed an autonomous robotic framework for high-throughput yield phenotyping of wheat in the field. The data were collected by a hyperspectral camera mounted on an unmanned aerial vehicle flying over three experimental fields containing hundreds of wheat plots during two consecutive growing seasons. A deep neural network was trained to predict the yield of wheat plots and estimate the yield variation at a sub-plot scale. The coefficient of determination for predicting the yield at sub-plot and plot scale were 0.79 and 0.41with normalized root-mean-square error of 0.24 and 0.14, respectively. In the fifth study (Chapter 6), we focused on developing a deep autoencoder network by leveraging a large unlabeled dataset (~ 8 million pixels) to learn an optimal feature representation of hyperspectral images in a low dimensional feature space for yield prediction. The result demonstrated that the trained autoencoder could substantially reduce the dimension of hyperspectral images onto a 3-, 5-, and 10-dimenionsal feature space with a mean squared error less than 7e-5, while retaining the relevant information for yield prediction. At a higher level, this dissertation contributes to improving economic, ecological, and social impacts by improving crop production, reducing pesticides use, and properly leveraging salt-affected farmlands. From an environmental perspective, a cultivar with high yield potential and a cultivar resistant to FHB disease both promote sustainability in crop production and environment by reducing the required fertilizer and pesticide to meet the anticipated farmers’ profit. The intelligent, automated phenotyping frameworks developed in this dissertation can help plant scientists and breeders identify crop varieties with the desired traits tailored around promoting crop production and mitigating food security concerns.Moghimi, Ali. (2019). Integrating Hyperspectral Imaging and Artificial Intelligence to Develop Automated Frameworks for High-throughput Phenotyping in Wheat. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/202435
SPIRITUAL CAPACITY AND SPIRITUAL LEADERSHIP IN THE IMPROVEMENT OF WORKING CONDITIONS OF ORGANIZATIONS
No full textConstant environment changes and the increases in the impact of information technology and communications have brought the human beings to the point of feeling being disorganized and alienated. Having a supporting system relying on which man can preserve his mental and spiritual calmness is now suggested as a necessity. Spirituality and faith are of the phenomena which are always encouraging people to be humanitarian and try to satisfy the needs of other people. When different dimensions of spirituality are applied in the working environment, individual satisfaction and creativity, organizational order, and long-term commercial success are increased. This paper mainly aims at studying the spiritual capacities and spiritual leadership in applied management, and after giving some general definitions of spirituality, it studies the role of spirituality and spiritual leadership in the efficiency of working environment and improvement of working conditions in the organizationsSpirituality, Spiritual Leadership, Spirituality at Work, Improvement in Working Condition
Preconcentration of Copper(II) Using Mesoporous Organo-Silicas and Determination by Flame Atomic Absorption Spectrometry
No full textSeparation of Trace Amount Cu (II) Using Octadecyl Silica Membrane Disks - Nano Graphene Modified N, N -disalicylideneethylenediamine
No full textA simple, highly sensitive, accurate and selective method was presented fordetermination of trace amounts of Cu (II) in water samples. The stability of an N, N´ - disalicylideneethylenediamine modified Nano Geraphene especially in concentrated hydrochloric acid was studied which used as a recycling and pre-concentration reagent for further uses of N, N´-disalicylideneethylenediamine modified Nano Geraphene. The method was based on N, N´ -disalicylideneethylenediamine modified Nano Gerapheneof Cu (II) on surfactant coated C18, modified with a N, N´ -disalicylideneethylenediamine modified Nano Geraphene. The retained ions were then eluted with 4 ml of 4 M nitric acid and determined by flame atomic absorption spectrometry (FAAS) at 283.3 nm for Cu. The influence of flow rates of sample and eluent solutions, pH, breakthrough volume, effect of foreign ions were investigated on chelation and recovery. (1.5 g of surfactant coated C18 adsorbs 40 mg of the Schiff’s base which in turn can retain 15.2±0.8mg of ion) The limit of detection (3σ) for Cu (II) was found to be 3.20 ng l -1. The enrichment factor for both ions was 100. The mentioned method was successfully applied on determination of Cu in different water samples. The ions were also speciated by means of three- column system
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