Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review

<span lang="EN-US">A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.</span>

A Unified Perspective for Disinformation Detection and Truth Discovery in Social Sensing: A Survey

With the proliferation of social sensing, large amounts of observation are contributed by people or devices. However, these observations contain disinformation. Disinformation can propagate across online social networks at a relatively low cost, but result in a series of major problems in our society. In this survey, we provide a comprehensive overview of disinformation and truth discovery in social sensing under a unified perspective, including basic concepts and the taxonomy of existing methodologies. Furthermore, we summarize the mechanism of disinformation from four different perspectives (i.e., text only, text with image/multi-modal, text with propagation, and fusion models). In addition, we review existing solutions based on these requirements and compare their pros and cons and give a sort of guide to usage based on a detailed lesson learned. To facilitate future studies in this field, we summarize related publicly accessible real-world data sets and open source codes. Last but the most important, we emphasize potential future research topics and challenges in this domain through a deep analysis of most recent methods.

Religion and Forced Displacement in Eastern Europe, the Caucasus, and Central Asia

This book examines the social and political mobilisation of religious communities towards forced displacement in Eastern Europe, the Caucasus, and Central Asia. It analyses religious strategies in relation to tolerance and transitory environments as a result of the breakup of the Soviet Union and Yugoslavia in the early 1990s, the post-2011 Syrian crisis and the 2014 Russian takeover of Crimea. How do religious actors and state bodies engage with refugees and migrants? What are the mechanisms of religious support towards forcibly displaced communities? The book argues that when states do not act as providers of human security, religious communities, as representatives of civil society and often closer to the grass roots level, can be well placed to serve populations in need. The book brings together scholars from across the region and provides a comprehensive overview of the ways in which religious communities tackle humanitarian crises in contemporary Armenia, Bulgaria, Greece, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Poland, Russia, Serbia, Ukraine and Uzbekistan.

A comprehensive overview of miRNA targeting drought stress resistance in plants

MicroRNAs (miRNAs) are essential nonprotein-coding genes. In a range of organisms, miRNAs has been reported to play an essential role in regulating gene expressions at post-transcriptional level. They participate in most of the stress responsive processes in plants. Drought is an ultimate abiotic stress that affects the crop production. Therefore understanding drought stress responses are essential to improve the production of agricultural crops. Throughout evolution, plants have developed their own defense systems to cope with the adversities of environmental stresses. Among defensive mechanisms include the regulations of gene expression by miRNAs. Drought stress regulates the expression of some of the functionally conserved miRNAs in different plants. The given properties of miRNAs provide an insight to genetic alterations and enhancing drought resistance in cereal crops. The current review gives a summary to regulatory mechanisms in plants as well as miRNAs response to drought stresses in cereal crops. Some possible approaches and guidelines for the exploitation of drought stress miRNA responses to improve cereal crops are also described.

Sentiment Analysis in Hindi—A Survey on the State-of-the-art Techniques

Sentiment Analysis (SA) has been a core interest in the field of text mining research, dealing with computational processing of sentiments, views, and subjective nature of the text. Due to the availability of extensive web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. It has become extremely significant to analyze this data and recover valuable and relevant information. Hindi being the first language of the majority of the population in India, SA in Hindi has turned out to be a critical task particularly for companies and government organizations. This research portrays a systematic review specifically in the field of Hindi SA. The major contribution of this article includes the categorization of numerous articles based on techniques that have attracted researchers in performing SA tasks in Hindi language. This survey classifies these state-of-the-art computational intelligence techniques into four major categories namely lexicon-based techniques, machine learning techniques, deep learning techniques, and hybrid techniques. It discusses the importance of these techniques based on different aspects such as their impact on the issues of SA, levels of analysis, and performance evaluation measures. The research puts forward a comprehensive overview of the majority of the work done in Hindi SA. This study will help researchers in finding out resources such as annotated datasets, linguistic resources, and lexical resources. This survey delivers some significant findings and presents overall future research directions in the field of Hindi SA.

Immune Checkpoint LAG3 and Its Ligand FGL1 in Cancer

LAG3 is the most promising immune checkpoint next to PD-1 and CTLA-4. High LAG3 and FGL1 expression boosts tumor growth by inhibiting the immune microenvironment. This review comprises four sections presenting the structure/expression, interaction, biological effects, and clinical application of LAG3/FGL1. D1 and D2 of LAG3 and FD of FGL1 are the LAG3-FGL1 interaction domains. LAG3 accumulates on the surface of lymphocytes in various tumors, but is also found in the cytoplasm in non-small cell lung cancer (NSCLC) cells. FGL1 is found in the cytoplasm in NSCLC cells and on the surface of breast cancer cells. The LAG3-FGL1 interaction mechanism remains unclear, and the intracellular signals require elucidation. LAG3/FGL1 activity is associated with immune cell infiltration, proliferation, and secretion. Cytokine production is enhanced when LAG3/FGL1 are co-expressed with PD-1. IMP321 and relatlimab are promising monoclonal antibodies targeting LAG3 in melanoma. The clinical use of anti-FGL1 antibodies has not been reported. Finally, high FGL1 and LAG3 expression induces EGFR-TKI and gefitinib resistance, and anti-PD-1 therapy resistance, respectively. We present a comprehensive overview of the role of LAG3/FGL1 in cancer, suggesting novel anti-tumor therapy strategies.

Review: The Effects of Supersonic Aviation on Ozone and Climate

When working towards regulation of supersonic aviation, a comprehensive understanding of the global climate effect of supersonic aviation is required in order to develop future regulatory issues. Such research requires a comprehensive overview of existing scientific literature having explored the climate effect of aviation. This review article provides an overview on earlier studies assessing the climate effects of supersonic aviation, comprising non-CO2 effects. An overview on the historical evaluation of research focussing on supersonic aviation and its environmental impacts is provided, followed by an overview on concepts explored and construction of emission inventories. Quantitative estimates provided for individual effects are presented and compared. Subsequently, regulatory issues related to supersonic transport are summarised. Finally, requirements for future studies, e.g., in emission scenario construction or numerical modelling of climate effects, are summarised and main conclusions discussed.

Tailoring Cardiac Synthetic Transcriptional Modulation Towards Precision Medicine

Molecular and genetic differences between individual cells within tissues underlie cellular heterogeneities defining organ physiology and function in homeostasis as well as in disease states. Transcriptional control of endogenous gene expression has been intensively studied for decades. Thanks to a fast-developing field of single cell genomics, we are facing an unprecedented leap in information available pertaining organ biology offering a comprehensive overview. The single-cell technologies that arose aided in resolving the precise cellular composition of many organ systems in the past years. Importantly, when applied to diseased tissues, the novel approaches have been immensely improving our understanding of the underlying pathophysiology of common human diseases. With this information, precise prediction of regulatory elements controlling gene expression upon perturbations in a given cell type or a specific context will be realistic. Simultaneously, the technological advances in CRISPR-mediated regulation of gene transcription as well as their application in the context of epigenome modulation, have opened up novel avenues for targeted therapy and personalized medicine. Here, we discuss the fast-paced advancements during the recent years and the applications thereof in the context of cardiac biology and common cardiac disease. The combination of single cell technologies and the deep knowledge of fundamental biology of the diseased heart together with the CRISPR-mediated modulation of gene regulatory networks will be instrumental in tailoring the right strategies for personalized and precision medicine in the near future. In this review, we provide a brief overview of how single cell transcriptomics has advanced our knowledge and paved the way for emerging CRISPR/Cas9-technologies in clinical applications in cardiac biomedicine.

Modeling and Simulation of Reaction Environment in Photoredox Catalysis: A Critical Review

From the pharmaceutical industry’s point of view, photoredox catalysis has emerged as a powerful tool in the field of the synthesis of added-value compounds. With this method, it is possible to excite the catalyst by the action of light, allowing electron transfer processes to occur and, consequently, oxidation and reduction reactions. Thus, in association with photoredox catalysis, microreactor technology and continuous flow chemistry also play an important role in the development of organic synthesis processes, as this technology offers high yields, high selectivity and reduced side reactions. However, there is a lack of a more detailed understanding of the photoredox catalysis process, and computational tools based on computational fluid dynamics (CFD) can be used to deal with this and boost to reach higher levels of accuracy to continue innovating in this area. In this review, a comprehensive overview of the fundamentals of photoredox catalysis is provided, including the application of this technology for the synthesis of added-value chemicals in microreactors. Moreover, the advantages of the continuous flow system in comparison with batch systems are pointed out. It was also demonstrated how modeling and simulation using computational fluid dynamics (CFD) can be critical for the design and optimization of microreactors applied to photoredox catalysis, so as to better understand the reagent interactions and the influence of light in the reaction medium. Finally, a discussion about the future prospects of photoredox reactions considering the complexity of the process is presented.

Causal Discovery in Manufacturing: A Structured Literature Review

Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed.