Fabrication and investigation of agricultural monitoring system with IoT & AI

Artificial intelligence (AI) can be used in a variety of fields and has the potential to alter how we currently view farming. Due to its emphasis on effectiveness and usability artificial intelligence has the largest impact on agriculture of all industries. We highlight the automation-supporting technologies such as Artificial Intelligence (AI), Machine Learning, and Long-Range (LoRa) technology which provides data integrity and protection. We also offer a structure for smart farming that depends on the location of data processing after a comprehensive investigation of numerous designs. As part of our future study we have divided the unresolved difficulties in smart agriculture into two categories such as networking issues and technology issues. Artificial Intelligence and Machine Learning are examples of technologies whereas the Moderate Resolution Imaging Spectroradiometer satellite and LoRa are used for all network-related jobs. The goal of the research is to deploy a network of sensors throughout agricultural fields to gather real-time information on a variety of environmental factors including temperature, humidity, soil moisture and nutrient levels. The seamless data transmission and communication made possible by these sensors’ integration with Internet of Things technologies. With the use of AI techniques and algorithms the gathered data is examined. The technology may offer practical insights and suggestions for improving agricultural practices because the AI models are trained to spot patterns, correlations, and anomalies in the data. We are also focusing on indoor farming by supplying Ultra Violet radiation and artificial lighting in accordance with plant growth. When a pest assault is detected using AI and LoRa even in poor or no network coverage area and notifies the farmer’s mobile in any part of the world. The irrigation system is put to the test with various plants at various humidity and temperature levels in both dry and typical situations. To keep the water content in those specific regions soil moisture sensors are used

Dendritic cells in cancer immunology and immunotherapy

Dendritic cells (DCs) are a diverse group of specialized antigen-presenting cells with key roles in the initiation and regulation of innate and adaptive immune responses. As such, there is currently much interest in modulating DC function to improve cancer immunotherapy. Many strategies have been developed to target DCs in cancer, such as the administration of antigens with immunomodulators that mobilize and activate endogenous DCs, as well as the generation of DC-based vaccines. A better understanding of the diversity and functions of DC subsets and of how these are shaped by the tumour microenvironment could lead to improved therapies for cancer. Here we will outline how different DC subsets influence immunity and tolerance in cancer settings and discuss the implications for both established cancer treatments and novel immunotherapy strategies.S.K.W. is supported by a European Molecular Biology Organization Long- Term Fellowship (grant ALTF 438– 2016) and a CNIC–International Postdoctoral Program Fellowship (grant 17230–2016). F.J.C. is the recipient of a PhD ‘La Caixa’ fellowship. Work in the D.S. laboratory is funded by the CNIC, by the European Research Council (ERC Consolidator Grant 2016 725091), by the European Commission (635122-PROCROP H2020), by the Ministerio de Ciencia, Innovación e Universidades (MCNU), Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional (FEDER) (SAF2016-79040-R), by the Comunidad de Madrid (B2017/BMD-3733 Immunothercan- CM), by FIS- Instituto de Salud Carlos III, MCNU and FEDER (RD16/0015/0018-REEM), by Acteria Foundation, by Atresmedia (Constantes y Vitales prize) and by Fundació La Marató de TV3 (201723). The CNIC is supported by the Instituto de Salud Carlos III, the MCNU and the Pro CNIC Foundation, and is a Severo Ochoa Centre of Excellence (SEV-2015-0505).S

Quantum Spacetime Phenomenology

I review the current status of phenomenological programs inspired by quantum-spacetime research. I stress in particular the significance of results establishing that certain data analyses provide sensitivity to effects introduced genuinely at the Planck scale. And my main focus is on phenomenological programs that managed to affect the directions taken by studies of quantum-spacetime theories.Comment: 125 pages, LaTex. This V2 is updated and more detailed than the V1, particularly for quantum-spacetime phenomenology. The main text of this V2 is about 25% more than the main text of the V1. Reference list roughly double

Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study

BACKGROUND: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future

Five insights from the Global Burden of Disease Study 2019

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3.5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.Peer reviewe

Some theoretical aspects on designing nickel free high nitrogen austenitic stainless steels

Nickel free high nitrogen austenitic stainless steel design calls for proper choice of alloying constituents that enhances nitrogen solubility and retention of the same after alloying. The nitrogen alloyed should be adequate enough to give a single phase austenitic matrix along with other alloying elemental constituents. The various studies that evaluated these aspects show certain inconsistencies. Certain empirical formulations have been evolved in this study by analyzing all various published data. The formulae obtained, enables choice of alloying elemental composition to get desired microstructure and strength in the solution annealed condition. The various inadequacies that exists in the data used for assessing nitrogen solubility calls for caution when they are applied to actual nitrogen steel production process conditions

Processing nickel free high nitrogen austenitic stainless steels through conventional electroslag remelting process

Nickel free high nitrogen austenitic stainless steels, made through air-induction melting were processed using conventional electroslag remelting (ESR) process without application of nitrogen gas pressure over the melt, it was found possible to retain the high nitrogen contents of the original steel. The loss in nitrogen content during ESR was found to increase with increasing melt rate. Electroslag remelting was carried out on eleven steels with a base composition at around 18wt%Cr-18wt%Mn-0.1 to 0.6wt%C-0.53 to 0.9wt%N. While the air-induction melted steel had extensive porosity, the ESR ingots were all sound and free from porosity. Thus, steels made in any other process route can be successfully remelted using conventional ESR. The cast structure analysis in a typical medium carbon high nitrogen steel showed that Cr and Mn has a tendency for microsegregation. The presence of microsegregation and residual carbides affect the ductility of the cast steel