Feasibility study on manganese nodules recovery in the Clarion-Clipperton Zone

The sea occupies three quarters of the area on the earth and provides various kinds of resources to mankind in the form of minerals, food, medicines and even energy. “Seabed exploitation” specifically deals with recovery of the resources that are found on the seabed, in the form of solids, liquids and gasses (methane hydrates, oil and natural gas). The resources are abundant; nevertheless the recovery process from the seabed, poses various challenges to mankind. This study starts with a review on three types of resources: polymetallic manganese nodules, polymetallic manganese crusts and massive sulphides deposits. Each of them are rich in minerals, such as manganese, cobalt, nickel, copper and some rare earth elements. They are found at many locations in the deep seas and are potentially a big source of minerals. No commercial seabed mining activity has been accomplished to date due to the great complexities in recovery. This book describes the various challenges associated with a potential underwater mineral recovery operation, reviews and analyses the existing recovery techniques, and provides an innovative engineering system. It further identifies the associated risks and a suitable business model.Chapter 1 presents a brief background about the past and present industrial trends of seabed mining. A description of the sea, seabed and the three types of seabed mineral resources are also included. A section on motivations for deep sea mining follows which also compares the latter with terrestrial mining.Chapter 2 deals with the decision making process, including a market analysis, for selecting manganese nodules as the resource of interest. This is followed by a case study specific to the location of interest: West COMRA in the Clarion-Clipperton Zone. Specific site location is determined in order to estimate commercial risk, environmental impact assessment and logistic challenge.Chapter 3 lists the existing techniques for nodule recovery operation. The study identifies the main components of a nodules recovery system, and organizes them into: collector, propulsion and vertical transport systems.Chapter 4 discusses various challenges posed by manganese nodules recovery, in terms of the engineering and environment. The geo-political and legal-social issues have also been considered. This chapter plays an important role in defining the proposed engineering system, as addressing the identified challenges will better shape the proposed solution.Chapter 5 proposes an engineering system, by considering the key components in greater details. An innovative component, the black box is introduced, which is intended to be an environmentally-friendly solution for manganese nodules recovery. Other auxiliary components, such as the mother ship and metallurgical processing, are briefly included. A brief power supply analysis is also provided.Chapter 6 assesses the associated risks, which are divided into sections namely commercial viability, logistic challenges, environmental impact assessment and safety assessment. The feasibility of the proposed solution is also dealt with.Chapter 7 provides a business model for the proposed engineering system. Potential customers are identified, value proposition is determined, costumer relation is also suggested. Public awareness is then discussed and finally a SWOT analysis is presented. This business model serves as an important bridge to reach both industry and research institutes.Finally, Chapter 8 provides some conclusions and recommendation for future work

Detailed Case Studies

Wireless body area networks (WBANs) are one of the key technologies that support the development of pervasive health monitoring (remote patient monitoring systems), which has attracted more attention in recent years. These WBAN applications requires stringent security requirements as they are concerned with human lives. In the recent scenario of the corona pandemic, where most of the healthcare providers are giving online services for treatment, DDoS attacks become the major threats over the internet. This chapter particularly focusses on detection of DDoS attack using machine learning algorithms over the healthcare environment. In the process of attack detection, the dataset is preprocessed. After preprocessing the dataset, the cleaned dataset is given to the popular classification algorithms in the area of machine learning namely, AdaBoost, J48, k-NN, JRip, Random Committee and Random Forest classifiers. Those algorithms are evaluated independently and the results are recorded. Results concluded that J48 outperform with accuracy of 99.98% with CICIDS dataset and random forest outperform with accuracy of 99.917, but it takes the longest model building time. Depending on the evaluation performance the appropriate classifier is selected for further DDoS detection at real-time

Suppression of NLRX1 in chronic obstructive pulmonary disease

Cigarette smoke (CS) and viruses promote the inflammation and remodeling associated with chronic obstructive pulmonary disease (COPD). The MAVS/RIG-I–like helicase (MAVS/RLH) pathway and inflammasome-dependent innate immune pathways are important mediators of these responses. At baseline, the MAVS/RLH pathway is suppressed, and this inhibition must be reversed to engender tissue effects; however, the mechanisms that mediate activation and repression of the pathway have not been defined. In addition, the regulation and contribution of MAVS/RLH signaling in CS-induced inflammation and remodeling responses and in the development of human COPD remain unaddressed. Here, we demonstrate that expression of NLRX1, which inhibits the MAVS/RLH pathway and regulates other innate immune responses, was markedly decreased in 3 independent cohorts of COPD patients. NLRX1 suppression correlated directly with disease severity and inversely with pulmonary function, quality of life, and prognosis. In murine models, CS inhibited NLRX1, and CS-induced inflammation, alveolar destruction, protease induction, structural cell apoptosis, and inflammasome activation were augmented in NLRX1-deficient animals. Conversely, MAVS deficiency abrogated this CS-induced inflammation and remodeling. Restoration of NLRX1 in CS-exposed animals ameliorated alveolar destruction. These data support a model in which CS-dependent NLRX1 inhibition facilitates MAVS/RHL activation and subsequent inflammation, remodeling, protease, cell death, and inflammasome responses

Hsc70 Focus Formation at the Periphery of HSV-1 Transcription Sites Requires ICP27

The cellular chaperone protein Hsc70, along with components of the 26S proteasome and ubiquitin-conjugated proteins have been shown to be sequestered in discrete foci in the nuclei of herpes simplex virus 1 (HSV-1) infected cells. We recently reported that cellular RNA polymerase II (RNAP II) undergoes proteasomal degradation during robust HSV-1 transcription, and that the immediate early protein ICP27 interacts with the C-terminal domain and is involved in the recruitment of RNAP II to viral transcription/replication compartments.Here we show that ICP27 also interacts with Hsc70, and is required for the formation of Hsc70 nuclear foci. During infection with ICP27 mutants that are unable to recruit RNAP II to viral replication sites, viral transcript levels were greatly reduced, viral replication compartments were poorly formed and Hsc70 focus formation was curtailed. Further, a dominant negative Hsc70 mutant that cannot hydrolyze ATP, interfered with RNAP II degradation during HSV-1 infection, and an increase in ubiquitinated forms of RNAP II was observed. There was also a decrease in virus yields, indicating that proteasomal degradation of stalled RNAP II complexes during robust HSV-1 transcription and replication benefits viral gene expression.We propose that one function of the Hsc70 nuclear foci may be to serve to facilitate the process of clearing stalled RNAP II complexes from viral genomes during times of highly active transcription

GEMv2 : Multilingual NLG benchmarking in a single line of code

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe

GEMv2 : Multilingual NLG benchmarking in a single line of code

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe

TLR2/MyD88/NF-κB Pathway, Reactive Oxygen Species, Potassium Efflux Activates NLRP3/ASC Inflammasome during Respiratory Syncytial Virus Infection

Human respiratory syncytial virus (RSV) constitute highly pathogenic virus that cause severe respiratory diseases in newborn, children, elderly and immuno-compromised individuals. Airway inflammation is a critical regulator of disease outcome in RSV infected hosts. Although “controlled” inflammation is required for virus clearance, aberrant and exaggerated inflammation during RSV infection results in development of inflammatory diseases like pneumonia and bronchiolitis. Interleukin-1β (IL-1β) plays an important role in inflammation by orchestrating the pro-inflammatory response. IL-1β is synthesized as an immature pro-IL-1β form. It is cleaved by activated caspase-1 to yield mature IL-1β that is secreted extracellularly. Activation of caspase-1 is mediated by a multi-protein complex known as the inflammasome. Although RSV infection results in IL-1β release, the mechanism is unknown. Here in, we have characterized the mechanism of IL-1β secretion following RSV infection. Our study revealed that NLRP3/ASC inflammasome activation is crucial for IL-1β production during RSV infection. Further studies illustrated that prior to inflammasome formation; the “first signal” constitutes activation of toll-like receptor-2 (TLR2)/MyD88/NF-κB pathway. TLR2/MyD88/NF-κB signaling is required for pro-IL-1β and NLRP3 gene expression during RSV infection. Following expression of these genes, two “second signals” are essential for triggering inflammasome activation. Intracellular reactive oxygen species (ROS) and potassium (K+) efflux due to stimulation of ATP-sensitive ion channel promote inflammasome activation following RSV infection. Thus, our studies have underscored the requirement of TLR2/MyD88/NF-κB pathway (first signal) and ROS/potassium efflux (second signal) for NLRP3/ASC inflammasome formation, leading to caspase-1 activation and subsequent IL-1β release during RSV infection

Soil Respiration in Tibetan Alpine Grasslands: Belowground Biomass and Soil Moisture, but Not Soil Temperature, Best Explain the Large-Scale Patterns

The Tibetan Plateau is an essential area to study the potential feedback effects of soils to climate change due to the rapid rise in its air temperature in the past several decades and the large amounts of soil organic carbon (SOC) stocks, particularly in the permafrost. Yet it is one of the most under-investigated regions in soil respiration (Rs) studies. Here, Rs rates were measured at 42 sites in alpine grasslands (including alpine steppes and meadows) along a transect across the Tibetan Plateau during the peak growing season of 2006 and 2007 in order to test whether: (1) belowground biomass (BGB) is most closely related to spatial variation in Rs due to high root biomass density, and (2) soil temperature significantly influences spatial pattern of Rs owing to metabolic limitation from the low temperature in cold, high-altitude ecosystems. The average daily mean Rs of the alpine grasslands at peak growing season was 3.92 µmol CO2 m−2 s−1, ranging from 0.39 to 12.88 µmol CO2 m−2 s−1, with average daily mean Rs of 2.01 and 5.49 µmol CO2 m−2 s−1 for steppes and meadows, respectively. By regression tree analysis, BGB, aboveground biomass (AGB), SOC, soil moisture (SM), and vegetation type were selected out of 15 variables examined, as the factors influencing large-scale variation in Rs. With a structural equation modelling approach, we found only BGB and SM had direct effects on Rs, while other factors indirectly affecting Rs through BGB or SM. Most (80%) of the variation in Rs could be attributed to the difference in BGB among sites. BGB and SM together accounted for the majority (82%) of spatial patterns of Rs. Our results only support the first hypothesis, suggesting that models incorporating BGB and SM can improve Rs estimation at regional scale