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Flexible Sensible Nonlinear Model Predictive Control regarding Echo Point out System Types.
g., stunted, overweight and obesity) of Pakistani children and adolescents. The dataset can also be used by researchers to calculate body surface area (BSA), body frame size (BFS), body shape index (BSI), and tri-ponderal mass index (TMI) of children and adolescents that are also some other reliable indicators of obesity and insulin resistance as well as cardiometabolic risk in children and adults.This article includes pulmonary function data collected via multiple breath nitrogen washout for 103 healthy U.S. adults recruited at National Jewish Health in Denver, Colorado. Testing was performed by certified technicians and reviewed by expert pulmonologists for quality and consistency. Data were collected from a diverse population that included 52 males and 51 females with an average age of 39 years (range 20-77 years). Participants were of non-Hispanic White (85%), African-American/Black (6%), Hispanic (4%), more than one race (4%) or American Indian/Alaskan Native (1%) race/ethnicity. The majority were never smokers (85%), but 12% were former smokers and 3% were current smokers. Height, weight, and body mass index (BMI) were collected in addition to multiple breath washout (MBW) test parameters such as the lung clearance index (LCI) score.This article elaborates on the life cycle assessment (LCA) protocol designed for formulating the life cycle inventories (LCIs) of fruit and vegetable (F&V) supply chains. As a set of case studies, it presents the LCI data of the processed vegetable products, (a) potato chips, frozen-fries, and dehydrated flakes, and (b) tomato-pasta sauce. learn more The data can support to undertake life cycle impact assessment (LCIA) of food commodities in a "cradle to grave" approach. An integrated F&V supply chain LCA model is constructed, which combined three components of the supply chain farming system, post-harvest system (processing until the consumption) and bio-waste handling system. We have used numbers of crop models to calculate the crop yields, crop nutrient uptake, and irrigation water requirements, which are largely influenced by the local agro-climatic parameters of the selected crop reporting districts (CRDs) of the United States. For the farming system, LCI information, as shown in the data are averaged from the respective CRDs. LCI data for the post-harvest stages are based on available information from the relevant processing plants and the engineering estimates. The article also briefly presents the assumptions made for evaluating future crop production scenarios. Future scenarios integrate the impact of climate change on the future productivity and evaluate the effect of adaptation measures and technological advancement on the crop yield. The provided data are important to understand the characteristics of the food supply chain, and their relationships with the life cycle environmental impacts. The data can also support to formulate potential environmental mitigation and adaptation measures in the food supply chain mainly to cope with the adverse impact of climate change.Cocoa bean (Theobroma cacao L.) is part of the global cocoa and chocolate industry valued at 44 billion US dollars in 2019. Cocoa pod borer (CPB), Conopomorpha cramerella is a major pest of cocoa in Malaysia and Indonesia that is responsible for the decline for cocoa production. They have been detected since 1980s. Unfortunately, current control strategies are inefficient for CPB management. Although biotechnological alternatives, including RNA interference (RNAi), have been proposed in recent years to control insect pests, characterizing the genetics of the target pest is essential for successful application of these emerging technologies. We generated a comprehensive RNA-seq dataset (135,915,430 clean reads) for larva and adult stages of CPB by using the Illumina HiseqTM 4000 system to increase the understanding of CPB in relation to molecular features. The CPB transcriptome was assembled de novo and annotated. The final assembled produced 249,280 unigenes, of which 75,929 unigenes annotated against NCBI NR database and were distributed among 156 KEGG pathways. The raw data were uploaded to SRA database and the BioProject ID is PRJNA553611. The transcriptomic dataset we present are the first reports of transcriptome information in CPB that is valuable for further exploration and understanding of CPB molecular pathways.We present the first dataset that aims to serve as a benchmark to validate the resilience of botnet detectors against adversarial attacks. This dataset includes realistic adversarial samples that are generated by leveraging two widely used Deep Reinforcement Learning (DRL) techniques. These adversarial samples are proved to evade state of the art detectors based on Machine- and Deep-Learning algorithms. The initial corpus of malicious samples consists of network flows belonging to different botnet families presented in three public datasets containing real enterprise network traffic. We use these datasets to devise detectors capable of achieving state-of-the-art performance. We then train two DRL agents, based on Double Deep Q-Network and Deep Sarsa, to generate realistic adversarial samples the goal is achieving misclassifications by performing small modifications to the initial malicious samples. These alterations involve the features that can be more realistically altered by an expert attacker, and do not compromise the underlying malicious logic of the original samples. Our dataset represents an important contribution to the cybersecurity research community as it is the first including thousands of automatically generated adversarial samples that are able to thwart state of the art classifiers with a high evasion rate. The adversarial samples are grouped by malware variant and provided in a CSV file format. Researchers can validate their defensive proposals by testing their detectors against the adversarial samples of the proposed dataset. Moreover, the analysis of these samples can pave the way to a deeper comprehension of adversarial attacks and to some sort of explainability of machine learning defensive algorithms. They can also support the definition of novel effective defensive techniques.
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