Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are nece...
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This work purpose is to study the influence of concrete buried structures in grounding systems. Comparison of soil characteristics between dry and rainy seasons and soil electrical behavior was carried out. Simulation...
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Venoms are a rich source for the discovery of molecules with biotechnological applications, but their analysis is challenging even for state-of-the-art proteomics. Here we report on a large-scale proteomic assessment ...
Venoms are a rich source for the discovery of molecules with biotechnological applications, but their analysis is challenging even for state-of-the-art proteomics. Here we report on a large-scale proteomic assessment of the venom of Loxosceles intermedia, the so-called brown spider. Venom was extracted from 200 spiders and fractioned into two aliquots relative to a 10 kDa cutoff mass. Each of these was further fractioned and digested with trypsin (4 h), trypsin (18 h), pepsin (18 h), and chymotrypsin (18 h), then analyzed by MudPIT on an LTQ-Orbitrap XL ETD mass spectrometer fragmenting precursors by CID, HCD, and ETD. Aliquots of undigested samples were also analyzed. Our experimental design allowed us to apply spectral networks, thus enabling us to obtain meta-contig assemblies, and consequently de novo sequencing of practically complete proteins, culminating in a deep proteome assessment of the venom. Data are available via ProteomeXchange, with identifier PXD005523.
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks an...
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Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecula...
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In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of w...
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In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex;2) The data, when communicated, are vulnerable to security and privacy issues;3) The communication of the continuously collected data is not only costly but also energy hungry;4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a serviceoriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the ove
Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (un...
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An approach for robustness analysis of non-dominated solutions to a multi-objective optimization model of an energy management system aggregator (EMSA) in face of uncertainty is presented. The EMSA is an intermediary ...
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ISBN:
(纸本)9781509042418
An approach for robustness analysis of non-dominated solutions to a multi-objective optimization model of an energy management system aggregator (EMSA) in face of uncertainty is presented. The EMSA is an intermediary entity between households and the System Operator (SO), capable of contributing to balance load and supply, and therefore coping with the intermittency of renewable energy sources (RES) and facilitating a load follows supply strategy in a Smart Grid environment. Household clusters provide load flexibility to satisfy system services requested by the SO, involving decreasing or increasing load in specific time slots. The EMSA multi-objective optimization model considers the maximization of profits and the minimization of the imbalance between the amounts of load flexibility provided by the end-user clusters to satisfy SO requests, taking into account revenues from the SO and payments to the clusters. A hybrid evolutionary approach combining Genetic Algorithms (GA) with Differential Evolution (DE) has been designed to deal with this model, and its behaviour subject to different scenarios of uncertainty is evaluated. The robustness analysis of non-dominated solutions produced by the hybrid evolutionary approach is based on the degree of robustness concept, taking into account the changes in the performance of the objective functions when small perturbations of the model nominal coefficients occur.
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