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The development of inorganic membranes has mainly found applicability in liquid separation technologies. However, only a few reports cite the permeation and separation of liquids through inorganic nanofiltration membranes compared with the more popular microfiltration membranes. Herein, we prepared silica membranes using 3,3,3-trifluoropropyltrimethoxysilane (TFPrTMOS) to investigate its liquid permeance performance using four different ion solutions (i.e., NaCl, Na2SO4, MgCl2, and MgSO4). The TFPrTMOS-derived membranes were deposited above a temperature of 175 °C, where the deposition behavior of TFPrTMOS was dependent on the organic functional groups decomposition temperature. The highest membrane rejection was from NaCl at 91.0% when deposited at 200 °C. For anions, the SO42- rejections were the greatest. It was also possible to separate monovalent and divalent anions, as the negatively charged groups on the membrane surfaces retained pore sizes &gt;1.48 nm. Ions were also easily separated by molecular sieving below a pore size of 0.50 nm. For the TFPrTMOS-derived membrane deposited at 175 °C, glucose showed 67% rejection, which was higher than that achieved through the propyltrimethoxysilane membrane. We infer that charge exclusion might be due to the dissociation of hydroxyl groups resulting from decomposition of organic groups. Pore size and organic functional group decomposition were found to be important for ion permeation.The recently growing progress in neuroscience research and relevant achievements, as well as advancements in the fabrication process, have increased the demand for neural interfacing systems. Brain-machine interfaces (BMIs) have been revealed to be a promising method for the diagnosis and treatment of neurological disorders and the restoration of sensory and motor function. Neural recording implants, as a part of BMI, are capable of capturing brain signals, and amplifying, digitizing, and transferring them outside of the body with a transmitter. The main challenges of designing such implants are minimizing power consumption and the silicon area. In this paper, multi-channel neural recording implants are surveyed. After presenting various neural-signal features, we investigate main available neural recording circuit and system architectures. The fundamental blocks of available architectures, such as neural amplifiers, analog to digital converters (ADCs) and compression blocks, are explored. We cover the various topologies of neural amplifiers, provide a comparison, and probe their design challenges. To achieve a relatively high SNR at the output of the neural amplifier, noise reduction techniques are discussed. Also, to transfer neural signals outside of the body, they are digitized using data converters, then in most cases, the data compression is applied to mitigate power consumption. We present the various dedicated ADC structures, as well as an overview of main data compression methods.Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. [https://www.selleckchem.com/products/bi-3802.html click here] The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. Tvels are satisfactory.The European Calcium Society (ECS) workshop, which is held every 2 years, is a dedicated meeting of scientists interested in the elucidation of the action of calcium binding, calcium signaling and the study of proteins and organelles, such as mitochondria and endoplasmic reticulum, thereby involved, either in health and disease conditions. The 8th edition of the ECS workshop was organized by a group of researchers from the University of Coimbra, Portugal, in close collaboration with ECS board members. Thanks to the central role of "Calcium Signaling in Aging and Neurodegenerative Disorders", the ECS 2019 workshop was attended by 62 experts who presented their results in a plenary lecture and five regular symposia, two oral communication sessions and two poster sessions, followed by a hands-on session on calcium imaging. All the scientific and social events were fully participated by the scientific community that allowed a close and fruitful interaction and discussion between junior researchers and senior experts in the field.