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Abstract Providing unrestricted access to sensitive content such as news and software is difficult in the presence of adaptive and resourceful surveillance and censoring adversaries. In this paper we leverage the distributed and resilient nature of commercial Satoshi blockchains to develop the first provably secure, censorship resistant, cost-efficient storage system with anonymous and private access, built on top of commercial cryptocurrency transactions. We introduce max-rate transactions, a practical construct to persist data of arbitrary size entirely in a Satoshi blockchain. We leverage max-rate transactions to develop UWeb, a blockchain-based storage system that charges publishers to self-sustain its decentralized infrastructure. UWeb organizes blockchain-stored content for easy retrieval, and enables clients to store and access content with provable anonymity, privacy and censorship resistance properties. We present results from UWeb experiments with writing 268.21 MB of data into the live Litecoin blockchain, including 4.5 months of live-feed BBC articles, and 41 censorship resistant tools. The max-rate writing throughput (183 KB/s) and blockchain utilization (88%) exceed those of state-of-the-art solutions by 2-3 orders of magnitude and broke Litecoin’s record of the daily average block size. Our simulations with up to 3,000 concurrent UWeb writers confirm that UWeb does not impact the confirmation delays of financial transactions.

In the recent years, Free Space Optics (FSO) technology has attracted significant research and commercial interest mostly because of its many advantages in comparison with other radio systems used for point-to-point connections. However, the reliable operation of these systems significantly depends on the conditions of the atmosphere in the area in which the optical beam propagates. The most important of these conditions are atmospheric turbulence and the misalignment between the optical beam and the receiver, which is also known as the pointing errors effect. In this work, in order to obviate the performance mitigation caused by these phenomena, we examined the most widely accepted and one of the most effective techniques, i.e., the implementation of receivers’ diversity. Various metrics have been investigated to evaluate the performance of such systems, but most of them do not take into account that the ultra-fast modern optical communication systems use blocks of bits for the transmission and codes for the detection and/or correction of erroneous bits. Thus, by taking these aspects into account, in this work, we investigated the combined impact of spatial jitter and atmospheric turbulence on the total average block error rate of an optical wireless system with receivers’ diversity. Novel closed-form analytical formulas were derived.

Background: The STOP-BANG questionnaire is a simple screening tool with high sensitivity for the detection of severe obstructive sleep apnea (OSA). Predicting airway obstruction would allow the safe management of sedative patients to prevent intraoperative hypoxia. This study was designed to check the correlation between the STOP-BANG score and oxygen saturation (SpO2) during sedation and confirm the availability of the STOP-BANG questionnaire as a preoperative exam for predicting the incidence of hypoxia in sedative patient management.Methods: This study included 56 patients who received spinal anesthesia. The pre-anesthesia evaluation was conducted using the STOP-Bang questionnaire. The patients were under spinal anesthesia with an average block level of T10. Dexmedetomidine was infused with a loading dose of 1 μg/kg over 10 min and a maintenance dose of 0.5 μg/kg/h until the end of the procedure. The SpO2 of the patients was recorded every 5 min.Results: The STOP-Bang score was negatively correlated with the lowest SpO2 (coefficient = –0.774, 95% confidence interval [CI]: –0.855 to –0.649, standard error [SE] = 0.054, P < 0.001). The item of “observed apnea” was the most correlated one with hypoxic events (odds ratio = 6.00, 95% CI: 1.086 to 33.145).Conclusions: The STOP-BANG score was significantly correlated with the lowest SpO2 during spinal anesthesia, which enabled the prediction of meaningful hypoxia before it occurred in the sedated patients.

AbstractThe idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there exists an explicit channel model for training that matches the actual channel model in the online transmission. The variation of the actual channel indeed imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. Specifically, we partition channel coefficient values into sub-intervals, train an AE for each partition in the offline phase, and constitute a bank of AEs. Then, based on the actual channel condition in the online phase and the average block error rate (BLER), the optimal pair of encoder and decoder is selected for data transmission. To gain knowledge about the actual channel conditions, we assume a realistic scenario in which the instantaneous channel is not known, and propose to blindly estimate it at the Rx, i.e., without any pilot symbols. Our simulation results confirm the superiority of the proposed adaptive scheme over existing methods in terms of the average power consumption. For instance, when the target average BLER is equal to $$10^{-4}$$ 10 - 4 , our proposed algorithm with 5 pairs of AE can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme.

abstract: It is extremely important that the quality control of the concrete block used in structural masonry is conducted based on standard procedures that allow reliable estimation of the properties of these components. This work aims to analyze and evaluate the influence of the concrete block moisture on the result of the compression test. Hollow concrete blocks were prepared and subsequently maintained in different environments for various periods of time and under different conditions of temperature and humidity to determine the influence of the type of drying on the relative humidity of the block at the time of testing and consequently on its compressive strength. As a conclusion, it can be stated that, because it is necessary to use water in the process, the grinding rectification of the faces of the blocks led them to have high humidity, above 70%. If tested in this condition, the results of the compressive strength tests will be lower than that of blocks under usual environmental conditions. No differences were found in the average block strength when they were kept dry in the controlled environment of the laboratory during periods of 24 or 48 h. After grinding, it is not necessary to dry the blocks inside an oven at 40ºC before the tests; simply leaving them at a usual room temperature of 23ºC and humidity of 40 ± 5% for 24h is sufficient. The attempt to accelerate drying in an oven at 100ºC is not adequate because this leads to an increase in the compressive strength. From the results, it was possible to determine expressions to correlate the compressive strength as a function of the moisture of the block at the time of the test. The best-fit expressions are distinct for each block type, but the formulations are consistent in indicating a considerable difference in resistance as a function of moisture.

Abstract The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there is an explicit channel model for training that matches the actual channel model in the online transmission. Since the actual channel varies over time, this imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. More precisely, we divide the interval of random channel coefficients into n sub-intervals. Subsequently, in the offline training phase, we employ an AE bank consisting of n pairs of encoder and decoder and perform training over the sub-intervals. Then, in the online transmission phase, based on the actual channel conditions, the optimal pair of encoder and decoder is selected for data transmission in terms of satisfying an average block error rate (BLER) constraint imposed on the system. To monitor actual channel conditions for adopting the adaptive scheme, we assume a realistic scenario where the instantaneous channel gain is not known to Tx/Rx and it is blindly estimated at the RX, i.e., without using any pilot symbols. Our simulation results confirms the superiority of the proposed adaptive scheme over a non-adaptive scenario in terms of average power consumption. For instance, when the target average BLER is equal to 10−4 , our proposed algorithm with n = 5 can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme

AbstractProteins display a wealth of dynamical motions that can be probed using both experiments and simulations. We present an approach to integrate side chain NMR relaxation measurements with molecular dynamics simulations to study the structure and dynamics of these motions. The approach, which we term ABSURDer (Average Block Selection Using Relaxation Data with Entropy Restraints) can be used to find a set of trajectories that are in agreement with relaxation measurements. We apply the method to deuterium relaxation measurements in T4 lysozyme, and show how it can be used to integrate the accuracy of the NMR measurements with the molecular models of protein dynamics afforded by the simulations. We show how fitting of dynamic quantities leads to improved agreement with static properties, and highlight areas needed for further improvements of the approach.

Abstract The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there is an explicit channel model for training that matches the actual channel model in the online transmission. Since the actual channel varies over time, this imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. More precisely, we divide the interval of random channel coefficients into n sub-intervals. Subsequently, in the offline training phase, we employ an AE bank consisting of n pairs of encoder and decoder and perform training over the sub-intervals. Then, in the online transmission phase, based on the actual channel conditions, the optimal pair of encoder and decoder is selected for data transmission in terms of satisfying an average block error rate (BLER) constraint imposed on the system. To monitor actual channel conditions for adopting the adaptive scheme, we assume a realistic scenario where the instantaneous channel gain is not known to Tx/Rx and it is blindly estimated at the RX, i.e., without using any pilot symbols. Our simulation results confirms the superiority of the proposed adaptive scheme over a non-adaptive scenario in terms of average power consumption. For instance, when the target average BLER is equal to 10−4, our proposed algorithm with n = 5 can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme.

Abstract Copper remains one of the key biocides for protecting timber in soil contact. Historically, copper-based preservatives relied on solubilized copper. Over the past decade, micronized copper systems have largely replaced soluble copper systems in this application. While micronized copper chemistries have been shown to perform well over time with reduced copper leaching compared to solubilized copper, data are lacking on the effects of repeated leaching cycles on resistance of the timber to fungal attack. The potential effects of repeated leaching cycles of micronized copper azole–treated southern pine (Pinus spp.) blocks on both copper losses and resistance to fungal attack were explored over 20 leaching cycles using Gloeophyllum trabeum and Rhodonia placenta as test fungi. Copper losses during leaching were elevated for the first two cycles and steadily declined with additional cycling. There were no noticeable differences in fungal-associated weight losses in blocks exposed to G. trabeum. There was only a slight increase in average block weight losses with R. placenta, although blocks exposed to this fungus experienced higher weight losses with increased leaching cycles. The results suggest that repeated leaching exposures may produce some localized increases in susceptibility to fungal attack, but the overall effects were minor.