Graph-Based Radio Resource Management for Vehicular Networks

This paper investigates the resource allocation problem in device-to-device (D2D)-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We propose a graph-based algorithm that uses graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problem as a weighted 3-dimensional matching problem, which is then solved through adapting a high-performance approximation algorithm.Comment: 7 pages; 5 figures; accepted by IEEE ICC 201

A lake ice phenology dataset for the Northern Hemisphere based on passive microwave remote sensing

Lake ice phenology (LIP) is an essential indicator of climate change and helps with understanding of the regional characteristics of climate change impacts. Ground observation records and remote sensing retrieval products of lake ice phenology are abundant for Europe, North America, and the Tibetan Plateau, but there is a lack of data for inner Eurasia. In this work, enhanced-resolution passive microwave satellite data (PMW) were used to investigate the Northern Hemisphere Lake Ice Phenology (PMW LIP). The Freeze Onset (FO), Complete Ice Cover (CIC), Melt Onset (MO), and Complete Ice Free (CIF) dates were derived for 753 lakes, including 409 lakes for which ice phenology retrievals were available for the period 1978 to 2020 and 344 lakes for which these were available for 2002 to 2020. Verification of the PMW LIP using ground records gave correlation coefficients of 0.93 and 0.84 for CIC and CIF, respectively, and the corresponding values of the RMSE were 11.84 and 10.07 days. The lake ice phenology in this dataset was significantly correlated (P < 0.001) with that obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data–the average correlation coefficient was 0.90 and the average RMSE was 7.87 days. The minimum RMSE was 4.39 days for CIF. The PMW is not affected by the weather or the amount of sunlight and thus provides more reliable data about the freezing and thawing process information than MODIS observations. The PMW LIP dataset provides the basic freeze–thaw data that is required for research into lake ice and the impact of climate change in the cold regions of the Northern Hemisphere. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00081.Peer reviewe

A Study of the Recurrent Neural Network Encoder-Decoder for Large Vocabulary Speech Recognition

Deep neural networks have advanced the state-of-the-art in automatic speech recognition, when combined with hidden Markov models (HMMs). Recently there has been interest in using systems based on recurrent neural networks (RNNs) to perform sequence modelling directly, without the require-ment of an HMM superstructure. In this paper, we study the RNN encoder-decoder approach for large vocabulary end-to-end speech recognition, whereby an encoder transforms a se-quence of acoustic vectors into a sequence of feature represen-tations, from which a decoder recovers a sequence of words. We investigated this approach on the Switchboard corpus us-ing a training set of around 300 hours of transcribed audio data. Without the use of an explicit language model or pronunciation lexicon, we achieved promising recognition accuracy, demon-strating that this approach warrants further investigation. Index Terms: end-to-end speech recognition, deep neural net-works, recurrent neural networks, encoder-decoder. 1

A study on influential factors of occupant window-opening behavior in an office building in China

Occupants often perform many types of behavior in buildings to adjust the indoor thermal environment. In these types, opening/closing the windows, often regarded as window-opening behavior, is more commonly observed because of its convenience. It not only improves indoor air quality to satisfy occupants' requirement for indoor thermal comfort but also influences building energy consumption. To learn more about potential factors having effects on occupants' window-opening behavior, a field study was carried out in an office building within a university in Beijing. Window state (open/closed) for a total of 5 windows in 5 offices on the second floor in 285 days (9.5 months) were recorded daily. Potential factors, categorized as environmental and non-environmental ones, were subsequently identified with their impact on window-opening behavior through logistic regression and Pearson correlation approaches. The analytical results show that occupants' window-opening behavior is more strongly correlated to environmental factors, such as indoor and outdoor air temperatures, wind speed, relative humidity, outdoor PM2.5 concentrations, solar radiation, sunshine hours, in which air temperatures dominate the influence. While the non-environmental factors, i.e. seasonal change, time of day and personal preference, also affects the patterns of window-opening probability. This paper provides solid field data on occupant window opening behavior in China, with high resolutions and demonstrates the way in analyzing and predicting the probability of window-opening behavior. Its discussion into the potential impact factors shall be useful for further investigation of the relationship between building energy consumption and window-opening behavior

An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

Funding Information: Funding Statement: This work was supported in part by the Natural Science Foundation of Hunan Province under Grant Nos. 2023JJ30316 and 2022JJ2029, in part by a project supported by Scientific Research Fund of Hunan Provincial Education Department under Grant No. 22A0686, and in part by the National Natural Science Foundation of China under Grant No. 62172058. This work was also funded by the Researchers Supporting Project (No. RSP2023R102) King Saud University, Riyadh, Saudi Arabia.Peer reviewedPublisher PD

The clinical application of metagenomic next-generation sequencing in sepsis of immunocompromised patients

BackgroundMetagenomic next-generation sequencing (mNGS) was commonly applied given its ability to identify and type all infections without depending upon culture and to retrieve all DNA with unbiasedness. In this study, we strive to compare outcomes of mNGS with conventional culture methods in adults with sepsis, investigate the differences between the immunocompromised and control group, and assess the clinical effects of mNGS.MethodsIn our study, 308 adult sepsis patients were included. We used both mNGS and conventional culture methods to analyze diagnostic results, pathogens, and sample types. The correlation between some laboratory tests and the frequency of pathogens by groups was also analyzed. Furthermore, the clinical impacts of mNGS were estimated.Results308 samples were assigned to an immunocompromised group (92/308,29.9%) and a control group (216/308,70.1%). There was the sensitivity of mNGS considered greater than that of the culture method in all samples (88.0% vs 26.3%; P &lt;​ 0.001), in the immunocompromised group (91.3% vs 26.1%; P &lt;​ 0.001), and the control group (86.6% vs 26.4%; P &lt;​ 0.001), particularly in all sample types of blood (P &lt;​ 0.001), BALF (P &lt;​ 0.001), CSF (P &lt;​ 0.001), sputum (P &lt;​ 0.001) and ascitic fluid (P = 0.008). When examining the mNGS results between groups, Pneumocystis jirovecii (P &lt; 0.001), Mucoraceae (P = 0.014), and Klebsiella (P = 0.045) all showed significant differences. On the whole, mNGS detected more pathogens than culture methods (111 vs 25), found 89 organisms that were continuously overlooked in entire samples by culture methods, and showed a favorable positive clinical effect in 76.3% (235 of 308) of patients. In 185 (60.1%) patients, mNGS prompted a modification in the course of management, which included antibiotic de-escalation in 61(19.8%) patients.ConclusionsThe research discovered that mNGS was more sensitive than the culture method, particularly in samples of blood, BALF, CSF, sputum, and ascitic fluid. When examining the mNGS results, Pneumocystis jirovecii and Mucoraceae were the pathogens seen more commonly in immunocompromised patients with sepsis, which required more attention from clinicians. There was a substantial benefit of mNGS in enhancing the diagnosis of sepsis and advancing patient treatment