Search Results (2,633)

To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter (PM) in specific particle size channels, providing essential information for estimating lung deposition of vaping aerosols. For the sensor’s input, wavelength-specific optical attenuation signals are acquired for three separate wavelengths in the ultraviolet, red, and near-infrared range, and the inhalation pressure is collected from a pressure sensor. The sensor’s outputs are PM mass in three size bins, specified as 100–300 nm, 300–600 nm, and 600–1000 nm. Reference measurements of electronic cigarette aerosols, obtained using a custom vaping machine and a scanning mobility particle sizer, provided the ground truth for size-binned PM mass. A lightweight two-layer feedforward neural network was trained using datasets acquired from a wide range of puffing conditions. The performance of the neural network was tested using unseen data collected using new combinations of puffing conditions. The model-predicted values matched closely with the ground truth, and the accuracy reached 81–87% for PM mass in three size bins. Given the sensor’s straightforward optical configuration and the direct collection of signals from undiluted vaping aerosols, the achieved accuracy is notably significant and sufficiently reliable for point-of-interest sensing of vaping aerosols. To the best of our knowledge, this work represents the first instance where machine learning has been applied to directly characterize high-concentration undiluted electronic cigarette aerosols. Our sensor holds great promise in tracking electronic cigarette users’ puff topography with quantification of size-binned PM mass, to support long-term personalized health and wellness. Full article

Overtaking situations are commonly encountered in maritime navigation, and the overtaking process involves various risk factors that significantly contribute to collision incidents. It is crucial to conduct research on the maneuvering behaviors and decision-making processes associated with ship overtaking. This paper proposes a method based on the analysis of ship maneuvering performance to investigate overtaking behaviors in navigational channels. A relative motion model is established for both the overtaking and the overtaken vessels, and the inter-vessel distance is calculated, taking into account the psychological perceptions of the ship’s driver. A decision-making model for ship overtaking is presented to provide a safety protocol for overtaking maneuvers. Applying this method to overtaking data from the South Channel shows that it effectively characterizes both the permissible overtaking space and the driver’s overtaking desire. Additionally, it enables the prediction of optimal overtaking timing and strategies based on short-term trajectory forecasts. Thus, this method not only offers a safe overtaking plan for vessels but also provides auxiliary information for decision making in intelligent ship navigation. Full article

Hyperspectral infrared atmospheric sounding data, characterized by their high vertical resolution, play a crucial role in capturing three-dimensional atmospheric spatial information. The hyperspectral infrared atmospheric detectors HIRAS/HIRAS-II, mounted on the FY3D/EF satellite, have established an initial global coverage network for atmospheric sounding. The collaborative observation approach involving multiple satellites will improve both the coverage and responsiveness of data acquisition, thereby enhancing the overall quality and reliability of the data. In response to the increasing number of channels, the rapid growth of data volume, and the specific requirements of multi-satellite joint observation applications with infrared hyperspectral sounding data, this paper introduces an efficient storage and indexing method for infrared hyperspectral sounding data within a distributed architecture for the first time. The proposed approach, built on the Kubernetes cloud platform, utilizes the Google S2 discrete grid spatial indexing algorithm to establish a grid-based hierarchical model for unified metadata-embedded documents. Additionally, it optimizes the rowkey design using the BPDS model, thereby enabling the distributed storage of data in HBase. The experimental results demonstrate that the query efficiency of the Google S2 grid-based embedded document model is superior to that of the traditional flat model, achieving a query time that is only 35.6% of the latter for a dataset of 5 million records. Additionally, this method exhibits better data distribution characteristics within the global grid compared to the H3 algorithm. Leveraging the BPDS model, the HBase distributed storage system adeptly balances the node load and counteracts the detrimental effects caused by the accumulation of time-series remote sensing images. This architecture significantly enhances both storage and query efficiency, thus laying a robust foundation for forthcoming distributed computing. Full article

This review article highlights the critical impact of surface roughness in modifying the structure of two-phase flow within mini- and microchannels, particularly in processes such as boiling and condensation. Channel surface roughness enhances flow resistance, affects the distribution of vapor bubbles, and enhances heat transfer by providing additional nucleation sites. Several experiments have shown that while increased surface roughness enhances the efficiency of heat transfer, increased flow resistance may hurt system performance. This is so because too high a surface roughness negatively impacts flow resistance, a factor of importance in the optimization for a balance between heat transfer and flow resistance, especially in high-performance compact heat exchangers. Furthermore, the review identifies that higher-degree measurement and characterization techniques of the surface roughness are increasingly required, as traditional 2D parameters may not fully represent the actual physics of complex surface interactions in two-phase flow systems. Consequently, the article calls for further research that can examine the exact relationship between roughness, flow structure, and thermal performance with the aim of improving design strategies for future heat exchanger technologies. Full article

In this paper, it is demonstrated that the AlGaN high electron mobility transistor (HEMT) based on silicon wafer exhibits excellent high-temperature performance. First, the output characteristics show that the ratio of on-resistance (R_ON) only reaches 1.55 when the working temperature increases from 25 °C to 150 °C. This increase in R_ON is caused by a reduction in optical phonon scattering-limited mobility (μ_OP) in the AlGaN material. Moreover, the device also displays great high-performance stability in that the variation of the threshold voltage (ΔV_TH) is only 0.1 V, and the off-state leakage current (I_D,off-state) is simply increased from 2.87 × 10⁻⁵ to 1.85 × 10⁻⁴ mA/mm, under the operating temperature variation from 25 °C to 200 °C. It is found that the two trap states are induced at high temperatures, and the trap state densities (D_T) of 4.09 × 10¹²~5.95 × 10¹² and 7.58 × 10¹²~1.53 × 10¹³ cm⁻² eV⁻¹ are located at E_T in a range of 0.46~0.48 eV and 0.57~0.61 eV, respectively, which lead to the slight performance degeneration of AlGaN HEMT. Therefore, this work provides experimental and theoretical evidence of AlGaN HEMT for high-temperature applications, pushing the development of ultra-wide gap semiconductors greatly. Full article

Ion channels are integral membrane proteins embedded in biological membranes, and they comprise specific proteins that control the flow of ion transporters in and out of cells, playing crucial roles in the biological functions of different cells. They maintain the homeostasis of water and ion metabolism by facilitating ion transport and participate in the physiological processes of neurons and glial cells by regulating signaling pathways. Neurodegenerative diseases are a group of disorders characterized by the progressive loss of neurons in the central nervous system (CNS) or peripheral nervous system (PNS). Despite significant progress in understanding the pathophysiological processes of various neurological diseases in recent years, effective treatments for mitigating the damage caused by these diseases remain inadequate. Increasing evidence suggests that ion channels are closely associated with neuroinflammation; oxidative stress; and the characteristic proteins in neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS). Therefore, studying the pathogenic mechanisms closely related to ion channels in neurodegenerative diseases can help identify more effective therapeutic targets for treating neurodegenerative diseases. Here, we discuss the progress of research on ion channels in different neurodegenerative diseases and emphasize the feasibility and potential of treating such diseases from the perspective of ion channels. Full article

We consider the wideband spectrum sensing within a multi-path propagation environment, where a multi-antenna base station (BS) is tasked with identifying the frequency positions of multiple narrowband transmissions distributed across a broad range of frequencies. To tackle this, we propose a sub-Nyquist sampling structure that incorporates a phased array system. Specifically, each antenna is connected to two separate sampling channels, i.e., one for direct sampling and another for delayed sampling, with the latter incorporating a specified time delay factor. The cross-correlation matrices associated with the samples, which are characterized by different time lags, are calculated. These matrices are represented in tensor form, and the factor matrices are extracted through CANDECOMP/PARAFAC (CP) decomposition. By these factor matrices, the carrier frequencies and the power spectra of the far-field signals of interest are estimated. Numerical simulations are conducted to evaluate the performance of the proposed method, and the results reveal the feasibility and effectiveness of the approach, demonstrating its potential for accurate and efficient wideband spectrum sensing in complex multi-path propagation environments. Full article

The present work shows the development of a comprehensive theoretical formulation for its application in the study of the internal flow of pressure-swirl atomizers with helical channels: “screw-conveyer”, which are characterized by presenting in their inlet channels, an angle of incidence or helix angle $\psi$. This angle originates a trigonometric factor ($cos\psi$) that must be considered in the geometrical characteristics parameter of pressure-swirl atomizer ($A_h$), which consequently involves other geometric parameters, such as the annular section coefficient ($\phi$), discharge coefficient ($C_d$), spray angle ($2\alpha$), etc., being relevant in the internal flow study and design of the pressure-swirl atomizers type screw-conveyer. This theoretical formulation integrates an internal ideal flow model (Abramovich theory) with a model that considers the influence of the liquid viscosity (Kliachko theory) and hydraulic resistance of Idelchik. For the validation of this theoretical formulation, numerical simulation was used, considering the commercial software Ansys Fluent 2023 R2 furthermore, hexahedral meshes were generated with the ICEM CFD software 2023, for four cases of helix angle $\psi$ ($15°$, $30°$, $45°$ and $60°$), with application of the RNG k-$\epsilon$ turbulence model and VOF multiphase model (volume of fluid) for the location of the liquid-gas interface and spray angle visualization. Full article

Chitin, an extracellular polysaccharide, is synthesized by membrane-embedded chitin synthase (CHS) utilizing intracellular substrates. The mechanism of the translocation of synthesized chitin across the membrane to extracellular locations remains unresolved. We prove that the chitin synthase from Phytophthora sojae (PsCHS) is a processive glycosyltransferase, which can rapidly produce and tightly bind with the highly polymerized chitin. We further demonstrate that PsCHS is a bifunctional enzyme, which is necessary and sufficient to translocate the synthesized chitin. PsCHS was purified and then reconstituted into proteoliposomes (PLs). The nascent chitin is generated and protected from chitinase degradation unless detergent solubilizes the PLs, showing that PsCHS translocates the newly produced chitin into the lumen of the PLs. We also attempted to resolve the PsCHS structure of the synthesized chitin-bound state, although it was not successful; the obtained high-resolution structure of the UDP/Mn²⁺-bound state could still assist in describing the characterization of the PsCHS’s transmembrane channel. Consistently, we demonstrate that PsCHS is indispensable and capable of translocating chitin in a process that is tightly coupled to chitin synthesis. Full article

Background: The single-channel sleep EEG has the advantages of convenient collection, high-cost performance, and easy daily use, and it has been widely used in the classification of sleep stages. Methods: This paper proposes a single-channel sleep EEG classification method based on long short-term memory and a hidden Markov model (LSTM-HMM). First, the single-channel EEG is decomposed using wavelet transform (WT), and multi-domain features are extracted from the component signals to characterize the EEG characteristics fully. Considering the temporal nature of sleep stage changes, this paper uses a multi-step time series as the input for the model. After that, the multi-step time series features are input into the LSTM. Finally, the HMM improves the classification results, and the final prediction results are obtained. Results: A complete experiment was conducted on the Sleep-EDFx dataset. The results show that the proposed method can extract deep information from EEG and make full use of the sleep stage transition rule. The proposed method shows the best performance in single-channel sleep EEG classification; the accuracy, macro average F1 score, and kappa are 82.71%, 0.75, and 0.76, respectively. Conclusions: The proposed method can realize single-channel sleep EEG classification and provide a reference for other EEG classifications. Full article

User-perceived throughput is a novel performance metric attracting a considerable amount of recent attention because it characterizes the quality of the experience in mobile multimedia services. For instance, it gives a data rate of video streaming with which a user will not experience any lag or outage in watching video clips. However, its performance limit remains open. In this paper, we are interested in the achievable upper bound of user-perceived throughput, also referred to as the user-perceived capacity, and how to achieve it in typical wireless channels. We find that the user-perceived capacity is quite limited or even zero with channel state information at the receiver (CSIR) only. When both CSIR and channel state information at the transmitter (CSIT) are available, the user-perceived throughput can be substantially improved by power or even rate adaptation. A constrained Markov decision process (CMDP)-based approach is conceived to compute the user-perceived capacity with joint power–rate adaptation. It is rigorously shown that the optimal policy obeys a threshold-based rule with time, backlog, and channel gain thresholds. With power adaptation only, the user-perceived capacity is equal to the hard-delay-constrained capacity in our previous work and achieved by joint diversity and channel inversion. Full article

Offshore oilfields are characterized by loose sandstone reservoirs, strong heterogeneity and high injection and production intensity. Water channeling gradually develops after entering the high water cut stage, which weakens production performance. Current identification methods usually have high computational costs and low efficiency. A quantitative identification model of water channeling based on inter-well connection units has been established by simplifying the complex reservoir system into a connection network between injectors and producers, which can quickly and accurately obtain strength characteristic parameters for waterflow channels. In addition, a comprehensive evaluation factor M and classification standard for water channeling suitable for offshore heterogeneous reservoirs have been proposed. It indicates a thief zone when M is larger than 0.65, a predominant waterflow channel when M is between 0.55 and 0.65, and no water channeling when M is smaller than 0.55. The application of (an) offshore S oilfield demonstrates that the new method successfully identifies 18 segments of the thief zone and 19 segments of the predominant waterflow channel and improves computational speed by 100 times compared with the conventional numerical modeling method. This novel method allows for rapid and accurate identification and prediction of water channeling, including location, directions, and strengths, thereby providing timely and practical guidance for inefficient water channel treatment. Full article

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication, restricted interests, and repetitive behaviors. Despite considerable research efforts, the genetic complexity of ASD remains poorly understood, complicating diagnosis and treatment, especially in the Arab population, with its genetic diversity linked to migration, tribal structures, and high consanguinity. To address the scarcity of ASD genetic data in the Middle East, we conducted genome sequencing (GS) on 50 ASD subjects and their unaffected parents. Our analysis revealed 37 single-nucleotide variants from 36 candidate genes and over 200 CGG repeats in the FMR1 gene in one subject. The identified variants were classified as uncertain, likely pathogenic, or pathogenic based on in-silico algorithms and ACMG criteria. Notably, 52% of the identified variants were homozygous, indicating a recessive genetic architecture to ASD in this population. This finding underscores the significant impact of high consanguinity within the Qatari population, which could be utilized in genetic counseling/screening program in Qatar. We also discovered single nucleotide variants in 13 novel genes not previously associated with ASD: ARSF, BAHD1, CHST7, CUL2, FRMPD3, KCNC4, LFNG, RGS4, RNF133, SCRN2, SLC12A8, USP24, and ZNF746. Our investigation categorized the candidate genes into seven groups, highlighting their roles in cognitive development, including the ubiquitin pathway, transcription factors, solute carriers, kinases, glutamate receptors, chromatin remodelers, and ion channels. Full article

Background: Dry eye disease (DED) is a chronic condition characterized by a decrease in tear production or an increase in tear evaporation, leading to inflammation and damage of the ocular surface. Dysfunction of ion channels, tear hyperosmolarity and immune cell-driven inflammation create a vicious circle responsible for the pathological changes in the eyes of DED patients. Mesenchymal stem cells (MSCs) are adult, rapidly proliferating stem cells that produce a large number of immunoregulatory, angiomodulatory, and growth factors that efficiently reduce tear hyperosmolarity-induced pathological changes, inhibit harmful immune response, and provide trophic support to the injured corneal and conjuctival epithelial cells, goblet cells and acinar cells in lacrimal glands of DED patients. Methods: An extensive research in the literature was implemented in order to elucidate the role of MSCs in the attenuation of tear hyperosmolarity and eye inflammation in patients suffering from DED. Results: Findings obtained in preclinical and pilot clinical studies demonstrated that MSCs reduced tear hyperomsolaity-induced pathological changes and suppressed immune cell-driven eye inflammation. Additionally, MSC-based therapy managed to successfully address the most severe DED-related conditions and complications. Conclusions: MSCs should be considered as potentially new therapeutic agents for the treatment of severe DED. Full article

The Pisha sandstone area, situated in the upper and middle reaches of the Yellow River in China, is characterized by severe soil and water erosion, making it one of the most critical regions on the Loess Plateau. The rugged terrain and exposed bedrock complicate management efforts for this area, posing challenges for accurate forecasting using soil erosion models. Through an analysis of terrain, vegetation, and precipitation impacts on soil erosion, this study offers theoretical support for predicting soil erosion within the exposed Pisha sandstone area of the Loess Plateau. This has substantial implications for guiding water and soil conservation measures in this region. Focusing on China’s exposed sandstone area within the Geqiugou watershed, temporal and spatial changes in vegetation cover and land use from 1990 to 2020 were analyzed. The result shows that, from 1990 to 2020, the grassland area has exhibited a consistent downward trend, with successive reductions of 64.86% to 59.46%. The area of low vegetation cover witnessed a significant decline of 59.29% in 2020 compared to that in 1990. The moderate erosion area decreased from 84.52 to 57.17 km². The significant reduction in soil and water loss can be attributed to the expansion of forest and grassland areas, with the implementation of the Grain for Green project serving as a key policy driver for facilitating this expansion. This study provided a good example of combining rainfall with vegetation coverage to fast estimation soil erosion. A mathematical relationship between the vegetation rainfall coupling index (RV) and soil erosion was established with strong fitting effects, enabling estimation of the soil erosion volume under varying slope conditions within Pisha sandstone areas. The main focus of future soil and water conservation in the Pisha sandstone area should be on effectively managing the channel slope and minimizing exposed bedrock areas through a combination of slope cutting, the application of anticorrosive materials, and the implementation of artificial vegetation planting. Full article