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The production of hydrogen from electrolytic water using renewable energy is a highly effective way to reduce carbon emissions and has garnered significant attention from scholars around the world. As the crucial link between the electrolyzer and the DC bus, the performance of the DC-DC buck converter is paramount. Therefore, a multiphase stacked interleaved buck converter (MSIBC) is proposed in this paper, which offers superior performance in terms of low voltage, high current, and minimal current ripple. This paper delves into the circuit design methods and current ripple elimination mechanisms of this topology. Furthermore, a detailed analysis is conducted to compare the performance of MSIBC with that of interleaved buck converter (IBC) and stacked interleaved buck converter (SIBC), covering factors such as efficiency, inductor current stress, and switching current stress. The results indicate that MSIBC offers superior efficiency, low inductor current stress, and switching current stress. Additionally, MSIBC enables redundant control of the system. Finally, experimental and MATLAB/SIMULINK simulation results confirm the accuracy of ripple cancellation.

Mammalian milk exosomal miRNAs play an important role in maintaining intestinal immune homeostasis and protecting epithelial barrier function, but the specific miRNAs and whether miRNA-mediated mechanisms are responsible for these benefits remain a matter of investigation. This study isolated sheep milk-derived exosomes (sheep MDEs), identifying the enriched miRNAs in sheep MDEs, oar-miR-148a, and oar-let-7b as key components targeting TLR4 and TRAF1, which was validated by a dual-luciferase reporter assay. In dextran sulfate sodium-induced colitis mice, administration of sheep MDEs alleviated colitis symptoms, reduced colonic inflammation, and systemic oxidative stress, as well as significantly increased colonic oar-miR-148a and oar-let-7b while reducing toll-like receptor 4 (TLR4) and TNF-receptor-associated factor 1 (TRAF1) level. Further characterization in TNF-α-challenged Caco-2 cells showed that overexpression of these miRNAs suppressed the TLR4/TRAF1-IκBα-p65 pathway and reduced IL-6 and IL-12 production. These findings indicate that sheep MDEs exert gastrointestinal anti-inflammatory effects through the miRNA-mediated modulation of TLR4 and TRAF1, highlighting their potential in managing colitis.

This study aimed to develop an ultrasound‐guided high‐intensity‐focused ultrasound (USgHIFU) probe for arterial sonication and to evaluate vascular contraction. The USgHIFU probe comprised two confocal spherical transducers for sonication and a US color Doppler flow imaging probe for guidance. A vessel‐mimicking phantom was sonicated in two directions. In the vascular radial direction, an isolated rabbit aorta embedded in ex vivo pork liver was sonicated at different acoustic powers (245 and 519 W), flow rates (25, 30, and 50 mL/minute), and sonication energies (519, 980, and 1038 J). Changes in the postsonication vessels were evaluated using US imaging, microscopic observation, and histopathological analysis. Beam focusing along the vascular radial direction caused significant deformation of both tube walls (n = 4), whereas focusing along the axial direction only affected the contraction of the anterior wall (n = 4). The contraction index (Dc) of the vessel sonicated at 245 W and 980 J was 56.2 ± 9.7% (n = 12) with 25 mL/minute. The Dcof the vessel sonicated at 519 W and 1038 J was 56.5 ± 7.8% (n = 17). The Dcof the vessel sonicated at 519 J total energy was 18.3 ± 5.1% (n = 12). The developed USgHIFU probe induced greater vascular contractions by covering a larger area of the vessel wall in the radial direction. Sonication energy affects vascular contraction through temperature elevation of the vessel wall. When the acoustic power was high, an increase in acoustic power, even with comparable sonication energy, did not result in greater vessel contraction.

Metallized film capacitors (MFCs) are known for their self-healing (SH) properties, enabling efficient and reliable operation, even under challenging conditions. These SH events have the potential to inflict damage on both the polypropylene (PP) film and the electrode layer. However, not all types of SH damage lead to catastrophic failure of the capacitor. Thus, finding the threshold of SH that has little impact on the reliability of the capacitor is important. This article classifies SH events based on their SH energy, ranging from safe to risky, and establishes thresholds for safe SH. We investigate the correlation between SH energy and critical factors, such as peeling stress (adhesion between films), damage to internal components, and the number of damaged layers. For instance, at safe SH levels (< 100 mJ), peeling stress on metallized films remains below 8 N/m, while risky SH events (>250 mJ) result in peeling stress as high as 14 N/m. Furthermore, this article associates the equivalent parallel resistance ( ${R}_{P}$ ) with SH safety, realizing the correlation between external measurable parameters of capacitor and internal damage caused by SH events. ${R}_{P}$ serves as a valuable tool for evaluating the safety of MFCs with an unknown SH history, contributing to the assessment of their reliability.

Byline: Yin. Xu, Xiao-yan. Fei, You-hua. Xue, Xue-mei. Wang, Xiao-yan. Wang, Yu-ting. Zheng, Ke. Chen, Jing-jing. He, Xiu-hong. Ren, Lei. Pan, Ling. Wang, Duo. Qian, Wei-feng. Li, Yan. Liang, Yan. [...]

Organic–inorganic hybrid halides flourish due to their thermal stability, plasticity, and optical properties. However, performance optimization through reasonable regulation has always been a challenge. Diverse anions offer a broad range of possibilities and options for the development of stimulus-responsive materials, thereby facilitating performance optimization. Here, we have successfully synthesized two organic–inorganic photoluminescent hybrid halides, (NNDP)2MnX4(NNDP = N,N-dimethylpiperidinium chloride; X = Cl, Br), with a significant enhancement in photoluminescence quantum yield (4.38% ? 39.91%) via halogen modulation. The increase in quantum yield is attributed to the distortion in the [MnX4]2–tetrahedron. Furthermore, the phase-transition temperatures of (NNDP)2MnCl4and (NNDP)2MnBr4were determined as 347 and 335 K, respectively, through thermodynamic measurement. Meanwhile, the intermolecular forces of (NNDP)2MnCl4and (NNDP)2MnBr4were analyzed using a Hirshfeld surface and a two-dimensional (2D) fingerprint. This study injects new innovative ideas for the design of novel photoluminescent materials.

Penpulimab is a novel programmed death (PD)-1 inhibitor. This study aimed to establish the efficacy and safety of first line penpulimab plus chemotherapy for advanced squamous non-small-cell lung cancer.

A high pulse energy and high power all-fiber-optic single frequency laser at 1572 nm has been demonstrated and delivered for CO2 LIDAR applications. The main amplifier is made with the company’s proprietary large mode area highly erbium-doped silicate glass fiber with a core diameter of 50 $\mu$ m. The pump laser is a high power 1480 nm Raman fiber laser. A maximum pulse energy of 2.2 mJ has been achieved at 2.5 kHz pulse repetition rate with diffraction limited beam quality, which is the highest pulse energy and average power combination for an all-fiber based laser at 1572 nm to the best of our knowledge. For purpose of extended lifetime and consistent performance, the output pulse energy from the delivered unit was limited to 1.8 mJ with a pulse duration of ~600 ns, corresponding to a peak power of ~3.0 kW.

Pulmonary fibrosis (PF) is a devastating lung disease with limited treatment options. During this pathological process, the profibrogenic macrophage subpopulation plays a crucial role, making the characterization of this subpopulation fundamentally important. The present study revealed a positive correlation between pulmonary macrophages with higher mitochondrial mass (Mømitohigh) and fibrosis. Among the Mømitohighsubpopulation of CD206+M2, characterized by higher expression of dynamin 1-like (Drp1), as determined by flow cytometry and RNA-seq analysis, a therapeutic intervention was developed using an exosome-based formula composed of pathfinder and therapeutics. A pathfinder exosome called “exosomeMMP19(ExoMMP19)”, was constructed to display matrix metalloproteinase-19 (MMP19) on the surface to locally break down the excessive extracellular matrix (ECM) in the fibrotic lung. A therapeutic exosome called “exosome therapeutics(ExoTx)”, was engineered to display D-mannose on the surface while encapsulating siDrp1 inside. Prior delivery of ExoMMP19degraded excessive ECM and thus paved the way for ExoTxto be delivered into Mømitohigh, where ExoTxinhibited mitochondrial fission and alleviated PF. This study has not only identified Mømitohighas profibrotic macrophages but it has also provided a potent strategy to reverse PF via a combination of formulated exosomes.

High-performance reusable materials from renewable resources are rare and urgently required in bioseparation. Herein, a series of tannic acid-chitosan composite membranes for the enrichment of phosphopeptides were fabricated by the freeze casting method. First, a tannic acid-chitosan composite membrane was acquired via the multiple hydrogen bonds between tannic acid and chitosan, which had a long-range aligned three-dimensional microstructure. Second, a covalent–hydrogen bond hybrid composite was also fabricated, with stable and aligned honeycomb-like microstructures that formed by the synergy of covalence and hydrogen bonding. Besides, a ternary composite membrane was “one-pot” synthesized by the copolymerization of tannic acid, chitosan, and Ti4+ions, indicating the feasibility of involving metal ions in the composition of the polymer skeleton in place of additional modification steps. The as-prepared chitosan composite membranes exhibited excellent performance in the enrichment of phosphopeptides from β-casein tryptic digest and human serum. Benefitting from the long-range aligned honeycomb-like structure coordinated by hydrogen bonds and covalent bonds, and a large number of pyrogallol functional groups provided by tannic acid, the covalent–hydrogen bond hybrid membrane showed excellent reusability and could be reused up to 16 times in phosphopeptide enrichment, as far as we know, which is the best reported result to date.

Graphic abstract:

The navigation and positioning system of mobile robots using multisensor fusion has become a research hotspot in the aspects of high accuracy, low computational complexity, and strong stability. In order to improve the accuracy of sensor asynchronous information fusion and meet the application requirements of geometric structure feature degradation in warehousing logistics, an adaptive weighted factor graph (AWFG) positioning method using inertial measurement unit (IMU), LiDAR, and stereo camera is proposed. Combining the dominant features of three sensors and factor graph theory, a new multisensor fusion factor graph model is established. By dynamically adjusting the reliability of sensor measurement information, an adaptive factor weight function is designed to improve positioning accuracy and system stability under abnormal sensor or environmental interference conditions. Besides, sliding window optimization is added to limit the factor scale, and the variable elimination algorithm is combined to optimize the factors in the window to further reduce the computational complexity. Compared with extended Kalman filter (EKF) and particle swarm optimization (PSO) algorithms, simulation and experimental results show that the proposed method not only reduces the mean location error by about 30%, but also effectively enhances the computational efficiency and system stability.

The electrification of automotive is growing rapidly, resulting in an increased need for aluminum alloys with high electrical conductivity. The core element of the electric vehicle, besides the batteries, is an electric motor, which replaces the internal combustion engines of a traditional gasoline vehicle. These applications require both high strength and high electrical conductivity. This paper presents the development of a new Al-Fe-Ni aluminum alloy that provides a good combination of mechanical properties and electrical conductivity. The alloy is well suited for high-pressure vacuum die casting (HPVDC). The as-cast microstructures, mechanical properties and electrical conductivity were studied for alloys with different Fe/Ni combination. The relationship between the microstructure and properties is analyzed, and the strengthening mechanisms of the studied alloy are discussed. Based on the experimental results, the new alloy demonstrates excellent potential for electric vehicle applications produced by high-pressure vacuum die casting.

The conventional surface wave inversion methodology relying upon dispersion curve analyses is frequently confounded by the phenomenon known as “mode kissing” as well as the absorptive properties of seismic waves traversing near-surface geologic strata. We propose a deep learning approach to overcome challenges in surface wave inversion due to complex near-surface geology. A convolutional neural network regression model is developed to directly map surface wave dispersion spectra to shear wave velocity profiles. The network takes entire spectrograms as input, avoiding issues from manual picking of individual dispersion curves. This circumvents problems of mode misidentification and crossover that hamper traditional inversion in layered media. Furthermore, we incorporate the quality factor Q into calculating the theoretical frequency dispersion spectrum to account for the absorptive influence of geologic formations. Through studying theoretical spectra, we found that when the Q-factor is less than 10, the absorption of the formation will have a greater impact on the dispersion imaging. Compared to existing workflows requiring manual parameter tuning and computationally expensive inversion calculations, the trained network directly infers velocities without user intervention or iterative optimization. The end-to-end relation learned from synthetic training data enables rapid interpretation of field records complicated by low-velocity zones and high-velocity inclusions, which inhibit conventional inversion algorithms.

The mining of meaningful features and effective fusion of multisource remote sensing (RS) data have always been the challenging research problems in the joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. In this article, we propose a multifrequency graph convolutional network with cross-modality mutual enhancement (MFGCN-CME) for multisource RS data classification. Specifically, we design an adaptive multifrequency graph feature learning module to capture the low- and high-frequency multiscale features of HSI and LiDAR in parallel and further adaptively aggregate them. Then, we propose a bipartite graph (BG) enhancement learning module to obtain the spatial-enhanced HSI features and spectral-enhanced LiDAR features by propagating intermodality information. To the best of our knowledge, the BG is first used to multisource RS data classification task. Furthermore, compared with traditional fusion methods, a gated fusion module is used to fully explore the complementarity of two data sources. Finally, a joint loss function combing a classification loss and a semisupervised contrastive loss is developed to improve the model robustness. Comprehensive experiments on different HSI and LiDAR datasets demonstrate that our proposed method can yield better performance compared with several state-of-the-art multisource RS data classification methods.

Research on orthodontic treatment monitoring from oralscan video is a new direction in dental digitalization. We designed an approach to reconstruct, segment, and estimate the pose of individual teeth to measure orthodontic treatment. To handle the semantic gap in heterogeneous data on the condition that they are combined linearly, we present a multimedia interaction network (MIN) to combine heterogeneous information in point cloud segmentation by extending the graph attention mechanism. Moreover, a structure-aware quadruple loss is designed to explore the relation between multiple and diverse unmatched points in point cloud registration. The performance of our approach is evaluated on multiple tooth registration datasets, and extensive experiments show that our approach improves the accuracy by a margin of 1.4% in the inlier ratio on the Aoralscan3 dataset when it is compared with prevailing approaches.

Metallized polypropylene film capacitors (MPPFCs) are well-suited for high-frequency and high-electric-field applications, such as electric vehicles, aerospace, and pulsed power systems, due to their self-healing (SH) properties. However, the SH process is affected by operating conditions such as voltage level and ramp rate. It is not well understood whether the SH mechanism is consistent across various operating conditions, which may lead to inaccurate assessments of MPPFC reliability. This article explores the effect of the dc voltage ramp rates on the SH performance of MPPFC. A more specific classification of SH processes under a high electrical field: independent SH (ISH) and related SH (RSH, composed of two or more sub-SHs). Additionally, the SH process of MPPFCs can be characterized using multiple Weibull subpopulation distributions for various voltage ramp rates. At slow ramp rates (< 500 V/s), both ISH and RSH processes are present in the MPPFC, whereas, at rapid ramp rates ( $\ge 500$ V/s), the SH process is mainly dominated by ISH. Moreover, the cumulative SH energy of the MPPFC at the 30 V/s ramp rate is about ten times that of the 900 V/s ramp rate. The ISH at the 900 V/s ramp rate causes several point-like damaged regions on the metalized polypropylene (PP) film ( $\sim $ 0.06% decrease in capacitance). In comparison, the RSH at the 30 V/s ramp rates induces clusters of holes across multiple layers in the MPPFC ( $\sim $ 9.85% decrease in capacitance), implying that the MPPFC has overreached its expected lifetime.

Lipid droplets (LDs) are highly dynamic intracellular organelles, which are involved in lots of biological processes. However, the dynamic morphogenesis and functions of intracellular LDs during persistent innate immune responses remain obscure. In this study, we induce long-term systemic immune activation in Drosophilathrough genetic manipulation. Then, the dynamic pattern of LDs is traced in the Drosophilafat body. We find that deficiency of Plin1, a key regulator of LDs’ reconfiguration, blocks LDs minimization at the initial stage of immune hyperactivation but enhances LDs breakdown at the later stage of sustained immune activation via recruiting the lipase Brummer (Bmm, homologous to human ATGL). The high wasting in LDs shortens the lifespan of flies with high-energy-cost immune hyperactivation. Therefore, these results suggest a critical function of LDs during long-term immune activation and provide a potential treatment for the resolution of persistent inflammation.

The term “mobile robot” or “intelligent mobile robot” refers to an autonomous system with specific sensory skills that can move independently in an environment that contains obstacles and can perform certain activities and belongs to the category of intelligent robots. The use of mobile robots combines expertise from many different academic fields, including strongly integrated techniques in biology, anthropology, computer science, control theory, power transmission, measurement, and sensing. At present, there are already cases of using mobile robots to perform various simple tasks, but due to the low efficiency of optimal path planning, the scope of application cannot be further expanded. Therefore, based on the improved gray wolf algorithm (IGWA) and self-powered sensor technology, this article optimized the optimal path planning method and energy driving method of the mobile robot, so that the mobile robot can be applied in complex environments. The experiment compared the improved path planning algorithm in this article with the traditional path planning algorithm. The results show that the optimal path planning using the IGWA can search the random tree algorithm, the improved bee colony algorithm, and the multiobjective based on edge distribution estimation faster. Compared with the optimization algorithm, the optimization effect was increased by 14.84%. It is beneficial to the optimal path planning of mobile robots in complex environments.

Nuclear pore complex in the nuclear envelope plays an important role in controlling the transportation of RNAs, proteins and other macromolecules between the nucleus and cytoplasm. The relationship between abnormal expression of nucleoporins and cardiovascular diseases is unclear. In this study we investigated how myocardial infarction affected the expression and function of nucleoporins in cardiomyocytes. We separately knocked down 27 nucleoporins in rat primary myocardial cells. Among 27 nucleoporins, knockdown of Nup93, Nup210 and Nup214 markedly increased the expression of ANPand BNP, two molecular markers of cardiomyocyte function. We showed that Nup93 was significantly downregulated in hypoxic cardiomyocytes. Knockdown of Nup93 aggravated hypoxia-induced injury and cell death of cardiomyocytes, whereas overexpression of Nup93 led to the opposite effects. RNA-seq and bioinformatics analysis revealed that knockdown of Nup93 did not affect the overall transportation of mRNAs from the nucleus to the cytoplasm, but regulated the transcription of a large number of mRNAs in cardiomyocytes, which are mainly involved in oxidative phosphorylation and ribosome subunits. Most of the down-regulated genes by Nup93 knockdown overlapped with the genes whose promoters could be directly bound by Nup93. Among these genes, we demonstrated that Nup93 knockdown significantly down-regulated the expression of YAP1. Overexpression of YAP1 partially rescued the function of Nup93 knockdown and attenuated the effects of hypoxia on cell injury and cardiomyocyte death. We conclude that down-regulation of Nup93, at least partially, contributes to hypoxia-induced injury and cardiomyocyte death through abnormal interaction with the genome to dynamically regulate the transcription of YAP1 and other genes. These results reveal a new mechanism of Nup93 and might provide new therapeutic targets for the treatment of ischemia-induced heart failure.

Xuanfei Baidu granule is an effective prescription for COVID-19, which is modified from classical famous prescriptions. In order to further standardize clinician’s rational use of Xuanfei Baidu granule, enhance clinical efficacy, improve rational medication level, and let it play a more significant role, the frontline clinical experts are invited for discussion on the major functions, usage, adverse reactions, and considerations of Xuanfei Baidu granule to form the consensus. The consensus is that: Xuanfei Baidu granule has the efficacy of promoting the lung and resolving dampness, clearing heat and removing pathogeneses, purging the lung and depoisoning; it can be used for the treatment of patients with mild, moderate, and heavy COVID-19 and other common upper respiratory tract infectious diseases such as influenza; acute tracheitis and bronchitis and community-acquired lower respiratory tract infectious diseases all belong to damp-toxin constraint in the lungs, Xuanfei Baidu granule can relieve cough, expectoration, and other symptoms caused by an acute exacerbation of the chronic obstructive pulmonary disease, with definite clinical efficacy and good safety. According to the principles of evidence-based medicine, this expert consensus will be updated regularly.

Log anomaly detection holds great significance in computer systems and network security. A large amount of log data is generated in the background of various information systems and equipment, so automated methods are required to identify abnormal behavior that may indicate security threats or system malfunctions. The traditional anomaly detection methods usually rely on manual statistical discovery, or match by regular expression which are complex and time-consuming. To prevent system failures, minimize troubleshooting time, and reduce service interruptions, a log template-based anomaly detection method has been proposed in this context. This approach leverages log template extraction, log clustering, and classification technology to timely detect abnormal events within the information system. The effectiveness of this method has been thoroughly tested and compared against traditional log anomaly detection systems. The results demonstrate improvements in log analysis depth, event recognition accuracy, and overall efficiency.

Evolving Android malware poses a severe security threat to mobile users, and machine-learning (ML)-based defense techniques attract active research. Due to the lack of knowledge, many zero-day families’ malware may remain undetected until the classifier gains specialized knowledge. The most existing ML-based methods will take a long time to learn new malware families in the latest malware family landscape. Existing ML-based Android malware detection and classification methods struggle with the fast evolution of the malware landscape, particularly in terms of the emergence of zero-day malware families and limited representation of single-view features. In this article, a new multiview feature intelligence (MFI) framework is developed to learn the representation of a targeted capability from known malware families for recognizing unknown and evolving malware with the same capability. The new framework performs reverse engineering to extract multiview heterogeneous features, including semantic string features, API call graph features, and smali opcode sequential features. It can learn the representation of a targeted capability from known malware families through a series of processes of feature analysis, selection, aggregation, and encoding, to detect unknown Android malware with shared target capability. We create a new dataset with ground-truth information regarding capability. Many experiments are conducted on the new dataset to evaluate the performance and effectiveness of the new method. The results demonstrate that the new method outperforms three state-of-the-art methods, including: 1) Drebin; 2) MaMaDroid; and 3) $N$ -opcode, when detecting unknown Android malware with targeted capabilities.

Los inhibidores de puntos de control inmunitarios mejoran enormemente el pronóstico del cáncer; sin embargo, los eventos adversos graves relacionados con el sistema inmunitario pueden causar la muerte de manera prematura. Las recomendaciones actuales para el tratamiento de neumonitis inducida por inhibidores de puntos de control (CIP) se basan principalmente en terapias inmunosupresoras, y también se requieren agentes antifibróticos, especialmente en pacientes con mala respuesta a los corticosteroides y curso más prolongado de neumonitis. Esto se debe a que los cambios fibróticos tienen un papel importante en la evolución patológica de la CIP. Presentamos un caso que demuestra que el nintedanib es un candidato prometedor para tratar o prevenir la CIP, por su potente efecto antifibrótico y su perfil de seguridad. Por otra parte, el nintedanib podría inhibir parcialmente el crecimiento tumoral en pacientes con cáncer de pulmón no-microcítico, y su eficacia puede mejorar aún más si se le combina con otras terapias antitumorales.

This paper proposes an iterative approach to separate a set of admissible markings of a nonlinear constraint into a number of subsets. At each iteration, we find a maximal subset of admissible markings that are separated from inadmissible markings by linear constraints. Then, the union of all the obtained subsets constitutes the set of all admissible markings. For each subset of admissible markings, we obtain a set of conjunctive linear constraints. Accordingly, we can equivalently transform a given nonlinear constraint to be a set of disjunctive/conjunctive linear constraints, which can deal with the case that both admissible and inadmissible marking spaces of a nonlinear constraint cannot be separated by linear constraints from each other. Furthermore, we propose a method to design a Petri net supervisor for a derived set of disjunctive/conjunctive constraints. Some examples are used to demonstrate the proposed approach. Note to Practitioners—Linear constraints on Petri nets have been widely studied in the literature. However, not all control specifications can be represented as linear constraints. This paper proposes to enforce nonlinear constraints on Petri nets. A maximally permissive supervisor is designed to make the controlled net live with all admissible markings with respect to a given nonlinear constraint. Meanwhile, the structural complexity is also considered by compressing the number of control places in the supervisor. Experimental results show that the proposed approach can implement the control specifications represented by nonlinear constraints.

The collector fields of large-scale commercial trough solar power stations often face inhomogeneities in environmental parameters and resistance to flow in the pipe network. Aiming at dealing with the problem of uneven distribution of hydraulic parameters and radiation in large collector fields, this study establishes a heat transfer model and a hydraulic model of the collector field and proposes a control strategy for the heat transfer fluid (HTF) flow in the parabolic trough collector (PTC) loops. Based on the proposed hydraulic model, the loop valve opening is directly calculated and fast control of the loop HTF flow is achieved. Simulation results show that the scheme significantly reduces the differences in HTF temperature at the outlet of the PTC loops.

Clear underwater images serve to explore and measure ocean resources. However, underwater images suffer from color deviations and hazy effects due to wavelength-dependent light attenuation and scattering. To address this problem, we propose a multiscale dual-color space underwater image enhancement network (MSDC-Net) comprising a color correction block and a deep learning-based network. Concretely, the color correction block compensates for the most absorbed color and limits the least one. First, the color histogram distributions of all channels are shifted to a similar range, which smooths and generalizes color deviations to a certain extent. Second, the corrected multiscale images are fed into a deep learning-based asymmetric multiscale encoder-decoder architecture, which works in RGB and HSV color spaces to extract rich and varied features. Then, the extracted features are integrated through the selective kernel concatenation (SKC) module. Finally, the decoder produces competitive outputs from the integrated features. Extensive experiments on real-world and synthetic underwater images demonstrate that the proposed MSDC-Net achieves outstanding results in subjective visual comparisons and objective metrics.

We present a wideband microwave vector network analyzer (VNA) based on photonic harmonic mixing (PHM) that can relax the demand for electrical equipment, such as bandwidth and sample rate. A single-tone radio frequency (RF) signal serves as the incident signal of the device under test (DUT). The response signal of the DUT is modulated on the optical harmonics, which is generated by modulating the local oscillation (LO) microwave signal on a continuous wave (CW) light. After beating the modulated optical harmonics at the photodetector (PD), the response signal of the DUT is down-converted to an intermediate frequency (IF) signal. The DUT’s scattering parameters ( $S$ parameters) at this frequency can be obtained from the IF signal. Then by synchronous frequency sweeping of LO and RF signal with IF frequency fixed, the scattering parameters ( $S$ parameters) within a frequency range are acquired. Experiments are demonstrated, in which the $S$ parameters of a 15-GHz low-pass filter (LPF) within 2.2–20.2 GHz is measured. And by employing higher order harmonics, the extension of measurable frequency range 4.2–40 GHz is achieved. The dynamic range (DR) of the proposed scheme exceeds 63 dB at an equivalent resolution bandwidth (RBW) of 16 kHz. The limit factors of the DR are discussed. The developed method offers a microwave photonic way for the researches of wideband and high DR microwave VNAs.

AbstractThe significance of the knowledge acquisition and knowledge transfer of Sustainable Development Education increase when their level of integration in the daily routine of the individuals is being evaluated. The complexity further enhances when such education is being given to the students whose first language is not English, however the mode and medium of such knowledge transfer in the higher education institution is English. Therefore, the present study aims to explore the significance of the intrinsic and extrinsic enablers of the sustainable development knowledge acquisition and its integration in the daily routine of the students whose first language is not English and the said education is being delivered in English Language. Following the survey research design, the data was collected from 349 students in the Sustainable Development Education course in the Chinese higher education institution and the hypothesis were assessed through the application of Structural Equation Modelling. The results reported the significant associations between all of the intrinsic and extrinsic predictors with the creation variables. Based on the findings, it has been recommended the development of the content should be made in the more simple and easy manner that are easy to comprehend by the students having English as foreign language.

Vehicular Ad-Hoc Networks (VANETs), a subset of Mobile Ad-Hoc Networks (MANETs), are wireless networks formed around moving vehicles, enabling communication between vehicles, roadside infrastructure, and servers. With the rise of autonomous and connected vehicles, security concerns surrounding VANETs have grown. VANETs still face challenges related to privacy with full-scale deployment due to a lack of user trust. Critical factors shaping VANETs include their dynamic topology and high mobility characteristics. Authentication protocols emerge as the cornerstone of enabling the secure transmission of entities within a VANET. Despite concerted efforts, there remains a need to incorporate verification approaches for refining authentication protocols. Formal verification constitutes a mathematical approach enabling developers to validate protocols and rectify design errors with precision. Therefore, this review focuses on authentication protocols as a pivotal element for securing entity transmission within VANETs. It presents a comparative analysis of existing protocols, identifies research gaps, and introduces a novel framework that incorporates formal verification and threat modeling. The review considers key factors influencing security, sheds light on ongoing challenges, and emphasises the significance of user trust. The proposed framework not only enhances VANET security but also contributes to the growing field of formal verification in the automotive domain. As the outcomes of this study, several research gaps, challenges, and future research directions are identified. These insights would offer valuable guidance for researchers to establish secure authentication communication within VANETs.

C16H12ClFO5, monclinic, P21/c(no. 14), a= 14.7536(6) Å, b= 7.8281(3) Å, c= 14.0501(7) Å, β= 111.174(5)°$111.174(5){}^{\circ}$ , V= 1513.13(12) Å3, Z= 4, Rgt(F) = 0.0367, wRref(F2) = 0.0865, T = 296(2) K.

• A novel ESO-SPTC combining ESO and SPTC is developed without destroying the overall optimality. • A new discrete-time generalized ESO is proposed to surmount higher-order disturbances. • In SPTC, the infinite horizon inputs fully satisfy the amplitude and rate constraints. • The ESO-SPTC effectively solves the control problem of the micro gas turbine cogeneration system. Control of micro gas turbine combined heating and power (MGT-CHP), i.e., cogeneration systems, is challenging because of large inertia, strong coupling, strict input constraints, nonlinearity, and complex disturbances. To overcome these problems, this paper develops an extended state observer (ESO) based stable predictive tracking control (SPTC) for MGT-CHP. Unlike traditional ESO-based control methods, ESO-SPTC guarantees overall optimality by using disturbance feedback compensation. A new discrete-time generalized ESO is developed for the ESO-SPTC to surmount higher-order disturbances and its bounded stability is demonstrated. Besides, the designed SPTC fully guarantees that the infinite horizon inputs fully satisfy the amplitude and rate constraints. The resulting ESO-SPTC can eliminate the impact of matched and unmatched disturbances in the output channel at a steady state. Simulation results on a numerical example and an 80 kW MGT-CHP verify the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc NETworks (VANETs) is an important research domain as it deals with the safety of vehicles moving on roads. The selection of the most prominent routing protocol in this field is a challenging job due to the high mobility rate of VANETs. The foremost motive for writing this paper is to recognize the most suitable protocol that can be used for a high-density traffic region. Therefore, the evaluation of six popular routing protocols (AODV, DSDV, DSR, GPSR, OLSR, and ZRP) of VANETs is examined in the congested city of Delhi. For this, a methodology is being presented that explains the creation of a network using the SUMO tool and assigning a trip and route to the obtained network through the Origin-Destination matrix. Then, by adopting the created network file, the TCL File and NAM File are generated using the NS-2. Finally, selected protocols' performance is examined and evaluated based on the metrics such as Throughput, Packet Delivery Ratio (PDR), Delay, Packet Loss Ratio (PLR), and Routing Overhead. The results showed that the AODV protocol acts as an appropriate routing protocol and could perform better with our presented approach. Also, the comparison is conducted with the results obtained for the AODV routing protocol using the presented approach and the existing approaches. The comparison results depict that the AODV routing protocol presents much better results by using the presented methodology than by using the existing methods in terms of throughput, delay, and PDR.

This study examined the effects of different temperatures (37°C and 45°C) and pH (uncontrolled, 8 and 10) on the co-fermentation of FeCl3, anionic polyacrylamide (a-PAM) settling chemical enhanced primary sedimentation (CEPS), and waste activated sludge (WAS) to produce volatile fatty acids (VFAs), which analyzed through characterization, microbial community, and untargeted metabolic pathways. Further, to investigate the interacting effects, temperatures of 37°C, 45°C, and pHs of 8 and 10 were chosen, with a sludge ratio of 7.5. The hydrolysis of sludge was aided by the concentration of soluble chemical oxygen demand (SCOD) rising as temperature and pH rose. Nevertheless, elevated SCOD concentrations failed to materialize into benefits for acid production. Therefore, considering practical applications, the optimal conditions for acid production were determined to be 37°C and pH 10, with a concentration of 3411.17 ± 119.92 mg COD/L. A thorough investigation of correlations between microbial communities, differential metabolites, and environmental factors was carried out, highlighting the significance of pH in relation to microbial and acid production performance. That instance, the pH affects acid generation efficiency more so than temperature parameters. Within the microbial community, there is a considerable positive correlation between several distinct metabolites and Steptococcus, Trichococcus, and Tepidimimicrobium. These metabolites are intimately associated with the materials and energy needed for biological cell production and metabolism. This could be as a result of how environmental influences alter the distribution of microbial populations, which influences various metabolites and ultimately the process of producing acid.

This paper proposes a fin electron-hole bilayer tunneling FET with a heterogate and an InAlAs barrier layer (HBF-EHBTFET). The heterogate can suppress off-state leakage caused by point tunneling, while the InAlAs barrier layer prevents source–drain direct tunneling, significantly reducing the off-state current (Ioff). P-type Gaussian doping can not only solve the problem of the inability to generate a hole layer during device fabrication, but also reduce the turn-on voltage of line-tunneling, ultimately increasing on-state current and reducing average subthreshold swing (SSavg). By optimizing parameters of the heterogate and InAlAs barrier layer, HBF-EHBTFET can obtain Ioffof 2.37 × 10−16A μm−1, SSavgof 17.97 mV dec−1, a cutoff frequency (fT) of 13.2 GHz, and a gain bandwidth product (GBW) of 1.58 GHz. Compared with traditional EHBTFET, HBF-EHBTFET exhibits a reduction in Ioffby four orders of magnitude, a decrease in SSavgby 65.27%, and an increase in fTand GBW by 78.59% and 93.62%, respectively.

Identifying and mitigating aberrant activities within the network traffic is important to prevent adverse consequences caused by cyber security incidents, which have been increasing significantly in recent times. Existing research mainly focuses on classical machine learning and deep learning-based approaches for detecting such attacks. However, exploiting the power of quantum deep learning to process complex correlation of features for anomaly detection is not well explored. Hence, in this paper, we investigate quantum machine learning and quantum deep learning-based anomaly detection methodologies to accurately detect network attacks. In particular, we propose three novel quantum auto-encoder-based anomaly detection frameworks. Our primary aim is to create hybrid models that leverage the strengths of both quantum and deep learning methodologies for efficient anomaly recognition. The three frameworks are formed by integrating the quantum autoencoder with a quantum one-class support vector machine, a quantum random forest, and a quantum k-nearest neighbor approach. The anomaly detection capability of the frameworks is evaluated using benchmark datasets comprising computer and Internet of Things network flows. Our evaluation demonstrates that all three frameworks have a high potential to detect the network traffic anomalies accurately, while the framework that integrates the quantum autoencoder with the quantum k-nearest neighbor yields the highest accuracy. This demonstrates the promising potential for the development of quantum frameworks for anomaly detection, underscoring their relevance for future advancements in network security.

ABSTRACT. To assess changes in lung function and airway reactivity resulting from exposure to cotton dust, and the role of atopic status in these changes, the authors observed a group [...]

Sustained anti-angiogenesis therapy increases the level of tumor hypoxia, leading to increased expression of HIF-1a, thereby contributing to the resistance to anti-angiogenesis therapy in hepatocellular carcinoma (HCC). Here, we report that phenazine biosynthesis-like domain-containing protein (PBLD) inhibits hypoxia-induced angiogenesis via ERK/HIF-1a/VEGF axis in HCC cells. Bioinformatic analysis of the TCGA database and clinical samples validation also identify a negative correlation between PBLD and angiogenesis-related genes expression including HIF-1a. Apart from the downregulation of HIF-1a/VEGF expression in HCC cells, PBLD also blocks VEGF receptor 2 (VEGFR2) on endothelial cells via HCC-derived exosomal miR-940. PBLD also activates TCF4 transcriptional promotion effects on miR-940 by directly interacting with it. Together, PBLD exerts an inhibitory effect on angiogenesis not only via blocking the VEGFR2 expression in endothelial cells, but also through downregulating HIF-1a-induced VEGF expression and secretion in HCC cells. These explorations may provide a theoretical basis for exploring new targets and strategies to overcome resistance to anti-angiogenesis therapy.

The hot deformation behavior and microstructure evolution of newly developed as-cast high-density steel were investigated by performing hot compression tests in the temperature range of 1000–1200 °C and strain rate range of 0.001–10 s−1. Flow stress data using hyperbolic sine equation was performed to obtain the constitutive relationship and the activation energy for the hot deformation of the steel. The novel steel shows higher activation energy due to the high tungsten content. The workability of as-cast steel was studied through processing map approach based on dynamic material model. When the strain is 0.8, the optimum procession condition is around 1160 °C/0.001 s−1with a peak efficiency of 0.52. With the increase of temperature and decrease of strain rate, the degree of dynamic recrystallization increases.

Background: Among women who practice breast self-examination (BSE), breast cancers may be detected when they are at an earlier stage and are smaller than in women who do not practice BSE. However, the efficacy of breast self-examination for decreasing breast cancer mortality is unproven. This study was conducted to determine whether an intensive program of BSE instruction will reduce the number of women dying of breast cancer. Methods: From October 1989 through October 1991, 266 064 women associated with 519 factories in Shanghai were randomly assigned to a BSE instruction group (132979 women) or a control group (133085 women). Initial instruction in BSE was followed by reinforcement sessions 1 and 3 years later, by BSE practice under medical supervision at least every 6 months for 5 years, and by ongoing reminders to practice BSE monthly. The women were followed through December 2000 for mortality from breast cancer. Cumulative risk ratios of dying from breast cancer were estimated using Cox proportional hazards models. All statistical tests were two-sided. Results: There were 135 (0.10%) breast cancer deaths in the instruction group and 131 (0.10%) in the control group. The cumulative breast cancer mortality rates through 10 to 11 years of follow-up were similar (cumulative risk ratio for women in the instruction group relative to that in the control group = 1.04, 95% confidence interval = 0.82 to 1.33; P = .72). However, more benign breast lesions were diagnosed in the instruction group than in the control group. Conclusions: Intensive instruction in BSE did not reduce mortality from breast cancer. Programs to encourage BSE in the absence of mammography would be unlikely to reduce mortality from breast cancer. Women who choose to practice BSE should be informed that its efficacy is unproven and that it may increase their chances of having a benign breast biopsy.

Aerodynamic interaction of rotor with stator is the primary cause of high cycle fatigue of radial turbine blade. However, few studies have been carried out on aerodynamic interaction in nozzleless radial turbines. This paper studies aerodynamic interaction in a nozzleless radial turbine via the experimentally validated numerical method. The results suggest that several flow phenomena are related to blade excitation. Firstly, when the blade sweeps by the volute tongue, the tongue wake induces a strong separation vortex at blade leading edge and interacts with it. As the tongue wake migrates downstream, it deforms into a triangular shape across the rotor passage. Secondly, the potential field of the volute leads to drastic fluctuations of blade load as well as tip leakage flow. Importantly, the tongue wake interacts with the tip leakage vortex in the vicinity of blade trailing edge. This phenomenon results in strong disturbance to pressure fluctuations on suction surface at low pressure ratio and the frequency of disturbance is roughly one order higher than that of potential field. However, its influence can be neglected at high pressure ratio. Furthermore, the details of vortex evolution in the rotor passage are discussed via the time-space contour plots of pressure difference. Various behaviors including propagation speed, direction and strength can be well manifested by this method. Finally, the strong influence of unsteady interaction on blade excitation in the nozzleless turbine is confirmed by a direct comparison of blade loads in unsteady condition with that in quasi-steady condition.

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With the booming of cyber attacks and cyber criminals against cyber-physical systems (CPSs), detecting these attacks remains challenging. It might be the worst of times, but it might be the best of times because of opportunities brought by machine learning (ML), in particular deep learning (DL). In general, DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data. DL models are adopted quickly to cyber attacks against CPS systems. In this survey, a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context. A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems. The methodology includes CPS scenario analysis, cyber attack identification, ML problem formulation, DL model customization, data acquisition for training, and performance evaluation. The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules. Moreover, excellent performance is achieved partly because of several high-quality datasets that are readily available for public use. Furthermore, challenges, opportunities, and research trends are pointed out for future research.

To mitigate cross-site scripting attacks (XSS), the W3C group recommends web service providers to employ a computer security standard called Content Security Policy (CSP). However, less than 3.7 percent of real-world websites are equipped with CSP according to Google’s survey. The low scalability of CSP is incurred by the difficulty of deployment and non-compatibility for state-of-art browsers. To explore the scalability of CSP, in this article, we propose JavaScript based CSP (JSCSP), which is able to support most of real-world browsers but also to generate security policies automatically. Specifically, JSCSP offers a novel self-defined security policy which enforces essential confinements to related items, including JavaScript functions, DOM elements and data access. Meanwhile, JSCSP has an efficient algorithm to automatically generate the policy directives and enforce them in a cascading way, which is more fine-grained and practical than the functionalities provided by CSP. We further implement JSCSP on a Chrome extension, and our evaluation shows that the extension is compatible with popular JavaScript libraries. Our JSCSP extension can detect and block the tested attacking vectors extracted from the prevalent web applications. We state that JSCSP delivers better performance compared to other XSS defense solutions.

• Identifying issues of external calls in Solidity smart contract programs in multi-agent robotic systems and defining three security rules. • Retrieving thousand smart contracts. • Proposing a novel security analysis plugin, named SolGuard, to integrate with Solidity linter. In the new era of blockchain-based multi-agent robotic systems, smart contract programs perform an influential role in implementing decentralized applications with required task allocations. Smart contract programs are developed using script-type of programming languages, and they have already deployed several vulnerable patterns without proper testing and audit. We studied Solidity smart contracts running on the Ethereum platform and identified that they had been exploited because of several programming issues, especially using low-level external calls to malicious sources. Since smart contracts are immutable after their deployment to autonomous multi-robot systems, they should be tested to fix possible development phase issues. We implemented a prototype plugin called SolGuard by extending the solhint linter to prevent three critical issues related to Solidity smart contract programs' usage of external calls. The SolGuard plugin checks state variable order in the smart contracts, participation of delegatecall invocations, address type parameters in the smart contract's constructor, and denial of service patterns. We empirically evaluate the SolGuard plugin with existing popular static analysis tools. Our results indicate that SolGuard outperformed the baseline tools in terms of efficiency and accuracy. [ABSTRACT FROM AUTHOR]