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Laccase, a multipurpose biocatalyst, is widely distributed across all kingdoms of life and plays a key role in essential biological processes such as lignin synthesis, degradation, and pigment formation. These functions are critical for fungal growth, plant-pathogen interactions, and maintenance of soil health. Due to its broad substrate specificity, multifunctional nature, and environmentally friendly characteristics, laccase is widely employed as a catalyst in various green chemistry initiatives. With its ability to oxidize a diverse range of phenolic and nonphenolic compounds, laccase has also been found to be useful as a food additive and for assessing food quality parameters. Ongoing advancements in research and technology are continually expanding the recognition of laccase’s potential to address global environmental, health, and energy challenges. This review aims to provide critical insights into the applications of laccases in the biotechnology and food industry.

To characterize Hirayama disease in female patients, and increase awareness among clinicians regarding the specifics of this disease.

Cooperative vehicle infrastructure is critical for facilitating multi-tier vehicular edge computing (VEC) systems to collaboratively process latency-sensitive intelligent tasks. However, the dynamic topology and the scarcity of available computing resources on real traffic roads pose significant challenges. While previous works have explored using public transit and parked vehicles as cooperative vehicles, they often overlook the incentive mechanisms and the specific tasks of these vehicles, rendering these solutions impractical. Motivated by the achievements in smart electric vehicles (EVs), this paper presents an electric vehicle cluster (EVC) assisted multi-tier VEC system that leverages the capabilities of the vehicle-to-grid (V2G) technology to provide stable computing resources to traffic areas. The proposed EVC-assisted multi-tier VEC system employs a cross-system two-level optimization method that jointly considers offloading and resource allocation decisions for road task vehicles (RTVs) in the VEC system and charging power decisions for the EVs in the EVC. At the lower level, we minimize the task latency for RTVs by utilizing the computing resources of idle EVs. At the upper level, we build a multi-agent two-step game model and introduce a potential game-based strategy and a Nash equilibrium (NE) based EV-RSU mapping method to derive the optimal decision strategy for the EVC. Extensive simulation results demonstrate the efficient reduction in total-task latency of RTVs and lower cost for the EVC while meeting the essential requirements in the V2G.

Atmospheric turbulence can often introduce phase errors into a propagating light field, thus resulting in anisoplanatic and temporally varying blur and distortion of images. Restoring such images degraded by atmospheric turbulence is extremely ill-posed, due to multiple plausible solutions for a given input image. Most methods offer a deterministic estimation of clean images and require high-computational costs. To address these challenges, this article proposes a fast turbulence mitigation network (FTMNet). It is a lightweight model for atmospheric turbulence mitigation. Differing other methods, it does not employ a strategy for producing a single deterministic reconstruction. Instead, it leverages the Monte Carlo method to enhance restoration performance and produces a different and reasonable set of reconstructed images for a given input. As a result, FTMNet effectively mitigates atmospheric turbulence effect while maintaining low-inference time and computational resource requirements. Experimental results demonstrate that FTMNet shows high-inference speed, reaching 90 fps, and outperforms the state-of-the-art peers.

For non-destructive testing (NDT) with X-ray computed tomography (XCT), the complex morphological segmentation of homogeneous defects is impossible for traditional threshold-based segmentation algorithm, due to their same absorbances. A variety of defect segmentation using convolutional neural networks (CNNs) has been suitable for such problems, and its accuracy is determined by image quality and comprehensiveness of training set. To achieve a high-performance measurement for quantitative defect evaluation, a modified U-Net-based segmentation model of XCT was proposed suitable for defects with various image quality resulting from different experimental efficiency. The dataset consisting of 24 subsets was acquired from the condition-varying measurements of aluminum alloy samples produced by laser 3D metal printing additive manufacturing. The structure and distribution of segmented pore and crack defects were accurately visualized by our model. Compared with traditional and main CNNs-based segmentation methods, our model exhibited a better recognition accuracy for pores and cracks, the Mean Intersection over Union and Pixel accuracy metrics reaching to 84.83 % and 98.87 % respectively. Practically, the option of efficiency or accuracy priority was quantitatively determined to carry out such a high-performance defect-related NDT. The proposed XCT-based segmentation mode has a great potential in fields of engineering, material science, biomedicine.

We consider the problem of channel estimation and joint active and passive beamforming for reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We show that, with a well-designed frame-based training protocol, the received pilot signal can be organized into a low-rank third-order tensor that admits a canonical polyadic decomposition (CPD). Based on this observation, we propose a CPD-based method for estimating the cascade channels associated with different subcarriers. The proposed method exploits the intrinsic low-rankness of the CPD formulation, which is a result of the sparse scattering characteristics of mmWave channels, and thus has the potential to achieve a significant training overhead reduction. Specifically, our analysis shows that the proposed method has a sample complexity that scales quadratically with the sparsity of the cascade channel. Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels. Simulation results show that the proposed CPD-based channel estimation method attains mean square errors that are close to the Cramér-Rao bound (CRB) and present a clear advantage over the compressed sensing-based methods. In addition, the proposed joint beamforming method can effectively utilize the estimated channel parameters to achieve superior beamforming performance.

Increasing research suggests a connection between gut microbiota and depressive disorders. Targeted changes to the intestinal flora may contribute to alleviating anxiety and depression. This study aimed to identify probiotics that could attenuate stress-induced abnormal behavior and explore potential mechanisms. The administration of LR.KY16 significantly reduced stress-induced abnormal behaviors and physiological dysfunction. The mechanism may be via regulating the structure of the intestinal microbiota in mice, increasing the abundance of Akkermansia muciniphila, prompting enterochromaffin cells to secrete 5-HTP in the gut, which enters the brain through the bloodstream and promotes the synthesis of 5-HT in the brain, and then activates brain-derived neurotrophic factor (BDNF)/tyrosine kinase receptor B (TrkB) through the 5-HT1A receptor. In addition, LR.KY16 also increased the expression of claudin-7, occludin, and zonula occludens-1 (ZO-1) in the colon, inhibited microglial M1 polarization, and inhibited systemic inflammation.

Fault diagnosis is crucial in monitoring machines within industrial processes. With the increasing complexity of working conditions and demand for safety during production, diverse diagnosis methods are required, and an integrated fault diagnosis system capable of handling multiple tasks is highly desired. However, the diagnosis subtasks are often studied separately, and the current methods still need improvement for such a generalized system. To address this issue, we propose the generalized out-of-distribution fault diagnosis (GOOFD) framework to integrate diagnosis subtasks. Additionally, a unified fault diagnosis method based on internal contrastive learning and Mahalanobis distance is put forward to underpin the proposed generalized framework. The method involves feature extraction through internal contrastive learning and outlier recognition based on the Mahalanobis distance. Our proposed method can be applied to multiple fault diagnosis tasks and achieve better performance than the existing single-task methods. Experiments are conducted on benchmark and practical process datasets, indicating the effectiveness of the proposed framework.

Electrostatic fields, as a new food physical processing technology, can effectively improve the quality of frozen food. The purpose of this study is to investigate the effect of electrostatic field‐assisted frozen storage (SEF) on dough rheological properties, gluten structure, and its mechanism. The results showed that the G′ and G" of the gluten were efficiently maintained by SEF throughout frozen storage. Meanwhile, after 8 weeks of frozen storage, the relative contents of α‐helices were 25.28% and 24.76%, respectively, and the relative contents of random coils were 40.58% and 41.25%, respectively, in SEFs compared with the control group. This suggested that SEFs might affect the recrystallization of ice crystals by changing the orientation of water molecules, which, in turn, alleviated the disruption of protein secondary structural order. SEF had no effect on the gauche‐gauche‐gauche conformation. SEF reduced damage to protein senior structure by frozen storage. The glutenin macropolymer (GMP) depolymerization of the control and SEF groups was 28.55% and 28.17%, respectively, and the SEF decelerated the GMP depolymerization. SEF slowed the disruption of the molecular structure by reducing the depolymerization of GMP and slowing the depolymerization of the α‐helix, thus maintaining the viscoelasticity of the gluten. Exploring the mechanism of the effect of electrostatic field‐assisted frozen storage on gluten quality.

Derived from camelid heavy-chain antibodies, nanobodies (Nbs) are the smallest natural antibodies and are an ideal tool in biological studies because of their simple structure, high yield, and low cost. Nbs possess significant potential for developing highly specific and user-friendly diagnostic assays. Despite offering considerable advantages in detection applications, knowledge is limited regarding the exclusive use of Nbs in lateral flow immunoassay (LFIA) detection. Herein, we present a novel double “Y” architecture, achieved by using the SpyTag/SpyCatcher and Im7/CL7 systems. The double “Y” assemblies exhibited a significantly higher affinity for their epitopes, as particularly evident in the reduced dissociation rate. An LFIA employing double “Y” assemblies was effectively used to detect the severe acute respiratory syndrome coronavirus-2 N protein, with a detection limit of at least 500 pg/mL. This study helps broaden the array of tools available for the development of Nb-based diagnostic techniques.

We here demonstrate that amine-functionalized hydrophobic cross-linked polymer microsphere-supported palladium nanoparticles (Pd/NFHCPs) are an efficient catalyst for the selective hydrogenation of citral to citronellal. The R3-N groups in NFHCPs offer abundant anchoring sites for the dispersion and stabilization of Pd nanoparticles. In addition, the surface of NFHCPs is lipophilic and hydrophobic, which is beneficial for mass transformation and diffusion. These characteristics enable the catalyst to display high activity and stability for reducing citral to citronellal under alkaline conditions. The effects of solvents, different auxiliaries, and wettability on the catalytic performance were systematically investigated, paving the way for its potential industrial applications.

AbstractCircular RNA (circRNA) ArfGAP with FG repeats 1 (circAGFG1) contributes to colorectal cancer (CRC) development. However, whether circAGFG1 regulates the resistance of CRC to oxaliplatin (L-OHP) remains unknown. CircAGFG1, microRNA-7-5p (miR-7-5p) and pyruvate kinase M2 (PKM2) RNA expression were quantified by quantitative real-time polymerase chain reaction. Protein expression was detected by western blot assay and immunohistochemistry assay. Glycolysis was analyzed through glucose uptake, lactate production and adenosine triphosphate (ATP) concentration assays. 50% inhibitory concentration of L-OHP was determined by cell counting kit-8 assay. Cell proliferation and apoptotic rate were analyzed by cell colony formation and flow cytometry analysis, respectively. Dual-luciferase reporter assay was used to identify the relationship among circAGFG1, miR-7- 5p and PKM2. The effect of circAGFG1 on L-OHP sensitivity in vivowas further evaluated by a xenograft model assay. CircAGFG1 and PKM2 expression were significantly increased, while miR-7-5p was decreased in L-OHP-resistant CRC tissues and cells. High circAGFG1 expression predicted a poor prognosis of CRC. CircAGFG1 knockdown or PKM2 depletion decreased glycolysis and cell proliferation and increased L-OHP sensitivity and cell apoptosis. PKM2 introduction rescued circAGFG1 silencing-induced effects in CRC cells. In terms of mechanism, circAGFG1 bound to miR-7-5p, which was identified to target PKM2. Also, circAGFG1 regulated PKM2 expression by interacting with miR-7-5p. Further, circAGFG1 knockdown improved the sensitivity of tumors to L-OHP in vivo. CircAGFG1 depletion inhibited L-OHP resistance by regulating the miR-7-5p/PKM2 pathway.

Bitter melon fruit is susceptible to yellowing, softening, and rotting under room-temperature storage conditions, resulting in reduced commercial value. Nitric oxide (NO) is an important signaling molecule and plays a crucial role in regulating the fruit postharvest quality. In this study, we investigated the effects of NO treatment on changes in sensory and firmness of bitter melon fruit during postharvest storage. Moreover, transcriptomic, metabolomic, and proteomic analyses were performed to elucidate the regulatory mechanisms through which NO treatment delays the ripening and senescence of bitter melon fruit. Our results show that differentially expressed genes (DEGs) were involved in fruit texture (CSLE, β-Gal, and PME), plant hormone signal transduction (ACS, JAR4, and AUX28), and fruit flavor and aroma (SUS2, LOX, and GDH2). In addition, proteins differentially abundant were associated with fruit texture (PLY, PME, and PGA) and plant hormone signal transduction (PBL15, JAR1, and PYL9). Moreover, NO significantly increased the abundance of key enzymes involved in the phenylpropanoid biosynthetic pathway, thus enhancing the disease resistance and alleviating softening of bitter melon fruit. Finally, differential metabolites mainly included phenolic acids, terpenoids, and flavonoids. These results provide a theoretical basis for further studies on the physiological changes associated with postharvest ripening and senescence of bitter melon fruit.

Hyperbolic polaritons have been attracting increasing interest for applications in optoelectronics, biosensing, and super-resolution imaging. Here, we report the in-plane hyperbolic exciton polaritons in monolayer black-arsenic (B-As), where hyperbolicity arises strikingly from two exciton resonant peaks. Remarkably, the presence of two resonances at different momenta makes overall hyperbolicity highly tunable by strain, as the two exciton peaks can be merged into the same frequency to double the strength of hyperbolicity as well as light absorption under a 1.5% biaxial strain. Moreover, the frequency of the merged hyperbolicity can be further tuned from 1.35 to 0.8 eV by an anisotropic biaxial strain. Furthermore, electromagnetic numerical simulation reveals a strain-induced hyperbolicity, as manifested in a topological transition of iso-frequency contour of exciton polaritons. The good tunability, large exciton binding energy, and strong light absorption exhibited in the hyperbolic monolayer B-As make it highly suitable for nanophotonics applications under ambient conditions.

This paper mainly focuses on the sensor fault diagnosis method based on H∞ observer in vehicle electronic stability control (ESC) system with disturbance and input delay. Firstly, the four-wheel steering vehicle model is established. Secondly, the system state and sensor fault are reconstructed into new system variables by a special matrix transformation method, based on which a new H∞ observer is developed to estimate system state and sensor fault signals. Then, based on Lyapunov method and linear matrix inequality (LMI) theory, the conditions for asymptotic stability and H∞ performance of the estimation error system are proved. Finally, Carsim and Matlab/Simulink co-simulation are used to show the effectiveness of the method.

An algorithm based on mixed signals is proposed, to solve the issues of low accuracy of identification algorithm, immeasurable intermediate variables of fractional order Hammerstein model, and how to determine the magnitude of fractional order. In this paper, a special mixed input signal is designed to separate the nonlinear and linear parts of the fractional order Hammerstein model so that each part can be identified independently. The nonlinear part is fitted by the neural fuzzy network model, which avoids the limitation of polynomial fitting and broadens the application range of nonlinear models. In addition, the multi-innovation Levenberg-Marquardt (MILM) algorithm and auxiliary recursive least square algorithm are innovatively integrated into the parameter identification algorithm of the fractional order Hammerstein model to obtain more accurate identification results. A simulation example is given to verify the accuracy and effectiveness of the proposed method.

ObjectiveThe objective of this study is to improve the efficacy of CLDN18.2/CD3 bispecific T-cell engagers (BiTEs) as a promising immunotherapy against pancreatic ductal adenocarcinoma (PDAC).DesignHumanised hCD34+/hCD3e+, Trp53R172HKrasG12DPdx1-Cre (KPC), pancreas-specific Cldn18.2 knockout (KO), fibroblast-specific Fcgr1 KO and patient-derived xenograft/organoid mouse models were constructed. Flow cytometry, Masson staining, Cell Titer Glo assay, virtual drug screening, molecular docking and chromatin immunoprecipitation were conducted.ResultsCLDN18.2 BiTEs effectively inhibited early tumour growth, but late-stage efficacy was significantly diminished. Mechanically, the Fc fragment of BiTEs interacted with CD64+cancer-associated fibroblasts (CAFs) via activation of the SYK-VAV2-RhoA-ROCK-MLC2-MRTF-A-α-SMA/collagen-I pathway, which enhanced desmoplasia and limited late-stage infiltration of T cells. Molecular docking analysis found that vilanterol suppressed BiTEs-induced phosphorylation of VAV2 (Y172) in CD64+CAFs and weakened desmoplasia. Additionally, decreased cyclic guanosine-adenosine monophosphate synthase/stimulator of interferon genes (STING) activity reduced proliferation of TCF-1+PD-1+stem-like CD8+T cells, which limited late-stage effects of BiTEs. Finally, vilanterol and the STING agonist synergistically boosted the efficacy of BiTEs by inhibiting the activation of CD64+CAFs and enriching proliferation of stem-like CD8+T cells, resulting in sustained anti-tumour activity.ConclusionVilanterol plus the STING agonist sensitised PDAC to CLDN18.2 BiTEs and augmented efficacy as a potential novel strategy.

Mass spectrometry (MS) stands as a pivotal tool for chemical analysis in the textile industry, capable of scrutinizing the vast majority of reported harmful chemicals. With the ever-increasing number of banned chemicals in textiles, there is a pressing need for a more systematic compilation of relevant literature on MS utilization to identify and quantify harmful substances in textiles. This article comprehensively reviews the role of MS in textile analysis over the past two decades, encompassing a wide range of sample preparation techniques, such as energy-assisted extraction, solid-phase microextraction, liquid-phase microextraction, and QuEChERS. Furthermore, it delves into MS techniques, such as gas chromatography–MS, liquid chromatography–MS, inductively coupled plasma–MS, ion mobility spectrometry–MS, and ambient ionization MS. Moreover, it offers a brief glimpse into the future of MS development in this field, highlighting ongoing research into additional sample preparation techniques for detecting hazardous chemicals in textiles. Notably, it underscores the growing interest in ambient ionization MS and the coupling of chromatography with high-resolution MS.

Although convolution neural network (CNN) has achieved certain success in fault detection and diagnosis (FDD) tasks in the chemical engineering industry, the performance and credibility of CNN-based FDD methods are greatly limited by two factors. First, CNN relies upon strong temporal/spatial correlation in data, which is very difficult to obtain in generic chemical tabular data. Second, most CNN methods have poor interpretability due to the encapsulation mechanism of feature extraction, and thus there is great difficulty in identifying the root-cause features from the results obtained using these methods. To address these difficulties, we propose an order-invariant and interpretable dilated convolution neural network (OIDLCNN), which is composed of feature clustering, dilated convolution, and a deep Shapley additive explanations (SHAP) explainer. Specifically, the feature clustering technique is adopted to reorder the features thus those with strong correlations are placed to be adjacent to each other. The large receptive field of dilated convolution can capture long-range correlations so then it can further recover the feature correlations and improve the classification performance. Last but not least, the proposed method provides interpretability by including the SHAP values to quantify feature contribution and identify the root-cause feature as the one with the highest contribution. Computational experiments are conducted on the Tennessee Eastman chemical process benchmark dataset. Compared with the other state-of-the-art methods, the proposed OIDLCNN-SHAP method achieves better performance in capturing feature correlations, detecting faults, and identifying the root-cause features. Note to Practitioners—Fault detection and diagnosis (FDD) is significant for reducing maintenance costs and improving safety in chemical processes. In this paper, we investigate the difficulty in detecting faults and identifying the root-cause features in chemical multivariate processes. This work was motivated by the fact that the existing CNN-based FDD algorithms are not designed for generic chemical tabular data and fail to capture vital information about feature correlations. In addition, the commonly used bayesian network-based root cause analysis methods are expensive since they require much prior knowledge and expert rules. We present a novel framework to capture the unique feature correlations and obtain the root-cause features without any expert knowledge. The proposed method provides an automatic and low-cost way for chemical process FDD tasks. It can be further integrated into the condition monitoring system of real chemical processes to analyze potential faults and identify the corresponding root causes in real time.

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become increasingly popular for the task of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a substantial quantity of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To develop a solution to the aforementioned problems, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. First, accurate classification results are obtained under limited fault samples. More specifically, we integrate the convolutional neural network (CNN) with attention mechanisms to provide strong intrinsic inductive biases of locality and spatial invariance, thereby strengthening the representational power under limited fault samples. In addition, we ulteriorly enhance the classification capability of the SCCAM method under limited fault samples by employing the supervised contrastive learning (SCL) loss. Second, a novel ante-hoc interpretable attention-based architecture is designed to directly obtain the root causes without expert knowledge. The convolutional block attention module (CBAM) is utilized to directly provide feature contributions behind each prediction thus achieving feature-level explanations. The proposed SCCAM method is testified on a continuous stirred tank heater (CSTH) and the Tennessee Eastman (TE) industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The effectiveness of the presented SCCAM method is evidenced by the comprehensive results that show our method outperforms the state-of-the-art methods in terms of fault classification and root cause analysis.

Urban Rail Transit Systems (URTS) have increasingly become the backbone of modern public transportation, attributed to their unparalleled convenience, high efficiency, and commitment to sustainable green energy. As we witness a global resurgence of urban rail transit, it becomes evident that most existing URTS still operate on a level of suboptimal intelligence, with their operation and maintenance methods lagging behind other advanced urban transit systems. URTS generate considerable data, offering substantial opportunities for service quality enhancements. Machine Learning (ML), with its demonstrated proficiency in extracting valuable insights from vast data, hold significant promise in the quest to empower URTS. This survey presents a comprehensive exploration of the potential application of ML in URTS. Initially, we delve into the existing challenges of URTS, thereby elucidating the compelling motivation behind the integration of ML into these systems. We then propose a taxonomy of ML paradigms and techniques, discussing in-depth their potential applications in URTS, encompassing perception, prediction, and optimization tasks. Subsequently, we scrutinize a plethora of ML-empowered URTS application scenarios, including but not limited to obstacle perception, infrastructure perception, communication and cybersecurity perception, passenger flow prediction, train delay prediction, fault prediction, remaining useful life (RUL) prediction, train operation and control optimization, train dispatch optimization, and train ground communication optimization. Finally, we present an insightful discussion on the challenges and future directions for URTS, aiming to harness the full potential of ML techniques to deliver superior service and performance.

Randomized controlled clinical trials are the gold standard for evaluating the safety and efficacy of pharmaceutical drugs, but in many cases their costs, duration, limited generalizability, and ethical or technical feasibility have caused some to look for real-world studies as alternatives. On the other hand, real-world data may be much less convincing due to the lack of randomization and the presence of confounding bias. In this article, we propose a statistical roadmap to translate real-world data (RWD) to robust real-world evidence (RWE). The Food and Drug Administration (FDA) is working on guidelines, with a target to release a draft by 2021, to harmonize RWD applications and monitor the safety and effectiveness of pharmaceutical drugs using RWE. The proposed roadmap aligns with the newly released framework for FDA’s RWE Program in December 2018 and we hope this statistical roadmap is useful for statisticians who are eager to embark on their journeys in the real-world research.

With the development of digitization, railways have also entered the era of intelligent construction. Intelligent construction can effectively improve the safety and efficiency of railway construction, but it has very high demands for communication and computing resources, especially in remote and uninhabited areas without public network coverage. Providing real-time and reliable computing services for intelligent railway construction in uninhabited areas is a considerable challenge. In this paper, we design an intelligent railway construction system based on edge computing, where unmanned aerial vehicles (UAV) are deployed in the construction scenario to provide spectrum and computing resources for intelligent construction devices. In order to improve the real-time performance of computing in the intelligent railway construction system, we formulate the communication and computing resources optimization process as a two level game model. In the low-level game, we address the inherent personal selfishness exhibited by construction devices. To enhance spectral efficiency, we propose a non-cooperative potential game among these devices. In the high-level game, a single UAV server faces challenges in promptly handling all incoming computation tasks. To address this issue, we present a cooperative game model aimed at optimizing load balancing among UAV servers. Our proposed algorithms are effectively evaluated in terms of spectral efficiency and average task delivery time, as evidenced by the performance assessments.

Gear fault detection based on encoder signals has attracted attention in recent years, in which it is important to capture the instantaneous angular speed (IAS) jitters caused by the gear faults. The central difference method (CDM) is widely used to calculate the IAS. However, due to the quantization error of the encoder and the measurement noise in practice, it is often hard to accurately estimate the jitters of the IAS caused by the gear fault using the CDM directly. To address this issue, the scheme of encoder signal reconstruction and the synchronous average merging is proposed to improve the estimation accuracy of the IAS. First, the encoder signal is reconstructed according to the square wavenumber of the encoder corresponding to each tooth of the gear. Then, the time-synchronous averaging (TSA) process is performed on the reconstructed signal. Third, the IAS signal is estimated by the CDM. Finally, the blind deconvolution based on the cyclostationarity maximization (CYCBD) method is used to process the IAS signal to enhance the jitters caused by the gear fault. The simulation and experimental results support the proposed method.

Accurate estimation of road peak adhesion coefficient is of great significance in vehicle active safety systems. In order to prevent the vehicle from running out of control in actual driving process, the estimation algorithm should be able to adapt to various working conditions, and transmit estimated information of road peak adhesion coefficient to Electronic Control Unit (ECU) of the vehicle timely and accurately. Therefore, a new preprocessment method for the fusion estimation algorithm of road adhesion coefficient is proposed. The core of this method is the equal ratio relationship between the longitudinal, lateral peak adhesion coefficients and the utilization adhesion coefficient under adjacent typical roads. According to this relationship, this method accomplishes normalization of the tyre model in the form of introducing parameters from the outside, so that the tyre model can be combined with the filtering algorithm to be applied to estimation, which solves the problem that the precise tyre model cannot be used for road adhesion coefficient estimation due to its complex construction. This method can adapt to most of tyre models in the field of vehicle dynamics. In addition, the existing estimation algorithms need sufficient excitation (when the comprehensive slip ratio is 0.15–0.20) to estimate the accurate road peak adhesion coefficient. This method can greatly reduce the system error caused by insufficient road excitation, and can also obtain accurate estimation results when the excitation is insufficient. Finally, in order to verify the preprocessment method, the magic formula tyre model is used to describe the tyre characteristics. After processing by the preprocessment method, the road peak adhesion coefficient is estimated in combination with Unscented Kalman Filter (UKF). The high accuracy and timeliness of the estimated results in simulation and real vehicle tests verify the effectiveness of the preprocessment method.

Rapid development of drug resistance after chemotherapy is a major cause of treatment failure in individuals with pancreatic ductal adenocarcinoma (PDAC). In this study, we illustrate that tumor-derived interleukin 35 (IL-35) mediates the accelerated resistance of PDAC to gemcitabine (GEM). We observe that GEM resistance can spread from GEM-resistant PDAC cells to GEM-sensitive cells, and that IL-35 is responsible for the propagation of chemoresistance, which is supported by sequencing and experimental data. Additionally, we discover that GEM-resistant cells have significantly higher levels of IL-35 expression. Mechanistically, aberrantly expressed IL-35 triggers transcriptional activation of SOD2 expression via GP130-STAT1 signaling, scavenging reactive oxygen species (ROS) and leading to GEM resistance. Furthermore, GEM treatment stimulates IL-35 expression through activation of the NF-κB pathway, resulting in acquired chemoresistance. In the mouse model, a neutralizing antibody against IL-35 enhances the tumor suppressive effect of GEM. Collectively, our data suggests that IL-35 is critical in mediating GEM resistance in pancreatic cancer, and therefore could be a valuable therapeutic target in overcoming PDAC chemoresistance.

Purpose: To investigate the value of Korean, American, and Chinese ultrasound risk stratification systems combined with BRAF(V600E) mutation in the detection of papillary thyroid carcinoma (PTC)within cytologically indeterminate thyroid nodules (CITNs). Methods: A single-center retrospective study encompassed 511 CITNs selected from 509 patients between January 2020 and July 2023.Each nodule underwent surgical treatment and was classified according to three distinct systems. Receiver operating characteristic (ROC) curves were plotted using histopathological diagnosis as the reference standard, and diagnostic performance was compared. Results: The three ultrasound stratification systems showed an elevated malignant risk with increasing grades (all Pfor trend2< 0.001). The cut-off values for Korean, American, and Chinese systems were 5, 5, and 4c, and their respective area under the curves (AUCs) were 0.735, 0.778, and 0.783.The combination of BRAF (V600E) mutation significantly enhanced the diagnostic efficacy for the Korean(0.773vs0.735, P< 0.001), American (0.809vs0.778, P< 0.001) and Chinese (0.815vs0.783, P< 0.001) stratification systems in distinguishing CITNs without compromising specificity. When the three stratification systems were applied individually or combined with BRAF (V600E) mutation, the AUCs of the American and Chinese systems were similar (all P> 0.05), both of which were higher than the AUC of the Korean system (all P< 0.05). The American system exhibited higher specificity compared to the Chinese and Korean systems (all P< 0.001), whereas the Chinese system demonstrated higher sensitivity and accuracy when compared to the American and Korean systems (all P< 0.001). Conclusion: Korean, American and Chinese stratification systems present potential in the differential diagnosis of CITNs. BRAF (V600E) mutation can significantly improve the detection rate of malignant nodules within CTNs, particularly PTC. Notably, the American and Chinese systems demonstrate superior overall diagnostic performance among these systems.

Grateloupia ramosaWang & Luan sp. nov. (Halymeniaceae, Rhodophyta) is newly described from Hainan Province, southern China. The organism has the following morphological features: (1) purplish red, cartilaginous and lubricous thalli 5–10 cm in height; (2) compressed percurrent axes bearing abundant branches with opposite arrangement; (3) claw-like apices on top, constricted to 2–4 cm at the base; (4) cortex consisting of 3–6 layers of elliptical or anomalous cells and a medulla covered by compact medullary filaments; (5) reproductive structures distributed throughout the thallus, especially centralized at the bottom of the end portion of the branches; and (6) 4-celled Carpogonial branches and 3-celled auxiliary-cell branches, both of the Grateloupia-type. The morphological differences were supported by molecular phylogenetics based on ribulose-1, 5-bisphosphate carboxylase/oxygenase ( rbc L) gene sequence analysis. There was only a 1 bp divergence between specimens collected from Wenchang and Lingshui of Hainan province. The new species was embedded in the large Grateloupiaclade of the Halymeniaceae. The pairwise distances between G. ramosaand other species within Grateloupiaranged from 26 to 105 bp, within pairwise distances of 13–111 bp between species of the large genus Grateloupiain Halymeniaceae. Thus, we propose this new species as G. ramosaWang & Luan sp. nov.

This article demonstrates a conformal miniaturized bandpass frequency-selective surface (FSS) with excellent frequency response stability. First, the simulation design for the finite FSS array is proposed. Next, the central connection patch, the ring patch, and the aperture elements, mapped from the first-order equivalent circuit model (ECM), are chosen to obtain transmission properties under conformal conditions. The influence law of elements on the performance stability and elements suitable for narrowband or wideband under conformal conditions are summarized. Based on these, a miniaturized wideband FSS for X-band with stable transmission performance is designed. The angular stability of the planar FSS can reach 75°. In the case of the conformal FSS with the bending angle of $5\pi $ /6, the resonance frequency deviation is 2.32% under ${y}$ -polarized and almost zero under ${x}$ -polarized. The transmission coefficients of the FSS prototype are also measured for both planar and curved surfaces. When the FSS is conformal on the cylindrical surface with a radius of 159 mm, the resonance frequency deviation can be almost negligible, and the transmission performance remains stable up to 70°. The measurement results are in good agreement with the simulation results, verifying the effectiveness of the proposed FSS structure and the simulation design.

In real applications, non-Gaussian distributions are frequently caused by outliers and impulsive disturbances, and these will impair the performance of the Rauch–Tung–Striebel (RTS) smoother. In this study, a modified RTS smoothing algorithm combined with the minimum error entropy (MEE) criterion (MEE-RTS) is developed, and by employing the Taylor series linearization method, it is also expanded to the state estimation of nonlinear systems. The proposed methods improve the robustness of the conventional RTS smoother against complex non-Gaussian noises. In addition, we examine the MEE-RTS smoother's mean error behavior, mean square error behavior, and computational complexity, and the performance of the proposed algorithms is verified by comparing it with existing RTS-type smoothers.

The increasingly intense cyber-attacks have always been a crucial issue to the communication-based train control (CBTC) system due to exposed wireless channels. Both cyber-attack intrusion detection and defense policy calculation demand substantial computing resources. Combined with high capacity and reliability 5G technologies, edge intelligence (EI) is believed to help empower CBTC systems in terms of security and efficiency. This paper proposes an EI-enabled structure for CBTCs to defend against cyber-attacks, where the EI server provides real-time intelligent computing services for trains to derive real-time defense policies. We formulate the cyber-attack and defense process in EI-enabled CBTCs as a two-level game model, where system security and edge computing resource allocation are jointly optimized. In the lower-level game, we model interactions between the cyber attacker and system defender as a discrete repeated security game (DRSG), which is also a non-zero sum and incomplete information game. The fictitious play (FP) is introduced to derive a Nash equilibrium (NE) based optimal defense scheme. In the upper-level game, considering that the EI server cannot simultaneously update the optimal defense scheme for all trains due to the limited computation resources, we construct a multi-stage computation resource allocation game (MCRAG). We derive the optimal computation resource allocation scheme by the neural fictitious self-play (NFSP), where a deep Q-learning network (DQN) and a supervised learning network are jointly built to learn the strategy. Extensive simulation results show that our proposed EI-enabled CBTC system and the two-level game model can effectively defend against various attacks.

Pulmonary nodule detection and segmentation are two important works for early diagnosis and treatment of lung cancer. The work of detection is to locate pulmonary nodules in a given chest CT scan, and the segmentation aims at extracting all the voxels from a CT scan within each nodule’s space. This paper propose a novel framework to process both nodule detection and segmentation integrately, which is implemented as the combination of SLIC supervoxel segmentation and CNN classification. The learning of CNN just require weakly labeled data annotations, where only a single coordinate is provided for each annotated nodule as the ground truth. The CNN architecture is designed as a 3D multi-level framework, which is able to comprehensively recognize nodules with variant sizes and shapes. Experiments on the dataset of LUNA16 challenge expressed prominent detecting performance, demonstrating the necessity and efficiency of 3D CNN architecture and multi-level framework for computer-aided detection of pulmonary nodules. Meanwhile the evaluation of segmentation presented impressive performance, producing elegant shapes of real nodules, which proves the great efficiency of SLIC technique.

The advanced 5G and mobile edge computing promote the development of the intelligent high-speed railway and enable computing-intensive and latency-sensitive tasks. Mobile edge computing can effectively release the pressure of tasks on on-board computing resources by migrating communication, computing, and storage to edge servers. However, the fluctuation of data transmission and task processing duration exists in practical projects. The current work assumes that the traditional communication model is ideal and that the latency can be derived as a fixed value, which is the most conservative bound. Obviously, there will be some system performance loss because the transmission latency is usually much shorter than this conservative bound. Therefore, an offloading strategy is proposed to maximize the task completion rate, considering the fluctuation of transmission latency and processing time in this paper. First, the stochastic network calculus is adopted to evaluate the fluctuation and probability of the transmission latency. Additionally, the task processing duration is regarded as an exponential distribution that is affected by the computing resource. Then, the task offloading model is generated based on the potential game to maximize the task completion rate. Moreover, the Nash Equilibrium and best response are derived. A reinforcement learning algorithm combined with a game is proposed to reach the Nash Equilibrium and obtain the task offloading strategy. Finally, extensive theoretical analysis and simulations are illustrated to prove the effectiveness of the model.

• Al–Si–Cu alloys with modified eutectic Si and unconventional nanocrystalline eutectics are prepared under 5 GPa. • High pressure results in a more stable growing interface of α-Al. • Dense nano-sized Si and Al 2 Cu precipitates lead to significantly enhanced mechanical properties. • The segregation of Si on θ'-Al 2 Cu phases is energetically favorable. This paper presents a systematic investigation of the microstructural evolution and mechanical properties of Al–7Si–3Cu alloys prepared under high pressure of 5 GPa. It is found that the dendritic structure disappears due to the enhanced stability of the growth interface under high pressure. Meanwhile, a considerable solute-induced modification effect is achieved in the eutectic Si. Besides, unconventional larger eutectic colonies and ultrafine nanocrystalline eutectics are observed within the intragranular area. Combined with detailed microstructural characterization, the crucial role of high pressure on the evolution of these isolated phases is discussed. The study also explores the precipitation behavior and strengthening mechanisms of alloys prepared under direct aging and solution-aging treatments. Upon the direct aging treatment, θ' phases, coarse Al 2 Cu particles, and Si particles form simultaneously in the matrix owing to the increased solid solubility of alloying elements, which results in the highest mechanical properties with a microhardness of 132.8 HV and compressive stress of 715 MPa. Solution-aging treatment leads to the segregation of Si on θ' phases, which proved to be energetically favorable according to first-principles calculations. This work provides new insights into the microstructural optimization of aluminum–silicon-based alloys through high-pressure manipulation and paves the way for further industrial applications. [Display omitted] [ABSTRACT FROM AUTHOR]

Rapid and accurate antimicrobial prescriptions are critical for bloodstream infection (BSI) patients, as they can guide drug use and decrease mortality significantly. The traditional antimicrobial susceptibility testing (AST) for BSI is time-consuming and tedious, taking 2–3 days. Avoiding lengthy monoclonal cultures and shortening the drug sensitivity incubation time are keys to accelerating the AST. Here, we introduced a bacteria separation integrated AST (BSI-AST) chip, which could extract bacteria directly from positive blood cultures (PBCs) within 10 min and quickly give susceptibility information within 3 h. The integrated chip includes a bacteria separation chamber, multiple AST chambers, and connection channels. The separator gel was first preloaded into the bacteria separation chamber, enabling the swift separation of bacteria cells from PBCs through on-chip centrifugation. Then, the bacteria suspension was distributed in the AST chambers with preloaded antibiotics through a quick vacuum-assisted aliquoting strategy. Through centrifuge-assisted on-chip enrichment, detectable growth of the phenotype under different antibiotics could be easily observed in the taper tips of AST chambers within a few hours. As a proof of concept, direct AST from artificial PBCs with Escherichia coliagainst 18 antibiotics was performed on the BSI-AST chip, and the whole process from bacteria extraction to AST result output was less than 3.5 h. Moreover, the integrated chip was successfully applied to the diagnosis of clinical PBCs, showing 93.3% categorical agreement with clinical standard methods. The reliable and fast pathogen characterization of the integrated chip suggested its great potential application in clinical diagnosis.

We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems. Spatial CCM is essential for two-timescale beamforming in IRS-assisted systems; however, estimating the spatial CCM is challenging due to the passive nature of reflecting elements and the large size of the CCM resulting from massive reflecting elements of the IRS. In this paper, we propose a CCM estimation method by exploiting the low-rankness as well as the positive semi-definite (PSD) 3-level Toeplitz structure of the CCM. Estimation of the CCM is formulated as a semidefinite programming (SDP) problem and an alternating direction method of multipliers (ADMM) algorithm is developed. Our analysis shows that the proposed method is theoretically guaranteed to attain a reliable CCM estimate with a sample complexity much smaller than the dimension of the CCM. Thus the proposed method can help achieve a significant training overhead reduction. Simulation results are presented to illustrate the effectiveness of our proposed method and the performance of two-timescale beamforming scheme based on the estimated CCM.

The objective of this study is to provide a model and method to assist fabricators in making appropriate precast production plans, coordinating factories prefabrication and on-site assembly in construction based on the just-in-time (JIT) philosophy. We propose a JIT precast production scheduling model for the precast production of steel box girders in the Hong Kong–Zhuhai–Macau (HZM) bridge construction project. In order to minimize the total early/tardy costs for this JIT scheduling model, we first present a job batching algorithm (JBA) to group series of jobs into several batches. Then, we model the batch cost function as a piecewise linear convex function and derive its optimal solution, which guides us to propose an optimal shifting algorithm (OSA) to locate the optimal starting time of each batch and minimize the batch cost. In order to get the best job sequence for minimizing the total early/tardy costs and improve search efficiency, we introduce a dominance rule for early/tardy scheduling problem and propose a dominance rule-based genetic algorithm (DBGA) embedded with an optimal timing algorithm, which can find the best job sequence as well as the corresponding optimal schedule. The real-world case study of HZM bridge project demonstrates that our proposed model and algorithm can assist construction practitioners to make better decision on precast production scheduling compared to empirical rule in current engineering practice. In addition, numerical studies prove that our proposed algorithm has a better performance on both effectiveness and efficiency due to the dominance rule.

In this letter, a design method for frequency selective surface (FSS) based on the equivalent circuit model (ECM) and deep learning is proposed. In the proposed method, the ECM depicting the target transmission characteristic is first established. Then, with the aid of the established ECM, the target transmission characteristic is converted into the desired transmission curve and the initial topology of the FSS structure is determined. The cascade network is adopted to realize the rapid inverse design of the FSS structure. Benefiting from the established ECM, the high quality of the training data and proper description of the design target can be ensured in deep learning. For verification, an ultrawideband FSS structure with a passband ranging from 3.1 to 12.05 GHz and a stopband ranging from 12.85 to 20 GHz is designed. Both simulated and experimental results show that the designed FSS provides an ultra-wide passband with flat-top and fast roll-off characteristics, covering 98.9% target passband and 95.3% target stopband.

The virtually coupled train sets (VCTS) have been proposed to improve transportation efficiency, passenger satisfaction and operational capability for communication-based train control (CBTC) systems. Reliable and real-time trainto-train (T2T) wireless communications are quite desirable to ensure the stability of VCTS. Moreover, with the development of industrial Internet of Things (IIoT), an IIoT-based VCTS is proposed. Due to the physically exposed wireless environment, T2T wireless communications are vulnerable to jamming attacks (JAs), causing packets loss, time delay, and interference with the operation of VCTS systems. The distance between adjacent trains in the IIoT-based VCTS would be violate the minimum relative braking distance regulated by the traditional moving block (MB) principle if JAs were not mitigated. This is a severe safety events, which is intolerable for train control systems. In this study, we develop an event-triggered control (ETC) based resilience control method to mitigate the impact of JAs on the IIoT-based VCTS. A cooperative control approach, stable IIoT-based VCTS range and T2T communication cycle are jointly designed in the ETC-based model. The resilience control approach enables the communication cycle to be adjusted by sacrificing part of the system performance to ensure the safety and stability of VCTS systems when JAs occurs. Extensive simulation results demonstrate that the proposed ETC-based resilience control model can successfully defend against jamming attacks, and the transportation efficiency in urban rail transits can be significantly improved even under JAs.

In this brief, a sliding mode control strategy is proposed to achieve the formation control for virtual coupling trains. Different from existing moving block based train control system, the headway between consecutive train sets is dynamically adjusted with respect to the velocity of the preceding train instead of just maintaining a large constant safe margin. By considering the uncertainty of the train model and external disruptions with unknown bound, the established formation control law can drive the position tracking error and the speed tracking error of the virtually coupled train sets to a predesigned sliding surface, and asymptotically converge to the equilibrium point. Meanwhile, estimations of the unknown train parameters and the bound of unknown external disturbances are derived on-line simultaneously. Simulation results are provided to demonstrate the effectiveness of the proposed strategy.

Purpose: To attain green economic efficiency, small, micro and medium-sized firms must follow environmental paradigms. This research aims to discover the relationship between green intellectual capital, green entrepreneurial orientation, green marketing, green organizational culture and competitive advantage strategies to attain sustainable manufacturing business success. Design/methodology/approach: Today, many companies have accepted their responsibilities, that their operations should not harm the environment. This paper aims to discover the relationship between green factors and competitive advantage strategies to succeed in sustainable manufacturing. The employees of the large manufacturing firms in China are the sample population of the present investigation. Through simple random sampling, surveys were distributed via email. The present study was a quantitative analysis. The analytical tool utilized here was structural equation modeling and SmartPLS program applications. Findings: The empirical outcomes find that green intellectual capital positively influences competitive advantages and sustainable success in business. In addition, the impact of green entrepreneurial orientation on competitive advantages and sustainable success is positive and significant. The findings illustrated that green marketing is an essential factor in competitive advantage and sustainable success in business. Another point is that green organizational culture positively affects competitive advantages and sustainable success. Finally, competitive advantages have significantly affected sustainable success in business. Practical implications: The outcomes help specialists enhance their practices to reflect sustainable business efficiency and competitive advantages. Originality/value: This is the first study that examined businesses' sustainable success and green factors in a comprehensive model and using a specific sample of manufacturing companies based on green technologies. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper investigates blast resistance and residual strength of concrete-filled steel tube (CFST) columns under close-range blast loads. A total of 8 CFST columns, including 4 with circular cross [...]

Coixol, a derivative of 2-benzoxazolinone extracted from coix (Coix lachryma-jobiL. var. ma-yuenStapf), has demonstrated promising anti-inflammatory activity and low cytotoxicity. In this study, 26 coixol derivatives were designed and synthesized by hybridization with cinnamic acid to identify new anti-inflammatory agents. The anti-inflammatory activities of the derivatives were screened using LPS-induced overexpression of nitric oxide (NO) in RAW264.7 macrophages. On the basis of the screening results, compounds containing furan (9c) or nitrofuran (9j) moieties displayed more pronounced activity than coixol and celecoxib. Mechanistic investigations revealed that 9cand 9jsuppressed the expression of induced nitric oxide synthase (iNOS), tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β, which was associated with the inhibition of the nuclear factor (NF)-κB signaling pathway. Invivostudies confirmed the anti-inflammatory activity of 9cand 9jin a xylene-induced mice auricles edema model. The preliminary invitroand invivoresearch findings suggest that 9cand 9jhave the potential to be developed as anti-inflammatory agents.

Offloading tasks in smart devices (SDs) to an edge intelligence-empowered service centre (EISC) is a promising solution to support burgeoning intelligent applications in large-scale intelligent urban rail transits (URTs). However, dynamic computation resource allocation is still a crucial challenge, facing that various large-scale SDs share the EISC computation resources and the reality that the allocated computation resource for an SD is coupled with the offloading rate of all SDs. This paper proposes a joint dynamic offloading rate control and computation resource optimization method for large-scale intelligent URTs. Firstly, we model the large-scale multi-agent computation resource competition problem by a multi-player differential game (MPDG) and prove that the Nash equilibrium (NE) based optimal solution exists for each SDs. Then, we transform the MPDG model into a mean-field game (MFG). By introducing the mean-field into the game, we can solve the multi-agent optimization problem with a single-agent optimization method. We illustrate the rationality of the MFG model and propose an iterative solution method based on the finite difference method to derive the solution. Finally, we propose a z-transforming-based control method to dynamically reschedule computation resources among intelligent applications to achieve a satisfactory quality of service (QoS). Extensive simulation results show that our proposed scheme can significantly improve the performance of intelligent URTs.

This paper addresses the challenge of accurately and timely determining the position of a train, with specific consideration given to the integration of the global navigation satellite system (GNSS) and inertial navigation system (INS). To overcome the increasing errors in the INS during interruptions in GNSS signals, as well as the uncertainty associated with process and measurement noise, a deep learning-based method for train positioning is proposed. This method combines convolutional neural networks (CNN), long short-term memory (LSTM), and the invariant extended Kalman filter (IEKF) to enhance the perception of train positions. It effectively handles GNSS signal interruptions and mitigates the impact of noise. Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.

ABSTRACTTo solve the snow problems on roads, conductive rubber composites are proposed to achieve active snow melting. The snow melting processes were analysed by numerical simulation and outdoor experiments. A thermal resistance model was used to analyse the heat transfer and phase change during snow melting. The snow melting effect under complex environments was evaluated by outdoor tests. Furthermore, the electric and thermal fields during snow melting of conductive rubber composites were simulated by COMSOL Multiphysics. The results show that the thermal resistance model reflects the heat transfer process of conductive rubber composites in roads. The conductive rubber composite melts 30 mm of snow in −5°C for 1.92 h. The conductive rubber composite melts snow more rapidly and evenly than existing snow melting methods. The model can predict the operation strategy for conductive rubber composites in engineering. Moreover, this study lays the theoretical and experimental foundation for practical applications.