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Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

Toxic gases, such as NOx, SOx, H2S and other S-containing gases, cause numerous harmful effects on human health even at very low gas concentrations. Reliable detection of various gases in low concentration is mandatory in the fields such as industrial plants, environmental monitoring, air quality assurance, automotive technologies and so on. In this paper, the recent advances in electrochemical sensors for toxic gas detections were reviewed and summarized with a focus on NO2, SO2 and H2S gas sensors. The recent progress of the detection of each of these toxic gases was categorized by the highly explored sensing materials over the past few decades. The important sensing performance parameters like sensitivity/response, response and recovery times at certain gas concentration and operating temperature for different sensor materials and structures have been summarized and tabulated to provide a thorough performance comparison. A novel metric, sensitivity per ppm/response time ratio has been calculated for each sensor in order to compare the overall sensing performance on the same reference. It is found that hybrid materials-based sensors exhibit the highest average ratio for NO2 gas sensing, whereas GaN and metal-oxide based sensors possess the highest ratio for SO2 and H2S gas sensing, respectively. Recently, significant research efforts have been made exploring new sensor materials, such as graphene and its derivatives, transition metal dichalcogenides (TMDs), GaN, metal-metal oxide nanostructures, solid electrolytes and organic materials to detect the above-mentioned toxic gases. In addition, the contemporary progress in SO2 gas sensors based on zeolite and paper and H2S gas sensors based on colorimetric and metal-organic framework (MOF) structures have also been reviewed. Finally, this work reviewed the recent first principle studies on the interaction between gas molecules and novel promising materials like arsenene, borophene, blue phosphorene, GeSe monolayer and germanene. The goal is to understand the surface interaction mechanism.

In this work, we have studied thermoelectric properties of monolayer and fewlayer MoS 2 in both armchair and zigzag orientations. Density functional theory (DFT) using non-equilibrium Green’s function (NEGF) method has been implemented to calculate the transmission spectra of mono- and fewlayer MoS 2 in armchair and zigzag directions. Phonon transmission spectra are calculated based on parameterization of Stillinger-Weber potential. Thermoelectric figure of merit, ZT, is calculated using these electronic and phonon transmission spectra. In general, a thermoelectric generator is composed of thermocouples made of both n-type and p-type legs. Based on our calculations, monolayer MoS 2 in armchair orientation is found to have the highest ZT value for both p-type and n-type legs compared to all other armchair and zigzag structures. We have proposed a thermoelectric generator based on monolayer MoS 2 in armchair orientation. Moreover, we have studied the effect of various dopant species on thermoelectric current of our proposed generator. Further, we have compared output current of our proposed generator with those of Silicon thin films. Results indicate that thermoelectric current of MoS 2 armchair monolayer is several orders of magnitude higher than that of Silicon thin films.

Objective Acute pancreatitis (AP) is a severe inflammatory disease associated with dysregulated glycolysis and mitochondrial dysfunction. This study investigates the therapeutic potential of quercetin, a novel PFKFB3 inhibitor, in modulating glycolysis and mitochondrial function to alleviate AP. Methods We conducted homology analysis of the PFKFB3 protein and identified quercetin as a potential inhibitor through molecular docking. In vitro experiments using a cerulein-induced inflammatory pancreatic cell model assessed the effects of quercetin on PFKFB3 expression, glycolysis, and mitochondrial function. In vivo validation was performed using an AP rat model to evaluate the impact on inflammation, tissue damage, and metabolic status. Results Quercetin significantly reduced PFKFB3 expression, inhibited glycolysis, and improved mitochondrial function in inflammatory pancreatic cells. In the AP rat model, quercetin treatment decreased serum amylase and lipase levels, reduced inflammatory markers (TNF-α and IL-6), and alleviated pancreatic tissue damage, as evidenced by histological analysis. Conclusion Quercetin effectively modulates glycolysis and mitochondrial function by inhibiting PFKFB3, thereby reducing inflammation and tissue damage in AP. These findings highlight the potential of quercetin as a novel therapeutic agent for AP.

A novel class of n-4R compliant parallel pointing mechanisms is proposed, and the compliance and kinetostatic model of the mechanism are established and analyzed successively. Firstly, the compliance model of a class of n-4R compliant parallel pointing mechanism is established based on the coordinate transformation. The model is verified by finite element analysis, and the influence of geometric parameter variations on the compliance performance of the mechanism is analyzed. Secondly, the mechanism is simplified to an equivalent spring system, and the governing equation of the equivalent spring system is constructed by utilizing the established compliance model. According to the governing equation, the mapping relationship between the input force and the output displacement of the mechanism is subsequently obtained, that is, the kinetostatic model. Then, the accuracy of the kinetostatic model is verified by two simulation examples: The spiral trajectory of the mobile platform center and the spatial pointing trajectory of the mechanism. The results of the two examples show that the deviations between the analytical results and the FE-results are within 0.038% and 0.857%, with the excellent consistency indicating the accuracy of the kinetostatic model. Finally, the influence of the geometric parameter values on the mapping matrix in the kinetostatic model is studied.

Background Acute pancreatitis (AP) is a severe inflammatory disease frequently accompanied by disturbances in glucose metabolism, which further complicate the disease prognosis. This study aims to explore the role of PFKFB3, a key glycolytic enzyme, in regulating glucose metabolism in AP and assess the potential of PFKFB3 inhibition via nanovesicle delivery to mitigate metabolic dysfunction. Methods Transcriptomic data from Gene Expression Omnibus (GEO), including single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing, were analyzed to investigate the molecular mechanisms involved in glucose metabolism dysregulation in AP. The therapeutic effects of PFKFB3 inhibition via nanovesicle-based delivery were evaluated using both in vivo and in vitro AP models. Results PFKFB3 inhibition significantly restored normal glycolytic function and improved glucose metabolism in AP models. Moreover, nanovesicle-mediated delivery also alleviated both inflammation and metabolic disturbances, highlighting its promise as a therapeutic strategy for managing glucose dysfunction in AP. Conclusion Our findings identify PFKFB3 as a critical therapeutic target for treating glucose metabolism disorders in acute pancreatitis. Nanovesicle-based PFKFB3 inhibition may serve as an innovative approach to address metabolic complications associated with AP, offering a new direction for therapeutic interventions in inflammatory diseases. Graphical Abstract Molecular Mechanism of EVs-mediated Delivery of PFKFB3 Inhibitor in Ameliorating Glucose Metabolism Disorder Post-AP.

Robust detection and recognition of water surfaces are critical for autonomous navigation of unmanned surface vehicles (USVs), since any none-water region is likely an obstacle posing a potential danger to the sailing vehicle. A novel water region visual detection method is proposed in this paper. First, the input image pixels are clustered into different regions and each pixel is assigned a label tag and a confidence value by adaptive multistage segmentation algorithm. Then the resulting label map and associated confidence map are fed into a convolutional neural network (CNN) as training samples to train the network online. Finally, the online trained CNN is used to segment the input image again but with greater precision and stronger robustness. Compared with other deep-learning image segmentation algorithms, the proposed method has two advantages. Firstly, it dispenses with the need of manual labeling training samples which is a costly and painful task. Secondly, it allows real-time online training for CNN, making the network adaptive to the navigational environment. Another contribution of this work relates to the training process of neuro network. An effective network training method is designed to learn from the imperfect training data. We present the experiments in the lake with a various scene and demonstrate that our proposed method could be applied to recognize the water region in the unknown navigation environment automatically.

Background Norovirus is an important cause of viral acute gastroenteritis (AGE) worldwide. Methods In order to characterize the molecular epidemiology and genetic diversity of norovirus in children in Beijing, 3634 anal swab samples of AGE patients from January 2023 to December 2024 were analyzed. Norovirus was detected using RT-PCR and genotyped by sequencing the partial RdRp and VP1 region. Results During the two-year period, norovirus was detected in 19.6% of AGE cases, with the highest detection rate in children under 3 years of age. GII.4 and GII.P16 were the dominant genotypes of VP1 and RdRp , with a detection rate of 36.39% and 44.59%, respectively. According to the dual-typing system combined the RdRp and VP1 , the dominant genotypes of norovirus changed between 2023 and 2024. In 2023, the most common genotype was GII.3[P12] (39.15%), followed by GII.4 Sydney[P16] (32.34%) and GII.4 Sydney[P31] (15.32%). However, in 2024, the dominant genotype was GII.17[P17] (41.43%), followed by GII.4 Sydney[P16] (34.29%) and GII.3[P12] (20.0%). The GII.17 variants in this study were divided into two clusters: cluster IIIa and IIIb, which shared high nucleotide identity with GII.17 variant emerged in 2014/2015. Significantly, GII.4 Sydney[P31] and novel GII.4 Sydney[P16] variants co-circulating in this region from 2023 to 2024. Conclusion The data provided useful information on the molecular epidemiology of norovirus in sporadic AGE among children and highlighted the necessary to continuously monitor the epidemiological characteristics of norovirus associated AGE.

Segregated incompressible large eddy simulation and acoustic perturbation equations were used to obtain the flow field and sound field of 1:25 scale trains with three, six and eight coaches in a long tunnel, and the aerodynamic results were verified by wind tunnel test with the same scale two-coach train model. Time-averaged drag coefficients of the head coach of three trains are similar, but at the tail coach of the multi-group trains it is much larger than that of the three-coach train. The eight-coach train presents the largest increment from the head coach to the tail coach in the standard deviation (STD) of aerodynamic force coefficients: 0.0110 for drag coefficient ( C d ), 0.0198 for lift coefficient ( C l ) and 0.0371 for side coefficient ( C s ). Total sound pressure level at the bottom of multi-group trains presents a significant streamwise increase, which is different from the three-coach train. Tunnel walls affect the acoustic distribution at the bottom, only after the coach number reaches a certain value, and the streamwise increase in the sound pressure fluctuation of multi-group trains is strengthened by coach number. Fourier transform of the turbulent and sound pressures presents that coach number has little influence on the peak frequencies, but increases the sound pressure level values at the tail bogie cavities. Furthermore, different from the turbulent pressure, the first two sound pressure proper orthogonal decomposition (POD) modes in the bogie cavities contain 90% of the total energy, and the spatial distributions indicate that the acoustic distributions in the head and tail bogies are not related to coach number.

This paper proposes two optimal design schemes for improving the kinematic and dynamic performance of the 3-PSS flexible parallel micromanipulator according to different application requirements and conditions. Firstly, the workspace, dexterity, frequencies, and driving forces of the mechanism are successively analyzed. Then, a progressive optimization design is carried out, in which the scale parameters of this mechanism are firstly optimized to maximize the workspace, combining the constraints of the minimum global dexterity of the mechanism. Based on the optimized scale parameters, the minimum thickness and the cutting radius of the flexure spherical hinge are further optimized for minimizing the required driving forces, combined with constraints of the minimum first-order natural frequency of the mechanism and the maximum stress of the flexure spherical hinge during the movement of the mechanism. Afterward, a synchronous optimization design is proposed, in which the scale parameters are optimized to maximize the first-order natural frequency of the mechanism, combined with the constraints of a certain inscribed circle of the maximum cross-section of the workspace, the maximum stroke of the selected piezoelectric stages, and the maximum ultimate angular displacement of the flexure spherical hinge. The effectiveness of both optimization methods is verified by the comparison of the kinematic and dynamic characteristics of the original and optimized mechanism. The advantage of the progressive optimization method is that both the workspace and the driving forces are optimized and the minimum requirements for global dexterity and first-order natural frequency are ensured. The merit of the synchronous optimization method is that only the scale parameters of the mechanism need to be optimized without changing the structural parameters of the flexible spherical hinge.