Search

Pressure sensors based on advanced materials have gained high attention for their potential applications in various fields, including sports training. This study focuses on the development of poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE))/graphene composite films as high-performance pressure sensors. The composite films were fabricated using a casting method, and their morphological, structural, thermal, mechanical, and electrical properties were comprehensively characterized. The incorporation of graphene nanoplatelets (GnPs) into the P(VDF-TrFE) matrix led to the formation of a percolated conductive network, resulting in enhanced electrical conductivity and pressure sensitivity. The composite film containing 5 wt% GnP exhibited remarkable sensing capabilities, boasting an elevated sensitivity of 0.85 kPa−1, a rapid response time of 50 ms, and exceptional resilience over 1000 loading-unloading cycles. Moreover, the incorporation of GnP substantially augmented the mechanical properties, with a 65 % enhancement in tensile strength and a 92 % surge in Young's modulus when juxtaposed against pristine P(VDF-TrFE) films. The superior property of these P(VDF-TrFE)/graphene composite pressure sensors, along with their excellent mechanical properties, make them promising candidates for sports training applications, enabling the monitoring and analysis of various biomechanical parameters to optimize athletic performance and prevent injuries.

Abstract Zero-shot stance detection is pivotal for autonomously discerning user stances on novel emerging topics. This task hinges on effective feature alignment transfer from known to unseen targets. To address this, we introduce a zero-shot stance detection framework utilizing multi-expert cooperative learning. This framework comprises two core components: a multi-expert feature extraction module and a gating mechanism for stance feature selection. Our approach involves a unique learning strategy tailored to decompose complex semantic features. This strategy harnesses the expertise of multiple specialists to unravel and learn diverse, intrinsic textual features, enhancing transferability. Furthermore, we employ a gating-based mechanism to selectively filter and fuse these intricate features, optimizing them for stance classification. Extensive experiments on standard benchmark datasets demonstrate that our model significantly surpasses existing baseline models in performance.

Road network plays an important role in the fields of navigation, urban planning, and transportation. Extracting road network data from imagery based on machine learning models is an efficient and economical method for obtaining road network data. In order to save labor costs, crowdsourced data can be employed to automatically acquire the labels for model training. In response to the current challenges in road extraction, such as the limited number of labeled samples, low precision of sample labels generated from crowdsourced data, and difficulty in obtaining accurate road label data, which lead to low-quality, incomplete, and inaccurate road extraction, this study proposes a label refining framework based on a road matching and integrate algorithm. Labels are generated from OpenStreetMap (OSM) vector data, and roads are extracted from very high resolution orthoimage using the U-net model. The extracted roads are then matched and integrated with the original data to generate refined labels, which are employed for further model training and road extraction. Experimental results demonstrate that this process can overcome the poor quality of samples directly generated from the OSM data, i.e., the label refining framework led to significant improvements with respect to the completeness, accuracy, and quality of the road network extraction results.

Hypercholesterolemia is a risk factor of cardiovascular diseases, which have been managed using statin drugs. Red yeast rice (RYR) as a traditional food in the Eastern Asia countries has anti-hyperlipidemia activity. Recently, a variety of food supplement products containing RYR have been developed to lower blood cholesterol, which is attributed to the presence of monacolins, especially monacolin K (lovastatin, a statin drug). This review was aimed to summarize the clinical trials using RYR products to investigate their effects on lipid profiles in humans. Relevant articles of human clinical trials were retrieved from PubMed and discussed here. Results showed that RYR and its extracts have been included in commercially available products alone and in combinations with various other materials, which include bioactive compounds such as coenzyme Q10 and berberine, vitamins, extracts from other plants such as phytosterols, polyunsaturated fatty acids or even probiotics. The durations of the trials ranged from 4 weeks to 60 months. The content of monacolin K ranged from 0.32 mg/pack to 10 mg/pack. The data of these human clinical trials demonstrated that these RYR supplement products were sufficient to reduce blood cholesterol in different populations. However, the inclusions of certain materials might not have additive effect. Additionally, studies including a lovastatin only positive control group with the equivalent dose as that of monacolin K in the RYR products remain to be done. Furthermore, variations of monacolin K contents and presence of toxic citrinin are still concerns. Last, bioactivities of other components in RYR should be investigated as well. More future studies will certainly help to fully explore the potentials of this traditional food in the combat against cardiovascular and other metabolic diseases.

IntroductionExercise is pivotal for maintaining physical health in contemporary society. However, improper postures and movements during exercise can result in sports injuries, underscoring the significance of skeletal motion analysis. This research aims to leverage advanced technologies such as Transformer, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs) to optimize sports training and mitigate the risk of injuries.MethodsThe study begins by employing a Transformer network to model skeletal motion sequences, facilitating the capture of global correlation information. Subsequently, a Graph Neural Network is utilized to delve into local motion features, enabling a deeper understanding of joint relationships. To enhance the model's robustness and adaptability, a Generative Adversarial Network is introduced, utilizing adversarial training to generate more realistic and diverse motion sequences.ResultsIn the experimental phase, skeletal motion datasets from various cohorts, including professional athletes and fitness enthusiasts, are utilized for validation. Comparative analysis against traditional methods demonstrates significant enhancements in specificity, accuracy, recall, and F1-score. Notably, specificity increases by ~5%, accuracy reaches around 90%, recall improves to around 91%, and the F1-score exceeds 89%.DiscussionThe proposed skeletal motion analysis method, leveraging Transformer and Graph Neural Networks, proves successful in optimizing exercise training and preventing injuries. By effectively amalgamating global and local information and integrating Generative Adversarial Networks, the method excels in capturing motion features and enhancing precision and adaptability. Future research endeavors will focus on further advancing this methodology to provide more robust technological support for healthy exercise practices.

Monitoring air pollution is important for human health and the environment. Previous studies on the prediction of air pollutants from satellite images have employed machine learning, yet there are few enhancements to the constructure of model. Moreover, the existing models have been successful in predicting pollutants like PM2.5, PM10, and O3. They have not been as effective in predicting other primary air pollutants. To improve the overall prediction performance of the existing model, a novel residual learning backpropagation model, abs. as BresNet, has been proposed in this research. This model has revealed the availability to precisely predict the ground-surface concentration of the six primary air pollutants, PM2.5, PM10, O3, NO2, CO, and SO2, based on the satellite imagery of MODIS AOD. Two of the most commonly used machine learning models so far, viz. the multilayer backpropagation neural network (MLBPN) and random forest (RF), were employed as the control. In the conducted experiments, the proposed BresNet model demonstrated significant improvements of 18.75%/31.94%, 33.82%/85.71%, 15.00%/35.29%, 39.06%/134.21%, 23.23%/68.00%, and 137.14%/260.87% in terms of R2 for the six primary air pollutants, compared to the RF/MLBPN model. Moreover, the convergence speed and loss function of the BresNet model compared to that of the MLBPN decreased by 55.15%, revealing superior convergence speed with the lower loss function.

Background: Previous studies have reported the direct or indirect relationship between the renin-angiotensin-aldosterone system (RAAS) and atrial fibrillation (AF). However, in patients with “apparently” idiopathic AF without possible external influence, whether RAAS is dysregulated at an early stage of AF and its relationship with the recurrence of AF after ablation have not been studied. Methods: This single-center, prospective, case-control study included apparently healthy individuals with AF (the case group) or paroxysmal supraventricular tachycardia (PSVT, the control group) referred for catheter ablation at the same period. The primary outcome was RAAS activation in these two groups. The secondary outcome was the 1-year recurrence of AF after ablation. Results: This study included 51 “apparently” idiopathic AF and 91 patients with PSVT. A greater proportion of patients in the case group had plasma renin activity (PRA) levels

Backgrounds: Hemorrhagic transformation (HT) is a complication after stroke and is associated with increased mortality and disability. Hyperglycemia is a risk factor for HT incidence and seriously affects the treatment of stroke. There are no effective drugs to cure HT in the clinic. The Xiaoshuan Tongluo effective component group (XECG) is a Chinese medicine formula reconstituted by active ingredients, which is clinically used for the treatment of stroke. However, the roles of XECG in hyperglycemia-enhanced HT are not clear. This study aims to evaluate the effects of XECG on hyperglycemia-enhanced HT and explore the potential mechanisms. Methods: In this study, a hyperglycemia-induced HT model was used. XECG (100, 200, 400 mg·kg−1) was orally administered for 5 days before the operation, and the incidence of HT was evaluated. Microglia polarization and related signaling pathways were measured to clarify the mechanisms of XECG on hyperglycemia-induced HT. Results: Our results revealed that pretreatment with XECG alleviated neurological deficits and BBB disruption and improved the incidence and severity of HT enhanced by hyperglycemia. Further studies confirmed that XECG regulated microglial polarization by inhibiting the M1 phenotype and promoting the M2 phenotype and inhibited the activation of the HMGB1-RAGE-NF-κB signaling pathway. Conclusions: These results suggested that pretreatment with XECG alleviated hyperglycemia-enhanced HT. XECG may be a potential drug for the treatment of HT.

Due to the genetic mutation (fa) in the gene encoding for leptin receptor, homozygous Zucker rats (fa−/−) develop excessive adiposity and become an experimental animal model in obesity and metabolic-related diseases research. Based on tetra-primer amplification refractory mutation system-polymerase chain reaction (ARMS-PCR), we developed a method to quickly genotype Zucker rats with a mutated fa allele from their wildtype littermates. The three genotypes are clearly discriminated on 2.0% agarose gel. Our method can be used as a reliable tool to set up and maintain the breeding colony in animal facilities as well as assign animals to control and treatment groups based on their genotypes for animal studies.

Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the “optimal” estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the “optimal” estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM2.5, PM10, NO2, and SO2 in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O3 slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area.

This present work aims to understand the strengthening mechanism of TiB2p reinforced rheological forming of Al matrix composites. The mechanism of in-situ TiB2p on microstructure evolution and mechanical properties strengthening of semi-solid Al–20Si (wt%) alloy was studied. The experiments show that the primary Si phase, α-Al phase, and eutectic Si are refined by in-situ TiB2p combined semi-solid forming. The primary Si phase is distributed uniformly in the matrix and the morphology of eutectic Si phase changed from needle-like to short rod-like. In addition, the Si/TiB2p interface is clear and well-bonded but no orientation relationship existed. On the contrary, the TiB2 are tightly bound to the α-Al and exist good lattice matching coherence in (01-1-1)TiB2p//(200)α-Al. The tensile strength and elongation are an increase from 110 MPa to 2.3% to 215.7 MPa and 4.2% with the content of in-situ TiB2p from 0 to 6%, respectively, own to dislocation strengthening, fine grain strengthening, and second phase grain strengthening induced by TiB2p. The fracture form from cleavage fracture to cleavage-ductile mixed-mode fracture due to the incorporation of TiB2p inhibits the crack propagation.

The issue of urban air quality in China has become increasingly significant due to industrialization and rapid urbanization. Although PM2.5 is the major air pollutant in most cities of northern China and has a direct negative impact on human health, there is a problem of under-representativeness at Chinese monitoring stations. In some cities, due to the relatively fewer national control stations and the fact that the stations are located closer to pollution sources, under the current assessment system, the monitoring data are not sufficient for the fairness of air quality assessment in different cities. In this article, the multispectral data of Landsat 8 data, air quality data, and meteorological data from ground monitoring stations have been integrated together and imported to different PM2.5-estimation models established based on the multi-layer back propagation neural network (MLBPN), support vector regression (SVR), and random forest (RF), respectively. According to the evaluation indices of R2, RMSE, and ME, the estimation model based on the MLBPN revealed the best PM2.5 estimation results and was therefore employed for the regional representativeness analysis in the study area of Xi’an, Shaanxi, China. The annual average PM2.5 concentration in the study area is depicted after error correction using Kriging interpolation, which can be further used to evaluate and analyze the representativeness of monitoring stations in Xi’an. By calculating the difference between the actual station annual average and the annual average of estimated PM2.5 concentration in the whole region, it can be found that the regional annual average value of PM2.5 in Xi’an is overestimated. To sum up, this article proposes a feasible method for the spatial positioning of the air quality monitoring stations to be established.

In this paper, the microstructures, mechanical properties, and corrosion behavior of xTiB2/Al–9Si–3Cu-0.8Zn composites after T6 heat treatment were invested in detail through the combination of different technologies, including optical microscopy (OM), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), tensile testing, corrosion testing, etc. The research results showed that the average size of the α-Al grains gradually decreased with the increase of TiB2 particles (TiB2p) content. In addition, there was a semi-coherent relationship between TiB2p and the α-Al matrix, and the nanoscale precipitates were identified as θ and θʹ phases by virtue of their crystal orientation relationship. Tensile testing results revealed that an increase in TiB2 content caused the elastic modulus, yield strength and tensile strength of the composites to increase and then decrease. And with a TiB2p content of 4 wt%, they reached 85.7 GPa, 360 MPa and 398 MPa, reflecting increases of 26.2%, 60.0%, and 30.9%, respectively. The contributions of thermal mismatch, grain boundary and Orowan strengthening mechanisms to the overall yield strength of the 4%TiB2/Al–9Si–3Cu–0.8Zn composite were 72.1, 3.8, and 35.6 MPa, respectively. Unfortunately, the corrosion resistance of xTiB2/Al–9Si–3Cu–0.8Zn composites gradually decreased with increasing TiB2p content. The corrosion morphology analysis indicated that the agglomeration of TiB2p and Si weakened the protection afforded by the oxide film, evolving into a point of nucleation for the subsequent corrosion reaction. Moreover, point–pair correlation was observed between the TiB2p and Al matrix, exacerbating the corrosion. The results implied that extra factors be considered in industrial application of TiB2 ceramic particles in a metallic system.

This paper addresses the synchronization problem of coupled harmonic oscillators via event-triggered impulsive control. Different from the existing results where both position and velocity measurements are required to achieve the synchronization, two kinds of event-triggered impulsive mechanisms, that is, the position-only and velocity-only approaches are proposed respectively, which effectively extends the event-triggered impulsive control strategy to the synchronization problem with limited information. Sufficient conditions are proposed to exhibit the convergence behavior of the coupled harmonic oscillators. Meanwhile, it is derived that Zeno behavior can be excluded by using the designed controller. Two simulation examples are illustrated to substantiate the performance of the proposed synchronization protocols.

The conjugate gradient method is very effective in solving large-scale unconstrained optimal problems. In this paper, on the basis of the conjugate parameter of the conjugate descent (CD) method and the second inequality in the strong Wolfe line search, two new conjugate parameters are devised. Using the strong Wolfe line search to obtain the step lengths, two modified conjugate gradient methods are proposed for general unconstrained optimization. Under the standard assumptions, the two presented methods are proved to be sufficient descent and globally convergent. Finally, preliminary numerical results are reported to show that the proposed methods are promising.

The self-developed dry-coupled rock ultrasonic monitoring system is adopted to set up a multidirectional and multiwaveform ultrasonic monitoring network, which aims to analyse the evolution law of acoustic spectrum parameters in the process of granite loading failure under uniaxial compression, to explore the dominant acoustic spectrum characteristic information at different stages of granite loading, and to verify in situ the damage monitoring of time-effect deformation. The results show that the wave velocity, amplitude, and amplitude-frequency of the first wave and the velocity of P-wave and S-wave show a significant upward trend in the rock compaction section. After entering the elastic stage, the three spectral parameters become peacefully stable, and the stage transformation is obvious. In the stable crack growth stage, with the initiation of the crack, the dominant frequency of S-wave shows a significant stage transition compared with the global ultrasonic wave velocity and the first arrived amplitude, and the dominant frequency decreases by 6%. In the unstable crack growth stage, the three acoustic spectrum parameters present obvious downward trend, and the first arrived wave amplitude of S-wave is found to have a significant decline of 39.1%. On the eve of failure, the amplitude-frequency of S-wave shows different feature from the P-wave; that is, S-wave transfers from the state of multipeak in wide frequency to the state of single peak in low frequency, which is the failure precursor of the rock sample.

Abstract In this paper, a class of nonlinear constrained optimization problems with both inequality and equality constraints is discussed. Based on a simple and effective penalty parameter and the idea of primal-dual interior point methods, a QP-free algorithm for solving the discussed problems is presented. At each iteration, the algorithm needs to solve two or three reduced systems of linear equations with a common coefficient matrix, where a slightly new working set technique for judging the active set is used to construct the coefficient matrix, and the positive definiteness restriction on the Lagrangian Hessian estimate is relaxed. Under reasonable conditions, the proposed algorithm is globally and superlinearly convergent. During the numerical experiments, by modifying the technique in Section 5 of (SIAM J. Optim. 14(1): 173-199, 2003), we introduce a slightly new computation measure for the Lagrangian Hessian estimate based on second order derivative information, which can satisfy the associated assumptions. Then, the proposed algorithm is tested and compared on 59 typical test problems, which shows that the proposed algorithm is promising.

Abstract Combining the techniques of the working set identification and generalized gradient projection, we present a new generalized gradient projection algorithm for minimax optimization problems with inequality constraints. In this paper, we propose a new optimal identification function, from which we provide a new working set. At each iteration, the improved search direction is generated by only one generalized gradient projection explicit formula, which is simple and could reduce the computational cost. Under some mild assumptions, the algorithm possesses the global and strong convergence. Finally, the numerical results show that the proposed algorithm is promising.

The application demand of lightweight high-quality aluminum alloy parts in automotive and aerospace fields is increasingly. In aluminum matrix composites, reinforcing particles can significantly improve the performance of the matrix. In this paper, the microstructures and mechanical properties of die-cast 4 wt%TiB _2 /Al-9Si-3Cu-0.8Zn composite were systematically analyzed by x-ray diffraction, optical microscope, scanning electron microscopy, energy dispersive spectrometer, transmission electron microscopy and tensile testing. The composite was successfully fabricated from an Al-K _2 TiF _6 -KBF _4 system by in situ melting technique. The research results show that the average grain sizes of the α -Al phase gradually decreased with the increase of filling distance. And the TiB _2 particles were distributed around eutectic Si in irregular polyhedral morphology or nearly circular shape. Meanwhile, the crystal structures of Ti-B compound and long needle shaped nano-sized precipitated were identified and analyzed, and they were found to be TiB _2 and Al _2 Cu phase, respectively. Tensile testing results show that the mechanical properties of die-cast composite clearly increase after T6 heat treatment. The yield strength, ultimate tensile strength and elongation could reach 311 MPa, 379 MPa and 2.8% respectively, with the best injection velocity (1.8 m s ^−1 ). The significantly enhancement of mechanical properties of composite after T6 heat treatment was mainly due to the introduction of TiB _2 reinforcing phase and the precipitation of Al _2 Cu precipitate in the aging stage. The results implied that the introduction of TiB _2 reinforced particles could improve the mechanical properties of die castings, which has an important guiding role for its practical application.

Background and objective Surgery is the best treatment for early and middle stage non-small cell lung cancer. The aim of this study is to summarize the experience of bronchial sleeve mordality lobectomy and carinal resection in the treatment of 92 patients with central lung cancer from January, 1996 to May, 2010. Methods A total of 92 patients with central lung cancer underwent pulmonary resection. Carinal resection and reconstruction were performed in 14 patients, bronchial sleeve resection in 70 patients, and bronchial sleeve combined with pulmonary artery sleeve lobectomy in 8 patients. Results There was no operative mortality. The average operation time was 2 hours and 43 minutes. Postoperative complications such as pulmonary atelectasis occurred in 6.94% (7/92) of total group, and hoarseness in 4.35% (4/92). The 1, 3 and 5 year survival rates were 80.7%, 59.6% and 31.5%. Conclusion Bronchial sleeve lobectomy and double sleeve lobectomy are capable of excising pulmonary tumor as much as possible while remaining healthy lung tissues. Carinal resection and reconstruction is helpful to extend the surgical indication, and increase the chance of successful resection.

Multiple lines of evidence suggest that a large portion of pancreatic cancer patients suffer from either hyperglycemia or diabetes, both of which are characterized by high blood glucose level. However, the underlying biological mechanism of this phenomenon is largely unknown. In the present study, we demonstrated that the proliferative ability of two human pancreatic cancer cell lines, BxPC-3 and Panc-1, was upregulated by high glucose in a concentration-dependent manner. Furthermore, the promoting effect of high glucose levels on EGF transcription and secretion but not its receptors in these PC cell lines was detected by using an EGF-neutralizing antibody and RT-PCR. In addition, the EGFR transactivation is induced by high glucose levels in concentration- and time-dependent manners in PC cells in the presence of the EGF-neutralizing antibody. These results suggest that high glucose promotes pancreatic cancer cell proliferation via the induction of EGF expression and transactivation of EGFR. Our findings may provide new insight on the links between high glucose level and PC in terms of the molecular mechanism and reveal a novel therapeutic strategy for PC patients who simultaneously suffer from either diabetes or hyperglycemia.

We describe a finite element (FE) simulation method for predicting the kinematics of individual knee joints that has been certified for use in vivo. Our goal is to advance treatment and restore natural joint kinematics by improving the way clinicians understand the complex individual living systems and potential diseases. The goal of this effort is to use the finite element method to dissect a more accurate human knee joint model. In order to complete the reconstruction of 3D knee models, computed tomography (CT) data from a sound subject was divided. When cartilaginous and cortical bones were separated, the Hounsfield unit (HU) was clearly visible. The femur and tibia bones, as well as ligaments, make up the knee model. The last step in improving the ligament was to realign and remove bone layers. Four ligaments at the knee joint were displayed through the use of direct spring elements. The models' finite element examinations were finished. On the proximal femur, critical powers somewhere in the range of 100 and 1000 N were applied. The review's discoveries, which uncovered that the most noteworthy greatest VMS at articular ligaments was 3.27 MPa and 2.930 MPa, separately, were in accordance with other distributed research. One could assume that the knee models were explored. [ABSTRACT FROM AUTHOR]

The microstructures of high-Si Al alloy cylindrical simples produced by rheological squeeze-casting with bottom-up filling were investigated. The effect of process parameters on the distribution of hard phases (primary Si and Fe-rich phase) was evaluated, and flow characteristics of semi-solid slurry of high-Si Al alloy were investigated. Differences in volume fractions (VFs) of hard phases between the middle and upper layers of cylindrical samples decreased as the squeezing speed increased. Moreover, the flow state of the semi-solid alloy slurry transitioned from laminar to turbulent flow as the squeezing speed was increased. Movement of gases throughout the semi-solid alloy slurry filling process was analysed under different process parameters. The results provide insights for improving rheological casting of high-Si Al alloy and other Al matrix composites.