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Introduction: Cherry extract has a high amount of anthocyanins and flavonoids containing antioxidant effects. Its high antioxidant characteristics have been shown to reduce markers of delayed muscle soreness (DOMS) and exercise-induced muscle damage (EIMD) to improve recovery after exercise. Objective: Verify the effects of the cherry extract on post-exercise muscle damage. Methods: Google scholar, Medline, and Scopus were systematically searched until February 2022. The Cochrane Collaboration tool was applied to determine the risks of bias. Results: The results showed that cherry extract administration did not have a decreasing impact on creatine kinase levels overall: (WMD = 12.85 IU. L-1, 95% CI: −35.94, 61.64; P = 0.606). Considerable heterogeneity was observed among the articles (Cochran's Q-test = 990.80, P = 0.000, I2 = 96.7 %). However, there is a significant reducing effect on pain sensation by the consumption of cherry extract (WMD = −6.105 mm; 95% CI: −11.193 −1.017; p = 0.019). Conclusion: Cherry extract consumption effectively reduced late-onset muscle pain among participants in the overall and subgroup analysis. Thus, the cherry extract may be a complementary alternative in recovery after exercise. Level of evidence II; Therapeutic studies - Manuscript review. RESUMO Introdução: O extrato de cereja tem uma alta quantidade de antocianinas e flavonóides contendo efeitos antioxidantes. Suas altas características antioxidantes demonstraram reduzir os marcadores de dor muscular retardada (DOMS) e dano muscular induzido pelo exercício (EIMD) para melhorar a recuperação após o exercício. Objetivo: Verificar os efeitos do extrato de cereja nos danos musculares pós-exercício. Métodos: Google scholar, Medline e Scopus foram sistematicamente pesquisados até fevereiro de 2022. A ferramenta de colaboração da Cochrane foi aplicada para determinar os riscos de viés. Resultados: Os resultados mostraram que a administração do extrato de cereja não teve um impacto decrescente nos níveis de creatina quinase em geral: (WMD = 12,85 IU. L-1, 95% CI: −35,94, 61,64; P = 0,606). Uma heterogeneidade considerável foi observada entre os artigos (teste Q da Cochran = 990,80, P = 0,000, I2 = 96,7 %). Porém, há um efeito redutor significativo na sensação de dor pelo consumo de extrato de cereja (WMD = −6,105 mm; 95% CI: −11,193 −1,017; p = 0,019). Conclusão: O consumo de extrato de cereja foi efetivo na redução de dores musculares de início tardio entre os participantes, na análise geral e nos subgrupos. Assim, o extrato de cereja pode ser uma alternativa complementar na recuperação após os exercícios. Nível de evidência II; Estudos terapêuticos - Revisão de manuscritos. RESUMEN Introducción: El extracto de cereza tiene una gran cantidad de antocianinas y flavonoides con efectos antioxidantes. Se ha demostrado que sus altas características antioxidantes reducen los marcadores de dolor muscular retardado (DOMS) y el daño muscular inducido por el ejercicio (EIMD) para mejorar la recuperación después del ejercicio. Objetivo: Verificar los efectos del extracto de cereza en el daño muscular posterior al ejercicio. Métodos: Se realizaron búsquedas sistemáticas en Google scholar, Medline y Scopus hasta febrero de 2022. Se aplicó la herramienta de colaboración Cochrane para determinar los riesgos de sesgo. Resultados: Los resultados mostraron que la administración de extracto de cereza no tuvo un impacto decreciente en los niveles de creatina quinasa en general: (WMD = 12,85 UI. L-1, IC del 95%: −35,94, 61,64; P = 0,606). Se observó una considerable heterogeneidad entre los artículos (prueba Q de Cochran = 990,80, P = 0,000, I2 = 96,7 %). Sin embargo, el consumo de extracto de cereza tiene un efecto significativo de reducción del dolor (WMD = −6,105 mm; IC del 95%: −11,193 −1,017; p = 0,019). Conclusión: El consumo de extracto de cereza fue eficaz para reducir el dolor muscular de aparición tardía entre los participantes en el análisis global y de subgrupos. Así, el extracto de cereza puede ser una alternativa complementaria en la recuperación después de los ejercicios. Nivel de evidencia II; Estudios terapéuticos - Revisión de manuscritos.

A growing amount of people are beginning to monitor themselves with the rapid emergence of a wide variety of cost-effective personal sensing instruments. To measure different facets of personal life, innovation helps people better understand their lifestyles, enhance their work quality, or maximize various health factors, allowing free-living. Although vast amounts of raw information on the provisioning and physiological parameters have been obtained much more straightforward, making use of all the information remains a significant task. The article introduces the Physical Activity Analysis Framework (PAAF) for the Elderly Person in Free-Living Conditions. In the framework, the acceleration signals split into overlapped windows and derive information in each frame’s frequency domain. The framework’s sensors sense the activity and evaluate a profound learning structure dependent on each window’s progressive networks. The proposed IoT model has multiple layers separately connected with each sensor, and the critical element integrates the outputs of all sensors for the classification of physical activity. In longer cycles, the model combines the window decision with a substantial increase in its efficiency. The model in the research has been evaluated using labelled free-living pilot data. Eventually, discover the use of the proposed models from a broader lifestyle intervention analysis in unlabeled, free-living data. The results show that the proposed model performs well for both labelled and unlabeled data. The experimental analyses of an older person in living conditions with their daily activities to be monitored via IoT system as Meditation effect analysis ratio is 86.6%, Physical activity ratio is 87.12%, Physical disability ratio is 87.1%, Exercise satisfaction ratio is 85.05%, and Self-efficacy ratio is 93.5%.

The paper aims to address output feedback problem for a class of nonlinear systems subjected to unknown dead-zone. High-gain K-filters is firstly designed such that unavailable states of system can be reconstructed. Then, with the help of dynamic surface control (DSC) method, an adaptive output feedback controller design is established, which can alleviate undesired influence of the dead-zone and guarantee the semi-global stability of the system. Furthermore, the $\mathcal {L_{\infty }}$ performance of tracking error is achieved by means of initialization technique. Ultimately, numerical and practical examples clarify the efficiency of the proposed solution.

We propose, to the best of our knowledge, a novel kind of tunable liquid crystal core refractive index (RI) sensor based on photonic crystal fiber (PCF) covered with a nanoring gold film. The finite element method is used to discuss and analyze the sensing performance of the RI sensor. Gold is used as the excitation material for surface plasmon resonance, and a gold nanoring is embedded around the first cladding of the PCF. The liquid analytes penetrate the outermost layer of the cladding, and the central hole is filled with liquid crystal E7. Complete coupling and incomplete coupling are excited as the analyte RI increases, and the resonance strength of complete coupling is stronger than that of incomplete coupling. It can be proved by calculation that at different wavelengths, resonant coupling of fifth-order and sixth-order surface plasmon polaritons is obtained. The RI of liquid analytes ranges from 1.405 to 1.445. The diameters of the liquid crystal cores are 0.2, 0.4, 0.6, and 0.8 µm; their average sensitivities are 10700, 10566, 10167, and 9166 nm/RIU; and the linear fitting constants are 0.98025, 0.97387, 0.96597, and 0.95507, respectively. In short, the RI sensor has the advantages of tunability, wide range, and high sensitivity, and is expected to be applied in various fields.

A high sensitivity of photonic crystal fiber (PCF) with a tunableliquid core refractive index sensor based on surface plasmon resonance (SPR) induced bythe symmetric metal films is proposed in this paper. The SPR excitation materials are chose as gold and silver. The transmission characteristics and sensing performance of the SPR-PCF sensor with different geometrical parameters were analyzed by using finite element method (FEM) and Maxwell equation. The final results show that when thediameter of the central hole is 1.6µm and the thickness of the gold film is 40nm, the SPR occurs at the wavelength of 1400nm, and the confinement loss can reach 17398dB/m. The average sensitivity of the SPR-PCFsensor can reach 7200nm/RIU, and the linear fitting constant is R2=0.99959, which is expected to have important applications in the direction of liquid refractive index detection.

This paper describes a large-size grid closed cavity composite payload bay frame moulding technology, focusing on the process research of the moulding process scheme, mould design scheme and layering curing mode. The research shows that: the moulding of large-size grid closed cavity composite frame can be realized by layering the layer of each unit separately and integral co-curing. Through the process research, the key technology of integral autoclave curing and individual silicon rubber pressurization has been broken through to meet the requirements of even and stable pressure at all positions in the of moulding process of composite products. The residual internal stress can be effectively removed after several cycles’ temperature variation, and then the dimensional accuracy of the frame tends to be stable in a specific temperature range. The vertical bar position deviates from 0.3mm-0.8mm, and the deviation of the frame’s thickness is ±2%. The overall flatness of the plane where the installation interfaces of all key equipment are located is better than 0.2mm. The testing shows that the mechanical properties of the specimen are better than the design index, meeting the user's requirements

Electromagnetic field inverse calculation plays a crucial role in solving the problem arising from the design and non-destructive testing of electromagnetic devices in engineering. In this study, an optimisation algorithm for electric field inverse calculation is proposed, which enables non-destructive detection of voltage on overhead transmission lines, enhances the robustness of the algorithm and improves the accuracy of calculation. Firstly, 2D and 3D calculation models of electric field are established. A multimedium calculation model with the operating environment of transmission lines considered is proposed. Based on the multimedium calculation method, the calculation environment is categorised into soil, vegetation, geology, and other media, and the correctness of the optimisation method is verified by the finite-element method-based model. Secondly, in order to eliminate the measurement noise and electric field distortion to the electric field measurement data, a modified model based on Kalman filter is constructed. Both multimedium calculation method and Kalman filter are applied to the inverse calculation of electric field as an optimisation electric field inverse solving algorithm. Finally, the inverse optimisation method is applied to 220 kV transmission lines and practical 500 kV ultra-high voltage transmission lines, respectively, to improve the accuracy and robustness of calculation.

To overcome the limitations of low sensitivity and instability of metal-medium-metal tunable surface plasmon resonance (SPR) sensors excited by planar waveguides, a silver-graphene-medium-graphene-silver film symmetrical SPR sensing structure excited by planar waveguides is proposed. And optimize sensor performance through modeling. The principle of symmetry structure for sensing tunability is analysed, finding that sensing with a silver film has a higher sensitivity than a gold film. However, since the silver film is easily oxidized, it affects the stability and repeatability of the sensor. To solve this problem, the graphene layer is added to the silver film to improve the disadvantage of easy oxidation of the silver film. A plane waveguide with a refractive index distribution of the residual error function of the excitation is prepared by ion exchange. The sensor structure was modeled and analyzed, and the influence of metal material, metal thickness, graphene layer number and thickness of the detection medium on the performance of the sensor was studied, and the sensing structure was better optimized. The results show that the silver film has a better sensing performance and higher sensitivity than the gold film. By adjusting the thickness of the detection medium, the refractive index range of the detection medium has a certain tunability: the choice of graphene thickness optimises sensor stability and detection sensitivity.

Based on double U-groove photonic crystal fiber (PCF), a surface plasmon resonance sensor with dual parametric detection of temperature and refractive index is proposed. The birefringence of PCF is increased by using germanium ions doped in the core and introducing U-shaped notches on both sides of the D-shaped fiber. The polished surface of the PCF is coated with gold film and PDMS as a temperature sensing channel, and the U-shaped groove is coated with gold film as a refractive index sensing channel. Through the design of the sensor, it is finally possible to achieve independent measurement of the two parameters. The sensor has a maximum wavelength sensitivity of 4715 nm/RIU in the analyte refractive index range of 1.32–1.4, and maximum wavelength sensitivity of 18 nm/°C in the ambient temperature range of − 30 ∘ C − 50 ∘ C . The proposed sensor has broad application prospects in scenarios such as blood analysis, DNA hybridization analysis, and microenvironmental cell interactions.

With the improvement of the quality of national life, the physical health problem is gradually placed in the same important position as the basic problems such as education, people’s livelihood, and safety. Improving the physical quality of college students and promoting the healthy growth of students are major events in the future of the country and the nation. Therefore, each school will conduct a physical test every year. The use of wireless sensors for data acquisition has played an important role in various fields. Therefore, for the physical health management of college students, this paper proposes to design a sensor system to collect physical measurement data. The measured data is collected through the sensor, such as the XGZP6847 gas pressure sensor to collect students’ vital capacity. Then, the sensor node and the coordinator are connected to the PC through the serial port, respectively, and the CC2530 chip transmits the data to the coordinator through the ZigBee module, establishes communication with the PC through the 2.4G wireless communication module, adopts the strategy of the thread pool to save resources, improves the response speed, and obtains students’ body test data in real time. Taking the data acquisition and transmission of vital capacity and body weight subjects as an example, the system realizes the accurate transmission of the collected test data from the sensor to the terminal, greatly improves the accuracy and real-time body test data acquisition, and reduces the probability of errors in the recording process, and the collected data can be used directly or for secondary development, and it promotes the establishment of college students’ physical health management system.

Ecdysteroids initiate the molting process in insects by binding to the ecdysone receptor (EcR), which is a promising target for identifying insect growth regulators. This paper presents an in silico/in vitro screening procedure for identifying new EcR ligands. The three-step virtual screening procedure uses a three-dimensional pharmacophore model, docking and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) rescoring routine. A novel hit (VS14) with good binding activity against Plutella xylostella EcR was identified from a library of over 200,000 chemicals. Subsequently, the 1-phenyl-4-cyano-5-aminopyrazole scaffold and twelve EcR ligands were synthesized. Their IC50 values against Plutella xylostella EcR ranged from 0.64 to 23.21 μm. Furthermore, a preliminary analysis of the structure-activity relationship for novel scaffolds provided a basis for designing new ligands with improved activity.

Image fusion and reconstruction from muldti-images taken by distributed or mobile cameras need accurate calibration to avoid image mismatching. This calibration process becomes difficult in fog when no clear nearby reference is available. In this work, the fusion of multi-view images taken in fog by two cameras fixed on a moving platform is realized. The positions and aiming directions of the cameras are determined by taking a close visible object as a reference. One camera with a large field of view (FOV) is applied to acquire images of a short-distance object which is still visible in fog. This reference is then adopted to the calibration of the camera system to determine the positions and pointing directions at each viewpoint. The extrinsic parameter matrices are obtained with these data, which are applied for the image fusion of distant images captured by another camera beyond visibility. The experimental verification was carried out in a fog chamber and the technique is shown to be valid for imaging reconstruction in fog without a prior in-plane. The synthetic image, accumulated and averaged by ten-view images, is shown to perform potential applicability for fog removal. The enhanced structure similarity is discussed and compared in detail with conventional single-view defogging techniques.

Five new phenolic polyketides, namely 3-O-methylwailupemycin G (1), wailupemycin J (2), R- and S-wailupemycin K (3 and 4) and wailupemycin L (5), as well as the known enterocin (6) and 5-deoxyenterocin (7) were isolated and identified from the fermentation broth of Streptomyces sp. OUCMDZ-3434 associated with the marine green algae, Enteromorpha prolifera. Their structures including absolute configurations were elucidated according to the spectroscopic analysis, X-ray single crystal diffraction and the Mosher's experiments. Compound 1 showed comparable α-glucosidase inhibition with an IC50 value of 0.86 mM to acarbose (IC50 1.12 mM), and compound 4 was cytotoxic on the HeLa cell with an IC50 value of 8.2 μM. Compounds 2, 3 and 7 showed anti-H1N1 virus activity with 47.8%, 42.5% and 60.6% inhibitions, respectively at 50 μg/mL (ribavirin, 45.3% inhibition).

The accurate calculation of electric field intensity under transmission lines is more and more important with the expansion of high voltage engineering, which largely determines the site selection and design of transmission projects. In order to get more accurate results, a methodology of a charge simulation method (CSM) combined with an intelligent optimization algorithm for a 3D calculation model is proposed in this paper. Three key points are emphasized for special mention in this optimized charge simulation method (OCSM). First, the number of sub-segments on the finite length conductor, the position and number of the simulating charge set on a sub-segment are taken as the optimization parameters for unified calculation. Secondly, the fitness function of optimization algorithm is constructed by two values, voltage relative error and electric field intensity relative error. Thirdly, a finite element method (FEM) was used to obtain the electric field intensities, which are compared with the results of the proposed algorithm. A simulation case is carried out on a 3D calculation model of 220 kV transmission lines, which verify the effectiveness of the optimization algorithm. The proposed OCSM solves the parameter optimization problem of CSM in the 3D computational model, which considers physical shape of wire span, and has the advantages of strong global search ability and higher calculation accuracy.

Neural networks have shown great successes in many fields. Due to the complexity of the training pipeline, however, using them in an industrial setting is challenging. In online advertising, the complexity arises from the immense size of the training data, and the dimensionality of the sparse feature space (both can be hundreds of billions). To tackle these challenges, we built TrainSparse (TS), a system that parallelizes the training of neural networks with a focus on efficiently handling large-scale sparse features. In this paper, we present the design and implementation of TS, and show the effectiveness of the system by applying it to predict the ad conversion rate (pCVR), one of the key problems in online advertising. We also compare several methods for dimensionality reduction on sparse features in the pCVR task. Experiments on real-world industry data show that TS achieves outstanding performance and scalability.

Estimating Click-through Rate (CTR) and Conversion Rate (CVR) are two essential user response prediction tasks in computing advertising and recommendation systems. The mainstream methods map sparse, high-dimensional categorical features (e.g., user id, item id) into low-dimensional representations with neural networks. Although they have achieved significant advancement in recent years, how to capture user’s diverse interests effectively from past behaviors is still challenging. Recently some works try using attention-based methods to learn the representation from user behavior history adaptively. However, it is insufficient to capture the diversity of user’s interests. As a step forward to improve this goal, we propose a method named Attentive Capsule Network (ACN). It uses Transformers for feature interaction and leverages capsule networks to capture multiple interests from user behavior history. To precisely obtain sequence representation related to the current advertisement, we further design a modified dynamic routing algorithm integrating with an attention mechanism. Experimental results on real-world datasets demonstrate the effectiveness of our proposed ACN with significant improvement over state-of-the-art approaches. Moreover, it also offers good explainability when extracting diverse interest points of users from behavior history.

Measurement ranges of the traditional surface plasmon resonance (SPR) sensors, which use intensity detection method and are excited by waveguides, are limited by the equivalent refractive index of the excited waveguide, not only makes it non-adjustable but also difficult to realize multi-range detection. To solve the above problems, tunable distributed SPR sensors with symmetrical structures excited by planar waveguides are designed. By studying several metrics of a metal-medium-metal SPR sensing structure exited by a planar waveguide, the influence of a metallic material’s thickness, and the medium thickness between the gold films on the measurement characteristics, are accordingly optimized to design the detection system. Double attenuation peaks can be generated under certain conditions, which embodies distributed sensing characteristics. Here, changing the resonance condition by changing the thickness of the measured medium between the metal films makes the measured refractive index range tunable. The planar waveguide with the Fermi refractive index distribution is prepared and the symmetrical sensing structures are arranged longitudinally alongside the direction of light propagation to achieve multiple attenuation peaks, and thus distributed sensing in different measured ranges is realized. The structure of different medium thicknesses excited by planar waveguides is used to test the honey solution with refractive indices varying between 1.33299 and 1.53246 in exciting the surface plasma waves with double attenuation peaks. For any refractive index range, the linear correlation coefficients are all above 0.99232, which demonstrates that the refractive index detection can be carried out within multiple ranges. This result constitutes a theoretical and practical basis for tunable distributed online measurement of the intensity SPR sensors.

Four new meroterpenoids 1&ndash, 4 and four new isocoumarinoids 5&ndash, 8, along with five known isocoumarinoids (9&ndash, 13), were isolated from the fungus Myrothecium sp. OUCMDZ-2784 associated with the salt-resistant medicinal plant, Apocynum venetum (Apocynaceae). Their structures were elucidated by means of spectroscopic analysis, X-ray crystallography, ECD spectra and quantum chemical calculations. Compounds 1&ndash, 5, 7, 9 and 10 showed weak &alpha, glucosidase inhibition with the IC50 values of 0.50, 0.66, 0.058, 0.20, 0.32, 0.036, 0.026 and 0.37 mM, respectively.

Aiming at the problems of high cost, slow response and unstable data in industrial wastewater quality monitoring in industrial parks, this paper proposes a water quality monitoring system for industrial parks based on Internet of Things and cloud computing technology by studying data acquisition technology of water quality sensors, wireless networking technology of water quality acquisition equipment, connection technology between equipment and cloud computing platform, and end of data model interface. The monitoring technology is also applied in the water quality monitoring system of an industrial park in Hebei Province, China. The data response is timely and the pollution alarm is accurate, which improves the level of water pollution control.