Great Application Value Research Articles

Interfaces do Método de Leitura da Paisagem e Sistemas de Informações Geográficas (SIG): contribuições do estudo das Unidades de Paisagem para o ensino-aprendizagem no Ateliê de Projeto Urbano

The interdisciplinary and multiscale methodological approach presented here seeks to understand the biogeophysical support in repeated interactions with society, highlighting the process of socio-spatial production. It outlines the structuring and adaptation of the Landscape Reading method for didactic workshops in Geographic Information Systems, as a result of the GIS research project applied to Landscape Planning developed in (omitted for blind reading). Structuring such method adapted to geotechnology tools and environmental modeling in a GIS environment proved to be of great value for application in Architecture and Urbanism undegraduate courses, which is undergoing an important process of teaching-learning design methodological adaptation, especially in the areas of landscape, urbanism, and urban and regional planning.

Isokinetic Force Modeling in Sports Based on Embedded System of Motion Recognition Model

With the gradual deepening of sports research, sports training methods integrating more scientific and technological means can greatly improve athletes’ professional ability. Combined with the isokinetic strength training method, it can effectively alleviate muscle recovery and body flexibility, which requires an analysis of the level of sports body promotion, and a more efficient training program for various sports parameters. In this study, taking strength enhancement as an example, the convolutional neural network is innovatively integrated into isokinetic strength training. Using the motion data obtained by wearable sensors in the training, we trained the neural network model by real-time analysis of data samples through an embedded system. Further, we optimized the network parameters to realize the extraction and recognition of EMG depth features. Compared with the traditional isokinetic strength classification model, this model lays a foundation for further constructing personalized strength training guidance methods. After the simulation test, the difference of the model’s accuracy requirement before and after the pre-training is 4.6%, and the difference of the test set before and after the pre-intensive training is [Formula: see text]1%. It is 16.2% higher than the TCAM algorithm, and the gap with STA-LSTM remains between [Formula: see text]. It is verified that the attention concentration control scheme can not only reduce the error rate in visual recognition tasks, but also improve the complexity of the neural network model. The optimized lightweight data structure is especially suitable for embedded systems with limited computing power. All kinds of test projects verify its rationality and make full use of the control ability of the embedded system and the characteristics of parallel computing and programmability, which has great practical application value for the development of the Internet of Things system network. The research results have made a thorough study of the physiological guidance of existing strength training, the identification and evaluation methods of exercise-induced muscle fatigue, and the current situation of the existing isokinetic equipment.

Study on crashworthiness of square frustum lattice structure under in-plane compression load

The direction of load on the component in engineering applications is often uncertain, so the excellent crashworthiness and smooth load transfer of energy-absorbing structures in various directions is of great application value. Square frustum lattice structure (SFLS) was proposed based on the control of cell deformation mode and the improvement of in-plane strength without increasing the overall mass. Compared with multi-cell tubes commonly used in engineering and origami-patterned structure with excellent performance in research, it is found that the SFLS shows high and stable energy absorption capacity with the same structural parameters. The SEA and CFE of SFLS are 1.7 times and 3.5 times of origami structure and multi-cell tube, respectively, and the fluctuation indicator of the force displacement curve of origami structure and multi-cell tube are 1.7 times and 2.7 times of that of SFLS. In-plane quasi-static compression experiment was carried out and the finite element model was verified. The results of parametric analysis and sensitivity show that, compared with the change of cell number and cell height, the crashworthiness of SFLS under in-plane compression was most sensitive to the ratio of side length, in the range of parameters studied, the maximum SEA value of SFLS can reach 79.35 J/g for k value equal to 0.3, while the best indicator of smoothness of force-displacement curve of SFLS is found at k equal to 0.5.

Stem Cells Treatment for Subarachnoid Hemorrhage.

Subarachnoid hemorrhage (SAH) refers to bleeding in the subarachnoid space, which is a serious neurologic emergency. However, the treatment effects of SAH are limited. In recent years, stem cell (SC) therapy has gradually become a very promising therapeutic method and advanced scientific research area for SAH. The SCs used for SAH treatment are mainly bone marrow mesenchymal stem cells (BMSCs), umbilical cord mesenchymal stem cells (hUC-MSCs), dental pulp stem cells (DPSCs), neural stem cells (NSCs)/neural progenitor cell (NPC), and endothelial progenitor cell (EPC). The mechanisms mainly included differentiation and migration of SCs for tissue repair; alleviating neuronal apoptosis; anti-inflammatory effects; and blood-brain barrier (BBB) protection. The dosage of SCs was generally 106 orders of magnitude. The administration methods included intravenous injection, nasal, occipital foramen magnum, and intraventricular administration. The administration time is generally 1 hour after SAH modeling, but it may be as late as 24 hours or 6 days. Existing studies have confirmed the neuroprotective effect of SCs in the treatment of SAH. SC has great potential application value in SAH treatment, a few case reports have provided support for this. However, the relevant research is still insufficient and there is still a lack of clinical research on the SC treatment for SAH to further evaluate the effectiveness and safety before it can go from experiment to clinical application.

Stress Evaluation of Ferromagnetic Materials Based on a New Barkhausen Noise Sensor Composed by High Entropy Alloy Magnetic Core

Abstract During the service of ferromagnetic structural steel materials, stress should be evaluated accurately. Although the magnetic Barkhausen noise (MBN) testing has the ability to sense stress, it can be easily interfered with by environment. In this paper, a new MBN sensor is fabricated by selecting FeCoNi(AlMn)0.25 high entropy alloy (HEA) as the case of magnetic core to improve the accuracy of stress evaluation. The process optimization results show that the stability of MBN signal characteristics is the largest when the excitation frequency is 4 Hz and the voltage is 6 V. The signal-to-noise ratio of MBN indicates that the HEA and Ni-Zn ferrite probes have better anti-interference capability. The MBN signal characteristic values peak voltage and root mean square measured by the HEA probe can linearly quantify the stress level with higher efficiency, stability, and accuracy. The underlying reason of high sensitivity of HEA probe to the variation of MBN signals is revealed based on the magnetic properties. The microstructure and the thermodynamic parameters are analyzed to clarify whether the additions of Al and Mn atoms can affect the short-ranged magnetic exchange interaction and lattice distortion, which affects the magnetization behavior of HEA. Finally, the availability of MBN sensor with HEA magnetic core to the stress evaluation on the retired slide rails of car seats is conducted, which demonstrates its great application value.

Active Fault Interpretation in the Northern Segment of the Red River Fault Based on Multisource Remote Sensing Data

High-resolution topographic and geomorphic data are important basic data for the study of active structures. Here, multisource remote sensing data were used to reinterpret the active faults in the northern segment of the Red River Fault (China). First, we obtained airborne light detection and ranging (LiDAR) data, high-resolution GaoFen-7 (GF-7) remote sensing image data, and historical aerial photographs, and a high-resolution digital elevation model (DEM) was generated based on the airborne LiDAR data and GF-7 data. According to the remote sensing interpretation, the main active faults were identified. We subsequently verified the faults in the field and constrained the geographic locations. The current activity was confirmed to be dominantly normal faulting, with some dextral strike-slip components, and the latest active age was the Late Holocene. It reflects the coordination of structural deformation between the rotation of the secondary block and the sliding of the boundary fault within the Sichuan–Yunnan Block. The results show that airborne LiDAR and GF-7 remote sensing data have a great application value in providing high-resolution topographic and geomorphologic data for the study of active structures. The comprehensive application of multisource remote sensing data can greatly improve the reliability of active fault interpretations and provide a reference for follow-up research within the study area.

Research on Blended Teaching of College English Writing Based on 'POA' Theory Under the Background of Big Data

This paper discusses the application value of the writing teaching mode combined with the mixed teaching mode in college English writing teaching against the background of big data. Focusing on production-oriented approach (POA) theory, this paper proposes a mixed learning writing model for English teaching and applies the POA mixed learning English writing teaching model to practical teaching by using the cloud classroom platform, collecting students' big data, and analyzing students' seriousness, classroom participation, satisfaction, and English writing performance. Although the overall student engagement is not high, their classroom satisfaction is very high. Research shows that after the teacher adjusted the lesson plan and encouraged students to participate more in class interactions, their scores improved in the second exam but declined in the third exam . The levels of engagement, participation, and satisfaction have significantly decreased across all grades. The POA combined with the blended learning mode has a great application value in practical teaching in the era of big data.

Leaf Moisture Content Detection Method Based on UHF RFID and Hyperdimensional Computing

Leaf moisture content (LMC) directly affects the life activities of plants and becomes a key factor to evaluate the growth status of plants. To explore a low-cost, real-time, rapid, and accurate method for LMC detection, this paper employs Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) sensor technology. By reading the tag information attached to the back of leaves, the parameters of the RSSI, phase, and reading distance of the tags are collected. In this paper, we propose an enhanced Multi-Feature Fusion algorithm based on Hyperdimensional Computing (HDC) called MFFHDC. In our proposed method, the real-valued features are encoded into hypervectors and then combined with Multi-Linear Discriminant Analysis (MLDA) for the feature fusion of different features. Finally, a retraining method based on Cosine Annealing with Warm Restarts (CAWR) is proposed to improve the model and further enhance its accuracy. Tests conducted in the experimental forest show that the proposed mechanism can effectively predict the LMC. The model’s Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) reached 0.0195, 0.0255, and 0.9131, respectively. Additionally, comparisons with other methods demonstrate that the presented system performs excellently in most aspects. As a lightweight model, this study shows great practical application value, particularly for the limited data volume and low hardware costs.

Magnetic Liquid Gating Valve Terminal for Patterned Droplet Generation and Transportation of Highly Viscous Bioactive Fluids.

As an open microfluidic technology with excellent anti-fouling and energy-saving properties, liquid gating technology can selectively separate or transfer multiphase fluids, which has shown great application value in the field of biomedical engineering. However, no study has demonstrated that liquid gating technology has the ability to transfer high-viscosity fluids and biologically active substances, and current liquid gating valves are unable to realize smart-responsive pulsed-patterned transfer, which severely limits their application scope. In this paper, liquid gating technology is combined with magnetically responsive materials to prepare a liquid-based magnetic porous membrane (LMPM) with excellent magnetostatic deformation capability and antifouling properties. On this basis, a magnetic liquid gating valve terminal (MLGVT) with patterning transfer capability is developed, and the feasibility of liquid gating technology for transferring high-viscosity fluids and hydrogel bioinks is explored. Meanwhile, a flexible MLGVT is prepared and realized for targeted drug delivery. This study expands the potential of liquid gating technology for drug delivery, cellular transport and smart patches.

GNSS spoofing detection based on frequency domain processing

Global Navigation Satellite System (GNSS) spoofing detection has attracted much attention in recent years to ensure security. However, most of the existing methods use the time domain feature and can be easily affected by interference, which limits the practical application. To solve this problem, the frequency domain processing approach is investigated and a novel spoofing detection method is proposed. The basic idea is to exploit the spoofing-induced feature by transforming the correlator output into the frequency domain. The proposed method has a simple implementation logic and requires only minor software upgrades to existing GNSS receivers. Simulation and experimental results are presented to verify the effectiveness of the proposed method. The results demonstrate that it exhibits superior detection performance and shows robustness to interference. Consequently, this method has great application value and can serve as a valuable complement to current detection methods.

Inflammatory regulation of squid cartilage gelatin with different molecular weights for treatment of chronic wounds in diabetes

Squid, as a very important economic marine species, accounts for 5 % of the total catch of fish and cephalopods. The waste from the processing process of squid can be used for collagen extraction, which has great application value in the field of biomedical materials. Here, we obtained squid cartilage gelatin (SCG) with different molecular weights by adjusti.ng the reaction conditions and used for the treatment of chronic wounds in diabetes. SCG extracted at low temperatures and short heating times demonstrated a more intact structure, higher molecular weight, and superior gel stability. Based on cell study and transcriptome analysis, SCG with high molecular weight significantly promoted cell adhesion, because it provided more contact sites for cells, whereas small molecules of SCG could directly reduce inflammation. Animal studies have demonstrated that SCG significantly promotes diabetic wound healing as evidenced by reducing inflammation, inducing vascular regeneration, promoting tissue growth, re-epithelialization, collagen deposition and remodeling. This study elucidated the immunoregulatory mechanisms of SCG with different molecular weights, and validated its potential application in chronic wound healing in diabetes.

Novel orthodontic adhesives with antibacterial, mineralization and fluorescence properties for enamel demineralization prevention

Enamel demineralization is one of the most common adverse effects of fixed orthodontic treatment, mainly because the appliances affect oral hygiene maintenance, potentially leading to plaque accumulation and oral flora dysbiosis. Adhesives are essential for fixed orthodontic treatment, but excessive adhesives on tooth surfaces might be stained aesthetically and serve as additional sites for plaque accumulation. Some research gaps exist for simultaneously addressing orthodontic enamel demineralization and proper adhesive removal. In this study, novel multifunctional orthodontic adhesives (MFOAs) were developed using amino-silane functionalized zinc oxide quantum dots (AF-ZnOQDS) with fluorescence property, antibacterial agent dimethylaminohexadecyl methacrylate (DMAHDM), and mineralizing material 45S5 bioactive glass (BG-45S5) as the modifiers. The three were incorporated into Transbond XT adhesive at different mass fractions and relevant properties were evaluated in vitro and in vivo. The results indicated that the MFOA-3 with 3 wt% AF-ZnOQDS, 5 wt% DMAHDM, and 7.5 wt% BG-45S5 possessed long-term antibacterial ability, dual-mineralization activity, and strong fluorescence properties. It had clinically acceptable physicochemical properties and good biocompatibility, providing a desirable anti-demineralization effect. Therefore, MFOA-3 provides a new strategy to solve the two major challenges of enamel demineralization and proper removal of adhesives during fixed orthodontics, and thus has great value in clinical application.

Research on the Transparency of Civil and Commercial Arbitration from the Perspective of Generative Artificial Intelligence

The emergence of GPT marks a significant progress in natural language processing in the field of generative artificial intelligence. Artificial intelligence has great application potential and value in legal retrieval, document review and production, arbitrator selection, and auxiliary arbitration decision-making. However, there is a contradiction between the development of artificial intelligence and the principle of arbitration confidentiality. Training a more accurate artificial intelligence model requires a large amount of diverse data, while the confidentiality of arbitration limits the acquisition and sharing of data. Therefore, in order to give full play to the potential of artificial intelligence, it is necessary to gradually increase the transparency of arbitration and achieve a balance between arbitration confidentiality and transparency through reasonable institutional design and legal norms.

A precise and efficient K-means-ELM model to improve ultra-short-term solar irradiance forecasting

To address the problems of intermittent and uncontrollable solar irradiance faced by grid-connected photovoltaic power plants, this paper proposes an ultra-short-term solar irradiance prediction model that combines K-means clustering with the Extreme Learning Machine (ELM). The K-means algorithm is first applied to cluster solar irradiance data from different seasons in Shanghai based on spatial similarity. Subsequently, the ELM algorithm is employed to train the model, significantly improving both prediction accuracy and training speed. Compared with the prediction accuracy of the model before clustering, the root-mean-square error (RMSE) of the clustered model is significantly reduced by 28.75 % in spring, and 0.30 %, 6.70 % and 5.92 % in summer, Autumn and winter, respectively. In addition, the model demonstrates stable predictive performance at different time resolutions (5, 10 and 15 minutes) with R2 values close to 1, confirming its accuracy and stability under small sample conditions. In terms of training speed, ELM’s training time is more than 100 times faster than Support Vector Regression (SVR) and significantly shorter than traditional models such as PSO-BP and Random Forest (RF), showing its great advantage in application scenarios that require fast response. Overall, the K-means-ELM model is able to accurately capture the overall trend and sudden changes in solar irradiance, which is of great application value for enhancing the efficiency and stability of solar power generation systems.

Broadband nanotubes-based nonlinear modulators for erbium- and thulium-doped lasers

Carbon-based nanomaterials have become a long-term research hotspot in the fields of material science and nanotechnology. Carbon nanotubes as one-dimensional nanomaterials have shown great application value in the fields of electronics and optoelectronics. Herein, stable mode-locked pulsed lasers in both 1.5- and 2-μm bands were achieved by combining and balancing the stimulated emission effect of rare-earth ions and saturable absorption effect of carbon nanotubes. In the Er-doped fiber laser, pulses with a SNR of 61 dB and a peak power of 2.97 W were achieved at the wavelength of 1565.8 nm. Meanwhile, bound states were observed in the same laser, where the time delay of pulses was adjustable from 9.84 to 21.77 ps by tuning the pump power and polarization state. In the Tm-doped laser, output pulses were obtained at the wavelength of 1921.0 nm with a 60-dB SNR and a 12.82-W peak power. These results demonstrate that carbon nanotubes are ideal candidates for saturable absorber in mode-locked fiber lasers, which can be applied to a variety of applications, including fiber optic sensing, optical communication, and nanoscale processing.

Efficient whole-cell biosynthesis of D-mannose by recombinant Bacillus subtilis

D-mannose is a natural hexose with great economic and application values in the food, medicine, and cosmetic fields. However, most biosynthesis methods of D-mannose rely on Escherichia coli as the host, which poses safety issues during the production process and imposes limitations on subsequent applications. This study compared the enzyme properties of mannose isomerases from multiple sources to select the most suitable source. B. subtilis 168/pMA5-EcMIaseA was constructed with "generally recognized as safe" (GRAS) Bacillus subtilis as the host and used as a whole-cell catalyst to synthesize D-mannose from d-fructose. Optimizing the conversion conditions such as culture temperature, pH, and substrate concentration increased the yield of D-mannose. The results showed that the conversion rates reached 27.75% and 27.22% and the yields of D-mannose were 138.74 g/L and 163.30 g/L after 6 h whole-cell transformation with d-fructose at the concentrations of 500 g/L and 600 g/L, respectively, in a 5 L fermentor. This study achieves the highest yield of D-mannose produced under the catalysis by recombinant B. subtilis that has ever been reported and provides a basis for the industrial production and application of D-mannose.

Line intensities of CO near 1560 nm measured with absorption and dispersion spectroscopy

Abstract High-precision line intensities are of great value in various applications, such as greenhouse gas metrology, planetary atmospheric analysis, and trace gas detection. 
Here we report simultaneous measurements of cavity-enhanced absorption and dispersion spectroscopy of the prototype molecule $^{12}$C$^{16}$O using the same optical resonant cavity. Nine lines were measured in the R branch of the $v=3-0$ band. The absorption and dispersion spectra were fitted separately with speed-dependent Voigt profiles, and the line intensities obtained by the two methods agree within the experimental uncertainty of about 1\textperthousand. 
The results demonstrate the feasibility of SI-traceable molecular density measurements based on laser spectroscopy.

Energy management optimization of hybrid electric vehicles based on deep learning model predictive control

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

Synthesis and Kinetics of CO2-Responsive Gemini Surfactants.

Surfactants are hailed as "industrial monosodium glutamate", and are widely used as emulsifiers, demulsifiers, water treatment agents, etc., in the petroleum industry. However, due to the unidirectivity of conventional surfactants, the difficulty in demulsifying petroleum emulsions generated after emulsification with such surfactants increases sharply. Therefore, it is of great significance and application value to design and develop a novel switchable surfactant for oil exploitation. In this study, a CO2-switchable Gemini surfactant of N,N'-dimethyl-N,N'-didodecyl butylene diamine (DMDBA) was synthesized from 1, 4-dibromobutane, dodecylamine, formic acid, and formaldehyde. Then, the synthesized surfactant was structurally characterized by infrared (IR) spectroscopy, hydrogen nuclear magnetic resonance (1H NMR) spectroscopy, and electrospray ionization mass spectrometry (ESI-MS); the changes in conductivity and Zeta potential of DMDBA before and after CO2/N2 injection were also studied. The results show that DMDBA had a good CO2 response and cycle reversibility. The critical micelle concentration (CMC) of cationic surfactant obtained from DMDBA by injecting CO2 was 1.45 × 10-4 mol/L, the surface tension at CMC was 33.4 mN·m-1, and the contact angle with paraffin was less than 90°, indicating that it had a good surface activity and wettability. In addition, the kinetic law of the process of producing surfactant by injecting CO2 was studied, and it was found that the process was a second-order reaction. The influence of temperature and gas velocity on the reaction dynamics was explored. The calculated values from the equation were in good agreement with the measured values, with a correlation coefficient greater than 0.9950. The activation energy measured during the formation of surfactant was Ea = 91.16 kJ/mol.

Crystal field optimization, luminescence enhancement and thermal stability of novel K2NaAl0.4Ga0.6F6:Mn4+ solid solution red phosphor for high-performance warm WLED

Mn4+-doped fluoride phosphors have important application value in the background light and WLED lighting fields. Therefore, the synthesis of efficient and stable Mn4+-doped red phosphor is of great application value. In this study, a series of red-emitting K2NaAl1-yGayF6:xMn4+ (y = 0, 0.2, 0.4, 0.6, 0.8 and 1) (x = 0.01, 0.03, 0.05, 0.07 and 0.09) solid solution phosphors are successfully synthesized by simple coprecipitation method. The synthesis strategy of simple crystal structure optimization is proposed to realize simultaneously the crystal field optimization, luminescence enhancement and thermal stability of red phosphor. The calculation and theoretical analysis of crystal field strength are used to prove that solid solution matrix K2NaAl0.4Ga0.6F6 is the most ideal matrix material. X-ray powder diffraction, energy-dispersive X-ray spectrometer and scanning electron microscope are employed to determine the crystal structure, composition and morphology of all samples. Optical properties are characterized using excitation spectra, emission spectra and fluorescence lifetime curves. Moreover, the CIE coordinates of red-light show that the phosphor has low correlated color temperature and excellent color purity. Intriguingly, the as-prepared K2NaAl0.4Ga0.6F6:Mn4+ phosphor possesses admirable thermal quenching behavior and color stability at high temperature. At a temperature of 423 K, the photoluminescence intensity can be maintained at 52 % of the original intensity (298 K). The activation energy, the chromaticity shift and chromaticity coordinate variation are also systematically calculated. More importantly, A high-performance warm WLED with low correlated color temperature (CCT = 3982 K) and high color rendering index (Ra = 93.7) is achieved by using K2NaAl0.4Ga0.6F6:Mn4+ as a red-emitting material. These results demonstrate that K2NaAl0.4Ga0.6F6:Mn4+ red phosphor provides a novel choice for the warm WLED required for indoor lighting.