Important Application Value Research Articles

Removal of antibiotics from aquaculture wastewater using a continuous flow/EC/PMS coupled system

The electricity activated peroxomonosulfate (ion) coupled system (EC/PMS) has important application value in water pollution treatment. However, the traditional batch system commonly used in this coupled system poses significant challenges in treating large volume and low concentration aquaculture wastewater, such as poor mass transfer, long treatment time, and increased risk of secondary pollution. Therefore, this study proposed to introduce the continuous flow operation mode into the EC/PMS system to form a continuous flow electrocatalytic peroxomonosulfate (ion) (CF/EC/PMS) coupling system, achieving 100 % removal of tetracycline (TC) with an initial concentration of 2 mg/L. Subsequently, through cathodic optimization, an 85 % removal efficiency was achieved for antibiotics with secondary pollution risks, such as norfloxacin and NOR. It is worth noting that the secondary pollutants (such as fluoride ions, F−) generated during the degradation process have also successfully achieved a removal efficiency of 70 %. In this system, singlet oxygen (1O2) was the main active species. This system has advantages such as a processing volume of at least 3L, a wide pH range (3–9), low oxidant dosage (PMS = 2 mM), and low energy consumption (EEO = 0.018 kWh/m3 log). The coupling system effectively removes the main pollutants, and the optimized cathode coupling system achieves safe removal of fluorinated drugs, reducing the risk of secondary pollution. In summary, this study has the characteristics of low cost and engineering applicability, providing a promising method for economically, efficiently, and environmentally friendly treatment of aquaculture wastewater.

Research on safety control technology of high-speed railway combined test based on threatening event analysis

PurposeSafety management is a key point and poses a challenge in joint testing. To detect and address potential accidents' hidden dangers early, this paper conducts research on the safety control technology for high-speed railway joint tests by incorporating the concept of hazardous events.Design/methodology/approachAiming at ensuring the safety of high-speed railway combined inspections and trials, this paper starts from the dual prevention mechanism. It introduces the concept of threatening events, defines them and analyzes the differences between threatening events and railway accidents. The paper also proposes a cause model for threatening events in high-speed railway combined inspections and trials, based on three types of hazard sources. Furthermore, it conducts research on the control strategies for these threatening events.FindingsThe research on safety control technology for high-speed railway combined operation and testing, based on the analysis of threatened events, offers a new perspective for safety management in these operations. It also provides theoretical and practical support for the transition from passive prevention to active risk pre-control, which holds significant theoretical and practical value.Originality/valueThe innovation mainly includes the following three aspects: (1) Building on the traditional dual prevention mechanism, which includes risk hierarchical management and control as well as hidden danger investigation and management, a triple prevention mechanism is proposed. This new mechanism adds the management of threatening events as the third line of defense. The aim is to more comprehensively identify and address potential security risks, thereby enhancing the efficiency and effectiveness of security management. (2) In this paper, the definition of a railway threatening event is clarified, and the causative model of a high-speed railway threatening event based on three kinds of danger sources is proposed. (3) This paper puts forward the control strategy of the high-speed railway combined operation and trial, which includes five key links: identification, reporting, analysis, rectification and feedback, which provides a new perspective for the safety management of the high-speed railway combined operation and trial and has important theoretical and application value.

Application of safety and stability optimization algorithms for charging connection devices in high-power charging systems

In response to the safety and stability issues of current electric vehicle charging connection devices, this study proposes a charging system planning for electric vehicles with different capacity charging piles based on the user behavior characteristics of electric vehicles and Monte Carlo methods. It is found that the predicted results under the set management strategy are most consistent with the trend of actual load changes. Moreover, in the prediction of weekly load, the research strategy has better performance than traditional unmanaged strategies. Under the research scheme, the average charging speed of charging piles with capacity of A and B in the peak period was 41.4 min/ and 18.8 min/, respectively, which increased by 29.3% and 11.7% respectively compared with 58.6 min/ and 21.3 min/ in the normal period. The total economic cost of the research plan was 4.871 million yuan, which was 67.0 million yuan and 3.833 million yuan lower than the control methods 1 and 2, respectively. The total number of charging stations of types a and b that need to be purchased for the research method decreased by 18.47% and 63.24% compared to the comparative method 3. The results indicate that the research method significantly improves the utilization rate of charging stations in the electric vehicle charging system. This study has important application value in the intelligent management of electric vehicle charging systems.

Dynamic Temporal Denoise Neural Network with Multi-Head Attention for Fault Diagnosis Under Noise Background

Fault diagnosis plays a crucial role in maintaining the operational safety of mechanical systems. As intelligent data-driven approaches evolve, deep learning (DL) has emerged as a pivotal technique in fault diagnosis research. However, the collected vibrational signals from mechanical systems are usually corrupted by unrelated noises due to complicated transfer path modulations and component coupling. To solve the above problems, this paper proposed the dynamic temporal denoise neural network with multi-head attention (DTDNet). Firstly, this model transforms one-dimensional signals into two-dimensional tensors based on the periodic self-similarity of signals, employing multi-scale two-dimensional convolution kernels to extract signal features both within and across periods. Secondly, for the problem of lacking denoising structure in traditional convolutional neural networks, a temporal variable denoise (TVD) module with dynamic nonlinear processing is proposed to filter the noises. Lastly, a multi-head attention fusion (MAF) module is used to weight the denoted features of signals with different periods. Evaluation on two datasets, Case Western Reserve University bearing dataset (single sensor) and Real aircraft sensor dataset (multiple sensors), demonstrates that the DTDNet can reduce the useless noises in signals and achieve a remarkable improvement in classification performance compared with the state-of-the-art method. DTDNet provides a high-performance solution for potential noise that may occur in actual fault diagnosis tasks, which has important application value.

Investigation on metal vapor characteristics and keyhole/melt pool dynamics in high-power laser welding with side gas flow

High-power laser welding processes involve intense interactions between the laser beam and the base material, resulting in severe welding defects such as humping, spattering, and undercutting. Utilizing side gas flow may provide an effective approach for minimizing these defects. However, the effects of side gas flow on the dynamic behavior and weld formation necessitate further investigation. In this study, an innovative analysis of metal vapor characteristics and keyhole/melt pool dynamics in high-power laser welding with side gas flow is presented, combining experimental and simulation results. A three-dimensional transient fluid flow model was developed, uniquely integrating an adaptive heat source, evaporation-condensation processes, and metal vapor energy attenuation to provide a novel perspective on the effects of side gas flow in high-power laser welding. The results display that side gas flow could suppress plasma, increase thermal input to the melt pool, and intensify the flow of liquid metal toward the pool bottom during high power laser welding, thereby enhancing weld seam penetration. The numerical results indicate that variations in flow rate significantly impact metal vapor morphology, with higher flow rates reducing both the average area and height of the metal vapor and effectively suppressing the size of metal vapor/plasma. When the side gas flow rate is 20 l/min, with the gas flow impact point located at the keyhole opening and the nozzle height set to 3 mm, the side gas flow significantly enhances the depth-to-width ratio of the weld seam in 316L stainless steel laser welding. This work can provide guidance for suppression of weld defects in high-power laser welding of medium-thick steel components, which has important theoretical significance and application value.

Energy-Efficient Separation of Xylene Isomers through Stacked 1-Aminopyrene Polymer Composite.

Developing high-performance adsorbents for energy-efficient separation of xylene isomers has important research and application value. Identification and separation of xylene isomers (PX, MX, and OX) at room temperature based on the different relative positions of two methyl groups on the benzene ring is an unprecedented attempt. Herein, 1-aminopyrene polymer (PAP) is designed and electro-synthesized composited with a supporting electrolyte as an adsorbent for the separation of xylene isomers using a multi-stage dispersed liquid-solid adsorption process at room temperature. Each -NH-pyrene-NH- unit can form a force field to identify and discriminate xylene isomer through π-π interaction combined with -CH3···-NH- interactions of different strength, thereby achieving separation of PX, MX, and OX isomers by amplifying the slight differences in the adsorption capacity and diffusion rates. The density functional theory (DFT) simulations provide a more quantitative analysis of the adsorbate-adsorbent interactions to illustrate PX > MX > OX order of adsorption selectivity. This study may offer an alternative strategy for the precise designing of energy-efficient and adsorption-based separation materials.

Impact and mechanisms of drag-reducing polymers on shear stress regulation in pulmonary hypertension.

Pulmonary hypertension (PH) is a refractory disease characterized by elevated pulmonary artery pressure and resistance. Drag-reducing polymers (DRPs) are blood-soluble macromolecules that reduce vascular resistance by altering the blood dynamics and rheology. Our previous work indicated that polyethylene oxide (PEO) can significantly reduce the medial wall thickness and vascular resistance of the pulmonary arteries, but the specific mechanism is still unclear. This study was designed to investigate the role and mechanism of PEO on intracellular calcium [Ca2+] i and cytoskeletal proteins of endothelial cells (ECs) induced by low shear stress (LSS) in PH. Primary Pulmonary Artery Endothelial Cells (PAECs) were subjected to steady LSS (1 dyn/cm2) or physiological shear stress (SS) (10 dyn/cm2) for 20 h in a BioFlux 200 flow system. Calcium influx assays were conducted to evaluate the mechanisms of PEO on [Ca2+] i. Subsequently, taking the key protein that induces cytoskeletal remodeling, the regulatory light chain (RLC) phosphorylation, as the breakthrough point, this study focused on the two key pathways of PEO that regulate phosphorylation of RLC: Myosin light chain kinase (MLCK) and Rho-associated kinase (ROCK) pathways. Our current research revealed that PEO at LSS (1 dyn/cm2) significantly suppressed LSS-induced [Ca2+] i and the expression level of transient receptor potential channel 1(TRPC1). In addition, ECs convert LSS stimuli into the upregulation of cytoskeletal proteins, including filamentous actin (F-actin), MLCK, ROCK, p-RLC, and pp-RLC. Further experiments using pharmacological inhibitors demonstrated that PEO at the LSS downregulated cytoskeleton-related proteins mainly through the ROCK and MLCK pathways. This study considered intracellular calcium and cytoskeleton rearrangement as entry points to study the application of PEO in the biomedical field, which has important theoretical significance and practical application value for the treatment of PH.

Discrete state transfer algorithm based on deep reinforcement learning for solving flexible job shop scheduling problem

Flexible Job Shop Scheduling (FJSP) is a scheduling problem widely used in real life. The research on its intelligent algorithm has important academic significance and application value. In order to solve FJSP, this paper proposes a discrete state transfer algorithm based on proximal policy optimization (DSTA-PPO) with the optimization goal of minimizing the maximum completion time. DSTA-PPO has the following three characteristics: (1) Considering that FJSP requires simultaneous scheduling of process sorting and machine allocation, a state feature that can fully express the current scheduling problem is designed by combining process coding and machine coding. (2) A variety of critical path-based search operations are designed for process sorting and machine allocation. (3) Through reinforcement learning training, it can effectively guide the agent to select the correct search operation to optimize the current scheduling sequence. Through simulation experiments based on different data sets, the effectiveness of each link of the algorithm was verified. At the same time, it was compared with the existing algorithms on the same example with minimizing the maximum completion time. The comparison results show that the proposed algorithm can solve the examples in most examples with a shorter completion time, and effectively solve the flexible job shop scheduling problem.

Application value of SOMATOM Force computed tomography in assisting the preoperative localization of colorectal cancer resection surgery

Background The objective of this study was to assess the application value of SOMATOM Force computed tomography (CT) in assisting the preoperative localization of colorectal cancer resection surgery. Method Retrospectively, the medical data of 120 inpatients with colorectal cancer were collected. The Kappa consistency test was used to evaluate diagnostic consistency in the localization and staging of colorectal cancer. The diagnostic value of preoperative SOMATOM Force CT detection was analyzed. Results In 120 colorectal cancer patients, the accuracy of SOMATOM Force CT for preoperative localization, T staging, and N staging of colorectal cancer were 91.7% (kappa = 0.837), 88.3% (kappa = 0.772) and 91.7% (kappa = 0.773), respectively. Among 45 rectum cancer patients, there were 19 positive cases with circumferential resection margin involvement, and the accuracy of SOMATOM Force CT detection was 86.7% (kappa = 0.767). The sensitivity, specificity, positive predictive value, and negative predictive value of SOMATOM Force CT detection in evaluating the circumferential resection margin involvement of rectum cancer were 78.95%, 96.15%, 93.75%, and 86.21%, respectively. Conclusions There was an important application value of SOMATOM Force CT in assisting the preoperative localization and tumor staging of colorectal cancer resection surgery. There was a good diagnostic value of preoperative SOMATOM Force CT detection in evaluating the circumferential resection margin involvement of rectum cancer.

Exploring the application of synchronous analysis of physiological signals and facial expressions in emotion recognition

In today's increasingly frequent human-computer interaction, the accurate recognition and response of intelligent systems to human emotions has become the key to improving the user experience. This paper aims to explore the application of physiological signals and facial expression synchronization analysis in emotion recognition. This paper adopts the method of literature review, and summarizes the acquisition techniques of physiological signals and facial expressions, synchronous analysis methods, and their application cases in emotion recognition. At the same time, the limitations and challenges of the existing technology are discussed. The review shows that the synchronous analysis of physiological signals and facial expressions has important application value in the field of emotion recognition, especially in mental health monitoring, educational feedback, and customer service automation. However, the practical application of the technology still faces many challenges, including the real-time nature of data collection, the adaptability of individual differences, and other identification problems. Therefore, future research needs to further optimize data acquisition technology and develop more accurate and personalized analysis algorithms.

Research on Damage Evolution Mechanism of Layered Rock Mass under Blasting Load

Rock mass consists of many discontinuities, such as faults, joints, etc., and layered joints are a common kind of rock mass structure. The joints affect the stress wave propagation, and blasting is an economical and efficient rock fragmentation method for rock mass engineering. So, the rock mass fragmentation effect and construction progress are affected by these layered joints. Numerical studies were carried out to analyze the damage evolution process of intact rock and rock mass with layered joints subjected to blasting loads based on the Riedel–Hiermaier–Thoma (RHT) model in LS-DYNA software (smp s R11.0.), and the effects of the location of initiation points and the fracture distribution on dynamic damage evolution of the rock mass were discussed. Bottom initiation tends to direct the blasting energy toward the blasthole mouth, resulting in effective rock fragmentation and ejection. Gradually adjusting the initiation point upward can improve the stress and damage distribution, allowing some of the blasting stress waves to propagate toward the bottom and enhance the fragmentation of the rock at the bottom. The distribution of layered joints exacerbates the damage to the rock mass on the upstream surface, but also acts as a certain shield to the propagation of stress waves, increasing the asymmetry of the damage distribution. It is useful to know the damage mechanism of the rock mass with layered joints to improve the effect of rock mass fragmentation by blasting. These results have very important theoretical significance and application value for the optimization of blasting construction technology.

Design and stability performance optimization of a novel hybrid inspection robot walking device for smart grid applications

Inspecting in high-altitude windy environments requires maintaining a stable attitude to avoid significant oscillations, which is a crucial factor in advancing the use of hybrid power transmission line inspection robots (PTLIRs) in real-world scenarios for the electrical power and energy industry. We developed and optimized a novel walking device to improve the safety of hybrid PTLIRs operating in strong wind conditions. First, we investigate the operational conditions of transmission line inspection and design a walking device capable of transporting a flight device to be suspended under the line for inspection purposes. Second, a force equilibrium analysis is performed to explore the relationship between the key structural parameters of the walking device and the wind-induced deflection of the robot. A multiobjective optimization model is formulated to minimize the maximum deflection angle of the robot along three axes under wind loads. Finally, an optimization algorithm, using an improved non-dominated sorting particle swarm optimization (NSPSO) approach, is proposed to solve the model with good global search ability and fast convergence speed. A test platform is constructed to collect the wind deflection angle of a self-developed hybrid PTLIR under the transmission line. The test results demonstrate that the walking device can carry 30 kg to move along the transmission line. The average deflection angle of the robot around the Z axis is the highest under the influence of wind force level 7 (wind speed of 15.5 m/s) perpendicular to the windward surface of the robot. After optimization, the maximum deflection angle is limited to 10.38°, aligning within safety thresholds. The optimization algorithm effectively reduces the maximum deflection angles around the X, Z, and Y axes by 33.19 %, 39.21 %, and 13.23 %, with minimal average errors of 2.40 %, 3.87 %, and 2.13 %, respectively. The research in this paper can solve the bottleneck problem of robot practicality, which has important theoretical significance and practical application value for the safety, stability, and intelligent operation and maintenance management of power transmission lines.

One-Step Carbonization Synthesis of N, S Co-Doped Carbon Materials Derived from Agricultural Waste Peanut Shells for High-Performance Symmetric Supercapacitors.

Biomass carbon has the advantages of wide source, low cost and environmental protection, and has been widely used in the field of electrochemical energy storage. In this work, N and S co-doped carbon materials were prepared by using peanut shell as carbon source and thiourea as activator. When the peanut shell and activator were 2 g and 4 g, respectively, the prepared NSPC-4 had the largest specific surface area and special pore structure. Elemental analysis showed that the activator introduced more N, S and O atoms to the carbon material, and more heteroatoms helped to improve the surface structure of the carbon material and provide additional pseudocapacitance. In addition, NSPC-4 contains a short-range ordered graphite structure, which can provide excellent electrical conductivity. The electrochemical test results show that NSPC-4 has the largest specific capacitance. When the mass of the activator is higher than or below 4 g, the electrochemical performance of the carbon material will be reduced. The symmetric supercapacitor (SSC) assembled by NSPC-4 has an energy density of 8.3 Wh kg-1 when the power density is 350 W kg-1. The synthesis method is not only simple, green and economical, but also has important application value.

Isolation of a novel Bacillus subtilis HF1 strain that is rich in lipopeptide homologs and has strong effects on the resistance of plant fungi and growth improvement of broilers.

Bacillus subtilis is an important probiotic microorganism that secretes a variety of antimicrobial compounds, including lipopeptides, which are a class of small molecule peptides with important application value in the fields of feed additives, food, biopesticides, biofertilizers, medicine and the biological control of plant diseases. In this study, we isolated a novel B. subtilis HF1 strain that is rich in lipopeptide components and homologs, has a strong antagonistic effect on a variety of plant fungi, and is highly efficient in promoting the growth of broilers. The live B. subtilis HF1 and its fermentation broth without cells showed significant inhibitory effects on 20 species of plant fungi. The crude extracts of lipopeptides in the fermentation supernatant of B. subtilis HF1 were obtained by combining acid precipitation and methanol extraction, and the lipopeptide compositions were analyzed by ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). The results showed that HF1 could produce 11 homologs of surfactin and 13 homologs of fengycin. Among the fengycin homologs, C13-C19 fengycin A and C15-C17 fengycin B were identified; among the surfactin homologs, C11-C17 surfactin A and C13-C16 surfactin B were characterized. C13 fengycin A, C11 surfactin A and C17 surfactin A were reported for the first time, and their functions are worthy of further study. In addition, we found that HF1 fermentation broth with and without live cells could be used as a feed additive to promote the growth of broilers by significantly increasing body weight up to 15.84%. HF1 could be a prospective strain for developing a biocontrol agent for plant fungal diseases and an efficient feed additive for green agriculture.

ILKD: An Incremental Learning and Knowledge Distillation Framework for Coronavirus Risk Prediction

Abstract The coronavirus pandemic has seriously affected public health and social order. Prediction methods based on machine learning can identify the infectivity phenotype and pandemic risk of coronavirus. Currently, six types of coronaviruses that infect humans have been discovered, with significant differences in viral genome sequences. Continuous genetic variation of the virus will lead to reduced performance of machine learning models and potential learning forgetting. To solve this challenge, we propose an incremental learning and knowledge distillation framework (ILKD). First, we employ Dna2Vec to extract virus features and encode the virus sequence into virus feature vector. Second, we use hierarchical clustering to continuously identify new coronavirus groups. Third, ILKD employ a combined strategy of incremental learning and knowledge distillation to transform the Back Propagation (BP) neural network to continuously learn and predict the phenotypes of human-to-human coronavirus infection. Experimental results show that ILKD can effectively alleviate the learning forgetting phenomenon. Further analysis reveals ILKD has better performance than other incremental learning models, and has important public health application value.

Growth Promoting Characteristics of Bacillus amyloliquefaciens GZA69 and Its Biocontrol Potential Against Soft Rot of Amorphophallus konjac

Plant growth-promoting rhizobacteria (PGPR) are beneficial bacteria colonizing the plant rhizosphere and can promote plant growth. PGPR have important application potential in the field of microbial fertilizers. This study isolated and characterized a PGPR strain GZA69 from konjac rhizosphere soil collected from Damogu Village, Luliang County, Qujing City, Yunnan Province, China. The strain was identified as Bacillus amyloliquefaciens. GZA69 showed diverse plant growth-promoting abilities, including nitrogen fixation, phosphorus solubilization, siderophore production, and indole-3-acetic acid (IAA) production. Tomato was used as an indicator crop to evaluate its growth-promoting effect, two GZA69 suspension concentrations (A1, OD600 = 0.3 and A2, 0.6) were used to treat tomato seeds and seedlings. Plate confrontation assay and konjac corm tissue inoculation were conducted to identify the antagonistic effect of GZA69 strain on the pathogen of konjac soft rot disease. The results showed that, both GZA69 concentrations significantly promoted tomato seed germination (A) and seedling growth, with growth increased of 8.2% and 9.66% in height, 20.87% and 22.77% in root length, and 90% and 130% in fresh weight, respectively. Additionally, GZA69 demonstrated a significant inhibitory effect on the konjac soft rot pathogen, with an inhibition zone of 1.47±0.07 cm. Furthermore, GZA69 effectively reduced disease incidence in inoculated corm tissues, with disease index decreased by 8.00%, 16.23%, 24.80% in co-inoculated with different concentrations of GZA69 suspensions (solution of soft rot pathogen and GZA69 bacterial at ratios of 1:1, 1:2, and 1:3, respectively) comparted to the control (sterile water). In summary, the B. amyloliquefaciens GZA69 screened from konjac rhizosphere soil has various plant growth promoting characteristics and has the potential to prevent and control konjac soft rot disease, which has important application value for developing konjac microbial fertilizers.

Fiber Bragg Grating Pulse and Systolic Blood Pressure Measurement System Based on Mach-Zehnder Interferometer.

A fiber Bragg grating (FBG) pulse and systolic blood pressure (SBP) measurement system based on the edge-filtering method is proposed. The edge filter is the Mach-Zehnder interferometer (MZI) fabricated by two fiber couplers with a linear slope of 52.45 dBm/nm. The developed system consists of a broadband light source, an edge filter, fiber Bragg gratings (FBGs), a coarse wavelength-division multiplexer (CWDM), and signal-processing circuits based on a field-programmable gate array (FPGA). It can simultaneously measure pulse pulsations of the radial artery in the wrist at three positions: Cun, Guan and Chi. The SBP can be calculated based on the pulse transit time (PTT) principle. The measurement results compared to a standard blood pressure monitor showed the mean absolute error (MAE) and standard deviation (STD) of the SBP were 0.93 ± 3.13 mmHg. The system meets the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) equipment standards. The proposed system can achieve continuous real-time measurement of pulse and SBP and has the advantages of fast detection speed, stable performance, and no compression sensation for subjects. The system has important application value in the fields of human health monitoring and medical device development.

Classification Model of Grassland Desertification Based on Deep Learning

Grasslands are one of the most important ecosystems on earth, and the impact of grassland desertification on the earth’s environment and ecosystem cannot be ignored. Accurately distinguishing grassland desertification types has important application value. The appropriate grazing strategies can be implemented based on these distinctions. Grassland conservation measures can be tailored accordingly. This contributes to further protecting and restoring grassland vegetation. This project takes color images labeled with the desertification types of grasslands as the research object, uses the currently popular deep learning model as the classification tool, and then establishes a color image-based grassland desertification classification model based on the feature extraction network, based on the Vision Transformer model, by comparing the various deep learning image classification models. The experimental results show that, despite the complex structure and large number of parameters of the grassland desertification classification model obtained in this project, the test accuracy rate reaches 88.72% and the training loss is only 0.0319. Compared with the popular classification models such as VGG16, ResNet50, ResNet101, DenseNet101, DenseNet169, and DenseNet201, and so on, the Vision Transformer demonstrates clear advantages in classification accuracy, fitting ability, and generalization capacity. By integrating with deep learning technology, the model can be applied to grassland management and ecological restoration. Mobile devices can be used to conveniently capture image data, and information can be processed quickly. This provides efficient tools for grazing managers, environmental scientists, and conservation organizations. These tools assist in quickly assessing the extent of grassland desertification, optimizing grassland management and conservation decisions. Furthermore, strong technical support is offered for the ecological restoration and sustainable management of desertification grasslands.

Effect of strain gradient and interface engineering on the high-temperature energy storage capacitors

The miniaturization and high integration of electronic devices pose new requirements for the energy storage density and high-temperature performance of dielectric capacitors. For thin film materials, internal stress and the interface layer often show a significant impact on their energy storage performance. Therefore, the capacitors with different stress gradient sequences and different periods were designed by BaHf0.17Ti0.83O3 (BHTO17), BaHf0.25Ti0.75O3 (BHTO25), and BaHf0.32Ti0.68O3 (BHTO32) to investigate the effect of stress gradient and interface engineering on the energy storage characteristics. Dielectric thin film structures with upward gradient, downward gradient, and periodic upward gradient (4N) were constructed. The study found that the upward gradient structure had higher breakdown field strength than the downward gradient structure. This is because the upward gradient structure can effectively extend the ending electric field of the Ohmic conduction mechanism and delay the activation electric field of the F–N tunneling mechanism. The 4N structure had a slightly higher breakdown field strength (reaching 9.22 MV/cm) compared to the pure upward gradient structure. The 4N structure thin film also exhibited higher energy storage density (115.44 J/cm3) and wide temperature (−100 to 400 °C) characteristics. These findings provide important guidance and application value for improving the energy storage characteristics of dielectric capacitors at high temperatures through structural design.

A Study of Library Mobile Robot Book Classification and Transportation by Integrating DA and RMM

As the complexity of modern library management tasks increases, it is difficult for traditional mobile robots to meet the task of moving and classifying books. In order to design a mobile robot that can autonomously classify and transport books, the study realizes the tasks of book classification and transportation in libraries by fusing the differential speed algorithm and the robot motion model algorithm. First, the robot operating system is utilized to scan the books, classify the books, and obtain the category information of the books. Then, the differential speed algorithm is used to control the motion of the robot to ensure that the robot can accurately transport the books to the designated location. At the same time, combined with the robot motion model algorithm, the motion trajectory of the robot is planned to ensure that the robot can avoid obstacles and stably complete the book transportation task. Finally, the deep reinforcement learning algorithm is used to train the decision-making model of the robot to improve the intelligence level of the robot. The results of simulation experiments show that the research method has the highest accuracy, with an average accuracy of 99.98%, and the robot is able to accurately categorize the books and quickly avoid obstacles with strong stability. The results of the application experiments show that the research method has the shortest moving distance, with an average moving distance of 132 m and an average completion time of 34 seconds, which are lower than the remaining three types of robots. The research robot showed high accuracy in the task of returning books in four time periods within 10 days in the library, with an average accuracy of 99.58%. The experimental results validate the superiority of the research methodology and show that the robots are capable of accurately recognizing and classifying books and can autonomously perform transportation tasks in libraries. The research results help to improve the automation level and management efficiency of libraries and have important application value.