Results Of Experiments Research Articles

The influence of pretend play on the cognitive and emotional development of children 3-4 years old

Introduction. Play is the leading activity of preschool age, and play with an imaginary situation (pretend play) is considered as the most significant type of play for children’s mental development, which allows this type of play to be used in the educational process as a means of teaching and developing preschool children. The purpose of this article was to assess the impact of pretend play on the cognitive and emotional development of children 3-4 years old. We assumed that (1) the play can influence the development of executive functions, imagination and understanding of emotions in children 3-4 years old, (2) the influence of the play can be different depending on which developmental mechanisms it contains more involved. Materials and methods. To test the hypotheses, a training controlled experiment design was developed. As an experimental intervention series of play sessions with an adult were conducted. Before and after the experimental intervention, the level of development of executive functions, imagination and understanding of emotions was assessed. 39 children aged 3-4 years took part in the play sessions. Play sessions were video recorded for later analysis. Results. The data obtained were analyzed in two stages. First, the results of the pre- and post-test in the experimental and control groups were compared. At the second stage, the analysis of video recordings was carried out in order to control the influence of the child's ability to stay in the play context. Thus, the test results of the same children before and after participating in the experiment (Student's paired t-test) were compared. Significant differences were identified in the indicator “Understanding the external causes of emotions” of the Test of Emotion Comprehension and the “Elaboration” of the test for imagination. Analysis of variance with control of the level of development of hot self-regulation in children at the time of participation in the experiment confirmed that the type of experimental exposure is a significant factor affecting the development of the ability to understand the external causes of emotions (F(1.36) = 3.87, p = 0.030, n2 = 0.035). Analysis of video recordings showed that children in both playing conditions to a large extent were involved in the playing and, as expected, in their play there was an emphasis on the meanings of human activity, the relationship between the characters and their feelings. The results of the conducted training experiment as a whole showed a low effectiveness of the pretend play impact even in conditions with active participation of the adult. Conclusion. The age of 3-4 years is the initial stage of the formation of both the pretend play itself, and such mental functions as imagination, self-regulation and understanding of emotions. The results of the training experiment generally showed the low effectiveness of the play influence even in conditions of active participation of the experimenter in the play. We can conclude that when planning to use the play for developmental purposes, it is important to take into account the age and individual zone of proximal development available to the child. This will allow to understand what format of pretend play with and what adult’s interventions will be most effective.

Automation of etch pit analyses on solid-state nuclear track detectors with machine learning for laser-driven ion acceleration.

Solid-state nuclear track detectors (SSNTDs) are often used as ion detectors in laser-driven ion acceleration experiments and are considered to be the most reliable ion diagnostics since they are sensitive only to ions and measure ions one by one. However, ion pit analyses require tremendous time and effort in chemical etching, microscope scanning, and ion pit identification by eyes. From a laser-driven ion acceleration experiment, there are typically millions of microscopic images, and it is practically impossible to analyze all of them by hand. This research aims to improve the efficiency and automation of SSNTD analyses for laser-driven ion acceleration. We use two sets of data obtained from calibration experiments with a conventional accelerator where ions with known nuclides and energies are generated and from actual laser experiments using SSNTDs. After chemical etching and scanning the SSNTDs with an optical microscope, we use machine learning to distinguish the ion etch pits from noises. From the results of the calibration experiment, we confirm highly accurate etch-pit detection with machine learning. We are also able to detect etch pits with machine learning from the laser-driven ion acceleration experiment, which is much noisier than calibration experiments. By using machine learning, we successfully identify ion etch pits ∼105 from more than 10 000 microscopic images with a precision of ≳95%. A million microscopic images can be examined with a recent entry-level computer within a day with high precision. Machine learning tremendously reduces the time consumption on ion etch pit analyses detected on SSNTDs.

Сегрегация или инклюзия: опыт экспериментального исследования условий психологического сопровождения профессиональной подготовки лиц с ограниченными возможностями здоровья

<p>The article reveals the degree of effectiveness of segregated and inclusive forms of psychological and pedagogical support for students with special educational needs. In the psychological experiment took part 68 students with disabilities, its purpose was to identify the dynamics of the psychological well-being of the students depending on the inclusive or segregated forms of psychological trainings they took part in. As a result of the study, carried out with the help of the Scale of psychological well-being K. Riffe (adaptation of T.D. Shevelenkova, T.P. Fesenko), questionnaire Life Style Index (<em>Plutchik</em>, <em>Kellerman</em>, & <em>Conte</em>, 1979) and the methodology of SUN (V.A. Doskin, N.A. Lavrentieva, V.B. Sharay), we found out the differences between the groups of students according to their mood (t=7.55; p<0.001), well-being (t=4.89; p<0.001), activity (t=4.78; p<0.001), psychological well-being (t=4.65; p<0.001), tension of psychological defenses (t=2.27; p<0.05). These factors indicate the negative dynamics of psychological well-being of students with disabilities who studied in segregated groups with no conditionally healthy students. Based on the results of the psychological experiment, we made a conclusion, that the segregation of students with disabilities from other students is inexpedient, even if the purpose was providing them with psychological assistance in higher education.</p>

Inland port and waterway capacity expansion model under demand uncertainty

We address the inland port and waterway capacity expansion (IPWCE) problem under demand uncertainty, the purpose of which is to determine the capacity expansion scales and timings for inland ports and waterways. To achieve this goal, we first build an inland waterway multimodal network (IWMN) that consists of physical and operational multimodal networks. Second, by viewing the IPWCE problem under deterministic demand as a subproblem of the IPWCE problem under uncertain demand, we develop a model for IPWCE under deterministic demand (IPWCEDD) based on bilevel programming and a model for IPWCE under uncertain demand (IPWCEUD) based on network real options theory. Third, we propose a sensitivity analysis-based descent search (SADS) algorithm to solve the IPWCEDD model. Moreover, in accordance with the optimal solutions of the IPWCEDD model, we then develop a least squares Monte Carlo simulation (LSM) method to solve the IPWCEUD model. Finally, we verify the effectiveness, efficiency, and applicability of the proposed models as well as the solution algorithm and methods through experiments of two different scales. The results of the small-scale experiment indicate that the proposed SADS algorithm and LSM method can solve the models optimally within the time limit. The large-scale experiment, which is conducted on the Yangtze River IWMN of mainland China, shows that capacity expansions at Chongqing Port, Nanjing Port, and Wuhan Port and on the waterway links connecting these ports can yield the maximum increased option values for the IPWCE problem under demand uncertainty. Moreover, the corresponding optimal expansion timing for implementing the capacity expansion plan is in the second year. Our research outcomes can help to guide IPWCE under demand uncertainty and further establish a high-quality inland river transport system.

Development Program of Basic Physical Movement Skills for Early Childhood in Guangzhou City, China

Background and Aim: The data highlights the persistent issue of high obesity rates among young children, aged 3-6, despite economic development and improved living standards. The overall physical fitness of children in this age group is deemed suboptimal, impacting their health, learning efficiency, and social adaptability. Recognizing early childhood as a critical phase for physical and mental development, the General Office of the Central Committee of the Communist Party of China and the General Office of the State Council emphasize the importance of physical education in the compulsory education stage. The recommended teaching model involves imparting health knowledge and basic motor skills, with an evolving focus on specialized motor skills. The study aims to investigate means and curriculum arrangements for physical fitness and skill exercises, intervening in basic motor skills to promote a holistic approach to physical and mental development in young children, ultimately contributing to the sustainable advancement of physical education. Materials and Methods: In this paper, using the methods of literature review, experimental method, expert interview method, observation method, and questionnaire survey method, an eight-week intervention experiment was carried out on 30, 5-6-year-old children, and the classroom teaching content was arranged in the way of physical fitness and basic motor skills integration. Results: The results of the two-month intervention experiment were as follows: the development of physical fitness and basic motor skills was better than that of the pre-intervention experiment; in terms of physical fitness: boys improved better than girls in standing long jump, 10-metre run, and throwing a tennis ball; girls improved better than boys in flexibility; and the development of coordination and balance was comparable between boys and girls. Regarding basic motor skills: boys outperformed girls in mobility skills; girls outperformed boys in biomechanical skills. Conclusion: In conclusion, the two-month intervention experiment exhibited positive overall outcomes in physical fitness and basic motor skills development, with distinct gender-specific variations. Boys showed superior progress in activities emphasizing strength and speed, while girls excelled in flexibility. Coordination and balance improvements were comparable between genders. The findings emphasize the importance of tailored approaches in physical education interventions to address diverse aspects of children's development.

On-line monitoring of membrane fouling based on an improved electrical measurement method.

On-line monitoring of membrane fouling is essential in the water treatment process. Drawbacks such as low-sensitivity and off-line limitations limit the application of existing methods. An on-line monitoring method based on Electrical Resistance Tomography (ERT) sensors is put forward in this paper. The Particle Swarm Optimization with Simulated Annealing (PSO-SA) algorithm is used in optimizing the topologies of finite element models in order to decrease the ill-posedness of sensitivity matrices. The deep denoising extreme learning machine with an auto-encoder model and the K-singular value decomposition algorithm are used in ERT reconstruction to improve imaging quality. The lift-wavelet is adopted in measuring the permeate flux to improve measuring accuracy. The ERT pixel values of the membrane module and the result of flux are used to analyze the fouling status. The results of membrane fouling experiments demonstrate the following: (1) Based on the local ERT pixels, the "two stage" phenomenon of membrane fouling can be observed. (2) In the early stage, the fouling distribution of the localized membrane module is consistent with its ERT pixels. (3) The deposition process of foulants for the localized membrane module is synchronized with the variation of ERT pixels. (4) The integrity of the membrane module can be detected according to the ERT pixels. Therefore, the novel method can effectively reflect the membrane fouling process, especially in the early stages of membrane fouling.

Role of Qufeng Tongqiao Prescription in the protection of cerebral ischemia and associated molecular network mechanism.

To explore the of Qufeng Tongqiao Prescription in the treatment of cerebral ischemia-reperfusion (CIR) and associated molecular network mechanism. Venny diagram, gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis, protein-protein interaction (PPI), hub genes mining, molecular docking, combined with animal experiments and Nissl stain were performed to determine the molecular network mechanism of Qufeng Tongqiao Prescription for CIR treatment. Fifty three intersecting genes between Qufeng Tongqiao Prescription and cerebral ischemia reperfusion were acquired from Venny analysis. GO analysis showed that the main biological process (BP) was response to lipopolysaccharide, and the main cell localization (CC) process was membrane raft, while the most important molecular function (MF) process is Cytokine receptor binding. Moreover, AGE-RAGE signaling pathway in diabetic complications is the most important signaling pathway in KEGG pathway. Through molecular docking, it was found that Astragalus membranaceus was docked with MAPK14, IL4, FOS, IL6, and JUN; pueraria membranaceus was directly docked with JUN and IL4; Acorus acorus was linked to JUN and MAPK14; Ganoderma ganoderma and human were involved in JUN docking, and Ligusticum chuanqi and pueraria could not be docked with MAPK14, respectively. The results of animal experiments showed that Qufeng Tongqiao Prescription significantly improved behavioral performance and reduced the number of neuronal deaths in rats subjected to CIR, and molecular mechanisms are associated with FOS, IL-6, IL4, JUN, and MAPK14, of there, IL-6, as a vital candidator, which has been confirmed by immunostaining detection. Together, Qufeng Tongqiao Prescription has positive therapeutic effect on CIR, and the underlying mechanism is involved MAPK14, FOS, IL4, and JUN network, while IL-6 may be as a vital target.

Comparing the Performance of Two Butcher-Based Block Hybrid Algorithms for the Solution of Initial Value Problems

In this paper, we compare the performances of two Butcher-based block hybrid methods for the numerical integration of initial value problems. We compare the condition numbers of the linear system of equations arising from both methods and the absolute errors of the solution obtained. The results of the numerical experiments illustrate that the better conditioned method outperformed its less conditioned counterpart based on the absolute errors. In addition, after applying our method on some examples, it was discovered that the absolute errors in this work were better than those of a recent study in the literature. Hence, we recommend this method for the numerical solution of stiff and non-stiff initial value problems.

Diversified Knowledge Transfer Strategy for Multitasking Particle Swarm Optimization.

Evolutionary multitasking optimization (EMTO) has capability of performing a population of individuals together by sharing their intrinsic knowledge. However, the existed methods of EMTO mainly focus on improving its convergence using parallelism knowledge belonging to different tasks. This fact may lead to the problem of local optimization in EMTO due to unexploited knowledge on behalf of the diversity. To address this problem, in this article, a diversified knowledge transfer strategy is proposed for multitasking particle swarm optimization algorithm (DKT-MTPSO). First, according to the state of population evolution, an adaptive task selection mechanism is introduced to manage the source tasks that contribute to the target tasks. Second, a diversified knowledge reasoning strategy is designed to capture the knowledge of convergence, as well as the knowledge associated with diversity. Third, a diversified knowledge transfer method is developed to expand the region of generated solutions guided by acquired knowledge with different transfer patterns so that the search space of tasks can be explored comprehensively, which is favor of EMTO alleviating local optimization. Finally, the performance of the proposed algorithm is evaluated in comparison with some other state-of-the-art EMTO algorithms on multiobjective multitasking benchmark test suits, and the practicality of the algorithm is verified in a real-world application study. The results of experiments demonstrate the superiority of DKT-MTPSO compared to other algorithms.

Traffic Fingerprints for Homogeneous IoT Traffic Based on Packet Payload Transition Patterns

Traffic fingerprint was considered an effective security protection mechanism in IoT scenarios because it can be used to automatically identify accessed devices. However, the results of replication experiments show that the classic traffic fingerprints based on simple network traffic attribute features have a significantly lower ability to identify accessed devices in real 5G IoT scenarios compared to what was stated in traditional IoT scenarios. The growing homogenization of IoT traffic caused by the application of 5G is believed to be the reason for the poor ability of traditional traffic fingerprints to identify 5G IoT terminals. Studying an enhanced traffic fingerprint is necessary to accommodate the homogeneous Internet of Things traffic. In addition, during the reproducing experiments, we noticed that the solution of overlap is a key factor that restricts the recognition ability of one-vs-all multi-classifiers, and the efficiency of existing methods still has some room for optimization. Based on targeted improvements to these two issues, we proposed an enhanced IoT terminal traffic fingerprint based on packet payload transition patterns to improve the device recognition ability in homogeneous IoT traffic. Additionally, we designed an improved solution for overlap based on density centers to expedite decision making. According to the experimental results, when compared with the existing traffic fingerprint, the proposed traffic fingerprint in this study demonstrated a Macro-Average Precision of close to 90% for network traffic from real 5G IoT terminals. The proposed overlap solution based on the density centers reduced the decision-making time from hundreds of seconds to tens of seconds while ensuring decision-making accuracy.

Impact of Texture Height and Ozone-Based Rounding on Silicon Heterojunction Solar Cells Performance

In this study, the effect of ozone-based rounding was investigated for three random-pyramid texture height ranges for Silicon Heterojunction (SHJ) at the cell and module levels. In a first experiment, 5 ozone-based rounding process times (0, 2, 4, 6, 8 and 10 minutes) were applied to two texture-height ranges (Small and Medium) to observe variations in the reflection response of the wafers. In a second experiment, 3 ozone-based rounding treatment times (0, 2 and 6 minutes) were applied to three texture-height ranges (Small, Medium and Large) and to observe the results both on the cell and on the module levels. The first experiment has shown that ozone-based rounding increases reflection and has a more pronounced effect for smaller than larger sized texture-heights. An increase in the minimum wavelength of reflection is seen after the Transparent Conductive Oxide (TCO) deposition, indicating that shorter deposition times can be used. The results of the second experiment show that, even though the Short-Circuit Current (Jsc) decreases for the three textures with rounding time, it is compensated by an increase in the pseudo-Fill-Factor (pFF). A maximum is seen at the cell level for 2 minutes of rounding for the small and large texture sizes, but which is leveled at the module. In conclusion, these results indicate that 2 minutes of ozone-based rounding can be beneficial for the cell’s final efficiency as the improvement in passivation still compensates the optical losses resulting in a lower Jsc.

A clustering effectiveness measurement model based on merging similar clusters.

This article presents a clustering effectiveness measurement model based on merging similar clusters to address the problems experienced by the affinity propagation (AP) algorithm in the clustering process, such as excessive local clustering, low accuracy, and invalid clustering evaluation results that occur due to the lack of variety in some internal evaluation indices when the proportion of clusters is very high. First, depending upon the "rough clustering" process of the AP clustering algorithm, similar clusters are merged according to the relationship between the similarity between any two clusters and the average inter-cluster similarity in the entire sample set to decrease the maximum number of clusters Kmax. Then, a new scheme is proposed to calculate intra-cluster compactness, inter-cluster relative density, and inter-cluster overlap coefficient. On the basis of this new method, several internal evaluation indices based on intra-cluster cohesion and inter-cluster dispersion are designed. Results of experiments show that the proposed model can perform clustering and classification correctly and provide accurate ranges for clustering using public UCI and NSL-KDD datasets, and it is significantly superior to the three improved clustering algorithms compared with it in terms of intrusion detection indices such as detection rate and false positive rate (FPR).

Assessing the effectiveness of non-pharmaceutical interventions in the SARS-CoV-2 pandemic: results of a natural experiment regarding Baden-Württemberg (Germany) and Switzerland in the second infection wave

Assessing the effectiveness of non-pharmaceutical interventions in the SARS-CoV-2 pandemic: results of a natural experiment regarding Baden-Württemberg (Germany) and Switzerland in the second infection wave

Software Implementation of Methods for Modeling the Interaction of Electromagnetic Radiation with Carbon Nanocomposites

Methods for modeling the interaction of electromagnetic radiation with a carbon nanostructured composite containing various structural elements, such as carbon fibers, nanoparticles, air gaps, and transition shells are considered. It is shown that for effective and adequate modeling of the interaction of electromagnetic radiation with a carbon nanostructured composite, it is necessary to create software due to the presence of a large set of input parameters, as well as the complexity of calculations. Functional requirements for the software are formulated, reflecting the choice of models and the presence of a convenient interactive interface. The architecture of the software that implements models of interaction of electromagnetic radiation with a carbon nanostructured composite is described, and the software implementation (class structure, methods, user interface) is considered. The developed code is based on proven physical and mathematical models, which make it possible to obtain results close to experimental data. The software architecture allows to effectively manage the modeling process, and also includes the ability to modify models and take into account new structural elements. An interactive interface for users has been developed that allows to easily manage the model parameters and analyze the results of numerical experiments.

Research of the FLC + PID switching control strategy based on real-time error for the pneumatic polishing force regulating system

This paper designs an active pneumatic polishing force control system and investigates its control strategy. A straightforward method based on the experiments is proposed for identifying and modeling the pneumatic system and a convenient and effective control strategy based on real-time error is proposed for pneumatic polishing force regulating. Corresponding function between the input duty cycles and the output force values is created based on the experiment data tested by the system. Then the pneumatic force regulating system is modeled by the corresponding function. Base on the results of simulations and experiments, a FLC (Fuzzy Logic Control) + PID (Proportion-Integration-Differentiation) switching control strategy is designed with a switching mechanism to choose different control algorithm based on real-time error which can take the advantages of both PID and FLC. The FLC + PID switching controller can eliminate the steady state errors occurring in FLC controller and achieve the same accuracy as the PID controller but with a faster response speed. The approach effectively stabilizes the polishing force, resulting in polishing force errors that are distributed within ± 1 N. The mean errors are less than 0.2 N and the absolute mean errors hover around 0.3 N. The pneumatic polishing force control system regulated by the FLC + PID switching control exhibits effectiveness, stability, high control precision and fast response speed, and satisfies the force control requirements of polishing process.

Исследование точности определения места повреждения воздушных линий по данным от устройств синхронизированных векторных измерений различных классов и производителей

The technology of synchronized phasor measurements has been widely used in the Russian power system to analyze the parameters of steady-state electrical power modes and to record electro-mechanical transient processes. Issues on fault location based on synchronized phasor measurements have mainly been discussed in foreign publications. A significant drawback of most research papers on this issue is simplified modeling of both overhead lines and current and voltage measurement channel as well as digital filters of phasor measurement units (PMUs). The goal of this research is to develop a fault location algorithm based on synchrophasor measurements and to study its accuracy by using specialized equipment including a real-time digital simulator (RTDS), PONOVO current and voltage amplifiers and production-grade PMUs. A PMU-based double-ended fault location algorithm is developed using long-line equations and well-known electromagnetic transient theory concepts. Currents and voltage oscillograms of both overhead transmission line ends are modelled in the MATLAB/Simulink software package. These oscillograms are saved as COMTRADE files and played back using the RTDS hardware and all-in-one software package RSCAD. In addition, the study uses two production-grade PMUs, the first one is TPA-02, and the second one is a merging unit ENMU acting as a PMU, thus imitating PMUs at the line terminals. To time-align all the measurements and to aggregate PMU data frames, various auxiliary software such as PMU Connection Tester, and hardware is used. A double-ended fault location (FL) method utilizing synchrophasors under a fault has been developed. The method is based on an overdetermined system of nonlinear equations that describes physical processes in an overhead power line and can be applied under various fault types. An integrated study of the efficiency of the developed FL method has been conducted. FL errors have been computed using production-grade PMUs ENMU and TPA-02, along with the RTDS and other equipment. The authors have considered the case of configuring PMUs of different classes at the overhead line terminals, and different phasor reporting rates as well. The conducted experiments make it possible to reveal that the fault location errors do not exceed the thresholds imposed by the standard STO 56947007-29.120.70.241-2017 in 88 % of all the analyzed fault scenarios. The developed FL method makes it possible to achieve the accuracy required by regulatory guide in calculating the distance to the short circuit point in most of the cases examined. Based on the results of numerical experiments for various types of fault cases, it can be concluded that the PMU class and phasor reporting rate do not have a significant impact on the FL accuracy, provided that the fault duration is enough for the PMU filter to approach a steady output.

Design and experiment of conduction cooling HTS current leads suited for superconducting Wiggler magnets

The cryogenic system of the 6T superconducting Wiggler magnets at the Hefei National Synchrotron Radiation Laboratory was upgraded. During the upgrade process, the original copper leads of the superconducting Wiggler magnets are replaced with two 460A high-temperature superconducting current leads and three 20A high-temperature superconducting current leads, reducing the heat load of the cryogenic system. Five HTS current leads can be cooled using a GM cryocooler based on the optimal design and experimental verification of their current-carrying capability and heat load. According to the loading current experimental data, the running voltage and operating temperature are stable. The results of the overcurrent capacity experiments demonstrate that the maximum loading current of the 20A high-temperature superconducting current lead can reach 142A and that of the 460A high-temperature superconducting current lead can reach 780A.

Adaptive and fuzzy locality discriminant analysis for dimensionality reduction

Linear discriminant analysis (LDA) uses labeled samples for acquiring a discriminant projection direction, by which data of different categories are separated into distinct groups in a lower-dimensional subspace. In response to the issue that LDA lacks robustness to non-Gaussian data with rich local information, improvements on LDA explore the subspace manifold structure by building a fully connected similarity graph. However, these methods are vulnerable to the interference of noisy and redundant information. In this paper, we propose a new local LDA method, named adaptive and fuzzy locality discriminant analysis (AFLDA), which aims at extracting precise and compact features. Firstly, an adaptive and fuzzy k-means strategy is adopted, where the membership between data and corresponding subclass centers for each class is learned to establish a hybrid bipartite graph and capture local information. Secondly, we design a discrete and probability constraint imposed on the membership matrix to explore the intricate structure of multimodal data. Moreover, the subblock partition for each class makes data accommodate to multimodal subclasses. Thirdly, the maximum total scatter regularization term is introduced, which amply disperses the data to enhance the recognition of local structure and avoid trivial solutions. Finally, to eliminate the interference of noisy and redundant information, AFLDA learns an optimized subspace, where cluster centers and the membership matrix are updated alternately. Promising results in experiments illustrate the efficacy of the model.

CrackTinyNet: A novel deep learning model specifically designed for superior performance in tiny road surface crack detection

AbstractWith the rapid advancement of highway construction, the maintenance of highway infrastructure has become particularly vital. During highway maintenance, the effective detection of tiny road surface cracks helps to extend the lifespan of roads and enhance traffic efficiency and safety. To elevate the performance of existing road detection models, the CrackTinyNet (CrTNet) algorithm is specifically proposed for detecting tiny road surface cracks. This algorithm utilizes the novel BiFormer general visual transformer, designed expressly for tiny objects, and optimizes the loss function to a normalized Wasserstein distance loss function. It replaces traditional downsampling with Space‐to‐Depth Conv to prevent the excessive loss of tiny object information in the network structure. To highlight the model's advantage in detecting tiny road cracks, ablation experiments and comparison trials were conducted with mainstream deep learning models for crack detection. The results of the ablation experiments show that, compared to the baseline, CrTNet improved the Mean Average Precision (MAP) by 0.22. When compared to other network models suitable for road detection, these results exhibited an improvement of over 8.9%. In conclusion, the CrTNet proposed in this study enables a more accurate detection of tiny road cracks, playing a significant role in the advancement of intelligent traffic management.

Electrospinning for producing antifungal nanofibers consisting of prochloraz/hydroxypropyl-γ-cyclodextrin inclusion complex

Prochloraz is an extensively used imidazole fungicide in agriculture, but low water solubility limits its application. In the present study, inclusion complex nanofibers of prochloraz and Hydroxypropyl-γ-Cyclodextrin (prochloraz/HPγCD-IC-NF) were prepared by electrospinning method. The results of various characterization experiments indicated that prochloraz was effectively encapsulated in the cyclodextrin cavity to form inclusion complex nanofibers. Phase solubility study demonstrated that HPγCD had a significant solubilization effect on prochloraz. In 10 mM HPγCD solution, the solubility of prochloraz was increased about 2.68 times. Thermogravimetric analysis experiments indicated the beginning thermos-decomposed temperature of prochloraz in prochloraz/HPγCD-IC-NF was raised by 31℃ (from 281℃ to 312℃). The molecular simulation visually showed the best state and the most stable model of prochloraz entering the cyclodextrin cavity. In addition, the fungal inhibitory activity of prochloraz/HPγCD-IC-NF against Gibberella was 4.559 times to that of prochloraz. In conclusion, the preparation of inclusion nanofibers can improve the water solubility, thermal stability and antifungal activity of mimosine, which has the potential to reduce environmental damage in agricultural applications.