计算机辅助普通话水平测试评分问题的研究——以二级甲等“机测”成绩为中心的调查分析

计算机辅助普通话水平测试将测试环境形式由人工转变为计算机,其突出作用是减少了人为因素干扰、增强了测试的客观性和公正性,进而提高了测试的规范性和权威性。然而通过对测试最终成绩进行分析和比较发现:计算机辅助普通话水平测试评判中,存在对应试者的"特殊发音错误"和发音缺陷"测不准"、对连读和语流"不评测"等问题。这一现象必须引起重视并加以纠正,否则不仅会失去普通话水平测试的严肃性、科学性和权威性,而且也会对普通话学习和训练带来错误的导向,进而严重偏离推广普通话工作的目标。要进一步加强对计算机辅助普通话水平测试的研究,扩大语音样本数据库语音样本类型和数量,以提高计算机网上评分的分辨识别能力,并通过加强人工复评来纠正部分计算机网上评分的偏差。

基于相关向量机的电站锅炉NO_x燃烧优化

为了降低电站锅炉NOx排放量,采用一种新的机器学习方法——相关向量机对某330 MW煤粉汽包锅炉的一、二次风速以及含氧量等26个输入参数和NOx输出结果进行建模,并用万有引力算法对模型的参数进行优化,获得最优模型。与粒子群算法、遗传算法优化相关向量机以及万有引力算法优化支持向量机等进行了比较,选择锅炉输入参数中的可调变量为优化变量,以NOx低排放量为目标进行优化,获得低NOx排放的输入参数。结果证明：万有引力优化相关向量机算法建立的模型精确度比其它几种算法高,对模型进行低NOx优化后,NOx输出值由最初的的906.65 mg/m3变为550.600 mg/m3,下降幅度约为38.9%,实现了NOx排放量大幅度降低。

Scoring and Consequential Validity Evidence of Computer-and Paper-Based Writing Tests in Times of Change

Little is known about how the assessment modality,i.e.,computer-based(CB)and paper-based(PB)tests,affects language teachers’scorings,perceptions,and preferences and,therefore,the validity and fairness of classroom writing assessments.The present mixed-methods study used Shaw and Weir’s(2007)sociocognitive writing test validation framework to examine the scoring and consequential validity evidence of CB and PB writing tests in EFL classroom assessment in higher education.Original handwritten and word-processed texts of 38 EFL university students were transcribed to their opposite format and assessed by three language lecturers(N=456 texts,152 per teacher)to examine the scoring validity of CB and PB tests.The teachers’perceptions of text quality and preferences for assessment modality accounted for the consequential validity evidence of both tests.Findings revealed that the assessment modality impacted teachers’scorings,perceptions,and preferences.The teachers awarded higher scores to original and transcribed handwritten texts,particularly text organization and language use.The teachers’perceptions of text quality differed from their ratings,and physical,psychological,and experiential characteristics influenced their preferences for assessment modality.The results have implications for the validity and fairness of CB and PB writing tests and teachers’assessment practices.

Research progress on fluorescence sensing properties of metal-organic frameworks

The metal-organic frameworks(MOFs)have the characteristics of high porosity and crystallinity,which also makes MOFs have the advantages of high sensitivity,high selectivity and high efficiency in the field of fluorescence sensing.It can detect the target substance efficiently and quickly,and has excellent anti-interference.Therefore,MOFs materials are also considered as ideal materials for fluorescence sensors.This paper introduces three common preparation methods of MOFs materials:hydrothermal/solvothermal method,mechanochemical method and microwave method.We focus on the application status of MOFs materials in the three fluorescence sensing fields of small molecules,metal cations and inorganic anions,as well as the shortcomings and improvement methods of MOFs materials in this application field.Finally,in view of the shortcomings of MOFs materials in the field of fluorescence sensing,such as poor stability,fluorescence quenching phenomenon susceptible to environmental interference,etc.,it is proposed that more in-depth research should be carried out in the future in terms of anti-interference and reuse of MOFs materials.

Bimetallic mixed-linker metal-organic frameworks for electrochemical detection of biomolecules

Because of their easy tunability in structure,porosity,and micro-environment,metal-organic frameworks(MOFs)have recently attracted numerous attentions in various fields.The detection of ascorbic acid(AA),dopamine(DA),and uric acid(UA)is of great significance not only in biomedicine and neurochemistry but also in disease diagnosis and pathology research.Herein,a series of bimetallic-organic frameworks,MIL-125(Ti-Fe)-x%NH_(2)(x=0,25,50,75,and 100),was successfully synthesized.MIL-125(Ti-Fe)-x%NH_(2)family was employed as electrochemical sensors for the detection of AA,DA,and UA,and MIL-125(Ti-Fe)-100%NH_(2)exhibited the most promising performance with 50%carbon black doping in 0.1 mol·L^(-1)PBS(pH=7.10).In addition,the as-prepared MIL-125(Ti-Fe)-100%NH_(2)/GCE exhibited excellent anti-interference performance and good stability,which provided a promising platform for future utilization in real sample analysis.

Anomaly Detection Method Using Feature Reconstruction Based Knowledge Distillation

In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationship between samples,resulting in poor accuracy in recognizing anomalous samples.To address this problem,a knowledge distillation anomaly detection method based on feature reconstruction was proposed in this study.Knowledge distillation was performed after inverting the structure of the teacher-student network to avoid the teacher-student network sharing the same inputs and similar structure.Representability was improved by using feature splicing to unify features at different levels,and the merged features were processed and reconstructed using an improved Transformer.The experimental results show that the proposed method achieves better performance on the MVTec dataset,verifying its effectiveness and feasibility in anomaly detection tasks.This study provides a new idea to improve the accuracy and efficiency of anomaly detection.

Improved YOLOv8s-Based Night Vehicle Detection

With the gradual development of automatic driving technology,people’s attention is no longer limited to daily automatic driving target detection.In response to the problem that it is difficult to achieve fast and accurate detection of visual targets in complex scenes of automatic driving at night,a detection algorithm based on improved YOLOv8s was proposed.Firsly,By adding Triplet Attention module into the lower sampling layer of the original model,the model can effectively retain and enhance feature information related to target detection on the lower-resolution feature map.This enhancement improved the robustness of the target detection network and reduced instances of missed detections.Secondly,the Soft-NMS algorithm was introduced to address the challenges of dealing with dense targets,overlapping objects,and complex scenes.This algorithm effectively reduced false and missed positives,thereby improved overall detection performance when faced with highly overlapping detection results.Finally,the experimental results on the MPDIoU loss function dataset showed that compared with the original model,the improved method,in which mAP and accuracy are increased by 2.9%and 2.8%respectively,can achieve better detection accuracy and speed in night vehicle detection.It can effectively improve the problem of target detection in night scenes.

Fire Detection Model Based on Improved RT-DETR

Fire detection has a great impact on people’s life safety.Fire Detection-DETR(FD-DETR)is a fire detection model based on RT-DETR for early fire identification in complex fire scenes.In this study,Adown sub-sampling module was selected to improve the original convolution module,which improved the detection accuracy and reduced the number of parameter values.Using LSKA attention module on the backbone network further improved the detection accuracy.The experimental results showed that compared with the original RT-DETR model,the precision and mAP of FD-DETR flame detection are increased by 0.8%and 0.1%,respectively,which proves that the improved method proposed in this study effectively improves the feature extraction and feature fusion capabilities of the network.In the complex scene fire detection task,the performance of the improved RT-DETR algorithm is better than the original RT-DETR algorithm.

Improved YOLOv8-Based Target Detection Algorithm for UAV Aerial Image

In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.

Development status and prospect of cold chain logistics of fruits,vegetables,and agricultural products based on intelligent technology

The cold chain in the production area of fruits and vegetables is the primary link to reduce product loss and improve product quality,but it is also a weak link.With the application of big data technology in cold chain logistics,intelligent devices,and technologies have become important carriers for improving the efficiency of cold chain logistics in fruit and vegetable production areas,extending the shelf life of fruits and vegetables,and reducing fruit and vegetable losses.They have many advantages in fruit and vegetable pre-cooling,sorting and packaging,testing,warehousing,transportation,and other aspects.This article summarizes the rapidly developing and widely used intelligent technologies at home and abroad in recent years,including automated guided vehicle intelligent handling based on electromagnetic or optical technology,intelligent sorting based on sensors,electronic optics,and other technologies,intelligent detection based on computer vision technology,intelligent transportation based on perspective imaging technology,etc.It analyses and studies the innovative research and achievements of various scholars in applying intelligent technology in fruit and vegetable cold chain storage,sorting,detection,transportation,and other links,and improves the efficiency of fruit and vegetable cold chain logistics.However,applying intelligent technology in fruit and vegetable cold chain logistics also faces many problems.The challenges of high cost,difficulty in technological integration,and talent shortages have limited the development of intelligent technology in the field of fruit and vegetable cold chains.To solve the current problems,it is proposed that costs be controlled through independent research and development,technological innovation,and other means to lower the entry threshold for small enterprises.Strengthen integrating intelligent technology and cold chain logistics systems to improve data security and system compatibility.At the same time,the government should introduce relevant policies,provide necessary financial support,and establish talent training mechanisms.Accelerate the development and improvement of intelligent technology standards in the field of cold chain logistics.Through technological innovation,cost control,talent cultivation,and policy guidance,we aim to promote the upgrading of the agricultural industry and provide ideas for improving the quality and efficiency of fruit and vegetable cold chain logistics.