Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

Analysis of Static Pressure in Area between Back Plate and Cylinder of a Carding Machine with CFD

To analyze static pressure between back plate and cylinder in an A186 carding machine,a fluid model is established. The model takes into account static pressure of airflow near back plate with the numerical simulation method of Computational Fluid Dynamics (CFD) in FLUENT software. The result of the simulation in the model shows that static pressure in this area quickly increases to its maximum then rapidly decreases to a lower fixed value from inlet to outlet along a zone between back plate and cylinder. Both rotating speeds of the cylinder and the taker-in affect static pressure from the inlet to the outlet,of which the cylinder rotating speed has more influence than that of taker-in. Numerical simulations reveal that static pressure on surface of back plate are in good agreement with the former result of experimental analysis.

Weighted Adaptive Generalized Predictive Control for Carding Autoleveler

In textile industry, carding process has decisive influence on produced yarn quality. From the system theoretic point of view, it is marked by stochastic disturbance, long delays, and parameter variation. So, a carding process is difficult to control with traditional control algorithms (such as PID). In this paper, a weighted adaptive generalized predictive control (GPC) law was developed to control such a process. The experimental results show that GPC autoleveller controller could greatly reduce the sliver’s standard deviation and reject disturbance.

A STUDY ON CARDING QUALITY BY MEASURING DEGREE OF FIBRE SEPARATION IN SLIVER

This paper discusses the degree of fibre separation in sliver, its testing principle and determi-nation of testing parameters. It also analyzes the correlativity between the testing indexes and thequality of yarn.

Impact of the Speed of Flat of a Typical Carding Machine on the Quality of Carded Sliver and 40 Ne Yarn

The carding cycle affects the sliver quality and the subsequent yarn attributes since it is the main sliver formation step. Processing parameters assume a significant part in affecting the nature of the eventual outcome in any sorts of production. In the case of carding machine, a higher production rate makes the operation more sensitive. And this will cause degradation in product quality. So optimization of speed is the talk of the town in spinning field [1]. Extreme higher speed can prompt fiber harm and unnecessary neps generation will corrupt the end result. Again lower speed will lessen the production rate which isn’t reasonable. So we need to discover the ideal speed which will be advantageous to both product quality and production rate. In carding machine, real operational activity happens between flats and cards [1]. From an ordinary perspective, high produce able cards generates higher level of speed. Speed of the cards impacts the carding cycle and the nature of the yarn and in practical point of view, flat’s level of speed is advanced and optimized. The aim of the project was to find out the optimum flat speed in the context of yarn quality. 40 Ne cotton yarns were produced with the slivers manufactured at different flat speeds such as 240, 260, 280, 300 and 320 mm/min. The quality parameters of slivers and yarns were tested and analyzed.

基于多交叉置换扩增和纳米生物传感技术快速检测肺炎支原体方法的建立

目的建立一种简单、灵敏、快速的肺炎支原体(MP)检测方法,并对其应用性进行验证和评价。方法利用多交叉置换扩增(MCDA)技术对肺炎支原体特异基因CARDS毒素基因进行扩增,利用侧流免疫层析生物传感(LFB)技术读取扩增结果,命名该方法为MP-MCDA-LFB。分析扩增反应在60~67℃(间隔1℃)的扩增效率,筛选最适反应温度;分析分别扩增10、20、30、40 min时能够检测到的最低核酸浓度,筛选最佳反应时间。利用10倍系列稀释的肺炎支原体核酸分析MP-MCDA-LFB方法的灵敏度和检测限,利用35株非肺炎支原体菌株分析MP-MCDA-LFB方法的特异性。利用MP-MCDA-LFB方法检测80份疑似MP感染的临床样本,并与RT-PCR法检测结果进行比较,分析MP-MCDA-LFB方法的临床应用性。结果MP-MCDA-LFB能够实现对肺炎支原体CARDS毒素基因的快速检测。其最佳反应温度为63℃,最短反应时间为40 min,整个检测过程可在1 h内。MP-MCDA-LFB方法具有较高的灵敏度和特异性,其检测限低至45 ng/L,与其他临床表现相似的病原体无交叉反应,特异性为100%。MP-MCDA-LFB方法从80份临床样本中检出45份阳性样本(56.3%),检出率与RT-PCR方法一致。结论本研究建立的以CARDS毒素基因为靶标的MP-MCDA-LFB检测方法具有简单、快速、灵敏度高、特异性强的优点,在基层医疗机构和现场检测具有较好的应用潜力。

CARD6在脓毒症心肌损伤中的机制研究

目的:探讨CARD6在脓毒症心肌损伤中的调节机制。方法:将C57BL/6小鼠随机分为对照组与模型组。采用LPS腹腔注射法构建脓毒症心肌损伤小鼠模型,RT-qPCR检测小鼠心肌组织中CARD6、caspase-1的mRNA水平。体外实验则采用LPS刺激转染CARD6过表达质粒的H9C2细胞,检测caspase-1、细胞活力、CK-MB水平。结果:与对照组相比,模型组小鼠心肌组织中LDH、CK-MB含量,caspase-1表达水平上升,提示脓毒症导致小鼠心肌损伤,而CARD6表达水平明显降低。体外实验发现CARD6表达水平呈下降趋势,转染含有CARD6的过表达质粒后细胞释放CK-MB的水平降低、细胞活力增加、caspase-1水平降低。结论:CARD6可改善脓毒症所致心肌损伤,其机制可能与caspase-1介导的细胞焦亡相关。

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different fields.Physical settings are probed for data,which is then transferred via linked networks.There are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny sensors.When it comes to deploying IoT,everyone agrees that security is the biggest issue.This is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square footage.This research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted circumstances.Using lightweight cryptography,the information sent between these gadgets may be secured.In order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance features.We also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

Credit Card Fraud Detection Using Improved Deep Learning Models

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

CARD11 serves as a therapeutic biomarker for the drug therapies of ccRCC

Background:The incidence of clear cell renal cell carcinoma(ccRCC)is globally high;however,despite the introduction of innovative drug therapies,there remains a lack of effective biomarkers for evaluating treatment response.Recently,Caspase recruiting domain-containing protein 11(CARD11)has garnered attention due to its significant association with tumor development and the immune system.Methods:The expression of CARD11 mRNA and protein in ccRCC were analyzed by public database and immunohistochemistry.The focus of this study is on the epigenomic modifications of CARD11,its expression of ccRCC immunophenotype,and its correlation with response to immunotherapy and targeted therapy.Furthermore,to investigate the mechanism of this molecule’s influence on different biological behaviors of cells,cell tests in vitro have been conducted to observe the impact of its expression level.Results:CARD11 expression was upregulated which may be mainly modified by body methylation and was correlated with poor prognosis in ccRCC.In the tumor microenvironment of ccRCC,CARD11 expression was positively correlated with increased T lymphocyte infiltration and increased expression of inhibitory immune checkpoints.Moreover,ccRCC patients with high CARD11 expression had a better response to immunotherapy and targeted therapy.The knockdown of CARD11 ultimately suppressed the proliferation,migration,and invasion capabilities of ccRCC cells while simultaneously enhancing tumor cell apoptosis.Conclusion:We identified CARD11 as a novel therapeutic biomarker for immunotherapy and targeted therapy in ccRCC.

Comparative Analysis of Metro Passengers’Mobility Patterns and Jobs-housing Balance of Metropolitan

The advent of the big data era has provided many types of transportation datasets,such as metro smart card data,for studying residents’mobility and understanding how their mobility has been shaped and is shaping the urban space.In this paper,we use metro smart card data from two Chinese metropolises,Shanghai and Shenzhen.Five metro mobility indicators are introduced,and association rules are established to explore the mobility patterns.The proportion of people entering and exiting the station is used to measure the jobs-housing balance.It is found that the average travel distance and duration of Shanghai passengers are higher than those of Shenzhen,and the proportion of metro commuters in Shanghai is higher than that of Shenzhen.The jobs-housing spatial relationship in Shenzhen based on metro travel is more balanced than that in Shanghai.The fundamental reason for the differences between the two cities is the difference in urban morphology.Compared with the monocentric structure of Shanghai,the polycentric structure of Shenzhen results in more scattered travel hotspots and more diverse travel routes,which helps Shenzhen to have a better jobs-housing balance.This paper fills a gap in comparative research among Chinese cities based on transportation big data analysis.The results provide support for planning metro routes,adjusting urban structure and land use to form a more reasonable metro network,and balancing the jobs-housing spatial relationship.

肺炎支原体CARDS毒素依赖KELED序列调控炎性反应

目的细胞内逆向运输系统是微生物毒素蛋白导致细胞毒性的关键。KELED序列是肺炎支原体CARDS毒素逆向转运至内质网,而后发挥细胞毒性作用的关键氨基酸基序。在小鼠模型中,体外重组的CARDS蛋白可以重现肺炎支原体肺炎患者的特征性症状。然而,CARDS是否依赖于KELED序列调控肺组织炎性反应仍不清楚。方法在大肠杆菌中表达并纯化CARDS及其KELED序列突变蛋白;随后在细胞水平验证其致空泡化活性;最后,在小鼠模型中评价KELED序列突变的CARDS蛋白是否影响炎性因子的表达以及是否造成肺组织病理学损伤。结果KELED序列氨基酸发生突变,减弱CARDS蛋白致细胞空泡化效应,下调肺组织炎性因子mRNA表达,减轻肺组织的病理学损伤。结论KELED序列对CARDS蛋白致细胞空泡化及炎性损伤活性的维持至关重要。

Real-Time Fraud Detection Using Machine Learning

Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit card dataset, I tackle class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) to enhance modeling efficiency. I compare several machine learning algorithms, including Logistic Regression, Linear Discriminant Analysis, K-nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector, Random Forest, XGBoost, and Light Gradient-Boosting Machine to classify transactions as fraud or genuine. Rigorous evaluation metrics, such as AUC, PRAUC, F1, KS, Recall, and Precision, identify the Random Forest as the best performer in detecting fraudulent activities. The Random Forest model successfully identifies approximately 92% of transactions scoring 90 and above as fraudulent, equating to a detection rate of over 70% for all fraudulent transactions in the test dataset. Moreover, the model captures more than half of the fraud in each bin of the test dataset. SHAP values provide model explainability, with the SHAP summary plot highlighting the global importance of individual features, such as “V12” and “V14”. SHAP force plots offer local interpretability, revealing the impact of specific features on individual predictions. This study demonstrates the potential of machine learning, particularly the Random Forest model, for real-time credit card fraud detection, offering a promising approach to mitigate financial losses and protect consumers.

Leveraging Zibo Barbecue’s Success to Develop Hebei’s Culinary Brand:A Strategic Path

In 2023,Zibo barbecue culture exploded across the entire country,allowing people nationwide to experience and enjoy Zibo barbecue.This phenomenon injected new vitality into the economic development of Zibo.To promote the economic development of Hebei Province,this paper fully analyzes the reasons behind the popularity of Zibo barbecue,combines these insights with the characteristics of traditional cuisine in Hebei,and draws lessons from Zibo barbecue’s success as a business card for Zibo.The paper then outlines a strategy for building a culinary business card for Hebei.

A Study on the Problem of Employee Punch Card Substitution in Enterprise Management:The Case of Jingdong Enterprise

With economic progress and the continuous advancement of science and technology,the issue of employees substituting punch cards has gradually become a significant challenge in enterprise management.The purpose of this paper is to discuss the causes,effects,and countermeasures of the employee punch card phenomenon,with the aim of providing effective management recommendations for Chinese enterprises.In practice,enterprises should flexibly apply the countermeasures proposed in this paper according to their specific circumstances to prevent substitute punch card incidents and improve overall management efficiency.

CARD8基因新发P367L失功能突变患者自身炎症性疾病的临床及免疫学特征

目的报道1例CARD8基因新发突变导致自身炎症性疾病患儿及其家系的临床特征、致病性鉴定与免疫致病机制。方法收集并分析1例CARD8基因突变患儿的突变位点以及临床资料,随访患儿治疗及预后;采用全外显子测序发现可疑致病基因并利用Sanger测序验证家系突变位点;利用体外实验进行致病性验证及免疫学分析。结果先证者为1名男性患儿,主要临床表现为极早发的食物蛋白过敏性肠炎、特应性皮炎及反复发作的上消化道出血。实验室检查提示血清IgE升高。全外显子测序发现CARD8基因新发杂合突变c.1100C>T(p.P367L),且该突变位点经Sanger测序验证证实来源于其父亲。该突变致病性不明确,且既往未见报道。实验室检查提示患儿血浆中多种促炎细胞因子水平显著升高。患儿原代外周血单个核细胞(PBMCs)及转染相同突变质粒的293T细胞CARD8蛋白表达均显著降低。患儿PBMCs中NLRP3炎性小体过度激活,而NF-κB通路无异常。以上研究均提示患儿的CARD8基因p.P367L突变为失功能性致病突变。结论本文报道全球第2例,中国第1例CARD8基因杂合突变患儿及家系,并明确CRAD8基因c.1100C>T的突变致病性。该CARD8基因失功能突变可导致NLRP3炎性小体的过度激活而诱发患儿发生以消化道症状起病的自身炎症性疾病。

子宫内膜癌患者NLRC5与自噬蛋白表达的相关性及其临床意义

目的 分析子宫内膜癌患者NLR家族含CARD结构域5(NLRC5)表达与自噬相关蛋白Beclin1、微管相关蛋白1轻链3(LC3)表达的相关性,并探讨NLRC5、Beclin1、LC3与子宫内膜癌预后的相关性。方法 采集2020年1月-2022年1月安徽医科大学第二附属医院90例子宫内膜癌患者癌组织作为子宫内膜癌组,另取90例正常子宫内膜作为子宫内膜组。采用实时荧光定量聚合酶链反应(qRT-PCR)测定两组NLRC5、Beclin1、LC3 mRNA的表达。子宫内膜癌组患者术后接受12个月的随访,按照实体瘤疗效标准分为预后良好组(72例)与预后不良组(18例);比较两组NLRC5、Beclin1、LC3 mRNA的表达,采用多因素一般Logistic回归模型分析影响子宫内膜癌预后的因素,绘制受试者工作特征(ROC)曲线分析NLRC5、Beclin1、LC3对子宫内膜癌患者患者预后的预测价值。结果 子宫内膜癌组与正常子宫内膜组NLRC5、Beclin1和LC3 mRNA相对表达量的比较,差异均有统计学意义(P <0.05);子宫内膜癌组NLRC5 mRNA相对表达量较正常子宫内膜组低,Beclin1和LC3 mRNA相对表达量较正常子宫内膜组高。NLRC5与Beclin1(r=-0.565,P <0.05)、LC3(r=-0.421,P <0.05)呈负相关;Beclin1与LC3呈正相关(r=0.462,P <0.05)。预后良好、预后不良组的年龄、体质量指数、脉管浸润、肿瘤直径比较,差异均无统计学意义(P>0.05);预后不良组FIGO分期、宫体肌层深肌层占比、淋巴结转移、Beclin1、LC3 mRNA相对表达量高于预后良好组,NLRC5 mRNA相对表达量低于预后良好组(P <0.05);FIGO分期Ⅱ、Ⅲ期[OR=3.760(95%CI:1.260,11.223)]、宫体肌层深肌层[OR=26.800(95%CI:7.043,101.984)]、有淋巴结转移[OR=11.324(95%CI:3.286,39.019)]及Beclin1 [OR=68.163(95%CI:2.537,1831.270)]、LC3高表达[OR=26.468(95%CI:1.444,485.127)]均是子宫内膜癌预后的危险因素(P <0.05);NLRC5高表达达[OR=0.001(95%CI:0.000,0.079)]为保护因素(P <0.05);ROC曲线分析结果显示,NLRC5敏感性为82.8%(95%CI:0.784,0.923)、特异性为73.9%(95%CI:0.670,0.938);Beclin1敏感性为56.8%(95%CI:0.638,0.853)、特异性为77.8%(95%CI:0.644,0.877);LC3敏感性为57.3(95%CI:0.680,0.815)、特异性为72.2%(95%CI:0.693,0.824);联合敏感性为94.4%(95%CI:0.824,0.971)、特异性为62.5%(95%CI:0.593,0.778)。结论 子宫内膜癌患者NLRC5呈低水平表达,NLRC5与自噬相关蛋白Beclin1、LC3呈负相关,3者联合检测可提高子宫内膜癌患者的预后预测价值。