Do companies influenced by smart cities pay more attention to green governance?Mechanism and optimal spatial correlation

Smart cities are a way for China to construct an innovative and environmentally conscious nation.The paper examines the impact of smart cities on corporate green governance and provides a theoretical foundation for formulating and executing smart city policy in China.Based on panel data from Chinese A-share listed companies in Shanghai and Shenzhen from 2008 to 2020,this study constructs a multiperiod double-difference model to examine the influence of smart cities on corporate green governance.Additionally,it uses a spatial double-difference model to investigate the spatial spillover effect of smart cities on neighboring areas.The findings indicate that smart cities effectively enhance corporate green governance.Analyzing the influencing mechanisms reveals that resource allocation efficiency,technological innovation,management environmental awareness,and regional environmental enforcement efforts act as mediators.Furthermore,the study reveals that the impact of smart cities on promoting corporate green governance is more pronounced in regions with lower levels of marketization and resource-based cities.Moreover,the research explores the spatial spillover effects of smart cities,with an effective radius of approximately 350 km.The optimal spatial correlation zone for green governance of businesses in neighboring areas in relation to smart cities is within a range of 250-350 km.This is manifested by the significant promotion of green governance in neighboring area businesses facilitated by smart cities.

An Integrated Model of Smart Home Applications User Experience Based on Cognitive Dissonance

This study constructs an integrated model of user experience in smart home applications(apps)to deeply explore the impact of cognitive dissonance on users’emotional responses,subsequent behaviors,and experiential outcomes.The research emphasizes the importance of addressing emotional management in the design and development of smart home apps.The findings indicate that emotional response plays a critical mediating role in the user experience of these apps,offering new insights for further optimization.By understanding users’emotional reactions and behavioral patterns under cognitive dissonance,developers can more effectively improve interface design and enhance the overall user experience.

Research on Source Maintenance Key Technology of the Smart Substation

The source maintenance technology of the smart substation offers the base for the models, data and graphs sharing between the substation and the dispatch center. This paper researches on the conversion technology between SCD model in IEC 61850 Ed. 2 and the CIM model in IEC 61970. The substation provides SVG and SCD documents to the dispatch center, which includes primary equipment information and the network topology. The dispatch center’s automation system completes the conversion between the two models. This paper researches on the smart remote technology, which uses IEC 61850 as communication protocol. It can filter and restructure communication data based on the needs of different dispatch center. At the same time, it can provide quality control of communication link, to ensure that the important data be sent in real time.

Smart Society and Artificial Intelligence:Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

The implementation of artificial intelligence(AI)in a smart society,in which the analysis of human habits is mandatory,requires automated data scheduling and analysis using smart applications,a smart infrastructure,smart systems,and a smart network.In this context,which is characterized by a large gap between training and operative processes,a dedicated method is required to manage and extract the massive amount of data and the related information mining.The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics(AD)for smart management,which is exploitable in any context of Society 5.0,thus reducing the risk factors at all management levels and ensuring quality and sustainability.We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes,for reducing training costs,for improving production yield,and for creating a human–machine cyberspace for smart infrastructure design.The results obtained in 12 international companies demonstrate a possible global standardization of operative processes,leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself.Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions,with the related smart maintenance and education.

Steel Surface Defect Recognition in Smart Manufacturing Using Deep Ensemble Transfer Learning-Based Techniques

Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product quality.In light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing process.In this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual weaknesses.We evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different classes.The ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted averaging.Our evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and F1-score.Comparative analysis with recent studies also showed the superior performance of our methodology.

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

Smart Gas Sensors:Recent Developments and Future Prospective

Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,etc.With the development of sensor technology,wireless communication,smart monitoring terminal,cloud storage/computing technology,and artificial intelligence,smart gas sensors represent the future of gassensing due to their merits of real-time multifunctional monitoring,earlywarning function,and intelligent and automated feature.Various electronicand optoelectronic gas sensors have been developed for high-performancesmart gas analysis.With the development of smart terminals and the maturityof integrated technology,flexible and wearable gas sensors play an increasingrole in gas analysis.This review highlights recent advances of smart gassensors in diverse applications.The structural components and fundamentalprinciples of electronic and optoelectronic gas sensors are described,andflexible and wearable gas sensor devices are highlighted.Moreover,sensorarray with artificial intelligence algorithms and smart gas sensors in“Internet of Things”paradigm are introduced.Finally,the challengesand perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.

Relationship between the use of smart medical services and mental health status

In this editorial,I comment on the article by Zhang et al.To emphasize the importance of the topic,I discuss the relationship between the use of smart medical devices and mental health.Smart medical services have the potential to positively influence mental health by providing monitoring,insights,and inter-ventions.However,they also come with challenges that need to be addressed.Understanding the primary purpose for which individuals use these smart tech-nologies is essential to tailoring them to specific mental health needs and prefe-rences.

Multi-Step Clustering of Smart Meters Time Series:Application to Demand Flexibility Characterization of SME Customers

Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.

New ISO standard on digital governance of smart cities published

ISO 37170:2022,Smart community infrastructures-Data framework for infrastructure governance based on digital technology in smart cities,with the leading efforts of Chinese experts,was officially published in December 2022.The new standard,managed by ISO/TC 268/SC 1 on smart community infrastructures,is developed based on the series of Chinese national standards on information system for digitized supervision and management of city(GB/T30428)with the collaborative efforts of various parties including Beijing EGOVA Technology Co.,Ltd.,and the support of SAC and Ministry of Housing and Urban-Rural Development.

The Construction and Application of Smart Class Teaching with Financial Features:Taking Guangxi University of Finance and Economics as an Example

Under the guidance of Education Informatization 2.0 with the policy background of“prioritizing the development of education,speed up the education modernization,and build an education powerhouse”,major universities have responded to the policy,and built a batch of smart classrooms in line with the development of university teaching,laying a solid foundation for promoting university teaching reformation.The author complied the necessity of smart classroom construction from the theoretical level,as well as the current construction status of the smart classroom at home and abroad,and finally,this paper takes Guangxi University of Finance and Economics as an example to analyze the teaching advantages of the school,how to promote the deep integration of information technology in education and teaching,and put forward a powerful plan for the construction of the smart classroom in school teaching,and the practical application of group discussion in smart classroom as an example to discuss the smart teaching.This kind of classroom teaching will provide a reference for leaders,students and teachers.

横腭弓-Nance弓联合支抗与口外弓增强磨牙支抗的比较

目的:比较研究横腭弓-Nance弓联合支抗与口外弓支抗对支抗磨牙的控制效果。方法:选择20例安氏Ⅱ类1分类上颌前突的患者,随机分为2组,分别使用横腭弓-Nance弓联合支抗和口外弓增强上颌磨牙的支抗,通过治疗前后X线头影测量分析,比较二者对磨牙的支抗控制效果。结果:联合支抗组磨牙平均前移1.87mm,切牙内收4.89mm;口外弓组磨牙平均前移1.34mm,切牙内收5.04mm,2组间差异无显著性(P>0.05)。结论:联合支抗可提供与传统口外弓强支抗同样强度的支抗作用,且制作简单,效果满意,患者易于接受。

Nance弓和横腭杆在拉尖牙远移中支抗效果的研究

目的:研究Nance弓和横腭杆在拉尖牙远移过程中,二者在三维方向上磨牙支抗效果的比较。方法:选择Angle I类中度拥挤,或Angle II类I分类软组织侧貌前突不明显者40人,随机分组,上、下颌减数双侧第一前磨牙,设计方案为中度支抗,两步法关闭拔牙间隙。排齐整平后和治疗阶段结束时取模型,拍摄口内像和头颅侧位片。测量各标志点的线距和磨牙角度变化。应用SPSS 17.0统计软件对测量结果行统计学分析。结果:2组在阻止上颌第一恒磨牙近中前移、水平宽度或垂直方向、近中倾斜差异无显著性,但在磨牙旋转的控制上,Nance弓组磨牙向近中舌向旋转3.70°±1.20°,横腭杆组向近中舌向旋转8.50°±3.59°(P<0.05)。结论:在磨牙旋转支抗控制上,Nance弓明显优于横腭杆。

Nance弓的支抗与支抗消耗分析

目的 :评价不同状态时Nance弓的支抗强弱和支抗消耗。方法 :43例分成J钩、Nance弓及J钩和Nance弓联合支抗 3组 ,分别测量矢状向和水平向的支抗消耗并进行统计分析。结果 :3种支抗时第一磨牙均有显著性前移 (P <0 .0 0 1) ,三者相互之间有显著性差异 (P <0 .0 1或P <0 .0 5 ) ,Nance弓水平支抗稳定 (P >0 .0 5 )。结论 :Nance弓是一种简单有效的支抗装置 ,具有三维支抗作用 。

Nance弓-横腭杆-双磨牙带环联合支抗在治疗安氏Ⅱ~1类错中的应用

目的：评价Nance弓－横腭杆－双磨牙带环联合支抗应用于安氏Ⅱ^1类错[牙合]正畸治疗中增强磨牙支抗的效果，并探讨其机制和使用方法。方法：临床选择15～23岁安氏Ⅱ^1类错[牙合]患者12名，通过应用口内联合支抗增强上颌磨牙的支抗，对治疗前后X线头影测量值的变化进行分析。结果：矢状方向上，U6A-RL和U6S-RL分别增加1．13mm和1．64mm，但支抗磨牙前移未达到拔牙间隙的1／3；磨牙近中倾斜角度的变化无显著性（P〉0．05）。U1A-RL和U1E-RL分别减少2．38mm和4．96mm，上颌切牙唇倾角度减小15．22°，上颌突度减小1．25mm，差异均具有统计学意义（P〈0．05）。垂直方向上，支抗磨牙伸长量、下颌平面角和面高比例的变化均无显著性（P〉0．05）。结论：口内联合支抗能够在三维方向获得满意的支抗效果，是一种简单、高效的非依赖性口内强支抗系统。

Nance弓-横腭杆-双磨牙带环联合支抗与口外弓增强磨牙支抗的比较研究

目的比较研究Nance弓-横腭杆-双磨牙带环联合支抗与口外弓增强磨牙支抗的效果。方法临床选择15-26岁安氏Ⅱ^1类错殆患者24例，随机分为两组，分别应用口内联合支抗和口外弓增强上颌磨牙的支抗，并对两组治疗前后X线头影测量值的变化进行分析。结果矢状方向上，口内联合支抗组的U6A-RL和U6s-RL分别增加1．13mm和1．64mm，口外弓组为0．95mm和1．34mm，但均未达到拔牙间隙的1／3；两组的磨牙近中倾斜角度的变化均无显著性（P〉0．05）。口内联合支抗组的U1A-RL和UiE-RL分别减小2．38mm和4．96mm，口外弓组为2．69mm和5．04mm，两组的上颌切牙唇倾角度分别减小15．22度和13．15度，上颌突度分别减小1．25mm和1．50mm，差异均具有统计学意义（P〈0．05）。垂直方向上，两组的支抗磨牙伸长量、下颌平面角和面高比例变化均无显著性（P〉0．05）。结论口内联合支抗具有与传统口外弓同样的强支抗效果，且戴用不需依赖患者配合，易被患者接受。

改良Nance弓矫治上颌埋伏尖牙对牙周硬组织及牙根的影响

目的:探讨改良Nance弓结合直丝弓矫治上颌埋伏尖牙对牙周硬组织及牙根的影响。方法:利用改良Nance弓矫治8例单侧尖牙埋伏阻生,以对侧自然萌出尖牙作为自身对照。治疗前、后拍摄全景片,测量患侧尖牙移动的距离和角度改变,治疗侧和对侧侧切牙、尖牙、相邻前磨牙的牙槽嵴顶至釉牙骨质界距离及牙根长度,采用SPSS11.0软件包对数据进行配对t检验。结果:埋伏尖牙平均移动距离为18.43mm,全景片上平均移动角度为32.7°,埋伏尖牙侧相邻侧切牙和前磨牙的牙槽嵴高度无降低,牙根无吸收(P>0.05)。结论:改良Nance弓结合直丝弓技术是矫治上颌埋伏尖牙的安全、有效方法。

反向Nance弓与横腭弓联合强化支抗在安氏Ⅱ类错(牙合)矫治中的应用

目的:评价安氏Ⅱ类1分类错(牙合)病例临床矫治中反向Nance弓与横腭弓联合应用的支抗控制效果。方法:选择18.7-24.6岁安氏Ⅱ类1分类女性错(牙合)患者16例,分析联合应用反向Nance弓与横腭弓加强支抗治疗前后的X线头影测量值的变化。结果:(1)UMD与UMRD均有增大,但磨牙前移量被控制在拔牙间隙的1／5以内。(2)UMVD、MP-FH与ANS-Me均无显著性改变,支抗磨牙的垂直方向变化得到有效控制。(3)磨牙在水平方向上无舌向倾斜、移位及扭转。结论:反向Nance弓与横腭弓联合应用,能在三维方向上较好地控制支抗磨牙。

Nance弓联合横腭杆在拉尖牙远移中支抗效果的研究

目的观察临床应用Nance弓联合横腭杆在拉尖牙远移中支抗的效果。方法采用前瞻性研究方法,选取2014年7月至2016年2月在安康市中心医院进行拔牙矫正的88例错颌患者,采用随机平行分组法随机分为对照组和观察组,每组各44例。对照组患者应用横腭杆治疗,观察组患者给予Nance弓联合横腭杆治疗。对比分析两组患者治疗后的支抗强度、模型测量结果、头影测量结果。结果观察组强支抗率(56.8%)明显高于对照组(38.7%),总体支抗强度较对照组明显提高,差异均具有统计学意义(P<0.05);观察组上颌第一恒磨牙矢状距(0.82±0.04 mm vs.1.88±0.13 mm)、第一恒磨牙宽度(0.64±0.01 mm vs.1.12±0.06 mm)以及第一恒磨牙轴角(3.73±0.15°vs.8.52±0.34°)明显小于对照组,差异均具有统计学意义(P<0.05);观察组上颌第一恒磨牙牙体长轴和前颅底平面交角(U6-SN)明显小于对照组(1.24±0.12°vs.1.58±0.21°),差异具有统计学意义(P<0.05)。结论在拉尖牙向远中移动过程中,应用Nance弓联合横腭杆治疗增加支抗效果显著,可在临床推广应用。

2种Nance弓临床应用效果及对牙周健康的影响研究

目的比较一种改良粘结式Nance弓与传统Nance弓在正畸临床上的使用效果及对牙周健康的影响。方法 40例上颌需要中度支抗的青少年正畸患者随机分为2组,分别戴用传统Nance弓和改良式Nance弓,戴用4周后使用VAS量表法评价佩戴舒适度,并分别于第4、8、12周检查2组患者上颌第一磨牙的菌斑指数、牙龈指数和探诊深度,配对t检验与χ2检验检测2组间差异。结果戴用改良Nance弓组在舒适度和牙周指数上显著优于传统Nance弓组。结论改良式Nance弓相比于传统Nance弓,戴用更为舒适,并能显著减少对牙周健康的影响。