Examining the Interplay between Trait Mindfulness and Thriving at Work among Teachers in Western China:The Mediating Role of General Self-Efficacy

This study explores the nuanced relationship between trait mindfulness and thriving at work among educators in Western China,emphasizing the mediating role of general self-efficacy.Employing a sample of 378 primary and secondary school teachers,this research utilizes the Five Facet Mindfulness Questionnaire(FFMQ),Thriving at Work Scale(TWS),and the General Self-Efficacy Scale(GSES)to conduct a thorough investigation.The findings indicate a significant positive correlation between trait mindfulness and thriving at work,between trait mindfulness and general self-efficacy,and between general self-efficacy and thriving at work.Additionally,trait mindfulness was found to have a positive predictive effect on both thriving at work and general self-efficacy,with general self-efficacy also showing a positive predictive effect on thriving at work.Importantly,general self-efficacy was identified as playing a partial mediating role in the relationship between trait mindfulness and thriving at work.These results underscore the importance of cultivating mindfulness and self-efficacy among teachers to enhance their enthusiasm for work,suggesting potential pathways for professional development and well-being in the educational sector.

Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelligence optimization.However,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not high.To solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal mine.First,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position prediction.Second,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground mines.Among them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,respectively.Meanwhile,compared with single-input ARIMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADI-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.

Internet of Lamps for Future Ubiquitous Communications:Integrated Sensing,Hybrid Interconnection,and Intelligent Illumination

The ubiquitous deployment and restricted consumption are the requirements restricting the development of Internet of Things.Thus,a promising technology named Internet of Lamps(Io L)is discussed in this paper to address these challenges.Compared with other communication networks,the remarkable advantage of Io L is that it can make full use of the existing lighting networks with sufficient power supply.The lamps can be connected to the Internet through wired power line communication and/or wireless communication,while the integration of integrated sensing,hybrid interconnection,and intelligent illumination is realized.In this paper,the Io L is discussed from three aspects including sensing layer,network layer,and application layer,realizing the comprehensive upgrade based on the conventional communication and illumination systems.Meanwhile,several novel technologies of Io L are discussed based on the requirements of sensing,communication,and control,which have put forward practical solutions to the issues faced by Io L.Moreover,the challenges and opportunities for Io L are highlighted from various parts of the system structure,so as to provide future insights and potential trends for researchers in this field.

Remote Sensing Image Encryption Using Optimal Key Generation-Based Chaotic Encryption

The Internet of Things(IoT)offers a new era of connectivity,which goes beyond laptops and smart connected devices for connected vehicles,smart homes,smart cities,and connected healthcare.The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users.With the increasing use of multimedia in communications,the content security of remote-sensing images attracted much attention in academia and industry.Image encryption is important for securing remote sensing images in the IoT environment.Recently,researchers have introduced plenty of algorithms for encrypting images.This study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption(ISCACE-RSI)technique in IoT Environment.The proposed model follows a three-stage process,namely pre-processing,encryption,and optimal key generation.The remote sensing images were preprocessed at the initial stage to enhance the image quality.Next,the ISCACERSI technique exploits the double-layer remote sensing image encryption(DLRSIE)algorithm for encrypting the images.The DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid(DNA)Strand Displacement(DNASD)approach.The chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext images.Then,the study presents three DNASD-related encryption rules based on the variety of DNASD,and those rules are applied for encrypting the images at the DNA sequence level.For an optimal key generation of the DLRSIE technique,the ISCA is applied with an objective function of the maximization of peak signal to noise ratio(PSNR).To examine the performance of the ISCACE-RSI model,a detailed set of simulations were conducted.The comparative study reported the better performance of the ISCACE-RSI model over other existing approaches.

服务天基感知业务的卫星互联网模拟验证系统设计与分析

提出具有遥感、星间星地组网、星载边缘计算功能的低轨卫星与地面云计算中心和天基、临近空间、空中、陆地、海面等各类型终端构成的卫星互联网总体架构,给出包括资源层、虚拟化层、服务层、服务管理层及应用层在内的星载边缘计算功能架构。针对星载计算能力有限、天基网络拓扑高动态等典型特征,在提出星载边缘计算模拟验证系统构建方法的基础上,设计服务天基感知业务的卫星互联网模拟验证系统,开展天基感知信息处理分发试验,并根据不同信息处理与分发方式对网络资源占用和用户服务时延的对比,分析不同能力条件下卫星互联网对天基感知业务服务的能力水平。

新型电力系统专用传感与边缘智能关键技术及其展望

电力物联网在电力系统的实时传感、透明感知与全局调控中发挥着重要作用。随着新型电力系统的不断演化,传统的设备传感与静态独立的智能系统难以应对多样化业务需求激增、多元化数据爆发式增长、传感器通信可靠性差与能效低下和算法模型迭代成本高等难题。因此,高度多元集成、绿色持续可靠、云边端深度协同的新型电力系统专用传感与边缘智能有望成为新范式。该文首先以新型电力系统专用传感与边缘智能的基本内涵为基础,构建了专用传感与边缘智能融合贯通的体系架构。然后重点阐述了国产化高频新型传感技术、绿色供能的高性能嵌入式芯片技术、高可靠性灵活组网技术、轻量化模型推理技术、云边协同资源共享技术和在线学习持续迭代技术。并进一步展望了专用传感与边缘智能在数据治理、隐私保护、软件定义硬件、业务-算力微服务、多层分布式泛在智能和电力大模型等发展方向,以期为新型电力系统建设提供参考。

物联网专业混合式实验教学模式探索

针对当前物联网专业高校在实验与实践教学活动开展过程中面临的师资力量有限、实验内容设计质量不高、自主训练不足且缺乏思维锻炼能力等问题,以新工科人才建设思想为指导,以培养工程实践思维和创新能力提升为目标,基于SPOC、虚拟仿真等现代化教学手段和先进的教学理念,探索虚实结合、多径培养的新型教学模式。通过案例引导、虚实结合、课堂翻转等多样化的教学手段提高学生参与课程的积极性和学习热情,为国内其他高校新工科专业的实践教学建设与开展提供新的解决思路与教学方法。

一种小型化人工表面等离激元传感系统——实现自适应和高灵敏的气体检测

Resonantly enhanced dielectric sensing has superior sensitivity and accuracy because the signal is measured from relative resonance shifts that are immune to signal fluctuations.For applications in the Internet of Things(IoT),accurate detection of resonance frequency shifts using a compact circuit is in high demand.We proposed an ultracompact integrated sensing system that merges a spoof surface plasmon resonance sensor with signal detection,processing,and wireless communication.A softwaredefined scheme was developed to track the resonance shift,which minimized the hardware circuit and made the detection adaptive to the target resonance.A microwave spoof surface plasmon resonator was designed to enhance sensitivity and resonance intensity.The integrated sensing system was constructed on a printed circuit board with dimensions of 1.8 cm×1.2 cm and connected to a smartphone wirelessly through Bluetooth,working in both frequency scanning mode and resonance tracking mode and achieving a signal-to-noise ratio of 69 dB in acetone vapor sensing.This study provides an ultracompact,accurate,adaptive,sensitive,and wireless solution for resonant sensors in the IoT.

基于SIRM算法的卫星结构板智能化生产管控系统设计

针对物联网技术在智能制造系统中的应用,通过在卫星结构研制设计过程中应用物联网标识和感知技术实现智能化生产管控.分析卫星结构研制流程中实物、信息的操作现状,提出了一种SIMR(Snapshot Integration Multipath Routing)路由算法的卫星研制流程自主感知优化技术,保证流量传输均衡和信息快速回传,解决卫星研制实物流与信息流难以深度融合的问题.围绕卫星零件、装配体、电子标签等实物定义了智能化生产管控系统,将系统分解为实物操作序列与数据操作序列,进而给出了基于物联网的卫星结构智能化生产管控系统架构,明确了本方法在卫星结构板成形生产和装配系统管控环节的应用模式,为提升卫星结构实物状态管控与全流程研制能力提供了一种可行的方法.

面向Internet的分布式遥感影像切割算法

为了实现遥感影像的网络快速发布,提出了一种高效的影像金字塔模型的构建算法,影像的切割与重采样均在内存中运算完成,尽可能地减少I/O操作,从而提高其执行效率。提出了面向Internet的分布式遥感影像的切割算法,并给出了基于B/S架构和基于C/S架构的两种实现方式。为了保证算法的效率与分布式结构,利用C++与Java联合编程进行实现。通过实例对算法性能进行测试,证明了算法的可操作性、实用性和高效性。

通感一体化赋能车联网:应用前景与关键技术

构建高效、安全且智能化的车联网系统是未来实现智慧交通的关键步骤。有限的频带资源和高冗余的硬件设计,对车联网系统的深化进程带来一定影响。随着6G通信技术的发展,通感一体化技术应用成为车联网系统构建的新研究方向。融入通感一体化技术,可以简化车联网系统中的硬件设计,同时实现资源智能配给和高效利用,推动车联网系统进步与发展。首先列举了通感一体化技术在车联网系统中的主要应用场景,然后介绍了融合通感一体化技术的车联网系统基础架构与关键技术,最后总结了在通感一体化车联网系统构建过程中面临的挑战,并对未来的发展方向进行了展望。

工业互联网平台资源重构机制研究——基于广度与深度的双重视角

新工业革命背景下传统企业如何利用工业互联网平台进行组织重构具有现实紧迫性。本文基于工业互联网平台广度与深度双重视角,构建企业市场感知能力通过平台连通性和兼容性增强资源重构能力的多重中介模型,并采用214名管理者的调研数据进行实证检验。研究结果表明:市场感知能力促使企业提高资源整合重构能力;企业除了通过工业互联网平台累积的海量数据提高连通性,在数据挖掘基础上满足潜在需求之外,还可以通过平台赋能的泛在化连接增强技术层面与组织层面的兼容性,从而助推企业实现组织重构;工业互联网平台的连通性和兼容性在市场感知与组织重构之间起中介作用。本文研究结论拓展了工业互联网平台的理论研究,为企业利用工业互联网平台实现数字化转型重构提供参考借鉴。

基于多学科交叉的材料设计加工实践教学体系研究

在新工科教育理念的指引下,通过开展具备多学科交叉融合的实践教学项目拓展大学生知识面的广度,培育综合运用多学科知识的深度,培养符合社会发展需求的卓越人才至关重要。南京信息工程大学材料物理专业以材料设计加工与传感系统构建作为实践教学载体,开展材料合成与测试、增材制造与加工、传感器件的封装与调试、系统编程与数据传输等多项实践教学内容,为大学生构建材料科学、电子技术、信息技术相结合的系统化教学体系。该教学改革实践有效提升学生的探索热情,培养创新思维和实践能力,为造就创新型卓越人才起到重要的作用。

基于无线WIFI的空调监测控制系统设计

针对国内能源问题日益凸显,其中空调能耗占比长年居高不下,基于此背景下,综合应用智能传感技术、信息处理技术与网络通信技术等物联网技术,开发出一套基于ESP32的空调节能监测控制系统。系统包括硬件部分和软件部分,硬件部分主要由智能传感模块、电量计算模块、红外发送接收模块组成;软件部分主要由温度预警系统、温度智能算法、网关软件设计组成。应用表明,系统既实现了智能化控制,又同时降低了空调能耗。

融合DAE-LSTM的认知物联网智能频谱感知算法

第五代(fifth-generation,5G)移动通信技术的兴起,推动了物联网(Internet of things,IoT)的发展。然而,随着物联网数据传输量的爆发式增长,频谱资源短缺问题越来越严重。频谱感知技术极大的提高了物联网频谱利用率。但是,物联网移动通信环境的复杂性高以及信号易畸变的特性,对现有的频谱感知算法提出了重大挑战。因此,提出了一种融合去噪自编码器(denoising autoencoder,DAE)和改进长短时记忆(long short term memory,LSTM)神经网络的智能频谱感知算法。DAE通过编码和解码过程挖掘移动信号的底层结构特征,改进的LSTM频谱感知分类器模型结合过去时刻信息特征对时序信号序列进行分类。与支持向量机(support vector machine,SVM)、循环神经网络(recurrent neural network,RNN)、LeNet5、学习矢量量化(learning vector quantization,LVQ)和Elman算法相比,该算法的感知性能提高了45%。

Cooperative Spectrum Sensing Deployment for Cognitive Radio Networks for Internet of Things 5G Wireless Communication

Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless communication.Spectrum sensing is a significant technology in CRN to identify underutilized spectrums.The CSS technique is highly applicable due to its fast and efficient performance.5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of Things(IoT)networks.5G wireless communication will potentially lead the way for next generation IoT communication.CSS has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in IoT.In this paper,an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access(OQAM/UFMC/NOMA)methodologies.Availability of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless communication.The optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS,low latency,Signal Noise Ratio(SNR)improvement,maximum capacity,offset synchronization,and Peak Average Power Ratio(PAPR)reduction.The Energy Efficient All-Pass Filter(EEAPF)algorithm is used to eliminate PAPR.The deployment approach improves Quality of Service(QoS)in terms of system reliability,throughput,and energy efficiency.Our in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

上行通感算融合的性能分析与速率区域刻画

对车联网场景下通信、感知和计算融合的性能进行分析。对于通感一体化(ISAC)上行链路,通过分析分集阶数和高信噪比(SNR)斜率来评估通信和速率(CMR)和感知速率(SR),同时推导了通信和速率(CMR)、感知速率(SR)和计算和速率(CPR)的一般形式。此外,对可实现的上行CMR-SR-CPR区域进行表征。仿真结果表明,通信、感知和计算融合可以为CMR、SR和CPR提供比传统的频分通信感知和计算系统更高的增益。

社区-家庭合作型护理模式在产褥期母婴居家护理管理中的应用

目的:探讨社区-家庭合作型护理模式在产褥期母婴居家护理管理中的应用。方法:选取本地2个社区的产褥期产妇为研究对象。按照分娩的时间顺序分为对照组和干预组,2022年1月1日—6月30日纳入的研究对象为对照组(n=43),2022年7月1日—12月31日纳入的研究对象为干预组(n=47)。对照组对产褥期产妇和新生儿进行常规的3次社区家庭访视,干预组在常规家庭访视的基础上,实施社区-家庭合作型护理模式的母婴居家护理。比较两组爱丁堡产后抑郁量表和中文版育儿胜任感量表得分情况。结果:干预后,干预组产妇爱丁堡产后抑郁量表得分低于对照组,差异有统计学意义(P<0.05);中文版育儿胜任感量表总分和各维度得分均高于对照组,差异均有统计学意义(P<0.05)。结论:社区-家庭合作型护理模式能提高产妇的育儿胜任感,缓解产妇产后抑郁情绪,降低产后抑郁率。

基于物联网技术的智能建筑管理系统研究

随着科技的不断进步和社会的发展,智能建筑管理系统在现代城市建设中扮演着越来越重要的角色,同时物联网技术的迅猛发展也为智能建筑管理系统的开发实现提供广阔的空间。文章从物联网技术与智能建筑管理系统简述入手,分析了物联网技术概述、智能建筑管理系统的定义和基本原理,进而探讨了物联网技术在智能建筑管理系统中的具体应用,并总结了其存在的问题及发展趋势。文章旨在为互联网及建筑领域的研究人员和实践者提供参考和启示,在未来的城市建设中发挥更加重要和广泛的作用。