Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards

As big data becomes an apparent challenge to handle when building a business intelligence(BI)system,there is a motivation to handle this challenging issue in higher education institutions(HEIs).Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources.This paper reviews big data and analyses the cases from the literature regarding quality assurance(QA)in HEIs.It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper.The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data.The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’QA systems.This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in glaucoma from 2013 to 2022

AIM:To conduct a bibliometric analysis of research on artificial intelligence(AI)in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions for future studies.METHODS:Relevant articles on the application of AI in the field of glaucoma from the Web of Science Core Collection were retrieved,covering the period from January 1,2013,to December 31,2022.In order to assess the contributions and co-occurrence relationships among different countries/regions,institutions,authors,and journals,CiteSpace and VOSviewer software were employed and the research hotspots and future trends within the field were identified.RESULTS:A total of 750 English articles published between 2013 and 2022 were collected,and the number of publications exhibited an overall increasing trend.The majority of the articles were from China,followed by the United States and India.National University of Singapore,Chinese Academy of Sciences,and Sun Yat-sen University made significant contributions to the published works.Weinreb RN and Fu HZ ranked first among authors and cited authors.American Journal of Ophthalmology is the most impactful academic journal in the field of AI application in glaucoma.The disciplinary scope of this field includes ophthalmology,computer science,mathematics,molecular biology,genetics,and other related disciplines.The clustering and identification of keyword nodes in the co-occurrence network reveal the evolving landscape of AI application in the field of glaucoma.Initially,the hot topics in this field were primarily“segmentation”,“classification”and“diagnosis”.However,in recent years,the focus has shifted to“deep learning”,“convolutional neural network”and“artificial intelligence”.CONCLUSION:With the rapid development of AI technology,scholars have shown increasing interest in its application in the field of glaucoma.Moreover,the application of AI in assisting treatment and predicting prognosis in glaucoma may become a future research hotspot.However,the reliability and interpretability of AI data remain pressing issues that require resolution.

Phosphorylated protein chip combined with artificial intelligence tools for precise drug screening

We have developed a protein array system,named"Phospho-Totum",which reproduces the phosphorylation state of a sample on the array.The protein array contains 1471 proteins from 273 known signaling pathways.According to the activation degrees of tyrosine kinases in the sample,the corresponding groups of substrate proteins on the array are phosphorylated under the same conditions.In addition to measuring the phosphorylation levels of the 1471 substrates,we have developed and performed the artificial intelligence-assisted tools to further characterize the phosphorylation state and estimate pathway activation,tyrosine kinase activation,and a list of kinase inhibitors that produce phosphorylation states similar to that of the sample.The Phospho-Totum system,which seamlessly links and interrogates the measurements and analyses,has the potential to not only elucidate pathophysiological mechanisms in diseases by reproducing the phosphorylation state of samples,but also be useful for drug discovery,particularly for screening targeted kinases for potential drug kinase inhibitors.

Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird Swarm Intelligence and DRL

TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and interception.To address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our work.By integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion strategies.To further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic planning.We also leverage self-play training to continuously optimize the performance of pursuit UAVs.During experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each scenario.Extensive simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed method.The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.

Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach

Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.

Emerging Professions:The Versatile Future of Jobs Embracing a new occupational paradigm with greater digitalization,intelligence,flexibility,and personalization

As technology continues to revolutionize industries,spawning new economic models and patterns,the internet and digital development have created a wave of new occupations and positions.Statistics show that in 2023,China’s total workforce stood at 402 million,with 84 million individuals engaged in new forms of employment.

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

Big Data 4.0: The Era of Big Intelligence

Big data has had significant impacts on our lives,economies,academia and industries over the past decade.The current equations are:What is the future of big data?What era do we live in?This article addresses these questions by looking at meta as an operation and argues that we are living in the era of big intelligence through analyzing from meta(big data)to big intelligence.More specifically,this article will analyze big data from an evolutionary perspective.The article overviews data,information,knowledge,and intelligence(DIKI)and reveals their relationships.After analyzing meta as an operation,this article explores Meta(DIKE)and its relationship.It reveals 5 Bigs consisting of big data,big information,big knowledge,big intelligence and big analytics.Applying meta on 5 Bigs,this article infers that 4 Big Data 4.0=meta(big data)=big intelligence.This article analyzes how intelligent big analytics support big intelligence.The proposed approach in this research might facilitate the research and development of big data,big data analytics,business intelligence,artificial intelligence,and data science.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

Artificial intelligence for characterization of diminutive colorectal polyps:A feasibility study comparing two computer-aided diagnosis systems

BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Polyps(AI4CRP)for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^(TM)(Fujifilm,Tokyo,Japan).CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm.Both CADxsystems exploit convolutional neural networks.Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard.AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value(range 0.0-1.0).A predefined cut-off value of 0.6 was set with values<0.6 indicating benign and values≥0.6 indicating premalignant colorectal polyps.Low confidence characterizations were defined as values 40%around the cut-off value of 0.6(<0.36 and>0.76).Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps.Self-critical AI4CRP,excluding 14 low confidence characterizations[27.5%(14/51)],had a diagnostic accuracy of 89.2%,sensitivity of 89.7%,and specificity of 87.5%,which was higher compared to AI4CRP.CAD EYE had a 83.7%diagnostic accuracy,74.2%sensitivity,and 100.0%specificity.Diagnostic performances of the endoscopist alone(before AI)increased nonsignificantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE(AI-assisted endoscopist).Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems,except for specificity for which CAD EYE performed best.CONCLUSION Real-time use of AI4CRP was feasible.Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.

铸态下Sn-Bi二元共晶焊料合金的组织特征及其力学性能

研究了四种不同Bi含量(45%、50%、55%、60%)的Sn-Bi二元共晶合金自由凝固下的组织特征和力学性能,探讨了Sn-Bi二元共晶合金的晶体生长机制以及Bi元素含量对合金显微组织的影响。结果表明:Sn-Bi二元亚共晶合金铸态下的金相组织为初生Sn固溶体相和Sn/Bi共晶层片集群构成,随着Bi含量的增加,呈树枝晶形态生长的初生Sn相数量减少,形态长大。初生Sn固溶体相内部脱溶析出大量的杆状或点状Bi相,形成杆状共晶结构;Sn-Bi二元过共晶合金铸态下的金相组织为初生Bi相和Sn/Bi共晶层片集群构成,初生Bi相中基本不固溶Sn元素,呈小平面方式生长,并在在初生Bi相周围包裹生长一层Sn晕圈相。在室温下,Sn-Bi二元共晶合金的硬度和强度随着Bi含量的增加而上升,延伸率随着Bi含量的增加先上升后下降。

放电等离子体烧结BaTiO_(3-x)Bi(Ni_(0.5)Zr_(0.5))O_(3)陶瓷的介电和阻抗性能

采用传统固相反应法合成BaTiO_(3-x)Bi(Ni_(0.5)Zr_(0.5))O_(3)粉体和放电等离子体烧结技术制备BaTiO_(3-x)Bi(Ni_(0.5)Zr_(0.5))O_(3)陶瓷,研究陶瓷的晶体结构、微观形貌、介电和阻抗性能。结果表明:BaTiO_(3)-0.10Bi(Ni_(0.5)Zr_(0.5))O_(3)陶瓷具有钙钛矿型,晶体结构为赝立方相,晶粒尺寸约为0.64μm,密度为5.81 g/cm^(3),最大介电常数为7149,且随频率升高相变温度向高温移动。在1 kHz下,BaTiO_(3)-0.10Bi(Ni_(0.5)Zr_(0.5))O_(3)陶瓷的ln(1/ε-1/ε_(m))与ln(T-T_(m))的拟合曲线斜率为1.61,在-41~169℃内,Δε/ε_(25℃)≤±15%,表明样品有良好的温度稳定性。此外,随温度和频率的升高,材料的阻抗降低,在50℃下,当频率为100 Hz时,电阻为2.33×10~6Ω,离子电导率为10^(-8)S/cm。

Mg-Bi系合金的研究进展

合金元素Bi的价格较低,在镁中有较高的固溶度,随着温度降低,其固溶度减小,析出Mg_(3)Bi_(2)相,提高了镁的力学性能,因此Mg-Bi系合金具有良好的固溶和时效硬化潜力。在Mg-Bi系合金中加入Sn、Mn、Al、Ca、Zn等元素,改善合金组织,能够进一步提高合金的力学性能及耐腐蚀性。文中介绍了国内外学者对Mg-Bi系合金的研究进展,在总结Mg-Bi二元合金研究成果基础上,系统的概述了Mg-Bi-Sn系、Mg-Bi-Mn系、Mg-Bi-Al系、Mg-Bi-Ca系、Mg-Bi-Zn系等合金的组织和性能,综述了合金化对合金第二相、晶粒尺寸、织构、动态再结晶的影响,阐述了合金元素种类、添加量及热加工参数与合金力学性能的关系。总结了Mg-Bi系合金研究中存在的问题,并对今后的研究工作进行了展望。

基于BIM技术与模拟退火算法的村镇轻钢框架结构智能设计方法

传统村镇住宅结构设计需要进行大量的人工试算与重复建模,而受制于建设成本,村镇住宅无法像城镇住宅一样通过设计师进行专业的结构设计与验算,其安全性与经济性均难以满足要求。为此,提出一种村镇轻钢框架结构智能设计方法,包括智能建模与智能优化两个环节。基于图层自动识别算法、光学字符识别技术、自适应分块算法提出村镇轻钢框架结构BIM智能建模方法,包括图层识别、轴文本数据提取、墙体轮廓提取等,智能建模结果基本满足实际工程要求。基于提出的两阶段模拟退火算法给出村镇轻钢框架结构的智能优化方法,优化速度较快,优化效果良好。通过实际工程案例对提出的智能设计方法进行验证,结果表明,提出的村镇轻钢框架结构智能设计方法具有可行性,与传统的人工设计方法相比,设计周期可缩短70%以上,材料用量、结构设计指标接近人工设计结果。

基于BIM和物联网技术的智慧工地平台在LNG码头施工中的应用

针对LNG(液化天然气)码头工程施工中存在的安全风险系数大、人员管控要求高、安装难度高、工期进度紧等问题,开展基于BIM和物联网技术在智慧工地平台的研究与应用。通过采用建立BIM技术与物联网技术融合的智慧工地管理方法,深化创建施工BIM模型,结合相应的智能化设备,实现模型与现场数据接入和集成的应用,解决了施工过程管控难、数据信息源集成多、劳务人员现场管理难等典型LNG码头工程施工问题。通过将BIM技术与智慧工地平台在施工阶段融合应用,有效提升了LNG码头工程建设项目的施工质量和技术管理水平。研究旨在为LNG码头领域的工程建设提供有益的经验和借鉴,推动智慧工地平台在该领域的应用和发展。

基于CNN-BiGRU-Attention的短期电力负荷预测

针对目前电力负荷数据随机性强,影响因素复杂,传统单一预测模型精度低的问题,结合卷积神经网络(Convolutional neural network,CNN)、双向门控循环单元(Bi-directional gated recurrent unit,BiGRU)以及注意力机制(Attention)在短期电力负荷预测上的不同优点,提出一种基于CNN-BiGRU-Attention的混合预测模型。该方法首先通过CNN对历史负荷和气象数据进行初步特征提取,然后利用BiGRU进一步挖掘特征数据间时序关联,再引入注意力机制,对BiGRU输出状态给与不同权重,强化关键特征,最后完成负荷预测。试验结果表明,该模型的平均绝对百分比误差(Mean absolute percentage error,MAPE)、均方根误差(Root mean square error,RMSE)、判定系数(R-square,R~2)分别为0.167%、0.057%、0.993,三项指标明显优于其他模型,具有更高的预测精度和稳定性,验证了模型在短期负荷预测中的优势。

老年腹部手术患者BIS监测下异丙酚闭环与开环靶控输注模式的麻醉效果比较

目的探讨脑电双频指数(BIS)监测下异丙酚闭环与开环靶控输注在老年腹部手术患者中的麻醉效果。方法将76例老年腹部手术患者按照随机数字表法分为对照组38例[采用异丙酚开环靶控输注(OLTCI)]和研究组38例[采用异丙酚闭环靶控输注(CLTCI)]。比较两组患者的麻醉效果、麻醉药物使用情况、不同时间阶段血流动力学指标以及手术相关指标。结果在麻醉诱导阶段和麻醉维持阶段,研究组患者麻醉满意时间占比高于对照组,麻醉过深时间占比低于对照组,且麻醉维持阶段的麻醉过浅时间占比低于对照组(P<0.05或0.01)。研究组患者异丙酚诱导总量少于对照组,每小时泵调整次数多于对照组(P<0.01)。两组患者意识消失、插管即刻、插管后1 min时的心率(HR)均慢于麻醉诱导前,但研究组患者意识消失、插管即刻、插管后1 min时的HR均快于对照组(P<0.05或0.01)。研究组患者诱导阶段各时期的平均动脉压(MAP)水平无明显变化(P>0.05)。对照组患者意识消失、插管即刻、插管后1 min时的MAP水平均低于麻醉诱导前,且插管即刻、插管后1 min时的MAP水平低于研究组(P<0.05或0.01)。两组患者手术时间、麻醉时间、拔管时间、苏醒时间以及恶心、呕吐发生率等手术相关指标比较,差异无统计学意义(P>0.05)。结论对老年腹部手术患者行BIS监测下异丙酚CLTCI时,可按照BIS的数值及时调整异丙酚用量,避免了血流动力学指标的大幅度波动,可减少麻醉药物的用量,值得临床大力推广。

融合改进Bi-RRT和DWA算法的无人机动态路径规划

为解决无人机在复杂环境中的避障问题,提出一种融合改进Bi-RRT和DWA的无人机动态路径规划算法。通过设置启发式函数、动态步长和安全距离改进Bi-RRT算法,提升全局路径的搜索效率和安全性;之后,修剪路径中的冗余路段并对修剪后的路径进行插值与平滑操作获得全局最优路径;对DWA修正障碍物距离评价函数并引入目标点距离评价函数,提升局部预测轨迹评分的准确性;然后,实时输出速度指令控制无人机跟踪全局最优路径并实现局部动态避障。仿真实验表明,改进Bi-RRT算法生成的路径更短更平滑、安全性更高且规划时间更少;在同时存在动、静态障碍物的复杂环境中,所提融合算法能控制无人机精准地跟踪全局最优路径并高效地完成局部动态避障。

基于Inception-BiLSTM和迁移学习的结构损伤识别

针对传统卷积神经网络(convolutional neural network,CNN)方法在时空特征提取存在不足,提出了一种改进的Inception与双向长短期记忆(bi-directional long short-term memory,BiLSTM)联合模型,以全面学习振动信号中的空间和时序信息。首先,构建具有多尺度感受野的Inception模块,自适应地提取不同尺度下的空间特征;其次,BiLSTM序列化处理时间特征,以深度挖掘时间相关性;最后,通过全局平均池化和Softmax分类器来实现钢框架结构的损伤识别。为评估该模型对噪声的鲁棒性,引入高斯白噪声作为干扰。此外,采用迁移学习策略来评估模型在不同强度激励和小样本下的泛化能力,确保适用于不同的损伤识别任务。结果表明,与传统的CNN方法相比,该模型在无噪声条件下及信噪比超过25 dB时保持了100%的识别精度。该方法解决了土木工程应用中样本量不足和不同强度激励的实际挑战。通过微调预训练模型的参数,实现了在不同强度激励和小样本情况下的知识迁移与泛化,从而增强了模型的实际适用性。

基于特征分解与Bi-LSTM-Attention模型的风向预测

为便于精准控制风电机组的偏航角度、充分利用风能提高机组发电量,提出一种基于历史数据深度学习的风向超短期预测方法。首先利用变分模态分解将风向数据分解成多个子序列,考虑分解后的残差分量仍保留大量信号特征,进一步采用自适应噪声完备集合经验模态分解方法对残差分量进行二次分解。在此基础上,结合风速、环境温度等特征,利用具有注意力机制的双向长短期记忆网络对风向进行超短期预测。采用河北某风电场SCADA真实数据,对风向进行5min的超短期预测实验,并与其他方法进行对比,结果表明所提方法具有更好的风向预测效果。