Research evolution of metal organic frameworks: A scientometric approach with human-in-the-loop

Purpose:This paper reports on a scientometric analysis bolstered by human-in-the-loop,domain experts,to examine the field of metal-organic frameworks(MOFs)research.Scientometric analyses reveal the intellectual landscape of a field.The study engaged MOF scientists in the design and review of our research workflow.MOF materials are an essential component in next-generation renewable energy storage and biomedical technologies.The research approach demonstrates how engaging experts,via human-in-the-loop processes,can help develop a comprehensive view of a field’s research trends,influential works,and specialized topics.Design/methodology/approach:Ascientometric analysis was conducted,integrating natural language processing(NLP),topic modeling,and network analysis methods.The analytical approach was enhanced through a human-in-the-loop iterative process involving MOF research scientists at selected intervals.MOF researcher feedback was incorporated into our method.The data sample included 65,209 MOF research articles.Python3 and software tool VOSviewer were used to perform the analysis.Findings:The findings demonstrate the value of including domain experts in research workflows,refinement,and interpretation of results.At each stage of the analysis,the MOF researchers contributed to interpreting the results and method refinements targeting our focus Research evolution of metal organic frameworks:A scientometric approach with human-in-the-loop on MOF research.This study identified influential works and their themes.Our findings also underscore four main MOF research directions and applications.Research limitations:This study is limited by the sample(articles identified and referenced by the Cambridge Structural Database)that informed our analysis.Practical implications:Our findings contribute to addressing the current gap in fully mapping out the comprehensive landscape of MOF research.Additionally,the results will help domain scientists target future research directions.Originality/value:To the best of our knowledge,the number of publications collected for analysis exceeds those of previous studies.This enabled us to explore a more extensive body of MOF research compared to previous studies.Another contribution of our work is the iterative engagement of domain scientists,who brought in-depth,expert interpretation to the data analysis,helping hone the study.

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

This paper considers the human-in-the-loop leader-following consensus control problem of multi-agent systems(MASs)with unknown matched nonlinear functions and actuator faults.It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory.Moreover,the leader’s input is nonzero and not available to all followers.By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults,respectively,the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed.It is proved that the state of each follower can synchronize with the leader’s state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded.Finally,simulation results are presented for verifying the effectiveness of the proposed control method.

Quality-of-Experience in Human-in-the-Loop Haptic Communications

With the worldwide rapid development of 5 G networks, haptic communications, a key use case of the 5 G, has attracted increasing attentions nowadays. Its human-in-the-loop nature makes quality of experience(QoE) the leading performance indicator of the system design. A vast number of high quality works were published on user-level, application-level and network-level QoE-oriented designs in haptic communications. In this paper, we present an overview of the recent research activities in this progressive research area. We start from the QoE modeling of human haptic perceptions, followed by the application-level QoE management mechanisms based on these QoE models. High fidelity haptic communications require an orchestra of QoE designs in the application level and the quality of service(QoS) support in the network level. Hence, we also review the state-of-the-art QoS-related QoE management strategies in haptic communications, especially the QoS-related QoE modeling which guides the resource allocation design of the communication network. In addition to a thorough survey of the literature, we also present the open challenges in this research area. We believe that our review and findings in this paper not only provide a timely summary of prevailing research in this area, but also help to inspire new QoE-related research opportunities in haptic communications.

A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe.However,effectively analyzing this vast amount of data poses a significant challenge.In response,astronomers are turning to deep learning techniques,but these methods are limited by their specific training sets,leading to considerable duplicate workloads.To overcome this issue,we built a framework for the general analysis of galaxy images based on a large vision model(LVM)plus downstream tasks(DST),including galaxy morphological classification,image restoration object detection,parameter extraction,and more.Considering the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories,we designed our LVM to incorporate a Human-in-the-loop(HITL)module,which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively.The proposed framework exhibits notable fewshot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging Surveys.In particular,for the object detection task,which was trained using 1000 data points,our DST in the LVM achieved an accuracy of 96.7%,while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%.For morphological classification,to obtain an area under the curve(AUC)of~0.9,LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 requested.In addition,multimodal data can be integrated,which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.

Dynamic event-triggered-based human-in-the-loop formation control for stochastic nonlinear MASs

The dynamic event-triggered(DET)formation control problem of a class of stochastic nonlinear multi-agent systems(MASs)with full state constraints is investigated in this article.Supposing that the human operator sends commands to the leader as control input signals,all followers keep formation through network topology communication.Under the command-filter-based backstepping technique,the radial basis function neural networks(RBF NNs)and the barrier Lyapunov function(BLF)are utilized to resolve the problems of unknown nonlinear terms and full state constraints,respectively.Furthermore,a DET control mechanism is proposed to reduce the occupation of communication bandwidth.The presented distributed formation control strategy guarantees that all signals of the MASs are semi-globally uniformly ultimately bounded(SGUUB)in probability.Finally,the feasibility of the theoretical research result is demonstrated by a simulation example.

A Novel Design Framework for Smart Operating Robot in Power System

This paper proposes the concept and framework of smart operating system based on the artificial intelligence(AI)techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.

基于Selenium的中药数据集构建

为了帮助药学实现智能化,解决药学领域缺失有效的数据集的困境。本文提出了构建一个中药数据集,同时为应对数据获取过程中存在的障碍,本文提出基于Selenium构建中药数据集。值得提出的是,在数据标注过程中,本文引入一种人在环路(human-in-the-loop)的数据标注方式。本文提出一个包含6112张图片的中药识别数据集,使用这种半自动的标注模式能自动标注多达64%的数据。经过抽样检测,标注错误率仅为1.4%。

HiTL-based adaptive fuzzy tracking control of MASs:A distributed fixed-time strategy

Human-in-the-loop(HiTL)control is promising for the cooperative control problem of multi-agent systems(MASs)under the complicated environment.By considering the effect of human intelligence and decision making,the system robustness and security are notably enhanced.Hence,a distributed fixed-time tracking control problem is investigated in this paper for heterogeneous MASs based on the HiTL idea.First,a lemma of practically fixed-time stable is given where an explicit relationship of settling time and convergence domain is clearly shown.Then,under the framework of the adaptive backstepping approach,a series of modified intermediate control signals is designed to avoid the singularity problem by taking advantage of power transformation,fuzzy logic systems,and inequality schemes.Finally,the numerical example and comparison results are utilized to testify the effectiveness of the proposed method.

Hybrid Mapping Method:from Human to Robotic Hands with Dissimilar Kinematics

Mapping grasps from human to anthropomorphic robotic hands is an open issue in research,because the master hand and the slave hand have dissimilar kinematics.This paper proposes a hybrid mapping method to solve this problem.In the proposed method,fingers in the master and the slave hands are divided into vital and synergic fingers according to their contribution to the grasping task.The tip of the vital finger of the master hand is first mapped to that of the slave hand while ensuring that both are in simultaneous contact with the object to be grasped.Following postural synergy theory,joints of the other synergic fingers of the slave hand are then used to generate an anthropomorphic grasping configuration according to the shape of the object to be grasped.Following this,a human-guided impedance controller is used to reduce the pre-grasping error and realize compliant interaction with the environment.The proposed hybrid mapping method can not only generate the posture of the humanoid envelope but can also carry out impedance-adaptive matching.It was evaluated using simulations and an experiment involving an anthropomorphic robotic slave hand.

S-ProvFlow.Storing and Exploring Lineage Data as a Service

We present a set of configurable Web service and interactive tools,s-ProvFlow,for managing and exploiting records tracking data lineage during workflow runs.It facilitates detailed analysis of single executions.It helps users manage complex tasks by exposing the relationships between data,people,equipment and workflow runs intended to combine productively.Its logical model extends the PROV standard to precisely record parallel data-streaming applications.Its metadata handling encourages users to capture the application context by specifying how application attributes,often using standard vocabularies,should be added.These metadata records immediately help productivity as the interactive tools support their use in selection and bulk operations.Users rapidly appreciate the power of the encoded semantics as they reap the benefits.This improves the quality of provenance for users and management.Which in turn facilitates analysis of collections of runs,enabling users to manage results and validate procedures.It fosters reuse of data and methods and facilitates diagnostic investigations and optimisations.We present S-ProvFlow's use by scientists,research engineers and managers as part of the DARE hyper-platform as they create,validate and use their data-driven scientific workflows.

Impact of occupant autonomy on satisfaction and building energy efficiency

The philosophy of building energy management is going through a paradigm change from traditional,often inefficient,user-controlled systems to one that is centrally automated with the aid of IoT-enabled technologies.In this context,occupants’perceived control and building automation may seem to be in conflict.The inquiry of this study is rooted in a proposition that while building automation and centralized control systems are assumed to provide indoor comfort and conserve energy use,limiting occupants’control over their work environment may result in dissatisfaction,and in turn decrease productivity.For assessing this hypothesis,data from the post-occupancy evaluation survey of a smart building in a university in Australia was used to analyze the relationships between perceived control,satisfaction,and perceived productivity.Using structural equation modeling,we have found a positive direct effect of occupants’perceived control on overall satisfaction with their working area.Meanwhile,perceived control exerts an influence on perceived productivity through satisfaction.Furthermore,a field experiment conducted in the same building revealed the potential impact that occupant controllability can have on energy saving.We changed the default light settings from automatic on-and-offto manual-on and automatic-off,letting occupants choose themselves whether to switch the light on or not.Interestingly,about half of the participants usually kept the lights off,preferring daylight in their rooms.This also resulted in a reduction in lighting electricity use by 17.8%without any upfront investment and major technical modification.These findings emphasize the important role of perceived control on occupant satisfaction and productivity,as well as on the energy-saving potential of the user-in-the-loop automation of buildings.