Reviewerly/exHarmony huggingface dataset:
Balance optimization method of energy shipping based on Hopfield neural network

It is of great significance for the optimization of transportation strategy to study the methods of energy shipping scheduling. Based on the Hopfield neural network (HNN) theory, this paper proposes Hopfield neural network energy transportation path optimization algorithm with improved activation function, which solves the problems of poor mapping ability, low flexibility and high sensitivity of neurons near zero to input of traditional activation function. The improved activation function can reduce the derivative value of the activation function and the sensitivity of neurons near zero to the input value by flexibly adjusting the steepness, positioning and mapping range of energy sea transportation at the same time. The experimental results show that: under the same initial conditions, the model proposed in this paper shows better error code performance, and the data length of the required transmission sequence is shorter. When sending 8PSK (Phase Shift Keying) signal with a data length of N = 40, the error rate of the 2-path synthesized random channel is less than 0.112. The error code performance and convergence speed of the energy shipping path planning algorithm are improved effectively.
Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data

This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments.
Building compact superconducting microwave resonators with Hilbert space-filling curves

Superconducting quantum computing is currently one of the most promising platforms for universal quantum information processing. The readout resonator is an essential integral part of a superconducting qubit, while its size is much larger compared to the Josephson junction. We propose and realize a new readout resonator using space-filling curves, specifically Hilbert space-filling curves. We introduce the frequency analysis method and demonstrate a qubit sample, in which the Hilbert-space-filling-curves resonator (HSFCR) is used to read out the qubit states. We also propose to fabricate the HSFCRs and Josephson junctions simultaneously in the same processes of E-beam lithography and E-beam evaporation. Our design reduces the resonator area sufficiently and, thus, will help to improve the integration of superconducting qubits, as well as to design other superconducting quantum devices.
Optimizing the propagation of mesoscopic twin-beam states for novel quantum communication protocols

Nowadays, optical Quantum Communication has reached a mature level, especially in free space and through optical fibers. Experimental implementations usually involve single-photon states or weak coherent states detected by single-photon detectors. In contrast to these standard configurations, in our work we consider mesoscopic twin-beam (TWB) states revealed by photon-number-resolving detectors. By properly acting on the natural divergence of the generated beams, we show that they remain nonclassical even at a moderate distance from the nonlinear crystal in which they are produced. We also consider the case where one of the two parties of TWB is partially transmitted through water, and show that the nonclassicality of the states is preserved. This result suggests that mesoscopic TWB states can be considered for the new and growing scenario of underwater Quantum Communication.
Synchronous Condenser-Based Intelligent Question Answering System Based on Knowledge Graph

As a human-recognizable and machine-friendly knowledge representation, knowledge graphs have been widely used in recent years. However, in the field of synchronous condenser adjustment, the construction and application of related knowledge graphs are seldom. Synchronous modulator is a synchronous motor in a special operating state. When applied to a power system, it can automatically increase reactive power output when the grid voltage drops according to the needs of the system. It is very necessary to develop a related synchronous condenser intelligent question and answer system. The question-answering system is mainly to solve the on-site operation and maintenance needs, on-site troubleshooting, fault diagnosis, and the processing mechanism after the system alarms. This paper proposes and develops an intelligent question answering system based on knowledge graph, which is the first research work of question answering system in the field of synchronous condenser adjustment.
Over Three Decades of Data Envelopment Analysis Applied to the Measurement of Efficiency in Higher Education: A Bibliometric Analysis

The higher education efficiency evaluation model using the data envelopment analysis method has interested many researchers. This paper uses bibliometric analysis on publications extracted from the Scopus database to provide a comprehensive overview of research publications on the measurement of higher education efficiency based on data envelopment analysis: its growth rate, major collaboration networks, the most important and popular research topic. A total of 169 related publications were collected and analyzed from 1988 to 2021. The analysis results show that: Publications published every year have increased sharply in the last six years; The quality of publications is relatively high as publications tend to be published in journals with high-ranking indexes; Countries with the most influence in studies on this topic are: Italy, China, Spain, the USA, and the United Kingdom; Authors with the most influence in this research direction are Agasisti T., Abbott M., Doucouliagos C., Avkiran N.K., and Johnes J.; The research cooperation among countries and among affiliations is not strong. Finally, the paper has provided recommendations for future studies based on the findings.
Data Forecasting with Application to Blockchain-based Local Energy Markets

The creation of distributed energy generation that eliminates conventional differences between energy producers and consumers creates a new role called prosumer. This study aims at deploying a general ABM simulation framework to facilitate electricity exchange and demonstrate the functionality of blockchain. The simulation involved a Transactive Energy Distributed Energy Resource in a block chain dependent robust multi-agent structure. The LEM proposal based on blockchain uses auction system to balance supply and demand. Prediction error impacts on market outcomes were reduced by the LTSM model. The prediction procedure works on blockchain based LEM with adjusted prediction procedure. In ten real-world household power consumption datasets, the proposed hybrid deep learning neural network outperforms the state-of-the-art methods. To complement the proposed framework for actual application use, we additionally offer a k-step power consumption forecasting technique. This research offers a scalable environment for analyzing an energy blockchain from the perspective of Qatari society, finance, and technology.
Automatic Method for Vickers Hardness Estimation by Image Processing

Hardness is one of the most important mechanical properties of materials, since it is used to estimate their quality and to determine their suitability for a particular application. One method of determining quality is the Vickers hardness test, in which the resistance to plastic deformation at the surface of the material is measured after applying force with an indenter. The hardness is measured from the sample image, which is a tedious, time-consuming, and prone to human error procedure. Therefore, in this work, a new automatic method based on image processing techniques is proposed, allowing for obtaining results quickly and more accurately even with high irregularities in the indentation mark. For the development and validation of the method, a set of microscopy images of samples indented with applied forces of 5N and 10N on AISI D2 steel with and without quenching, tempering heat treatment and samples coated with titanium niobium nitride (TiNbN) was used. The proposed method was implemented as a plugin of the ImageJ program, allowing for obtaining reproducible Vickers hardness results in an average time of 2.05 seconds with an accuracy of 98.3% and a maximum error of 4.5% with respect to the values obtained manually, used as a golden standard.
Artificial Intelligence Applications in English Language Teaching: A Short Survey

Artificial intelligence is one of the most popular and influential sciences in many fields. It works continuously to contemporise computer systems to operate with high efficiency and to think like what a human think. In addition, this science seeks to make the work of the machine simulate the work of the human brain in thinking and making decisions, according to the environment in which they live. Therefore, it has become necessary to have artificial intelligence applications in all areas, including education, especially the English language teaching electronically. In this regard, the most influential applications and programs that contribute to the development of teaching English electronically and their effectiveness in developing e-learning will be reviewed. This article concluded that there are applications of artificial intelligence in teaching English electronically, which are of great importance and a great future in the development of language teaching.
A balanced allocation method of English MOOC teaching resources based on QoS constraints

In order to overcome the problems of long allocation time and high bandwidth consumption in traditional resource allocation methods, this paper proposes a new balanced allocation method of English MOOC teaching resources based on QoS constraints. With the support of big data and cloud computing technology, MOOC English teaching resources are mined and collected, and the resource balanced allocation model is constructed by using MMPs algorithm. The resource balanced allocation of virtual machine is realised through the selection of QoS constraint parameters, physical resource mapping and QESA resource allocation system. On the basis of experiments, the proposed method has the advantages of balanced resource allocation and execution time.
Mixed Reality and IoT for Physical Rehabilitation

This article describes a solution for physical rehabilitation of upper members using hot technologies such as mixed reality (MR) and Internet of Things (IoT). Serious games were developed in Unity for MR scenario using Meta Quest 2 as MX interface, and smart sensors inserted onto a pair of gloves that are used to perform real-time measurements. The smart sensors based on Arduino Nano boards that receive the data from measurement channels that include finger force and finger flexion sensors. The smart sensors are connected through Bluetooth with the MR headset. The obtained data is sent to a cloud data-base created in Amazon RDS. The communications with the database are done using .php scripts allocated on a virtual machine created by Amazon EC2. The obtained data is then observable on a cross-platform application for smartphones where the user can visualize the exercise data and create new users. Experimental results and discussions are included in the paper as part of system validation.
Multi-contrast High Quality MR Image Super-Resolution with Dual Domain Knowledge Fusion

Multi-contrast high quality high-resolution (HR) Magnetic Resonance (MR) images enrich available information for diagnosis and analysis. Deep convolutional neural network methods have shown promising ability for MR image super-resolution (SR) given low-resolution (LR) MR images. Methods taking HR images as references (Ref) have made progress to enhance the effect of MR images SR. However, existing multi-contrast MR image SR approaches are based on contrasting-expanding backbones, which lose high frequency information of Ref image during downsampling. They also failed to transfer textures of Ref image into target domain. In this paper, we propose Edge Mask Transformer UNet (EMFU) for accelerating MR images SR. We propose Edge Mask Transformer (EMF) to generate global details and texture representation of target domain. Dual domain fusion module in UNet aggregates semantic information of the representation and LR image of target domain. Specifically, we extract and encode edge masks to guide the attention in EMF by re-distributing the embedding tensors, so that the network allocates more attention to image edge area. We also design a dual domain fusion module with self-attention and cross-attention to deeply fuse semantic information of multiple protocols for MRI. Extensive experiments show the effectiveness of our proposed EMFU, which surpasses state-of-the-art methods on benchmarks quantitatively and visually. Codes will be released to the community.
A Flower Pollination Optimization Algorithm Based on Cosine Cross-Generation Differential Evolution

The flower pollination algorithm (FPA) is a novel heuristic optimization algorithm inspired by the pollination behavior of flowers in nature. However, the global and local search processes of the FPA are sensitive to the search direction and parameters. To solve this issue, an improved flower pollination algorithm based on cosine cross-generation differential evolution (FPA-CCDE) is proposed. The algorithm uses cross-generation differential evolution to guide the local search process, so that the optimal solution is achieved and sets cosine inertia weights to increase the search convergence speed. At the same time, the external archiving mechanism and the adaptive adjustment of parameters realize the dynamic update of scaling factor and crossover probability to enhance the population richness as well as reduce the number of local solutions. Then, it combines the cross-generation roulette wheel selection mechanism to reduce the probability of falling into the local optimal solution. In comparing to the FPA-CCDE with five state-of-the-art optimization algorithms in benchmark functions, we can observe the superiority of the FPA-CCDE in terms of stability and optimization features. Additionally, we further apply the FPA-CCDE to solve the robot path planning issue. The simulation results demonstrate that the proposed algorithm has low cost, high efficiency, and attack resistance in path planning, and it can be applied to a variety of intelligent scenarios.
Separate the primary aberration for the deformation error of optical flat in 300-mm-aperture vertical Fizeau interferometer

As a key component in a phase-shifting interferometer, the optical flat is often affected by supporting methods and generates deformation. In this paper, we propose a method to separate the deformation error through rotation measurement and Zernike polynomial fitting. We studied the principles, simulations, and experiments of this method. First, a support model is established in Comsol, and the deformation of the optical flat is analyzed. Then, the numerical simulation of the separation method for the deformation is performed, and the corresponding experiment is carried out on a Φ300 mm vertical Fizeau interferometer to verify the correctness of the method. The results show that numerical calculation and experimental measurement results are in good agreement, the difference between the PV value and the RMS value are only 0.004λ and 0.002λ, respectively. Finally, the error term that affected the calculation accuracy was analyzed. This proves that the proposed method can effectively separate the deformation introduced by the support.
Blockchain in Healthcare: A Systematic Review and Future Perspectives

Healthcare, being a basic necessity and a fundamental human right, is a paramount discipline of research today. During the past decade, blockchain technology has gained an increased interest from academic scholars for its use in the healthcare sector. Applications such as healthcare supply chain, identification of fake drugs, managing and securing electronic health records, managing patient appointments with healthcare providers, regular patient monitoring using various tools, and telemedicine come into picture with respect to healthcare. A thorough survey of related research works in this domain is required in order to gain insights into the future ideas and recommendations for the use of blockchain technology in the healthcare sector. In this book chapter, we aim to provide an exhaustive systematic review of research publications related to smart healthcare applications by the use of blockchain technology. With a thorough review and study of the research works selected by systematic approach, this book chapter aims 212to answer a set of research questions focusing on blockchain platforms, consensus methods, smart contracts, system evaluation methods, and their drawbacks. A tabular comparison of all the research articles on the basis of the research questions is provided. In the end, we give our observations of the challenges and limitations of the research works under this study, and their possible solutions. Thus, we reach up to a future scope of blockchain technology in healthcare applications.
Classification of Medical Image Notes for Image Labeling by Using MinBERT

The lack of labeled image data poses a serious challenge to the application of artificial intelligence (AI) in medical image diagnosis. Medical image notes contain valuable patient information that could be used to label images for machine learning tasks. However, most image note texts are unstructured with heterogeneity and short-paragraph characters, which fail traditional keyword-based techniques. We utilized a deep learning approach to recover missing labels for medical image notes automatically by using a combination of deep word embedding and deep neural network classifiers. Bidirectional encoder representations from transformers trained on medical image notes corpus (MinBERT) were proposed. We applied the proposed techniques to two typical classification tasks: Medical image type identification and clinical diagnosis identification. The two methods significantly outperformed baseline methods and presented high accuracies of 99.56% and 99.72% in image type identification and of 94.56% and 92.45% in clinical diagnosis identification. Visualization analysis further indicated that word embedding could efficiently capture semantic similarities and regularities across diverse expressions. Results indicated that our proposed framework could accurately recover the missing label information of medical images through the automatic extraction of electronic medical record information. Hence, it could serve as a powerful tool for exploring useful training data in various medical AI applications.
Palliative Care Is Proven

Palliative care was officially recognized by the World Health Organization in 1990 as a distinct specialty dedicated to relieving suffering and improving quality of life for patients with serious illnesses. Journal of Palliative Medicine (JPM) was founded in 1997 in response to the need for a scientifically rigorous peer-reviewed journal to advance the field. In our first quarter of the century, JPM has become a leading global peer-reviewed scientific journal. What is the way forward? We engaged with this question in two ways. First, we utilized artificial intelligence techniques to analyze the trends of the articles published in JPM for the past two decades to discern key topic themes. Second, we applied human intelligence by convening seven panels of experts to discuss current topics of interest to the field as a separate strategy for discerning the future. Taken together, the way forward is clear. The field of palliative care has become broader and more subspecialized than anyone ever imagined at the beginning. The expansion of new knowledge has accelerated in all directions from its origins in the end-of-life care of patients with cancer. Although implementation science is of paramount importance, the barriers to implementation of this growing body of specialized knowledge lie not just with the need for more science. Some of the barriers lie within our field. The way forward requires confidence in what we know and the establishment of new collaborations outside of our field, including with people outside of traditional health care.
How does interactive virtual reality enhance learning outcomes via emotional experiences? A structural equation modeling approach

Introduction Interest in interactive virtual reality (IVR) is increasing due to its potential for embodied learning and group-led teaching. However, few studies have investigated the internal mechanism by which IVR technology features and learning experiences affect learning outcomes in terms of psychological and emotional value. Based on media technology models and the control value theory of achievement emotions (CVTAE), this study uses structural equation modeling (SEM) to investigate the correlations among the internal elements of IVR technology features, learning experiences, and learning outcomes. It also emphasizes the role played by emotional experience in this context. Methods The sample referenced by this study consisted of 480 college students (193 males) who were simultaneously engaged in guided inquiry and learning in an IVR-based COVID-19 pandemic science museum in groups of 10. Results The findings suggest that presence and perceived enjoyment have a key mediating effect on the relationship between virtual reality (VR) features and perceived learning outcomes in an IVR-based learning simulation. In addition, the results indicate that presence is more strongly correlated with perceived learning effects, while enjoyment is more strongly correlated with learning satisfaction. Discussion These findings provide intellectual support and theoretical backing for VR-based instructional design and environmental development. Moreover, this study has practical value with regard to the future large-scale application of IVR to experiential teaching, group-led teaching, and the promotion of the digital transformation and intelligence upgrading in education.