Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.

Research and Practice on the Multi-dimensional Course Goal Achievement Evaluation of Software Engineering

According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteristics of hybrid teaching in Kunming University of Science and Technology.Then a multi-dimensional evaluation system for course goal achievement of software engineering is proposed.The practice’s results show that the multi-dimensional course goal achievement evaluation is helpful to the continuous improvement of course teaching,which can effectively support the evaluation of graduation outcomes.

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

Envelope Method and More General New Global Structures of Solutions for Multi-dimensional Conservation Law

For the two-dimensional(2D)scalar conservation law,when the initial data contain two different constant states and the initial discontinuous curve is a general curve,then complex structures of wave interactions will be generated.In this paper,by proposing and investigating the plus envelope,the minus envelope,and the mixed envelope of 2D non-selfsimilar rarefaction wave surfaces,we obtain and the prove the new structures and classifications of interactions between the 2D non-selfsimilar shock wave and the rarefaction wave.For the cases of the plus envelope and the minus envelope,we get and prove the necessary and sufficient criterion to judge these two envelopes and correspondingly get more general new structures of 2D solutions.

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.

Recent Ventures in Interdisciplinary Arctic Research:The ARCPATH Project

This paper celebrates Professor Yongqi GAO's significant achievement in the field of interdisciplinary studies within the context of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies-ARCPATH(https://www.svs.is/en/projects/finished-projects/arcpath). The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers. ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities.

关于现阶段我国英文期刊办刊模式的思考——以Research等期刊为例

近年来,国内兴起了创办英文科技期刊热潮。本文以此为视角,分析了我国创办英文期刊现状及问题。通过查阅文献和新闻报道,以中国科协和美国科学促进会合作共同创办的英文期刊Research等近年来国内创办的高水平英文新刊为例,详细分析了案例从创刊到快速发展成为高影响力科技期刊的路径,主要包括选择办刊人才、明确办刊定位、组建编委会、邀约优质稿源、加大媒体宣传等,以期为正在筹备创办新刊的同行提供借鉴,也为相关部门决策提供建议参考。

Can ChatGPT evaluate research quality?

Purpose:Assess whether ChatGPT 4.0 is accurate enough to perform research evaluations on journal articles to automate this time-consuming task.Design/methodology/approach:Test the extent to which ChatGPT-4 can assess the quality of journal articles using a case study of the published scoring guidelines of the UK Research Excellence Framework(REF)2021 to create a research evaluation ChatGPT.This was applied to 51 of my own articles and compared against my own quality judgements.Findings:ChatGPT-4 can produce plausible document summaries and quality evaluation rationales that match the REF criteria.Its overall scores have weak correlations with my self-evaluation scores of the same documents(averaging r=0.281 over 15 iterations,with 8 being statistically significantly different from 0).In contrast,the average scores from the 15 iterations produced a statistically significant positive correlation of 0.509.Thus,averaging scores from multiple ChatGPT-4 rounds seems more effective than individual scores.The positive correlation may be due to ChatGPT being able to extract the author’s significance,rigour,and originality claims from inside each paper.If my weakest articles are removed,then the correlation with average scores(r=0.200)falls below statistical significance,suggesting that ChatGPT struggles to make fine-grained evaluations.Research limitations:The data is self-evaluations of a convenience sample of articles from one academic in one field.Practical implications:Overall,ChatGPT does not yet seem to be accurate enough to be trusted for any formal or informal research quality evaluation tasks.Research evaluators,including journal editors,should therefore take steps to control its use.Originality/value:This is the first published attempt at post-publication expert review accuracy testing for ChatGPT.

Research funding and citations in papers of Nobel Laureates in Physics, Chemistry and Medicine, 2019-2020

Purpose:The goal of this study is a comparative analysis of the relation between funding(a main driver for scientific research)and citations in papers of Nobel Laureates in physics,chemistry and medicine over 2019-2020 and the same relation in these research fields as a whole.Design/methodology/approach:This study utilizes a power law model to explore the relationship between research funding and citations of related papers.The study here analyzes 3,539 recorded documents by Nobel Laureates in physics,chemistry and medicine and a broader dataset of 183,016 documents related to the fields of physics,medicine,and chemistry recorded in the Web of Science database.Findings:Results reveal that in chemistry and medicine,funded researches published in papers of Nobel Laureates have higher citations than unfunded studies published in articles;vice versa high citations of Nobel Laureates in physics are for unfunded studies published in papers.Instead,when overall data of publications and citations in physics,chemistry and medicine are analyzed,all papers based on funded researches show higher citations than unfunded ones.Originality/value:Results clarify the driving role of research funding for science diffusion that are systematized in general properties:a)articles concerning funded researches receive more citations than(un)funded studies published in papers of physics,chemistry and medicine sciences,generating a high Matthew effect(a higher growth of citations with the increase in the number of papers);b)research funding increases the citations of articles in fields oriented to applied research(e.g.,chemistry and medicine)more than fields oriented towards basic research(e.g.,physics).Practical implications:The results here explain some characteristics of scientific development and diffusion,highlighting the critical role of research funding in fostering citations and the expansion of scientific knowledge.This finding can support decision-making of policymakers and R&D managers to improve the effectiveness in allocating financial resources in science policies to generate a higher positive scientific and societal impact.

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

Global trends in diabetic eye disease research from 2012 to 2021

Diabetic eye disease refers to a group of eye complications that occur in diabetic patients and include diabetic retinopathy, diabetic macular edema, diabetic cataracts, and diabetic glaucoma. However, the global epidemiology of these conditions has not been well characterized. In this study, we collected information on diabetic eye disease-related research grants from seven representative countries––the United States, China, Japan, the United Kingdom, Spain, Germany, and France––by searching for all global diabetic eye disease journal articles in the Web of Science and Pub Med databases, all global registered clinical trials in the Clinical Trials database, and new drugs approved by the United States, China, Japan, and EU agencies from 2012 to 2021. During this time period, diabetic retinopathy accounted for the vast majority(89.53%) of the 2288 government research grants that were funded to investigate diabetic eye disease, followed by diabetic macular edema(9.27%). The United States granted the most research funding for diabetic eye disease out of the seven countries assessed. The research objectives of grants focusing on diabetic retinopathy and diabetic macular edema differed by country. Additionally, the United States was dominant in terms of research output, publishing 17.53% of global papers about diabetic eye disease and receiving 22.58% of total citations. The United States and the United Kingdom led international collaborations in research into diabetic eye disease. Of the 415 clinical trials that we identified, diabetic macular edema was the major disease that was targeted for drug development(58.19%). Approximately half of the trials(49.13%) pertained to angiogenesis. However, few drugs were approved for ophthalmic(40 out of 1830;2.19%) and diabetic eye disease(3 out of 1830;0.02%) applications. Our findings show that basic and translational research related to diabetic eye disease in the past decade has not been highly active, and has yielded few new treatment methods and newly approved drugs.

New roles of research data infrastructure in research paradigm evolution

Research data infrastructures form the cornerstone in both cyber and physical spaces,driving the progression of the data-intensive scientific research paradigm.This opinion paper presents an overview of global research data infrastructure,drawing insights from national roadmaps and strategic documents related to research data infrastructure.It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem.The four new missions of research data infrastructures are:(1)as a pioneer,to transcend the disciplinary border and address complex,cutting-edge scientific and social challenges with problem-and data-oriented insights;(2)as an architect,to establish a digital,intelligent,flexible research and knowledge services environment;(3)as a platform,to foster the high-end academic communication;(4)as a coordinator,to balance scientific openness with ethics needs.

Brain Research on Mental Disorders: A Criticism

Objective: To expose the problems and inherent limitations of neuroscience-based brain research on mental disorders. Method: Discussion of the theory underlying brain research on mental disorders, followed by a systematic evaluation of typical studies. Results: The fundamental problem is that brain researchers fail to differentiate between biological mental disorders in which brain processes cause the disorder (notably schizophrenia, bipolar disorder, and melancholic depression) and learned mental disorders in which brain processes mediate but do not cause the disorder (which is the case with reactive depression, reactive anxiety, OCD, and PTSD). Researchers have been unsuccessful in identifying mechanisms in the brain that cause biological mental disorders, and will never be able to locate the innumerable specific neural connections that mediate learned mental disorders. Moreover, the author’s review of typical studies in this field shows that they have serious problems with theory, measurement, and data analysis, and that their findings cannot be trusted. Conclusions: Neuroscience-based brain research on mental disorders, unlike other neurological research, has been an expensive failure and it is not worth continuing.

Global Scientific Trends in Research Related to Diabetic Sarcopenia from 1993 to 2023: A Bibliometric Analysis

Objectives: In this study, bibliometric approach was used to explore the literature in the field of research related to Diabetic Myasthenia Gravis in order to identify the current research progress in Diabetic Myasthenia Gravis related research and to help the researchers to predict the future hotspots in the field of research and to provide reference for the research. Methods: Literature related to diabetic sarcopenia published from 1993 to 2023 since the inception of the repository was extracted from the Web of Science Core Collection (WoSCC) and bibliometric analyses were performed. We have analysed the published literature of the last approximately almost 30 years, as well as publication and citation analyses from different countries, institutions, journals and authors. For keywords, we performed co-occurrence, clustering, timeline view and citation burst analysis. Results: On the basis of 1564 publications, we found a continuous increase in the number of publications and citations, especially in the last six years. The United States is the most representative country, and Seoul National University (SNU) is the most representative institution. The most popular journal in the field is Diabetes Care;Fukui, Michiaki is the most prolific author, leading many studies related to diabetic sarcopenia. The most frequently cited reference was a revised European consensus on the definition and diagnosis of sarcopenia;the most cited keywords were related to physiological factors of diabetes, sarcopenia and related conditions, such as “insulin resistance”, “skeletal muscle”, “body composition”, “risk” and “prevalence”. Conclusion: With more and more studies on the relationship between diabetic sarcopenia, this study presents the current status and trend of research related to diabetic sarcopenia over the past nearly 30 years through the visualization software CiteSpace. It can help researchers identify potential collaborators and partner institutions, hotspots and research frontiers in the field of diabetic sarcopenia. However, our work is only based on the English language literature in the WoSCC database, and for future studies, we recommend that researchers explore the literature from multiple databases to enhance the scope of their research.

Navigating interdisciplinary research:Historical progression and contemporary challenges

Interdisciplinary research plays a crucial role in addressing complex problems by integrating knowledge from multiple disciplines.This integration fosters innovative solutions and enhances understanding across various fields.This study explores the historical and sociological development of interdisciplinary research and maps its evolution through three distinct phases:pre-disciplinary,disciplinary,and post-disciplinary.It identifies key internal dynamics,such as disciplinary diversification,reorganization,and innovation,as primary drivers of this evolution.Additionally,this study highlights how external factors,particularly the urgency of World War II and the subsequent political and economic changes,have accelerated its advancement.The rise of interdisciplinary research has significantly reshaped traditional educational paradigms,promoting its integration across different educational levels.However,the inherent contradictions within interdisciplinary research present cognitive,emotional,and institutional challenges for researchers.Meanwhile,finding a balance between the breadth and depth of knowledge remains a critical challenge in interdisciplinary education.

Glucocorticoids-based prodrug design:Current strategies and research progress

Attributing to their broad pharmacological effects encompassing anti-inflammation,antitoxin,and immunosuppression,glucocorticoids(GCs)are extensively utilized in the clinic for the treatment of diverse diseases such as lupus erythematosus,nephritis,arthritis,ulcerative colitis,asthma,keratitis,macular edema,and leukemia.However,longterm use often causes undesirable side effects,including metabolic disorders-induced Cushing's syndrome(buffalo back,full moon face,hyperglycemia,etc.),osteoporosis,aggravated infection,psychosis,glaucoma,and cataract.These notorious side effects seriously compromise patients'quality of life,especially in patients with chronic diseases.Therefore,glucocorticoid-based advanced drug delivery systems for reducing adverse effects have received extensive attention.Among them,prodrugs have the advantages of low investment,low risk,and high success rate,making them a promising strategy.In this review,we propose the strategies for the design and summarize current research progress of glucocorticoid-based prodrugs in recent decades,including polymer-based prodrugs,dendrimer-based prodrugs,antibody-drug conjugates,peptide-drug conjugates,carbohydrate-based prodrugs,aliphatic acid-based prodrugs and so on.Besides,we also raise issues that need to be focused on during the development of glucocorticoid-based prodrugs.This review is expected to be helpful for the research and development of novel GCs and prodrugs.

Biobanks and biomarkers:Their current and future role in biomedical research

The importance and utility of biobanks has increased exponentially since their inception and creation.Initially used as part of translational research,they now contribute over 40%of data for all cancer research papers in the United States of America and play a crucial role in all aspects of healthcare.Multiple classification systems exist but a simplified approach is to either classify as population-based or disease-oriented entities.Whilst historically publicly funded institutions,there has been a significant increase in industry funded entities across the world which has changed the dynamic of biobanks offering new possibilities but also new challenges.Biobanks face legal questions over data sharing and intellectual property as well as ethical and sustainability questions particularly as the world attempts to move to a low-carbon economy.International collaboration is required to address some of these challenges but this in itself is fraught with complexity and difficulty.This review will examine the current utility of biobanks in the modern healthcare setting as well as the current and future challenges these vital institutions face.

Visual Analysis of Domestic and Foreign Building Brick Skin Research Based on Citespace

With the progress of the times and the leap of science and technology,the application of brick materials and the research on the brick skin in modern architectural design have shown a dual-track development trend of returning to tradition and innovation.Based on the core collection database resources of Web of Science and the CiteSpace visual analysis tool,this paper constructed and analyzed the spatio-temporal map of keyword co-occurrence network,cluster structure,mutation phenomenon,time course and regional distribution map of building brick skin research.The study revealed that in recent years,the research on brick materials has spanned the study of single material properties and extensively involved in the broad world of construction,especially in the integration of green energy-saving technology,the innovation of fine construction technology of brick skin,and the frontier exploration of digital technology in brick masonry,which has shown particularly significant research vitality and development potential.

Challenges and Suggestions of Ethical Review on Clinical Research Involving Brain-Computer Interfaces

Brain-computer interface(BCI)technology is rapidly advancing in medical research and application.As an emerging biomedical engineering technology,it has garnered significant attention in the clinical research of brain disease diagnosis and treatment,neurological rehabilitation,and mental health.However,BCI also raises several challenges and ethical concerns in clinical research.In this article,the authors investigate and discuss three aspects of BCI in medicine and healthcare:the state of international ethical governance,multidimensional ethical challenges pertaining to BCI in clinical research,and suggestive concerns for ethical review.Despite the great potential of frontier BCI research and development in the field of medical care,the ethical challenges induced by itself and the complexities of clinical research and brain function have put forward new special fields for ethics in BCI.To ensure"responsible innovation"in BCI research in healthcare and medicine,the creation of an ethical global governance framework and system,along with special guidelines for cutting-edge BCI research in medicine,is suggested.