Multiple Intelligences——a Disputable and Utilizable theory

According to the brief understanding of Multiple Intelligences Theory, the author states that the eight intelligences should be awaked and utilized in teaching and reality. The teacher's job is to observe his students, diagnose his students and offer a prescription for their educational problems through exerting all his multiple intelligences skills.

Application of New Concept for Multiple Intelligences Calculation for Personality and Social Groups Comparison Research

A person is considered as information-energy system with a host of feedbacks. The possibility of determining the statistical characteristics in a multiple intelligences profile of various social groups’ representatives using the vibraimage technology is investigated. Theft and alcohol abuse have been chosen as examples of significant social problems including deviant behavior and the trigger of formation of various socially vulnerable groups. The comparative analysis of conscious and unconscious attitudes in multiple intelligences structure of individuals prone to deviant behavior and the control group allows differentiating professional preferences and the impact of society on different social groups.

Automobile Industry Ushers in Reform and Internet of Intelligences

In recent years, electric vehicles have become popular among national governments, sectors and the general public. During January to October of 2017, the production output of China＇s new energy vehicles had surpassed 0.5 million units,

Using Multiple Intelligences Approach in Designing Classroom Activities

In 1983, Horward Gardner, a professor of education at Harvard Universiy introduced the theory of Multiple Intelligence. This theory states that people have a unique blend of capabilities and skills （intelligences）. In the classroom context, it implies that students possess different kinds of minds and therefore learn, remember, perform, and understand in different ways. Based on the theory, Gardner challenged the previous practices that gauged intelligence using IQ test which focused more on the ability to use language and/or logic. According to him, the measurement of people＇s intelligence should not be limited to linguistic and logical-mathematical abilities only, but should also cover other abilities. In addition to language and logic, Gardner proposed six other intelligences, which in the past were known as aptitudes or interests, namely： Visual-Spatial Intelligence, Musical Intelligence, Bodily-Kinesthetic Intelligence, Intrapersonal Intelligence, Interpersonal Intelligence, and Naturalist Intelligence. As an English teacher, we often have to deal with the fact that our classes consist of students who posses different intelligences. To help the students reach their maximum learning potential, teacher needs to respect students＇ uniqueness and accommodate the variety of intelligences. This can be done by applying multiple intelligences approach in designing classroom activities.

Research on Novel and Innovative Music Education Model based on Multiple Intelligences Theory and New Media

With the rapid development of new media and education theories, the combination of the two aspects is urgently needed. In this paper, we conduct theoretical analysis on novel and innovative music education model based on multiple intelligences theory and new media. The theory of multiple intelligence education concept is to change too pay attention to the tendency of knowledge. The novelty of new media, not only in the form of new information carrier and communication channel, also include technological progress brings new propagation mode. Our proposed combined model will enhance the performance of the current teaching activity which is meaningful.

Research on the Sports Teaching Reform in Colleges and Universities based on Theory of Multiple Intelligences and Interactive Teaching

In this paper, we conduct research on the sports teaching reform in colleges and universities based on theory of the multiple intelligences and interactive teaching. In colleges and universities curriculum reform as an opportunity to introduce outward bound training in colleges and universities sports curriculum, not only can make the college students in the smooth finish school and get good physical and psychological education, and to the healthy positive attitude in the face of social reality, rational choosing, reasonable employment, by providing the best social services, fully realize their social values and personal goals to obtain satisfactory teaching effect. We adopt primary advantage of the multiple intelligences and interactive teaching model to form the novel revised teaching pattern for the college PE classes which will holds significant meaning.

Trying the Theory of Multiple Intelligences in English Teaching

Multiple intelligences theory provides English teaching with a new theoretical perspective.This theory has important significance in correcting student view,changing the teaching style,setting up a new assessment view in English teaching reform.It is necessary to apply eight kinds of intelligences proposed in this theory as teaching strategies in teaching practice.

The Application of Multiple Intelligences Theory in College English Reading Course

This paper,based on Gardner's Multiple Intelligences Theory,aims to investigate whether the application of MI Theory in col lege English reading class can improve students'English reading proficiency.MI-categorized activities are adopted in the class.The paper explores college Enlgish reading teaching strategy based on MI Theory to improve English teaching proficiency.The paper sheds some light on how MI theory could be applied in EFL.

When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

Colorectal cancer screening:A review of current knowledge and progress in research

Colorectal cancer(CRC)is one of the most prevalent malignancies worldwide,being the third most commonly diagnosed malignancy and the second leading cause of cancer-related deaths globally.Despite the progress in screening,early diagnosis,and treatment,approximately 20%-25%of CRC patients still present with metastatic disease at the time of their initial diagnosis.Furthermore,the burden of disease is still expected to increase,especially in individuals younger than 50 years old,among whom early-onset CRC incidence has been increasing.Screening and early detection are pivotal to improve CRC-related outcomes.It is well established that CRC screening not only reduces incidence,but also decreases deaths from CRC.Diverse screening strategies have proven effective in decreasing both CRC incidence and mortality,though variations in efficacy have been reported across the literature.However,uncertainties persist regarding the optimal screening method,age intervals and periodicity.Moreover,adherence to CRC screening remains globally low.In recent years,emerging technologies,notably artificial intelligence,and non-invasive biomarkers,have been developed to overcome these barriers.However,controversy exists over the actual impact of some of the new discoveries on CRC-related outcomes and how to effectively integrate them into daily practice.In this review,we aim to cover the current evidence surrounding CRC screening.We will further critically assess novel approaches under investigation,in an effort to differentiate promising inno-vations from mere novelties.

人工智能加速GPCR配体的发现

G protein-coupled receptors(GPCRs)are crucial players in various physiological processes,making them attractive candidates for drug discovery.However,traditional approaches to GPCR ligand discovery are time-consuming and resource-intensive.The emergence of artificial intelligence(AI)methods has revolutionized the field of GPCR ligand discovery and has provided valuable tools for accelerating the identification and optimization of GPCR ligands.In this study,we provide guidelines for effectively utilizing AI methods for GPCR ligand discovery,including data collation and representation,model selection,and specific applications.First,the online resources that are instrumental in GPCR ligand discovery were summarized,including databases and repositories that contain valuable GPCR-related information and ligand data.Next,GPCR and ligand representation schemes that can convert data into computer-readable formats were introduced.Subsequently,the key applications of AI methods in the different stages of GPCR drug discovery were discussed,ranging from GPCR function prediction to ligand design and agonist identification.Furthermore,an AI-driven multi-omics integration strategy for GPCR ligand discovery that combines information from various omics disciplines was proposed.Finally,the challenges and future directions of the application of AI in GPCR research were deliberated.In conclusion,continued advancements in AI techniques coupled with interdisciplina ry collaborations will offer great potential for improving the efficiency of GPCR ligand discovery.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

通智测试——基于动态具身物理社会交互环境的通用人工智能测试

The release of the generative pre-trained transformer(GPT)series has brought artificial general intelligence(AGI)to the forefront of the artificial intelligence(AI)field once again.However,the questions of how to define and evaluate AGI remain unclear.This perspective article proposes that the evaluation of AGI should be rooted in dynamic embodied physical and social interactions(DEPSI).More specifically,we propose five critical characteristics to be considered as AGI benchmarks and suggest the Tong test as an AGI evaluation system.The Tong test describes a value-and ability-oriented testing system that delineates five levels of AGI milestones through a virtual environment with DEPSI,allowing for infinite task generation.We contrast the Tong test with classical AI testing systems in terms of various aspects and propose a systematic evaluation system to promote standardized,quantitative,and objective benchmarks and evaluation of AGI.

Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma:A step towards precision medicine

The high rate of early recurrence in hepatocellular carcinoma(HCC)post curative surgical intervention poses a substantial clinical hurdle,impacting patient outcomes and complicating postoperative management.The advent of machine learning provides a unique opportunity to harness vast datasets,identifying subtle patterns and factors that elude conventional prognostic methods.Machine learning models,equipped with the ability to analyse intricate relationships within datasets,have shown promise in predicting outcomes in various medical disciplines.In the context of HCC,the application of machine learning to predict early recurrence holds potential for personalized postoperative care strategies.This editorial comments on the study carried out exploring the merits and efficacy of random survival forests(RSF)in identifying significant risk factors for recurrence,stratifying patients at low and high risk of HCC recurrence and comparing this to traditional COX proportional hazard models(CPH).In doing so,the study demonstrated that the RSF models are superior to traditional CPH models in predicting recurrence of HCC and represent a giant leap towards precision medicine.

Toward a Learnable Climate Model in the Artificial Intelligence Era

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

Mental health in the virtual world:Challenges and opportunities in the metaverse era

Current rates of mental illness are worrisome.Mental illness mainly affects females and younger age groups.The use of the internet to deliver mental health care has been growing since 2020 and includes the implementation of novel mental health treatments using virtual reality,augmented reality,and artificial intelligence.A new three dimensional digital environment,known as the metaverse,has emerged as the next version of the Internet.Artificial intelligence,augmented reality,and virtual reality will create fully immersive,experiential,and interactive online environments in the metaverse.People will use a unique avatar to do anything they do in their“real”lives,including seeking and receiving mental health care.In this opinion review,we reflect on how the metaverse could reshape how we deliver mental health treatment,its opportunities,and its challenges.

May ChatGPT be a tool producing medical information for common inflammatory bowel disease patients’questions?An evidencecontrolled analysis

Artificial intelligence is increasingly entering everyday healthcare.Large language model(LLM)systems such as Chat Generative Pre-trained Transformer(ChatGPT)have become potentially accessible to everyone,including patients with inflammatory bowel diseases(IBD).However,significant ethical issues and pitfalls exist in innovative LLM tools.The hype generated by such systems may lead to unweighted patient trust in these systems.Therefore,it is necessary to understand whether LLMs(trendy ones,such as ChatGPT)can produce plausible medical information(MI)for patients.This review examined ChatGPT’s potential to provide MI regarding questions commonly addressed by patients with IBD to their gastroenterologists.From the review of the outputs provided by ChatGPT,this tool showed some attractive potential while having significant limitations in updating and detailing information and providing inaccurate information in some cases.Further studies and refinement of the ChatGPT,possibly aligning the outputs with the leading medical evidence provided by reliable databases,are needed.

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

A review of artificial intelligence applications in high-speed railway systems

In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.