A scoping review of methodologies for applying artificial intelligence to physical activity interventions

Purpose This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence(AI)applications in physical activity(PA)interventions;introduce them to prevalent machine learning(ML),deep learning(DL),and reinforcement learning(RL)algorithms;and encourage the adoption of AI methodologies.Methods A scoping review was performed in PubMed,Web of Science,Cochrane Library,and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes.AI methodologies were summarized and categorized to identify synergies,patterns,and trends informing future research.Additionally,a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application.Results The review included 24 studies that met the predetermined eligibility criteria.AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes.Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data.Comparisons of different AI models yielded mixed results,likely due to model performance being highly dependent on the dataset and task.An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed,addressing complex human–machine communication,behavior modification,and decision-making tasks.Six key areas for future AI adoption in PA interventions emerged:personalized PA interventions,real-time monitoring and adaptation,integration of multimodal data sources,evaluation of intervention effectiveness,expanding access to PA interventions,and predicting and preventing injuries.Conclusion The scoping review highlights the potential of AI methodologies for advancing PA interventions.As the field progresses,staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.

Catalyzing Random Access at Physical Layer for Internet of Things:An Intelligence Enabled User Signature Code Acquisition Approach

Exploiting random access for the underlying connectivity provisioning has great potential to incorporate massive machine-type communication(MTC)devices in an Internet of Things(Io T)network.However,massive access attempts from versatile MTC devices may bring congestion to the IIo T network,thereby hindering service increasing of IIo T applications.In this paper,an intelligence enabled physical(PHY-)layer user signature code acquisition(USCA)algorithm is proposed to overcome the random access congestion problem with reduced signaling and control overhead.In the proposed scheme,the detector aims at approximating the optimal observation on both active user detection and user data reception by iteratively learning and predicting the convergence of the user signature codes that are in active.The crossentropy based low-complexity iterative updating rule is present to guarantee that the proposed USCA algorithm is computational feasible.A closed-form bit error rate(BER)performance analysis is carried out to show the efficiency of the proposed intelligence USCA algorithm.Simulation results confirm that the proposed USCA algorithm provides an inherent tradeoff between performance and complexity and allows the detector achieves an approximate optimal performance with a reasonable computational complexity.

The Mathematical and Physical Theory of Rational Human Intelligence: Complete Empirical-Digital Properties;Full Electrochemical-Mechanical Model (Part I: Mathematical Foundations)

The design of this paper is to present the first installment of a complete and final theory of rational human intelligence. The theory is mathematical in the strictest possible sense. The mathematics involved is strictly digital—not quantitative in the manner that what is usually thought of as mathematics is quantitative. It is anticipated at this time that the exclusively digital nature of rational human intelligence exhibits four flavors of digitality, apparently no more, and that each flavor will require a lengthy study in its own right. (For more information,please refer to the PDF.)

Artificial Intelligence and Internet of Things Enabled Intelligent Framework for Active and Healthy Living

Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss.State-of-the-art technologies have the potential for long-term benefits in post-surgery living.In this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight.The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients.It also attempts to automate the data analysis and represent the facts about a patient.The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system.The proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.

Artificial intelligence and machine learning in motor recovery: A rehabilitation medicine perspective

Artificial intelligence(AI)and machine learning(ML)are powerful technologies with the potential to revolutionize motor recovery in rehabilitation medicine.This perspective explores how AI and ML are harnessed to assess,diagnose,and design personalized treatment plans for patients with motor impairments.The integration of wearable sensors,virtual reality,augmented reality,and robotic devices allows for precise movement analysis and adaptive neurorehabilitation approaches.Moreover,AI-driven telerehabilitation enables remote monitoring and consultation.Although these applications show promise,healthcare professionals must interpret AI-generated insights and ensure patient safety.While AI and ML are in their early stages,ongoing research will determine their effectiveness in rehabilitation medicine.

An overview of artificial intelligence in medical physics and radiation oncology

Artificial intelligence(AI)is developing rapidly and has found widespread applications in medicine,especially radiotherapy.This paper provides a brief overview of AI applications in radiotherapy,and highlights the research directions of AI that can potentially make significant impacts and relevant ongoing research works in these directions.Challenging issues related to the clinical applications of AI,such as robustness and interpretability of AI models,are also discussed.The future research directions of AI in the field of medical physics and radiotherapy are highlighted.

Factors Related to Physical Strength of Banana Pseudostem

Banana is an important fruit in China. Banana production played important role in economic development in tropical region. Banana production in China was always cut because of lodging caused by typhoon. Getting new cultivars with high resistance to lodging is the basic resolution to resolve this problem. Screening and identifying the germ plasm resource is the first step to breed new cultivars. Banana plant height was high. A single banana plant needs large area. It is difficult to screen the germ plasm resource by identifying the physical strength of banana pseudostem. This research focused on studying the relationship between pseudostem and plant height, pseudostem diameter, acid soluble lignin, acid insoluble lignin, total lignin, pore numbers of pseudostem cross section, and the expression of 4-coumarate:CoA ligase (4CL). Results showed that the plant with high physical strength in seedling stage always has high physical strength in mature stage. The physical strength of banana seedling pseudostem was closely related to pseudostem diameter and total lignin. Pseudostem diameter and total lignin can be used to predict the physical strength of mature banana pseudostem. Work on identifying and screening the physical strength of banana germ plasm pseudostem can be reduced by measuring seedling pseudostem diameter and total lignin in pseudostem of banana germplasm.

The association of physical activity duration and intensity on emotional intelligence in 10-13 year-old Children

Previous studies have shown that Physical Activity(PA)has a positive association with emotional health and intelligence in adolescents but none have focused on the relationship of PA duration and intensity on Emotional Intelligence(EI).The purpose of this study was to cross-sectionally assess the association of PA measures on overall EI and its domains in a cohort of 2029 adolescents aged 10-13 years of age in the National Longitudinal Survey for Children and Youth(NLSCY)from Canada.Multivariable linear regression analysis of EI was adjusted for age,sex,annual household income,and health status.One-way analysis of variance(ANOVA)was used to relate PA duration measured in minutes,frequency,and intensity categories with continuous GEI scores and also the corresponding scores for domains of GEI.The mean GEI scores were(28.3±6.6)for 0-30 minute(min)PA duration,(30.0±6.5)for 30 to<60 min,(30.8±6.7)for 60-120 min,and(30.1±6.5)for≥121 min.There was a statistically significant linear trend across PA duration categories,p?0.0004.Post-hoc pairwise comparison revealed that compared to the referent category(<30 min PA category)was statistically significantly lower GEI than each of the other two PA categories(30-59 min;and 60-120 min),both p-values<0.01.Meeting World Health Organization(WHO)guidelines for duration and vigorous intensity were positively associated with the higher overall EI and its domains except for Stress Management.

Cognitive Intelligence Based 6G Distributed Network Architecture

5G is envisioned to guarantee high transmission rate,ultra-low latency,high reliability and massive connections.To satisfy the above requirements,the 5G architecture is designed with the properties of using service-based architecture,cloud-native oriented,adopting IT-based API interfaces and introduction of the Network Repository Function.However,with the wide commercialization of 5G network and the exploration towards 6G,the 5G architecture exposes the disadvantages of high architecture complexity,difficult inter-interface communication,low cognitive capability,bad instantaneity,and deficient intelligence.To overcome these limitations,this paper investigates 6G network architecture,and proposes a cognitive intelligence based distributed 6G network architecture.This architecture consists of a physical network layer and an intelligent decision layer.The two layers coordinate through flexible service interfaces,supporting function decoupling and joint evolution of intelligence services and network services.With the above design,the proposed 6G architecture can be updated autonomously to deal with the future unpredicted complex services.

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.

Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy

Artificial intelligence(AI)has seen tremendous growth over the past decade and stands to disrupts the medical industry.In medicine,this has been applied in medical imaging and other digitised medical disciplines,but in more traditional fields like medical physics,the adoption of AI is still at an early stage.Though AI is anticipated to be better than human in certain tasks,with the rapid growth of AI,there is increasing concerns for its usage.The focus of this paper is on the current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy.Topics on AI for image acquisition,image segmentation,treatment delivery,quality assurance and outcome prediction will be explored as well as the interaction between human and AI.This will give insights into how we should approach and use the technology for enhancing the quality of clinical practice.

Steps toward Parallel Intelligence

The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the ''new normal'' of cyber-social-physical systems (CPSS), in which the system behaviors are guided by Merton's Laws. Thus, the ACP-based parallel intelligence consisting of Artificial societies, Computational experiments and Parallel execution are introduced to bridge the big modeling gap in CPSS. © 2014 Chinese Association of Automation.

An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

Unlocking the power of physical activity in easing psychological distress

The severity of the current global mental health situation and the importance of maintaining psychological well-being call for more powerful,convenient,and efficient solutions for addressing psychological issues and relieving mental stress.Physical activity not only effectively improves physical fitness and reduces negative emotions such as anxiety and depression but also increases the improvement of psychological health and sense of well-being.At the same time,physical activity interventions for mental health have unique advantages,including reducing the side effects of psychological interventions and increasing necessity,convenience,and cost-effectiveness,as well as flexible adaptability across multiple methods,groups,and age ranges,providing stronger support for relieving psychological stress and addressing psychological issues.Although physical activity is an important intervention measure in relieving psychological stress,its value and role in mental health care seem to have not yet received sufficient attention,and its potential remains to be further revealed.Given the significant advantages and effectiveness of physical activity in mental health intervention practices,it is necessary to stimulate its potential in relieving psychological stress through various means in future studies to better safeguard the public’s physical and mental health.Developing guidelines for physical activity for improved mental health,enhancing organic integration with other intervention measures,and providing necessary respect,encouragement,and support are important directions to consider.

Determination of shear strength of steel fiber RC beams:application of data-intelligence models

Accurate prediction of shear strength of structural engineering components can yield a magnificent information modeling and predesign process.This paper aims to determine the shear strength of steel fiber reinforced concrete beams using the application of data-intelligence models namely hybrid artificial neural network integrated with particle swarm optimization.For the considered data-intelligence models,the input matrix attribute is one of the central element in attaining accurate predictive model.Hence,various input attributes are constructed to model the shear strength"as a targeted variable".The modeling is initiated using historical published researches steel fiber reinforced concrete beams information.Seven variables are used as input attribute combination including reinforcement ratio(ρ%),concrete compressive strength(f′c),fiber factor(F1),volume percentage of fiber(Vf),fiber length to diameter ratio(lf/ld)effective depth(d),and shear span-to-strength ratio(a/d),while the shear strength(SS)is the output of the matrix.The best network structure obtained using the network having ten nodes and one hidden layer.The final results obtained indicated that the hybrid predictive model of ANN-PSO can be used efficiently in the prediction of the shear strength of fiber reinforced concrete beams.In more representable details,the hybrid model attained the values of root mean square error and correlation coefficient 0.567 and 0.82,respectively.

Application of Physical Unclonable Function for Lightweight Authentication in Internet of Things

IoT devices rely on authentication mechanisms to render secure message exchange.During data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT devices.The application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped agreement.This paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT devices.PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication.An IoT network gathers information of interest from multiple cluster members selected by the proposed framework.In addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform.Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance ratio.By enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.

Evaluation of empirical estimation of uniaxial compressive strength of rock using measurements from index and physical tests

The uniaxial compressive strength(UCS) of rock is an important parameter required for design and analysis of rock structures,and rock mass classification.Uniaxial compression test is the direct method to obtain the UCS values.However,these tests are generally tedious,time-consuming,expensive,and sometimes impossible to perform due to difficult rock conditions.Therefore,several empirical equations have been developed to estimate the UCS from results of index and physical tests of rock.Nevertheless,numerous empirical models available in the literature often make it difficult for mining engineers to decide which empirical equation provides the most reliable estimate of UCS.This study evaluates estimation of UCS of rocks from several empirical equations.The study uses data of point load strength(Is(50)),Schmidt rebound hardness(SRH),block punch index(BPI),effective porosity(n) and density(ρ)as inputs to empirically estimate the UCS.The estimated UCS values from empirical equations are compared with experimentally obtained or measured UCS values,using statistical analyses.It shows that the reliability of UCS estimated from empirical equations depends on the quality of data used to develop the equations,type of input data used in the equations,and the quality of input data from index or physical tests.The results show that the point load strength(Is(50)) is the most reliable index for estimating UCS among the five types of tests evaluated.Because of type-specific nature of rock,restricting the use of empirical equations to the similar rock types for which they are developed is one of the measures to ensure satisfactory prediction performance of empirical equations.

Predicting uniaxial compressive strength of serpentinites through physical,dynamic and mechanical properties using neural networks

The uniaxial compressive strength(UCS)of intact rock is one of the most important parameters required and determined for rock mechanics studies in engineering projects.The limitations and difficulty of conducting tests on rocks,specifically on thinly bedded,highly fractured,highly porous and weak rocks,as well as the fact that these tests are destructive,expensive and time-consuming,lead to development of soft computing-based techniques.Application of artificial neural networks(ANNs)for predicting UCS has become an attractive alternative for geotechnical engineering scientists.In this study,an ANN was designed with the aim of indirectly predicting UCS through the serpentinization percentage,and physical,dynamic and mechanical characteristics of serpentinites.For this purpose,data obtained in earlier experimental work from central Greece were used.The ANN-based results were compared with the experimental ones and those obtained from previous analysis.The proposed ANN-based formula was found to be very efficient in predicting UCS values and the samples could be classified with simple physical,dynamic and mechanical tests,thus the expensive,difficult,time-consuming and destructive mechanical tests could be avoided.

Perioperative respiratory muscle training improves respiratory muscle strength and physical activity of patients receiving lung surgery:A meta-analysis

BACKGROUND The clinical role of perioperative respiratory muscle training(RMT),including inspiratory muscle training(IMT)and expiratory muscle training(EMT)in patients undergoing pulmonary surgery remains unclear up to now.AIM To evaluate whether perioperative RMT is effective in improving postoperative outcomes such as the respiratory muscle strength and physical activity level of patients receiving lung surgery.METHODS The PubMed,EMBASE(via OVID),Web of Science,Cochrane Library and Physiotherapy Evidence Database(PEDro)were systematically searched to obtain eligible randomized controlled trials(RCTs).Primary outcome was postoperative respiratory muscle strength expressed as the maximal inspiratory pressure(MIP)and maximal expiratory pressure(MEP).Secondary outcomes were physical activity,exercise capacity,including the 6-min walking distance and peak oxygen consumption during the cardio-pulmonary exercise test,pulmonary function and the quality of life.RESULTS Seven studies involving 240 participants were included in this systematic review and meta-analysis.Among them,four studies focused on IMT and the other three studies focused on RMT,one of which included IMT,EMT and also combined RMT(IMT-EMT-RMT).Three studies applied the intervention postoperative,one study preoperative and the other three studies included both pre-and postoperative training.For primary outcomes,the pooled results indicated that perioperative RMT improved the postoperative MIP(mean=8.13 cmH_(2)O,95%CI:1.31 to 14.95,P=0.02)and tended to increase MEP(mean=13.51 cmH_(2)O,95%CI:-4.47 to 31.48,P=0.14).For secondary outcomes,perioperative RMT enhanced postoperative physical activity significantly(P=0.006)and a trend of improved postoperative pulmonary function was observed.CONCLUSION Perioperative RMT enhanced postoperative respiratory muscle strength and physical activity level of patients receiving lung surgery.However,RCTs with large samples are needed to evaluate effects of perioperative RMT on postoperative outcomes in patients undergoing lung surgery.

Physical Methods and Parameters for Assessing the Strength, Fatigue, Durability and Damage to a Structural Material

The methods and criteria of the physical theory of strength are used. The initial physical and mechanical parameters of the strength of steel 45 were determined analytically. Strength, fatigue and damage to steel were calculated for non-stationary mechanical and various thermal loads. The ratio between the physical and generally accepted mechanical parameters of the material strength is determined analytically. The result of the calculation of the new characteristics of the strength of the damaged material is given. The method takes into account plastic deformation, an arbitrary form of stress cycle, temperature mode. Additional physical criteria for evaluating the strength properties are proposed. We use our own calculation programs, which allow us to take into account the changed characteristics of the damaged material for various stress functions. The physical method allows you to analyze and quickly process the rheological data of sensors that control the parameters of the material under load. A method for rapid analysis and comparison of the results of indentation into the material in accordance with ISO 14577 using various indenters is proposed. Physical parameters of the material and new theoretical methods of calculation can be used to assess the properties of materials, monitor the condition and predict the strength and durability of the material of machines during operation.