A Meta-analysis of the spirit turtle eight method Acupuncture Therapy for Insomnia

Objective:To systematically evaluate the efficacy and safety of the spirit turtle eight method acupuncture therapy in the treatment of insomnia,to better serve the clinic and provide reference ideas for the treatment of insomnia.Methods:Search CNKI,CBM,Wanfang,Weipu,China Clinical Trial Registry databases on the use of the spirit turtle eight method compared with others interventions to treat insomnia.The search period is from the library until August 1,2021,two researchers will conduct independent quality evaluation and effect size extraction on the literature,and use STATA16.0 and Review5.3 to conduct Meta analysis and quality evaluation on the literature.Through screening,a total of 18 eligible documents participated in the meta-analysis,with a total of 1583 cases.Results:①The effective rate of the spirit turtle eight method in the treatment of insomnia is higher than the control group[RR=1.213,95%CI(1.155,1.274),Z=7.71,P<0.01];②The Pittsburgh Sleep Quality Index(PSQI)score is lower than other acupuncture treatments[WMD=-2.33,95%CI(-2.75,-1.91),Z=10.97,P<0.01];③The one-month follow-up PSQI score after the treatment of insomnia by the spirit turtle eight method is lower than the control group[WMD=-3.263,95%CI(-3.446,-3.080),Z=34.96,P<0.01];④No studies have reported serious adverse reactions in the treatment group and control group.Conclusion:Based on the current research,the spirit turtle eight method are better than the control group in the treatment of insomnia,and are suitable for clinical application.However,more high-quality clinical studies are still needed to prove its efficacy.

Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy.

AI-Enhanced Performance Evaluation of Python, MATLAB, and Scilab for Solving Nonlinear Systems of Equations: A Comparative Study Using the Broyden Method

This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core objectives include comparing software performance using standardized benchmarks, employing key performance metrics for quantitative assessment, and examining the influence of varying hardware specifications on software efficiency across HP ProBook, HP EliteBook, Dell Inspiron, and Dell Latitude laptops. Results from this investigation reveal insights into the capabilities of these software tools in diverse computing environments. On the HP ProBook, Python consistently outperforms MATLAB in terms of computational time. Python also exhibits a lower robustness index for problems 3 and 5 but matches or surpasses MATLAB for problem 1, for some initial guess values. In contrast, on the HP EliteBook, MATLAB consistently exhibits shorter computational times than Python across all benchmark problems. However, Python maintains a lower robustness index for most problems, except for problem 3, where MATLAB performs better. A notable challenge is Python’s failure to converge for problem 4 with certain initial guess values, while MATLAB succeeds in producing results. Analysis on the Dell Inspiron reveals a split in strengths. Python demonstrates superior computational efficiency for some problems, while MATLAB excels in handling others. This pattern extends to the robustness index, with Python showing lower values for some problems, and MATLAB achieving the lowest indices for other problems. In conclusion, this research offers valuable insights into the comparative performance of Python, MATLAB, and Scilab in solving nonlinear systems of equations. It underscores the importance of considering both software and hardware specifications in real-world applications. The choice between Python and MATLAB can yield distinct advantages depending on the specific problem and computational environment, providing guidance for researchers and practitioners in selecting tools for their unique challenges.

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

Research on the College English Teaching Innovation Methods based on the Theory of Multiple Intelligence and Language Cognitive

In this paper, we conduct research on the college English teaching innovation methods based on the theory of multiple intelligence and language cognitive. Gardner Suggestions to teaching points out: “the relevant content can be shown by a variety of ways, such as teachers, books, software, hardware, or other media. In many cases, the choice of the above shows patterns, lead to the success of education experience. History can have language, logic, space, or the general pattern of personal understanding to teaching, and even can also use the space geometry teaching classroom, logic, the language and digital methods such as ability to implement teaching.” This paper combines primary theories of the corresponding issues to propose the novel understanding of the modern college English education mode that will be benefi cial for the education mode optimization.

Investigation on behavior of crack penetration/deflection at interfaces in intelligent coating system

Based on the three-phase model, the propagation behavior of a matrix crack in an intelligent coating system is investigated by an energy criterion. The effect of the elastic mismatch parameters and the thickness of the interface layer on the ratio of the energy release rate for infinitesimal deflected and penetrated crack is evaluated with the finite element method. The results show that the ratio of the energy release rates strongly depends on the elastic mismatch al between the substrate and the driving layer. It also strongly depends on the elastic mismatch a2 between the driving layer and the sensing layer for a thinner driving layer when a primary crack reaches an interface between the substrate and the driving layer. Moreover, with the increase in the thickness of the driving layer, the dependence on a2 gradually decreases. The experimental observation on aluminum alloys monitored with intelligent coating shows that the established model can better explain the behavior of matrix crack penetration and can be used in optimization design of intelligent coating.

Prediction of landslide displacement with dynamic features using intelligent approaches

Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.

Real-time determination of sandy soil stiffness during vibratory compaction incorporating machine learning method for intelligent compaction

An emerging real-time ground compaction and quality control, known as intelligent compaction(IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional(3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are discussed.The proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.

THE DEVELOPING METHOD OF AI SOFTWARE FOR APPLICATION OF AERIAL PHOTOGRAPH(AIAAP)

This paper put forward a framework of artificial intelligence (AI) for app;ication of aerial photograph. The framwork contains structures of knowledge base and inference mechanism.The functions of framework about how to convert expert’s knowledge into logical rules and how to inference using the rules wer discussed. The regeneraion pltring of fired land in Dwi’an Mountains area was taken as an example to have a ditail discuss on the contation of knowledge base and the steps of inference mechaism. the program was written using the basic langUae and the running result proyed tha the software can take the place of expert’s work with high efficiellcy.

Physics-data-driven intelligent optimization for large-aperture metalenses

Metalenses have gained significant attention and have been widely utilized in optical systems for focusing and imaging,owing to their lightweight,high-integration,and exceptional-flexibility capabilities.Traditional design methods neglect the coupling effect between adjacent meta-atoms,thus harming the practical performance of meta-devices.The existing physical/data-driven optimization algorithms can solve the above problems,but bring significant time costs or require a large number of data-sets.Here,we propose a physics-data-driven method employing an“intelligent optimizer”that enables us to adaptively modify the sizes of the meta-atom according to the sizes of its surrounding ones.The implementation of such a scheme effectively mitigates the undesired impact of local lattice coupling,and the proposed network model works well on thousands of data-sets with a validation loss of 3×10^(−3).Based on the“intelligent optimizer”,a 1-cm-diameter metalens is designed within 3 hours,and the experimental results show that the 1-mm-diameter metalens has a relative focusing efficiency of 93.4%(compared to the ideal focusing efficiency)and a Strehl ratio of 0.94.Compared to previous inverse design method,our method significantly boosts designing efficiency with five orders of magnitude reduction in time.More generally,it may set a new paradigm for devising large-aperture meta-devices.

Design and Application of an Intelligent Agent for Web Information Discovery

With the propagation of applications on the internet, the internet has become a great information source which supplies users with valuable information. But it is hard for users to quickly acquire the right information on the web. This paper an intelligent agent for internet applications to retrieve and extract web information under user's guidance. The intelligent agent is made up of a retrieval script to identify web sources, an extraction script based on the document object model to express extraction process, a data translator to export the extracted information into knowledge bases with frame structures, and a data reasoning to reply users' questions. A GUI tool named Script Writer helps to generate the extraction script visually, and knowledge rule databases help to extract wanted information and to generate the answer to questions.

Global Inverse Optimal Tracking Control of Underactuated Omni-directional Intelligent Navigators （ODINs）

这份报纸关于一个有意义的费用函数论述最佳的控制器的一个图案强迫 underactuated 在到磁道的未知经常的环境负担下面的全向的聪明的导航者(最高之神) 在二维的空格的一条引用轨道。由驾驶实践的车辆激发了，当加巨浪的虚拟控制插入了力量，考虑的 yaw 角度被用来强迫车辆的位置全球性追踪它的参考书轨道。控制设计基于为反的最佳的控制和非线性的系统，围住的骚乱观察员的一个新图案，和 backstepping 和 Lyapunovs 直接方法的稳定性分析开发的几最近的结果。州反馈并且产量反馈控制图案被探讨。模拟被包括说明建议结果的有效性。

Distributed Intelligent Lighting System by Performing New Model for Illuminance and Color Temperature in the Workplace

A new approach has been proposed to improve the performance of the in-telligent lighting system by estimating personal illuminance and desired color temperature at the workplace. We are considering the problem of using the sensing devices manually for the intelligent lighting system. The lighting control system has not become useful without sensing devices to measure the provided illuminance and color temperature. In this paper, we have used the property of light for the color temperature to estimate the level of color temperature for each user at the workplace. The new method will give personal illuminance for each user at the workplace and decrease the power consumption of the environment as well. As a result, the proposed method of the intelligent lighting system has realized the target of illuminance and color temperature for each user at the workplace by adapting dimming levels using illuminance sensing information for each user. Thus, the energy of the workplace has reduced by using a distributed luminance to realize the target for each user.

Strategic information perception methods and practices in the open source intelligence

Open source intelligence is one of the most important public data sources for strategic information analysis. One of the primary and core issues of strategic information research is information perception,so this paper mainly expounds the perception method for strategic information perception in the open source intelligence environment as well as the framework and basic process of information perception. This paper argues that in order to match the information perception result with the information depiction result,it conducts practical exploration for the results of information acquisition,perception,depiction and analysis. This paper introduces and develops a monitoring platform for information perception. The results show that the method proposed in this paper is feasible.

Interaction and the Genesis of Experience: A Phenomenological Contribution for Meaningful Embodied Artificial Intelligence

In this article I will address the issue of the meaning of Embodied Artificial Intelligence(EAI)as it is configured today.My starting point is the refined interactive perspective on the semantics of EAI,as was recently suggested by Froese and colleagues.This perspective rests on the assumption that the concept of human bodily subjectivity must be extended to include meaning-making processes,which are enabled by advanced AI systems that may be incorporated in the human biological body.After having clarified the technical background,I will introduce the genetic component of the phenomenological method as a suitable tool to face the aforementioned issue.Towards this end,I will place the genetic method in the context of the so-called New Human-Machine Interaction(New HMI).I will further outline a genetic phenomenology of visual embodiment,suggesting a futuristic application based on the thesis of the“technological supplementation of phenomenological methodology”through the synthetic method.The case at stake is that of patients with a severe clinical picture characterised by the loss of corneal function,who in the near future could be treated with synthetic corneal prosthetic implants produced by a 3D bio-printing process by using an advanced EAI technique.I will conclude this article with a brief review of the main problems that still remain open.

Design methods for antimicrobial peptides with improved performance

The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult.In this regard,developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority.Antimicrobial peptides(AMPs),a ubiquitous class of naturally occurring compounds with broadspectrum antipathogenic activity,hold significant promise as an effective solution to the current antimicrobial resistance(AMR)crisis.Several AMPs have been identified and evaluated for their therapeutic application,with many already in the drug development pipeline.Their distinct properties,such as high target specificity,potency,and ability to bypass microbial resistance mechanisms,make AMPs a promising alternative to traditional antibiotics.Nonetheless,several challenges,such as high toxicity,lability to proteolytic degradation,low stability,poor pharmacokinetics,and high production costs,continue to hamper their clinical applicability.Therefore,recent research has focused on optimizing the properties of AMPs to improve their performance.By understanding the physicochemical properties of AMPs that correspond to their activity,such as amphipathicity,hydrophobicity,structural conformation,amino acid distribution,and composition,researchers can design AMPs with desired and improved performance.In this review,we highlight some of the key strategies used to optimize the performance of AMPs,including rational design and de novo synthesis.We also discuss the growing role of predictive computational tools,utilizing artificial intelligence and machine learning,in the design and synthesis of highly efficacious lead drug candidates.

Management Method and Intelligent Technology Analysis of Construction Management

The construction industry is a critical lifeline for China’s national economy’s development.The growth of the building business has piqued the interest of a variety of industries.To ensure the quality of construction engineering,it is necessary to improve the technical level of construction engineering management by strengthening construction engineering management through various effective methods,following the development of The Times,and strengthening the introduction of intelligent technology.The author investigates and analyses the importance of incorporating intelligent technology into construction project management,and proposes effective strategies for incorporating intelligent technology into construction project management in the hopes of improving the quality of construction project management.

IMPROVED EXECUTION METHOD OF TENTACLE ALGORITHM FOR INTELLIGENT VEHICLE

In order to solve some deficiencies in tentacle execution,an improved execution method of tentacle algorithm is presented.The method uses a short trajectory to match the curvature between the path of vehicle and tentacle,rather than computing a whole steady state.To control vehicle motion via wheel force and steering angle,two parameters should be discretized under certain area and these discrete values can form 18×20 groups.Then the curvature between the trajectory and tentacle should be matched,and the corresponding group of wheel force and steering angle can be found.The flow chart of the improved execution method is given,and simulation is performed on a platform named″pro-sivic″.The simulation results show that the improved method can maintain the advantage of the tentacle algorithm in terms of computation speed,and avoid the errors such as endless loop and data overflow,which proves the method more efficient.

Key problems in validation of intelligent models

To provide a realistic simulation environment for users,intelligent models have become key components in military simulations. After the analysis of modeling nature of intelligent models,the validation criteria for defining the validation points and validation metrics for measuring the agreements between human experts and intelligent models were presented. Further,such methods as graphical comparison,feature analysis and face validation were discussed according to the characteristics of intelligent models. Based on the validation criteria,validation metrics and validation methods,the intelligent models can be effectively validated,which has been proved in current developed intelligent models.

Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

Soil salinity is a serious land degradation issue in agriculture.It is a major threat to agriculture productivity.Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction(LF)of irrigation water.For the leaching process to be effective,the LF of irriga-tion water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration(ET)rate.The relationship between environmental conditions and ET rate is hard to be defined by a linear relationship and data-driven Machine learning(ML)based decisions are required to determine the calibrated Evapotranspiration(ETc)rate.ML-assisted ETc is pro-posed to adjust the LF according to the ETc and soil salinity level.A regression model is proposed to determine the ETc rate according to the prevailing tempera-ture,humidity,and sunshine,which would be used to determine the smart LF according to the ETc and soil salinity level.The proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration(ETo)determination.The validation of the model from the test dataset reveals the accu-racy of the ML model in terms of Root mean squared errors(RMSE)are 0.41,Mean absolute errors(MAE)are 0.34,and Mean squared errors(MSE)are 0.28 mm day-1.The applications of the proposed solution in a real-time environ-ment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.