Changes in human settlement environments and their drivers in valley cities located in arid and semi-arid regions:A case study of Lanzhou in Western China

Development of urban human settlement environments(HSEs)is an integral part of promoting high-quality and sustainable regional development and constructing a beautiful China.The city of Lanzhou,located at the geometric center of China,is the only provincial capital traversed by the Yellow River.Given the constraints posed by the valley topography and the need for economic development,the development of this HSE,which is located within an arid region,poses considerable challenges.Evidently,an understanding of the evolution of HSEs and drivers of changes in them contributes to high-quality,sustainable urban development in arid and semi-arid regions.An analytical model was developed using the parameters of relief degree of land surface,human comfort days,the land cover index,nighttime light index,and precipitation.This model was used in combination with population density and the gross domestic product to analyze the spatial distribution of Lanzhou's HSE and its drivers.The results showed that landscapes in Lanzhou underwent significant changes between 2000 and 2022,with an increase in building-up land(+0.946%),cultivated land(+0.134%),and forest land(+0.018%)and a decrease in grassland(-1.10%).There was significant outward expansion of the main urban zone of Lanzhou and of various county towns,with the increase in building-up land being most prominent.During this period,there were significant changes in the periphery of the core urban area and county towns in Lanzhou,with decreases moving from the urban center(the highest value)to the surrounding areas(Yongdeng County had the lowest value).The correlation between the HSE and population density grew stronger in Anning and Chengguan Districts but became weaker in Xigu and Qilihe Districts.Spatiotemporal variations in the HSE were primarily caused by climate change,followed by human activities,and were also influenced by the valley topography.Overall,the spatial distribution of population density and the HSE in Lanzhou demonstrated good consistency under the in-fluence of economic development and urbanization.

Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.

Reflections on the Improvement of Rural Human Settlement Environment from the Perspective of Beauty and Livability

The construction of beautiful and livable villages is one of the important tasks of ruralrevitalization in the new era, and the development of rural areas attaches more and more importanceto the improvement of human settlement environment. By investigating the construction status andexisting problems of Zhaoba Village in Nanjing, it tries to take “beauty” and “livability” as the planningrequirements. The village positioning is planned accurately, and the functional layout is carried outreasonably. According to the characteristic industries, landscape features and infrastructure of ZhaobaVillage, humanized construction strategies are put forward, thus improving the quality of human settlementenvironment in Zhaoba Village.

Human-Machine Symbiosis:Philosophical Reflection on Virtual Human

Virtual human is the simulation of human under the synthesis of virtual reality,artificial intelligence,and other technologies.Modern virtual human technology simulates both the external characteristics and the internal emotions and personality of humans.The relationship between virtual human and human is a concrete expression of the modern symbiotic relationship between human and machine.This human-machine symbiosis can either be a fusion of the virtual human and the human or it can cause a split in the human itself.

Predict the evolution of mechanical property of Al-Li alloys in a marine environment

The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learning(ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment.We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments.We obtained the strain evolution by digital image correlation(DIC).The strain field images are voxelized using 2D-Convolutional Neural Networks(CNN)autoencoders as input data for Long Short-Term Memory(LSTM)neural networks.Then,the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation(BP)neural network to predict the mechanical properties of Al-Li alloys.The main conclusions are as follows:1.The variation law of mechanical properties of2297-T8 in the Marine atmosphere is revealed.With the increase in outdoor exposure test time,the tensile elastic model of 2297-T8 changes slowly,within 10%,and the tensile yield stress changes significantly,with a maximum attenuation of 23.6%.2.The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%.3.This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments.The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials.

Developing an atmospheric aging evaluation model of acrylic coatings:A semi-supervised machine learning algorithm

To study the atmospheric aging of acrylic coatings,a two-year aging exposure experiment was conducted in 13 representative climatic environments in China.An atmospheric aging evaluation model of acrylic coatings was developed based on aging data including11 environmental factors from 567 cities.A hybrid method of random forest and Spearman correlation analysis was used to reduce the redundancy and multicollinearity of the data set by dimensionality reduction.A semi-supervised collaborative trained regression model was developed with the environmental factors as input and the low-frequency impedance modulus values of the electrochemical impedance spectra of acrylic coatings in 3.5wt%NaCl solution as output.The model improves accuracy compared to supervised learning algorithms model(support vector machines model).The model provides a new method for the rapid evaluation of the aging performance of acrylic coatings,and may also serve as a reference to evaluate the aging performance of other organic coatings.

Integrating linear and nonlinear models to assess the built environment’s impact on older adults’behaviors in China

China is experiencing rapid population aging.The one contributing factor affecting senior citizens’lives is the disconnect between the built environment in urban and rural areas and the behavioral preferences of older adults.However,research on the relation between the built environment and the behavior of older individuals has been limited.Thus,this paper uses the most recent health tracking data on factors influencing aging in China released in 2020(China Senior Health Survey Tracking Survey).Applying traditional regression,least absolute shrinkage and selection operator regression,and two decision tree optimization models from machine learning,a comprehensive comparative study is carried out to investigate the correlation between the built environment and the physical activity,dietary habits,and social interactions of older age groups.The findings reveal that built environment variables most significantly impact physical activity,accounting for 52.525%,followed by social interaction behaviors at 50.202%and dietary intake at 47.991%.Furthermore,the authors identify population density and greenness rate as the built environment factors having considerable effects on the behavior of older adults.Thus,this study establishes a theoretical foundation for developing age-friendly community environments for older adults.

Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision

Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

Conformal Human–Machine Integration Using Highly Bending‑Insensitive,Unpixelated,and Waterproof Epidermal Electronics Toward Metaverse

Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals,which is critical to the breakthrough development of metaverse.Interactive electronics face common dilemmas,which realize highprecision and stable touch detection but are rigid,bulky,and thick or achieve high flexibility to wear but lose precision.Here,we construct highly bending-insensitive,unpixelated,and waterproof epidermal interfaces(BUW epidermal interfaces)and demonstrate their interactive applications of conformal human–machine integration.The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection,high flexibility,rapid response time,excellent stability,and versatile“cut-and-paste”character.Regardless of whether being flat or bent,the BUW epidermal interface can be conformally attached to the human skin for real-time,comfortable,and unrestrained interactions.This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics,which offers a new technology route and may further broaden human–machine interactions toward metaverse.

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

The development of artificial intelligence(AI)and smart home technologies has driven the need for speech recognition-based solutions.This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes.Speech recognition allows users to control devices and perform everyday actions through spoken commands,eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light,heating,or lowering the blinds.The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home.It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories,subcategories,and actions from sentences.A dataset labeled with relevant information about categories,subcategories,and actions related to human actions in the smart home is used.The methodology uses machine learning techniques implemented in Python,extracting features using CountVectorizer to convert sentences into numerical representations.The results show that the classification model is able to accurately predict categories,subcategories,and actions based on sentences,with 82.99%accuracy for category,76.19%accuracy for subcategory,and 90.28%accuracy for action.The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home,supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.

Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

The Protection of Human Rights in the Construction of Barrier-free Environment--Viewed from the perspective of the Marxist Human Rights Concept

The construction of a barrier-free environment contains a rich connotation of human rights protection,and is closely related to the study of human rights protection in China.With the evolution of the concept of barrier-free design and the expansion of its beneficiary groups,constructing a barrier-free environment has become a basic task that benefits all members of society and meets the public needs of all the people,with its core goal expanding from the protection of the rights of the disabled to the protection of the basic rights and interests of all members of society.Building a barrier-free environment from the perspective of the Marxist human rights concept is conducive to safeguarding the people’s right to subsistence and development,and at the same time,it satisfies their demand for the right to pursue a high-quality and better life and reflects the common vision of everyone in society fully enjoying human rights.promoting the construction of a barrier-free environment in terms of legal protection based on China’s institutional advantages can improve and innovate the country’s legislation on the construction of a barrier-free environment,so as to play an exemplary and guiding role in enhancing the international barrier-free level and enable all people to enjoy the good life created through the drive to build a barrier-free environment.

Operation and Consideration of a Pipe Corrosion Inspection System Based on Human-in-the-Loop Machine Learning

The aim of this study is to improve the efficiency of external corrosion inspection of pipes in chemical plants.Currently,the preferred method involves manual inspection of images of corroded pipes;however,this places significant workload on human experts owing to the large number of required images.Furthermore,visual assessment of corrosion levels is prone to subjective errors.To address these issues,we developed an AI(artificial intelligence)-based corrosion-diagnosis system(AI corrosion-diagnosis system)and implemented it in a factory.The proposed system architecture was based on HITL(human-in-the-loop)ML(machine learning)[1].To overcome the difficulty of developing a highly accurate ML model during the PoC(proof-of-concept)stage,the system relies on cooperation between humans and the ML model,utilizing human expertise during operation.For instance,if the accuracy of the ML model was initially 60%during the development stage,a cooperative approach would be adopted during the operational stage,with humans supplementing the remaining 40%accuracy.The implemented system’s ML model achieved a recall rate of approximately 70%.The system’s implementation not only contributed to the efficiency of operations by supporting diagnosis through the ML model but also facilitated the transition to systematic data management,resulting in an overall workload reduction of approximately 50%.The operation based on HITL was demonstrated to be a crucial element for achieving efficient system operation through the collaboration of humans and ML models,even when the initial accuracy of the ML model was low.Future efforts will focus on improving the detection of corrosion at elevated locations by considering using video cameras to capture pipe images.The goal is to reduce the workload for inspectors and enhance the quality of inspections by identifying corrosion locations using ML models.

Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.

Using Support Vector Machine to Predict Eco-environment Burden:A Case Study of Wuhan,Hubei Province,China

Objective The human socio-economic development depends on the planet＇s natural capital. Humans have had a considerable impact on the earth, such as resources depression and environment deterioration. The objective of this study was to assess the impact of socio-economic development on the ecological environment of Wuhan, Hubei Province, China, during the general planning period 2006-2020. Methods Support vector machine （SVM） model was constructed to simulate the process of eco-economic system of Wuhan. Socio-economic factors of urban total ecological footprint （TEF） were selected by partial least squares （PLS） and leave-one-out cross validation （LOOCV）. Historical data of socio-economic factors as inputs, and corresponding historical data of TEF as target outputs, were presented to identify and validate the SVM model. When predicted input data after 2005 were presented to trained model as generalization sets, TEFs of 2005, 2006,…, till 2020 were simulated as output in succession. Results Up to 2020, the district would have suffered an accumulative TEF of 28.374 million gha, which was over 1.5 times that of 2004 and nearly 3 times that of 1988. The per capita EF would be up to 3.019 gha in 2020. Contusions The simulation indicated that although the increase rate of GDP would be restricted in a lower level during the general planning period, urban ecological environment burden could not respond to the socio-economic circumstances promptly. SVM provides tools for dynamic assessment of regional eco-environment. However, there still exist limitations and disadvantages in the model. We believe that the next logical step in deriving better dynamic models of ecosystem is to integrate SVM and other algorithms or technologies.

Humanizing the Culture of Technology Teams: Strategies for Creating Healthier and More Productive Work Environments

With the advancement of technology, companies are increasingly dependent on technology teams to stay competitive. However, members of these teams often work in stressful and unhealthy environments, which can undermine their productivity and well-being. The humanization of the culture of technology teams is an approach that aims to create healthier and more productive work environments for team members, promoting balance between personal and professional life. Despite the importance of a healthy and productive work environment, many companies do not invest in strategies to humanize the culture of their technology teams. This can lead to high levels of stress, staff turnover and low productivity. The objective of this project is to identify effective strategies to humanize the culture of technology teams and create healthier and more productive work environments in digital companies. For this, factors such as management styles, psychological safety, human-centered development, individual beliefs and time and energy management will be analyzed. The project’s methodology will include a literature review on the subject and qualitative data analysis will be performed using a content analysis approach. This project will contribute to the advancement of knowledge about the humanization of the culture of technology teams in digital companies. The results can be applied to companies that want to create healthier and more productive work environments for their team members.

The Application of Landscape Plants in Humanized Interior Environmental Design

Landscape plants play a role of decorating and beautifying the indoor environment.From the perspective of humanization,the functions and application patterns of landscape plants in the interior environmental design are analyzed in this study,and finally the application rules of landscape plants in the humanized interior environmental design are summarized.

Behavior Recognition of the Elderly in Indoor Environment Based on Feature Fusion of Wi-Fi Perception and Videos

With the intensifying aging of the population,the phenomenon of the elderly living alone is also increasing.Therefore,using modern internet of things technology to monitor the daily behavior of the elderly in indoors is a meaningful study.Video-based action recognition tasks are easily affected by object occlusion and weak ambient light,resulting in poor recognition performance.Therefore,this paper proposes an indoor human behavior recognition method based on wireless fidelity(Wi-Fi)perception and video feature fusion by utilizing the ability of Wi-Fi signals to carry environmental information during the propagation process.This paper uses the public WiFi-based activity recognition dataset(WIAR)containing Wi-Fi channel state information and essential action videos,and then extracts video feature vectors and Wi-Fi signal feature vectors in the datasets through the two-stream convolutional neural network and standard statistical algorithms,respectively.Then the two sets of feature vectors are fused,and finally,the action classification and recognition are performed by the support vector machine(SVM).The experiments in this paper contrast experiments between the two-stream network model and the methods in this paper under three different environments.And the accuracy of action recognition after adding Wi-Fi signal feature fusion is improved by 10%on average.

Natural environment suitability for human settlements in China based on GIS

With the degeneration of environment and acceleration of urbanization,human environment has attracted great attention worldwide.This paper sets up the Human Settlements Environment Index(HEI) model and evaluates the natural environment suitability for Human Settlements in China based on GIS technology.The results show that the HEI of China decreases from southeast to northwest in general,HEI is significantly correlated with population density at grid size and the correlation coefficient(r) between them reaches 0.93,which indicates that natural environment suitability for Human Settlements has a significant influence on population distribution.Most people in China mainly concentrate in areas with high natural environment suitability.The total suitable area is 430.47×104 km2,which accounts for nearly 45% of the total land area in China,while the proportion of people living in the area reaches 96.56%.With a population density of 18 people per square kilometer,the critical area is the transitional region for people to live in China.The critical area covers 225.11×104 km2 with a population of 41.12 million.The non-suitable area covers 304.42×104 km2 with a population of only 2.49 million.

Environment and Human Behaviors——Taking “The Acorn Gatherer” as a Case Study

Human living environment interacts with human behaviors.Environment to some degree determines human behaviors which in return reshape and sustain the dynamic environment.Taking Richard Jefferies' The Acorn Gatherer as a case study,this paper makes an argument that the behaviors of the villagers in the story can be more adequately interpreted in view of the backward countryside environment,and the same is true with the author Richard Jefferies' non-interference in the omniscient third-person point of view in writing the story.It is Richard Jefferies' family environment that makes up his viewpoint of writing,reflecting his critical loving of the countryside,where lives boom and conventions are extremely followed.