Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image dis...Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.展开更多
1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic inf...1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic information sciences, remote sensing and cartography. Manuscripts come from different parts of the world.展开更多
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noti...Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.展开更多
Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating t...Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating terrains;3) Small fragmented land;4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion;2) extraction research on color features;3) extraction research on vegetation index;4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.展开更多
With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration wi...With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the con...Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.展开更多
Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors.These vehicles are crucial in various fields,including environ...Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors.These vehicles are crucial in various fields,including environmental science research,ecological and environmental monitoring projects,disaster response,and emergency management.A key method employed in these vehicles for achieving high-precision positioning is LiDAR(lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping(SLAM).However,maintaining highprecision localization in complex scenarios,such as degraded environments or when dynamic objects are present,remains a significant challenge.To address this issue,we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data registration.Specifically,semantic information simplifies the modeling of scene elements,reducing the reliance on dense point clouds,which can be less efficient.Meanwhile,visual texture information complements LiDAR-Visual localization by providing additional contextual details.By incorporating semantic and texture details frompaired images and point clouds,we significantly improve the quality of data association,thereby increasing the success rate of localization.This approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts.展开更多
Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detectio...Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detection accuracy.Using multiple methods to carry out a comprehensive evaluation can eff ectively improve the accuracy of advanced geological prediction results.In this study,geological information is combined with the detection results of geophysical methods,including transient electromagnetic,induced polarization,and tunnel seismic prediction,to establish a comprehensive analysis method of adverse geology.First,the possible main adverse geological problems are determined according to the geological information.Subsequently,various physical parameters of the rock mass in front of the tunnel face can then be derived on the basis of multisource geophysical data.Finally,based on the analysis results of geological information,the multisource data fusion algorithm is used to determine the type,location,and scale of adverse geology.The advanced geological prediction results that can provide eff ective guidance for tunnel construction can then be obtained.展开更多
Objective: This study evaluates the impact of handshake and information support on patients’ outcomes during laparoscopic cholecystectomy. It examines the effects on their physiological and psychological responses an...Objective: This study evaluates the impact of handshake and information support on patients’ outcomes during laparoscopic cholecystectomy. It examines the effects on their physiological and psychological responses and overall satisfaction with nursing care. Methods: A total of 84 patients scheduled for laparoscopic cholecystectomy were selected through convenient sampling and randomly assigned to either the control group or the intervention group using a random number table. Each group consisted of 42 patients. The control group received standard surgical nursing care. In addition to standard care, the intervention group received handshake and information support from the circulating nurse before anesthesia induction. Vital signs were recorded before surgery and before anesthesia induction. Anxiety levels were measured using the State-Trait Anxiety Inventory (STAI) and the State-Anxiety Inventory (S-AI), while nursing satisfaction was assessed using a numerical rating scale. Results: No significant differences were found between the two groups in systolic and diastolic blood pressures before surgery and anesthesia induction (P > 0.05). However, there was a significant difference in heart rate before anesthesia induction (P Conclusion: Providing handshake and information support before anesthesia induction effectively reduces stress, alleviates anxiety, and enhances comfort and satisfaction among patients undergoing laparoscopic cholecystectomy.展开更多
This study analyzes the User Interface(UI)and User Experience(UX)of information systems that provide local government information.The systems analyzed are the Local Administrative Comprehensive Information Disclosure ...This study analyzes the User Interface(UI)and User Experience(UX)of information systems that provide local government information.The systems analyzed are the Local Administrative Comprehensive Information Disclosure System(Zheripan),the Integrated Local Financial Disclosure System(Qinching Online),and the Local Regulations Information System(12348 Zhejiang Legal Network).The Local Administrative Comprehensive Information Disclosure System offers public service and personnel information,while the Integrated Local Financial Disclosure System provides financial information,and the Local Regulations Information System offers legal information as its main content.The analysis framework utilized three elements:objective data,psychological factors,and heuristic evaluation.The results of the first objective data analysis show that approximately 70%of visits to Zheripan and Qinching Online are through search,and the time spent on the homepage is short.In contrast,about 70%of visits to the 12348 Zhejiang Legal Network are direct visits,with users browsing multiple pages with a clear purpose.In terms of data provision methods,Zheripan provides two types of data in three formats,Qinching Online offers 28 types of data in five formats,and 12348 Zhejiang Legal Network provides one type of information in a single format.The second psychological factor analysis found that all three websites had a number of menus suitable for short-term cognitive capacity.However,only one of the sites had a layout that considered the user’s eye movement.Finally,the heuristic evaluation revealed that most of the evaluation criteria were not met.While the design is relatively simple and follows standards,feedback for users,error prevention,and help options were lacking.Moreover,the user-specific usability was low,and the systems remained at the information-providing level.Based on these findings,both short-term and long-term improvement measures for creating an interactive system beyond simple information disclosure are proposed.展开更多
This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data a...This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.展开更多
Nowadays,spatiotemporal information,positioning,and navigation services have become critical components of new infrastructure.Precise positioning technology is indispensable for determining spatiotemporal information ...Nowadays,spatiotemporal information,positioning,and navigation services have become critical components of new infrastructure.Precise positioning technology is indispensable for determining spatiotemporal information and providing navigation services.展开更多
The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland im...The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.展开更多
Background: For nursing students, gathering social information is essential for understanding healthcare and social issues and developing critical thinking and decision-making skills. However, the choice of informatio...Background: For nursing students, gathering social information is essential for understanding healthcare and social issues and developing critical thinking and decision-making skills. However, the choice of information sources varies by age and individual habits. With the widespread use of the internet, there are notable differences between younger and older generations in their reliance on the internet versus traditional media sources like newspapers and television. Given the wide age range and diverse backgrounds of nursing students, understanding generational differences in information-gathering methods is important for implementing effective education. Purpose: The purpose of this study is to identify how nursing students in different age groups obtain social information and to examine media usage trends by age group. Additionally, we aim to use the findings to provide insights into effective information dissemination methods in nursing education. Results: The results showed that nursing students in their teens to forties, regardless of gender, primarily relied on the internet as their main information source, with television playing a secondary role. In contrast, students in their fifties tended to obtain information more often from newspapers and television than from the internet. This highlights an age-related difference in preferred information sources, with older students showing a greater reliance on traditional media. Conclusions: This study demonstrates that nursing students use different information-gathering methods based on their age, suggesting a need to custo-mize information dissemination strategies in nursing education. Digital media may be more effective for younger students, while traditional media or printed materials might better serve older students. Educational institutions should consider these generational differences in media usage and adopt strategies that meet the diverse needs of their student populations.展开更多
Background: The availability of essential medicines and medical supplies is crucial for effectively delivering healthcare services. In Zambia, the Logistics Management Information System (LMIS) is a key tool for manag...Background: The availability of essential medicines and medical supplies is crucial for effectively delivering healthcare services. In Zambia, the Logistics Management Information System (LMIS) is a key tool for managing the supply chain of these commodities. This study aimed to evaluate the effectiveness of LMIS in ensuring the availability of essential medicines and medical supplies in public hospitals in the Copperbelt Province of Zambia. Materials and Methods: From February to April 2022, a cross-sectional study was conducted in 12 public hospitals across the Copperbelt Province. Data were collected using structured questionnaires, checklists, and stock control cards. The study assessed LMIS availability, training, and knowledge among pharmacy personnel, as well as data accuracy, product availability, and order fill rates. Descriptive statistics were used to analyse the data. Results: All surveyed hospitals had LMIS implemented and were using eLMIS as the primary LMIS. Only 47% and 48% of pharmacy personnel received training in eLMIS and Essential Medicines Logistics Improvement Program (EMLIP), respectively. Most personnel demonstrated good knowledge of LMIS, with 77.7% able to log in to eLMIS Facility Edition, 76.6% able to locate stock control cards in the system, and 78.7% able to perform transactions. However, data accuracy from physical and electronic records varied from 0% to 60%, and product availability ranged from 50% to 80%. Order fill rates from Zambia Medicines and Medical Supplies Agency (ZAMMSA) were consistently below 30%. Discrepancies were observed between physical stock counts and eLMIS records. Conclusion: This study found that most hospitals in the Copperbelt Province of Zambia have implemented LMIS use. While LMIS implementation is high in the Copperbelt Province of Zambia, challenges such as low training levels, data inaccuracies, low product availability, and order fill rates persist. Addressing these issues requires a comprehensive approach, including capacity building, data quality improvement, supply chain coordination, and investment in infrastructure and human resources. Strengthening LMIS effectiveness is crucial for improving healthcare delivery and patient outcomes in Zambia.展开更多
The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions a...The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.展开更多
In this work,general definition and meaning of knowledge,information,data and symbol are expressed generally/specifically,and the differences/relationships between them are briefly discussed.The general definition of ...In this work,general definition and meaning of knowledge,information,data and symbol are expressed generally/specifically,and the differences/relationships between them are briefly discussed.The general definition of system is briefly interpreted,and the semantic contents of the concept“system”expressed with nine perspectives generally.The meaning and importance of philosophy of information are then defined according to the general approaches.Some of the important philosophers of information and their professional interests are evaluated.The meaning and importance of mind,and philosophy of mind are discussed due to general approaches.Some of the philosophers of mind and their interests are evaluated and compared with a table.Systems philosophy is defined in line with general approaches,and its relationships with four main areas are stated.The new perspective of philosophy is then defined by the author generally,and the eight basic branches of philosophy and hybrid philosophy,along with their relevant theories,are briefly outlined.R-Philosophy,R-Science,R-Information,R-Mind,and R-System new disciplines are shortly expressed.New perspective for philosophy of information is defined as complementary branch with other seven basic philosophies.Types of information due to method,size,and content are given with a table.The 23 sub-branches of philosophy of information are defined generally/specifically.Philosophy of basic senses and some other branches are new defined,and new perspectives for philosophy of mind and for some other branches are expressed specifically.18 hybrid philosophies for information are defined,and their relations with philosophy of information explained generally/specifically.General disciplines and concepts about information are defined shortly,and information science(s),2D-6D hybrid information sciences,information system(s),and information&communication systems are given with details.New perspective for philosophy of system is defined,and types of system due to methods,size,and content are given with a table.Hybrid philosophies for systems,and some disciplines and concepts about systems are shortly outlined.Systems science(s)are defined due to four categories and each of these categories is explained with detailed tables.Hybrid systems defined by the author are shortly interpreted.展开更多
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX24_1332)Jiangsu Province Education Science Planning Project in 2024(Grant No.B-b/2024/01/122)High-Level Talent Scientific Research Foundation of Jinling Institute of Technology,China(Grant No.jit-b-201918).
文摘Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.
文摘1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic information sciences, remote sensing and cartography. Manuscripts come from different parts of the world.
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
基金The paper is supported by the Research Foundation for OutstandingYoung Teachers , China University of Geosciences ( Wuhan) ( No .CUGQNL0616) Research Foundationfor State Key Laboratory of Geo-logical Processes and Mineral Resources ( No . MGMR2002-02)Hubei Provincial Depart ment of Education (B) .
文摘Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.
文摘Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating terrains;3) Small fragmented land;4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion;2) extraction research on color features;3) extraction research on vegetation index;4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.
基金Under the auspices of National Natural Science Foundation of China(No.42330510)。
文摘With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
基金supported by the National Natural Science Foundation of China(62222212).
文摘Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.
基金supported by the project“GEF9874:Strengthening Coordinated Approaches to Reduce Invasive Alien Species(lAS)Threats to Globally Significant Agrobiodiversity and Agroecosystems in China”funding from the Excellent Talent Training Funding Project in Dongcheng District,Beijing,with project number 2024-dchrcpyzz-9.
文摘Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors.These vehicles are crucial in various fields,including environmental science research,ecological and environmental monitoring projects,disaster response,and emergency management.A key method employed in these vehicles for achieving high-precision positioning is LiDAR(lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping(SLAM).However,maintaining highprecision localization in complex scenarios,such as degraded environments or when dynamic objects are present,remains a significant challenge.To address this issue,we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data registration.Specifically,semantic information simplifies the modeling of scene elements,reducing the reliance on dense point clouds,which can be less efficient.Meanwhile,visual texture information complements LiDAR-Visual localization by providing additional contextual details.By incorporating semantic and texture details frompaired images and point clouds,we significantly improve the quality of data association,thereby increasing the success rate of localization.This approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts.
基金National Natural Science Foundation of China(grant numbers 42293351,41877239,51422904 and 51379112).
文摘Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detection accuracy.Using multiple methods to carry out a comprehensive evaluation can eff ectively improve the accuracy of advanced geological prediction results.In this study,geological information is combined with the detection results of geophysical methods,including transient electromagnetic,induced polarization,and tunnel seismic prediction,to establish a comprehensive analysis method of adverse geology.First,the possible main adverse geological problems are determined according to the geological information.Subsequently,various physical parameters of the rock mass in front of the tunnel face can then be derived on the basis of multisource geophysical data.Finally,based on the analysis results of geological information,the multisource data fusion algorithm is used to determine the type,location,and scale of adverse geology.The advanced geological prediction results that can provide eff ective guidance for tunnel construction can then be obtained.
文摘Objective: This study evaluates the impact of handshake and information support on patients’ outcomes during laparoscopic cholecystectomy. It examines the effects on their physiological and psychological responses and overall satisfaction with nursing care. Methods: A total of 84 patients scheduled for laparoscopic cholecystectomy were selected through convenient sampling and randomly assigned to either the control group or the intervention group using a random number table. Each group consisted of 42 patients. The control group received standard surgical nursing care. In addition to standard care, the intervention group received handshake and information support from the circulating nurse before anesthesia induction. Vital signs were recorded before surgery and before anesthesia induction. Anxiety levels were measured using the State-Trait Anxiety Inventory (STAI) and the State-Anxiety Inventory (S-AI), while nursing satisfaction was assessed using a numerical rating scale. Results: No significant differences were found between the two groups in systolic and diastolic blood pressures before surgery and anesthesia induction (P > 0.05). However, there was a significant difference in heart rate before anesthesia induction (P Conclusion: Providing handshake and information support before anesthesia induction effectively reduces stress, alleviates anxiety, and enhances comfort and satisfaction among patients undergoing laparoscopic cholecystectomy.
文摘This study analyzes the User Interface(UI)and User Experience(UX)of information systems that provide local government information.The systems analyzed are the Local Administrative Comprehensive Information Disclosure System(Zheripan),the Integrated Local Financial Disclosure System(Qinching Online),and the Local Regulations Information System(12348 Zhejiang Legal Network).The Local Administrative Comprehensive Information Disclosure System offers public service and personnel information,while the Integrated Local Financial Disclosure System provides financial information,and the Local Regulations Information System offers legal information as its main content.The analysis framework utilized three elements:objective data,psychological factors,and heuristic evaluation.The results of the first objective data analysis show that approximately 70%of visits to Zheripan and Qinching Online are through search,and the time spent on the homepage is short.In contrast,about 70%of visits to the 12348 Zhejiang Legal Network are direct visits,with users browsing multiple pages with a clear purpose.In terms of data provision methods,Zheripan provides two types of data in three formats,Qinching Online offers 28 types of data in five formats,and 12348 Zhejiang Legal Network provides one type of information in a single format.The second psychological factor analysis found that all three websites had a number of menus suitable for short-term cognitive capacity.However,only one of the sites had a layout that considered the user’s eye movement.Finally,the heuristic evaluation revealed that most of the evaluation criteria were not met.While the design is relatively simple and follows standards,feedback for users,error prevention,and help options were lacking.Moreover,the user-specific usability was low,and the systems remained at the information-providing level.Based on these findings,both short-term and long-term improvement measures for creating an interactive system beyond simple information disclosure are proposed.
基金This work is supported by the Ministry of Education of Humanities and Social Science projects in China(No.20YJCZH124)Guangdong Province Education and Teaching Reform Project No.640:Research on the Teaching Practice and Application of Online Peer Assessment Methods in the Context of Artificial Intelligence.
文摘This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.
文摘Nowadays,spatiotemporal information,positioning,and navigation services have become critical components of new infrastructure.Precise positioning technology is indispensable for determining spatiotemporal information and providing navigation services.
基金supported by National Natural Science Foundation of China(No.61761027)Gansu Young Doctor’s Fund for Higher Education Institutions(No.2021QB-053)。
文摘The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.
文摘Background: For nursing students, gathering social information is essential for understanding healthcare and social issues and developing critical thinking and decision-making skills. However, the choice of information sources varies by age and individual habits. With the widespread use of the internet, there are notable differences between younger and older generations in their reliance on the internet versus traditional media sources like newspapers and television. Given the wide age range and diverse backgrounds of nursing students, understanding generational differences in information-gathering methods is important for implementing effective education. Purpose: The purpose of this study is to identify how nursing students in different age groups obtain social information and to examine media usage trends by age group. Additionally, we aim to use the findings to provide insights into effective information dissemination methods in nursing education. Results: The results showed that nursing students in their teens to forties, regardless of gender, primarily relied on the internet as their main information source, with television playing a secondary role. In contrast, students in their fifties tended to obtain information more often from newspapers and television than from the internet. This highlights an age-related difference in preferred information sources, with older students showing a greater reliance on traditional media. Conclusions: This study demonstrates that nursing students use different information-gathering methods based on their age, suggesting a need to custo-mize information dissemination strategies in nursing education. Digital media may be more effective for younger students, while traditional media or printed materials might better serve older students. Educational institutions should consider these generational differences in media usage and adopt strategies that meet the diverse needs of their student populations.
文摘Background: The availability of essential medicines and medical supplies is crucial for effectively delivering healthcare services. In Zambia, the Logistics Management Information System (LMIS) is a key tool for managing the supply chain of these commodities. This study aimed to evaluate the effectiveness of LMIS in ensuring the availability of essential medicines and medical supplies in public hospitals in the Copperbelt Province of Zambia. Materials and Methods: From February to April 2022, a cross-sectional study was conducted in 12 public hospitals across the Copperbelt Province. Data were collected using structured questionnaires, checklists, and stock control cards. The study assessed LMIS availability, training, and knowledge among pharmacy personnel, as well as data accuracy, product availability, and order fill rates. Descriptive statistics were used to analyse the data. Results: All surveyed hospitals had LMIS implemented and were using eLMIS as the primary LMIS. Only 47% and 48% of pharmacy personnel received training in eLMIS and Essential Medicines Logistics Improvement Program (EMLIP), respectively. Most personnel demonstrated good knowledge of LMIS, with 77.7% able to log in to eLMIS Facility Edition, 76.6% able to locate stock control cards in the system, and 78.7% able to perform transactions. However, data accuracy from physical and electronic records varied from 0% to 60%, and product availability ranged from 50% to 80%. Order fill rates from Zambia Medicines and Medical Supplies Agency (ZAMMSA) were consistently below 30%. Discrepancies were observed between physical stock counts and eLMIS records. Conclusion: This study found that most hospitals in the Copperbelt Province of Zambia have implemented LMIS use. While LMIS implementation is high in the Copperbelt Province of Zambia, challenges such as low training levels, data inaccuracies, low product availability, and order fill rates persist. Addressing these issues requires a comprehensive approach, including capacity building, data quality improvement, supply chain coordination, and investment in infrastructure and human resources. Strengthening LMIS effectiveness is crucial for improving healthcare delivery and patient outcomes in Zambia.
基金supported by the National Natural Science(No.U19A2063)the Jilin Provincial Development Program of Science and Technology (No.20230201080GX)the Jilin Province Education Department Scientific Research Project (No.JJKH20230851KJ)。
文摘The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.
文摘In this work,general definition and meaning of knowledge,information,data and symbol are expressed generally/specifically,and the differences/relationships between them are briefly discussed.The general definition of system is briefly interpreted,and the semantic contents of the concept“system”expressed with nine perspectives generally.The meaning and importance of philosophy of information are then defined according to the general approaches.Some of the important philosophers of information and their professional interests are evaluated.The meaning and importance of mind,and philosophy of mind are discussed due to general approaches.Some of the philosophers of mind and their interests are evaluated and compared with a table.Systems philosophy is defined in line with general approaches,and its relationships with four main areas are stated.The new perspective of philosophy is then defined by the author generally,and the eight basic branches of philosophy and hybrid philosophy,along with their relevant theories,are briefly outlined.R-Philosophy,R-Science,R-Information,R-Mind,and R-System new disciplines are shortly expressed.New perspective for philosophy of information is defined as complementary branch with other seven basic philosophies.Types of information due to method,size,and content are given with a table.The 23 sub-branches of philosophy of information are defined generally/specifically.Philosophy of basic senses and some other branches are new defined,and new perspectives for philosophy of mind and for some other branches are expressed specifically.18 hybrid philosophies for information are defined,and their relations with philosophy of information explained generally/specifically.General disciplines and concepts about information are defined shortly,and information science(s),2D-6D hybrid information sciences,information system(s),and information&communication systems are given with details.New perspective for philosophy of system is defined,and types of system due to methods,size,and content are given with a table.Hybrid philosophies for systems,and some disciplines and concepts about systems are shortly outlined.Systems science(s)are defined due to four categories and each of these categories is explained with detailed tables.Hybrid systems defined by the author are shortly interpreted.
