Knowledge Of Map Research Articles

Future Prospects of Large Language Models: Enabling Natural Language Processing in Educational Robotics

Large language models (LLMs) have recently shown considerable promise in educational robotics by offering generic knowledge necessary in situations when prior programming is not possible. In general, mobile education robots cannot perform tasks like navigation or localization unless they have a working knowledge of maps. In this letter, we tackle the issue of making LLMs more applicable in the field of mobile education robots by helping them to understand Space Graph, a text-based map description. This study, which focuses on LLMs, is divided into several sections. It explores basic natural language processing (NLP) techniques and highlights how they can help create smooth education discussions. Examining the development of LLMs inside NLP systems, the paper explores the benefits and implementation issues of important models utilized in the education sector. Applications useful in educational discussions are described in depth, ranging from patient-focused tools like diagnosis and treatment recommendations to systems that support education providers. We provide thorough instructions and real-world examples for quick engineering, making LLM-based educational robotics solutions more accessible to novices. We demonstrate how LLM-guided upgrades can be easily included in education robotics applications using tutorial-level examples and structured prompt creation. This survey provides a thorough review and helpful advice for leveraging language models in automation development, acting as a road map for researchers navigating the rapidly changing field of LLM-driven educational robotics.

Knowledge Mapping and Trend Analysis of Disaster Mitigation Research in the Sulawesi Islands of Indonesia: A Bibliometric Study Using VOSviewer

Indonesia, as an archipelagic country located on the Pacific Ring of Fire, faces a high risk of natural disasters, including earthquakes, tsunamis and volcanic eruptions. Sulawesi Island, which frequently experiences disasters, is the focus of this research to analyze trends and map knowledge related to disaster mitigation. The aim of this research is to highlight the effectiveness of existing mitigation strategies and identify areas that require further attention. The method used is bibliometric analysis with VOSviewer software, using Publish or Perish 8, of key terms in scientific literature. Data includes articles published between 2019 and 2024, with strict inclusion criteria to ensure relevance to disaster mitigation in Sulawesi. Research findings show that the four main types of disasters are floods, landslides, volcanic eruptions and tsunamis. disaster mitigation in Sulawesi. In addition, there is a lack of knowledge and implementation of sustainable mitigation strategies, as well as the need for active involvement of local communities in mitigation programs. The implications of this research emphasize the importance of evaluating existing disaster mitigation programs and more effective development policies, which can increase community preparedness and resilience to disasters. It is hoped that this research can make a significant contribution to efforts to reduce disaster risk in the region, as well as encourage collaboration between government, institutions and society in improving disaster mitigation.

Human prior knowledge estimation from movement cues for information-based control of mobile robots during search

Robotic search often involves teleoperating vehicles into unknown environments. In such scenarios, prior knowledge of target location or environmental map may be a viable resource to tap into and control other autonomous robots in the vicinity towards an improved search performance. In this paper, we test the hypothesis that despite having the same skill, prior knowledge of target or environment affects teleoperator actions, and such knowledge can therefore be inferred through robot movement. To investigate whether prior knowledge can improve human-robot team performance, we next evaluate an adaptive mutual-information blending strategy that admits a time-dependent weighting for steering autonomous robots. Human-subject experiments show that several features including distance travelled by the teleoperated robot, time spent staying still, speed, and turn rate, all depend on the level of prior knowledge and that absence of prior knowledge increased workload. Building on these results, we identified distance travelled and time spent staying still as movement cues that can be used to robustly infer prior knowledge. Simulations where an autonomous robot accompanied a human teleoperated robot revealed that whereas time to find the target was similar across all information-based search strategies, adaptive strategies that acted on movement cues found the target sooner more often than a single human teleoperator compared to non-adaptive strategies. This gain is diluted with number of robots, likely due to the limited size of the search environment. Results from this work set the stage for developing knowledge-aware control algorithms for autonomous robots in collaborative human-robot teams.

Visual Communication Method of Multi-Frame Film and Television Special Effects Images Based on Deep Learning

In the view of art form change, there is a perspective problem in the detail part of multi-frame film and television special effects (TSEs) images in the special effects rendering module, which leads to the greater ambiguity of multi-frame film and TSEs images. In order to advance the talent of identifying the detail structures and characteristics of multi-frame film and TSEs images, a visual communication technology of multi-frame film and TSEs images in the view of art form change grounded on deep learning is suggested. The transmission relation model of detail characteristics of multi-frame film and TSEs images in the field of artistic form change is constructed, and the attenuation treatment of detail action special effects of multi-frame film and TSEs images in the field of artistic form change is carried out with the assistance of deep learning approaches. The detailed information and color of multi-frame film and TSEs images are obtained by using prior map knowledge instead of rendering basic color. Over the technique of filtering and preserving, the multi-frame film and TSEs images are directed to screen the detailed feature quantity of the input film and TSEs images in the field of artistic form change. In addition, the Sobel operator is practiced to perceive the scrawny edge information of multi-frame film and TSEs images, and block fitting interpolation and depth filtering analysis are used in the edge area to realize the visual communication and color feature parameter identification and amplification of multi-frame film and TSEs images in the field of artistic form change, so as to improve the fuzzy enhancement of multi-frame film and TSEs images. The test results and results indicate that the average Peak signal-to-noise ratio (PSNR) of the proposed method is about 37.7166[Formula: see text]dB, which is 49.08% and 52.08% higher than the PSNRs of the Harris method and SUFR method, respectively. It has been confirmed that this scheme can successfully solve the edge blurring and distortion problems of multi-frame movies and image images in the field of art form change.

A Scoping Review Protocol to Map Knowledge and Attitudes about Patient Safety among Students in Healthcare Undergraduate Courses.

Patient safety is a global challenge in healthcare, with adverse events representing a significant concern. The integration of patient safety education in undergraduate curricula is crucial in developing a culture of safety and safe practices among future professionals. However, there is a gap in research assessing the levels of knowledge and attitudes about patient safety among undergraduate students in healthcare using validated instruments. This scoping review aims to map the levels of knowledge and attitudes about patient safety among students in healthcare courses, allowing for national and international comparisons. The review will follow the methodological frameworks proposed by the Joanna Briggs Institute (JBI) for scoping reviews. The search will include published and unpublished studies in six databases, with no date or language restrictions. This scoping review was prospectively registered with the Open Science Framework on 17 July 2024. This scoping review will provide a comprehensive overview of knowledge and attitudes about patient safety among undergraduate students in healthcare, identifying gaps in patient safety education and areas for improvement in curricula. The results may guide teachers in creating teaching strategies to prepare future healthcare professionals, reducing knowledge gaps and improving the quality of care.

Descent modulus and applications

The norm of the gradient ‖∇f(x)‖ measures the maximum descent of a real-valued smooth function f at x. For (nonsmooth) convex functions, this is expressed by the distance dist(0,∂f(x)) of the subdifferential to the origin, while for general real-valued functions defined on metric spaces by the notion of metric slope |∇f|(x). In this work we propose an axiomatic definition of descent modulus T[f](x) of a real-valued function f at every point x, defined on a general (not necessarily metric) space. The definition encompasses all above instances as well as average descents for functions defined on probability spaces. We show that a large class of functions are completely determined by their descent modulus and corresponding critical values. This result is already surprising in the smooth case: a one-dimensional information (norm of the gradient) turns out to be almost as powerful as the knowledge of the full gradient mapping. In the nonsmooth case, the key element for this determination result is the break of symmetry induced by a downhill orientation, in the spirit of the definition of the metric slope. The particular case of functions defined on finite spaces is studied in the last section. In this case, we obtain an explicit classification of descent operators that are, in some sense, typical.

Global Trends in Research Related to Emergence Agitation From 1978 to 2023: A Bibliometric Analysis

PurposeEmergence agitation (EA) is a postoperative behavioral disorder characterized by a lack of connection between consciousness and behavior. Despite its clinical importance and prevalence in the postoperative setting, the research focus and knowledge structure remain to be systematically mapped. Therefore, we conducted a bibliometric analysis from 1978 to 2023 to address this gap. This study aims to excavate research hotspots, map knowledge structure, uncover emerging trends, and provide invaluable insights for guiding future research endeavors. DesignA bibliometric analysis of emergency agitation. MethodsWe conducted a literature search in the Web of Science Core Collection for articles on EA published between 1978 and 2023. We collected data on countries, institutions, journals, authors, citations, and keywords. Subsequently, we comprehensively analyzed the gathered knowledge units using three tools, including VOSviewer, HistCite Pro, and ArcGIS Pro. FindingsFrom 1978 to 2023, we retrieved 948 pieces of literature on EA, with the number of publications increasing yearly. China ranked first in the number of publications (n = 253, 26.69%). The United States was the most cited and influential country, closely collaborating with other countries (5,661 citations, H-index 57, citations per publication 37.99, total link strength [TLS] 24). Yonsei University was the most productive and influential institution (n = 18, 1.9%, 427 citations, H-index 12, citations per publication 23.72). Shanghai Jiao Tong University had close collaborative relationships with other universities (TLS 7). The journal with the most publications and influence was Pediatric Anaesthesia (n = 46, 4.92%, H-index 32). Anaesthesia and Analgesia was the most cited journal (2,849 citations). M.T. Aouad was the most influential and productive author devoted to studying EA (n = 6, 0.63%, 173 citations, TLS 3,002). The most influential reference was “Greater incidence of delirium during recovery from sevoflurane anaesthesia in preschool boys” (author J. Aono, No. 19, IF = 8.8, Q1, local citation score 147). Hot research directions in EA include EA and emergence delirium, propofol and EA, risk factors of EA, EA in pediatric anaesthesia, dexmedetomidine, and EA induced by sevoflurane. Esketamine, nalbuphine, sugammadex, and dezocine are drugs that have received increased attention in recent years. ConclusionsThe study mapped the knowledge structure of EA and identified six prominent research directions, four drugs of significant research interest, and emerging thematic trends. These findings offer insights into the current state of research on EA, providing a comprehensive view of the field's landscape.

Mineral Prospectivity Mapping Based on Spatial Feature Classification with Geological Map Knowledge Graph Embedding: Case Study of Gold Ore Prediction at Wulonggou, Qinghai Province (Western China)

Prospectivity mapping based on deep learning typically requires substantial amounts of geological feature information from known mineral deposits. Due to the limited spatial distribution of ore deposits, the training of predictive models is often hampered by insufficient positive samples. Meanwhile, data-driven mineral prospectivity mapping often overlooks domain knowledge and expert experience, leading to poor interpretability of predictive results. To address this problem, we employed the Gaussian mixture model (GMM) for spatial feature classification to expand the number of positive samples. The approach integrated the embedding of geological map knowledge graphs with geological exploration data to enhance the knowledge constraints of the prospecting model, which enabled the integration of knowledge with data. Considering the complex spatial structure of geological elements, a bi-branch utilizing the 1-dimensional convolutional neural network (CNN1D) and graph convolutional network (GCN) was used to extract geological spatial features for model training and prediction. To validate the effectiveness of the method, a gold mineralization prediction study was conducted in the Wulonggou area (Qinghai province, western China). The results indicate that, when the number of GMM spatial feature classifications was 17, the positive-to-negative sample ratio was optimal, and the embedding of the knowledge graph controlled the prediction area distribution effectively, which demonstrated strong consistency between the prospecting area and the known mineral deposits. Compared with the predictions by CNN1D, the fused prediction model of CNN1D and GCN yielded higher accuracy. Our model identified 11 classes of mineralization potential areas and provides geological interpretations for different prediction categories.

After effects of historical grassland on soil organic carbon content and plant growth in croplands in southern Germany determined using satellite data

Numerous studies have reported that grasslands harbor higher soil organic carbon (SOC) stocks compared with arable land; however, the relevant carbon dynamics and sink persistence remain unclear.Herein, arable fields characterized by historical grassland zones (h_GL; grassland use decades ago) and permanent arable land zones (h_CL) were examined. The h_GL zones were determined using historical maps. The change in land use from grassland to cropland occurred 30–50 years ago.In eight arable fields, SOC and total nitrogen (TN) stocks in the topsoil were analyzed at a high spatial resolution. Additionally, remote sensing via satellites was employed to determine the biomass yield at a high spatial resolution using the normalized difference vegetation index (NDVI). In all the fields, the mean SOC content of the h_GL zones (1.81 %, n = 97 measuring points) was higher than the mean SOC content of the h_CL zones (1.52 %, n = 220). Furthermore, the mean relative NDVI was higher in the h_GL zones than in the h_CL zones. SOC and NDVI were positively correlated (up to r = 0.79), as well as TN and NDVI (up to r = 0.72). To evaluate the first dataset, zonal soil samples were collected from the h_GL and h_CL zones from 14 arable fields to determine the SOC and TN content. The mean SOC content of the h_GL zones was 1.92 % and that of the h_CL zones was 1.39 %—a difference of absolute SOC stocks in the topsoil of 23.8 t ha−1 (bulk density: 1.5 g cm−3).The work combines the knowledge of historical soil maps, remote sensing applications and georeferenced soil sampling and shows that SOC stocks in grassland have a high persistence and can have positive impact on yields even decades after a land use change. Historical land use proved to be a major factor for spatial SOC variability at the study site.

Locally-adaptive mapping for network alignment via meta-learning

Network alignment (NA), discovering anchor nodes that represent the same entities across different networks, plays a fundamental role in information fusion. Most existing embedding-based methods rarely study the alignment module, which learns a global mapping to unify embedding spaces. However, global mapping is a holistic solution for nodes, incapable of optimally projecting each node, thus deteriorating alignment accuracy. To solve this problem, this paper proposes a local mapping framework named MANA, which fine-tunes global mapping by meta-learning to obtain node-level local mappings. One advantage of MANA is that it tailors local mapping adapted to the embedded locality of each node while maintaining the general knowledge of global mapping. The main challenge of applying meta-learning to network alignment is paradigm incompatibility; that is, how to construct effective meta-tasks and support sets for zero-shot NA. Therefore, the paper constructs meta-tasks with similarity-based support sets. A support set is taken from pairs of anchor nodes, the source nodes of which are embedded close to the query node. Our framework can be applied to existing mapping-based NA models. Experimental results show that mapping-based models with MANA improve evaluation scores by 1%–59% relative to their original models, demonstrating the effectiveness of local mapping. Some simple mapping-based models improved by MANA even outperform sophisticated sharing-based NA approaches.

Multi-order feature interaction-aware intrusion detection scheme for ensuring cyber security of intelligent connected vehicles

The evolution of technology has raised concerns regarding cybersecurity for intelligent connected vehicles (ICVs). In-vehicle network in ICVs lacks robust protection mechanisms, making it vulnerable to cyber threats. In response, intrusion detection systems (IDSs) for ICVs have been developed to protect vehicles from malicious cyber attacks. However, current IDS methods solely rely on independent features, limiting their learning capabilities and increasing the number of false detections. Moreover, many IDSs require the knowledge of mapping between network messages and contents, which restricts their application. To address these limitations, we propose the Multi-order Feature Interaction-aware Intrusion Detection (MIFI) scheme for ICVs. Feature attention cross network is designed to address higher-order feature interactions, while factorization machine is used for second-order interactions. Then a discriminator is utilized to detect the attacks. MIFI expands the feature space through features interaction, thereby enhancing its ability to detect attacks. Moreover, it perceives the relationships of vehicle messages, facilitating intrusion detection without knowing the corresponding rules of vehicle messages. The performance of the proposed method is evaluated on two real-vehicle datasets, affirming its effectiveness and robustness. MIFI achieves an accuracy of over 99% in detecting different attacks. The proposed method can improve the accuracy of traditional IDS to a maximum of 99.99%, and increase the highest F1-score to 97.18%, demonstrating the model’s ability of achieving multi-order feature interactions. Ultimately, MIFI is suitable for intrusion detection in different types of ICV networks, significantly contributing to the cybersecurity of ICVs.

Storage estimation in morphology modeling of the human whole brain at the nanoscale

The human brain is an enormous scientific challenge. Knowledge of the complete map of neuronal connections (connectome) is essential for understanding how neuronal circuits encode information and the brain works in health and disease. Nanoscale connectomes are created for a few small animals but not yet for the human. The key challenges in the development of a whole human brain model at the nanoscale are data acquisition and computing including big data and high performance computing. This work focuses on big data and volumetric and geometric modeling of brain morphology at the micro- and nanoscales. It presents the volumetric and four geometric neuronal models and estimates the storage required for them. It introduces four geometric neuronal models: straight wireframe, enhanced wireframe, straight polygonal, and enhanced polygonal. The volumetric model requires approximately from 4.2 to 33.6 petabytes (PB) at the microscale up to 5,600,000 exabytes (EB) at the nanoscale. The straight wireframe model requires 18 PB at the microscale and 24 PB at the nanoscale. The enhanced parabolic wireframe model needs 36 PB at the microscale and 48 PB at the nanoscale, whereas the enhanced cubic model requires 54 PB at the microscale and 72 PB at the nanoscale. The straight polygonal model requires 24 PB at the microscale and 32 PB at the nanoscale. The enhanced parabolic polygonal model needs 48 PB at the microscale and 64 PB at the nanoscale, while the enhanced cubic model needs 72 PB at the microscale and 96 PB at the nanoscale. The straight wireframe model of 18 PB is sufficient to enable computing of the human synaptome and subsequently the connectome. The only operational supercomputer able to provide such storage is the world’s first exascale supercomputer Frontier. The sizes of the volumetric and geometric models are comparable at the microscale, however, their difference is dramatic at the nanoscale; for the 10 nm resolution the geometric models are smaller approximately from 58 to 233 thousand times, and for the 1 nm resolution from 58 to 233 million times. This novel work is an extended version of a conference paper [15] and it represents a step forward toward the development of the human whole brain model at the nanoscale.

Lyapunov-like functions for almost global convergence in discrete-time systems

This paper presents new results on discrete-time almost global convergence to an invariant set. First, we present new sufficient conditions for almost global convergence using density functions. In contrast to existing density-function results, the new results in this paper do not rely on a local convergence assumption, and they do not require knowledge of the inverse map of the difference equation. Next, we present new sufficient conditions for almost global convergence using Lyapunov-like functions rather than density functions. These Lyapunov-like results can be useful because constructing and analyzing density functions is often difficult in comparison to Lyapunov-like analysis. This paper also presents a variety of simple examples to illustrate these new methods for almost global convergence analysis.

The significance of radiative heat and mass transfer through a vertical sheet with chemical reaction: Designing by artificial approach Levenberg-Marquardt

The present study examines the impact of incorporating soft computing algorithms during neural network training on the evaluation and prediction performance of artificial neural networks. The research centers on a magneto hydrodynamic flow model (RHMT-VSCR) that depicts the movement of rotating fluid along a vertical sheet in a permeable medium. Furthermore, the model considers the impact of heat source-sink interactions, thermal radiation, and reactive species in conjunction with the effects of Hall current on the enhancement of energy and solute profiles. Because the induced magnetic field has a negligible magnetic Reynolds number, it is disregarded. A set of governing nonlinear PDEs is converted to a system of ODEs by applying an appropriate postulate of similarity variables in order to analyze the system. The multilayer perceptron network utilizes a supervised Levenberg–Marquardt Backpropagation algorithm (LMLA-BPNN) to ascertain the ideal quantity of neurons to be included in the hidden layer of artificial neural networks intended for modeling purposes. The bvp4c numerical approach is utilized to establish a continuous neural network mapping, which produces datasets that are utilized for the purposes of authentication, training, and testing. The range i.e., 10−2−10−8 of absolute error of the reference and target data demonstrates the optimal accuracy performance of LMLA-BPNN networks. After acquiring knowledge of neural network mapping, it is applied to approximate solutions for a wide range of scenarios through the manipulation of physical constraints, including suction/injection quantities, magnetic parameter, and porosity parameter, which influence the characteristics of flow, energy, and concentration.To assess the precision of the proposed method, statistical graphs based on regression, mean squared error analysis graphs, and error histogram graphs are employed. The results of the research demonstrate that the Levenberg-Marquardt Backpropagation neural network mappings' derivation, convergence, authentication, and stability were effectively validated.

Fractional-order quantum game chaotic map and its synchronization with application

In this paper, according to the knowledge of discrete fractional calculus and quantum game chaotic map, we first get the fractional quantum game chaotic map, which describes the dynamics of a quantum duopoly game. Then, the phase space, maximum Lyapunov exponent, and bifurcation diagram of the proposed map are obtained for the purpose of observing dynamic behaviors compared with the original quantum game chaotic map. After that, the synchronous controller is designed and the chaos synchronization of the fractional quantum game chaotic map is observed. Finally, we develop the fractional quantum game chaotic map into the chaotic sequence generator and image encryption scheme.

Visualization of students’ cognitive knowledge in digital concept mapping

<span lang="EN-US">Meaningful learning through the creation a digital concept map is the easiest way to gain cognitive knowledge, new information, and possibility how to expand among learners. Only a few empirical studies have been conducted in the field of nursing regarding learning through concept mapping to achieve higher levels of knowledge and digital skills. The study evaluated the student’s ability to visualize cognitive knowledge by creating a digital concept map. The objectives were to identify the perception using digital technologies and conceptual mapping, differences and relationships between gender, age, years of study, and branches. We used the ContextMinds program map creation. There were 92 Slovak students of nursing, public health and physiotherapy participated in the study. Data were collected and analyzed using IBM SPSS version 25.0. Perception of learning using digital concept mapping strategy was assessed using descriptive statistics, where paired t-test, analysis of variance (ANOVA) and Chi-square were used. The study found a positive perception of creating the digital concept map, but a low level of digital skills. First-year students took one or more weeks to submit the final version of the concept map in comparison to third-year students who took one or more hours. For second-year students, a creation of a concept map was less problematic than for first-year students. Students prefer standard teaching and learning methods. Healthcare absolvents are required to be proficient in digital literacy. Applying cognitive knowledge to create a digital concept map is a challenging strategy procedure.</span>

Voorbij de nationale kerkenstamboom: naar een inclusieve genealogie van het christendom in Nederland

Since the sixteenth-century Reformation, Christianity in the Netherlands is characterized by pluriformity. Besides the Catholic Church of Rome and the Reformed ‘public church’, there were ecclesial bodies of ‘Protestant Dissenters’. The 1834 Secession provoked a lot of splits and (re)unions up to the present, whereas the breakthrough of religious freedom in 1848 enabled the establishment of many new groups (partly imported from abroad). In the past fifty years, several didactic and sometimes ecumenical or apologetical schemes of denominations were published, showing in particular the labyrinth of the Reformed world. At the same time, the Christian landscape underwent structural changes because of repatriation from former colonies, immigration from numerous countries, and regrouping of evangelical believers. Given the developments of secularization (and, as a result, declining knowledge of the national church map), internationalization (rising non-Dutchdenominations), and inclusivity (dealing with the colonial past), a new design of the Dutch ‘Christian family tree’ seems to be welcome. Therefore, this article offers a basis by discussing earlier examples, presenting a historically justified alternative, while welcoming feedback from the field and recommending a creative elaboration with digital tools.

‘We find our way!’ Preschool children express their mapping skills

ABSTRACT Raising consciousness about the concept of the physical environment and learning ways to find a street or targeted destination in early childhood are important in developing children’s cognitive skills. Map skills and spatial thinking can help children to solve a wide variety of problems in their daily lives (Gökçe, 2015). This study aimed to explore how preschool (five-year-old) children understand and use geographic knowledge in practice. A case study using the Mosaic approach was run to collect data from a series of seven carefully planned sessions, conducted in a rural public preschool in a city of southern Turkey. Pre- and post-activity tests were used to gauge children’s knowledge of maps and mapping, and observations were made as to how children reacted to mapping skills activities. Utilisation of mapping tools was taught and children’s knowledge about mapping skills was increased.

A Lightweight Object Counting Network Based On Density Map Knowledge Distillation

A Lightweight Object Counting Network Based On Density Map Knowledge Distillation

M-FSDistill: A Feature Map Knowledge Distillation Algorithm for SAR Ship Detection

M-FSDistill: A Feature Map Knowledge Distillation Algorithm for SAR Ship Detection