Mapping Method Research Articles

Application of Blockchain in Procurement and Management of Building Services: A Critical and Systematic Review

Building services systems are essential for creating a comfortable and safe living environment in buildings. These are complex systems with high stakeholder involvement, a lengthy lifecycle, and high financial costs. This leads to building services systems having complex procurement and management (P&M) requirements which create a multitude of challenges. Blockchain technology has emerged as a revolutionary digital technology under “Procurement 4.0”. The purpose of this paper is to investigate the applicability of blockchain technology for systematic procurement and management of building services. The science mapping method was used to systematically and quantitatively analyse 102 publications related to bibliographic records retrieved from Scopus and the Web of Science databases. This includes content analysis of the existing issues, current trends of technologies, and applicability of blockchain in the P&M of building services. The results of the bibliometric analysis indicate that publications had grown significantly faster in 2021 related to the P&M of building services with new technologies while there is a minimal collaboration of countries, organisations and authors in publishing research in this area. Further, it is observed that Building Information Modelling (BIM) is the main technology utilised in general P&M. As a result of content analysis, a total of 28 issues that affect the performance of procurement and management of building services are identified. The paper critically evaluates blockchain technology in terms of peer-to-peer networks, hashing algorithms, public key cryptography, consensus mechanisms, smart contracts, and distributed ledger. It indicates that blockchain provides a perfect match for resolving these issues. The findings of the research will open a path to apply blockchain technology in building services. The study offers a readily available point of reference for practitioners, policymakers and research and development bodies.

Lack of Small-Scale Changes in Breeding Birds after a Fire: Does the Resilience of Cork Oaks Favor Rapid Recolonization in Suburban Wood Patches?

Forest fires are disturbance events that can impact biological assemblages at multiple scales. In this study, the structures of breeding bird communities in cork oak patches located in an agro-mosaic suburban landscape of central Italy (Rome) were compared at the local scale with a fine-grained mapping method before (2018) and after (2023) a fire event occurred in July 2022. The analyses did not reveal any significant changes in the density of territorial pairs or in the diversity metrics, both univariate (Shannon–Wiener index, evenness, Margalef normalized richness) and bivariate (Whittaker and k-dominance plots, abundance/biomass curves) of diversity. Even when the guilds of strictly forest-related species were compared, no differences emerged before and after the fire. This counterintuitive phenomenon may be due to the characteristics of the dominant tree, the cork oak (Quercus suber), a sclerophilous tree that is very resilient to fires and able to recover foliage in the following spring season, thus allowing rapid bird recolonization. However, other small-scale phenomena (e.g., the ‘crowding effect’ and local dispersal of territorial pairs from remnant wood patches not affected by fire) may explain this lack of change in breeding bird density and diversity. Further studies should be carried out at larger spatial and temporal scales and at different levels of fire frequency and intensity to confirm these responses at the guild/community level in suburban cork oak wood patches.

Multi-Channel Signals in Dynamic Force-Clamp Mode of Microcantilever Sensors for Detecting Cellular Peripheral Brush.

The development of numerous diseases, such as renal cyst, cancer, and viral infection, is closely associated with the pathological changes and defects in the cellular peripheral brush. Therefore, it is necessary to develop a potential new method to detect lesions of cellular peripheral brush. Here, a piecewise linear viscoelastic constitutive model of cell is established considering the joint contribution of the peripheral brush and intra-cellular structure. By combining the Laplace transformation and its inverse transformation, and the differential method in the temporal domain and differential quadrature method (DQM) in the spatial domain, the signal interpretation models for quasi-static and dynamic signals of microcantilever are solved. The influence mechanisms of the peripheral brush on the viscoelastic properties of cells and quasi-static/dynamic signals of microcantilever are clarified. The results not only reveal that the peripheral brush has significant effects on the complex modulus of the cell and multi-channel signals of the microcantilever, but also suggest that an alternative mapping method by collecting multi-channel signals including quasi-static and higher frequency signals with more brush indexes could be potentially used to identify cancerous cells.

Mangrove mapping in China using Gaussian mixture model with a novel mangrove index (SSMI) derived from optical and SAR imagery

As an important shoreline vegetation and highly productive ecosystem, mangroves play an essential role in the protection of coastlines and ecological diversity. Accurate mapping of the spatial distribution of mangroves is crucial for the protection and restoration of mangrove ecosystems. Supervised classification methods rely on large sample sets and complex classifiers and traditional thresholding methods that require empirical thresholds, given the problems that limit the feasibility and stability of existing mangrove identification and mapping methods on large scales. Thus, this paper develops a novel mangrove index (spectral and SAR mangrove index, SSMI) and Gaussian mixture model (GMM) mangrove mapping method, which does not require training samples and can automatically and accurately map mangrove boundaries by utilizing only single-scene Sentinel-1 and single-scene Sentinel-2 images from the same time period. The SSMI capitalizes on the fact that mangroves are differentiated from other land cover types in terms of optical characteristics (greenness and moisture) and backscattering coefficients of SAR images and ultimately highlights mangrove forest information through the product of three expressions (f(S) = red egde/SWIR1, f(B) = 1/(1 + e-VH), f(W)=(NIR-SWIR1)/(NIR+SWIR1)). The proposed SSMI was tested in six typical mangrove distribution areas in China where climatic conditions and mangrove species vary widely. The results indicated that the SSMI was more capable of mapping mangrove forests than the other mangrove indices (CMRI, NDMI, MVI, and MI), with overall accuracys (OA) higher than 0.90 and F1 scores as high as 0.93 for the other five areas except for the Maowei Gulf (S5). Moreover, the mangrove maps generated by the SSMI were highly consistent with the reference maps (HGMF_2020、LASAC_2018 and IMMA). In addition, the SSMI achieves stable performance, as shown by the mapping results of the other two classification methods (K-means and Otsu’s algorithm). Mangrove mapping in six typical mangrove distribution areas in China for five consecutive years (2019–2023) and experiments in three Southeast Asian countries with major mangrove distributions (Thailand, Vietnam, and Indonesia) demonstrated that the SSMIs constructed in this paper are highly stable across time and space. The SSMI proposed in this paper does not require reference samples or predefined parameters; thus, it has great flexibility and applicability in mapping mangroves on a large scale, especially in cloudy areas.

Optimalisasi Kemampuan Penalaran Matematis melalui Metode Mind Mapping

Increasingly rapid global developments raise complex problems that require effective and innovative solutions. Mathematical reasoning is the key to solving the problem. But on the other hand, learning in schools is still a lot of conventional learning and the development of the students' mathematical reasoning skills is not optimal. The aim of this study was to explore the effectiveness of using mind map-based learning methods that were judged to be effective in developing mathematical reasoning skills. With a qualitative approach to literature studies, research results were obtained which showed that the method of mind mapping had a significant influence on the mathematical reasoning ability of the students. Library study sources are derived from previous research that had the same focus of study. Library reviews are used to analyze several studies related to the use of mind mapping-based learning methods and their influence on mathematical reasoning capabilities. This research is expected to provide an understanding of the importance of developing mathematical reasoning skills to form intelligent and critical generations

A Classification and Interpretation of Methodological Approaches to Pursue Natural Capital Valuation in Forest Research

This paper reviews natural capital (NC) valuation approaches in the context of woodland, forest, and riparian ecosystems, emphasising the need for participatory methods to take priority over neoclassical economics approaches. Focusing on research carried out in Scotland, the study analyses findings according to a classification of natural capital initiatives that we have developed, building on ideas proposed by the UK ENCA initiative, a guideline proposed to help researchers and practitioners understand NC and take it into account in valuation, decision-making and policy. We have found that landscape-scale initiatives that address the relationships between people and place to inform value and decision-making beyond the economic (monetary) benefits generated by ecosystem services (ES) are becoming popular. For instance, recent methods employed to capture stakeholders’ non-utilitarian preferences include the use of participatory GIS mapping, scenario planning, and other participatory methods to identify, explore and quantify less tangible cultural ecosystem services (CES). The review shows that many studies provide information relevant to the formulation of a place-based NC approach, working towards the integration of contextual and relational values into land management decisions to help formulate management strategies that maximise ES delivery. Conversely, we have not found evidence of the integration of shared values arising from an eco-centric perspective of nature valuation into the more classical, instrumental value lens. Such an approach would help inform broader, overarching aspects of woodland and forest management that may foster more effective conservation and help to manage conflicts.

A Resource Allocation Algorithm for Cloud-Network Collaborative Satellite Networks with Differentiated QoS Requirements

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.

‘Learning from what my Mentor Teachers were Doing in the Classroom to Include Diverse learners’: Experiences that Contribute to the Use of Inclusive Instruction in Pre-Service Teachers

ABSTRACT All learners benefit when schools foster an environment for teachers to enhance their teaching skills. Pre-service education programs are often where this important process begins. Using a Global Concept Mapping method, pre-service teachers across Canada were interviewed about the personal and professional experiences that contributed to their instructional practices within inclusive classrooms. Participants sorted 93 unique statements into categories. They were asked to rate on a scale of 1 (not at all important) to 6 (very important) how important each experience would be to their instructional practices. Mentoring relationship statements were rated significantly more important than the others. Practicum experiences and those within the course part of the education program were rated as equally important and more important than professional development. Their perceived least important experiences were those related to past jobs/positions and personal experiences with people identified with diverse learning needs. Understanding the specific experiences that influence pre-service teachers’ perspectives of their inclusive instructional practices will help teacher education programs connect to what motivates teachers’ early experiences in becoming inclusive educators.

Triangular Fuzzy Cognitive Maps Method for Modelling Interrelation between Causal and Trigger Factors Towards Students' Mathematics Problem-Solving Ability

The purpose of this study is to model the interrelation between causal and trigger factors towards students' mathematics problem-solving ability by using the triangular fuzzy cognitive maps (TrFCM) method. Selection weaknesses and limitations in the method of relational analysis cause the interrelation and influence between variables not to be visualised and do not reveal the characteristics of the actual interaction. As a result, this study demonstrates TrFCM as a more effective way of analysing the relationship between variables based on the complexity that happens in analysing causal factors and triggers for students' problem-solving abilities in mathematics. The results of the influential relations map (IRM) demonstrate that emotion and metacognition are the triggers for problem-solving ability. While executive function is the main cause of success in completing mathematics problems, it is also influenced by additional factors such as motivation, attention, and working memory. These causal and triggering factors also mobilise parts of students' cognitive and behavioural performance to improve the process of solving mathematics problems. Based on the outcomes of this study, computational intelligence methods like fuzzy systems give useful procedures for analysing data from expert surveys. The TrFCM method offers a more accurate relational analysis procedure in modelling interrelation between human factors.

A multi-strategy ontology mapping method based on cost-sensitive SVM

As the core of ontology integration, the task of ontology mapping is to find the semantic relationship between ontologies. Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregarding semantic nuances and necessitating substantial manual intervention. Therefore, this paper introduces an ontology mapping method. Based on the traditional ontology mapping method, the process employs a deep learning model to mine the semantic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the similarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similarity matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. However, since the number of mapped pairs is much larger than the number of non-mapped pairs, the number of positive samples in the data set is much smaller than the number of negative samples. Therefore, based on the traditional SVM, the paper adopts cost-sensitive strategy to deal with the class imbalance problem. In comparative evaluations against contemporary ontology mapping techniques, our method demonstrates a noteworthy 5.0% enhancement in recall and a 3.0% improvement in F1 score when tested on both public benchmark datasets and domain-specific datasets.

Social perception of the process of rural shrinkage: Application of loss mapping method in one of Hungary's inner peripheral regions

Social perception of the process of rural shrinkage: Application of loss mapping method in one of Hungary's inner peripheral regions

A Holistic Perspective on the AI-Education Nexus: A Science Mapping Study

This study examines how the relationship between artificial intelligence (AI) and education in scientific literature is evolving around common key concepts. For this purpose, the science mapping method was employed. Data were obtained from the Web of Science Core Collection. The search terms included “artificial intelligence,” “education,” “instruction,” and “teaching,” as well as "OpenAI," "ChatGPT," and "Chatbot." Bibliographic data from 14,682 scientific documents were extracted, forming the dataset for this study. Analyses were conducted using the VOSviewer software tool, and co-occurrence analyses were performed on the data. These analyses produced both co-occurrence maps and detailed outputs. With the contribution of these outputs, the general and emerging concepts in the map were identified. The results indicate that the AI-education relationship is predominantly discussed in the context of instructional methods rather than as a technology or tool. In recent years, this discourse has particularly enriched and deepened in related fields, learning environments/contexts, issues/skills related to teaching and learning, and research. This richness supports the AI-education relationship from pedagogical integration, applicability, and ethics perspectives. Additionally, it strengthens the theoretical foundations of this relationship by linking it to educational technology and incorporating socio-psychological elements. However, there remains potential for further development in areas such as impact dynamics and human-AI collaboration

Combined assembloid modeling and 3D whole-organ mapping captures the microanatomy and function of the human fallopian tube.

The fallopian tubes play key roles in processes from pregnancy to ovarian cancer where three-dimensional (3D) cellular and extracellular interactions are important to their pathophysiology. Here, we develop a 3D multicompartment assembloid model of the fallopian tube that molecularly, functionally, and architecturally resembles the organ. Global label-free proteomics, innovative assays capturing physiological functions of the fallopian tube (i.e., oocyte transport), and whole-organ single-cell resolution mapping are used to validate these assembloids through a multifaceted platform with direct comparisons to fallopian tube tissue. These techniques converge at a unique combination of assembloid parameters with the highest similarity to the reference fallopian tube. This work establishes (i) an optimized model of the human fallopian tubes for in vitro studies of their pathophysiology and (ii) an iterative platform for customized 3D in vitro models of human organs that are molecularly, functionally, and microanatomically accurate by combining tunable assembloid and tissue mapping methods.

Mineral prospectivity mapping susceptibility evaluation based on interpretable ensemble learning

In the present study, an interpretable ensemble learning-based method for mineral prediction mapping is proposed to address the limitations of traditional mineralization prediction modeling. A stacking ensemble learning model was constructed, employing random forest (RF), extreme gradient boosting (XGBoost), and AdaBoost as primary learners, and logistic regression as the secondary learner. The model’s interpretability was analyzed using local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) algorithms. The lead–zinc deposits in the Changba mining area of Gansu Province served as a case study. By integrating geological and geochemical data, and selecting 18 evaluation factors, the effectiveness and interpretability of the ensemble learning model in mineralization prediction were validated. The results demonstrate that the lead–zinc prospecting map generated using the stacking model effectively correlates geological and geochemical data with known lead–zinc deposit locations, significantly enhancing the accuracy of identifying potential lead–zinc prospecting areas.

Utilization of Open Polygon Method for Land Mapping in Mojoroto Kediri with Geospatial Approach

Land mapping is an important research activity to determine the location of points on the earth's surface, describing the physical condition of parts of the surface that resemble the actual condition. The objective of this process is to gain a comprehensive understanding of the land to be built and used through land area measurements, topographic maps, and land volume analysis at various altitudes. In this mapping, the open polygon method is used for calculations which are conventional methods using data point values as data centers to represent the area of influence. During this mapping, there are three main points used with the open polygon method. A total of 26 ground contours were recorded, and 4 road contours recorded. During the mapping process, corrections are required on the open polygon calculations to ensure the accuracy of the results. This correction involves adjusting the data to take into account field variability and inaccuracies of measuring instruments, so that the resulting data can be reliable for further analysis and correct decision-making. The land that has been painted can be used for various fields such as construction, and mining. Land mapping allows partial surface physical state imaging, classification of land, and can be used to plan, build, and maintain infrastructure such as highways and bridges. The result of this practice obtained data of the maximum height of 90,0945 masl and minimum 88,76 masl with the average ground height 90,05059 masl, while the average highway height is at 88,86175 masl.

Lines of Flight and Nomadic Trajectories: Visualising Rhizomatic Learning with Cartography

Aim. This article presents the research on using cartography as a research method based on Gilles Deleuze and Félix Guattari’s (2004) insights. The aim of the study – to unfold and visualise students' learning experiences in non-formal and informal learning contexts beyond the classroom. Methods. Cartography and rhizoanalysis. Cartography is a method of mapping (Wilmott, 2020). In order to create rhizomatic maps we used Deleuze and Guattari (2004) theoretical insights. The rhizoanalysis (Masny, 2015) was carried out according to Deleuze and Guattari’s (2004) rhizome principles: connectivity and heterogeneity multiplicity and rupture, cartography and decalcomania. The rhizoanalysis simultaneously reveals Alecia Youngblood Jackson and Lisa Mazzei's (2012) process of "thinking with theory". Thirty-eight students from upper-grade classes participated in this research. Each student’s learning path was depicted as their rhizomatic journeys, using various concepts such as nomads, lines of flight, becoming. Results. The rhizoanalysis makes it possible to reveal students’ rhizomatic learning experiences, areas of interest and learning styles. Such a map challenges an existing hierarchical framework and conveys how students’ freely make connections through lines of flight and create their own learning paths. Conclusions. This study demonstrates cartography as a useful methodology for visualising students’ learning experiences. Also, this study is unique and distinctive because it reveals the nomadic trajectories of individual learners and offers a thorough investigation of their activity destinations. The researchers of this study advise that students' non-formal and informal learning experiences be included into their formal education by using nomadic pedagogy.

Measuring Multidimensional Poverty of the Population of the Regions of Russia: A Hybrid Approach

The research paper is devoted to the measurement of poverty (and its depth) of the population of Russian regions on the basis of a hybrid approach. This approach involves both an objective assessment of the phenomenon under study (according to Rosstat data) and taking into account the subjective perception of poverty by the population. In the first case, the index method is used in combination with the method of self-organising maps by T. Kohonen and an ensemble of multilayer perseptrons of various configurations. Artificial intelligence allows not only to deepen the results of retrospective assessment of multidimensional poverty through cluster analysis, but also to realise the predictive function. In the second case, a sociological survey is conducted on the example of a certain region of Russia (the Republic of Bashkortostan). According to the results of the cluster analysis, taking into account the earlier retrospective assessment, we can conclude that there is a high degree of polarisation of Russian regions based on the actual level of ‘multidimensional poverty’. ‘Oases’ (St. Petersburg, Moscow and the Republic of Tatarstan) contrast with “centres” (the Republic of Ingushetia, the Republic of Tyva, the Karachay-Cherkess Republic and the Republic of Altai) of multidimensional poverty. At the same time, the Republic of Bashkortostan (one of the old-industrial regions with a rich mineral and raw material base) in the formed rating regularly occupied the median position among other constituent entities of the Russian Federation. The main (key) problems in the sphere of poverty for this region of Russia are specified based on the results of sociological research. Such a study also makes it possible to deepen the retrospective assessment of ‘multidimensional poverty’, since not all the necessary data for a comprehensive analysis of the phenomenon under study are generated by Rosstat on a regular basis. The results obtained in the framework of the study can be used in the actualisation of the most important direction of the state social policy of modern Russia – the fight against poverty. In the long term, the top management of the country needs to ensure a significant reduction in the poverty level of the population regardless of the territorial factor (constituent entity of the Russian Federation).

A scoping review of coaching in occupational therapy: Mapping methods, populations and outcomes.

Coaching is proposed as a core enabling skill of occupational therapy and increasingly suggested for a diverse range of health populations. In recent years, research on coaching has proliferated, emerging from within and outside of occupational therapy. Yet, concern has been raised about the absence of theoretical underpinning and diversity of descriptions of coaching in occupational therapy, as well as low-quality evidence supporting its effectiveness. The aim of this study is to map the use of coaching by occupational therapists from 2007 to 2022 to identify the populations, settings and outcomes it is applied to, differences between coaching interventions and evidence supporting its use. This systematic scoping review applied Arksey and O'Malley's five-stage methodological framework with further methodological direction using the Levac framework. There was no consumer involvement in undertaking this review. Findings from 60 research publications on coaching indicate its use with populations across the lifespan in primarily community settings to improve occupational performance, participation and self-efficacy. A wide range of coaching interventions are designed or used by occupational therapists, with Occupational Performance Coaching being the most researched (16/27, 59% of named interventions). Common elements of coaching interventions included collaborative goal setting and problem-solving; however, a lack of described theory or coaching methods persists. High-quality study designs indicate its effectiveness; however, an absence of fidelity definition or monitoring limits interpretation of many studies. The language of coaching is becoming common practice within occupational therapy, and evidence indicates effectiveness of some coaching interventions. Advances in coaching research and use by occupational therapists will be aided by an explicit statement of the theoretical basis for coaching interventions, with congruent coaching methods, mechanisms and outcomes stated. This scoping review maps the use of coaching by occupational therapists in current published research. Key aspects of coaching examined include how coaching is defined, who it is used with and how effective it is. The kinds of outcomes that coaching is intended to improve are also reported. From 633 publications, 60 research publications were fully analysed. The use of coaching by occupational therapists has grown rapidly over the past 10years. Strong evidence of its effectiveness exists for achieving the personal goals of people receiving occupational therapy when goals describe changes in everyday life activities. Coaching is being used with people across the lifespan with a variety of disabilities and chronic conditions and their caregivers/parents, most commonly parents of children with developmental issues. The key features of coaching interventions used by occupational therapists include collaborative goal setting, shared problem-solving and client-led action planning. Some elements of coaching are also used within other interventions with a wider range of methods. Coaching is believed to work by supporting an individual's self-belief that they can make changes. Theories that underpin coaching vary but commonly focus on self-determination, behaviour change and occupational therapy theory. However, many of the coaching interventions do not describe any theoretical basis nor coaching methods in detail. Future research on coaching in occupational therapy should ensure a clear explanation of the theory that underpins it, coaching methods and monitoring of how well coaching is applied.

Феномен релокации (Модели экономического поведения российских мигрантов после февраля 2022 г.)

The events of February 2022 became the starting point of a new wave of Russian emigration. On the one hand, this phenomenon can be considered within a series of previous migration waves, on the other hand, the modern context leaves its imprint on the current migration process, making it very special from the economic and sociocultural perspectives. The article presents an attempt to analyze this specificity with an emphasis on the characteristics of the economic behavior of emigrants. Empirically, the study is based on a series of in-depth interviews with people who left Russia after February 2022. On the basis of these interviews, the authors built idealized cognitive models of possible options for the economic behavior of migrants. The authors considered a total of four models: relocation, which involves searching for a job in the host country; relocation, which involves remote work for a Russian company; relocation, which involves creating or moving one’s own business; relocation to a branch of a Russian company. Since the economic strategies of migrants are influenced by signals broadcast by the Russian government structures, the article analyzed the statements of representatives of these structures using the method of network cognitive mapping, which made it possible to reveal two types of official discourse in relation to those who left Russia, which differed in the extent of their criticism towards them. In general, the study showed that migrants of the latest wave are internally heterogeneous and fall into several groups that differ markedly in their motivation and patterns of economic behavior. At the same time, a significant part of them continues to cooperate with Russia in one way or another and, in principle, is ready to return to Russia. For them, migration is driven largely by security and/or economic reasons. Only a minority of emigrants left the country due to their disagreement with the current political course and consider the possibility of coming back only if the political course changes radically.

Efficient and low-drift point-cloud mapping method for GNSS weak and obstructed areas

Abstract Road modelling based on point-cloud data often encounters signal-rejection areas, where the absence of absolute position constraints leads to rapid error accumulation in the constructed map. To mitigate error growth, the manual setting of control points is necessary, which compromises automation in the mapping process and causes a challenging trade-off between efficiency and accuracy. To address these issues, in the present study, we designed a fast and efficient map-construction method that provides absolute pose constraints for simultaneous localisation and mapping (SLAM) based on the bidirectional filter position of global navigation satellite system (GNSS)/inertial measurement unit (IMU) integrated navigation. First, we designed an absolute position constraint factor (BF_Factor) updated at each epoch according to the reference-position difference and time-delay error, which can significantly mitigate error divergence during mobile mapping in a GNSS-rejected region. An adaptive noise model was then constructed for the above factors so that the weights of different factors could be adjusted automatically to achieve a more accurate mapping result. Finally, we conducted experimental tests on the proposed mapping approach in two scenarios that included multiple GNSS-rejected areas. Even when several hundred metres of GNSS signal-obstructed areas were present in the scene, the relocation results indicated that the three-dimensional position error obtained using point-cloud maps was <0.15 m and the pitch/roll error was better than 2°. Compared with Fastlio2 and Fastlio2-SC (Fastlio2 with Scan Context), the proposed method had a better mapping effect and higher mapping accuracy in the aforementioned scenarios. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications.