Mapping Applications Research Articles

Looking into the black mirror of the overdose crisis: Assessing the harms of collaborative surveillance technologies in the United States response.

Drug overdose is a leading cause of death among adults in the United States, prompting calls for more surveillance data and data sharing across public health and law enforcement to address the crisis. This paper integrates Black feminist science and technology studies (STS) into an anthropological analysis of the collision of public health, policing, and technology as embedded in the US National Overdose Response Strategy and its technological innovation, the Overdose Detection Mapping Application Program (ODMAP). The dystopian Netflix series "Black Mirror," which explores the seemingly useful but quietly destructive potential of technology, offers a lens through which to speculate upon and anticipate the harms of collaborative surveillance projects. Ultimately, I ask: are such technological interventions a benevolent approach to a public health crisis or are we looking into a black mirror of racialized surveillance and criminalization of overdose in the United States?

Building Better Models: Benchmarking Feature Extraction and Matching for Structure from Motion at Construction Sites

The popularity of Structure from Motion (SfM) techniques has significantly advanced 3D reconstruction in various domains, including construction site mapping. Central to SfM, is the feature extraction and matching process, which identifies and correlates keypoints across images. Previous benchmarks have assessed traditional and learning-based methods for these tasks but have not specifically focused on construction sites, often evaluating isolated components of the SfM pipeline. This study provides a comprehensive evaluation of traditional methods (e.g., SIFT, AKAZE, ORB) and learning-based methods (e.g., D2-Net, DISK, R2D2, SuperPoint, SOSNet) within the SfM pipeline for construction site mapping. It also compares matching techniques, including SuperGlue and LightGlue, against traditional approaches such as nearest neighbor. Our findings demonstrate that deep learning-based methods such as DISK with LightGlue and SuperPoint with various matchers consistently outperform traditional methods like SIFT in both reconstruction quality and computational efficiency. Overall, the deep learning methods exhibited better adaptability to complex construction environments, leveraging modern hardware effectively, highlighting their potential for large-scale and real-time applications in construction site mapping. This benchmark aims to assist researchers in selecting the optimal combination of feature extraction and matching methods for SfM applications at construction sites.

Robust Invariance Conditions of Uncertain Linear Discrete Time Systems Based on Semidefinite Programming Duality

This article proposes a novel robust invariance condition for uncertain linear discrete-time systems with state and control constraints, utilizing a method of semidefinite programming duality. The approach involves approximating the robust invariant set for these systems by tackling the dual problem associated with semidefinite programming. Central to this method is the formulation of a dual programming through the application of adjoint mapping. From the standpoint of semidefinite programming dual optimization, the paper presents a novel linear matrix inequality (LMI) conditions pertinent to robust positive invariance. Illustrative examples are incorporated to elucidate the findings.

Integrating knowledge systems for holistic approaches to addressing knowledge and health inequities: combining fuzzy cognitive maps.

This paper describes fuzzy cognitive mapping as an accessible and robust tool to strengthen community engagement in health promotion research. We outline how fuzzy cognitive mapping can combine, compare, and contextualize knowledge and priorities from diverse population groups as well as from evidence syntheses. We present procedures to represent a shared perspective across populations or population groups through reconciling maps by simple or weighted averaging. We present a novel second approach to reconciling derived from discourse analysis. We then present two procedures to contextualize one knowledge in another knowledge. The first procedure draws on Bayesian updating, providing a formal way to account for stakeholder knowledge in contextualizing other knowledge sources, including evidence syntheses. A second approach compares discourse patterns across maps derived from different sources. We provide examples of each procedure, describe how each may contribute to greater incorporation of patient- and community-level input in decision-making, and share tools for researchers interested in applications of fuzzy cognitive mapping.

Maximising the value of hyperspectral drill core scanning through real-time processing and analysis

Hyperspectral imaging is gaining widespread use in the resource sector, with applications in mineral exploration, geometallurgy and mine mapping. However, the sheer size of many hyperspectral datasets (>1 Tb) and associated correction, visualisation and analysis challenges can limit the integration of this technique into time-critical exploration and mining workflows. In this contribution, we propose and demonstrate a novel open-source workflow for rapidly processing hyperspectral data acquired on exploration drillcores. The resulting products are adaptable to the varied needs of geologists, geophysicists and geological engineers, facilitating better integration of hyperspectral data during decision making. These tools are applied to process hyperspectral data of 6.4 km of exploration drill cores from Stonepark (Ireland), Collinstown (Ireland) and Spremberg (Germany). The results are presented via an open-source web-viewing platform that we have developed to facilitate easy on and off-site access to hyperspectral data and its derivatives. We suggest that maximum value can be extracted from hyperspectral data if it is acquired shortly after drilling and processed on-site in real time, so that results can be quickly validated and used to inform time-critical decisions on sample selection, geological interpretation (logging) and drillhole continuation or termination. This timeliness and accessibility is key to ensure rapid data availability for decision makers during mineral exploration and exploitation. Finally, we discuss several remaining challenges that limit the real-time integration of hyperspectral drill core scanning data, and explore some opportunities that may arise as these rich datasets become more widely collected.

Mapping agricultural tile drainage in the US Midwest using explainable random forest machine learning and satellite imagery

There has been an increase in tile drained area across the US Midwest and other regions worldwide due to agricultural expansion, intensification, and climate variability. Despite this growth, spatially explicit tile drainage maps remain scarce, which limits the accuracy of hydrologic modeling and implementation of nutrient reduction strategies. Here, we developed a machine-learning model to provide a Spatially Explicit Estimate of Tile Drainage (SEETileDrain) across the US Midwest in 2017 at a 30-m resolution. This model used 31 satellite-derived and environmental features after removing less important and highly correlated features. It was trained with 60,938 tile and non-tile ground truth points within the Google Earth Engine cloud-computing platform. We also used multiple feature importance metrics and Accumulated Local Effects to interpret the machine learning model. The results show that our model achieved good accuracy, with 96 % of points classified correctly and an F1 score of 0.90. When tile drainage area is aggregated to the county scale, it agreed well (r2 = 0.69) with the reported area from the Ag Census. We found that Land Surface Temperature (LST) along with climate- and soil-related features were the most important factors for classification. The top-ranked feature is the median summer nighttime LST, followed by median summer soil moisture percent. This study demonstrates the potential of applying satellite remote sensing to map spatially explicit agricultural tile drainage across large regions. The results should be useful for land use change monitoring and hydrologic and nutrient models, including those designed to achieve cost-effective agricultural water and nutrient management strategies. The algorithms developed here should also be applicable for other remote sensing mapping applications.

Assessing Geospatial Accuracy in Mapping Applications: A Focus on Google Earth

Google Earth, among other online mapping platforms, offers an interactive mapping platform that has become indispensable for academic and research applications. It serves as a primary reference and a foundational tool for map creation, providing open-source, cost-free imagery that meets the user needs of the mapping community. As a contemporary repository of high-resolution images of Earth's landmass, Google Earth has vast potential for scientific exploration and remains an underexploited resource. Its rapid expansion and consistent reliability make it a favored source for mapping and routing tasks. However, this research underscores the crucial aspect of Google Earth's positional accuracy, which is at the heart of this study. A comparative analysis between the positional accuracy of Google Earth and traditional ground surveying maps was conducted. The Wilcoxon rank test and quantitative methods were used to evaluate coordinate discrepancies, revealing significant discrepancies between the two datasets. This study aims to provide a rigorous assessment of Google Earth's utility and accuracy in scientific and academic contexts, emphasizing its role and reliability as a critical resource for researchers and practitioners in the field of mapping. The results revealed displacement changes in both the northing and the easting coordinates. For the northing coordinates, the displacement increases when moving eastward and decreases when moving westward. For the easting coordinates, the displacement increases when moving northward and decreases when moving southward. This pattern highlights spatial discrepancies and the varying impact of location on the dataset's accuracy, emphasizing the need for targeted corrections to enhance data accuracy. These key findings provide valuable insights that could significantly contribute to optimizing mapping practices and efficiently exploiting this vast, yet underexplored, digital resource. Doi: 10.28991/CEJ-2024-010-08-012 Full Text: PDF

Congestion-Aware Vertical Link Placement and Application Mapping Onto 3-D Network-on-Chip Architectures

Congestion-Aware Vertical Link Placement and Application Mapping Onto 3-D Network-on-Chip Architectures

The influence of cluster mind map on ESL university students’ writing skills

In academic writing, harnessing creativity and effectively organizing thoughts are pivotal for producing coherent content. Cluster mind mapping is a useful tool that can aid students’ writing skills from the beginning such as drafting to the final stages of writing. Therefore, this research investigates to what extent planning, brainstorming, and idea matching can influence students’ writing. This research specifically examines the application of the cluster mind map in student’s academic writing. This study involves a total of 60 participants from the institutions of higher education in the state of Perlis. A quantitative research approach was employed to evaluate the effectiveness of Cluster Mind Map using inferential statistics through Statistical Package for Social Sciences (SPSS). The findings of the study highlight that effective planning, brainstorming, and idea generation were the key categories that significantly influenced students' writing performance in a cluster mind map. This study also points out that cluster mind map acts as a valuable resource for educators seeking to improve students’ academic writing and seeking to enhance students’ learning outcomes. It is useful in aiding English language practitioners in equipping students with metacognitive processing skills particularly in the area of academic writing.

Application of data element mapping and analysis for system definition to enable model‐based systems engineering

AbstractIn the connected age of the model‐based enterprise and model‐based systems engineering (MBSE), new systems engineering tools are needed to move from a functional, document‐centric, hierarchical view of data and information to the individual units of data or data element level view. Data element mapping and analysis (DEMA) is a technology‐agnostic analytical methodology that combines traditional functional analysis techniques, systems engineering elicitation practices, and novel data mapping techniques to provide a holistic view of a system's data and information flows at the individual units of data (data element level). In this research, DEMA was utilized to enable an enhanced system definition for the development of a verification and validation process as applied to a modeling and simulation environment. DEMA uncovered and visually mapped the hidden flow of approximately 1600 data vessel occurrences as inputs or outputs to 79 functional activities in 23 disparate storage locations. The results reveal that DEMA is a practical tool for both improving existing systems and defining new systems. The data element level view captured by DEMA can be used to define the interconnections between the system elements that are input to systems modeling language (SysML) models. Therefore, DEMA is a necessary and novel tool that can be used to enable system digitalization.

Geometry-modulated all organic 3D printed smart PLA fibers for flextension amplified giant mechanical energy harvesting and Machine learning assisted pressure mapping

Piezoelectret sensors offer a promising avenue for harvesting abundant mechanical energy in the environment, providing a sustainable power solution for low-powered electronics. However, effectively harnessing large impact mechanical energy remains a challenge. In this study, we introduce a novel 3D cylindrical self-powered piezoelectret sensor capable of converting irregular axial impact forces into uniform radial pressures, enabling efficient large-scale impact mechanical energy harvesting. The cylindrical piezoelectret sensor, operating via a flextensional mechanism, was fabricated using 3D printed poly(lactic acid) (PLA) fibers as the piezoelectret layer and poly(3,4-ethylenedioxythiophene) (PEDOT)-coated PLA as the electrode layer. Our cylindrical sensor demonstrates the output voltage, and current of ∼ 11.4 V and 7.2 μA, representing remarkable improvement of 250 % and 160 % respectively, compared to flat fiber membrane device. Furthermore, we leverage IoT connectivity and machine learning techniques to utilize the fabricated robotic sensor for pressure mapping applications. Through machine learning algorithms, we achieve a remarkable accuracy of 100 % in character identification based on sensor data. This work highlights the potential of organic robotic sensors for efficient energy harvesting and intelligent pressure mapping applications, paving the way for the development of sustainable and adaptive sensor systems for various real-world scenarios.

A high-level simulator for Network-on-Chip

This study presents a high-level simulator for Network-on-Chip (NoC), designed for many-core architectures, and integrated with the PlatEMO platform. The motivation for developing this tool arose from the need to evaluate the behavior of application mapping algorithms and the routing, both aspects are essential in the implementation and design of NoC architectures. This analysis underscored the importance of having effective NoC simulators as tools that allow for studying and comparing various network technologies while ensuring a controlled simulation environment. During this investigation and evaluation, some simulators, such as Noxim, NoCTweak, and NoCmap, among others, offered configurable parameters for application traffic, options to synthetically define topology and packet traffic patterns. Additionally, they include mapping options that optimize latency and energy consumption, routing algorithms, technological settings such as the CMOS process, and measurement options for evaluating performance metrics such as throughput and power usage. However, while these simulators meet detailed technical demands, they are mostly restricted to analyzing the low-level elements of the architecture, thus hindering quick and easy under- standing for non-specialists. This insight underscored the challenge in developing a tool that balances detailed analysis with a comprehensive learning perspective, considering the specific restrictions of each simulator analyzed. Experiments demonstrated the proposed simulator efficacy in handling algorithms like GA, PSO, and SA variant, and, surprisingly, revealed limitations of the XY algorithm in Mesh topologies, indicating the need for further investigation to confirm these findings. Future work will expand the simulator functionalities, incorporating a broader range of algorithms and performance metrics, to establish it as an indispensable tool for research and development in NoCs.

The Impact of Mind Mapping on Young Children's Writing Skills: An Experimental Study

This study investigates the application of the mind-mapping learning model to enhance children's writing skills. Conducted over two weeks during the 2022/2023 academic year at a school in Medan, North Sumatra, the research employed a quantitative experimental design. The study population included 106 children aged 3-8 years, with samples systematically selected from two classes. Data collection involved observation sheets to assess student engagement and activity levels. Statistical tests ensured data validity and reliability, with normality tests (Kolmogorov-Smirnov and Shapiro-Wilk) revealing normal and non-normal distributions and homogeneity tests confirming data uniformity. The t-test results indicated a significant positive impact of mind mapping on writing skills (Sig. = 0.000; t = 24.810). These findings suggest that mind mapping in early education can significantly enhance writing abilities, improving academic performance and cognitive development. The implications are substantial for educational practice, advocating for integrating mind mapping into teaching methods and supporting teacher training programs. Despite limitations such as a small sample size and potential sampling bias, this study provides a foundation for further research. Future studies should involve larger samples to validate these results and explore additional applications of mind mapping in educational settings, aiming to refine and enhance teaching strategies for early childhood education. Additionally, educational policymakers should consider integrating innovative teaching strategies like mind mapping to improve academic outcomes.

The application of B1 inhomogeneity-corrected variable flip angle T1 mapping for assessing liver fibrosis

PurposeThe aim of this study was to evaluate the diagnostic accuracy of the B1 inhomogeneity-corrected variable flip angle (VFA) method using native T1 values in the staging of liver fibrosis. MethodsEighty-three patients who presented for liver biopsy due to varying degrees of liver damage, underwent MR examinations and had T1-mapping images of the liver acquired using the B1 inhomogeneity-corrected VFA VIBE method. Among them, 65 patients underwent Fibroscan, and their results were used to evaluate the elasticity of liver tissue. Additionally, T1-mapping images were collected from 19 normal control patients. Independent sample t-tests were used to analyze the correlation between T1 mapping and Fibroscan. The diagnostic efficacy of T1 mapping in patients with different stages of liver fibrosis was evaluated using receiver operating characteristic (ROC) curves. ResultsThe consistency between different observer groups was intraclass correlation coefficient (ICC) =0.802. T1 mapping demonstrated significant differences between mid-stage liver fibrosis (S = 2) and late-stage liver fibrosis (S = 3), as well as moderate inflammation (G = 2) and severe inflammation (G = 3), P < 0.05. The Area Under Curve(AUC) values of T1 mapping for early liver fibrosis (S ≥ 1), significant liver fibrosis (S ≥ 2), advanced liver fibrosis (S ≥ 3), and end-stage liver fibrosis (S = 4) were 0.760, 0.709, 0.790, and 0.768, respectively. T1 mapping combined with Fibroscan had an AUC value of 0.860. ConclusionsThe B1 inhomogeneity-corrected VFA T1 mapping may be useful for the staging of liver fibrosis. It has a superior diagnostic efficiency for diagnosing advanced fibrosis (≥S3), while native T1 values combined with Fibroscan have potential value for the staging of liver fibrosis.

ODMAP: Stakeholder Perspectives on a Novel Public Health and Public Safety Overdose Surveillance System.

This pilot study explores the utilization of the Overdose Detection Mapping Application Program (ODMAP) as a tool for enhancing collaboration between the public health and public safety sectors to address the overdose epidemic in the United States. Through qualitative interviews with ODMAP users, key themes emerged, including the role of data sharing in facilitating collaboration, challenges posed by divergent data privacy standards, and the need for clearer guidance on cross-sector data sharing. Findings highlight ODMAP's potential to integrate data for targeted interventions at individual and population levels. Future research directions include overcoming data sharing barriers, strategically utilizing data across sectors, and rigorously evaluating the impact of cross-sector partnerships on overdose morbidity and mortality. Overall, this study underscores the importance of ODMAP in fostering coordinated responses to the overdose crisis and provides valuable insights for improving overdose surveillance and intervention efforts.

Proposal for a managed aquifer recharge feasibility index for southern Portugal using multi-criteria decision analysis

Water scarcity in the Mediterranean region requires the adoption of new and optimized water resources management tools that allow a progressive climate change adaptation, being one of the main drivers for the employment of alternative water storage measures such as Managed Aquifer Recharge (MAR). To promote the implementation of these measures, this study evaluates the application of a MAR feasibility index mapping to southern Portugal, employing Multi-Criteria Decision Analysis (MCDA). A participatory approach – developed within the AGREEMAR project – aims for the characterization of the intrinsic site suitability, water availability and demand, where the stakeholder inputs are embedded in the weighting computation process.The results obtained demonstrate prevalent moderate MAR feasibility across the entire region (78% of the area) with an increased percentage of the area corresponding to high MAR feasibility (22%) strongly related to highly suitable geological features. A comparative analysis with the Analytical Hierarchical Process (AHP) showed minor high MAR feasibility areas (7%), and feasibility class transitions between the directly above or below classes.A sensitivity analysis, conducted based on a one-at-a-time (OAT) variation of the criteria weights, assessed the impact of the weighting methods on feasibility class changes. For the AGREEMAR method, changes in the weight of 10 of the 28 considered criteria resulted in negligible changes when compared to the original feasibility map, while for AHP 3 out of 28 produced minor changes. The remaining criteria showed important modifications in the feasibility maps, which underlined AGREEMAR method weights provided higher stability in the results.The study provides information about a MAR site selection process procedures, allowing replication. The dissemination of the results is expected to raise awareness of MAR among stakeholders and support scientifically-based water resources management decision-making.

European beech spring phenological phase prediction with UAV-derived multispectral indices and machine learning regression

Acquiring phenological event data is crucial for studying the impacts of climate change on forest dynamics and assessing the risks associated with the early onset of young leaves. Large-scale mapping of forest phenological timing using Earth observation (EO) data could enhance our understanding of these processes through an added spatial component. However, translating traditional ground-based phenological observations into reliable ground truthing for training and validating EO mapping applications remains challenging. This study explored the feasibility of predicting high-resolution phenological phase data for European beech (Fagus sylvatica) using unoccupied aerial vehicle (UAV)-based multispectral indices and machine learning. Employing a comprehensive feature selection process, we identified the most effective sensors, vegetation indices, training data partitions, and machine learning models for phenological phase prediction. The model that performed best and generalized well across various sites utilized Green Chromatic Coordinate (GCC) and Generalized Additive Model (GAM) boosting. The GCC training data, derived from the radiometrically calibrated visual bands of a multispectral sensor, were predicted using uncalibrated RGB sensor data. The final GCC/GAM boosting model demonstrated capability in predicting phenological phases on unseen datasets within a root mean squared error threshold of 0.5. This research highlights the potential interoperability among common UAV-mounted sensors, particularly the utility of readily available, low-cost RGB sensors. However, considerable limitations were observed with indices that implement the near-infrared band due to oversaturation. Future work will focus on adapting models to better align with the ICP Forests phenological flushing stages.

Understanding marine litter origin in a coastal area: Can Gio beaches, Ho Chi Minh City, Vietnam, as the case study

To decrease the leakage of waste and harmful compounds into marine ecosystems, it is imperative to identify the source of marine litter and implement appropriate mitigation strategies. In this study, bottles and single-use containers presented at four beaches in Can Thanh Town, Can Gio District, Ho Chi Minh City were recorded the abundance, country of production, and lifetime from the manufacturing stamp date on the bottles and containers. Instead of collecting items and bringing them to the laboratory like most studies on marine litter, this study used the Mergin Maps application to record data. A total of 522 items were recorded during two surveys. The identification of litter sources has been made considerably more accessible by using trademarks and logos. Among the 60.5 % of items with recorded brands, items with domestic production addresses (Vietnam) accounted for the most significant proportion (78.2 % of items with brands). In addition, Thailand accounted for the most significant proportion of items originating from foreign countries. Lids and caps are essential for long-distance transportation. Epibionts and bite marks indicated that most bottles and containers drifted along the ocean currents in the area. Marine litter from beaches in Can Gio District originated mainly from waste drifting from rivers and oceans. This study not only illustrated where the waste came from but also provided insightful data that would support managers' decisions to illustrate where the waste came from and provided insightful data that would support managers' decisions to regulate and lessen marine litter in the area effectively.

Application of T2 mapping to assess articular cartilage in patients at risk of developing chondromalacia

Application of T2 mapping to assess articular cartilage in patients at risk of developing chondromalacia

Artificial Intelligence in Agricultural Mapping: A Review

Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time and increases efficiency in agricultural management activities, which improves the food industry. Agricultural mapping is necessary for resource management and requires technologies for farming challenges. The mapping in agricultural AI applications gives efficiency in mapping and its subsequent use in decision-making. This study analyses AI’s current state in agricultural mapping through bibliometric indicators and a literature review to identify methods, agricultural resources, geomatic tools, mapping types, and their applications in agricultural management. The methodology begins with a bibliographic search in Scopus and the Web of Science (WoS). Subsequently, a bibliographic data analysis and literature review establish the scientific contribution, collaboration, AI methods, and trends. The United States (USA), Spain, and Italy are countries that produce and collaborate more in this area of knowledge. Of the studies, 76% use machine learning (ML) and 24% use deep learning (DL) for agricultural mapping applications. Prevailing algorithms such as Random Forest (RF), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs) correlate mapping activities in agricultural management. In addition, AI contributes to agricultural mapping in activities associated with production, disease detection, crop classification, rural planning, forest dynamics, and irrigation system improvements.