Creation Of Maps Research Articles

Predictive GAM seabed maps can account for defined and fuzzy boundaries to improve accuracy in a Scottish sea loch seascape

Marine seabed mapping is an important element in marine spatial and conservation planning. Recent large scale mapping programmes have greatly increased our knowledge of the seafloor, yet at finer resolutions, large gaps remain. Loch Eriboll, Scotland, is an area of conservation interest with a diverse marine environment supporting habitats and species of conservation importance. Here we test and present strategies for a predictive seabed substrata map for Loch Eriboll using drop down Stereo Baited Remote Underwater Video (SBRUV) imagery collected as part of systematic underwater survey of the Loch. A total of 216 SBRUV deployments were made across the study site in depths of 3 m–117 m, with six seabed classes identified using an adaptation of the EUNIS (European Nature Information System) hierarchical habitat classification scheme. Four statistical learning approaches were tested, we found, Generalised Additive Models (GAMs) provided the optimal balance between over- and underfitted predictions. We demonstrate the creation of a predictive substratum habitat map covering 63 km2 of seabed which predicts the probability of presence and relative proportion of substratum types. Our method enables naturally occurring edges between habitat patches to be described well, increasing the accuracy of mapping habitat boundaries when compared to categorical approaches. The predictions allow for both defined boundaries such as those between sand and rock and fuzzy boundaries seen among fine mixed sediments to exist in the same model structure. We demonstrate that SBRUV imagery can be used to generate cost effective, fine scale predictive substrata maps that can inform marine planning. The modelling procedure presented has the potential for a wide adoption by marine stakeholders and could be used to establish baselines for long term monitoring of benthic habitats and further research such as animal distribution and movement work which require detailed benthic maps.

Co-producing Sustainable Supply Chain Transitions: Proposing a template for interdisciplinary systemic service design facilitation

This article aims to explore the underutilised potential of systemic service design methods in providing the necessary overview and facilitating green transitions in sustainable supply chains by being a facilitator of the interdisciplinary process. We will introduce a ‘Mess/Gigamap’ template with an influence from Geels´ (2005)1 ‘Multi-Level Perspective’ model. In addition, we will show how ‘Mess/Gigamaps’ are tools that can aid conversations between stakeholders to visually understand the complexities that they are facing. The aim of this template is to enable the creation of systemic maps as boundary objects to facilitate discussions between multiple stakeholders.

Https://wjarr.com/ArchiveIssue-2024-Vol23-Issue2

The objective of this study was to elucidate the mechanism by which Citri Reticulatae Pericarpium (CRP) treated Alzheimer’s disease (AD) using network pharmacology and molecular docking. Initially, a screening process identified 98 target compounds and 628 related component targets within CRP. Additionally, 2483 AD targets were retrieved from disease databases. Subsequently, an overlapping targets map was constructed, integrating CRP and AD targets, followed by the creation of a protein-protein interaction network map to identify 66 targets closely associated with the treatment of AD using CRP. These targets were identified through topological attribute analysis. To gain further insights, GO function and KEGG pathway enrichment analyses were conducted on the 66 identified targets. The results revealed enrichment in various biological pathways, including the relaxin signaling pathway, calcium signaling pathway, HIF-1 signaling pathway, and IL-17 signaling pathway. Finally, molecular docking verification was performed on the targets and active components of CRP. Active components, such as flavanone, tangeretin, flavonol, carvacrol, and perillaldehyde, were found to form hydrogen bonds with targets, with binding energies below 0 kg/mol. This study utilized network pharmacology and molecular docking methods to systematically elucidate the mechanism by which CRP treats AD. The findings provided a theoretical foundation for future research and clinical investigations

Exploring risk factors of food loss and waste: A comprehensive framework using root cause analysis tools

Food loss and waste occurring early in the food supply chain leads to increased resource wastage, including land, water, fertilisers, pesticides, energy, and labour. Targeting food loss and waste prevention benefits various aspects such as food security, productivity, economic growth, climate change mitigation, resource conservation, and waste management. Understanding the causes of food loss and waste is crucial for the design of effective solutions and their prioritisation. The purpose of this study was to investigate the risk factors and underlying causes of FLW in leafy vegetables, spanning from agricultural production to retail. To achieve this, the research methodology encompassed a case study conducted among small-scale producers and retailers. A comprehensive approach was adopted by integrating a systematic literature review of global food loss and waste causes and those specific to the Latin American context. This approach was complemented by exploratory research, involving interviews with various stakeholders, coupled with root-cause analysis. A methodology was developed for the creation of a causal map, incorporating the 'Five Whys' technique, the current reality tree, and an interrelationship diagram. The study's findings underscore the critical role of retailer-supplier dynamics in the management and flow of products, identifying a significant research gap in the development of comprehensive strategies for waste reduction. By exploring these dynamics, particularly in the context of take-back agreements and the lack of public policies incentivizing food loss and waste reduction, the research reveals the multifaceted nature of food loss and waste and the necessity for collaborative efforts across the supply chain to mitigate its impacts. The application of the structured methodology provides valuable insights into the root causes and critical risk factors of FLW, offering a foundation for the development of integrated strategies aimed at reducing food loss and waste and advancing towards global sustainability goals.

Fostering strategic synergy: Empirical insights on aligning innovation activities with competitive strategies

An effective innovation strategy is crucial for firm performance and national economic growth, yet many firms still face the challenge of misalignment between their innovation activities and strategies. While previous studies focused on the mediation role of innovation activities on strategy and performance, research is needed to examine the complex interdependencies between strategies and innovation activities to better understand their impact on firm performance.From a configurational perspective, this study investigates how different combinations of innovation activities and strategies influence firm performance. Twenty combinative effects of four categories of innovation activities and five strategic types on two firm performance measures (growth and profit) were explored, resulting in the creation of an innovation-strategy alignment map. This study contributes to innovation strategy literature by providing the alignment map, where four configurational clusters with equifinality were identified. It also contributes to competitive strategy literature by offering empirical verification for the “pure” versus “hybrid” debate and identifying two new types of hybrids with more and different strategic dimensions by using an empirical method, thus advancing the call for more detailed hybrid strategy research in this area. In addition, the innovation-strategy alignment map provides options to managers with different resources and capacities for effective planning.

Vehicle Localization Using Crowdsourced Data Collected on Urban Roads.

Vehicle localization using mounted sensors is an essential technology for various applications, including autonomous vehicles and road mapping. Achieving high positioning accuracy through the fusion of low-cost sensors is a topic of considerable interest. Recently, applications based on crowdsourced data from a large number of vehicles have received significant attention. Equipping standard vehicles with low-cost onboard sensors offers the advantage of collecting data from multiple drives over extensive road networks at a low operational cost. These vehicle trajectories and road observations can be utilized for traffic surveys, road inspections, and mapping. However, data obtained from low-cost devices are likely to be highly inaccurate. On urban roads, unlike highways, complex road structures and GNSS signal obstructions caused by buildings are common. This study proposes a reliable vehicle localization method using a large amount of crowdsourced data collected from urban roads. The proposed localization method is designed with consideration for the high inaccuracy of the data, the complexity of road structures, and the partial use of high-definition (HD) maps that account for environmental changes. The high inaccuracy of sensor data affects the reliability of localization. Therefore, the proposed method includes a reliability assessment of the localized vehicle poses. The performance of the proposed method was evaluated using data collected from buses operating in Seoul, Korea. The data used for the evaluation were collected 18 months after the creation of the HD maps.

Introducing a climate, demographics, and infrastructure multi-module workflow for projected flood risk mapping in the greater Pamba River Basin, Kerala, India

Enhanced understanding of extreme events is essential for comprehensive risk assessment promoting informed decision-making and early warning to ensure the resilience against challenges imposed by changing climate. This study proposes a novel multi-module integration framework for the comprehensive evaluation of future flood risk in the Greater Pamba River basin, Kerala, India. The integrative modelling framework is developed by assembling a Cellular Automata-Markov Chain (CAMC) based and Use/Land Cover (LULC) projections, Reliability Ensemble Averaging (REA) of Global Climate Model (GCM) rainfall projections, Bayesian approach-based population projection, and spatial analysis of projected infrastructure and Digital Elevation Models (DEMs). By seamlessly interweaving the associated datasets, we illustrate the intricacies of flood vulnerability, exposure, and frequency across multiple return periods (2, 5, 10, and 100, 200 and 500 year) leading to the creation of diagnostic and prognostic high-resolution flood risk maps. The resultant flood analytics and analyses shown that around 36 % of the villages/municipalities and between 70 and 80 % of the critical facilities like schools, hospitals, and flood relief camps are falling under high-risk zones, for the 100-year return period flood. This finding is in line with Kerala's devastating flood episode in 2018, which exhibited characteristics of a nearly 100-year return period flood. In light of a changing climate, population growth, and shifting landscapes, these approaches provide a roadmap for more informed decision-making and adaptive flood risk management measures.

Satellitome Analysis of Adalia bipunctata (Coleoptera): Revealing Centromeric Turnover and Potential Chromosome Rearrangements in a Comparative Interspecific Study.

Eukaryotic genomes exhibit a dynamic interplay between single-copy sequences and repetitive DNA elements, with satellite DNA (satDNA) representing a substantial portion, mainly situated at telomeric and centromeric chromosomal regions. We utilized Illumina next-generation sequencing data from Adalia bipunctata to investigate its satellitome. Cytogenetic mapping via fluorescence in situ hybridization was performed for the most abundant satDNA families. In silico localization of satDNAs was carried out using the CHRISMAPP (Chromosome In Silico Mapping) pipeline on the high-fidelity chromosome-level assembly already available for this species, enabling a meticulous characterization and localization of multiple satDNA families. Additionally, we analyzed the conservation of the satellitome at an interspecific scale. Specifically, we employed the CHRISMAPP pipeline to map the satDNAs of A. bipunctata onto the genome of Adalia decempunctata, which has also been sequenced and assembled at the chromosome level. This analysis, along with the creation of a synteny map between the two species, suggests a rapid turnover of centromeric satDNA between these species and the potential occurrence of chromosomal rearrangements, despite the considerable conservation of their satellitomes. Specific satDNA families in the sex chromosomes of both species suggest a role in sex chromosome differentiation. Our interspecific comparative study can provide a significant advance in the understanding of the repeat genome organization and evolution in beetles.

Design and Application Research of a UAV-Based Road Illuminance Measurement System

This paper presents a UAV-based road illumination measurement system and evaluates its performance through experiments. The system employs a HUBSAN Zino 2+ UAV, STM32F103RCT6 microcontroller, BH1750 illuminance sensor, and GPS and integrates flight, processing, measurement, cloud platform, obstacle avoidance, communication, and power supply units via the OneNET cloud platform. Both hardware and software designs were implemented, using the Z-score algorithm to handle outliers in illumination data. The system showed a single-point measurement error rate of 1.14% and a MAPE of 5.08% for multi-point measurements. In experiments, the system’s horizontal and vertical illuminance RMSE were 1.92 lx and 1.75 lx, respectively. The real-time visualization interface improved operational efficiency, cutting labor costs by half and time costs by nearly four-fifths. UAV control and monitoring from the roadside ensured safety during measurements. The system’s efficiency and wide measurement range enabled extended experiments, collecting illuminance data across multiple horizontal and vertical planes. This resulted in the creation of both horizontal and innovative vertical-plane illuminance distribution maps. These findings provide valuable data for evaluating road lighting quality, enhancing road traffic safety, and improving road illumination design.

Effectiveness of Information and Communication Programmes for Children with Special Educational Needs in the Context of Speech Development

The work aims to determine the effectiveness of information and communication programs for special educational needs (SENs) in the context of speech development. The aim was achieved through the use of general theoretical methods of analysis, observations, and calculations of the ranking factor, efficiency coefficient, and Phillips correlation coefficient. It was established that the most effective approaches for the development of children's speech are the use of information technologies (2.5), social interaction (2.37), and group approach (2.25). Results: The results provided grounds for developing a speech and general development program for children aged 4 to 7. The program provided for the creation of mind maps, provision of joint reading, performance of educational songs, and development of speech in accordance with the specified topic. Training became possible due to the use of modern technologies, namely the applications MindMeister, Digital Inclusion, and speech synthesis from Google and YouTube. It was established that the developed program had a positive effect on the improvement of children’s phonetic speech compared to the results at the beginning of the study. Conclusions: The practical significance of the work implies the possibility of using the developed program for speech correction among a larger number of children. Prospects: Research prospects are related to elaborating speech development mechanisms depending on the degree of speech impairment.

Geomorphological Compartmentation of Pereiro Massif and its surroundings, in Northern Northeast of Brazil

The Pereiro Massif (PM), located between the states of Ceará, Rio Grande do Norte and Paraíba, is a granitoid body that occupies an area of ​​approximately 2,200 km2, and and has maximum altitudes of 861 meters. This work aimed to elaborate and discuss the geomorphological compartmentalization of the Pereiro Massif and its surroundings. To this end, the following procedures were followed: 1) bibliographic and cartographic review on the geology and relief of the study area; 2) creation of the geomorphological map in a GIS environment, through SRTM image processing; 3) field activities for mapping conference, obtaining information to characterize the shapes and photographic survey. Fifteen units were mapped and grouped into three dimensions that consider the resulting processes and forms: accumulation, flattening and erosion surfaces. The compartments are classified recognizing processes associated with continental denudation with erosion, transport and deposition of material, based on structural geomorphology. The mapping carried out enhances the scientific notoriety of the PM and is expected to serve for environmental planning actions, such as the creation of protected areas and environmental licensing procedures.

A Bibliometric Analysis Of Emotional Intelligence And Leadership

This research aims to conduct a bibliometric analysis of studies on the relationship between emotional intelligence and leadership. The investigation encompasses a compilation of 1136 articles sourced from Scopus, spanning from 1996 to 2023. The data analysis process involved utilizing the VOSviewer software, which facilitated the creation of maps depicting co-authorship and keywords. A combination of descriptive statistics and social network analysis techniques were employed to analyze the data. Within the 1136 articles, 105 author keywords were referenced at least five times. These keywords were subsequently categorized into 11 clusters. The prevalent terms included “training,” “competence,” “job satisfaction,” “self-awareness,” and “organizational citizenship behavior.” An exploration of the temporal aspect of the data revealed that recent scholarly interest has gravitated toward topics such as “higher education,” “job performance,” “leader development,” “soft skill,” and “employee engagement.” This study’s findings suggest potential avenues for further research in the realm of emotional intelligence and leadership. Subsequent researchers might consider conducting studies in these domains to contribute to the evolving knowledge system.

Inverse radius weighting and its python package “IRWPy”: A new topography-informed interpolation to enhance geological interpretations

The extent and volume of geochemical sampling are inherently constrained by numerous factors, including budgetary limitations, analytical costs, and access restrictions. These constraints result in sampling networks that vary in density and regularity, dividing regions into sampled and unsampled areas. The creation of continuous geochemical maps is essential for geochemical prospectivity, which aims to identify geochemical anomalies, distinguish between background levels and anomalies, and define mineralization patterns. Therefore, it is necessary to interpolate data from sampled areas to estimate values in unsampled regions. Although several interpolation models exist, including Inverse Distance Weighting and various kriging methods, Inverse Distance Weighting is often used in two-dimensional ground modeling because, unlike kriging methods, Inverse Distance Weighting has no smoothing effect on edges. Inverse Distance Weighting’s reliance solely on horizontal Euclidean distances between samples overlooks critical factors such as topography and the ensuing effects on dilution, transportation, and element mobility, rendering it less effective over varied elevations. This study introduces Inverse Radius Weighting, a new interpolation technique that incorporates both Euclidean and elevation fluctuations, therefore Pythagorean distance, to help with better geological interpretations. We assessed the efficacy of Inverse Radius Weighting compared to Inverse Distance Weighting across three elements with varying mobility (Arsenic − highly mobile, Copper − moderately mobile, and Barium − nearly immobile), using different numbers of neighbors and by comparing three different evaluation measures, namely R2, Mean Absolute Error and Mean Absolute Percentage Error. By evaluating the spatial uncertainty of the generated maps and selecting the configurations with the least uncertainty as the final maps, our analysis reveals an improvement in correlation between interpolated and actual values with Inverse Haversine Radius Weighting. With this advancement, Inverse Haversine Radius Weighting overcomes the limitations of traditional Inverse Distance Weighting by accounting for elevation and its associated effects, thereby paving the way for more accurate and geochemically meaningful interpolation in geochemical prospectivity mapping.

A Diffusion Tensor Imaging White Matter Atlas of the Domestic Canine Brain

Abstract There is increasing reliance on magnetic resonance imaging (MRI) techniques in both research and clinical settings. However, few standardized methods exist to permit comparative studies of brain pathology and function. To help facilitate these studies, we have created a detailed, MRI-based white matter atlas of the canine brain using diffusion tensor imaging. This technique, which relies on the movement properties of water, permits the creation of a three-dimensional diffusivity map of white matter brain regions that can be used to predict major axonal tracts. To generate an atlas of white matter tracts, thirty neurologically and clinically normal dogs underwent MRI imaging under anesthesia. High-resolution, three-dimensional T1-weighted sequences were collected and averaged to create a population average template. Diffusion weighted imaging sequences were collected and used to generate diffusivity maps, which were then registered to the T1-weighted template. Using these diffusivity maps, individual white matter tracts—including association, projection, commissural, brainstem, olfactory and cerebellar tracts—were identified with reference to previous canine brain atlas sources. To enable the use of this atlas, we created downloadable overlay files for each white matter tract identified using manual segmentation software. In addition, using diffusion tensor imaging tractography, we created tract files to delineate major projection pathways. This comprehensive white matter atlas serves as a standard reference to aid in the interpretation of quantitative changes in brain structure and function in clinical and research settings.

Shree Anna (Millets): Promoting Nutrition and Sustainability in Agricultural Millets Map of India

Shree Anna, an initiative dedicated to promoting the cultivation and consumption of millets in India, stands at the forefront of efforts to enhance nutrition and sustainability in agriculture. Central to its mission is the creation of the Millets Map of India, which showcases the diverse regions where millets are cultivated across the country. From Karnataka to Rajasthan, and Tamil Nadu to Uttar Pradesh, the map highlights the geographical spread of millet cultivation, emphasizing their nutritional value and environmental resilience. Through educational programs, farmer training, and consumer awareness campaigns, Shree Anna aims to underscore the nutritional benefits of millets while advocating for their sustainable cultivation. By fostering market demand and economic opportunities for millet farmers, the initiative seeks to create a more inclusive and resilient agricultural ecosystem. The Millets Map of India serves as a visual testament to the transformative potential of millets in promoting nutrition, sustainability, and food security in India's agricultural landscape.

UAV survey mapping of illegal deforestation in Madagascar

Societal Impact StatementUnmanned aerial vehicle (UAV) imagery highlights the extent of illegal deforestation in protected areas for the biodiverse humid forest of central Madagascar. The ultra‐high‐resolution (<10‐cm pixel) images enable the creation of detailed forest 3D base maps and provide the means to quantify forest stand losses. To help communities safeguard their forests, local non‐governmental organisations can use UAV maps in combination with weekly deforestation alerts to facilitate an immediate on‐ground response that significantly restricts illegal activity. Integrating ultra‐high‐resolution UAV mapping and coarse‐resolution freely available satellite imagery should have much wider applications in Madagascar and the humid tropics for community‐based conservation.Summary This study of the Ambohimahamasina humid forest shows that small UAVs offer a detailed (<10‐cm pixel), rapid and cost‐effective solution to provide maps of detailed deforestation patterns not visible in satellite imagery. Calculating forest extent and volume are valuable ways to rapidly assess forest losses and prioritise areas for ground patrols. The use of 3‐dimensional measurements for above ground carbon estimates indicate how, in the future, these metrics could be used to calculate carbon payments for conservation programs. By combining UAV and free satellite imagery, an effective alert system has been developed that supports community initiatives in the protection of their natural forest resources. The wealth of ultra‐high‐resolution UAV data collected in this study provides insights into forest dynamics, supports local community forest management, and has the potential to measure the value of the forest.

CMDS-SLAM: real-time efficient centralized multi-robot dense surfel SLAM

Abstract Real-time dense mapping technology for multi-robot systems is crucial in scenarios like search and rescue. This paper presents CMDS-SLAM, a centralized multi-robot dense surfel SLAM system aimed at overcoming limitations in hardware constraints, data transmission, and real-time creation and updating of dense maps in multi-robot SLAM. CMDS-SLAM reduces the transmission of dense information by employing a dense information filtering mechanism based on co-visual keyframes, in conjunction with the extraction and compression of superpixels. Additionally, the method employs a three-stage superpixel segmentation approach to optimize transmission and enhance the efficiency of surfel map generation. Finally, a surfel co-visibility graph is established, and multi-robot surfel map maintenance and updates are achieved through co-visibility graph and map optimization. A comprehensive evaluation of CMDS-SLAM indicates that the method enables multi-robot surfel mapping and significantly alleviates data transmission pressures while achieving real-time updates and maintenance of the surfel map.

Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity

Abstract Road transportation accounts for up to 35% of carbon dioxide and 49% of nitrogen oxides emissions in the Paris region. However, estimates of city traffic patterns are often incomplete and of coarse spatio-temporal resolution, even where extensive networks of sensors exist. This study uses a machine learning approach to analyze data from 2086 magnetic road sensors across Paris, generating a detailed dataset of hourly traffic flow and road occupancy covering 6846 road segments from 2018 to 2022. Our model captures flow and occupancy with a Symmetric Mean Absolute Percentage Error of 37% and 54% respectively, providing high-resolution insights into traffic patterns. These insights allow for the creation of a comprehensive map of hourly transportation patterns in Paris, offering a robust framework for assessing traffic variables for each significant road link in the city. The model's ability to incorporate an emission factor based on the mean speed of the vehicle fleet, derived from flow and occupancy data, holds promise for developing a detailed CO2 and pollutant inventory. This methodology is not limited to Paris; it can be applied to other urban centers with similar data availability, highlighting its potential as a versatile tool for sustainable urban monitoring.

An idea to explore: A systematic approach for solving plasmid double-digest puzzles.

A common exercise given to students early in a molecular biology course is the creation of a restriction map of a plasmid "digested" by two restriction enzymes (RE). Meanwhile, students have learned from an early age about the properties and analyses of circles in their mathematics courses. But it is rare for students to learn using puzzle-based assignments at the intersection of molecular biology and mathematics. Therefore, we should present students with a puzzle that allows them to combine knowledge and skills from these seemingly disconnected disciplines. Here, we present a method for analyzing RE digests of circular plasmids using basic geometric principles.

Research on agricultural monitoring and progressive map creation in the context of ground-air coordination

Abstract By combining network RTK technology, 2D LiDAR, and UAV, this study investigates how to achieve agricultural monitoring effectively. A collaborative agricultural monitoring scheme was designed to accurately and comprehensively gather agricultural information in response to the demand for targeted sequential monitoring at specified agricultural locations. To establish a low-cost and low computational power-dependent 3D map of obstacle point clouds, a progressive 3D mapping scheme based on 2D LiDAR was designed and implemented. The effectiveness of the design was validated through simulation experiments and partly through real-world testing.