Cartography Research Articles

Hermeneutic Cartography for the Restitution of a Lost Antique Seaport

This article deals with the reconstruction of an ancient seaport in the town of Bejaia, known as Muslubium Horrea. This settlement, ignored by the official historiography, would nevertheless have played an important role in wheat exports and trade with the Roman port of Ostia. Moreover, of all the ancient ports known to date, the Algerian coast has only two port warehouses, and Muslubium Horrea is the only granary belonging to the Roman province of Mauretania Sitifensis (400AD). The methodology adopted to locate and reconstruct the topography and the settlement’s structures is based on the concept of cartographic hermeneutics. The compilation of existing historical accounts and the interpretation derived from the superimposition of ancient geographical maps and 19th century archaeological and geological maps identified all the natural resources necessary for its construction, in particular the abundance of water sources and deposits of geomaterials for construction (clay, stone, gypse, marble, etc.). These data were confirmed by field and photogrammetric surveys, resulting in a map showing the archaeological context of the Muslubium Horrea port and the remains of structures still visible today. The approach adopted allowed to delineate the size of the port site and scale the structures that make it up, as well as identify the water supply source and its primitive topography prior to its establishment.

Determinants of Visual Impairment Among Chinese Middle-Aged and Older Adults: Risk Prediction Model Using Machine Learning Algorithms.

Visual impairment (VI) is a prevalent global health issue, affecting over 2.2 billion people worldwide, with nearly half of the Chinese population aged 60 years and older being affected. Early detection of high-risk VI is essential for preventing irreversible vision loss among Chinese middle-aged and older adults. While machine learning (ML) algorithms exhibit significant predictive advantages, their application in predicting VI risk among the general middle-aged and older adult population in China remains limited. This study aimed to predict VI and identify its determinants using ML algorithms. We used 19,047 participants from 4 waves of the China Health and Retirement Longitudinal Study (CHARLS) that were conducted between 2011 and 2018. To envisage the prevalence of VI, we generated a geographical distribution map. Additionally, we constructed a model using indicators of a self-reported questionnaire, a physical examination, and blood biomarkers as predictors. Multiple ML algorithms, including gradient boosting machine, distributed random forest, the generalized linear model, deep learning, and stacked ensemble, were used for prediction. We plotted receiver operating characteristic and calibration curves to assess the predictive performance. Variable importance analysis was used to identify key predictors. Among all participants, 33.9% (6449/19,047) had VI. Qinghai, Chongqing, Anhui, and Sichuan showed the highest VI rates, while Beijing and Xinjiang had the lowest. The generalized linear model, gradient boosting machine, and stacked ensemble achieved acceptable area under curve values of 0.706, 0.710, and 0.715, respectively, with the stacked ensemble performing best. Key predictors included hearing impairment, self-expectation of health status, pain, age, hand grip strength, depression, night sleep duration, high-density lipoprotein cholesterol, and arthritis or rheumatism. Nearly one-third of middle-aged and older adults in China had VI. The prevalence of VI shows regional variations, but there are no distinct east-west or north-south distribution differences. ML algorithms demonstrate accurate predictive capabilities for VI. The combination of prediction models and variable importance analysis provides valuable insights for the early identification and intervention of VI among Chinese middle-aged and older adults.

Appraisal of contamination, hydrogeochemistry, and Monte Carlo simulation of health risks of groundwater in a lithium-rich ore area.

This study incorporated hydrogeochemical facies, the entropy-weighted water quality index (EWQI), multivariate statistics, and probabilistic human exposure assessment to investigate hydrogeochemistry, analyze groundwater quality, and estimate potential risks to human health in a lithium-rich ore area (Jadar River basin, Serbia). The findings designated the Ca·Mg-HCO3 hydrogeochemical type as the predominant type of groundwater, in which rock weathering and evaporation control the major ion chemistry. Due to the weathering of a lithium-rich mineral (Jadarite), the lithium content in the groundwater was very high, up to 567mg/L, with a median value of 4.3mg/L. According to the calculated EWQI, 86.4% of the samples belong to poor and extremely poor quality water for drinking. Geospatial mapping of the studied area uncovered several hotspots of severely contaminated groundwater. The risk assessment results show that groundwater contaminants pose significant non-carcinogenic and carcinogenic human health risks to residents, with most samples exceeding the allowable limits for the hazard index (HI) and the incremental lifetime cancer risk (ILCR). The ingestion exposure pathway has been identified as a critical contaminant route. Monte Carlo risk simulation made apparent that the likelihood of developing cancerous diseases is very high for both age groups. Sensitivity analysis highlighted ingestion rate and human body weight as the two most influential exposure factors on the variability of health risk assessment outcomes.

Orthohantavirus diversity in Central-East Argentina: Insights from complete genomic sequencing on phylogenetics, Geographic patterns and transmission scenarios.

Hantavirus Pulmonary Syndrome (HPS), characterized by its high fatality rate, poses a significant public health concern in Argentina due to the increasing evidence of person-to-person transmission of Andes virus. Several orthohantaviruses were described in the country, but their phylogenetic relationships were inferred from partial genomic sequences. The objectives of this work were to assess the viral diversity of the most prevalent orthohantaviruses associated with HPS cases in the Central-East (CE) region of Argentina, elucidate the geographic patterns of distribution of each variant and reconstruct comprehensive phylogenetic relationships utilizing complete genomic sequencing. To accomplish this, a detailed analysis was conducted of the geographic distribution of reported cases within the most impacted province of the region. A representative sample of cases was then selected to generate a geographic map illustrating the distribution of viral variants. Complete viral genomes were obtained from HPS cases reported in the region, including some from epidemiologically linked cases. The phylogenetic analysis based on complete genomes defined two separate clades in Argentina: Andes virus in the Southwestern region and Andes-like viruses in other parts of the country. In the CE region, Buenos Aires virus and Lechiguanas virus clearly segregate in two subclades. Complete genomes were useful to distinguish person-to-person transmission from environmental co-exposure to rodent population. This study enhances the understanding of the genetic diversity, geographical spread, and transmission dynamics of orthohantaviruses in Central Argentina and prompt to consider the inclusion of Buenos Aires virus and Lechiguanas virus in the species Orthohantavirus andesense, as named viruses.

Assessing drinking water quality in Eloued, south-East Algeria, using the groundwater pollution index (GPI) and the synthetic pollution index (SPI) model.

Groundwater quality degradation is a significant environmental issue worldwide, with potentially severe economic consequences and harm to ecosystems and biodiversity. This can directly affect human health, particularly in developing countries where rapid and uncontrolled urbanization is on the rise. Groundwater is the primary resource for meeting the water needs of the Eloued region, located in southeastern Algeria. Water is considered unfit for human consumption if its physico-chemical elements exceed national or international standards or guidelines. We used the GPI and SPI indices to evaluate the quality of groundwater suited for drinking. Groundwater samples were obtained from 22 wells at depths of more than 250m. Standard analytical procedures were used to determine the physicochemical characteristics of the collected samples, which included pH, EC, TDS, Na+, Ca+2, Mg+2, K+, Cl-, HCO3-, SO4-2, NO3-, NO2-, NH4+ and PO4-3. Multivariate statistical analysis and GIS techniques were used to process the results. The results of the selected physicochemical parameters were compared with World Health Organization (WHO) guidelines to determine the quality of drinking water. The findings indicate that the waters of the terminal complex aquifer are salty and contain medium to high quantities of main ions that surpass the established drinking water limits. The primary ions' relative abundance is Cl- > SO4-2 > HCO3- > NO3 for anions and Na+ > Ca+2 > Mg+2 > K+ for cations. Groundwater chemical types were dominated by Na+, Ca+2, Cl-, and SO4-2. Principal Component Analysis (PCA) showed that alteration and dissolution of carbonates, evaporates, salts, partly silicates, and evaporation, are the main reasons affecting the chemical composition of water in Eloued. The GPI results show that 18.18%, 54.54%, and 27.27% of the water samples were classed as lightly polluted, moderately polluted, or substantially polluted for drinking purposes, respectively. According to the SPI study, 9.09%, 36.36%, 36.36%, and 18.18% were considered drinkable, mildly contaminated, moderately polluted, and seriously polluted for drinking purposes, respectively. According to the GPI and SPI models' geographical distribution maps, potable water is generally scarce and concentrated in the northeastern section of the research area, near the town of Ourmes.

A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh

Flash floods are catastrophic global events, especially in northeast Bangladesh, and assessing flash flood susceptibility is crucial for preparedness and mitigation. Traditional geographic system-based flash flood susceptibility mapping struggles to capture flash floods’ non-linear and complex nature. However, machine learning models have recently emerged as an efficient alternative to address these limitations. This study evaluates four machine learning models—Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Random Forest – Gradient Boosting (RFGB) Hybrid, and Categorical Boosting (CatBoost)—for flash flood susceptibility. It categorizes areas into five susceptibility levels: very low, low, moderate, high, and very high. Covering 24,424.25 km2 across eight districts, the study uses 400 points (200 flood and 200 non-flood) for training and validation, based on field investigation, historical flood information, Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar data using Google Earth Engine, and insights from the local people. The models’ predictive performances are evaluated by incorporating topographical attributes and rainfall indices and using accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) score, with 70% of the data for training and 30% for testing. The ANN model performs best with rainfall indices, achieving high accuracy (maximum AUC = 0.802). The RFGB hybrid model shows excellent training accuracy (AUC ≥ 0.971) but suffers from overfitting during validation (AUC ≤ 0.674), requiring careful hyperparameter adjustment. The CatBoost model effectively uses both rainfall indices and terrain features, achieving AUC = 0.701 in training and AUC = 0.667 in validation. The ANN model conservatively includes the largest area (2198.3 km2) under ’very high’ susceptibility. This study’s flash flood susceptibility maps, which include rainfall extremes, are more robust than those without, helping local administrative authorities and national flood practitioners prepare for flash floods.

Spatial heterogeneity and interacting intensity of drivers for trade-offs and synergies between carbon sequestration and biodiversity

Global crises of biodiversity loss and climate change highlighted the urgent need to understand the trade-off and synergy between carbon sequestration and biodiversity and the driving mechanisms behind them. However, existing research on driving mechanisms primarily focused on the influence of independent variables on dependent variables, neglecting the interaction between independent variables. The InVEST and PLUS models were utilized in this study to evaluate the dynamics of carbon sequestration and biodiversity in the Chishui River Basin, Southwest of China. By integrating the factor detection of the Geographic Detector and the Multi-scale Geographically Weighted Regression (MGWR) model, this study identified the key drivers affecting trade-off and synergy and revealed their spatial heterogeneity. Furthermore, utilizing the interaction detection of the Geographic Detector and the Geographic Cross-Convergence Mapping (GCCM) model, this study analyzed the interaction direction and interaction intensity among key drivers. The results showed that: (1) From 2012–2035, the total values of carbon sequestration and biodiversity exhibited an overall increasing trend, with 58.49 % of the area showing trade-off effects, 23.86 % showing positive synergy, and 17.65 % showing negative synergy. (2) Elevation, slope, topographic relief, temperature, and land use intensity were identified as key factors influencing trade-off and synergy, and these factors displayed significant spatial heterogeneity. (3) When multiple drivers interacted, their explanatory power for trade-off and synergy increased significantly. The interaction patterns among drivers presented a “topography➙climate↔human activity↔distance➙soil” structure, where human activity interacted bidirectionally with other factors, with the strongest interaction occurring between climate factors and human activity. This study advanced the understanding of the driving mechanisms behind trade-off and synergy through MWGR and GCCM model. Based on the supply-demand theory, a synergistic management framework was proposed to emphasize the “pattern-process-service-feedback” nexus. The hotspot areas identified through scenario simulation could be used for synergistic enhancement of carbon sequestration and biodiversity.

Allium taiseba, a new species of Allium sect. Scorodon (Amaryllidaceae) from Van province (Turkey)

A new species, Allium taiseba (A. sect. Scorodon), is described from Hoşap (Xoşap)/Van province, Turkey. It is morphologically similar to A. bingoelense, but it differs in several morphological features including inner tunic's colour of bulb, spathe length, tepal colours and size, stamen colour and length, ovary shape, style colour and length, capsule and seeds size. A comprehensive description of the new species is provided, including detailed photographs, geographical distribution map, habitat and ecology, vernacular names and IUCN conservation status.

THE ROLE OF GEOSPATIAL ARTIFICIAL INTELLIGENCE (GEOAI) IN SMART BUILT ENVIRONMENT MAPPING: AUTOMATIC OBJECT DETECTION OF RASTER TOPOGRAPHIC MAPS IN MALAYSIA

Smart built environment mapping is integrating Geospatial Artificial Intelligence (GeoAI) to enable advanced analysis, pattern recognition, and decision-making processes. This shift in understanding, planning, designing, and managing the built environment is paving the way for a smarter, more sustainable future. This commentary explores the current role of AI in enhancing technology use within the geospatial field, focusing specifically on the application of GeoAI in mapping the built environment. Additionally, the paper presents a selection of case studies related to the implementation of AI in developing automatic vectorization, particularly for geospatial mapping in built environments. This research demonstrates the effectiveness of using Convolutional Neural Network (CNN) models for sorting objects in scanned, old topographic maps of the built environment. The findings of this study are valuable for making informed decisions, devising effective strategies, and identifying opportunities for further research and exploration within the dynamic field of GeoAI in smart built environment mapping and applications.

Multivariate Statistical Analysis and Geospatial Mapping for Assessing Irrigation Water Quality in the Aquifers of the South-Atlas Tinghir-Errachidia-Boudenib basin (Pre-African Trough, Morocco)

This study investigates the hydrogeochemical characteristics of the South-Atlas Tinghir-Errachidia-Boudenib trough situated in the arid southeastern region of Morocco. The primary objective is to assess the quality of groundwater intended for both drinking and irrigation purposes. Advanced multivariate statistical techniques such as Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) will be utilized to decipher the hydrogeochemical processes influencing groundwater mineralization. Furthermore, the research will determine the suitability of groundwater for irrigation using various indices, including Sodium Adsorption Ratio (SAR), Percent Sodium (Na%), Permeability Index (PI), and the Piper trilinear diagram.During February 2023, a systematic collection of fifty groundwater samples took place, spanning different aquifers such as those from the Senonian, Infracenomanian, Quaternary, and Paleozoic formations. These samples were subjected to thorough analysis to evaluate their quality, typology, origin, and irrigation suitability.The study's findings reveal that the majority of groundwater constituents in the South- Atlas Tinghir-Errachidia-Boudenib trough exceed the World Health Organization's (WHO) drinking water standards. The Piper diagram shows three major types of hydrogeochemical facies detected in different aquifers of the study area. The majority of the samples belonged to Ca-Mg-Cl-SO4 followed by Na-K-Cl-SO4 and Ca-Mg-HCO3. Three unique processes governing groundwater hydrochemistry were identified using multivariate techniques: The first group (which matches PC1), which consists of [EC, Na+, Cl-, TDS, TH, Mg2+, SO42-, and Ca2+], represents the mineralization axis found in the southern portion of the research area's Infracenomanian aquifer (between Aoufous and Erfoud) and the Goulmima region in the south. Al, K+, and pH are found in the second group (matches PC2), which is situated in the southern portion of the research area on the Paleozoic aquifer. Some samples are also found on the Senonian aquifer in the eastern Boudnib region. The third group (PC3 matches) is made up of [HCO3- and Fe2+], and it is mostly found in the Senonian aquifer in the northeastern region of the study area (Boudenib and its surroundings). The Senonian samples demonstrated excellent SAR, Na%, and PI, values, whereas most groundwater samples from other aquifers showed good to permissible suitability for irrigation. Furthermore, the water-rock interactions and agricultural fertilizers were highlighted as key factors influencing groundwater compositions.

Journey towards equity: Unveiling the relationship between patient travel distance and social vulnerability among patients with cancer.

158 Background: Patient travel distance is a significant barrier to accessing high-quality cancer care, impacting patient outcomes. Factors such as geographic location, transportation infrastructure, and socio-economic status contribute to variations in patient travel distance. The relationship between patient travel distance and the area deprivation index (ADI), a measure of socioeconomic disadvantage (based on income, education, employment, and housing quality) and social vulnerability of neighborhoods developed by the Health Resources & Services Administration (HRSA), remains largely unexplored. This abstract seeks to unveil the intricate relationship between patient travel distance and ADI, shedding light on the impact of social vulnerability on cancer care access. Methods: Using patient ZIP code data and practice location data from April 2023 to March 2024, we calculated the straight-line, one-way, patient-clinic travel distance (travel distance). We then derived the ADI for patient ZIP codes using geospatial mapping techniques and compared travel distances by area deprivation vulnerability categories. Results: While analyzing geographic data for 20,000 patients at 5 locations, we observed that 89% of patients traveled 15 miles or less one-way to reach the practice location, with a median travel distance of 5 miles. Median travel distance in miles for patients within 15 miles of each practice location by ADI vulnerability category is provided (Table). Median travel distances between ADI categories were statistically significant (Kruskal Wallis H-test, p <0.001). Patients from the most disadvantaged neighborhoods generally travel longer distances to access cancer care. Conclusions: Patients in socially vulnerable locales generally travel longer distances to access cancer care. By considering the ADI alongside patient travel distance, healthcare providers and policymakers can understand barriers faced by disadvantaged populations and allocate resources to reduce disparities in access to care. Targeted interventions and policies, like improving transportation services/infrastructure, empowering community-based care locations, and expanding telehealth, are necessary to address travel distance disparities for vulnerable populations. The effects of increased travel distance on timely access to care, treatment adherence, health outcomes, finances, caregivers, and patient satisfaction warrant further investigation. Location Site Category Within 15 miles of the practice location % of patients Median travel distance (in miles) by overall ADI vulnerability Highest (8, 9, 10 ADI Rank) Mid High (5, 6, 7 ADI Rank) Low (1, 2, 3, 4 ADI Rank) A Suburban Metro 89.8% 13.5 2.2 4.7 B Suburban Small Town 86.7% 12.5 13.03 8.5 C Suburban Metro 85.1% 14.9 9.4 5.9 D Suburban Metro 90.9% 12.9 9.6 4.7 E Urban Metro 93.6% 12.9 4.8 2.7 ALL 89% 12.5 6.3 5.0

Geospatial Mapping to Identify Primary Glomerulonephritis Hot Spots in Saskatchewan

Geospatial Mapping to Identify Primary Glomerulonephritis Hot Spots in Saskatchewan

Increasing access to biomarker-driven treatment for lung cancer in Alabama: The Alabama Access initiative.

159 Background: Lung cancer is the leading cause of cancer death in Alabama, with an estimated 2,550 deaths in 2024. Non-small cell lung cancer (NSCLC) represents 85% of these cases. Despite national guidelines recommending comprehensive biomarker testing (CBT) for metastatic NSCLC, significant disparities persist in the utilization of CBT and targeted therapies among Alabama residents. Methods: In March 2024, we launched the "Alabama Access" initiative – a partnership between the LUNGevity Foundation and the O’Neal Comprehensive Cancer Center at the University of Alabama at Birmingham – to foster academic and community oncology partnerships to increase access to biomarker-driven treatments. We used a population health science and geospatial mapping approach to visualize oncology practices serving counties with Alabama's highest lung cancer burden. Practices in Walker, Dothan, Marengo, Morgan, Jackson, Marshall, and Montgomery counties were identified as the highest priority based on annual mortality rates. Key interventions will include virtual molecular tumor boards and oncology navigation to facilitate the integration of resources and expertise and ensure that patients in nonacademic practices benefit from the latest advances in precision oncology. After roll-out, we will collect feasibility and acceptability data around program integration. Results: Our analysis revealed that 62.0% of the state’s population resides in Primary Care Health Professional Shortage Areas (HPSAs), and 58 out of 67 counties are classified as Medically Underserved Areas. The poverty rate in 89.6% of counties surpasses the national average, contributing to higher lung cancer incidence and mortality rates, with 62.0% of cases diagnosed at advanced stages. The state also shows a higher smoking prevalence, affecting 97.0% of counties compared to national figures. We will present practice-level testing data, including barriers to CBT. Conclusions: This pilot aims to mitigate disparities in CBT through enhanced collaboration between academic and community oncology settings. Integrating molecular navigation into community practices will be pivotal in disseminating program information and collecting data on current testing practices to improve overall lung cancer care quality in Alabama.

Intercomparisons of the Latest Precipitation Estimates from Satellite, Reanalysis, and Merged Products over Alaska

Abstract This study uses National Centers for Environmental Prediction (NCEP) Stage IV (Stage IV) precipitation data over the state of Alaska to assess and cross compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW) [Special Sensor Microwave Imager/Sounder (SSMIS)–F17, Microwave Humidity Sounder (MHS)–MetOp-B, MHS–NOAA-19, Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced Technology Microwave Sounder (ATMS), and Global Precipitation Measurement Microwave Imager (GMI) in V05 and V07], active microwave [AMW or radar; Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) in V06 and V07], combined active and passive microwave (DPRGMI in V06 and V07), infrared [Atmospheric Infrared Sounder (AIRS)], reanalysis [fifth major global reanalysis produced by ECMWF (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and satellite–gauge [Global Precipitation Climatology Project (GPCP) V1.3 and GPCP V3.2] products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multisensor products.

Embodied Mapping: Negotiating Epistemic Access during Initial Place Reference

ABSTRACT This study explores the negotiation of locational epistemic access during initial reference sequences through embodied mapping, a practice in which co-participants (re)construct geographical maps by using their bodies and the space around them in concert with talk. Like a physical map, an embodied map becomes a mutually available resource that can be scrutinized or amended and therefore constitutes a resource for managing intersubjectivity and addressing epistemic asymmetry. Although embodied maps are ephemeral and physically intangible, they are treated as perceptible by co-participants and are made amenable to correction through the practices of visualization. Embodied mapping in novice second language interaction is a sophisticated, jointly accomplished resource for constructing gesture utterance packages that bolsters participants’ limited linguistic resources. The data are in English and Japanese and originate from conversations video-recorded in a paired English as a Foreign Language (EFL) test setting.

Генетические портреты хантов и манси по гаплогруппам Y-хромосомы в контексте генофондов России

Khanty and Mansi are small indigenous peoples of Western Siberia with the unique cultural, anthropological, and linguistic characteristics. The study of their gene pool will make it possible to reconstruct the genetic structure of the Ugric-speaking population, of which in modern times only Khanty and Mansi remain, along with Hungarians, in whose gene pool there are traces of medieval migration of the Ugric-speaking Magyars. The detailed characterization of the gene pool of Khanty and Mansi is important for reconstruction of Ugric populations and genetic history of the region. The study was aimed to assess representative samples of Khants (n = 83) and Mansi (n = 74) based on the standard panel of 60 SNP markers and the extended panel of 74 Y-chromosomal SNP markers by statistical and cartographic methods in the context of indigenous population of Urals and Western Siberia. The differences between the gene pools of Khanty and Mansi have been revealed based on both standard panel of Y chromosome haplogroups and branches of haplogroups N2 and N3a4. Most of the Khanty gene pool is evenly distributed between N2-Y3195 (26%), N2-VL67 (23%), and N3a4-Z1936 (23%). The “Western” branch N2-Y3195 predominates in the Mansi gene pool (69%). Mansi gravitate towards populations of the Urals-Volga region in the multidimensional genetic space. Based on the standard panel of Y haplogroups, Khanty are close to the populations of Western and South Siberia. However, the analysis of branches N3a4 has shown that Khanty are intermediate between the “Uralic” and “Siberian” clusters: when the ancestors of Khanty moved from the Ural region to the northeast, these acquired both genetic components. The gene geographic maps of 10 haplogroup N3a4 branches in the populations of Urals and Western Siberia reflect the dynamic changes of the gene pool that took place 4–2 kya.

Association Between Household Food Insecurity and Low Birth Weight: A Population-Based Case-Control Study.

Low birth weight (LBW) is a major public health issue associated with increased neonatal morbidity and mortality. This study aimed to examine the association between household food insecurity and LBW in Mashhad, Iran. Study Design: A population-based case-control study. This study involved 6294 mothers (3247 cases and 3247 controls) who visited healthcare centers affiliated with Mashhad University of Medical Sciences for term births between March 2019 and March 2022. Cases included women who delivered neonates weighing<2500 g, while controls delivered babies≥2500 g. Food security was measured using the validated Household Food Insecurity Access Scale. Logistic regression models examined the associations between food insecurity and LBW. Geographic information system techniques mapped LBW distribution in Mashhad. Household food insecurity was significantly associated with higher odds of LBW (adjusted odds ratio [AOR]=1.25, 95% confidence interval [CI]: 1.03, 1.53). Other risk factors included younger maternal age (AOR=1.03, 95% CI: 1.01, 1.04), lower maternal education (AOR=0.55, 95% CI: 0.43, 0.71), cesarean delivery (AOR=1.70, 95% CI: 1.40, 2.06), and exposure to secondhand smoke (AOR=1.68, 95% CI: 1.02, 2.75). Gestational diabetes demonstrated a protective effect (AOR=0.37, 95% CI: 0.15, 0.91). Geographic mapping revealed that regions with higher food insecurity had elevated LBW prevalence. These findings underscore the importance of addressing food insecurity among pregnant women to reduce the risk of LBW and improve newborn outcomes.

A robust correspondence-based registration method for large-scale outdoor point cloud

ABSTRACT Large-scale outdoor point cloud registration is essential for the 3D reconstruction of outdoor scenes. Its central objective is to achieve accurate point cloud registration by determining accurate spatial transformation parameters. While feature-based methods eliminate the need for initial position estimation, they encounter challenges in handling high outlier rates. Therefore, a method capable of effectively managing outliers is crucial for enhancing the efficiency and accuracy of large-scale outdoor point cloud registration. This paper introduces the maximal clique with adaptive voting (MCAV) method, which leverages graph-based inlier compatibility to optimize potential matches. MCAV employs adaptive parameter voting (APV) to enhance computational efficiency, demonstrating significant speedup characteristics in datasets with a significant number of inliers. To further reduce outliers in potential matches, we integrate Black-Rangarajan Duality (BRD) and graduated non-convexity (GNC) into the truncated least squares (TLS) framework (BG-TLS). Accordingly, we propose the efficient BG-TLS (EBG-TLS) method for computing the registration model. Comparative analyses with traditional and deep learning-based methods across various real-world environments demonstrate that the proposed method outperforms existing algorithms in terms of rotation error, translation error, and efficiency, particularly in complex, high-noise settings. This method finds broad applications in geospatial mapping and surveying, autonomous navigation, and environmental monitoring.

Differences that Make a Difference: Adapting a Professional Development Program for the Post-COVID-New-Normal

The unpredictable nature of the COVID-19 pandemic created unprecedented challenges including a need for new curricula, pedagogy, and more accessible forms of professional development (PD). A gap in research exists regarding professional development of educators. This qualitative study used data collected from two surveys, a focus group, and a geographical distribution map. The analysis focuses on identifying factors that influence in-service educators when choosing and participating in an extended, elective PD that focuses on writing and its instruction over two years. Analysis of two online summer National Writing Project institutes revealed several motivators for participation including desires to improve pedagogical skills, online accessibility, and desire to participate in personal writing. Participants indicated a strong desire for the trainings be moved to a later date in the summer and to be offered online. These actionable requests were implemented in the second summer PD offering. Findings will be used to reframe professional development formats.

A comparison of data visualization tools: A case study in health-related research

Data visualization tools are important tools for making health decisions based on large amounts of data. However, very few comparisons of data visualization tools have been done in the context of health-related research. This paper compares commonly available visualization tools from the perspective of a dashboard developer in the context of scientific health research using real-world data and to provide general suggestions for dashboard developers’ future visualization efforts in health-related scientific discoveries. We evaluated four commonly available visualization tools (Tableau, Spotfire, PowerBI, and R Shiny). Each tool was evaluated on visualization outcomes (comparing the generated outputs of tables, scatterplots, correlation plots, treemaps, heatmaps, and geographic maps), usability, Cloud compatibility, analytic integration, and a few other factors. All tools generated comparable visualization outcomes in our tests and were equally usable with the exception of R Shiny which is not recommended for users with limited coding experience. Every tool except R Shiny supports connections to PostgreSQL, gSheets, and Athena without additional configurations. All tools support integration with R and Python; however, PowerBI requires some additional configuration. Every tool is capable of data pre-processing and supports data transformation, null detection and outlier detection. Each tool has its own unique advantages and limitations.