Environmental Variables Research Articles

Multi-population mortality modeling with economic, environmental and lifestyle variables

AbstractThe premise of multipopulation mortality models is that pooling multiple populations can help identify more stable trends and diminish statistical noise. However, many existing models fail to contextualize mortality trends, treating them as isolated phenomena. This article introduces a comprehensive multipopulation mortality model that incorporates a broad spectrum of economic, environmental, and lifestyle factors to predict mortality trends. The factors are obtained with principal components analysis, extending current models which employ external variables beyond GDP. The model is applied to 33 countries present in the Human Mortality Database, divided into 9 clusters. Expanding the scope of covariates improves model fit for 29 countries out of 33 compared to GDP alone, and consistently outperforms the Li-Lee model. Furthermore, forecasting accuracy surpasses that of the Li-Lee model across various jump-off years and matches or exceeds models limited to GDP as a covariate. This study advances the field by demonstrating that a multipopulation approach, enriched with a wide array of covariates, significantly refines mortality forecasts, challenging the reliance on extrapolative or GDP-only models. It offers actuarial practitioners and policymakers a more nuanced tool for scenario planning, emphasizing the interconnectedness of mortality rates with broader socio-economic and environmental factors.

Insights into Blue Whale (Balaenoptera musculus L.) Population Movements in the Galapagos Archipelago and Southeast Pacific

The Galapagos Marine Reserve is vital for cetaceans, serving as both a stopover and residency site. However, blue whales, occasionally sighted here, exhibit poorly understood migratory behavior within the Galapagos and the broader Eastern Tropical Pacific. This study, the first to satellite tag blue whales in the Galapagos (16 tagged between 2021 and 2023), explored their behavior in relation to environmental variables like chlorophyll-a concentration, sea surface temperature (SST), and productivity. Key findings show a strong correlation between foraging behavior, high chlorophyll-a levels, productivity, and lower SSTs, indicating a preference for food-rich areas. Additionally, there is a notable association with geomorphic features like ridges, which potentially enhance food abundance. Most tagged whales stayed near the Galapagos archipelago, with higher concentrations observed around Isabela Island, which is increasingly frequented by tourist vessels, posing heightened ship strike risks. Some whales ventured into Ecuador’s exclusive economic zone, while one migrated southward to Peru. The strong 2023 El Niño–Southern Oscillation event led to SST and primary production changes, likely impacting whale resource availability. Our study provides crucial insights into blue whale habitat utilization, informing adaptive management strategies to mitigate ship strike risks and address altered migration routes due to climate-driven environmental shifts.

Anuran occupancy varies with stream characteristics and flow across Arizona wilderness areas

Abstract Riparian ecosystems comprise less than 2% of the landscape in the arid western United States, yet they provide habitat and resources to over half of arid‐land wildlife species, including a broad diversity of anurans (frogs and toads). Despite this, relatively few studies describe riparian herpetofaunal communities in the Southwest, and remote wilderness areas are particularly undersurveyed. We employed passive acoustic monitoring to capture anuran advertisement calls in wilderness area tributaries of the Verde River, Arizona, USA. In the spring and summer of 2021 and 2022, 13–29 autonomous recording units (ARUs) were deployed along perennial, intermittent and ephemeral tributary stream reaches. We characterized sites based on the per cent of pool, riffle, run and side channel habitat within 100 m of each ARU and quantified substrate, wetted channel width and canopy cover. To relate anuran occupancy to environmental and hydrological variables, we evaluated acoustic detection data using single‐species, single‐season occupancy models. Four species were detected in this study: canyon treefrog (Hyla arenicolor), red‐spotted toad (Anaxyrus punctatus), Woodhouse's toad (Anaxyrus woodhousii) and non‐native American bullfrog (Lithobates catesbeianus), with canyon treefrog being the most ubiquitous species observed. Vocal activity of canyon treefrog was higher at perennial and intermittent sites compared to ephemeral sites, and the presence of pools was most strongly associated with canyon treefrog occupancy. Persistent, slow‐moving water best predicted the presence of breeding canyon treefrogs. Notably, this study did not detect several species with historical records in the middle Verde River catchment, including Arizona toad (Anaxyrus microscaphus) and northern leopard frog (Lithobates pipiens). Given climate change‐related flow declines and intensifying demands for water in arid lands globally, maintaining stream flows that provide consistent and suitable hydroregimes for anuran breeding and larval development is of increasing importance. Determining habitat use and flow regimes necessary to support anuran populations can aid in prioritization of conservation actions related to water management and predict how changes in water availability may impact stream‐breeding anurans.

Understanding processes underlying cross-taxon congruence in species composition along elevational gradients

Changes in species diversity of different taxa along environmental gradients are usually correlated, resulting in a pattern called cross-taxon congruence. This pattern can be due to functional relationships between taxa, a common response to niche-related processes, or stochastic processes. However, it remains unclear the extent to which they contribute to the association among patterns of changes in species composition, (i.e., beta diversity), and whether these changes are related to species nestedness and turnover. Here we described patterns of change in the taxonomic composition of plant and orthopteran assemblages along an elevational gradient in Cordoba province, central Argentina. We assessed cross-taxon congruence and identified the main environmental variables accounting for such patterns. Mantel correlations showed congruence between the patterns of taxonomic dissimilarity of plants and orthopterans. According Generalized disiimilarity models (GDM) the main environmental variables driving the patterns were temperature for both taxa, and changes in soil nutrient content for plants, spatial effects were also found. Beta diversity was mainly due to species turnover for orthopterans and plants, indicating replacement by species adapted to elevational conditions. Niche-related process, such as environmetal filtering, along with neutral processes may have contributed to cross-taxon congruence in beta diversity.

Ecological dynamics and coexistence patterns of wild and domestic mammals in an abandoned landscape

The issue of agricultural land abandonment in southern Europe has raised concerns about its impact on biodiversity. While abandoned areas can lead to positive developments like creating new habitats and restoring native vegetation, they can also result in human–wildlife conflicts, particularly in areas with extensive farming and free‐ranging livestock. To understand habitat selection and use of livestock and wild ungulates, it is essential to study their spatial and temporal distribution patterns. In this context, we conducted a long‐term large mammal monitoring project using camera traps in the Peneda‐Gerês National Park in northern Portugal. Our primary focus was on exploring habitat preferences, occupancy dynamics, and potential spatial use correlations between domestic and wild species, utilizing dynamic occupancy models. Most wild species exhibited stable area use patterns, while domestic species experienced marginal declines, and the Iberian ibex displayed signs of repopulation. We observed distinct effects of habitat variables on occupancy, colonization, and extinction, revealing species‐specific patterns of habitat utilization. Human disturbance had a notable impact on domestic species but did not affect wild ones. Camera sensitivity emerged as a critical factor, enhancing detection probability for all species. Additionally, habitat and weather variables exerted varying effects on detection probabilities, underscoring the necessity of accounting for these factors in modeling the detection process. We found shared habitat preferences between cattle and horses, both positively correlated with wolves, suggesting potential human–wildlife conflicts. Despite extensive spatial overlap, domestic and wild species seem to exhibit ecological independence possibly due to distinct strategies and low predation pressure. Overall, the study emphasizes the multifaceted factors influencing habitat use. The observed species associations contribute to understanding ecological relationships and potential resource competition, emphasizing the importance of considering environmental variables for effective wildlife conservation and management.

Four decades in the vineyard: the impact of climate change on grapevine phenology and wine quality in northern Italy

The wine sector, among the most profitable agricultural segments, has been markedly affected by the ongoing climate change impacts, such as warmer climate conditions with higher frequency of extreme temperatures and a trend of decreasing precipitation. All this results in higher evaporative demand and therefore higher occurrence of water stress events leading to advancement of temperature-sensitive phenological stages (e.g., budburst and ripening). Such negative effects eventually affect berry development and quality, especially in historically valuable viticultural areas, forcing winegrowers to work within a compressed harvest period to maintain wine typicity. In this work we examined the relationship between environmental variables (air and soil temperature, relative humidity, precipitation, and solar radiation), phenology, berry, and wine quality for the two varieties (Chardonnay and Teroldego) in Trentino Alto-Adige/South Tyrol (Italy) over 36 years. Huglin Index (a bioclimatic heat index), growing degree days (measure of heat accumulation), and overall mean temperature showed linear increase (p < 0.001) in the last years, while no variations were recorded for precipitations. Despite no major effects being observed for phenological interval lengths, the onset of most of the phenological stages for both varieties had significantly (p < 0.001) advanced. However, i) early budburst pushed the budburst-flowering interphase by -1.2 days every two years toward putative colder periods with increased late frost probability and potential slower phenological progression towards flowering, and ii) early veraison shifted the veraison-ripening interphase by 0.25 day per year into warmer periods that oppositely impose faster phenological advancement. Hence, a substantial equilibrium in the seasonal growing length over years was maintained. Potential carry-over effects from the previous season were observed, particularly associated with heat requirements to unlock early phenological events, raising additional concerns on the additive effects of climate change to viticulture. Generally, white wine quality increased (p < 0.05) over the years, while red and sparkling wines remained unaffected. This was putatively related to accurate harvest date decision-making dictated by berry quality parameters: sugar-to-acidity ratio for Chardonnay and bunch sanitary status for Teroldego. Overall, this work provides evidence of the dynamics involved in climate change, and, to our knowledge, its overlooked effects on viticulture, thus providing new insights that can contribute to further developing adaptive strategies.

The Impact of an Automation System Built with Jenkins on the Efficiency of Container-Based System Deployment

This paper evaluated deployment efficiency by comparing manual deployment with automated deployment through a CI/CD pipeline using Jenkins. This study involved moving from a manual deployment process to an automated system using Jenkins and experimenting with both deployment methods in a real-world environment. The results showed that the automated deployment system significantly reduced the deployment time compared to manual deployment and significantly reduced the error rate. Manual deployment required human intervention at each step, making it time-consuming and prone to mistakes, while automated deployment using Jenkins automated each step to ensure consistency and maximized time efficiency through parallel processing. Automated testing verified the stability of the code before deployment, minimizing errors. This study demonstrates the effectiveness of adopting a CI/CD pipeline and shows that automated systems can provide high efficiency in real-world production environments. It also highlights the importance of security measures to prevent sensitive information leakage during CI/CD, suggesting the use of secrecy management tools and environment variables and limiting access rights. This research will contribute to exploring the applicability of CI/CD pipelines in different environments and, in doing so, validate the universality of automated systems.

Inter-algal associations and nutrients linked by Scenedesmus and Desmodesmus structure eukaryotic algal communities.

Eutrophication reduces the variability of the community composition of plankton. However, the mechanisms underlying the diversity and restructuring of eukaryotic algal communities remain unknown. This study analysed the diversity and compositional patterns of algal communities in shallow eutrophic lakes. It investigated how these communities were modified by key genera through mediating inter-algal associations under the influence of abiotic factors. Inter-algal associations explained more variance in algal communities than environmental variables, and variation in composition and diversity was primarily derived from Scenedesmus, Desmodesmus and Cryptomonas, rather than nutrients. Scenedesmus and Desmodesmus were positively correlated with the genera of Chlorophyta and formed the hub of the algal association network. When the relative abundance of Scenedesmus and Desmodesmus increased from 0.41% to 13.74%, communities enriched in biomarkers of Bacillariophyta, Chrysophyceae and Cryptophyta transitioned to communities enriched in biomarkers of Chlorophyta. Moreover, negative associations between the Chlorophyta hub genera and other non-Chlorophyta genera increased. High concentrations of total phosphorus altered the composition of algal communities by increasing the abundance of Scenedesmus and Desmodesmus, which in turn had cascading effects through inter-algal associations. Additionally, algal communities with higher abundances of Scenedesmus and Desmodesmus were more susceptible to the effects of total phosphorus. Our study suggested that inter-algal associations, centred on Scenedesmus and Desmodesmus, had a greater influence on algal diversity and community structure than other factors. Nutrient levels were not a direct driver of algal diversity and community structure adjustments, but acted indirectly by enhancing the influence of Scenedesmus and Desmodesmus.

Spatiotemporal relationship between agriculture, livestock, deforestation, and visceral leishmaniasis in Brazilian legal Amazon

Visceral leishmaniasis (VL) is an urgent public health concern in Brazil. We evaluated the spatiotemporal distribution of VL to better understand the effects of economic activities related to agriculture, livestock, and deforestation on its incidence in the Brazilian Legal Amazon (BLA). The data on newly confirmed cases of VL in Brazilian municipalities from 2007 to 2020 were extracted from the Brazilian Notifiable Diseases Information System (SINAN) and analyzed. The data on agricultural production (planted area in hectares) and livestock (total number of cattle) were obtained from the Brazilian Institute of Geography and Statistics (IBGE), whereas deforestation data (in hectares) were obtained from the Amazon Deforestation Estimation Project (PRODES). SatScan and the local indicators of spatial association (LISA) were used to identify the spatial and temporal patterns of VL and its relationships with economic and environmental variables. The cumulative incidence rate was found to be 4.5 cases per 100,000 inhabitants. Based on the LISA results, areas with a high incidence of VL and deforestation were identified in the states of Roraima, Pará, and Maranhão. Strengthening deforestation monitoring programs and environmental enforcement actions can help implement public policies to control illegal deforestation and mitigate the socio-environmental vulnerability in the BLA. Therefore, areas identified in this study should be prioritized for controlling VL.

Modern pollen distribution along a Himalayan elevation gradient in Central Nepal

Modern pollen distribution and its relationship to environmental variables in tropical to alpine vegetation zones are investigated to provide a reference for palaeoenvironmental reconstructions and to improve the understanding of pollen dispersal patterns in Central Nepal. A total of 100 samples (from soil and moss) were collected from along an elevational gradient ranging from 173 to 4615 m above sea level. Results show that the alpine zone is dominated by arboreal pollen (AP) and the sub-alpine zone also shows good representation of coniferous taxa pollen such as Pinus, Abies, Tsuga, and Picea along with Ericaceae. The temperate zone has a good representation of Quercus (Evergreen) and Pinus pollen, whereas the sub-tropical zone is characterized by Alnus pollen and agricultural taxa such as Cerealia (planted Poaceae) and Brassicaceae. In the tropical zone, non-arboreal pollen (NAP), mainly Cerealia dominate, reflecting intensive agricultural practices. Shorea is completely silent in pollen representation. A redundancy analysis indicates that elevation is the primary factor influencing pollen distribution in this region. Overall, the pollen data reflect the vegetation distribution but Pinus is over-represented and Shorea is unrepresented, which should be taken into consideration when interpreting fossil pollen in Central Nepal.

PSX-22 Boosting techniques for prediction of dry matter intake of cattle in confinement settings and SHAP analysis on the models’ output

Abstract Measurement of individual dry matter intake (DMI) is critical to improving the management of the beef herd and is necessary to determine the feed efficiency of individual cattle. Combining machine learning models and proxies for DMI (e.g., water intake, body weight, climatic variables) has the potential to replace the need for dedicated feed intake equipment. It would also open the possibility of measuring DMI in situations like grazing where quantifying DMI directly is not possible. In this study, we examined the relationship between DMI, animal variables and environmental variables. In this study, we developed and tested machine learning models based boosting techniques to develop approaches to predict DMI in cattle, using the same data we have previously published results from Random Forest techniques in the past. The dataset included both bulls and steers. The developed models include both regular boosting regression models and random effects boosting models. The regular boosting technique models such as gradient boosting regression, light gradient boosting regression, extreme gradient boosting regression. Similarly, random effects boosting machine learning model Gaussian process boosting (GPBoost) was developed to consider the random effects associated with the intake behavior of the animals. The extreme gradient boosting model outperformed other regular boosting regression models with a r2 of 0.81 and 0.48 on training and testing datasets respectively. However, it does not consider the random effects associated with the longitudinal nature of the animal intake and weather variables and prone to overfitting. GPBoost model variants were tested and developed to avoid overfitting and to incorporate a random effects model, which we propose as the overall best performing model. The GPBoost generalized well on the training and testing datasets with r2 scores of 0.55 and 0.54 respectively. To further understand the model’s performance on the unseen dataset, we used SHAP analysis on the GPBoost. The SHAP analysis revealed that water intake, full body weight and age of the animals contributed significantly to predicting unseen data.

Blockchain on Sustainable Environmental Measures: A Review

Blockchain has emerged as a solution for ensuring accurate and truthful environmental variable monitoring needed for the management of pollutants and natural resources. The immutability property of blockchain helps protect the measured data on pollution and natural resources to enable truthful reporting and effective management and control of polluting agents. However, specifics on what to measure, how to use blockchain, and highlighting which blockchain frameworks have been adopted need to be explored to fill the research gaps. Therefore, we review existing works on the use of blockchain for monitoring and managing environmental variables in this paper. Specifically, we examine existing blockchain applications on greenhouse gas emissions, solid and plastic waste, food waste, food security, water usage, and the circular economy and identify what motivates the adoption of blockchain, features sought, used blockchain frameworks and consensus algorithms, and the adopted supporting technologies to complement data sensing and reporting. We conclude the review by identifying practical works that provide implementation details for rapid adoption and remaining challenges that merit future research.

A Quantitative Assessment Approach to Implement Pneumatic Waste Collection System Using a New Expert Decision Matrix Related to UN SDGs

An innovative decision matrix has been developed to guide the selection and implementation of Pneumatic Urban Solid Waste Collection Systems (PUSWCS) in smart city projects. This study comprehensively collects and analyzes data on the advantages and disadvantages of pneumatic collection systems from technical, economic, and social perspectives. A decision-making tool was created to address the complexities of evaluating the desirability of incorporating PUSWCS in municipalities or specific areas, using a holistic approach. The tool assesses the technical, economic, and social feasibility of implementing PUSWCS, aligning it with the United Nations’ Sustainable Development Goals (SDGs). Specific variables are measured to assess compliance with the SDGs, distinguishing technical aspects from economic and social aspects. The methodology includes surveys of system users and technicians, expert assessments, and the development of a decision matrix that cross-references study variables and SDGs. The matrix assigns numerical values to the Magnitude (M) and Impact (I) of each variable, enabling quantitative interpretation. This holistic approach accommodates the complexities of waste management and diverse stakeholder perspectives. The results demonstrate the matrix’s effectiveness in accurately assessing the desirability of implementing PUSWCS. This confirms the matrix’s ability to optimally integrate with innovative smart city concepts and align with long-term sustainability goals. The study concludes that the design of the decision matrix allows the collection of information from experts, users, and stakeholders about economic, social, and environmental variables and relates them to the SDGs, to obtain a numerical result that allows to decide whether in a given urban environment it is advisable to implement a PUSWCS.

Comprehensive identification of genomic and environmental determinants of phenotypic plasticity in maize.

Maize phenotypes are plastic, determined by the complex interplay of genetics and environmental variables. Uncovering the genes responsible and understanding how their effects change across a large geographic region are challenging. In this study, we conducted systematic analysis to identify environmental indices that strongly influence 19 traits (including flowering time, plant architecture, and yield component traits) measured in the maize nested association mapping (NAM) population grown in 11 environments. Identified environmental indices based on day length, temperature, moisture, and combinations of these are biologically meaningful. Next, we leveraged a total of more than 20 million SNP and SV markers derived from recent de novo sequencing of the NAM founders for trait prediction and dissection. When combined with identified environmental indices, genomic prediction enables accurate performance predictions. Genome-wide association studies (GWASs) detected genetic loci associated with the plastic response to the identified environmental indices for all examined traits. By systematically uncovering the major environmental and genomic factors underlying phenotypic plasticity in a wide variety of traits and depositing our results as a track on the MaizeGDB genome browser, we provide a community resource as well as a comprehensive analytical framework to facilitate continuing complex trait dissection and prediction in maize and other crops. Our findings also provide a conceptual framework for the genetic architecture of phenotypic plasticity by accommodating two alternative models, regulatory gene model and allelic sensitivity model, as special cases of a continuum.

Bird metacommunities of urban parks in the pampean region, Argentina

Metacommunities are the set of local communities that are linked by the dispersion of potentially interacting species. The study of metacommunities is important to elucidate the relationship between processes that occur at different spatial scales. However, bird metacommunities in urban parks have been little studied. The objectives of this study were: 1) to analyze the relative role of species dispersal, environmental selection, and stochastic processes shaping urban bird metacommunities; and 2) to analyze the structure of the metacommunities of birds in urban parks. Bird surveys were made in 51 parks of six cities in Buenos Aires province, Argentina. To obtain the metacommunity structure, three elements were analyzed: coherence, turnover, and boundary clumping. We found that the metacommunity conforms to a clementsian structure, in which groups of species respond in a similar way to environmental gradients. The environmental and connectivity variables explained a greater proportion of the variance than the spatial variable. The composition of species was related to the location of the cities, the distance to the rural area, the distance to the urban center, the number of cars passing near parks, and habitat diversity. Due to the clementsian structure of metacommunities, they probably were structured under a species sorting and mass effect process. The number of cars and habitat diversity in the parks would be acting as environmental filters for bird species, while the distance to rural areas and the distance to the urban center would affect species dispersal to the parks. These findings emphasize the importance of maintaining and improving park connectivity and habitat diversity for bird species, managing them as cities grow.

The Intersection of the Internet of Things and Smart Cities: A Tertiary Study

Since the transition from an agricultural to an industrial economy, cities have attracted large masses of people in search of their facilities. Cities are symbols of progress and opportunities. However, urbanization also brings with it several problems and challenges. Smart Cities (SC) offer a way to address these challenges by using technology to make cities more efficient, sustainable, and livable. There are numerous technologies that enable the concept of smart cities, including the Internet of Things (IoT). The IoT provides the fundamental sensing infrastructure that allows connecting and virtualizing the physical world, extracting environmental variables that serve as initial inputs for decision-making processes. Such processes are provided by software systems whose construction and execution need to deal with the dynamism, heterogeneity and often serendipitous nature that permeate both the domains of smart cities and IoT. As the integration of IoT and Smart Cities paradigms is still at an early stage, and there are not yet holistic solutions to explore the full potential of such a synergy, we carried out a literature review on the topic. In particular, the objective of this article is to describe the results of a structured literature review to identify general concepts about quality attributes, applications, technologies, and challenges of IoT solutions applied to the SC domain. Our main goal is to assist in understanding the basic concepts of the research area through the search for secondary studies. This review is a tertiary study covering 17 reviews and aims to promote a high-level discussion on the identified characteristics and provide an overview of the area to promote a better perception of current development needs and opportunities.

Effects of Global Climate Warming on the Biological Characteristics of Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae)

Spodoptera frugiperda (J.E. Smith) is a significant economic pest that has recently invaded Africa and Asia. However, much of the information regarding its ecological capabilities in these newly invaded environments remains largely unknown. In this study, the life history traits of the fall armyworm under conditions of increased temperature, different photoperiods, and varying humidity levels were systematically evaluated. Among 43 studies, a total of 20 studies were included in the analysis by passing the screening criteria, and random-effects meta-analysis, fixed-effects meta-analysis, and meta-regression were conducted. It has been found that with the increase in temperature above 20 °C, various physiological indicators of the fall armyworm are significantly enhanced. When the temperature reaches 32 °C, the physiological activities of S. frugiperda are at their highest point. As the temperature increases, the duration of each developmental stage of the fall armyworm decreases significantly, accompanied by an increase in oviposition quantity and period in females. Additionally, the pupal development time is shortened, which leads to an increase in the lifespan of the adult moth. Using temperature and relative humidity as environmental variables, the optimal survival conditions for each insect state of the fall armyworm were calculated. These findings can assist in predicting the population dynamics of the fall armyworm and in formulating appropriate and practical management strategies.

The relationship between homicides from armed conflict and efficiency of educational quality in Colombia

This study analyzes the relationship between homicides due to the armed conflict and educational efficiency in 912 municipalities in Colombia between 2014 and 2018, through the Saber 11 standardized test and school pass rates. The main objective is to calculate the potential loss of efficiency in the municipalities due to the armed conflict; to do this, a conditional robust non-parametric approach is applied, incorporating armed conflict as an environmental variable. Results show, primarily, that armed conflict deaths in the municipality are associated with lower educational efficiency; additionally, conflict in both local and neighboring municipalities is found to be significantly correlated with level of educational inefficiency.

Bayesian spatio-temporal analysis of dengue transmission in Lao PDR

Dengue, a zoonotic viral disease transmitted by Aedes mosquitoes, poses a significant public health concern throughout the Lao People’s Democratic Republic (Lao PDR). This study aimed to describe spatial–temporal patterns and quantify the effects of environmental and climate variables on dengue transmission at the district level. The dengue data from 2015 to 2020 across 148 districts of Lao PDR were obtained from the Lao PDR National Center for Laboratory and Epidemiology (NCLE). The association between monthly dengue occurrences and environmental and climate variations was investigated using a multivariable Zero-inflated Poisson regression model developed in a Bayesian framework. The study analyzed a total of 72,471 dengue cases with an incidence rate of 174 per 100,000 population. Each year, incidence peaked from June to September and a large spike was observed in 2019. The Bayesian spatio-temporal model revealed a 9.1% decrease (95% credible interval [CrI] 8.9%, 9.2%) in dengue incidence for a 0.1 unit increase in monthly normalized difference vegetation index at a 1-month lag and a 5.7% decrease (95% CrI 5.3%, 6.2%) for a 1 cm increase in monthly precipitation at a 6-month lag. Conversely, dengue incidence increased by 43% (95% CrI 41%, 45%) for a 1 °C increase in monthly mean temperature at a 3-month lag. After accounting for covariates, the most significant high-risk spatial clusters were detected in the southern regions of Lao PDR. Probability analysis highlighted elevated trends in 45 districts, emphasizing the importance of targeted control strategies in high-risk areas. This research underscores the impact of climate and environmental factors on dengue transmission, emphasizing the need for proactive public health interventions tailored to specific contexts in Lao PDR.

Analyzing shared e-scooter trip frequency on urban road segments in Austin, TX

The expansion of e-scooter sharing system presents a mix of advantages and challenges to the urban transportation system. This research delves into the frequency of shared e-scooter trips on urban road segments in Austin, TX, leveraging a Random Forest model to dissect the influence of built environment and demographic variables on e-scooter trip frequencies. The model was then interpreted using Shapley Additive Explanations and Partial Dependence Plots. Results indicated that presence of bike lanes, distance to city center, violent crime, walkability, and land use are the most important variables. Notably, high shared e-scooter trip frequency often coincides with high incidence of violent crimes. The study further explores the non-linear relationships between e-scooter trip frequency and these key variables, revealing threshold effects and significant shifts in usage patterns. These insights offer valuable guidance for cities in the strategic development and regulation of shared e-scooter services.