Environmental Measures Research Articles

Young Children's Participation and Environment Measure (YC-PEM): Cross-Cultural Adaptation, Reliability, and Validity of Turkish Version.

We investigated the psychometric properties of a Turkish version of the Young Children's Participation and Environment Measure (YC-PEM-T). Our participants were 367 children (212 children with disabilities and 155 children without disabilities; M age = 40.76 months, SD = 20.33; range 0-71 months) and their parents, who were selected by a convenience sampling method. We translated and cross-culturally adapted the original YC-PEM from English to Turkish (YC-PEM-T). We found the internal consistency and test-retest reliability of the YC-PEM-T to be good with a Croanbach's alpha ranging from .68 to .94, and intraclass correlation coefficients ranging from .69 to .89 after the YC-PEM-T was administered twice to 100 parents within a three week period. In a two-way analysis of variance (ANOVA) we compared YC-PEM-T scores between children with and without the presence of a disability and found significant differences (p < .05). While we found significant differences between three different age groups, they did not follow consistently. To evaluate discriminant validity, we followed up these ANOVAs with post-hoc analyses when results were statistically significant. Regarding concurrent validity, there were strong to very strong relationships between scores on the participation part of the YC-PEM-T and both the Pediatric Evaluation of Disability Inventory (PEDI; .701 < r < .854, p < .001), and the European Child Environment Questionnaire (ECEQ; - .632 < r < - .690, p < .001). We concluded that the YC-PEM-T is a valid, reliable, and culturally adapted tool for evaluating daily life participation and adaptation to environmental factors in young Turkish children. We can recommend the YC-PEM-T for use in clinical settings and for epidemiological research to assess young children's participation in activities in the home, preschool and community. We also recommend that future investigators examine other psychometric properties of this scale, including its interpretation and capacity to differentiate varied diagnostic and disability groups.

Spread spectrum lighting for excitation-emission matrix measurements in ambient light environments

Spread spectrum lighting for excitation-emission matrix measurements in ambient light environments

Indoor environment and user perceptions in offices in Greenland compared to Denmark

Most of the Greenlandic building stock comprises residential houses and apartments. Consequently, the indoor environment in Greenlandic office buildings has not been subject to wide-ranging investigations. The purpose of this study was to investigate the indoor environment in office buildings in Greenland and to characterise the buildings, their indoor environment, and occupant well-being, health and self-assessed performance. This was done through a post occupancy evaluation (POE), where 22 office buildings, including 70 offices in Nuuk and Sisimiut, were evaluated based on indoor environment measurements and questionnaires of occupants' perceived indoor environment. This was compared with data and results from a related study in office buildings in Denmark (26 office buildings, including 83 offices) collected in the same period. The Danish office buildings performed slightly better than the Greenlandic office buildings regarding the perceived indoor environment. The measured indoor environment differed in performance between the countries when benchmarking towards the requirements and guidelines of the Danish Working Environment Authority (DWEA). It was concluded that the office buildings in Greenland did not differ much from the office buildings in Denmark regarding the measured and perceived indoor environment.

Fluctuating selection in a monkeyflower hybrid zone

Abstract While hybridization was viewed as a hindrance to adaptation and speciation by early evolutionary biologists, recent studies have demonstrated the importance of hybridization in facilitating evolutionary processes. However, it is still not well-known what role spatial and temporal variation in natural selection play in the maintenance of naturally occurring hybrid zones. To identify whether hybridization is adaptive between two closely related monkeyflower species, Mimulus guttatus and Mimulus laciniatus, we performed repeated reciprocal transplants between natural hybrid and pure species’ populations. We planted parental genotypes along with multiple experimental hybrid generations in a dry (2021) and extremely wet (2023) year in the Sierra Nevada, CA. By taking fine-scale environmental measurements, we found that the environment of the hybrid zone is more similar to M. laciniatus’s seasonally dry rocky outcrop habitat than M. guttatus’s moist meadows. In our transplants hybridization does not appear to be maintained by a consistent fitness advantage of hybrids over parental species in hybrid zones, but rather a lack of strong selection against hybrids. We also found higher fitness of the drought-adapted species, M. laciniatus, than M. guttatus in both species’ habitats, as well as phenotypic selection for M. laciniatus-like traits in the hybrid habitat in the dry year of our experiment. These findings suggest that in this system, hybridization might function to introduce drought-adapted traits and genes from M. laciniatus into M. guttatus, specifically in years with limited soil moisture. However, we also find evidence of genetic incompatibilities in second generation hybrids in the wetter year, which may balance a selective advantage of M. laciniatus introgression. Therefore, we find that hybridization in this system is both potentially adaptive and costly, and that the interaction of positive and negative selection likely determines patterns of gene flow between these Mimulus species.

Identifying Potential Factors Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data.

Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted. This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach. In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium's data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure. The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.

Ecological vulnerability assessment and spatial-temporal variations analysis in typical ecologically vulnerable areas of China

Ecological vulnerability assessment is crucial for environment protection, ecological restoration and resource utilization. However, many former studies have limitations in the indicator system of the assessment, which were not comparable for different types of ecologically vulnerable areas. It is difficult to apply directly to the ecological vulnerability assessment of different types and in various regions. Aiming to solve these problems, the study proposed a well-established and comprehensive indicator system for ecological vulnerability assessment and conducted ecological vulnerability assessment application of five types of typical ecologically vulnerable areas of China based on remote sensing, meteorological, geographic and other data. The results show that the average EVIs value of Zhangbei County ranging from 0.525 to 0.559 are the highest among the five research areas during the four periods, followed by Zoige region and Xiamen bay. However, the region with the lowest average EVI value varies. In 2005 and 2015, it was Taihe County, while in 2010 and 2020, it was the Sanjiangyuan region. The variation of average EVIs in the five typical areas presents slight fluctuation and remains generally stable from 2005 to 2020. It indicates that the environmental protection measures and projects undertaken by the Chinese government in recent years have had a striking effect, curbing the trend of ecological environment deterioration.

Participation Strategies Used by Young People With and Without Physical Disabilities.

Participation strategies used by young people (YP) are understudied. This study aimed to identify strategies used by YP with and without physical disabilities to promote participation at home, school, community, and workplace. In this qualitative descriptive study, 106 participants ages 12 to 30 ( = 22.7) years, with (n = 52) and without (n = 54) physical disabilities, reported up to three strategies to facilitate their participation in four settings using the Youth, Young-Adult Participation and Environment Measure. A total of 1,235 strategies were analyzed using inductive content analysis. As a result, 11 categories emerged; seven were environment-focused and four were person-focused. Both groups used time management, built on intrinsic factors, and utilized social support across all settings. At school, those with disabilities uniquely benefited from accommodative institutional environments. They also reported more strategies involving advocacy, analyzing a situation, and seeking accommodative physical and social environments. The findings illustrate a range of participation strategies YP use that may inform ways to promote participation more effectively.

Study on the Precise Evaluation of Environmental Impacts of Air Pollution in Cold Regions Using the Cost Control Method

Objective: With the acceleration of industrialization, air pollution has become a global environmental issue, particularly in cold regions where the unique climatic and geographical conditions give rise to distinctive types of air pollution and impacts. Considering the economic evaluation of environmental damage is crucial for effective pollution control policies, this study aims to provide a more precise environmental damage assessment method through the Improved Virtual Control Cost Method (IVCCM) to optimize air pollution governance strategies in cold regions. Method: This study utilizes a case study of a major company producing methanol and coal-based natural gas, where the emissions from the boiler exhaust exceeded the prescribed standards for particulate matter, sulfur dioxide, and nitrogen oxides during a specific period. By employing a segmented counting approach that accounts for downtime, precise calculations were conducted for the actual periods of excess emissions. Adjustments were made to the calculation coefficients within the Virtual Control Cost Method to more accurately reflect the ecological damage caused by air pollution. Results: The IVCCM calculations revealed that the total environmental loss caused by the company’s excessive air pollution emissions amounted to USD 1.6844 million, significantly lower than the original calculation method (USD 2.1885 million). Specifically, the environmental losses due to particulate matter, sulfur dioxide, and nitrogen oxides were USD 0.0032 million, USD 0.3600 million, and USD 1.3212 million, respectively. Conclusions: The IVCCM enables a more precise assessment and prediction of ecological environmental damage caused by air pollution in cold regions. Compared to traditional methods, it effectively reduces assessment costs, mitigates disputes arising from unclear parameter values and calculation methods, and facilitates the development of more rational environmental protection policies and measures.

Development of indoor CO2 monitoring system with an integrated fixed sensor and a mobile measuring robot

Abstract This paper proposes an advanced system for optimizing indoor environmental quality (IEQ) in office environments that integrates fixed sensors with a mobile measuring robot (MMR). A demand-based measurement strategy that uses human detection and predictive analytics via machine learning is used to enhance data collection accuracy and efficiency. The system incorporates voice notifications to prompt occupants to perform actions that improve IEQ. The MMR's operational capabilities and coordination with fixed sensors allow the system to achieve high precision and efficiency in office environments. The system's effectiveness is validated through empirical studies (two preliminary experiments and two main experiments) in real office settings. The first preliminary experiment identified measurement blind spots and the second preliminary experiment tested the equivalence of environmental measurements between the MMR and fixed sensors. The first main experiment showed the system's human detection function for efficient and precise environmental measurement and the voice notification function for prompting occupants to perform actions that improve IEQ. The second main experiment showed the system's predictive accuracy in forecasting CO2 levels using neural network models. The main experiments demonstrate that the system can effectively guide MMR operations, reduce measurement times, and accurately predict environmental changes. The proposed system is a comprehensive solution for IEQ enhancement in office buildings.

Pig farming and the quest for decent work and economic growth: Evidences from Buffalo city

This study delves into the role of pig farming in advancing Sustainable Development Goal (SDG) 8—Decent work and economic growth in Buffalo City, Eastern Cape. The absence of meaningful employment opportunities and genuine economic progress has remained a significant economic obstacle in South Africa for an extended period. Through a mixed-method approach, the study examines the transformative impact of pig farming as an economic avenue in achieving SDG 8. Through interviews and questionnaires with employed individuals engaged in pig farming in Buffalo City, the study further examines pig farming’s vital role as a source of decent work and economic growth. The study reveals inadequate government support and empowerment for pig farming in Buffalo City despite pig farming’s resilience and potential in mitigating socio-economic vulnerabilities and supporting community’s livelihoods. To enhance pig farming initiatives, this study recommends government’s prioritization of an enabling environment and empowerment measures for the thriving of pig farming in Buffalo City. By facilitating supportive policies and infrastructures, the government can empower locals in Buffalo City to leverage pig farming’s potential in achieving SDG 8.

FBSM: Foveabox-based boundary-aware segmentation method for green apples in natural orchards

Image segmentation of target fruits is an essential part of machine vision systems, aiming to facilitate more accurate early fruit measurement and machine harvesting in natural orchard environments. Given that instance-level pooling and down-sampling operations in conventional segmentation models often lead to the loss of detailed information, resulting in coarse partitioning masks, we endeavor to restore the boundary information of the masks. To achieve high-quality fruit segmentation with clear boundaries and refined masks, a Foveabox-based boundary-aware segmentation model (FBSM) is constructed by adding a multi-stage mask prediction head incorporating fine-grained features to the anchor-free detection model FoveaBox to realize accurate segmentation of green apple fruits. At each stage, a bi-layer fusion structure (BFS) is employed to fully fuse the fine-grained features in a double-fusion manner to guide the subsequent instance mask prediction. Finally, the boundary recovery module (BRM) is leveraged to recover the lost boundary detail information and obtain more accurate boundaries for the continuous optimization process of the fruit instance mask. Experimental results demonstrate that the proposed FBSM model achieves a mean average precision (mAP) of 60.7 % for green apple instance segmentation in unstructured natural orchard environments, surpassing traditional instance segmentation models. Moreover, it enhances base model detection accuracy by 1.3 % while striking a better balance between detection and segmentation performance.

CONCEPT FOR THE DEVELOPMENT OF A GREEN URBAN ECOSYSTEM: MAIN THEORETICAL APPROACHES

The article reveals the basic theoretical approaches to the formation of a green urban ecosystem in the conditions of modern urbanization. The importance of ecological planning in cities and the impact of green spaces on improving the urban environment are substantiated. The necessity of introducing new methods of managing urban ecosystems, taking into account modern environmental challenges, has been established. Based on the set goal, modern approaches to planning and management of green infrastructure are considered. It is argued that the integration of natural components into urban structures is a key prerequisite for the sustainable development of urban areas. The main principles of creation and maintenance of the urban ecosystem were studied, in particular in the context of adaptation to climate change. The main trends in the development of green spaces in cities have been determined. Further perspectives of research should be directed to the integration of innovative solutions to improve the ecological condition of urban areas. The article also discusses the concept of urban ecosystems and what functions they perform. The latest scientific studies on the growing interest in the problems of green infrastructure development in cities were analyzed. Fifteen principles of green urbanism are analyzed in detail and examples of how they are implemented in some countries of the European Union are given. It is proved that the implementation of the concept of "green" economy is possible only if state mechanisms are introduced to encourage investment in environmentally safe production. Attention is focused on the need to change the methods and forms of fiscal policy at the government level, adopt appropriate laws and regulations, radically reform the policy of subsidizing and supporting renewable energy sources, and further increase investment in the "green" sectors of the economy. It is shown that the development and implementation of the concept of "green" economy in Ukraine should be considered as the main reference point and the basis of state policy. To improve environmental safety in the capital, it is necessary to consistently implement a range of organizational, technical, innovative and other environmental measures with mandatory definition of the social optimum of environmental quality, which will be accompanied by increasing environmental costs and significantly increasing society's requirements for economical use.

Abstract C009: An exploratory ecologic analysis of the association of neighborhood obesogenic factors with prostate cancer mortality in Chicago, Illinois: Challenges and opportunities

Abstract Introduction: Obesity has been associated with aggressive prostate cancer (PCa). Environments that promote obesity, known as obesogenic environments, may also contribute to PCa progression at the population level. However, studies exploring these factors are limited. The purpose of this study was to examine the association between neighborhood obesogenic factors with PCa mortality rates in Chicago. Methods: We used an ecological study design with Chicago Community Areas (n=77) serving as the geographical area unit of analysis. All community area-level data were obtained from the Chicago Health Atlas public data portal. For each area, two obesogenic factors were examined: food environment and park access. The number of healthy and unhealthy grocery stores was used to characterize the food environment. The retail food establishments data was retrieved for the year 2013. The stores were classified into chain grocery stores, non-chain grocery stores, liquor stores, and convenience stores which included dollar tsores, corner stores and gas stations. Chain and non-chain grocery stores were grouped as healthy food access, and other (convenience store, dollar store and liquor store) as unhealthy food access. The area of public parks per 100,000 community area population was used as a measure of park access. An exclusive park area for each community was calculated by intersecting the community area and park area shape files from the year 2016. The outcome variable was community area-level PCa mortality rate age-adjusted to the community area male population. To normalize the data, all included variables were standardized to zero mean and unit standard deviation. Linear regression models analyzed the association of the standardized measures of obesogenic environment (individually and simultaneously) with age-adjusted rates using the Statistical Analysis Software 9.4. Results: The mean number of healthy (0 - 11) and unhealthy (0 - 10) food stores in each community area were 3.1 and 2.6 respectively and mean park area (14.0 - 4096.5 acres) available in each community area was 453.5 acres per 100,000 population. The mean (0 – 29.6) PCa mortality was 9.8 per 100,000 population. Healthy food access was inversely associated with PCa mortality rates, where for every for one 1-standard deviation increase in healthy food access standard deviation, there is a 0.40 unit decrease in community area PCa mortality rate standard deviation (p=0.002) while accounting for the other obesogenic factors (unhealthy food access and park access). Conclusion: The findings of this study suggest that the neighborhood access to healthy food may be a determinant of neighborhood PCa mortality rates. Ecological studies are helpful to understand the impact at the geographical level. Methodological challenges and opportunity in ecological analyses, such as addressing bias and multivariable control of confounding, will be discussed. Citation Format: Ravneet Kaur, Jiehuan Sun, Nebiyou Y. Tilahun, Vincent L. Freeman. An exploratory ecologic analysis of the association of neighborhood obesogenic factors with prostate cancer mortality in Chicago, Illinois: Challenges and opportunities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C009.

Sustainable development and environmental protection in the Absheron peninsula: a geo-ecological perspective

The research focuses on the ecological and geographical aspects of the Absheron region in Azerbaijan, highlighting the significant environmental challenges and the need for sustainable resource management. Given the region’s rapid economic development and urbanization, there is a crucial need to balance economic growth with environmental conservation to ensure long-term ecological stability. The Absheron region faces numerous environmental issues due to intensive industrial activities, particularly in the oil and gas sectors, and increasing urbanization. These activities have led to significant anthropogenic impacts, necessitating comprehensive studies to assess and mitigate their effects on the natural environment. The article’s primary aim is to analyze the current dynamics of economic progress in the Absheron peninsula, assess the anthropogenic impacts, and identify efficient strategies for environmental management. The study also aims to provide a comprehensive ecological survey of the region, focusing on sustainable green economic practices. There has been significant urban growth in cities like Baku, Sumgayit, and Khyrdalan, contributing to increased environmental pressure. Modern settlements and infrastructure development have played a crucial role in organizing advanced systems for economic purposes. The economic activities, particularly in the oil sector, have significantly impacted the region’s natural complexes, necessitating the implementation of green economy principles. Effective management practices are essential to prevent pollution, maintain ecological balance, and protect natural resources. The study highlights the importance of greening initiatives and emergency response plans to manage environmental risks. The author emphasizes the need for a balanced approach to economic development and environmental conservation in the Absheron region. The study provides valuable insights into the ecological and geographical challenges of the Absheron region and offers practical solutions for achieving sustainable development. The article outlines the potential of geographic information systems and remote sensing technologies in monitoring and managing environmental change, in particular, in assessing the impact of industrial activity and urban expansion. The results of the study indicate the need for a comprehensive policy that combines economic development goals with environmental protection measures to ensure long-term environmental sustainability and economic prosperity in the Absheron Peninsula.

Predicting indoor personalized heat stress using wearable sensors and data-driven models

Rising global temperatures pose heightened heat stress challenges, especially impacting vulnerable populations such as older adults in low-income urban areas of color, who disproportionately face energy inefficiencies and environmental burdens. Traditional research often overlooks individual metabolic rates and behaviors in heat stress assessments, relying instead on isolated environmental measures in laboratory settings. This paper presents a novel, occupant-centric approach that integrates wearable sensor data, building information, and air-conditioning usage to predict extreme heat events and assess their impact on occupants through calibrated thermal building simulations for seven older adults in Houston, Texas. We developed a personalized heat stress model based on existing heat index equations and dynamic predicted mean votes, enhanced by measured metabolic rates. A data-driven machine learning algorithm was then used to predict extreme heat events with this model serving as the ground truth. Our findings reveal that elevated activity levels significantly contribute to discomfort during extreme heat events, often undetected by the heat index equation—an environmental metric—alone. Furthermore, we observe that combining metabolic rates with measured indoor temperatures consistently predicts extreme heat events with over 95 % accuracy, demonstrating almost 13 % improvement compared to using indoor temperature in isolation. This study provides a valuable proof of concept that underscores the merits of integrating multiple data sources to raise the accuracy and predictability of indoor extreme heat events. Our research not only offers potential to enhance early warning systems and guide architectural decisions but also influences building material choices, significantly mitigating the adverse effects of extreme heat.

A Multi-Omics, Machine Learning-Aware, Genome-Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction.

Given the extensive heterogeneity and variability, understanding cellular functions and regulatory mechanisms through the analysis of multi-omics datasets becomes extremely challenging. Here, a comprehensive modeling framework of multi-omics machine learning and metabolic network models are proposed that covers various cellular biological processes across multiple scales. This model on an extensive normalized compendium of Bacillus subtilis is validated, which encompasses gene expression data from environmental perturbations, transcriptional regulation, signal transduction, protein translation, and growth measurements. Comparison with high-throughput experimental data shows that EM_iBsu1209-ME, constructed on this basis, can accurately predict the expression of 605 genes and the synthesis of 23 metabolites under different conditions. This study paves the way for the construction of comprehensive biological databases and high-performance multi-omics metabolic models to achieve accurate predictive analysis in exploring complex mechanisms of cell genotypes and phenotypes.

A longitudinal study on the predictors of early grades learning attainments in low-and-middle-income countries: Evidence from Tanzania

Strands of evidence from high-income countries has consistently indicated that early learning attainments predict later learning achievements. However, less are known about these issues from low-and-middle-income countries, particularly from the sub-Saharan region. This longitudinal study examined the extent to which children’s school-entry (N = 597; mean age = 6.34 years) early reading and early math skills predicted their later learning achievement in a low-income country, Tanzania. Further, given the existing disparities of quality of educational services, families’ income, and early learning achievements, the sample included children and families from both rural and urban contexts. Data were collected using Measure of Early Learning Environments (MELE) for controlling of school quality, Early Grade Reading Assessment (EGRA) and Early Grade Math Assessment (EGMA) for children’s tests, and parents’ questionnaire for home-related variables. Controlling for family socioeconomic status, intervention status, urbanicity settings, and parental education, findings indicated that school-entry math skills were significantly predictive of children’s end of Grade 2 reading and math achievement. However, while school-entry early reading skills predicted end of Grade 2 reading achievement, it did not predict later math achievements. This article extends research on links between elements of transition to school and later achievement across countries, thus, its implications are discussed.

Research on SLAM Localization Algorithm for Orchard Dynamic Vision Based on YOLOD-SLAM2

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines was less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on a four-zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results showed that within a range of 6 m, the average relative error of distance measurement did not exceed 1%, and within a range of 9 m, the maximum relative error was 2.03%. The average distance measurement time was 30 ms, which could accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduced the RMSE index of absolute trajectory error compared to the ORB-SLAM2 algorithm, which was less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 had a higher degree of agreement between the estimated trajectory and the true trajectory when the vehicle was traveling in straight and circular directions. The RMSE index of the absolute trajectory error was less than 0.03 m/s, and the average tracking time was 47 ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper could meet the accuracy and real-time requirements of agricultural vehicle positioning in orchard environments.

National trends in pediatric drowning — insights from the Israeli Ministry of Health registry-based cohort

This study analyzes the epidemiology of pediatric drowning in Israel from 2010 to 2022, focusing on differences across age groups, sex, and regions. We conducted a retrospective cohort study using data from the Ministry of Health’s administrative databases, covering all children aged 0–17 years who were seen in the emergency department, discharged after hospitalization, or died at the scene due to drowning, excluding cases of intentional harm or suicide. The primary outcome was the annual drowning rate per 100,000 children, categorized by age, sex, and region, along with hospitalization duration, intensive care unit admissions, and mortality rates. A total of 2101 drowning incidents were identified, with 9% resulting in death. Higher drowning rates were found in younger children (1–4 years) and teenagers (15–17 years), with notable differences by sex. Drowning rates were higher in coastal regions and more frequent during winter and spring. An increase in drowning rates was observed over the last 3 years of the study (2020–2022). Children hospitalized for more than 3 days had significantly higher mortality rates, both in general wards and intensive care units.Conclusions: Pediatric drowning is a persistent public health concern in Israel, with distinct seasonal, regional, age-specific, and sex-specific patterns in incidence and mortality. This study underscores the need for a comprehensive prevention strategy that includes year-round public education, environmental safety measures, and targeted interventions for high-risk groups to reduce drowning incidents and fatalities among children effectively.What is Known:• Pediatric drowning represents a significant public health challenge globally, with varying rates.• In Israel, pediatric drowning is the second leading cause of death from all injury-related deaths. Thus far, the measures and interventions to reduce fatalities were not proven effective enough.What is New:• Pediatric drowning in Israel, with a 9% mortality rate, revealed a biphasic rate varied by sex and is higher in coastal regions and during winter and spring.• There was a notable increase in drowning incidents during the last 3 years of the study period (2020–2022), coinciding with the COVID-19 pandemic.

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.