Geographical Features Research Articles

Spatial Distribution Characteristics and Ecological Risks of Typical Drugs in the Yongjiang River Basin

In recent years, due to large-scale production and widespread use, drugs have posed a potential threat to biological and human health and have received increasing attention. The occurrence and distribution of drugs in urban rivers are influenced by various factors, such as urbanization level, population density, regional geographical and climatic characteristics, and drug consumption habits. In October 2022, the Yongjiang River Basin was divided into four sub-basins： upstream, midstream, tributaries, and downstream. A total of 20 surface water and sediment samples were collected to explore the occurrence, spatial distribution, and ecological risks of drugs in water and sediment. Among the 40 target drugs, 22 drugs were detected in water samples, accounting for 55% of the target drugs, with a maximum concentration of 207.25 ng·L-1, and 12 drugs were detected in sediment, accounting for 30% of the target drug, with a maximum concentration of 153.63 μg·kg-1. The dominant species in water samples were metabolic drugs. In sediment, the dominant species in the upstream and midstream were quinolone antibiotics, while the dominant species in the tributaries and downstream were cardiovascular drugs. Nine drugs were detected in both water and sediment, with an apparent distribution coefficient Kd value of 5.0-385 029 L·kg-1. According to the average lgKoc value, the order from highest to lowest was： sertraline hydrochloride > ofloxacin > clarithromycin > D,L-venlafaxine > roxithromycin > sulfamethoxazole > fluoxetine > metoprolol > carbamazepine. Principal component analysis was used to analyze the source of drugs in water and four principal components explained 76.24% of the variation, corresponding to a source of medical wastewater, a mixed source of domestic wastewater and aquaculture wastewater, an untreated source of rural domestic wastewater, and an agricultural non-point source. Principal component analysis coupled multiple linear regression analysis well predicted the total concentration of drugs in 20 water samples （R2 = 0.868）. Three drugs were present in the water sample that posed ecological risks, with risk quotients in the order of 1,7-dimethylxanthine > dehydrated erythromycin > carbamazepine, whereas only carbamazepine in sediment posed ecological risks. The cumulative risk quotient of multiple drugs in the water at 20 sampling sites was 1.22-16.27, all of which belonged to high risk, while the cumulative risk quotient of multiple drugs in sediment was 0.009~0.064, with no risk at site S11 and low risk at all other sites. The research results can be used to analyze the distribution mechanism of drugs between water and sediment, providing a technical basis for the prevention and control of drug pollution and environmental risks in urban rivers.

Circumpolar and Regional Seascape Drivers of Genomic Variation in a Southern Ocean Octopus.

Understanding how ecological, environmental and geographic features influence population genetic patterns provides crucial insights into a species' evolutionary history, as well as their vulnerability or resilience under climate change. In the Southern Ocean, population genetic variation is influenced across multiple spatial scales ranging from circum-Antarctic, which encompasses the entire continent, to regional, with varying levels of geographic separation. However, comprehensive analyses testing the relative importance of different environmental and geographic variables on genomic variation across these scales are generally lacking in the Southern Ocean. Here, we examine genome-wide single nucleotide polymorphisms of the Southern Ocean octopus Pareledone turqueti across the Scotia Sea and the Antarctic continental shelf, at depths between 102 and 1342 m, throughout most of this species' range. The circumpolar distribution of P. turqueti is biogeographically structured with a clear signature of isolation-by-geographical distance, but with long-distance genetic connectivity also detected between East and West Antarctica. Genomic variation of P. turqueti was also associated with bottom water temperature at a circumpolar scale, driven by a genotype-temperature association with the warmer sub-Antarctic Shag Rocks and South Georgia. Within the Scotia Sea, geographic distance, oxygen and fine-scale isolation-by-water depth were apparent drivers of genomic variation at regional scales. Putative positive selection of haemocyanin (oxygen transport protein), calcium ion transport and genes linked to RNA modification, detected within the Scotia Sea, suggest physiological adaptation to the regional sharp temperature gradient (~0-+2°C). Overall, we identified seascape drivers of genomic variation in the Southern Ocean at circumpolar and regional scales in P. turqueti and contextualised the role of environmental adaptations in the Southern Ocean.

Genetic diversity of populations of Simulium reptans s.l. (Diptera: Simuliidae) in the territory of Russia and Northern Kazakhstan

The presence of sibling (cryptic) species among bloodsucking black flies can be a serious problem in assessing both their diversity and the role of individual species in the transmission of dangerous human and animal pathogens. The peculiarities of molecular genetic structure of populations and ecology of black flies of Simulium reptans s.l. (complex of Simulium reptans (Linnaeus) and Simulium reptantoides Carlsson) in European and Asian parts of Russia and Northern Kazakhstan in comparison with populations from Central and Northern Europe were investigated. Using restriction analysis of the COI gene and subsequent sequencing, we searched for unique mtDNA variants that could belong to previously undescribed forms of the studied group. In the north of the European part of Russia (north of Eastern Europe), only S. reptans A was registered, and in the Asian part of Russia (Siberia) and Northern Kazakhstan, only S. reptans B was found. Using phylogenetic analysis of the total data, three branches of S. reptans B were revealed and named in accordance with the areas of their distribution–East European, Eurasian and Asian. Simulium reptantoides Carlsson has currently been found only in Europe. The peculiarities of distribution and possible adaptations to different habitats of S. reptantoides and representatives of individual groups within S. reptans are discussed. Based on the characteristics of the sampling sites of the studied specimens, as well as the samples available in the NCBI database, the ecological and geographical features of the habitats of the molecular forms of Simulium reptans A and B were revealed. To obtain more accurate information on the distribution of S. reptans A and B forms in Eurasia, taking into account the elevational zonation, it is necessary to conduct additional studies in the mountainous regions of South-Western Europe, Siberia and Northern China. Based on the sequences presented in the DNA database, a method (AS-PCR COI variant) for separating the cryptic species–Simulium reptans (Linnaeus) and S. reptantoides Carlsson–was proposed. During testing of this method on the material collected in Russia and Northern Kazakhstan, only S. reptans was identified (S. reptantoides was absent), which completely coincided with the sequencing results. To further confirm the effectiveness of the method, it is necessary to conduct testing on material including representatives of the both species.

Predicting Depressive Symptoms Using GPS-Based Regional Data in Germany With the CORONA HEALTH App During the COVID-19 Pandemic: Cross-Sectional Study.

Numerous studies have been conducted to predict depressive symptoms using passive smartphone data, mostly integrating the GPS signal as a measure of mobility. Environmental factors have been identified as correlated with depressive symptoms in specialized studies both before and during the pandemic. This study combined a data-based approach using passive smartphone data to predict self-reported depressive symptoms with a wide range of GPS-based environmental factors as predictors. The CORONA HEALTH app was developed for the purpose of data collection, and this app enabled the collection of both survey and passive data via smartphone. After obtaining informed consent, we gathered GPS signals at the time of study participation and evaluated depressive symptoms in 249 Android users with the Patient Health Questionnaire-9. The only GPS-based data collected were the participants' location at the time of the questionnaire, which was used to assign participants to the nearest district for linking regional sociodemographic data. Data collection took place from July 2020 to February 2021, coinciding with the COVID-19 pandemic. Using GPS data, each dataset was linked to a wide variety of data on regional sociodemographic, geographic, and economic characteristics describing the respondent's environment, which were derived from a publicly accessible database from official German statistical offices. Moreover, pandemic-specific predictors such as the current pandemic phase or the number of new regional infections were matched via GPS. For the prediction of individual depressive symptoms, we compared 3 models (ie, ridge, lasso, and elastic net regression) and evaluated the models using 10-fold cross-validation. The final elastic net regression model showed the highest explained variance (R2=0.06) and reduced the dataset from 121 to 9 variables, the 3 main predictors being current COVID-19 infections in the respective district, the number of places in nursing homes, and the proportion of fathers receiving parental benefits. The number of places in nursing homes refers to the availability of care facilities for the elderly, which may indicate regional population characteristics that influence mental health. The proportion of fathers receiving parental benefits reflects family structure and work-life balance, which could impact stress and mental well-being during the pandemic. Passive data describing the environment contributed to the prediction of individual depressive symptoms and revealed regional risk and protective factors that may be of interest without their inclusion in routine assessments being costly.

State of the population of Pulsatilla patens (L.) Mill. on the territorу National Nature Park «Podilski Tovtry»

The population of Pulsatilla patens has been described in the NNP «Podilski Tovtry» national nature park on the territory of the «Ivakhnovetskiy» protected area. The article analyzes the data from the literature on the possible systematic position of P. patens and its range. Literary sources and herbarium specimens stored in local herbaria were processed, and probable localities in the territory of the national park were described. The only place of growth on the «Ivakhnovetskiy» of the NNP «Podilski Tovtry» has been confirmed. The ecological and coenotic relationships are characterized, and the biological features of the studied species are discussed. The geobotanical descriptions within the «Ivakhnovetskiy» protected area are carried out. The habitats and communities from the Green Book of Ukraine with P. patens were identified. The physical and geographical features of the habitat of the studied species are characterized. The results of statistical processing of morphometric parameters of P. patens, which were analyzed within the NNP «Podilski Tovtry», are processed and presented. It was found that the population on the «Ivakhnovetskiy» is predominantly homogeneous. The largest coefficient of variation was determined for the number of flowers and the height of the pedunculate shoot, which is due to the biological characteristics of the species. Based on the results of accounting for the age structure of the P. patens population in this locality, it was found that the recovery index is moderate. The factors of influence on the population of Pulsatilla patens on the «Ivakhnovetskiy» were determined. The conservation measures for the restoration and conservation of the rare species P. patens in the territory of NNP «Podilski Tovtry» and in Ukraine in particular at the eastern limit of its distribution are proposed. It is proposed to propagate the germinated seeds at the collection site with further return to the natural habitat of «Ivakhnovetskiy» and other localities identified during the study of literary and herbarium materials – to reintroduce it. Controlled spontaneous succession in abandoned and/or still active limestone quarries with large areas devoid of dense vegetation can be an effective tool for population recovery.

Construction and Evaluation of Urban Green Infrastructure in Ecologically Vulnerable Areas Based on Multi-Scale and Multi-Objective Approaches: Taking the Four-Lake Hydrographic Network in the Upper Yellow River Basin as an Example

The construction and optimization of urban green infrastructure (UGI) are regarded as effective strategies for harmonizing the natural landscape with human activities, particularly in ecologically vulnerable areas in the Upper Yellow River Basin, China. However, there is little attention paid to the scale effects and object effects as well as an absence of comprehensive assessments regarding landscape stability. Taking the Four-Lake Hydrographic Network (FLHN) in Shizuishan, a prefecture-level city, as an example, this study focuses on identifying the important sources of UGI by integrating both regional and interregional perspectives utilizing morphological spatial pattern analysis (MSPA). UGI networks were constructed and optimized based on trade-offs and synergizing relationships between individual objects using the minimum cumulative resistance (MCR) model, and the UGI network’s stability combined centrality and connectivity aspects, which were subsequently assessed. The results showed that a total of 19 important sources covering an area of 105.07 km2 were identified in the FLHN, integrating both regional and interregional levels. It was deemed unnecessary to maintain lengths of 7.79 km key corridors, 9.42 km general corridors, and 29.89 km fragile corridors; furthermore, there was no longer a requirement to upgrade an additional 5.51 km of general corridors and 25.78 km of any corridor, as UGI corridors were extracted based on a trade-off and synthesized objective methodology. The overall connectivity index value (OG) of UGI stability with respect to the multi-objective model demonstrated superior performance compared to the same index in scenarios involving the use of a single-objective approach and the straightforward overlay of each object. This study reveals the multifaceted requirements of urban landscape security and sustainability, indicating that multi-scale and multi-objective approaches in territorial space planning not only ensure the integrity of the landscape patterns but also reduce the costs associated with landscape construction. This model can be utilized to implement urban landscape entity protection and restoration for landscapes with various geographical characteristics, and it can provide valuable guidance for similar areas.

Performance comparison of GPM IMERG V07 with its predecessor V06 and its application in extreme precipitation clustering over Türkiye

Defining regions with similar characteristics for extreme precipitation is crucial for understanding the impacts of climate change, planning and managing water resources, and designing hydraulic structures. However, studies on the regionalization of extreme precipitation for Türkiye are limited, and regional extreme precipitation characteristics are not well defined. In this study, motivated by the need to contribute to this field, homogenous regions for extreme precipitation across Türkiye were determined using the latest version (V07) of Integrated Multi-satellitE Retrievals for GPM (IMERG). We initially validated IMERG V07 estimates using data from 214 ground-based stations and compared the results with its predecessor V06. The results revealed that IMERG showed some notable improvements from V06 to V07 for all seasons, especially in winter. During this season, the correlation coefficient increased from 0.57 to 0.64, the mean absolute bias decreased from 78.22 % to 69.27 %, and the RMSE decreased from 11.10 mm/day to 9.70 mm/day. In V07, while the trend of decreasing accuracy with increasing elevation observed in V06 continues, it has been shown that some notable improvements were achieved in continuous and categorical metrics. We then applied widely used non-hierarchical (K-means) and hierarchical (Ward's method) clustering techniques. To perform this, we first applied Principal Component Analysis (PCA) to reduce the number of variables related to extreme precipitation (e.g. amount, frequency, standard deviation, and seasonality) and geographic characteristics to identify the most significant variables for analysis. The K-means method delineated Türkiye into eight extreme precipitation regions, while the Ward's method resulted in six distinct extreme precipitation regions. We evaluated the results based on the existing extreme precipitation climatology literature for Türkiye and by associating them to known precipitation dynamics, and as a result, we recommended eight precipitation regions determined by the K-means. The identified precipitation regions are expected to contribute to future studies analyzing the effects of climate change and to regional studies on natural disasters resulting from extreme precipitation.

Kebijakan Pemerintah Dalam Mewujudkan Strategi Coastal Defense dengan pendekatan Anti-Access Area Denial (A2/AD) Sebagai Pertahanan Di Kawasan Indonesia

This study examines Indonesia’s coastal defense policy using an Anti-Access/Area Denial (A2/AD) strategy to protect its coastal regions. The A2/AD approach is adapted to the geographical characteristics of Indonesia as an archipelagic state, involving maritime radar surveillance, strengthening coastal patrol fleets, and developing access denial tactics in strategic areas like the Malacca Strait and the North Natuna Sea. Key threats, such as foreign vessel infiltration and illegal activities, drive the adoption of this strategy. A qualitative method is employed, analyzing policy documents, including the Indonesian Navy’s Strategic Plan, interviews with relevant officials, and case studies of the Nusantara Maritime Surveillance System (SPILN). The analysis reveals progress in infrastructure development, such as coastal base construction and defense equipment modernization. However, challenges remain, particularly in inter-agency coordination involving the Navy, Maritime Security Agency (Bakamla), and the Ministry of Marine Affairs and Fisheries, as well as limited resource allocation. The Fast Patrol Boat program exemplifies these issues, with operational constraints due to inadequate integration with national surveillance systems. Recommendations include enhancing policy synergy through a unified command center, optimizing maritime defense budgets, and improving tactical training for coastal units. These findings aim to guide policymakers in formulating a more effective coastal defense strategy to safeguard Indonesia’s maritime sovereignty and security.

Geospatial Mapping of Head and Neck Cancer Research: Assessing Access, Disparities, and Characteristics of Head and Neck Cancer Clinical Trials Across the United States.

To report geographic distribution and characteristics of head and neck cancer (HNC) clinical trials in the United States. We conducted a retrospective analysis of U.S. HNC clinical trials searching ClinicalTrials.gov from January 1, 2017 to December 31, 2021 using the terms "head and neck cancer" or "head and neck neoplasms." A total of 381 clinical trials met inclusion criteria with 2181 trial opportunities, which were correlated with population density. Of the U.S. population, 72% live within a 25-mile radius of trials. California, Pennsylvania, and New York had the greatest number of clinical trial entries. The majority of patients living more than 25 miles from an HNC clinical trial site are located in rural areas. One hundred sixty-five (43.3%) trials were about systemic therapy, of which 138 (83.6%) involved targeted immunotherapy. There were 286 unique principal investigators. One hundred six (37.1%) were females and 180 (62.9%) were males. We demonstrate disparity in the geographic distribution of HNC trials favoring densely populated urban areas, which may limit patient access due to travel burden. Studies are skewed towards immunotherapy drug trials, with fewer radiation and surgery investigations. Level III.

A tensor decomposition method based on embedded geographic meta-knowledge for urban traffic flow imputation

Accurate and reliable traffic flow data are essential for intelligent transportation systems; however, limitations arising from hardware and communication costs often lead to missing data. Tensor decomposition is widely used to address these issues. However, existing imputation methods employ a fixed geographic feature similarity matrix to constrain the tensor decomposition process, which fails to accurately capture the spatial heterogeneity of traffic flows, thus limiting the imputation accuracy and robustness. This study proposes a tensor decomposition method embedded with geographic meta-knowledge (Meta-TD) to accurately determine the spatial heterogeneity of traffic flows. The key innovation is establishing a dynamic relationship between the geographic meta-knowledge and spatial heterogeneity of traffic flows, and then using the spatial heterogeneity of the traffic flows to constrain the tensor decomposition process. Experimental results based on real urban traffic flows demonstrated the superiority of Meta-TD over fifteen baseline models under random, block, and long time-series missing patterns, achieving reductions in MAE, RMSE, and MAPE of 6.97–97.05%, 3.33–94.68%, and 0.72–90.89%, respectively. Notably, Meta-TD maintained high accuracy for sudden changes in traffic flow states, evidencing its robustness to varying missing data rates and distribution patterns. This adaptability makes it highly suitable for complex and dynamic urban traffic environments.

Projection of future gastric cancer incidence and health-care service demand by geographic area in Kanagawa, Japan.

Projections of future gastric cancer incidence and the demand for health-care services for gastric cancer patients by geographic area will assist local authorities in determining health-care needs, allocating medical resources, and planning services. This study aims to project the future incidence of gastric cancer, estimate the number of patients per medical institution, and decompose the net changes in cases to assess the impact of population aging by geographic area. Our projections are based on population-based cancer registry data, census data from 2000 to 2020, and the projected population for 2025-2045 in Kanagawa, Japan. We classified Kanagawa into urban, town, outer city, and rural areas based on geographic and population features. The number of medical institutions providing gastric cancer treatment was used to estimate the number of patients per medical institution. We projected a decrease of 25%, 52%, and 5% in gastric cancer cases in towns, outer cities, and rural areas from 2020 to 2045, respectively. However, cases are expected to increase by 9% in urban areas, primarily due to population aging. The annual number of gastric cancer patients per medical institution in urban areas is expected to increase from 54 to 59, while numbers in other areas are predicted to decline from 2020 to 2045. Our long-term projections indicate that the number of older gastric cancer patients will continue to increase in urban areas. While current measures effectively reduce gastric cancer risk, they need to be revised to address the impact of population aging.

Prospects of Agrotourism Development in the Region

For every nation, the era of transition to a market economy is marked by a severe lack of financial resources. Bringing in sectors of the economy that yield the fastest and most efficient returns is one strategy to develop and overcome this issue. Agrotourism, which is seen as a new trend in the tourism business, is one of these industries. The Samarkand region is used as an example to examine the scientific and theoretical underpinnings of agrotourism as well as its geographical characteristics in this article. Furthermore, an agrotourism map of the region was created by evaluating each location’s potential for agrotourism, classifying the regions based on how desirable they are for agrotourism, and identifying the facilities and resources available for agrotourism. Factors affecting the market of tourist services in the area were studied, and forecast values of the volume of tourist services were determined.

Inequalities in geographic barriers and patient representation in lymphoma clinical trials across England.

The distribution of trial site locations may lead to disparities in geographic access and affect patient representativeness in clinical trials. We utilised trial data covering 1993-2022 from the National Institute for Health and Care Research (NIHR) Open Data Platform, 2011 and 2021 English Census and geographic data and English individual-patient cancer registry data for patients diagnosed with lymphoma between 1997 and 2017. To assess representation, we compared patient age and sex between trial participants and the incident population. We mapped the distance and travel times of English lower layer super output areas (LSOAs) to their nearest research active NHS Trusts and assessed associations between distance and travel times and the geographic and sociodemographic characteristics of the LSOAs. Trial participants were younger than the incident population and more likely to be male. The closest NHS Trust to more than half of English LSOAs was not research active. Greater LSOA mean age, male percent, White British percent, rurality and coastal/border status were positively associated with distance and travel time (at prespecified p < 0.05 level), while greater deprivation was negatively associated. Female and older lymphoma patients in England are underrepresented in trials, with the latter facing a higher burden of geographic barriers.

Structure shielding of school and gymnasium buildings against fallout gamma radiation from the Fukushima Daiichi Nuclear Power Plant accident

The radiation dose reduction factor represents a building’s shielding capability against radiation in scenarios of potential nuclear accidents. Notably, the radiation dose reduction for school buildings and gymnasiums have not yet been determined, even though the building types are planned to be used as evaluation shelters in Japan. This study evaluated dose reduction factors in relation to the accident at the Fukushima Daiichi Nuclear Power Plant (FDNPP). In this study, we separately measured the indoor and outdoor ambient dose equivalent rates and the dose rates from natural radionuclides at two school sites in an evacuation-order-lifted area in Fukushima Prefecture, Japan, to evaluate the dose reduction factors of the school buildings and gymnasiums. The factors depended not only on the structure and materials of the buildings, but also on the geographical characteristics of the building locations, which could govern the geometries of irradiation. We noted a non-negligible overestimation of the dose reduction factors by the conventional method, due to the presence of natural radiation in areas with low dose rates (<1 μSv/h). Note that the dose reduction factor is used as an uncertainty source for external dose evaluation related to the FDNPP accident. This study can serve as a foundation for future investigations on improving the resilience of buildings confronted by artificial radiation due to scenarios of nuclear plant accidents.

Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data

In this study, we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal. We analyze how these features influence crop yields by utilizing remotely sensed data. Our methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies, offering a comprehensive understanding of the factors affecting agricultural productivity in Senegal. To optimize the model's performance and identify the optimal hyperparameters, we implemented a comprehensive grid search across four distinct machine learning regressors: Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient-Boosting Machine (LightGBM). Each regressor offers unique functionalities, enhancing our exploration of potential model configurations. The top-performing models were selected based on evaluating multiple performance metrics, ensuring robust and accurate predictive capabilities. The results demonstrated that XGBoost and CatBoost perform better than the other two. We introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural datasets. By achieving high similarity scores with real-world data, our synthetic samples enhance model robustness, mitigate overfitting, and provide a viable solution for small dataset issues in agriculture. Our approach distinguishes itself by creating a flexible model applicable to various crops together. By integrating five crop datasets and generating high-quality synthetic data, we improve model performance, reduce overfitting, and enhance realism. Our findings provide crucial insights for productivity drivers in key cropping systems, enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in data-scarce regions.

Advancing Coastal Flood Risk Prediction Utilizing a GeoAI Approach by Considering Mangroves as an Eco-DRR Strategy

Traditional coastal flood risk prediction often overlooks critical geographic features, underscoring the need for accurate risk prediction in coastal cities to ensure resilience. This study enhances the prediction of coastal flood occurrence by utilizing the Geospatial Artificial Intelligence (GeoAI) approach. This approach employed models—random forest (RF), k-nearest neighbor (kNN), and artificial neural networks (ANN)—and compared them to the IPCC risk framework. This study used El Salvador as a demonstration case. The models incorporated seven input variables: extreme sea level, coastline proximity, elevation, slope, mangrove distance, population, and settlement type. With a recall score of 0.67 and precision of 0.86, the RF model outperformed the other models and the IPCC approach, which could avoid imbalanced datasets and standard scaler issues. The RF model improved the reliability of flood risk assessments by reducing false negatives. Based on the RF model output, scenario analysis predicted a significant increase in flood occurrences by 2100, mainly under RCP8.5 with SSP5. The study also highlights that the continuous mangrove along the coastline will reduce coastal flood occurrences. The GeoAI approach results suggest its potential for coastal flood risk management, emphasizing the need to integrate natural defenses, such as mangroves, for coastal resilience.

Soil erosion projection and response to changed climate and land use and land cover on the Loess Plateau

The Grain for Green Project has profoundly altered land use and Land cover (LULC), climate, and soil loss rates on the Loess Plateau over the past few decades. Evaluation and projection of soil erosion with the impacts of climate and LULC are essential for soil erosion control. This study focuses on the Jiuyuangou watershed (JYG), a typical representative of the climate and geographical characteristics of the Loess Plateau. Utilizing the Coupled Model Intercomparison Project Phase 6 (CMIP6) and environmental data, the Revised Universal Soil Loss Equation (RUSLE) coupled with the Future land use simulation (FLUS) model was used to assess soil loss rates for historical periods and various future scenarios. Furthermore, the qualitative and quantitative analysis methods were explored to evaluate the impacts of LULC and climate change on soil erosion. The forest and grass in the JYG increased from 33.4 % in 1995 to 74.1 % in 2020. The soil loss rate significantly decreased, from 64.95 t ha−1·yr−1 in 1995–4.52 t ha−1·yr−1 in 2020. The trend of converting cropland to forest and grass is predicted to continue under different future scenarios. The soil erosion will increase from 2030 to 2050 for different scenarios compared to 2020. The LULC development scenario aiming at ecological protection will effectively curb the increase in soil erosion compared with other scenarios. LULC exerted its influence in various forms across different historical phases. Attribution analysis indicated that the construction of check dams effectively reduced soil erosion from 1995 to 2005, while afforestation from 2005 to 2020 accounted for 64.6 % of the overall contribution. The increase in soil erosion is primarily attributed to more erosive rainfall events and unreasonable allocation of LULC. The correlation between LULC and soil erosion will decrease compared to climate. These findings elucidate the long-term mechanisms of soil erosion changes, emphasizing the urgency of sustainable practices to mitigate soil erosion in the face of environmental challenges.

Doubly robust estimation of policy-relevant causal effects under interference

Abstract To comprehensively evaluate a public policy intervention, researchers must consider the effects of the policy not just on the implementing region, but also nearby, indirectly affected regions. For example, an excise tax on sweetened beverages in Philadelphia, Pennsylvania was shown to not only be associated with a decrease in volume sales of taxed beverages in Philadelphia, but also an increase in sales in nontaxed bordering counties. The latter association may be explained by cross-border shopping behaviours of Philadelphia residents and indicate a causal effect of the tax on nearby regions, which may drastically offset the total effect of the intervention. In this paper, we adapt doubly robust difference-in-differences methodology to estimate distinct causal effects on the implementing and neighbouring control regions when they are geographically separable and data exists from an unaffected control region. Our approach adjusts for potential confounding in quasi-experimental evaluations and relaxes standard assumptions on model specification while accounting for geographically separable interference, repeated observations, spatial correlation, and unknown effect heterogeneity. We apply these methods to evaluate the effect of the Philadelphia beverage tax on taxed beverage sales in 231 Philadelphia and bordering county stores. We also use our methods to explore effect heterogeneity across geographical features.

Demographic and Geographic Characteristics Associated with the Type of Prescription and Drug Expenditure: Real World Evidence for Greece During 2015-2021.

Electronic prescribing has allowed for the collection of prescription data in real time in Greece for the first time. Hence, the aim of the current study was to present the characteristics of prescriptions for the Greek population during the period from 2015 to 2021. This retrospective study was based on data extracted from the nationwide Greek electronic prescription database between January 2015 and December 2021. Descriptive statistics methods were used for the needs of the study. As the basic figures examined depend on the size of the population, in order for the results to be comparable, we estimated the corresponding measures per inhabitant, using population data from the Greek Statistical Authority. Appropriate indicators for the comparison of consumption and expenditure over time were estimated. A study of the trend was also carried out using time series and linear regression models. In order to facilitate the design and implementation of specialized policies, it is useful to identify the drug categories with the highest consumption and expenditure, as well as the geographical areas that present similar characteristics. For the first, ABC analysis was used, which helps to identify the most popular categories of drugs, while for the second, cluster analysis was carried out. Agglomerative clustering was used to divide the regions into similar groups. This hierarchical clustering algorithm classifies the population into several clusters, with areas in the same cluster being more similar, and areas in different clusters being dissimilar. The Ward linkage method with Euclidean distance was used. The analysis of prescription drug consumption and expenditure from 2015 to 2021 revealed significant fluctuations and trends across various drug categories, age groups, and geographical areas. Notably, the quantity of prescriptions increased by 20% since 2015, while expenditure surged by over 30%, with significant spikes following the end of the MoU in 2019 and the onset of the pandemic in 2020. In terms of expenditure, antineoplastic and immunomodulation agents (category L) held the largest share, driven by the introduction of new, costly drugs. The expenditure per inhabitant revealed gender and age disparities, with older populations, particularly women, incurring higher costs. Geographically, drug expenditure, and consumption varied significantly, with distinct regional clusters identified. These clusters, while showing some overlap in consumption and expenditure patterns, also highlighted unique regional characteristics. The insights into prescription drug consumption and expenditure trends offer a valuable basis for developing targeted interventions aimed at optimizing healthcare resource allocation. Moreover, the findings underscore the importance of addressing regional and demographic disparities in pharmaceutical use, thereby contributing to more equitable and cost-effective healthcare strategies. More specifically, the age distribution of prescriptions shows the increase in younger ages, which, as a result, anticipates the overall increase in prescriptions. The knowledge of the most convex categories of medicine, as well as the percentages of the use of generic drugs, shows where interventions should be made, with financial incentives and information through new information channels. The geographic disparities recorded should lead to policies that help the residents of hard-to-reach areas to access prescriptions. In addition, the present study provides a strategic framework for policymakers and healthcare managers to guide future studies and inform decision-making processes.

GENDER AND IDENTITY IN POSTMODERNITY: EARLY CONSIDERATIONS

It is urgent to reflect on identity and how it can be generated by personal characteristics. The dichotomy that emerges when it comes to identity between men and women reveals a segregation in which positive value is attributed to one and negative value to the other, placing gender binarism in our society as a unique condition. To this end, the guiding thread of this article will seek to understand the relations of identity in postmodernity and the relations that gender has in the post-structuralist movement, in a qualitative methodological proposal that encompasses basic texts on gender and identity in which the authors weave considerations. Based on the conception of identities, we see that differentiating individuals is a process intertwined with meanings that reinforces the historical and geographical characteristics to which the subjects are subjected, which presents constant modifications, which gives rise to new identities. Regarding gender, the quest for it to become an analytical category involves the struggle of minorities affected by inequalities in power relations and the redefinition of the term so that it does not merely differentiate between masculine and feminine, opening space for new concepts and categories. Current social construction, therefore, demands that meanings loaded with absolute truths be remodeled, countering modern thinking and redefining identities and gender, in order to deconstruct the ambiguities and generalizations rooted in each of us.