Search Results (137)

Efforts to establish Pakistan’s National Spatial Data Infrastructure (NSDI) have been underway for the past 15 years, and therefore it is necessary to gauge the current progress to channelize efforts into areas that need improvement. This article assessed Pakistan’s NSDI implementation efforts through well-established approaches, including the SDI readiness model, organizational aspects, and state of play. The data were collected from Spatial Data Infrastructure (SDI) and Geographic Information System (GIS) experts. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, lack of awareness of geoinformation (GI), sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering NSDI development in Pakistan. However, certain factors exhibit favorable standings, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, the unavailability of core spatial datasets, and institutional leadership. Thus, to enhance the development of NSDI in Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI. Full article

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates. Full article

The relationship between natural resource development and sustainable development has long been a focus in academia. In the context of a new global economic cooperation system, many scholars argue that such cooperation can lead to a “resource curse” effect in partner countries, hindering their sustainable development. This study analyzed panel data from 64 countries from 2008 to 2020, using the Belt and Road Initiative as a representative of multilateral economic cooperation (MEC) policies. The aim was to examine the actual impact of multilateral economic cooperation on the sustainable development levels of partner countries and to explore the underlying mechanisms influencing these outcomes. First, we measured and identified the sustainable development index (SDI) under natural resource development schemes and the “resource curse” effect in these countries. Then, we employed a double machine learning approach to evaluate the policy effects of MEC on sustainable resource development. We constructed an interactive double machine learning model to examine and validate the specific mechanisms of resource development effects. The results indicate that the level of sustainable resource development in MEC countries is relatively low, and a “resource curse” effect already exists. However, participating in MEC suppresses this “curse” effect. By promoting innovation cooperation, institutional improvement, structural optimization, trade openness, and pollution reduction, MEC effectively enhances the sustainable development levels of partner countries. Full article

Hydrological droughts occur as a result of various hydrometeorological conditions, such as precipitation deficits, reduced snow cover, and high evapotranspiration. Droughts caused by precipitation deficits and occurring during warm seasons are usually longer in duration. This important observation raises the question that climate change associated with global warming may increase drought conditions. Consequently, it is important to understand changes in the processes leading to dry periods in order to predict potential changes in the future. This study is a scientific analysis of the impact of climate change on drought conditions in the Zhaiyk–Caspian, Tobyl–Torgai, Yesil, and Nura–Sarysu water management basins using the standardized precipitation index (SPI) and streamflow drought index (SDI). The analysis methods include the collection of hydrometeorological data for the entire observation period up to and including 2021 and the calculation of drought indices to assess their intensity and duration. The results of this study indicate an increase in the intensity and frequency of drought periods in the areas under consideration, which is associated with changes in climatic conditions. The identified trends have serious implications for agriculture, ecological balance, and water resources. The conclusions of this scientific study can be useful for the development of climate change adaptation strategies and the sustainable management of natural resources in the regions under consideration. Full article

Poplar (Populus L.) anthracnose is an infectious disease that seriously affects the growth and yields of poplar trees, and large-scale poplar infections have led to huge economic losses in the Chinese poplar industry. To efficiently and accurately detect poplar anthracnose for improved prevention and control, this study collected hyperspectral data from the leaves of four types of poplar trees, namely healthy trees and those with black spot disease, early-stage anthracnose, and late-stage anthracnose, and constructed a poplar anthracnose detection model based on machine learning and deep learning. We then comprehensively analyzed poplar anthracnose using advanced hyperspectral-based plant disease detection methodologies. Our research focused on establishing a detection model for poplar anthracnose based on small samples, employing the Design of Experiments (DoE)-based entropy weight method to obtain the best preprocessing combination to improve the detection model’s overall performance. We also analyzed the spectral characteristics of poplar anthracnose by comparing typical feature extraction methods (principal component analysis (PCA), variable combination population analysis (VCPA), and the successive projection algorithm (SPA)) with the vegetation index (VI) method (spectral disease indices (SDIs)) for data dimensionality reduction. The results showed notable improvements in the SDI-based model, which achieved 89.86% accuracy. However, this was inferior to the model based on typical feature extraction methods. Nevertheless, it achieved 100% accuracy for early-stage anthracnose and black spot disease in a controlled environment respectively. We conclude that the SDI-based model is suitable for low-cost detection tasks and is the best poplar anthracnose detection model. These findings contribute to the timely detection of poplar growth and will greatly facilitate the forestry sector’s development. Full article

Driven by economic expansion, urbanization, and population growth, the world is witnessing an escalating demand for water, energy, land, and food, posing substantial threats to the sustainable development of societies and economies. Given the intricate interdependencies inherent within the water–energy–land–food (WELF) system, it is imperative to conduct comprehensive assessments of the coupling coordination and sustainable development of the WELF system over long time scales and diverse characteristic dimensions. This study selects Hebei province, China, as the research region, constructing a comprehensive indicator system spanning from 1980 to 2020 using three dimensions: reliability (Rel), robustness (Rob), and equilibrium (Equ). The degree of coupling coordination (DCC) and sustainable development index (SDI) were developed using the comprehensive evaluation index and coupling coordination degree model. Additionally, the obstacle degree model and gray relational degree model were employed to assess the indicators that hinder or promote the SDI. The results indicate that: (1) The DCC (range of 0–1, bigger the better) of the WELF system increased from 0.65 to 0.75 between 1980 and 1998, then fluctuated between 0.75 and 0.69, stabilizing at a moderate level of coordinated development after 2015. (2) For the WELF system in Hebei, as Rel increased, Rob decreased, and Equ increased; similarly, as Rob increased, Equ also increased. (3) The SDI (range of 0–1, bigger the better) rose from 0.45 in 1980, initially increased, then decreased, and eventually stabilized. After 2014, it experienced rapid growth, reaching 0.54 by 2020, indicating an improvement in sustainable development capability. (4) Indicators related to the Equ dimension and the land subsystem were more critical limiting factors for SDI development, while indicators related to the Rel dimension and the food subsystem were more significant contributors to SDI development. These findings offer a scientific foundation and practical insights for Hebei and comparable regions, aiding in the resolution of resource conflicts, optimization of resource allocation, and enhancement of regional sustainable development. Full article

In response to the challenges of remote sensing imagery, such as unmanned aerial vehicle (UAV) aerial imagery, including differences in target dimensions, the dominance of small targets, and dense clutter and occlusion in complex environments, this paper optimizes the YOLOv8n model and proposes an innovative small-object-detection model called DDSC-YOLO. First, a DualC2f structure is introduced to improve the feature-extraction capabilities of the model. This structure uses dual-convolutions and group convolution techniques to effectively address the issues of cross-channel communication and preserving information in the original input feature mappings. Next, a new attention mechanism, DCNv3LKA, was developed. This mechanism uses adaptive and fine-grained information-extraction methods to simulate receptive fields similar to self-attention, allowing adaptation to a wide range of target size variations. To address the problem of false and missed detection of small targets in aerial photography, we designed a Semantics and Detail Infusion Feature Pyramid Network (SDI-FPN) and added a dedicated detection scale specifically for small targets, effectively mitigating the loss of contextual information in the model. In addition, the coordinate adaptive spatial feature fusion (CASFF) mechanism is used to optimize the original detection head, effectively overcoming multi-scale information conflicts while significantly improving small target localization accuracy and long-range dependency perception. Testing on the VisDrone2019 dataset shows that the DDSC-YOLO model improves the mAP0.5 by 9.3% over YOLOv8n, and its performance on the SSDD and RSOD datasets also confirms its superior generalization capabilities. These results confirm the effectiveness and significant progress of our novel approach to small target detection. Full article

Brazil reported 18,282 suspected congenital Zika syndrome (CZS) cases up to 2018 and accounts for 61.4% of the total reported Zika cases in the Americas in the period. To detect high-risk areas for children with CZS in the city of Rio de Janeiro, we used cluster detection and thematic maps. We analyzed data using a Poisson model in Satscan 10.1.3 software. We also analyzed the records of children with CZS from 2015 to 2016 to describe the clinical and epidemiological maternal and child profile, as well as live births in 2016 and the social development index (SDI) by neighborhood. In 2015 and 2016, the incidence rates of CZS were 8.84 and 46.96 per 100,000 live births in the city, respectively. Severe congenital findings such as microcephaly and brain damage, osteoarticular impairment, ocular abnormalities, and hearing loss were observed in 47 children. The spatial distribution of CZS was concentrated in the north and west zones in heterogeneous neighborhoods. The neighborhoods with the highest occurrence of CZS cases were found to have the worst SDIs. Stascan detected three spatial clusters in the north zone, where the SDI is lower. The clusters presented high relative risks for CZS (7.86, 1.46, and 2.08), although they were not statistically significant. Our findings highlight a higher occurrence of CZS in areas with less favorable socioeconomic conditions. Full article

Concrete defects have a significant impact on concrete constructions. These defects should be considered not only aesthetic defects but also structural defects. In this study, a novel Surface Defect Index (SDI) method was developed to quantify the defect volume according to liquids’ penetrating properties by applying ready-mixed plaster (RMP). The SDI refers to the volumetric proportion of all apparent and unapparent defects in a given area of concrete, and it is expressed as a percentage of the total volume affected by defects. The proposed SDI method was validated and tested under various controlled defect configurations. Regardless of the specific characteristics of each defect configuration, the SDI method consistently demonstrated a high level of consistency, repeatability, and reproducibility, with coefficients of variation (CVr and CVR) below 5% and with correlation coefficients of R² = 0.9968. The method succeeded in assessing the surface quality levels through the SDI, demonstrating a significant correlation between this index and the volume of defects. The proposed index was tested on real concrete surfaces, affirming its efficacy in accurately quantifying the volume of surface defects; thus, it can provide an important metric for quality control. Moreover, it provides an excellent evaluation of the quality of concrete surfaces. Full article

Water scarcity is one of the significant constraints on sustainable agricultural development in arid and semi-arid regions. The challenges faced in forage production are even more severe than those encountered with general crops. The industry still struggles to achieve water-efficient, high-yield quality forage in water-scarce pastoral areas. This study focuses on alfalfa, a high-quality forage crop, employing a combination of “subsurface drip irrigation (SDI) + alternate partial root-zone irrigation (APRI)” and establishing three water supply gradients (full irrigation, 75% deficit, 50% deficit), in comparison with the widely used subsurface drip irrigation, to study the effects of two irrigation methods and three moisture gradients on alfalfa. The aim is to provide some theoretical basis and data support for achieving water-saving and high-yield quality forage in water-scarce pastoral areas. The main findings are as follows: First, compared with SDI, the two-year alternate dry and wet environment provided by alternate partial root-zone drip irrigation (ARDI) significantly increased the specific root length, specific surface area, and root length density of alfalfa at 20~40 cm depth, increasing by 33.3~76.8%, 6.4~32.97%, and 15.2~93.9%, respectively, compared to SDI. Under ARDI irrigation, the alfalfa root system has a greater contact area with the soil, which lays a solid foundation for the water and nutrient supply needed for the accumulation of its above-ground biomass. Secondly, over the two-year production process, the plant height of alfalfa under ARDI treatment was 12~14.5% higher than that under SDI, the total fresh forage yield was 43.5~64% higher, and the total dry forage yield was 23.2~33.8% higher than SDI. Under ARDI, the 75% water deficit treatment could still maintain the plant height and stem thickness of alfalfa compared to full irrigation with SDI and increased the dry forage yield by 6.6% without significantly reducing the quality, significantly enhancing the productive performance of alfalfa. Moreover, during the two years of production and utilization, the nutritional quality of alfalfa under the ARDI irrigation mode did not significantly decrease compared to SDI, maintaining the stable nutritional quality of alfalfa over multiple years of production. Lastly, thanks to the improved root system and increased yield of alfalfa under ARDI irrigation, and based on this, its water evapotranspiration did not significantly increase compared to SDI; the annual average Alfalfa Water Productivity Index (AWP_I) and Alfalfa Water Productivity of Crop (AWP_C) under ARDI irrigation increased by 28.8% and 37.2%, respectively, improving the water use efficiency of alfalfa production. In summary, in the production of alfalfa in water-scarce pastoral areas, ARDI and its water deficit treatment have more potential for water-saving than SDI as a water-saving irrigation strategy. Full article

Background: Cystic echinococcosis (CE) is a neglected tropical parasitic disease that poses huge disease, social and economic burdens worldwide; however, there has been little knowledge on the global morbidity, mortality and disability-adjusted life years (DALYs) of CE until now. This study aimed to collect the most up-to-date data about the global, regional and national disease burden due to CE from 1990 to 2019 and to project trends in the next 10 years. Methods: We measured the global, regional and national morbidity, mortality and DALYs of CE from 1990 to 2019 based on the Global Burden of Disease Study 2019 (GBD 2019) data, and we examined the correlation between socioeconomic development levels and the disease burden of CE. In addition, the disease burden due to CE was projected from 2020 to 2030. Results: The age-standardized incidence rate (ASIR) of CE reduced from 2.65/10⁵ [95% UI: (1.87/10⁵ to 3.7/10⁵)] in 1990 to 2.6/10⁵ [95% UI: (1.72/10⁵ to 3.79/10⁵)] in 2019 (EAPC = −0.18%). The number of deaths, DALYs, age-standardized mortality rate (ASMR) and age-standardized DALY rate due to CE all showed a tendency to decline from 1990 to 2019. A higher disease burden of CE was measured in women than in men in 2019. There was a significant difference in the ASMR of CE by region according to the socio-demographic index (SDI), and lower burdens of CE were estimated in high-SDI regions. The global ASIR of CE is projected to decline from 2020 to 2030; however, the ASMR and age-standardized DALY rate are projected to rise. Conclusions: The global burden of CE remains high, and it is recommended that more health resources are allocated to low-SDI regions, women and the elderly aged 55 to 65 years to reduce the disease burden of CE. Full article

Heteroresistance (HR) to colistin is especially concerning in settings where multi-drug-resistant (MDR) K. pneumoniae are prevalent and empiric use of colistin might lead to treatment failures. This study aimed to assess the frequency of occurrence of colistin HR (CHR) among (MDR) K. pneumoniae (n = 676) isolated from patients hospitalized in 13 intensive care units (ICUs) in six European countries in a clinical trial assessing the impact of decolonization strategies. All isolates were whole-genome-sequenced and studied for in vitro colistin susceptibility. The majority were colistin-susceptible (CS) (n = 597, MIC ≤ 2 µg/mL), and 79 were fully colistin-resistant (CR) (MIC > 2 µg/mL). A total of 288 CS isolates were randomly selected for population analysis profiling (PAP) to assess CHR prevalence. CHR was detected in 108/288 CS K. pneumoniae. No significant association was found between the occurrence of CHR and country, MIC-value, K-antigen type, and O-antigen type. Overall, 92% (617/671) of the K. pneumoniae were MDR with high prevalence among CS (91%, 539/592) and CR (98.7%, 78/79) isolates. In contrast, the proportion of carbapenemase-producing K. pneumoniae (CP-Kpn) was higher among CR (72.2%, 57/79) than CS isolates (29.3%, 174/594). The proportions of MDR and CP-Kpn were similar among CHR (MDR: 85%, 91/107; CP-Kpn: 29.9%, 32/107) and selected CS isolates (MDR: 84.7%, 244/288; CP-Kpn: 28.1%, 80/285). WGS analysis of PAP isolates showed diverse insertion elements in mgrB or even among technical replicates underscoring the stochasticity of the CHR phenotype. CHR isolates showed high sequence type (ST) diversity (Simpson’s diversity index, SDI: 0.97, in 52 of the 85 STs tested). CR (SDI: 0.85) isolates were highly associated with specific STs (ST101, ST147, ST258/ST512, p ≤ 0.003). The widespread nature of CHR among MDR K. pneumoniae in our study urge the development of rapid HR detection methods to inform on the need for combination regimens. Full article

Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more oriented towards the professional users in the implementation process and final application. Building appropriate geographic applications for non-professionals remains a challenge. In this study, a geospatial data service architecture is designed that links desktop geographic information system (GIS) software and cloud-based platforms to construct an efficient user collaboration platform. Based on the scalability of the platform, four web apps with different themes are developed. Data in the fields of ecology, oceanography, and geology are uploaded to the platform by the users. In this pilot phase, the gap between non-specialized users and experts is successfully bridged, demonstrating the platform’s powerful interactivity and visualization. The paper finally evaluates the capability of building spatial data infrastructures (SDI) based on GeoNode and discusses the current limitations. The support for three-dimensional data, the improvement of metadata creation and management, and the fostering of an open geo-community are the next steps. Full article

Background: Antimalarial agents (AMAs) are cornerstone drugs in the treatment of systemic lupus erythematosus (SLE), and their use has established benefits, such as improved prognosis and decelerated accrual of organ damage. The aim of this study was to investigate the frequency of discontinuation of AMAs and associated factors in a Swedish SLE population. Methods: We retrieved data from a regional SLE register where all patients fulfilled the 1982 ACR and/or the 2012 SLICC classification criteria. A total of 328 subjects were included in the analysis. Results: Altogether, 92.4% (303/328) had been prescribed AMAs at some point during their disease. At the last available visit, 67.7% (222/328) were currently prescribed AMAs. Among individuals who had discontinued use, 24.7% (20/81) had developed a contraindication. Side effects were also common reasons for discontinuation (n = 38); gastrointestinal symptoms (52.6%, 20/38) were most common. Patients who discontinued had accrued more organ damage at the last visit (mean SDI: 2.9; SD: 2.8) compared with those still on AMAs (mean SDI: 1.4; SD: 1.8; p = 0.001). Conclusions: Most patients had been exposed to AMAs, but 25% discontinued therapy. Among side effects leading to discontinuation, >50% were gastrointestinal, calling for adequate gastroprotection towards drug retention and prevention of organ damage progression. Full article

The field of planetary mapping and cartography builds almost exclusively on remote-sensing data and can be defined by three distinct concepts: systematic imaging as performed through spacecraft surveying, reference mapping as performed through the compilation of reference maps, i.e., regional to global image and topographic maps, and thematic mapping, which aims at abstracting and contextualizing spatial information to generate complex thematic maps, such as geologic or geomorphologic maps. While thematic mapping represents the highest form of abstraction of information that is provided through systematic mapping, thematic mapping also provides scientific reasoning in support of systematic mapping and exploration through spatially contextualized knowledge. For the development of knowledge, it is paramount to manage and exploit the value of thematic maps as research products, and to design a reliable and transparent development process from the beginning of the mapping phase as there is almost no validation for thematic maps. A key element in accomplishing these objectives is well-designed structures and metadata which are maintained within spatial data infrastructures (SDI) and shared as a coordinated process in research data management through data models. In this contribution, we focus on the need to transfer planetary thematic maps into findable, accessible, interoperable, reusable (FAIR), as well as transparent research data assets to facilitate improved knowledge extraction and also to compensate for limitations caused by the lack of conventional validation options. We review the current status of planetary thematic mapping, and we discuss the principles and roles of mappers and publishers in the process of creating and stewarding digital planetary maps and associated data products. We then present and discuss a set of recommendations that are closely tied to the FAIR concepts in research data management to accomplish such tasks. Full article