Search Results (6)

Background/Objectives: Long-acting injections (LAIs) are innovative drug delivery systems that improve patient compliance by maintaining therapeutic drug levels over extended periods. Micro- and nanosuspensions are commonly used in LAIs to enhance bioavailability, but their thermodynamic instability poses challenges, including particle aggregation and growth. This study aimed to evaluate the impact of two helping processes—vehicle thermal treatment and high-shear homogenization—on the stability and manufacturing efficiency of aripiprazole monohydrate (AM) suspensions. Methods: AM suspensions containing sodium carboxymethyl cellulose (CMCNa), mannitol and disodium phosphate in water for injections (WFIs) were prepared using a combination of thermal treatment of the vehicle solution, high-shear homogenization and bead milling. Four manufacturing variants were tested to assess the influence of these processes on particle size distribution (PSD), viscosity and stability during a 3-month accelerated stability study. Molecular weight changes in CMCNa from thermal treatment were analyzed using size exclusion chromatography with multiangle scattering (SEC-MALS), and PSD was measured using laser diffraction. Results: Thermal treatment of the vehicle solution had minimal impact on CMCNa molecular weight, preserving its functionality. High-shear homogenization during bead milling significantly reduced particle aggregation, resulting in improved PSD and reduced viscosity. Synergistic effects of the two helping processes used in one manufacturing process were observed, which led to superior stability and minimal changes in PSD and viscosity during storage. Batches without the helping processes exhibited increased particle size and viscosity over time, indicating reduced suspension stability. Conclusions: Incorporating vehicle thermal treatment and high-shear homogenization during bead milling enhances the stability and manufacturing efficiency of AM suspensions. These findings underscore the importance of optimizing laboratory-scale processes to ensure the quality and safety of pharmaceutical suspensions. Full article

Work–Family Integration (WFI) is the decision-making process that enables an individual to effectively balance work, family, and personal responsibilities, generating a level of personal satisfaction aligned with the management of these demands. This research aims to explore the potential links between personal competencies facilitating work and family integration (WFI Competencies), employer-provided support (WFI Support), perceived satisfaction in role integration (WFI Satisfaction), and their association with organizational performance indicators and the overall health of professionals in dependent employment. Data were obtained via an online questionnaire administered to 270 professionals possessing a university education or higher, employed in public or private organizations spanning various sectors in Venezuela. The data were subsequently analyzed utilizing Structural Equation Modeling (SEM). The study was divided into two main parts: the factorial analysis (both exploratory and confirmatory) of measurement models and the analysis of the relationships and modeling inherent to the structural model. Initially, two diagnostic instruments were developed, one for WFI Competencies and another for WFI Indicators; though applied simultaneously, their structuring and validation were conducted separately. In the subsequent phase, conceptual models for structural analysis were defined. A positive relationship was observed between WFI Support and WFI Satisfaction, corroborating findings from previous research. The relationships between WFI Competencies and Satisfaction led to insights into the necessity of training to strengthen the personal decision-making process under the dual pressures of work and family roles. Future longitudinal studies could elucidate the effects of relationships within such programs on WFI Satisfaction. Concerning organizational indicators, this study found that WFI Satisfaction positively correlates with organizational commitment, enhancing work productivity and mitigating negative health effects. This research presents a model that could be replicated in other countries and with various sample types, facilitating comparative analyses that enrich the body of knowledge on this subject. Full article

This article aims to estimate the water footprint (WF) of cereals—specifically, wheat and barley—in the Kairouan plain, located in central Tunisia. To achieve this objective, two components must be determined: actual evapotranspiration (ET_a) and crop yield. The study covers three growing seasons from 2010 to 2013. The ET_a estimation employed the S-SEBI (simplified surface energy balance index) model, utilizing Landsat 7 and 8 optical and thermal infrared spectral bands. For yield estimation, an empirical model based on the normalized difference vegetation index (NDVI) was applied. Results indicate the effectiveness of the S-SEBI model in estimating ET_a, demonstrating an R² of 0.82 and an RMSE of 0.45 mm/day. Concurrently, yields mapped over the area range between 6 and 77 qx/ha. Globally, cereals’ average WF varied from 1.08 m³/kg to 1.22 m³/kg over the three study years, with the majority below 1 m³/kg. Notably in dry years, the importance of the blue WF is emphasized compared to years with average rainfall (WF_b-2013 = 1.04 m³/kg, WF_b-2012 = 0.61 m³/kg, WF_b-2011 = 0.41 m³/kg). Moreover, based on an in-depth agronomic analysis combining yields and WF, four classes were defined, ranging from the most water efficient to the least, revealing that over 30% of cultivated areas during the study years (approximately 40% in 2011 and 2012 and 29% in 2013) exhibited low water efficiency, characterized by low yields and high WF. A unique index, the WFI, is proposed to assess the spatial variability of green and blue water. Spatial analysis using the WFI highlighted that in 2012, 40% of cereal plots with low yields but high water consumption were irrigated (81% blue water compared to 6% in 2011). Full article

This study aimed to show the influence of cultural landscape structure on species richnessand the conservation value of vascular flora.The analyses are based on 3201 original floristic lists (relevés) and 83,875 floristic data collected since 1994 within Gopło Millennium Park (Nadgoplański Park Tysiąclecia) in a rural area in central Poland. Descriptions of landscape composition in grid cells (0.5 km × 0.5 km) include land use structure, mean deviation of uneven proportions of various land use types, and Shannon index of diversity (H’). Vascular plant diversity was described using total species richness and contributions of groups of native and alien species. Assessment of floristic conservation value was based on qualitative and quantitative floristic index (Wfj and Wfi), mean coefficient of conservatism (C), and floristic quality index (FQI). Floristic analyses were conducted in relation to the whole study area and within grid cells, basing on numbers of species and number of floristic data. The results suggest that species richness in grid cells depends more strongly on diversity and evenness of contributions of land use types, irrespective of which land use types were present. Species richness is strongly dependent on land use structure. Larger contributions of arable fields and built-up areas are linked with a decrease in species richness of nonsynanthropic native plants and species of floristic conservation value. Regularity in this respect is very well illustrated by indices excluding the influence of species richness on floristic value (quantitative floristic index Wfi and mean coefficient of conservatism C). According to the algorithm of FQI, the most valuable floras are characterized by a large number of species with a high contribution of conservative ones. In the study area, this condition was met by floras of surface waters and wetlands. Full article

Fire in Brazilian Pantanal represents a serious threat to biodiversity. The Brazilian National Institute of Spatial Research (INPE) has a program named Queimadas, which estimated from January 2020 to October 2020, a burned area in Pantanal of approximately 40,606 km${}^2$. This program also provides daily data of active fire (fires spots) from a methodology that uses MODIS (Aqua and Terra) sensor data as reference satellites, which presents limitations mainly when dealing with small active fires. Remote sensing researches on active fire dynamics have contributed to wildfire comprehension, despite generally applying low spatial resolution data. Convolutional Neural Networks (CNN) associated with high- and medium-resolution remote sensing data may provide a complementary strategy to small active fire detection. We propose an approach based on object detection methods to map active fire in the Pantanal. In this approach, a post-processing strategy is adopted based on Non-Max Suppression (NMS) to reduce the number of highly overlapped detections. Extensive experiments were conducted, generating 150 models, as five-folds were considered. We generate a public dataset with 775-RGB image patches from the Wide Field Imager (WFI) sensor onboard the China Brazil Earth Resources Satellite (CBERS) 4A. The patches resulted from 49 images acquired from May to August 2020 and present a spatial and temporal resolutions of 55 m and five days, respectively. The proposed approach uses a point (active fire) to generate squared bounding boxes. Our findings indicate that accurate results were achieved, even considering recent images from 2021, showing the generalization capability of our models to complement other researches and wildfire databases such as the current program Queimadas in detecting active fire in this complex environment. The approach may be extended and evaluated in other environmental conditions worldwide where active fire detection is still a required information in fire fighting and rescue initiatives. Full article

Mangroves play an important role in many aspects of ecosystem services. Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area. However, popular mangrove extraction methods, such as the object-oriented method, still have some defects for remote sensing imagery, such as being low-intelligence, time-consuming, and laborious. A pixel classification model inspired by deep learning technology was proposed to solve these problems. Three modules in the proposed model were designed to improve the model performance. A multiscale context embedding module was designed to extract multiscale context information. Location information was restored by the global attention module, and the boundary of the feature map was optimized by the boundary fitting unit. Remote sensing imagery and mangrove distribution ground truth labels obtained through visual interpretation were applied to build the dataset. Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove. Finally, comparative experiments were conducted to prove the potential for mangrove extraction. We selected the Sentinel-2A remote sensing data acquired on 13 April 2018 in Hainan Dongzhaigang National Nature Reserve in China to conduct a group of experiments. After processing, the data exhibited 2093 $\times$ 2214 pixels, and a mangrove extraction dataset was generated. The dataset was made from Sentinel-2A satellite, which includes five original bands, namely R, G, B, NIR, and SWIR-1, and six multispectral indices, namely normalization difference vegetation index (NDVI), modified normalized difference water index (MNDWI), forest discrimination index (FDI), wetland forest index (WFI), mangrove discrimination index (MDI), and the first principal component (PCA1). The dataset has a total of 6400 images. Experimental results based on datasets show that the overall accuracy of the trained mangrove extraction network reaches 97.48%. Our method benefits from CNN and achieves a more accurate intersection and union ratio than other machine learning and pixel classification methods by analysis. The designed model global attention module, multiscale context embedding, and boundary fitting unit are helpful for mangrove extraction. Full article