Search Results (1,305)

Background: Since the introduction of the first GLP-1 receptor agonist (GLP-1 RA) in 2005, there has been a steady increase in the number of drugs available in this group, as well as an expansion of their indications and routes of administration. Aim: The aim of the study was to assess the clinical characteristics of patients treated with GLP-1 RA in Poland in 2018–2024, with particular emphasis on the disease entities constituting indications for treatment (like obesity and diabetes), and to analyse the frequency of use of individual drugs during the study period. Methods: A cohort study was conducted based on anonymised medical data from 300 outpatient clinics the largest private healthcare facilities in Poland (Luxmed), on consecutive patients who had at least one prescription for GLP-1 RA. The analysis covered the period from 1 January 2018 to 31 December 2024. Results: The number of patients using GLP-1 RA increased from 212 in 2018 to 12,836 in 2024. Obesity was diagnosed in 78% of all patients, most often in the groups using liraglutide and tirzepatide. The highest percentage of patients with type 2 diabetes was observed in the dulaglutide group (67%), while the lowest was in the tirzepatide group (15%). From 2022, the share of oral semaglutide steadily increased, reaching 50% of all semaglutide applications in 2024 in Poland. Conclusions: In the analysed group, GLP-1 RAs were most commonly used to treat obesity. The oral form of semaglutide was more frequently used in younger females with less aggravating medical history. Full article

Analysis of historical and future land use/land cover (LULC) dynamics using spatiotemporal data is crucial for better management of natural resources and environmental monitoring. This study investigated LULC transformations over a span of 60 years (1984–2044) for the Giba basin in northern Ethiopia. ArcGIS and the Cellular Automata and Artificial Neural Network (CA-ANN) model were used to develop the historical (1984, 2004, 2014, and 2024) and projected future (2034 and 2044) LULC maps of the basin, respectively. The results show that LULC categories experienced shifts from one class to another by 35%, 33%, and 40% in 2004–2014, 2014–2024, and 2004–2024, respectively. During 1984–2024, the largest and smallest percentage of positive changes were observed in settlement (7700%) and shrubs and bushes (25%), which increased from negligible to 78 km² and from 1668 km² to 2082 km², respectively. Furthermore, barren land and forestland showed the largest (−80%) and smallest (−37%) declines, which decreased from 956 km² to 187 km² and from 164 km² to 103 km² during the same period, respectively. Overall, the last 40 years witnessed considerable changes to LULC dynamics in the Giba basin. Cropland, water bodies, and settlements showed a continuously increasing trend throughout the historical study period, while grassland exhibited a continuous decreasing trend. Results of the CA-ANN model showed that the majority of the LULC categories (including water body, forest, bushes and shrubs, grassland, and barren land) will decrease, except for a slight increase of cropland (+6%) and settlements (+16%), which is projected to increase from 2570 km² to 2733 km² and from 78 km² to 91 km², respectively, in the next two decades, from 2024 to 2044. In general, high population increase, changes in government policies, and armed conflicts were found to be the most influential driving factors of LULC changes in the basin. Full article

Bug reports in open-source projects are often incomplete or low in quality, which reduces maintenance efficiency. To address this issue, we propose AgentReport, a multi-agent pipeline based on large language models (LLMs). AgentReport integrates QLoRA-4bit lightweight fine-tuning, CTQRS (Completeness, Traceability, Quantifiability, Reproducibility, Specificity) structured prompting, Chain-of-Thought reasoning, and one-shot exemplar within seven modules: Data, Prompt, Fine-tuning, Generation, Evaluation, Reporting, and Controller. Using 3966 summary–report pairs from Bugzilla, AgentReport achieved 80.5% in CTQRS, 84.6% in ROUGE-1 Recall, 56.8% in ROUGE-1 F1, and 86.4% in Sentence-BERT (SBERT). Compared with the baseline (77.0% CTQRS, 61.0% ROUGE-1 Recall, 85.0% SBERT), AgentReport improved CTQRS by 3.5 percentage points, Recall by 23.6 points, and SBERT by 1.4 points. The inclusion of F1 complemented Recall-only evaluation, offering a balanced framework that covers structural completeness (CTQRS), lexical coverage and precision (ROUGE-1 Recall/F1), and semantic consistency (SBERT). This modular design enables consistent experimentation and flexible scaling, providing practical evidence that multi-agent LLM pipelines can generate higher-quality bug reports for software maintenance. Full article

This research presents a detailed Life Cycle Assessment (LCA) of 26 mm Crown cork metal closures used in glass bottle packaging, with the objective of quantifying and comparing their environmental impacts across all life cycle stages. This study adheres to ISO 14040 and ISO 14044 standards and utilizes Microsoft Excel for structuring and documenting input–output data across each phase. The LCA encompasses three primary stages: raw material production (covering iron ore extraction and steel manufacturing), manufacturing processes (including metal sheet printing, forming, and packaging of closures), and the transport phase (distribution to bottling facilities). During the Life Cycle Inventory (LCI), steel production emerged as the most environmentally burdensome phase. It accounted for the highest emissions of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_x), and sulphur oxides (SO_x), while emissions of heavy metals and volatile organic compounds were found to be negligible. The Life Cycle Impact Assessment (LCIA) was carried out using the Eco-Indicator 99 methodology, which organizes emissions into impact categories related to human health, ecosystem quality, and resource depletion. Final weighting revealed that steel production is the dominant contributor to overall environmental impact, followed by the manufacturing stage. In contrast, transportation exhibited the lowest relative impact. The interpretation phase confirmed these findings and emphasized steel production as the critical stage for environmental optimization. This study highlights the potential for substantial environmental improvements through the adoption of low-emission steel production technologies, particularly Electric Arc Furnace (EAF) processes that incorporate high percentages of recycled steel. Implementing such technologies could reduce CO₂ emissions by up to 68%, positioning steel production as a strategic focus for sustainability initiatives within the packaging sector. Full article

As climate disasters intensify, their financial shockwaves increasingly threaten residential stability and the resilience of the U.S. mortgage market. While prior research links natural disasters to payment delinquency, far less is known about foreclosure—the terminal outcome of housing distress. We construct a novel property-level panel covering 55 flood, wildfire, and hurricane events, integrating transactional, mortgage, and insurance data. A difference-in-differences framework compares foreclosure rates for damaged parcels with nearby undamaged controls within narrowly defined hazard perimeters. Results show that flooding substantially increases foreclosure risk: inundated properties experience a 0.29-percentage-point rise in foreclosure likelihood within three years, with effects concentrated outside federally mandated flood-insurance zones. In contrast, wildfire and hurricane wind damage are associated with lower foreclosure incidence, likely reflecting standard insurance coverage and rapid post-event price recovery. These findings suggest that physical destruction alone does not drive credit distress; rather, insurance liquidity and post-disaster equity dynamics mediate outcomes. Policy interventions that expand flood insurance coverage, stabilize insurance markets, and embed climate metrics in mortgage underwriting could reduce systemic exposure. Absent such measures, climate-driven foreclosures could account for nearly 30% of lender losses by 2035, posing growing risks to both household wealth and financial stability. Full article

Transmission lines often traverse mountainous regions prone to frequent geological hazards, making it of great practical significance to analyze the spatial distribution patterns of landslide traces along the transmission line corridors. This study focuses on the Hubei section of the ±800 kV ultra-high-voltage (UHV) transmission line from the upper reaches of the Jinsha River to Hubei. Based on high-resolution remote sensing imagery provided by Google Earth, a landslide traces inventory was constructed through visual interpretation. In addition, 13 factors, such as elevation, slope, aspect, relief, soil type and land cover, were selected to analyze the spatial distribution of landslides. The results indicate the following: (1) There are at least 18,598 landslides in the study area, with a total area of approximately 2671.82 km². The spatial distribution is uneven, exhibiting a general pattern of “dense in the west, sparse in the east”. The maximum landslide number density (LND) reaches 4.16 km⁻², and the maximum landslide area percentage (LAP) is 0.83%. (2) Landslides are predominantly distributed in areas with elevations of 278–1059 m, slope gradients of 20–30°, northwest and southeast aspects, surface roughness values of 400–600, Triassic and Jurassic strata, evergreen coniferous forest and sparse forest, as well as lixisols and ferrallitic soil. This study established a landslide traces database for the region, preliminarily revealing the distribution characteristics of landslides and their dominant controlling factors. It provides a scientific basis for geological hazard risk assessment and prevention for UHV transmission lines. Full article

This study employs an integrated framework that combines field-based measurements, remote sensing, and Geographic Information Systems (GISs) to monitor vegetation dynamics and assess the suitability of a steppe range reserve for livestock grazing. Forty-three surface and subsurface soil samples were collected in April and November 2021 to capture seasonal variations. Above-ground biomass (AGB) measurements were recorded at five sampling locations across the reserve. Six Sentinel-2 satellite imageries, acquired around mid-March 2016–2021, were processed to derive time-series Normalized Difference Vegetation Index (NDVI) data, capturing temporal shifts in vegetation cover and density. The GIS-based Multi-Criteria Decision Analysis (MCDA) was employed to model the suitability of the reserve for livestock grazing. The results showed higher salinity, total dissolved solids (TDSs), and nitrate (NO₃) values in April. However, the percentage of organic matter increased from approximately 7% in April to over 15% in November. The dry forage productivity ranged from 111 to 964 kg/ha/year. On average, the reserve’s dry yield was 395 kg/ha/year, suggesting moderate productivity typical of steppe rangelands in this region. The time-series NDVI analyses showed significant fluctuations in vegetation cover, with lower NDVI values prevailing in 2016 and 2018, and higher values estimated in 2019 and 2020. The grazing suitability analysis showed that 13.8% of the range reserve was highly suitable, while 24.4% was moderately suitable. These findings underscore the importance of tailoring grazing practices to enhance forage availability and ecological resilience in steppe rangelands. By integrating satellite-derived metrics with in situ vegetation and soil measurements, this study provides a replicable methodological framework for assessing and monitoring rangelands in semi-arid regions. Full article

This work presents optimized decision tree-based ensemble machine learning models for predicting and quantifying the effects of greenhouse gas (GHG) emissions in Bangladesh. It aims to identify policy implications in response to significant environmental changes. The models analyze the emissions of CO₂, N₂O, and CH₄ from sectors including energy, industry, agriculture, and waste. We consider many parameters, including energy consumption, population, urbanization, gross domestic products, foreign direct investment, and per capita income. The data covers the period from 1971 to 2019. The model is trained using 80% of the dataset and validated using the remaining 20%. The hyperparameters, such as the number of estimators, maximum samples, maximum depth, learning rate, and minimum samples leaf, were optimized via particle swarm optimization. The models were tested, and their forecasts were extended till 2041. An examination of feature importance has identified energy consumption as a critical factor in greenhouse gas emissions, acknowledging the positive effects of clean energy in accordance with the clean development mechanism. The results demonstrate a robust model performance, with an R² score of approximately 0.90 for both the training and testing datasets. The bagging decision tree model showed the lowest mean squared error of 151.3453 and the lowest mean absolute percentage error of 0.1686. The findings of this study will help decision-makers understand the complex connections between socioeconomic conditions and the elements that contribute to greenhouse gas emissions. The discoveries will enable more precise monitoring of national greenhouse gas (GHG) inventories, allowing for focused efforts to mitigate climate change in Bangladesh. Full article

Background and Objectives: Midlife represents a critical window for the emergence of metabolic risk factors. This study aimed to investigate age- and sex-related changes in lipid profiles, body composition, oxidative stress, and fatty acid content. Materials and Methods: This cross-sectional study included adults grouped by age: <30, 30–39, and 40–49 years. The assessments covered body composition (fat mass, fat distribution, and lean mass), fasting lipids, inflammation markers measurements, and platelet fatty acids evaluation. Results: In total, 169 adults took part in this study (60 men and 109 women), aged 36.30 ± 6.25 years. Fat mass and its regional distribution were higher after age 40, especially in females. In women, fat mass was lower in the thirties and higher again in the forties, while, in men, fat accumulation was progressive. Participants aged 40–49 had a significantly worse metabolic profile than younger individuals. Statistically significant higher total cholesterol, LDL cholesterol, triglycerides, and glucose were shown in the 40–49-years group when compared to younger groups. Malondialdehyde was higher in the 40–49-years vs. 30–39-years group (105.83 vs. 99.72, p = 0.034). In women aged 40–49, a more adverse lipid and glycemic profile was observed compared to younger groups. Platelet fatty acids in the 40–49-years group showed higher polyunsaturated fatty acids and ω6 percentages (12.85% vs. 10.14%, p = 0.046 and 11.44% vs. 8.79%, p = 0.031), including higher linoleic (8.80 ± 5.18 vs. 6.97 ± 5.05, p = 0.045), arachidonic (2.64 ± 2.64 vs. 1.82 ± 1.73, p = 0.030), and docosahexaenoic (0.61 ± 0.86 vs. 0.31 ± 0.49, p = 0.008) acids, when compared to younger groups. Fat mass strongly correlated with insulin resistance, triglycerides, and CRP, and inversely with HDL-C. Conclusions: Significant age-related changes in body composition, metabolic biomarkers, and platelet fatty acid profiles occur after the age of 40, with distinct gender-specific patterns. The fifth decade of life is a transitional period characterized by central adiposity, deteriorating metabolic profiles, and altered fatty acid composition, especially in women. Full article

As climate conditions and agricultural technologies change, the soil seed bank may increase or decrease, which may affect the species composition and abundance of weeds in crops. The research was carried out in order to evaluate the influence of hillside parts on the number of viable seeds during different seasons (spring and autumn) in agrophytocenoses, which differ in the duration of the land’s covering with plants. Soil samples have been taken out in spring and autumn at the summit, midslope, and footslope of the hill. The time of the soil sample collection and covering of agrophytocenoses had a significant effect on soil seed numbers. In autumn, the average seed amount in the soil was higher by 6.38% than in spring. The largest seed number (in spring and autumn) was evaluated in the soil of cereal–grass crop rotation with a 2.0- and 6.9-times higher seed amount compared to the rotation with a row crop and permanent grassland. During the years, hill parts had a significant effect on the seed bank in autumn. In spring, the viable seeds comprised 67.10%, and in autumn, they comprised 65.33% of the total seed number. Significantly, the highest percentage of viable seeds was estimated in the footslope of the hill. This can be related to more favorable microclimatic conditions and higher soil moisture at the footslope, where more fertile soil and organic matter naturally accumulate, creating better conditions for seed viability preservation. Full article

Quantifying and removing urbanization-induced biases in existing precipitation datasets is critical for climate change detection, model assessment, and attribution studies in Northwest China (NWC). The precipitation observational stations of NWC were divided into rural (reference) stations and urban stations using the percentage of urban areas calculated from the land use/land cover (LULC) satellite data of the European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover project. The annual extreme precipitation index series for urban stations (all stations) and rural stations from 1961 to 2022 were calculated based on the categorization of meteorological stations, and the urbanization effects and their contributions to precipitation index series were quantitatively evaluated through estimating trends in the difference series between all stations and the rural stations. The results showed that the urbanization effect varies among different regions and indices. The R10mm, R95pTOT, R99pTOT, and PRCPTOT indices in the sampled urban areas of NWC exhibited statistically significant negative urbanization effects, reaching −0.075 days decade⁻¹, −0.038 % decade⁻¹, −0.024 % decade⁻¹, and −0.035 % decade⁻¹, respectively. However, the R95pTOT, SDII, CDD, and CWD indices at the urban station of the largest city, Urumqi, have been significantly positively affected by urbanization, which is inconsistent with the sampled urban areas of NWC, where the urbanization effect reached 0.069 % decade⁻¹, 0.054 mm·d⁻¹ decade⁻¹, 2.319 days decade⁻¹, and 0.112 days decade⁻¹, respectively. Our analysis shows that the previously reported regional increase in total precipitation and extremes has been underestimated due to the negative urbanization effects in the precipitation data series of urban stations. Full article

Scenarios of climate change, extensive land use, soil degradation, the loss of native forest cover due to monoculture expansion, and pasture scarcity pose new challenges to livestock farming worldwide. Associated crops emerge as an alternative to mitigate these factors; however, selecting compatible species that do not generate competition and optimize the attributes of the forage is a necessity. Therefore, this study evaluated the effect of a maize and bean association, and cutting time on the morphological variables, yield, and nutritional composition of forage. A randomized complete block design (RCBD) with a 3A × 3C factorial arrangement and three blocks was used. Factor A (associations) had three levels: INIA-604-Morocho maize monoculture (M), M+PER1003544 chaucha bean association (M+F1), and M+PER1003551 chaucha bean association (M+F2). Factor C (maize cutting stage) had three levels: R2 (blister grain), R3 (milky grain), and R4 (pasty grain). A total of 27 experimental units were established. No silage was made; the nutritional quality was evaluated as the raw material for silage. The treatments modulated key attributes for silage. In R4, the M+F2 association (INIA-604-Morocho + PER1003551) showed a higher percentage of dry matter in the system (32.36%) and better mixture quality due to a lower NDF and ADF (48.22% and 23.29%) and higher digestibility and protein values (62.10% and 9.53%). In addition, dry matter yields increased compared with R2 in M+F1 (134.16%), M+F2 (90.56%), and M (138.48%). Although R3 maximized green forage, R4 offered the best combination of quantity and quality for silage (as raw material), reducing the risk of deterioration and improving forage use efficiency. In general, combining maize with beans and adjusting the cut to R4 optimizes the production and quality of the raw material for silage, with the criterion that these findings pertain to pre-ensiled material and should be validated in future studies. Full article

The global transition toward carbon neutrality has accelerated the adoption of electric vehicles (EVs), prompting the need for smarter infrastructure planning in urban environments. This study presents a novel framework that integrates machine learning–based EV adoption forecasting with multi-objective optimization (MOO) using the NSGA-II algorithm. The forecasting component leverages neural networks to predict the percentage of EV sales relative to total vehicle sales, which is then used to derive infrastructure demand, energy consumption, and traffic congestion. These derived forecasts inform the optimization model, which balances conflicting objectives—namely infrastructure costs, energy usage, and traffic congestion—to support data-driven decision-making for smart city planners. A comprehensive dataset covering EV metrics from 2011 to 2024 is used to validate the framework. Experimental results demonstrate strong predictive performance for EV adoption, while downstream derivations highlight expected patterns in infrastructure cost and energy usage, and greater variability in traffic congestion. The NSGA-II algorithm successfully identifies Pareto-optimal trade-offs, offering urban planners flexible strategies to align infrastructure development with sustainability goals. This research underscores the benefits of integrating adoption forecasting with optimization in dynamic, real-world planning contexts. These results can significantly inform future smart city planning and optimization of EV infrastructure deployment in rapidly urbanizing regions. Full article

Background/Objectives: We here describe the impact of navigated care on utilization patterns of pharmacologic and minimally invasive overactive bladder therapies. Methods: This retrospective observational cohort study used electronic medical record data from the Precision Point Specialty Analytics Portal in the United States. Eligible patients were adults (≥18 years) newly diagnosed and treated for non-neurogenic overactive bladder (1 January 2015 to 31 December 2019). Categorical endpoints were analyzed by chi-square test or Fisher exact test. Of 170,000 eligible patients, 8982 (≈5%) were randomly selected and stratified by navigation status (navigated: 1150 [12.8%]; non-navigated: 7832 [87.2%]). Results: Overall, 60.0% of patients were female, 69.9% were White, and 42.7% had Medicare coverage. Navigated care was more common among women, Black patients, and those covered by Medicaid/Medicare. Initial pharmacologic treatment rates were similar between navigated and non-navigated groups (anticholinergic: 57.0% vs. 57.4%; beta-3 agonist: 43.0% vs. 42.6%). Greater percentages of navigated versus non-navigated patients received minimally invasive therapy (23.8% vs. 10.8%, respectively; p < 0.0001). Discontinuation rates were lower for navigated versus non-navigated patients undergoing pharmacologic treatment (62.5% vs. 71.3%; p < 0.0001). Conclusions: Patient navigation for overactive bladder may help increase access to minimally invasive therapies and may be a tool to address treatment disparities. Full article

Tropical forests play an essential role in the carbon and water cycles of terrestrial ecosystems, but they are increasingly threatened by human activities and climate change. For places where ground observations are scarce, like in Equatorial Africa, remote sensing is a key source of information for monitoring the temporal and spatial dynamics of forests over large areas. Several Earth Observation-based global maps were developed in recent decades using different definitions of the land-use/land-cover (LULC) classes. While such products are widely used for monitoring land use and planning land management, the consistency of these LULC maps for the Congo Basin has never been analyzed and quantified at the ecosystem level. Here, we selected seven of the most-used global maps and analyzed their consistency over the Congo Basin. After reclassification into forest/non-forest masks and spatial resampling, we assessed the agreement and disagreement percentage across the different tropical ecoregions of Africa, from moist forest to miombo, including savanna. The datasets showed differences in forest area as a function of spatial resolution, with higher forest area levels at coarser resolutions (e.g., from 74.1% to 88.5% forest cover when upscaling the GLCLU from 30 m to 1 km over the Congo Basin). A higher agreement between the datasets was found for forest area over moist forest (between 88.18% and 99.38%) in comparison to savanna (32.82%–99.84%) and miombo (53.83%–99.7%). These discrepancies led to large differences in forest cover, varying from a net loss of 205,704 km² to a net gain of 50,726 km² over 2001–2019 depending on the dataset used. This study draws attention to the uncertainty associated with these products with regard to forests, particularly in regions of biological importance, such as the miombo and savanna regions, which remain poorly understood. Indeed, the two major uncertainties affecting the quality of LULC products are related to the different spatial resolutions and biological definition of “forest” adopted by each product. Full article