Search Results (4,932)

Previous studies about the COVID-19 pandemic on STEMI patient outcomes have conflicting results. It remains unclear if this may be attributed to regional differences and/or differences during COVID-19 wave periods. Using the American Heart Association Get With The Guidelines–Coronary Artery Disease registry data, we evaluated (1) time metrics related to STEMI system goals and (2) regional variation in STEMI incidence and in-hospital mortality during pandemic wave time periods. The study included all patients 18–100 years old admitted with STEMI (n = 72,516) to 1 of 435 American Heart Association Get With The Guidelines–Coronary Artery Disease hospitals (1 October 2019–31 December 2021). Of these, 70.8% were male and 73.0% non-Hispanic White, with a median age of 63 (IQR 18) years. Compared to pre-pandemic time frames, patients with STEMI had a higher risk profile, delayed time to treatment, were treated with fibrinolytic therapy or primary PCI, and were transferred for primary PCI at similar rates, and had higher adjusted in-hospital mortality (during the second wave in the South and Midwest). Preservation of STEMI systems of care resulted in an overall lower in-hospital mortality rate than predicted, although opportunities exist to improve treatment delays. Regional differences in mortality rates require further study. Full article

Breast cancer (BRCA) remains a significant cause of mortality among women, particularly in developing and underdeveloped regions, where early detection is crucial for effective treatment. This research introduces an innovative hybrid model that combines Improved Grey Wolf Optimizer (IGWO) with the Seagull Optimization Algorithm (SOA), forming the IGWO–SOA technique to enhance BRCA detection accuracy. The hybrid model draws inspiration from the adaptive and strategic behaviors of seagulls, especially their ability to dynamically change attack angles in order to effectively tackle complex global optimization challenges. A deep neural network (DNN) is fine-tuned using this hybrid optimization method to address the challenges of hyperparameter selection and overfitting, which are common in DL approaches for BRCA classification. The proposed IGWO–SOA model demonstrates optimal performance in identifying key attributes that contribute to accurate cancer detection using the CBIS-DDSM dataset. Its effectiveness is validated using performance metrics such as loss, F1-score, precision, accuracy, and recall. Notably, the model achieved an impressive accuracy of 99.4%, outperforming existing methods in the domain. By optimizing both the learning parameters and model structure, this research establishes an advanced deep learning framework built upon the IGWO–SOA approach, presenting a robust and reliable method for early BRCA detection with significant potential to improve diagnostic precision. Full article

Analyses of student mobility are typically conducted in an urban environment and are informed by socio-demographic or trip attributes. The prevailing focus is on individual modes of transport, different groups of commuters travelling to campus, students’ behavioural perceptions, and the totality of student trips. This paper starts with the identification of the determinants of student mobility that have received insufficient research attention. Utilising surveys, the study captures the mobility patterns of a sample of 1014 students and calculates their carbon footprint (CF; in kg/academic year) to assess whether the factors neglected in previous studies influence differences in the actual environmental load of student commuting. A regression analysis is employed to ascertain the significance of these factors as predictors of sustainable student mobility. This study exclusively focuses on the group of student commuters to campus and analyses the trips associated with compulsory activities at a suburban campus that is distant from the university centre and student facilities, which changes the mobility context in terms of commuting options. The under-researched factors identified in this research have not yet been quantified as CF. The findings confirm that only some of the factors neglected in previous research are statistically significant predictors of the local environmental load of student mobility. Specifically, variables such as student employment, frequency of class attendance, and propensity for ride-sharing could be utilised to forecast and regulate students’ mobility towards more sustainable patterns. However, all of the under-researched factors (including household size, region of origin (i.e., past experiences), residing at term-time accommodation while studying, and the availability of a family car) have an influence on the differences in CF magnitude in the studied campus. Full article

Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, these studies fail to concurrently consider activity and mobility patterns of populations in both space and time, which results in many studies only being able to employ static geostatistical analytical methods, neglecting the transmission risks associated with human mobility. This study utilized the mobile phone signaling data of Shenzhen residents from 2019 to 2020 and developed a CP tensor decomposition algorithm to decompose the long-sequence spatiotemporal trajectory data to detect high risk zones in terms of detecting overlapped community structures. Tensor decomposition algorithms revealed community structures in 2020 and the overlapping regions among these communities. Based on the overlap in spatial distribution and the similarity in temporal rhythms of these communities, we identified regions with spatiotemporal co-location as high–risk zones. Furthermore, we calculated the degree of population mixing in these areas to indicate the level of risk. These areas could potentially lead to rapid virus spread across communities. The research findings address the shortcomings of currently used static geographic statistical methods in delineating risk zones, and emphasize the critical importance of integrating spatial and temporal dimensions within behavioral big data analytics. Future research should consider utilizing non-aggregated individual trajectories to construct tensors, enabling the inclusion of individual and environmental attributes. Full article

In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor. Full article

Underground brine as a liquid mineral resource available for development and utilization has attracted widespread attention. However, how the mining process affects the hydrochemical characteristics and evolution of underground brine has yet to be fully understood. Herein, 207 underground brine samples were collected from the Luobei mining area of the Lop Nur region during pre-exploitation (2006), exploitation (2019), and late exploitation (2023) to explore the dynamic change characteristics and evolution mechanisms of the underground brine hydrochemistry using the combination of statistical analysis, spatial interpolation, correlation analysis, and ion ratio analysis. The results indicated that Na⁺ and Cl⁻ were the dominant ionic components in the brine, and their concentrations remained relatively stable throughout the mining process. However, the content of Mg²⁺ increased gradually during the mining process (increased by 45.08% in the middle stage and 3.09% in the later stage). The elevation in Mg²⁺ concentration during the mining process could be attributed to the dissolution of Mg-bearing minerals, reverse cation exchange, and mixed recharge. This research furnishes a scientific foundation for a more in-depth comprehension of the disturbance mechanism of brine-mining activities on the groundwater chemical system in the mining area and for the sustainable exploitation of brine resources. Full article

This study examines how beliefs about the causes of climate change relate to emotions, perceptions of its effects, and willingness to engage in collective action among residents of the Valparaíso Region in Chile, a territory particularly vulnerable to this phenomenon. A survey was conducted with 809 individuals using stratified probabilistic sampling. Analysis of variance revealed significant differences among those who attribute climate change to human, mixed, or natural causes. The results show that individuals who believe in the anthropogenic origin of climate change report higher levels of negative emotions, anxiety, perceived impacts, and willingness to participate in both direct and institutional collective actions. Moreover, these individuals perceive greater negative effects of climate change on their surroundings and daily lives. In contrast, those who attribute the phenomenon to natural causes show a lower predisposition to act and a lower risk perception. The study concludes that causal attribution of climate change significantly influences people’s emotional and behavioral responses, highlighting the importance of strengthening climate education and communication based on scientific evidence as key tools for fostering civic engagement in the face of the environmental crisis. The findings contribute to sustainability by strengthening environmental education, participatory governance, and collective action in vulnerable contexts. Full article

Urban and regional ecosystems are increasingly challenged by the compounded effects of climate change and intensive land use. In this study, a predictive assessment framework for ecosystem resilience in the Beijing–Tianjin–Hebei region was developed by integrating multi-source remote sensing data, with the aim of quantitatively evaluating the coupled effects of climate change and land use change on future ecosystem resilience. In the first stage of the study, the SD-PLUS coupled modeling framework was employed to simulate land use patterns for the years 2030 and 2060 under three representative combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Building upon these simulations, ecosystem resilience was comprehensively evaluated and predicted on the basis of three key attributes: resistance, adaptability, and recovery. This enabled a quantitative investigation of the spatio-temporal dynamics of ecosystem resilience under each scenario. The results reveal the following: (1) Temporally, ecosystem resilience exhibited a staged pattern of change. From 2020 to 2030, an increasing trend was observed only under the SSP1-2.6 scenario, whereas, from 2030 to 2060, resilience generally increased in all scenarios. (2) In terms of scenario comparison, ecosystem resilience typically followed a gradient pattern of SSP1-2.6 > SSP2-4.5 > SSP5-8.5. However, in 2060, a notable reversal occurred, with the highest resilience recorded under the SSP5-8.5 scenario. (3) Spatially, areas with high ecosystem resilience were primarily distributed in mountainous regions, while the southeastern plains and coastal zones consistently exhibited lower resilience levels. The results indicate that climate and land use changes jointly influence ecosystem resilience. Rainfall and temperature, as key climate drivers, not only affect land use dynamics but also play a crucial role in regulating ecosystem services and ecological processes. Under extreme scenarios such as SSP5-8.5, these factors may trigger nonlinear responses in ecosystem resilience. Meanwhile, land use restructuring further shapes resilience patterns by altering landscape configurations and recovery mechanisms. Our findings highlight the role of climate and land use in reshaping ecological structure, function, and services. This study offers scientific support for assessing and managing regional ecosystem resilience and informs adaptive urban governance in the face of future climate and land use uncertainty, promotes the sustainable development of ecosystems, and expands the applicability of remote sensing in dynamic ecological monitoring and predictive analysis. Full article

The Raohe subduction–accretion complex (RSAC) in the Wandashan Block, NE China, comprises ultramafic rocks, gabbro, mafic volcanic rocks, deep-sea and hemipelagic sediments, and trench–slope turbidites. We investigate the basalts within the RSAC to resolve debates on its origin. Zircon U-Pb dating of pillow basalt from Dadingzi Mountain yields a concordant age of 117.5 ± 2.1 Ma (MSWD = 3.6). Integrating previous studies, we identify three distinct basalt phases. The Late Triassic basalt (210 Ma–230 Ma) is characterized as komatites–melilitite, exhibiting features of island arc basalt, as well as some characteristics of E-MORB. It also contains high-magnesium lava, suggesting that it may be a product of a juvenile arc. The Middle Jurassic basalt (around 159 Ma–172 Ma) consists of a combination of basalt and magnesium andesite, displaying features of oceanic island basalt and mid-ocean ridge basalt. Considering the contemporaneous sedimentary rocks as hemipelagic continental slope deposits, it is inferred that these basalts were formed in an arc environment associated with oceanic subduction, likely as a result of subduction of the young oceanic crust. The Early Cretaceous basalt (around 117 Ma) occurs in pillow structures, exhibiting some characteristics of oceanic island basalt but also showing transitional features towards a continental arc. Considering the regional distribution of the rocks, it is inferred that this basalt likely formed in a back-arc basin. Integrating the formation ages, nature, and tectonic attributes of the various structural units within the RSAC, as well as previous research, it is inferred that subduction of the Paleo-Pacific Ocean had already begun during the Late Triassic and continued into the Early Cretaceous without cessation. Full article

Background/Objectives: Cardiovascular diseases are a global health issue with an increasing burden and are exacerbated by hypertension. High blood pressure is partly attributed to genetic variants that are generally not well understood or extensively studied in sub-Saharan African populations. Variants linked to blood pressure have been found through genome-wide association studies (GWASs), which were mostly conducted among European ancestry populations; however, limited research has been undertaken in Africa. The current study evaluated single-nucleotide polymorphisms (SNPs) of PCSK9, ABCA1, LPL, and PON1 in relation to blood pressure measurements of 1839 Ghanaian adults. Methods: Genotypes were extracted from data generated by the H3Africa SNP array. After adjusting for sex, age, smoking, and body mass index (BMI), inferential statistics were used to investigate the relationships between SNPs and blood pressure (BP) indices. Additionally, Bonferroni correction was used to adjust for multiple testing. Results: Diastolic blood pressure (DBP) and the minor allele T of the PCSK9 variant (rs17111557) were positively associated at p = 0.006 after covariate adjustments. Although this novel DBP-associated variant is located in the 3′ untranslated region (3′ UTR) of the PCSK9 gene, in silico functional prediction suggests it is an expression quantitative trait locus (eQTL) that may change the binding site of transcription factors, potentially altering the rate of transcription and impacting DBP in this Ghanaian population. Conclusions: Our findings highlight the role of genetics in hypertension risk and the potential of discovering new therapies targeting isolated diastolic blood pressure in this rural African population. Full article

Background/Objectives: The increased accessibility of information has resulted in a rise in patients trying to self-diagnose and opting for self-medication, either as a primary treatment or as a supplement to medical care. Our objective was to evaluate the reliability, comprehensibility, and readability of the responses provided by ChatGPT 4.0 when queried about the most prevalent orthopaedic problems, thus ascertaining the occurrence of misguidance and the necessity for an audit of the disseminated information. Methods: ChatGPT 4.0 was presented with 26 open-ended questions. The responses were evaluated by two observers using a Likert scale in the categories of diagnosis, recommendation, and referral. The scores from the responses were subjected to subgroup analysis according to the area of interest (AoI) and anatomical region. The readability and comprehensibility of the chatbot’s responses were analyzed using the Flesch–Kincaid Reading Ease Score (FRES) and Flesch–Kincaid Grade Level (FKGL). Results: The majority of the responses were rated as either ‘adequate’ or ‘excellent’. However, in the diagnosis category, a significant difference was found in the evaluation made according to the AoI (p = 0.007), which is attributed to trauma-related questions. No significant difference was identified in any other category. The mean FKGL score was 7.8 ± 1.267, and the mean FRES was 52.68 ± 8.6. The average estimated reading level required to understand the text was considered as “high school”. Conclusions: ChatGPT 4.0 facilitates the self-diagnosis and self-treatment tendencies of patients with musculoskeletal disorders. However, it is imperative for patients to have a robust understanding of the limitations of chatbot-generated advice, particularly in trauma-related conditions. Full article

InGaN-based photovoltaic devices have attracted great attention due to their remarkable theoretical potential for high efficiency. In this paper, the influence of different distributions of step-gradient indium content within the intrinsic region on the photovoltaic performance of P-I-N type InGaN/GaN solar cells is numerically investigated. Through the comprehensive analysis of carrier dynamics, it is found that for the device with the indium content decreasing stepwise from 50% at the top to 10% at the bottom in intrinsic region, the photovoltaic conversion efficiency is increased to 10.29%, which can be attributed to joint influence of enhanced photon absorption, reduced recombination rate, and optimized carrier transport process. Full article

Amman’s rapid population growth and sprawling urbanization have resulted in car-centric, fragmented neighborhoods that lack social cohesion and are vulnerable to the impacts of climate change. This study reframes walkability as a climate adaptation strategy, demonstrating how pedestrian-oriented spatial planning can reduce vehicle emissions, mitigate urban heat island effects, and enhance the resilience of green infrastructure in peri-urban contexts. Using Deir Ghbar, a rapidly developing marginal area on Amman’s western edge, as a case study, we combine objective walkability metrics (street connectivity and residential and retail density) with GIS-based spatial regression analysis to examine relationships with residents’ sense of community. Employing a quantitative, correlational research design, we assess walkability using a composite objective walkability index, calculated from the land-use mix, street connectivity, retail density, and residential density. Our results reveal that higher residential density and improved street connectivity significantly strengthen social cohesion, whereas low-density zones reinforce spatial and socioeconomic disparities. Furthermore, the findings highlight the potential of targeted green infrastructure interventions, such as continuous street tree canopies and permeable pavements, to enhance pedestrian comfort and urban ecological functions. By visualizing spatial patterns and correlating built-environment attributes with community outcomes, this research provides actionable insights for policymakers and urban planners. These strategies contribute directly to several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action), by fostering more inclusive, connected, and climate-resilient neighborhoods. Deir Ghbar emerges as a model for scalable, GIS-driven spatial planning in rural and marginal peri-urban areas throughout Jordan and similar regions facing accelerated urban transitions. By correlating walkability metrics with community outcomes, this study operationalizes SDGs 11 and 13, offering a replicable framework for climate-resilient urban planning in arid regions. Full article

With the increasing market demand for spinels of various colors, purple spinel—long regarded as a symbol of nobility—has attracted growing attention. In this study, pinkish-purple to purple spinels from the Mogok region of Myanmar were systematically examined using conventional gemological, spectroscopic, and chemical analytical techniques. Raman analysis reveals that these spinels commonly contain octahedral inclusions composed of calcite, dolomite, magnesite, and graphite. Chemically, the samples are primarily magnesia-alumina spinels. Color variation is influenced by trace elements: increasing Cr and V contents enhance the red hue, while higher Fe concentrations intensify the purple tone. UV–Vis spectra show that Cr³⁺ and V³⁺ jointly contribute to absorptions at 388 nm and 548 nm, with Fe²⁺ and Fe³⁺ responsible for the bands at 371 nm and 457 nm, respectively, together controlling the pink-to-purple color variation. Most samples display four Cr³⁺-related peaks near 700 nm; however, these are absent in deeply purple spinels. In contrast, light pink spinels show weaker absorption at 371 nm and 457 nm, attributed to Fe²⁺ and Fe³⁺. Fluorescence spectra confirm characteristic Cr³⁺ emission bands at 673 nm, 684 nm, 696 nm, 706 nm, and 716 nm, indicating a strong crystal field environment. Raman spectra have peaks mainly around 312 cm⁻¹, 406 cm⁻¹, 665 cm⁻¹, and 768 cm⁻¹. The peaks of the infrared spectrum mainly appear around 840 cm⁻¹, 729 cm⁻¹, 587 cm⁻¹, 545 cm⁻¹, and 473 cm⁻¹. Full article

Soil microbes play a crucial role in driving biogeochemical cycles and are closely linked with aboveground plants during forest succession. Moso bamboo (Phyllostachys edulis) encroachment into adjacent forests of varying composition is known to alter plant diversity in subtropical and tropical regions. However, how soil microbial communities respond to this vegetation type transformation has not fully explored. To address this knowledge gap, a time-alternative spatial method was employed in the present study, and we investigated the effect of Moso bamboo expansion into subtropical broad-leaved forest and coniferous forest on soil microbial phospholipid fatty acids (PLFAs). We also measured the dynamics of key soil properties during the Moso bamboo expansion processes. Our results showed that Moso bamboo encroachment into subtropical broad-leaved forest induced an elevation in soil bacterial PLFAs (24.78%) and total microbial PLFAs (22.70%), while decreasing the fungal-to-bacterial (F:B) ratio. This trend was attributed to declines in soil NO₃⁻-N (18.63%) and soil organic carbon (SOC) concentrations (28.83%). Conversely, expansion into coniferous forests promoted soil fungal PLFAs (40.41%) and F:B ratio, primarily driven by increases in soil pH (4.83%) and decreases in SOC (36.18%). These results provide mechanistic insights into how contrasting expansion trajectories of Moso bamboo restructure soil microbial communities and highlight the need to consider vegetation context-dependency when evaluating the ecological consequences of Moso bamboo expansion. Full article