Search Results (96)

We consider constraints on the Hubble parameter $H_0$ and the matter density parameter ${\mathrm{\Omega }}_{\mathrm{M}}$ from the following: (i) the age of the Universe based on old stars and stellar populations in the Galactic disc and halo; (ii) the turnover scale in the matter power spectrum, which tells us the cosmological horizon at the epoch of matter-radiation equality; and (iii) the shape of the expansion history from supernovae (SNe) and baryon acoustic oscillations (BAOs) with no absolute calibration of either, a technique known as uncalibrated cosmic standards (UCS). A narrow region is consistent with all three constraints just outside their $1\sigma$ uncertainties. Although this region is defined by techniques unrelated to the physics of recombination and the sound horizon then, the standard Planck fit to the CMB anisotropies falls precisely in this region. This concordance argues against early-time explanations for the anomalously high local estimate of $H_0$ (the ‘Hubble tension’), which can only be reconciled with the age constraint at an implausibly low ${\mathrm{\Omega }}_{\mathrm{M}}$. We suggest instead that outflow from the local KBC supervoid inflates redshifts in the nearby universe and, thus, the apparent local $H_0$. Given the difficulties with solutions in the early universe, we argue that the most promising alternative to a local void is a modification to the expansion history at late times, perhaps due to a changing dark energy density. Full article

Introduction: Acromegaly is a chronic endocrine disorder caused by excessive secretion of growth hormone (GH) and insulin-like growth factor 1 (IGF-1). Acromegaly leads to a wide range of systemic alterations, including metabolic disturbances, abnormalities in bone microarchitecture, soft tissue overgrowth, and morphological changes in the maxilla and mandible. All these factors may significantly complicate the planning and success of implant therapy. Study Aim: This narrative review aimed to critically analyze the impact of acromegaly on bone healing and osseointegration, with particular emphasis on the stability of implant biomaterials, and to assess whether the disease constitutes a contraindication to implant prosthetic treatment. Methods: A narrative literature review was conducted using the PubMed, Scopus, and Web of Science databases, covering publications from 2000 to August 2025. Manual screening of reference lists from key articles was also performed. Peer-reviewed publications in English, including experimental and preclinical studies, case reports, biomaterials research, and conceptual reviews, were included based on their relevance to acromegaly, bone metabolism, stomatognathic alterations, and implant therapy outcomes. No formal inclusion or exclusion criteria were applied, and methodological quality was not formally assessed, reflecting the exploratory and conceptual nature of this review. Results: Patients with acromegaly exhibit persistent structural bone deficits, such as reduced trabecular number, irregular trabecular distribution, and increased cortical porosity, despite normal or even elevated bone mineral density. In parallel, profound changes in soft tissues and dentition are observed, including macroglossia, diastemas, gingival overgrowth, and mandibular prognathism, which further complicate prosthetic rehabilitation. Animal studies suggest that GH and IGF-1 may support early osseointegration, although the long-term effects of their excess remain inconclusive. Clinical data, although limited, indicate that implant placement in patients with acromegaly is feasible when treatment is meticulously planned and carried out within an interdisciplinary setting. Standard biomaterials, such as titanium and its alloys, may undergo degradation under conditions of chronic inflammation and oxidative stress, underscoring the need for innovative solutions integrating bioactive and immunomodulatory materials, as well as patient-specific implants manufactured using 3D printing technologies. Conclusions: Acromegaly should not be regarded as an absolute contraindication to implant therapy; however, the current evidence is limited. Implant placement requires individualized planning, endocrine control, and interdisciplinary coordination. Further clinical and preclinical studies are needed to establish reliable treatment protocols for this population. Full article

Research on self-imagined emotional imagery supports the development of practical affective brain–computer interface (BCI) systems. This study proposes a hybrid emotion induction approach that combines facial expression image cues with subsequent emotional imagery, involving six positive and six negative emotions across two- or four-class valence and arousal categories. Machine learning (ML) techniques were applied to interpret these self-generated emotions from electroencephalogram (EEG) signals. Experiments were conducted to observe brain activity and validate the proposed feature and classification algorithms. The results showed that absolute beta power features computed from power spectral density (PSD) across EEG channels consistently achieved the highest classification accuracy for all emotion categories with the K-nearest neighbors (KNN) algorithm, while alpha–beta ratio features also contributed. The nonlinear parametric ML models achieved high effectiveness; the K-nearest neighbor (KNN) classifier performed best in detecting neutral states, while the artificial neural network (ANN) achieved balanced accuracy across emotional stages. The proposed system supports the use of the hybrid emotion induction paradigm and PSD-derived EEG features to develop reliable, subject-independent affective BCI systems. In future work, we will expand the datasets, employ advanced feature extraction and deep learning models, integrate multi-modal signals, and validate the proposed approaches across broader populations. Full article

Objective: This study aimed to compare the effects of empagliflozin and dapagliflozin on classical lipid parameters—including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG)—as well as on atherogenic risk indices, including the atherogenic index of plasma (AIP), Castelli Risk Index I (CRI-I), Castelli Risk Index II (CRI-II), atherogenic coefficient (AC), and triglyceride-glucose index (TyG), in patients with heart failure and a history of coronary artery bypass grafting (CABG). To our knowledge, this is the first study to comprehensively evaluate these parameters in this high-risk population. Methods: This single-center, retrospective study included 484 patients with preserved ejection fraction heart failure and prior CABG who were treated with sodium–glucose cotransporter-2 (SGLT2) inhibitors. Patients were allocated to empagliflozin (n = 201) or dapagliflozin (n = 283) groups. All patients were receiving statin therapy. Lipid parameters and atherogenic indices were evaluated at baseline and after 12 weeks of treatment. Results: Both empagliflozin and dapagliflozin significantly reduced TC and LDL-C at 12 weeks (p < 0.001). No significant changes were observed in HDL-C or TG. Both agents produced significant improvements in CRI-I, CRI-II, AC, and TyG index (all p < 0.001), while AIP remained unchanged. Dapagliflozin achieved a greater reduction in TC (p = 0.044). Conclusions: This study represents the first direct comparison of empagliflozin and dapagliflozin on lipid profiles and atherogenic indices in patients with heart failure and prior CABG. Both agents significantly improved TC, LDL-C, and atherogenic indices. Dapagliflozin achieved a greater reduction in TC compared with empagliflozin, but overall both drugs demonstrated favorable and largely comparable effects. Beyond improvements in absolute values, both agents also contributed to favorable shifts in risk categories of lipid-derived indices. These findings suggest that clinical decision-making between empagliflozin and dapagliflozin may rely on factors other than lipid modulation. Larger multicenter prospective trials are warranted to confirm these results and clarify their long-term cardiovascular implications. Full article

Background/Objectives: The neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation, is a known predictor of cardiovascular disease and overall mortality. We examined the relationship between the NLR and the metabolic activity of hematopoietic organs and visceral fat, and their association with the risk of atherosclerotic cardiovascular disease (ASCVD) in an asymptomatic healthy population. Methods: We retrospectively analyzed individuals who underwent F-18-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) as part of their health check-ups. Metabolic activity was quantified using standardized uptake values (SUVs) from the lumbar vertebral bone marrow, spleen, visceral, and subcutaneous fat, normalized to target-to-background ratios (TBRs) using the superior vena cava. NLR was calculated from absolute neutrophil and lymphocyte counts. Correlations between NLR, clinical parameters, organ TBRs, and ASCAD risk were analyzed. Results: Among 303 participants from three hospitals, the median NLR was 1.5 (range: 0.5–5.55). NLR showed weak correlation with the TBRs of bone marrow, visceral fat, and subcutaneous fat, as well as high-density lipoprotein cholesterol and body mass index (BMI). In logistic regression analysis adjusted for age and sex, BMI and the TBRs of bone marrow and visceral fat were independent predictors of elevated NLR (≥ 1.5). When integrating these parameters, NLR demonstrated strong predictive performance for identifying a high ASCVD risk (≥20% over 10 years), with an area under the curve of 0.826. Conclusions: In an asymptomatic healthy population, NLR is associated with FDG metabolic parameters of hematopoietic organs and adipose tissue. These combined measures may serve as valuable marker for identifying individuals at elevated ASCVD risk. Full article

Cocultivation of microalgae and aerobic methanotrophs represents an emerging biotechnology platform to produce high-protein biomass, yet quantifying individual species in mixed cultures remains challenging. Here, we present a rapid, low-cost method—differential sedimentation, optical density, and fluorescence (DSOF)—to determine the abundance of coculture members. DSOF exploits differences in cell size and pigment autofluorescence between the thermoacidophilic microalga and methanotrophic species Galdieria sp. RTK37.1 and Methylacidiphilum sp. RTK17.1, respectively, to selectively sediment algal cells and estimate population contributions via OD₆₀₀ and phycocyanin fluorescence. Evaluation with model suspensions across a wide cell density range (0 ≤ [Galdieria]: ≤ 3.23 A.U., and 0 ≤ [Methylacidiphilum] ≤ 1.54 A.U.) showed strong agreement with known values, with most absolute errors < 0.1 A.U. and relative errors < 10% at moderate biomass levels. Application to live batch cocultures under microalga or methanotroph growth-suppressed conditions, and during simultaneous growth, demonstrated accurate tracking of population dynamics and revealed enhanced methanotroph growth in the presence of oxygenic microalgae. While DSOF accuracy decreases at very concentrated biomass (>2.0 A.U. for Galdieria) or under nitrogen-limiting conditions, the model provides a practical, scalable alternative to more complex, invasive or expensive techniques, enabling near real-time monitoring of microalgae–methanotroph cocultures. Full article

The conservation of plant species requires detailed knowledge of their reproductive behaviour and population demographic structure. This is particularly important for species such as Cardamine bulbifera, which depend on old-growth forest habitats and rely predominantly or entirely on vegetative reproduction through axillary bulbils. Although C. bulbifera has a wide native range, little is known about its population structure and dynamics. The aim of this study was to assess the demographic composition, density and main traits of C. bulbifera individuals in six populations occurring in three types of forest habitats in southern Lithuania: Fennoscandian hemiboreal natural old broadleaved deciduous forests, Fennoscandian herb-rich forests with Picea abies and Galio-Carpinetum oak–hornbeam forests. Field studies were conducted in 2023, during which a total of 20 sampling plots (each 1 m²) were analysed in each population, arranged in a transect. The study revealed an absolute dominance of young (juvenile and immature) individuals in the populations (89.2%), whereas mature individuals comprised only a small fraction (10.8%). The proportion of mature individuals was significantly larger in hornbeam forests than in the other two forest types. The highest density of individuals was recorded in broadleaved forest, while the lowest density was found in spruce forest habitat. Mature C. bulbifera individuals in hornbeam habitats were significantly taller and had longer inflorescences than those in other habitats. The highest mean number of bulbils was produced by individuals of the studied species in spruce habitats, while bulbil production was lowest in hornbeam habitats. The strongest negative contribution to the number of C. bulbifera individuals was the area of bare soil in the sampling plot, whereas herb cover had the strongest positive effect. These results highlight habitat-specific differences in C. bulbifera population structure and suggest that the long-term viability of its populations is closely associated with forest type, as well as stability of the habitat and plant community. The optimum habitat conditions for C. bulbifera are found in old broadleaved forests, and habitats with natural succession are the most favourable for its growth and conservation. Full article

Urban pluvial waterlogging has become a major challenge for densely populated cities due to increasingly extreme rainfall events and the rapid expansion of impervious surfaces. In response to the growing demand for localized waterlogging risk assessments, an automated evaluation framework is proposed that integrates high-resolution digital elevation models (DEMs), rainfall scenarios, and classified building data within a GIS-based modeling system. The methodology consists of four modules: (i) design of rainfall scenarios and runoff estimation, (ii) waterlogging depth simulation based on volume-matching algorithms, (iii) construction of depth–damage curves for residential and commercial buildings, and (iv) building-level economic loss estimation though differentiated depth–damage functions for residential/commercial assets—a core innovation enabling sector-specific risk precision. A case study was conducted in the Lixia District, Jinan City, China, involving 15,317 buildings under a 50-year return period rainfall event. The total economic losses were shown to reach approximately USD 327.88 million, with residential buildings accounting for 88.6% of the total. The model achieved a mean absolute percentage error within 5% for both residential and commercial cases. The proposed framework supports high-precision, building-level urban waterlogging damage assessment and demonstrates scalability for use in other high-density urban areas. Note: all monetary values were converted from Chinese Yuan (CNY) to U.S. Dollars (USD) using an average exchange rate of 1 USD = 7.28 CNY. Full article

Fine particulate matter (PM_2.5) and ozone (O₃) are the main pollutants affecting the air quality in China, yet their common influencing factors and spatial patterns remain unclear. Focusing on the year 2020, this study adopted the least absolute shrinkage and selection operator algorithm to construct land use regression models with 34 environmental variables for the O₃ concentration at the air quality monitoring stations in the Shenyang Metropolitan Area. For comparison, PM_2.5 models had been developed in our previous work using the same approach. Model performance was satisfactory (cross-validated R² = 0.49–0.81 for O₃; 0.56–0.65 for PM_2.5 in our previous study), confirming the robustness of the approach. The results showed that: (1) Tree cover and grassland exerted synergistic, co-directional mitigation on both pollutants, whereas built-up areas and permanent water bodies were positively associated with their concentrations; (2) Longitude, elevation, and population, as well as atmospheric components such as nitrous dioxide column density and aerosol optical depth, displayed opposite effects on both pollutants, indicating trade-offs; (3) Spatially, PM_2.5 played the dominant role in shaping the pattern of combined pollution, with higher PM_2.5 levels than O₃ in nearly half of the area (46.97%), while O₃-dominant regions were rare (4.27%) and mostly confined to localized zones. This study contributes to a deeper understanding of the synergies and trade-offs driving PM_2.5 and O₃ pollution as well as providing a scientific basis for formulating policies on integrated control measures against combined pollution. Full article

Urban networks are a key force in reshaping regional resilience patterns. However, existing research has not yet systematically elucidated, from a physical–virtual integration perspective, the underlying mechanisms through which composite urban networks shape multidimensional urban resilience in regions confronted with severe environmental and infrastructural challenges. Northwest China, characterized by its extreme arid climate, pronounced core–periphery structure, and heavy reliance on overland transportation, provides an important empirical context for examining the unique relationship between network centrality and the mechanisms of resilience formation. Based on the panel data of 33 prefecture-level cities in northwest China from 2011 to 2023, this article empirically examines the impact of the composite urban network constructed by traffic and information flows on urban resilience from the perspective of network node centrality using a two-way fixed-effects model. It is found that (1) the spatial evolution of urban resilience in northwest China is characterized by “core leadership—gradient agglomeration”: provincial capitals demonstrate significantly the highest resilience levels, while non-provincial cities are predominantly characterized by medium resilience and contiguous distribution, and the growth rate of low-resilience cities is faster, which pushes down the relative gap in the region, but the absolute gap persists; (2) the urban network in this region is characterized by a highly centralized topology, which improves the efficiency of resource allocation yet simultaneously introduces systemic vulnerability due to its over-reliance on a limited number of core hubs; (3) urban network centrality exerts a significant positive impact on resilience enhancement (β = 0.002, p < 0.01) and the core nodes of the city through the control of resources to strengthen the economic, ecological, social, and infrastructural resilience; (4) multi-dimensional factors synergistically drive the resilience, with the financial development level, economic density, and informationization level as a positive pillar. The population size and rough water utilization significantly inhibit the resilience of the region. Accordingly, the optimization path of “multi-center resilience network reconstruction, classified measures to break resource constraints, regional wisdom, and collaborative governance” is proposed to provide theoretical support and a practical paradigm for the construction of resilient cities in northwest China. Full article

The analysis of soil nematode communities provides information on their impact on soil quality and the health of different agricultural cropping systems and soil management practices, which is necessary to evaluate their sustainability. Here, we evaluated the status of nematode communities and trophic groups’ abundance in fifteen fields hosting different cropping systems and managed according to organic or conventional practices. The nematode population densities differed significantly across cropping systems and management types covering various European climatic zones (spanning 121 to 799 individuals per sample). Population density was affected by the duration of the cropping system, with the lowest value in the vegetable cropping system (on average about 300 individuals) and the highest in the long-term fruiting system (on average more than 500 individuals). The occurrence and abundance of the different trophic groups was partly dependent on the cropping system or the management method, particularly for the bacteria, fungal and plant feeders. The taxonomical classification of a subset of samples allowed us to identify 22 genera and one family (Dorylaimidae) within the five trophic groups. Few taxa were observed in all fields and samples (i.e., Rhabditis and Cephalobus), while Aphelenchoides or Pratylenchus were present in the majority of samples. Phosphorus content was the only soil chemical parameter showing a positive correlation with total nematode population and bacterial feeders’ absolute abundance. Based on the nematological ecological indices, all three cropping systems were characterized by disturbed soil conditions, conductive and dominated by bacterivorous nematodes. This knowledge could lead to a choice of soil management practices that sustain a transition toward healthy soils. Full article

This study investigates the anthropogenic transformation of small- and medium-sized river basins within the Caspian Sea catchment. The basins of seven rivers—Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan—were selected as key study areas. For both the broader Caspian region, particularly its southwestern sector, and the selected study sites, trends in land cover types were analyzed, natural resource use practices were assessed, and population density dynamics were examined. Furthermore, a range of indices were calculated to quantify the degree of anthropogenic transformation, including the coefficient of anthropogenic transformation, the land degradation index, the urbanity index, the degree of anthropogenic transformation, coefficients of absolute and relative tension of the ecological and economic balance, and the natural protection coefficient. The study was conducted using geoinformation research methods and sets of geodata databases—the global LandScan population density database, the GHS Population Grid database, the ESRI land cover type dynamics database, and OpenStreetMap (OSM) data. The analysis was performed using the geoinformation programs QGIS and ArcGIS, and a large amount of literary and statistical data was additionally analyzed. It is shown that within the studied region, there has been a decrease in the number and density of the population, as a result of which the territories of river basins are experiencing an increasing anthropogenic impact, the woody type of land cover is decreasing, and the agricultural type is increasing. The most anthropogenically transformed river basins are Karachay, Haraz, and Gorgan. Full article

Carbon emissions from global warming pose significant threats to both regional ecology and sustainable development. Understanding the factors affecting emissions is critical to developing effective carbon neutral strategies. This study constructed a precise 1 km resolution net carbon emissions map of Hubei Province, China (2000–2020), and compared the ten distinct machine learning models to identify the most effective model for revealing the relationship between carbon emissions and their influencing factors. The random forest regressor (RFR) demonstrates optimal performance, achieving root mean square error (RMSE) and mean absolute error (MAE) values that are nearly 10 times lower on average than the other models. The results are interpreted using Shapley additive explanation (SHAP), revealing dynamic factor impacts. Our findings include the following. (1) Between 2000 and 2020, net carbon emissions in Hubei increased threefold, with emissions from construction land rising by approximately 7.5 times over the past two decades. Woodland, a major carbon sink, experienced a downward trend. (2) Six key factors are population, the normalized difference vegetation index (NDVI), road density, PM_2.5, the degree of urbanization, and the industrial scale, with only the NDVI reducing emissions. (3) Net carbon emissions displayed significant spatial differences and aggregation and are mainly concentrated in the central urban areas of Hubei Province. Overall, this study evaluates various regression models and identifies the primary factors influencing net carbon emissions. The net carbon emission map we have developed can visually identify and locate high-emission hotspots and vulnerable carbon sink areas, thereby providing a direct basis for provincial land use planning. Full article

To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI requires first determining the sub-indices for several pollutants, including respirable suspended particulates (PM₁₀), fine suspended particulates (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), and carbon monoxide (CO). Accurate prediction of AQI is crucial in providing early warnings to the public before pollution episodes occur. To improve AQI prediction accuracy, deep learning methods such as artificial neural networks (ANNs) and long short-term memory (LSTM) models were applied to forecast the six pollutants commonly found in the AQI. The data for this study was accessed from the Macau High-Density Residential Air Quality Monitoring Station (AQMS), which is located in an area with high traffic and high population density near a 24 h land border-crossing facility connecting Zhuhai and Macau. The novelty of this work lies in its potential to enhance operational AQI forecasting for Macau. The ANN and LSTM models were run five times, with average pollutant forecasts obtained for each model. Results demonstrated that both models accurately predicted pollutant concentrations of the upcoming 24 h, with PM₁₀ and CO showing the highest predictive accuracy, reflected in high Pearson Correlation Coefficient (PCC) between 0.84 and 0.87 and Kendall’s Tau Coefficient (KTC) between 0.66 and 0.70 values and low Mean Bias (MB) between 0.06 and 0.10, Mean Fractional Bias (MFB) between 0.09 and 0.11, Root Mean Square Error (RMSE) between 0.14 and 0.21, and Mean Absolute Error (MAE) between 0.11 and 0.17. Overall, the LSTM model consistently delivered the highest PCC (0.87) and KTC (0.70) values and the lowest MB (0.06), MFB (0.09), RMSE (0.14), and MAE (0.11) across all six pollutants, with the lowest SD (0.01), indicating greater precision and reliability. As a result, the study concludes that the LSTM model outperforms the ANN model in forecasting air pollutants in Macau, offering a more accurate and consistent prediction tool for local air quality management. Full article

Scatterplots can reveal important data relationships, but their visual complexity can make pattern identification challenging. Systematic analytical approaches help structure interpretation by dividing scatterplots into meaningful regions. This paper introduces the region partitioning framework (RCF), a systematic method for dividing scatterplots into interpretable regions using k × k grids, in order to enhance visual data analysis and quantify structural changes through transformation metrics. RCF partitions the x and y dimensions into k × k grids (e.g., 4 × 4 or 16 regions), balancing granularity and readability. Each partition is labeled using an R(p, q) notation, where p and q indicate the position along each axis. Two perspectives are supported: the absolute mode, based on raw values (e.g., “very short, narrow”), and the relative mode, based on min–max normalization (e.g., “short relative to population”). I propose a set of transformation metrics—density, net flow, relative change ratio, and redistribution index—to quantify how data structures change between modes. The framework is demonstrated using both the Iris dataset and a subset of the airquality dataset, showing how RCF captures clustering behavior, reveals outlier effects, and exposes normalization-induced redistributions. Full article