Search Results (3,732)

This study assesses the quality of financial reporting in ten Central and Eastern European countries using a methodology based on natural language processing (NLP) techniques. 570 annual reports of companies listed on the main index on the stock exchanges of 10 Central and Eastern European (CEE) countries, over the period 2019–2023, were evaluated to determine the degree of convergence of the following four measurable qualitative characteristics: relevance, exact representation, comparability and understandability. The main objective is to identify consistency in the quality of accounting information based on the application of an international financial reporting framework. The applied methodology eliminates subjective variability by implementing a standardized scoring system, aligned with the criteria developed by NiCE, using libraries such as spaCy and NLTK for term extraction, respective sentiment analysis and word frequency evaluation. The results reveal significant heterogeneity in all characteristics examined, with statistical tests confirming substantial differences between countries. The investigation of relevance revealed partial convergence, with three dimensions achieving complete uniformity, while the exact representation showed the highest variability. The assessment of comparability showed a significant difference between countries’ extreme values, and in terms of comprehensibility a formalistic approach was evident, with technical dimensions outweighing user-oriented aspects. The overall quality index varied significantly across countries, with a notable average deterioration in 2023, indicating structural vulnerabilities in financial reporting systems. These findings support initial hypotheses on the lack of homogeneity in the quality of financial reporting in the selected region, despite the implementation of international standards. Full article

This study analyzes spatiotemporal aridity dynamics in Central Kazakhstan (1960–2022) using a monthly Aridity Index (AI = P/PET), where P is precipitation and PET is potential evapotranspiration, Mann–Kendall trend analysis, and climate zone classification. Results reveal a northeast–southwest aridity gradient, with Aridity Index ranging from 0.11 to 0.14 in southern deserts to 0.43 in the Kazakh Uplands. Between 1960–1990 and 1991–2022, southern regions experienced intensified aridity, with Aridity Index declining from 0.12–0.15 to 0.10–0.14, while northern mountainous areas became more humid, where Aridity Index increased from 0.40–0.44 to 0.41–0.46. Seasonal analysis reveals divergent patterns, with winter showing improved moisture conditions (52.4% reduction in arid lands), contrasting sharply with aridification in spring and summer. Summer emerges as the most extreme season, with hyper-arid zones (8%) along with expanding arid territories (69%), while autumn shows intermediate conditions with notable dry sub-humid areas (5%) in northwestern regions. Statistical analysis confirms these observations, with northern areas showing positive Aridity Index trends (+0.007/10 years) against southwestern declines (−0.003/10 years). Key drivers include rising temperatures (with recent degradation) and variable precipitation (long-term drying followed by winter and spring), and PET fluctuations linked to temperature. Since 1991, arid zones have expanded from 40% to 47% of the region, with semi-arid lands transitioning to arid, with a northward shift of the boundary. These changes are strongly seasonal, highlighting the vulnerability of Central Kazakhstan to climate-driven aridification. Full article

In this study, a combined approach joining the machine learning model Extreme Gradient Boosting (XGBoost) with Shapley Additive Explanation (SHAP) is adopted to simulate the temporal pattern of the air quality index (AQI) and subsequently explore the key factors affecting AQI variability. Based on the analysis of air pollutants and meteorological data acquired from two air quality monitoring stations in Rome (Italy), over the 2018–2022 period, the results demonstrate the effectiveness of the proposed methodological approach in elucidating the role of the main factors driving AQI evolution, and their interaction effects. Full article

With the rapid pace of urbanization, cities are increasingly facing severe challenges related to environmental pollution, ecological degradation, and climate change. Extreme climate events—such as heatwaves, droughts, heavy rainfall, and wildfires—have intensified public concern about sustainability, environmental protection, and low-carbon development. Ensuring environmental preservation while maintaining residents’ quality of life has become a central focus of urban governance. In this context, evaluating green indicators and predicting urban greenness is both necessary and urgent. This study incorporates international frameworks such as the EU Green City Index, the European Green Capital Award, and the United Nations Sustainable Development Goals to assess urban sustainability. The Extreme Gradient Boosting (XGBoost) algorithm is employed to predict the green level of cities and to develop multiple optimized models. Comparative analysis with traditional models demonstrates that XGBoost achieves superior performance, with an accuracy of 0.84 and an F1-score of 0.81. Case study findings identify “Greenhouse Gas Emissions per Person” and “Per Capita Emissions from Transport” as the most critical indicators. These results provide practical guidance for policymakers, suggesting that targeted regulations based on these key factors can effectively support emission reduction and urban sustainability goals. Full article

The demand for functional beverages is increasing as consumers seek options that offer health benefits, and plant-based beverages are gaining popularity for their associated advantages. The objective of this study was to optimize the formulation of a chickpea and hibiscus beverage to maximize flavor sensory acceptance, antioxidant capacity, and anthocyanin content using a mixture design and characterize the optimal formulation. An extreme vertices mixture design was employed, with fixed proportions of chickpea beverage (66.5%) and inulin (2%), while varying the proportions of hibiscus decoction, monk fruit, and cinnamon powder. Additionally, the Kano model was used to classify the beverage’s attributes. The optimized formulation consisted of 31.41% hibiscus decoction, 0.48% monk fruit, and 0.61% cinnamon powder, achieving 329.2 µmol TE/100 mL (antioxidant capacity), 3.567 mg C3GE/100 mL (anthocyanin content), and a flavor rating of 6.2. The Kano model classified good taste, functional properties, monk fruit sweetening, and chickpeas as attractive attributes, with functional properties obtaining the highest satisfaction index (0.88). These results demonstrate that employing a mixture design is an effective tool to enhance health-related aspects and consumer acceptance. Additionally, the incorporation of the Kano model provides a broader perspective on the development of functional beverages by identifying key attributes that influence product acceptance and market success. Full article

Drought and water scarcity are some of the most important challenges facing agricultural producers in dry environments. This study aimed to evaluate the effect of algae extract and zeolite in terms of their biostimulant action on water stress tolerance to obtain better growth and production of tomato Lycopersicum esculentum L. grown in an open field under suboptimum and deficient soil moisture content. Large plots had a suboptimum soil moisture content (SSMC) of 25% ± 2 [28% below field capacity (FC)] and deficient soil moisture content (DSMC) of 20% ± 2 [11% above permanent wilting point (PWP)]; both soil moisture ranges were based on field capacity FC (32%) and PWP (18%). Small plots had four treatments: algae extract (AE) 50 L ha⁻¹ and zeolite (Z) 20 t ha⁻¹, a combination of both products (AE + Z) 25 L ha⁻¹ and 10 t h⁻¹, and a control (without application of either product). By applying AE, Z, and AE + Z, plant height, plant vigor, and chlorophyll index were significantly higher compared to the control by 20.3%, 10.5%, and 22.3%, respectively. The effect on relative water content was moderate—only 2.6% higher than the control applying AE, while the best treatment for the photosynthesis variable was applying Z, with a value of 20.9 μmol CO₂ m⁻² s⁻¹, which was 18% higher than the control. Consequently, tomato yield was also higher compared to the control by 333% and 425% when applying AE and Z, respectively, with suboptimum soil moisture content. The application of the biostimulants did not show any mitigating effect on water stress under soil water deficit conditions close to permanent wilting. These findings are relevant to water-scarce agricultural areas, where more efficient irrigation water use is imperative. Plant biostimulation through organic and inorganic extracts plays an important role in mitigating environmental stresses such as those caused by water shortages, leading to improved production in vulnerable agricultural areas with extreme climates. Full article

Landslide susceptibility modelling represents an efficient approach to enhance disaster management and mitigation strategies. The focus of this paper lies in the development of a landslide susceptibility evaluation in northwestern Tunisia using the Analytical Hierarchy Process (AHP) and Artificial Neural Network (ANN) approaches. The used database covers 286 landslides, including ten landslide factor maps: rainfall, slope, aspect, topographic roughness index, lithology, land use and land cover, distance from streams, drainage density, lineament density, and distance from roads. The AHP and ANN approaches were applied to classify the factors by analyzing the correlation relationship between landslide distribution and the significance of associated factors. The Landslide Susceptibility Index result reveals five susceptible zones organized from very low to very high risk, where the zones with the highest risks are associated with the combination of extreme amounts of rainfall and steep slope. The performance of the models was confirmed utilizing the area under the Relative Operating Characteristic (ROC) curves. The computed ROC curve (AUC) values (0.720 for ANN and 0.651 for AHP) convey the advantage of the ANN method compared to the AHP method. The overlay of the landslide inventory data locations of historical landslides and susceptibility maps shows the concordance of the results, which is in favor of the established model reliability. Full article

The process of urbanization has intensified the urban heat environment, with the degradation of thermal conditions closely linked to the morphological characteristics of different functional zones. This study delineated urban functional areas using a multivariate dataset and investigated the seasonal and threshold effects of landscape and architectural features on land surface temperature (LST) through boosted regression tree (BRT) modeling and Spearman correlation analysis. The key findings are as follows: (1) LST exhibits significant seasonal variation, with the strongest urban heat island effect occurring in summer, particularly within industry, business, and public service zones; residence zones experience the greatest temperature fluctuations, with a seasonal difference of 24.71 °C between spring and summer and a peak temperature of 50.18 °C in summer. (2) Fractional vegetation cover (FVC) consistently demonstrates the most pronounced cooling effect across all zones and seasons. Landscape indicators generally dominate the regulation of LST, with their relative contribution exceeding 45% in green land zones. (3) Population density (PD) exerts a significant, seasonally dependent dual effect on LST, where strategic population distribution can effectively mitigate extreme heat events. (4) Mean building height (MBH) plays a vital role in temperature regulation, showing a marked cooling influence particularly in residence and business zones. Both the perimeter-to-area ratio (LSI) and frontal area index (FAI) exhibit distinct seasonal variations in their impacts on LST. (5) This study establishes specific indicator thresholds to optimize thermal comfort across five functional zones; for instance, FVC should exceed 13% in spring and 31.6% in summer in residence zones to enhance comfort, while maintaining MBH above 24 m further aids temperature regulation. These findings offer a scientific foundation for mitigating urban heat waves and advancing sustainable urban development. Full article

With the continuous increase in urban population, the subway is the main way to alleviate traffic congestion. However, the construction environment of subway stations is complex, and the safety risks are extremely high. Therefore, it is of great practical significance to scientifically and systematically evaluate the safety vulnerability of subway station construction. This paper takes the Chengdu subway project as an example, and establishes a metro station construction safety vulnerability evaluation index system based on the driving forces–pressures–state–impacts–responses (DPSIR) theory with 5 first-level indexes and 23 second-level indexes, and adopts the fuzzy hierarchical analysis method (FAHP) to calculate the subjective weights, and the improved Harris Hawks optimization–projection pursuit method (HHO-PPM) to determine the objective weights, combined with game theory to calculate the comprehensive weights of the indicators, and finally uses the improved cloud model of Bayesian feedback to determine the vulnerability level of subway station construction safety. The study found that the combined empowerment–improvement cloud model assessment method is reliable, and the case study verifies that the vulnerability level of the project is “very low risk”, and the investigations of safety hazards and the pressure of surrounding traffic are the key influencing factors, allowing for the proposal of more scientific and effective management strategies for the construction of subway stations. Full article

Springs are unique ecosystems found in lowland areas. In urban environments, these springs often have niches that are heavily transformed by human activity. In this study, we identified and compared the taxonomic diversity of diatom communities across various microhabitats—epilithon, epipelon, epipsammon, epibryon, and epixylon—within altered lowland urban springs. Our results revealed differences in diatom communities among the microhabitats, with the highest species richness observed in the epibryon. Notably, the presence of extremely rare species such as Amphora eximia, Caloneis aerophila, and Stauroneis muriella suggest that, even under urban conditions, springs continue to serve a refugial function for diatom diversity. These findings underscore the important role of urban springs in maintaining diatom diversity despite high anthropogenic pressure. We also assessed the ecological status of the springs using the Polish Multimetric Diatom Index (IO), which incorporates indicators of trophy, saprobity, and the abundance of reference species. All studied springs were classified as having very good ecological status. Full article

Our study was conducted in the Valle Nuevo National Park and included four habitat classes: tussock grass (Sabapa), pine forest (Pinoc), broadleaf forest (Boslat), and agricultural ecosystem (Ecoag). We had two main objectives: to comparatively describe millipede communities and to determine the relationships between population density/diversity and soil physicochemical variables. The research was cross-sectional and non-manipulative, with a descriptive and correlational scope; sampling followed a stratified systematic design, with eight transects and 32 quadrats of 1 m², covering 21.7 km. We found a sandy loam soil with an extremely acidic pH. The highest population density of millipedes was recorded in Sabapa, and the lowest in Ecoag. The highest alpha diversity was shared between Boslat (Margalef = 1.72) and Pinoc (Shannon = 2.53); Sabapa and Boslat showed the highest Jaccard similarity (0.56). The null hypothesis test using the weighted Shannon index revealed a statistically significant difference in diversity between the Boslat–Sabapa and Pinoc–Sabapa pairs. Two of the species recorded highly significant indicator values (IndVal) for two habitat classes. We found significant correlations (p < 0.05) between various soil physicochemical variables and millipede density and diversity. Full article

Background: Patients diagnosed with melanoma are at increased risk of developing multiple primary melanomas (MPMs). Identifying clinical and genetic factors associated with MPM is critical for implementing personalized surveillance strategies. This study aims to describe the clinical, histopathological, and genetic characteristics of patients with MPM managed in a tertiary hospital and to contextualize findings within the current literature. Methods: We conducted a retrospective review of patients diagnosed with two or more primary melanomas between 2010 and 2023 at a tertiary dermatology unit. Demographic data, personal and family cancer history, phototype, melanoma characteristics, genetic testing, staging, treatments, and outcomes were collected. These data were compared with findings from the recent literature. Results: Thirteen patients (ten males, three females; median age: 59 years) were found to have a total of 33 melanomas. Most patients had Fitzpatrick phototype II and no immunosuppression. The number of melanomas per patient ranged from two to five. Synchronous lesions were observed in two patients. Common locations included the trunk and extremities. Histologically, 57% were in situ melanomas, and subsequent melanomas were generally thinner than the index lesion. Two patients showed progression to advanced disease. One patient was positive for MC1R mutation; the rest were negative or inconclusive. Additional phenotypic and environmental risk factors were extracted from patient records and are summarized as follows: Ten patients (76.9%) had Fitzpatrick skin phototype II, and three (23.1%) had phototype III. Chronic occupational sun exposure was reported in four patients (30.8%), while five (38.5%) recalled having suffered multiple sunburns during childhood or adolescence. Eight patients (61.5%) presented with a total nevus count exceeding 50, and five (38.5%) exhibited clinically atypical nevi. None of the patients reported use of tanning beds. Conclusions: Our findings are consistent with the existing literature indicating that patients with MPM often present with thinner subsequent melanomas and require long-term dermatologic follow-up. The inclusion of genetic testing and phenotypic risk factors enables stratified surveillance and supports the application of personalized medicine in melanoma management. Full article

Accurate prediction of the coal abrasive index (AI) is critical for optimizing coal processing efficiency and minimizing equipment wear in industrial applications. This study explores tree-based machine learning models; Random Forest (RF), Gradient Boosting Trees (GBT), and Extreme Gradient Boosting (XGBoost) to predict AI using selected coal properties. A database of 112 coal samples from the KwaZulu-Natal Coalfield in South Africa was used. Initial predictions using all eight input properties revealed suboptimal testing performance (R²: 0.63–0.72), attributed to outliers and noisy data. Feature importance analysis identified calorific value, quartz, ash, and Pyrite as dominant predictors, aligning with their physicochemical roles in abrasiveness. After data cleaning and feature selection, XGBoost achieved superior accuracy (R² = 0.92), outperforming RF (R² = 0.85) and GBT (R² = 0.81). The results highlight XGBoost’s robustness in modeling non-linear relationships between coal properties and AI. This approach offers a cost-effective alternative to traditional laboratory methods, enabling industries to optimize coal selection, reduce maintenance costs, and enhance operational sustainability through data-driven decision-making. Additionally, quartz and Ash content were identified as the most influential parameters on AI using the Cosine Amplitude technique, while calorific value had the least impact among the selected features. Full article

Early rehabilitation is essential for restoring functional recovery in patients with stroke, particularly during the early phase of post-acute care (PAC), or the subacute stage. We aimed to evaluate the effectiveness of a 7-week PAC rehabilitation program in improving muscle strength, physical performance, and functional recovery. A total of 219 inpatients with stroke in the subacute stage were initially recruited from the PAC ward of a regional teaching hospital in Northern Taiwan, with 79 eligible patients—within 1 month of an acute stroke—included in the analysis. The program was delivered 5 days per week, with 3–4 sessions daily (20–30 min each, up to 120 min daily), comprising physical, occupational, and speech–language therapies. Sociodemographic data, muscle strength, physical performance (Berg Balance Scale [BBS], gait speed, and 6-minute walk test [6MWT]), and functional recovery (modified Rankin Scale [mRS], Barthel Index [BI], Instrumental Activities of Daily Living [IADL], and Fugl–Meyer assessment: sensory and upper extremity) were collected at baseline, 3 weeks, and 7 weeks. Generalized estimating equations analyzed program effectiveness. Among the 56 patients (70.9%) who completed the program, significant improvements were observed in the muscle strength of both the affected upper (B = 0.93, p < 0.001) and lower limbs (B = 0.88, p < 0.001), as well as in their corresponding unaffected limbs; in physical performance, including balance (BBS score: B = 9.70, p = 0.003) and gait speed (B = 0.23, p = 0.024); and in functional recovery, including BI (B = 19.5, p < 0.001), IADL (B = 1.48, p < 0.001), and mRS (B = −0.13, p = 0.028). These findings highlight the 7-week PAC rehabilitation program as an effective strategy during the critical recovery phase for patients with stroke. Full article

It is well-known that the necessary and sufficient condition for a connected graph to be unicyclic is that its omega invariant, a recently introduced graph invariant useful in combinatorial and topological calculations, is zero. This condition could be stated as the condition that the order and the size of the graph are equal. Using a recent result saying that the length of the unique cycle could be any integer between 1 and n − a₁ where a₁ is the number of pendant vertices in the graph, two explicit labeling algorithms are provided that attain these extremal values of the first and second Zagreb indices by means of an application of the well-known rearrangement inequality. When the cycle has the maximum length, we obtain the situation where all the pendant vertices are adjacent to the support vertices, the neighbors of the pendant vertices, which are placed only on the unique cycle. This makes it easy to calculate the second Zagreb index, as the contribution of the pendant edges to such indices is fixed, implying that we can only calculate these indices for the edges on the cycle. Full article