Search Results (9,421)

This study examines the application of Geographic Information Systems (GIS) in tourism planning and sustainable destination management, using Gelnica, Slovakia, as a case study. The research highlights a key challenge—the absence of systematic visitor data collection—which hinders tourism market analysis, demand assessment, and strategic decision-making. The study integrates alternative data sources, including the Google Places API, to address this gap to analyse Points of Interest (POIs) based on user-generated reviews, ratings, and spatial attributes. The methodological framework combines data acquisition, spatial analysis, and GIS-based visualisation, employing thematic and heat maps to assess tourism resources and visitor behaviour. The findings reveal critical spatial patterns and tourism dynamics, identifying high-demand zones and underutilised locations. Results underscore the potential of GIS to optimise tourism infrastructure, enhance visitor management, and inform evidence-based decision-making. This study advocates for systematically integrating GIS technologies with visitor monitoring and digital tools to improve destination competitiveness and sustainability. The proposed GIS-driven approach offers a scalable and transferable model for data-informed tourism planning in similar historic and environmentally sensitive regions. Full article

The porcine reproductive and respiratory syndrome virus (PRRSV) possesses an inherent ability to adapt to environmental transformations and undergo evolutionary changes, which has imposed significant economic pressure on the global pig industry. Given the potential for recombination among PRRSV genomes and variations in pathogenicity, newly emerging PRRSV isolates are of considerable clinical importance. In this study, we successfully isolated a novel strain named XJ-Z5 from PRRSV-positive samples collected in Xinjiang province in 2022. Through comprehensive genomic sequencing, phylogenetic analysis, and recombination analysis, we confirmed that this strain belongs to the NADC30-like recombinant PRRSV. During pathogenicity tests in piglets, this strain exhibited moderate virulence, causing symptoms such as reduced appetite, persistent fever, and weight loss; however, no mortality cases were observed. Tests conducted at various time points detected the presence of PRRSV nucleic acid in nasal swabs, rectal swabs, tissue samples, and blood, with the highest viral loads found in lung tissue and blood. Serum biochemical tests indicated significant impairment of liver and kidney function. PRRSV antibodies began to appear gradually after 10 days post infection. Hematoxylin and eosin staining revealed substantial pathological changes in lung tissue and lymph nodes. This study enhances our understanding of the epidemiology of PRRSV and underscores the importance of ongoing monitoring and research in light of the challenges posed by the continuous evolution of viral strains. Furthermore, the research emphasizes the urgency of the rapid genomic analysis of emerging viral strains. Through these comprehensive research and monitoring strategies, we aimed to curb the spread of PRRSV more effectively and thus reduce the huge economic losses it caused to the pig industry. Full article

Background/Objectives: The recent COVID-19 pandemic posed a significant psychological challenge for healthcare workers. Resilience and the extent of psychological stress varied across professional groups and individual circumstances. This study aims to longitudinally capture the trajectories of psychological stress and resilience among medical personnel during the pandemic and identify various contributing factors. Methods: Over a period of three years, healthcare workers from five locations (Bonn, Cologne, Ulm, Erlangen, and Dresden) were surveyed regarding their psychological stress (PHQ-4) and other aspects of mental health. Data were collected at five different points during the pandemic. Using Growth Mixture Modeling (GMM), various stress trajectories during the crisis were modeled without initial adjustment for covariates to allow for an unbiased identification of latent classes. Differences in demographic and occupational factors (e.g., age, gender, profession) were analyzed across the identified trajectory groups in subsequent steps. Results: The application of GMM revealed three distinct profiles of psychological stress and resilience among the respondents, largely consistent with the literature. The largest group was the ‘resilience’ group (81%), followed by the ‘recovery’ (10%) and ‘delayed’ groups (9%). Group membership was consistent with self-reported trajectories over the course of the pandemic. It was not possible to predict individual trajectories based on the results of a short resilience questionnaire (RS-5). Conclusions: The COVID-19 pandemic had multiple psychological impacts on healthcare workers, manifesting in clearly differentiated group trajectories of distress over time. While a majority of respondents in this sample exhibited a stable trajectory with low distress, other groups showed varying stress responses over time. These findings highlight the necessity of longitudinal approaches to understand the complex interplay of stressors and coping mechanisms during prolonged crises. Full article

The sea surface displays dynamic characteristics, such as waves and various formations. As a result, images of the sea surface usually have few stable feature points, with a background that is often complex and variable. Moreover, the sea surface undergoes significant changes due to variations in wind speed, lighting conditions, weather, and other environmental factors, resulting in considerable discrepancies between images. These variations present challenges for identification using traditional methods. This paper introduces an algorithm based on the phase-consistency model. We utilize image data collected from a specific maritime area with a high-frame-rate surface array infrared camera. By accurately detecting images with identical names, we focus on the subtle texture information of the sea surface and its rotational invariance, enhancing the accuracy and robustness of the matching algorithm. We begin by constructing a nonlinear scale space using a nonlinear diffusion method. Maximum and minimum moments are generated using an odd symmetric Log–Gabor filter within the two-dimensional phase-consistency model. Next, we identify extremum points in the anisotropic weighted moment space. We use the phase-consistency feature values as image gradient features and develop feature descriptors based on the Log–Gabor filter that are insensitive to scale and rotation. Finally, we employ Euclidean distance as the similarity measure for initial matching, align the feature descriptors, and remove false matches using the fast sample consensus (FSC) algorithm. Our findings indicate that the proposed algorithm significantly improves upon traditional feature-matching methods in overall efficacy. Specifically, the average number of matching points for long-wave infrared images is 1147, while for mid-wave infrared images, it increases to 8241. Additionally, the root mean square error (RMSE) fluctuations for both image types remain stable, averaging 1.5. The proposed algorithm also enhances the rotation invariance of image matching, achieving satisfactory results even at significant rotation angles. Full article

This systematic review answers questions about whether or not a model of metacognition is well accepted and if it can be used in cognitive architecture design. Self-planning, self-monitoring, and self-evaluation are the model concepts, which are viewed as metacognitive experiences. A newly formulated theoretical approach named Attention as Action was targeted, as it is shown to be used in cognitive architecture design. In order to link the model to the theoretical approach, specific concepts like mental imagery and learning experience were researched. The method includes the statistical analysis of key phrases in articles that were collected based on a system of criteria. Data were retrieved from 91 scientific papers to allow statistical analysis of the relationship between the model of metacognition and the theoretical approach to cognitive architecture design. Several observations from the data show that the model is applicable for designing cognitive monitoring systems that depict experiences of metacognition. Furthermore, the results point out that the researched fields require explanations about the concepts defined in the theoretical approach of Attention as Action. Systematically formulated as types of internal attentional experiences, new relations are provided for researching cognitive and metacognitive concepts in terms of the cognitive cycle. Full article

This study focuses on the preservation of indoor murals, which can be supported by combining RTK and total station technology to explore the optimization of image geometric accuracy based on a control points layout. The study involves placing varying numbers of control points on the mural surface and processing the collected data using a spatial coordinate transformation model to assess the impact of different layouts on image accuracy. Some control points are used to ensure the spatial positioning accuracy of the images, while others serve as check points to validate the geometric precision of the images. After data processing, high-precision digital orthophotos are generated using Agisoft PhotoScan2.0.1 software, with accuracy verified by the check points. The experimental results show that as the number of control points increases, image accuracy improves gradually. When the number of control points reaches 24, the geometric accuracy of the images stabilizes, and further increases in the number of control points have a limited effect on improving accuracy. Therefore, the study proposes an optimal layout scheme: 24 control points for every 16 square meters. This scheme not only meets millimeter-level precision requirements but also effectively optimizes resource allocation and reduces time costs. The research provides reliable data support for the high-precision preservation and restoration of murals and offers important references for similar cultural heritage preservation projects. Full article

Some plants belonging to the Leontodon sect. Asterothrix were collected from southern Albania. They were compared with the closest taxon (L. albanicus s.str.) from morphological and molecular (AFLPseq fingerprinting) points of view. Uni- and multivariate statistical analyses of morphological data revealed distinctive discontinuities—especially in terms of the characteristics of the indumentum–that are paralleled by separation into two genetic clusters in AFLPseq fingerprinting. Following an integrated taxonomic approach based on morphological, genetic, and geographical sources of evidence, we show that the newly discovered population should be regarded as a new subspecies named Leontodon albanicus subsp. acroceraunicus. The new taxon is described and illustrated, and its relationship with L. albanicus subsp. albanicus is also discussed. We have no data to assess conservation status according to IUCN categories and criteria; however, considering that it is probably limited to the Acroceraunian Mountains, it deserves particular conservation interest. Full article

Soil salinization is a significant threat to agricultural production, making accurate salinity prediction essential. This study addresses key challenges in the Yellow River Delta (YRD) soil salinity inversion, including (1) determining which Landsat 8 OLI level performs better, (2) identifying the most suitable month for salinity inversion, and (3) improving model performance and identifying important variables in modeling. Thus Landsat 8 OLI images (Level-1 and Level-2) for 12 months were collected, then images having less than 10% cloud cover were selected and processed to extract spectral values. A total of 86 sampled points were processed to measure soil salinity. Using Pearson correlation and expert insights, January 15 and August 26 were identified as suitable dates for inversion. Then, seven original bands, 29 spectral indicators, and 39 derived variables which created through six mathematical transformations, were used to construct the following three models: partial least squares regression (PLSR), random forest (RF), and backpropagation neural network (BPNN). The results showed the following: (1) The Level-1 data, after FLAASH atmospheric correction, outperforms Level-2 data. (2) January is optimal for salinity inversion. (3) Among the three models, RF outperformed the others, achieving test set R² = 0.55, RMSE = 3.4, suggesting that the combination of spectral indicators and mathematically transformed variables can effectively enhance model accuracy for predicting soil salinity in the YRD. Furthermore, SWIR1, SWIR2, CLEX, second-order difference of SWIR1, and first-order difference of SWIR2 along with NIR played a key role in modeling. Full article

In this study, modification of poly(adipic anhydride) through branching its chains was carried out via melt condensation polymerization with D-mannitol. The percentage of mannitol was varied (3, 4, 5, 10, 15, and 20 Wt.%) and the resulting copolymers were purified and characterized by FT-IR and ¹³C-NMR. These analyses indicated that linear chains of poly(adipic anhydride) can react with strong nucleophiles and dissociate to produce highly branched poly(adipic anhydride-co-mannitol adipate) which confirms the validity of the proposed mechanism. The copolymer’s molecular weight characteristics have been also examined using GPC analysis. Thermal properties of copolymers were also investigated using TGA, DTG, and DCS analyses. TGA/DTG revealed that the thermal degradation of copolymers proceeds in multi-stage decomposition, whereas the shift and pattern change of the melting point peak of DSC curves can identify the weight percentage of mannitol for homogenous copolymers. Two non-isothermal models, the Flynn–Wall–Ozawa and Kissinger methods, have been also employed to analyze thermogravimetric data collected from the thermal decomposition of the copolymers and found that Flynn–Wall–Ozawa method provides better results with R² correlation up to 99.3%. The activation energy in the region of T_max was determined and found that an increase in mannitol contents in copolymer has a positive impact on its thermal stability. Full article

Natural fiber composites have gained significant attention in recent years due to their environmental benefits and unique mechanical properties. These materials combine natural fibers with polymer matrices to create sustainable alternatives to traditional synthetic composites. In addition to natural fiber reinforcement, the usage of recycled aggregates in concrete has been proposed as a remedy to combat the rapidly increasing amount of construction and demolition waste in recent years. However, the accurate prediction of the structural performance metrics, such as tensile strength, remains a challenge for concrete composites reinforced with natural fibers and containing recycled aggregates. This study aims to develop predictive models of natural-fiber-reinforced recycled aggregate concrete based on experimental results collected from the literature. The models have been trained on a dataset consisting of 482 data points. Each data point consists of the amounts of cement, fine and coarse aggregate, water-to-binder ratio, percentages of recycled coarse aggregate and natural fiber, and the fiber length. The output feature of the dataset is the splitting tensile strength of the concrete. Extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and extra trees regressor models were trained to predict the tensile strength of the specimens. For optimum performance, the hyperparameters of these models were optimized using the blended search strategy (BlendSearch) and cost-related frugal optimization (CFO). The tensile strength could be predicted with a coefficient of determination greater than 0.95 by the XGBoost model. To make the predictive models accessible, an online graphical user interface was also made available on the Streamlit platform. A feature importance analysis was carried out using the Shapley additive explanations (SHAP) approach. Full article

The majority of metagenomic studies are based on the study of bacterial biota. At the same time, the COVID-19 pandemic has prompted interest in the study of both individual fungal pathogens and fungal communities (i.e., the mycobiome) as a whole. Here, in this work, we investigated the human gut mycobiome during COVID-19. Stool samples were collected from patients at two time points: at the time of admission to the hospital (the first time point) and at the time of discharge from the hospital (the second time point). The results of this study revealed that Geotrichum sp. is more represented in a group of patients with COVID-19. Therefore, Geotrichum sp. is elevated in patients at the time of admission to the hospital and underestimated at the time of discharge. Additionally, the influence of factors associated with the diversity of fungal gut microbiota was separately studied, including disease severity and age factors. Full article

The aim of this study was to investigate the effects of storage temperature and preservation time on the microbial diversity and community composition of rumen fluid. Rumen fluid samples were collected from six Hu sheep fed on a high-forage diet and stored at −80 °C and −20 °C for intervals of 0, 7, 14, 30, 60, 120, and 240 days. DNA was extracted at each time point for 16S rRNA gene sequencing to evaluate the rumen microbial diversity and community composition. The results showed that storage temperature affected only the relative abundance of Proteobacteria, with no substantial impact on alpha-diversity or other microbial groups (p > 0.05), and no significant interaction effects were observed between storage temperature and preservation time (p > 0.05). Alpha-diversity indices such as Chao1, observed species, and PD whole tree showed dynamic changes after 7 days of storage, while the relative abundances of Verrucomicrobiota and Christensenellaceae R-7 group, as well as the energy metabolism metabolic pathway, exhibited significant alterations after 14 days of storage (p < 0.05). Notably, Patescibacteria, Rikenellaceae RC9 gut group, and Veillonellaceae UCG-001 abundances demonstrated significant changes after 240 days of storage (p < 0.05). Both principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS) showed distinct overlaps. This study suggests that storing rumen fluid at −80 °C and −20 °C does not influence rumen microbial diversity and community composition, whereas the storage time significantly impacts these factors, with most differences emerging after 14 days of preservation. Consequently, it is advised that the analysis of microbial diversity and community composition in rumen fluid samples be conducted within 14 days post-collection. Full article

We consider a particular model of a Source of independent particles and a macroscopic Detector that are both tuned to the same resonance frequency ${\nu }_0\equiv 1/P$. Particles are emitted by the Source at exact multiples of the resonance period P, and the Detector absorbs them with a certain probability at any one of its points. The Detector may also announce the detection of the absorbed particle. Any particle that is not absorbed at a certain point passes through to a deeper layer in the interior of the Detector. Eventually, all particles will be absorbed, i.e., detected. We calculate the probability of detection for two particle time series generated by the same Source reaching the Detector with a time delay of $\delta t$ and show that it manifests the illusion of collective (wave-like) interference with particle number conservation. We conclude that wave phenomena may reflect the nature of detectors rather than fundamental laws of physics. Full article

Research in environmental education points out the need for an improvement in pre-service teacher education training in this area. This proposal follows PBL methodology and focuses on the development of an environmental education project whose final product is the organization of a science fair for elementary school students. The 5-week project addresses the topic of urban biodiversity decline and uses the butterfly garden as an initiative to mitigate it. Four months after the program ended, a survey was administered to the 86 participating pre-service teachers. A mixed-methods approach was used, collecting quantitative data on perceptions of urban biodiversity decline, the One Health concept, and environmental education, along with qualitative keyword responses to open-ended questions about the butterfly garden’s impact and the project’s value for self-learning and professional development. Participants reported positive perceptions regarding butterfly gardens after participating in our environmental program, recognizing them as both a valuable educational resource and an effective initiative to mitigate urban biodiversity decline. Respondents showed a strong pro-environmental attitude, taking seriously their role in transmitting environmental values. Using a butterfly garden for teaching purposes offers insight into environmental literacy, connection with nature, and improvements in well-being and is a powerful platform for deep and meaningful pedagogical learning. Full article

Using density functional theory (DFT) and scanning tunneling microscopy (STM), the intrinsic point defects, formation energy, and electronic structure of 1T-TiS₂ were investigated. Defect systems include single-atom vacancies, interstitial and adatom additions, and direct atomic substitution. Using a collective approach for analyzing realistic systems for point defect investigation, we provide a more straightforward comparison to the experimental measurements, reproducing more realistic environmental conditions related to thin film growth. STM images are compared to computationally simulated electron density images to identify specific geometries that result from favorable point defects. DFT suggests that titanium interstitials are the most energetically favorable intrinsic defect, and sulfur vacancies are more likely to form than titanium vacancies within this realistic analysis, which is in agreement with STM data. A pristine, stoichiometric monolayer system is calculated to have a direct band gap of 0.422 eV, which varies based on local point defects. Local semiconducting-to-metallic electronic transitions are predicted to occur based on the presence of Ti interstitials. Full article