Search Results (8,941)

This study investigates residential segregation and housing types in Craiova, Romania, with a particular focus on the disparities shaped by historical and contemporary urban developments. Using collected data from former hostels built for young workers during the communist era, this research maps and analyzes the spatial distribution and living conditions of these housing types at a neighborhood level. Key metrics such as the number of inhabitants, the surface area of rooms, the current occupancy rates, and the number of unoccupied rooms were collected. Additionally, residential segregation is measured using indices of dissimilarity, isolation, exposure, concentration, and centralization, providing a comprehensive view of the socio-spatial divides within the city. The findings indicate significant disparities between these buildings with unsuitable living conditions and the newer residential developments, revealing a clear urban divide. No differences have been identified in terms of access to urban services like education, health, green areas, banks, or supermarkets, despite the appropriate location differences being noted in access to water and gas supply, and internet services. This study contributes to the understanding of how housing types and access to services in Craiova shape patterns of residential segregation, and it suggests policy interventions aimed at mitigating the negative impacts of these urban divides. Full article

Background: Fungal infections, particularly among immunocompromised individuals, present significant challenges due to rising incidence rates, treatment costs, and increasing resistance to antifungal agents. This study evaluates trends in antifungal use among Medicaid beneficiaries, focusing on prescribing patterns, costs, and pricing to optimize therapy. Methods: Using the national Medicaid outpatient pharmacy claims data collected by the US Center of Medicare and Medicaid Services, a retrospective drug utilization analysis was conducted for antifungal medications from 2009 to 2023. Antifungal medications were categorized based on therapeutic use. The study examined annual utilization, reimbursement, and pricing trends, along with the market share. Results: Overall Medicaid utilization of superficial fungal infections’ (SFIs’) medications increased from 3.95 million prescriptions in 2009 to 6.16 million in 2023. Nystatin was the most frequently utilized SFI agent, while fluconazole emerged as the most commonly prescribed agent for invasive fungal infections (IFIs). In 2022, a notable spike occurred in the number of prescriptions for both SFIs and IFIs. Medicaid’s total expenditure on SFI medications rose from USD 121.9 million in 2009 to USD 155 million in 2023, while spending on IFI medications fluctuated substantially, peaking at USD 156.8 million in 2022 before declining to USD 80.7 million in 2023. After being introduced to the market, efinaconazole became the most expensive SFI agent over the years. Isavuconazole, the latest approved IFI medication, demonstrated sustained utilization, reimbursement, and price increases. Conclusions: The substantial rise in antifungal utilization and spending underscores the growing financial burden on Medicaid, emphasizing the need for policy interventions to manage costs and generic drug substitution while ensuring equitable access to these essential treatments. However, this study is limited by the lack of clinical outcome data and information on off-label use. Additionally, reimbursement data may not accurately reflect actual drug prices. Full article

This study investigates a multi-scenario combustion state diagnosis system based on acoustic feature extraction techniques. In this study, the voiceprint technology is applied to combustion condition monitoring for the first time, and an integrated approach for monitoring and diagnosis is proposed by combining multiple acoustic features, such as acoustic pattern features, step index P, and frequency-domain monitoring. In this study, a premixed hydrogen combustion test bed was built to simulate common combustion faults, and the corresponding acoustic features were collected and extracted. In this study, step index P and acoustic features are used for parallel diagnostic analysis, and CNN, ANN, and BP models are used to train the four states of flameout, flameback, thermoacoustic oscillation, and stable combustion, and the training diagnostic performance of each model is compared and analyzed using a confusion matrix. It is found that CNN has the strongest classification ability, can accurately distinguish the four states, has the lowest misclassification rate, has very strong generalization ability, and has a diagnostic accuracy of 93.49%. The classification accuracy of ANN is not as good as that of CNN, and there are local fluctuations during the training process. The BP neural network has a slower convergence speed and a high error rate in recognizing the flameback and thermoacoustic oscillations. In summary, the combustion state diagnosis system based on CNN model combined with acoustic features has optimal performance, and the combination of step index P and frequency-domain monitoring in the flameback diagnosis can improve the accuracy of combustion state identification and safety control level, which provides an important theoretical basis and practical reference in the field of combustion state diagnosis and is of profound significance to ensure the safe and efficient operation of the combustion process. Full article

With an increasing number of illegal radio stations, connected cars, and IoT devices, high-accuracy radio source localization techniques are in demand. Traditional methods such as GPS positioning and triangulation suffer from accuracy degradation in NLOS (non-line-of-sight) environments due to obstructions. In contrast, the fingerprinting method builds a database of pre-collected radio information and estimates the source location via pattern matching, maintaining relatively high accuracy in NLOS environments. This study aims to improve the accuracy of fingerprinting-based localization by optimizing UAV flight paths. Previous research mainly relied on RSSI-based localization, but we introduce an AOA model considering AOA (angle of arrival) and EOA (elevation of arrival), as well as a HYBRID model that integrates multiple radio features with weighting. Using Wireless Insite, we conducted ray-tracing simulations based on the Institute of Science Tokyo’s Ookayama campus and optimized UAV flight paths with PSO (Particle Swarm Optimization). Results show that the HYBRID model achieved the highest accuracy, limiting the maximum error to 20 m. Sequential estimation improved accuracy for high-error sources, particularly when RSSI was used first, followed by AOA or HYBRID. Future work includes estimating unknown frequency sources, refining sequential estimation, and implementing cooperative localization. Full article

Amphipterygium adstringens (cuachalalate) is a medicinal plant widely used in traditional Mexican medicine for its anti-inflammatory, gastroprotective, and antimicrobial properties. In this study, we applied qualitative and quantitative NMR-based metabolomics profiling, combined with multivariate statistical analyses, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and S-plots, to evaluate the chemical composition and authenticity of A. adstringens samples collected from different commercial sources sold in Mexico City. Metabolomic profiles in organic and aqueous extracts revealed highly similar spectral patterns among all collected samples, supporting the consistency of commercially available A. adstringens in Mexico. The presence of 3α-hydroxy-masticadienoic acid (3α-HMDA) and anacardic acids, biomarkers of the genus, was confirmed by ¹H NMR in hexane extracts; in the aqueous extract they were not observed with the same analytical platform. These findings suggest that the traditional infusion method may not effectively extract the above-mentioned key bioactive compounds. This approach enhances quality control and ensures the reliability of A. adstringens products in the commercial market. Full article

The present study analyzed the growth pattern of the stingray Urotrygon chilensis caught as bycatch by the shrimp fishery in the southeastern Mexican Pacific. From January to December 2012, the thoracic vertebrae of 491 females and 205 males were collected. Female ages ranged from 0 to 14 years, whereas male ages ranged from 0 to 12 years. The marginal increment and edge analyses suggested the annual formation of growth bands in the vertebrae. The size-at-age data were analyzed using the multimodel inference approach; six candidate growth models were compared, including models with a theoretical age-at-zero total length, mean size-at-birth, and generalized models. Based on Akaike’s information criterion, the best statistical fit to the size-at-age data was the two-phase Gompertz growth model (k = −0.13, G = 1.59, $L_0$ = 10.40) for males and the two-parameter Gompertz growth model (k = 1.42, α = 0.15, $L_0$ = 10.90) for females. In this study, we compare the growth parameters among batoid species, finding that U. chilensis has a relatively short lifespan, slower growth, and that females are larger than males. Full article

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), poses a significant threat to wheat production in China. Previous epidemic studies have demonstrated the potential of high genetic diversity in the southwest regions of China. Among this epidemic region, the eastern Xizang (Tibet) region holds particular significance, as both wheat and barley crops are susceptible to Pst. However, limited information exists regarding the level of population genetic diversity, reproduction model, and migration patterns of the rust in eastern Xizang. The present study seeks to address this gap by analyzing 146 Pst isolates collected from the Basu, Zuogong, and Mangkang regions, genotyping by the 20K target Gene Array (Genobait). Our results showed relatively low genotypic diversity in the Basu region, while the highest genetic diversity was observed in the Mangkang area. Structural analysis revealed the abundance of admixed groups in Mangkang, which exhibited this population occurred due to sexual recombination between two different ancestor groups. Gene flow was observed between Zuogong and Basu populations, but it almost did not occur between Mangkang and Zuogong/Basu populations. This region is the world’s highest-altitude epidemic area, thus facilitating the evolution of the rust and possessing the potential to transmit newly evolved Pst races to lower wheat-growing regions. Implementing disease management strategies in this area is of potential importance to prevent the transmission of Pst races to other parts of Xizang, even neighboring regions possibly. This study facilitates our understanding of epidemiological and population genetic knowledge and the evolution of Pst in Xizang. Full article

Background/Objectives: Digital transformation is reshaping dentistry by improving clinical efficiency, diagnostic accuracy, and patient care. However, the adoption of digital technologies in dental clinics varies widely, influenced by multiple factors, including technology readiness. This study aimed to assess the relationship between technology readiness and digital technology adoption among German dentists, focusing on the impact of clinic characteristics and professional development. Methods: A cross-sectional survey was conducted among 200 licensed German dentists. Technology readiness was measured using the validated Technology Readiness Index (TRI 2.0), encompassing four dimensions: optimism, innovativeness, discomfort, and insecurity. Data on the current use of digital technologies were collected, including digital radiography, CAD/CAM systems, AI-supported tools, and patient management solutions. Statistical analyses included correlation and quartile-based comparisons to identify patterns and significant associations. Results: Clinics with higher TRI scores demonstrated significantly greater adoption of digital technologies. Larger clinics (MVZs) showed higher levels of digital integration compared to solo practices. Younger dentists and those engaged in continuous professional development exhibited higher technology readiness and usage of advanced digital tools. No significant gender-based differences were identified in technology readiness or digital adoption. While basic technologies like digital radiography and CAD/CAM systems were widely used, AI-based diagnostics and 3D printing remained underutilized. Key barriers included financial constraints and limited training opportunities. Conclusions: Technology readiness plays a critical role in shaping digital adoption in dental clinics. The findings highlight the need for targeted support, especially for smaller clinics, through professional training and investment in digital infrastructure. This study contributes to a better understanding of digital transformation in dentistry and supports strategies aligned with global health goals to improve access to digital care. Full article

From the urban sustainability perspective and from the steps essential for regulating/balancing the microclimate features, the creation and maintenance of urban green spaces (UGS) are vital. The UGS include vegetation of any kind in urban areas such as parks, gardens, vertical gardens, trees, hedge plants, and roadside plants. This “urban green infrastructure” is a cost-effective and energy-saving means for ensuring sustainable development. The relationship between urban landscape patterns and microclimate needs to be sufficiently understood to make urban living ecologically, economically, and ergonomically justifiable. In this regard, information on diverse patterns of land use intensity or spatial growth is essential to delineate both beneficial and adverse impacts on the urban environment. With this background, the present study aimed to address water requirements of UGS plants and trees during the non-rainy months from Panaji city (Koppen classification: Am) situated on the west coast of India, which receives over 2750 mm of rainfall, almost exclusively during June–September. During the remaining eight months, irrigating the plants in the UGS becomes a serious necessity. In this regard, the daily water requirements (DWR) of 34 tree species, several species of hedge plants, and lawn areas were estimated using standard methods that included primary (field survey-based) and secondary (inputs from key-informant survey questionnaires) data collection to address water requirement of the UGS vegetation. Monthly evapotranspiration rates (ET_o) were derived in this study and were used for calculating the water requirement of the UGS. The day–night average ET_o was over 8 mm, which means that there appears to be an imminent water stress in most UGS of the city in particular during the January–May period. The DWR in seven gardens of Panaji city were ~25 L/tree, 6.77 L/m² hedge plants, and 4.57 L/m² groundcover (=lawns). The water requirements for the entire UGS in Panaji city were calculated. Using this information, the estimated total daily volume of water required for the entire UGS of 1.86 km² in Panaji city is 7.10 million liters. The current supply from borewells of 64,200 L vis a vis means that the ET_o-based DWR of 184,086 L is at a shortage of over 2.88 times and is far inadequate for meeting the daily demand of hedge plants and lawn/groundcover. Full article

Silicone structural glazing (SSG) sealants are crucial sealing materials in modern building curtain walls, whose performance degradation may lead to functional and safety issues, posing significant challenges to building safety maintenance. This study comprehensively investigated the effects of temperature, humidity, stress, and ultraviolet (UV) irradiance on the durability of SSG sealants through multi-gradient matrix aging tests, revealing the influence patterns of these four aging factors on tensile bond strength (TBS). Based on aging test data and degradation patterns, a novel degradation model for TBS aging was established by incorporating all four aging factors as variables, enabling the model to reflect their combined effects on TBS degradation. The unknown parameters in the model were calculated using the Markov chain Monte Carlo (MCMC) algorithm and validated against experimental data. A recursive algorithm was developed to predict TBS degradation under actual service conditions based on the degradation model and environmental records, with verification through outdoor aging tests. This study established a service life prediction methodology that combines the degradation model with environmental data through recursive computation and standard-specified strength limits. The results demonstrate that increasing temperature, humidity, stress, and UV irradiation accelerates TBS changes, with influence intensity ranking as UV irradiation > temperature > humidity > stress. Synergistic effects exist among all four factors, where UV irradiation shows the most significant coupling effect by amplifying other factors’ combined impacts, while UV’s primary influence manifests through such synergies rather than independent action. Among temperature, humidity, and stress combined effects, temperature contributes approximately 50%, temperature–humidity interaction about 35%, with temperature-related terms collectively accounting for 90%. The degradation model calculation results show excellent agreement with experimental data (R² > 0.9, MAE = 0.019 MPa, RMSE = 0.0245 MPa). The characteristic TBS minimum value considering material discreteness and strength assurance rate serves as a reliable criterion for service life evaluation. The proposed prediction method provides essential theoretical and methodological foundations for ensuring long-term safety and maintenance strategies for glass curtain walls. Full article

This study explored the impact of lifestyle habits and sensory processing patterns on sleep quality by analyzing body movements (BMs) during the first and last 3 h of sleep in toddlers. We collected cross-sectional data about sleep-related habits from 58 toddlers using a mobile application. Actigraphy measured BMs during nighttime sleep and 1 h before bedtime, as well as sleep latency, over 8 consecutive days. The Infant/Toddler Sensory Profile was used to assess the toddlers’ sensory processing patterns. The participants had a mean age of 22.0 ± 2.0 months. BMs were significantly lower during the first 3 h of sleep. Longer sleep latency was significantly associated with media use and higher activity levels before bedtime. Ending a nap earlier and consuming a substantial breakfast were correlated with lower BMs during the first 3 h of sleep. Auditory and oral sensory scores were positively correlated with BMs during the first 3 h of sleep. However, no specific factors related to lifestyle habits or sensory processing patterns were found to correlate with BMs during the last 3 h of sleep. Lifestyle habits and sensory processing patterns have a significant impact on toddlers’ sleep quality, emphasizing the importance of appropriate routines and environments. Full article

Maize (Zea mays L.) silage in the irrigated and flat areas of Italy represents the most important large ruminant feed crop due to the high dry matter yield and nutritive value per hectare. The aim of the investigation was to evaluate the chemical composition and the in vitro fermentation patterns of five maize varieties (Tiesto, R700 1, MAS 78.T, DKC 7074 and KWS Kantico) freshly chopped and preserved via ensiling. The results indicated that the chemical composition was not significantly different among varieties. The substrates were incubated for 72 h with buffered rumen fluid collected from cow. The ensiling process slightly reduced gas production and fermentation kinetics, likely due to the consumption of soluble sugars during fermentation. Organic matter loss (OM loss) differed significantly (p < 0.01) among varieties in ensiled maize, correlating with their neutral detergent fiber (NDF) content. While total volatile fatty acid (VFA) production showed no significant differences between varieties, the buffer capacity ratio (BCR), an indicator of protein degradation, varied significantly. Ammonia production (NH₃) was significantly higher in ensiled samples, supporting previous findings that ensiling increases non-protein nitrogen (NPN) due to microbial proteolysis and plant enzyme activity. The gas production profiles and fermentation rates over time showed minor differences between fresh and ensiled samples, with fresh material exhibiting faster fermentation kinetics due to the presence of soluble sugars. These findings highlight the importance of evaluating maize silage quality to optimize ruminant nutrition and feed efficiency. Full article

This study explores the thematic structure and editorial focus of the digital media landscape in Bulgaria by analyzing one year of online news publications from eight major media outlets. The data were collected through a custom-built web scraping application developed in Java, which enabled the automated extraction and processing of full-text articles from publicly accessible news websites. The structured dataset, generated during the scraping process, records word-level occurrences in both article titles and bodies, along with publication dates and URLs. By applying lexical frequency analysis and temporal tracking, this study identifies the most frequently used words and platform-specific usage patterns. The findings reveal clear distinctions in editorial focus between public broadcasters, private national media, and international outlets. Additionally, the analysis highlights how title construction and word prominence vary depending on platform type and media strategy. This study demonstrates the potential of web scraping and computational text analysis as scalable tools for investigating media systems in small and transitional democracies. Full article

This work presents the development and implementation of a remote UV radiation monitoring station using LoRaWAN technology at the Universidad de las Américas. The main objective was to establish a system capable of measuring UV radiation levels through solarimeters, ensuring the remote transmission of data to protect the health and safety of students and staff exposed to solar radiation. To achieve this, several activities were conducted, including analyzing the architecture and communication components of LoRaWAN technology, designing a prototype based on this architecture, implementing the prototype based on the proposed design, and conducting functional tests to validate the system’s operability. The system included the installation of a solarimeter and a receiver or gateway, configured to operate from 8 a.m. to 6 p.m. The data collected by the prototype were validated through comparisons with measurements from the environmental monitoring system of the Secretariat of Environment of the Metropolitan District of Quito, which allowed for the verification of the prototype’s reliability. With this system, it was possible to identify patterns of high UV radiation and calculate error percentages in comparison with reference systems. Full article

Precise analysis of electroencephalogram (EEG) signals is critical for advancing the understanding of neurological conditions and mapping brain activity. However, accurately visualizing brain regions and behavioral patterns from neural signals remains a significant challenge. The present study proposes a novel methodology for the automated calculation of the average power of EEG signals, with a particular focus on the beta frequency band which is known for its pronounced activity during cognitive tasks such as 2D content engagement. An optimization algorithm is employed to determine the most appropriate digital filter type and order for EEG signal processing, thereby enhancing both signal clarity and interpretability. To validate the proposed methodology, an experiment was conducted with 22 students, during which EEG data were recorded while participants engaged in cognitive tasks. The collected data were processed using MATLAB (version R2023a) and the EEGLAB toolbox (version 2022.1) to evaluate various filters, including finite impulse response (FIR) and infinite impulse response (IIR) Butterworth and IIR Chebyshev filters with a 0.5% passband ripple. Results indicate that the IIR Chebyshev filter, configured with a 0.5% passband ripple and a fourth-order design, outperformed the alternatives by effectively reducing average power while preserving signal fidelity. This optimized filtering approach significantly improves the accuracy of neural signal visualizations, thereby facilitating the creation of detailed brain activity maps. By refining the analysis of EEG signals, the proposed method enhances the detection of specific neural behaviors and deepens the understanding of functional brain regions. Moreover, it bolsters the reliability of real-time brain activity monitoring, potentially advancing neurological diagnostics and insights into cognitive processes. These findings suggest that the technique holds considerable promise for future applications in brain–computer interfaces and advanced neurological assessments, offering a valuable tool for both clinical practice and research exploration. Full article