Search Results (46,367)

Social networks are now integral to corporate strategy and daily social life. They enable the rapid and extensive dissemination of information, proving highly effective for promoting hotel marketing content. Consequently, they facilitate interaction and engagement between hotels and their customers, serving both advertising and evaluation purposes. This study aims to analyse the use of the Facebook social network by luxury wine hotels located in countries associated with the Mediterranean Diet. An analytical model examining the variables of content, interactivity, and visibility was employed. A total of 17 luxury hotel pages were analysed, with data collected using the Karma Fanpage platform, an online tool for social media analysis and monitoring. The findings indicate that the majority of profile posts were photographs, and that this format generated the highest number of user reactions. It is recommended that hotels publish more photographic content to foster greater engagement and conduct further analysis of the specific types of posts that elicit the most reactions. Full article

Producing high-quality analytical reports for the environmental domain is typically time-consuming and requires significant human expertise. This paper describes MeteoChat, a semi-automatic framework for efficiently generating specialized environmental reports from heterogeneous environmental data. MeteoChat utilizes a Large Language Model (LLM) fine-tuned and integrated with Retrieval-Augmented Generation (RAG). The system’s core is its plug-and-play philosophy, which separates analytical reasoning from the data source and the report’s intended audience. The fine-tuning phase uses data-agnostic, parameterized question–context–answer triples defined by an environmental expert to teach the LLM domain-specific analytical logic and audience-appropriate communication styles. Subsequently, the RAG phase integrates the model with actual datasets, which are processed via an Extract–Transform–Load (ETL) workflow to generate statistical summaries. This architectural separation ensures that the same reporting engine can operate on different sources, such as meteorological time series, satellite imagery, or geographical data, without additional training. Users interact with the system via a web-based conversational interface, where responses are tailored for either technical experts (using explicit calculations and tables) or the general public (using simplified, narrative language). MeteoChat has been tested with real data extracted from the micrometeorological network of ARPA Lazio. Full article

Background: Current sepsis guidelines recommend the best supportive treatment for severe sepsis, but they are limited on the effectiveness of immunomodulatory therapies. Recent data suggest that IgM-enriched immunoglobulin preparations may decrease mortality, but the exact pathomechanism remains unknown. The present experimental study aims to test the hypothesis that IgM-enriched immunoglobulin may improve hemodynamics in E-coli-induced severe sepsis. Subjects and methods: Sepsis was induced in the E. coli bacteriemia (n = 8), E. coli-parallel Pentaglobin treatment (PR-PG; n = 8), and E. coli-delayed Pentaglobin treatment (D-PG; n = 8). Sepsis was induced in the sepsis, PR-PG, and D-PG groups by infusing 38 mL of an E. coli suspension (2.5 × 10⁵/mL) over 3 h. The PR-PG group received a 0.75 g/kg Pentaglobin bolus over 20 min concurrently with the start of E. coli infusion. The D-PG group was given a 0.67 g/kg Pentaglobin bolus one hour after starting E. coli, followed by a continuous infusion at 0.02 g/kg/h for 240 min. Hemodynamic parameters were monitored every 2 h using a pulse contour cardiac output monitoring technique (PiCCo™). Results: Heart rate increased in all groups to varying extents. Mean arterial pressure (MAP) remained stable in controls but declined in untreated sepsis. Both Pentaglobin-treated groups showed higher MAP than untreated septic animals. Mild cardiac index increases occurred in controls and untreated sepsis, whereas the treated groups maintained a consistently elevated CI after Pentaglobin administration. Systemic vascular resistance index (SVRI) transiently increased in controls before normalizing, while untreated septic animals experienced continuous SVRI decline. Treated animals showed an initial transient SVRI rise followed by a decline; yet, SVRI remained higher than in untreated sepsis. Conclusions: IgM-enriched immunoglobulin led to a slight stabilization of some hemodynamic parameters, probably due to the reduced extpnfiravasation of fluids into the interstitium and, hence, had an effect on preload. Full article

This study analyzed the hydroclimatic functioning of the Lake Fitri basin (Chad) by combining rainfall records, in situ hydrological observations, water balance analysis, and spatial remote sensing data. Results show a strong Sahelian climatic control, with rainfall concentrated in a short-wet season (July–September) and potential evapotranspiration largely exceeding precipitation. Batha River flows are highly seasonal, generating short flood pulses that drive lake level fluctuations and aquifer recharge. Water balance estimates indicate that recharge is limited and episodic (approximately 70–120 mm in 2020), representing only 14–24% of annual rainfall, occurring almost exclusively during extreme rainfall events. Compared with Lake Chad, Lake Fitri is more directly sensitive to local rainfall variability, reflecting its dependence on a single tributary. Overall, the findings underline the fragility of this hydrosystem and the need for reinforced monitoring and integrated management to ensure sustainable water resources under increasing climatic variability. This work constitutes the initial reference for the hydroclimatic characterization of Lake Fitri, thanks to a methodology combining in situ and satellite data. Full article

Introduction: The availability of toxicokinetic data is critical for detecting and monitoring the intake of psychoactive substances. Timely characterization of novel psychoactive substances (NPS) is particularly important to assess their abuse potential and inform public health responses. Methods: Toxicometabolomics offers a powerful approach to characterize xenobiotic metabolism through high-resolution profiling of biochemical transformations. It thus allows the finding of exogenous biomarkers, such as new drug metabolites, and endogenous biomarkers, which could be indications of acute drug ingestions or sample manipulation, as well as offering information on the mode of action of drugs. In this study, we applied a liquid chromatography–high-resolution mass spectrometry workflow to investigate the toxicometabolomics of two N1-sulfonated N,N-dimethyltryptamine derivatives with potential for both therapeutic use and recreational abuse. Results: Zebrafish (Danio rerio), an increasingly valuable model for preclinical pharmacology and toxicology studies, along with pooled human liver S9 fractions were used to elucidate metabolic pathways and identify key phase I and phase II biotransformations. Furthermore, untargeted metabolomics revealed significant downregulation of L-threonine associated with compound exposure. Conclusions: These findings advance the current understanding of tryptamine metabolism and underscore the utility of toxicometabolomics in the analytical evaluation of NPS. Full article

Background/Objectives: Cutaneous leishmaniasis (CL) remains a major public health and economic challenge in Morocco, where its transmission dynamics are increasingly influenced by climatic variability. This study aimed to evaluate the impact of meteorological factors on CL incidence in the province of Essaouira, a high-incidence region, to identify the environmental drivers behind recent epidemic trends. Methods: Epidemiological data (N = 834 cases) were collected from the Hygiene and Health Laboratory of Essaouira for the period between January 2014 and December 2023. Climatic variables were obtained from the Moroccan Directorate of National Meteorology. Data were analyzed at annual, seasonal, and monthly scales using the Spearman rank correlation in R 4.5.0 software to account for non-normal distributions and non-linear associations. Results: CL incidence remained stable from 2014 to 2021 before an unprecedented surge in cases during 2022–2023. Annual analysis indicated that warm and dry years pose a higher risk, with incidence positively correlated with temperatures and negatively associated with humidity and precipitation. Monthly results identified a biphasic regulatory mechanism: a winter hydric constraint phase with strong negative correlations with January rainfall and humidity (p < 0.05), followed by a summer thermal promotion phase where minimum temperature (T_min) emerged as the dominant driver (rho = 0.53), peaking in September (rho = 0.59). Conclusions: Our findings confirm the significant influence of climatic factors on CL incidence through complex seasonal dynamics. These results highlight the necessity of integrating high-resolution meteorological monitoring and predictive modeling into public health surveillance to anticipate future outbreaks in the context of increasing Mediterranean aridification. Full article

Groundwater, a vital resource for drinking water supply, must be managed sustainably to ensure its availability and quality. In France, the SISE-Eaux database on water intended for human consumption, archived by the Regional Health Agencies (ARS) since 1990, constitutes a rich source of information. This study focused on the groundwater of the Nouvelle-Aquitaine region, the largest administrative region in metropolitan France, covering 84,061 km² with 6 million inhabitants. It is based on a 22-year data extraction, resulting in a matrix of 121,649 observations and 51 physico-chemical and bacteriological parameters. Following logarithmic transformation of the data and fitting of variograms using the mean value of each parameter for each sampling point, the spatial distribution of numerous parameters across the region is presented. From this initial sparse matrix, a dense matrix of 23,319 samples (rows) and 15 key parameters (columns) was selected for a multivariate approach. A Principal Component Analysis (PCA) was used to condense the information and create summary maps capturing over 68% of the information contained in the dense matrix. The combined results of the multivariate analysis (dense matrix) and the distribution of individual parameters (sparse matrix) highlight the diversity of sources contributing to the spatial variability of groundwater, such as the role of lithology, the origin and pathways of fecal contamination, and the influence of redox processes. Neither the large size of the study area nor the high number of parameters proved to be an obstacle to the analysis. The understanding of ongoing processes and the factorial axis distribution maps, which enable the spatial representation of these mechanisms, can be used to facilitate groundwater monitoring and protection. Full article

Frequent accidents involving heavy trucks and the inadequacy of existing dynamic monitoring technologies pose significant challenges to accurate early warning risk and safety management. To address these issues, this study proposes a multi-dimensional risk measurement and real-time early warning method for heavy truck driving behavior based on trajectory data. By extracting multi-dimensional trajectory features such as lateral position, speed, and acceleration, quantitative indicators for driving stability and car-following risk were constructed. Integrated with the CRITIC objective weighting method and the K-means++ clustering algorithm, a comprehensive risk measurement model was established to systematically characterize the dynamic evolution of driving behavior, overcoming the limitations of single-dimensional risk analysis. Experimental results based on the CQSkyEyeX trajectory dataset demonstrate that the proposed method categorizes driving behavior into six risk levels. Low-risk behavior accounted for 66.70%, while medium- to high-risk behaviors mainly included serpentine driving (26.69%) and close following (4.18%). High-risk behavior constituted only 0.03%. A multi-strategy real-time warning mechanism was further developed, achieving a warning accuracy of 98.36% with the final-value method, significantly outperforming the mode method (83.62%). The outcomes of this study demonstrate the effectiveness and practical utility of the proposed model for risk identification and early warning. On a practical level, the developed risk classification framework and management strategy establish a quantitative basis for differentiated supervision, enabling a closed-loop management process of “identification–intervention–optimization”. Future work will focus on three key directions: integrating multi-source data, extending the model to other typical operational scenarios, and incorporating advanced machine learning techniques to further enhance its generalization capability and warning accuracy. Overall, this research provides a feasible technical pathway for the precise quantification, dynamic monitoring, and tiered intervention of driving behavior in heavy-duty trucks, thereby contributing to enhanced safety in road freight transportation. Full article

With the continuous advancement of the “dual-carbon” strategy, the penetration of renewable energy sources such as wind and photovoltaic (PV) power has steadily increased, imposing more stringent requirements on the safe and stable operation of modern power systems. As the core components of these systems, critical electrical devices operate under harsh conditions characterized by high voltage, strong electromagnetic interference (EMI), and confined high-temperature environments. Their operating status directly affects the reliability of the power supply, and any fault may trigger cascading failures, resulting in significant economic losses. To address the issues of low inspection efficiency, limited fault-identification accuracy, and unstable data transmission in strong-EMI environments, this study proposes an intelligent inspection system for power equipment based on multi-technology integration. The system incorporates a redundant dual-mode wireless transmission architecture combining Wireless Fidelity (Wi-Fi) and Fourth Generation (4G) cellular communication, ensuring reliable data transfer through adaptive link switching and anti-interference optimization. A You Only Look Once version 8 (YOLOv8) object-detection algorithm integrated with Open Source Computer Vision (OpenCV) techniques enables precise visual fault identification. Furthermore, a multi-source data-fusion strategy enhances diagnostic accuracy, while a dedicated monitoring scheme is developed for the water-cooling subsystem to simultaneously assess cooling performance and fault conditions. Experimental validation demonstrates that the proposed system achieves a fault-diagnosis accuracy exceeding 95.5%, effectively meeting the requirements of intelligent inspection in modern power systems and providing robust technical support for the operation and maintenance of critical electrical equipment. Full article

Romerillo (Retrophyllum rospigliosii), a vulnerable conifer native to the cloud forests of Cajamarca, Peru, persists in small remnants at high altitudes in San Ignacio province, where its integration into agroforestry systems may support both conservation and sustainable production. This study aimed to model the growth of R. rospigliosii associated with coffee (Coffea arabica L.) using diameter and height as indicators. Field data were collected over 18 months in two experimental plots and the study analyzed 329 individuals selected from 600 initially planted, with monthly monitoring to evaluate early growth and survival dynamics. The data were analyzed with nonlinear mathematical models, including Schumacher, Chapman–Richards, and Weibull, with model selection based on goodness-of-fit and prediction statistics such as R², AIC, and BIC. Results showed that Schumacher provided the best performance for height (R² = 0.98, AIC = 27,978.54), while Weibull (R² = 0.80, AIC = 27,204.63) and Chapman–Richards (R² = 0.80, AIC = 27,207.97) also yielded consistent estimates. For diameter, Schumacher was the most accurate (R² = 0.92, AIC = 2627.87). Survival analysis revealed significant differences between plots (p = 0.011), with higher survival at 1820 m (87.8% at 18 months) compared to 1540 m (77.3%). These findings indicate that the Schumacher model is most suitable for growth estimation, while altitude plays a critical role in survival, underscoring its importance in establishing R. rospigliosii within coffee-based agroforestry systems. Full article

Coal mining is a major source of methane emissions globally, and monitoring these emissions has become a sustained area of interest in both scientific research and policy-making. Coal mine methane emissions typically manifest as discrete point sources, such as individual mines or ventilation shafts, and spatially concentrated area sources, such as mining clusters. In recent years, satellite remote sensing technology has become a key tool for monitoring and assessing methane emissions from coal mines. Notable progress has been made in quantifying emissions through point-source inversion using high-resolution satellite data, such as GF-5B/AHSI, and in estimating regional-scale area-source emissions using wide-swath instruments, such as S5P/TROPOMI. However, there remains a lack of systematic comparison between inversion results derived from these two types of satellite data with differing spatial resolutions. This study comprehensively analyzes the strengths and limitations of the GF-5B/AHSI and S5P/TROPOMI sensors for quantifying methane emissions. It conducts a spatiotemporal comparative analysis of point-source and area-source methane emission datasets from the coal-mining regions of Shanxi Province. The research aims to clarify the intrinsic relationship between remote-sensing data at different observational scales and to systematically evaluate how prior information on emission-source locations influences emission quantification results. The comparative analysis between TROPOMI grid-level emissions and GF-5B/AHSI point-source emissions indicates that TROPOMI-gridded emission data, owing to its longer time series, can more effectively characterize the annual-average methane emission levels in mining areas. Meanwhile, high-resolution observations from GF-5B/AHSI show distinct advantages in detecting small-scale plumes and attributing emissions to specific facilities. Although the regional-average emissions derived from TROPOMI are significantly higher than point-source emission rate estimates, their data ranges overlap within their uncertainty intervals, demonstrating substantial consistency between the monitoring results of the two methods. Furthermore, the study reveals that when key emission facilities, such as ventilation shafts, are located far from the core operational areas of mines, relying solely on point-source observations may not fully capture the spatial distribution pattern of methane emissions at the mine scale. Full article

Background/Objectives: Amyotrophic lateral sclerosis is a progressive neurodegenerative disease in which respiratory failure is the leading cause of death. Mechanical ventilation improves both survival and quality of life; however, the prognostic implications of built-in ventilator software monitoring remain insufficiently characterized. The aim of the study was to determine whether built-in ventilator software-based monitoring is associated with enhanced survival in amyotrophic lateral sclerosis subjects. Methods: Cohort study of amyotrophic lateral sclerosis subjects, stratified into two groups: those monitored through detailed built-in ventilator software and those not monitored. Clinical and ventilatory data were systematically evaluated during a 24-month follow-up. Results: Among 120 ALS subjects (57 detailed built-in ventilator software, 63 non-detailed ventilator software), median survival from diagnosis was significantly longer in the detailed built-in ventilator software group (3.42 vs. 2.12 years; p < 0.001). Survival from mechanical ventilation initiation was also significantly longer in the built-in ventilator software group (2.79 years vs. 0.78 years). Greater daily mechanical ventilation usage was associated with shorter survival (p < 0.003). Paradoxically, subjects with the lowest proportion of spontaneous inspirations exhibited superior survival outcomes (p = 0.04). Neither persistent leaks nor asynchronies were independently predictive of survival. Conclusions: BVS-monitoring was associated with improved survival in amyotrophic lateral sclerosis subjects receiving home mechanical ventilation. Its integration into clinical practice may enable timely, data-driven ventilatory adjustments, ultimately contributing to more individualized and optimized patient management. Full article

Focusing on the construction of a 58-m-diameter double-layer steel space frame dome at the Han Culture Museum Assembly Hall, this study addresses scheme selection and safety control challenges in staggered jacking of large-span spatial structures. A three-dimensional finite element model in MIDAS Gen simulated the three-stage jacking process to compare three temporary support layouts. Numerical evaluation metrics included maximum vertical displacements, peak internal forces, the proportion of members undergoing stress state transitions, and spatio-temporal evolution of stress concentrations. Scheme B demonstrated superior performance, reducing peak vertical displacement by 44% under critical conditions, lowering peak stresses, and enabling more uniform internal force redistribution—effectively mitigating tension–compression cycling and buckling risks. Crucially, only nodal displacements and support elevations were monitored in situ using a 3D system based on magnetic prisms and total stations; no strain or force measurements were conducted due to practical constraints during construction. Monitoring data show good agreement with simulated displacements and support elevations under Scheme B, validating the model’s deformation response. However, localized deviations—including a 29 mm deflection discrepancy and elevation errors up to 28 mm—reveal the influence of uneven boundary conditions, with potential implications for long-term structural behavior. The findings confirm that numerical predictions of deformation are reliable, while internal forces remain unvalidated by field data. The integrated approach of “scheme comparison–construction simulation–full-process displacement monitoring” proves effective for safety control and decision-making in complex jacking operations, offering a transferable framework for similar large-span double-layer space frame projects. Full article

In September 2021, significant changes in the geophysical and geochemical parameters on Vulcano Island were recorded by the surveillance network activities and periodic surveys. Between October 2021 and June 2024, additional surveys were conducted to acquire LIDAR, thermal, and RGB datasets for the generation of Digital Terrain Models (DTMs), orthophotos, and fumarole field maps. These data were collected using DJI Matrice 300 UAS platforms. Precision positioning was ensured through a POS/NAV RTK georeferencing approach. The instrumentation included Genius R-Fans-16 and DJI Zenmuse L1 laser scanners for structural mapping, alongside Zenmuse H20T infrared cameras for the thermal detection of potential instabilities on the volcano flanks, focused on the northern area and summit of Gran Cratere La Fossa, and these were subsequently repeated in May 2022, October 2022, October 2023, and June 2024. Additionally, 3D reconstruction targeted morphological variations in unstable areas like the cone top, Forgia Vecchia, and the 1988 landslide site. In May 2022, anomalous degassing in the Eastern Bay led to increased gas and hydrothermal fluid emissions, causing water whitening in front of Baia di Levante. Optical-thermal monitoring, both on land and at sea, detected multiple hydrothermal gas streams, aiding in assessing the magnitude and areal extension of fumarolic fields. These findings contribute to establishing a comprehensive monitoring approach for understanding the volcanic unrest evolution cost-effectively and safely. Full article

Measuring water motion is essential for oceanography, coastal engineering, and marine environmental monitoring. A wide range of sensing technologies is used to quantify water velocity, wave motion, and flow dynamics, each suited to specific spatial and temporal scales. This paper presents a comprehensive review of modern sensor technologies for marine flow measurement, covering mechanical, electromagnetic, pressure-based, acoustic, optical, MEMS-based, inertial, Lagrangian, and remote-sensing approaches. The operating principles, strengths, and limitations of each technology are examined alongside their suitability for different environments and deployment platforms, including moorings, buoys, vessels, autonomous underwater vehicles, and drifters. Special attention is given to rapidly advancing fields such as MEMS flow sensors, multi-sensor fusion, and hybrid systems that combine inertial, acoustic, and optical data. Applications range from high-resolution turbulence measurements to large-scale current mapping and wave characterization. Remaining challenges include biofouling, performance degradation in energetic shallow waters, uncertainties in indirect velocity estimation, and long-term calibration stability. By synthesizing the state of the art across sensing modalities, this review provides a unified perspective on current technological capabilities and identifies key trends shaping the future of marine flow measurement. Full article