Search Results (3,462)

Background and Aim: Passive smoking (PS) is a well-established risk factor associated with systemic and oral health impairments in children. However, its influence on perioperative physiological stability and recovery profiles during pediatric dental sedation remains insufficiently elucidated. This study investigated the association between PS exposure and perioperative vital parameters, recovery characteristics, and emergence behavioral outcomes in children undergoing dental extractions under sedation. Methods: This prospective cohort study (ClinicalTrials.gov: NCT06780189) included 100 ASA I children aged 4–6 years scheduled for primary molar extraction under midazolam-remifentanil-propofol sedation. Participants were stratified into three groups: no exposure, caregiver and household exposure, and household exposure only. An exposure-related relationship was evaluated based on daily household cigarette consumption. Perioperative vital signs (HR, blood pressure, and SpO₂) were continuously monitored. Postoperative recovery and emergence profiles were assessed using the Modified Aldrete Recovery Score (MASS), Richmond Agitation–Sedation Scale (RASS), and Pediatric Anesthesia Emergence Delirium (PAED) scale. Results: Children exposed to PS demonstrated significantly lower SpO₂ levels across all perioperative phases compared with non-exposed counterparts (p < 0.001), reflecting an exposure-related effect. In contrast, no statistically significant differences were observed in cardiovascular parameters (p > 0.05). Recovery time was significantly prolonged in PS-exposed children (p = 0.002). Furthermore, PS exposure was associated with significantly higher RASS and PAED scores, indicating increased agitation and emergence delirium (p < 0.001). Conclusions: Passive smoking adversely affects perioperative oxygenation, delays recovery, and exacerbates emergence neurobehavioral disturbances in children undergoing dental sedation. Environmental tobacco exposure must be integrated into preoperative risk assessments. Full article

Tree root-system architecture is vital for forest resilience under rising climate stress, yet techniques like excavation are destructive, slow, and unsuitable for large surveys. We evaluated how Scots pine (Pinus sylvestris) root architecture varies across contrasting environments using non-invasive, high-resolution multichannel ground-penetrating radar (GPR). Plots in the Olkusz Forest District (southern Poland) spanned gradients of soil fertility and stand age. A multichannel radar array produced 3D subsurface volumes, from which two traits were derived: the 2D planar root extent and the 3D rooting-envelope volume. Generalized additive models linked these metrics to site, stand, and tree characteristics. Multichannel GPR revealed clear site-driven differences in root structure and delivered markedly better data quality than single-channel systems. Selective excavation of visible roots confirmed close agreement between radar estimates and true root positions. Root architecture shifted along the fertility gradient and depended strongly on tree size, stand density, and age: rooting volume increased with site productivity and diameter at breast height but declined with stand age and relative spacing. Overall, Scots pine shows strong adaptive plasticity, and multichannel GPR provides a powerful way to integrate below-ground traits into monitoring, modeling, and climate-smart forest management. Full article

The agricultural sector, particularly animal production, faces numerous unprecedented challenges driven by climate change, resource depletion, and an ever-growing global demand for quality food. These challenges are further compounded by the increasing environmental impact of livestock farming, including greenhouse gas emissions, overuse of water and land resources, and the destruction of vital ecosystems. Ensuring the sustainability of animal production systems while mitigating the negative environmental impacts of these factors is essential for future global food security. As the demand for animal-derived products continues to rise, there is a pressing need for innovations that can enhance productivity without compromising environmental integrity or animal welfare. Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize the animal production industry. AI-driven solutions offer promising avenues for optimizing production efficiency, enhancing animal health and welfare, and reducing the environmental footprint of livestock farming. Machine learning, sensor technologies, and advanced data analytics are being increasingly utilized to monitor and predict various aspects of animal farming, such as feed efficiency, disease prevention, and climate resilience. These technologies enable farmers to make data-driven decisions, fostering more sustainable and environmentally responsible practices. This review examines the integration of AI into animal production systems, emphasizing its applications in climate change mitigation, resource management, and advancing sustainability. The discussion addresses how AI technologies can be utilized to improve productivity while minimizing environmental impact and enhancing animal welfare. Additionally, the paper outlines future opportunities, challenges, and potential barriers to integrating AI technologies into livestock farming, thereby ensuring long-term sustainability amid global challenges. Full article

Gnathotrichus materiarius Fitch, 1858 is a North American ambrosia beetle that has progressively expanded its distribution across Europe during the twentieth and twenty-first centuries. The aim of this study was to assess its occurrence in Slovakia based on multi-year surveys conducted in the Záhorie region and to document its first national record. Monitoring was carried out in 2021 and 2025 in pine-dominated stands using traps baited with pheromone and kairomone attractants. No individuals were detected in 2021. In 2025, two adults of G. materiarius were captured at a single site during the first inspection of traps baited with a (+)-α-pinene and ethanol blend. A large volume of stored pine timber was present in close proximity to the capture site, providing suitable breeding material. The findings suggest a recent introduction or early stage of establishment in Slovakia. Although the species is regarded as a technical pest of coniferous wood, it does not colonize healthy trees and currently poses a limited risk to vital forest stands. Continued targeted monitoring is important to assess its further spread. Full article

Background: Fiberoptic bronchoscopy (FOB) is a procedure routinely performed in clinical practice for both diagnostic and therapeutic purposes. FOB frequently impairs respiratory function, which may exacerbate respiratory failure. Currently, conventional oxygen therapy (COT) is the most commonly used form of respiratory support; however, non-invasive ventilation (NIV) and high-flow nasal cannula (HFNC) are being used increasingly. The optimal settings and indications for NIV and HFNC in patients with respiratory acidosis undergoing FOB have not yet been determined. Methods: This is a prospective, multicenter, randomized controlled trial including two parallel study populations defined by the indication for bronchoscopy and the type of respiratory acidosis. Therapeutic FOB (Study 1): Patients with decompensated type 2 respiratory failure (pH < 7.35 and PaCO₂ > 45 mmHg) will be randomized to receive one of four methods of respiratory support during bronchoscopy: COT, NIV, HFNC, or invasive mechanical ventilation (IMV) (n = 315). Diagnostic FOB (Study 2): Patients with chronic respiratory acidosis (pH ≥ 7.35, PaCO₂ > 45 mmHg, and/or HCO₃⁻ > 27 mmol/L) will be randomized to receive COT, NIV, or HFNC during bronchoscopy (n = 210). Before FOB, patients in both groups will undergo arterial blood gas (ABG) analysis. During FOB, vital signs will be continuously monitored, including SpO₂, FiO₂, TcCO₂, ECG, and heart rate. After FOB, ABG analysis will be repeated, and study endpoints and complications, if any, will be recorded. The planned study period is from April 2026 to April 2029. Results: Based on the study results, we aim to evaluate the effectiveness and safety of different respiratory support strategies during flexible bronchoscopy, with the primary objective of comparing the rate of treatment failure among COT, HFNC, NIV, and IMV. Treatment failure is defined as the need for endotracheal intubation, premature termination of the procedure, or escalation of respiratory support. Additionally, we aim to identify the optimal NIV and HFNC settings, as well as complication rates in both study groups. Conclusions: The results of this study will help define the role of optimal respiratory support in patients with respiratory acidosis undergoing FOB, potentially leading to a shorter time from admission to diagnosis, better tolerance of the procedure, and faster recovery afterward. Full article

The transition to a low-carbon economy is the cornerstone of global sustainability, requiring high-emission enterprises to shift from carbon-intensive production to genuine green innovation. However, this study uncovers a significant structural impediment to this transition: the “defensive greenwashing” response to climate stress. Focusing on listed companies in China’s high-emission industries (2009–2024), we employ a Debiased Machine Learning (DML) framework and Causal Forest analysis to capture the non-linear impacts of multi-dimensional climate risks. Our findings reveal a robust “threshold-trigger” mechanism: once climate pressures—whether physical shocks or policy-induced transition risks—exceed corporate endurance levels, firms aggressively pivot toward strategic “information arbitrage” rather than substantive decarbonization. We identify a profound “capability paradox” in sustainability governance, where firms with higher digital maturity and resource slack leverage their technical prowess to “calibrate” sophisticated narratives, thereby widening the monitoring gap and distorting green asset pricing. Furthermore, CEO risk preference acts as a psychological accelerator, amplifying strategic decoupling, particularly under transition-risk-induced uncertainty. By demonstrating how climate stress inadvertently incentivizes symbolic compliance over sustainable transformation, this research offers critical micro-level insights for policymakers. These findings are vital for refining sustainability oversight and ensuring that capital allocation fosters a resilient, equitable transition toward true ecological and economic decoupling. Full article

Blood disorders encompass a wide range of diseases including anemia, hemophilia, thrombotic disorders, platelet dysfunction, and hematological cancers, making blood disorders a major global health concern. These conditions can impair processes vital to human physiology including oxygenation, coagulation, and immune defense. Hematologic malignancies, both chronic and acute, require timely diagnosis and ongoing disease monitoring for effective clinical management. Microfluidic technologies have emerged as promising alternatives to benchtop techniques for diagnosing and monitoring hematological disorders. For example, microfluidic assays can be used for the isolation and characterization of liquid biopsy markers such as rare cells, extracellular vesicles, and cell-free molecules to support disease management in a minimally invasive manner while the process automation afforded by microfluidics decentralizes healthcare, making it more accessible. Advances in lab-on-a-chip technologies, including large-scale fabrication methods and novel design strategies, will provide tools for the clinical validation of biomarkers and the translation of these technologies from the laboratory bench to the patient bedside. In this review, we will show that microfluidic devices enable disease monitoring via high-throughput analysis of liquid biopsy samples for the detection of rare disease-specific biomarkers found in blood, plasma, urine, etc., providing an alternative to standard benchtop testing using specimens secured via invasive bone marrow procedures, typically used for managing blood-based diseases. A key advantage of microfluidics is their ability to manipulate blood components at scales that closely mimic the body’s microvascular environment. Not surprisingly, microfluidic vascular models have been developed to replicate physiological rheology enabling quantitative assessment of blood cell deformability, aggregation, or clot formation. We provide a critical perspective on the use of the microfluidic “organ-on-chip” designed for blood disorders’ modeling and employed to recapitulate the blood cancer microenvironment. A summary of advances in microfluidic strategies for detection, diagnosis, drug screening, and mechanistic investigations of blood disorders, and future directions for precision testing, will be presented. Full article

Lake Villarrica, located in southern Chile, is a vital freshwater resource whose ecological status requires continuous evaluation. Chlorophyll-a (Chl-a) is a key indicator of phytoplankton biomass and estimating it using satellite sensors enables efficient and large-scale monitoring. This study compared the performance of different empirical models based on reflectance data obtained from atmospherically corrected satellite images using ACOLITE software (Generic Version 20231023.0), calibrated with in situ measurements of Chl-a collected during the spring and summer seasons between 2014 and 2024. For each sensor, the best combination of spectral bands was selected, and retrieval models were generated using a bootstrapping procedure with 1000 iterations to obtain robust regression coefficients; the final models were defined using the median of these coefficients. The top-performing model for Landsat-8 and 9 was based on a blue-red band combination (R² = 0.79, RMSE = 2.1 µg·L⁻¹, MAE = 1.2 µg·L⁻¹, n = 74). In contrast, the optimal model for Sentinel-2A utilized green and blue bands, yielding higher precision (R² = 0.75, RMSE = 0.8 µg·L⁻¹, MAE = 0.72 µg·L⁻¹, n = 112). In general, the results obtained through remote sensing reveal a gradual increase in Chl-a levels over the last decade, reflected in recurrent summer biomass increases primarily along the shoreline near the urban area of Pucón and in the vicinity of the Pucón River inflow into Lake Villarrica. These results support the development of an operational satellite-based monitoring framework for inland lake water quality assessment. Full article

Seagrasses are vital ecosystem engineers and habitat architects in coastal environments, with Posidonia oceanica in the Mediterranean playing a crucial role as an indicator of ecological health. As an endemic and vulnerable species, P. oceanica meadows are highly susceptible to environmental degradation, underscoring the importance of non-destructive monitoring techniques. Traditional SCUBA-based surveys are accurate but resource-intensive and difficult to scale, especially for estimating shoot density and leaf length. This study applies a conservative acoustic-based approach to assess Posidonia oceanica biometrics, habitat characteristics, and ecological status along the Turkish Levant coast. The method offers a non-destructive alternative to SCUBA surveys and addresses a regional knowledge gap in Mediterranean seagrass monitoring. Acoustic data collected during winter and summer 2019 along the Turkish Levant coast were analyzed to estimate seagrass biometrics and derive ecological indicators, with validation via SCUBA observations. Results show that acoustic methods can reliably estimate shoot density, leaf area index, and canopy height. They provide broad-scale coverage and efficiency, though further refinement is required to improve calibration across depths and substrates. While acoustic methods provide broad, non-invasive coverage, they are affected by spatial and temporal variability that SCUBA surveys capture more reliably. Calibration of the POSIBIOM (vers 1.1) algorithm was based on specimens collected at 15 m depth on rocky substrates. While this provided consistent regression relationships, it may limit accuracy when extrapolated to habitats such as sand, mud, or matte. This study represents the first high-resolution, spatiotemporal mapping of P. oceanica meadows and benthic habitats along a significant portion of the Turkish Levant coast using acoustics alone. Overall, the study highlights the potential of acoustics as a scalable, non-invasive tool for seagrass monitoring. This approach contributes to ecosystem-based management and conservation strategies in the Mediterranean. Future work will focus on refining models to address bottom type- and depth-dependent acoustic responses and improve biometric accuracy. Full article

Monitoring marine organisms plays a vital role in biodiversity conservation, marine environmental management, and fisheries resource management. However, the underwater environment is often low-light and turbid, leading to indistinct target boundaries. Moreover, the wide variety of marine organisms—with significant differences in color, scale, texture, and morphology—can easily result in missed detections. To address these challenges, this paper proposes a multi-class marine organism detection method using multi-scale attention-enhanced You Only Look Once 11 nano (YOLO11n). The method incorporates the Convolutional Block Attention Module (CBAM) into the YOLO11n network, enabling the model to better focus on key feature regions while effectively suppressing background noise interference in complex marine environments. In addition, the model is trained using the Complete Intersection over Union (CIoU) loss function, which enhances bounding box regression accuracy, especially in handling targets of varying scales. The effectiveness of the proposed method is validated on the publicly available BrackishMOT dataset. The proposed model achieves an overall mAP@0.5 of 0.481, computed as the average AP across six organism categories. Category-wise results indicate stronger performance on visually distinguishable targets, such as Jellyfish, Starfish, and Small fish, with AP values of 0.808, 0.678, and 0.677, respectively. In contrast, performance remains limited for rare or visually ambiguous categories. These results suggest that the proposed method is effective for multi-class marine organism detection, particularly when discriminative visual features are present. Full article

Acoustic emission (AE) is a vital non-destructive technique for monitoring damage in materials, yet its simulation via the Discrete Element Method (DEM) has historically been limited to material-scale analysis. This research presents a novel application of block-based DEM to simulate AE signals in masonry structures at the structural scale under quasi-static in-plane loading. Using a simplified micro-modeling approach, the study first validates the method by monitoring crack initiation and AE energy in single mortar bed joints under tensile and shear conditions. The methodology is then scaled to a large-scale masonry wall panel (1.835 × 1.170 × 0.15 m³) subjected to monotonic shear loading. A critical finding is the influence of local damping; a reduced damping ratio of 0.3 is recommended to preserve the kinetic energy necessary for capturing clear velocity signals. Numerical results show strong agreement with experimental force-displacement and cumulative AE energy curves, confirming the model’s robustness. Furthermore, frequency analysis of the simulated signals successfully distinguishes between tensile and shear failure modes. This study fills a significant gap in the literature by demonstrating that DEM is an effective predictive tool for structural-scale failure analysis and AE monitoring in heterogeneous masonry. Full article

As urbanization picks up pace and the public demand for security keeps climbing, video surveillance systems have emerged as a vital tool for maintaining social stability and safeguarding public safety. Person Re-Identification (Re-ID), as one of the core technologies in intelligent monitoring, mainly aims to accurately match pedestrian identities across cameras without overlapping fields of view. However, in practical applications, occlusion remains a primary challenge that severely degrades Re-ID performance. Especially in high-density crowds, pedestrians are often partially or completely obscured by other objects or individuals, resulting in incomplete image information and impaired feature representation, which significantly reduces recognition accuracy and reliability. Aiming at the problems of excessive reliance on external pose estimation models and asymmetric information matching in occluded Re-ID, this paper proposes a transformer-based pedestrian background decoupling network. The algorithm achieves foreground–background separation and multi-scale feature matching through the synergy of three modules. Meanwhile, a two-stage training strategy is adopted: the first stage optimizes the decoupling module to ensure clean feature separation, while the second stage jointly fine-tunes the correlation module to enhance matching accuracy. Extensive experimental results show that the proposed algorithm outperforms existing methods. Full article

Contactless vital signs monitoring is becoming increasingly relevant in scenarios where conventional sensors are impractical or not recommended. In this manuscript, a radar-based contactless system for the joint reconstruction of phonocardiogram (PCG) waveforms and cardiac state segmentation is illustrated. The proposed method exploits a self-attention one-dimensional (1D) U-Net fed by a pre-processed radar-derived input to estimate a PCG-like waveform, its envelope, and the four main cardiac phases: S1, systole, S2, and diastole. The accuracy of our method has been assessed on a public synchronized radar–PCG dataset acquired by means of a 24 GHz Doppler radar and a digital stethoscope. On the test subset, the proposed model achieved a 13.4885 dB reduction in log-spectral distance relative to the radar input signal, indicating a marked improvement in waveform fidelity. Segmentation performance also improved, with Micro-${\mathrm{F}}_1$ increasing from 74.41% to 84.17% and Macro-${\mathrm{F}}_1$ from 68.40% to 80.43% on average. Experimental results demonstrated the viability of real-time low-power embedded hardware deployment for contactless auscultation and continuous cardiac monitoring applications. The findings confirm that respiratory interference and low-amplitude signals complicate S2 detection, especially when exacerbated by subject motion. Full article

Benthic foraminifera, single-cell marine organisms found worldwide, represent an important component of seabed ecosystems. Due to their sensitivity to environmental pollution, they are often used as bioindicators, providing an efficient tool in toxicity studies. Among the pollutants affecting marine coastal and estuarine environments, persistent flame retardants, such as polybrominated diphenyl ethers (PBDEs), are frequently found. Low-level exposure to BDE-47, a PBDE congener, is known to affect organismal development. In this framework, this study aims to assess the effects of BDE-47 exposure on benthic foraminifera from coastal marine environments. Foraminifera specimens belonging to the symbiont-bearing Peneroplidae family were sampled and exposed to two different BDE-47 concentrations for up to 48 h. Vitality indicators such as changes in pseudopodial activity, movement, reproduction, loss of symbiont algae, and occasional mortality events were monitored during the experiment. Exposure to BDE-47 induced alterations in pseudopodial activity, movement, reproduction, and symbiont retention, with the progressive loss of vitality and limited mortality at increasing exposure levels, highlighting the sensitivity of this species to BDE-47. These findings suggest the harmful repercussions of PBDE pollution on marine coastal ecosystems, affecting benthic organisms and potentially contributing to biomagnification processes within the food web, with possible implications for human health. Full article

Underwater Wireless Sensor Networks (UWSNs) play a vital role in ocean monitoring and exploration. However, harsh underwater conditions and frequent topology changes caused by node and sink mobility pose significant challenges for reliable routing. Conventional routing protocols that depend on global route reconstruction and static paths generate excessive control overhead and degrade performance in large-scale underwater environments. In this paper, we propose an energy-efficient virtual cell-based mobile-sink adaptive routing (VC-MAR) protocol for UWSNs. The sensing field is logically partitioned into a three-dimensional grid of virtual cells, where a cell-gateway is elected in each cell to construct a low-overhead routing backbone. To support sink mobility, VC-MAR introduces a localized route-adjustment mechanism that updates only the affected backbone segments rather than reconstructing the entire routing structure. By confining routing updates to neighboring cells influenced by sink movement, the proposed protocol significantly reduces control packet exchanges while ensuring stable and reliable data delivery. Simulation results show that the proposed VC-MAR improves the packet delivery ratio by up to 20% and reduces routing control overhead by about 34% compared with traditional grid-based routing methods. These results confirm the suitability of VC-MAR for dynamic and realistic underwater sensing scenarios. Full article