Search Results (5,963)

Deep coalbed methane (CBM) reservoirs are characterized by high hydrocarbon content and are considered an important strategic resource. Due to their inherently low permeability and porosity, horizontal well drilling is commonly employed to enhance production, with the length of the horizontal section playing a critical role in determining CBM output. However, during extended horizontal drilling, wellbore instability frequently occurs as a result of drilling fluid invasion into the coal formation, posing significant safety challenges. This instability is primarily caused by the physical intrusion of drilling fluids and their interactions with the coal seam, which alter the mechanical integrity of the formation. To address these challenges, interpenetrating and semi-interpenetrating network (IPN/s-IPN) hydrogels have gained attention due to their superior physicochemical properties. This material offers enhanced sealing and support performance across fracture widths ranging from micrometers to millimeters, making it especially suited for plugging applications in deep CBM reservoirs. A self-degradable interpenetrating double-network hydrogel particle plugging agent (SSG) was developed in this study, using polyacrylamide (PAM) as the primary network and an ionic polymer as the secondary network. The SSG demonstrated excellent thermal stability, remaining intact for at least 40 h in simulated formation water at 120 °C with a degradation rate as high as 90.8%, thereby minimizing potential damage to the reservoir. After thermal aging at 120 °C, the SSG maintained strong plugging performance and favorable viscoelastic properties. A drilling fluid containing 2% SSG achieved an invasion depth of only 2.85 cm in an 80–100 mesh sand bed. The linear viscoelastic region (LVR) ranged from 0.1% to 0.98%, and the elastic modulus reached 2100 Pa, indicating robust mechanical support and deformation resistance. Full article

Waste from zinc−carbon batteries poses a serious environmental protection problem. One of the main problems is also the reliable and rapid determination of some compounds that may be present in food and beverages consumed worldwide. This study addresses these problems and presents a possible solution for the electrochemical detection of xanthine using carbon from spent batteries. Cyclic voltammetry and differential pulse voltammetry are electrochemical methods used for the detection of xanthine. The techniques used demonstrate the mechanism of xanthine oxidation in the tested environment. A linear correlation was found between the oxidation current peaks and the xanthine concentration in the range of 5·10⁻⁷ to 1·10⁻⁴ M, as well as the values for the limit of detection and the limit of quantification, 7.86·10⁻⁸ M and 2.62·10⁻⁷ M, respectively. The interference test shows that the electrode obtained from waste Zn-C batteries has good selectivity, which means that the electrode can be used for xanthine determination in the presence of various ions. The data obtained show that carbon sensors from used zinc−carbon batteries can be used to detect xanthine in real samples. Full article

The historical use of toxic chemicals to cause intentional harm has evolved from blister agents in World War I to highly lethal organophosphates and emerging families of chemicals, such as Novichok. In turn, medical or recreational substances like fentanyl, lysergamides, and phencyclidine pose a growing risk of hostile use, particularly related to the rapid proliferation of new psychoactive substances (NPSs). A narrative literature review was conducted covering specialized databases (PubMed, ScienceDirect, SciELO, Google Scholar) and sources from international organizations (OPCW, UNODC, ONU), analyzing historical and recent cases of the use of nerve agents in conflicts and the use of NPSs for hostile purposes. The main families of conventional agents (G, V, A series, and Novichok) and NPSs (lysergamides, PCP, fentanyl derivatives) were identified, highlighting their ease of synthesis, high toxicity profiles, and the regulatory gaps that facilitate their illicit production. In this scenario, it is essential to strengthen regulatory frameworks, surveillance systems, and ethical protocols in chemical research, as well as to promote international cooperation to prevent these substances from becoming chemical threats. Full article

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being. Full article

In recent years, phylogenomic approaches have significantly deepened our understanding of moss diversity. These techniques have uncovered numerous previously overlooked species and provided greater clarity in resolving complex taxonomic relationships. In this context, the genus Rehubryum is particularly outstanding, because of its close morphological similarity to both Ulota and Atlantichella. The challenges posed by its segregation are addressed in this study, which integrates morphological and molecular data to reassess the circumscription of Rehubryum and its phylogenetic placement within the subtribe Lewinskyinae. Our results support the recognition of a new species, R. kiwi, and show that its inclusion within the genus further complicates the morphological delimitation of Rehubryum from Ulota, as both genera are distinguishable by only two consistent gametophytic characteristics: a submarginal leaf band of elongated cells, and the presence of geminate denticulations in the margins of the basal half of the leaf. Moreover, R. kiwi challenges the current morphological circumscription of Rehubryum itself, as it overlaps in key characteristics with its sister genus Atlantichella, rendering their morphological separation untenable. The striking interhemispheric disjunction between Rehubryum and Atlantichella raises new questions about long-distance dispersal and historical biogeography in mosses, despite these complexities at the generic level. Nevertheless, species-level distinctions remain well defined, especially in sporophytic traits and geographic distribution. These findings highlight the pervasive cryptic diversity within Orthotrichaceae, underscoring the need for integrative taxonomic frameworks that synthesize morphology, molecular phylogenetics, and biogeography to resolve evolutionary histories. Full article

Per- and polyfluoroalkyl substances (PFASs) have gained significant attention due to their widespread distribution in the environment and potential adverse health effects. While ingestion, especially through contaminated drinking water, is considered the primary route of human exposure, recent research suggests that other pathways, such as inhalation and dermal absorption, also play a significant role. This review provides a concise overview of the toxicological impacts of both legacy and emerging PFASs, such as GenX and perfluorobutane sulfonic acid (PFBS), with a particular focus on their effects on the liver, kidneys, and immune and nervous systems, based on findings from recent in vivo, in vitro, and epidemiological studies. Despite the transition to PFAS alternatives, much of the existing toxicity data focus on a few legacy compounds, such as perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), which have been linked to adverse immune outcomes, particularly in children. However, evidence for carcinogenic risk remains limited to populations with extremely high exposure levels, and data on neurodevelopmental effects remain underexplored. While epidemiological and experimental animal studies supported these findings, significant knowledge gaps persist, especially regarding emerging PFASs. Therefore, this review examines the visceral, neural, and immunotoxicity data for emerging PFASs and mixtures from recent studies. Given the known risks from well-studied PFASs, a precautionary principle should be adopted to mitigate human health risks posed by this large and diverse group of chemicals. Full article

Pupillometry is commonly used to evaluate cognitive effort, attention, and facial expression response, offering valuable insights into human performance. The combination of eye tracking and facial expression data under the iMotions platform provides great opportunities for multimodal research. However, there is a lack of standardized pipelines for managing pupillometry data on a multimodal platform. Preprocessing pupil data in multimodal platforms poses challenges like timestamp misalignment, missing data, and inconsistencies across multiple data sources. To address these challenges, the authors introduced a systematic preprocessing pipeline for pupil diameter measurements collected using iMotions 10 (version 10.1.38911.4) during an endoscopy simulation task. The pipeline involves artifact removal, outlier detection using advanced methods such as the Median Absolute Deviation (MAD) and Moving Average (MA) algorithm filtering, interpolation of missing data using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and mean pupil diameter calculation through linear regression, as well as normalization of mean pupil diameter and integration of the pupil diameter dataset with facial expression data. By following these steps, the pipeline enhances data quality, reduces noise, and facilitates the seamless integration of pupillometry other multimodal datasets. In conclusion, this pipeline provides a detailed and organized preprocessing method that improves data reliability while preserving important information for further analysis. Full article

Sustainable education requires addressing the challenges posed by digital transformation, including technostress among university students. This study evaluates technostress levels in higher education through the validation of the TS4US scale and its implications for sustainable learning environments. A cross-sectional study was conducted with 328 university students from four districts in Lima, Peru, using an online survey to measure technostress. Confirmatory factor analysis (CFA) was performed to assess the psychometric properties of the TS4US scale, resulting in a refined model with two latent factors and thirteen validated items. Findings indicate that 28% of students experience high technostress levels, while 5% report very high levels, though no significant associations were found between technostress and sociodemographic variables such as campus location, employment status, gender, and academic level. The TS4US instrument had been previously validated in Chile; this study confirms its structure in a new sociocultural context, reinforcing its cross-cultural applicability. These results highlight the need for sustainable strategies to mitigate technostress in higher education, including institutional support, digital literacy programs, and policies fostering a balanced technological environment. Addressing technostress is essential for promoting sustainable education (SDG4) and enhancing student well-being (SDG3). This study directly contributes to the achievement of Sustainable Development Goals 3 (Good Health and Well-being) and 4 (Quality Education) by providing validated tools and evidence-based recommendations to promote mental health and equitable access to digital education in Latin America. Future research should explore cross-country comparisons and targeted interventions, including digital well-being initiatives and adaptive learning strategies, to ensure a resilient and sustainable academic ecosystem. Full article

Laboratory-based assessment of cardiorespiratory function is a widely applied method in sports science. Most performance evaluations focus on oxygen uptake parameters. Despite the well-established concept of oxygen deficit introduced by Hill in the 1920s, relatively few studies have examined its behavior during submaximal exercise, with limited exploration of deficit dynamics. The present study aimed to analyze the behavior of oxygen deficit in young female handball players (N = 42, age: 15.4 ± 1.3 years) during graded exercise. Oxygen deficit was estimated using the American College of Sports Medicine (ACSM) algorithm, restricted to subanaerobic threshold segments of a quasi-ramp exercise protocol. Cardiorespiratory parameters were measured with the spiroergometry test on treadmills, and body composition was assessed via Dual Energy X-ray Absorptiometry (DEXA). Cluster and principal component analyzes revealed two distinct athlete profiles with statistically significant differences in both morphological and physiological traits. Cluster 2 showed significantly higher relative VO₂ peak (51.43 ± 3.70 vs. 45.70 ± 2.87 mL·kg⁻¹·min⁻¹; p < 0.001; Cohen’s d = 1.76), yet also exhibited a greater oxygen deficit per kilogram (39.03 ± 16.71 vs. 32.56 ± 14.33 mL·kg⁻¹; p = 0.018; d = 0.80). Cluster 1 had higher absolute body mass (69.67 ± 8.13 vs. 59.66 ± 6.81 kg; p < 0.001), skeletal muscle mass (p < 0.001), and fat mass (p < 0.001), indicating that body composition strongly influenced oxygen deficit values. The observed differences in oxygen deficit profiles suggest a strong influence of genetic predispositions, particularly in cardiovascular and muscular oxygen utilization capacity. Age also emerged as a critical factor in determining the potential for adaptation. Oxygen deficit during submaximal exercise appears to be a multifactorial phenomenon shaped by structural and physiological traits. While certain influencing factors can be modified through training, others especially those of genetic origin pose inherent limitations. Early development of cardiorespiratory capacity may offer the most effective strategy for long-term optimization. Full article

The extended operation of high-speed railways has led to an increased incidence of tunnel lining defects, with falling debris posing a significant safety threat. Within tunnels, single-train passage and trains-crossing events constitute the most frequent operational scenarios, both generating extreme aerodynamic environments that alter debris trajectories from free fall. To systematically investigate the aerodynamic differences and underlying mechanisms governing falling debris behavior under these two distinct conditions, a three-dimensional computational fluid dynamics (CFD) model (debris–air–tunnel–train) was developed using an improved delayed detached eddy simulation (IDDES) turbulence model. Comparative analyses focused on the translational and rotational motions as well as the aerodynamic load coefficients of the debris in both single-train and trains-crossing scenarios. The mechanisms driving the changes in debris aerodynamic behavior are elucidated. Findings reveal that under single-train operation, falling debris travels a greater distance compared with trains-crossing conditions. Specifically, at train speeds ranging from 250–350 km/h, the average flight distances of falling debris in the X and Z directions under single-train conditions surpass those under trains crossing conditions by 10.3 and 5.5 times, respectively. At a train speed of 300 km/h, the impulse of C_Fx and C_Fz under single-train conditions is 8.6 and 4.5 times greater than under trains-crossing conditions, consequently leading to the observed reduction in flight distance. Under the conditions of trains crossing, the falling debris is situated between the two trains, and although the wind speed is low, the flow field exhibits instability. This is the primary factor contributing to the reduced flight distance of the falling debris. However, it also leads to more pronounced trajectory deviations and increased speed fluctuations under intersection conditions. The relative velocity (CRV) on the falling debris surface is diminished, resulting in smaller-scale vortex structures that are more numerous. Consequently, the aerodynamic load coefficient is reduced, while the fluctuation range experiences an increase. Full article

Construction and demolition byproducts include substantial amounts of wood panel waste (WPW) that pose environmental challenges. They also create opportunities for sustainable resource recovery. This study investigates the potential of WPW-derived biochar as an efficient adsorbent for phenol removal from aqueous solutions. Biochar was produced via pyrolysis at 450 °C and subsequent activation at 750, 850, and 950 °C. The biochar’s physicochemical properties, including surface area, pore volume, and elemental composition, were characterized using advanced methods, including BET analysis, elemental analysis, and adsorption isotherm analysis. Activated biochar demonstrated up to nine times higher adsorption capacity than raw biochar, with a maximum of 171.9 mg/g at 950 °C under optimal conditions: pH of 6 at 25 °C, initial phenol concentration of 200 mg/L, and biochar dosage of 1 g/L of solution for 48 h. Kinetic and isotherm studies revealed that phenol adsorption followed a pseudo-second-order model and fit the Langmuir isotherm, indicating chemisorption and monolayer adsorption mechanisms. Leaching tests confirmed the biochar’s environmental safety, with heavy metal concentrations well below regulatory limits. Based on these findings, WPW biochar offers a promising, eco-friendly solution for wastewater treatment in line with circular economy and green chemistry principles. Full article

This study evaluates the potential health risks posed by geogenic arsenic in environments suitable for leisure activities, such as walking, bathing, and playing, for adults and children alike, as well as in neighbouring agricultural areas. The study includes an analysis of environmental characteristics and the main stream originating in the adjacent mining area, with water and sediment samples taken. The study area is representative of other areas in the vicinity of the Mar Menor Lagoon, which is one of the largest and most biodiverse coastal lagoons in the Mediterranean Sea. The general characteristics of the soil and water were determined for this study, as was the concentration of As in the soil and water samples. A granulometric separation was carried out into four different fractions (<2 mm, <250 µm, <100 µm, and <65 µm). The mineralogical composition, total As content, and bioaccessible As content are analysed in each of these fractions. This provides data with which to calculate the danger of arsenic (As) to human health by ingestion and to contribute to As bioaccessibility studies and the role played by the mineralogical composition and particle size of soil ingestion. The conclusions rule out residential use of this environment, although they allow for eventual tourist use and traditional agricultural use of the surrounding soils. Full article

Background: Unlike most cancers, breast cancer poses a persistent risk of distant recurrence—often years after initial treatment—making long-term risk stratification uniquely challenging. Current tools fall short in predicting late metastatic events, particularly for early-stage patients. Methods: We present an interpretable machine learning (ML) pipeline to predict distant recurrence-free survival at 5, 10, and 15 years, integrating Bayesian network-based causal feature selection, deep feed-forward neural network models (DNMs), and SHAP-based interpretation. Using electronic health record (EHR)-based clinical data from over 6000 patients, we first applied the Markov blanket and interactive risk factor learner (MBIL) to identify minimally sufficient predictor subsets. These were then used to train optimized DNM classifiers, with hyperparameters tuned via grid search and benchmarked against models from 10 traditional ML methods and models trained using all predictors. Results: Our best models achieved area under the curve (AUC) scores of 0.79, 0.83, and 0.89 for 5-, 10-, and 15-year predictions, respectively—substantially outperforming baselines. MBIL reduced input dimensionality by over 80% without sacrificing accuracy. Importantly, MBIL-selected features (e.g., nodal status, hormone receptor expression, tumor size) overlapped strongly with top SHAP contributors, reinforcing interpretability. Calibration plots further demonstrated close agreement between predicted probabilities and observed recurrence rates. The percentage performance improvement due to grid search ranged from 25.3% to 60%. Conclusions: This study demonstrates that combining causal selection, deep learning, and grid search improves prediction accuracy, transparency, and calibration for long-horizon breast cancer recurrence risk. The proposed framework is well-positioned for clinical use, especially to guide long-term follow-up and therapy decisions in early-stage patients. Full article

Wearable sensors are central to health-monitoring systems, but the limited capacity of compact batteries poses a challenge for long-term and maintenance-free operation. In this study, we investigated ambient electromagnetic wave (AEMW) energy harvesting using a human body antenna (HBA) as a means to supply power to wearable sensors. The power density and frequency distribution of AEMWs were measured in diverse indoor, outdoor, and basement environments. We designed and fabricated a flexible HBA–circuit interface electrode, optimized for broadband impedance matching when worn on the body. Experimental comparisons using a simulated AEMW source demonstrated that the HBA outperformed a conventional small whip antenna, particularly at frequencies below 300 MHz. Furthermore, the outdoor measurements indicated that the power harvested by the HBA was estimated to be −31.9 dBm (0.64 μW), which is sufficient for the intermittent operation of low-power wearable sensors and Bluetooth Low Energy modules. The electromagnetic safety was also evaluated through numerical analysis, and the specific absorption rate was confirmed to be well below the international safety limits. These findings indicate that HBA-based AEMW energy harvesting provides a practical and promising approach to achieving battery-maintenance-free wearable devices. Full article

Background: The spread of ESBL-producing Enterobacteriaceae (ESBL-PE) strains in food poses a potential risk to human health. The aim of the study was to determine the occurrence of ESBL-PE and to investigate their distribution on foods. Methods: A total of 1000 food samples, including both raw and ready-to-eat products, was analyzed for the presence of ESBL-producing Enterobacteriaceae using chromogenic selective agar. Antibiotic resistance in the isolated strains was assessed using conventional methods, while whole-genome sequencing was employed to predict antimicrobial resistance and virulence genes. Results: The overall occurrence of ESBL-PE strains was 2.8%, with the highest contamination in raw meat samples (10%). A total of 31 multidrug-resistant (MDR) strains was isolated, mainly Escherichia coli, followed by Klebsiella pneumoniae, Salmonella enterica, and Enterobacter hormaechei. All strains exhibited high levels of resistance to at least four different β-lactam antibiotics, as well as to other antimicrobial classes including sulfonamides, tetracyclines, aminoglycosides, and quinolones. Whole-genome sequencing identified 63 antimicrobial resistance genes, with bla_CTX-M being the most prevalent ESBL gene. Twenty-eight (90%) isolates carried Inc plasmids, known vectors of multiple antimicrobial resistance genes, including those associated with ESBLs. Furthermore, several virulence genes were identified. Conclusions: The contamination of food with ESBL-PE represents a potential public health risk, underscoring the importance of the implementation of genomic surveillance to monitor and control the spread of antimicrobial resistance. Full article