Search Results (281)

Theophylline represents a significant public health challenge due to its dual acute and chronic toxicity resulting from therapeutic, environmental, and dietary exposures. Effective monitoring across the bio-environmental–food triad requires analytical methods that are highly sensitive, universally applicable, and capable of overcoming complex matrix interferences. This study introduces a flat membrane-based liquid-phase microextraction (LPME) technique combined with LC–MS/MS for the determination of theophylline in diverse matrices. The method eliminates the need for specialized adsorbents or equipment, offering a simple and cost-effective solution for high-throughput sample clean-up. Under optimized conditions, the method demonstrated exceptional sensitivity (LOD: 0.2 ng mL⁻¹) and a wide linear range (0.01–10 μg mL⁻¹). It was successfully applied to plasma, urine, hospital sewage, and green tea, providing accurate (recoveries of 86.7–111.3%) and reproducible (RSD < 10%) results across all matrices. This unified and robust approach effectively addresses matrix interferences and provides a reliable tool for the monitoring and risk assessment of theophylline across multiple domains. Full article

Food allergy is a growing global health concern, with seafood representing one of the most significant sources of allergic reactions. The primary allergens responsible for fish and shellfish allergies are β-parvalbumins and tropomyosin, respectively. Therefore, ensuring food safety requires precise and reliable methods for the detection and quantification of these molecules. Traditional approaches, such as ELISA and PCR, have notable limitations in terms of specificity, sensitivity, and multiplexing capabilities. In contrast, liquid chromatography coupled with mass spectrometry (LC-MS) has emerged over the past decade as a powerful alternative, offering enhanced accuracy and analytical depth. Various LC-MS-based strategies have been developed for the identification and quantification of seafood allergens, contributing to improved allergen monitoring and risk assessment. Nevertheless, the routine implementation of these methods in analytical laboratories still faces several challenges, including high equipment costs, complex workflows, and the need for standardized reference materials and protocols. Continued technological advances and validation efforts are necessary to overcome these barriers and to integrate LC-MS-based techniques into routine food allergen testing. Full article

The initial part of this study fills a notable research gap by investigating the substantial impact of numerical diffusion errors from different schemes on sloshing tank models. Multiple numerical models were developed: first- and higher-order upwind schemes equipped with precise wall treatment using ghost nodes, MacCormack and central methods that are explicit second-order finite difference methods, and Preissmann and staggered methods employed in full-implicit and semi-implicit modes. Furthermore, the separation of variables technique was proposed for simulating sloshing tanks and deriving an analytical equation for the tank’s natural period. An analytical solution to the perturbation was employed to examine the numerical diffusion of the schemes. Subsequently, two sloshing tests, resonant and near-resonant excitations, were employed to determine the numerical diffusion and calibrate the physical diffusion coefficients, respectively. Finally, an efficient and accurate numerical scheme was applied to a linear shallow water model including physical diffusion and coupled with a single degree of freedom (SDOF), to simulate tuned liquid dampers (TLDs). It shows that the efficiency of TLD is associated with a compact domain around resonance excitation. Contrary to SDOF alone, when SDOF interacts with TLD the impact of structural damping on reducing the response is minimal in resonance excitation. Full article

As MEMS design encounters growing challenges, particularly stiction between movable and stationary electrodes, dielectric charging, pull-in instability, and multi-valued response characteristics, the integration of dimple-equipped structures has emerged as a pivotal solution to mitigate these fundamental issues. Consequently, this study investigates the dynamic behavior of an electrostatically actuated double-clamped microbeam incorporating dimples and contact pads. While the dimples enhance the beam’s travel range, they may also induce an impact mode upon contact with the landing pads, leading to complex nonlinear dynamic phenomena. A reduced-order model was developed to numerically solve the governing equation of motion. The microbeam’s response was analyzed both with and without dimples using multiple analytical techniques, including bifurcation diagrams and discrete excitation procedures near the impacting regime. The findings demonstrate that the inclusion of dimples effectively suppresses stiction, pull-in instability, and multi-valued responses. The results indicate that upon contacting the landing pads, the beam exhibits pronounced nonlinear dynamic behaviors, manifesting as higher-period oscillations such as period-3, period-4 and period-5 and then fully developed chaotic attractors. Indeed, this specifically demonstrates the potential of using the dynamic transition from a steady-state to a chaotic response to build novel MEMS sensors. Full article

The deep carbonate reservoirs in the Yingzhong Block of the Qaidam Basin exhibit strong vertical heterogeneity and complex natural fracture development. Conventional fracability evaluation methods struggle to accurately characterize formation features, thereby affecting the stimulation effectiveness. To enhance the evaluation accuracy of fracturing sweet spot intervals, automatic mineral scanning equipment is employed to obtain formation micro-physical property parameters at continuous depths. Considering the temperature-pressure coupling effect under deep conditions, a rock mechanics computational model based on mineral composition was established to derive macroscopic mechanical parameters such as brittleness index and in situ stress. Based on a combined algorithm of the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP), a fracturing sweet spot prediction model integrating micro- and macro-multi-factors is established, and sweet spot index levels are classified. The research results indicate that the rock mechanics computational model demonstrates high accuracy, the calculated macroscopic parameters are reliable, and the fracturing sweet spot index model can fracability and meticulously evaluate the characteristics of deep carbonate formations. The fracturing sweet spots can be classified into three levels: Level I with an index higher than 0.50, Level II with an index between 0.35 and 0.50, and Level III with an index lower than 0.35. After using this method for layer selection, the fracture pressure decreases by 11.6%, and the sand addition success rate increases by 24%. Applying this method to guide the optimization of fracturing intervals demonstrates good on-site practical value, providing an important reference for identifying fracturing sweet spots in deep carbonate reservoirs. Full article

The global rise in population, increased life expectancy, and heightened international mobility have escalated disease prevalence and pharmaceutical demand. This growth intensifies energy consumption and chemical waste production within the pharmaceutical industry, challenging environmental sustainability and operational efficiency. Chromatography, a vital analytical technique for ensuring product quality and regulatory compliance, can also contribute to material waste and energy inefficiencies if not properly maintained and optimized. This study applies Failure Mode and Effects Analysis (FMEA) to chromatographic equipment maintenance within Hovione’s Engineering and Maintenance Department, aiming to identify and mitigate failure risks. By integrating environmental metrics derived from Life Cycle Assessment (LCA) into the FMEA framework, a hybrid risk evaluation tool was developed that prioritizes both equipment reliability and sustainability performance. The findings demonstrate how this integrated approach reduces unplanned downtime, lowers solvent waste, and improves energy efficiency. Additionally, the study proposes a conceptual dashboard to support proactive, sustainability-driven asset management in pharmaceutical laboratories. By bridging reliability engineering and environmental sustainability, this research offers a strategic model for optimizing resource use, minimizing chemical waste, and enhancing long-term operational resilience in regulated pharmaceutical environments. Full article

The reaction of zirconium tetrachloride with 2-amino-1,4-benzenedicarboxylic acid in N,N-dimethylformamide with the addition of HCl leads to the formation of zirconium 2-amino-1,4-benzenedicarboxylate. Zirconium 2-amino-1,4-benzenedicarboxylate was characterized by elemental analysis, infrared spectrometry, X-ray diffraction, scanning electron microscopy, and volumetric nitrogen adsorption/desorption. The sample has a constant porosity with an average pore diameter of 7.97 nm and both microporous and mesoporous structure with a large surface area (820 m²/g) corresponding to the type IV adsorption. Zirconium 2-amino-1,4-benzenedicarboxylate was used for solid-phase extraction (SPE) of chlortetracycline hydrochloride from the aqueous solution. The obtained results confirmed the possibility of using the proposed analytical technique as a new, convenient approach to the extraction of chlortetracycline hydrochloride from industrial or other wastewaters, where such substance is contained in insignificant concentrations and its determination requires expensive and complex equipment. In the future, this method can be used not only for the effective removal of pollutants from industrial wastewater with subsequent regeneration of the sorbent, but also as a sample-preparation method for concentrating chlortetracycline hydrochloride from dilute solutions with its subsequent elution and analysis by available methods, for example, spectrophotometry, since the limit of detection is 0.06 mg/L. Experimental data are described by the isotherm of SPE (R² = 0.998–0.999) and show the ability of zirconium 2-amino-1,4-benzenedicarboxylate to extract up to 578 mg/g of sorbent at 5 °C under optimal conditions. Full article

Robust statistical analysis is essential for advancing evidence-based nutrition research, particularly when investigating the complex relationships between dietary exposure and health outcomes. Multiple regression is a widely used analytical technique in nutrition studies due to its ability to control for confounding variables and assess multiple predictors simultaneously. However, the reliability, validity, and generalizability of findings from regression analyses depend heavily on having an appropriate sample size. Despite its importance, many published nutrition studies do not include formal sample size justifications or power calculations, leading to a high risk of Type II errors and reduced interpretability of results. This methodological review examines three commonly used approaches to sample size determination in multiple regression analysis: the rule of thumb, variance explained (R²) method, and beta weights approach. Using a consistent hypothetical example, rather than empirical data, this paper illustrates how sample size recommendations can differ depending on the selected approach, highlighting the advantages, assumptions, and limitations of each. This review is intended as an educational resource to support methodological planning for applied researchers rather than to provide new empirical findings. The aim is to equip nutrition researchers with practical tools to optimize sample size decisions based on their study design, research objectives, and desired power. The rule of thumb offers a simple and conservative starting point, while the R² method ties sample size to anticipated model performance. The beta weights approach allows for more granular planning based on the smallest effect of interest, offering the highest precision but requiring more detailed assumptions. By encouraging more rigorous and transparent sample size planning, this paper contributes to improving the reproducibility and interpretability of quantitative nutrition research. Full article

Polycyclic aromatic hydrocarbon (PAH) derivatives, specifically azaarenes and nitrated and oxygenated PAHs, are emerging contaminants of concern due to their increased toxicity and persistence compared to the parent PAHs. Despite their toxicity, their simultaneous analysis in complex matrices, such as in fumes emitted from bituminous mixtures, remains challenging due to limitations of conventional analytical techniques. To address this, an advanced methodology was developed using Ultra-High-Performance Liquid Chromatography coupled with High-Resolution Mass Spectrometry (UHPLC-HRMS Orbitrap Eclipse) equipped with an APCI source for the simultaneous identification and quantification of 14 PAH derivatives. Chromatographic and ionization parameters were optimized to ensure maximum sensitivity and selectivity. Following ICH Q2(R2) guidelines, the method was validated, demonstrating excellent linearity (R² > 0.99), high mass accuracy (≤5 ppm), strong precision (<15%), and excellent sensitivity. Limits of detection (LODs) ranged from 0.1 µg L⁻¹ to 0.6 µg L⁻¹ and limits of quantification (LOQs) ranged from 0.26 µg L⁻¹ to 1.87 µg L⁻¹. The validated method was successfully applied to emissions from asphalt pavement materials collected on quartz filters under controlled conditions, enabling the identification and quantification of all 14 targeted compounds. These results confirm the method’s robustness and suitability for trace-level analysis of PAH derivatives in complex environmental matrices. Full article

This article investigates the combined effects of different common defects on the structural integrity and operational and environmental safety in the operation of an existing Light Cycle Oil (LCO) storage tank. This study correlates all the tank defects (like corrosion and local plate thinning, deformations, and local stress concentrators) against the loads and their combinations that occur during the tank’s lifetime. All the information gathered by various inspection techniques is used together to create a digital twin of the equipment that will be further analyzed by Finite Element Analysis. A tank condition assessment is a complex activity, and it is based on the experience of the engineer performing it. Since there are multiple methods for performing a comprehensive analysis, starting from the basic visual inspection (which is the most important) and some measurements followed by analytical calculations, up to full wall thickness measurements, 3D scan of deformations and FEA analysis of the tank digital twin, it depends on the engineer performing the evaluation to chose the best method for each particular case from technical and economical point of views. The goal of this article is to demonstrate that analytical and FEA methods have the same result and also to establish a well-determined standard calculation model for future applications. Full article

Unmanned maritime vehicles (UMVs) have become essential tools in marine research and monitoring, significantly enhancing operational efficiency and reducing risks and costs. Fiber-reinforced composites have been widely used in marine applications due to their excellent characteristics. However, environmental concerns and the pursuit of sustainable development goals have driven the development of environmentally friendly materials. The development of eco-friendly biocomposites for UMV construction can effectively reduce the environmental impact of marine equipment. This study investigates the effects of seawater aging on kenaf/flax/glass-fiber-reinforced composites under artificial seawater conditions and determines their ranking for UMVs using the Analytic Hierarchy Process (AHP). These hybrid composites, fabricated with various stacking sequences, were prepared using a combination of hand lay-up and vacuum bagging techniques. All plant fibers underwent sodium hydroxide treatment to eliminate impurities and enhance interfacial bonding, while nano-silica was incorporated into the epoxy matrix to improve overall performance. After 50 days of immersion in artificial seawater, mechanical tests were conducted to evaluate the extent of changes in mechanical properties. Subsequently, the AHP analysis was performed based on three main criteria and thirteen sub-criteria to determine the most suitable configuration for marine applications. The results demonstrate that the stacking sequence plays a critical role in resisting seawater-induced degradation and maintaining mechanical performance. GKFKG exhibited the highest retention rates for both tensile strength (86.77%) and flexural strength (88.36%). Furthermore, the global priority vector derived from the AHP analysis indicates that hybrid composites consisting of kenaf, flax, and glass fibers consistently ranked highest. The optimum configuration among these hybrid composites was determined to be GKFKG, followed by GFKFG, GKKKG, and GKGKG. Full article

Sewage sludge (SS) is commonly applied as a soil amendment. This practice has raised concern about the dissemination of emerging pollutants (EPs). EPs include compounds such as flame retardants, plasticizers, pharmaceuticals, and personal care products, among others, which may pose risks to human health and ecosystems. The complexity of the SS matrix, combined to the absence of an international legislation framework, makes it necessary to evaluate the techniques available for detecting these contaminants. Detection is typically performed using sensitive analytical techniques; however, the extraction strategy selected remains a crucial step. This review aims to compile different methodologies for the determination of EPs in SS, focusing on extraction strategies reported between 2010 and 2025. Ultrasound-assisted extraction (UAE), pressurized liquid extraction (PLE), and microwave-assisted extraction (MAE) are the most widely used strategies for EPs. UAE is considered the most preferable option, as it enables the extraction of a wide range of compounds without the need for expensive equipment. Among novel techniques, the quick, easy, cheap, effective, rugged, and safe (QuEChERS) method is especially promising, as it is applicable to multiple target compounds. This review provides up-to-date information that can support the development of routine and standardized methodologies for the characterization of EPs in SS. Full article

Monitoring water quality is crucial for achieving clean water and sanitation goals, particularly in remote areas. The project “Morbidity vs. Water Quality for Human Consumption in Tonosí: A Pilot Study” aimed to enhance water quality assessments in Panama using advanced analytical techniques to assess volatile organic compounds, heavy metals, and microbiological pathogens. To support this, the Technical Unit for Water Quality (UTECH) was established, featuring a novel mobile laboratory with cutting-edge technology for accurate testing, minimal chemical reagent use, reduced waste generation, and equipped with a solar-powered battery system. The aim of this paper is to explore the design, deployment, and impact of the UTECH. Furthermore, this study presents results from three sampling points in Tonosí, where several parameters exceeded regulatory limits, demonstrating the capabilities of the UTECH and highlighting the need for ongoing monitoring and intervention. The study also assesses the environmental, social, and economic impacts of the UTECH in alignment with the Sustainable Development Goals and national initiatives. Finally, a SWOT analysis illustrates the UTECH’s potential to improve water quality assessments in Panama while identifying areas for sustainable growth. The study showcases the successful integration of advanced mobile laboratory technologies into water quality monitoring, contributing to sustainable development in Panama and offering a replicable model for similar initiatives in other regions. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

The traditional approach that separates remaining useful life prediction from maintenance strategy design often fails to support efficient decision-making. Effective maintenance requires a comprehensive consideration of prediction accuracy, cost control, and equipment safety. To address this issue, this paper proposes a multi-objective maintenance optimization method based on stochastic modeling. First, a multi-sensor data fusion technique is developed, which maps multidimensional degradation signals into a composite degradation state indicator using evaluation metrics such as monotonicity, tendency, and robustness. Then, a linear Wiener process model is established to characterize the degradation trajectory of equipment, and a closed-form analytical solution of its reliability function is derived. On this basis, a multi-objective optimization model is constructed, aiming to maximize equipment safety and minimize maintenance cost. The proposed method is validated using the NASA aircraft engine degradation dataset. The experimental results demonstrate that, while ensuring system reliability, the proposed approach significantly reduces maintenance costs compared to traditional periodic maintenance strategies, confirming its effectiveness and practical value. Full article