Search Results (429)

This study presents an advanced analytical method for the simultaneous quantification of malondialdehyde (MDA), a biomarker of oxidative stress, and diphenyl phosphate (DPhP), a metabolite of the organophosphate flame retardant triphenyl phosphate (TPhP), in human urine. The method integrates hydrophilic interaction liquid chromatography (HILIC), a type of liquid chromatography suitable for polar compounds, for MDA separation, and an online restricted access material (RAM), a preconcentration column, for DPhP isolation, achieving high specificity and sensitivity. Validation with certified urine samples confirmed its robustness across diverse analyte concentrations and complex biological matrices. The optimized clean-up steps effectively minimized carryover, allowing for high-throughput analysis. Application to 72 urine samples revealed a significant positive correlation (ρ = 0.702, p-value = 1.9 × 10⁻⁷) between MDA and DPhP levels, supporting a potential link between oxidative stress and TPhP exposure. The subset analysis demonstrated a statistically significant moderate positive correlation in women (ρ = 0.622, p-value = 0.020), although this result should be interpreted with caution because of the limited sample size (N = 14). This method provides a powerful tool for biomonitoring oxidative stress and environmental contaminants, offering valuable insights into exposure-related health risks. Full article

Mass spectrometry (MS) and coupled gas chromatography-mass spectrometry (GC-MS) are globally recognized as the primary techniques for the analysis of gases or vapors due to their selectivity, sensitivity, accuracy, and reproducibility. When thermal stress is applied, vapors or gases are released as a result of the reactions and changes that occur. The analysis of these gases during the thermally induced reaction is scientifically referred to as evolved gas analysis (EGA), which is essential for confirming the occurrence of the induced reactions. Pyrolyzers, thermobalances, or simple heaters can increase the temperature of the analyzed samples according to a programmed and software-managed ramp, allowing for control over both the heating rate and isothermal stages. The atmosphere can also be varied to simulate pyrolysis or thermo-oxidative processes. This way, each induced reaction generates a unique evolved gas, which can be linked to a theoretically hypothesized mechanism. Mass spectrometry (MS) and coupled gas chromatography–mass spectrometry (GC-MS) are fundamental analytical methods used for on-line thermally induced evolved gas analysis (OLTI-EGA). Full article

Background and Aim: Digital surveillance, which utilizes data from social media, search engines, and other online platforms, has emerged as an innovative approach for the early detection of infectious disease outbreaks. This scoping review aimed to systematically map and characterize the methodologies, performance metrics, and limitations of digital surveillance tools compared to traditional epidemiological monitoring. Methods: A scoping review was conducted in accordance with the Joanna Briggs Institute and PRISMA-SCR guidelines. Scientific databases including PubMed, Scopus, and Web of Science were searched, incorporating both empirical studies and systematic reviews without language restrictions. Key elements analyzed included digital sources, analytical algorithms, accuracy metrics, and validation against official surveillance data. Results: The reviewed studies demonstrate that digital surveillance can provide significant lead times (from days to several weeks) compared to traditional systems. While performance varies by platform and disease, many models showed strong correlations (r > 0.8) with official case data and achieved low predictive errors, particularly for influenza and COVID-19. Google Trends and X (formerly Twitter) emerged as the most frequently used sources, often analyzed using supervised regression, Bayesian models, and ARIMA techniques. Conclusions: While digital surveillance shows strong predictive capabilities, it faces challenges related to data quality and representativeness. Key recommendations include the development of standardized reporting guidelines to improve comparability across studies, the use of statistical techniques like stratification and model weighting to mitigate demographic biases, and leveraging advanced artificial intelligence to differentiate genuine health signals from media-driven noise. These steps are crucial for enhancing the reliability and equity of digital epidemiological monitoring. Full article

Industrial production often involves complex time-varying operating conditions that result in continuous time-series production data. The traditional soft sensor approach has difficulty adjusting to such dynamic changes, which makes model performance less optimal. Furthermore, online analytical systems have significant operational and maintenance costs and entail a substantial delay in measurement output, limiting their ability to provide real-time control. In order to deal with these challenges, this paper introduces a multivariate multi-step predictive multilayer perceptron regression soft-sensing model, referred to as incremental MVMS-MLP. This model incorporates incremental learning strategies to enhance its adaptability and accuracy in multivariate predictions. As part of the method, a pre-trained MVMS-MLP model is developed, which integrates multivariate multi-step prediction with MLP regression to handle temporal data. Through the use of incremental learning, an incremental MVMS-MLP model is constructed from this pre-trained model. The effectiveness of the proposed method is demonstrated by benchmark problems and real-world industrial case studies. Full article

Modern database systems require autonomous CPU scheduling frameworks that dynamically optimize resource allocation across heterogeneous workloads while maintaining strict performance guarantees. We present a novel hierarchical deep reinforcement learning framework augmented with graph neural networks to address CPU scheduling challenges in mixed database environments comprising Online Transaction Processing (OLTP), Online Analytical Processing (OLAP), vector processing, and background maintenance workloads. Our approach introduces three key innovations: first, a symmetric two-tier control architecture where a meta-controller allocates CPU budgets across workload categories using policy gradient methods while specialized sub-controllers optimize process-level resource allocation through continuous action spaces; second, graph neural network-based dependency modeling that captures complex inter-process relationships and communication patterns while preserving inherent symmetries in database architectures; and third, meta-learning integration with curiosity-driven exploration enabling rapid adaptation to previously unseen workload patterns without extensive retraining. The framework incorporates a multi-objective reward function balancing Service Level Objective (SLO) adherence, resource efficiency, symmetric fairness metrics, and system stability. Experimental evaluation through high-fidelity digital twin simulation and production deployment demonstrates substantial performance improvements: 43.5% reduction in p99 latency violations for OLTP workloads and 27.6% improvement in overall CPU utilization, with successful scaling to 10,000 concurrent processes maintaining sub-3% scheduling overhead. This work represents a significant advancement toward truly autonomous database resource management, establishing a foundation for next-generation self-optimizing database systems with implications extending to broader orchestration challenges in cloud-native architectures. Full article

This article proposes a method to solve nonlinear optimal control problems with arbitrary performance indices and terminal constraints, which is based on the neighboring extremal method and Gauss pseudospectral collocation. Firstly, a quadratic performance index is formulated, which minimizes the second-order variation of the nonlinear performance index and fully considers the deviations in initial states and terminal constraints. Secondly, the first-order necessary conditions are applied to derive the perturbation differential equations involving deviations in state and costate variables. Therefore, a quadratic optimal control problem is formulated, which is subject to such perturbation differential equations. Thirdly, the Gauss pseudospectral collocation is used to transform the differential and integral operators into algebraic operations. Therefore, an analytical solution of the control correction can be successfully derived in the polynomial space, which comes close to the optimal solution. This method has a fast computation speed and low computational complexity due to the discretization at orthogonal points, making it suitable for online applications. Finally, some simulations and comparisons with the optimal solution and other typical methods have been carried out to evaluate the performance of the method. Results show that it not only performs well in computational efficiency and accuracy but also has great adaptability and optimality. Moreover, Monte Carlo simulations have been conducted. The results demonstrate that it has strong robustness and excellent performance even in highly dispersed environments. Full article

Heterozygous mutations in the GBA1 gene, encoding the enzyme glucocerebrosidase (GCase), are major risk factors for Parkinson’s Disease (PD). Ambroxol, a small chaperone originally used as a mucolytic agent, has been shown to cross the blood–brain barrier, enhance GCase activity, and reduce α-synuclein levels, making it a promising therapeutic candidate for disease-modifying effects in GBA1-associated PD (GBA1-PD). This study aimed to develop a method to quantify ambroxol levels in human plasma and cerebrospinal fluid (CSF) using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Ambroxol was determined by online solid-phase extraction (SPE), coupled with LC-MS/MS, by gradient elution on a monolithic column. Detection employed a 3200 QTRAP tandem mass spectrometer in the positive electrospray ionization mode. Calibration curves exhibited linearity across the analyzed ranges in both plasma and CSF. The recovery rate ranged from 106.7% to 113.5% in plasma and from 99.0% to 103.0% in CSF. No significant matrix effect was observed. Intra-day and inter-day precisions were below 11.8% in both matrices, and accuracy ranged from 89.9% to 103.1% in plasma and from 96.3% to 107.8% in CSF. We evaluated and confirmed the stability of the analyte in plasma and CSF across various storage conditions. The method was successfully validated according to European Medicine Agency (EMA) guidelines and its applicability was confirmed in the context of a multicenter, randomized, double-blind, placebo-controlled, phase II study, designed to monitor the ambroxol levels in the plasma and CSF of GBA1-PD. Full article

Background/Objectives: Work engagement is essential for quality care and workforce retention in professional nursing. While job demands–resources theory has guided extensive research on job-related antecedents, personal resources have received comparatively less empirical attention, primarily focusing on self-efficacy, self-esteem, and optimism. This study examined the unique association between ikigai—the Japanese concept of life purpose—as a novel personal resource and work engagement in professional nursing practice, controlling for established job resources, demands, and personal resources. Methods: An analytical cross-sectional study was conducted with vocational nursing trainees in Germany (N = 166). Data were collected via online questionnaire using validated instruments to assess ikigai, job resources (autonomy, interpersonal relations, professional resources), job demands (work overload, lack of formal rewards), other personal resources (self-efficacy, organization-based self-esteem, optimism), and work engagement. Hierarchical multiple linear regression examined ikigai’s unique association with work engagement. Results: The final model explained 40.3% of variance in engagement, with ikigai accounting for a statistically significant increase in explained variance (ΔR² = 0.033, p < 0.01). Ikigai demonstrated a unique positive association with work engagement (β = 0.24, p < 0.01), comparable in strength to job resources and other personal resources. Conclusions: Findings support ikigai as a distinct personal resource associated with work engagement among nursing trainees. This extends the job demands–resources model by highlighting the relevance of existential constructs. Supporting ikigai development may offer a complementary strategy for promoting engagement in professional nursing. Full article

Land managers across the United States (U.S.) are developing plans to mitigate climate change. Effective implementation and monitoring of these climate action plans require standardized methods and timely, accurate geospatial data at appropriate resolutions. Despite the abundance of geospatial and statistical data in the U.S., a significant gap remains in translating these data into actionable insights. To address this gap, we developed the Land Emissions and Removals Navigator (LEARN), an online tool that automates subnational greenhouse gas (GHG) inventories of forests and trees in nonforest lands using a standardized analytical framework consistent with national and international guidelines. LEARN integrates multiple datasets to calculate land cover and tree canopy changes, delineate areas of forest disturbance, and estimate carbon emissions and removals. To demonstrate the application of LEARN, this paper presents case studies in Jefferson County, Washington; Montgomery County, Maryland; and federally owned forests across the conterminous U.S. Our results highlight LEARN’s capacity to provide localized insights into carbon dynamics, enabling subnational entities to develop tailored climate strategies. By enhancing accessibility to standardized data, LEARN empowers community land managers to more effectively mitigate climate change. Future developments aim to expand LEARN’s scope to cover nonforest landscapes and incorporate additional decision-making functionalities. Full article

The eddy current testing (ECT) technique enables efficient and non-destructive conductivity measurement. However, conventional ECT is significantly influenced by the thickness of the material, often resulting in the arbitrary selection of excitation frequency. In addition, complex inverse calculations in the eddy current analytical model pose challenges for practical application. This paper proposes a method for measuring the conductivity of non-ferromagnetic materials based on a simplified analytical model. Firstly, the classical Dodd–Deeds analytical model is simplified based on the electromagnetic properties of materials under high-frequency conditions, resulting in a simplified model that directly relates the coil impedance phase to the material’s conductivity. Furthermore, in combination with a finite element method (FEM) analysis, a frequency selection criterion is proposed, and a corresponding measurement method is developed. This method enables direct conductivity calculation by substituting the measured coil impedance phase into the simplified model. Finally, experiments were conducted to verify the effectiveness of the proposed method. The results demonstrate that the proposed method accurately measures the conductivity of non-ferromagnetic materials over a range of 0.5–58.5 MS/m, achieving absolute and relative errors less than 1.05 MS/m and 1.83%, respectively, without requiring complex inversion calculations or multiple calibrations. This advancement in measurement principles provides a new theoretical foundation and technical pathway for developing online inspection systems and portable instrumentation. Full article

Real-time mining (RTM) has become increasingly significant in response to the growing need for sustainable mineral resource extraction, driven by global population growth and technological progress. This innovative approach addresses critical challenges, such as declining ore grades, deeper and less accessible deposits, and rising energy costs, by integrating advanced online grade monitoring, data analysis, and process optimization. By employing real-time grade control, dynamic mine planning, and production optimization, it enhances the efficiency of resource extraction while minimizing environmental and social impacts. Originally proposed about a decade ago, RTM has gained attention for its potential to revolutionize the industry. This review examines recent advancements in closed-loop concepts, emphasizing the integration of advanced sensors and data analytics to enable continuous monitoring and adaptive decision making across the mining value chain. It highlights the role of online sensor technologies in providing high-resolution data for process optimization and evaluates various mining optimization techniques. The paper also explores data assimilation methods, such as Kalman filters and artificial intelligence (AI), showcasing their ability to continuously update models and reduce operational uncertainties. Ultimately, it proposes a comprehensive framework for adaptive, data-driven mining operations that promote sustainable development, enhance profitability, and improve decision-making capabilities. Full article

Scientific culture in pedagogical work involves the integration of scientific principles, values, and practices into education to promote critical thinking, evidence-based reasoning, and curiosity. It fosters an environment where students develop as independent learners, problem-solvers, and informed citizens. Teachers play a key role in cultivating this culture, acting as facilitators and guides who equip students with the tools to think critically and engage with the world scientifically. While often associated with STEM disciplines, scientific literacy extends beyond these areas, emphasizing the integration of scientifically grounded knowledge into all subject areas. To achieve this, teachers must continually enhance their own understanding and skills in scientific thinking. Staying updated with the latest scientific discoveries, critically evaluating information, and applying innovative pedagogical methods are essential. Professional development can provide a vital avenue for teachers to acquire these competencies. Approaches such as reading scientific literature, collaborating with colleagues, and attending specialized training programs can improve teaching strategies and promote scientific thinking in the classroom. This study investigated professional development programs provided for in-service teachers to understand how they contribute to fostering a scientific culture. The researchers collected data from municipalities in Latvia and online resources to summarize the information on the professional development programs provided to in-service teachers. This study examined how elements of scientific culture are reflected in teacher professional development programs in Latvia, using Merton’s CUDOS norms as an analytical framework. The acronym CUDOS refers to four foundational principles of scientific ethos: communalism, universalism, disinterestedness, and organized skepticism. These norms guided the evaluation of whether and how scientific inquiry values are embedded in the design and delivery of training programs for in-service teachers. Using quantitative and qualitative methods for data analysis, it was found that in-service teacher training in Latvia is highly eclectic and often lacks alignment with scientifically grounded principles. There are a lot of programs provided to develop transversal competencies, but very few subject-specific programs are available. The findings highlight the need for more structured and cohesive professional development programs to support in-service teachers in developing competence in cultivating scientific inquiry, nurturing curiosity, and empowering students to navigate an increasingly complex and technology-driven society. These insights offer practical implications for education policymakers and program designers aiming to enhance the scientific orientation of teacher training. By identifying specific gaps in content and alignment with scientific culture, this study provides an original contribution to the discourse on evidence-informed teacher development and supports a more conceptually grounded and equitable approach to lifelong professional learning in Latvia. Full article

The global sleep aids market has grown significantly due to rising stress and increased awareness of sleep’s importance. Melatonin, available in the EU as a prescription or over-the-counter medicine, depending on the country, is also permitted in dietary supplements with country-specific limits. Recent reports indicate concerning levels of excessive melatonin in EU dietary supplements, necessitating accurate quantification methods. We developed and validated, by applying accuracy profiles, ISO17025-compliant, rapid ultra-high performance liquid chromatography (UHPLC) methodologies coupled with either diode array detection (DAD) or high-resolution accurate mass spectrometry (HRAM MS). The cost-effective UHPLC-DAD method is suitable for medicines and most dietary supplements, except those more complex herbal matrices containing passionflower, hop, hemp, lime tree or lavender or their extracts, where UHPLC-HRAM MS is recommended due to selectivity issues of the DAD methodology. To demonstrate the applicability, we analyzed 50 dietary supplements claiming to contain melatonin—25 from legal supply chains and 25 from suspicious sources claiming therapeutic melatonin content. Our findings confirmed previous reports of high melatonin content in online products, especially when purchased through rogue internet pharmacies. Moreover, 12% of legal supply chain samples violated current legislation through unauthorized health claims or contained at least triple the melatonin amount permitted in Belgium. This research provides reliable analytical methods for regulatory bodies and confirms the circulation of non-compliant melatonin-containing dietary supplements in the EU market, even in the legal supply chain. Full article

Flood risk to rice production has previously been examined in terms of river basins or administrative units, incorporating data about the flood year, inundated area, precipitation, elevation, and impacts. However, there is limited knowledge about this topic, as most flood impact studies have focused on loss and damage to people and the economy. It remains important to identify how flood risk to rice production can be better identified within a long-term, community-based, analytical framework. In addition, flood risk studies in Sri Lanka tend to focus on single-year flood events within an administrative boundary, making it difficult to fully comprehend risks to rice production. This paper aims to fill these gaps by investigating long-term flood risk levels on rice production. With this aim, we collected and analyzed information about rice production, geospatial data, and 15-year precipitation records. Temporal-spatial maps were generated using Google Earth Engine JavaScript coding, Google Earth Pro, and OpenStreetMap. In addition, focus group discussions with farmers and key informant interviews were conducted to verify the accuracy of online information. The collected data were analyzed using descriptive statistics, GIS, and linear regression analysis methods. Regarding rice production impacts, we found that floods in the years 2006–2007, 2010–2011, and 2014–2015 had significant impacts on rice production with 20.5%, 75.8%, and 16.6% reductions, respectively. Flood risk maps identified low-, medium-, and high-risk areas based on 15-year flood events, elevation, proximity to water bodies, and 15-year flood-induced damage to rice fields. High risk areas were further studied through field discussions and interviews, showing the connection between past floods and poor water governance practices in terms of dam management. Our linear regression analysis found a marginal negative correlation between total seasonal rainfall and rice production. Full article

Permanent magnet flux harmonics in Permanent Magnet Synchronous Motors (PMSMs) can cause torque ripple. Traditional torque ripple suppression methods based on analytical models are highly dependent on the accuracy of motor parameters, while existing flux harmonic identification techniques often suffer from limited precision, compromising the effectiveness of ripple suppression. This paper proposes an online flux harmonic identification method that considers the dead-time effect of inverters. A dead-time compensation algorithm is introduced to effectively mitigate current harmonics induced by inverter dead-time. The current harmonic signals are extracted using a multi-synchronous rotating coordinate system. A harmonic controller is employed to suppress current harmonics, and its output voltage is used to identify the permanent magnet flux harmonics, from which a flux harmonic lookup table is constructed. Based on the identified flux harmonics, the torque ripple suppression strategy using analytical methods is further optimized. Experimental results validate the effectiveness of the proposed method in improving flux harmonic identification accuracy and reducing torque ripple. Full article