Analysis Method Research Articles

Heterogeneity of metabolic phenotypes in adolescent boys with obesity due to excess calories

The aim of the study was to determine the heterogeneity of metabolic parameters in teenage boys diagnosed with “obesity due to excess calories” using cluster analysis. Materials and methods. One hundred and twenty-three randomly selected patients aged 14 to 17 years diagnosed with “obesity due to excess calories” were examined. The statistical analysis included descriptive and analytical methods, in particular aimed to identify clusters of biochemical indicators, including serum fasting glucose level, insulin level, total cholesterol level, low density lipoproteins (LDLP), very low-density lipoproteins (VLDL), high density lipoproteins (HDLP) and triglycerides. Results. Two clusters of observations have been identified. The first cluster consisted of 72 patients (58.5%) having fasting glucose levels slightly below average, insulin levels above average, total cholesterol, triglycerides, LDLP and VLDL above average, and HDLP levels below average (“metabolically unhealthy” obesity). The second cluster included 51 teenagers (41.5%) who presented with opposite trends in metabolic characteristics (“metabolically healthy” obesity). The adolescents from the first cluster were slightly older, had higher body mass, body mass index (BMI) and z-BMI scores, and higher HOMA-IR scores. Arterial hypertension and non-alcoholic fatty liver disease were diagnosed statistically significantly more frequently in adolescents from this cluster. Conclusion. Teenage boys with obesity are a heterogeneous group in terms of the values of major metabolic indicators.

Multimass Analysis of Adeno-Associated Virus Vectors by Orbitrap-Based Charge Detection Mass Spectrometry.

Adeno-associated virus (AAV) vectors have attracted significant attention as the main platform for gene therapy. To ensure the safety and efficacy of AAV vectors when used as gene therapy drugs, it is essential to assess their critical quality attributes (CQAs). These CQAs include the genome packaging status, the size of the genome encapsidated within the AAV capsid, and the stoichiometry of viral proteins (VPs) that constitute the AAV capsids. Analytical methods have been established for evaluating CQAs, such as analytical ultracentrifugation, capillary gel electrophoresis with laser-induced fluorescence detection, and capillary gel electrophoresis using sodium dodecyl sulfate with UV detection. Here, we present a multimass analysis of AAV vectors using orbitrap-based charge detection mass spectrometry (CDMS), a single-ion mass spectrometry. Orbitrap-based CDMS facilitates the quantitative evaluation of the genome packaging status based on the mass distribution of empty and full particles. Additionally, we established a novel method to analyze the encapsidated genome directly without pretreatment, such as protein digestion or heat treatment, and to estimate the stoichiometric variation of VP for the capsid based on the mass distribution constituted by the single peak corresponding to AAV particles. Orbitrap-based CDMS is a distinctive method that allows multiple mass characterizations of AAV vectors with a small sample volume of 20 μL for 1013 cp/mL in a short time (30 min), and it holds the potential to become a new standard method in the assessment of CQAs for AAV vectors.

Improved catalytic stability of immobilized Candida antarctica lipase B on macroporous resin with organic polymer coating for biodiesel production.

Lipase is one of the most widely studied and applied biocatalysts. Due to the high enzyme leakage rate of the immobilization method of physical adsorption, we propose a new lipase immobilization method, based on the combination of macroporous resin adsorption and organic polymer coating. The immobilized Candida antarctica lipase B (CALB@resin-CAB) was prepared by combining the macroporous resin adsorption with cellulose acetate butyrate coating, and its structure was characterized by various analytic methods. Immobilized lipase was applied for biodiesel production using acidified palm oil as the starting material, the conversion rate achieved as high as 98.5% in two steps. Furthermore, the immobilized lipase displayed satisfactory stability and reusability in biodiesel production. When the aforementioned reaction was carried out in a continuous flow packed bed system, the yield of biodiesel was 94.8% and space-time yield was 2.88g/(mL∙h). The immobilized lipase CALB@resin-CAB showed high catalytic activity and stability, which has good potential for industrial application in the field of oil processing.

AI meets academia: transforming systematic literature reviews

PurposeThis study synthesizes the role of artificial intelligence (AI) and automation in systematic literature reviews (SLRs), focusing in particular on efficiency, methodological quality and human–machine collaboration.Design/methodology/approachA systematic review methodology was applied, analyzing studies from Scopus and Web of Science databases to explore the use of AI and automation in SLRs. A final sample of 28 articles was selected through a rigorous and interdisciplinary screening process.FindingsOur analysis leads to seven themes: human and machine collaboration; efficiency and time savings with AI; methodological quality; analytical methods used in SLRs; analytical tools used in SLRs; SLR stages AI is utilized for and living systematic reviews. These themes highlight AI’s role in enhancing SLR efficiency and quality while emphasizing the critical role of human oversight.Research limitations/implicationsThe rapid advancement of AI technologies presents a challenge in capturing the current state of research, suggesting the need for ongoing evaluation and theory development on human–machine collaboration.Practical implicationsThe findings suggest the importance of continuously updating AI applications for SLRs and advocating for living systematic reviews to ensure relevance and utility in fast-evolving fields.Social implicationsIntegrating AI and automation in SLRs could democratize access to up-to-date research syntheses, informing policy and practice across various disciplines and redefining the researcher’s role in the digital age.Originality/valueThis review offers a unique synthesis of AI and automation contributions to SLRs, proposing a conceptual model emphasizing the synergy between human expertise and machine efficiency to improve methodological quality.

Statistical monitoring applied to data science in classification: continuous validation in predictive models

PurposeThis study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.Design/methodology/approachThe research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.FindingsValidation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.Originality/valueThe main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

The effect of national school health strategy components on the quality of health services: An Analytical research in Baghdad Governorate

Purpose: The research objective is to diagnose the extent to which the National School Health Strategy contributes to the quality of health services. Theoretical framework: The quality of health services can provide schools with benefits through building a healthy school community in line with the sustainable development goals, especially regarding quality education, good health, and well-being and through school health, we may be improving the health and well-being of elementary and secondary students, and the necessity of providing healthy foods reduction of fat content in school meals, and awareness to prevent the use of tobacco products and drugs. Design/methodology/approach: The research relied on the descriptive -analytical method and the research sample is a random sample of primary schools (governmental and private). The research tool is represented by using a questionnaire to collect data. Findings: Although there is a big school community represented by students compared to their society, studies in most countries of the world are still limited in discussing the quality of health services provided to them and the limited solutions. Research, Practical & Social implications: We suggest future research to evaluate the national school health strategy: a comparative study between two governorates. Originality/value: The results indicate that commitment to implementing school health programs positively affects the quality of health services provided to students of school age and that any defect in their application reflects negatively on the health of students currently and society in the future.

Force-extension and longitudinal response of wormlike chains with single cross-link

Certain important biopolymers, such as actin filaments, are known to have cross-links at their interfaces, which significantly influence their mechanical properties. To explore these effects, the force-extension and longitudinal response of wormlike chains (WLCs) with a single cross-link under tension in two-dimension are examined using both analytical methods and Brownian dynamics simulations. The cross-link is modeled as a spring in the analytical method, and mode analysis is used to calculate the path integrals associated with the partition function. These theoretical results are then validated through Brownian dynamics simulations. Final results indicate that the simulation results are consistent with the theoretical predictions, particularly for situations involving large tensile force and short chain, which are prerequisites for the application of the weak bending approximation.

Dynamic Analysis of Metamorphic Mechanisms with Impact Effects During Configuration Transformation

AbstractMetamorphic mechanisms have attracted considerable attention owing to their capability to switch their topology to adapt to different operational tasks. One feature of topological change is the re-contact of different bodies, which inevitably causes collisions affecting operation accuracy and service life. Consequently, in this study, a collision incidence matrix was introduced to describe the topology of a system involved in collisions, and a method for reducing the closed-loop system to an open-loop system was proposed. The complex movement of the metamorphic mechanism in a changing topology was classified into two different running stages of the source metamorphic mechanism. Based on the relative coordinate method, dynamic modeling of the source metamorphic mechanism considering the impact effects was conducted. Combining the classical collision theory and Newton–Euler equation, the generated impact impulse and the motion after collision were determined. Subsequently, a dynamic analytical method for the full configuration of metamorphic mechanisms was proposed to reflect the changes in the topological structure in the dynamic model. Finally, two typical metamorphic mechanisms used in packaging and spinning were considered as examples to verify the correctness and effectiveness of the proposed method, and their impact characteristics during configuration transformation were analyzed. The proposed analytical method of internal impact for a variable topology process provides effective theoretical guidance for the stability analysis of configuration transformation and structural design aimed at minimizing impacts.

Nutshell Materials as a Potential Eco-Friendly Biosorbent for the Effective Extraction of UV Filters and Parabens from Water Samples

UV filters and parabens, as ingredients of cosmetics, are commonly occurring water pollutants. In our work, nutshells were used as biosorbents in the developed analytical procedure for the determination of UV filters and parabens in water samples. The shells obtained from walnuts, hazelnuts, peanuts and pistachios were applied as biosorbents. The proposed analytical method can be used as a powerful alternative to other methods for the analysis of UV filters and parabens in water samples. A method of carrying out the sorption step and its parameters, i.e., the effect of time, pH, and salt addition, was developed. A method for the desorption of analytes was also developed, in which the type and volume of solvent, and the desorption time, were established. The recoveries were in the range of 59–117% for benzophenones and lower recoveries from 14 to 75% for parabens. The results showed that nutshells can be used as low-cost, efficient and eco-friendly biosorbents for the determination of parabens and UV filters in water samples. These materials can be used as a ‘greener’ replacement for the commercially available adsorbents for the extraction of cosmetic ingredients from the environment.

Modeling of an inductive displacement sensor based on 1DCNN-LSTM-AT

Abstract The input-output relationship of inductive displacement sensors is typically nonlinear, which demands numerous computational resources for precise calculation. Therefore, the traditional analytical methods for the inductive displacement sensors cannot meet the real-time high-precision measurement requirements. In response to the aforementioned issues, this paper proposes an optimized Long Short-Term Memory (LSTM) neural networks based on one-dimensional convolutional neural networks (1DCNN) to modeling the input-output relationship of the inductive displacement sensor. First, the measurement principle of the inductive displacement sensors was analyzed, and the analytical model of the sensor output was derived. And the influences of the key parameters of sensors on the relationship between the induced voltage and the displacement were studied. Then, the 1DCNN-LSTM-AT network for modelling the input-output relationship of the inductive displacement sensor was studied. The spatial features of historical induced electromotive force (EMF) data generated by the induction coil were initially extracted using the 1DCNN network. And these spatial features were then utilized as input to the LSTM neural network to capture the temporal features of the historical induced EMF data. Subsequently, the spatiotemporal features of the induced EMF data were fed into the regression prediction layer to compute the displacement measurement results corresponding to the current input. Moreover, the attention mechanism was used for the 1DCNN-LSTM model to enhance the prediction accuracy and stability of the model. Finally, the experimental results demonstrate that the proposed 1DCNN-LSTM-AT model achieves an average absolute percentage error (MAPE) of 3.1%, significantly outperforming traditional models such as LSTM (29.3%), CNN (4.7%), and ANN (4.3%). This paper provides a new method for modelling the nonlinear relationships of the inductive displacement sensor, and presents a fresh perspective for research in the data processing of nonlinear sensors.

Greenness and whiteness appraisal for bioanalysis of quetiapine, levodopa and carbidopa in spiked human plasma by high performance thin layer chromatography.

A sustainable HPTLC-densitometric method was developed for quantitative determination of Quetiapine (QUET), Levodopa (LD) and Carbidopa (CD) in presence of Dopamine (DOP) as an internal standard. This applicable technique was achieved by spiking human plasma and extraction was performed using the protein precipitation approach. The mobile phase used was acetone, dichloromethane, n-butanol, glacial acetic acid and water (3: 2.5: 2: 2: 1.75, by volume). Method validation was done according to US-FDA guidelines and was able to quantify Quetiapine, Levodopa and Carbidopa in the ranges of 100-4000, 200-8000 and 30-1300 ng/mL, respectively. Bioanalytical method validation parameters were assessed for the studied drugs. Finally, the analytical suggested methodology was evaluated using various green and white analytical chemistry metrics and other tools, such as the green solvent selection tool, analytical eco-scale, green analytical procedure index, analytical greenness metric approach and the red-green-blue algorithm tool. The results revealed that the applied analytical method had a minor impact on the environment and is a relatively greener option than other previously reported chromatographic methods.

Surface Deformation Analysis by Fusion of Multi-temporal and Multi-source Data under Disaster Emergency Conditions

Abstract. Rapid analysis of surface deformation is crucial for rescue operations following natural disasters. However, the lack of recent terrain data and the discrepancy between data types and field-collected data often hinder timely surface deformation analysis. To enhance data usability, this paper proposes an analytical method that integrates multiple sources of remote sensing data, including satellite data, oblique photography data, and LiDAR data. By merging oblique images with grayscale point clouds, true-color point clouds are generated. The method optimizes old data through threshold segmentation and median filtering, then converts and unifies the resolution of multi-source data via data interpolation. Elevation interpolation matrices are employed for overlay analysis, and a combination of bilateral filtering and threshold processing is used. This groundbreaking approach enables the completion of surface deformation analysis in emergency geospatial surveys and has been validated in various typical regions, demonstrating its application potential across different surface environments. Experimental results indicate that this method can quickly utilize multi-temporal and multi-source remote sensing data to effectively identify surface deformations following natural disasters.

Negotiation Strategies and Their Effect on Organizational Success: Analytical Research in The Iraqi Ministry of Industry and Minerals

Purpose: This study aims to determine the impact of Negotiation Strategies (NS) on Organizational Success(OS) in the Iraqi Ministry of Industry and Minerals. Conceptual Framework: The NS includes dimensions (cooperative strategy, competitive strategy, avoidance strategy, bargaining strategy and absorption strategy) measured by (Thomas, 2008), and OS includes dimensions (resources, organizational structure, management, process and knowledge management) measured by (Hamedi & Azimi, 2013)​. Design/Methodology/approach: The descriptive analytical method was adopted in completing the current research, on a sample of senior and middle leaders in the Iraqi Ministry of Industry and Minerals under study, which numbered (120) individuals, and they were selected using the comprehensive survey stratified sample method. Findings: We found that NS plays a positive role in achieving organizational success in the Iraqi Ministry of Industry and Minerals. NS enhances cooperation and coordination between parties within the ministry, which has led to improved operations. Thus, the main and sub-hypotheses of the research were proven correct. Research, Practical & Social implications: Identify the latest developments and fundamental ideas related to the variables of the study (NS - organizational success) to find interrelationships between the variables. Originality/value: Providing those concerned and interested with practical and theoretical information that explains the nature of the research variables, their sources and causes, the extent of their reflection on the research sample, and the extent of the interest of the researched organization (the Iraqi Ministry of Industry and Minerals) in their dimensions.

Current perspectives on the use of milk fat globule membrane in infant milk formula.

Sources of milk fat globule membrane (MFGM) are desirable to include in infant milk formula (IMF) to mimic the composition and functionality of human milk MFGM. MFGM in its natural form consists of a trilayer structure containing lipids (e.g., cholesterol, phospholipids, gangliosides, ceramides), proteins (e.g., butyrophilin, xanthine oxidase, mucin-1, adipophilin) and glycans (e.g., sialic acid). Components of MFGM have been associated with various biological benefit areas including intestinal, neurocognitive, and immune health. There are many aspects to consider when supplementing IMF with MFGM ingredients, of which the major ones are highlighted and critiqued in this review from an industrial research perspective. Features include compositional unknowns, discussion on how best to incorporate MFGM to IMF, analytical method needs, biological function unknowns, and considerations on how best to communicate MFGM in different contexts. It is hoped that by identifying the key scientific gaps outstanding in this subject area, collective efforts can proceed to ensure the potential impact of MFGM on infant health is realized.

Synthesis, Structural Characterization, Optical Properties, and Biological Potency of Co(II), Cu(II), and Cd(II) Complexes With Heterocyclic Thiosemicarbazide Ligand: Insights From Molecular Docking Studies

ABSTRACTA novel heterocyclic thiosemicarbazide ligand, [(Z)‐N‐(2‐oxoindolin‐3‐ylidene)nicotinohydrazide] (H2L), and its Co(II), Cu(II), and Cd(II) metal chelates were synthesized and characterized using a variety of analytical methods, including elemental analysis, FT‐IR, UV–Visible spectroscopy, EI‐mass, fluorescence spectroscopy, X‐ray powder diffraction, and TGA. The structures and geometries of the complexes were determined using these methods as [Co2(H2L)2Cl2(H2O)4]·2H2O, [Cu(H2L)2Cl2(H2O)2]·2H2O, and [Cd(H2L)2Cl2(H2O)2], all having octahedral geometry. The particulate diameters for [Co2(H2L)2Cl2(H2O)4]·2H2O, [Cu(H2L)2Cl2(H2O)2]·2H2O, and [Cd(H2L)2Cl2(H2O)2] were 192.68, 68.48, and 192.74 nm, respectively using Debye–Scherrer equation and FWHM approach. The corresponding FWHM values were 0.0078, 0.0217, and 0.0075, whereas the crystal strain was calculated as (ε) = 0.0018, 0.0056, and 0.0054, and the dislocation density (δ) as 2.69 × 10−5, 2.13 × 10−4, 2.69 × 10−5 Å−2. Quantum chemical calculations for the ligand and its solid metal complexes were carried out utilizing DFT with the DMOL3 module. Furthermore, the optical sensing response of the free ligand H2L to Co(II), Cu(II), and Cd(II) ions was investigated, revealing that the presence of these metal ions enhanced the fluorescence spectra of the ligand. Additionally, the antibacterial effects of these compounds, as well as their cytotoxicity in human cancer cells, were investigated. Compared to its metal complexes, the ligand, H2L exhibits increased antibacterial activity against various bacteria. Tests on HepG‐2 and MCF‐7 cells were conducted to evaluate the compounds' anticancer efficacy against the standard, Doxorubicin. The ligand, H2L, demonstrated greater cytotoxicity against the HepG‐2 cell line than its metal complexes. Docking experiments were conducted to assess the interaction of the ligand and its complexes with HepG‐2 (PDB ID: 2OH4) and MCF‐7 (PDB ID: 3W2S) cells, using the XP glide technique and Schrödinger suite software.

Analyzing missingness patterns in real-world data using the SMDI toolkit: application to a linked EHR-claims pharmacoepidemiology study

BackgroundMissing data in confounding variables present a frequent challenge in generating evidence using real-world data, including electronic health records (EHR). Our objective was to apply a recently published toolkit for characterizing missing data patterns and based on the toolkit results about likely missingness mechanisms, illustrate the decision-making process for analyses in an empirical case example.MethodsWe utilized the Structural Missing Data Investigations (SMDI) toolkit to characterize missing data patterns in the context of a pharmacoepidemiology study comparing cardiovascular outcomes of initiating sodium-glucose-cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase‐4 inhibitors (DPP‐4i) among older adults. The study used a linked EHR-Medicare claims dataset from Duke Health patients (2015–2017), focusing on partially observed confounders from EHR data (HbA1c lab and body mass index [BMI] values). Our analysis incorporated SMDI's descriptive functions and diagnostic tests to explore missingness patterns and determine missingness mitigation approaches. We used findings from these investigations to inform estimation of adjusted hazard ratios comparing the two classes of medications.ResultsHigh levels of missingness were noted for important confounding variables including HbA1c (63.6%) and BMI (16.5%). Diagnostic tests resulted in output that described: 1) the distributions of patient characteristics, exposure, and outcome between patients with or without an observed value of the partially observed covariate, 2) the ability to predict missingness based on observed covariates, and 3) estimate if the missingness of a partially observed covariate is differential with respect to the outcome. There was evidence that missingness could be sufficiently described using observed data, which allowed multiple imputation by chained equations using random forests to address missing confounder data in estimating treatment effects. Multiple imputation resulted in improved alignment of effect estimates with previous studies.ConclusionsWe were able to demonstrate the practical application of the SMDI toolkit in a real-world setting. Application of the SMDI toolkit and the resulting insights of potential missingness patterns can inform the choice of appropriate analytic methods and increase transparency of research methods in handling missing data. This type of approach can inform analytic decision making and may increase our ability to generate evidence from real-world data.

Ultrasonic-Assisted Extraction and Antioxidant Evaluation of Resveratrol from Peanut Sprouts

The orthogonal array design method was used to optimize ultrasonic-assisted extraction of resveratrol from peanut sprouts. The results showed that the highest extraction yield of resveratrol using ultrasonic-assisted extraction could be up to 1.1%. The optimal extraction conditions were liquid to solid ratio of 30:1 (mL/g) and ethanol concentration of 80% (v/v) as solvent for 40 min at the temperature of 70 °C. AB-8 macroporous adsorption resin was used to purify the crude extract and the resveratrol content increased to 47.5% after one treatment run. The optimal adsorption parameters were initial concentrations in the sample solution of 2 mg/mL, a pH of 5.0, a flow rate of 2 mL/min, and a temperature of 25 °C. The optimal desorption parameters were 60% ethanol and a flow rate of 1 mL/min. The chemical composition of the peanut sprout’s resveratrol sample was investigated via HPLC, and the predominant constituents were found to be protocatechuic acid, catechins, caffeic acid, epicatechuic acid, resveratrol, and rutin. The antioxidant activities of the resveratrol were measured via the following different analytical methods: reducing power, 2,2-diphenyl-1-picrylhdrazyl (DPPH), hydroxyl radical-scavenger activity, superoxide radical-scavenger activity, the β-carotene bleaching test, and the scavenging nitrite test. The results indicated that the resveratrol in peanut sprouts have significant antioxidant activities and can be used as a source of potential antioxidant. And peanut sprout’s resveratrol has the potential and valuable application to be used as a new type of resveratrol resource. The finding of this study can provide some theoretical reference for the comprehensive utilization of peanut resources in the development of antioxidant health foods.

Advanced Analytical Methods for Risk Mitigation in Multimodal Freight Transport

Stakeholders in multimodal freight transport encounter significant challenges due to the multitude of unknowns and inherent risks that can adversely affect operations. The subjective nature of the information complicates the identification and assessment of these risks, making them particularly challenging in the context of multimodal transport, where the potential consequences can be substantial. This research intends to provide a comprehensive understanding of the asymmetries in risks associated with multimodal freight transport by identifying and evaluating quantitative hazards. By integrating fuzzy set theory and failure mode and effects analysis (FMEA), the study offers a structured approach to statistically forecast risks, addressing imprecision in traditional risk assessments. Qualitative interviews conducted with multimodal freight transport operators in Thailand reveal critical insights, including the identification of high-priority risks such as delays from regulatory compliance, inadequate infrastructure, and inefficiencies in stakeholder communication. The findings of this study not only highlight these pressing issues but also provide actionable strategies to mitigate risks, thereby enhancing the operational resilience of multimodal freight transport systems.

A Method for Analyzing Transverse Stress in Link Slabs of Simply Supported Steel–Concrete Composite Bridges

The cracking of link slabs in jointless bridges presents significant challenges due to the complexity of their stress conditions. This study focused on analyzing the transverse stresses in link slabs of jointless steel–concrete composite bridges. Utilizing linear elasticity theory and partial differential equations of plates, the deflection and stress distribution functions for the link slabs were determined. The validity of these analytical solutions was confirmed through comparisons with finite element models and load tests. Results from both the load tests and the finite element model indicate that the upper face of the girder end link slabs experiences maximum tensile stresses in both transverse and longitudinal directions. The stress values obtained from the analytical method align well with these results, showing that the total stress, when considering transverse stresses, reaches 107% of the longitudinal stresses alone. Furthermore, a 40% reduction in longitudinal girder spacing or a 50% increase in girder end length can lead to link slab stresses of 128% and 145% of the longitudinal stresses, respectively. This finding suggests that even loads lower than those designed based solely on longitudinal stresses can result in cracking. Therefore, it is recommended that transverse stresses be considered in the design of link slabs for jointless bridges. Relying solely on conventional longitudinal stress analyses may underestimate actual stress conditions and contribute to the formation of cracks.

Simultaneous analysis of several plasticizer classes in different matrices by on-line turbulent flow chromatography-LC-MS/MS.

The development of methodologies for the determination of plasticizers is essential for assessing the environmental and human impact resulting from the use of plastics. A fast analytical method with on-line purification based on turbulent flow chromatography (TFC) coupled to tandem mass spectrometry (MS-MS) has been developed for the analysis of ten phthalates, four alternative plasticizers (including adipates and citrates), and 20 organophosphate esters (OPEs). The method has been validated for the determination of plasticizers across different matrices. Analytical parameters showed acceptable recoveries ranging between 50 and 125%, RSDs lower than 20%, and mLODs of 0.001-2.08ngg-1 wet weight (ww), 0.002-0.30ngg-1, and 0.001-0.93ngm-3 for foodstuffs, face masks, and ambient air, respectively. These methodologies were applied to foodstuff samples purchased in grocery stores, reusable and self-filtering masks, and indoor air measured in different locations. Plasticizers were detected in all the analyzed samples, with values up to 22.0μgg-1 ww, 6.78μgg-1, and 572ngm-3 for foodstuffs, face masks, and indoor air, respectively. The contribution of each family to the total plasticizer content varied between 1.3 and 87%, 0.5 and 98%, and 0.5 and 65% for phthalates, alternative plasticizers, and OPEs, respectively. These findings highlighted the need for analytical methodologies capable of simultaneously assessing a wide number of plasticizers with minimal extractionsteps. This capability is crucial in order to obtain more conclusive insights into the impact of these pollutants on both the environment and human health, arising from different sources of exposure such as foodstuffs, plastic materials, and atmospheric air.