Classical Analysis Methods Research Articles

Rapid assessment of main agronomic indicators in the grain of winter and spring forms of triticale using infrared spectroscopy

Background. Calibration models were developed for rapid assessment of crucial agronomic indicators in the grain of winter and spring triticale from the VIR collection using near-infrared spectroscopy (NIRS). These calibration models will enable users to find the most optimal utilization trends for individual triticale accessions. Materials and methods. Indicators of agronomic value (coloration, moisture, and the content of ash, protein, starch, amylose and fiber) were studied on a set of 32 grain samples collected from winter and spring triticale forms (Triticosecale Wittm. & A. Camus) reproduced in 2021–2022 at the Dagestan Experiment Station, a branch of the N.I. Vavilov All-Russian Institute of Plant Genetic Resources (VIR). Calibration models for the assessment of the said agronomic characters in triticale grain were developed on the platform of the MATRIX-I IR analyzer (Bruker Optics, Germany) and based on the values obtained by classical methods of biochemical analysis adopted at VIR. Results. Reliability of the calibration models was tested on a randomized batch of triticale grain samples: the results of the new calibrations were compared with those of the conventional biochemical analysis. This verification showed that the data obtained through the use of calibration curves were reliable for protein and ash content, while other models required further improvement. Conclusion. Calibration models for measuring protein and ash content developed on the basis of the MATRIX-I IR analyzer can be used to perform rapid assessment of the winter and spring triticale grain. This method makes it possible to accelerate the process of obtaining crop characteristics for a number of basic agronomic characters and preserve valuable breeding material.

Biosensor development for diabetes diagnosis: Determining relevant miRNA using a newly developed N-annulated perylene fluorescent dye

MicroRNAs (miRNAs) have emerged as essential biomarkers for disease diagnosis, and several techniques are available to determine type 2 diabetes (T2D) relevant miRNAs. However, detecting circulating miRNAs can be challenging due to their small size, low abundance, and high sequence similarity, often requiring sensitive detection approaches combined with additional amplification processes. Laser-induced fluorescence (LIF) is a classic analytical method suitable for sensitively detecting trace amounts of nucleotide acid. Duplex-specific nuclease (DSN)-mediated amplification recently gained attention due to its catalytic activity based on target recycling, demonstrating a promising approach for miRNA amplification. This work developed a novel N-annulated perylene fluorescent dye to create a biosensor to analyze the miRNA (miR-223) relevant to T2D. The amine-reactive fluorescent dye assists the amidation reaction for nucleotide labeling, giving the oligonucleotide probe a high fluorescence quantum yield and sufficient water solubility. By combining the locked nucleic acid (LNA) modified oligonucleotide fluorescent probe to enhance the stability of LNA/RNA hybrids, thereby improving the DSN-mediated target miR-223 recycling for signal amplification, the proposed biosensor can highly selectively determine miR-223 with a limit of detection (LOD, S/N = 3) of 9.5 pM. When applied to real-world samples, the biosensor demonstrated its potential to distinguish between T2D patients and healthy controls.

The connection between the accumulation of structural defects caused by proton irradiation and the destruction of the near-surface layer of Li4SiO4 – Li2TiO3 ceramics

The key problem of using lithium-containing ceramics as materials of breeders for the propagation of tritium in thermonuclear reactors is phase stability, as well as the preservation of strength and thermophysical parameters of ceramics during their operation, which is accompanied by the accumulation of fission products in the near-surface layers, alongside mechanical influences from the outside. Moreover, in contrast to other types of ceramics, the presence of lithium in the composition of the samples under study leads to limitations in the use of classical analysis methods (scanning electron microscopy, energy dispersive analysis or optical spectroscopy) of structural changes caused by the accumulation of radiation damage, which requires the use of more complex methods for the assessment of the defect concentration in the structure, as well as establishing their relationship with the deterioration of strength and thermophysical parameters, playing a key role in determining the stability mechanisms and further exploitation of ceramics for tritium production. In this regard, the aim of the study is to determine the kinetics of changes in the near-surface layer of two-phase Li4SiO4 – Li2TiO3 ceramics associated with the accumulation of structural distortions caused by irradiation, as well as their relationship with strain embrittlement and disorder. During the studies, it was found that the accumulation of implanted hydrogen in the near-surface layer under high-dose irradiation initiates deformation distortion processes, the intensity of which depends on the ratio of components in the ceramics, according to which the optimal compositions of two-phase ceramics are ratios of components from 0.3 to 0.6. Determination of the type of defects in the composition of the damaged layer, as well as their concentration, was carried out using the electron spin resonance (ESP) method. During the studies, it was found that at low irradiation fluences, the dominant role in the accumulation of structural defects is played by vacancy defects associated with E′-centers, the formation of which is associated with structural distortions, while as the fluence grows, the structure is dominated by deformation disordering caused by the accumulation of Ti3+ defects and HC2 centers (SiO43-), the concentrations of which have a clear dependence on the phase composition of the ceramics.

The association of psychological and trauma-related factors with biological and facial aging acceleration: evidence from the UK Biobank

BackgroundPsychological and trauma-related factors are associated with many diseases and mortality. However, a comprehensive assessment of the association between psycho-trauma exposures and aging acceleration is currently lacking.MethodsUsing data from 332,359 UK Biobank participants, we calculated biological aging acceleration, indexed by the presence of leukocyte telomere length (LTL) deviation (i.e., the difference between genetically determined and observed LTL > 0). The acceleration of facial aging (i.e., looking older than the chronological age) was assessed using a self-report question. Then, we estimated the associations of each psycho-trauma factor with biological and facial aging acceleration, using logistic regression models adjusted for multiple important covariates. Furthermore, restricted to 99,180 participants with complete psychological and trauma-related data, we identified clusters of individuals with distinct psycho-trauma patterns using the latent class analysis method and assessed their associations with aging acceleration using similar models.ResultsWe observed most of the studied psycho-trauma factors were associated with biological and facial aging acceleration. Compared to the “Absence of trauma and psychopathology” cluster, the “adverse childhood experiences (ACEs) with psychopathology” cluster showed strong associations with those aging measurements (odds ratio [OR] = 1.13 [1.05 − 1.23] for biological and 1.52 [1.18 − 1.95] for facial aging acceleration), while no such association was observed for the “ACEs without psychopathology” cluster (1.04 [0.99 − 1.09] and 1.02 [0.84 − 1.24].ConclusionsOur study demonstrated significant associations of psycho-trauma factors with both biological and facial aging acceleration. The differential aging consequences observed among ACEs exposed individuals with and without psychopathology prompt interventions aimed to improve individuals’ psychological resilience to prevent aging acceleration.

Tracking induced pluripotent stem cell differentiation with a fluorescent genetically encoded epigenetic probe

Epigenetic modifications (methylation, acetylation, etc.) of core histones play a key role in regulation of gene expression. Thus, the epigenome changes strongly during various biological processes such as cell differentiation and dedifferentiation. Classical methods of analysis of epigenetic modifications such as mass-spectrometry and chromatin immuno-precipitation, work with fixed cells only. Here we present a genetically encoded fluorescent probe, MPP8-Green, for detecting H3K9me3, a histone modification associated with inactive chromatin. This probe, based on the chromodomain of MPP8, allows for visualization of H3K9me3 epigenetic landscapes in single living cells. We used this probe to track changes in H3K9me3 landscapes during the differentiation of induced pluripotent stem cells (iPSCs) into induced neurons. Our findings revealed two major waves of global H3K9me3 reorganization during 4-day differentiation, namely on the first and third days, whereas nearly no changes occurred on the second and fourth days. The proposed method LiveMIEL (Live-cell Microscopic Imaging of Epigenetic Landscapes), which combines genetically encoded epigenetic probes and machine learning approaches, enables classification of multiparametric epigenetic signatures of single cells during stem cell differentiation and potentially in other biological models.

A novel theoretical analysis method for the longitudinal cascaded piezoelectric transducer based on equivalent circuit

Abstract High power ultrasonic transducers are widely used in the fields of ultrasonic cleaning, ultrasonic welding and therapeutic ultrasound. To further improve the ultrasonic intensity and power of piezoelectric transducers for power ultrasound applications, the cascaded piezoelectric transducer have received tons of attention. The electromechanical equivalent circuit, as a classical transducer analysis method, translates the structural and mechanical parameters into the corresponding circuit parameters and helps us to better understand and analyze the performance characteristics of the transducer, which is of great significance for the design and optimization of the transducer. In this paper, a new theoretical analysis method based on the equivalent circuit is proposed for cascaded piezoelectric transducer. In this method, the equivalent circuit of each component of the longitudinal piezoelectric transducer is cascaded, and equivalent circuit is analyzed using the Kirchhoff’s law to obtain the frequency resonance equation of the piezoelectric transducer. This analysis method avoids the complex equivalent transformation of the equivalent circuit including multiple excitation sources and also obtains the vibration velocity of the piezoelectric transducer radiation surfaces, more information on the vibration characteristics of the transducer was gained from the theoretical aspect. Finally, the theoretical analysis results are compared with the finite element simulation analysis and the traditional analysis results for verification.

Deep learning-based instance segmentation for improved pepper phenotyping

Vegetable breeding companies invest a considerable amount of their resources in phenotyping. The advancement of computer vision technology has made it possible to digitalize these processes, leading to improved efficiency and quality. However, phenotyping activities often take place in outdoor fields or greenhouses, where the environmental/illumination conditions are constantly changing. This lack of standardization presents a problem for automatically isolating the relevant elements in the images, which is an important first step for phenotyping. Classical image analysis methods have shown not to be robust enough for that in these changing conditions. However, in the last years, deep learning models have demonstrated to be able to identify and learn meaningful features that are more robust and representative of the underlying patterns, enabling them to handle diverse and changeable conditions effectively.In this work, we propose a pepper instance segmentation solution based on deep learning after harvest under field conditions. We implement the method and validate it for three pepper varieties: Blocky Bell, Jalapeño and Lamuyo. We compare the performance of this new method for each variety with a previous solution based on classical image processing techniques, with the objective of measuring and demonstrating the superiority of deep learning-based instance segmentation over traditional methods as a first step for phenotyping.The instance segmentation deep learning based models outperform the results obtained by classical image processing algorithms for the three pepper varieties: in Blocky Bell mAP is increased from 0.63 to 0.97, in Jalapeño from 0.39 to 0.52 and in Lamuyo from 0.67 to 0.97.

Evaluating the effectiveness of safety countermeasures at highway–railway grade crossing based on a machine learning framework

Objective This research aims to cluster similar highway–railway grade crossings (HRGCs) to examine the safety countermeasures at HRGCs. Methods The methodology integrates inventory and collision data from Federal Railroad Association (FRA) data set during years 2010 to 2022 . The XGBoost and random forest (RF) algorithms are employed to identify influential collision severity factors. Then, the deep latent class analysis (DLCA) method is utilized on selected inventory factors as important features to cluster similar HRGCs. Afterward, collision modification factor (CMF) and standard error (SE) measures are computed for each countermeasure through collisions within each HRGC cluster. Results XGBoost successfully identified 20 important collision and inventory factors with importance levels exceeding 94%, such as the number of daily trains and the surface material. Then, the DCLA method achieved 4 distinct clusters optimized by high similarity within each cluster and significant independence among clusters. The effectiveness of countermeasures was computed in terms of CMF and SE. The CMF results demonstrated that bells achieved superior safety compared to other countermeasures in clusters with sharper track angles and high maximum train speeds. Implementing bells decreased collisions across Clusters 1 and 4, with reductions of 53% (CMF = 0.47) and 46% (CMF = 0.54), respectively. Conclusions The results highlight XGBoost’s capability to identify important collision and inventory factors, successfully uncovering 20 of the most important factors. The DCLA clustering method forms 4 distinct groups marked by substantial internal similarity within each cluster. This approach contributes to a clearer understanding of how each countermeasure impacts collision frequency. The findings highlight the varying effectiveness of different countermeasures across clusters, improving decision making for safety at HRGCs. The study highlights the efficacy of crossbucks in addressing safety concerns during moderate traffic conditions, particularly evident in environments with a highway speed limit between 100 and 125 mph. Additionally, bells demonstrate notable effectiveness in areas with sharper track angles.

Temperature Control of Concrete Structure in Bridge Construction by Renewable Energy Technology

In this study, the classical display nonlinear dynamic analysis method was used to detect the temperature of concrete structures in bridge construction. Firstly, two different bridge construction test methods, the Winfrith subfunction and CSCM subfunction, were selected to detect the nonlinear temperature of the simulated concrete, and the temperature of the bridge concrete structure and the residual velocity of solidification were detected and analyzed by combining the nonlinear dynamic algorithm for data processing. The results show that the data of the nonlinear dynamic test in this paper agree with the actual requirements, and the accuracy rate is about 90~95%. In addition, the Winfrith subfunction calculates the data of the large strain force of photovoltaic temperature control energy on concrete. It identifies the nonlinear characteristics of high strain rate, especially the effect of photovoltaic heating, battery stable power supply heating, and the effect of wind on water vapor dissipation, which further proves that renewable energy technology can monitor the temperature of concrete structures in bridge construction, and effectively detect and identify concrete problems to support the construction and later use of bridge concrete.

Effects of conservation agriculture technologies on soil macrofauna community attributes in rainfed agriculture system

AbstractInappropriate rainfed crop management, especially wrong tillage and crop residue management, led to reduce the diversity of soil biological quality, especially soil macrofauna community, in semiarid region. This study was conducted to investigate the effect of 5 year conservation agriculture operation techniques on the quality and the diversity indices of soil macrofauna community in the rainfed lands in the west of Iran. This experiment was carried out in the statistical format of split‐split plots based on a randomized completely block design with three replications. The main treatments include control or conventional tillage, compound tillage, chisel tillage, and direct cultivation (no tillage) with three sub‐treatments of plant residues (no residues, one‐third, and two‐thirds of plant residues) in rainfed wheat (Triticum aestivum) rotation with rainfed chickpea (Cicer arietinum). Changes in soil biological quality indicators, such as soil microbial respiration were measured. Also, the indicators related to the population of soil macrofauna, including biodiversity index, uniformity index, and species richness, were measured and the effect of treatments on the measured indicators were compared with the classical statistical method of analysis of variance and Duncan's mean comparison test. The results showed that conservation agricultural treatments, including no‐tillage treatment, have led to significant increase of 19% and 15% of macrofauna biodiversity index in wheat and chickpea, respectively. Also, keeping one‐third of the wheat residues has led to an 18% increase in the biodiversity index of the soil macrofauna during the evaluation period. Biodiversity index and taxonomic richness of soil macrofauna were generally higher in the years of wheat than chickpea, and on the contrary, the species uniformity index of soil macrofauna biological community was higher in chickpea than wheat.

Causal inference with hidden mediators

Summary Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators that are not observed, yet error prone proxies of the hidden mediators are measured. Specifically, (i) we establish causal hidden mediation analysis, which extends classical causal mediation analysis methods for identifying natural direct and indirect effects under no unmeasured confounding to a setting where the mediator of interest is hidden, but proxies of it are available; (ii) we establish a hidden front-door criterion, criterion to allow for hidden mediators for which proxies are available; (iii) we show that the identification of a certain causal effect called the population intervention indirect effect remains possible with hidden mediators in settings where challenges in (i) and (ii) might co-exist. We view (i)–(iii) as important steps towards the practical application of front-door criteria and mediation analysis as mediators are almost always measured with error and, thus, the most one can hope for in practice is that the measurements are at best proxies of mediating mechanisms. We propose identification approaches for the parameters of interest in our considered models. For the estimation aspect, we propose an influence function-based estimation method and provide an analysis for the robustness of the estimators.

Advances in methods for characterizing dietary patterns: A scoping review.

There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns.

FICTION AS SYNTHESIS OF MULTI-MODE MEANS OF PRESENTING NARRATIVE INFORMATION

Despite a considerable number of discoveries made in the XX and XXI centuries in the field of linguistics, there still remain a few global problems demanding their solution or treatment on the new level of scientific and social development. Amongst those, we shall mention the issue of giving a clearcut definition of fiction. This challenge has become even more difficult due to such new literary forms as digital literature, comics, fanfiction, narrative videogames, etc. Thus, the topicality of the article follows from the linguo-gnoseological importance of defining the very term “fiction” in a world with the dominance of computer technologies, providing more and more new ways of transmitting information. Our work is also motivated by the lack of serious research suggesting objective criteria for a modern piece of fiction. Hence, the research aims at specifying what can be understood as fiction in modern conditions and at characterizing the peculiarities of narrative representation in fiction today. The work is grounded on the postulates of information theory and narrative studies, coupled with classical methods of linguistic analysis. Thus, the results obtained in the process of investigation can be summarized in the following conclusions. A piece of fiction is a work of art which is built around a certain narrative. Its aesthetic component is shaped by the latter and by the communicative goal set up by the author. Narrative here is codified by means of verbal (and sometimes non-verbal) signs and presents an extra-lingual situation arousing the reader’s interest since it reflects or has nothing to do with their own cognitive experience, teases their ability to predict the plotline and influences their world pictures in the process of indirect communication with the author. The very existence of a number of modes objectivizing one and the same narrative (or its close invariants), like movies, comics, fanfiction, gamebooks, videogames, etc., enhances the reader’s immersion and the impact made by the narrative, granting the audience multi-existence and generating a new Gestalt-like quasi-universe in their minds.

Non-parametric fragility curves for probabilistic risk assessment of rainfall-triggered landslides

Fragility analysis of slopes under rainfall condition express the conditional probabilities of exceeding prescribed slope limit states under a range of rainfall intensities, which is the basic and pivotal procedure in rainfall-triggered landslide risk assessment. Classical fragility analysis methods of slopes always develop fragility curves with the assumption of lognormal distribution. The validation of assuming the shape of fragility curves as lognormal distribution remains a open question in practice. In addition, the existing fragility analysis of slopes under rainfall condition have not dealt with the effect of spatially-variable geo-material parameters. In the present study, a novel fragility analysis methodology is proposed for the construction of non-parametric fragility curves of slopes under rainfall condition, in which the spatially-variable soil properties are taken into account. The non-parametric fragility curves are developed by the calculated slope failure probabilities under various rainfall intensities on the basis of probability density evolution theory, without restricting fragility functions to some assumed distribution models. With the proposed method and a set of probabilistic slope rainfall seepage and stability analyses, non-parametric fragility curves, fragility surface using two rainfall intensity measures and time-dependent fragility curves of a slope subjected to various rainfall scenarios are obtained for rainfall-triggered landslides risk assessment.

High Frequency of Ancestral Haplotype A of Fatty Acid Desaturase Genes in the Yakut Population.

Aims: The purpose of this study was to study the correlation of the body weight of Yakuts with the variability of polymorphisms rs174537, rs174546 and rs3834458 of the FADS1 - FADS2 region to identify the connection of certain genotypes with obesity. Materials and Methods: For genotyping, classical methods of PCR-RFLP analysis were used. A sample of 446 DNA samples from Yakut volunteers without chronic diseases (143 women and 303 men) was studied. Results: The predominance of the ancestral alleles of SNPs rs174537, rs174546 and rs3834458 was established in all of our studied groups. Analysis of the odds ratio of allele and genotype frequencies in patients with normal BMI, high BMI and obesity did not show statistically significant values. We did not find an association between rs174537, rs174546 and rs3834458 with obesity, but we did not take into account the diet of the subjects, which may have had a stronger effect on BMI. Analysis of pairwise linkage disequilibrium and assessment of haplotypes for 3 SNPs in the FADS1 and FADS2 genes showed strong linkage of all three SNPs to each other (r2 = 0.93-0.96). Conclusions: According to the result of genotyping of SNP rs174537, the frequency of haplotype A in the Yakut population was 0.76 and, in comparison with other world data, is quite high. Which in turn is associated with lower conversion of short-chain polyunsaturated fatty acid to long-chain polyunsaturated fatty acid. Accordingly, a shift in nutrition towards more plant foods can negatively impact the health of the Yakuts. At the moment, the exact dosage of polyunsaturated fatty acids (PUFAs) for humans has not yet been established, but judging by the fact that all recommendations are mainly made on the basis of European populations, in connection with the results of the study, the Yakuts have a particularly high need for PUFAs.

Intelligent multiple-attributes decision support for classroom teaching quality evaluation in dance aesthetic education based on the GRA and information entropy

Abstract Dance education in colleges and universities is the most important means of inheriting dance skills, cultivating dance talents, and promoting the prosperity and development of dance art. In the new era, the country’s emphasis on “aesthetic education” has provided fertile policy soil for the development of dance majors in universities. Based on the spiritual and cultural needs of the people and the development needs of the national dance art, it is of great urgency for colleges and universities to explore the future oriented Chinese dance higher education and dance creation. Dance education and dance creation are closely linked and interdependent. In the process of Dance education, dance creation inspiration is stimulated. Dance creation and innovation inject new soul into Dance education. College Dance education should combine the two organically to promote the high-quality development of Chinese dance art. The classroom teaching quality evaluation in dance aesthetic education is classical multiple-attributes decision-making (MADM). The probabilistic hesitancy fuzzy sets (PHFSs) are used as a tool for characterizing uncertain information during the classroom teaching quality evaluation in dance aesthetic education. In this paper, we extend the classical grey relational analysis (GRA) method to the probabilistic hesitancy fuzzy MADM with unknown weight information. Firstly, the basic concept, comparative formula and Hamming distance of PHFSs are introduced. Then, the information entropy is used to compute the attribute weights based on the expected values and deviation degree. Then, probabilistic hesitancy fuzzy GRA (PHF-GRA) method is built for MADM under PHFSs. Finally, a practical case study for classroom teaching quality evaluation in dance aesthetic education is designed to validate the proposed method and some comparative studies are also designed to verify the applicability.

Damping Optimization Method of Combine Harvester Frame Undergoing Multi-Source Excitation

The complex mechanical system of a rice combine harvester not only has various excitation sources, but also, the vibration transmission path between each working device and the vibration contribution characteristics to the frame are not clear, so it is difficult to perform a reduction vibration design for the sharp vibration of the rice combine harvester frame. Therefore, based on the comparison and improvement of multiple classical transfer path analysis methods, this paper analyzed the vibration transfer characteristics and transfer characteristics of each harvester by the discrete time matrix method and operating path method. In the Experimental section, through the vibration characteristic experiment firstly, this paper obtained the power spectrum variation and the most needed optimized path in the transmission path of each device under each operating condition. Secondly, through frame simulation analysis under the exciting force, we obtained the vibration damping areas that needs to be optimized. Finally, the damping optimization experiment connected with the vibration characteristic experiment, and the excitation force simulation analysis was performed. The results of the damping optimization experiment displayed that the maximum change value of the vibration acceleration of the cutting table decreased from 7.862 m·s−2 to 3.522 m·s−2, decreasing by 55.2%, and the peak amplitude of the multipoint test in the cab was 5.4, 5.3, 1.7 and 2.0 μm, respectively, which was significantly reduced, so the optimization effect was significant. This study provides theoretical support for the vibration reduction optimization of a rice combine harvester frame.

Hybrid Machine Learning Algorithms for Prediction of Failure Modes and Punching Resistance in Slab-Column Connections with Shear Reinforcement

This study presents a data-driven model for identifying failure modes (FMs) and predicting the corresponding punching shear resistance of slab-column connections with shear reinforcement. An experimental database that contains 328 test results is used to determine nine input variables based on the punching shear mechanism. A comparison is conducted between three typical machine learning (ML) approaches: random forest (RF), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and two hybrid optimized algorithms: grey wolf optimization (GWO) and whale optimization algorithm (WOA). It was found that the XGBoost classifier had the highest accuracy rate, precision, and recall values for FM identification. In testing, WOA-XGBoost has the best accuracy in predicting punching shear resistance, with R2, MAE, and RMSE values of 0.9642, 0.087 MN, and 0.126 MN, respectively. However, a comparison between experimental values and calculated values derived from classical analytical methods clearly demonstrates that existing design codes need to be improved. Additionally, Shapley additive explanations (SHAP) were applied to explain the model’s predictions, with factors categorized according to their impact on failure modes and punching shear resistance. By modifying these parameters, punching resistance can be improved while reducing unpredictable failure. With the proposed hybrid algorithms, it is possible to determine the failure modes and the punching shear resistance of slabs during the preliminary stages of the construction.

Association between movement behavior patterns and cardiovascular risk among Chinese adults aged 40–75: a sex-specific latent class analysis

BackgroundCardiovascular disease (CVD) is a major global health threat, particularly in China, contributing to over 40% of deaths. While sleep behaviors, sedentary behaviors, and physical activities are recognized as independent lifestyle risk factors for CVD, there remains limited understanding of specific movement behavior patterns and their CVD risks, especially considering sex-specific differences. This study examines movement behavior patterns among Chinese adults (40–75) and their associations with cardiovascular risk, with a focus on sleep, physical activity (PA), and sedentary behavior (SB).MethodsData pertaining to 13,465 male participants and 15,613 female participants, collected from the Chronic Disease and Risk Factor Surveillance Survey in Nanjing from February 2020 to December 2022. The latent class analysis method was employed to identify underlying movement patterns across sexes. Multinomial logistic regression models assessed CVD risk, and the China-PAR model calculated 10-year risk.ResultsThree male and four female movement patterns emerged. Active Movers (17.10% males, 5.93% females) adhered to PA recommendations but had poorer sleep quality. Moderate Achievers (61.42% males, 45.32% females) demonstrated moderate behavior. Sedentary Sleepers (21.48% males, 10.20% females) exhibited minimal PA but good sleep. Female Moderate Physical Activity (MPA) Dominant Movers demonstrated a prevalent adherence to recommended MPA levels. Active movers had the lowest CVD risk. After adjusting for potential confounders, moderate achievers (OR = 1.462, 95% CI 1.212, 1.764) and sedentary sleepers (OR = 1.504, 95% CI 1.211, 1.868) were both identified as being associated with a high-risk of cardiovascular diseases (CVDs) compared to active movers in males, demonstrating a similar trend for intermediate risk. Such associations were not statistically significant among females.ConclusionsOur study revealed sex-specific movement patterns associated with CVD risks among middle-aged Chinese adults. We suggest that adopting an active movement behavior pattern, characterized by meeting or exceeding recommended levels of vigorous physical activity (VPA) and reducing sedentary behavior, is beneficial for all middle-aged adults, particularly males. An active lifestyle could help counteract the adverse effects of relatively poor sleep quality on the risk of developing CVD in this population. Integrating sleep, PA, and SB information provides a holistic framework for understanding and mitigating CVD risks.

Review—Novel Trends in the Determination of Pharmaceutical Compounds Commonly Found in Topical Treatments using Electrochemical Sensing Approaches

Abstract Topical treatments rely on drugs that play a crucial role in addressing skin and mucous membrane disorders. Therefore, it is highly needed to utilize accurate analytical techniques that can determine the concentration of these chemicals in various sample matrices, including pharmaceuticals, food, and water. Currently, electrochemical sensors are predominantly used in specific fields such as biomedical, industrial, and environmental monitoring, while they have not yet been incorporated into the pharmaceutical manufacturing industry. However, electrochemical methods employing an expanding range of sensors provide a reliable, cost-effective, and efficient substitute for classical analytical methods. Their potential is highly favorable, offering possibilities for simultaneous determination, miniaturization, and real-time on-site monitoring. This work covers numerous sensors designed between 2020 and 2023 for the determination of topical drugs, highlighting their respective benefits and drawbacks while illuminating emerging trends. Moreover, it discusses the correlation between the used materials and the ease of manufacturing, to the achieved results, including dynamic range, detection limit, sensitivity, and selectivity. This work aims to serve as a valuable resource for researchers, engineers, and policymakers in the evolving field of electrochemical sensing by providing guidance and facilitating decision-making, which could lead to significant innovations in sensor technology.