Precise Methodology Research Articles

Optimization of Jiyang depression block X shale condensate reservoir well spacing based on geology–engineering integration

Appropriate well spacing is crucial for the efficient development of shale reservoirs, as it is closely related to the degree of resource utilization. Well spacing design is influenced by both fracturing processes and geological characteristics. While increasing well density can enhance reservoir recovery, it may lead to higher investment costs and significant well interference issues. In this study, we adopted an integrated geological–engineering approach, combining fracture propagation simulation, EDFM (Embedded Discrete Fracture Modeling), and numerical simulation methods to comprehensively analyze well interference under different well spacings in shale condensate reservoirs. Development well spacing was optimized using the degree of resource utilization and well interference rate as key indicators. There are three main research findings: (1) The geological engineering integration approach allows for differentiated well spacing according to specific research areas. Combining this integrated approach with EDFM and leveraging quantitative evaluation, we have developed an efficient and precise methodology for well spacing optimization. (2) When well spacing is less than the length of hydraulic fractures, inter-well fractures exhibit an entangled pattern, reducing the effectiveness of fracturing treatments and causing severe well interference. As well spacing increases, interference between fractures from different wells diminishes, but unstimulated volumes gradually emerge, leading to a decrease in reservoir recovery. (3) Considering both well interference and resource utilization, a well spacing of 400 m is recommended in the study area. At this spacing, interference between hydraulic fractures from different wells is minimal. After 10 years of production, the estimated reservoir recovery is 39.16%, with a production rate of 25.58% and a well interference rate of 13.58%. These research outcomes provide valuable insights for optimizing the well spacing of hydraulic fractured horizontal wells in shale condensate reservoirs.

Forestat: An R package for computing forest carbon sequestration and potential productivity

1. Forest ecosystem is a basic component of terrestrial ecosystem that mitigates the impact of climate change through absorbing significant amount of carbon dioxide. Forest is crucial in maintaining ecological equilibrium, reducing greenhouse gas emission, and preserving biodiversity. Thus, standardised and precise methodologies for measuring forest carbon sequestration and assessing its potential are of paramount importance in evaluating the impacts of climate change.2. We presented the “forestat”, an approach for assessing forest carbon sequestration and its potential based on annual growth of biomass, considering site class and forest age conditions, wrapped in an R package, which is applicable to all forest types. By using a comprehensive system of forest stand models, “forestat” contributes to a refined comprehension of relationships between forest biomass and carbon sequestration and offers an evaluation of the carbon sequestration potential in forest ecosystem, unifying the methods of carbon measurement and its potential estimation in both natural and plantation forests.3. Based on the two applications, this study (a) introduces and demonstrates the methods in R package “forestat”; (b) uses the 6th, 7th, 8th, and 9th National Forest Inventory data to investigate carbon sequestration and potential productivity of degraded forests in China; and (c) conducts suitability analysis for tree species in the Yellow River Basin.4. “forestat” provides a unified, efficient, and standardized analytical tool for forest carbon sequestration and its potential. This study also highlights the primary forest functionalities.

Measurement Method of Whole Mechanical Automatic Vertical Drilling Tools

The inaccurate positioning of a plat valve is a crucial factor affecting the efficiency and safety of drilling operations. Because of the complicated structure of vertical drilling, it is difficult to obtain the actual position of a plat valve during the working process. This work effectively addresses the challenge of accurately measuring the deflection angle of a plat valve in mechanical automatic vertical drilling. Initially, a comprehensive mathematical model was proposed for determining the angle range of the plat valve, which was founded on the thrust generated by vertical drilling. Furthermore, this work proposed a precise measurement methodology for acquiring the crucial parameters of the MVDT. The actual deviation angle range for the N-MVDT and O-MVDT vertical drilling was calculated using data obtained from measurements of the tools. At the same time, the influence of TID on the performance of the MVDT was determined. Then, a simulation was carried out to compare the calculation results. From this, it was determined that the deflection angle range of the O-MVDT at a higher rotational speed was [86°, 58°], and the deflection angle range of the N-MVDT was [−14°, 14°]. By comparing the two methods, the error in the angle range was derived. The error of the N-MVDT was less than 16.67%, and the error of O-MVDT was less than 9.43%, proving that the measurement method and the mathematical model can accurately estimate the deflection angle range of a plat valve for mechanical automatic vertical drilling. The experimental and mathematical model results consistently showed that the deflection angle φ of the N-MVDT was significantly smaller than that of the O-MVDT, so the TID significantly improved the stability of the MVDT.

Development of a Voltammetric Methodology Based on a Methacrylic Molecularly Imprinted Polymer-Modified Carbon-Paste Electrode for the Determination of Aflatoxin B1

Aflatoxin B1 (AFB1) is one of the most dangerous mycotoxins found in food, necessitating the development of precise and reliable methodologies for its detection. In this study, a novel electrochemical sensor based on a molecularly imprinted polymer (MIP) integrated with a carbon-paste electrode was developed for the voltammetric determination of AFB1. The innovative aspect of this work lies in the use of methacrylic acid (MAA) as the functional monomer, which enhances the sensor’s selectivity and binding affinity. The developed electrochemical sensor exhibited a linear response range from 20.8 to 80 ng/L, with a limit of detection (LOD) of 2.31 ng/L and a sensitivity of 19.83 µA (ng/L)−1 cm−2. The sensor demonstrated outstanding analytical performance, with reproducibility and repeatability yielding relative standard deviations (RSDs) of 3.24% and 1.41%, respectively. To validate the sensor’s practical applicability, its performance was tested in real samples of corn and wheat using the standard addition method. Samples were prepared following official Mexican standard methods. Detected AFB1 concentrations were 0.0147 μg/L and 0.0138 μg/L for corn and wheat, respectively. A statistical comparison using the Student’s t-test confirmed no significant matrix effects, underscoring the high selectivity and accuracy of the MIP-modified sensor. This work introduces a highly selective, sensitive, and reproducible methodology for AFB1 detection, which could significantly advance food safety monitoring.

Rapid Color Quality Evaluation of Needle-Shaped Green Tea Using Computer Vision System and Machine Learning Models.

Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea.

Data Collection for Cultural Heritage Risk Management: the Damage Map through Heritage Building Information Modeling (HBIM) Project Applied to the Façade of St Francis of Assisi, Ouro Preto, Brazil

ABSTRACT The development of precise analytical methodologies, together with the systematic use of verifiable data, aims to raise standards for treatment, risk management, and maintenance programs. This endeavor aims to diminish the frequency of subsequent restoration cycles, thereby positively impacting public spending and fostering the preservation of the authentic characteristics of historical buildings, particularly by mitigating the effects of decay. The Damage Map through Heritage Building Information Modeling (HBIM) project, funded by the National Council for Scientific and Technological Development (CNPq) and Minas Gerais Research Support Foundation (FAPEMIG), both Brazil, aims to establish protocols for risk management in historical buildings, with a focus on decorative stone elements integrated into the construction. For this reason, the stone relief on the façade of the church of St Francis of Assisi in Ouro Preto, Brazil, was chosen as a case study. This paper outlines the data collection methodology applied to study the stone. This approach encompasses historical examinations of restoration cycles, along with preliminary findings derived from extensive fieldwork, conducted by a multidisciplinary team in 2023. It elucidates the data collection stage, employing photography and aerial photogrammetry, ultraviolet fluorescence photography, mobile laser scanning, and thermography. Subsequently, all collected data will be used to construct a diagnostic model within the HBIM.

Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs).

Mental illnesses extract a high personal and societal cost, and thus explorations of the links between mental illness and functional connectivity in the brain are critical. Investigating major mental illnesses, believed to arise from disruptions in sophisticated neural connections, allows us to comprehend how these neural network disruptions may be linked to altered cognition, emotional regulation, and social interactions. Although neuroimaging has opened new avenues to explore neural alterations linked to mental illnesses, the field still requires precise and sensitive methodologies to inspect these neural substrates of various psychological disorders. In this study, we employ a hierarchical methodology to derive double functionally independent primitives (dFIPs) from resting state functional magnetic resonance neuroimaging data (rs-fMRI). These dFIPs encapsulate canonical overlapping patterns of functional network connectivity (FNC) within the brain. Our investigation focuses on the examination of how combinations of these dFIPs relate to different mental disorder diagnoses. The central aim is to unravel the complex patterns of FNC that correspond to the diverse manifestations of mental illnesses. To achieve this objective, we used a large brain imaging dataset from multiple sites, comprising 5805 total individuals diagnosed with schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and controls. The key revelations of our study unveil distinct patterns associated with each mental disorder through the combination of dFIPs. Notably, certain individual dFIPs exhibit disorder-specific characteristics, while others demonstrate commonalities across disorders. This approach offers a novel, data-driven synthesis of intricate neuroimaging data, thereby illuminating the functional changes intertwined with various mental illnesses. Our results show distinct signatures associated with psychiatric disorders, revealing unique connectivity patterns such as heightened cerebellar connectivity in SCZ and sensory domain hyperconnectivity in ASD, both contrasted with reduced cerebellar-subcortical connectivity. Utilizing the dFIP concept, we pinpoint specific functional connections that differentiate healthy controls from individuals with mental illness, underscoring its utility in identifying neurobiological markers. In summary, our findings delineate how dFIPs serve as unique fingerprints for different mental disorders.

Detachable acoustofluidic droplet-sorter

BackgroundCell sorting is crucial in isolating specific cell populations. It enables detailed analysis of their functions and characteristics and plays a vital role in disease diagnosis, drug discovery, and regenerative medicine. Fluorescence-activated cell sorting (FACS) is considered the gold standard for high-speed single-cell sorting. However, its high cost, complex instrumentation, and lack of portability are significant limitations. Additionally, the high pressure and electric fields used in FACS can harm cell integrity. In this work, an acoustofluidic device was developed in combination with surface acoustic wave (SAW) and droplet microfluidics to isolate single-cell droplets with high purity while maintaining high cell viability. ResultHuman embryonic kidney cells, transfected with fluorescent reporter plasmids, were used to demonstrate the targeted droplet sorting containing single cells. The acoustofluidic sorter achieved a recovery rate of 81 % and an accuracy rate higher than 97 %. The device maintained a cell viability rate of 95 % and demonstrated repeatability over 20 consecutive trials without compromising efficiency, thus underscoring its reliability. Thermal image analysis revealed that the temperature of the interdigital transducer (IDT) during SAW operation remained within the permissible range for maintaining cell viability. SignificanceThe findings highlighted the sensitivity and effectiveness of the developed acoustofluidic device as a tool for single-cell sorting. The detachable microfluidic chip design enables the reusability of the expensive IDT, making it cost-effective and reducing the risk of cross-contamination between different biological samples. The results underscore its capability to accurately isolate individual cells on the basis of specific criteria, showcasing its potential to advance research and clinical applications requiring precise cell sorting methodologies.

Sentiment to Stocks: Rule-Based and Deep Learning Sentiment Analysis for LSTM-Driven Stock Prediction

How to accurately predict stock prices is a persistent problem in the financial realm. According to behavioral economics, human actions in the stock market tend to be irrational, emotional, and easily misled. In such context, this papers approach involves adopting various sentiment analysis models in subsequent sections of the paper, which aims to synthesize a precise methodology for forecasting stock prices aligned with the available data, namely structured technical analysis, while considering and quantifying investor sentiment, namely unstructured fundamental analysis. This work points out that by adopting sentiment analysis using the VADER and FinBERT models separately, there are accuracy improvements in both integration of VADER or FinBERT and transactional data compared with merely doing stock price prediction by forecasting using historical data alone. This outcome resemble an valuable proposition of modeling and prediction on irrational human behavior. Nevertheless, this paper provides insights on future possible enhancements in this area, which analyzing additional emotional dimensions in textual data and recognizing the multifaceted nature of human emotions is needed.

Regional freight accessibility analysis based on truck trajectories—A case study of Hunan Province in China

Freight transport is crucial in fostering economic growth and enhancing societal well-being, but it also poses challenges for traffic management and environmental sustainability. For freight planning and policy formulation, it is important to measure and evaluate regional freight accessibility. This paper proposes a novel framework for analyzing regional freight accessibility based on truck trajectory data. The framework is structured around two principal components: extracting freight trip information and constructing a freight accessibility model. It introduces precise and effective methodologies for identifying truck parking zones based on trajectory data, thereby facilitating the extraction of complete freight trip information. Afterward, the framework integrates a distance-weighted topological analysis with multiple relevant indicators to comprehensively assess the freight accessibility of a region. Additionally, it incorporates spatial lag modeling to examine the factors influencing the spatial distribution of freight accessibility within a region. Applied to a case study in Hunan Province, China, the framework demonstrates its efficacy. The analysis reveals that accessibility in Changsha and two other cities stands out significantly higher than in other cities. The study offers valuable insights for strategic freight planning and policy formulation.

Integration of single-cell and bulk RNA sequencing revealed immune heterogeneity and its association with disease activity in rheumatoid arthritis patients.

Rheumatoid arthritis (RA) is a chronic, inflammatory, systemic autoimmune disease characterized by cartilage, bone damage, synovial inflammation, hyperplasia, autoantibody production, and systemic features. To obtain an overall profile of the immune environment in RA patients and its association with clinical features, we performed single-cell transcriptome and T-cell receptor sequencing of mononuclear cells from peripheral blood (PBMC) and synovial fluid (SF) from RA patients, integrated with two large cohorts with bulk RNA sequencing for further validation and investigation. Dendritic cells (DCs) exhibited relatively high functional heterogeneity and tissue specificity in relation to both antigen presentation and proinflammatory functions. Peripheral helper T cells (TPHs) are likely to originate from synovial tissue, undergo activation and exhaustion, and are subsequently released into the peripheral blood. Notably, among all immune cell types, TPHs were found to have the most intense associations with disease activity. In addition, CD8 effector T cells could be clustered into two groups with different cytokine expressions and play distinct roles in RA development. By integrating single-cell data with bulk sequencing from two large cohorts, we identified interactions among TPHs, CD8 cells, CD16 monocytes, and DCs that strongly contribute to the proinflammatory local environment in RA joints. Of note, the swollen 28-joint counts exhibited a more pronounced association with this immune environment compared to other disease activity indexes. The immune environment alternated significantly from PBMCs to SF, which indicated that a series of immune cells was involved in proinflammatory responses in the local joints of RA patients. By integrating single-cell data with two large cohorts, we have uncovered associations between specific immune cell populations and clinical features. This integration provides a rapid and precise methodology for assessing local immune activation, offering valuable insights into the pathophysiological mechanisms at play in RA.

A deep learning-based U-Net 3+ technique for segmentation blood cell

This study introduces a novel and enhanced UNet3Plus model tailored for the precise segmentation of blood cells in medical images. The architecture incorporates structural modifications, including strengthened connections between convolutional layers, increased filter numbers, and integration of Bayesian optimization for hyperparameter tuning. The model's generalization capability is optimized through the dynamic adjustment of dropout rates and learning rates. Bayesian optimization facilitates the exploration of optimal hyperparameter combinations, allowing the model to adapt effectively to diverse datasets. Advanced training strategies, such as adaptive learning rate adjustment and early stopping, are employed to mitigate overfitting and enhance training efficiency. The proposed model exhibits exceptional performance across multiple folds, achieving low training and validation losses, high accuracy metrics, and robust segmentation indices. Evaluation metrics, including Mean IoU (Jaccard Index), Dice score, Pixel Accuracy, and Precision, affirm the model's proficiency in accurately delineating blood cell boundaries. The study contributes to the field of deep learning-based medical image segmentation by showcasing the effectiveness of customized architectures and optimization techniques. The proposed UNet3Plus model stands as a promising solution for accurate and reliable blood cell segmentation, demonstrating adaptability and robust performance across various datasets. This work sets the stage for future research in the domain of medical image segmentation, emphasizing the potential for continued advancements in precise and efficient segmentation methodologies.

Detection Of The Thermal Front Using The Cayula-Cornillon Alghorithm: A Case Study Of The State Fisheries Management Area 713, Indonesia

Thermal fronts are pivotal in shaping the marine ecosystem, influencing the presence and distribution of marine species. This study delves into the fluctuating patterns of thermal fronts in Indonesian waters, specifically focusing on the State Fisheries Management Area of the Republic of Indonesia, known as WPPNRI, 713. We identified the distribution and frequency of thermal fronts across different monsoon seasons. Utilizing the extended Cayula Cornillon Algorithm – Single Image Edge Detection (CCA-SIED), we deploy a precise methodology for detecting these fronts, relying on sea surface temperature (SST) gradients to identify specific periods and regions. Our analysis encompasses extensive satellite data collected from Moderate Resolution Imaging Spectroradiometer (MODIS) level 3. Our findings unveil distinct seasonal variations, with a decrease in thermal fronts during the west monsoon and a surge during the east monsoon. Moreover, we identify regional disparities, with denser thermal fronts observed in the southern and middle regions compared to the northern areas. Furthermore, our study underscores the critical need to integrate oceanographic data with fisheries management strategies to address the impacts of climate variability on marine resources. The observed relationships between thermal fronts, nutrient distribution, and fish migration emphasize the necessity for ongoing, localized monitoring to develop adaptive management solutions. This research marks a significant step in characterizing thermal fronts in WPPNRI 713, laying the groundwork for future investigations into other ecologically significant fronts, including those related to chlorophyll, salinity, and nutrients, thereby enhancing our understanding of marine ecosystem dynamics.Keywords: Cayula Cornillon; Fishing Zone; Marine Ecosystem; Sea Surface Temperature; Thermal Fronts

Controlled Reversible Visceral Arterial Ischemia, Venous Congestion and Combined Malperfusion via Midline Laparotomy in Rats.

Besides sepsis and malignancy, malperfusion is the third leading cause of tissue degradation and a major pathomechanism for various medical and surgical conditions. Despite significant developments such as bypass surgery, endovascular procedures, extracorporeal membrane oxygenation, and artificial blood substitutes, tissue malperfusion, especially of visceral organs, remains a pressing issue in patient care. The demand for further research on biomedical processes and possible interventions is high. Valid biological models are of utmost importance in enabling this kind of research. Due to the multifactorial aspects of tissue perfusion research, which include not only cell biology but also vascular microanatomy and rheology, an appropriate model requires a degree of biological complexity that only an animal model can provide, rendering rodents the obvious model of choice. Tissue malperfusion can be differentiated into three distinct conditions: (1) isolated arterial ischemia, (2) isolated venous congestion, and (3) combined malperfusion. This article presents a detailed step-by-step protocol for the controlled and reversible induction of these three types of visceral malperfusion via midline laparotomy and clamping of the abdominal aorta and caval vein in rats, underscoring the significance of precise surgical methodology to guarantee uniform and dependable results. Prime examples of possible applications of this model include the development and validation of innovative intraoperative imaging modalities, such as Hyperspectral Imaging (HSI), to objectively visualize and differentiate malperfusion of gastrointestinal, gynecological, and urological organs.

Agro-Silvo-Pastoral Heritage Conservation and Valorization—A Comparative Analysis of the Chinese Nationally Important Agricultural Heritage Systems and of the Italian Register of Historical Rural Landscapes

Agricultural heritage systems are receiving increasing attention due to their multifunctional role, their capability to provide ecosystem services, and for representing sustainable development models for rural areas. At the international level, the Food and Agriculture Organization (FAO) of the United Nations launched in 2002 the Globally Important Agricultural Heritage Systems (GIAHS) Program to identify and safeguard traditional agro-silvo-pastoral systems that are the result of the adaptation of rural communities to the surrounding environments. Following this approach, similar national programs have been developed, among which the Italian National Register of Historical Rural Landscapes and the China Nationally Important Agricultural Heritage Systems (China-NIAHS) initiative stand out. This research compares the China-NIAHS initiative and the Italian Register of Historical Rural Landscapes, highlighting similarities and differences and identifying strengths and weaknesses, to contribute to the development of proper tools for the conservation and valorization of agricultural heritage systems. Both initiatives were established in 2012 and share similar aims and inscription criteria, including historical and current significance, knowledge system and social structure, economic and productive framework, and causes of vulnerability. The main differences are related to the delimitation of the boundaries of the proposed sites and to the inscription process; in addition, while for the Italian National Register spatial analyses of land-use changes and landscape structure are mandatory according to a precise methodology, for the China-NIAHS no specific landscape analyses are required. The main critical issues are the lack of public participation and the lack of a monitoring plan after the inscription. The absence of monitoring is also the main vulnerability of the FAO GIAHS Programme. This research can offer important information for different stakeholders at the international, national, and local levels dealing with agricultural heritage conservation and valorization. In particular, it highlights the need of developing a multidisciplinary monitoring system with a standard methodology based on different indicators that can contribute to maximizing the impact of these initiatives. This research also provides useful information for the countries that are interested in developing a national initiative for agricultural heritage systems’ identification and valorization/conservation or for countries that intend to improve their national programs.

P-333 Combining deep shotgun metagenomic sequencing and culturomics for in-depth characterization of a potential endometrial microbiome

Abstract Study question Does the endometrium host a microbiome, and if so, are these taxa distinct from the vaginal/cervical microbiome? Summary answer Multifaceted analysis of endometrial tissue reveals no distinct microbial taxa compared to the vaginal/cervical samples, questioning whether detected microbiota are endometrium-related or result from cross-contamination. What is known already Recent studies suggest a role for the endometrial microbiota in reproductive health, yet the existence of an endometrial microbiome and its specific bacterial taxa remain enigmatic. The female reproductive tract, with a bacterial abundance gradient, is predominantly Lactobacillus-dominated in the lower region. Inconsistent findings on Lactobacillus dominance in endometrial samples are often ascribed to cross-contamination and methodological constraints, highlighting the need for precise methodologies in studying the low-biomass endometrial environment. Study design, size, duration Fifteen women, undergoing a diagnostic hysteroscopy and endometrial biopsy, as part of the subfertility work-up, were included in this study. Microbiota profiles of endometrial biopsies were compared to paired vaginal and cervical swabs from nine patients using 16S amplicon sequencing. Furthermore, the microbial composition of six additional endometrium biopsies was characterized using both a culturomics-based approach and deep metagenomic sequencing. Participants/materials, setting, methods Endometrial biopsies (n = 15) were taken with a sterile Pipelle de Cornier and snap-frozen in liquid nitrogen, followed by -80 °C storage and 16S amplicon sequencing (n = 9) targeting the V4-region or deep shotgun metagenomic sequencing (n = 6) (Prebiomics, Italy). Simultaneously, in nine women, paired cervical and vaginal swabs (Copan Diagnostics, Italy) were taken and preserved in eNAT medium. In six participants, an additional endometrial biopsy was collected and stored in Amies medium for immediate culturomics. Main results and the role of chance We observed a high similarity in the microbial composition of endometrial biopsies, cervical, and vaginal samples using 16 amplicon sequencing, but also some taxa were only found in the endometrial samples. Notably, the presence of Gemmobacter, a waterborne genus, was only found in endometrial biopsies and could be a source of contamination from water used during hysteroscopy to flush the uterus. Most endometrial microbiome profiles were Lactobacillus dominated, with exceptions observed in three patients. No significant differences for alpha diversity measures (richness and evenness) of the microbiota composition were observed between the different niches. For the first time we applied both culturomics and deep shotgun metagenomic sequencing for the characterization of the microbial composition in the same endometrial biopsies. Commonly identified genera using both techniques include Lactobacillus, Prevotella, Bifidobacterium, Anaerococcus, and Cuticubacterium. Remarkably, at the species level, the two approaches revealed almost no overlapping species within each biopsy, underscoring the importance of combining multiple methodologies in examining low-biomass niches like the endometrium. Limitations, reasons for caution A limitation of this study is the low-biomass nature of endometrial biopsies which are prone to cross-contamination with the lower female reproductive tract. This challenges downstream analysis, as it becomes difficult to distinguish between a relevant microbiological signal and contaminating noise. Wider implications of the findings This study emphasizes the need to consider a multifaceted microbiome approach when exploring low-biomass environments, like the endometrium. The critical consideration of potential cross-contamination by the lower female reproductive tract and its implications for downstream analysis is paramount in clinical microbiology research and demands meticulous handling. Trial registration number not applicable

Dynamic flexural strength of Aluminosilicate glass with a perspective of impulsive and quasi-impulsive responses: An experimental–numerical coupled evaluation

The flexural strength of glass is a critical design parameter for applications encountering impact loadings. However, the micro defects, specimen geometry, loading rate, and load transformation from a quasi-dynamic to quasi-impulsive state may influence the measurement accuracy. Due to the stochastic and amorphous nature of the material, an accurate determination of the flexural strength remains a challenge. In this two-fold study, a coupled experimental–numerical strategy was devised to evaluate the dynamic flexural strength. In the first phase, three-point bending experiments were conducted on a novel “Electromagnetic Split Hopkinson Pressure Bar (ESHPB)”. The incident stress signal and fracture time were recorded from experimental data, while the flexural strength was indirectly computed from a numerical algorithm. A quantitative comparison of the flexural strength with those in existing literature established the accuracy of the proposed methodology. Results of the study indicate that the specimen response became independent of the support conditions under impulsive loading. That being said, the specimen behaved like it had an infinite span length, and the measured flexural strength remained the same whether the specimen was supported or not. Besides, the specimen also maintained contact at the interfaces of the incident bar and fixture supports for the entire loading duration. In the second part of this study, the computed flexural strength was used to calibrate the existing JH-2 model. Numerical prediction of the damage propagation corroborated with that obtained from reprography images, though qualitatively. This work presents a precise and robust methodology to determine the dynamic flexural strength of brittle ceramics like Aluminosilicate glass over traditional experimental procedures to facilitate its adoption.

Estimation of aggregated inertia constant and load damping: A PMU-based analytical approach

A precise, straightforward, and efficient data-driven methodology is introduced to estimate the most important aggregated generation-load parameters of the power system frequency response model i.e., the inertia constant and the load damping. The estimation method depends on defining specific base systems characterized by particular frequency features. A sensitivity analysis is carried out to determine the most suitable base system for each real-time application. The data collected from PMUs is then compared to the attributes of the base system to make accurate estimations of the parameters of interest. The effectiveness of the proposed methodologies is assessed using datasets that include both simulated data from the New York-New England benchmark and actual measurements collected from six historical power system disturbances within a national power grid.

Unraveling the role of Raman modes in evaluating the degree of reduction in graphene oxide via explainable artificial intelligence

This paper evaluated the degree of reduction in graphene oxide, leveraging deep learning and machine learning models on over 15,000 Raman scattering spectra along with validation using density functional theory calculations. We addressed the limitations of previous studies, such as the consideration of an insufficient number of spectra as well as the lack of a comprehensive analysis of the contribution of individual Raman modes, by introducing machine learning and deep learning. Moreover, our models succeeded in predicting the carbon-to-oxygen ratio and classifying the reduction temperatures using the Raman scattering spectra as input. Employing the partial dependence plot and the feature importance, we interpreted the models and obtained consistent results on the significance of D* mode in graphene oxide. The intensity of the D* mode stands out by not only displaying the highest feature importance value for the reduction temperatures but also by correlating proportionally with the widest range of carbon-to-oxygen ratios among the various Raman modes in graphene oxide. Finally, we validated our findings through quantum mechanical calculations and confirmed the significance of the D* mode. Our study presents a comprehensive insight into the role of Raman modes in the degree of reduction as well as a precise methodology for evaluating the carbon-to-oxygen ratio of graphene oxide, a step towards its further industrial applications.

Antibiotics in honey: a comprehensive review on occurrence and analytical methodologies.

Honey is a food of great nutritional importance and has always been used for human consumption. The production of honey and other beekeeping products depends on the proper functioning of this extremely important sector, as it has a direct impact on other sectors such as agriculture. The decline in bee colony numbers has been linked, among other factors, to bacterial diseases affecting bees, including American and European foulbrood, and Nosema spp. disease. In this matter, prophylactic or therapeutic use of veterinary drugs in apiculture is common but can lead to their accumulation in bees and in honey. Consumption of contaminated honey can have adverse effects such as allergic or hypersensitivity reactions, carcinogenicity, reproductive effects, and teratogenicity. Commission Regulation (EU) N⍛ 37/2010 sets MRLs for antibiotics in various foods, but these limits are not set for api-products. The lack of harmonized rules has led some countries to set recommended concentrations and minimum performance limits. Nonetheless, to achieve this goal, development of accurate and precise analytical methodologies is crucial. In recent years, the analysis of antibiotics in honey has led to the development of methods in an extensive range of families, including aminoglycosides, amphenicols, lincosamides, macrolides, nitroimidazoles, quinolones, sulfonamides, tetracyclines and nitrofurans. This review work entails an in-depth exploration of occurrence studies, extraction methodologies, and analytical techniques for the determination of antibiotics in apiculture products. It was found that the most used extraction methods include solid-phase extraction, dispersed solid or liquid phase extraction and QuEChERS. Due to the complexity of the honey matrix, samples are often diluted or acidified using McIlvaine buffer, H2O, MeOH, acidified ACN and TCA solution. This is usually followed by a purification step using SPE cartridges or PSA. Golden analytical methodologies include high-performance liquid chromatography coupled to a triple quadrupole mass spectrometer (MS/MS) with Orbitrap or Q-ToF detectors.