Two-phase Process Research Articles

Detonation flows of explosives containing compressible inert particles

The present study employs a combination of numerical and analytical techniques to examine the detonation flows of explosives containing compressible inert particles. A two-phase numerical model incorporating the compressibility of the particles is developed, and the two-phase detonation process is simulated under the Lagrangian framework, where the explosive and the particles are treated as a fluid and discrete spheres, respectively. For small velocity difference between phases, a perturbation method is utilized for the analysis of the dynamics of the detonation front, the detonation product, and the particles. The effects of the particles' compressibility and material density are examined in detail. The results show that the material density of the particles exerts a linear influence on the detonation flow of the explosive, while the effects of the particle compressibility are much more complex. An increase in the particle's material density or compressibility can reduce the velocities of both the detonation front and the flowing-stagnant boundary. The jump of the particle volume fraction across the detonation front relies on the particle compressibility. For more compressible particles, the particle volume fraction exhibits a non-monotonic behavior in the flowing region. A concise scaling law is also obtained for the velocity difference between phases. The present research provides a quantitative prediction to the effects of compressible particles on the detonation flows of explosives.

Hybrid improved fuzzy C-means and watershed segmentation to classify Alzheimer’s using deep learning

Brain damage and deficits in interactions among brain cells are the primary causes of dementia and Alzheimer’s disease (AD). Despite ongoing research, no effective medications have yet been developed for these conditions. Therefore, early detection is crucial for managing the progression of these disorders. In this study, we introduce a novel tool for detecting AD using non invasive medical tests, such as magnetic resonance imaging (MRI). Our method employs fuzzy C-means clustering to identify features that enhance image accuracy. The standard fuzzy C-means algorithm has been augmented with fuzzy components to improve clustering performance. This enhanced approach optimizes segmentation by extracting image information and utilizing a sliding window to calculate center coordinates and establish a stable group matrix. These critical features are subsequently integrated with a two-phase watershed segmentation process. The resulting segmented images are then used to train an optimal convolutional neural network (CNN) for AD classification. Our methodology demonstrated a 98.20% accuracy rate in the detection and classification of segmented MRI brain images, highlighting its efficacy in identifying disease types.

Improving Web Readability Using Video Content: A Relevance-Based Approach

With the increasing integration of multimedia elements into webpages, videos have emerged as a popular medium for enhancing user engagement and knowledge retention. However, irrelevant or poorly placed videos can hinder readability and distract users from the core content of a webpage. This paper proposes a novel approach leveraging natural language processing (NLP) techniques to assess the relevance of video content on educational websites, thereby enhancing readability and user engagement. By using a cosine similarity-based relevance scoring method, we measured the alignment between video transcripts and webpage text, aiming to improve the user’s comprehension of complex topics presented on educational platforms. Our results demonstrated a strong correlation between automated relevance scores and user ratings, with an improvement of over 35% in relevance alignment. The methodology was evaluated across 50 educational websites representing diverse subjects, including science, mathematics, and language learning. We conducted a two-phase evaluation process: an automated scoring phase using cosine similarity, followed by a user study with 100 participants who rated the relevance of videos to webpage content. The findings support the significance of integrating NLP-driven video relevance assessments for enhanced readability on educational websites, highlighting the potential for broader applications in e-learning.

Impact of glyphosate on soil bacterial communities and degradation mechanisms in large-leaf tea plantations

This study investigated the impact of glyphosate on bacterial communities and their degradation mechanisms in large-leaf tea soil, through exposure microcosm and enrichment culture experiments. Soils from three tea gardens in Yunnan, China, were used: two glyphosate-free (JM and KL) for microcosm study and one long-term exposed (G2) for enrichment culture experiment. The results revealed a two-phase degradation process with half-lives of 12.7 to 268 days, while the metabolite AMPA was notably persistent. The acidic conditions and high organic content of tea soils may retard glyphosate microbial availability and degradation. Glyphosate initially stimulated bacterial growth but led to abundance declines with prolonged exposure. It tended to enhance bacterial diversity at lower doses. Network complexity increased in JM soil where strong adsorption moderated glyphosate exposure, yet decreased in KL soil where weak adsorption enabled greater microbial-glyphosate interactions. Community structure analysis revealed soil-specific responses, with decreased Proteobacteria in JM soil and Actinobacteria in KL soil, while several phyla including Proteobacteria, Acidobacteriota, Chloroflexi, Myxococcota, and Verrucomicrobiota showed increased abundance. PICRUSt2 analysis indicated enhanced biosynthesis and cell growth pathways, while carbohydrate metabolism, nitrogen metabolism, and xenobiotics biodegradation pathways were reduced. LEfSe analysis identified potential degrading biomarkers primarily from Proteobacteria, Acidobacteriota, Myxococcota, Chloroflexi, and Actinobacteriota, suggesting their putative role in degradation. The enriched consortium G2 efficiently degraded 400mg/L glyphosate within 7 days, with notable increases in Afipia, Dokdonella, and Cohnella abundance. This study provides insights into bacterial interactions with glyphosate in tea soils, suggesting strategies for contamination mitigation and environmental restoration.

Two-Phase Perchlorate Activation Enabled by a Dinuclear Fe-NHC (N-Heterocyclic Carbene) Complex.

Perchlorate, initially regarded as a weakly coordinating counterion rather than a reactive oxidizing reagent due to its kinetic stability, has garnered attention for its potential in microbial systems. Under anaerobic conditions, microbes utilize perchlorate as a terminal oxidant for methane oxidation, involving two distinct stages: extraction and release of oxidizing ability. This two-phase activation process necessitates the collaborative action of multiple enzymes, a phenomenon not extensively explored in artificial systems. To address this issue, a dinuclear Fe-NHC (N-heterocyclic carbene) complex 1 was designed to enable the two-phase activation of perchlorate. Initially, complex 1 extracts the oxidative potential of perchlorate, leading to the formation of Fe(III)-O-Fe(III) complex 2 as the oxidation product. Subsequently, the extracted oxidative potential can be released by photolyzing a mixture of complex 2 and 9,10-dihydroanthracene. In contrast to the commonly observed selectivity, the homocoupling product 5 was identified as the major product in this C-H activation reaction. Further, a catalytic C-H activation reaction is initiated under anaerobic conditions to selectively form the C-C coupling product, achieving the complete two-phase activation of perchlorate using a single artificial catalyst. This work provides a new paradigm for constructing biomimetic anaerobic oxidation using kinetically inert high-valent oxygenated acid anions as oxidants.

Exploring the Impact of Additive Shortcuts in Neural Networks via Information Bottleneck-like Dynamics: From ResNet to Transformer.

Deep learning has made significant strides, driving advances in areas like computer vision, natural language processing, and autonomous systems. In this paper, we further investigate the implications of the role of additive shortcut connections, focusing on models such as ResNet, Vision Transformers (ViTs), and MLP-Mixers, given that they are essential in enabling efficient information flow and mitigating optimization challenges such as vanishing gradients. In particular, capitalizing on our recent information bottleneck approach, we analyze how additive shortcuts influence the fitting and compression phases of training, crucial for generalization. We leverage Z-X and Z-Y measures as practical alternatives to mutual information for observing these dynamics in high-dimensional spaces. Our empirical results demonstrate that models with identity shortcuts (ISs) often skip the initial fitting phase and move directly into the compression phase, while non-identity shortcut (NIS) models follow the conventional two-phase process. Furthermore, we explore how IS models are still able to compress effectively, maintaining their generalization capacity despite bypassing the early fitting stages. These findings offer new insights into the dynamics of shortcut connections in neural networks, contributing to the optimization of modern deep learning architectures.

Adsorption Removal of Organophosphates from Water by Steel Slag: Modification, Performance, and Energy Site Analysis

Organophosphates are a type of emerging environmental contaminant, which can be removed effectively by adsorption. Here, modified steel slag was examined for its adsorptive performance in the removal of hydroxyethylidene diphosphonic acid (HEDP) from water. Compared to acid (55.3%, maximum removal rate) and base (85.5%) modification, high-temperature modification (90.6%) significantly enhanced steel slag’s adsorption capacity for HEDP, surpassing that of unmodified slag (71.2%). Kinetic analyses elucidated a two-phase adsorption process—initial rapid adsorption followed by a slower equilibrium phase. The results of adsorption energy analysis showed that modified steel slag preferentially occupied the sites with higher energy, which promoted the adsorption. After five regeneration cycles, the adsorption properties of the material were not significantly reduced, which indicates that the material has good application potential. Microscopic and spectroscopic techniques, including SEM-EDS, FTIR, and XPS, were employed to uncover the surface chemistry and structural changes responsible for the enhanced adsorption efficiency. The adsorption mechanism of HEDP on steel slag is a complete process guided by hydrogen bonding interactions, strengthened surface complexation, and optimized ligand exchange. This study advances the sustainable utilization of industrial waste materials and contributes significantly to the development of innovative water treatment technologies.

Thermodynamically consistent interfacial curvatures in real pore geometries: Implications for pore-scale modeling of two-phase displacement processes

In conventional Pore-network Modeling (PNM) approaches, fluid flow and transport are solved in a network of pore elements with idealized geometries. Such simplification can lead to inaccurate predictions when the original pore space features complex geometries. To overcome this limitation, this study introduces a novel workflow that integrates four key components: (i) an enhanced pore network extraction (PNE) platform capable of identifying and extracting pore cross-sections from high-resolution micro-computed tomography (micro-CT) images of the pore space, (ii) a computationally-efficient semi-analytical model that can faithfully predict capillary entry pressure and the corresponding fluid configuration of piston-like displacements using real two-dimensional cross-sections of pores, (iii) a PNM approach for two-phase flow modeling that utilizes the capillary entry pressure of pores predicted by the semi-analytical model, and (iv) an Artificial Intelligence (AI)-driven model that paves the way for future advancements in efficiently predicating fluid displacement properties in intricate pore structures. To validate this new workflow, we constructed various pore networks containing real pore and throat cross-sections over a diverse group of sandstone and carbonate rock samples. Subsequently, we simulate capillary pressure curves of Mercury Intrusion Capillary Pressure (MICP) and oil–water primary drainage displacements in Bentheimer and Berea sandstones, respectively, using both the conventional and enhanced PNM approaches. The latter demonstrated improved prediction accuracy compared to conventional methods. Next, primary drainage simulations are conducted for two carbonates, and the resulting capillary pressure curves from both PNM approaches are compared. In addition, we conduct an in-depth analysis of fourteen geometric features of the pore space, identifying key factors of hydraulic radius, circumradius, sphericity, and area, that significantly impact capillary entry pressure of pores. After that, we construct an Artificial Neural Network (ANN) to predict the capillary entry pressure of pores using their critical geometric features. This AI model, trained using data derived from the semi-analytical model, exhibits excellent predictive accuracy (with a R2 of 0.995 for the test data set) in estimating capillary entry pressure of pores. Overall, our newly proposed, integrated workflow represents a significant step forward in the field of digital rock technology (DRT), offering an accurate and efficient method for modeling fluid flow in rock samples with complex pore geometries.

Kitchen Waste Digestate and Digestate Biochar Fertilizer for Turfgrass Management and Nutrient Leaching

Slow-release fertilizer is generally used in turfgrass management to maintain quality turf without inducing excess vertical growth, to avoid frequent mowing. This study was designed to verify whether kitchen waste digestate and digestate biochar could serve as slow-release fertilizers to promote turf quality and reduce nutrient leaching risk in two turfgrass species over two years in controlled pot experiments. The results showed that the nutrient release of granular fertilizers made from kitchen waste digestate and digestate biochar was a two-phase process (an initial rapid phase, followed by a slow-release phase). The kitchen waste digestate showed higher nitrogen and phosphorus release ratios than the biochar and sheep manure. Compared to the reference sheep manure, fertilization with kitchen waste digestate and biochar increased the leaf chlorophyll content by 48.6% and 27.9%, and reduced senescent leaves by 15.6% and 11.1%, respectively, at 23 d after fertilization, leading to higher turf quality. Fertilization with kitchen waste digestate biochar significantly reduced the nutrient leaching risk compared to fertilization with kitchen waste digestate and sheep manure. Our results demonstrated the potential of reusing kitchen waste digestate and digestate biochar as slow-release fertilizers for promoting turf quality in turfgrass management.

Challenges and Opportunities in the Adoption of Flexible Manufacturing Systems in Indian Manufacturing Industries

The manufacturing sector is increasingly adopting automation technologies to enhance productivity and flexibility. Among these technologies, Flexible Manufacturing Systems (FMS) offer substantial benefits, such as reduced lead times, improved production flexibility, and lowered operational costs. Despite its advantages, FMS adoption in Indian manufacturing industries has been slow due to several barriers, including high capital costs, technological complexity, and inadequate government support. This study investigates the current status of FMS adoption in India, identifies the key barriers hindering its implementation, and proposes a decision-making model to facilitate its wider adoption. The research gap highlights the lack of a comprehensive framework to quantify the barriers and assess the performance of FMS adoption. To address this, a two-phase Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology was used. This approach allowed for the classification and prioritization of barriers, as well as the development of a model for FMS selection based on statistical analysis. A case study was conducted using this methodology to evaluate various FMS models and provide a systematic decision-making framework for manufacturers. The results revealed that the most significant barriers to FMS adoption in India include high upfront costs, insufficient infrastructure, and the challenges posed by rapidly advancing FMS technology. By applying the AHP-TOPSIS approach, this study offers insights into overcoming these barriers and presents actionable recommendations for manufacturers and policymakers. The findings emphasize the need for increased government support and industry collaboration to accelerate FMS adoption in India. This research contributes to the development of a structured decision-making framework, which could help manufacturers implement FMS more effectively, thereby enhancing competitiveness in an increasingly dynamic global market. Keywords— Flexible Manufacturing Systems, Barriers to FMS, Indian Manufacturing, AHP-TOPSIS, Decision-Making Model

Virtual Lifelines: Filipino Psychologists' Experiences with Tele-Counseling for Suicide Prevention

The COVID-19 pandemic forced a rapid shift to tele-counseling, presenting unique challenges for psychologists, especially those working with high-risk clients. This exploratory multiple-case study investigated the experiences of three Filipino psychologists providing tele-counseling for suicide prevention. The study revealed initial struggles with technology, ethical concerns, and perceived clinical effectiveness. However, the psychologists demonstrated resilience through a two-phase continuous professional development process: orientation and recalibration. Key outcomes included increased clinician enthusiasm, new professional opportunities, and improved ethical responses to crises. Tele-counseling emerged as a client-responsive tool that expands access to mental health services. Despite challenges, tele-counseling can be effective for suicide prevention with proper adaptation and training. These findings underscore the importance of integrating tele-counseling competencies into professional training curricula and developing evidence-based practices for remote crisis intervention.

Efficient removal of nanoplastics by iron-modified biochar: Understanding the removal mechanisms

Tiny plastic particles, particularly nanoplastics, are becoming major threats to aquatic and biotic life owing to their unique physico-chemical characteristics. Thus, in the present work, biochar (BC) was fabricated using “Ulva prolifera green tide” as a biowaste raw material by slow pyrolysis technique to examine its potential in removing nanoplastics from the environment. The findings depicted that nanoplastics removal efficiency by BC was V-shaped with initial pH increased from 2 to 11, and the main removal mechanism changed from adsorption to heterogeneous aggregation between nanoplastics, biochar colloids, and leached substances from BC. When the solution pH crossed the pHpzc of BC (2.3), the aggregation kinetics were well-fitted by the logistic model and displayed as an S-shaped curve with a lag period. Characterization results indicated that biochar colloids were the key enabler with a critical concentration of 72.01 mg L−1 at neutral pH. Keeping in mind the removal mechanisms and contribution of biochar colloids, iron-modified biochar (Fe-BC) was produced to enhance the overall removal efficiency. The Fe-BC demonstrated a two-phase removal process of pre-adsorption and post-aggregation, successfully realized to minimize lag time and enhance aggregation performance. The theoretical removal capacity of Fe-BC against nanoplastics could reach up to 1626.3 mg g−1, which was three-fold higher than that of BC. Further, the Fe-BC was suggested to be recycled and reused at least three times by ultrasound, followed by co-pyrolysis for green and efficient degradation of nanoplastics. Overall, the findings offer a promising approach for removing and recycling nanoplastics in the environment.

Active mission assignment for improving the effectiveness of opportunistic maintenance in a fleet of mission-critical systems

In a fleet of mission-critical systems, maintaining high availability for each individual system is vital to ensure consistent fleet readiness to meet mission demands. This study introduces a novel mission assignment strategy aimed at actively aligning maintenance schedules within each system to maximise opportunistic maintenance potential, thus enhancing system availability. However, solely focusing on system-level assignments may result in simultaneous maintenance needs across the fleet, leading to resource conflicts. The proposed strategy addresses this issue by coordinating mission assignments to consider both the alignment of schedules within a system and the even distribution of schedules among systems to prevent fleet-level conflicts. Two operational conditions, intra-system and inter-system balance, are defined to evaluate the criticality of each system at both system and fleet levels. Based on these conditions, scheduling rules are developed to determine assignment priority across the fleet in a two-phase process. Simulation studies demonstrate the effectiveness of this approach, showing a 2% improvement in system availability, equivalent to a two-month reduction in downtime across the fleet over a one-year period. The results underscore the advantages of the proposed strategy in optimising maintenance operations in complex fleet environments with diverse mission requirements.

A molecular dynamics study on the supercritical evaporation of liquid ammonia under forced convection conditions

Although the behavior of the supercritical transition during the evaporation of liquid fuels in engine combustion chambers has been studied by many researchers, the effects of convection on the phase transition of liquid fuel in supercritical environments have been little studied. Previous studies mainly focused on hydrocarbon fuels for supercritical evaporation, with fewer on the zero-carbon liquid ammonia fuel. Aiming at the above deficiencies, this study used molecular dynamics simulations (MD) to explore the phase transition of liquid ammonia in a nitrogen supercritical environment under forced convection conditions. The Peng-Robinson equation of state (PR-EoS) was used to calculate the vapor–liquid equilibrium of the ammonia–nitrogen system to determine the supercritical transition time of liquid ammonia under forced convection conditions. The effects of forced convection on the heat and mass transfer process and the evolution of the liquid-phase region and the vapor–liquid interface region were analyzed. It was found that forced convection can promote the mass and heat transfer process and accelerate the phase transition of liquid ammonia to some extent. Forced convection may have a greater effect on the single-phase diffusive mixing process compared to the two-phase evaporation process. The increase in the interfacial thickness under forced convection conditions is mainly attributed to the temperature increase promoted by convection. The interfacial temperature dominates the development of its thickness and the thickness is approximately linear with temperature during the diffusive mixing stage. The shear effect induced by convection has an inhibitory effect on the diffusion of ammonia molecules within the interface region along the vertical convection direction. The cases in this study indicate that forced convection may promote the supercritical transition of liquid ammonia. In addition, a dimensionless number κ was proposed to explore the possibility of scaling up the MD results to the engineering scale.

Synergistic release of Cd(II) and As(V) from ternary sorption systems containing ferrihydrite nanoparticles: The role of binary and ternary surface complexes

Cadmium (Cd) and arsenic (As) generally exhibit mutually beneficial co-sorption behavior on iron oxyhydroxides through multiple mechanisms, including surface precipitation, ternary surface complexes, and electrostatic interactions. However, the numerous factors that control the immobilization of Cd and As in turn complicated the processes and mechanisms involved in their co-desorption from iron minerals, which hindered the full understanding of their geochemical behaviors. Here, the simultaneous release of Cd(II) and As(V) from newly precipitated ferrihydrite nanoparticles by either Ca or P was investigated through kinetics and isothermal desorption experiments. We showed that the Cd(II) and As(V) present two-phase desorption processes (rapid desorption and slow desorption) in both binary (Fe–Cd or Fe–As alone) and ternary systems (Fe–Cd–As co-presence). Compared to their binary counterparts, Cd(II) and As(V) in the ternary systems are more prone to detachment from ferrihydrite. Further desorption of Cd(II) and As(V) at different co-presence scenarios (different initial concentrations) demonstrated mutual promotion behaviour towards their counterparts; the co-presence of Cd(II) facilitates the desorption of As(V), while the co-presence of As(V) also promotes the desorption of Cd(II). XPS and FTIR results demonstrated that either Ca or P showed minor effects on the binding environment of Cd and As. Further results from the in-situ ATR-FTIR experiment and second derivative peak fitting analysis indicate that the enhanced detachment of Cd(II) and As(V) from the ternary system may be due to the synergistic desorption of the ternary surface complexes and other surface complex species. Our results provide new insights into the prediction of the environmental behaviour of the coexistence of Cd(II) and As(V) in iron-rich geological settings. The potential environmental risks of iron-based remediation methods should be considered due to the enhanced bioavailability of Cd(II) and As(V) in co-presence circumstances.

CFD simulation of liquid-liquid extraction mechanism and enhancement schemes in microchannels based on three mass transfer models

Microreactors are highly efficient devices with a large specific surface area to improve the mass transfer efficiency. In this paper, a comprehensive three-dimensional CFD model was constructed based on three mass transfer theories to simulate the liquid-liquid two-phase flow and mass transfer process in a microchannel reactor, and two enhancement schemes were proposed. The multiphase flow and mass transfer characteristics were investigated by visualization and extraction experiments. The results indicated that the extraction efficiencies (E) and overall mass transfer coefficients (KLa) calculated by the penetration model and surface renewal model were within ±15 % errors compared to the experimental values. Moreover, E primarily increases with the increase of fluid residence time, while KLa increases with increasing flow rate. As the flow rate increases from 0.3 ml/min to 1.5 ml/min, E decreases by 15 %, and KLa rises from 0.78 s⁻¹ to 2.65 s⁻¹. Whereas the channel width decreases from 0.8 mm to 0.3 mm, E decreases by 3 %, and KLa rises from 0.44 s⁻¹ to 2.77 s⁻¹. Finally, microchannel with necked structure and baffles in mixing zone both improve the mass transfer efficient to some extent.

Polycystic Ovary Syndrome Markers and Mechanism

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder characterized by hormonal imbalances, metabolic dysregulation, and reproductive abnormalities. This study aims to elucidate the significance of PCOS markers and their mechanisms while outlining the methodology employed. A comprehensive literature review was conducted from 2020 to 2023 using databases like PubMed, ScienceDirect, EMBASE, and Google Scholar. Key search terms included "pathophysiology," "pathogenesis," "PCOS," "marker," and "biomarker," aligned with the PICOT criteria. After removing duplicates, articles underwent a two-phase screening process based on predefined inclusion criteria. Additionally, a marker mechanism flowchart was created using the Biorender application. Results highlighted the pivotal role of various markers in understanding PCOS pathophysiology and guiding clinical management. Anthropometric, visual, metabolic, inflammatory, endocrine, and oxidative stress markers were analyzed for their diagnostic, prognostic, and therapeutic implications in PCOS. The study underscores the importance of marker interactions in personalized PCOS management. Limitations in marker interpretation warrant further research to refine diagnostic accuracy and optimize therapeutic interventions. Integrating marker mechanisms enhances understanding of PCOS heterogeneity and informs targeted treatment approaches tailored to individual phenotypic variations.

505 Utilizing porcine hepatocyte cultures to test the effect of short chain fatty acids on androstenone metabolism

Abstract Boar taint is an off-putting odor found in the meat of entire male pigs due to the accumulation of androstenone and skatole in the fat. Development of boar taint is dependent on the balance between the rate of synthesis and metabolism of these compounds. Prior studies have shown that feed ingredients high in fermentable carbohydrates, including chicory root, sugar beet pulp, and Jerusalem artichoke decreased the synthesis of skatole in the hindgut and altered the metabolism in the liver, resulting in a skatole concentrations comparable with castrated males. However, the mechanism of how these dietary fermentable carbohydrates affect liver metabolism is not well understood. Metabolism of boar taint compounds takes place in a two-phase process, regulated by various enzymes and cytochrome families. Short-chain fatty acids (SCFAs) are produced from the microbial fermentation of fiber and have been shown to have regulatory effects on histone deacetylases and gene expression in humans, providing a potential mechanism in which feed high in dietary fiber reduces boar taint. The purpose of this study was to assess if SCFA directly affect the metabolism of androstenone in liver hepatocytes. Primary hepatocytes were isolated from the liver of 6 [(Yorkshire x Landrace) x Duroc] boars weighing around 90 kg. Cells were treated in triplicate with acetate, butyrate, propionate, and various combinations for 24 h to alter gene expression. Two sets of wells acted as a control, not being treated with SCFAs. The cells were then incubated with androstenone for 3 h, and media was extracted, and metabolite production was analyzed using high-performance liquid chromatography. The effects of treatments were using a one-way ANOVA with a Dunnet’s adjustment for multiple comparisons to a control. Results showed the combined acetate, butyrate, and propionate treatment had the greatest effect on androstenone glucuronide production when compared with the control, resulting in significant increases (P < 0.05) ranging from 82 ± 1.6% to 1 ± 2.4% in individual boars. Other treatments had a similar effect, significantly increasing the production of androstenone glucuronide but to a lesser degree. Butyrate increased production ranging from 40 ± 2.2% to 5±1.5% (P < 0.05), propionate from 38 ± 3.6% to 3 ± 2.6% (P < 0.05), acetate and butyrate from 61±3.9% to 1 ± 2.8% (P < 0.05), acetate and propionate from 70±3.5% to 5±1.7% (P < 0.05), and butyrate and propionate from 82 ± 2.5% to 4 ± 1.1% (P < 0.05) when compared with the control. In conclusion, this trial provided evidence that SCFAs produced by the microbial fermentation of fiber can significantly increase hepatic androstenone metabolism in some animals, with a wide range in these effects among individual boars. Future research will involve assessing the effects of SCFAs on hepatic skatole metabolism.

Evaluating the quality of multiple-choice question pilot database: A global educator-created tool for concept-based pharmacology learning.

The Core Concepts of Pharmacology (CCP) initiative is developing educational resources to transform pharmacology education into a concept-based approach. This study evaluated the quality of global educator-created MCQs in generating items for the pharmacology concept inventory (PCI) instrument and developed as a resource for learning pharmacology fundamental concepts. A panel of 22 global pharmacology experts recruited from the CCP initiative research team participated in the MCQ pilot database design and evaluation. The quality analysis framework of the MCQs in the pilot database included four assessment tools: item writing guidelines (IWGs), Bloom's taxonomy, the CCP, and the MCQ design format. A two-phase evaluation process was involved, including inter-rater agreement on item quality, followed by resolving conflicts that occurred in quality assessment. The chi-square (χ2) test of independence and Cramer's V correlation tests were utilized to measure the relationship among quality assessment attributes. About 200 MCQs were gathered and 98% underwent expert evaluation. Nearly 80% addressed one or more CCP, with 52% designed using a context-dependent format. However, only 40% addressed higher levels of Bloom's cognitive domain and 10% adhered to all IWGs. A strong positive correlation was observed between the context-based item format and its effectiveness in assessing the higher cognitive domain, the main CCP and improved IWGs adherence. Context-based item construction can assess the higher cognitive skills and fundamental pharmacology concepts, showing potential for rigorous PCI development. The pilot database will store items to create the PCI, aiding the development of a concept-based pharmacology curriculum.

Health Disparities Curricula in General Surgery Residency Programs: A Critical Scoping Review

IntroductionIn 2021, the structural determinants of health (SDOH) were added to the Accreditation Council of Graduate Medical Education common program requirements for all accredited residency programs, including general surgery. In this study, we sought to explore the current scope of, and concepts used in, health disparities curricula for general surgery residents, specifically investigating how general surgery residents learn about health disparities and the SDOH. MethodsWe searched PubMed, EMBASE, Education Research Complete (EBSCOhost), and Web of Science Core Collection using keywords related to health disparities and the SDOH. Inclusion criteria consisted of all studies published after 2005 that discussed health disparities curricula for Accreditation Council of Graduate Medical Education–accredited general surgery residency programs. Five thousand three hundred seventeen articles were screened using a two-phase process. Data extraction and analysis was performed using critical review methods. ResultsSeventeen articles were identified. Within these articles, seven unique health disparities curricula were found. All seven of the identified curricula employed cultural frameworks as methods to mitigate health disparities. Three curricula, all published after 2011, included education on the SDOH. A wide variety of educational methods were utilized; in-person didactics was the most common. ConclusionsIn the current literature, culture continues to play a large role in health disparities training for general surgery residents. Though further efforts are needed to understand the methods used in programs that have not published scholarly work, it is imperative to ensure that residents are provided with the sociopolitical perspective needed to understand the SDOH and serve all patients, including those affected by health disparities.