Tapping into Efficient Learning: An Exploration of the Impact of Sequential Learning on Skill Gains and Learning Curves in Central Venous Catheterization Simulator Training.

OBJECTIVE: Medical residents learn how to perform many complex procedures in a short amount of time. Sequential learning, or learning in stages, is a method applied to complex motor skills to increase skill acquisition and retention but has not been widely applied in simulation-based training (SBT). Central venous catheterization (CVC) training could benefit from the implementation of sequential learning. CVC is typically taught with task trainers such as the dynamic haptic robotic trainer (DHRT). This study aims to determine the impact of sequential learning on skill gains and learning curves in CVC SBT by implementing a sequential learning walkthrough into the DHRT. METHODS: 103 medical residents participated in CVC training in 2021 and 2022. One group (N = 44) received training on the original DHRT system while the other group (N = 59) received training on the DHRTsequential with interactive videos and assessment activities. All residents were quantitatively assessed on (e.g. first trial success rate, distance to vein center, overall score) the DHRT or DHRTsequential systems. RESULTS: Residents in the DHRTsequential group exhibited a 3.58 times higher likelihood of successfully completing needle insertion on their first trial than those in the DHRT only group and required significantly fewer trials to reach a pre-defined mastery level of performance. The DHRTsequential group also had fewer significant learning curves compared to the DHRT only group. CONCLUSION: Implementing sequential learning into the DHRT system significantly benefitted CVC training by increasing the efficiency of initial skill gain, reducing the number of trials needed to complete training, and flattening the slope of the subsequent learning curve.

Feasibility, clinical outcomes, and learning curves of robotic-assisted colorectal cancer surgery in a high-volume district general hospital: a cohort study.

Introduction: Robotic-assisted surgery (RAS) is one of the most influential surgical advances with widespread clinical and health-economic benefits. West Hertfordshire Teaching Hospital NHS Trust was the first in the UK to simultaneously integrate two CMR Surgical Versius robots. This study aims to investigate clinical outcomes of RAS, explore surgeon learning curves and assess the feasibility of implementation within a district general hospital (DGH). Methods: A prospective cohort study of 100 consecutive patient data were collected between July 2022 and August 2023, including demographics, operative and clinical variables, and compared with laparoscopic surgery (LS) data from the National Bowel Cancer Audit. Surgeon learning curves were analysed using sequential surgical and console times. Results: In the RAS cohort, the median age was 70 (IQR 57-78 years) and 60% were male. Retrieval of a minimum of 12 lymph nodes significantly increased in RAS compared to LS (95% vs. 88%, P=0.05). The negative mesorectal margin rate was similar between RAS and LS (97% vs. 91%, P=0.10), as well as length of stay greater than 5 days (42% vs. 39%, P=0.27). For anterior resections performed by the highest volume surgeon (n=16), surgical time was reduced over 1 year by 35% (304.9-196.9 min), whilst console time increased by 111% (63.0-132.8 min). Conclusions: Key quality performance indicators were either unchanged or improved with RAS. There is potential for improved theatre utilisation and cost-savings with increased RAS. This study demonstrates the feasibility and easy integration of robotic platforms into DGHs, offering wider training opportunities for the next generation of surgeons.

A new class of Lindley distribution: System reliability, simulation and applications.

This paper presents a new probability distribution called the DUS Lindley distribution, created by applying the DUS transformation to the traditional Lindley distribution. The study provides an in-depth analysis of the distribution's statistical properties. These properties include a variety of statistical measures such as the probability density function, cumulative distribution function, failure rate, survival function, reverse hazard function, Mills ratio, mean residual life, mean past life, moments, conditional moments, characteristic function, order statistics, entropy measures, likelihood ratio test and Lorenz and Bonferroni curves. Parameter estimation is performed using several methods including weighted least squares, maximum likelihood estimation, Cramer-Von Mises estimation, least squares and Anderson-Darling estimation. The paper also explores the estimation of system reliability and evaluates the performance of maximum likelihood estimators through simulation studies across different sample sizes. Finally, the DUS Lindley distribution is applied to two real-world datasets, demonstrating a better fit than other well-known distributions.

Thermoluminescence and Kinetic Parameters of Beta Rays Irradiated Egyptian Muscovite.

The current work was going to study the dosimetric properties of Egyptian muscovite minerals irradiated with ß-rays using the thermoluminescence technique (TL). The analysis of the TL glow curves was done using the (Tm - Tstop) and the initial rise methods. The deconvolution of the muscovite glow curves was analyzed to have eight superimposed trapping peaks. These trapes were located at 0.72, 0.80, 0.98, 1.09, 1.19, 1.30, 1.50, and 1.59 eV. Muscovite samples were studied at doses up to 330 Gy and exhibited good linearity in relation to beta radiation. The kinetic parameters, sensitivity, and dosimetric characteristics were evaluated. High accuracy was achieved through the repeatability of TL measurements.

Clinical utility of novel anthropometric indices in identifying type 2 diabetes mellitus among South African adult females.

AIM: To assess the clinical utility of novel anthropometric indices and other traditional anthropometric indices in identifying the risk of type 2 diabetes mellitus (T2D) among South African adult females. METHODS: In the first South African National Health and Nutrition Examination Survey (SANHANES-1), traditional [body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR)] and novel [a-body shape index (ABSI), abdominal volume index (AVI), body adiposity index (BAI), body roundness index (BRI), conicity index (CI), and Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE)] anthropometric indices were assessed. T2D was diagnosed using glycated haemoglobin (HbA1c) ≥ 6.5% among participants without known T2D. Basic statistics and multiple regression analyses were explored the association between anthropometric indices and newly diagnosed T2D. Receiver operating characteristic (ROC) curve analysis was used to measure the predictive ability of both traditional and novel indices. RESULTS: Among 2 623 participants, 384 (14.6%) had newly diagnosed T2D. All anthropometric indices mean values were significantly higher among participants with T2D (most p < 0.001). Higher mean values increased T2D odds e.g., in the model adjusted for age, employment, residence, and population group, odds ratio (OR) and 95% confidence interval (CI) for T2D with some of anthropometric indices were: 1.86 (1.60-2.15) for WC, 1.84 (1.59-2.13) for WHtR, 1.73 (1.51-1.99) for AVI, 1.71 (1.49-1.96) for BRI and 1.86 (1.57-2.20) for CUN-BAE. The top quartile for all indices had the highest T2D odds (p < 0.05). These outcomes were the highest for WC, AVI, and CUN-BAE and remained so even after removing the confounding effects of age, employment, population group, and residence. Based on the ROC analysis, none of the anthropometrical indices performed excellently (i.e., had an area under the curve [AUC] > 0.80). The WC, WHtR, AVI, BRI, and CUN-BAE, however, performed acceptably (AUCs 0.70-0.79), while also exhibiting corresponding cutoff values of 86.65 cm, 0.57, 15.52, 3.83, and 38.35, respectively. CONCLUSIONS: The data shows that traditional and novel anthropometric indices similarly identifying newly diagnosed T2D among adult South African females. We recommend the continuing the use of traditional indices, as they are affordable and easy to use in our setting.

Optimizing sheep growth curves using a meta-heuristic algorithm.

Sheep were among the first animals domesticated by humans, and to this day, small ruminants are primarily raised for their meat, milk, and wool. This study evaluated the goodness of fit for growth curve models using observed age and weight data from crossbred lambs of various breeds based on the mean values between paired breeds. We employed a hybrid metaheuristic algorithm, combining a simulated annealing (SA) algorithm and a genetic algorithm (GA) called SAGAC, to determine the optimal parameter values for growth models, ensuring the best alignment between simulated and observed curves. The goodness of fit and model accuracy was assessed using the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Errors were measured by comparing the criteria differences between simulated and observed data. Thirty crossbreed combinations were simulated, considering the average weight. Analysis of the observed and simulated growth curves indicated that specific crossbreeding scenarios produced promising results. This simulation approach is believed to assist geneticists in predicting potential crossbreeding outcomes, thereby saving time and financial resources in field research.

Machine Learning-based Macrophage Signature for Predicting Prognosis and Immunotherapy Benefits in Cholangiocarcinoma.

AIMS: We aimed to develop a macrophage signature for predicting clinical outcomes and immunotherapy benefits in cholangiocarcinoma. BACKGROUND: Macrophages are potent immune effector cells that can change phenotype in different environments to exert anti-tumor and anti-tumor functions. The role of macrophages in the prognosis and therapy benefits of cholangiocarcinoma was not fully clarified. OBJECTIVE: The objective of this study is to develop a prognostic model for cholangiocarcinoma. METHODS: The macrophage-related signature (MRS) was developed using 10 machine learning methods with TCGA, GSE89748 and GSE107943 datasets. Several indicators (TIDE score, TMB score and MATH score) and two immunotherapy datasets (IMvigor210 and GSE91061) were used to investigate the performance of MRS in predicting the benefits of immunotherapy. RESULTS: The Lasso + CoxBoost method's MRS was considered a robust and stable model that demonstrated good accuracy in predicting the clinical outcome of patients with cholangiocarcinoma; the AUC of the 2-, 3-, and 4-year ROC curves in the TCGA dataset were 0.965, 0.957, and 1.000. Moreover, MRS acted as an independent risk factor for the clinical outcome of cholangiocarcinoma cases. Cholangiocarcinoma cases with higher MRS scores are correlated with a higher TIDE score, higher tumor escape score, higher MATH score, and lower TMB score. Further analysis suggested high MRS score indicated a higher gene set score correlated with cancer-related hallmarks. CONCLUSION: With regard to cholangiocarcinoma, the current study created a machine learning-based MRS that served as an indication for forecasting the prognosis and therapeutic advantages of individual cases.

Enhancing diagnostic accuracy in symptom-based health checkers: a comprehensive machine learning approach with clinical vignettes and benchmarking.

Introduction: The development of machine learning models for symptom-based health checkers is a rapidly evolving area with significant implications for healthcare. Accurate and efficient diagnostic tools can enhance patient outcomes and optimize healthcare resources. This study focuses on evaluating and optimizing machine learning models using a dataset of 10 diseases and 9,572 samples. Methods: The dataset was divided into training and testing sets to facilitate model training and evaluation. The following models were selected and optimized: Decision Tree, Random Forest, Naive Bayes, Logistic Regression and K-Nearest Neighbors. Evaluation metrics included accuracy, F1 scores, and 10-fold cross-validation. ROC-AUC and precision-recall curves were also utilized to assess model performance, particularly in scenarios with imbalanced datasets. Clinical vignettes were employed to gauge the real-world applicability of the models. Results: The performance of the models was evaluated using accuracy, F1 scores, and 10-fold cross-validation. The use of ROC-AUC curves revealed that model performance improved with increasing complexity. Precision-recall curves were particularly useful in evaluating model sensitivity in imbalanced dataset scenarios. Clinical vignettes demonstrated the robustness of the models in providing accurate diagnoses. Discussion: The study underscores the importance of comprehensive model evaluation techniques. The use of clinical vignette testing and analysis of ROC-AUC and precision-recall curves are crucial in ensuring the reliability and sensitivity of symptom-based health checkers. These techniques provide a more nuanced understanding of model performance and highlight areas for further improvement. Conclusion: This study highlights the significance of employing diverse evaluation metrics and methods to ensure the robustness and accuracy of machine learning models in symptom-based health checkers. The integration of clinical vignettes and the analysis of ROC-AUC and precision-recall curves are essential steps in developing reliable and sensitive diagnostic tools.

Anthropometric indices growth references (length, Weight and Head Circumferences) of children aged 0-24 months in North-East of Iran by GAMLSS.

BACKGROUND: Growth references play a crucial role in the screening, evaluation, and surveillance of children, aiding in the early identification of the requirement for diverse growth-promoting interventions. Variations in human growth across distinct ethnic cohorts arise from genetic disparities, lifestyle variances, nutritional diversity, and diverse social and environmental contexts. Consequently, the selection of growth references markedly influences the prevalence of developmental disorders and nutritional imbalances in children. The study aims to assess the growth percentile of children in the north-east of Iran and establish population-specific reference charts for length, weight, and head circumference spanning from birth to 24 months. METHODS: This cross-sectional population-based research conducted in the north-east of Iran, from 2016 to 2023. The Data extracted from the electronic health records of Mashhad University of Medical Sciences. All apparently healthy children aged from birth to 24 months who were measured at least once by health staff at the ages of birth,1,2,4,6,7,9,12,15,18,24 months were included. The target population of the study were 479,089 children (96.21%), encompassing 233,565 girls (48.75%) and 245,524 boys (51.25%). Gender-specific percentile curves for length, weight, and head circumference concerning age, as well as weight concerning length, were derived using the GAMLSS approach. RESULTS: From the anthropometric information of 479,089 children (245,524 boys and 233,565 girls), growth charts were constructed. In comparison to the standard WHO chart, Iranian neonates displayed lower weight across all percentiles during the first month after birth, exhibited decreased head circumference at the 3rd percentile, and boys showed reduced length across all percentiles. After this age, Iranian children demonstrated increased weight, length, and head circumference. CONCLUSIONS: This research introduces the inaugural large-scale endeavor for indigenous reference charts. Through the noted distinctions from the international reference, the utilization of this novel resource offers the potential to enhance the surveillance of children's growth within the area. Moreover, by accurately assessing growth anomalies such as underweight, stunting, and wasting, it expands the domain of impactful policies in this sphere. Simultaneously, it enables the exploration of the secular trend of children's growth in the forthcoming years.

Canadian adult reference data for body composition, trabecular bone score and advanced hip analysis using DXA.

DXA-derived reference data for visceral adipose tissue (VAT) and advanced hip analysis (AHA) parameters spanning the entire adult lifespan are limited. The purpose of this study was to develop age-, site- and sex-specific reference data for dual X-ray absorptiometry (DXA) -derived body composition, trabecular bone score (TBS) and advanced hip analysis (AHA) parameters across the adult lifespan. Adults (N = 908; female: 561 and male: 347) from Calgary and the surrounding area over the age of 20 years participated in this study. Participants received DXA scans of their hip (total hip [TH] and femoral neck [FN]), lumbar spine [LS], forearm [33 % site] and total body (iDXA, GE Lunar, GE Healthcare). Areal bone mineral density (aBMD, g/cm2) was captured at all sites, and body composition variables, including lean mass, fat mass and percent fat, were analyzed from the total body scan. VAT mass was assessed from total body DXA scans. Advanced hip analysis (AHA) was performed on hip scans and trabecular bone score (TBS) on the LS scans to assess bone quality. Site- and sex-specific centile curves and tables were generated using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) method. Clinicians and researchers can use these Canadian reference data as a tool to assess body composition, TBS and AHA parameters across the adult lifespan.

Over 30 years of using functional data analysis in human movement. What do we know, and is there more for sports biomechanics to learn?

Functional data analysis (FDA) is a contemporary area of statistics designed for analysis of functions or curves. FDA has grown in human movement applications over the last three decades, with it being applied across a range of sport applications including rowing, weightlifting, diving, race-walking, jumping and running. Functional principal components analysis (fPCA) has been the most commonly used technique in sports biomechanics, often being applied to better understand characteristics of variability present in curves from biomechanical variables sampled from sporting movements. Given that FDA is an area of statistics with specific techniques for processing and analysing data, it provides one valuable platform for biomechanists to understand and think about their data more holistically. Further, the visual interpretability that FDA techniques provide, there is great potential for FDA to be used beyond research contexts, as a suite of practical tools to assist practical sports biomechanists in making decisions in sport. This review aims to demonstrate some methods yet to be applied in sports biomechanics, with simple sports biomechanics data applications taken from rowing. This article aims to showcase the value that FDA may have in assisting practitioners as they make decisions with athletes regarding their movement characteristics.

Simultaneous comparison of three methods for bone quality assessment.

PURPOSE: The study aims to investigate the correlations between the T score, Hounsfield units (HU) value, and vertebral bone quality (VBQ) score, and to compare their discrimination capability for patients with osteoporotic vertebral compression fracture (OVCF). METHODS: One hundred and sixty-three eligible participants were enrolled (49 OVCF group,114 non-OVCF group). The T score, HU value, and VBQ score were collected retrospectively. Then, those three parameters were compared between the OVCF and non-OVCF groups and the correlations among the three were assessed. Finally, the discrimination capability of those parameters was compared by the receiver operating characteristic (ROC) curves. RESULTS: The OVCF group showed a lower T score, lower HU value, and higher VBQ score (all P < 0.001) than the non-OVCF group. Correlations were observed among the T score, HU value, and VBQ score (all P < 0.001, HU VS. mean T score, r = 0.66; HU VS. minimum T score, r = 0.67; VBQ VS. mean/ minimum T score, r=-0.33; VBQ VS. HU, r=-0.45). The HU value indicated the maximum area under curve (AUC), followed by the VBQ score and then the T score. Moreover, the AUC of combining the VBQ score and the HU value was similar to that of the HU value. CONCLUSIONS: Both the HU value and the VBQ score had superior discrimination capability for patients with OVCF compared to the T score, especially for the HU value. For patients with routinely performed lumbar MRI or CT scans, the HU value or the VBQ score may provide alternative options for assessing the bone condition.

A modified analysis protocol for the PhNR test.

PURPOSE: Several studies have reported that glaucoma patients have abnormal photopic negative response (PhNR) results compared to reference control subjects. The International Society for Clinical Electrophysiology of Vision (ISCEV) released an extended protocol for PhNR (I-PhNR) in 2018. The purpose of this study was to compare the I-PhNR protocol to a similar protocol modified (M-PhNR) to enhance the performance of the method in detecting glaucomatous damage. METHODS: Thirty subjects were enrolled in this study (12 glaucoma patients, 10 glaucoma suspects, 8 normal controls). PhNR tests were conducted with a Diagnosys E3 mobile system (Diagnosys LLC, Lowell, MA). I-PhNR tests utilized all parameters specified by the ISCEV requirement. M-PhNR tests used the same parameters as the ISCEV tests with the exceptions of a 5-45 Hz bandpass filter and a novel, objective sweep-selection parameter. According to the ISCEV protocol, the PhNR relative to baseline (i.e., BT), a-wave and b-wave response amplitudes and BT/b-wave amplitude ratios were measured. Coefficients of variation, receiver operating characteristic (ROC) curves, and t-tests were used to assess the data from one randomly chosen eye per subject. RESULTS: The M-PhNR protocol resulted in a decrease in the intra-subject repeat test coefficient of variation and a decrease in the average inter-subject coefficient of variation for the glaucoma subjects. The ROC curves demonstrated an increase in the area under the curve (AUC) for the M-PhNR compared to the I-PhNR protocol. The sensitivity and specificity were also greater for the M-PhNR protocol. CONCLUSIONS: The M-PhNR protocol resulted in a decrease in intra-subject and inter-subject data variability which resulted in a significant increase in the ROC AUC, sensitivity, and specificity for glaucoma. Thus, the M-PhNR protocol shows promise as a better diagnostic tool than the I-PhNR protocol for detecting glaucoma.

Why the Fermi paradox may not be well explained by Wong and Bartlett's theory of civilization collapse. A Comment on: 'Asymptotic burnout and homeostatic awakening: a possible solution to the Fermi paradox?' (2022) by Wong and Bartlett.

Wong and Bartlett explain the Fermi paradox by arguing that neither human nor extra-terrestrial civilizations can escape the time window singularity which, they claim, results from the way in which social characteristics of civilizations follow super-linear growth curves of cities. We question if data at the city level necessarily can lead to conclusions at the civilization level. More specifically, we suggest ways in which learnings from research, foresight, diversity and effective future government might act outside of their model to regulate super-linear growth curves of civilizations, and thus substantively increase the likelihood of civilizations progressing towards higher levels of the Kardashev scale. Moreover, we believe their claimed history of the collapse of terrestrial societies used to evidence their model is difficult to justify. Overall, we cast reasonable doubt on the ability of their proposed model to satisfactorily explain the Fermi paradox.

Oxygen enriched PAni-based counter electrode network toward efficient dye-sensitized solar cells (DSSCs).

Dye-sensitized solar cells (DSSCs) have great potential as a renewable energy technology assisting combat climate change due to its low cost, adaptability, and sustainability. Oxygen plasma ion doping is a promising strategy to improve the capacity of a low-cost, platinum-free counter-electrodes (CEs) to absorb photons and drive high-performance DSSCs via generating an abundance of active absorption sites. In this instance, novel PAni-ZnO (PZ) composite layers were designed as a CE material and received various in-situ oxygen plasma dosages, including 0, 2, 4, 6, 8, and 10 min, to improve their physiochemical and microstructural feature for the first time, to the best of our knowledge. Physical evaluations of the microstructure, porosity, morphology, contact angle, roughness, electrical, and optical, electrochemical impedance spectroscopy (EIS) features of CEs were conducted in along with an evaluation of J-V variables. Compared to pristine CE substance, the surface nature of the modified hybrids was gradually enhanced as the plasma level rose, reaching an optimum after 8 min (i.e. 0.2 µm for average pore size and average roughness Ra = 7.21 µm). Expanded plasma treatment doses also improved PV cell performance even further: after 4 min at a plasma level, η = 5.41% was obtained, and after 6 min in a oxygen plasma environment, η = 5.81% was obtained. Mixing high energetic plasma ions increased the mobility of charge carriers in PAni composites along with lowered charge carrier recombination through generating an environment that was conducive to charge dissociation. Therefore, longer lifespans and more effective charge transfer inside the photovoltaic cell as a consequence of the increased mobility less resistive losses. In this respect, following 8 min of plasma surface modification of the PZ CE, the optimized efficiency of 6.31% and Jsc of 15.6 mA/cm2 were obtained. The improvement in efficiency equated to a proportion growth of 77% versus a pristine one. This gain was explained by the reality that suffusing a quantity of oxygen plasma free radicals into the PAni system developed continuous channels that enabled the mixture to move electrons more rapidly, hence raising the photovoltaic efficiency. Overall, this study highlights the advantages of regulating heteroatom species and their co-doping, offering a new perspective for the application of heteroatom-doped CE in DSSCs.

Rapid Analysis for α-Tocopherol and Its Oxidative Products in the Pisum sativum L. Leaf Using Supercritical Fluid Chromatography-Medium Vacuum Chemical Ionization Tandem Mass Spectrometry.

A method for the rapid determination of α-tocopherol (α-T) and its oxidative products in plant tissue has been developed using supercritical fluid extraction (SFE) coupled with supercritical fluid chromatography (SFC) and medium vacuum chemical ionization (MVCI) with tandem mass spectrometry. The method is designed to study changes in levels for α-T and its oxidative products in plant cells during photosynthesis, aiming to observe the light response curves. α-T oxidation is a non-enzymatic self-defense mechanism in plant cells. Unlike enzyme-involved reactions, it cannot be stopped, so the oxidation continues in crude extracts even after extraction. Therefore, a real-time in-situ method is essential for tracking the light response curves. To optimize the selective reaction monitoring method, the reaction mixture of α-T and singlet oxygen (1O2), generated by rose Bengal under light illumination, was used as the source of oxidative products. The relative abundance changes in α-tocopherylquinone and 8a-hydroperoxy tocopherone in Pisum sativum L. (Pea) leaves under excessive light illumination have been preliminarily analyzed as part of the light response curve study. The method archives a throughput of 10-15 minutes for analyzing duplicate leaf samples. This process includes cutting off the leaf, sectioning it, placing the sample in a frozen SFE vessel, and conducting SFE/SFC analysis. Consequently, the average throughput is approximately 5-7 minutes per sample.

CO2 capture enhancement by metal oxides impregnated coal fly ash: a breakthrough adsorption study.

Coal fired power plants are significant contributors to CO2 emissions and produce solid waste in the form of coal fly ash, posing severe environmental challenges. This study explores the application of dry-impregnated coal fly ash for CO2 capture from gas stream. The modification of coal fly ash was achieved using alkaline earth metal oxides, specifically CaO and MgO, to alter its physical and chemical properties. Characterization techniques like X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and BET (Brunauer-Emmett-Teller) analysis were employed for physio-chemical changes in the adsorbent. Breakthrough experiments were conducted using a laboratory-scale fixed packed-bed reactor to assess the influence of temperature and gas flow rate on CO2 adsorption. Among the synthesized sorbents, calcium oxide-impregnated ash showed the highest CO2 uptake capacity, achieving 9.41 mg/g at 30 °C and a flow rate of 20 L/hr under atmospheric pressure. Isotherm modeling indicated a heterogeneous adsorbent surface, with the data best fitting the Sips isotherm model. Furthermore, the adsorption data conformed well to the Yoon-Nelson and Thomas kinetic models, affirming their relevance in characterizing the adsorption process under varying conditions. This research emphasizes the potential of coal fly ash-an abundant, cost-free material-as an effective CO2 adsorbent, contributing to both CO2 mitigation and landfill waste reduction.

Brain-machine convergent evolution: Why finding parallels between brain and artificial systems is informative.

Central nervous system neurons manifest a rich diversity of selectivity profiles-whose precise role is still poorly understood. Following the striking success of artificial networks, a major debate has emerged concerning their usefulness in explaining neuronal properties. Here we propose that finding parallels between artificial and neuronal networks is informative precisely because these systems are so different from each other. Our argument is based on an extension of the concept of convergent evolution-well established in biology-to the domain of artificial systems. Applying this concept to different areas and levels of the cortical hierarchy can be a powerful tool for elucidating the functional role of well-known cortical selectivities. Importantly, we further demonstrate that such parallels can uncover novel functionalities by showing that grid cells in the entorhinal cortex can be modeled to function as a set of basis functions in a lossy representation such as the well-known JPEG compression. Thus, contrary to common intuition, here we illustrate that finding parallels with artificial systems provides novel and informative insights, particularly in those cases that are far removed from realistic brain biology.

Comparison of four light-response models using relative curvature measures of nonlinearity.

Photosynthetic light response curves serve as powerful mathematical tools for quantitatively describing the rate of photosynthesis of plants in response to changes in irradiance. However, in practical applications, the daunting task of selecting an appropriate nonlinear model to accurately fit these curves persists as a significant challenge. Thus, there arises a need for a method to systematically evaluate the efficacy of such models. In the present study, four distinct nonlinear models, namely Exponential Model (EM), Rectangular Hyperbola Model (RHM), Nonrectangular Hyperbola Model (NHM), and Modified Rectangular Hyperbola Model (MRHM), were used to fit the relationship between light intensity and the rate of photosynthesis across 42 empirical datasets. The goodness of fit for each model was assessed using the root-mean-square error, and relative curvature measures of nonlinearity were employed to assess the nonlinear behavior of the models. In terms of goodness of fit, pairwise difference tests of the root-mean-square error revealed that there was little to choose among the four models, although RHM gave a marginally poorer fit. However, in terms of nonlinear behavior, EM not only provided the most favorable linear approximation performance at the global level, but also exhibited the best close-to-linear behavior at the individual parameter level among the four models across the 42 datasets. Consequently, the results strongly advocate for EM as the most suitable mathematical framework for fitting photosynthetic light response curves. These findings provide insights into the model assessment for nonlinear regression in describing the relationship between the photosynthetic rate and light intensity.

Quantifying the sweetness intensity and impact of aroma in honey from four floral sources.

Unlike many commercial sweeteners for which sweetness dose-response curves have been constructed, honey's sweetness has yet to be quantified. Honey differs from most commercial sweeteners in that it has a robust aroma; this aroma may impact its perceived sweetness. This study quantified the sweetness intensity and the impact of aroma on the perceived sweetness of four different honey varieties (clover, wildflower, alfalfa, and orange) compared to sucrose. Each sweetener evaluated was diluted to six concentrations in water ranging from 12.5 g/L to 125 g/L. Panelists (n = 55) rated the sweetness intensities with and without aroma, in replicate, on the Global Sensory Intensity Scale. Additionally, the volatile organic compounds in the honey samples were profiled using gas chromatography-mass spectrometry (GC/MS) analysis. Honey and sugar were equivalently sweet at a given concentration (g/L), with aroma present (p = 0.251). Additionally, honey and sugar were not equivalently sweet without aroma; aroma significantly increased sweetness intensities for all sweeteners (p = 0.042) and especially honeys. In a 100 g/L solution, the aromas in honey increased its sweetness by 23%-43%, depending on the floral source. Compounds with sweet aroma characteristics were identified at high concentrations in all honey samples using GC/MS analysis, including furfural, benzaldehyde, benzene acetaldehyde, and dimethyl sulfide. Additionally, (S)-limonene and toluene were present in high quantities in the orange and alfalfa samples. This study can inform appropriate honey usage levels and identify major volatiles that may enhance sweetness. PRACTICAL APPLICATION: Honey sweetness has not been determined quantitatively, despite the widespread use of honey among consumers and product formulators. Sweetness enhancement by honey aroma volatiles may support a reduction in added sugars while maintaining sweetness intensity.