bluuebunny/crossref_metadata_2025 huggingface dataset:
Studio as a Catalyst for Incremental and Ambitious Teacher Learning
Authors: Elliott Rebekah , Roberts Sarah A.
Published: October 2024

This article explores how the practice-focused Studio professional learning (PL) model can provide incremental and ambitious teacher learning opportunities. We argue that when the model’s structures and practices are grounded in ambitious and equitable teaching, they catalyze incremental teacher learning. Studio, like lesson study, supports teachers in considering the entailments of lessons, focusing on the live shared enactment to strengthen teaching and learning through collaborative analysis and reflection. To build our argument, we drew from two Studio projects that had shared structures of cycles of learning and routines, as well as shared practices of using rich representations and collective interpretations of teaching. While both projects’ structures and practices take up ambitious and equitable teaching, they use different routines and attend to different features of equitable teaching. Building on a history of PL models, such as lesson study, which use similar structures and practices as powerful catalysts of teacher learning, we argue that Studio’s structures and practices can catalyze teachers’ incremental learning of ambitious and equitable teaching. We discuss the implications for future research based on this argument and for those leading PL.
The Role of Cognitive Learner Prerequisites for Cognitive Load and Learning Outcomes in AR-Supported Lab Work
Authors: Altmeyer Kristin , Brünken Roland , Kuhn Jochen , Malone Sarah
Published: October 2024

Augmented Reality (AR) can enhance student-centered lab work by bridging the spatial and temporal split between virtual information and observed real-world phenomena. While the Cognitive Theory of Multimedia Learning and the Cognitive Load Theory suggest that AR can reduce extraneous cognitive load (ECL) and foster learning, the empirical results remain inconsistent. This re-analysis of three related studies with different target groups and AR devices explores whether learners’ spatial abilities and verbal working memory capacity moderate the effect of AR support in lab work settings on ECL and conceptual knowledge gains. Although these moderators could not be confirmed consistently, the results indicate that tablet-based AR holds the potential to support learners with low spatial abilities. Moreover, low verbal working memory learners were demonstrated to be particularly vulnerable to the spatial contiguity failure that can be caused by smartglasses AR. Moderation effects were only observed for ECL but not for conceptual knowledge gains. The findings highlight that the benefit of AR support can depend on learners’ cognitive prerequisites and additional contextual factors, such as the AR device used and the age of the target group. The design and implementation of AR-supported lab work environments should account for these factors to optimize the learning outcomes.
Concepts of Mental Disorders Among Psychiatrists, Psychologists, and Theologians
Authors: Kralj Žana , Kardum Goran
Published: October 2024

The objective of this study was to compare the perspectives of psychiatrists, psychologists, and theologians on schizophrenia, depression, anxiety, and antisocial personality disorder. A cross-sectional research design was utilized, involving a random sample selected from the official registries of these professionals. The findings revealed significant differences in how these groups conceptualize mental disorders. The preferred concepts varied depending on the specific disorder. For instance, when it came to schizophrenia, psychiatrists showed a greater inclination towards the psychodynamic concept compared to psychologists, who leaned more towards the cognitive and social constructivist concepts. In the case of depression, psychiatrists favored biological, psychodynamic, and spiritual concepts, while psychologists tended to prefer the cognitive concept. Theologians consistently favored the spiritual concept across all four diagnoses, in comparison to both psychiatrists and psychologists. This research holds significant value for both theoretical understanding and practical applications, and future studies should consider incorporating qualitative, in-depth research to explore the complexities of these concepts related to mental disorders.
Utilizing Attention-Enhanced Deep Neural Networks for Large-Scale Preliminary Diabetes Screening in Population Health Data
Authors: Hu Hongwei , Dong Wenbo , Yu Jianming , Guan Shiyan , Zhu Xiaofei
Published: October 2024

Early screening for diabetes can promptly identify potential early stage patients, possibly delaying complications and reducing mortality rates. This paper presents a novel technique for early diabetes screening and prediction, called the Attention-Enhanced Deep Neural Network (AEDNN). The proposed AEDNN model incorporates an Attention-based Feature Weighting Layer combined with deep neural network layers to achieve precise diabetes prediction. In this study, we utilized the Diabetes-NHANES dataset and the Pima Indians Diabetes dataset. To handle significant missing values and outliers, group median imputation was applied. Oversampling techniques were used to balance the diabetes and non-diabetes groups. The data were processed through an Attention-based Feature Weighting Layer for feature extraction, producing a feature matrix. This matrix was subjected to Hadamard product operations with the raw data to obtain weighted data, which were subsequently input into deep neural network layers for training. The parameters were fine-tuned and the L2 regularization and dropout layers were added to enhance the generalization performance of the model. The model’s reliability was thoroughly assessed through various metrics, including the accuracy, precision, recall, F1 score, mean squared error (MSE), and R2 score, as well as the ROC and AUC curves. The proposed model achieved a prediction accuracy of 98.4% in the Pima Indians Diabetes dataset. When the test dataset was expanded to the large-scale Diabetes-NHANES dataset, which contains 52,390 samples, the test precision of the model improved further to 99.82%, with an AUC of 0.9995. A comparative analysis was conducted using multiple models, including logistic regression with L1 regularization, support vector machine (SVM), random forest, K-nearest neighbors (KNNs), AdaBoost, XGBoost, and the latest semi-supervised XGBoost. The feature extraction method using attention mechanisms was compared with the classical feature selection methods, Lasso and Ridge. The experiments were performed on the same dataset, and the conclusion was that the Attention-based Ensemble Deep Neural Network (AEDNN) outperformed all the aforementioned methods. These results indicate that the model not only performs well on smaller datasets but also fully leverages its advantages on larger datasets, demonstrating strong generalization ability and robustness. The proposed model can effectively assist clinicians in the early screening of diabetes patients. This is particularly beneficial for the preliminary screening of high-risk individuals in large-scale, extensive healthcare datasets, followed by detailed examination and diagnosis. Compared to the existing methods, our AEDNN model showed an overall performance improvement of 1.75%.
ADYOLOv5-Face: An Enhanced YOLO-Based Face Detector for Small Target Faces
Authors: Liu Linrunjia , Wang Gaoshuai , Miao Qiguang
Published: October 2024

Benefiting from advancements in generic object detectors, significant progress has been achieved in the field of face detection. Among these algorithms, the You Only Look Once (YOLO) series plays an important role due to its low training computation cost. However, we have observed that face detectors based on lightweight YOLO models struggle with accurately detecting small faces. This is because they preserve more semantic information for large faces while compromising the detailed information for small faces. To address this issue, this study makes two contributions to enhance detection performance, particularly for small faces: (1) modifying the neck part of the architecture by integrating a Gather-and-Distribute mechanism instead of the traditional Feature Pyramid Network to tackle the information fusion challenges inherent in YOLO-based models; and (2) incorporating an additional detection head specifically designed for detecting small faces. To evaluate the performance of the proposed face detector, we introduce a new dataset named XD-Face for the face detection task. In the experimental section, the proposed model is trained using the Wider Face dataset and evaluated on both Wider Face and XD-face datasets. Experimental results demonstrate that the proposed face detector outperforms other excellent face detectors across all datasets involving small faces and achieved improvements of 1.1%, 1.09%, and 1.35% in the AP50 metric on the WiderFace validation dataset compared to the baseline YOLOv5s-based face detector.
Resonance Capacitance Selection Method for Minimizing Leakage Magnetic Fields and Achieving Zero Phase Angles in Wireless Power Transfer Systems
Authors: Shin Yujun , Rhee Jaewon , Woo Seongho
Published: October 2024

This study proposes a novel approach for selecting the resonance capacitance of wireless power transfer systems, aiming to achieve a zero phase angle (ZPA) while simultaneously minimizing the leakage magnetic field. The performance of the method is validated across two key topologies: series–series (S–S or SS) and the double-sided inductor–capacitor–capacitor (LCC, LCC–LCC) topologies. By minimizing the vector phasor sum of the coil currents, the proposed approach effectively mitigates magnetic field leakage. The method is further validated through mathematical derivations, simulations, and experimental tests. The results reveal that using the proposed method to select resonance capacitances reduces the leakage magnetic field by up to 35.2% in the SS topology and by 42.0% in the double-sided LCC topology. Furthermore, the method improves the ZPA by more than 20° in both cases. These outcomes affirm the effectiveness of the proposed resonance tuning approach.
Signal Processing to Characterize and Evaluate Nonlinear Acoustic Signals Applied to Underwater Communications
Authors: Campo-Valera María , Diego-Tortosa Dídac , Rodríguez-Rodríguez Ignacio , Useche-Ramírez Jorge , Asorey-Cacheda Rafael
Published: October 2024

Nonlinear acoustic signals, specifically the parametric effect, offer significant advantages over linear signals because the low frequencies generated in the medium due to the intermodulation of the emitted frequencies are highly directional and can propagate over long distances. Due to these characteristics, a detailed analysis of these signals is necessary to accurately estimate the Time of Arrival (ToA) and amplitude parameters. This is crucial for various communication applications, such as sonar and underwater location systems. The research addresses a notable gap in the literature regarding comparative methods for analyzing nonlinear acoustic signals, particularly focusing on ToA estimation and amplitude parameterization. Two types of nonlinear modulations are examined: parametric Frequency-Shift Keying (FSK) and parametric sine-sweep modulation, which correspond to narrowband and broadband signals, respectively. The first study evaluates three ToA estimation methods—threshold, power variation (Pvar), and cross-correlation methods for the modulations in question. Following ToA estimation, the amplitude of the received signals is analyzed using acoustic signal processing techniques such as time-domain, frequency-domain, and cross-correlation methods. The practical application is validated through controlled laboratory experiments, which confirm the robustness and effectiveness of the existing methods proposed under study for nonlinear (parametric) acoustic signals.
RNEP: Random Node Entropy Pairing for Efficient Decentralized Training with Non-IID Local Data
Authors: Kang Jo-In , Lee Seong-Won
Published: October 2024

The proliferation of edge devices and advancements in Internet of Things technology have created a vast array of distributed data sources, necessitating machine learning models that can operate closer to the point of data generation. Traditional centralized machine learning approaches often struggle with real-time big data applications, such as climate prediction and traffic simulation, owing to high communication costs. In this study, we introduce random node entropy pairing, a novel distributed learning method for artificial neural networks tailored to distributed computing environments. This method reduces communication overhead and addresses data imbalance by enabling nodes to exchange only the weights of their local models with randomly selected peers during each communication round, rather than sharing the entire dataset. Our findings indicate that this approach significantly reduces communication costs while maintaining accuracy, even when learning from non-IID local data. Furthermore, we explored additional learning strategies that enhance system accuracy by leveraging the characteristics of this method. The results demonstrate that random node entropy pairing is an effective and efficient solution for distributed learning in environments where communication costs and data distribution present significant challenges.
Recent Status and Prospects of Low-Temperature Drift Resistors
Authors: Liu Fang , Zhang Lei , Wu Bo , Deng Yongfeng , Xu Kai
Published: October 2024

With the rapid development of modern science and technology, the stability and reliability of electronic components become essential. Low-temperature drift resistors (LTDRs) are of importance owing to their excellent performance and stability in different temperature environments. LTDR technology is now widely used in the industrial field. This paper reviews the research status of LTDRs in order to provide reference for researchers and engineers in related fields. First, the basic principle of LTDRs is briefly discussed. A brief explanation of the mechanism behind low-temperature drift is illustrated. Second, the materials, types, and manufacturing processes of LTDRs are classified and discussed. The review ends with a brief conclusion concerning the challenges from mechanism to application and the future outlook.
Bit Sufi-Dance: Covert Data Exfiltration from Air-Gapped Networks via Electricity Meter
Authors: Liang Yongyu , Shan Hong , Liu Zetao , Xu Chengxi
Published: October 2024

To protect important data and files, people often use air gap isolation, also known as air gap separation, to block external threats. However, internal networks may still introduce pollution due to supply chain contamination, human error, or social engineering. Although internal devices cannot directly communicate with the outside world. This paper proposes a new technology called Bit Sufi-Dance that utilizes electricity meters and optical devices to detect exfiltrated data. Most electricity meters have power indicator mechanical turntables or LED lights which can be indirectly controlled by the device’s power consumption oscillation. This allows for information encoding and the extraction of data from the air-gapped computer. It is important to note that this exfiltration channel does not require any hardware or firmware modifications and cannot be detected by existing Data Leakage Prevention (DLP) systems. The article discusses its design and implementation issues while evaluating it using different types of electricity meters. Our experiment demonstrates that data can be exfiltrated from the air-gap isolated computer through an electricity meter at a bit rate of 101 b/h. Finally, we assess this security threat and discuss defense mechanisms and preventive measures.
The Sustainable Configuration Optimisation of Office Multi-Angled Façade Systems
Authors: Hannoudi Loay , Saleeb Noha , Dafoulas George
Published: October 2024

This research paper optimises the configuration of multi-angled façade systems to achieve the sustainability goals of reduced energy consumption and improved indoor climate quality. The concept of a multi-angled façade system is based on proposing the use of two different orientations of windows in each façade on a vertical axis, but not tilted up and down. The large part of the multi-angled façade is oriented more to the north to optimise the use of daylight and the small part more to the south to optimise the use of solar radiation. In order to evaluate the performance of the façade, the software program IDA ICE version 4.8 is used. (EQUA, Stockholm, Sweden). Two groups of scenarios were simulated: the first group consisted of nine scenarios (A1 to A9) that included changing the area and the orientation of the two façade parts, and the second group consisted of three scenarios (B1 to B3) by changing the window to wall ratio (WWR) of these scenarios. According to the results of the simulation, two scenarios from the first group are recommended: A3 for optimal daylight penetration and A7 for optimal energy performance. Regarding the second group, scenarios B1 for optimal daylight penetration and B3 for optimal energy performance are recommended.
The Causal Nexus Among Energy Dependency, Human Capital, and Renewable Energy: An Empirical Analysis for EU Members
Authors: Sart Gamze , Özekicioğlu Halil , Danilina Marina , Aytemiz Levent , Bayar Yilmaz
Published: October 2024

Countries have turned to developing renewable energy production, avoiding the risks posed by the disruptions in global energy trade, the high volatility in energy prices, and the remarkable environmental impairment. Numerous economic, environmental, institutional, and social factors have been put forward as driving factors toward renewable energy. The goal of this research article is to study the causal nexus among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy of the 27 EU members between 2000 and 2020 through Emirmahmutoglu and Kose causality test. The results of the panel-level causality tests demonstrate feedback interplay among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy use. However, the results of the country-level causality analysis unveil that the interplay among renewable energy utilization, energy dependency, real GDP per capita, CO2 emissions, and human capital remarkably varies among EU members. The results of this study suggest that renewable energy investments are significant instruments to make progress in energy security, human capital, real GDP per capita, and CO2 emissions. Furthermore, energy security, human capital, real GDP per capita, and CO2 emissions are significant drivers of renewable energy development.
Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells
Authors: Sunderraj Niranjan , Dhanushkodi Shankar Raman , Chidambaram Ramesh Kumar , Węglowski Bohdan , Skrzyniowska Dorota , Schmid Mathias , Fowler Michael William
Published: October 2024

We introduce a theoretical model for the photocurrent-voltage (I-V) characteristics designed to elucidate the interfacial phenomena in photoelectrochemical cells (PECs). This model investigates the sources of voltage losses and the distribution of photocurrent across the semiconductor–electrolyte interface (SEI). It calculates the whole exchange current parameter to derive cell polarization data at the SEI and visualizes the potential drop across n-type cells. The I-V model’s simulation outcomes are utilized to distinguish between the impacts of bulk recombination and space charge region (SCR) recombination within semiconductor cells. Furthermore, we develop an advanced deep neural network model to analyze the electron–hole transfer dynamics using the I-V characteristic curve. The model’s robustness is evaluated and validated with real-time experimental data, demonstrating a high degree of concordance with observed results.
Point of Common Connection Voltage Modulated Direct Power Control with Disturbance Observer to Increase in Renewable Energy Acceptance in Power System
Authors: Jeong Yong Woo , Choi Woo Young
Published: October 2024

In this paper, we present a disturbance observer-based point of common connection voltage-modulated direct power control (PCCVM-DPC) system, which increases the robustness of the PCCVM-DPC system. First, the mathematical analysis of the disturbances for the step-up transformer’s nonlinearity, the grid voltage harmonics, and the parameter uncertainties is presented. By analyzing the disturbance terms of the PCCVM-DPC system, we present the disturbance observer (DOB) for the PCCVM-DPC system. To assess the efficacy of our approach, we perform comparative studies of the PCCVM-DPC without DOB and PCCVM-DPC with DOB by constructing the simulation environment based on the commercial step-up transformer and ESS inverter datasheet. We have validated that the active and reactive power control performance of the PCCVM-DPC with DOB outperforms the PCCVM-DPC without DOB from the observation that the current total harmonic distortion reduced by more than 40% compared to the PCCVM-DPC without the DOB.
Review of Low Voltage Ride-Through Capabilities in Wind Energy Conversion System
Authors: Ntuli Welcome Khulekani , Kabeya Musasa , Moloi Katleho
Published: October 2024

The significance of low voltage ride-through (LVRT) capability in wind energy conversion systems (WECSs) is paramount for ensuring grid stability and reliability during voltage dips. This systematic review delves into the advancements, challenges, and methodologies associated with LVRT capabilities in WECSs. By synthesizing recent research findings, this review highlights technological innovations, control strategies, and regulatory requirements that influence LVRT performance. Key insights include the efficacy of various LVRT techniques, the role of grid codes in shaping LVRT standards, and the integration of advanced control algorithms to improve system resilience. The study offers a comprehensive understanding of the current landscape of LVRT in WECSs and pinpoints future research directions to optimize their performance in increasingly complex grid environments. During the LVRT process, the stator of a double-fed induction generator (DFIG) is directly linked to the power grid. When the external power grid experiences a failure, the stator flux produces a significant transient component, resulting in substantial overvoltage and overcurrent on the rotor side of the DFIG. Failure to implement preventative measures may result in damage to the converter, therefore compromising the safety and stability of how the power system functions.
Influence of Terrain on MODIS and GLASS Leaf Area Index (LAI) Products in Qinling Mountains Forests
Authors: Zheng Jiaman , Wang Mengyuan , Liang Mingyue , Gao Yuyang , Tan Mou Leong , Liu Mengyun , Wang Xiaoping
Published: October 2024

Leaf Area Index (LAI), as a pivotal parameter in characterizing the structural properties of vegetation ecosystems, holds significant importance in assessing the carbon sink function. Given the availability of multiple long-term LAI products, validating these LAI products with consideration of topographic factors is a prerequisite for enhancing the quality of LAI products in mountainous areas. Therefore, this study aims to evaluate the performance of MODIS LAI and GLASS LAI products from 2001 to 2021 by comparing and validating them with ground-measured LAI data, focusing on the spatio-temporal and topographic aspects in the Qinling Mountains. The results show that the GLASS LAI product is a better choice for estimating LAI in the Qinling Mountains. The GLASS LAI product has better completeness and generally higher values compared to the MODIS LAI product. The time-series curve of the GLASS LAI product is more continuous and smoother than the MODIS LAI product. Both products, however, face challenges in quantifying LAI values of evergreen vegetation during winter. The MODIS and GLASS LAI products exhibit differences between sunny and shady slopes, with mean LAI values peaking on sunny slopes and reaching their lowest on shady slopes. When the slope ranges from 0 to 10°, the mean values of GLASS LAI product show a higher increasing trend compared to the MODIS LAI product. At elevations between 1450 and 2450 m, the mean LAI values of the GLASS LAI product are higher than the MODIS LAI product, primarily in the southern Qinling Mountains. Compared to ground-measured LAI data, the GLASS LAI product (R² = 0.33, RMSE = 1.62, MAE = 0.61) shows a stronger correlation and higher accuracy than the MODIS LAI product (R² = 0.24, RMSE = 1.61, MAE = 0.68).
Genome-Wide Analysis of the MAPKKK Gene Family Under Abiotic Stresses in Moso Bamboo (Phyllostachys edulis)
Authors: Huang Feiyi , Wang Jiaxin
Published: October 2024

Mitogen-activated protein kinase kinase kinases (MAPKKKs) are the upstream components of MAPK cascades and are involved in mediating stress responses and developmental processes. Although MAPKKK genes have been investigated in many plants, the identification and characterization of MAPKKKs in moso bamboo were still limited. Here, 134 MAPKKKs were identified as unevenly distributed on 23 chromosomes (except for chromosome 1) of moso bamboo and divided into three subfamilies by phylogenetic analysis. The gene structure and conserved motif of PeMAPKKKs were investigated. The expansions of PeMAPKKKs were driven by whole-genome duplication (WGD) or segmental duplication events. The duplicated gene pairs were under purifying selection based on the Ka/Ks ratios, suggesting they underwent functional conservation. Most PeMAPKKKs contained cis-elements related to development, hormones, and stress responses. Tissue expression patterns showed that PeMAPKKKs had multiple expression patterns. The qPCR analysis showed distinct expression patterns of PeMAPKKKs under drought, salt, and cold stress conditions. Taken together, this study provides a solid foundation for future functional characterizations of MAPKKKs and identifies candidate stress-responsive genes for further study in moso bamboo.
(S)-2-Hydroxyisovalerate Production from d-Xylose with CO-Converting Clostridium ragsdalei
Authors: Schwarz Irina , Rupp Markus , Frank Oliver , Daschner Andreas , Weuster-Botz Dirk
Published: October 2024

Clostridium ragsdalei was found to produce (S)-2-hydroxyisovalerate (2-HIV) as a novel product in addition to acetate, ethanol, and d-2,3-butanediol in heterotrophic (d-xylose), autotrophic (CO), and mixotrophic (d-xylose + CO) conditions. Mixotrophic batch processes in stirred-tank bioreactors with continuous gassing resulted in improved production of this alpha-hydroxy acid compared to batch processes solely with either d-xylose or CO. The maximal CO uptake rate was considerably reduced in mixotrophic compared to autotrophic processes, resulting in a concomitant decreased total CO2 production. Simultaneous conversion of 9.5 g L−1 d-xylose and 320 mmol CO enabled the production of 1.8 g L−1 2-HIV in addition to 1.1 g L−1 d-2,3-butanediol, 2.0 g L−1 ethanol, and 1.8 g L−1 acetate. With reduced initial d-xylose (3.1 g L−1), l-valine production started when d-xylose was depleted, reaching a maximum of 0.4 g L−1 l-valine. Using l-arabinose or d-glucose instead of d-xylose in mixotrophic batch processes reduced the 2-HIV production by C. ragsdalei. Considerable amounts of meso-2,3-butanediol (0.9–1.3 g L−1) were produced instead, which was not observed with d-xylose. The monomer 2-HIV can form polyesters that make the molecule attractive for application as bioplastic (polyhydroxyalkanoates) or new composite material.
Self-Heating Risk of Coals and Metal Powders: A Comparison
Authors: Taraba Boleslav , Maršálek Roman
Published: October 2024

The self-heating risk of four coal samples (two bituminous and two subbituminous) and five metal powder samples (four industrial and one laboratory-prepared) were studied calorimetrically using oxidation heat at 30 °C. All of the samples were measured in fresh (as-received), vacuum-dried and wetted states. The heat effects of fresh and/or dried coals were found to be significantly higher than those of the metals. On the other hand, wetting the samples markedly increased the oxidation heat mainly of the metals, making their oxidation potential comparable to or even exceeding that of the subbituminous coals. As a practical consequence, a comprehensive index to assess the self-heating risk of the materials in respect to both their oxidation ability and the effect of moisture is proposed.
A Deep Learning Approach for Predicting Aerial Suppressant Drops in Wildland Firefighting Using Automatic Dependent Surveillance–Broadcast Data
Authors: Magstadt Shayne , Wei Yu , Pietruszka Bradley M. , Calkin David E.
Published: October 2024

This study utilizes Automatic Dependent Surveillance–Broadcast (ADS-B) data sourced by the OpenSky Network to curate a dataset aimed at enhancing the precision of aerial suppressant drop predictions in wildland firefighting. By amalgamating ADS-B data with Automated Telemetry Unit (ATU) drop information, this research constructs a reliable base for analyzing the spatial aspects of aerial firefighting operations. Using sequential machine learning models, specifically Long Short-Term Memory (LSTM) networks and 1D Convolutional Neural Networks (1DCNN), the study interprets complex flight dynamics to predict drop locations. The dataset, covering 2017 to 2023, is labeled and segmented to reflect accurate suppressant release events, facilitating the distinction between drop and non-drop activities in fixed-wing aircraft. The LSTM model demonstrated strong predictive performance with an F1 score of 0.922, effectively identifying suppressant drop events with high accuracy. This model’s reliable predictions can significantly improve situational awareness in real-time aerial firefighting operations, enabling more informed decision-making and better coordination of resources during wildfire events.
Improved Delayed Detached-Eddy Simulation of Turbulent Vortex Shedding in Inert Flow over a Triangular Bluff Body
Authors: McConnell Matthew R. , Knight Jason , Buick James M.
Published: October 2024

The Improved Delayed Detached-Eddy Simulation (IDDES) is a modification of the original Detached-Eddy Simulation (DES) design to incorporate Wall Modeled Large Eddy Simulation (WMLES) capabilities and to extend the class of flows suitable for this methodology. For thin attached boundary layers, typically seen in external aerodynamic flows, the DES branch of the model is active, whereas with thick boundary layers, typically seen in internal flows and also wake flows, the WMLES branch is active, thus providing a numeric method suited to handling most flow cases automatically. The flow over a triangular bluff body is used to validate the suitability of the IDDES model and compare the results with experimental, DDES, and LES data. The IDDES model is found to be relatively accurate when compared with the experimental results, with recirculation length, streamwise velocity, and Reynolds stresses all showing good agreement with the experimental data. However, when compared with the DDES model, there is a ~4% overprediction of the recirculation length using the same mesh and numerical scheme. The code, with its extra complexity, is also ~3% slower to solve. The IDDES model has also been tested against different meshes, and the results show that even for a coarse mesh, there is still good agreement with the experimental data.
On the Use of Different Sets of Variables for Solving Unsteady Inviscid Flows with an Implicit Discontinuous Galerkin Method
Authors: Alberti Luca , Cammalleri Emanuele , Carnevali Emanuele , Nigro Alessandra
Published: October 2024

This article presents a comparison between the performance obtained by using a spatial discretization of the Euler equations based on a high-order discontinuous Galerkin (dG) method and different sets of variables. The sets of variables investigated are as follows: (1) conservative variables; (2) primitive variables based on pressure and temperature; (3) primitive variables based on the logarithms of pressure and temperature. The solution is advanced in time by using a linearly implicit high-order Rosenbrock-type scheme. The results obtained using the different sets are assessed across several canonical unsteady test cases, focusing on the accuracy, conservation properties and robustness of each discretization. In order to cover a wide range of physical flow conditions, the test-cases considered here are (1) the isentropic vortex convection, (2) the Kelvin–Helmholtz instability and (3) the Richtmyer–Meshkov instability.
Non-Spherical Cavitation Bubbles: A Review
Authors: Jia Boxin , Soyama Hitoshi
Published: October 2024

Cavitation is a phase-change phenomenon from the liquid to the gas phase due to an increased flow velocity. As it causes severe erosion and noise, it is harmful to hydraulic machinery such as pumps, valves, and screw propellers. However, it can be utilized for water treatment, in chemical reactors, and as a mechanical surface treatment, as radicals and impacts at the point of cavitation bubble collapse can be utilized. Mechanical surface treatment using cavitation impacts is called “cavitation peening”. Cavitation peening causes less pollution because it uses water to treat the mechanical surface. In addition, cavitation peening improves on traditional methods in terms of fatigue strength and the working life of parts in the automobile, aerospace, and medical fields. As cavitation bubbles are utilized in cavitation peening, the study of cavitation bubbles has significant value in improving this new technique. To achieve this, many numerical analyses combined with field experiments have been carried out to measure the stress caused by bubble collapse and rebound, especially when collapse occurs near a solid boundary. Understanding the mechanics of bubble collapse can help to avoid unnecessary surface damage, enabling more accurate surface preparation, and improving the stability of cavitation peening. The present study introduces three cavitation bubble types: single, cloud, and vortex cavitation bubbles. In addition, the critical parameters, governing equations, and high-speed camera images of these three cavitation bubble types are introduced to support a broader understanding of the collapse mechanism and characteristics of cavitation bubbles. Then, the results of the numerical and experimental analyses of non-spherical cavitation bubbles are summarized.
Effects of Zeaxanthin on the Insulin Resistance and Gut Microbiota of High-Fat-Diet-Induced Obese Mice
Authors: Jin Zhibo , Liu Meihong , Zhao Hongyu , Xie Jiahan , Yin Wandi , Zheng Mingzhu , Cai Dan , Liu Huimin , Liu Jingsheng
Published: October 2024

Obesity-induced insulin resistance (IR) can precipitate metabolic disorders such as diabetes. Zeaxanthin, a crucial member of the carotenoid family, has been found to mitigate the damage caused by obesity. However, reports on the effects of zeaxanthin on obesity-induced IR are lacking. Our objective was to examine the metabolic regulatory impacts of zeaxanthin on mice subjected to a high-fat diet (HFD) that triggered IR and to explore their influence on gut microbiota regulation. This study constructed a mouse model of metabolic dysfunction caused by lipid-rich nutritional patterns to investigate physiological and biochemical indices, liver pathway expression, and the intestinal microbiota. The mechanisms by which zeaxanthin improved both IR and glucose metabolic disorders were elucidated. The results demonstrate that zeaxanthin effectively suppressed obesity. The fasting blood glucose, area under curve of oral glucose tolerance test and insulin tolerance test, and homeostatic model assessment–insulin resistance (HOMA-IR) indices in the HFDZEA group decreased by 14.9%, 25.2%, 28.9%, and 29.8%. Additionally, zeaxanthin improved the lipid metabolism and alleviated damage to the liver and pancreas while also activating the PI3K/Akt pathway, regulating hepatic gluconeogenesis and the glycogen metabolism. The number of OTUs in the HFDZEA group increased by 29.04%. Zeaxanthin improved the structure and profile of the gastrointestinal microbiome and enhanced its diversity, increasing probiotics abundance, decreasing pathogen abundance, and thereby ameliorating the dysbiosis of enteric microbial communities in rodents with obesity resulting from excessive fat consumption. The outcomes of our analysis provide a rational basis for advancing zeaxanthin-based nutritional products.
How Helpful May Be a CRISPR/Cas-Based System for Food Traceability?
Authors: Farinati Silvia , Devillars Aurélien , Gabelli Giovanni , Vannozzi Alessandro , Scariolo Francesco , Palumbo Fabio , Barcaccia Gianni
Published: October 2024

Genome editing (GE) technologies have the potential to completely transform breeding and biotechnology applied to crop species, contributing to the advancement of modern agriculture and influencing the market structure. To date, the GE-toolboxes include several distinct platforms able to induce site-specific and predetermined genomic modifications, introducing changes within the existing genetic blueprint of an organism. For these reasons, the GE-derived approaches are considered like new plant breeding methods, known also as New Breeding Techniques (NBTs). Particularly, the GE-based on CRISPR/Cas technology represents a considerable improvement forward biotech-related techniques, being highly sensitive, precise/accurate, and straightforward for targeted gene editing in a reliable and reproducible way, with numerous applications in food-related plants. Furthermore, numerous examples of CRISPR/Cas system exploitation for non-editing purposes, ranging from cell imaging to gene expression regulation and DNA assembly, are also increasing, together with recent engagements in target and multiple chemical detection. This manuscript aims, after providing a general overview, to focus attention on the main advances of CRISPR/Cas-based systems into new frontiers of non-editing, presenting and discussing the associated implications and their relative impacts on molecular traceability, an aspect closely related to food safety, which increasingly arouses general interest within public opinion and the scientific community.
Designing of an Oat-Mango Molded Snack with Feasible Nutritional and Nutraceutical Properties
Authors: Aviles-Rivera Yudit Aimee , Valdez-Torres José Benigno , Campos-Sauceda Juan Pedro , Heredia José Basilio , Hinojosa-Gómez Jeny , Muy-Rangel María Dolores
Published: October 2024

In recent years, the market has seen a growing demand for healthy and convenient food options, such as fruit and cereal bars, driven by shifts in eating habits. These changes are primarily attributed to time constraints in meal preparation and the need for ready-to-eat foods. Consequently, this has promoted interest in creating a nutritious, high-quality snack combining oats and mango. This study employed a response surface analysis of extreme vertex mixtures, incorporating constraints and three components: oats, mango peel, and dehydrated mango pulp. This resulted in ten different mixtures, each with unique combinations and proportions of the three components. It evaluated the microbiological quality, proximal composition, total phenolic content, tannins, Aw, color, texture, and chemical properties during storage at room temperature. The optimal blend, which demonstrated the best quality characteristics, consisted of 44.38% oats, 5.36% mango peel, and 29.24% mango pulp. This formulation yielded a protein content of 7.1 g, dietary fiber of 20.3 g per 100 g, total phenols of 3.4 mg gallic acid per g, and no pathogenic microorganisms. According to the obtained data, Aw > 0.3, the estimated shelf life could be 12 months at room temperature. Developing a stable oat-mango snack with excellent nutritional, nutraceutical, chemical quality, and microbiological properties is technologically feasible.
Fractional Einstein–Gauss–Bonnet Scalar Field Cosmology
Authors: Micolta-Riascos Bayron , Millano Alfredo D. , Leon Genly , Droguett Byron , González Esteban , Magaña Juan
Published: October 2024

Our paper introduces a new theoretical framework called the Fractional Einstein–Gauss–Bonnet scalar field cosmology, which has important physical implications. Using fractional calculus to modify the gravitational action integral, we derived a modified Friedmann equation and a modified Klein–Gordon equation. Our research reveals non-trivial solutions associated with exponential potential, exponential couplings to the Gauss–Bonnet term, and a logarithmic scalar field, which are dependent on two cosmological parameters, m and α0=t0H0 and the fractional derivative order μ. By employing linear stability theory, we reveal the phase space structure and analyze the dynamic effects of the Gauss–Bonnet couplings. The scaling behavior at some equilibrium points reveals that the geometric corrections in the coupling to the Gauss–Bonnet scalar can mimic the behavior of the dark sector in modified gravity. Using data from cosmic chronometers, type Ia supernovae, supermassive Black Hole Shadows, and strong gravitational lensing, we estimated the values of m and α0, indicating that the solution is consistent with an accelerated expansion at late times with the values α0=1.38±0.05, m=1.44±0.05, and μ=1.48±0.17 (consistent with Ωm,0=0.311±0.016 and h=0.712±0.007), resulting in an age of the Universe t0=19.0±0.7 [Gyr] at 1σ CL. Ultimately, we obtained late-time accelerating power-law solutions supported by the most recent cosmological data, and we proposed an alternative explanation for the origin of cosmic acceleration other than ΛCDM. Our results generalize and significantly improve previous achievements in the literature, highlighting the practical implications of fractional calculus in cosmology.
Calibration of Luminosity Correlations of Gamma-Ray Bursts Using Quasars
Authors: Purohit Sarveshkumar , Desai Shantanu
Published: October 2024

In order to test the efficacy of gamma-ray bursts (GRBs) as cosmological probes, we characterize the scatter in the correlations between six pairs of GRB observables. However, some of these observables depend on the luminosity distance, for which one needs to assume an underlying cosmological model. In order to circumvent this circularity problem, we use X-ray and UV fluxes of quasars as distance anchors to calculate the luminosity distance in a model-independent manner, which, in turn, is used to calculate the GRB-related quantities. We find that all six pairs of regression relations show high intrinsic scatter for both low- and high-redshift samples. This implies that these GRB observables cannot be used as model-independent high-precision cosmological probes.
The Effects of Polymerization on the Performance of Viologen-Based Electrochromic Devices
Authors: Nucera Antonello , Rizzuto Carmen , Pipita Mario Michele , Barba Castagnaro Irene , Termine Roberto , Barberi Riccardo C. , Castriota Marco
Published: October 2024

In this work, electrochromic devices were prepared using the redox couple ethyl viologen diperchlorate and 1,1′-diethyl ferrocene in propylene carbonate as an aprotic solvent to facilitate ions separation and diffusion inside the devices. Electrochromic devices were made using electrochromic gel mixtures at the concentrations of 55%, 60% and 65% with respect to the bisphenol A polymer. In particular, two sets of gels were made: one set contained the bisphenol A not-polymerized while and the second one contained the polymerized polymer. Different techniques, such as cyclic voltammetry, UV-vis-NIR, and Raman spectroscopy, were used to study such systems to understand the differences in terms of performances between the different sets of electrochromic devices. Cyclic voltammetry confirmed that the oxidation process of the 1,1′-diethyl ferrocene and the reduction of the ethyl viologen diperchlorate occurred at about 0.4 V. Interesting variations in the transmittances were found between the two groups of samples. The best values of CE were provided by the electrochromic devices based on the polymerized electrochromic gel mixture at a concentration of 60% (EM60). The EM60 device result was CE = 92.82 C/cm2 in the visible region and CE = 80.38 C/cm2 in the near–infrared region, confirming that these devices can be used for energy-saving applications. A structural characterization of the materials used in the two sets of electrochromic devices was made using Raman spectroscopy, and the analysis supports the electrochemical models used to explain the processes involved during operation of the electrochromic systems.
Genetic Diversity and Population Structure Analysis of Soybean [Glycine max (L.) Merrill] Genotypes Using Agro-Morphological Traits and SNP Markers
Authors: Chiemeke Felicity Kido , Olasanmi Bunmi , Agre Paterne A. , Mushoriwa Hapson , Chigeza Godfree , Abebe Abush Tesfaye
Published: October 2024

Background/Objectives: Understanding the genetic diversity of soybean genotypes can provide valuable information that guides parental selection and the design of an effective hybridization strategy in a soybean breeding program. In order to identify genetically diverse, complementary, and prospective parental lines for breeding, this study set out to ascertain the genetic diversity, relationships, and population structure among 35 soybean genotypes based on agro-morphological traits and Single Nucleotide Polymorphic (SNP) marker data. Methods/Results: Cluster analysis, based on agro-morphological traits, grouped the studied genotypes into four clusters. The first two principal components accounted for 62.8% of the total phenotypic variation, where days to 50% flowering, days to 95% maturity, grain yield, shattering score, and lodging score had high and positive contributions to the total variation. Using the SNP marker information, mean values of 0.16, 0.19, 0.067, and 0.227 were obtained for minor allele frequency (MAF), polymorphic information content (PIC), observed heterozygosity (Ho), and expected heterozygosity (He), respectively. Using different clustering approaches (admixture population structure, principal component scatter plot, and hierarchical clustering), the studied genotypes were grouped into four major clusters. Conclusions:The agro-morphological and molecular analysis results indicated the existence of moderate genetic diversity among the studied genotypes. The traits identified to be significantly related to yield provide valuable information for the genetic improvement of soybeans for yield.