bluuebunny/crossref_metadata_2025 huggingface dataset:
Extraction and Surface Activity of Australian Native Plant Extracts: Alphitonia excelsa
Authors: Sebben Damien A. , Semple Susan J. , Condina Mark R. , Dilmetz Brooke A. , Hoffmann Peter , Claudie David J. , Krasowska Marta , Beattie David A.
Published: August 2024

Saponin surfactants extracted from plants have significant potential applications in many industries. The interfacial properties of extracts of Alphitonia excelsa, a native Australian plant rich in saponins, have been characterised to assess their suitability as dual-purpose foaming and antibacterial additives. Two sources of the plant (Adelaide Botanic Gardens and homelands of Chuulangun Aboriginal Corporation) were investigated to look for alteration of properties as a result of differences in cultivation and geographic location. Two methods of saponin extraction (water and water/ethanol mixtures) were investigated to determine differences in extraction efficiency and performance. Distinct differences were observed between the traditional analytical analysis (for saponin content) of the extracts based on source and extraction method; however, these differences were not as stark when considering the effect of the extracts on air–water interfacial tension and dilatational rheology, with extraction method proving to be the single biggest factor in extract efficacy. The data obtained point toward the presence of an altered array of surface-active species (different relative amounts of particular saponins in the water/ethanol extracted material) as a function of the extraction method. All extracts presented some antibacterial effect, albeit modest. This work highlights that the extraction method needs to be carefully considered and tailored for a given application.
Distribution of Red Blood Cells Deformability: Study on Density-Separated Cell Subpopulations
Authors: Barshtein Gregory , Arbell Dan , Gural Alexander , Livshits Leonid
Published: August 2024

Aging-related processes lead to significant metabolic and structural changes in red blood cells (RBCs) and, as a result, to heterogeneity in cell populations. Using the Percoll linear density gradient, separating the RBC population and obtaining fractions enriched with cells of different ages is possible. Previously, cells from the “light” fraction were characterized by increased deformability. However, the distribution of RBC deformability in subpopulations possessing a different density has not been studied. In this study, we measured the deformability of RBCs from cell fractions characterized by different densities. RBC deformability was determined using a computerized cell flow-properties analyzer, which provides the deformability distribution in a population of 10,000–15,000 cells. Our results demonstrate a strong correlation between the cytosol hemoglobin concentration and the cell deformation indexes. In addition, we show that the “lightest” fraction of RBCs contains the lowest number of deformable and the highest number of highly deformable cells. In contrast, the “dense” fraction is enriched with undeformable RBCs, with a minimal presence of highly deformable cells. In summary, we have shown that RBC fractions depleted or enriched with undeformable cells can be obtained by using a density gradient. However, these fractions are not homogeneous in their deformability properties.
Numerical Modeling and Simulation of Vehicular Crashes into Three-Bar Metal Bridge Rail
Authors: Fang Howie , Jaus Christopher , Wang Qian , Palta Emre , Pachocki Lukasz , Bruski Dawid
Published: August 2024

Advanced finite element (FE) modeling and simulations were performed on vehicular crashes into a three-bar metal bridge rail (TMBR). The FE models of a sedan, a pickup truck, and a TMBR section were adopted in the crash simulations subject to Manual for Assessing Safety Hardware (MASH) Test Level 2 (TL-2) and Test Level 3 (TL-3) requirements. The test vehicle models were first validated using full-scale physical crash tests conducted on a two-bar metal bridge using a sedan and a pickup truck with similar overall physical properties and sizes to their respective vehicles used in the simulations. The validated vehicular models were then used to evaluate the crash performance of the TMBR using MASH evaluation criteria for structural adequacy, occupant risk, and post-impact trajectory. The TMBR met all MASH TL-2 requirements but failed to meet the MASH TL-3 Criteria H and N requirements when impacted by the sedan. The TMBR was also evaluated under in-service conditions (behind a 1.52 m wide sidewalk) and impacted by the sedan under MASH TL-3 conditions. The simulation results showed that the TMBR behind a sidewalk met all safety requirements except for the occupant impact velocity in the longitudinal direction, which exceeded the MASH limit by 3.93%.
Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review
Authors: Fransisca Diandra Chika , Sukono  , Chaerani Diah , Halim Nurfadhlina Abdul
Published: August 2024

Traditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations and uncertainties in asset returns and covariances. This study aims to perform a Systematic Literature Review (SLR) on robust portfolio mean-variance (RPMV) in stock investment utilizing genetic algorithms (GAs). The SLR covered studies from 1995 to 2024, allowing a thorough analysis of the evolution and effectiveness of robust portfolio optimization methods over time. The method used to conduct the SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The result of the SLR presented a novel strategy to combine robust optimization methods and a GA in order to enhance RPMV. The uncertainty parameters, cardinality constraints, optimization constraints, risk-aversion parameters, robust covariance estimators, relative and absolute robustness, and parameters adopted were unable to develop portfolios capable of maintaining performance despite market uncertainties. This led to the inclusion of GAs to solve the complex optimization problems associated with RPMV efficiently, as well as fine-tuning parameters to improve solution accuracy. In three papers, the empirical validation of the results was conducted using historical data from different global capital markets such as Hang Seng (Hong Kong), Data Analysis Expressions (DAX) 100 (Germany), the Financial Times Stock Exchange (FTSE) 100 (U.K.), S&P 100 (USA), Nikkei 225 (Japan), and the Indonesia Stock Exchange (IDX), and the results showed that the RPMV model optimized with a GA was more stable and provided higher returns compared with traditional MV models. Furthermore, the proposed method effectively mitigated market uncertainties, making it a valuable tool for investors aiming to optimize portfolios under uncertain conditions. The implications of this study relate to handling uncertainty in asset returns, dynamic portfolio parameters, and the effectiveness of GAs in solving portfolio optimization problems under uncertainty, providing near-optimal solutions with relatively lower computational time.
Methods for Detecting the Patient’s Pupils’ Coordinates and Head Rotation Angle for the Video Head Impulse Test (vHIT), Applicable for the Diagnosis of Vestibular Neuritis and Pre-Stroke Conditions
Authors: Mamykin G. D. , Kulesh A. A. , Barkov Fedor L. , Konstantinov Y. A. , Sokol’chik D. P. , Pervadchuk Vladimir
Published: August 2024

In the contemporary era, dizziness is a prevalent ailment among patients. It can be caused by either vestibular neuritis or a stroke. Given the lack of diagnostic utility of instrumental methods in acute isolated vertigo, the differentiation of vestibular neuritis and stroke is primarily clinical. As a part of the initial differential diagnosis, the physician focuses on the characteristics of nystagmus and the results of the video head impulse test (vHIT). Instruments for accurate vHIT are costly and are often utilized exclusively in healthcare settings. The objective of this paper is to review contemporary methodologies for accurately detecting the position of pupil centers in both eyes of a patient and for precisely extracting their coordinates. Additionally, the paper describes methods for accurately determining the head rotation angle under diverse imaging and lighting conditions. Furthermore, the suitability of these methods for vHIT is being evaluated. We assume the maximum allowable error is 0.005 radians per frame to detect pupils’ coordinates or 0.3 degrees per frame while detecting the head position. We found that for such conditions, the most suitable approaches for head posture detection are deep learning (including LSTM networks), search by template matching, linear regression of EMG sensor data, and optical fiber sensor usage. The most relevant approaches for pupil localization for our medical tasks are deep learning, geometric transformations, decision trees, and RASNAC. This study might assist in the identification of a number of approaches that can be employed in the future to construct a high-accuracy system for vHIT based on a smartphone or a home computer, with subsequent signal processing and initial diagnosis.
Dislocation Interactions with Hcp- and χ-Phase Particles in Tungsten: Molecular Dynamics Insights into Mechanical Strengthening Mechanisms
Authors: Sharapova Yu. R. , Kazakov A. M. , Babicheva R. I. , Semenov A. S. , Izosimov A. A. , Korznikova E. A.
Published: August 2024

Our study investigates the interaction of dislocations with hexagonal close-packed (hcp) and chi-phase (χ) particles in body-centred cubic (bcc) tungsten (W) using molecular dynamics simulations. The research aims to understand how these interactions influence the mechanical properties of W, particularly in the context of neutron irradiation environments. The simulations were conducted with spherical and cylindrical particles at various temperatures and cell sizes to observe the effects on critical shear stress. Results indicate that the shape and size of the particles significantly affect the critical shear stress required for dislocation movement, with cylindrical particles requiring higher stresses than spherical ones. Additionally, the study found that temperature variations have a more pronounced effect on χ-phase particles compared to hcp-phase particles. Our findings provide insights into the strengthening mechanisms in W-Re alloys and suggest potential pathways for enhancing the material’s performance under extreme conditions.
Student Teachers’ Perceptions of a Game-Based Exam in the Genial.ly App
Authors: Gravelsina Elina , Daniela Linda
Published: August 2024

This research examines student teachers’ perceptions of a game-based exam conducted in the Genial.ly app in the study course ”Legal Aspects of the Pedagogical Process”. This study aims to find out the pros and cons of game-based exams and understand which digital solutions can enable the development and analysis of digital game data. At the beginning of the course, students were introduced to the research and asked to provide feedback throughout the course on what they saw as the most important aspects of each class and insights on how they envisioned the game-based exam could proceed. The game-based exam was built using the digital platform Genial.ly after its update, which provided the possibility to include open-ended questions and collect data for analyses. It was designed with a narrative in which a new teacher comes to a school and is asked for help in different situations. After reading a description of each situation, the students answered questions about how they would resolve them based on Latvia’s regulations. After the exam, students wrote feedback indicating that the game-based exam helped them visualize the situations presented, resulting in lower stress levels compared to a traditional exam. This research was structured based on design-based principles and the data were analyzed from the perspective of how educators can use freely available solutions to develop game-based exams to test students’ knowledge gained during a course. The results show that Genial.ly can be used as an examination tool, as indicated by positive student teachers’ responses. However, this research has limitations as it was conducted with only one test group due to administrative reasons. Future research could address this by including multiple groups within the same course as well as testing game-based exams in other subject courses for comparison.
Would Rutin be a Feasible Strategy for Environmental-Friendly Photoprotective Samples? A Review from Stability to Skin Permeability and Efficacy in Sunscreen Systems
Authors: Ariede Maíra Bueno , Gomez Junior Wallace Androm , Cândido Thalita Marcílio , de Aguiar Michelle Maria Gonçalves Barão , Rosado Catarina , Rangel-Yagui Carlota de Oliveira , Pessoa Fabiana Vieira Lima Solino , Velasco Maria Valéria Robles , Baby André Rolim
Published: August 2024

Rutin, a flavonoid found in various plants, has gained attention for its potential applications in the cosmetic industry due to its antioxidant, anti-inflammatory, and potential photoprotective properties. Our review explored the use of rutin (rutoside, 3-O-rutinoside-quercetin) in cosmetics/dermocosmetics, focusing on its physicochemical properties and stability, cutaneous permeability, and efficacy in sunscreen systems, involving in vitro tests and the current state of clinical trials. Rutin’s ability to scavenge free radicals, prevent peroxidation, and reduce vascular fragility makes this flavonoid a promising ingredient for photoprotection. Studies have shown that rutin can enhance the sun protection factor (SPF) of sunscreen formulations, especially when combined with organic UV filters. The encapsulation of rutin in nanostructures has also been investigated to improve its applicability. Overall, rutin shows potential as a safe and effective ingredient in cosmetics/dermocosmetics, offering protection against the harmful effects of UV radiation and oxidative stress on the skin, as well as being a feasible strategy for developing environmentally friendly multifunctional sunscreens.
Creation of a Spatiotemporal Algorithm and Application to COVID-19 Data
Authors: Bou Sakr Natalia , Mansour Gihane , Salhi Yahia
Published: August 2024

This study offers an in-depth analysis of the COVID-19 pandemic’s trajectory in several member countries of the European Union (EU) in order to assess similarities in their crisis experiences. We also examine data from the United States to facilitate a larger comparison across continents. We introduce our new approach, which uses a spatiotemporal algorithm to identify five distinct and recurring phases that each country underwent at different times during the COVID-19 pandemic. These stages include: Comfort Period, characterized by minimal COVID-19 activity and limited impacts; Preventive Situation, demonstrating the implementation of proactive measures, with relatively low numbers of cases, deaths, and Intensive Care Unit (ICU) admissions; Worrying Situation, is defined by high levels of concern and preparation as deaths and cases begin to rise and reach substantial levels; Panic Situation, marked by a high number of deaths relative to the number of cases and a rise in ICU admissions, denoting a critical and alarming period of the pandemic; and finally, Epidemic Control Situation, distinguished by limited numbers of COVID-19 deaths despite a high number of new cases. By examining these phases, we identify the various waves of the pandemic, indicating periods where the health crisis had a significant impact. This comparative analysis highlights the time lags between countries as they transitioned through these different critical stages and navigated the waves of the COVID-19 pandemic.
Inocybaceae (Basidiomycota) in Ectomycorrhizal Symbiosis with Halimium (Cistaceae), and the Description of Two New Species of Inocybe from Sardinia (Italy)
Authors: Sanna Massimo , Mua Alberto , Casula Marco , Rinaldi Andrea C.
Published: August 2024

Halimium, Cistaceae, is a genus of shrubs restricted to the western part of the Mediterranean basin, where it thrives in diverse habitats. Despite this ecological adaptability, little attention has been devoted to understanding the mycorrhizal biology of Halimium. Through the examination of both sporocarps and ectomycorrhizal root tip collections, together with a thorough study of data previously reported in the relevant literature, we revealed a rich diversity of ectomycorrhizal mycobiota associated with Halimium spp. In this study, we describe the noteworthy diversity of the basidiomycetous family Inocybaceae in Halimium stands. Some 17 species of Inocybe and Pseudosperma are currently reported as linked to Halimium spp., mostly on the basis of sporocarp occurrence in the proximity of the potential host plant. Of these species, over 40% (7 of 17) come from our own study based on observations in pure Halimium stands in southwestern Sardinia, Italy. We also describe two new species of Inocybe, I. halimiphila sp. nov. and Inocybe rupribes sp. nov., that were collected under Halimium and demonstrated to establish ectomycorrhizal symbiosis with the shrub. We discuss the main morphological and ecological characteristics that distinguish the new species, unveiling their evolutionary relationships by inferring a phylogeny based on multiple loci.
The Interaction between Climate Change and Biodiversity Can Be Assessed from a Material Cycle Perspective
Authors: Kim Ah-Young , Lee Who-Seung , Son Yowhan
Published: August 2024

This study focused on understanding biodiversity variability in response to climate change as an environmental stressor from a carbon cycle perspective. As the frequency and magnitude of environmental stresses are expected to increase due to rising carbon concentrations, these changes may affect the variability in biodiversity. However, several studies have argued that conventional methods for biodiversity assessment, primarily based on arithmetic indices utilized in national policies, are inadequate for accurately assessing these interactions among species, abiotic changes, and material cycling. Here, we indicate that environmental stressors that occur at various scales and domains, from macroscopic to microscopic, and their effects on ecosystems are multi-dimensional. Furthermore, environmental stresses not only affect organisms inhabiting these ecosystems but these organisms, in return, influence the carbon cycle through life history changes resulting from these stresses. The results of our review suggest that these processes are sequentially interconnected, forming self-reinforcing feedback loops. Ultimately, an integrated approach is necessary to understand biodiversity variability in complex and open ecosystems. This approach should be capable of comprehending changes in the overall flow by considering ecosystem characteristics.
The Feasibility of Prenatal Ultrasonic Screening for Developmental Dysplasia of the Hip
Authors: Bevilacqua Claudia , Boscarato Virginia , Pieroni Giovanni , Fraternali Eva , Lattanzi Giuliano , Aspriello Simone Domenico , Gigante Antonio Pompilio , Cecchi Alessandro
Published: August 2024

Background: developmental dysplasia of the hip (DDH) is a condition characterized by abnormal hip development in infancy. Early diagnosis allows for effective treatment, while late presentation often necessitates complex surgical interventions. Current recommendations advise screening between the 6th and 8th week postnatal using an ultrasound, typically employing the Graf method. However, there is no universal consensus on whether ultrasound screening significantly increases treatment likelihood compared to clinical examination-guided ultrasound. This study aims to explore the feasibility of prenatal ultrasound for the early identification of DDH risk. Methods: This prospective observational study involved 100 pregnant women undergoing fetal hip ultrasounds during the second and third trimesters. Using the modified Graf method, alpha and beta angles were calculated on the fetus. Postnatally, alpha and beta angles were compared with the prenatal values. Results: Prenatal ultrasound at the 24th week showed inconclusive results because of the difficulty in identification of Graf landmarks, while ultrasound at the 34th week proved to be a reliable and safe method for the quantitative determination of alpha and beta angles. Significant correlations were found between prenatal and postnatal alpha and beta angles. Moreover, significant differences in prenatal alpha and beta values were observed in patients developing mature/immature hips postnatally. Conclusions: Prenatal diagnostics show promise for predicting infant hip development. Further research is warranted to validate correlation strength and clinical applicability.
Free Energy Evaluation of Cavity Formation in Metastable Liquid Based on Stochastic Thermodynamics
Authors: Shimizu Issei , Matsumoto Mitsuhiro
Published: August 2024

Nucleation is a fundamental and general process at the initial stage of first-order phase transition. Although various models based on the classical nucleation theory (CNT) have been proposed to explain the energetics and kinetics of nucleation, detailed understanding at nanoscale is still required. Here, in view of the homogeneous bubble nucleation, we focus on cavity formation, in which evaluation of the size dependence of free energy change is the key issue. We propose the application of a formula in stochastic thermodynamics, the Jarzynski equality, for data analysis of molecular dynamics (MD) simulation to evaluate the free energy of cavity formation. As a test case, we performed a series of MD simulations with a Lennard-Jones (LJ) fluid system. By applying an external spherical force field to equilibrated LJ liquid, we evaluated the free energy change during cavity growth as the Jarzynski’s ensemble average of required works. A fairly smooth free energy curve was obtained as a function of bubble radius in metastable liquid of mildly negative pressure conditions.
Singular-Value-Decomposition-Based Matrix Surgery
Authors: Ghafuri Jehan , Jassim Sabah
Published: August 2024

This paper is motivated by the need to stabilise the impact of deep learning (DL) training for medical image analysis on the conditioning of convolution filters in relation to model overfitting and robustness. We present a simple strategy to reduce square matrix condition numbers and investigate its effect on the spatial distributions of point clouds of well- and ill-conditioned matrices. For a square matrix, the SVD surgery strategy works by: (1) computing its singular value decomposition (SVD), (2) changing a few of the smaller singular values relative to the largest one, and (3) reconstructing the matrix by reverse SVD. Applying SVD surgery on CNN convolution filters during training acts as spectral regularisation of the DL model without requiring the learning of extra parameters. The fact that the further away a matrix is from the non-invertible matrices, the higher its condition number is suggests that the spatial distributions of square matrices and those of their inverses are correlated to their condition number distributions. We shall examine this assertion empirically by showing that applying various versions of SVD surgery on point clouds of matrices leads to bringing their persistent diagrams (PDs) closer to the matrices of the point clouds of their inverses.
Investigating the Impact of the Stratified Cognitive Apprenticeship Model on High School Students’ Math Performance
Authors: Wang Ruimei , Zulkifli Nurul Nadwa , Mohd Ayub Ahmad Fauzi
Published: August 2024

This study assessed the impact of a cognitive apprenticeship model (CAM)-based stratified teaching module on the mathematical proficiency of high school students. The stratified cognitive apprenticeship model teaching module (SCTM) first involves grouping students based on their mathematical abilities. Students with higher performance are placed in one class, while those with lower scores are placed in another. Instruction for each group is then conducted using the cognitive apprenticeship model, tailoring the teaching approach to align with the specific needs and abilities of each group. A quasi-experimental design was adopted and 150 students were recruited. This study compared the outcomes of a control group, which was instructed using conventional teaching methods (CI), with those of two experimental groups—one instructed using a stratified cognitive teaching method (SCTM)-based on the CAM—and another instructed using the CAM alone. Students’ performance was evaluated based on a mathematics test including the following dimensions: knowing and understanding, investigating, communication, and application (of mathematical knowledge to real-life problems). The data were analyzed using an analysis of covariance (ANCOVA). The results indicated that students instructed using the SCTM outperformed their peers in mathematical achievement, thereby validating SCTM’s effectiveness as a comprehensive educational strategy for mathematics education at the senior high school level.
Affective Regulation and Trait Anger Personalities: The Buffering Effect of the Companion Animal Bond
Authors: Bochicchio Vincenzo , Scandurra Cristiano , Dolce Pasquale , Scandurra Anna , Freda Maria Francesca , Mezzalira Selene
Published: August 2024

Emotional dysregulation involving anger can have severe consequences on the individual’s psychosocial and emotional functioning. This study aimed to investigate the role that the companion animal bond and the personality dimension of trait anger play in explaining affective dysregulation. A cross-sectional online survey was administered to 365 participants. Using the PROCESS macro for SPSS, a moderated model was tested to analyze the hypothesis that affective dysregulation depends on trait anger and that the companion animal bond moderates the relationship between trait anger and affective dysregulation. The results showed that the effect of trait anger on affective dysregulation increases especially when the degree of bonding to an animal companion is low, suggesting that a strong bond to a companion animal may protect individuals with trait anger from the likelihood of experiencing affective regulation problems. The psychological, health-related, and educational implications of the current anthrozoological study include the potential of the human–animal bond in acting as a facilitator of adaptive affective regulation processes, which can reduce the levels of uncontrolled anger-related emotions and the subsequent risk of out-of-control behaviors.
Long Short-Term Memory-Based Non-Uniform Coding Transmission Strategy for a 360-Degree Video
Authors: Guo Jia , Li Chengrui , Zhu Jinqi , Li Xiang , Gao Qian , Chen Yunhe , Feng Weijia
Published: August 2024

This paper studies an LSTM-based adaptive transmission method for a 360-degree video and proposes a non-uniform encoding transmission strategy based on LSTM. Our goal is to maximize the user’s video experience by dynamically dividing the 360-degree video into tiles of different numbers and sizes, and selecting different bitrates for each tile. This aims to reduce buffering events and video jitter. To determine the optimal number and size of tiles at the current moment, we constructed a dual-layer stacked LSTM network model. This model predicts, in real-time, the number, size, and bitrate of the tiles needed for the next moment of the 360-degree video based on the distance between the user’s eyes and the screen. In our experiments, we used an exhaustive algorithm to calculate the optimal tile division and bitrate selection scheme for a 360-degree video under different network conditions, and used this dataset to train our prediction model. Finally, by comparing with other advanced algorithms, we demonstrated the superiority of our proposed method.
A Novel Deep Learning Approach for Real-Time Critical Assessment in Smart Urban Infrastructure Systems
Authors: Almaleh Abdulaziz
Published: August 2024

The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies and temporal dynamics inherent in these systems, risking urban resilience. This study aims to enhance the criticality assessment of geographic zones within smart cities by introducing a novel deep learning architecture. Utilizing Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependency modeling, the proposed framework processes inputs such as total electricity use, flooding levels, population, poverty rates, and energy consumption. The CNN component constructs hierarchical feature maps through successive convolution and pooling operations, while the LSTM captures sequence-based patterns. Fully connected layers integrate these features to generate final predictions. Implemented in Python using TensorFlow and Keras on an Intel Core i7 system with 32 GB RAM and an NVIDIA GTX 1080 Ti GPU, the model demonstrated a superior performance. It achieved a mean absolute error of 0.042, root mean square error of 0.067, and an R-squared value of 0.935, outperforming existing methodologies in real-time adaptability and resource efficiency.
Design of Small-Size Lithium-Battery-Based Electromagnetic Induction Heating Control System
Authors: Liang Yuanjie , Song Shihao , Xu Bocheng , Li Zhuangzhuang , Li Xuelin , Mo Zonglai , Li Jun
Published: August 2024

This paper presents the design and optimization of a small-size electromagnetic induction heating control system powered by a 3.7 V–900 mAh lithium battery and featuring an LC series resonant full-bridge inverter circuit, which can be used for small metal material heating applications, such as micro medical devices. The effects of the resonant capacitance, inductor wire diameter, heating tube material, and wall thickness were studied to maximize the heating rate of the workpiece and simultaneously reduce the temperature rise of the NMOS transistor. The optimal circuit configuration meeting the design requirements was finally identified by comparing the operational parameters and NMOS transistor loss under different circuit conditions. Validation experiments were conducted on designed electromagnetic induction smoking devices. The results indicate that under an output current of 4.6 A, the heating tube can reach the temperature target of 250 °C within 11 s, and all NMOS transistors stay below 50 °C in a 5 min heating process.
Metaheuristic Algorithm-Based Proportional–Integrative–Derivative Control of a Twin Rotor Multi Input Multi Output System
Authors: Cabuker Ali Can , Almalı Mehmet Nuri
Published: August 2024

Metaheuristic algorithms are computational techniques based on the collective behavior of swarms and the study of organisms acting in communities. These algorithms involve different types of organisms. Finding controller values for nonlinear systems is a challenging task using classical approaches. Hence, using metaheuristics to find the controller values of a twin rotor multi-input multi-output system (TRMS), one of the nonlinear systems studied in the literature, seems to be more appropriate than using classical methods. In this study, different types of metaheuristic algorithms were used to find the PID controller values for a TRMS, including a genetic algorithm (GA), a dragonfly algorithm, a cuckoo algorithm, a particle swarm optimization (PSO) algorithm, and a coronavirus optimization algorithm (COVIDOA). The obtained graphs were analyzed based on certain criteria for the main rotor and tail rotor angles to reach the reference value in the TRMS. The experimental results show that when the rise and settlement times of the TRMS are compared in terms of performance, the GA took 1.5040 s (seconds) and the COVIDOA took 9.59 s to increase the pitch angle to the reference value, with the GA taking 0.7845 s and the COVIDOA taking 2.4950 s to increase the yaw angle to the reference value. For the settling time, the GA took 11.67 s and the COVIDOA took 28.01 s for the pitch angle, while the GA took 14.97 s and the COVIDOA took 26.69 s for the yaw angle. With these values, the GA and COVIDOA emerge as the foremost algorithms in this context.
A Review and Prospective Study on Modeling Approaches and Applications of Virtual Energy Storage in Integrated Electric–Thermal Energy Systems
Authors: Fu Qitong , Xing Zuoxia , Zhang Chao , Xu Jian
Published: August 2024

The increasing use of renewable energy sources introduces significant fluctuations in power generation, demanding enhanced regulatory capabilities to maintain the balance between power supply and demand. To promote multi-energy coupling and the local consumption of renewable energy, integrated energy systems have become a focal point of multidisciplinary research. This study models adjustable sources, networks, and loads within electric–thermal integrated energy systems as energy storage entities, forming virtual energy storage systems to participate in the optimization and scheduling of integrated energy systems. This paper investigates the modeling and control strategies of virtual energy storage systems within electric–thermal integrated energy systems. Initially, it introduces the definition, logical architecture, and technical connotations of virtual energy storage. Next, it models temperature-controlled loads as virtual energy storage systems and compares them with traditional energy storage systems, analyzing their characteristic differences and summarizing virtual energy storage system modeling methods and characteristic indicators. This paper then focuses on the specific applications of virtual energy storage systems in four typical scenarios. Finally, it explores the future development directions of virtual energy storage.
CO2 Storage in Deep Oceanic Sediments in the form of Hydrates: Energy Evaluation and Advantages Related to the Use of N2-Containing Mixtures
Authors: Di Giuseppe Alessia , Gambelli Alberto Maria
Published: August 2024

Permanent storage in suitable geological sites and/or deep aquifers is emerging as the most concrete and effective solution to mitigate its increasing concentration in the atmosphere. This article experimentally investigated its storage in deep marine environments in the form of hydrates. Gas hydrates were formed into a small-scale reactor, designed to reproduce marine seafloors. Hydrates were formed with pure carbon dioxide and with CO2-based gaseous mixture containing nitrogen at different concentrations, equal, respectively, to 30, 50 and 60 vol%. The results obtained for each mixture were then compared to each other. In particular, the quantity of hydrates formed was evaluated as a function of the thermodynamic conditions selected for the experiments. The energy spent for the process, calculated by considering the cooling and compression phases, was calculated for the unit quantity of hydrates formed and for the unit quantity of carbon dioxide stored. Finally, the energy requirements for gas cooling and for gas compression were calculated separately in order to comprehend the contribution of the single process for each mixture.
A Method to Design an Efficient Airfoil for Small Wind Turbines in Low Wind Speed Conditions Using XFLR5 and CFD Simulations
Authors: Sang Le Quang , Phengpom Tinnapob , Thin Dinh Van , Duc Nguyen Huu , Hang Le Thi Thuy , Huyen Cu Thi Thanh , Huong Nguyen Thi Thu , Tran Quynh T.
Published: August 2024

Small wind turbines operating in low wind speed regions have not had any significant success. In addition, small wind speed regions occupy a large area of the world, so they represent a potential area for installing small wind turbines in the future. In this paper, a method to design an efficient airfoil for small wind turbines in low wind speed conditions using XFLR5 and CFD simulations is implemented. Because the impact of the airflow on the blade surface under low Re number conditions can change suddenly for small geometries, designing the airfoil shape to optimize the aerodynamic performance is essential. The tuning of the key geometric parameters using inversion techniques for better aerodynamic performance is presented in this study. A two-dimensional model was used to consider the airflow on the airfoil surface with differences in the angle of attack. The original S1010 airfoil was used to design a new airfoil for increasing the aerodynamic efficiency by using V6.57 XFLR5 software. Subsequently, the new VAST-EPU-S1010 airfoil model was adjusted to the maximum thickness and the maximum thickness position. It was simulated in low wind speed conditions of 4–6 m/s by a computational fluid dynamics simulation. The lift coefficient, drag coefficient, and CL/CD coefficient ratio were evaluated under the effect of the angle of attack and the maximum thickness by using the k-ε model. The simulation results show that the VAST-EPU-S1010 airfoil achieved the greatest aerodynamic efficiency at an angle of attack of 3°, a maximum thickness of 8%, and a maximum thickness position of 20.32%. The maximum value of CL/CD of the new airfoil at 6 m/s was higher than at 4 m/s by about 6.25%.
A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems
Authors: Kiasari Mahmoud , Ghaffari Mahdi , Aly Hamed
Published: August 2024

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future.
A Review on Multi-Objective Mixed-Integer Non-Linear Optimization Programming Methods
Authors: Jaber Ahmed , Younes Rafic , Lafon Pascal , Khoder Jihan
Published: August 2024

This paper provides a recent overview of the exact, approximate, and hybrid optimization methods that handle Multi-Objective Mixed-Integer Non-Linear Programming (MO-MINLP) problems. Both the domains of exact and approximate research have experienced significant growth, driven by their shared goal of addressing a wide range of real-world problems. This work presents a comprehensive literature review that highlights the significant theoretical contributions in the field of hybrid approaches between these research areas. We also point out possible research gaps in the literature. Hence, the main research questions to be answered in this paper involve the following: (1) how to exactly or approximately solve a MO-MINLP problem? (2) What are the drawbacks of exact methods as well as approximate methods? (3) What are the research lines that are currently underway to enhance the performances of these methods? and (4) Where are the research gaps in this field? This work aims to provide enough descriptive information for newcomers in this area about the research that has been carried out and that is currently underway concerning exact, approximate, and hybrid methods used to solve MO-MINLP problems.
Behavior of Retained Austenite and Carbide Phases in AISI 440C Martensitic Stainless Steel under Cavitation
Authors: Brunatto Silvio Francisco , Cardoso Rodrigo Perito , Santos Leonardo Luis
Published: August 2024

In this work emphasis was given to determine the evolution of the retained austenite phase fraction via X-ray diffractometry technique in the as-hardened AISI 440C martensitic stainless steel surface subjected to cavitation for increasing test times. Scanning electron microscopy results confirmed the preferential carbide phase removal along the prior/parent austenite grain boundaries for the first cavitation test times on the polished sample surface during the incubation period. Results suggest that the strain-induced martensitic transformation of the retained austenite would be assisted by the elastic deformation and intermittent relaxation action of the harder martensitic matrix on the austenite crystals through the interfaces between both phases. In addition, an estimation of the stacking fault energy value on the order of 15 mJ m−2 for the retained austenite phase made it possible to infer that mechanical twinning and strain-induced martensite formation mechanisms could be effectively presented in the studied case. Finally, incubation period, maximum erosion rate, and erosion resistance on the order of 7.0 h, 0.30 mg h−1, and 4.8 h μm−1, respectively, were determined for the as-hardened AISI 440C MSS samples investigated here.
Exploration of Co-Inoculation of Lactiplantibacillus plantarum and Kazachstania bulderi for Potential Use in Mushroom Pleurotus eryngii Pickle Fermentation
Authors: Gou Xuelei , Zhang Weisi , Luo Xiaoli , Zhou Pei , Zhang Shasha , He Rong , Hua Rong , Wu Surui , Sun Dafeng
Published: August 2024

This study investigated the impact of Lactiplantibacillus plantarum PC-004 (PL) and Kazachstania bulderi PY-1 (PY), selected from home-made Paocai, on the physicochemical properties and volatile flavor compounds of fermented Pleurotus eryngii. P. eryngii, fermented by PY, was maintained at about pH 5 throughout fermentation, while co-fermentation exhibited a higher acid production rate and lower post-acidification value compared to mono-fermentation by PL. The viable cell counts of PY exhibited no difference between the mono- and co-fermentation, while PL significantly decreased in the co-fermentation after 168 h. Compared to mono-fermented P. eryngii, co-fermentation had lower levels of certain biogenic amines. Co-fermented P. eryngii had a higher value of nitrite in the early fermentation stage, but this decreased during mono-fermentations. Additionally, among the three different groups, PY fermentation had more esters, PL fermentation had more ketones and aldehydes, and co-fermentation had more abundance and diverse volatile flavor compounds than mono-fermentations. Our results suggest that co-fermentation with K. bulderi and L. plantarum is suitable for producing fermented P. eryngii, highlighting their potential for the fermentation of edible mushrooms.
Dynamic Changes in Microbial Communities and Chemical Compounds during the Semi-Dry Fermentation Processing of Coffea arabica
Authors: Shen Xiaojing , Yuan Wenjuan , Wang Qi , Peng Bintao , Guo Yi , Liu Kunyi , Jiang Weiwei
Published: August 2024

The semi-dry fermentation processing (SFP) of Coffea arabica is a traditional primary processing method in the coffee industry, which crucially impacts the coffee’s flavor. To further obtain useful information on microbial communities and chemical compounds during the SFP of C. arabica from Yunnan, China, the microbial community structures and the differentially changed non-volatile compounds (DCnVCs) were comprehensively analyzed. The results showed that Tatumella, Staphylococcus, Klebsiella, Brevundimonas, and Gluconobacter were the most prevalent bacteria genera, and Candida, Hannaella, Hanseniaspora, Pichia, and Lachancea were the most abundant fungal genera. Furthermore, 117 DCnVCs were found in the fermentation-finished samples compared to the raw materials. Therefore, this study can provide useful information for understanding the SFP of coffee beans, and its impact on coffee’s quality parameters.
Unpacking the Influence of Visual Density on Pizza Packaging: Sensory Expectations and Purchase Intentions
Authors: Sun Cong , Ding Yuechun , Meng Xing
Published: August 2024

Visual density, defined as the number of identifiable elements per unit area within a visual design, significantly influences consumer perceptions. This study investigates the effects of varying visual densities in pizza packaging, encompassing both food-related and decorative elements, on consumers’ expectations regarding taste and texture, ultimately influencing their purchase decisions. We conducted a controlled experiment where participants were presented with pizza boxes of differing visual densities. Participants rated their expectations regarding the taste and texture of the pizza, as well as their purchase intentions. Additionally, we measured consumption frequency to evaluate its moderating influence on the observed effects. Results indicate that high-visual-density packaging significantly heightened expectations of taste and texture, independent of the element’s nature—whether food-related or decorative. Enhanced sensory expectations fully mediated the relationship between visual density and purchase intentions. Additionally, high consumption frequency amplified the effect of high visual density on sensory expectations and purchase intentions. These findings contribute to sensory marketing theory by highlighting the importance of visual density in packaging design and the role of consumption frequency. They provide practical implications for food packaging strategies aimed at enhancing consumer experience and satisfaction.
K-Carrageenan/Locust Bean Gum Gels for Food Applications—A Critical Study on Potential Alternatives to Animal-Based Gelatin
Authors: Russo Spena Simona , Pasquino Rossana , Grizzuti Nino
Published: August 2024

Among hydrocolloids used in the food industry, gelatin (an animal protein) is remarkably known for its unique gel forming ability. Creating a perfect, green substitute for animal gelatin is extremely difficult if not impossible, because this versatile hydrocolloid offers many special properties that are not easily imitated by other vegetable-based systems. The combination of more than one type of hydrocolloid is commonly used in food either to bridge the above-mentioned gap or to impart novel organoleptic characteristics (such as mouthfeel) to food products, to modify rheological characteristics, and to satisfy processing requirements in the industry. In this work, we study the rheology and the texture of water mixtures of κ-Carrageenan (κ-C) and Locust Bean Gum (LBG). By fixing different κ-C concentrations and varying the LBG/κ-C ratio, we explore a wide range of potentially useful textures. The results obtained for the green systems are also compared to those exhibited by animal gelatin formulations.