bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review of Environmental Factors for an Ontology-Based Risk Analysis for Pandemic Spread
Authors: Cheung Liege , Lau Adela S. M. , Lam Kwok Fai , Ng Pauline Yeung
Published: April 2024

Contact tracing is a method used to control the spread of a pandemic. The objectives of this research are to conduct an empirical review and content analysis to identify the environmental factors causing the spread of the pandemic and to propose an ontology-based big data architecture to collect these factors for prediction. No research studies these factors as a whole in pandemic prediction. The research method used was an empirical study and content analysis. The keywords contact tracking, pandemic spread, fear, hygiene measures, government policy, prevention programs, pandemic programs, information disclosure, pandemic economics, and COVID-19 were used to archive studies on the pandemic spread from 2019 to 2022 in the EBSCOHost databases (e.g., Medline, ERIC, Library Information Science & Technology, etc.). The results showed that only 84 of the 588 archived studies were relevant. The risk perception of the pandemic (n = 14), hygiene behavior (n = 7), culture (n = 12), and attitudes of government policies on pandemic prevention (n = 25), education programs (n = 2), business restrictions (n = 2), technology infrastructure, and multimedia usage (n = 24) were the major environmental factors influencing public behavior of pandemic prevention. An ontology-based big data architecture is proposed to collect these factors for building the spread prediction model. The new method overcomes the limitation of traditional pandemic prediction model such as Susceptible-Exposed-Infected-Recovered (SEIR) that only uses time series to predict epidemic trend. The big data architecture allows multi-dimension data and modern AI methods to be used to train the contagion scenarios for spread prediction. It helps policymakers to plan pandemic prevention programs.
Hemp Seed Yield Responses to Nitrogen Fertility Rates
Authors: Podder Swarup , Shafian Sanaz , Thomason Wade E. , Wilson T. Bain , Fike John H.
Published: April 2024

Industrial hemp (Cannabis sativa L.) holds promise as a crop for more sustainable supply chains given its potential as a source of high-strength fibers, adsorbents, and nutrient-dense feedstuffs. Developing nutrient management guidelines for hemp will be an important part of optimizing the crop’s sustainability attributes. This study measured hemp seed yield in response to N fertilization rate (0, 60, 120, 180, and 240 kg N ha−1). Treatments were tested with four hemp cultivars (‘Joey’ and ‘Grandi’ in 2020, 2021, and 2022 and ‘NWG 2463’ and ‘NWG 4113’ in 2023) in Virginia. Nitrogen input influenced (p ≤ 0.0177) seed yield in all four experimental years, although the pattern of response varied substantially. In 2020, following delayed seeding, hemp showed a weak quadratic (p = 0.0113) response to N inputs, with peak yield (1640 kg ha−1) occurring with 120 kg N ha−1. In 2021, hemp displayed a strong linear (p < 0.0001) response to N inputs, with the highest seed yield (2510 kg ha−1) at 240 kg N ha−1. In 2022, a season characterized by low precipitation and high weed pressure, a weak, linear (p = 0.0111) response to the N rate was observed. The greatest seed yield (380 kg ha−1) was again observed with 240 kg N ha−1. In 2023, weed pressure remained an issue, but the response to N was strong and linear (p < 0.0001), with the greatest seed yield (831 kg ha−1) again measured at 240 kg N ha−1. These findings indicate hemp can be quite responsive to N inputs but that the magnitude of response is sensitive to other factors such as available soil moisture, weed pressure, and growing period.
A Genome-Wide Association Study of Seed Morphology-Related Traits in Sorghum Mini-Core and Senegalese Lines
Authors: Ahn Ezekiel , Park Sunchung , Hu Zhenbin , Ellur Vishnutej , Cha Minhyeok , Lee Yoonjung , Prom Louis K. , Magill Clint
Published: April 2024

Sorghum (Sorghum bicolor L.) ranks fifth as the most crucial cereal crop globally, yet its seed morphology remains relatively unexplored. This study investigated seed morphology in sorghum based on 115 mini-core and 130 Senegalese germplasms. Eight seed morphology traits encompassing size, shape, and color parameters were assessed. Statistical analyses explored potential associations between these traits and resistance to three major sorghum diseases: anthracnose, head smut, and downy mildew. Furthermore, genome-wide association studies (GWAS) were conducted using phenotypic data from over 24,000 seeds and over 290,000 publicly available single nucleotide polymorphisms (SNPs) through the Genome Association and Prediction Integrated Tool (GAPIT) R package. Significant SNPs associated with various seed morphology traits were identified and mapped onto the reference sorghum genome to identify novel candidate defense genes.
Anatomical Location of the Vesical Branches of the Inferior Hypogastric Plexus in Human Cadavers
Authors: Day Emily P. , Johnston Benjamin R. , Bazarek Stanley F. , Brown Justin M. , Lemos Nucelio , Gibson Eve I. , Hurban Helaina N. , Fecho Susan B. , Holt-Bright Lewis , Eun Daniel D. , Pontari Michel A. , De Elise J. , McGovern Francis J. , Ruggieri Michael R. , Barbe Mary F.
Published: April 2024

We have demonstrated in canines that somatic nerve transfer to vesical branches of the inferior hypogastric plexus (IHP) can be used for bladder reinnervation after spinal root injury. Yet, the complex anatomy of the IHP hinders the clinical application of this repair strategy. Here, using human cadavers, we clarify the spatial relationships of the vesical branches of the IHP and nearby pelvic ganglia, with the ureteral orifice of the bladder. Forty-four pelvic regions were examined in 30 human cadavers. Gross post-mortem and intra-operative approaches (open anterior abdominal, manual laparoscopic, and robot-assisted) were used. Nerve branch distances and diameters were measured after thorough visual inspection and gentle dissection, so as to not distort tissue. The IHP had between 1 to 4 vesical branches (2.33 ± 0.72, mean ± SD) with average diameters of 0.51 ± 0.06 mm. Vesical branches from the IHP arose from a grossly visible pelvic ganglion in 93% of cases (confirmed histologically). The pelvic ganglion was typically located 7.11 ± 6.11 mm posterolateral to the ureteral orifice in 69% of specimens. With this in-depth characterization, vesical branches from the IHP can be safely located both posterolateral to the ureteral orifice and emanating from a more proximal ganglionic enlargement during surgical procedures.
Efficacy and Safety of Plasma Rich in Growth Factor in Patients with Congenital Aniridia and Dry Eye Disease
Authors: Lozano-Sanroma Javier , Barros Alberto , Alcalde Ignacio , Alvarado-Villacorta Rosa , Sánchez-Ávila Ronald M. , Queiruga-Piñeiro Juan , Cueto Luis Fernández-Vega , Anitua Eduardo , Merayo-Lloves Jesús
Published: April 2024

Congenital aniridia is a rare bilateral ocular malformation characterized by the partial or complete absence of the iris and is frequently associated with various anomalies, including keratopathy, cataract, glaucoma, and foveal and optic nerve hypoplasia. Additionally, nearly 50% of individuals with congenital aniridia experience symptoms of ocular dryness. Traditional treatment encompasses artificial tears and autologous serum. This study aimed to assess the effectiveness and safety of using platelet rich in growth factors (PRGF) plasma in patients with congenital aniridia and ocular dryness symptoms. Methods: The included patients underwent two cycles of a 3-month PRGF treatment. At 6 months, symptomatology was evaluated using the OSDI and SANDE questionnaires, and ocular surface parameters were analyzed. Results: The OSDI and SANDE values for frequency and severity demonstrated statistically significant improvements (p < 0.05). Ocular redness, corneal damage (corneal staining), and tear volume (Schirmer test) also exhibited statistically significant improvements (p < 0.05). No significant changes were observed in visual acuity or in the grade of meibomian gland loss. Conclusion: The use of PRGF in patients with congenital aniridia and ocular dryness symptoms led to significant improvements in symptomatology, ocular redness, and ocular damage. No adverse effects were observed during the use of PRGF.
Heat Treatment’s Vital Role: Elevating Orthodontic Mini-Implants for Superior Performance and Longevity—Pilot Study
Authors: Panaite Tinela , Savin Carmen , Olteanu Nicolae Daniel , Karvelas Nikolaos , Romanec Cristian , Vieriu Raluca-Maria , Balcos Carina , Baltatu Madalina Simona , Benchea Marcelin , Achitei Dragos , Zetu Irina
Published: April 2024

Orthodontic mini-implants are devices used for anchorage in various orthodontic treatments. We conducted a pilot study which aimed to observe preliminary trends regarding the impact of heat treatment on the elastic modulus of Ti6Al4V alloy and stainless steel 316L mini-implants. The initial phase involved testing the impact of heat treatment on the mechanical properties of Ti6Al4V alloy and stainless steel 316L mini-implants. Material and methods: Ten self-drilling mini-implants sourced from two distinct manufacturers (Jeil Medical Corporation® and Leone®) with dimensions of 2.0 mm diameter and 10 mm length were tested. They were separated into two material groups: Ti6Al4V and 316L. Using the CETRUMT-2 microtribometer equipment, indentation testing was conducted employing a diamond-tipped Rockwell penetrator at a constant force of 4.5 N. Results: Slight differences were observed in the elastic modulus of the Ti6Al4V alloy (103.99 GPa) and stainless steel 316L (203.20 GPa) compared to natural bone. The higher elastic moduli of these materials indicate that they are stiffer, which could potentially lead to stress-shielding phenomena and bone resorption. Heat treatment resulted in significant changes in mechanical properties, including elastic modulus reductions of approximately 26.14% for Ti6Al4V and 24.82% for 316L, impacting their performance in orthodontic applications. Conclusion: Understanding the effects of heat treatment on these alloys is crucial for optimizing their biomechanical compatibility and longevity in orthodontic treatment. To fully evaluate the effects of heat treatment on mini-implants and to refine their design and efficacy in clinical practice, further research is needed.
Perceptions in Digital Smile Design: Assessing Laypeople and Dental Professionals’ Preferences Using an Artificial-Intelligence-Based Application
Authors: Buduru Smaranda , Cofar Florin , Mesaroș Anca , Tăut Manuela , Negucioiu Marius , Almășan Oana
Published: April 2024

Digital Smile Design (DSD) is used in many fields of dentistry. This prospective observational study assessed laypeople’s and dental professionals’ perceptions of a DSD application. SmileCloud, an online DSD platform, was used to create two different designs for three patients; after that, the participants, in a 30-question online illustrated survey, were asked about the most attractive design and other features of the smile. Dentists’ and laypeople’s perceptions about specific DSD features were assessed. The Kolmogorov–Smirnov normality test was used. Descriptive and crosstab analyses compared the respondents’ opinions for each statement. Chi-square tests were used to determine the relationship between the questions and any association with age, gender, and profession. The test results were rated as significant at a p-value < 0.05. A total of 520 participants (dental professionals, students, dental technicians, and laypeople) were enrolled. The statistically significant features were self-esteem related to appearance (p = 0.05), facial and smile symmetry (p = 0.42, p < 0.0001), tooth color (p = 0.012), and symmetry of gums (p < 0.001). For each patient, the design with dominant round upper incisors and perfect symmetry was preferred (p < 0.001). Digital pre-visualization benefits diagnosis and enriches treatment planning. The dentist–dental technician–patient team should be involved in the decision-making process of pre-visualization.
Harnessing Information Thermodynamics: Conversion of DNA Information into Mechanical Work in RNA Transcription and Nanopore Sequencing
Authors: Tsuruyama Tatsuaki
Published: April 2024

Recent advancements in information thermodynamics have revealed that information can be directly converted into mechanical work. Specifically, RNA transcription and nanopore sequencing serve as prime examples of this conversion, by reading information from a DNA template. This paper introduces an information thermodynamic model in which these molecular motors can move along the DNA template by converting the information read from the template DNA into their own motion. This process is a stochastic one, characterized by significant fluctuations in forward movement and is described by the Fokker–Planck equation, based on drift velocity and diffusion coefficients. In the current study, it is hypothesized that by utilizing the sequence information of the template DNA as mutual information, the fluctuations can be reduced, thereby biasing the forward movement on DNA and, consequently, reducing reading errors. Further research into the conversion of biological information by molecular motors could unveil new applications, insights, and important findings regarding the characteristics of information processing in biology.
Enhancing Zero-Shot Stance Detection with Contrastive and Prompt Learning
Authors: Yao Zhenyin , Yang Wenzhong , Wei Fuyuan
Published: April 2024

In social networks, the occurrence of unexpected events rapidly catalyzes the widespread dissemination and further evolution of network public opinion. The advent of zero-shot stance detection aligns more closely with the characteristics of stance detection in today’s digital age, where the absence of training examples for specific models poses significant challenges. This task necessitates models with robust generalization abilities to discern target-related, transferable stance features within training data. Recent advances in prompt-based learning have showcased notable efficacy in few-shot text classification. Such methods typically employ a uniform prompt pattern across all instances, yet they overlook the intricate relationship between prompts and instances, thereby failing to sufficiently direct the model towards learning task-relevant knowledge and information. This paper argues for the critical need to dynamically enhance the relevance between specific instances and prompts. Thus, we introduce a stance detection model underpinned by a gated multilayer perceptron (gMLP) and a prompt learning strategy, which is tailored for zero-shot stance detection scenarios. Specifically, the gMLP is utilized to capture semantic features of instances, coupled with a control gate mechanism to modulate the influence of the gate on prompt tokens based on the semantic context of each instance, thereby dynamically reinforcing the instance–prompt connection. Moreover, we integrate contrastive learning to empower the model with more discriminative feature representations. Experimental evaluations on the VAST and SEM16 benchmark datasets substantiate our method’s effectiveness, yielding a 1.3% improvement over the JointCL model on the VAST dataset.
Prediction Horizon-Varying Model Predictive Control (MPC) for Autonomous Vehicle Control
Authors: Chen Zhenbin , Lai Jiaqin , Li Peixin , Awad Omar I. , Zhu Yubing
Published: April 2024

The prediction horizon is a key parameter in model predictive control (MPC), which is related to the effectiveness and stability of model predictive control. In vehicle control, the selection of a prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the proximal policy optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. We established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared them with an MPC with fixed prediction horizons. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong adaptability and trajectory tracking capability.
Simple Modeling and Analysis of Total Ionizing Dose Effects on Radio-Frequency Low-Noise Amplifiers
Authors: Kim Taeyeong , Ryu Gyungtae , Lee Jongho , Cho Moon-Kyu , Fleetwood Daniel M. , Cressler John. D. , Song Ickhyun
Published: April 2024

In this study, the degradation characteristics of radio frequency (RF)-low-noise amplifiers (LNA) due to a total ionizing dose (TID) is investigated. As a device-under-test (DUT), sample LNAs were prepared using silicon–germanium (SiGe) heterojunction bipolar transistors (HBTs) as core elements. The LNA was based on a cascode stage with emitter degeneration for narrowband applications. By using a simplified small-signal model of a SiGe HBT, design equations such as gain, impedance matching, and noise figure (NF) were derived for analyzing TID-induced degradations in the circuit-level performance. To study radiation effects in circuits, the SiGe-RF-LNAs fabricated in a commercial 350 nm SiGe technology were exposed to 10-keV X-rays to a total ionizing dose of up to 3 Mrad(SiO2). The TID-induced performance changes of the LNA were modeled by applying degradation to device parameters. In the modeling process, new parameter values after irradiation were estimated based on information in the literature, without direct measurements of SiGe HBTs used in the LNA chip. As a result, the relative contributions of parameters on the circuit metrics were compared, identifying dominant parameters for degradation modeling. For the TID effects on input matching (S11) and NF, the base resistance (RB) and the base-to-emitter capacitance (Cπ) of the input transistor were mostly responsible, whereas the transconductances (gm) played a key role in the output matching (S22) and gain (S21). To validate the proposed approach, it has been applied to a different LNA in the literature and the modeling results predicted the TID-induced degradations within reasonable ranges.
A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures
Authors: Liu Xing , Zio Enrico , Borgonovo Emanuele , Plischke Elmar
Published: April 2024

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities.
A Training-Free Estimation Method for the State of Charge and State of Health of Series Battery Packs under Various Load Profiles
Authors: Pei Lei , Yu Cheng , Wang Tiansi , Yang Jiawei , Wang Wanlin
Published: April 2024

To ensure the accuracy of state of charge (SOC) and state of health (SOH) estimation for battery packs while minimizing the amount of pre-experiments required for aging modeling and the scales of computation for online management, a decisive-cell-based estimation method with training-free characteristic parameters and a dynamic-weighted estimation strategy is proposed in this paper. Firstly, to reduce the computational complexity, the state estimation of battery packs is summed up to that of two decisive cells, and a new selection approach for the decisive cells is adopted based on the detection of steep voltage changes. Secondly, two novel ideas are implemented for the state estimation of the selected cells. On the one hand, a set of characteristic parameters that only exhibit local curve shrinkage with aging is chosen, which keeps the corresponding estimation approaches away from training. On the other hand, multiple basic estimation approaches are effectively combined by their respective dynamic weights, which ensures the estimation can maintain a good estimation accuracy under various load profiles. Finally, the experimental results show that the new method can quickly correct the initial setting deviations and have a high estimation accuracy for both the SOC and SOH within 2% for a series battery pack consisting of cells with obvious inconsistency.
Correction: Abbas et al. Analysis of Entropy Generation on Magnetohydrodynamic Flow with Mixed Convection through Porous Media. Energies 2022, 15, 1206
Authors: Abbas Munawwar Ali , Ahmed Bashir , Chen Li , Rehman Shamas ur , Saleem Muzher , Khudair Wissam Sadiq
Published: April 2024

In the original paper [...]
Research on Fracturing Optimization of Coalbed Methane Wells Aiming at Economic Benefit—A Case Study of Liulin Block
Authors: Liu Jianzhong , Su Yanchun , Sun Lichun , Li Chen , Wang Lei , Meng Yanjun , Li Yong
Published: April 2024

Hydraulic fracturing is an essential technology in the development of coalbed methane reservoirs. Hydraulic fracturing can create a highly conductive fracture in the reservoir and increase its permeability. At present, the focus of coalbed methane reservoir fracturing optimization is gradually shifting to the fracturing scale. In the current development process, more and more coalbed methane blocks try to increase the fracturing scale to increase the gas production of coalbed methane wells. Field tests show that gas production of coalbed methane wells will increase to a certain extent with the increase of fracturing scale. However, the increase in the scale of fracturing also increases its cost. Therefore, the most economical fracturing scale is not necessarily the optimal fracturing scale for gas production. The field test usually pays more attention to the gas production effect, but the development of a coalbed methane field should pay more attention to the economic benefit, and the optimization of fracturing should take the economic benefit as the goal. Taking economic benefits as the starting point, this paper uses fracturing simulation to calculate the fracture extension under different geological conditions and different fracturing scales. It also uses numerical simulation to calculate the gas well productivity under different fracture extension conditions. The economic evaluation model was established to calculate the economic benefits under different fracturing scales, and the optimal fracturing scale was obtained. Finally, the typical maps of fracturing optimization under different geological conditions are formed. The optimization method of fracturing scale integrating economy, fracturing, and gas reservoir is realized. The research results have been successfully applied to the optimization scheme of Liulin block development, and very good results have been achieved. Because this method is targeted at different geological conditions, it can be used to guide the fracturing optimization of other coalbed methane blocks and has very important significance for the development and optimization of coalbed methane reservoirs.
Pumped Storage Hydropower as a Part of Energy Storage Systems in Poland—Młoty Case Study
Authors: Kulpa Jarosław , Kopacz Michał , Stecuła Kinga , Olczak Piotr
Published: April 2024

The increase in the share of renewable energy sources (RES) leads to a growing need for sources or systems/actions to stabilize the national energy grid. Such stabilizing actions include market tools, such as prices and demand-side response (DSR) tools, as well as flexible energy sources (e.g., gas). In addition, energy storage, where pumped storage hydroelectricity (PSH) accounts for 90% of global storage capacity, plays an important role. Therefore, the authors presented a detailed analysis of PSH in the context of the dynamic growth of installed capacity in renewable energy sources. They analyzed the economic viability of this type of power plant, with a particular emphasis on operational costs, energy production, and revenue. The Młoty case study and market data, including historical data on various PSH, were presented and analyzed. This study uses copulas, simulation, and statistical analysis. The authors proved that market prices and arbitrage actions alone are not sufficient to achieve profitability of the investment; however, additional benefits, such as fees for available power, enable the achievement of economic profitability. The reason for this is the fact that one of the main goals of PSH is to serve as a power reserve. In addition, this paper presents the analysis of the utilization of existing PSH in the form of full pumping and energy generation cycles (charging and discharging storage).
A Robust Switching Control Strategy for Three-Phase Voltage Source Converters with Uncertain Circuit Parameters
Authors: Guo Xin , Qiao Jichen , Li Yankai , Jiao Shangbin
Published: April 2024

This study proposes a novel double closed-loop robust control strategy based on a power switching affine model of three-phase voltage source converters (VSCs). The aim is to overcome the challenges posed by inaccurate mathematical models, complex controller configurations, indirect switching control, and performance degradation under circuit parameters uncertainty or load variation in conventional methods. These conventional methods rely on linearization models, duty ratio regulation, and pulse width modulation (PWM) technologies. The contributions of work are the following: (1) A two-dimensional (2D) power switching affine model is constructed without any approximation or averaging. (2) The proposed approach achieves direct switching control of three-phase VSCs, eliminating the need for complex rotation coordinate transformation, PWM, and phase locking loop (PLL), which are utilized in traditional control methods. (3) The rigor of the system stability analysis is enhanced based on the 2D power switching model compared to the existing three-dimensional (3D) current switching model. (4) A simple control structure with only two control parameters is employed to address circuit parameter uncertainties. The effectiveness and superiority of the proposed method is validated through simulation and experimental comparison results.
Modified Multimachine Power System Design with DFIG-WECS and Damping Controller
Authors: Sabo Aliyu , Ebuka Odoh Theophilus , Veerasamy Veerapandiyan , Abdul Wahab Noor Izzri
Published: April 2024

Rotor angle stability, which involves electromechanical oscillation damping and control, is very important in maintaining the stability of modern power grid systems. Renewable energy sources like wind energy are undergoing massive integration into modern power grid systems to meet energy demands and decarbonize power grid systems of carbon emissions from fossil fuel generators. To enable increased integration of wind renewable energy sources, precise models are needed for research and analytical purposes. Wind renewable energy is generated through a wind energy conversion system (WECS); one such conversion system is the doubly fed induction generator (DFIG) system. In this study, a precise model of a DFIG-WECS was modeled and integrated into the IEEE’s two-area Kundur power test system, which represents the available power grid system, and is also a multimachine power system using the Matlab/Simulink 2023 software. A damping controller known as the power system stabilizer (PSS), whose optimal parameters were obtained using artificial eco-system optimization (AEO), was also incorporated into the integrated power grid system to control and damp electromechanical oscillations. The results showed that the PSS damping controller effectively damped electromechanical oscillations in the integrated power grid system.
Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network
Authors: Wang Qinghai , Wang Wei , He Yan , Li Meng
Published: April 2024

The physical and mechanical properties of heat-treated wood are essential factors in assessing its appropriateness for different applications. While back-propagation (BP) neural networks are widely used for predicting wood properties, their accuracy often falls short of expectations. This paper introduces an improved Beluga Whale Optimisation (IBWO)-BP model as a solution to this challenge. We improved the standard Beluga Whale Optimisation (BWO) algorithm in three ways: (1) use Bernoulli chaos mapping to explore the entire search space during population initialization; (2) incorporate the position update formula of the Firefly Algorithm (FA) to improve the position update strategy and convergence speed; (3) apply the opposition-based learning based on the lens imaging (lensOBL) mechanism to the optimal individual, which prevents the algorithm from getting stuck in local optima during each iteration. Subsequently, we adjusted the weights and thresholds of the BP model, deploying the IBWO approach. Ultimately, we employ the IBWO-BP model to predict the swelling and shrinkage ratio of air-dry volume, as well as the modulus of elasticity (MOE) and bending strength (MOR) of heat-treated wood. The benefit of IBWO is demonstrated through comparison with other meta-heuristic algorithms (MHAs). When compared to earlier prediction models, the results revealed that the mean square error (MSE) decreased by 39.7%, the root mean square error (RMSE) by 22.4%, the mean absolute percentage error (MAPE) by 9.8%, the mean absolute error (MAE) by 31.5%, and the standard deviation (STD) by 18.9%. Therefore, this model has excellent generalisation ability and relatively good prediction accuracy.
Research on the Exposure Risk Analysis of Wildfires with a Spatiotemporal Knowledge Graph
Authors: Ge Xingtong , Peng Ling , Yang Yi , Wang Yinda , Chen Deyue , Yang Lina , Li Weichao , Chen Jiahui
Published: April 2024

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as ‘exposures’, which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, ‘exposure risk’ denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method’s efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O (m×n×p) to O (m×n).
Relationships of Fire Rate of Spread with Spectral and Geometric Features Derived from UAV-Based Photogrammetric Point Clouds
Authors: Carbonell-Rivera Juan Pedro , Moran Christopher J. , Seielstad Carl A. , Parsons Russell A. , Hoff Valentijn , Ruiz Luis Á. , Torralba Jesús , Estornell Javier
Published: April 2024

Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R2 in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models.
Valorization of Agricultural By-Products (Fragaria vesca) through the Production of Value-Added Micro/Nanostructures Using Electrohydrodynamic Techniques
Authors: Couto Ana Francisca , Estevinho Berta N.
Published: April 2024

An innovative approach for the production of bio-micro/nanostructures with high-value compounds from agricultural by-products was studied. This research aimed to valorize bioactive compounds existing in the by-products of the plants of Fragaria vesca (wild strawberry). The particle characteristics, morphology, size, release properties, and antioxidant activity of micro/nanostructures containing the extract of by-products of the plants of Fragaria vesca or quercetin (one of the main polyphenols in the plant) were analyzed. The electrohydrodynamic (EHD) technique was utilized for encapsulation. The results showed that the morphology and size of the structures were influenced by the concentration of zein, with 10% w/v zein concentration leading to irregular and non-uniform nanostructures, while 20% w/v zein concentration resulted in a mixture of microparticles and thin fibers with an irregular surface. The type and concentration of the core material did not significantly affect the morphology of the micro/nanostructures. In vitro release studies demonstrated the controlled release of the core materials from the zein micro/nanostructures. The release profiles were analyzed using the Korsmeyer–Peppas and Weibull models, which provided insights into the release mechanisms and kinetics. The most relevant release mechanism is associated with “Fickian Diffusion”. The antioxidant activity of the structures was evaluated using an ABTS radical-scavenging assay, indicating their potential as antioxidants. In conclusion, the EHD technique enabled the successful encapsulation of Fragaria vesca by-product extract and quercetin with zein, resulting in micro/nanostructures with different morphologies.
Assessing Seasonal Effects on Identification of Cultivation Methods of Short–Growth Cycle Brassica chinensis L. Using IRMS and NIRS
Authors: Liu Xing , Fan Kai , Lu Yangyang , Zhao Hong , Rao Qinxiong , Geng Hao , Chen Yijiao , Rogers Karyne Maree , Song Weiguo
Published: April 2024

Seasonal (temporal) variations can influence the δ13C, δ2H, δ18O, and δ15N values and nutrient composition of organic (ORG), green (GRE), and conventional (CON) vegetables with a short growth cycle. Stable isotope ratio mass spectrometry (IRMS) and near-infrared spectroscopy (NIRS) combined with the partial least squares-discriminant analysis (PLS-DA) method were used to investigate seasonal effects on the identification of ORG, GRE, and CON Brassica chinensis L. samples (BCs). The results showed that δ15N values had significant differences among the three cultivation methods and that δ13C, δ2H, and δ18O values were significantly higher in winter and spring and lower in summer. The NIR spectra were relatively clustered across seasons. Neither IRMS-PLS-DA nor NIRS-PLS-DA could effectively identify all BC cultivation methods due to seasonal effects, while IRMS-NIRS-PLS-DA combined with Norris smoothing and derivative pretreatment had better predictive abilities, with an 89.80% accuracy for ORG and BCs, 88.89% for ORG and GRE BCs, and 75.00% for GRE and CON BCs. The IRMS-NIRS-PLS-DA provided an effective and robust method to identify BC cultivation methods, integrating multi-seasonal differences.
Matcha Green Tea: Chemical Composition, Phenolic Acids, Caffeine and Fatty Acid Profile
Authors: Kika Joanna , Jakubczyk Karolina , Ligenza Alicja , Maciejewska-Markiewicz Dominika , Szymczykowska Kinga , Janda-Milczarek Katarzyna
Published: April 2024

Matcha—Japanese powdered tea—is a variety of green tea (Camellia sinensis L.), one of the most popular beverages in the world. Due to the unique way it is grown, it contains high concentrations of health-promoting phytochemicals. The aim of this study was to determine the basic nutritional and phenolic composition of dry matcha green tea powder. The fibre content was determined according to the enzymatic–gravimetric method. Crude protein was measured by the Kjeldahl method. The total fat content was measured by the Soxhlet method, and the fatty acid profile was defined by the GC method. The determination of the phenolic acid and caffeine content was performed using high-performance liquid chromatography. The total fibre content of matcha was 56.1 g/100 g, with 52.8 g/100 g (94.1% of total fibre) of insoluble dietary fibre and 3.3 g/100 g of soluble fibre (5.9% of total fibre). The total protein content was 17.3 g/100 g. The total fat content in dry matcha was 7.285 g/100 g, comprising varying proportions of individual fatty acids, the highest ones being those of linoleic acid and α-linolenic acid. The caffeine content of matcha tea was 2213.492 µg/g. With regard to phenolic acids, the highest content was estimated for gallic acid (252.3755 µg/g). Matcha showed a particularly high content of total dietary fibre, with a predominance of the insoluble fraction. Matcha was found to be a valuable source of plant protein and unsaturated fatty acids, mostly of the omega-3 fatty acid family.
Consumer Preferences and Willingness to Pay for High-Quality Vegetable Oils: A Cross-Sectional Analysis of Chinese Residents
Authors: Tang Shiting , Wang Pei , Xing Huan , Huang Zeying , Liu Peng , Li Tai , Huang Jiazhang
Published: April 2024

The consumption of edible vegetable oil is an important source of essential fatty acids and vitamin E for the human body. Guiding residents to consume scientifically and reasonably control the intake of edible oils is an important part of promoting the construction of a healthy China. Currently, Chinese residents have an insufficient understanding of the scientific consumption of edible oils, leading to an intake exceeding the dietary recommendations, resulting in excessive fat intake and increasing the risk of chronic diseases such as obesity and cardiovascular diseases. Based on the Theory of Planned Behavior (TPB) and using Structural Equation Modeling (SEM), this study analyzed the cognitive preferences and willingness to pay a premium for edible vegetable oils among urban consumers in China. The survey included 1098 Chinese urban consumers of vegetable oils. The research found that attitudes, health value, monetary value, and environmental endowment of urban residents are the main predicting factors of the intention to purchase high-quality vegetable oils. This study confirms the applicability of the Theory of Planned Behavior in the consumption of high-quality vegetable oils and provides theoretical contributions and insights for Chinese enterprises and policymakers in formulating marketing strategies for vegetable oils.
Quality Characteristics of Vegan Mayonnaise Produced Using Supercritical Carbon Dioxide-Processed Defatted Soybean Flour
Authors: Lee Kyo-Yeon , Han Chae-Yeon , Rahman Wasif Ur , Harinarayanan Nair Chithra , Park Chae-Eun , Choi Sung-Gil
Published: April 2024

Emulsifiers, like egg yolk (EY), are necessary for the formation of mayonnaise, which is an oil-in-water type of colloid. This study aimed to assess the potential of defatted soybean powder treated with supercritical carbon dioxide (DSF) to enhance the quality of plant-based mayonnaise as plant-based alternatives gain popularity. This study involved the production of DSF and the comparison of its quality attributes to those of mayonnaise made with varying amounts of control soy flour (CSF), DSF, and EY. It was found that mayonnaise made with an increased quantity of DSF showed better emulsion stability, viscosity, and a smaller, more uniform particle size when compared with CSF mayonnaise. Additionally, DSF mayonnaise was generally rated higher in sensory evaluation. The addition of approximately 2% DSF positively influenced the emulsion and sensory properties of the vegan mayonnaise, indicating that DSF is a promising plant-based alternative emulsifier for the replacement of animal ingredients.
Ozaki-Type Bi-Close-to-Convex and Bi-Concave Functions Involving a Modified Caputo’s Fractional Operator Linked with a Three-Leaf Function
Authors: Vijaya Kaliappan , Murugusundaramoorthy Gangadharan , Breaz Daniel , Oros Georgia Irina , El-Deeb Sheza M.
Published: April 2024

The focus of the present work is on the establishment and investigation of the coefficient estimates of two new subclasses of bi-close-to-convex functions and bi-concave functions; these are of an Ozaki type and involve a modified Caputo’s fractional operator that is associated with three-leaf functions in the open unit disc. The classes are defined using the notion of subordination based on the previously established fractional integral operators and classes of starlike functions associated with a three-leaf function. For functions in these classes, the Fekete-Szegö inequalities and the initial coefficients, |a2| and |a3|, are discussed. Several new implications of the findings are also highlighted as corollaries.
A High-Stretching, Rapid-Self-Healing, and Printable Composite Hydrogel Based on Poly(Vinyl Alcohol), Nanocellulose, and Sodium Alginate
Authors: Li Mingyang , Wang Yanen , Wei Qinghua , Zhang Juan , Chen Xiaohu , An Yalong
Published: April 2024

Hydrogels with excellent flexibility, conductivity, and controllable mechanical properties are the current research hotspots in the field of biomaterial sensors. However, it is difficult for hydrogel sensors to regain their original function after being damaged, which limits their practical applications. Herein, a composite hydrogel (named SPBC) of poly(vinyl alcohol) (PVA)/sodium alginate (SA)/cellulose nanofibers (CNFs)/sodium borate tetrahydrate was synthesized, which has good self-healing, electrical conductivity, and excellent mechanical properties. The SPBC0.3 hydrogel demonstrates rapid self-healing (<30 s) and achieves mechanical properties of 33.92 kPa. Additionally, it exhibits high tensile strain performance (4000%). The abundant internal ions and functional groups of SPBC hydrogels provide support for the good electrical conductivity (0.62 S/cm) and electrical response properties. In addition, the SPBC hydrogel can be attached to surfaces such as fingers and wrists to monitor human movements in real time, and its good rheological property supports three-dimensional (3D) printing molding methods. In summary, this study successfully prepared a self-healing, conductive, printable, and mechanically superior SPBC hydrogel. Its suitability for 3D-printing personalized fabrication and outstanding sensor properties makes it a useful reference for hydrogels in wearable devices and human motion monitoring.
Foot Posture Index Does Not Correlate with Dynamic Foot Assessment Performed via Baropodometric Examination: A Cross-Sectional Study
Authors: Origo Daniele , Buffone Francesca , Montini Gabriele , Belluto Daniele , Tramontano Marco , Dal Farra Fulvio
Published: April 2024

Background. Clinicians employ foot morphology assessment to evaluate the functionality of the method and anticipate possible injuries. This study aims to correlate static foot posture and the dynamic barefoot evaluation in a sample of healthy adult participants. Methods. The foot posture was evaluated using the Foot Posture Index-6 (FPI-6) and the dynamics were evaluated through baropodometric examination. Two operators independently assessed the participants’ foot posture through FPI-6, and then a dynamic evaluation was performed by asking them to walk 8 times across a platform. One hundred participants (mean age: 32.15 ± 7.49) were enrolled. Results. The inter-rater agreement between the two assessors was found to be excellent. The majority of the feet belonged to the 0 < FPI < 4 class (32%), followed by the 4 < FPI < 8 (31%) and the FPI > 8 ranges (19.5%). Our “area of contact” analysis showed a significant poor correlation between FPI and total foot, midfoot, and the second metatarsophalangeal joint (MTPJ) (−0.3 < r < 0). Regarding “force” parameters, the analysis showed a poor correlation between the midfoot, hallux, and the second toe (−0.2 < r < 2); finally the “pressure” analysis showed a poor correlation between FPI, the fourth MTPJ, and the second toe (−0.2 < rs < 0.3) and a moderate correlation between the hallux (r = 0.374) and the fifth MTPJ (r = 0.427). Conclusions. This study emphasizes the constrained correlation between static foot posture observation and dynamic barefoot examination.