bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Data Compression Methodology Focused on Power Quality Signals Using Compressive Sampling Matching Pursuit
Authors: Ruiz Milton , Jaramillo Manuel , Aguila Alexander , Ortiz Leony , Varela Silvana
Published: December 2022

In this research a new data compression technique for electrical signals was proposed. The methodology combined wavelets and compressed sensing techniques. Two algorithms were proposed; the first one was designed to find specific characteristics of any type of energy quality signal such as the number of samples per cycle, zero-crossing indices, and signal amplitude. With the data obtained, the second algorithm was designed to apply a biorthogonal wavelet transform resulting in a shifted signal, and its amplitude was modified with respect to the original. The errors were rectified with the attributes found in the early stage, and the application of filters was conducted to reduce the ripple attached. Then, the third algorithm was designed to apply Compressive Sampling Matching Pursuit, which is a greedy algorithm that creates a dictionary with orthogonal bases representing the original signal in a sparse vector. The results exhibited excellent features of quality and were accomplished by the suggested compression and reconstruction technique. These results were a compression ratio of 1020:1, that is, the signal was compressed by 99.90% with respect to the original one. The quality indicators achieved were RTE = 0.9938, NMSE = 0.0098, and COR = 0.99, exceeding the results of the most relevant research papers published in Q1 high-impact journals that were further discussed in the introduction section.
A Combined Fuzzy Optimization Model for the Location of an Intelligent Energy-Efficient Manufacturing Industrial Park
Authors: He Chufeng , Liu Aijun , Xu Lei , Yuan Shuailei , Cheng Mingbao , Wang Huan , Wang Fang , Lu Hui , Liu Xiaoxue
Published: December 2022

With the background of implementing carbon peaking and carbon neutralization, identifying methods to realize energy-saving and carbon reduction effectively has become an important issue in the intelligent energy-conservation manufacturing industry. During the process of achieving this goal, determining an optimal location for a low-carbon and intelligent manufacturing industrial park is a foremost decision-making problem for manufacturing corporations’ energy-efficient development. The article established a multi-criteria decision framework to assist manufacturing companies when selecting suitable industrial park sites. To begin with, an evaluation criteria framework is confirmed by literature search. Then, a fuzzy optimization model, which combines the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is presented, where fuzzy TOPSIS is used to determine the decision-maker criteria weights. Then, criteria weights are calculated by the optimization model with construction of a Lagrange function. Moreover, the fuzzy VIKOR method is applied to sort alternatives and choose the best alternative location. In addition, five alternative sites for a manufacturing company are evaluated and ranked according to the values of the ranking index as a numerical case to demonstrate the proposed framework’s application. Finally, a comprehensive analysis of diverse methods and sensitivity analyses for the volatility in criteria weights and decision-maker weights is illustrated to confirm that the framework is practicable for the problem of intelligent and sustainable manufacturing industrial park-site selection.
Fundamental Understanding of Heat and Mass Transfer Processes for Physics-Informed Machine Learning-Based Drying Modelling
Authors: Khan Md Imran H. , Batuwatta-Gamage C. P. , Karim M. A. , Gu YuanTong
Published: December 2022

Drying is a complex process of simultaneous heat, mass, and momentum transport phenomena with continuous phase changes. Numerical modelling is one of the most effective tools to mechanistically express the different physics of drying processes for accurately predicting the drying kinetics and understanding the morphological changes during drying. However, the mathematical modelling of drying processes is complex and computationally very expensive due to multiphysics and the multiscale nature of heat and mass transfer during drying. Physics-informed machine learning (PIML)-based modelling has the potential to overcome these drawbacks and could be an exciting new addition to drying research for describing drying processes by embedding fundamental transport laws and constraints in machine learning models. To develop such a novel PIML-based model for drying applications, it is necessary to have a fundamental understanding of heat, mass, and momentum transfer processes and their mathematical formulation of drying processes, in addition to data-driven modelling knowledge. Based on a comprehensive literature review, this paper presents two types of information: fundamental physics-based information about drying processes and data-driven modelling strategies to develop PIML-based models for drying applications. The current status of physics-based models and PIML-based models and their limitations are discussed. A sample PIML-based modelling framework for drying application is presented. Finally, the challenges of addressing simultaneous heat, mass, and momentum transport phenomena in PIML modelling for optimizing the drying process are presented at the end of this paper. It is expected that the information in this manuscript will be beneficial for further advancing the field.
Aerodynamic Performance of VAWT Airfoils: Comparison between Wind Tunnel Testing Using a New Three-Component Strain Gauge Balance and CFD Modelling
Authors: Santamaría Luis , Galdo Vega Mónica , Pandal Adrián , González Pérez José , Velarde-Suárez Sandra , Fernández Oro Jesús Manuel
Published: December 2022

Vertical axis wind turbines are an emerging and in-development wind energy technology which are characterized by their complicated aerodynamics. Detached flow conditions, which are typically developed at operational tip speed ratios, demand a rigorous characterization of the airfoils for an accurate prediction of the turbine performance. In this work, a custom-built, three-component external strain gauge balance, specifically developed for airfoil testing, is validated. The physical reasons responsible for discrepancies with reference data are also analyzed. Two- and three-dimensional flat plates, as well as the DU06-W-200 airfoil, are tested in a wind tunnel. Lift and drag coefficients and pitching moments are obtained for a wide angular range at Re = 200,000. The results are compared with data from the bibliography and CFD simulations, performed with the recently developed GEKO (generalized k-omega) turbulence model, achieving remarkable agreement. Instantaneous forces are also analyzed with both experimental and CFD techniques, providing interesting results of the unsteady fluid dynamics. Finally, critical factors affecting the measurements are identified and enhancements are proposed for future works. In summary, a thorough evaluation of this new balance design is provided, showing its valuable potential for VAWT applications.
Virtual Battery Modeling of Air Conditioning Loads in the Presence of Unknown Heat Disturbances
Authors: Hahm Seung-Jun , Jang Ye-Eun , Kim Young-Jin
Published: December 2022

Air conditioning loads (ACLs) are potential flexible resources that can provide various grid services to the power system. Recent studies have attempted to represent their flexibility using a virtual battery (VB) model for quantification, but the modeling process requires information on thermal parameters and heat disturbances (e.g., solar irradiation and internal heat load) that are difficult to measure. In this paper, we present a new method that models a VB without prior knowledge of such information. First, we construct a thermal dynamic model of an individual ACL using historical input-output data. The linear regression model parameters are identified without using the measurements of disturbances. Second, we derive a VB model from the linear regression parameters using a change of variable technique. We show that the VB can be directly modeled from the regression model of thermal dynamics without estimating the exact thermal parameters and heat disturbances. Third, aggregation of the VB models is implemented. The energy limits of aggregate VB models are designed considering the baseline load prediction error caused by disturbance uncertainty. Finally, simulation results verify the accuracy and effectiveness of the proposed VB modeling strategy.
Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System
Authors: Sun Lijuan , Chen Menggang , Shi Yawei , Zheng Lifeng , Li Songyang , Li Jun , Xu Huijuan
Published: December 2022

As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented in a better way. This paper investigates a price-driven charging management model in which all plug-in electric vehicles are informed of the charging strategies of neighboring plug-in electric vehicles and adjust their own strategies to minimize the cost, while an aggregator determines the unit price based on overall electricity consumption to coordinate the charging strategies of the plug-in electric vehicles. In this article, we used an asynchronous distributed generalized Nash game algorithm to investigate a charging management model for plug-in electric vehicles in a smart charging station (SCS). In a charging management model, we need to consider constraints on the charge and discharge rates of plug-in electric vehicles, the battery capacity, the amount of charge per plug-in electric vehicle, and the maximum electrical load that the whole system can allow. Meeting the constraints of plug-in electric vehicles and smart charging stations, the model coordinates the charging strategy of each plug-in electric vehicle to ultimately reduce the cost of smart charging stations, which is the cost that the smart charging station should pay to the higher-level power supply facility. To the best of our knowledge, this algorithm used in this paper has not been used to solve this model, and it has better performance than the generalized Nash equilibria (GNE) seeking algorithm originally used for this model, which is called a fast alternating direction multiplier method (Fast-ADMM). In the simulation results, the asynchronous algorithm we used showed a correlation error of 0.0076 at the 713th iteration, compared to 0.0087 for the synchronous algorithm used for comparison, and the cost of the smart charging station was reduced to USD 4800.951 after coordination using the asynchronous algorithm, which was also satisfactory. We used an asynchronous algorithm to better implement a plug-in electric vehicle charging management model; this also demonstrates the potential advantages of using an asynchronous algorithm for solving the charging management model for plug-in electric vehicles.
Self-Starting Characteristics and Flow-Induced Rotation of Single- and Dual-Stage Vertical-Axis Wind Turbines
Authors: Khalid Muhammad Saif Ullah , Wood David , Hemmati Arman
Published: December 2022

Despite offering promising opportunities for wind energy harvesting in urban environments, vertical axis wind turbines face limitations in terms of poor starting characteristics. In this study, we focus on analyzing improvements offered by dual-stage turbines for a range of wind velocities. Numerical simulations are performed for different phase angles between the rotors (a measure of relative angular positions of the blades in the two rotors) to quantify the response time for their starting behavior. These simulations rely on a through sliding mesh technique coupled with flow-induced rotations. We find that for U∞=4m/s, the phase angles of 30∘ and 90∘ substantially reduce starting time in comparison to a single-stage turbine. Dual-stage turbines with a phase angle of 90∘ exhibit similar or better starting behavior for other wind speeds. The phase angle of 0∘ in double-rotor turbines shows the poorest starting response. Moreover, it is revealed that stabilization of shear layers generated by the blades passing through the windward side of the turbine, vortex-entrapment by these rotating blades, and suppressing of flow structures in the middle of the wake enhance the capacity of VAWTs to achieve faster steady angular speed.
Designing Efficient Renewable Energy Portfolios for Optimal Coverage of European Power Demand under Transmission Constraints
Authors: Thomaidis Nikolaos S. , Moukas Alexios-Ioannis
Published: December 2022

In this paper, we consider spatially and technologically diversified portfolios of renewable energy resources (RES) on a European scale. These portfolios efficiently allocate wind and solar production capacity with a view on matching load and restricting conventional generation capacity in each country. We investigate the composition of optimal portfolios taking into account the existing cross-border transmission capacity as well as future expansion scenarios. For the purposes of our analysis, we develop several multi-criteria optimization models and apply them to a rich dataset of daily electricity load, RES capacity factors, and transmission capacity constraints for 32 European countries during the period 2010–2015. By exploring different scenarios for the composition of efficient portfolios and cross-border network connectivity, we quantify the effectiveness of the resource aggregation strategy in terms of reducing the energy production deficit or surplus in each country. Empirical evidence shows that the portfolio perspective provides a solid basis for strategic planning on the future expansion of the European grid.
Demand Response Using Disturbance Estimation-Based Kalman Filtering for the Frequency Control
Authors: Wu Xuehua , Qian Qianqian , Bao Yuqing
Published: December 2022

Demand response (DR) has a great potential for stabilizing the frequency of power systems. However, the performance is limited by the accuracy of the frequency detection, which is affected by measurement disturbances. To overcome this problem, this paper proposes a disturbance estimation-based Kalman filtering method, which is utilized for the frequency control. By using the rate of change of frequency (RoCoF), the Kalman filtering method can estimate the state of the ON/OFF loads well. In this way, the influence of detection error can be reduced, and the DR performance can be improved. Test results show that the proposed disturbance estimation-based Kalman filtering method has a higher accuracy of frequency detection than existing methods (such as the low-pass filter method) and therefore improves the frequency control performance of DR.
Optimal Control of the Diesel–Electric Propulsion in a Ship with PMSM
Authors: Zwierzewicz Zenon , Tarnapowicz Dariusz , German-Galkin Sergey , Jaskiewicz Marek
Published: December 2022

The article presents and compares two different control methods for a permanent magnet synchronous motor (PMSM) for diesel–electric ship propulsion. The main focus of the article is on control optimization, which allows improving energy efficiency by reducing reactive power in the mechatronic propulsion system. The first method consists in modifying the commonly used field-oriented control (FOC) strategy to ensure zero reactive power in the inverter–PMSM system. Since a characteristic of ship propulsion systems, unlike those used on land, is the step load on the propulsion motor, the system’s performance in dynamic states is particularly important. Unfortunately, control strategies based on FOC do not take into account the dynamics of the system, since they apply only to steady states. Therefore, the authors of this paper, based on control theory methods, proposed an approach that also optimizes control in dynamic states, while minimizing reactive power in the steady state. The analytical studies were confirmed in simulation studies using the MATLAB Simulink package.
Antioxidant and Functional Features of Pre-Fermented Ingredients Obtained by the Fermentation of Milling By-Products
Authors: Siroli Lorenzo , Giordani Barbara , Rossi Samantha , Gottardi Davide , McMahon Helena , Augustyniak Aleksandra , Menon Abhay , Vannini Lucia , Vitali Beatrice , Patrignani Francesca , Lanciotti Rosalba
Published: December 2022

The use of milling by-products as ingredients in food formulations has increased gradually over the past years, due to their well-recognized health properties. Fermentation performed with selected microbial strains or microbial consortia is the most promising way to reduce antinutritional factors of cereals and bran, while increasing their nutritional and functional properties. This work, developed within the BBI project INGREEN, was aimed to study the functional, nutritional and technological features of a pre-fermented ingredient obtained from the fermentation of a mixture of rye bran and wheat germ by a selected microbial consortium composed of yeasts (Kazachstania unispora and Kazachstania servazii) and lactic acid bacteria (Latilactobacillus curvatus) using as reference the unfermented mixture and the same mixture fermented by a baker’s yeast. The selected microbial consortium improved the complexity of the volatile molecules such as acids, alcohols and esters. A better retention of color parameters was maintained compared to the product fermented by a baker’s yeast. In addition, the fermentation by the selected consortium showed a significant increase in short chain fatty acids (more than 5-fold), antioxidant activity (22–24%), total phenol content (53–71%), bioactive peptides (39–52%), a reduction of 20–28% in phytic acid content and an increase in prebiotic activity not only compared to the unfermented product but also compared to the preferment obtained with a baker’s yeast. Overall, the fermentation by the selected microbial consortium can be considered a valuable way to valorize milling by-products and promote their exploitation as food ingredients.
Fermented Plant Beverages Stabilized with Microemulsion: Confirmation of Probiotic Properties and Antioxidant Activity
Authors: Merenkova Svetlana , Zinina Oksana , Potoroko Irina
Published: December 2022

The purpose of this study was to experimentally confirm the probiotic properties and antioxidant activity of plant fermented beverages stabilized with microemulsion. The object of the study were beverages obtained from hemp seeds and fermented with Bifidobacterium longum. To stabilize the plant base, the microemulsion with a bioactive substance (curcumin) was introduced with simultaneous ultrasound treatment. A significant increase in the viscosity of beverages with microcellulose-stabilized microemulsion was noted. Non-fermented plant beverages are characterized by their smaller diameter and distribution of particles in the micro-range, from 0.81 to 6.5 µm. When Twin-stabilized microemulsion was added to beverages, a monodisperse distribution of particles sufficiently small in diameter was observed. A significant increase of 29.4–33.6% in the antioxidant activity of plant beverages stabilized by ME with curcumin was established. A maximum concentration of flavonoids was noted in non-fermented plant beverages containing microemulsion. The results of this study proved the possibility of obtaining fermented plant beverages with identified probiotic and antioxidant properties. A positive effect of stabilizing with a microemulsion loaded with biologically active components on the development of probiotic microorganism cultures in the system of fermented plant products and the formation of their antioxidant activity was established.
Analysis of the Genome Architecture of Lacticaseibacillus paracasei UNQLpc 10, a Strain with Oenological Potential as a Malolactic Starter
Authors: Iglesias Nestor Gabriel , Navarro Marina Edith , Brizuela Natalia Soledad , Valdés La Hens Danay , Semorile Liliana Carmen , Tymczyszyn Emma Elizabeth , Bravo Ferrada Bárbara Mercedes
Published: December 2022

The Lacticaseibacillus paracasei UNQLpc 10 strain was isolated from a Malbec wine produced in North Patagonia, Argentina, and identified by 16S rRNA gene sequencing. The aim of this work was to obtain the fully assembled genome of the UNQLpc 10 strain, analyze its structure, and evaluate the possible functions of the predicted genes with regard to its oenological potential as a malolactic starter. UNQLpc10 is the first whole assembled genome of an oenological strain of Lcb. paracasei reported in databases. This information is of great interest inexpanding the knowledge of diversity of oenological lactic acid bacteria and in searching for new candidate species/strains to design starter cultures. The in silico genome-wide analysis of UNQLpc 10 confirms the existence of genes encoding enzymes involved in the synthesis of several metabolites of oenological interest, and proteins related to stress responses. Furthermore, when UNQLpc 10 was incubated in synthetic wine, it exhibited a very good survival and L-malic acid consumption ability.
Disentangling the Drivers of the Sampling Bias of Freshwater Fish across Europe
Authors: Rodríguez-Rey Marta , Grenouillet Gaël
Published: December 2022

The Wallacean shortfall refers to the knowledge gap in biodiversity distributions. There is still limited knowledge for freshwater fish species despite the importance of focusing conservation efforts towards this group due to their alarming extinction risk and the increasing human pressure on freshwater ecosystems. Here, we addressed the Wallacean shortfall for freshwater fish faunas across Europe by using the completeness indicator derived from species accumulation curves to quantify the fish sampling efforts. The multiple potential drivers of completeness that were previously related to the sampling efforts for other species (i.e., population density, nature reserves, or distance to cities) were tested using a 10 × 10 km2 grid resolution, as well as environmental (e.g., climatic) factors. Our results suggested that although there was an overall spatial pattern at the European level, the completeness was highly country-dependent. Accessibility parameters explained the sampling efforts, as for other taxa. Likewise, climate factors were related to survey completeness, possibly pointing to the river conditions required for fish sampling. The survey effort map we provide can be used to optimize future sampling, aiming at filling the data gaps in undersampled regions like the eastern European countries, as well as to account for the current bias in any ecological modeling using such data, with important implications for conservation and management.
Six-Field Theory for a Polyatomic Gas Mixture: Extended Thermodynamics and Kinetic Models
Authors: Pavić-Čolić Milana , Simić Srboljub
Published: December 2022

Polyatomic gases may be characterized by internal molecular degrees of freedom. As a consequence, at a macroscopic level, dynamic pressure appears, which may be related to the bulk viscosity of the gas. Inspired by the models of a single polyatomic gas with six fields, developed within rational extended thermodynamics (RET) and the kinetic theory of gases, this paper presents a six-field theory for the mixture of polyatomic gases. First, the macroscopic mixture model is developed within the framework of RET. Second, the mixture of gases with six fields is analyzed in the context of the kinetic theory of gases, and corresponding moment equations are derived. Finally, complete closure of the RET model, i.e., computation of the phenomenological coefficients, is achieved by means of a combined macroscopic/kinetic closure procedure.
Untargeted Profiling and Differentiation of Volatiles in Varieties of Meat Using GC Orbitrap MS
Authors: Yang Youyou , Li Jing , Xing Jiangtao , Xing Weihai , Tang Chaohua , Rao Zhenghua , Zhang Junmin
Published: December 2022

Volatile compounds play vital roles in food sensory attributes and food quality. An analysis of volatile compounds could illustrate the sensory attributes at the microscale level. Here, untargeted profiling approaches for volatiles in five most-consumed meat species were established using headspace SPME-GC/high resolution Orbitrap MS. An extended high-resolution database of meat volatile compounds was established to enhance the qualification accuracy. Using sulfur-containing compounds, aldehydes, and ketones as the research model, the parameters including fiber coating types, extraction temperature, extraction time, and desorbing time were optimized. Principle component analysis, volcano analysis and partial least squares discriminant analysis were applied to run the classification and the selection of discriminant markers between meat varieties, respectively. Different varieties could be largely distinguished according to the volatiles’ profiles. 1-Octen-3-ol, 1-octen-3-one, 2-pentyl furan and some other furans degraded from n-6 fatty acids would contribute to distinguishing duck meat from other categories, while methyl esters mainly from oleic acid as well as dimethyl sulfoxide and carbon disulfide possibly produced from the sulfur-containing amino acids contributed to the discrimination of beef. Therefore, volatiles’ profiling not only could interpret the aroma style in meat but also could be another promising method for meat differentiation and authentication.
Spent Yeast Valorization for Food Applications: Effect of Different Extraction Methodologies
Authors: Oliveira Ana Sofia , Odila Pereira Joana , Ferreira Carlos , Faustino Margarida , Durão Joana , Pereira Ana Margarida , Oliveira Carla Maria , Pintado Manuela E. , Carvalho Ana P.
Published: December 2022

Over the years, synthetic biology has been growing with the use of engineered yeast strains for the production of sustainable ingredients to meet global healthcare, agriculture, manufacturing and environmental challenges. However, as seen from the brewing industry perspective, these processes generate a substantial amount of spent yeast that contains high nutritional value related to its high protein content, showing its potential to be used as an alternative protein source. Taking into account the rising demand for protein because of the growth in the global population, the present study aims to produce peptide-rich extracts by different potentially scalable and sustainable methodologies in a circular economy approach for the food and nutraceutical industries. The results demonstrated that extraction from genetically modified strains allowed the production of extracts with an excellent nutritional profile and low molecular weight peptides. Furthermore, autolysis was shown to be a potential sustainable approach for this production, though other green metrics need to be explored in order to establish this process at an industrial level.
Modified Atmosphere Packaging and 1-Methylcyclopropene Treatments Maintain the Fruit Quality of ‘Wonmi’ Persimmons during Export Simulation
Authors: Kumarihami H. M. Prathibhani C. , Shin Mi Hee , Jang Kyeong Eun , Kim Yun-Hee , Ma Kyeong Bok , Kim Jin Gook
Published: December 2022

Persimmons are one of the most important export fruits in South Korea, where several tons are exported across the globe each year. In this study, the quality attributes of ‘Wonmi’ persimmon fruits were evaluated during an export simulation at 0 °C, 10 °C, and 24 °C with a combination of 1-Methylcyclopropene (1-MCP) and modified atmosphere packaging (MAP) treatments. The relative humidity during the export simulation was greater at room temperature (75–92%) and 0 °C (85% to 93%) than at 10 °C (42% to 60%). The results show that the application of 1-MCP and MAP treatments during the export simulation were effective in delaying the ripening of ‘Wonmi’ persimmons by reducing respiration and ethylene production. The suppressed expression of ethylene synthesis genes and cell wall modification genes reduced the ethylene production and maintain the fruit firmness, respectively. In addition, 1-MCP and MAP treatments were effective in maintaining SSC and color of ‘Wonmi’ persimmon fruits during the export simulation. Thus, by adopting these treatments, the overall quality of persimmon exports from South Korea can significantly improve.
Characterization, Sensory and Oxidative Stability Analysis of Vegetable Mayonnaise Formulated with Olive Leaf Vinegar as an Active Ingredient
Authors: De Leonardis Antonella , Macciola Vincenzo , Iftikhar Ayesha , Lopez Francesco
Published: December 2022

Development of novel food products represents a basic meeting point for health and business requirements. Mayonnaise sauce is well-suited to be a healthy and tasty dressing. In this study, mayonnaise was formulated by using unconventional ingredients, such as olive leaf vinegar (OLV), soybean/high oleic sunflower oil blend, and soymilk (as an egg substitute). An 18% alcoholic vinegar was used as the control sample. OLV is a rich source of bioactive substances, especially polyphenols and represents a possible way to enhance the olive oil by-product valorisation. For this new typology of vinegar an high level of phenolic compounds (7.2 mg/mL GAE), especially oleuropein (6.0 mg/mL oleuropein equivalent) was found. OLV mayonnaise had 57% fat, composed of 11%, 64%, and 23% saturated, monounsaturated, and polyunsaturated fatty acids, while linolenic acid was up to 1.7%. The phenol and oleuropein contents were 68 and 52 mg/100 g, respectively. Sensory panellists expressed a moderate overall acceptability for both samples but attested more distinctive and positive sensations for the colour, odour, and taste attributes of OLV mayonnaise. Finally, oxidative stability and shelf life were better in OLV mayonnaise than in the control. Specifically, the peroxide value remained low (around 4.5 meqO2/kg) after 12 months of storage at room and low (4 °C) temperatures.
Sustainable Future Protein Foods: The Challenges and the Future of Cultivated Meat
Authors: Chodkowska Karolina A. , Wódz Karolina , Wojciechowski Jakub
Published: December 2022

Global pressure from consumers to improve animal welfare, and reduce microbiological risks or the use of antibiotics pose new challenges for the meat industry. Today’s livestock production, despite many undertaken measures, is still far from being sustainable. This forced the need to work on alternative protein types that come from plants, insects, fungi, or cell culture processes. Due to some technical and legal barriers, cultivated meat is not present on the European market, however, in 2020 it was approved in Singapore and in 2022 in the USA. While the technology of obtaining cell cultures from animal muscles has been known and successfully practiced for years, the production of a stable piece of meat with appropriate texture, taste, and smell, is still a problem for several scientific groups related to subsequent companies trying to obtain the highest quality product, in line with the expectations of customers. Although the work on optimal cell meat production has been going on for years, it is still in an early stage, mainly due to several limitations that represent milestones for industrial production. The most important are: the culture media (without animal serum), which will provide an environment for optimal muscle development, natural or close to natural (but still safe for the consumer) stable scaffolds for growing cells. Here, we review the actual knowledge about the above-mentioned challenges which make the production of cellular meat not yet developed on an industrial scale.
Herglotz Variational Problems Involving Distributed-Order Fractional Derivatives with Arbitrary Smooth Kernels
Authors: Cruz Fátima , Almeida Ricardo , Martins Natália
Published: December 2022

In this paper, we consider Herglotz-type variational problems dealing with fractional derivatives of distributed-order with respect to another function. We prove necessary optimality conditions for the Herglotz fractional variational problem with and without time delay, with higher-order derivatives, and with several independent variables. Since the Herglotz-type variational problem is a generalization of the classical variational problem, our main results generalize several results from the fractional calculus of variations. To illustrate the theoretical developments included in this paper, we provide some examples.
Estimation and Testing of Random Effects Semiparametric Regression Model with Separable Space-Time Filters
Authors: Li Shuangshuang , Chen Jianbao , Li Bogui
Published: December 2022

This paper focuses on studying a random effects semiparametric regression model (RESPRM) with separable space-time filters. The model cannot only capture the linearity and nonlinearity existing in a space-time dataset, but also avoid the inefficient estimators caused by ignoring spatial correlation and serial correlation in the error term of a space-time data regression model. Its profile quasi-maximum likelihood estimators (PQMLE) for parameters and nonparametric functions, and a generalized F-test statistic for checking the existence of nonlinear relationships are constructed. The asymptotic properties of estimators and asymptotic distribution of test statistic are derived. Monte Carlo simulations imply that our estimators and test statistic have good finite sample performance. The Indonesian rice farming data are used to illustrate our methods.
Does Supramolecular Gelation Require an External Trigger?
Authors: Van Lommel Ruben , Van Hooste Julie , Vandaele Johannes , Steurs Gert , Van der Donck Tom , De Proft Frank , Rocha Susana , Sakellariou Dimitrios , Alonso Mercedes , De Borggraeve Wim M.
Published: December 2022

The supramolecular gelation of small molecules is typically preceded by an external stimulus to trigger the self-assembly. The need for this trigger stems from the metastable nature of most supramolecular gels and can limit their applicability. Herein, we present a small urea-based molecule that spontaneously forms a stable hydrogel by simple mixing without the addition of an external trigger. Single particle tracking experiments and observations made from scanning electron microscopy indicated that triggerless gelation occurred in a similar fashion as the archetypical heat-triggered gelation. These results could stimulate the search for other supramolecular hydrogels that can be obtained by simple mixing. Furthermore, the mechanism of the heat-triggered supramolecular gelation was elucidated by a combination of molecular dynamics simulations and quantitative NMR experiments. Surprisingly, hydrogelation seemingly occurs via a stepwise self-assembly in which spherical nanoparticles mature into an entangled fibrillary network.
Development of Chincho (Tagetes elliptica Sm.) Essential Oil Organogel Nanoparticles through Ionic Gelation and Process Optimization with Box–Behnken Design
Authors: Cerrón-Mercado Francis , Salva-Ruíz Bettit K. , Nolazco-Cama Diana , Espinoza-Silva Clara , Fernández-López Juana , Pérez-Alvarez Jose A. , Viuda-Martos Manuel
Published: December 2022

The aim of this work was to obtain chitosan nanoparticles (<1000 nm) with chincho (Tagetes elliptica Sp.) essential oil (CEO-CSNPs) using the ionic gelation method. A Box–Behnken design (BBD) was applied, using chitosan solution (CS) pH (4.0, 4.4, 4.8); the mass ratio of CS/CEO (1:0.7, 1:0.85, 1:1.0) and the mass ratio of CS/CS-tripolyphosphate (1:0.46, 1:0.58, 1:0.7) as independent variables. The formulation-dependent variables, encapsulation efficiency (EE) and loading capacity (LC) of the CEO-CSNPs were evaluated. BBD determined that optimal conditions for CEO-CSNPs were pH: 4.4, CS/CEO mass ratio 1:0.7 and CS/TPP mass ratio 1:0.46. Once the optimization was defined, particle size (PS), zeta potential (ZP), polydispersity index (PDI), CEO-CSNPs morphological studies, in vitro CEO release, and antibacterial activity were determined. The CEO-CSNPs showed an EE of 52.64% and a LC of 11.56%, with a diameter of 458.5 nm, with a ZP of 23.30mV, and a PDI of 0.418. The SEM studies showed that the nanoparticles were rounded and had uniform shapes. In addition, CEO-CSNPs showed a minimum inhibitory concentration against Staphylococcus aureus, Salmonella infantis and Escherichia coli of 5.29, 10.57 and 10.57 µg/mL, respectively. These results could be very useful for the stabilization of chincho essential oil for food industry purposes. However, several studies about the release, as well as interaction with food matrices, will be necessary.
Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model
Authors: Kashyap Ramgopal , Nair Rajit , Gangadharan Syam Machinathu Parambil , Botto-Tobar Miguel , Farooq Saadia , Rizwan Ali
Published: December 2022

Glaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that are still building enough healthcare infrastructure to cope with glaucoma, the ailment is misdiagnosed nine times out of ten. To aid in the early diagnosis of glaucoma, the creation of a detection system is necessary. In this work, the researchers propose using a technology known as deep learning to identify and predict glaucoma before symptoms appear. The glaucoma dataset is used in this deep learning algorithm that has been proposed for analyzing glaucoma images. To get the required results when using deep learning principles for the job of segmenting the optic cup, pretrained transfer learning models are integrated with the U-Net architecture. For feature extraction, the DenseNet-201 deep convolution neural network (DCNN) is used. The DCNN approach is used to determine whether a person has glaucoma. The fundamental goal of this line of research is to recognize glaucoma in retinal fundus images, which will aid in assessing whether a patient has the condition. Because glaucoma can affect the model in both positive and negative ways, the model’s outcome might be either positive or negative. Accuracy, precision, recall, specificity, the F-measure, and the F-score are some of the metrics used in the model evaluation process. An extra comparison study is performed as part of the process of establishing whether the suggested model is accurate. The findings are compared to convolution neural network classification methods based on deep learning. When used for training, the suggested model has an accuracy of 98.82 percent and an accuracy of 96.90 percent when used for testing. All assessments show that the new paradigm that has been proposed is more successful than the one that is currently in use.
Dietary Antioxidants and Lung Cancer Risk in Smokers and Non-Smokers
Authors: Alsharairi Naser A.
Published: December 2022

Smoking is considered a major risk factor in the development of lung diseases worldwide. Active smoking and secondhand (passive) smoke (SHS) are related to lung cancer (LC) risk. Oxidative stress (OS) and/or lipid peroxidation (LP) induced by cigarette smoke (CS) are found to be involved in the pathogenesis of LC. Meta-analyses and other case-control/prospective cohort studies are inconclusive and have yielded inconsistent results concerning the protective role of dietary vitamins C and E, retinol, and iron intake against LC risk in smokers and/or non-smokers. Furthermore, the role of vitamins and minerals as antioxidants with the potential in protecting LC cells against CS-induced OS in smokers and non-smokers has not been fully elucidated. Thus, this review aims to summarize the available evidence reporting the relationships between dietary antioxidant intake and LC risk in smokers and non-smokers that may be used to provide suggestions for future research.
Nursing Diagnoses of Individuals with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Research Protocol for a Qualitative Synthesis
Authors: Oter-Quintana Cristina , Esteban-Hernández Jesús , Cuéllar-Pompa Leticia , Gil-Carballo María Candelas , Brito-Brito Pedro Ruymán , Martín-García Angel , Alcolea-Cosín María Teresa , Martínez-Marcos Mercedes , Alameda-Cuesta Almudena
Published: December 2022

Although previously developed qualitative studies have explored the experience of illness of individuals with myalgic encephalomyelitis/chronic fatigue syndrome, these findings have not been undertaken for the purpose of enabling the identification of nursing care needs in such patients. This study aims to identify NANDA-I nursing diagnoses of adults with myalgic encephalomyelitis/chronic fatigue syndrome based on a qualitative literature review of their experience of illness. The protocol includes: searches in the electronic databases Medline, Embase, CINAHL, PsycINFO, SCI-EXPANDED, SSCI, SciELO, LILACS, and Cuiden; and manual searches in specialised journals and the references of the included studies. The authors will systematically search qualitative research studies published in databases from 1994 to 2021. Searches are limited to studies in Spanish and English. All stages of the review process will be carried out independently by two reviewers. Any disagreements shall be resolved through joint discussions, involving a third reviewer if necessary. The findings will be synthesised into a thematic analysis informed by the Domains and Classes of the NANDA-I Classification of Nursing Diagnoses, which will then serve to identify nursing diagnoses. This review will enable nursing professionals to identify the care needs of individuals with myalgic encephalomyelitis/chronic fatigue syndrome by taking into consideration their experience of illness in its entirety.
Association between Nutrition Literacy and Bangladeshi Adults’ Healthy Eating Behaviors: Evidence from the Nutrition Literacy Study 2021
Authors: Al Banna Md. Hasan , Hamiduzzaman Mohammad , Kundu Satyajit , Sultana Mst. Sadia , Seidu Abdul-Aziz , Brazendale Keith , Abid Mohammad Tazrian , Ara Tasnu , Rifat M. A. , Mozumder N. H. M. Rubel , Hagan John Elvis , Khan Md Shafiqul Islam , Schack Thomas
Published: December 2022

This study investigated the association between healthy eating behaviors and nutrition literacy in a sample of Bangladeshi adults. A cross-sectional survey was conducted among 400 adults from two districts of Bangladesh (Dhaka and Chattogram). Data were generated by in-person interviews using a structured questionnaire. The Nutrition Literacy Scale and National Dietary Guidelines for Bangladesh were used to assess nutrition literacy and healthy eating behaviors, respectively. Multiple linear regression models were used to observe the association. The mean score for healthy eating behavior was 21.8 (SD = 4.8, Range: 5–33) on a scale of 34. A moderate positive correlation was found between nutrition literacy and healthy eating behavior of participants (r = 0.28, p < 0.001). The adjusted regression model showed that a 1 unit increase in nutrition literacy reflected an increase in the healthy eating behavior score of participants by 0.22 units (β = 0.223, p < 0.001). Findings showed an association between nutrition literacy and eating behaviors in Bangladeshi adults. Future research could be carried out to establish a causal relationship that may help inform the necessity of educational interventions for Bangladeshi adults to assist with meeting national nutrition-related targets.
Clinical Diagnosis and Early Medical Management for Endometriosis: Consensus from Asian Expert Group
Authors: Kim Mee-Ran , Chapron Charles , Römer Thomas , Aguilar Angela , Chalermchockcharoenkit Amphan , Chatterjee Siddharta , Dao Le Thi Anh , Fong Yoke Fai , Hendarto Hendy , Hidayat Syarief Taufik , Khong Su Yen , Ma Li , Kumar Pratap , Primariawan Relly Yanuari , Siow Anthony , Sophonsritsuk Areepan , Devi Thirunavukarasu Ramani , Thuong Bui Chi , Yen Chih-Feng
Published: December 2022

This work provides consensus guidance regarding clinical diagnosis and early medical management of endometriosis within Asia. Clinicians with expertise in endometriosis critically evaluated available evidence on clinical diagnosis and early medical management and their applicability to current clinical practices. Clinical diagnosis should focus on symptom recognition, which can be presumed to be endometriosis without laparoscopic confirmation. Transvaginal sonography can be appropriate for diagnosing pelvic endometriosis in select patients. For early empiric treatment, management of women with clinical presentation suggestive of endometriosis should be individualized and consider presentation and therapeutic need. Medical treatment is recommended to reduce endometriosis-associated pelvic pain for patients with no immediate pregnancy desires. Hormonal treatment can be considered for pelvic pain with a clinical endometriosis diagnosis; progestins are a first-line management option for early medical treatment, with oral progestin-based therapies generally a better option compared with combined oral contraceptives because of their safety profile. Dienogest can be used long-term if needed and a larger evidence base supports dienogest use compared with gonadotropin-releasing hormone agonists (GnRHa) as first-line medical therapy. GnRHa may be considered for first-line therapy in some specific situations or as short-term therapy before dienogest and non-steroidal anti-inflammatory drugs as add-on therapy for endometriosis-associated pelvic pain.
Varieties of Lettuce Forming Distinct Microbial Communities Inhabiting Roots and Rhizospheres with Various Responses to Osmotic Stress
Authors: Žiarovská Jana , Urbanová Lucia , Moravčíková Dagmar , Artimová Renata , Omelka Radoslav , Medo Juraj
Published: December 2022

A plant microbiome is an important factor in plant growth, stress resistance, health status, and consumer quality and safety. The rhizosphere microbiome evolves in a negotiation between microbial communities that inhabit soil and plant root tissue. In this study, the rhizosphere and root internal tissue microbiome of six varieties of lettuce were analyzed in normal conditions and under salinity stress. The metabarcoding analysis used 16S rRNA gene and ITS2 region sequencing. The microbiomes of root samples were significantly less diverse with different members of the community compared to those of the rhizosphere. A significant effect of lettuce variety was found on the diversity index for bacteria and fungi. Varieties formed very different communities of bacteria in roots. Pseudomonas, Herbaspirillum, Mycobacterium, potentially pathogenic Enterobacter, and other genera were more prevalent in certain varieties. Salinity stress had a significant negative impact on bacterial diversity and community composition, whereas the diversity of fungi has not changed significantly, and the fungal community has changed less than the bacterial one. Changes were more evident in varieties that were more resistant to salinity stress than in sensitive varieties.