bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Scientific Quality and Usability of Digital Dietary Assessment Tools
Authors: Böse Vanessa , Frenser Marius , Schumacher Melanie , Fischer Tobias
Published: May 2024

The importance of digital tools for dietary assessment has increased in recent years, both commercially and scientifically. In the field of nutrition research, the digitization of dietary assessment methods presents many opportunities and risks. One of the main challenges is ensuring scientific quality while maintaining good usability. In this context, an evaluation tool was developed based on the guidelines of the European Food Safety Authorization (EFSA; 2009 and 2014), complemented by the usability aspect of health-related applications. This was followed by a literature search concerning the available dietary assessment tools, which were analyzed according to the evaluation criteria. Eight applications were included in the study after reviewing the inclusion and exclusion criteria for the digital tools. A total of thirty-eight requirements in eight main categories were defined for the evaluation, which the best possible dietary assessment tool should meet. The evaluation showed that none of the tested tools currently meet all the defined requirements or categories. The aspects of usability and the accuracy of data collection showed a positive correlation, suggesting a direct link between the two categories and providing an important approach for future developments.
FASD: The Living Experience of People with Fetal Alcohol Spectrum Disorder—Results of an Anonymous Survey
Authors: Hargrove Emily , Lutke C. J. , Griffin Katrina , Himmelreich Myles , Mitchell Justin , Lutke Anique , Choate Peter
Published: May 2024

Fetal Alcohol Spectrum Disorder (FASD) is considered a lifelong disability that has been framed with neurobiological descriptions focused on the brain. These are important features but fail to tell the story of living with FASD. By surveying those with FASD, this work expanded upon prior survey work which illustrated a multitude of early-onset physiological issues occurring at rates much higher than is typical of the general population. The current project, again using an anonymous survey methodology, sought to open up other direct experiences to better understand the complexity of living with FASD. An anonymous online survey was used to gather data on adversity in childhood, schooling, employment, housing and finances, and involvement with the criminal justice system, as well as relationships and parenting. Results indicate high levels of adversity throughout the life span; vulnerability to manipulation, which is connected to involvement in the criminal justice system; struggles with housing; economic instability; and struggles maintaining employment, as well as difficulties with social and familial relationships. Systemic stigma was also identified. Suggestions are offered to inform others on how support can be enhanced and targeted with a goal of improving quality of life, as well as dealing with self-imposed stigma. The survey was developed by adults living with FASD who have served as a long-standing advocacy and educational group influencing policy and practice in the field.
Designing Holistic and Multivoiced Online Learning: Higher Education Actors’ Pedagogical Decisions and Perspectives
Authors: Papageorgiou Vasiliki , Meyer Edgar , Ntonia Iro
Published: May 2024

Higher education has witnessed continuous growth in online learning, further catalysed by the COVID-19 pandemic. Moving forward, it is important to transition from remote teaching to sustainable, high-quality and mature online learning practices for impactful student learning. This paper presents the findings of a qualitative multiple case study research that investigated the pedagogical decisions and rationales of educators and digital learning professionals in deliberately designed online learning contexts. Data were collected through 31 interviews, observations and documents from seven interdisciplinary design teams across six UK universities over an extended period. Three themes were constructed to convey key research insights including: (1) embracing a multi-level view of student learning journeys, (2) embedding multiple and diverse ‘voices’ and (3) creating a complex web of social learning opportunities and ‘spaces’. The findings from this study offer a revitalised understanding of pedagogies suggesting holistic and multivoiced approaches to online learning. Findings pointed to the need for narrative-based approaches to online learning design, attention to purposeful hybrid learning spaces and an expansive view of educators’ role. The insights presented in this paper can be enlightening for educators, teaching teams, digital learning teams, academic developers, researchers and university leadership, opening up dialogue and new directions for online learning practices and research.
Examining the Influence of Secondary Math and Science Teacher Preparation Programs on Graduates’ Instructional Quality and Persistence in Teaching
Authors: Rhemer Danielle Marie , Rogers Will , Southerland Sherry Ann
Published: May 2024

This quantitative, non-experimental study explored the relationship between the features of math and science teachers’ preparation programs and their graduates’ instructional rigor and persistence in teaching. Five math and science teacher preparation programs from across the United States were examined. Six sets of instructional tasks were collected from forty-six recent graduates of these programs to provide insights into novices’ instructional rigor, and employment data were collected for thirty-seven of these graduates three to eight years after graduation. Regardless of the program’s features, all teachers could design and implement instruction with moderate to high rigor. However, this ability was not the norm. Mixed-effect models suggest the strongest evidence between degree types (bachelor versus post-bachelor) was related to teachers’ persistence: novices from graduate programs were more likely to persist in the work. However, no program feature was strongly associated with instructional rigor. Further research is needed to determine if the differences we found in teacher persistence are due to the nature of applicants drawn to particular programs (undergraduate versus graduate) or the program’s structure. Future research is also needed to explore the influence of instructional context (i.e., district, school, and department norms for instruction) on math and science teachers’ instructional rigor.
Caregiver Burden and Family Quality of Life in Early Intervention: The Role of Mothers and Family Confidence
Authors: García-Grau Pau , Martínez-Rico Gabriel , González-García Rómulo J. , Escorcia-Mora Claudia Tatiana , Cañadas-Pérez Margarita
Published: May 2024

(1) Background: Because life events when there is a family member with a disability can affect the overall family wellbeing, contributing to enhance family quality of life (FQoL) in the field of early childhood intervention has become a priority. However, it is a distal outcome that needs other short-term outcomes to be addressed, some of them under the potential impact of support services. This study examines the relationships between caregiver burden, family confidence, and FQoL, as well as the influence of child and family variables. (2) Method: A total of 58 families with children in early intervention from four Spanish communities participated. Hierarchical regression was conducted to assess the relevance of each predictor. Also, a mediation was performed to investigate the mediating role of family confidence. (3) Results: The family income impacted FQoL scores, and when burden and confidence were added, it was no longer relevant. Mothers with higher levels of confidence predicted a higher FQoL. Finally, we found a complete mediation of family confidence in the relations between severity and caregiver burden on FQoL. (4) Conclusions: Caregiver burden and family confidence affect FQoL. Building families’ confidence contributes to attenuating the impact of burden on FQoL.
Automatic Evaluation Method for Functional Movement Screening Based on Multi-Scale Lightweight 3D Convolution and an Encoder–Decoder
Authors: Lin Xiuchun , Liu Yichao , Feng Chen , Chen Zhide , Yang Xu , Cui Hui
Published: May 2024

Functional Movement Screening (FMS) is a test used to evaluate fundamental movement patterns in the human body and identify functional limitations. However, the challenge of carrying out an automated assessment of FMS is that complex human movements are difficult to model accurately and efficiently. To address this challenge, this paper proposes an automatic evaluation method for FMS based on a multi-scale lightweight 3D convolution encoder–decoder (ML3D-ED) architecture. This method adopts a self-built multi-scale lightweight 3D convolution architecture to extract features from videos. The extracted features are then processed using an encoder–decoder architecture and probabilistic integration technique to effectively predict the final score distribution. This architecture, compared with the traditional Two-Stream Inflated 3D ConvNet (I3D) network, offers a better performance and accuracy in capturing advanced human movement features in temporal and spatial dimensions. Specifically, the ML3D-ED backbone network reduces the number of parameters by 59.5% and the computational cost by 77.7% when compared to I3D. Experiments have shown that ML3D-ED achieves an accuracy of 93.33% on public datasets, demonstrating an improvement of approximately 9% over the best existing method. This outcome demonstrates the effectiveness of and advancements made by the ML3D-ED architecture and probabilistic integration technique in extracting advanced human movement features and evaluating functional movements.
Current-Prediction-Controlled Quasi-Z-Source Cascaded Multilevel Photovoltaic Inverter
Authors: Lei Shanshan , Jin Ningzhi , Jiang Jiaxin
Published: May 2024

To address problems that traditional two-stage inverters suffer such as high cost, low efficiency, and complex control, this study adopts a quasi-Z-source cascaded multilevel inverter. Firstly, the quasi-Z-source inverter utilizes a unique impedance network to achieve single-stage boost and inversion without requiring a dead zone setting. Additionally, its cascaded multilevel structure enables independent control of each power unit structure without capacitor voltage sharing problems. Secondly, this study proposes a current-predictive control strategy to reduce current harmonics on the grid side. Moreover, the feedback model of current and system state is established, and the fast control of grid-connected current is realized with the deadbeat control weighted by the predicted current deviation. And a grid-side inductance parameter identification is added to improve control accuracy. Also, an improved multi-carrier phase-shifted sinusoidal PWM method is adopted to address the issue of switching frequency doubling, which is caused by the shoot-through zero vector in quasi-Z-source inverters. Finally, the problems of switching frequency doubling and high harmonics on the grid side are solved by the improved deadbeat control strategy with an improved MPSPWM method. And a seven-level simulation model is built in MATLAB (2022b) to verify the correctness and superiority of the above theory.
Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks
Authors: Dang Xuan-Toan , Shin Oh-Soon
Published: May 2024

Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.
Engineering, Emulators, Digital Twins, and Performance Engineering
Authors: Kenett Ron S.
Published: May 2024

Developments in digital twins are driven by the availability of sensor technologies, big data, first principles knowledge, and advanced analytics. In this paper, we discuss these changes at a conceptual level, presenting a shift from nominal engineering, aiming at design optimisation, to performance engineering, aiming at adaptable monitoring diagnostic, prognostic, and prescriptive capabilities. A key element introduced here is the role of emulators in this transformation. Emulators, also called surrogate models or metamodels, provide monitoring and diagnostic capabilities. In particular, we focus on an optimisation goal combining optimised and robust performance derived from stochastic emulators. We demonstrate the methodology using two open-source examples and show how emulators can be used to complement finite element and computational fluid dynamic models in digital twin frameworks. The case studies consist of a mechanical system and a biological production process.
Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis
Authors: Hachache Rachida , Labrahmi Mourad , Grilo António , Chaoub Abdelaali , Bennani Rachid , Tamtaoui Ahmed , Lakssir Brahim
Published: May 2024

Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy.
A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed
Authors: Didier Flavie , Liu Yong-Chao , Laghrouche Salah , Depernet Daniel
Published: May 2024

This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field.
MnO/Mn2O3 Aerogels as Effective Materials for Supercapacitor Applications
Authors: Ramkumar Ramya , Suganthi Sanjeevamuthu , Milton Ahamed , Park Jungbin , Shim Jae-Jin , Oh Tae Hwan , Kim Woo Kyoung
Published: May 2024

Mixed-oxide transition-metal aerogels (AGLs), particularly manganese-based AGLs, have attracted considerable interest over the past decade owing to their extraordinary properties, including high porosity, good surface area, and ultralow density. To develop easy and lightweight materials for the ever-increasing energy storage demands of the near future, we designed a novel Mn-based electrode material to meet these rising requirements. MnO/Mn2O3 AGLs were synthesized using a novel borohydride hydrolysis method and then annealed at 200, 400, and 550 °C. The as-synthesized AGLs yielded flower-like network structures, but their porosity increased with increasing temperatures, to a high temperature of 400 °C. This increased porosity and network structure facilitate a high capacitance. A supercapacitor (SC) constructed with the three-electrode material yielded 230 F/g for the MnAGL@400 sample, followed by yields from the MnAGL@200 and MnAGL@550 electrodes. Furthermore, the device constructed with MnAGL@400 exhibited an energy density of 9.8 Wh/kg and a power density of ~16,500 W/kg at a current density of 20 A/g. The real-time applicability of the AGL was demonstrated by engineering a two-electrode device employing MnAGL@400 as the positive electrode, which exhibited 97% capacity retention and 109% Coulombic efficiency over 20,000 cycles.
Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques
Authors: Akbulut Osman , Cavus Muhammed , Cengiz Mehmet , Allahham Adib , Giaouris Damian , Forshaw Matthew
Published: May 2024

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.
Performance Analysis Based on Fuel Valve Train Control Optimization of Ammonia-Fuel Ships
Authors: Seungtaek Lim , Hosaeng Lee , Youngkyun Seo
Published: May 2024

In order to reduce carbon emissions, which are currently a problem in the shipping and offshore plant sectors, the international community is strengthening regulations such as the Energy Efficiency Design Index (EEDI) and Energy Efficiency Existing Ship Index (EEXI). To cope with this, eco-friendly fuel propulsion technology is being developed, and the development of an ammonia fuel supply system is in progress. Among them, fuel valve train (FVT) technology was researched for the final supply and cutoff of fuel and purging through nitrogen for ammonia engines. In this paper, we analyzed the change in ammonia supply due to FVT opening and the change in nitrogen supply due to closure. In addition, a plan to minimize risk factors was presented by applying a control method to remove residual fuel in FVT. According to the presented FVT model, the difference in the flow rate of supplied fuel was as much as 17.8 kg/s. Additionally, by opening the gas bleed valve at intervals during the closing process and purging about 0.28 kg of nitrogen, the internal fuel could be completely discharged. This is expected to have an impact on improving the marine environment through the application of eco-friendly fuels and the development of fuel supply system technology.
Advancing Smart Lithium-Ion Batteries: A Review on Multi-Physical Sensing Technologies for Lithium-Ion Batteries
Authors: Wang Wenwei , Liu Shuaibang , Ma Xiao-Ying , Jiang Jiuchun , Yang Xiao-Guang
Published: May 2024

Traditional battery management systems (BMS) encounter significant challenges, including low precision in predicting battery states and complexities in managing batteries, primarily due to the scarcity of collected signals. The advancement towards a “smart battery”, equipped with diverse sensor types, promises to mitigate these issues. This review highlights the latest developments in smart sensing technologies for batteries, encompassing electrical, thermal, mechanical, acoustic, and gas sensors. Specifically, we address how these different signals are perceived and how these varied signals could enhance our comprehension of battery aging, failure, and thermal runaway mechanisms, contributing to the creation of BMS that are safer and more reliable. Moreover, we analyze the limitations and challenges faced by different sensor applications and discuss the advantages and disadvantages of each sensing technology. Conclusively, we present a perspective on overcoming future hurdles in smart battery development, focusing on appropriate sensor design, optimized integration processes, efficient signal transmission, and advanced management systems.
Automatic Reconstruction of 3D Models from 2D Drawings: A State-of-the-Art Review
Authors: Feist Sofia , Jacques de Sousa Luís , Sanhudo Luís , Poças Martins João
Published: May 2024

Among the methods of 3D reconstruction, the automatic generation of 3D models from building documentation is one of the most accessible and inexpensive. For 30 years, researchers have proposed multiple methods to automatically generate 3D models from 2D drawings. This study compiles this research and discusses the different methods used to generate 3D models from 2D drawings. It offers a critical review of these methods, focusing on the coverage and completeness of the reconstruction process. This review allows us to identify the research gaps in the literature, and opportunities for improvement are identified for future research.
MART3D: A Multilayer Heterogeneous 3D Radiative Transfer Framework for Characterizing Forest Disturbances
Authors: Ouyang Lingjing , Qi Jianbo , Wang Qiao , Jia Kun , Cao Biao , Zhao Wenzhi
Published: May 2024

The utilization of radiative transfer models for interpreting remotely sensed data to evaluate forest disturbances is a cost-effective approach. However, the current radiative transfer modeling approaches are either too abstract (e.g., 1D models) or too complex (detailed 3D models). This study introduces a novel multilayer heterogeneous 3D radiative transfer framework with medium complexity, termed MART3D, for characterizing forest disturbances. MART3D generates 3D canopy structures accounting for the within-crown clumping by clustering leaves, which is modeled as a turbid medium, around branches, applicable for forests of medium complexity, such as temperate forests. It then automatically generates a multilayer forest with grass, shrub and several layers of trees using statistical parameters, such as the leaf area index and fraction of canopy cover. By employing the ray-tracing module within the well-established LargE-Scale remote sensing data and image Simulation model (LESS) as the computation backend, MART3D achieves a high accuracy (RMSE = 0.0022 and 0.018 for red and Near-Infrared bands) in terms of the bidirectional reflectance factor (BRF) over two RAMI forest scenes, even though the individual structures of MART3D are generated solely from statistical parameters. Furthermore, we demonstrated the versatility and user-friendliness of MART3D by evaluating the band selection strategy for computing the normalized burn ratio (NBR) to assess the composite burn index over a forest fire scene. The proposed MART3D is a flexible and easy-to-use tool for studying the remote sensing response under varying vegetation conditions.
Correction: Zhang et al. A Forest Fire Prediction Method for Lightning Stroke Based on Remote Sensing Data. Forests 2024, 15, 647
Authors: Zhang Zhejia , Tian Ye , Wang Guangyu , Zheng Change , Zhao Fengjun
Published: May 2024

There was an error in the original publication [...]
Hydrological Variability in the El Cielo Biosphere Reserve, Mexico: A Watershed-Scale Analysis Using Tree-Ring Records
Authors: Villanueva-Díaz José , Correa-Díaz Arian , Gutiérrez-García Jesús Valentín , Astudillo-Sánchez Claudia C. , Martínez-Sifuentes Aldo R.
Published: May 2024

The El Cielo Biosphere Reserve (CBR) stands as a vital forested region in eastern Mexico due to its high biodiversity in flora and fauna and provision of environmental services. This study established a network of 10 ring-width chronologies of different species within the CBR and adjacent watersheds. The objective was to analyze their climatic response and reconstruct the seasonal streamflow contribution of each sub-basin to the main stream, utilizing data from a gauge network of eight hydrological stations located at strategic locations of the CBR. With chronologies ranging from 116 to 564 years, most exhibited association with the accumulated streamflow between January and June. Based on the adjusted R2, Akaike Information Criteria, and Variance Inflation Factor, the stepwise regression procedure was selected among different statistical methods for developing the reconstruction model. In spite of differences in the seasonal reconstructed periods, all the species showed potential to develop hydrological reconstructions as indicated by their common response to streamflow variability, as occurred in the wet years of 1976, 1993, 2000, and 2008, and dry years of 1980, 1982, 1996, and 2011. It was found that the response of the chronologies to gauge records increased as a function of the chronologies’ interseries correlation, average mean sensitivity, and distance of the tree-ring series to the gauge station. Streamflow reconstructions at the sub-basin level allowed a better understanding of the hydroclimatic variability characterizing the CBR, but also suggested the need to increase the network of chronologies for some particular sub-basins lacking tree-ring series to improve the reconstructed models.
Study on the Effect of Pore Evolution on the Coal Spontaneous Combustion Characteristics in Goaf
Authors: Li Jinglei , Xu Hao , Wu Genshui
Published: May 2024

Understanding the characteristics of coal spontaneous combustion (CSC) in goaf under different porosities is crucial for comprehending the mechanism of CSC and its prevention and control. In this paper, a multi-field coupled model of CSC in the goaf, considering porosity variation, is developed to investigate the effect of porosity on the CSC characteristics in the goaf. The results indicate that, as the goaf depth increases, both porosity and permeability decrease. When the highest goaf porosity is 25%, the average airflow velocity is between 0.00134 and 0.00139 m/s. In contrast, the average airflow velocity in the goaf with a porosity of 40% is approximately six times greater than that of the goaf with a porosity of 25%. As the goaf porosity increases, the overall oxygen concentration, temperature, and oxidized zone area also rise. Moreover, the oxidation zone area can be quantified and visualized, thereby enabling more effective prediction of the CSC risk in the goaf. The findings of the study have a positive significance in guiding the prevention and control of coal fires.
Sensory and Physical Properties of Fibrous Meat Analogs Made from Faba Bean, Pea, and Oat Using High-Moisture Extrusion
Authors: Knaapila Antti , Kantanen Katja , Ramos-Diaz Jose Martin , Piironen Vieno , Sandell Mari , Jouppila Kirsi
Published: May 2024

Faba bean is a promising source of ingredients for the production of meat analogs. However, sensory properties of faba bean, especially the bitter taste of the protein concentrate, restrict its use. Our aim was to assess the feasibility of two types of faba bean ingredients—flour (from germinated, gently heat-treated beans) and groat (from non-germinated, roasted beans)—in combination with pea protein isolate and oat fiber concentrate for producing meat analogs using high-moisture extrusion. We produced six samples using varying recipes, while maintaining constant process parameters. An untrained panel (55 participants) evaluated the samples for key sensory attributes (check-all-that-apply) and rated their pleasantness. The water absorption capacity and mechanical properties of the samples were assessed using instrumental measurements. The samples were frequently described as ‘beany’ and ‘tasteless’, but very rarely as ‘bitter’. The most frequently cited attributes for mouthfeel varied between the samples containing 30% (‘tough’, ‘gummy’) and 50% (‘crumbly’, ‘floury’) of faba bean flour/groat and were associated with corresponding mechanical properties. On average, the sample containing a blend of faba bean groat and pea protein isolate (50% each) appeared to be the most pleasant. Our results suggest that faba bean groat with pea protein isolate enables the production of fibrous meat analogs with acceptable taste and texture, without the bitter off-taste.
Global Approach to Follow-Up of Celiac Disease
Authors: Perez-Junkera Gesala , Ruiz de Azua Lorea , Vázquez-Polo Maialen , Lasa Arrate , Fernandez Gil María Pilar , Txurruka Itziar , Navarro Virginia , Larretxi Idoia
Published: May 2024

Celiac disease, an autoimmune disorder induced by the ingestion of gluten, affects approximately 1.4% of the population. Gluten damages the villi of the small intestine, producing symptoms such as abdominal pain, bloating and a subsequent loss of nutrient absorption, causing destabilization of the nutritional status. Moreover, gluten can trigger extra intestinal symptoms, such as asthma or dermatitis, but also mental disorders such as depression or anxiety. Moreover, people suffering from celiac disease sometimes feel misunderstood by society, mainly due to the lack of knowledge about the disease and the gluten-free diet. Thus, the treatment and follow-up of patients with celiac disease should be approached from different perspectives, such as the following: (1) a clinical perspective: symptomatology and dietary adherence monitorization; (2) nutritional assessment: dietary balance achievement; (3) psychological assistance: mental disorders avoidance; and (4) social inclusion: educating society about celiac disease in order to avoid isolation of those with celiac disease. The aim of this narrative review is to gain deep insight into the different strategies that currently exist in order to work on each of these perspectives and to clarify how the complete approach of celiac disease follow-up should be undertaken so that the optimum quality of life of this collective is reached.
Innovative Applications of Tenebrio molitor Larvae in the Production of Sustainable Meat Sausages: Quality and Safety Aspects
Authors: Jankauskienė Agnė , Kiseliovienė Sandra , Aleknavičius Dominykas , Miliūnaitė Ieva , Kerzienė Sigita , Gaižauskaitė Žydrūnė , Juknienė Ignė , Zaviztanavičiūtė Paulina , Kabašinskienė Aistė
Published: May 2024

With the world’s population continuing to grow, ensuring sustainable protein sources for everyone is becoming increasingly challenging. Despite meat being considered unsustainable, people find it challenging to abstain from consuming it. However, one solution to this dilemma could be the incorporation of mealworms into conventional meat products, i.e., sausages. The incorporation of mealworms into sausage formulations appears to shift the fatty acid profile towards higher levels of monounsaturated fats and polyunsaturated fatty acids (PUFAs), particularly omega-3s, potentially enhancing the nutritional value and offering health benefits. Therefore, our study aimed to improve the nutritional value and safety parameters of traditional sausages by enriching them with the flour of mealworm larvae. For this purpose, the larvae were reared on a sustainable substrate with brewery by-products, brewer’s yeast, and carrots. They were used frozen and freeze-dried in sausage recipes, replacing pork in different proportions. The analysis of the product’s chemical safety parameters (biogenic amines, nitrates and nitrites, volatile fatty acids (FA), and peroxide) and nutritional value (including collagen, cholesterol, amino acids, FA, and hydroxyproline) was carried out in an accredited laboratory. The results of our study have demonstrated that the incorporation of mealworms into sausages, particularly through freeze-drying, increased fat content and enhanced the profile of FA, including omega-3s while reducing protein and cholesterol levels, and altering collagen content, suggesting improved nutritional value and potential health benefits without compromising the safety of the product. Therefore, we are highlighting that the addition of mealworms influences the quality of amino acids positively and maintains biogenic amine levels within safe limits, alongside a negligible impact on nitrates and nitrites and a reduction in peroxide values. These findings indicate an overall improvement in sausage quality and safety without compromising safety.
Detection of Gate Valve Leaks through the Analysis Fractal Characteristics of Acoustic Signal
Authors: Zagretdinov Ayrat , Ziganshin Shamil , Izmailova Eugenia , Vankov Yuri , Klyukin Ilya , Alexandrov Roman
Published: May 2024

This paper considers the possibility of using monofractal and multifractal analysis of acoustic signals to detect water leaks through gate valves. Detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MF-DFA) were used. Experimental studies were conducted on a ½-inch nominal diameter wedge valve, which was fitted to a ¾-inch nominal diameter steel pipeline. The water leak was simulated by opening the valve. The resulting leakage rates for different valve opening conditions were 5.3, 10.5, 14, 16.8, and 20 L per minute (L/min). The Hurst exponent for acoustic signals in a hermetically sealed valve is at the same level as a deterministic signal, while the width of the multifractal spectrum closely matches that of a monofractal process. When a leak occurs, turbulent flow pulsations appear, and with small leak sizes, the acoustic signals become anticorrelated with a high degree of multifractality. As the leakage increases, the Hurst exponent also increases and the width of the multifractal spectrum decreases. The main contributor to the multifractal structure of leak signals is small, noise-like fluctuations. The analysis of acoustic signals using the DFA and MF-DFA methods enables determining the extent of water leakage through a non-sealed gate valve. The results of the experimental studies are in agreement with the numerical simulations. Using the Ansys Fluent software (v. 19.2), the frequencies of flow vortices at different positions of gate valve were calculated. The k-ω SST turbulence model was employed for calculations. The calculations were conducted in a transient formulation of the problem. It was found that as the leakage decreases, the areas with a higher turbulence eddy frequency increase. An increase in the frequency of turbulent fluctuations leads to enhanced energy dissipation. Some of the energy from ordered processes is converted into the energy of disordered processes.
Fractional Scalar Field Cosmology
Authors: Rasouli Seyed Meraj Mousavi , Cheraghchi Samira , Moniz Paulo
Published: May 2024

Considering the Friedmann–Lemaître–Robertson–Walker (FLRW) metric and the Einstein scalar field system as an underlying gravitational model to construct fractional cosmological models has interesting implications in both classical and quantum regimes. Regarding the former, we just review the most fundamental approach to establishing an extended cosmological model. We demonstrate that employing new methodologies allows us to obtain exact solutions. Despite the corresponding standard models, we cannot use any arbitrary scalar potentials; instead, it is determined from solving three independent fractional field equations. This article concludes with an overview of a fractional quantum/semi-classical model that provides an inflationary scenario.
Forward Starting Option Pricing under Double Fractional Stochastic Volatilities and Jumps
Authors: Zhang Sumei , Xiao Haiyang , Yong Hongquan
Published: May 2024

This paper aims to provide an effective method for pricing forward starting options under the double fractional stochastic volatilities mixed-exponential jump-diffusion model. The value of a forward starting option is expressed in terms of the expectation of the forward characteristic function of log return. To obtain the forward characteristic function, we approximate the pricing model with a semimartingale by introducing two small perturbed parameters. Then, we rewrite the forward characteristic function as a conditional expectation of the proportion characteristic function which is expressed in terms of the solution to a classic PDE. With the affine structure of the approximate model, we obtain the solution to the PDE. Based on the derived forward characteristic function and the Fourier transform technique, we develop a pricing algorithm for forward starting options. For comparison, we also develop a simulation scheme for evaluating forward starting options. The numerical results demonstrate that the proposed pricing algorithm is effective. Exhaustive comparative experiments on eight models show that the effects of fractional Brownian motion, mixed-exponential jump, and the second volatility component on forward starting option prices are significant, and especially, the second fractional volatility is necessary to price accurately forward starting options under the framework of fractional Brownian motion.
Normalized Clinical Severity Scores Reveal a Correlation between X Chromosome Inactivation and Disease Severity in Rett Syndrome
Authors: Merritt Jonathan K. , Fang Xiaolan , Caylor Raymond C. , Skinner Steven A. , Friez Michael J. , Percy Alan K. , Neul Jeffrey L.
Published: May 2024

Rett Syndrome (RTT) is a severe neurodevelopmental disorder predominately diagnosed in females and primarily caused by pathogenic variants in the X-linked gene Methyl-CpG Binding Protein 2 (MECP2). Most often, the disease causing the MECP2 allele resides on the paternal X chromosome while a healthy copy is maintained on the maternal X chromosome with inactivation (XCI), resulting in mosaic expression of one allele in each cell. Preferential inactivation of the paternal X chromosome is theorized to result in reduced disease severity; however, establishing such a correlation is complicated by known MECP2 genotype effects and an age-dependent increase in severity. To mitigate these confounding factors, we developed an age- and genotype-normalized measure of RTT severity by modeling longitudinal data collected in the US Rett Syndrome Natural History Study. This model accurately reflected individual increase in severity with age and preserved group-level genotype specific differences in severity, allowing for the creation of a normalized clinical severity score. Applying this normalized score to a RTT XCI dataset revealed that XCI influence on disease severity depends on MECP2 genotype with a correlation between XCI and severity observed only in individuals with MECP2 variants associated with increased clinical severity. This normalized measure of RTT severity provides the opportunity for future discovery of additional factors contributing to disease severity that may be masked by age and genotype effects.
Neuropsychological Profile of 25 Brazilian Patients with 22q11.2 Deletion Syndrome: Effects of Clinical and Socioeconomic Variables
Authors: Pimenta Larissa Salustiano Evangelista , Mello Claudia Berlim de , Benedetto Luciana Mello Di , Soares Diogo Cordeiro de Queiroz , Kulikowski Leslie Domenici , Dantas Anelisa Gollo , Melaragno Maria Isabel , Kim Chong Ae
Published: May 2024

The 22q11.2 deletion syndrome (22q11.2DS) is associated with a heterogeneous neurocognitive phenotype, which includes psychiatric disorders. However, few studies have investigated the influence of socioeconomic variables on intellectual variability. The aim of this study was to investigate the cognitive profile of 25 patients, aged 7 to 32 years, with a typical ≈3 Mb 22q11.2 deletion, considering intellectual, adaptive, and neuropsychological functioning. Univariate linear regression analysis explored the influence of socioeconomic variables on intellectual quotient (IQ) and global adaptive behavior. Associations with relevant clinical conditions such as seizures, recurrent infections, and heart diseases were also considered. Results showed IQ scores ranging from 42 to 104. Communication, executive functions, attention, and visuoconstructive skills were the most impaired in the sample. The study found effects of access to quality education, family socioeconomic status (SES), and caregiver education level on IQ. Conversely, age at diagnosis and language delay were associated with outcomes in adaptive behavior. This characterization may be useful for better understanding the influence of social-environmental factors on the development of patients with 22q11.2 deletion syndrome, as well as for intervention processes aimed at improving their quality of life.
Study on the Characteristics of Coarse Feeding Tolerance of Ding’an Pigs: Phenotypic and Candidate Genes Identification
Authors: Song Yanxia , Xue Mingming , Wang Feng , Tang Qiguo , Luo Yabiao , Zheng Meili , Wang Yubei , Xue Pengxiang , Dong Ningqi , Sun Ruiping , Fang Meiying
Published: May 2024

Ding’an (DA) pig, a prominent local breed in Hainan Province, exhibits notable advantages in coarse feeding tolerance and high-quality meat. To explore the potential genetic mechanism of coarse feeding tolerance in DA pigs, 60-day-old full sibling pairs of DA and DLY (Duroc-Landrace-Yorkshire) pigs were subjected to fed normal (5%) and high (10%) crude fiber diets for 56 days, respectively. The findings showed that increasing the crude fiber level had no impact on the apparent digestibility of crude fiber, intramuscular fat, and marbling scores in DA pigs, whereas these factors were significantly reduced in DLY pigs (p < 0.05). Through differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) of the colonic mucosal transcriptome data, 65 and 482 candidate genes with coarse feeding tolerance in DA pigs were identified, respectively. Joint analysis screened four key candidate genes, including LDHB, MLC1, LSG1, and ESM1, potentially serving as key regulated genes for coarse feeding tolerance. Functional analysis revealed that the most significant pathway enriched in differential genes associated with coarse feeding tolerance in Ding’an pigs was the signaling receptor binding. The results hold substantial significance for advancing our understanding of the genetic mechanisms governing coarse feeding tolerance in Ding’an pigs.
Genetic Screening Revealed the Negative Regulation of miR-310~313 Cluster Members on Imd Pathway during Gram-Negative Bacterial Infection in Drosophila
Authors: Li Yao , Sun Yixuan , Li Ruimin , Zhou Hongjian , Li Shengjie , Jin Ping
Published: May 2024

Innate immune response is the first line of host defense against pathogenic microorganisms, and its excessive or insufficient activation is detrimental to the organism. Many individual microRNAs (miRNAs) have emerged as crucial post-transcriptional regulators of immune homeostasis in Drosophila melanogaster. However, the synergistical regulation of miRNAs located within a cluster on the Imd-immune pathway remains obscured. In our study, a genetic screening with 52 transgenic UAS-miRNAs was performed to identify ten miRNAs or miRNA clusters, including the miR310~313 cluster, which may function on Imd-dependent immune responses. The miRNA RT-qPCR analysis showed that the expression of miR-310~313 cluster members exhibited an increase at 6–12 h post E. coli infection. Furthermore, the overexpression of the miR-310~313 cluster impaired the Drosophila survival. And the overexpression of miR-310/311/312 reduced Dpt expression, an indication of Imd pathway induced by Gram-negative bacteria. Conversely, the knockdown of miR-310/311/312 led to increases in Dpt expression. The Luciferase reporter expression assays and RT-qPCR analysis confirmed that miR-310~313 cluster members directly co-targeted and inhibited Imd transcription. These findings reveal that the members of the miR-310~313 cluster synergistically inhibit Imd-dependent immune responses by co-targeting the Imd gene in Drosophila.