bluuebunny/crossref_metadata_2025 huggingface dataset:
Wall Materials and Manufacturing Techniques for Korean Ancient Mural Paintings (Great Gaya, 6th Century)—Discovery of Shells Used in Wall Plaster and Identification of Their Processing Status
Authors: Lee Hwa Soo , Yu Yeong Gyeong , Lee Han Hyoung , Han Kyeong Soon
Published: July 2022

This study used morphological analysis to identify the materials used for the wall plaster in the Goa-ri Tomb and elucidate the wall manufacturing techniques by determining the processing conditions for the plaster ingredients through a calcination experiment. A glossy nacreous layer (or folia layer) and an adductor muscle scar were identified on the ventral of the shell. The shell section was confirmed in the form of a lens-like prismatic layer (or chalky layer) inserted between the nacreous layer (or folia layer). The calcinated samples showed clusters of polygonal particles of different sizes. The experiment confirmed a process of particles in plate-type–bedded structures converting into shapeless particles followed by recrystallization. The samples calcinated at 650 °C and 750 °C mostly showed plate-type–bedded structures. According to the results of the study, the plaster used on the Goa-ri Tomb walls was confirmed to be made of oyster shells. The existence of many incompletely calcinated shell particles in the plaster indicates that the calcination temperature was not high enough or the heat was not evenly distributed. The Goa-ri Tomb used weaker and less durable plaster made of incompletely calcinated shells, which seems to have caused the extensive exfoliation of the plaster from the wall surfaces.
Algorithmic Assessments in Deciding on Voluntary, Assisted or Involuntary Psychiatric Treatment
Authors: Grobler Gerhard , Van Staden Werdie
Published: July 2022

The challenges in assessing whether psychiatric treatment should be provided on voluntary, assisted or involuntary legal bases prompted the development of an assessment algorithm that may aid clinicians. It comprises a part that assesses the incapacity to provide informed consent to treatment, care or rehabilitation. It also captures the patient’s willingness to receive these treatments, the risk posed to the patient’s health or safety, financial interests or reputation and risks of serious harm to self or others. By following various decision paths, the algorithm yields one of four legal states: a voluntary, assisted, or involuntary state or that the proposed intervention should be declined. This study examined the predictive validity and the reliability of this algorithm. It was applied 4052 times to 135 clinical case narratives by 294 research participants. The legal states yielded by the algorithm had high statistical significance when matched with the gold standard (Chi-squared = 6963; df = 12; p < 0.001). It was accurate in yielding the correct legal state for the voluntary, assisted, involuntary and decline categories in 94%, 92%, 88% and 86% of the clinical case narratives, respectively. For internal reliability, a correspondence model accounted for 99.8% of the variance by which the decision paths clustered together fittingly with each of the legal states. Inter-rater reliability testing showed a moderate degree of agreement among participants on the suitable legal state (Krippendorff’s alpha = 0.66). These results suggest the algorithm is valid and reliable, which warrant a subsequent randomised controlled study to investigate whether it is more effective in clinical practice than standard assessments.
Neural Adaptive Funnel Dynamic Surface Control with Disturbance-Observer for the PMSM with Time Delays
Authors: Li Menghan , Li Shaobo , Zhang Junxing , Wu Fengbin , Zhang Tao
Published: July 2022

This paper suggests an adaptive funnel dynamic surface control method with a disturbance observer for the permanent magnet synchronous motor with time delays. An improved prescribed performance function is integrated with a modified funnel variable at the beginning of the controller design to coordinate the permanent magnet synchronous motor with the output constrained into an unconstrained one, which has a faster convergence rate than ordinary barrier Lyapunov functions. Then, the specific controller is devised by the dynamic surface control technique with first-order filters to the unconstrained system. Therein, a disturbance-observer and the radial basis function neural networks are introduced to estimate unmatched disturbances and multiple unknown nonlinearities, respectively. Several Lyapunov-Krasovskii functionals are constructed to make up for time delays, enhancing control performance. The first-order filters are implemented to overcome the “complexity explosion” caused by general backstepping methods. Additionally, the boundedness and binding ranges of all the signals are ensured through the detailed stability analysis. Ultimately, simulation results and comparison experiments confirm the superiority of the controller designed in this paper.
Impact of Mask Mandates on K-12 and Higher-Ed Teaching along with the Recommendation for Mask-Wearing during an Infectious Disease Outbreak
Authors: Khandelwal Megha , Apodaca Theresa
Published: July 2022

Mask mandates were used as a multilayered approach to prevent the spread of the COVID-19. The state of New Mexico had one of the longest indoor mask mandates during the pandemic. We surveyed the K-12 teachers located throughout the state as well as higher-ed faculty members at New Mexico Tech to determine the impact of mask mandates on their teaching. Many instructors indicated minor problems, especially their ability to judge students’ engagement and/or participation. While some reported that their classrooms adopted masks as a new norm and it has become part of their school lives with no major issues. Some instructors did report negative impacts because of the participants’ opposition to mask-wearing. Two of the instructors provided unique perspectives by comparing secondary school vs. higher-education (higher-ed) teaching during the pandemic. Moreover, three K-12 teachers who worked with hearing-impaired students reported the initial issues of being unable to use facial expressions; however, they also noted their students learned to sign with masks on. Collectively the benefits of masks in slowing the spread of COVID-19 outweigh many of the communication issues noted here. After the mask mandate was lifted in February 2022, instructors reported general excitement and/or social awkwardness in some cases.
Fault Diagnosis of Coal Mill Based on Kernel Extreme Learning Machine with Variational Model Feature Extraction
Authors: Zhang Hui , Pan Cunhua , Wang Yuanxin , Xu Min , Zhou Fu , Yang Xin , Zhu Lou , Zhao Chao , Song Yangfan , Chen Hongwei
Published: July 2022

Aiming at the typical faults in the coal mills operation process, the kernel extreme learning machine diagnosis model based on variational model feature extraction and kernel principal component analysis is offered. Firstly, the collected signals of vibration and loading force, corresponding to typical faults of coal mill, are decomposed by variational model decomposition, and the intrinsic model functions at different scales are obtained. Then, the eigenvectors consisting of feature energy and sample entropy in these functions are respectively calculated, and the kernel principal component analysis is used for noise removal and dimensionality reduction. Finally, the kernel extreme learning machine model is trained and tested with the dimension reduced feature vector as input and the corresponding coal mill state as output. The results show that the variational model decomposition extraction can improve the input features of the model compared with the single eigenvector model, and the kernel principal component analysis method can significantly reduce the information redundancy and the correlation of eigenvectors, which can effectively save time and cost, and improve the prediction performance of the model to some extent. The establishment of this model provides a new idea for the study of coal mill fault diagnosis.
Critical Review on Nanomaterials for Enhancing Bioconversion and Bioremediation of Agricultural Wastes and Wastewater
Authors: Salehi Bahare , Wang Lijun
Published: July 2022

Anaerobic digestion (AD), microalgae cultivation, and microbial fuel cells (MFCs) are the major biological processes to convert organic solid wastes and wastewater in the agricultural industry into biofuels, biopower, various biochemical and fertilizer products, and meanwhile, recycle water. Various nanomaterials including nano zero valent irons (nZVIs), metal oxide nanoparticles (NPs), carbon-based and multicompound nanomaterials have been studied to improve the economics and environmental sustainability of those biological processes by increasing their conversion efficiency and the quality of products, and minimizing the negative impacts of hazardous materials in the wastes. This review article presented the structures, functionalities and applications of various nanomaterials that have been studied to improve the performance of AD, microalgae cultivation, and MFCs for recycling and valorizing agricultural solid wastes and wastewater. The review also discussed the methods that have been studied to improve the performance of those nanomaterials for their applications in those biological processes.
A Sequential Multi-Staged Approach for Developing Digital One-Stop Shops to Support Energy Renovations of Residential Buildings
Authors: Sequeira Miguel Macias , Gouveia João Pedro
Published: July 2022

Buildings account for 40% of the European Union’s energy consumption. Deep energy renovation of residential buildings is key for decarbonization and energy poverty alleviation. However, renovation is occurring at far below the needed pace and depth. In this context, building renovation one-stop shops, which bring all project phases under one roof and provide advice, support, and finance to households, are highlighted as a promising solution. Nevertheless, this model is still absent or under-developed in most European countries and remains understudied in the scientific literature. Therefore, the present research goals are as follows: (i) to provide a critical review of emerging one-stop shop models; (ii) to streamline the deployment of building renovation digital one-stop shops by piloting a sequential multi-staged approach for Portuguese households and proposing it for replication elsewhere; and (iii) to compare case-study insights with other one-stop shops and discuss the notion in the context of the European Renovation Wave. In total, for the Portuguese case-study, five steps were conducted. The first three—stakeholder mapping, expert interviews, and customer journey—aimed to gather intel on the local energy renovation market. The results from these stages informed the design of the platform (fourth step). Finally, a post-launch market consultation survey gathered user feedback (fifth step). Insights from this study suggest that digital one-stop shops, while providing a helpful tool to close information gaps and activate specific audiences, may be insufficient on their own. As such, a more comprehensive set of instruments supporting households is needed to accelerate building renovation.
Biomimetic Design for Adaptive Building Façades: A Paradigm Shift towards Environmentally Conscious Architecture
Authors: Faragalla Ali M. A. , Asadi Somayeh
Published: July 2022

A change in thinking has been ongoing in the architecture and building industry in response to growing concern over the role of the building industry in the excessive consumption of energy and its devastating effects on the natural environment. This shift changed the thinking of architects, engineers, and designers in the initial phases of a building’s design, with a change from the importance of geometry and form to assessing a building’s performance, from structure to a building’s skin, and from abstract aesthetics to bio-climatic aesthetics. In this context, sustainable, intelligent, and adaptive building façades were extensively researched and developed. Consequently, several typologies, strategies, and conceptual design frameworks for adaptive façades were developed with the aim of performing certain functions. This study focuses on the biomimetic methodologies developed to design adaptive façades because of their efficiency compared to other typologies. A comprehensive literature review is performed to review the design approaches toward those façades at the early stage of design. Then, the theoretical bases for three biomimetic frameworks are presented to gain an overall understanding of the concepts, opportunities, and limitations.
Analysis of Perovskite Solar Cell Degradation over Time Using NIR Spectroscopy—A Novel Approach
Authors: Gąsiorowski Marek , Dasgupta Shyantan , Bychto Leszek , Ahmad Taimoor , Szymak Piotr , Wojciechowski Konrad , Patryn Aleksy
Published: July 2022

In recent years, there has been a dynamic development of photovoltaic materials based on perovskite structures. Solar cells based on perovskite materials are characterised by a relatively high price/performance ratio. Achieving stability at elevated temperatures has remained one of the greatest challenges in the perovskite solar cell research community. However, significant progress in this field has been made by utilising different compositional engineering routes for the fabrication of perovskite semiconductors such as triple cation-based perovskite structures. In this work, a new approach for the rapid analysis of the changes occurring in time in perovskite structures was developed. We implemented a quick and inexpensive method of estimating the ageing of perovskite structures based on an express diagnosis of light reflection in the near-infrared region. The possibility of using optical reflectance in the NIR range (900–1700 nm) to observe the ageing of perovskite structures over time was investigated, and changes in optical reflectance spectra of original perovskite solar cell structures during one month after PSC production were monitored. The ratio of characteristic pikes in the reflection spectra was determined, and statistical analysis by the two-dimensional correlation spectroscopy (2D-COS) method was performed. This method allowed correctly detecting critical points in thermal ageing over time.
Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach
Authors: Sorouri Hoda , Oshnoei Arman , Novak Mateja , Blaabjerg Frede , Anvari-Moghaddam Amjad
Published: July 2022

This paper proposes a learning-based finite control set model predictive control (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in a DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented to address one of the ongoing challenges in FCS-MPC of the converters, i.e., optimal design of the weighting coefficients appearing in the FCS-MPC objective function for each converter. A deep deterministic policy gradient method is employed to learn the optimal weighting coefficient design policy. A Markov decision method formulates the DRL problem. The DRL agent is trained for each converter in the MG, and the weighting coefficients are obtained based on reward computation with the interactions between the MG and agent. The proposed strategy is wholly distributed, wherein agents exchange data with other agents, implying a multi-agent DRL problem. The proposed control scheme offers several advantages, including preventing the dependency of the converter control system on the operating point conditions, plug-and-play capability, and robustness against the MG uncertainties and unknown load dynamics.
Integrated Optimization for Biofuel Management Associated with a Biomass-Penetrated Heating System under Multiple and Compound Uncertainties
Authors: Fu Dianzheng , Yang Tianji , Huang Yize , Tong Yiming
Published: July 2022

The biofuel management of a biofuel-penetrated district heating system is complicated due to its association with multiple and polymorphic uncertainties. To handle uncertainties and system dynamic complexities, an inexact two-stage compound-stochastic mixed-integer programming technique is proposed, innovatively based on the integration of different uncertain optimization approaches. The proposed technique can not only address the inexact recourse problems sourced from multiple and compound uncertainties existing in the pre-regulated biofuel supply–demand match mode, but can also quantitatively analyze the conflicts between the economic target that minimizes the system cost and the risk preference that maximizes the heating service satisfaction. The developed model is applied to a real-world biofuel management case study of a district heating system to obtain the optimal biofuel management schemes subject to supply–demand, policy requirement constraints, and the financial minimization objective. The results indicate that biofuel allocation and expansion schemes are sensitive to the multiple and compound uncertainty inputs, and the corresponding biofuel-deficit change trends of three heat sources are obviously distinct with the system’s condition, varying due to the complicated interactions of the system’s components. Beyond that, a potential trade-off relationship between the heating cost and the constraint-violation risk can be obtained by observing system responses with thermalization coefficient varying.
A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement
Authors: Nair Divya R. , Nair Manjula G. , Thakur Tripta
Published: July 2022

The widespread popularity of renewable and sustainable sources of energy such as solar and wind calls for the integration of renewable energy sources into electrical power grids for sustainable development. Microgrids minimize power quality issues in the main grid by linking with an active filter and furnishing reactive power compensation, harmonic mitigation, and load balancing at the point of common coupling. The reliability issues faced by standalone DC microgrids can be managed by interlinking microgrids with a power grid. An artificial intelligence-based Icosϕ control algorithm for power sharing and power quality improvement in smart microgrid systems is proposed here to render grid-integrated power systems more intelligent. The proposed controller considers various uncertainties caused by load variations, state of charge of the battery of microgrids, and power tariff based on the availability of power in microgrids. This paper presents a detailed analysis of the integration of wind and solar microgrids with the grid for dynamic power flow management in order to improve the power quality and to reduce the burden, thereby strengthening the central grid. A smart grid system with multiple smart microgrids coupled with a renewable energy source with tariff control and judicious power flow management was simulated for power-sharing and power quality improvement. A hardware prototype of the artificial intelligence-based Icosϕ control algorithm with nonlinear load was also implemented successfully. Furthermore, the economic viability was investigated to ensure the feasibility of the smart microgrid system with the proposed controller design for power flow management and power quality improvement.
A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection
Authors: Wu Kaitong , Peng Xiangang , Li Zilu , Cui Wenbo , Yuan Haoliang , Lai Chun Sing , Lai Loi Lei
Published: July 2022

High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.
Techno-Economic Analysis of a Wind-Energy-Based Charging Station for Electric Vehicles in High-Rise Buildings in Malaysia
Authors: Abdelrahim Misbah , Alkawsi Gamal , Alkahtani Ammar Ahmed , Alhasan Ali M. W. , Khudari Mohammad , Abdul Kadir Mohd Rizuan , Ekanayake Janaka , Tiong Sieh Kiong
Published: July 2022

Renewable energy sources have become necessary for long-term energy sustainability due to the increased demand for electric cars and worrisome rises in carbon dioxide emissions from traditional energy sources. Furthermore, transportation is one of the sectors that uses the most energy on the planet, accounting for 24% of overall consumption. Fossil fuels are still the dominant energy source for balancing global demand/supply dynamics. Supporting laws and regulations have enhanced the first phase of environmentally friendly energy-resource consumption. This has spurred the development of new solutions that cut greenhouse-gas emissions and reduce the air pollution produced by internal combustion engines that are fuelled by fossil fuels. Wind energy is one of the clean energy sources that may be utilised for this purpose. Wind energy has been used to power electric-car-charging infrastructure, generally in a hybrid mode with another renewable source. This research examines the possibility of using wind energy as a standalone energy source to support electric-vehicle-charging infrastructure. Using data from Malacca, Malaysia, and HOMER software, the project will build and optimise a standalone wind-powered charging station. An RC-5K-A wind turbine coupled to a battery and converter is the appropriate choice for the system. The findings demonstrate that the turbine can produce 214,272 kWh per year at the cost of USD 0.081/kWh, confirming wind’s future feasibility as an energy-infrastructure support source.
High and Low Air Temperatures and Natural Wildfire Ignitions in the Sierra Nevada Region
Authors: Petrie Matthew D. , Savage Neil P. , Stephen Haroon
Published: July 2022

The Sierra Nevada region has experienced substantial wildfire impacts. Uncertainty pertaining to fire risk may be reduced by better understanding how air temperature (Ta: °C) influences wildfire ignitions independently of other factors. We linked lightning-ignited wildfires to Ta patterns across the region from 1992 to 2015 and compared monthly high- and low-air-temperature patterns between ignition and non-ignition locations at local scales (4 km). Regionally, more ignitions occurred in springs with a greater number of high-Ta months and fewer cool Ta months (analyzed separately) and in summers with fewer cool Ta months. Locally, summer ignition locations experienced warmer summer months on a normalized scale than non-ignition locations. The probability of a wildfire ignition was positively associated with a greater number of high-Ta months during and prior to fire seasons. Regionally, springs with a greater number of high-Ta months had more wildfire ignitions. Locally, as individual locations in the region experienced a greater number of high-Ta months preceding and including the fire season, they exhibited substantial increases in spring (+1446%), summer (+365%), and fall (+248%) ignitions. Thus, the frequent occurrence of high-Ta months is positively associated with lightning-ignited wildfires in the Sierra Nevada region.
Pathogenicity of Three Bursaphelenchus xylophilus (Steiner & Buhrer) Nickle. Isolates in Pinus koraiensis (Siebold & Zucc.) Seedlings
Authors: Cao Ye-Fan , Wang Lai-Fa , Wang Xi-Zhuo , Wang Xiang , Xu Ming
Published: July 2022

The pine wood nematode (PWN), Bursaphelenchus xylophilus (Steiner & Buhrer) Nickle., is one of the most dangerous invasive species in the world, causing devastating pine wilt disease (PWD) in pine trees from many countries. The PWN is now established in 18 provinces in China from the south to north, and it has expanded to some areas of Liaoning Province with temperatures that are beyond the ideal range. It has been reported that Pinus koraiensis Siebold & Zucc., one of the representative pine trees of Liaoning Province, has been infected by PWNs. To investigate the pathogenicity of the PWN in P. koraiensis, the reproductive ability of PWNs on fungal culture was compared among three isolates: QH-1, NM-1, and CM-1 (QH-1 from Liaoning Province, NM-1 from Nanjing Province, and CM-1 from Chongqing Municipality). Four-year-old P. koraiensis seedlings were inoculated with QH-1, NM-1, and CM-1 at a rate of 2000 per seedling. Pathogenicity, external symptoms, and nematode migration were all monitored on a daily basis over the next few days. The results from the experiment showed that all three PWN isolates caused wilt in P. koraiensis seedlings, with QH-1 being more virulent than NM-1 and CM-1. In addition, QH-1 exhibited greater reproductive and migration abilities in the seedlings than NM-1 and CM-1. These results indicate that the virulence of the B. xylophilus isolates QH-1, NM-1, and CM-1 can differ in terms of seedling mortality, migration ability, and reproductive ability (in trees).
Hybrid Sensing Platform for IoT-Based Precision Agriculture
Authors: Bagha Hamid , Yavari Ali , Georgakopoulos Dimitrios
Published: July 2022

Precision agriculture (PA) is the field that deals with the fine-tuned management of crops to increase crop yield, augment profitability, and conserve the environment. Existing Internet of Things (IoT) solutions for PA are typically divided in terms of their use of either aerial sensing using unmanned aerial vehicles (UAVs) or ground-based sensing approaches. Ground-based sensing provides high data accuracy, but it involves large grids of ground-based sensors with high operational costs and complexity. On the other hand, while the cost of aerial sensing is much lower than ground-based sensing alternatives, the data collected via aerial sensing are less accurate and cover a smaller period than ground-based sensing data. Despite the contrasting virtues and limitations of these two sensing approaches, there are currently no hybrid sensing IoT solutions that combine aerial and ground-based sensing to ensure high data accuracy at a low cost. In this paper, we propose a Hybrid Sensing Platform (HSP) for PA—an IoT platform that combines a small number of ground-based sensors with aerial sensors to improve aerial data accuracy and at the same time reduce ground-based sensing costs.
The Determinants of Global Value Chain Participation in Developing Seafood-Exporting Countries
Authors: Eegunjobi Ruth , Ngepah Nicholas
Published: July 2022

Global value chain (GVC) participation has played a significant role in boosting the trade gains of both developed and developing seafood-exporting countries over the past three decades. In addition, the extent of GVC participation has become the most important platform for addressing gains from trade in developing seafood-exporting countries to ensure that their participation enhances economic growth. Recent studies on GVC participation in developing countries have highlighted the importance of domestic institutions. However, the literature is silent on the quality of the domestic institutions–GVC participation nexus. This paper aims to investigate the determinants of GVC participation and the effect of the quality of domestic institutional governance on seafood-exporting developing countries’ GVC participation indices. Using the Hausman–Taylor (HT) estimator and the system generalised method of moments (GMM) dynamic panel data methodology to examine seafood export data from 32 countries from 2009 to 2018, we find that economic potential drives backward GVC participation, while low forward participation might not only lead to lower gains from trade, but also limit countries to the supply of primary seafood products with little value addition. In addition, the quality of domestic institutional governance constrains GVC participation. Overall the results indicate that the quality of domestic institutional governance matters for the GVC participation of seafood-exporting developing countries.
Beak Microstructure Estimates of the Age, Growth, and Population Structure of Purpleback Flying Squid (Sthenoteuthis oualaniensis) in the Xisha Islands Waters of the South China Sea
Authors: Chen Ziyue , Lu Huajie , Liu Wei , Liu Kai , Chen Xinjun
Published: July 2022

This study aimed to explore the feasibility of using an upper beak microstructure to estimate the age of purpleback flying squid (Sthenoteuthis oualaniensis). From these microstructures, the age and growth of squid caught from January to March and May to August in 2018, 2019, and 2020 in the waters surrounding the Xisha Islands in the South China Sea were determined. We found three typical growth zones (the hood region, crest region, and axis), abnormal increments (checks), and erosion in the beak examination. The average dorsal mantle length (ML) of males and females was 112.13 (±15.23 mm) and 119.67 mm (±24.50 mm), respectively, and no squid were older than 10 months. The peak hatching dates, according to back calculations, were from October to January of the next year. All sampled squid belonged to the autumn/winter cohort. Significant sex differences were found in the relationship between ML and age in squid with similar growth patterns. Exponential models best described the relationships of ML with age and body weight (BW) for both sexes. However, a linear model best described the relationship between age and upper rostrum length (URL). The maximum absolute daily growth rates (AGR) of BW were reached during days 240–270 for both sexes. The maximum AGRs in ML were reached during days 180–210 and 240–270 for males and females, respectively. The period of 120–150 days (4–5 months) was considered the sub-adult stage of S. oualaniensis in the Xisha Islands waters of the South China Sea. This study confirmed that the beak microstructure provides good age estimates for purpleback flying squid (S. oualaniensis).
Fortification of Chinese Steamed Bread with Glycyrrhizauralensis Polysaccharides and Evaluation of Its Quality and Performance Attributes
Authors: Guo Dongqi , Yin Xiuxiu , Cheng Huan , Chen Jianle , Ye Xingqian
Published: July 2022

Natural polysaccharides are new popular healthy food material, and the materials are widely used in various functional foods. The influences of polysaccharides from Glycyrrhiza uralensis on the quality and sensory properties of Chinese steamed bread (CSB), as well as the performance (starch digestion in vitro and starch staling) of CSB, were investigated in this study. The addition of Glycyrrhiza polysaccharide (GP) increased the specific volume of CSB in a dose-dependent manner, and the specific volume of CSB-2 was 2.55 mL/g. GP also contributed to the increase in hardness (from 1240.17 to 2539.34 g) and chewiness (893.85 to 1959.27 g) of fresh CSB. In addition, GP could maintain the integrity of the protein network within the CSB. The scores for sensory evaluation indicators of CSB-1 were relatively balanced. More importantly, the addition of GP altered starch digestive properties, and the content of the resistant starch (RS) was increased from 8.62 (CSB-0) to 43.46% (CSB-2). GP led to a significant reduction of the expected glycemic index (eGI) of CSB, and the eGI of CSB was decreased from 97.50 (CSB-0) to 73.8 (CSB-2), which was classified as a medium-GI (MGI) food. In addition, X-ray diffraction (XRD) and differential scanning calorimeter (DSC) revealed the addition of GP delayed the staling of CSB during storage. In general, adding the proper amount of GP could improve the quality of CSB and show the potential as a functional component of CSB to reduce the postprandial blood glucose level resulted by the CSB.
Effect of Respite Time before Live Transportation on Muscle Quality of Blunt Snout (Wuchang) Bream
Authors: Peng Ling , You Juan , Wang Lan , Xiong Shanbai , Huang Qilin , Yin Tao
Published: July 2022

To provide scientific support for improving the muscle quality of blunt snout bream, ultrahigh performance liquid chromatography, texture analyzer, and optical electron microscopy were applied to explore the effects of respite time (0, 24, 48, and 72 h) on the muscle quality of blunt snout bream before live transportation. The energy compounds (ATP and glycogen) of muscle significantly decreased with the respite time (p < 0.05). Lactic acid content declined and then increased, leading to a rise and then a fall in pH (p < 0.05). Water-holding capacity of fish muscle increased progressively when the respite time was increased to 48 h and then dramatically decreased when the respite time was further increased to 72 h (p < 0.05). Shear force gradually increased (p < 0.05), while the whiteness and lightness values remained stable (p > 0.05). Both the content of umami compounds (IMP) and bitter compounds (HxR, Hx), and the calculated K value decreased steadily with the respite time (p < 0.05). The extracellular gap of the fish muscles gradually decreased with the respite time. The most uniform and intact cellular structure occurred at 48 h. However, when the respite time was extended to 72 h, the extracellular gap and muscle fragmentation rate of the muscle increased considerably. The findings indicated that a 48 h respite time was suitable to improve the muscle quality of blunt snout bream after live transportation.
An Easy and Cheap Kiwi-Based Preparation as Vegetable Milk Coagulant: Preliminary Study at the Laboratory Scale
Authors: Nicosia Fabrizio Domenico , Puglisi Ivana , Pino Alessandra , Baglieri Andrea , La Cava Rosita , Caggia Cinzia , Fernandes de Carvalho Antonio , Randazzo Cinzia Lucia
Published: July 2022

In the present study, a kiwifruit aqueous extract was developed and used as a coagulant enzyme in cheesemaking. In detail, polyacrylamide gel electrophoresis (SDS-PAGE) was used to investigate the presence of actinidin, the kiwifruit enzyme involved in κ-casein hydrolysis, in different tissues (pulp, peel, and whole fruit) of ripe and unripe kiwifruits. Data revealed the presence of the enzyme both in the peel and in the pulp of the fruit. Although the aqueous extract obtained from the kiwifruit peel was able to hydrolyze semi-skimmed milk, it did not break down κ-casein. The aqueous extract obtained from the pulp showed a hydrolytic activity toward both κ-casein and semi-skimmed milk. The values for milk-clotting and proteolytic activity of the kiwifruit pulp extract were evaluated at different temperatures and pH parameters in order to obtain a high value of the MCA/PA ratio; we found that a temperature of 40 °C in combination with a pH value of 5.5 allowed us to obtain the best performance. In addition, the data revealed a higher hydrolytic activity of the enzymatic preparation from ripe kiwifruits than that from unripe ones, suggesting the use of the extract from pulp of ripe kiwifruits in the laboratory-scale cheesemaking. The data showed that 3% (v/v) of the ripe kiwifruit pulp extract determined a curd yield of 20.27%, comparable to chymosin yield. In conclusion, the extraction procedure for kiwifruit aqueous extract proposed in the present study was shown to be a fast, cheap, chemical-free, and ecofriendly technology as a plant coagulant for cheese manufacturing.
Characterization and Quantification of Capsaicinoids and Phenolic Compounds in Two Types of Chili Olive Oils, Using HPLC/MS
Authors: Zamljen Tilen , Slatnar Ana , Hudina Metka , Veberic Robert , Medic Aljaž
Published: July 2022

Chili olive oil is a popular addition to various foods in many countries. In a detailed study, the content of phenols and capsaicinoids in chili olive oil was determined using chili flakes and whole chilies. A total of 99.8% of the phenols in chili olive oil were secoiridoids, with elenolic acid, oleuropein aglycones, and ligostride aglycones being the most abundant. Chili olive oil with chili flakes contained higher levels of capsaicinoids (+21.6%) compared to whole chili olive oil. Capsaicin and dihydrocapsaicin accounted for about 95% of all capsaicinoids in the chili olive oil. The extraction rate of dry “Cayenne” chili was 7.1% in whole chili olive oil and 9% in chili olive oil with flakes, confirming that chili flakes are better extracted in olive oil. With the determination of 29 individual phenols and five individual capsaicinoids, the study provided a detailed insight into the secondary metabolites of chili olive oil and confirmed that it is a health source.
Suitability of Banana and Plantain Fruits in Modulating Neurodegenerative Diseases: Implicating the In Vitro and In Vivo Evidence from Neuroactive Narratives of Constituent Biomolecules
Authors: Oyeyinka Barnabas Oluwatomide , Afolayan Anthony Jide
Published: July 2022

Active principles in plant-based foods, especially staple fruits, such as bananas and plantains, possess inter-related anti-inflammatory, anti-apoptotic, antioxidative, and neuromodulatory activities. Neurodegenerative diseases affect the functionality of the central and peripheral nervous system, with attendant cognitive deficits being hallmarks of these conditions. The dietary constitution of a wide range of bioactive compounds identified in this review further iterates the significance of the banana and plantain in compromising, halting, or preventing the pathological mechanisms of neurological disorders. The neuroprotective mechanisms of these biomolecules have been identified by using protein expression regulation and specific gene/pathway targeting, such as the nuclear and tumor necrosis factors, extracellular signal-regulated and mitogen-activated protein kinases, activator protein-1, and the glial fibrillary acidic protein. This review establishes the potential double-edged neuro-pharmacological fingerprints of banana and plantain fruits in their traditionally consumed pulp and less utilized peel component for human nutrition.
Convergence Rate of the High-Order Finite Difference Method for Option Pricing in a Markov Regime-Switching Jump-Diffusion Model
Authors: Liu Jun , Yan Jingzhou
Published: July 2022

The high-order finite difference method for option pricing is one of the most popular numerical algorithms. Therefore, it is of great significance to study its convergence rate. Based on the relationship between this algorithm and the trinomial tree method, as well as the definition of local remainder estimation, a strict mathematical proof is derived for the convergence rate of the high-order finite difference method for option pricing in a Markov regime-switching jump-diffusion model. The theoretical result shows that the convergence rate of this algorithm is O(Δτ) . Moreover, the results also hold in the case of Brownian motion and jump-diffusion models that are specialized forms of the given model.
Dynamic Behavior Investigation of a Novel Epidemic Model Based on COVID-19 Risk Area Categorization
Authors: Pan Jiaji , Sun Siqiang , He Yixuan , Ren Shen , Li Qing , Chen Zhongxiang , Feng Hao
Published: July 2022

This study establishes a compartment model for the categorized COVID-19 risk area. In this model, the compartments represent administrative regions at different transmission risk levels instead of individuals in traditional epidemic models. The county-level regions are partitioned into High-risk (H), Medium-risk (M), and Low-risk (L) areas dynamically according to the current number of confirmed cases. These risk areas are communicable by the movement of individuals. An LMH model is established with ordinary differential equations (ODEs). The basic reproduction number R0 is derived for the transmission of risk areas to determine whether the pandemic is controlled. The stability of this LHM model is investigated by a Lyapunov function and Poincare–Bendixson theorem. We prove that the disease-free equilibrium (R0 < 1) is globally asymptotically stable and the disease will die out. The endemic equilibrium (R0 > 1) is locally and globally asymptotically stable, and the disease will become endemic. The numerical simulation and data analysis support the previous theoretical proofs. For the first time, the compartment model is applied to investigate the dynamics of the transmission of the COVID-19 risk area. This work should be of great value in the development of precision region-specific containment strategies.
Efficiency of Diagnostic Test for SARS-CoV-2 in a Nursing Home
Authors: Salmerón Sergio , López-Escribano Alonso , García-Nogueras Inmaculada , Lorenzo Joaquina , Romero Juan Manuel , Hernández-Martínez Antonio , García-Alcaraz Francisco
Published: July 2022

Background: there is no consensus on how to optimally use diagnostic tests in each stage of COVID-19 pandemic. The objective of this research is to determine the efficiency of sorting positive antibody test quarterly. Methods: this research uses a retrospective, observational study. COVID-19 diagnostic tests performed and avoided refer to a Spanish nursing home. Population: 261 employees and 107 residents. A quarterly antibody test was performed on subjects who had tested positive during the first wave of coronavirus, and a antibody rapid test on the remaining subjects. Results: during the first wave, 24.0% of the employees and 51.4% of the residents had a positive antibody test. Seronegativization was observed in 7.6% of employees and 1.6% of residents. An employee was infected with COVID-19 in September 2020, followed by a nursing home outbreak in October: 118 Polymerase Chain Reactions tests were avoided in residents and 18 in employees, which in turn prevented 15 workers from going on sick leave and the quarantine of 59 residents. This represents savings of about $15,000. Conclusions: our study supports the need to know and apply the strategies for early detection, surveillance and control of COVID-19 for future outbreaks. We conclude that surveillance for positive COVID-19 serology among long-term care staff and residents may be a cost-effective strategy during a pandemic.
New Insights in the Diagnosis of Rare Adenocarcinoma Variants of the Cervix—Case Report and Review of Literature
Authors: Secosan Cristina , Balint Oana , Ilian Aurora , Balan Lavinia , Balulescu Ligia , Motoc Andrei , Zahoi Delia , Grigoras Dorin , Pirtea Laurentiu
Published: July 2022

We report the case of a 29-year-old patient with low-grade squamous intraepithelial lesion (L-SIL), negative human papilloma virus (HPV), positive p16/Ki-67 dual-staining and colposcopy suggestive for severe dysplastic lesion. The patient underwent a loop electrosurgical excision procedure (LEEP), the pathology report revealing mesonephric hyperplasia and adenocarcinoma. The patient also opted for non-standard fertility-sparing treatment. The trachelectomy pathology report described a zone of hyperplasia at the limit of resection towards the uterine isthmus. Two supplementary interpretations of the slides and immunohistochemistry (IHC) were performed. The results supported the diagnosis of mesonephric adenocarcinoma, although with difficulty in differentiating it from mesonephric hyperplasia. Given the discordant pathology results that were inconclusive in establishing a precise diagnosis of the lesion and the state of the limits of resection, the patient was referred to a specialist abroad. Furthermore, the additional interpretation of the slides and IHC were performed, the results suggesting a clear cell carcinoma. The positive p16/Ki-67 dual-staining prior to LEEP, the non-specific IHC and the difficulties in establishing a diagnosis made the case interesting. Given the limitations of cytology and the fact that these variants are independent of HPV infection, dual staining p16/Ki-67 could potentially become useful in the diagnosis of rare adenocarcinoma variants of the cervix, however further documentation is required.
Food Intake Changes and Their Impact on Quality of Life in Spanish Citizens with and without COVID-19 during Lockdown
Authors: García-de-Miguel María , Huertas-Hoyas Elisabet , Pérez-Corrales Jorge , Rodríguez-Rivas Cristina , García-Bravo Cristina , García-Bravo Sara , Camacho-Montaño Lucía Rocío
Published: July 2022

The pervasive impact of the COVID-19 pandemic is just beginning to be analyzed. To date, only a handful of these studies have investigated the relationship between an individual’s quality of life (QoL) and their changes in food intake due to the virus (from the associated symptomatology of diagnosis to the universal impact of lockdown on individual lives, regardless of a person’s health status). Therefore, the purpose of this study is to identify changes in food intake resulting from the pandemic and the corresponding impact on QoL in the Spanish population. This study focuses its investigation on the 3-month time period within which lockdown was its most restrictive, March–May 2020. Survey questions ask participants to compare their eating habits, general health status, and QoL during these 3 months to times prior to the pandemic. We conducted an online survey amongst the Spanish population over 18 years old. Three surveys were administered: (1) the adult eating behavior questionnaire (EABQ), (2) EuroQoL-5D-5L, and (3) The determinants lifestyle changes during lockdown period (March to May 2020). A total of 86 participants were included, with a mean age of 34 years. In the analysis of QoL, significant differences were found according to age, sex, and the presence of a COVID-19 diagnosis. Likewise, in the analysis of food intake, significant differences were found by age (greater changes in the 18–29-years-old population) (p< 0.01) and by sex (women p < 0.03), as well as greater changes in those with a diagnosis of COVID-19. Furthermore, both food intake and COVID-19 diagnosis were variables that predicted QoL outcomes. In sum, forced home lockdown can cause changes in food intake, which can predict a lower QoL. It follows that the lockdown situation caused by the worldwide COVID-19 pandemic has affected the food intake and overall QoL of the Spanish population.
A Bibliometric and Visualization Analysis of Motor Learning in Preschoolers and Children over the Last 15 Years
Authors: Xu Fei , Xu Jing , Zhou Daliang , Xie Hao , Liu Xuan
Published: July 2022

Motor learning enables preschoolers and children to acquire fundamental skills that are critical to their development. The current study sought to conduct a bibliometric and visualization analysis to provide a comprehensive overview of motor-learning progress in preschoolers and children over the previous 15 years. The number of studies is constantly growing, with the United States and Australia, as well as other productive institutions and authors, at the leading edge. The dominant disciplines were Neurosciences and Neurology, Psychology, Rehabilitation, and Sport Sciences. The journals Developmental Medicine & Child Neurology, Human Movement Science, Physical Therapy, Neuropsychology, Journal of Motor Behavior, and Journal of Experimental Child Psychology have been the most productive and influential in this regard. The most common co-citations for clinical symptoms were for cerebral palsy, developmental coordination disorder, and autism. Research has focused on language impairment (speech disorders, explicit learning, and instructor-control feedback), as well as effective intervention strategies. Advances in brain mechanisms and diagnostic indicators, as well as new intervention and rehabilitation technologies (virtual reality, transcranial magnetic stimulation, and transcranial direct current stimulation), have shifted research frontiers and progress. The cognitive process is critical in intervention, rehabilitation, and new technology implementation and should not be overlooked. Overall, our broad overview identifies three major areas: brain mechanism research, clinical practice (intervention and rehabilitation), and new technology application.