bluuebunny/crossref_metadata_2025 huggingface dataset:
Supporting Healthier Grocery-Shopping Habits for Adults with Intellectual and Developmental Disabilities Using Self-Management and Technology: A Single-Case Pilot
Authors: Savage Melissa N.
Published: June 2023

While there are many benefits to healthy nutrition, adults with intellectual and developmental disabilities often have poor nutrition habits. Identifying effective practices to promote healthy nutrition for individuals with intellectual and developmental disabilities is critical. The purpose of this study was to examine the use of a health app and self-management intervention in supporting adults with intellectual and developmental disabilities in making healthier nutrition choices. A changing criterion single-case research design was used with three adults with intellectual and developmental disabilities to determine if there was a functional relation between a health app and self-management intervention, and the number of healthy food and drink items purchased when grocery shopping. The combination of the health app with self-management intervention was effective in increasing the number of healthy food and drink items two participants bought at the grocery store. Participants reported that they enjoyed using the app, scanning food and drink items was fun, and the goal-setting and self-monitoring components were easy to understand. Implications for practice and future research are discussed.
Hyper-Local Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool
Authors: Adkins Kevin A. , Becker William , Ayyalasomayajula Sricharan , Lavenstein Steven , Vlachou Kleoniki , Miller David , Compere Marc , Muthu Krishnan Avinash , Macchiarella Nickolas
Published: June 2023

This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations.
Joint Hovering Height, Power, and Rate Optimization for Air-to-Ground UAV-RSMA Covert Communications
Authors: Ma Kang , Chang Heng , Chen Pengxu , Bai Zhiquan , Liu Hongwu
Published: June 2023

In this paper, we investigate covert communications for an unmanned aerial vehicle (UAV)-aided rate-splitting multiple access (RSMA) system in which the UAV transmits to the covert and public users separately while shielding covert transmissions from a warden. Under the RSMA principles, the messages of the covert and public users are converted to common and private streams for air-to-ground transmissions. Subject to the quality of service (QoS) requirements of the public user and covertness constraint of the UAV-aid RSMA (UAV-RSMA) system, the aim of covert communication design is to maximize the covert rate by jointly optimizing the transmit power allocation, common rate allocation, and UAV hovering height, which each contribute to the uncertainty of the warden’s binary decision and attempts to enhance communication performance. To address the non-convex covert rate maximization problem in addition to the highly coupled system parameters, we decouple the original problem into three subproblems of transmit power allocation, common rate allocation, and UAV hovering height. We derive the optimal solutions for each of the subproblems of the transmit power and rate allocations and formulate a signomial programming problem to tackle UAV hovering height optimization. The simulation results indicate the superior covert rate performance achieved by the proposed AO algorithm and demonstrate that the proposed UAV-RSMA scheme achieves a higher covert rate than the benchmark schemes.
Individualized Indicators and Estimation Methods for Tiger Nut (Cyperus esculentus L.) Tubers Yield Using Light Multispectral UAV and Lightweight CNN Structure
Authors: Li Dan , Wu Xiuqin
Published: June 2023

Tiger nuts are a non-genetically modified organism crop with high adaptability and economic value, and they are being widely promoted for cultivation in China. This study proposed a new yield-estimation method based on a lightweight convolutional neural network (CNN) named Squeeze Net to provide accurate production forecasts for tiger nut tubers. The multispectral unmanned aerial vehicle (UAV) images were used to establish phenotypic datasets of tiger nuts, comprising vegetation indices (VIs) and plant phenotypic indices. The Squeeze Net model with a lightweight CNN structure was constructed to fully explore the explanatory power of the spectral UAV-derived information and compare the differences between the parametric and nonparametric models applied in tiger nut yield predictions. Compared with stepwise multiple linear regression (SMLR), both algorithms achieved good yield prediction performances. The highest obtained accuracies reflected an R2 value of 0.775 and a root-mean-square error (RMSE) value of 688.356 kg/ha with SMLR, and R2 = 0.780 and RMSE = 716.625 kg/ha with Squeeze Net. This study demonstrated that Squeeze Net can efficiently process UAV multispectral images and improve the resolution and accuracy of the yield prediction results. Our study demonstrated the enormous potential of artificial intelligence (AI) algorithms in the precise crop management of tiger nuts in the arid sandy lands of northwest China by exploring the interactions between various intensive phenotypic traits and productivity.
The Smart Classroom: Learning Challenges in the Digital Ecosystem
Authors: Pardo-Baldoví M. Isabel , San Martín-Alonso Ángel , Peirats-Chacón José
Published: June 2023

Digital devices are causing the emergence of new learning ecosystems, including the smart classroom. This model changes the pedagogical intermediation paradigm, redefining educational values and principles. The present paper delves into this scenario through the results of a doctoral thesis funded by the Ministry of Science, Innovation and Universities of the Spanish Government. The research is based on a qualitative approach, involving a field study in four schools in the Valencian Community. The information is collected through the study of the school documentation, in-depth interviews with 23 teachers and participant observation sessions. This information is analysed under the postulates of discourse analysis. The results show that teachers see the smart classroom as an innovative paradigm that facilitates active learning. However, the focus tends to be on technologies, which are assumed to optimise learning. Teachers opt for the implementation of technology-mediated methodologies, such as the flipped classroom, PBL or gamification, but these often operate more at the formal level than at the applied level. Despite this, a learning ecosystem is emerging that is significantly different from the traditional one, involving a change of logic in which digital technologies adopt the centrality formerly held by the textbook or the teacher.
Sustainable Teacher Training and SDGs Knowledge: A Study from the Reading Perspective
Authors: Encabo-Fernández Eduardo , Maestre-Espinosa Marina , Jerez-Martínez Isabel , Hernández-Delgado Lourdes
Published: June 2023

This study aims at analysing the relevance of the Sustainable Development Goals (SDGs) in initial teacher education and the prominence given to reading, as well as the relationship between the two issues. The research modality chosen is non-experimental, descriptive (survey) and mixed. On the one hand, a questionnaire designed for 118 students of the different teaching-related degrees offered at the Faculty of Education of Universidad de Murcia (Spain) was used to collect information. On the other hand, the interview script was prepared for five professors from the University who teach in these degrees or postgraduate studies, also related to teaching. The results highlight the poor treatment and general lack of knowledge about the SDGs and the little consideration given to them in the literature. There is a pressing need to change the approach and to advocate for a cross-cutting approach to both issues, also taking advantage of the relationship between literature and SDGs for the benefit of the education community.
Parkinson’s Disease Detection Using Hybrid LSTM-GRU Deep Learning Model
Authors: Rehman Amjad , Saba Tanzila , Mujahid Muhammad , Alamri Faten S. , ElHakim Narmine
Published: June 2023

Parkinson’s disease is the second-most common cause of death and disability as well as the most prevalent neurological disorder. In the last 15 years, the number of cases of PD has doubled. The accurate detection of PD in the early stages is one of the most challenging tasks to ensure individuals can continue to live with as little interference as possible. Yet there are not enough trained neurologists around the world to detect Parkinson’s disease in its early stages. Machine learning methods based on Artificial intelligence have acquired a lot of popularity over the past few decades in medical disease detection. However, these methods do not provide an accurate and timely diagnosis. The overall detection accuracy of machine learning-related models is inadequate. This study collected data from 31 male and female patients, including 195 voices. Approximately six recordings were created per patient, with the length of each recording extending from 1 to 36 s. These voices were recorded in a soundproof studio using an Industrial Acoustics Company (IAC) AKG-C420 head-mounted microphone. The data set was collected to investigate the diagnostic significance of speech and voice abnormalities caused by Parkinson’s disease. An imbalanced dataset is the main contributor of model overfitting and generalization errors, and hence one class has the majority of samples and the other class has minority samples. This problem is addressed in this study by utilizing the three sampling techniques. After balancing the datasets, each class has the same number of samples, which has proven valuable in improving the model’s performance and reducing the overfitting problem. Four performance metrics such as accuracy, precision, recall and f1 score are used to evaluate the effectiveness of the proposed hybrid model. Experiments demonstrated that the proposed model achieved 100% accuracy, recall and f1 score using the balanced dataset with the random oversampling technique and 100% precision, 97% recall, 99% AUC score and 91% f1 score with the SMOTE technique.
Doppler Radar-Based Human Speech Recognition Using Mobile Vision Transformer
Authors: Li Wei , Geng Yongfu , Gao Yang , Ding Qining , Li Dandan , Liu Nanqi , Chen Jinheng
Published: June 2023

As one of the important vital features of the human body, the acquisition of a speech signal plays an important role in human–computer interaction. In this study, voice sounds are gathered and identified using Doppler radar. The skin on the neck vibrates when a person speaks, which causes the vocal cords to vibrate as well. The vibration signal received by the radar will produce a unique micro-Doppler signal according to words with different pronunciations. Following the conversion of these signals into micro-Doppler feature maps, these speech signal maps are categorized and identified. The speech recognition method used in this paper is on neural networks. CNN convolutional neural networks have a lower generalization and accuracy when there are insufficient training samples and sample extraction bias, and the training model is not suitable for use on mobile terminals. MobileViT is a lightweight transformers-based model that can be used for image classification tasks. MobileViT uses a lightweight attention mechanism to extract features with a faster inference speed and smaller model size while ensuring a higher accuracy. Our proposed method does not require large-scale data collection, which is beneficial for different users. In addition, the learning speed is relatively fast, with an accuracy of 99.5%.
Molecular Simulation Techniques as Applied to Silica and Carbon-Based Adsorbents for Carbon Capture
Authors: Wadi Basil , Golmakani Ayub , N.Borhani Tohid , Manovic Vasilije , Nabavi Seyed Ali
Published: June 2023

There has been ongoing interest in research to mitigate climate change through carbon capture (CC) by adsorption. This guideline is meant to introduce computational chemistry techniques in CC by applying them to mesoporous structures and disordered morphologies. The molecular simulation techniques presented here use examples of literature studies on silica and carbon-based adsorbents. An initial summary of molecular simulation techniques and concepts is first presented. This is followed by a section on molecular simulation applications in mesoporous amorphous silica, both functionalized and not. Novel strategies to validate and output useful results are discussed, specifically when modelling chemisorption. The use of computational chemistry to build upon experimental results is reviewed, and a similar summation is presented for carbon-based adsorbents. The final section provides a short review of computational chemistry methods in novel applications and highlights potential complications. Computational chemistry techniques provide a streamlined method of gathering data across a range of conditions. Alongside experimental studies, these techniques can provide valuable information on underlying molecular mechanisms. This paper aims to be a starting point for navigating these numerical methods by providing an initial understanding of how these techniques can be applied to carbon capture while clarifying the current and inherent limitations present.
Dynamic Accounting Model and Method for Carbon Emissions on the Power Grid Side
Authors: He Hengjing , Zhou Shangli , Zhang Leping , Zhao Wei , Xiao Xia
Published: June 2023

Currently, in China’s power grid, the accounting of carbon emissions has shortcomings such as unclear accounting boundaries, slow updating of carbon emission factors (EFs), and a lack of spatiotemporal characteristics. In this study, a dynamic accounting model for carbon emission was constructed based on carbon flow theory and the QIO (Quasi-Input-Output) model using the transmission side, the substation side, and the distribution side as accounting nodes. By utilizing the electricity metering data and carbon EF on the input side of the node, the total carbon emissions flowing into the node could be calculated. Furthermore, based on the electricity metering data on the output side of the node, the carbon emissions and carbon EF flowing out of the node could be calculated. The accounting results of carbon emissions and carbon EF are characterized by flexibility and dynamicity in both spatial and temporal dimensions. Finally, the practicality of the method has been demonstrated through a substation node. The accounting model has a positive impact on accurate carbon emission accounting for the power grid, better tracing of carbon emissions, and effective user guidance on active carbon emission reduction.
Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory
Authors: Tegani Ilyes , Kraa Okba , Ramadan Haitham S. , Ayad Mohamed Yacine
Published: June 2023

This paper proposes a practical solution to address the energy management issue in fuel cell hybrid electric vehicles (FCHEVs). This solution revolves around a powertrain system that contains a fuel cell (FC) as the main supply, a photovoltaic cell (PC) as the secondary energy source, and a battery bank (Batt) as backup storage to compensate for the FC’s low response rate. The energy in this hybrid powertrain system alternated between the designated elements and the load via a DC bus, and to maintain a stable output voltage, the DC link was adjusted using a nonlinear approach that is based on the flatness theory and the nonlinear autoregressive moving average (NARMA-L2) neuro-controller. As for the current regulation loops, the sliding mode technique was employed to attain the high dynamic of the reference signals produced by the energy manager loop. To validate the accuracy of the proposed energy management approach (EMA), a test bench was equipped with digital, electronic circuits and a dSPACE DS-1104 unit. This experimental bench contained a fuel cell emulator FC of 1200 W and 46 A, lithium-ion batteries of 24 V, and a solar source capable of 400 W. The obtained results, indeed, attested to the validity of the approach used, yielding a notable performance during multiple charge variations. This ultimately demonstrated that the management approach enhanced the efficiency of the hybrid powertrain.
Comparative Investigation on the Thermophysical Property and System Performance of R1234yf
Authors: Li Gailian , Jin Tingxiang , Xu Ran , Lv Zijian
Published: June 2023

In this paper, an experimental study of R1234yf replacing R22 in window air conditioning was carried out. The optimum types of capillary tubes and the charge amount were obtained, and the system performance of the window air conditioning charged with R1234yf was tested in an air conditioner performance teat facility under nominal and high-temperature conditions. The results revealed that the cooling capacity and coefficient of performance (COP) of the air conditioner charged with R1234yf were 28.5% and 11.1%, respectively, under nominal conditions, which were lower than that of R22. Under high-temperature conditions, the cooling capacity of R1234yf was still lower than that of R22, but the COP was slightly higher. This suggests that R1234yf may be a suitable substitution for R22 in high-temperature environment.
Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling
Authors: Lu Siyue , Zhang Baoqun , Ma Longfei , Xu Hui , Li Yuantong , Yang Shaobing
Published: June 2023

Central air conditioning in large buildings is an important demand-response resource due to its large load power and strong controllability. Demand-response-oriented air conditioning load modeling needs to calculate the room temperature. The room temperature calculation models commonly used in the existing research cannot easily and accurately calculate the room temperature change of large buildings. Therefore, in order to obtain the temperature change of a large building and its corresponding power potential, this paper first proposes a building model based on CNN (convolutional neural network). Then, in order to fully apply the demand-response potential of the central air conditioning load, this paper puts forward an evaluation method of the load-reduction potential of the central air conditioning cluster based on pre-cooling and develops an economic load-reduction strategy according to the different energy consumption of different buildings in the pre-cooling stage. Finally, multiple building examples with different building parameters and temperature comfort ranges are set up, and the economic advantages of the proposed strategy are illustrated by Cplex solution examples.
Fungal Community Taxa Differ in Diversity and Number between Live and Dead Prunus serotina Ehrh. Wood in a Protected Forest within Its Secondary Range of Distribution
Authors: Baranowska Marlena , Korzeniewicz Robert , Behnke-Borowczyk Jolanta , Kowalkowski Wojciech , Krupiński Dariusz , Purcel Andrzej , Łukowski Adrian
Published: June 2023

Prunus serotina is an important invasive plant species in Poland. It was introduced to European forests, even in places currently protected by law, such as national parks. One major factor contributing to the spread of this species is the lack of infecting pathogens and incomplete knowledge about the relationship between the plant and microorganisms. This study aimed to identify fungal communities collected from freshly cut stumps of live and dead black cherry tree specimens growing in a protected forest. The main working hypotheses were as follows: (i) fungal communities inhabiting the wood of dead and living trees will differ in diversity and the number of taxa; and (ii) saprotrophic fungi will dominate in the wood of dead tree stumps. This study applied Illumina sequencing based on the amplification of the fungal ribosomal ITSI region. The average number of sequences (OTU) obtained from the analysis of dead tree wood was 101,758, while that of living trees was 94,150. These sequences belonged to 312 taxa, among which 254 were isolated from the wood of dead trees and 171 from that of living trees. Among the saprotrophs on dead trees, the following species were identified: Stereum rugosum, Ganoderma adspersum, G. applanatum, Peniophora cinerea, and Ascocoryne cylichnium. On the other hand, in the wood of living trees, Cytospora leucostoma and Botrytis cinerea were the most abundant saprotrophic species. The fungal communities inhabiting the wood of dead and living trees differed in the diversity and abundance of taxa, thus confirming our hypothesis. The results of our research conducted in a protected area indicate that black cherry wood can be naturally colonized by many pathogen species that can further limit its expansion.
Wildfire Susceptibility Mapping Using Deep Learning Algorithms in Two Satellite Imagery Dataset
Authors: Bahadori Nazanin , Razavi-Termeh Seyed Vahid , Sadeghi-Niaraki Abolghasem , Al-Kindi Khalifa M. , Abuhmed Tamer , Nazeri Behrokh , Choi Soo-Mi
Published: June 2023

Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algorithms were leveraged to generate WFSMs using two distinct remote sensing datasets. Specifically, the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 images were utilized to monitor wildfires that transpired during the year 2021. To develop an effective WFSM, two datasets were created by incorporating 599 wildfire locations with Landsat-8 images and 232 sites with MODIS images, as well as twelve factors influencing wildfires. Deep learning algorithms, namely the long short-term memory (LSTM) and recurrent neural network (RNN), were utilized to model wildfire susceptibility using the two datasets. Subsequently, four WFSMs were generated using the LSTM (MODIS), LSTM (Landsat-8), RNN (MODIS), and RNN (Landsat-8) algorithms. The evaluation of the WFSMs was performed using the area under the receiver operating characteristic (ROC) curve (AUC) index. The results revealed that the RNN (MODIS) (AUC = 0.971), RNN (Landsat-8) (AUC = 0.966), LSTM (MODIS) (AUC = 0.964), and LSTM (Landsat-8) (AUC = 0.941) algorithms demonstrated the highest modeling accuracy, respectively. Moreover, the Gini index was employed to assess the impact of the twelve factors on wildfires in the study area. The results of the random forest (RF) algorithm indicated that temperature, wind speed, slope, and topographic wetness index (TWI) parameters had a significant effect on wildfires in the study region. These findings are instrumental in facilitating efficient wildfire management and enhancing community resilience against the detrimental effects of wildfires.
Energy Consumption and Cutting Performance of Battery-Powered Chainsaws
Authors: Pandur Zdravko , Bačić Marin , Šušnjar Marijan , Landekić Matija , Šporčić Mario , Jambreković Branimir , Lepoglavec Kruno
Published: June 2023

The aim of this research is to measure the energy consumption and compare the cutting performance of three types of battery-powered chainsaws (Stihl MSA 200, Stihl MSA 220, and Stihl MSA 300). Tested chainsaws were powered by two different battery packs (Stihl AP 300 S and Stihl AP 500 S) with different energy capacity and output current when cutting two wooden beams, European beech (Fagus sylvatica L.) and black locust (Robinia pseudoacacia L.) with different densities and moisture content. Energy consumption was measured using a multimeter built-in battery charger housing, and consumed energy was noted in watt-hours. Each individual cut was recorded with an action camera at 60 frames per second, and the duration of the cuts recorded in centiseconds was observed on the obtained recordings. Results show significant differences in energy consumption and cutting duration between individual chainsaw/battery combinations. Energy consumption ranged from 2.45 to 4.50 Wh per cut for beech (Stihl MSA300E_AP500S and Stihl MSA220_AP300S, respectively) and from 3.10 to 5.00 Wh per cut for black locust (Stihl MSA300M_AP500S and Stihl MSA220_AP500S, respectively). Duration of the cut ranged from 3.48 to 9.24 s per cut for beech (Stihl MSA300M/H_AP500S and Stihl MSA220_AP300S, respectively) and from 3.74 to 9.35 s per cut for the black locust (Stihl MSA300M_AP500S and Stihl MSA220_AP300S, respectively). In general, it can be concluded that more powerful chainsaw/battery combinations (Stihl MSA300_AP500S) consumed less energy per cut and had better cutting performance (shorter cut). In that regard, more powerful chainsaws had greater cutting efficiency, i.e., more cuts on one charge (battery) but shorter cutting time on one charge. The effect of tree species (fresh-sawed beech/air-dried black locust) on energy consumption and cutting performance is absent on the most powerful chainsaw/battery combinations. Today, lithium-ion batteries still have 70 times less energy density than petrol. However, if there is a significant increase in the energy density of the batteries in the coming times, battery chainsaws will most certainly displace petrol chainsaws from use in the future.
Impacts of Forest Management on the Biodiversity and Sustainability of Carya dabieshanensis Forests
Authors: Huang Cheng , Fu Songling , Tong Yinhao , Ma Xiaomin , Yuan Feiyang , Ma Yuhua , Feng Chun , Liu Hua
Published: June 2023

Natural secondary Carya dabieshanensis forests comprise the natural wealth of the Dabie Mountains, which maintain the ecological balance of this region and enhance the incomes of farmers. However, forest ecosystems are being increasingly challenged due to increasing long-term anthropogenic development and management. Elucidating the impacts of management intensity and duration on the diversity and stability of vegetation communities in natural secondary Carya dabieshanensis forests is of great significance toward achieving sustainable forest management. For this study, we compared the effects of three forest management intensities (no management (CK), extensive management (EM), and intensive management (IM)), and five management durations (0, 3, 8, 15, and 20 y) on species diversity and the stability of vegetation communities (trees, shrubs, and herbaceous layers) of a Carya dabieshanensis forest. Our results revealed that the diversity of the vegetation communities continued to decline following the onset of management activities. The diversity, dominance, evenness, and richness indices of the vegetation communities decreased by 53.37%–62.77%, 46.04%–59.17%, 32.58%–53.55%, and 50.18%–51.30%, respectively, after 20 y of forest management. Intensive management translated to species diversity loss more so than extensive management. All vegetation communities of the forest stands under study were generally unstable; however, the stability they did have was not at risk under greater management intensity and duration, and neither did greater species diversity enhance it. This study provides new evidence to support the non-synchronicity of species diversity and community stability in forest resident vegetation communities. Since the species diversity and structural composition of tree layers have a positive effect on community stability, so we suggest that both extensive and intensive forest lands need to retain or replant more tree species other than Carya dabieshanensis.
Physiological and Biochemical Traits of Needles Imply That Understory Light Conditions in the Growing Season May Be Favorable to Pinus koraiensis Trees
Authors: Li Wenkai , Li Bei , Ma Xiao , Saha Sudipta , Wu Haibo , Zhang Peng , Shen Hailong
Published: June 2023

Light is often considered the primary factor leading to the regeneration failure of Korean pines (Pinus koraiensis) under the forest canopy. However, studies on the effect of light on Korean pines mainly focus on the use of an artificial sunshade net to control shade; field studies on the canopy are extremely scarce, and the current experimental results are contradictory. For a deeper understanding of the relationship between light conditions and understory Korean pine trees, the conditions of low, middle, high and full light (control) under the forest were tested at 18 years of age. The photosynthetic pigments, chlorophyll fluorescence, non-structural carbohydrate metabolism, antioxidant enzyme activity, and nutrient concentrations of current-year needles from Korean pine trees were measured. From June to September, light intensity and quality decreased under full light, but following leaf fall, understory light conditions improved slightly. As the light conditions improved, the photosynthetic pigments in the needles decreased, but Car/Chl were highest in the needles under full light. All light conditions had a positive correlation with glucose concentrations and Rubisco activity. Full-light needles had the highest APX activity, DPPH scavenging capacity, and proline concentration, as well as higher NPQ and lower Fv/Fm readings. This indicated that full-light Korean pine trees were stressed and inhibited photosynthesis to some extent, while the understory light environment may alleviate stress. The conservative strategy of storing more starch and using less glucose in understory Korean pine trees may be one of the reasons for the observed differences in growth rates among Korean pine trees under varying light conditions. Overall, this study implies that understory light during the growing season is not always unfavorable to 18-year-old Korean pine trees; this means that 18-year-old Korean pine trees still have shade tolerance to some extent and are capable of living under a canopy of deciduous trees.
Concentrated Manipueira as an Alternative Low-Cost Substrate to Rhodotorula glutinis for Biotechnological Production of High Contents of Carotenoids
Authors: Maia Felipe de Andrade , Igreja Willen Silva , Xavier Ana Augusta Odorissi , Mercadante Adriana Zerlotti , Lopes Alessandra Santos , Chisté Renan Campos
Published: June 2023

Carotenoids are fat-soluble bioactive compounds found in plants, animals, and microorganisms, which have a range of health benefits and great technological and industrial importance in the fields of pharmaceuticals, food, and biotechnology. Yeasts of the genus Rhodotorula can biosynthesize large amounts of carotenoids under environmental stress conditions, and this process may become industrially feasible if production costs are minimized using low-cost by-products as nutrient sources. As cassava roots are produced and consumed in large quantities in the Amazon biome, our research aimed to investigate the best cultivation conditions in a benchtop bioreactor for the biotechnological production of carotenoids by R. glutinis using manipueira as a low-cost substrate. Manipueira is wastewater generated during cassava flour production, and it was concentrated at 12 °Brix for this study. In addition, the carotenoid composition, at the best cultivation condition, was analyzed by LC-MS. Higher carotenoid contents (1410 μg/g of dried biomass) than described in the literature, and biomass (10 g), were produced by R. glutinis using concentrated manipueira as a low-cost substrate after cultivation at pH 5, 35 °C and agitation at 150 rpm. At the best cultivation conditions, β-carotene, lutein, and 5,8-epoxy-lutein were the major carotenoids, with the latter two compounds being identified for the first time in the biomass of R. glutinis. Therefore, concentrated manipueira can be seen as a feasible alternative and low-cost substrate to be used for the biotechnological production of high carotenoid contents by R. glutinis.
A Comparison of the Physiological Responses to Heat Stress of Two Sizes of Juvenile Spotted Seabass (Lateolabrax maculatus)
Authors: Qin Huihui , Long Zhongying , Huang Zhangfan , Ma Jianrong , Kong Lumin , Lin Yi , Lin Hao , Zhou Sishun , Li Zhongbao
Published: June 2023

Temperature affects the metabolism of fish, and fish of different sizes have different tolerances to temperature. The aim of this experiment was to compare two sizes of juvenile spotted seabass, Lateolabrax maculatus (with average weights of 57.91 ± 11.57 g and 13.92 ± 2.77 g, respectively) for changes in physiological, biochemical, and molecular mechanisms under acute heat stress. Experimental fish were exposed to acute temperature increasing from 23 °C to 32 °C, and the mortality rate was noted at various heat stress exposures (0, 3, 6, 12, 24, 48, and 72 h). Moreover, serum and liver were obtained before and after heat stress. The activities of aspartate aminotransferase (AST), alanine aminotransferase (ALT), superoxide dismutase (SOD), malondialdehyde (MDA), lactic acid (LD), lactate dehydrogenase (LDH), glucose, and hepatic glycogen, and the expression of heat shock proteins (HSP70, HSP90) and apoptosis-related genes (BAX, caspase-3) in two sizes of spotted seabass were measured. Results showed that the contents of AST, ALT, SOD, MDA, LD, and glucose as well as the expression level of BAX and mortality were higher in large spotted seabass than in small spotted seabass within 12 h. These results indicate that the large spotted seabass had higher levels of oxidative stress and more severe liver damage, resulting in a higher mortality. Furthermore, the HSPs expression level of small spotted seabass was higher and the mortality was lower than that of large spotted seabass. Therefore, we considered that the large spotted seabass has lower levels of HSPs expression, causing their physiological response to be elevated to resist heat stress. In conclusion, spotted seabass with larger size has a poorer tolerance to heat stress compared with spotted seabass with smaller size. The smaller fish size was possibly resistant to heat stress by regulating the HSPs expression level in a more active extent.
Nutritional Composition of Hass Avocado Pulp
Authors: Ford Nikki A. , Spagnuolo Paul , Kraft Jana , Bauer Ella
Published: June 2023

Avocados (Persea americana) are a unique fruit that can provide health benefits when included in a healthy diet. As health care moves towards precision health and targeted therapies or preventative medicine, it is critical to understand foods and their dietary components. The nutritional composition and plant physiology of the Hass avocado is strikingly different from other fruits. This paper reviews the nutrient and bioactive composition of the edible portion of the Hass avocado (pulp) reported in the literature and from commercial lab analyses of the current market supply of fresh Hass avocados. These results provide comprehensive data on what nutrients and bioactives are in avocado and the quantity of these nutrients. We discuss the reasons for nutrient composition variations and review some potential health benefits of bioactive compounds found in Hass avocados.
Biochemical Characterization of Novel GH6 Endoglucanase from Myxococcus sp. B6-1 and Its Effects on Agricultural Straws Saccharification
Authors: Huang Zhen , Ni Guorong , Dai Longhua , Zhang Weiqi , Feng Siting , Wang Fei
Published: June 2023

Cellulase has been widely used in many industrial fields, such as feed and food industry, because it can hydrolyze cellulose to oligosaccharides with a lower degree of polymerization. Endo-β-1,4-glucanase is a critical speed-limiting cellulase in the saccharification process. In this study, endo-β-1,4-glucanase gene (CelA257) from Myxococcus sp. B6-1 was cloned and expressed in Escherichia coli. CelA257 contained carbohydrate-binding module (CBM) 4-9 and glycosyl hydrolase (GH) family 6 domain that shares 54.7% identity with endoglucanase from Streptomyces halstedii. The recombinant enzyme exhibited optimal activity at pH 6.5 and 50 °C and was stable over a broad pH (6–9.5) range and temperature < 50 °C. CelA257 exhibited broad substrate specificity to barley β-glucan, lichenin, CMC, chitosan, laminarin, avicel, and phosphoric acid swollen cellulose (PASC). CelA257 degraded both cellotetrose (G4) and cellppentaose (G5) to cellobiose (G2) and cellotriose (G3). Adding CelA257 increased the release of reducing sugars in crop straw powers, including wheat straw (0.18 mg/mL), rape straw (0.42 mg/mL), rice straw (0.16 mg/mL), peanut straw (0.16 mg/mL), and corn straw (0.61 mg/mL). This study provides a potential additive in biomass saccharification applications.
Bioproducts from Passiflora cincinnata Seeds: The Brazilian Caatinga Passion Fruit
Authors: Reis Carolina Cruzeiro , Freitas Suely Pereira , Lorentino Carolline Margot Albanez , Fagundes Thayssa da Silva Ferreira , da Matta Virgínia Martins , dos Santos André Luis Souza , Moreira Davyson de Lima , Kunigami Claudete Norie , Jung Eliane Przytyk , Ribeiro Leilson de Oliveira
Published: June 2023

The present work aimed to obtain bioproducts from Passiflora cincinnata seeds, the Brazilian Caatinga passion fruit, as well as to determine their physical, chemical and biological properties. The seeds were pressed in a continuous press to obtain the oil, which showed an oxidative stability of 5.37 h and a fatty profile rich in linoleic acid. The defatted seeds were evaluated for the recovery of antioxidant compounds by a central rotation experimental design, varying temperature (32–74 °C), ethanol (13–97%) and solid–liquid ratio (1:10–1:60 m/v). The best operational condition (74 °C, 58% ethanol, 1:48) yielded an extract composed mainly of lignans, which showed antioxidant capacity and antimicrobial activity against Gram-positive and Gram-negative bacteria. The microencapsulation of linoleic acid-rich oil through spray drying has proven to be an effective method for protecting the oil. Furthermore, the addition of the antioxidant extract to the formulation increased the oxidative stability of the product to 30% (6.97 h), compared to microencapsulated oil without the addition of the antioxidant extract (5.27 h). The microparticles also exhibited favorable technological characteristics, such as low hygroscopicity and high water solubility. Thus, it was possible to obtain three bioproducts from the Brazilian Caatinga passion fruit seeds: the oil rich in linoleic acid (an essential fatty acid), antioxidant extract from the defatted seeds and the oil microparticles added from the antioxidant extract.
Fractal Features of Fracture Networks and Key Attributes of Their Models
Authors: Mondragón-Nava Hugo , Samayoa Didier , Mena Baltasar , Balankin Alexander S.
Published: June 2023

This work is devoted to the modeling of fracture networks. The main attention is focused on the fractal features of the fracture systems in geological formations and reservoirs. Two new kinds of fracture network models are introduced. The first is based on the Bernoulli percolation of straight slots in regular lattices. The second explores the site percolation in scale-free networks embedded in the two- and three-dimensional lattices. The key attributes of the model fracture networks are sketched. Surprisingly, we found that the number of effective spatial degrees of freedom of the scale-free fracture network models is determined by the network embedding dimension and does not depend on the degree distribution. The effects of degree distribution on the other fractal features of the model fracture networks are scrutinized.
Ice-Templated and Cross-Linked Xanthan-Based Hydrogels: Towards Tailor-Made Properties
Authors: Raschip Irina Elena , Fifere Nicusor , Lazar Maria Marinela , Hitruc Gabriela-Elena , Dinu Maria Valentina
Published: June 2023

The use of polysaccharides with good film-forming properties in food packaging systems is a promising area of research. Xanthan gum (XG), an extracellular polysaccharide, has many industrial uses, including as a common food additive (E415). It is an effective thickening agent, emulsifier, and stabilizer that prevents ingredients from separating. Nevertheless, XG-based polymer films have some disadvantages, such as poor mechanical properties and high hydrophilic features, which reduce their stability when exposed to moisture and create difficulties in processing and handling. Thus, the objective of this work was to stabilize a XG matrix by cross-linking it with glycerol diglycidyl ether, 1,4-butanediol diglycidyl ether, or epichlorohydrin below the freezing point of the reaction mixture. Cryogelation is an ecological, friendly, and versatile method of preparing biomaterials with improved physicochemical properties. Using this technique, XG-based cryogels were successfully prepared in the form of microspheres, monoliths, and films. The XG-based cryogels were characterized by FTIR, SEM, AFM, swelling kinetics, and compressive tests. A heterogeneous morphology with interconnected pores, with an average pore size depending on both the nature of the cross-linker and the cross-linking ratio, was found. The use of a larger amount of cross-linker led to both a much more compact structure of the pore walls and to a significant decrease in the average pore size. The uniaxial compression tests indicated that the XG-based cryogels cross-linked with 1,4-butanediol diglycidyl ether exhibited the best elasticity, sustaining maximum deformations of 97.67%, 90.10%, and 81.80%, respectively.
Liquid Biopsy at the Frontier of Kidney Diseases: Application of Exosomes in Diagnostics and Therapeutics
Authors: Agborbesong Ewud , Bissler John , Li Xiaogang
Published: June 2023

In the era of precision medicine, liquid biopsy techniques, especially the use of urine analysis, represent a paradigm shift in the identification of biomarkers, with considerable implications for clinical practice in the field of nephrology. In kidney diseases, the use of this non-invasive tool to identify specific and sensitive biomarkers other than plasma creatinine and the glomerular filtration rate is becoming crucial for the diagnosis and assessment of a patient’s condition. In recent years, studies have drawn attention to the importance of exosomes for diagnostic and therapeutic purposes in kidney diseases. Exosomes are nano-sized extracellular vesicles with a lipid bilayer structure, composed of a variety of biologically active substances. In the context of kidney diseases, studies have demonstrated that exosomes are valuable carriers of information and are delivery vectors, rendering them appealing candidates as biomarkers and drug delivery vehicles with beneficial therapeutic outcomes for kidney diseases. This review summarizes the applications of exosomes in kidney diseases, emphasizing the current biomarkers of renal diseases identified from urinary exosomes and the therapeutic applications of exosomes with reference to drug delivery and immunomodulation. Finally, we discuss the challenges encountered when using exosomes for therapeutic purposes and how these may affect its clinical applications.
Cryptic Diversity and Demographic Expansion of Plasmodium knowlesi Malaria Vectors in Malaysia
Authors: Pramasivan Sandthya , Low Van Lun , Jeyaprakasam Nantha Kumar , Liew Jonathan Wee Kent , Ngui Romano , Vythilingam Indra
Published: June 2023

Although Malaysia is considered free of human malaria, there has been a growing number of Plasmodium knowlesi cases. This alarming trend highlighted the need for our understanding of this parasite and its associated vectors, especially considering the role of genetic diversity in the adaptation and evolution among vectors in endemic areas, which is currently a significant knowledge gap in their fundamental biology. Thus, this study aimed to investigate the genetic diversity of Anopheles balabacensis, Anopheles cracens, Anopheles introlatus, and Anopheles latens—the vectors for P. knowlesi malaria in Malaysia. Based on cytochrome c oxidase 1 (CO1) and internal transcribed spacer 2 (ITS2) markers, the genealogic networks of An. latens showed a separation of the haplotypes between Peninsular Malaysia and Malaysia Borneo, forming two distinct clusters. Additionally, the genetic distances between these clusters were high (2.3–5.2% for CO1) and (2.3–4.7% for ITS2), indicating the likely presence of two distinct species or cryptic species within An. latens. In contrast, no distinct clusters were observed in An. cracens, An. balabacensis, or An. introlatus, implying a lack of pronounced genetic differentiation among their populations. It is worth noting that there were varying levels of polymorphism observed across the different subpopulations, highlighting some levels of genetic variation within these mosquito species. Nevertheless, further analyses revealed that all four species have undergone demographic expansion, suggesting population growth and potential range expansion for these vectors in this region.
miRNAs: Potential as Biomarkers and Therapeutic Targets for Cancer
Authors: Chakrabortty Atonu , Patton Daniel J. , Smith Bruce F. , Agarwal Payal
Published: June 2023

MicroRNAs (miRNAs) are single-stranded, non-coding RNA molecules that regulate gene expression post-transcriptionally by binding to messenger RNAs. miRNAs are important regulators of gene expression, and their dysregulation is implicated in many human and canine diseases. Most cancers tested to date have been shown to express altered miRNA levels, which indicates their potential importance in the oncogenic process. Based on this evidence, numerous miRNAs have been suggested as potential cancer biomarkers for both diagnosis and prognosis. miRNA-based therapies have also been tested in different cancers and have provided measurable clinical benefits to patients. In addition, understanding miRNA biogenesis and regulatory mechanisms in cancer can provide important knowledge about resistance to chemotherapies, leading to more personalized cancer treatment. In this review, we comprehensively summarized the importance of miRNA in human and canine cancer research. We discussed the current state of development and potential for the miRNA as both a diagnostic marker and a therapeutic target.
Coupling Geotechnical Numerical Analysis with Machine Learning for Observational Method Projects
Authors: Mitelman Amichai , Yang Beverly , Urlainis Alon , Elmo Davide
Published: June 2023

In observational method projects in geotechnical engineering, the final geotechnical design is decided upon during actual construction, depending on the observed behavior of the ground. Hence, engineers must be prepared to make crucial decisions promptly, with few available guidelines. In this paper, we propose coupling numerical analysis with machine learning (ML) algorithms for enhancing the decision process in observational method projects. The proposed methodology consists of two main computational steps: (1) data generation, where multiple numerical models are automatically generated according to the anticipated range of input parameters, and (2) data analysis, where input parameters and model results are analyzed with ML models. Using the case study of the Semel tunnel in Tel Aviv, Israel, we demonstrate how this computational process can contribute to the success of observational method projects through (1) the computation of feature importance, which can assist with better identifying the key features that drive failure prior to project execution, (2) providing insights regarding the monitoring plan, as correlative relationships between various results can be tested, and (3) instantaneous predictions during construction.