bluuebunny/crossref_metadata_2025 huggingface dataset:
MicroRNA-126-3p/5p and Aortic Stiffness in Patients with Turner Syndrome
Authors: Abu-Halima Masood , Oberhoffer Felix Sebastian , Wagner Viktoria , Abd El Rahman Mohamed , Jung Anna-Maria , Zemlin Michael , Rohrer Tilman R. , Meese Eckart , Abdul-Khaliq Hashim
Published: July 2022

Background: Turner Syndrome (TS) is a relatively rare X-chromosomal disease with increased cardiovascular morbidity and mortality. This study aimed to identify whether the circulating miR-126-3p/5p are involved in the pathophysiology of vascular dysfunction in TS. Methods: Using the RT-qPCR, the abundance levels of miR-126-3p and miR-126-5p were determined in 33 TS patients and 33 age-matched healthy volunteers (HVs). Vascular screening, including the assessment of blood pressure, pulse wave velocity, augmentation index, aortic deformation, arterial distensibility, and arterial elastance, was conducted in TS patients and HVs. Results: The abundance levels of miR-126-3p and miR-126-5p were significantly higher in TS patients compared to HVs (p < 0.0001). Within the TS cohort, miR-126-3p/5p correlated significantly with aortic deformation (r = 0.47, p = 0.01; r = 0.48, p < 0.01) and arterial distensibility (r = 0.55, p < 0.01; r = 0.48, p < 0.01). In addition, a significant negative correlation was demonstrated between miR-126-3p and arterial elastance (r = −0.48, p = 0.01). The receiver operating characteristic analysis showed that miR-126-3p and miR-126-5p separated the tested groups with high sensitivity and specificity. Conclusions: The abundance levels of miR-126-3p and miR-126-5p were significantly higher in TS patients compared to HVs. Within the TS cohort, a lower abundance level of miR-126-3p and miR-126-5p was linked with a significantly higher aortic stiffness.
Diagnosis of the Extreme Climate Events of Temperature and Precipitation in Metropolitan Lima during 1965–2013
Authors: Giráldez Lucy , Silva Yamina , Flores-Rojas José L. , Trasmonte Grace
Published: July 2022

The most extreme precipitation event in Metropolitan Lima (ML) occurred on 15 January 1970 (16 mm), this event caused serious damage, and the real vulnerability of this city was evidenced; the population is still not prepared to resist events of this nature. This research describes the local climate variability and extreme climate indices of temperature and precipitation. In addition, the most extreme precipitation event in ML is analyzed. Extreme climate indices were identified based on the methodology proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Some extreme temperature indices highlight an initial trend toward warm conditions (1965–1998); this trend has changed towards cold conditions since 1999, consistent with the thermal cooling during the last two decades in ML (−0.5 °C/decade) and other coastal areas of Peru. The variations of extreme temperature indices are mainly modulated by sea-surface temperature (SST) alterations in the Niño 1 + 2 region (moderate to strong correlations were found). Extreme precipitation indices show trends toward wet conditions after the 1980s, the influence of the Pacific Ocean SST on the extreme precipitation indices in ML is weak and variable in sign. The most extreme precipitation event in ML is associated with a convergence process between moisture fluxes from the east (Amazon region) at high and mid levels and moisture fluxes from the west (Pacific Ocean) at low levels, and near the surface.
N-Doped and Sulfonated Reduced Graphene Oxide Supported PtNi Nanoparticles as Highly Efficient Electrocatalysts for Oxygen Reduction Reaction
Authors: Ouyang Chun , Xun Damao , Jian Gang
Published: July 2022

N-doping and sulfonation is prepared on the reduced graphene oxide (rGO) support for PtNi nanoparticles (PtNi/S-(N)rGO) by a simple method of hydrothermal synthesis and thermal decomposition. The specific surface area increases from 180.7 m2/g of PtNi/rGo to 293.5 m2/g of PtNi/S-(N)rGO. The surface morphology shows wrinkles sites, which are separated by the sulfonated groups. The catalytic stability and efficiency are improved by the anchoring effect of sulfonated groups and evenly distribution of nanoparticles, respectively. The synergistic effect of N-doping and sulfonation can be in favor of catalytic efficiency by the increase of number of electron transfer. The half-wave potential of the PtNi/S-(N)rGO catalyst is up to 0.632 V, a small positive shift compared to the Pt/C catalyst. The durability of the PtNi/S-(N)rGO is 2.6 times higher than of the Pt/C catalyst after 5000 repeated cycles. The peak power of the PtNi/S-(N)rGO catalyst increased 37.5% compared to the Pt/C catalyst. Therefore, the stability and catalytic efficiency are improved by the PtNi/S-(N)rGO catalyst applied in proton exchange membrane fuel cell (PEMFC) compared to the commercial Pt/C catalyst.
Influence of Oxygen Flow Rate on the Properties of FeOXNY Films Obtained by Magnetron Sputtering at High Nitrogen Pressure
Authors: Grafoute Moussa , N’Djoré Kouamé , Petitjean Carine , Pierson Jean , Rousselot Christophe
Published: July 2022

Fe-O-N films were successfully deposited by magnetron sputtering of an iron target in Ar-N2-O2 reactive mixtures at high nitrogen partial pressure 1.11 Pa (Q(N2) = 8 sccm) using a constant flow rate of argon and an oxygen flow rate Q(O2) varying from 0 to 1.6 sccm. The chemical composition and the structural and microstructural nature of these films were characterized using Rutherford Backscattering Spectrometry, X-ray diffraction, and Conversion Electron Mössbauer Spectrometry, respectively. The results showed that the films deposited without oxygen are composed of a single phase of γ″-FeN, whereas the other films do not consist of pure oxides but oxidelike oxynitrides. With higher oxygen content, the films are well-crystallized in the α-Fe2O3 structure. At intermediate oxygen flow rate, the films are rather poorly crystallized and can be described as a mixture of oxide γ-Fe2O3/Fe3O4. In addition, the electrical behavior of the films evolved from a metallic one to a semiconductor one, which is in total agreement with other investigations. Comparatively to a previous study carried out at low nitrogen partial pressure (0.25 Pa), this behavior of films prepared at higher nitrogen partial pressure (1.11 Pa) could be caused by a catalytic effect of nitrogen on the crystallization of the hematite structure.
Performance Investigation of the Incorporation of Ground Granulated Blast Furnace Slag with Fly Ash in Autoclaved Aerated Concrete
Authors: Bernard Vijay Antony Raj , Renuka Senthil Muthalvan , Avudaiappan Siva , Umarani Chockkalingam , Amran Mugahed , Guindos Pablo , Fediuk Roman , Ivanovich Vatin Nikolai
Published: July 2022

Autoclaved aerated concrete (AAC) is one of the most common types of lightweight cellular concrete, having a density of approximately one-fourth of that of conventional plain cement concrete. The use of industrial waste materials in concrete as a replacement for cement has garnered a lot of attention in recent years as a way to reduce the environmental effect of concrete. In this study, an attempt has been made to study the effect of AAC blocks made of industrial wastes such as fly Ash (FA) and ground granulated blast furnace slag (GGBS). Fly ash, along with different dosages of GGBS, was used as a partial replacement for cement in the production of AAC. For all the different dosages, microstructural analysis was performed using a Scanning electron microscope (SEM), X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDAX), and Fourier transform infrared spectroscopy (FTIR). Mechanical performances of AAC were determined by conducting various tests like compressive strength, modulus of rupture, dry density, and water absorption. The results revealed that the dosage of “15% GGBS + 85% cement” has maximum compressive strength, modulus of elasticity, and modulus of rupture made of Class F Fly Ash when compared to Class C Fly Ash based AAC blocks. Besides, the incorporation of GGBS in the manufacturing process would increase the compressive strength of AAC up to 68%. Hence, it is recommended to use 15% GGBS + 85% cement as a potential rate of replacement, to improve the mechanical properties of AAC blocks significantly.
Intratemporal Facial Nerve Schwannomas: A Review of 45 Cases in A Single Center
Authors: Kitama Tsubasa , Hosoya Makoto , Noguchi Masaru , Nishiyama Takanori , Wakabayashi Takeshi , Shimanuki Marie N. , Yazawa Masaki , Inoue Yasuhiro , Kanzaki Jin , Ogawa Kaoru , Oishi Naoki
Published: July 2022

There are no established indications for facial nerve schwannoma treatment, including surgery, radiation and follow-up observation, and it is difficult to determine treatment policy uniformly. The treatment policy was examined from each treatment course. Data of patients with facial nerve schwannomas at our hospital from 1987 to 2018 were retrospectively examined. Their age, sex, clinical symptoms, tumor localization, treatment policies and outcomes were reviewed. In total, 22 patients underwent surgery and 1 patient underwent radiotherapy; 22 patients were followed up without treatment. After total resection, there were no tumor recurrences, and most patients had grade 3 or 4 postoperative facial paralysis. After subtotal resection, tumor regrowth was observed in four patients and reoperation was required in two patients. Facial nerve function was maintained in four patients and was decreased in two patients. During follow-up, six patients showed tumor growth. Only one patient had worsening facial nerve paralysis; four patients underwent facial nerve decompression owing to facial nerve paralysis during follow-up. If the tumor compresses the brain or it is prone to growth, surgery may be indicated, and when the preoperative facial nerve function is grade ≤ 3, consideration should be given to preserving facial nerve function and subtotal resection should be indicated. If the preoperative facial nerve function is grade ≥ 3, total resection with nerve grafting is an option to prevent regrowth. If there is no brain compression or tumor growth, the follow-up is a good indication, and decompression should be considered in facial nerve paralysis cases.
Assessing the Accuracy of an Artificial Intelligence-Based Segmentation Algorithm for the Thoracic Aorta in Computed Tomography Applications
Authors: Artzner Christoph , Bongers Malte N. , Kärgel Rainer , Faby Sebastian , Hefferman Gerald , Herrmann Judith , Nopper Svenja L. , Perl Regine M. , Walter Sven S.
Published: July 2022

The aim was to evaluate the accuracy of a prototypical artificial intelligence-based algorithm for automated segmentation and diameter measurement of the thoracic aorta (TA) using CT. One hundred twenty-two patients who underwent dual-source CT were retrospectively included. Ninety-three of these patients had been administered intravenous iodinated contrast. Images were evaluated using the prototypical algorithm, which segments the TA and determines the corresponding diameters at predefined anatomical locations based on the American Heart Association guidelines. The reference standard was established by two radiologists individually in a blinded, randomized fashion. Equivalency was tested and inter-reader agreement was assessed using intra-class correlation (ICC). In total, 99.2% of the parameters measured by the prototype were assessable. In nine patients, the prototype failed to determine one diameter along the vessel. Measurements along the TA did not differ between the algorithm and readers (p > 0.05), establishing equivalence. Inter-reader agreement between the algorithm and readers (ICC ≥ 0.961; 95% CI: 0.940–0.974), and between the readers was excellent (ICC ≥ 0.879; 95% CI: 0.818–0.92). The evaluated prototypical AI-based algorithm accurately measured TA diameters at each region of interest independent of the use of either contrast utilization or pathology. This indicates that the prototypical algorithm has substantial potential as a valuable tool in the rapid clinical evaluation of aortic pathology.
Machine Learning for Anxiety Detection Using Biosignals: A Review
Authors: Ancillon Lou , Elgendi Mohamed , Menon Carlo
Published: July 2022

Anxiety disorder (AD) is a major mental health illness. However, due to the many symptoms and confounding factors associated with AD, it is difficult to diagnose, and patients remain untreated for a long time. Therefore, researchers have become increasingly interested in non-invasive biosignals, such as electroencephalography (EEG), electrocardiogram (ECG), electrodermal response (EDA), and respiration (RSP). Applying machine learning to these signals enables clinicians to recognize patterns of anxiety and differentiate a sick patient from a healthy one. Further, models with multiple and diverse biosignals have been developed to improve accuracy and convenience. This paper reviews and summarizes studies published from 2012 to 2022 that applied different machine learning algorithms with various biosignals. In doing so, it offers perspectives on the strengths and weaknesses of current developments to guide future advancements in anxiety detection. Specifically, this literature review reveals promising measurement accuracies ranging from 55% to 98% for studies with sample sizes of 10 to 102 participants. On average, studies using only EEG seemed to obtain the best performance, but the most accurate results were obtained with EDA, RSP, and heart rate. Random forest and support vector machines were found to be widely used machine learning methods, and they lead to good results as long as feature selection has been performed. Neural networks are also extensively used and provide good accuracy, with the benefit that no feature selection is needed. This review also comments on the effective combinations of modalities and the success of different models for detecting anxiety.
An Unsupervised Neural Network Feature Selection and 1D Convolution Neural Network Classification for Screening of Parkinsonism
Authors: Mian Tariq Saeed
Published: July 2022

Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease. It has a slow progressing neurodegenerative disorder rate. PD patients have multiple motor and non-motor symptoms, including vocal impairment, which is one of the main symptoms. The identification of PD based on vocal disorders is at the forefront of research. In this paper, an experimental study is performed on an open source Kaggle PD speech dataset and novel comparative techniques were employed to identify PD. We proposed an unsupervised autoencoder feature selection technique, and passed the compressed features to supervised machine-learning (ML) algorithms. We also investigated the state-of-the-art deep learning 1D convolutional neural network (CNN-1D) for PD classification. In this study, the proposed algorithms are support vector machine, logistic regression, random forest, naïve Bayes, and CNN-1D. The classifier performance is evaluated in terms of accuracy score, precision, recall, and F1 score measure. The proposed 1D-CNN model shows the highest result of 0.927%, and logistic regression shows 0.922% on the benchmark dataset in terms of F1 measure. The major contribution of the proposed approach is that unsupervised neural network feature selection has not previously been investigated in Parkinson’s detection. Clinicians can use these techniques to analyze the symptoms presented by patients and, based on the results of the above algorithms, can diagnose the disease at an early stage, which will allow for improved future treatment and care.
A Sustainable Deep Learning-Based Framework for Automated Segmentation of COVID-19 Infected Regions: Using U-Net with an Attention Mechanism and Boundary Loss Function
Authors: Ahmed Imran , Chehri Abdellah , Jeon Gwanggil
Published: July 2022

COVID-19 has been spreading rapidly, affecting billions of people globally, with significant public health impacts. Biomedical imaging, such as computed tomography (CT), has significant potential as a possible substitute for the screening process. Because of this, automatic segmentation of images is highly desirable as clinical decision support for an extensive evaluation of disease control and monitoring. It is a dynamic tool and performs a central role in precise or accurate segmentation of infected areas or regions in CT scans, thus helping in screening, diagnosing, and disease monitoring. For this purpose, we introduced a deep learning framework for automated segmentation of COVID-19 infected lesions/regions in lung CT scan images. Specifically, we adopted a segmentation model, i.e., U-Net, and utilized an attention mechanism to enhance the framework’s ability for the segmentation of virus-infected regions. Since all of the features extracted or obtained from the encoders are not valuable for segmentation; thus, we applied the U-Net architecture with a mechanism of attention for a better representation of the features. Moreover, we applied a boundary loss function to deal with small and unbalanced lesion segmentation’s. Using different public CT scan image data sets, we validated the framework’s effectiveness in contrast with other segmentation techniques. The experimental outcomes showed the improved performance of the presented framework for the automated segmentation of lungs and infected areas in CT scan images. We also considered both boundary loss and weighted binary cross-entropy dice loss function. The overall dice accuracies of the framework are 0.93 and 0.76 for lungs and COVID-19 infected areas/regions.
Enhanced Machine Learning Approach for Accurate and Fast Resolution of Inverse Scattering Problem in Breast Cancer Detection
Authors: Costanzo Sandra , Flores Alexandra
Published: July 2022

An improved machine learning approach is presented in this paper to guarantee the fast convergence of the Born Iterative Method, even in the presence of strong scatterers, by assuming a single operating frequency and a reduced number of antennas in the scattering setup. The initial estimation of the dielectric profile, provided by the Born Iterative Method, was processed by a specific convolutional neural network to improve the reconstruction using a fast machine learning approach. To ensure rapid convergence, a proper choice of the initial guess in terms of the minimum permittivity value over the entire domain was also made. Numerical validations on realistic breast phantoms were illustrated, demonstrating an average error of 2.4% and an accuracy greater than 96% for all considered tests, even when considering a single operating frequency and a reduced amount of training data.
Coupling Effects on Distributed Multi-Propeller Channel Wing at Low Speed Condition
Authors: Zhao Junmin , Fan Zhongyun , Chang Min , Wang Gang
Published: July 2022

Channel wing is a propeller-coupling layout that has good low-speed performance and S/VTOL potential. Focused on the application of this layout at the S/VTOL stage, this paper attempts to find the interaction mechanism for the distributed propeller channel wing. Firstly, the computation method based on RANS equations for propeller–wing integration was established with Momentum Source Method, which was compared with the unsteady Sliding Mesh method and validated by a ducted propeller. Secondly, the performances and aerodynamic characteristics of the single-propeller channel wings with two different airfoils were analyzed, and a ground test for the scaled model was conducted. Finally, a four-propeller channel wing was analyzed and compared with single-propeller channel wing, then the flow field characteristics were discussed in depth. The study shows that the airfoil shape will strongly affect the lift of channel wing at S/VTOL stage. Multi-propeller channel wing analysis indicates that rotational direction plays an important role in outside propeller interaction, where outboard-up rotation increases outside channel lift. In addition, the propeller wake also shows special distortion and dissipation behaviors, which are strongly affected by adjacent propellers.
Event-Triggered Security Consensus for Multi-Agent Systems with Markov Switching Topologies under DoS Attacks
Authors: Tian Yuan , Tian Sheng , Li Huaqing , Han Qi , Wang Xiaonan
Published: July 2022

This paper studies secure consensus control for multi-agent systems subject to denial-of-service (DoS) attacks. The DoS attacks cause changes in topologies, which will destroy the channels of communication and result in network paralysis. Unlike the existing publications with Markov switching, this paper mainly studies the topological structure changes of the subsystem models after DoS attacks. To ensure the consensus of systems, this paper designs an event triggered to reduce the use of the controller and decrease the influence of channel breaks off caused by DoS attacks. On this basis, different Lyapunov functions are established in each period of attack. Then, stochastic and Lyapunov stable theory is used to form the consensus criteria. Moreover, Zeno behavior is excluded by theoretical analysis. Finally, the simulation experiment proves the effectiveness of the proposed protocol.
Preparation and Characterization of Microencapsulated Phase Change Materials for Solar Heat Collection
Authors: Chen Hongbing , Zhao Rui , Wang Congcong , Feng Lianyuan , Li Shuqian , Gong Yutong
Published: July 2022

In this paper, a new type of microencapsulated phase change materials (MPCMs) with docosane as the core and titanium dioxide (TiO2) as the shell was prepared by in situ polymerization. Its phase transition temperature was approximately 40 °C, and it can be used as a phase change material (PCM) in a low-temperature solar heat collection system. The properties of the new material were examined including the microstructure, the chemical elements on the surface of the microcapsules, and thermal conductivity. In addition, to obtain the optimized formula of the microcapsules, single-factor analysis on the emulsifier type, its mass fraction, ultrasonic oscillation time, pH, and core–shell ratio were performed. The results showed that the MPCMs prepared in this paper had a particle size of 2–5 μm and were spherical. Its surface was uniform and smooth without cracks, and the TiO2 was well dispersed around the docosane, completely coating the docosane without impurities. The MPCMs had good performance in terms of thermal properties and heat storage when using 0.40% SDS as an emulsifier, 10 min ultrasonic, a 3.5 pH value, and a 1:1 core–shell ratio. However, the stirring method, time, and experimental reaction temperature also affected the properties of the material, which was not studied in this experiment. We will continue to study these factors in the future.
A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology
Authors: Hasan Md. Mahamudul , Berseneff Boris , Meulenbroeks Tim , Cantero Igor , Chakraborty Sajib , Geury Thomas , Hegazy Omar
Published: July 2022

This paper develops a multi-objective co-design optimization framework for the optimal sizing and selection of battery and power electronics in hybrid battery energy storage systems (HBESSs) connected to the grid. The co-design optimization approach is crucial for such a complex system with coupled subcomponents. To this end, a nondominated sorting genetic algorithm (NSGA-II) is used for optimal sizing and selection of technologies in the design of the HBESS, considering design parameters such as cost, efficiency, and lifetime. The interoperable framework is applied considering three first-life battery cells and one second-life battery cell for forming two independent battery packs as a hybrid battery unit and considers two power conversion architectures for interfacing the hybrid battery unit to the grid with different power stages and levels of modularity. Finally, the globally best HBESS system obtained as the output of the framework is made up of LTO first-life and LFP second-life cells and enables a total cost of ownership (TCO) reduction of 29.6% compared to the baseline.
Hierarchical Low-Carbon Economic Dispatch with Source-Load Bilateral Carbon-Trading Based on Aumann–Shapley Method
Authors: Nan Junpei , Feng Jieran , Deng Xu , Wang Chao , Sun Ke , Zhou Hao
Published: July 2022

Introducing carbon trading is an essential way to decarbonize the power system. Many existing studies mainly consider source-side unilateral carbon trading (UCT). However, there are still rare studies considering source-load bilateral carbon trading (BCT). The effect of source-load BCT on system-wide carbon mitigation is worth studying. To fill this research gap, a hierarchical low-carbon economic-dispatch model with source-load BCT based on the Aumann–Shapley method was proposed. In the first layer, economic-dispatch was conducted to minimize the power-generation costs and source-side carbon-trading costs. Then, based on the carbon-emission flow (CEF) theory, the actual load carbon emissions can be obtained and passed to the second layer. At the second layer, the demand-response optimization was performed to minimize the load-side carbon-trading costs. Finally, the proposed model was tested on the modified New England 39-bus and IEEE 118-bus systems using the MATLAB/YALMIP platform with the Gurobi solver. The results indicate that the proposed model can effectively facilitate peak-load shifting, wind-power consumption, and carbon mitigation. Furthermore, compared with the models only considering source-side or load-side UCT, the proposed source-load BCT model has obvious advantages in carbon mitigation.
Thermal Aging Study of an Anodized Aluminum Strip Wire for Winding and High Temperature Use
Authors: Ait-Amar Sonia , Saoudi Rania , Vélu Gabriel
Published: July 2022

Electrical machines are commonly used in industries and transport and are manufactured with enameled wire coils with polymer insulation. In extreme conditions, as in aeronautics and aerospace, where temperatures exceed 280 °C, the organic insulation deteriorates and it is necessary to find another way to insulate winding wires, in order to ensure a longer life duration of the insulation system and a better operating reliability of electric motors in these environments. Ceramics are more resistant to high temperatures than polymers and the proposed study focuses on the temperature dependence of the dielectric properties of anodized aluminum strips for use in high-temperature electrical machine windings. Different dielectric parameters are measured during thermal constraint. The purpose is to determine the temperature index of the anodized strip and propose a thermal stress life model.
The Impact of Nanostructured Silicon and Hybrid Materials on the Thermoelectric Performance of Thermoelectric Devices: Review
Authors: Ramadan Rehab , Martín-Palma Raúl J.
Published: July 2022

Nanostructured materials remarkably improve the overall properties of thermoelectric devices, mainly due to the increase in the surface-to-volume ratio. This behavior is attributed to an increased number of scattered phonons at the interfaces and boundaries of the nanostructures. Among many other materials, nanostructured Si was used to expand the power generation compared to bulk crystalline Si, which leads to a reduction in thermal conductivity. However, the use of nanostructured Si leads to a reduction in the electrical conductivity due to the formation of low dimensional features in the heavily doped Si regions. Accordingly, the fabrication of hybrid nanostructures based on nanostructured Si and other different nanostructured materials constitutes another strategy to combine a reduction in the thermal conductivity while keeping the good electrical conduction properties. This review deals with the properties of Si-based thermoelectric devices modified by different nanostructures and hybrid nanostructured materials.
Interkingdom Plant–Soil Microbial Ecological Network Analysis under Different Anthropogenic Impacts in a Tropical Rainforest
Authors: Yu Jingjing , Cong Wei , Ding Yi , Jin Lixiao , Cong Jing , Zhang Yuguang
Published: July 2022

Plants and their associated soil microorganisms interact with each other and form complex relationships. The effects of slash-and-burn agriculture and logging on aboveground plants and belowground microorganisms have been extensively studied, but research on plant–microbial interkingdom ecological networks is lacking. In this study, using old growth forest as a control, we used metagenomic data (ITS and 16S rRNA gene amplified sequences) and plant data to obtain interdomain species association patterns for three different soil disturbance types (slash-and-burn, clear cutting and selective cutting) in a tropical rainforest based on interdomain ecological network (IDEN) analysis. Results showed that the soil bacterial–fungal and plant–microbe ecological networks had different topological properties among the three forest disturbance types compared to old growth forest. More nodes, links, higher modularity and negative proportion were found in the selective cutting stand, indicating higher stability with increasing antagonistic relationships and niche differentiation. However, the area of slash-and-burn forest yield opposite results. Network module analysis indicated that different keystone species were found in the four forest types, suggesting alternative stable states among them. Different plant species had more preferential associations with specific fungal taxa than bacterial taxa at the genus level and plant–microbe associations lagged behind bacterial–fungal associations. Overall, compared with old growth forests, the bacterial–fungal and plant–microbe ecological networks in the slash-and-burn and clear cutting stands were simpler, while the network in the selective cutting stand was more complex. Understanding the relationships between aboveground plants and belowground microorganisms under differing disturbance patterns in natural ecosystems will help in better understanding the surrounding ecosystem functions of ecological networks.
Comparison of Modeling Approaches for the Height–diameter Relationship: An Example with Planted Mongolian Pine (Pinus sylvestris var. mongolica) Trees in Northeast China
Authors: Lin Fucheng , Xie Longfei , Hao Yuanshuo , Miao Zheng , Dong Lihu
Published: July 2022

In the process of modeling height–diameter models for Mongolian pine (Pinus sylvestris var. mongolica), the fitting abilities of six models were compared: (1) a basic model with only diameter at breast height (D) as a predictor (BM); (2) a plot-level basic mixed-effects model (BMM); (3) quantile regression with nine quantiles based on BM (BQR); (4) a generalized model with stand or competition covariates (GM); (5) a plot-level generalized mixed-effects model (GMM); and (6) quantile regression with nine quantiles based on GM (GQR). The prediction bias of the developed models was assessed in cases of total tree height (H) predictions with calibration or without calibration. The results showed that extending the Chapman–Richards function with the dominant height and relative size of individual trees improved the prediction accuracy. Prediction accuracy was improved significantly when H predictions were calibrated for all models, among which GMM performed best because random effect calibration provided the lowest prediction bias. When at least 8% of the trees were selected from a new plot, relatively accurate and low-cost prediction results were obtained by all models. When predicting the H values of Mongolian pine for a new stand, GMM and BMM were preferable if there were available height measurements for calibration; otherwise, GQR was the best choice.
Effects of Aluminum Chloride Impregnating Pretreatment on Physical and Mechanical Properties of Heat-Treated Poplar Wood under Mild Temperature
Authors: Wang Xujie , Luo Cuimei , Mu Jun , Qi Chusheng
Published: July 2022

The acid formed by thermal degradation of wood can autocatalyze its heat treatment. In this study, exogenous acid was introduced by impregnation into poplar wood to investigate its effect on the physical and mechanical properties of wood. Equilibrium moisture content (EMC), dimensional stability, mass loss (ML), color, modulus of rupture (MOR), and modulus of elasticity (MOE) of heat-treated poplar were tested under mild temperature (130–160 °C) for different pretreatment concentrations of aluminum chloride (AlCl3). The results show that the EMC of the heat-treated wood diminishes by 2.7%–47.8%, and dimensional stability improves significantly after AlCl3 impregnation pretreatment. The samples impregnated with 0.5 mol/L AlCl3 and heat treated at 160 °C achieved the best dimensional stability, which was better than for the samples only heat-treated at 220 °C. The color changed significantly as the impregnating concentration increased, achieving a color effect similar to that of wood only heat-treated at a high temperature such as 200 or 220 °C. Heat-treatment temperature under the same ML of wood samples was reduced, which also mitigated the reduction of MOR. MOE of heat-treated wood with 0.5 mol/L impregnation pretreatment was 11.4%–30.7% more than for samples heat-treated at 160–220 °C. After exogenous acidic AlCl3 impregnation pretreatment, the cell wall structure of the heat-treated wood was found to remain relatively intact. Thus, AlCl3 impregnating pretreatment exerted a substantial and beneficial effect on the physical and mechanical properties of poplar and achieved good performance of poplar wood treated at a mild temperature.
Effects of Konjac Flour and Lactiplantibacillus plantarum on Fermentation Quality, Aerobic Stability, and Microbial Community of High-Moisture Forage Rape Silages
Authors: Du Encun , Zhao Na , Guo Wanzheng , Fan Qiwen , Wei Jintao , Xu Zhiyu
Published: July 2022

To obtain high-quality silage and better understand the mechanism underlying silage fermentation, a study was conducted to investigate the effects of konjac flour (KF), Lactiplantibacillus plantarum (LP) and their combination on fermentation quality, aerobic stability, and microbial community of high-moisture forage rape after 60 days of ensiling. Results showed that the KF and LP treatments increased the lactic acid content, decreased the pH value, and inhibited the production of butyric acid in ensiled forage rape (p < 0.05). The additives also altered the bacterial community of forage rape silages, showing reduced Shannon and Simpson indexes (p < 0.05), while the abundance of desirable Lactobacillus was increased, and the abundance of undesirable bacteria, such as enterobacteria and clostridia, was decreased (p < 0.05). In addition, their combination significantly improved the aerobic stability (96 h vs. 28 h, p < 0.05) and exhibited notable influence on the bacterial community, with the highest abundance of Lactobacillus. These results indicated that KF and LP improved the silage quality of high-moisture forage rape, and their combination displayed a beneficial synergistic effect.
Comparative Study of Bioactive Lipid Extraction from Squid (Doryteuthis gahi) by-Products by Green Solvents
Authors: Aubourg Santiago P. , Trigo Marcos , González María Jesús , Lois Salomé , Medina Isabel
Published: July 2022

A novel approach of bioactive lipid extraction by different green solvents was carried out on squid (Doryteuthis gahi) by-products. By-products (viscera, heads, skin, tails, etc.), considered as a single product, were subjected to the following solvent systems: ethanol, acetone, ethyl acetate, 1/1 ethanol/acetone, 1/1 ethanol/ethyl acetate, and 1/1 acetone/ethyl acetate. Analyses carried out included lipid yield, lipid class content, and fatty acid (FA) composition. Results were compared to the lipid extract obtained by the traditional procedure (1/1 chloroform/methanol). Lipid yields obtained by green solvents led to a 33.4–73.2% recovery compared to traditional extraction; the highest values (p < 0.05) were obtained by ethanol-containing systems. Compared to the traditional procedure, ethanol systems showed an 85.8–90.3% recovery of phospholipid compounds and no differences (p > 0.05) in the ω3/ω6 ratio. Green-extracting systems led to higher average values for eicosapentaenoic acid content (15.66–18.56 g·100 g−1 total FAs) and polyene index (1.93–3.29) than chloroform/methanol extraction; differences were significant (p < 0.05) for systems including acetone and ethyl acetate. No differences (p > 0.05) were detected for docosahexaenoic acid content between the traditional procedure and green systems, with all values being included in the 31.12–32.61 g·100 g−1 total FA range. The suitability of EtOH-containing green systems for extraction of bioactive lipid compounds from squid by-products was concluded.
Polyphenols as Plant-Based Nutraceuticals: Health Effects, Encapsulation, Nano-Delivery, and Application
Authors: Zhang Zhiheng , Li Xiaojing , Sang Shangyuan , McClements David Julian , Chen Long , Long Jie , Jiao Aiquan , Jin Zhengyu , Qiu Chao
Published: July 2022

Plant polyphenols have attracted considerable attention because of their key roles in preventing many diseases, including high blood sugar, high cholesterol, and cancer. A variety of functional foods have been designed and developed with plant polyphenols as the main active ingredients. Polyphenols mainly come from vegetables and fruits and can generally be divided according to their structure into flavonoids, astragalus, phenolic acids, and lignans. Polyphenols are a group of plant-derived functional food ingredients with different molecular structures and various biological activities including antioxidant, anti-inflammatory, and anticancer properties. However, many polyphenolic compounds have low oral bioavailability, which limits the application of polyphenols in nutraceuticals. Fortunately, green bio-based nanocarriers are well suited for encapsulating, protecting, and delivering polyphenols, thereby improving their bioavailability. In this paper, the health benefits of plant polyphenols in the prevention of various diseases are summarized, with a review of the research progress into bio-based nanocarriers for the improvement of the oral bioavailability of polyphenols. Polyphenols have great potential for application as key formulations in health and nutrition products. In the future, the development of food-grade delivery carriers for the encapsulation and delivery of polyphenolic compounds could well solve the limitations of poor water solubility and low bioavailability of polyphenols for practical applications.
Improving Soy Sauce Aroma Using High Hydrostatic Pressure and the Preliminary Mechanism
Authors: Zhang Yaqiong , Zhang Zhi-Hong , He Ronghai , Xu Riyi , Zhang Lei , Gao Xianli
Published: July 2022

Using high hydrostatic pressure (HHP) to treat liquid foods can improve their aroma; however, no information about the effects of HHP on soy sauce aroma has yet been reported. The effects of HHP on the aroma of soy sauce fermented for 30 d were investigated using quantitative descriptive analysis (QDA), SPME–GC–olfactometry/MS, hierarchical cluster analysis (HCA) and principal component analysis (PCA). Results showed that the pressure used during HHP treatment had a greater influence on soy sauce aroma than the duration of HHP. Compared to the control, soy sauce that was treated with HHP at 400 MPa for 30 min (HHP400–30) obtained the highest sensory score (33% higher) by increasing its sour (7%), malty (9%), floral (27%) and caramel-like (47%) aromas, while decreasing its alcoholic (6%), fruity (6%) and smoky (12%) aromas; moreover, the aroma of HHP400–30 soy sauce was comparable with that of soy sauce fermented for 180 d. Further investigation demonstrated that HHP (400 MPa/30 min) enhanced the OAVs of compounds with sour (19%), malty (37%), floral (37%), caramel-like (49%) and other aromas (118%), and lowered the OAVs of compounds with alcoholic (5%), fruity (12%) and smoky (17%) aromas. These results were consistent with the results of the QDA. HHP treatment positively regulated the Maillard, oxidation and hydrolysis reactions in raw soy sauce, which resulted in the improvement and accelerated formation of raw soy sauce aroma. HHP was capable of simultaneously improving raw soy sauce aroma while accelerating its aroma formation, and this could treatment become a new alternative process involved in the production of high-quality soy sauce.
Quality Properties of Dry-Aged Beef (Hanwoo Cattle) Crust on Pork Patties
Authors: Lee Jeong-Ah , Kim Hack-Youn , Seol Kuk-Hwan
Published: July 2022

This study evaluated the effects of crust derived from dry-aged beef (Hanwoo cattle) on the quality of pork patties. Pork patty samples were prepared with different amounts of crust (0—control, 1, 2, and 3%). The protein, fat, and ash contents in the crust samples were significantly higher than those in the control sample (p < 0.05). The CIE b* value of uncooked pork patties with crust added was significantly lower than that of the control patties (p < 0.05). The pH and CIE L* values of uncooked patty batter samples decreased with increasing concentrations of crust (p < 0.05). However, the viscosity increased proportionally with an increase in crust (p < 0.05). Samples containing 3% crust showed significantly higher uncooked and cooked CIE a*, water-holding capacity, cooking yield, and shear force than the control sample (p < 0.05). Moreover, samples containing 2% and 3% crust showed significantly lower diameter and thickness reductions than those of the control sample (p < 0.05). The sensory evaluation conferred by the crust was significantly higher than that of the control sample (p < 0.05). Overall, our results suggest that pork patties supplemented with 3% crust have improved properties.
Influence of Dietary Algae Meal on Lipid Oxidation and Volatile Profile of Meat from Lambs with Competent Reticular Groove Reflex
Authors: Avilés-Ramírez Carmen , Vioque Amor Montserrat , Polvillo Polo Oliva , Horcada Alberto , Gómez-Cortés Pilar , de la Fuente Miguel Ángel , Núñez-Sánchez Nieves , Martínez Marín Andrés Luis
Published: July 2022

Dietary lipid sources influence intramuscular fatty acid composition, which in turn may affect the volatile profile of meat. The aim of this work was to investigate the effects of marine algae supplementation (Aurantiochytrium limacinum) on volatile compounds of cooked lamb meat. Forty-eight lambs with 42 days of age were divided into three groups: lambs fed a conventional diet without algae meal supplementation (NOALG), lambs with competent reticular groove reflex (RGR) fed the same diet supplemented with 2.5% marine algae meal mixed in the concentrate (ALGCON), and lambs with competent RGR, receiving the same diet and fed with 2.5% marine algae meal in a milk replacer to bypass the rumen (ALGMILK). Lipid and protein oxidation in raw meat was assessed and volatile compounds in grilled meat were determined. The highest and lowest lipid oxidations were observed in the ALGMILK and NOALG groups, respectively. Protein oxidation was unaffected. Out of 56 identified compounds, 12 volatiles significantly increased in both algae groups and 6 of them exclusively in the ALGCON treatment. Algae meal supplementation and its form of administration, either protected or not from rumen degradation, are important factors to consider in lipid oxidation and the aromatic profile of lamb meat.
Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment
Authors: Yang Ke , Peng Baoliang , Gu Fengwei , Zhang Yanhua , Wang Shenying , Yu Zhaoyang , Hu Zhichao
Published: July 2022

Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning—a convolutional neural network—is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs with varied brightness, surface attachment, and shape, which enabled sufficient learning of the detector. The optimum minibatches and epochs were obtained by comparing the test results of different training parameters. Research shows that IRM-YOLOv2 is superior to the SqueezeNet, ShuffleNet, and YOLOv2 models of classical neural networks, as well as the YOLOv3 and YOLOv4 algorithm models. The confidence score, average accuracy, deviation, standard deviation, detection time, and storage space of IRM-YOLOv2 were 0.98228, 99.2%, 2.819 pixels, 4.153, 0.0356 s, and 24.2 MB, respectively. In addition, this study provides an important reference for the application of the YOLO algorithm in food research.
Heat Stress and an Immune Challenge Influence Turkey Meat Quality, but Conspecific-Directed Pecking Behavior Does Not
Authors: Davis Melissa , Stevenson Rachel , Ford Emily , Erasmus Marisa , Zuelly Stacy M. S.
Published: July 2022

Heat stress (HS), immune challenges (IC) and pecking behavior are some of the many stressors poultry can experience in commercial settings that may affect bird welfare and meat quality after harvest. The first objective was to determine if HS or IC turkeys displayed greater negative effects on meat quality, and the second objective was to determine if the frequency of non-aggressive pecking behaviors among the birds was related to meat quality. Ninety-two, commercial male, beak-trimmed turkeys were used with a total of 15 rooms and 4–7 birds per room. Each treatment was applied for 1 week prior to harvest: the Control (CON) group had no stressors added, the HS group ambient temperature was approximately 29 °C for 120 min, and the IC group involved inoculating birds with a live vaccine for hemorrhagic enteritis virus. Birds were recorded and scored to quantify pecking behavior. Once harvested, carcasses were evaluated for feather retention force, pH, color, proximate analysis, fatty acid composition, shear force, and drip loss. Stress treatment resulted in HS breasts having the lowest protein content, and IC breasts having the lowest CIE L* values and the greatest shear force values. Pecking behavior had no impact on any meat quality attributes.
Simpson’s Type Inequalities for s-Convex Functions via a Generalized Proportional Fractional Integral
Authors: Desalegn Henok , Mijena Jebessa B. , Nwaeze Eze R. , Abdi Tadesse
Published: July 2022

In this paper, we give new Simpson’s type integral inequalities for the class of functions whose derivatives of absolute values are s-convex via generalized proportional fractional integrals. Some results in the literature are particular cases of our results.
Uncertain Currency Option Pricing Based on the Fractional Differential Equation in the Caputo Sense
Authors: Liu Qinyu , Jin Ting , Zhu Min , Tian Chenlei , Li Fuzhen , Jiang Depeng
Published: July 2022

The foreign exchange market comprises the largest global volume, so the pricing of foreign exchange options has always been a hot issue in the foreign exchange market. This paper treats the exchange rate as an uncertain process that is described by an uncertain fractional differential equation, and establishes a new uncertain fractional currency model. The uncertain process is driven by Liu process, and, with the application of the Mittag-Leffler function, the solution of the fractional differential equation in a Caputo sense is presented. Then, according to the uncertain fractional currency model, the pricing formulas of European and American currency options are given. Lastly, the two numerical examples of European and American currency options are given; the price of the currency option increased when p changed from 1.0 to 1.1, and prices with different p were all decreasing functions of exercise price K.