bluuebunny/crossref_metadata_2025 huggingface dataset:
Characteristics of Deep Coal Reservoir and Key Control Factors of Coalbed Methane Accumulation in Linxing Area
Authors: Tao Chuanqi , Li Yong , Wang Yanbin , Ni Xiaoming , Wu Xiang , Zhao Shihu
Published: August 2023

Deep coalbed methane (CBM, commonly accepted as >1500 m) has enormous exploration and development potential, whereas the commercial development of deep CBM exploration areas wordwide has been quite limited. The Linxing area, with coals buried approximately 2000 m deep, shows great development potential. Based on a basic geological analysis of structural and hydrodynamic conditions, combining field tests of reservoir temperature and pressure and indoor measurements of maceral composition, proximate analysis, thermal maturity, porosity and permeability, the factors controlling deep CBM accumulations were discussed. The results show that the present burial depth of the No. 8 + 9 coal seam, mainly between 1698 and 2158 m, exhibits a high reservoir temperature (45.0–64.0 °C) and pressure (15.6–18.8 MPa), except for the uplift area caused by the Zijinshan magma event (with coal depth approximately 1000 m). The maximum vitrinite reflectance (Ro,max) of the coal varies from 1.06% to 1.47%, while the magma-influenced areas reach 3.58% with a relatively high ash content of 31.3% (air-dry basis). The gas content calculated by field desorption tests shows a wide range from 7.18 to 21.64 m3/t. The key factors controlling methane accumulation are concluded from regional geological condition variations. The north area is mainly controlled by structural conditions and the high gas content area located in the syncline zones. The center area is dominated by the Zijinshan magma, with relatively high thermal maturity and a high gas content of as much as 14.5 m3/t. The south area is developed with gentle structural variations, and the gas content is mainly influenced by the regional faults. Furthermore, the groundwater activity in the eastern section is stronger than that in the west, and the hydrodynamic stagnant areas in the western are more beneficial for gas accumulation. The coals vary from 3.35% to 6.50% in porosity and 0.08 to 5.70 mD in permeability; thus, hydrofracturing considering high temperature and pressure should be applied carefully in future reservoir engineering, and the co-production of gas from adjacent tight sandstones also should be evaluated.
Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques
Authors: Mumtaz Rafia , Amin Arslan , Khan Muhammad Ajmal , Asif Muhammad Daud Abdullah , Anwar Zahid , Bashir Muhammad Jawad
Published: August 2023

Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning models, including long short-term memory (LSTM) and bidirectional LSTM, and machine learning models, including decision tree and random forest regression, were utilized to forecast the levels of NO2, SO2, and CO in the atmosphere. Our results demonstrate that implementing green energy transportation systems in urban centers of developing countries can enhance air quality by approximately 98%. Notably, the bidirectional LSTM model outperformed others in predicting NO2 and SO2 concentrations, while the LSTM model excelled in forecasting CO concentration. These results offer valuable insights into predicting air pollution levels and guiding green energy policies to mitigate the adverse health effects of air pollution.
Robust Multiobjective Decision Making in the Acquisition of Energy Assets
Authors: Bambirra Rafael , Schiavo Lais , Lima Marina , Miranda Giovanna , Reis Iolanda , Cassemiro Michael , Andrade Antônio , Laender Fernanda , Silva Rafael , Vieira Douglas , Ekel Petr
Published: August 2023

In asset management for energy portfolios, quantitative methodologies are typically employed. In Brazil, the NEWAVE computational model is universally used to generate scenarios of hydraulic production and future prices, which result in revenue distributions. These distributions are then used to estimate the portfolio’s revenue and assess its risk. Although this is a well-established analysis, it has some shortcomings that are not always considered. The validity of the revenue series constructed by NEWAVE, especially in long-term analysis, is a real problem for agents concerning the acquisition of assets such as power plants. Another issue is the disregard for other objectives that are important for the operationality of the management task and are often ignored, such as operational risk. To address these limitations, this work combines the areas of multicriteria decision making under uncertainty and risk management and presents a methodology for evaluating the acquisition of long-term energy assets, as well as a practical application of the proposed method. Investment alternatives are evaluated in multiple developed scenarios, so it is possible to measure how robust a given option is. By analyzing several scenarios simultaneously, a larger region of uncertainties can be covered, and therefore, decision making becomes more secure. The proposed methodology includes six objectives, designed to address a wider range of stakeholder needs. This approach is applied to an illustrative portfolio, producing results that allow for a more comprehensive understanding of decision attributes. Therefore, this work not only addresses the current limitations in the field but also adds an original contribution by considering simultaneously several scenarios and integrating multiple objectives in a robust and secure decision-making framework.
Physiological Response to Low Temperature of Four Genotypes of Cyclocarya paliurus and Their Preliminary Evaluation to Cold Resistance
Authors: Zhang Zanpei , Gu Yueying , Mao Qianxing , Wang Ji
Published: August 2023

Cyclocarya paliurus is a versatile tree species with immense potential for development, as it combines edible, medicinal, and ornamental functions. Low temperature is one of the important abiotic factors that affect plant survival and flourishing but their response mechanism to low temperature is not yet clear. In this study, we utilized annual shoots of four genotypes of C. paliurus, namely T2, W10, M31 and S12, as materials. The physiological responses of annual shoots of C. paliurus to low temperature stress were elaborated by determining and comparing indicators related to cold resistance, such as relative electric conductivity, semi-lethal low temperature, malondialdehyde, soluble sugar, soluble protein, proline, superoxide dismutase and peroxidase. The contents of malondialdehyde, proline, soluble protein and peroxidase activities were not only correlated with the treatment temperature but also related to the genotypes. Osmotic substance (soluble sugar, soluble protein and proline) contents and antioxidant enzyme activities (peroxidase and superoxide dismutase) of the four genotypes showed a trend of increasing and then decreasing with the five decreasing temperatures. Furthermore, a comprehensive evaluation of cold resistance was performed by using a combination of principal component analysis and membership function, with the cold resistance ranked as W10 > M31 > S12 > T2. Results from this study would provide some references for extending the plantation areas.
Effects of Myo-Inositol on NaCl Stress in Tamarix ramosissima: Insights from Transcriptomics and Metabolomics
Authors: Li Haijia , Fan Yunlong , Zhang Huanchao , Chen Yahui
Published: August 2023

NaCl stress adversely affects plant growth. Tamarix ramosissima Ledeb (T. ramosissima), a halophyte, thrives in saline-alkali areas. Myo-inositol, a lipid-soluble compound, is crucial for stress response, but its role in mitigating NaCl damage remains underexplored. We analyzed transcriptome sequencing and metabolites in T. ramosissima roots under NaCl stress at various intervals (0 h, 48 h, and 168 h). We identified ten Myo-inositol oxygenase-related genes. Nine of these genes, linked to metabolic pathways involving Myo-inositol, showed differential expression. Myo-inositol accumulation increased over time, suggesting its role as an osmotic regulator and reactive oxygen species (ROS) scavenger. This accumulation likely shields T. ramosissima from NaCl-induced osmotic and oxidative damage. Notably, Unigene0002140 and Unigene0095980, associated with Myo-inositol oxygenase, appear to regulate Myo-inositol accumulation and correlate significantly with its levels. We hypothesize they are key genes in controlling Myo-inositol levels, warranting further study. This research illuminates the role of Myo-inositol oxygenase-related genes in T. ramosissima roots combating NaCl stress, offering insights for selecting salt-tolerant tree species.
Identification of Bird Habitat Restoration Priorities in a Central Area of a Megacity
Authors: Wang Yuncai , Lu Xinghao , Wang Ruojing , Jia Yifei , Huang Junda
Published: August 2023

Rapid global urbanization has caused habitat degradation and fragmentation, resulting in biodiversity loss and the homogenization of urban species. Birds play a crucial role as biodiversity indicators in urban environments, providing multiple ecosystem services and demonstrating sensitivity to changes in habitat. However, construction activities often disrupt urban bird habitats, leading to a decline in habitat quality. This paper proposes a framework for prioritizing habitat restoration by pinpointing bird hotspots that demand attention and considering the matching relationship between bird richness and habitat quality. Shanghai represents a typical example of the high-density megacities in China, posing a significant challenge for biodiversity conservation efforts. Utilizing the random forest (RF) model, bird richness patterns in central Shanghai were mapped, and bird hotspots were identified by calculating local spatial autocorrelation indices. From this, the habitat quality of hotspot areas was evaluated, and the restoration priority of bird habitats was determined by matching bird richness with habitat quality through z-score standardization. The results were as follows: (1) Outer-ring green spaces, large urban parks, and green areas along coasts or rivers were found to be the most important habitats for bird richness. Notably, forests emerged as a crucial habitat, with approximately 50.68% of the forested areas identified as hotspots. (2) Four habitat restoration types were identified. The high-bird-richness–low-habitat-quality area (HBR-LHQ), mainly consisting of grassland and urban construction land, was identified as a key priority for restoration due to its vulnerability to human activities. (3) The Landscape Shannon’s Diversity Index (SHDI) and Normalized Difference Vegetation Index (NDVI) are considered the most significant factors influencing the bird distribution. Our findings provide a scientifically effective framework for identifying habitat restoration priorities in high-density urban areas.
Response of the Radial Growth of Woody Plants in the West Siberian Plain and Adjacent Mountainous Territories to the Characteristics of the Snow Cover
Authors: Bykov Nikolay I. , Shigimaga Anna A. , Rygalova Natalia V.
Published: August 2023

The dependence of the width of annual rings of woody plants on the characteristics of the snow cover is analyzed in various natural zones of the West Siberian Plain and adjacent mountainous areas: the maximum depth and water reserve for the entire winter period and for individual months, the dates of disappearing, establishment, and duration of the occurrence of a stable snow cover. It has been shown that the role of the depth and water content of snow cover for the radial growth of trees is differentiated by geographical location. On the plain, it intensifies in the forest-tundra and dry steppe. The response of radial growth to snow cover in the upper and lower parts of the forest belt is often the opposite. Dates of establishment of stable snow cover are more important for tree growth compared to dates of disappearance. Dates of disappearance of stable snow cover are more significant in the southern regions than in the northern ones. The value of the duration of the period with stable snow cover for tree growth is higher in the southern regions.
Identification and Characterization of the AREB/ABF/ABI5 Gene Family in Sandalwood (Santalum album L.) and Its Potential Role in Drought Stress and ABA Treatment
Authors: Liu Xiaojing , Cheng Renwu , Chen Yu , Wang Shengkun , Qin Fangcuo , Wang Dongli , Liu Yunshan , Hu Lipan , Meng Sen
Published: August 2023

AREB/ABF/ABI5 (ABA-responsive element-binding protein/ABRE binding factors and ABA INSENSITIVE 5) transcription factors are involved in regulating the expression of ABA (abscisic acid)-related genes and improving plant adaptability to environmental stress. To explore the influence of AREB/ABF transcription factors on santalol synthesis, we conducted a genome-wide analysis of the AREB gene family in sandalwood, identified 10 SaAREB genes, and divided them into five subfamilies. We found that all SaAREB genes encoded unstable hydrophilic proteins and the subcellular localization prediction of SaAREBs was that they are located in the nucleus. AREB/ABF genes belong to the bZIP-A subfamily and we found that the 10 AREB proteins all contained bZIP (basic region leucine zipper) and four potential phosphorylation sites (RXXS/T). According to the collinearity analysis results, four of the SaAREB genes were involved in two fragment duplication events. Through qRT-PCR (real-time fluorescence quantitative PCR), we explored the expression profile of SaAREB in different tissues; the effects of ABA treatment and drought treatment on AREB transcription factors were predicted. From the expression of different tissues, we found that SaAREB1 not only responded to prolonged drought but also was highly expressed in stems. Moreover, SaAREB3, SaAREB7, and SaAREB8 specifically respond to ABA treatment. Based on RNA-seq (RNA sequencing) data, we found that SaAREB6 and SaAREB8 were highly expressed in the sapwood and transition regions. Regarding SaCYP736A167, as a key gene in santalol synthesis, its promoter contains the most ABRE cis-reactive elements. These results provide a basis for further analysis of the role of the Santalum album L. (S. album) ABRE/ABF/ABI5 genes in the formation of santalols.
Co-Fermentation of Glucose–Xylose–Cellobiose–XOS Mixtures Using a Synthetic Consortium of Recombinant Saccharomyces cerevisiae Strains
Authors: Yan Ning , Luan Tao , Yin Mengqi , Niu Yaping , Wu Longhao , Yang Shuo , Li Zailu , Li Hongxing , Zhao Jianzhi , Bao Xiaoming
Published: August 2023

The efficient conversion of cellulosic sugars is vital for the economically viable production of biofuels/biochemicals from lignocellulosic biomass hydrolysates. Based on comprehensive screening, Saccharomyces cerevisiae RC212 was chosen as the chassis strain for multiple integrations of heterologous β-glucosidase and β-xylosidase genes in the present study. The resulting recombinant BLN26 and LF1 form a binary synthetic consortium, and this co-culture system achieved partial fermentation of four sugars (glucose, xylose, cellobiose, and xylo-oligosaccharides). Then, we developed a ternary S. cerevisiae consortium consisting of LF1, BSGIBX, and 102SB. Almost all four sugars were efficiently fermented to ethanol within 24 h, and the ethanol yield is 0.482 g g−1 based on the consumed sugar. To our knowledge, this study represents the first exploration of the conversion of mixtures of glucose, xylose, cellobiose, and xylo-oligosaccharides by a synthetic consortium of recombinant S. cerevisiae strains. This synthetic consortium and subsequent improved ones have the potential to be used as microbial platforms to produce a wide array of biochemicals from lignocellulosic hydrolysates.
Effects of Different Cutting Stages and Additives on the Fermentation Quality and Microbial Community of Sudangrass (Sorghum sudanense Stapf.) Silages
Authors: Yu Qiang , Li Mengxin , Zhang Yu , Xu Jinyi , Li Ping , Sun Hong , Xie Yixiao , Dong Rui , Zheng Yulong , Chen Chao
Published: August 2023

(1) Background: Previous studies have indicated that ferulic acid esterase (FAE), cellulase and xylanase have synergistic effects in lignocellulose degradation, and the cutting stage has a major impact on silages. Whether these additives affect the silages at different cutting stages is unclear. (2) Methods: Sudangrass height at the tested cutting stages was 1.8 m (S1) and 2.0 m (S2). The silage from the two cutting stages was treated with FAE-producing Lactobacillus plantarum (LP), cellulase and xylanase (CX) and a combination of LP and CX (LP+CX) for 30 and 60 days. (3) Results: Compared with CK, adding LP+CX significantly decreased the pH and the content of neutral detergent fiber (NDF) and acidic detergent fiber (ADF) (p < 0.05) and increased the lactic acid (LA) concentration (p < 0.05), dry matter (DM) content and crude protein content. Adding LP+CX effectively degraded lignocellulose in sudangrass, and the NDF and ADF degradation rates at the two stages were all more than 30%. In comparison, cutting at the S2 stage led to a lower pH and higher LA and DM contents (p < 0.05). Additives and the cutting stage exerted a strong effect on the silage microbial community, and Firmicutes and Lactiplantibacillus became the most dominant bacterial phyla and genera, especially at the S2 stage. (4) Conclusions: The results suggest that FAE-producing L. plantarum, cellulase and xylanase had synergistic effects on sudangrass silages, especially at the S2 stage, and their use can thus serve as an efficient method for ensiling.
Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
Authors: Fragiadakis George , Filiopoulou Evangelia , Michalakelis Christos , Kamalakis Thomas , Nikolaidou Mara
Published: August 2023

When exploring alternative cloud solution designs, it is important to also consider cost. Thus, having a comprehensive view of the cloud market and future price evolution allows well-informed decisions to choose between alternatives. Cloud providers offer various service types with different pricing policies. Currently, infrastructure-as-a-Service (IaaS) is considered the most mature cloud service, while reserved instances, where virtual machines are reserved for a fixed period of time, have the largest market share. In this work, we employ a machine-learning approach based on the CatBoost algorithm to explore a price-prediction model for the reserve instance market. The analysis is based on historical data provided by Amazon Web Services from 2016 to 2022. Early results demonstrate the machine-learning model’s ability to capture the underlying evolution patterns and predict future trends. Findings suggest that prediction accuracy is not improved by integrating data from older time periods.
An Improved Deep Learning Model for DDoS Detection Based on Hybrid Stacked Autoencoder and Checkpoint Network
Authors: Mousa Amthal K. , Abdullah Mohammed Najm
Published: August 2023

The software defined network (SDN) collects network traffic data and proactively manages networks. SDN’s programmability makes it excellent for developing distributed applications, cybersecurity, and decentralized network control in multitenant data centers. This exceptional architecture is vulnerable to security concerns, such as distributed denial of service (DDoS) attacks. DDoS attacks can be very serious due to the fact that they prevent authentic users from accessing, temporarily or indefinitely, resources they would normally expect to have. Moreover, there are continuous efforts from attackers to produce new techniques to avoid detection. Furthermore, many existing DDoS detection methods now in use have a high potential for producing false positives. This motivates us to provide an overview of the research studies that have already been conducted in this area and point out the strengths and weaknesses of each of those approaches. Hence, adopting an optimal detection method is necessary to overcome these issues. Thus, it is crucial to accurately detect abnormal flows to maintain the availability and security of the network. In this work, we propose hybrid deep learning algorithms, which are the long short-term memory network (LSTM) and convolutional neural network (CNN) with a stack autoencoder for DDoS attack detection and checkpoint network, which is a fault tolerance strategy for long-running processes. The proposed approach is trained and tested with the aid of two DDoS attack datasets in the SDN environment: the DDoS attack SDN dataset and Botnet dataset. The results show that the proposed model achieves a very high accuracy, reaching 99.99% in training, 99.92% in validation, and 100% in precision, recall, and F1 score with the DDoS attack SDN dataset. Also, it achieves 100% in all metrics with the Botnet dataset. Experimental results reveal that our proposed model has a high feature extraction ability and high performance in detecting attacks. All performance metrics indicate that the proposed approach is appropriate for a real-world flow detection environment.
Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks Using Machine Learning Algorithms
Authors: Obonna Ugochukwu Onyekachi , Opara Felix Kelechi , Mbaocha Christian Chidiebere , Obichere Jude-Kennedy Chibuzo , Akwukwaegbu Isdore Onyema , Amaefule Miriam Mmesoma , Nwakanma Cosmas Ifeanyi
Published: August 2023

Recently, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks. Examples include the denial-of-service (DoS), distributed denial-of-service (DDoS), and man-in-the-middle (MitM) attacks, and this may have largely been caused by the integration of open network to operation technology (OT) as a result of low-cost network expansion. The connection of OT to the internet for firmware updates, third-party support, or the intervention of vendors has exposed the industry to attacks. The inability to detect these unpredictable cyber-attacks exposes the PCN, and a successful attack can lead to devastating effects. This paper reviews the different forms of cyber-attacks in PCN of oil and gas installations while proposing the use of machine learning algorithms to monitor data exchanges between the sensors, controllers, processes, and the final control elements on the network to detect anomalies in such data exchanges. Python 3.0 Libraries, Deep-Learning Toolkit, MATLAB, and Allen Bradley RSLogic 5000 PLC Emulator software were used in simulating the process control. The outcomes of the experiments show the reliability and functionality of the different machine learning algorithms in detecting these anomalies with significant precise attack detections identified using tree algorithms (bagged or coarse ) for man-in-the-middle (MitM) attacks while taking note of accuracy-computation complexity trade-offs.
Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
Authors: Jadhav Hindavi Kishor , Kumaravelu Vinoth Babu , Murugadass Arthi , Imoize Agbotiname Lucky , Selvaprabhu Poongundran , Chandrasekhar Arunkumar
Published: August 2023

The sixth-generation (6G) network is supposed to transmit significantly more data at much quicker rates than existing networks while meeting severe energy efficiency (EE) targets. The high-rate spatial modulation (SM) methods can be used to deal with these design metrics. SM uses transmit antenna selection (TAS) practices to improve the EE of the network. Although it is computationally intensive, free distance optimized TAS (FD-TAS) is the best for performing the average bit error rate (ABER). The present investigation aims to examine the effectiveness of various machine learning (ML)-assisted TAS practices, such as support vector machine (SVM), naïve Bayes (NB), K-nearest neighbor (KNN), and decision tree (DT), to the small-scale multiple-input multiple-output (MIMO)-based fully generalized spatial modulation (FGSM) system. To the best of our knowledge, there is no ML-based antenna selection schemes for high-rate FGSM. SVM-based TAS schemes achieve ∼71.1% classification accuracy, outperforming all other approaches. The ABER performance of each scheme is evaluated using a higher constellation order, along with various transmit antennas to achieve the target ABER of 10−5. By employing SVM for TAS, FGSM can achieve a minimal gain of ∼2.2 dB over FGSM without TAS (FGSM-NTAS). All TAS strategies based on ML perform better than FGSM-NTAS.
Tremella Polysaccharide Has Potential to Retard Wheat Starch Gel System Retrogradation and Mechanism Research
Authors: Wang Jiaxun , Zhang Shanshan , Wang Nan , Fan Hongxiu , Wang Hanmiao , Liu Tingting
Published: August 2023

This study investigated the effects of adding different concentrations of TP (tremella polysaccharide) on the water distribution, rheological, thermal, microstructure, and retrogradation properties of WS (wheat starch) gels. The results showed that the starch aging increased during storage, and the addition of TP reduced the rate of change of the elastic modulus of the starch gel and delayed the short-term aging of WS. In the same storage period, the hardness value of the gel decreased and the texture became softer with the increase in the mass fraction of TP. TP increased the T0 (starting temperature) of the system and decreased the enthalpy of retrogradation (ΔHr). No new groups were formed after the retrogradation of the compound system, the hydrogen bonding force increased with the increase in polysaccharide, and the relative crystallinity and the degree of ordering of the system decreased. The addition of TP increased the content of bound water and immobile water, decreased the content of free water, and increased the gel water-holding capacity, indicating that it could effectively inhibit the long-term retrogradation of WS. The findings provide new theoretical insights for the production of starch-based foods.
Optimization of Hazelnut Spread Based on Total or Partial Substitution of Palm Oil
Authors: Marra Francesco , Lavorgna Arianna , Incarnato Loredana , Malvano Francesca , Albanese Donatella
Published: August 2023

Palm oil is widely used in the manufacturing of hazelnut-based spreads due to its unique fatty acid and triacylglycerol profile and, thus, its crystallization behaviour, which makes it suitable for use in fat-based spreadable products. An interesting solution that enables the replacement of palm oil is given by oleogels made with high nutritional quality oil. In this study, the influence of the replacement of palm oil with different glycerol monostearate/olive oil-based oleogels, as well as the influence of the different amounts of GMS employed in oleogel preparation, on the oil binding capacity, spreadability, and rheological and sensory parameters of hazelnut cocoa spreads was investigated. A design of experiment (DoE) approach, with the adoption of the D-optimal design, was used to plan the cocoa hazelnut spread formulations, with the aim being to identify the optimal formulation with desirable quality parameters in terms of Casson’s viscosity, spreadability, and oil binding capacity. The resulting optimized formulation was identified in a spread characterized by a total replacement of palm oil with an oleogel made of 95% olive oil and 5% GMS.
A Comprehensive Quality Analysis of Different Colors of Medicinal and Edible Honeysuckle
Authors: Liu Zhenying , Cheng Yunxia , Chao Zhimao
Published: August 2023

Honeysuckle (the dried flower bud or opening flower of Lonicera japonica Thunb.), a medicinal and edible substance, has is greatly popular among consumers for its remarkable health effects, such as antioxidant, antibacterial, and anti-inflammatory effects. However, due to the influences of processing methods, storage conditions, and other factors, honeysuckles show different colors which can directly reflect the quality and the price on the market. In order to comprehensively compare the quality of different colors, 55 batches of honeysuckle samples were collected and analyzed. Their color parameters, chlorophyll content (chl), total phenol content (TPC), total flavonoid content (TFC), antioxidant activity (AA), main active compounds, and metabolites were measured. As a result, the initial green-white (GW) samples, a kind of highest-quality honeysuckle, had the smallest a* value, largest h*, chl, TPC, TFC, and AA values, and highest content of chlorogenic acid and cynaroside. There was a significant difference between GW samples and a series of discolored samples. As the color darkened or lightened, the quality gradually decreased. The yellow-brown (YB) samples were of the worst quality and were no longer available for clinical and health purposes. A series of differential metabolites, such as quercetin-7-O-glucoside and secologanoside, could be used as important references to evaluate the quality of differently colored samples. The metabolic profile of honeysuckle provided new insights into the process of color change and laid a foundation for further honeysuckle quality control. The correlation results showed that the a* and h* values significantly affect the abovementioned quality indicators and the 10 main active compounds. In other words, the color difference could directly reflect the quality and clinical efficacy. Multiple regression analysis was carried out using combined L*, a*, and b* values to predict the quality of honeysuckle. This is the first time the quality of different color honeysuckle samples on the post-harvest link has been systematically compared and a demonstration of medicinal and edible substances with different colors has been provided.
Delivery Systems to Enhance Bioaccessibility and Bioavailability of Bioactive Factors: Structure, Property, and Food Applications
Authors: Zhang Yaqiong , Jia Xin
Published: August 2023

Incorporating bioactive factors to strengthen food nutrition is important for functional food development [...]
A Nationwide Survey and Risk Assessment of Ethyl Carbamate Exposure Due to Daily Intake of Alcoholic Beverages in the Chinese General Population
Authors: Wang Zifei , Qu Pengfeng , Zhao Yunfeng , Wu Yongning , Lyu Bing , Miao Hongjian
Published: August 2023

Ethyl carbamate (EC) is carcinogenic, and, in China, oral intake of EC mainly occurs as a result of the consumption of alcoholic beverages. To obtain the latest EC intake and risk analysis results for the general population in China, the China National Center for Food Safety Risk Assessment (CFSA) conducted the sixth total diet study (TDS) as a platform to analyze EC contents and exposure due to the intake of alcoholic beverages. A total of 100 sites in 24 provinces were involved in the collection and preparation of alcohol mixture samples for the sixth TDS. There were 261 different types of alcohol collected across the country, based on local dietary menus and consumption survey results. Ultimately, each province prepared a mixed sample by mixing their respective samples according to the percentage of local consumption. The EC levels of these twenty-four mixed samples were determined using our well-validated gas chromatography-mass spectrometry (GC-MS) method. The values ranged from 1.0 μg/kg to 33.8 μg/kg, with 10.1 μg/kg being the mean. China’s EC daily intake ranged from 0.001 ng/kg bw/d to 24.56 ng/kg bw/d, with a mean of 3.23 ng/kg bw/d. According to the margin of exposure (MOE), virtually safe dose (VSD), and T25 risk assessments of the carcinogenicity of EC, the mean lifetime cancer risk for the Chinese population was 9.8 × 104, 1.5 × 10−7, and 8.6 × 10−8, respectively. These data show that the carcinogenicity of EC in the general Chinese population due to alcoholic intake is essentially minimal.
Microbial Dynamics during labneh Ambaris Production in Earthenware Jars
Authors: Abi Khalil Reine , Couderc Christel , Yvon Sophie , Sicard Delphine , Bigey Frédéric , Jard Gwenaelle , El Rammouz Rabih , Abi Nakhoul Pierre , Eutamène Hélène , Ayoub Marie-José , Tormo Hélène
Published: August 2023

The responses of various microbial populations to modifications in the physicochemical properties of a food matrix, as well as interactions between these populations already present, are the main factors that shape microbial dynamics in that matrix. This work focused on the study of microbial dynamics during labneh Ambaris production, a traditional Lebanese concentrated fermented goat milk made in jars during 3 months. This was assessed in two earthenware jars at a production facility. DNA metabarcoding of the ITS2 region as well as the V3–V4 region of the 16S rRNA gene was used to characterize the fungal and bacterial communities, respectively. Viable bacterial isolates were also identified by Sanger sequencing of the V1–V4 region of the 16S rRNA gene. Our results showed that the dominant microorganisms identified within labneh Ambaris (Lactobacillus kefiranofaciens, Lentilactobacillus kefiri, Lactococcus lactis, Geotrichum candidum, Pichia kudriavzevii and Starmerella sp.) settle early in the product and remain until the end of maturation with varying abundances throughout fermentation. Microbial counts increased during early fermentation stage, and remained stable during mid-fermentation, then declined during maturation. While microbial compositions were globally comparable between the two jars during mid-fermentation and maturation stages, differences between the two jars were mainly detected during early fermentation stage (D0 until D10). No significant sensorial differences were observed between the final products made in the two jars. Neither coliforms nor Enterobacteriaceae were detected in their viable state, starting D7 in both jars, suggesting the antimicrobial properties of the product.
Whey Protein Hydrolysate Improved the Structure and Function of Myofibrillar Protein in Ground Pork during Repeated Freeze–Thaw Cycles
Authors: Yu Pengjuan , Yan Jiayan , Kong Lingru , Yu Juan , Zhao Xinxin , Peng Xinyan
Published: August 2023

Whey protein hydrolysate (WPH) has made a breakthrough in inhibiting oxidative deterioration and improving the quality of meat products during storage. Based on our previous study of extracting the most antioxidant active fraction I (FI, the molecular weight < 1 kDa) from whey protein hydrolysates of different molecular weights, the present study continued to delve into the effects of WPH with fraction I on the structure and function of myofibrillar proteins (MP) in ground pork during the freeze–thaw (F-T) cycles. With the number of F-T cycles raised, the total sulfhydryl content, the relative contents of α-helix, Ca2+-ATPase activity, K+-ATPase activity, solubility, emulsion activity index (EAI), and emulsion stability index (ESI) of MP gradually decreased. Conversely, the carbonyl content and the relative content of random curl showed an increasing trend. In particular, the damage to the structure and the function of MP became more pronounced after three F-T cycles. But, during F-T cycles, FI stabilized the structure of MP. Compared to the control group, the 10% FI group showed a remarkable improvement (p < 0.05) in the total sulfhydryl content, Ca2+-ATPase activity, K+-ATPase activity, solubility, EAI and ESI after multiple F-T cycles, suggesting that 10% FI could effectively inhibit protein oxidation and had the influence of preserving MP function properties. In conclusion, WPH with fraction I can be used as a potential natural antioxidant peptide for maintaining the quality of frozen processed meat products.
Post-Harvest Application of Nanoparticles of Titanium Dioxide (NPs-TiO2) and Ethylene to Improve the Coloration of Detached Apple Fruit
Authors: Wang Yongxu , Chen Guolin , Wang Daru , Zhang Jing , You Chunxiang , Wang Xiaofei , Liu Huaifeng
Published: August 2023

In this study, we analyzed the effects of treatments with titanium dioxide nanoparticles (NPs-TiO2) and ethylene on anthocyanin biosynthesis and reactive oxygen species (ROS) metabolism during light exposure in ripe ‘red delicious’ apples. Both treatments led to improved anthocyanins biosynthesis in detached mature apples, while the NPs-TiO2 had less impact on the fruit firmness, TSS, TA, and TSS/TA ratio. Furthermore, the effects of both treatments on the expression of anthocyanin-related enzymes and transcription factors in the apple peel were evaluated at the gene level. The differentially expressed genes induced by the two treatments were highly enriched in the photosynthesis and flavonoid biosynthesis pathways. The expression of structural genes involved in anthocyanin biosynthesis and ethylene biosynthesis was more significantly upregulated in the ethylene treatment group than in the NPs-TiO2 treatment group, and the opposite pattern was observed for the expression of genes encoding transcription factors involved in plant photomorphogenesis pathways. In addition, the ROS levels and antioxidant capacity were higher and the membrane lipid peroxidation level was lower in fruit in the NPs-TiO2 treatment group than in the ethylene treatment group. The results of this study reveal differences in the coloration mechanisms induced by NPs-TiO2 and ethylene in apples, providing new insights into improving the color and quality of fruits.
Fractional Pricing Models: Transformations to a Heat Equation and Lie Symmetries
Authors: Champala Reginald , Jamal Sameerah , Khan Suhail
Published: August 2023

The study of fractional partial differential equations is often plagued with complicated models and solution processes. In this paper, we tackle how to simplify a specific parabolic model to facilitate its analysis and solution process. That is, we investigate a general time-fractional pricing equation, and propose new transformations to reduce the underlying model to a different but equivalent problem that is less challenging. Our procedure leads to a conversion of the model to a fractional 1 + 1 heat transfer equation, and more importantly, all the transformations are invertible. A significant result which emerges is that we prove such transformations yield solutions under the Riemann–Liouville and Caputo derivatives. Furthermore, Lie point symmetries are necessary to construct solutions to the model that incorporate the behaviour of the underlying financial assets. In addition, various graphical explorations exemplify our results.
Complex Dynamical Characteristics of the Fractional-Order Cellular Neural Network and Its DSP Implementation
Authors: Cao Hongli , Chu Ran , Cui Yuanhui
Published: August 2023

A new fractional-order cellular neural network (CNN) system is solved using the Adomian decomposition method (ADM) with the hyperbolic tangent activation function in this paper. The equilibrium point is analyzed in this CNN system. The dynamical behaviors are studied as well, using a phase diagram, bifurcation diagram, Lyapunov Exponent spectrum (LEs), and spectral entropy (SE) complexity algorithm. Changing the template parameters and the order values has an impact on the dynamical behaviors. The results indicate that rich dynamical properties exist in the system, such as hyperchaotic attractors, chaotic attractors, asymptotic periodic loops, complex coexisting attractors, and interesting state transition phenomena. In addition, the digital circuit implementation of this fractional-order CNN system is completed on a digital signal processing (DSP) platform, which proves the accuracy of ADM and the physical feasibility of the CNN system. The study in this paper offers a fundamental theory for the fractional-order CNN system as it applies to secure communication and image encryption.
Emergence of Nano-Based Formulations for Effective Delivery of Flavonoids against Topical Infectious Disorders
Authors: Dwivedi Khusbu , Mandal Ashok Kumar , Afzal Obaid , Altamimi Abdulmalik Saleh Alfawaz , Sahoo Ankit , Alossaimi Manal A. , Almalki Waleed H. , Alzahrani Abdulaziz , Barkat Md. Abul , Almeleebia Tahani M. , Mir Najib Ullah Shehla Nasar , Rahman Mahfoozur
Published: August 2023

Flavonoids are hydroxylated phenolic substances in vegetables, fruits, flowers, seeds, wine, tea, nuts, propolis, and honey. They belong to a versatile category of natural polyphenolic compounds. Their biological function depends on various factors such as their chemical structure, degree of hydroxylation, degree of polymerization conjugation, and substitutions. Flavonoids have gained considerable attention among researchers, as they show a wide range of pharmacological activities, including coronary heart disease prevention, antioxidative, hepatoprotective, anti-inflammatory, free-radical scavenging, anticancer, and anti-atherosclerotic activities. Plants synthesize flavonoid compounds in response to pathogen attacks, and these compounds exhibit potent antimicrobial (antibacterial, antifungal, and antiviral) activity against a wide range of pathogenic microorganisms. However, certain antibacterial flavonoids have the ability to selectively target the cell wall of bacteria and inhibit virulence factors, including biofilm formation. Moreover, some flavonoids are known to reverse antibiotic resistance and enhance the efficacy of existing antibiotic drugs. However, due to their poor solubility in water, flavonoids have limited oral bioavailability. They are quickly metabolized in the gastrointestinal region, which limits their ability to prevent and treat various disorders. The integration of flavonoids into nanomedicine constitutes a viable strategy for achieving efficient cutaneous delivery owing to their favorable encapsulation capacity and diminished toxicity. The utilization of nanoparticles or nanoformulations facilitates drug delivery by targeting the drug to the specific site of action and exhibits excellent physicochemical stability.
Bromelain- and Silver Nanoparticle-Loaded Polycaprolactone/Chitosan Nanofibrous Dressings for Skin Wound Healing
Authors: Saghafi Yasaman , Baharifar Hadi , Najmoddin Najmeh , Asefnejad Azadeh , Maleki Hassan , Sajjadi-Jazi Sayed , Bonkdar Alireza , Shams Forough , Khoshnevisan Kamyar
Published: August 2023

A cutaneous wound is caused by various injuries in the skin, which can be wrapped with an efficient dressing. Electrospinning is a straightforward adjustable technique that quickly and continuously generates nanofibrous wound dressings containing antibacterial and anti-inflammatory agents to promote wound healing. The present study investigated the physicochemical and biological properties of bromelain (BRO)- and silver nanoparticle (Ag NPs)-loaded gel-based electrospun polycaprolactone/chitosan (PCL/CS) nanofibrous dressings for wound-healing applications. Electron microscopy results showed that the obtained nanofibers (NFs) had a uniform and homogeneous morphology without beads with an average diameter of 176 ± 63 nm. The FTIR (Fourier transform infrared) analysis exhibited the loading of the components. Moreover, adding BRO and Ag NPs increased the tensile strength of the NFs up to 4.59 MPa. BRO and Ag NPs did not significantly affect the hydrophilicity and toxicity of the obtained wound dressing; however, the antibacterial activity against E. coli and S. aureus bacteria was significantly improved. The in vivo study showed that the wound dressing containing BRO and Ag NPs improved the wound-healing process within one week compared to other groups. Therefore, gel-based PCL/CS nanofibrous dressings containing BRO and Ag NPs could be a promising solution for healing skin wounds.
Bovine Serum Albumin Molecularly Imprinted Electrochemical Sensors Modified by Carboxylated Multi-Walled Carbon Nanotubes/CaAlg Hydrogels
Authors: Cheng Letian , Guo Zhilong , Lin Yuansheng , Wei Xiujuan , Zhao Kongyin , Yang Zhengchun
Published: August 2023

In this paper, sodium alginate (NaAlg) was used as functional monomers, bovine serum albumin (BSA) was used as template molecules, and calcium chloride (CaCl2) aqueous solution was used as a cross-linking agent to prepare BSA molecularly imprinted carboxylated multi-wall carbon nanotubes (CMWCNT)/CaAlg hydrogel films (MIPs) and non-imprinted hydrogel films (NIPs). The adsorption capacity of the MIP film for BSA was 27.23 mg/g and the imprinting efficiency was 2.73. The MIP and NIP hydrogel film were loaded on the surface of the printed electrode, and electrochemical performance tests were carried out by electrochemical impedance spectroscopy (EIS) and differential pulse voltammetry (DPV) using the electrochemical workstation. The loaded MIP film and NIP film effectively improved the electrochemical signal of the bare carbon electrode. When the pH value of the Tris HCl elution solution was 7.4, the elution time was 15 min and the adsorption time was 15 min, and the peak currents of MIP-modified electrodes and NIP-modified electrodes reached their maximum values. There was a specific interaction between MIP-modified electrodes and BSA, exhibiting specific recognition for BSA. In addition, the MIP-modified electrodes had good anti-interference, reusability, stability, and reproducibility. The detection limit (LOD) was 5.6 × 10−6 mg mL−1.
SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach
Authors: Soriano Beatriz , Hafez Ahmed Ibrahem , Naya-Català Fernando , Moroni Federico , Moldovan Roxana Andreea , Toxqui-Rodríguez Socorro , Piazzon María Carla , Arnau Vicente , Llorens Carlos , Pérez-Sánchez Jaume
Published: August 2023

Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome–host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem.