bluuebunny/crossref_metadata_2025 huggingface dataset:
Feature Reduction Networks: A Convolution Neural Network-Based Approach to Enhance Image Dehazing
Authors: Yu Haoyang , Yuan Xiqin , Jiang Ruofei , Feng Huamin , Liu Jiaxing , Li Zhongyu
Published: December 2023

Image dehazing represents a dynamic area of research in computer vision. With the exponential development of deep learning, particularly convolutional neural networks (CNNs), innovative and effective image dehazing techniques have surfaced. However, in stark contrast to the majority of computer vision tasks employing CNNs, the output from a dehazing model is often treated as uninformative noise, even though the model’s filters are engineered to extract pertinent features from the images. The standard approach of end-to-end models for dehazing involves noise removal from the hazy image to obtain a clear one. Consequently, the model’s dehazing capacity diminishes, as the noise is progressively filtered out throughout the propagation phase. This leads to the conception of the feature reduction network (FRNet), which is a distinctive CNN architecture that incrementally eliminates informative features, thereby resulting in the output of noise. Our experimental results indicate that the CNN-driven FRNet surpasses previous state-of-the-art (SOTA) methods in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) evaluation metrics. This highlights the effectiveness of the FRNet across various image dehazing datasets. With its reduced overhead, the CNN-based FRNet demonstrates superior performance over current SOTA methods, thereby affirming the efficacy of CNNs in image dehazing tasks.
Experimental Investigation of the Metasurfaced Reverberation Chamber
Authors: Sun Hengyi , Li Zhuo , Gu Changqing , Xu Qian , Zhang Yaoran , Xu Zhengran , Luo Xiaoping , Sun Changjiang , Chan Alfred
Published: December 2023

This paper explores the concept of a metasurfaced reverberation chamber (MRC) through empirical measurements, utilizing a 1-bit random coding metasurfaced stirrer within a standard reverberation chamber (RC). The study rigorously compares the performance of the MRC against traditional mechanical stirrers, both horizontal and vertical, in terms of key metrics. These metrics include the quality factor (Q factor), number of samples, standard deviation, angle autocorrelation, average K factor, total scattering cross section (TSCS), and the enhanced back scattering coefficient (eb). The results demonstrate the practicality and effectiveness of the MRC in the operational frequency range of the RC. Notably, the use of the 1-bit random coding metasurface stirrer shows potential in significantly expanding the test volume of the RC, marking a promising advancement in reverberation chamber technology.
Impact of Injection Gas on Low-Tension Foam Process for EOR in Low-Permeability Oil-Wet Carbonates
Authors: Hachem Dany , Nguyen Quoc P.
Published: December 2023

Low-tension gas (LTG) flooding has been proven in lab-scale experiments to be a viable tertiary enhanced oil recovery (EOR) technique in low-permeability (~10 mD) oil-wet carbonates. Work carried out previously almost exclusively focused on water-wet cores. The application of LTG in oil-wet carbonates is investigated in this study along with the impact of a hydrocarbon (HC) mixture as the injection gas on oil–water microemulsion phase behavior. The optimum injection gas fraction (ratio of gas injection rate to total injection rate of gas and water) for the hydrocarbon gas mixture in oil-wet carbonates regarding the oil recovery rate was determined to be 60% as it resulted in around 50% residual oil in place (ROIP) recovery. It was shown that proper mobility control can be achieved under these conditions even in the absence of strong foam. The effect of HC gas dissolution in oil was clearly shown by replacing the injection HC gas with nitrogen under the same conditions. Furthermore, the importance of ultra-low interfacial tension (IFT) produced by the injection gas and surfactant slug is proven by comparing injection at sub-optimum salinity to injection at optimum salinity.
Monte Carlo Simulation of Percolation Phenomena for Direct Current in Large Square Matrices
Authors: Zukowski Pawel , Okal Pawel , Kierczynski Konrad , Rogalski Przemyslaw , Bondariev Vitalii , Pogrebnjak Alexander D.
Published: December 2023

In this study, an in-depth analysis of the percolation phenomenon for square matrices with dimensions from L = 50 to 600 for a sample number of 5 × 104 was performed using Monte Carlo computer simulations. The percolation threshold value was defined as the number of conductive nodes remaining in the matrix before drawing the node interrupting the last percolation channel, in connection with the overall count of nodes within the matrix. The distributions of percolation threshold values were found to be normal distributions. The dependencies of the expected value (mean) of the percolation threshold and the standard deviation of the dimensions of the matrix were determined. It was established that the standard deviation decreased with the increase in matrix dimensions, ranging from 0.0262253 for a matrix with L = 50 to 0.0044160 for L = 600, which is almost six-fold lower. The mean value of the percolation threshold was practically constant and amounted to approximately 0.5927. The analysis involved not only the spatial distributions of nodes interrupting the percolation channels but also the overall patterns of node interruption in the matrix. The distributions revealed an edge phenomenon within the matrices, characterized by the maximum concentration of nodes interrupting the final percolation channel occurring at the center of the matrix. As they approached the edge of the matrix, their concentration decreased. It was established that increasing the dimensions of the matrix slowed down the rate of decrease in the number of nodes towards the edge. In doing so, the area in which values close to the maximum occurred was expanded. Based on the approximation of the experimental results, formulas were determined describing the spatial distributions of the nodes interrupting the last percolation channel and the values of the standard deviation from the matrix dimensions. The relationships obtained showed that with increasing matrix dimensions, the edge phenomenon should gradually disappear, and the percolation threshold standard deviation values caused by it will tend towards zero.
Feasibility Study of Exhaust Energy Recovery System for Mobile Carbon Capture Operations in Commercial Engines through 1D Simulation
Authors: Woo Seungchul , Jeong Yusin , Lee Kihyung
Published: December 2023

The global proportion of eco-friendly vehicles continues to increase; however, regarding hybrid vehicles, the vehicle powertrains in most countries include internal combustion engines. Therefore, research on reducing the carbon emissions from internal combustion engines must be conducted. Carbon capture technology must be developed for e-fuel, which has recently attracted attention, to achieve carbon neutrality. In this study, a turbo compound system capable of recovering waste exhaust gas energy was selected as the most appropriate energy supply system to operate a mobile carbon capture system. The feasibility was reviewed by analyzing the turbo compound speed, pressure drop, power generation, etc., using a one-dimensional simulation method. The maximum power generation of the configured turbo compound system was approximately 9 kW, and approximately 1–3 kW of energy could be recovered under medium speed and load conditions, which are the optimal operating conditions for a test engine with the displacement of a 4 L.
Dibenzofulvene Derivatives as Promising Materials for Photovoltaic and Organic Electronics
Authors: Szlapa-Kula Agata , Ledwon Przemyslaw , Krawiec Agnieszka , Kula Slawomir
Published: December 2023

This review aimed to summarize the current knowledge regarding dibenzofulvene derivatives (DBF) investigated for photovoltaics and organic electronics applications. The work begins with a detailed analysis of the synthesis and modification methods for dibenzofulvene derivatives’ structure. Then, the physicochemical properties (thermal, electrochemical, and optical) of the selected compounds are discussed in detail. Moreover, this article also presents the DFT calculations performed so far. Finally, the review presents the latest research on the applications of dibenzofulvene derivatives as dyes for DSSC cells, hole transport materials (HTMs) for perovskite solar cells (PSCs), organic light-emitting diodes (OLEDs), and luminescent and electrochromic materials. Considering the above, this review may be helpful when designing new organic compounds for photovoltaic and organic electronic applications.
Vehicle Underhood Environment Investigations Using a Simplified Test Rig
Authors: Vdovin Alexey , Kadri Sathiyan Tarun
Published: December 2023

Energy efficiency and thermal management continue to be critical areas in the vehicle development process. Independent of whether it is a vehicle with an internal combustion engine, hybrid, or fully electrical, engineers require proper and accurate tools to comprehend heat transfer in engine bays. However, developing such tools using a fully detailed production geometry can be challenging. This work presents a simplified and flexible test rig design of the vehicle underhood environment in two different variants: a passenger car and a commercial truck. The rig design in CAD and experimental data for the two rig configurations are made available upon request. These data will allow for the validation of different simulation approaches against the experimental dataset. An example of such a validation is presented in this paper. The load case scenario represented includes constant speed driving under heavy loads continued by a complete halt, which is followed by soaking for 100 min. The differences in experimental results for different rig variants are shown and analyzed. A good correlation between the experiments and CFD is obtained.
Model-Based Calibration and Control of Tailpipe Nitrogen Oxide Emissions in a Light-Duty Diesel Engine and Its Assessment through Model-In-The-Loop
Authors: d’Ambrosio Stefano , Di Dio Cosimo , Finesso Roberto
Published: December 2023

The present paper investigates two different strategies for model-based calibration and control of tailpipe nitrogen oxide emissions in a light-duty 3.0 L diesel engine equipped with an aftertreatment system (ATS). The latter includes a diesel oxidation catalyst (DOC), a diesel particulate filter (DPF), and an underfloor selective catalytic reduction (SCR) device, in which the injection of diesel exhaust fluid (DEF), marketed as ‘AdBlue’, is also taken into account. The engine was modeled in the GT-SUITE environment, and a previously developed model-based combustion controller was integrated in the model, which is capable of adjusting the start of injection of the main pulse and the total injected fuel mass, in order to achieve desired targets of engine-out nitrogen oxide emissions (NOx) and brake mean effective pressure (BMEP). First, a model-based calibration strategy consisting of the minimization of an objective function that takes into account fuel consumption and AdBlue injection was developed and assessed by exploring different weight factors. Then, a direct model-based controller of tailpipe nitrogen oxide emissions was designed, which exploits the real-time value of the SCR efficiency to define engine-out NOx emission targets for the combustion controller. Both strategies exploit the model-based combustion controller and were tested through a Model-in-the-Loop (MiL) under steady-state and transient conditions. The advantages in terms of tailpipe NOx emissions, fuel consumption, and AdBlue injection were finally discussed.
Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks
Authors: Lee Donghun , Kim Jongeun , Kim Suhee , Kim Kwanho
Published: December 2023

As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model’s performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption.
Improvement of Fuzzy Newton Power Flow Convergence
Authors: Zhao Ligang , Zheng Hua , Zhen Hongyue , Xie Li , Xu Yuan , Huang Xianchao
Published: December 2023

In order to address the convergence issue in fuzzy power flow calculations, this paper proposes an analytical approach based on the Levenberg–Marquardt method, aiming to improve the convergence of the fuzzy Newton power flow method. Firstly, a detailed analysis is conducted on the convergence theorem and convergence behavior of the fuzzy Newton method, revealing its poor convergence when the initial values are not properly selected. The Levenberg–Marquardt method is then selected as a means to enhance the convergence of the fuzzy Newton power flow calculations, specifically to tackle the problem of initial value deviation. Since the Jacobian matrix has a significant impact on the convergence region of the power flow, this paper reconstructs the Jacobian matrix based on the Levenberg–Marquardt method, effectively enlarging the convergence region. Through validation experiments on the IEEE 118 standard nodes and simulation comparative analysis, the results confirm the method’s effectiveness in resolving the problem of initial value deviation and notably enlarging the convergence region, thereby improving the convergence of power flow calculations.
From Global Health to Global Warming: Tracing Climate Change Interest during the First Two Years of COVID-19 Using Google Trends Data from the United States
Authors: Hoffmann Lena , Bressem Keno K. , Cittadino Jonas , Rueger Christopher , Suwalski Phillip , Meinel Jakob , Funken Simon , Busch Felix
Published: December 2023

Climate change mitigation depends on actions that affect the public interest and lead to widespread changes in public attitudes and behavior. With the global outbreak of the COVID-19 pandemic, humanity faced a more imminent threat to its well-being and viability. This retrospective cross-sectional study examines how public interest in climate change was attenuated by the severity of the COVID-19 pandemic using Google Trends Search Volume Index (SVI), weather, and climate data on a United States state-level basis during the first two years of the pandemic from 2020 to 2022. To identify channels through which the COVID-19 pandemic affected information demand on climate change, a novel fixed effect regression model of public climate change interest was developed. The measure captures changes in the climate change SVI independent of weather and climate conditions, comprising pandemic-related changes in living circumstances such as COVID-19-related cases and deaths, mask mandates, and the proportion of the vaccinated population. Our results indicate that public interest in climate change was systematically attenuated by the severity of the COVID-19 pandemic. In addition, this study provides an approach for identifying drivers of public interest in climate change.
Utilizing Grid Data and Deep Learning for Forest Fire Occurrences and Decision Support: A Case Study in the Ningxia Hui Autonomous Region
Authors: Shao Yakui , Zhu Qin , Feng Zhongke , Sun Linhao , Yue Peng , Wang Aiai , Zhang Xiaoyuan , Su Zhiqiang
Published: December 2023

In order to investigate the geographical distribution of forest fire occurrences in the Ningxia Hui Autonomous Region, this study employs advanced modeling techniques, utilizing diverse data sources, including fuel, Gross Domestic Product (GDP), population, meteorology, buildings, and grid data. This study integrates deep learning Convolutional Neural Networks (CNNs) to predict potential fire incidents. The research findings can be summarized as follows: (i) The employed model exhibits very good performance, achieving an accuracy of 84.35%, a recall of 86.21%, and an Area Under the Curve (AUC) of 87.67%. The application of this model significantly enhances the reliability of the forest fire occurrence model and provides a more precise assessment of its uncertainty. (ii) Spatial analysis shows that the risk of fire occurrence in most areas is low-medium, while high-risk areas are mainly concentrated in Longde County, Jingyuan County, Pengyang County, Xiji County, Yuanzhou District, Tongxin County, Xixia District, and Yinchuan City, which are mostly located in the southern, southeastern, and northwestern regions of Ningxia Hui Autonomous Region, with a total area of 2191.2 square kilometers. This underscores the urgent need to strengthen early warning systems and effective fire prevention and control strategies in these regions. The contributions of this research include the following: (i) The development of a highly accurate and practical provincial-level forest fire occurrence prediction framework based on grid data and deep learning CNN technology. (ii) The execution of a comprehensive forest fire prediction study in the Ningxia Hui Autonomous Region, China, incorporating multi-source data, providing valuable data references, and decision support for forest fire prevention and control. (iii) The initiation of a preliminary systematic investigation and zoning of forest fires in the Ningxia Hui Autonomous Region, along with tailored recommendations for prevention and control measures.
A Seedling Collection Unit of a Mobile Automatic Device for Forest Tree Planting—An Extended Operating Concept
Authors: Sobocki Sebastian , Adamczyk Florian , Tylek Paweł , Szaroleta Michał , Żurowski Kamil , Kormanek Mariusz , Szewczyk Grzegorz , Tabor Sylwester , Zwierzyński Mikołaj , Kaźmierczak Michał , Mac Jarosław
Published: December 2023

Agrotechnological procedures related to forest management, including planting, are burdensome and the energy expenditure of the people working is very high. Nowadays, planting is usually carried out manually, and sometimes with the use of planters coupled to universal tractors. Occasionally, planters mounted on high-power carriers are used, and usually the seedlings are removed from their cassettes and placed manually into the planter’s storage unit. In connection with the above, the prototype of a mobile automatic device for performing forest regeneration tasks and afforestation of post-agricultural and reclaimed areas (RoboFoR) was designed. The main task of this vehicle is planting forest tree seedlings with a covered root system, which are collected directly from their nursery cassettes. This study presents the structure, principle of operation and results of operational tests of the seedling storage and feeding unit. It was found that the unit achieved the expected work efficiency. However, imperfections in the system related to the non-centralized distribution of seedlings in containers and the high variability of biometric characteristics of the seedlings were also identified. A new solution for this unit was proposed, which will increase the reliability of collecting the seedlings. A new solution requires greater computational power of the control unit as well as equipping the sensor system with a technologically advanced root neck detection system. The new concept also assumes the possibility of independent movement of each gripper.
An Updated Cyclic CBR Test with Realistic Stress Values under the Plunger for Resilient Modulus Calculation
Authors: Florian Aleš , Ševelová Lenka , Žáková Kateřina , Žák Jaroslav
Published: December 2023

The design and assessment of forest and rural road pavements made from unbound natural and recycled materials require the careful determination of their physical-mechanical properties. The fundamental parameter for characterizing the soil material property is considered to be the resilient modulus Mr. The generally accepted alternative solution for its calculation is the cyclic CBR test, which uses standard CBR testing equipment. To perform tests on intact specimens and under stress conditions corresponding to expected states of stress in a pavement structure, an updated cyclic CBR test is proposed. In contrast to the standard cyclic CBR test, the applied loading force for repeated loadings is not determined in the first loading step by the plunger penetration to the prescribed depth, but by the stress value that is expected in an actual pavement structure. The updated test was verified on 240 tested specimens taken from a total of 40 soil samples belonging to nine soil types according to the USCS system. The resilient modulus results obtained are compared with those obtained using the standard cyclic CBR test and the cyclic triaxial test.
Aboveground Biomass Prediction of Plots in the Natural Forests of Arid Mountains Based on Large Trees
Authors: Xiong Shimei , Yi Lubei , Bao Anming , Wang Zhengyu , Tao Zefu , Xu Wenqiang
Published: December 2023

While the use of large tropical trees to predict aboveground biomass (AGB) in forests has previously been studied, the applicability of this approach in arid regions remains unquantified. In the natural forests of arid mountains of Northwestern China, this study collected individual tree data from 105 plots across 11 sites through field measurements. The objective was to assess the feasibility of using large trees for predicting plot AGB in these natural forests of arid mountains. This entailed determining the contribution of large trees, based on which a plot AGB prediction model was constructed. This study also aimed to identify the optimal number of large trees needed for accurate AGB prediction. The findings indicate that within the natural forests of arid mountains, only seven large trees (approximately 12% of the trees in a plot) are necessary to account for over 50% of the plot AGB. By measuring 18 large trees within a plot, this study achieved a precise plot AGB estimation, resulting in a model rRMSE of 0.27. The regression fit R2 for the predicted AGB and the estimated AGB was 0.79, effectively aligning the predicted and measured AGB. In the Tianshan Mountains’ natural forests, the prediction model yielded further improvements with an rRMSE of 0.13 and a remarkable regression R2 of 0.92 between predicted and estimated AGB. However, due to variances in tree size distribution and tree species biomass, the Altai Mountains’ natural forest was found to be unsuitable for predicting plot AGB using large trees. This study establishes that large trees can effectively represent plot AGB in the natural forests of arid mountains. Employing forest surveys or remote sensing to collect data from a few large trees instead of the entire tree population enables accurate plot AGB prediction. This research serves as the initial quantification of large tree utilization for plot AGB prediction in the natural forests of arid mountains, carrying substantial implications for future arid forest inventories, carbon accounting, and the formulation of prudent conservation strategies.
Forest Canopy Structures and Bamboo Rhizome Internodes Impact the Appearance Quality of Bamboo Shoots
Authors: She Chunyan , Yu Yulin , Wan Maji , Abubakar Adamu Abdullahi , Luo Weixue , Liu Jinchun , Tao Jianping
Published: December 2023

Bamboo shoots are a healthy vegetable with significant commercial value, and their appearance quality is a key factor influencing consumer preference and market pricing. Their growth characteristics—after being unearthed, they maintain basal diameter while rapidly growing in height—affect the taste and nutritional quality. However, little attention has been given to the impact of bamboo forest management on shoot appearance. Therefore, this study addressed this research gap through a comprehensive investigation across three types of bamboo forests: evergreen broad-leaved forest (EBF), evergreen deciduous broad-leaved mixed forest (MBF), and pure bamboo forest (PBF). In addition, we further assessed factors that potentially affect the appearance quality of bamboo shoots, including canopy structures, understory light factors and understory soil factors, mother bamboo factors, and shoot internal factors (pigments and cells). The basal diameters of shoots in PBF and MBF were 1.89 cm and 1.97 cm, respectively, which were significantly larger than those in EBF by 0.27 cm and 0.35 cm, respectively. The linear mixed effect model identified the number of bamboo rhizome internodes and the chlorophyll a content as primary factors influencing basal diameter thickening and elongation growth of shoots, respectively. In addition, increasing the bamboo canopy and mean leaf angle reduced the chlorophyll a content and increased the carotenoid content, thereby benefiting the improvement in or maintenance of the taste and quality of shoots. This study highlighted that increasing the number of bamboo rhizome internodes, bamboo canopy, and mean leaf angle is helpful to improve the appearance quality of shoots. These findings offer a scientific foundation for bamboo forest management, contributing to both ecological sustainability and economic benefits.
Distributed Denial of Service Classification for Software-Defined Networking Using Grammatical Evolution
Authors: Spyrou Evangelos D. , Tsoulos Ioannis , Stylios Chrysostomos
Published: December 2023

Software-Defined Networking (SDN) stands as a pivotal paradigm in network implementation, exerting a profound influence on the trajectory of technological advancement. The critical role of security within SDN cannot be overstated, with distributed denial of service (DDoS) emerging as a particularly disruptive threat, capable of causing large-scale disruptions. DDoS operates by generating malicious traffic that mimics normal network activity, leading to service disruptions. It becomes imperative to deploy mechanisms capable of distinguishing between benign and malicious traffic, serving as the initial line of defense against DDoS challenges. In addressing this concern, we propose the utilization of traffic classification as a foundational strategy for combatting DDoS. By categorizing traffic into malicious and normal streams, we establish a crucial first step in the development of effective DDoS mitigation strategies. The deleterious effects of DDoS extend to the point of potentially overwhelming networked servers, resulting in service failures and SDN server downtimes. To investigate and address this issue, our research employs a dataset encompassing both benign and malicious traffic within the SDN environment. A set of 23 features is harnessed for classification purposes, forming the basis for a comprehensive analysis and the development of robust defense mechanisms against DDoS in SDN. Initially, we compare GenClass with three common classification methods, namely the Bayes, K-Nearest Neighbours (KNN), and Random Forest methods. The proposed solution improves the average class error, demonstrating 6.58% error as opposed to the Bayes method error of 32.59%, KNN error of 18.45%, and Random Forest error of 30.70%. Moreover, we utilize classification procedures based on three methods based on grammatical evolution, which are applied to the aforementioned data. In particular, in terms of average class error, GenClass exhibits 6.58%, while NNC and FC2GEN exhibit average class errors of 12.51% and 15.86%, respectively.
In Vitro Assessment of the Bioaccessibility of Zn, Ca, Mg, and Se from Various Types of Nuts
Authors: Moskwa Justyna , Naliwajko Sylwia Katarzyna , Puścion-Jakubik Anna , Soroczyńska Jolanta , Socha Katarzyna , Koch Wojciech , Markiewicz-Żukowska Renata
Published: December 2023

The bioaccessibility of zinc (Zn), calcium (Ca), magnesium (Mg), and selenium (Se) from various nuts (Brazil nuts, walnuts, peanuts, almonds, cashews, pecans, hazelnuts, macadamia nuts, and pistachios) was assessed using a simulated two-phase model of enzymatic digestion in vitro. The levels of Zn, Mg, and Ca were determined by atomic absorption spectrometry, and Se was measured by inductively coupled plasma-mass spectrometry. All tested nuts were good sources of Mg, and most, except macadamia nuts, were also good sources of Zn (the standard portion covers over 15% of NRV–R (UE) 1924/2006). Brazil nuts had the highest Se content. Almonds and Brazil nuts were rich in Ca. Se demonstrated the highest bioaccessibility from nuts (27.7% to 70.65%), whereas Ca exhibited the lowest bioaccessibility (below 9%). Pistachios had the highest Zn bioavailability, while cashews excelled in Mg bioaccessibility. Macadamia and pistachios were top for Ca bioaccessibility, and Brazil nuts for Se. Bioaccessibility is positively correlated with fat (for Zn: r = 0.23), carbohydrates (for Mg: 0.44; for Ca: 0.35), and sugar content (for Zn: r = 0.36; for Mg: 0.46; for Ca: 0.40).
Effect of Solar Pre-Drying and Yeast Starter Inoculation Treatments on the Chemical Composition of Cocoa (Theobroma cacao L.) Beans from Southwestern Colombia
Authors: Tejeda Juan Florencio , Arango-Angarita Jéssica , Cuervo Jairo Leonardo
Published: December 2023

In Southwestern Colombia, cocoa clones are cultivated in which productivity characteristics predominate over bean quality. In this study, cocoa beans of the CCN-51 clone harvested in the Tumaco region (Nariño, Colombia) were fermented following four different treatments: (1) spontaneously (at room temperature for 120 h) in traditional conditions (Control); (2) traditional processing with a pre-drying (sun-dried for 24 h) treatment (PD); (3) with fermentation (for 120 h) after inoculation of a yeast starter culture (YS); and (4) including both treatments, pre-drying and yeast inoculation (PD + YS). Chemical composition, fatty acids, total polyphenol, methylxanthines (theobromine and caffeine) and lactic acid content of cocoa beans were determined. Chemical and fatty acid composition and theobromine content were not affected by the different fermentation treatments applied (p > 0.05). When analyzing total polyphenol content, YS (16.62 mg/g) and PD + YS (17.74) treatments significantly decreased (p < 0.05) the content of these compounds at the end of the fermentation process, affecting cocoa flavour, while PD treatment decreased (p < 0.05) the caffeine content (0.68 mg/g) of cocoa beans. Finally, lactic acid content decreased because of both inoculation of yeast starter (1.11 mg/g) and mainly the pre-drying treatment (0.60). In conclusion, solar pre-drying in the open air and the inoculation of yeast starter treatments could improve the final quality of cocoa beans.
Response Surface Methodology to Optimize the Extraction of Carotenoids from Horticultural By-Products—A Systematic Review
Authors: Cano-Lamadrid Marina , Martínez-Zamora Lorena , Mozafari Laleh , Bueso María Carmen , Kessler Mathieu , Artés-Hernández Francisco
Published: December 2023

Response Surface Methodology (RSM) is a widely used mathematical tool for process optimization, setting their main factorial variables. The current research analyzes and summarizes the current knowledge about the RSM in the extraction of carotenoids from fruit and vegetable by-products, following a systematic review protocol (Prisma 2020 Statement). After an identification of manuscripts in Web of Science (September 2023) using inclusion search terms (“carotenoids”, “extraction”, “response-surface methodology”, “ultrasound”, “microwave” and “enzyme”), they were screened by titles and abstracts. Finally, 29 manuscripts were selected according to the PRISMA methodology (an evidence-based minimum set of items for reporting in systematic reviews), then, 16 questions related to the quality criteria developed by authors were applied. All studies were classified as having an acceptable level of quality criteria (≤50% “yes answers”), with four of them reaching a moderate level (>50 to ≤70% “yes answers”). No studies were cataloged as complete (>70% “yes answers”). Most studies are mainly focused on ultrasound-assisted extraction, which has been widely developed compared to microwave or enzymatic-assisted extractions. Most evidence shows that it is important to provide information when RSM is applied, such as the rationale for selecting a particular design, the specification of input variables and their potential levels, a discussion on the statistical model’s validity, and an explanation of the optimization procedure. In addition, the principles of open science, specifically data availability, should be included in future scientific manuscripts related to RSM and revalorization.
Synthesis of Effect Sizes on Dose Response from Ultra-Processed Food Consumption against Various Noncommunicable Diseases
Authors: Bestari Fairuz Firda , Andarwulan Nuri , Palupi Eny
Published: December 2023

Ultra-processed foods (UPFs), according to the NOVA classification food system, are food products that are processed using advanced processing technology, which improves palatability and sensory quality. However, UPFs increase the daily intake of energy, sodium, sugar, and total fat (including saturated fat), and decrease the intake of fiber. This might trigger overweight and obesity, the initial stages of noncommunicable diseases (NCDs). However, the effect of UPF consumption on NCDs remains under debate. This study aimed to synthesize the effect size of UPF dose response on various NCDs by using a meta-analysis method. The main output was a hazard ratio (HR) and a 95% confidence interval (CI). Using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) selection guidelines, 18 research articles were chosen for further effect size synthesis. The results showed that UPF consumption significantly increased the daily intake of carbohydrates, added sugar, saturated fat, sodium, energy, cholesterol, and total fat; increases of 49.64, 40.78, 30.00, 27.76, 26.67, 25.69, and 15.77%, respectively, were observed. Moreover, with UPF consumption, the fiber intake was way below the daily dietary recommendation (DR), at −38.55%. Further, a 10% increment in UPF consumption significantly affects diabetes, cardiovascular diseases, obesity, and cancer (HR ± 95% CI: 1.115 ± 0.044, 1.096 ± 0.053, 1.068 ± 0.050, and 1.020 ± 0.020, respectively). Thus, limiting daily UPF consumption could help prevent obesity and various NCDs.
Improving the Flavour of Enzymatically Hydrolysed Beef Liquid by Sonication
Authors: Ye Chao , Zhang Zhankai , Zhang Zhi-Hong , He Ronghai , Zhao Xue , Gao Xianli
Published: December 2023

Beef potentiator is an important flavour enhancer in the food industry, while it is prone to generating insufficient compounds with umami and sweet tastes and compounds with a fishy odour during enzymatic hydrolysis of beef, resulting in poor flavour of beef potentiator. It has been extensively reported that sonication is capable of improving food flavour. However, the effect of sonication on the flavour of enzymatically hydrolysed beef liquid (EHBL) was scarcely reported. Herein, we investigated the effect of sonication on the flavour of EHBL using quantitative descriptive analysis (QDA), physicochemical analysis and SPME-GC-olfactometry/MS. QDA showed that sonication had a significant effect on taste improvement and off-odour removal of EHBL. Compared with the control, sonication (40 kHz, 80 W/L) increased the contents of total nitrogen, formaldehyde nitrogen, total sugars, reducing sugars, free amino acids (FAAs) and hydrolysis degree of EHBL by 19.25%, 19.80%, 11.83%, 9.52%, 14.37% and 20.45%. Notably, sonication markedly enhanced the contents of sweet FAAs, umami FAAs and bitter FAAs of EHBL by 19.66%, 14.04% and 9.18%, respectively, which contributed to the taste improvement of EHBL. SPME-GC-olfactometry/MS analysis showed that aldehydes and alcohols were the main contributors to aroma compounds of EHBL, and sonication significantly increased the contents of key aroma compounds and alcohols (115.88%) in EHBL. Notably, sonication decreased the contents of fishy odorants, hexanoic acid and nonanal markedly by 35.29% and 26.03%, which was responsible for the aroma improvement of EHBL. Therefore, sonication could become a new potential tool to improve the flavour of EHBL.
Protein Hydrolysates from Fishery Processing By-Products: Production, Characteristics, Food Applications, and Challenges
Authors: Nikoo Mehdi , Regenstein Joe M. , Yasemi Mehran
Published: December 2023

Fish processing by-products such as frames, trimmings, and viscera of commercial fish species are rich in proteins. Thus, they could potentially be an economical source of proteins that may be used to obtain bioactive peptides and functional protein hydrolysates for the food and nutraceutical industries. The structure, composition, and biological activities of peptides and hydrolysates depend on the freshness and the actual composition of the material. Peptides isolated from fishery by-products showed antioxidant activity. Changes in hydrolysis parameters changed the sequence and properties of the peptides and determined their physiological functions. The optimization of the value of such peptides and the production costs must be considered for each particular source of marine by-products and for their specific food applications. This review will discuss the functional properties of fishery by-products prepared using hydrolysis and their potential food applications. It also reviews the structure–activity relationships of the antioxidant activity of peptides as well as challenges to the use of fishery by-products for protein hydrolysate production.
Investigation of Acid Tolerance Mechanism of Acetobacter pasteurianus under Different Concentrations of Substrate Acetic Acid Based on 4D Label-Free Proteomic Analysis
Authors: Li Tian , Wang Xinwei , Li Chunyan , Fu Qingquan , Shi Xuewei , Wang Bin
Published: December 2023

Acetobacter pasteurianus is always used to brew vinegar because of its ability of producing and tolerating a high concentration of acetic acid. During vinegar fermentation, initial acetic acid contributes to acetic acid accumulation, which varies with initial concentrations. In this study, to investigate the mechanisms of tolerating and producing acetic acid of Acetobacter pasteurianus under different concentrations of substrate acetic acid, four-dimensional label-free proteomic technology has been used to analyze the protein profiles of Acetobacter pasteurianus at different growth stages (the lag and exponential phases) and different substrate acetic acid concentrations (0%, 3%, and 6%). A total of 2093 proteins were quantified in this study. The differentially expressed proteins were majorly involved in gene ontology terms of metabolic processes, cellular metabolic processes, and substance binding. Under acetic acid stress, strains might attenuate the toxicity of acetic acid by intensifying fatty acid metabolism, weakening the tricarboxylic acid cycle, glycerophospholipid and energy metabolism during the lag phase, while strains might promote the assimilation of acetic acid and inter-conversion of substances during the exponential phase by enhancing the tricarboxylic acid cycle, glycolysis, pyruvate, and energy metabolism to produce and tolerate acid. Besides, cell cycle regulation and protein translation might be potential acid tolerance pathways under high acid stress. The result contributes to the exploration of new potential acid tolerance mechanisms in Acetobacter pasteurianus from four-dimensional label-free relative quantitative proteomics analysis.
Identification of Superior Haplotypes and Haplotype Combinations for Grain Size- and Weight-Related Genes for Breeding Applications in Rice (Oryza sativa L.)
Authors: Liu Gang , Qiu Dongfeng , Lu Yuxia , Wu Yan , Han Xuesong , Jiao Yaru , Wang Tingbao , Yang Jinsong , You Aiqing , Chen Jianguo , Zhang Zaijun
Published: December 2023

The identification of superior haplotypes and haplotype combinations is essential for haplotype-based breeding (HBB), which provides selection targets for genomics-assisted breeding. In this study, genotypes of 42 functional genes in rice were analyzed by targeted capture sequencing in a panel of 180 Indica rice accessions. In total, 69 SNPs/Indels in seven genes were detected to be associated with grain length (GL), grain width (GW), ratio of grain length–width (L/W) and thousand-grain weight (TGW) using candidate gene-based association analysis, including BG1 and GS3 for GL, GW5 for GW, BG1 and GW5 for L/W, and AET1, SNAC1, qTGW3, DHD1 and GW5 for TGW. Furthermore, two haplotypes were identified for each of the seven genes according to these associated SNPs/Indels, and the amount of genetic variation explained by different haplotypes ranged from 3.24% to 27.66%. Additionally, three, three and eight haplotype combinations for GL, L/W and TGW explained 25.38%, 5.5% and 22.49% of the total genetic variation for each trait, respectively. Further analysis showed that Minghui63 had the superior haplotype combination Haplotype Combination 4 (HC4) for TGW. The most interesting finding was that some widely used restorer lines derived from Minghui63 also have the superior haplotype combination HC4, and our breeding varieties and lines using the haplotype-specific marker panel also confirmed that the TGW of the lines was much higher than that of their sister lines without HC4, suggesting that TGW-HC4 is the superior haplotype combination for TGW and can be utilized in rice breeding.
Application of OpenArray Technology to Assess Changes in the Expression of Functionally Significant Genes in the Substantia Nigra of Mice in a Model of Parkinson’s Disease
Authors: Troshev Dmitry , Kolacheva Anna , Pavlova Ekaterina , Blokhin Victor , Ugrumov Michael
Published: December 2023

Studying the molecular mechanisms of the pathogenesis of Parkinson’s disease (PD) is critical to improve PD treatment. We used OpenArray technology to assess gene expression in the substantia nigra (SN) cells of mice in a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of PD and in controls. Among the 11 housekeeping genes tested, Rps27a was taken as the reference gene due to its most stable expression in normal and experimental conditions. From 101 genes encoding functionally significant proteins of nigrostriatal dopaminergic neurons, 57 highly expressed genes were selected to assess their expressions in the PD model and in the controls. The expressions of Th, Ddc, Maoa, Comt, Slc6a3, Slc18a2, Drd2, and Nr4a2 decreased in the experiment compared to the control, indicating decreases in the synthesis, degradation, and transport of dopamine and the impaired autoregulation of dopaminergic neurons. The expressions of Tubb3, Map2, Syn1, Syt1, Rab7, Sod1, Cib1, Gpx1, Psmd4, Ubb, Usp47, and Ctsb genes were also decreased in the MPTP-treated mice, indicating impairments of axonal and vesicular transport and abnormal functioning of the antioxidant and ubiquitin-proteasome systems in the SN. The detected decreases in the expressions of Snca, Nsf, Dnm1l, and Keap1 may serve to reduce pathological protein aggregation, increase dopamine release in the striatum, prevent mitophagy, and restore the redox status of SN cells.
Phenotypic and Proteomic Insights into Differential Cadmium Accumulation in Maize Kernels
Authors: Guo Huanle , Deng Min , Yu Feng , Li Han , Cao Zhongyang , Zeng Qiang , Chen Zhihui , Luo Hongbing , Tang Bin
Published: December 2023

The contamination of agricultural soil with cadmium (Cd), a heavy metal, poses a significant environmental challenge, affecting crop growth, development, and human health. Previous studies have established the pivotal role of the ZmHMA3 gene, a P-type ATPase heavy metal transporter, in determining variable Cd accumulation in maize grains among 513 inbred lines. To decipher the molecular mechanism underlying mutation-induced phenotypic differences mediated by ZmHMA3, we conducted a quantitative tandem mass tag (TMT)-based proteomic analysis of immature maize kernels. This analysis aimed to identify differentially expressed proteins (DEPs) in wild-type B73 and ZmHMA3 null mutant under Cd stress. The findings demonstrated that ZmHMA3 accumulated higher levels of Cd compared to B73 when exposed to varying Cd concentrations in the soil. In comparison to soil with a low Cd concentration, B73 and ZmHMA3 exhibited 75 and 142 DEPs, respectively, with 24 common DEPs shared between them. ZmHMA3 showed a higher induction of upregulated genes related to Cd stress than B73. Amino sugar and nucleotide sugar metabolism was specifically enriched in B73, while phenylpropanoid biosynthesis, nitrogen metabolism, and glyoxylate and dicarboxylate metabolism appeared to play a more significant role in ZmHMA3. This study provides proteomics insights into unraveling the molecular mechanism underlying the differences in Cd accumulation in maize kernels.
BnPLP1 Positively Regulates Flowering Time, Plant Height, and Main Inflorescence Length in Brassica napus
Authors: Ding Ting , Cai Lei , He Yuqi , Li Yuanhong , Tian Entang , Zhou Qianhui , Zhou Xufan , Wang Xiaodong , Yu Kunjiang , Shen Xinjie
Published: December 2023

Protein prenylation mediated by the Arabidopsis thaliana PLURIPETALA (AtPLP) gene plays a crucial role in plant growth, development, and environmental response by adding a 15-carbon farnesyl group or one to two 20-carbon geranylgeranyl groups onto one to two cysteine residues at the C-terminus of the target protein. However, the homologous genes and their functions of AtPLP in rapeseed are unclear. In this study, bioinformatics analysis and gene cloning demonstrated the existence of two homologous genes of AtPLP in the Brassica napus L. genome, namely, BnPLP1 and BnPLP2. Evolutionary analysis revealed that BnPLP1 originated from the B. rapa L. genome, while BnPLP2 originated from the B. oleracea L. genome. Genetic transformation analysis revealed that the overexpression of BnPLP1 in Arabidopsis plants exhibited earlier flowering initiation, a prolonged flowering period, increased plant height, and longer main inflorescence length compared to the wild type. Contrarily, the downregulation of BnPLP1 expression in B. napus plants led to delayed flowering initiation, shortened flowering period, decreased plant height, and reduced main inflorescence length compared to the wild type. These findings indicate that the BnPLP1 gene positively regulates flowering time, plant height, and main inflorescence length. This provides a new gene for the genetic improvement of flowering time and plant architecture in rapeseed.