bluuebunny/crossref_metadata_2025 huggingface dataset:
Rural Latinx Students’ Spatial Imaginations of Their College Choices: Toward a Student Understanding of “Rural-Serving Institutions” in California’s San Joaquin Valley
Authors: Puente Mayra , Nuñez Martinez Mayra , Rios Arroyo Daniel , Torres Sarahy
Published: June 2023

Rural students face multiple issues pursuing higher education, including financial hardships, inadequate college preparation, and geographic isolation from postsecondary institutions. These issues are further complicated for rural Latinx students, especially those from immigrant farm working backgrounds, who are positioned at the intersection of multiple systems of oppression. Yet, rural Latinx students’ college choice and transition experiences are rarely centered in the higher education literature. This article examined the college choices of nine rural Latinx high school seniors from California’s San Joaquin Valley who chose to attend a public higher education institution in this agricultural region. This article drew on three indicators from the culturally engaging campus environments (CECE) model and employed a Chicana/Latina feminist pláticas methodology to analyze the campus elements that motivated rural Latinx students to enroll in public institutions in the San Joaquin Valley. Findings demonstrate that rural Latinx students purposely chose these institutions because they imagined that such institutions would (a) be racially and spatially familiar, (b) allow them to give back to their rural communities through relevant majors, and (c) offer tight-knit collegiate environments. Recommendations for higher education researchers and practitioners interested in increasing college success for rural Latinx students and expanding traditional definitions of rural-serving institutions (RSIs) are provided.
Word-of-Mouth Engagement in Online Social Networks: Influence of Network Centrality and Density
Authors: Anastasiei Bogdan , Dospinescu Nicoleta , Dospinescu Octavian
Published: June 2023

This paper investigates the effect of network centrality and network density on the propensity to engage in positive and negative eWOM, using social networks usage as a moderating variable. The research method was Structural Equation Modeling, and the data were collected through a survey conducted on 436 respondents from Romania. Findings showed that centrality and density only affect negative eWOM intent, the relationship being stronger at higher levels of network usage. In consequence, influential network members are more readily inclined to produce unfavorable eWOM. Subsequently, companies should make continuous efforts to spot and turn around bad publicity online.
Biased-Power Allocation and Shared-Antenna Selection Techniques for Spatial Modulation-Based Layer Division Multiplexing Systems
Authors: Al-Ansi Mohammed , Kolhar Manjur , Sourour Essam , Chatzinotas Symeon
Published: June 2023

This study proposes two approaches for improving the effectiveness of spatial modulation integrated into layer division multiplexing (SM-LDM) in broadcasting systems: biased-power allocation (Bi-PA) and shared antenna selection (SAS). Even though different data rates are employed in SM-LDM systems, Bi-PA enhances bit error rate (BER) fairness across layers. The ideal power ratios are adaptively determined by balancing signal-to-interference plus noise ratios with a preference for the lower layer (LL) that involves a higher modulation order. SAS alleviates the complexity of successive interference cancellation and enhances spectral and energy efficiencies. Both the LL and upper layer (UL) share the antenna selection decision and transmit using a single antenna. The UL carries a space shift keying signal while the entire power is allocated for the LL. We analyze the spectral efficiency for the SAS-based SM-LDM system with finite alphabet inputs. Numerical results demonstrate the advantages of the proposed approaches. Compared to pre-assigned-PA (Pre-PA), Bi-PA shows nearly identical BERs for both layers and solves the error floor problem. The sharing property and common layer transmission of SAS-based SM-LDM yield a significant BER reduction relative to conventional SM-LDM. It provides gains ranging from 7 to 15 dB for LL at BER equal to 10−3, while UL performance ranges from slight gain to minor loss. Furthermore, both Bi-PA and SAS techniques enhance the achievable LL rate and sum-rate at low and intermediate signal-to-noise ratio values. They can achieve an improvement of up to two bits in LL rate and less than one bit in sum-rate at a signal-to-noise ratio of −0.5 dB. These findings show that both proposed techniques have a considerable impact on enhancing the fairness, BER performance, and feasible rates of SM-LDM systems, making them promise for broadcast system designs.
Generation of Terahertz OAM Waves with Six Modes Based on Three-Layer Z-Shaped Reflective Metasurface
Authors: Zhu Qibiao , Xiao Xiangzhong , Yu Lisu , Zhou Nanrun
Published: June 2023

In this paper, a broadband and efficient three-layer Z-shaped reflective metasurface for linear polarization conversion is designed and six different modes of orbital angular momentum (OAM) waves are generated in the terahertz band. The designed metasurface consisted of several units, and it is divided into twelve regions. The phase difference is achieved by changing the structural parameters of the units, and then different modes of OAM waves are generated. The terahertz OAM waves with the modes of ±1, ±2, and ±3 are generated by metasurface with high efficiency and wide bandwidth. The results show that the designed metasurface could produce high purity terahertz OAM waves with six different modes, and the reflection amplitude of the metasurface unit is more than 0.9 in the frequency range of 1.0 THz to 1.8 THz. The generated OAM waves with the modes of ±1 and ±2 have a mode purity more than 90%. The designed metasurface has good wavefront control ability, which provides an effective method to generate multimode OAM waves.
Complete Bifurcation Analysis of the Vilnius Chaotic Oscillator
Authors: Ipatovs Aleksandrs , Victor Iheanacho Chukwuma , Pikulins Dmitrijs , Tjukovs Sergejs , Litvinenko Anna
Published: June 2023

The paper is dedicated to the numerical and experimental study of nonlinear oscillations exhibited by the Vilnius chaotic generator. The motivation for the work is defined by the need for a comprehensive analysis of the dynamics of the oscillators being embedded into chaotic communication systems. These generators should provide low-power operation while ensuring the robustness of the chaotic oscillations, insusceptible to parameter variations and noise. The work focuses on the investigation of the dependence of nonlinear dynamics of the Vilnius oscillator on the operating voltage and component parameter changes. The paper shows that the application of the Method of Complete Bifurcation Groups reveals the complex smooth and non-smooth bifurcation structures, forming regions of robust chaotic oscillations. The novel tool—mode transition graph—is presented, allowing the comparison of experimental and numerical results. The paper demonstrates the applicability of the Vilnius oscillator for the generation of robust chaos, and highlights the need for further investigation of the inherent trade-off between energy efficiency and robustness of the obtained oscillations.
Research on a Novel High-Torque-Density Axial–Radial-Flux Permanent-Magnet Motor with Annular Winding for an Elevator-Traction Machine
Authors: Zhang Jiaxu , Liu Junfeng , Wu Hongbing
Published: June 2023

The traditional radial flux PMSM and axial flux PMSM have an effective air gap on only one side, between the stator and rotor, and only the effective air gap generates electromagnetic torque. There are defects in the magnetic-field utilization, and it is difficult to improve the torque density. Therefore, this paper proposes an axial–radial-flux permanent-magnet synchronous motor (ARF-PMSM), which combines radial flux with axial flux, to be used in an elevator-traction machine-drive motor. The characteristics of the ARF-PMSM are that the stator core is made of a soft magnetic composite material and the winding is annular. The motor has three effective air gaps, which can achieve high torque density without increasing the overall dimensions. In this paper, the mechanical structure and operation mechanism of the ARF-PMSM are introduced, and the characteristics of its magnetic circuit structure are analyzed by using the equivalent magnetic circuit method. The torque characteristics and other electromagnetic characteristics of the ARF-PMSM, the traditional surface-mounted PMSM, and the spoke-type PMSM are compared and analyzed using the finite element method. The research results show that the proposed motor has high torque density, which provides a new design idea in the form of a high-torque-density PMSM for use in elevator-traction machines.
Effective Digital Predistortion (DPD) on a Broadband Millimeter-Wave GaN Power Amplifier Using LTE 64-QAM Waveforms
Authors: Somasundaram Gokul , Mayeda Jill C. , Sweeney Clint , Lie Donald Y. C. , Lopez Jerry
Published: June 2023

We demonstrate in this work effective linearization on a millimeter-wave (mm-Wave) broadband monolithic gallium nitride (GaN) power amplifier (PA) using digital predistortion (DPD). The PA used is a two-stage common-source (CS)/2-stack PA that operates in the mm-Wave 5G FR2 band, and it is linearized with the generalized memory polynomial (GMP) DPD and tested using 4G (4th generation) long-term-evolution (LTE) 64-QAM (quadrature amplitude modulation) modulated signals with a PAPR (peak-to-average power ratio) of 8 dB. Measurement results after implementing GMP DPD indicate considerable broadband improvement in the adjacent channel leakage power ratio (ACLR) of 16.9 dB/17.3 dB/16.5 dB/15.1 dB at 24 GHz/28 GHz/37 GHz/39 GHz, respectively, with a common average POUT of 15 dBm using a 100 MHz LTE 64-QAM input signal. At a fixed frequency of 28 GHz, the GaN PA after GMP DPD achieved signal bandwidth-dependent ACLR improvement and root-mean-square (rms) EVM (error vector magnitude) reduction using 20 MHz/40 MHz/80 MHz/100 MHz LTE 64-QAM waveforms with a common average POUT of 15 dBm. The GaN PA thus achieved very good linearization results compared to that in other state-of-the-art mm-Wave PA DPD studies in the literature, suggesting that GMP DPD should be rather effective for linearizing mm-Wave 5G broadband GaN PAs to improve POUT, Linear.
Joint Optimization of Massive MIMO System Resources Based on Service QoS
Authors: Liu Qingli , Li Rui , Li Mengqian
Published: June 2023

Aiming at the problem of low throughput and energy efficiency caused by the mutual restriction of energy efficiency and spectral efficiency in massive MIMO systems and the fact that resource allocation does not consider the factors of user service QoS and the upper and lower speed limits, a resource joint optimization method based on user service QoS guarantee is proposed. The method first performs user scheduling according to service delay and channel state under the condition of equal power distribution and calculates the current system capacity, and then combines transmit antenna power and service QoS constraints to redistribute power, and corrects the system capacity, establishing the objective function for the joint optimization of the spectral efficiency and energy efficiency. An algorithm combining deep learning and Q learning is used to solve the problem, and finally, the purpose of joint optimization is achieved. The simulation shows that the joint optimization method proposed in this paper can control the timeout of user data packets more finely and, at the same time, obtain greater energy efficiency and throughput.
Analysis and Suppression of Rectifier Diode Voltage Oscillation Mechanism in IPOS High-Power PSFB Converters
Authors: Sun Fei , Chen Jun , Lin Xinchun , Liao Dongchu
Published: June 2023

Parasitic oscillations in the rectifier diode voltage of phase-shifted-full-bridge (PSFB) converters limit their application in high-voltage and high-power situations. The conventional analysis method for parasitic oscillation in rectifier diode voltage in PSFB converters treats the filter inductor as a constant current source and fails to consider the impact of changes in filter inductor current on the rectifier diode’s parasitic oscillation. Consequently, this approach does not apply when analyzing the rectifier diode voltage’s parasitic oscillations in high-power PSFB converters employing an input-parallel output-series (IPOS) configuration with interleaved drive. This research paper introduces an innovative equivalent circuit model for analyzing the parasitic oscillations of rectifier diode voltage in IPOS high-power PSFB converters. The model takes into account the mutual influence of rectifier diode voltage oscillations between submodules under interleaved control, considering the influence of changes in filter inductor current on rectifier diode parasitic oscillation. Based on the circuit model, we explain the mechanism of multiple oscillations of the rectifier diode voltage and the reason for the high peak of the first oscillation. Consequently, the interplay of rectifier diode voltage oscillations in IPOS high-power k-module PSFB converters under interleaved control is analyzed. To mitigate the adverse effects of rectifier diode voltage parasitic oscillation, a buffering strategy involving the connection of a resistor capacitor diode (RCD) circuit in parallel after the rectifier bridge is adopted, considering the structure of the IPOS high-power PSFB converter. The study provides a detailed analysis of the circuit’s operation mechanism upon incorporating the RCD buffer circuit and establishes the relationship between buffer capacitance, resistance, and spike voltage. Furthermore, a design method for buffer capacitors and discharge resistors in buffer circuits is presented. Finally, a 100 kW prototype is tested to verify the rectifier diode voltage oscillation mechanism of the IPOS high-power PSFB converter and the rationality of the buffer capacitor and discharge resistor design method under the interleaved drive approach.
Experimental Investigation on Deformation and Permeability of Clayey–Silty Sediment during Hydrate Dissociation by Depressurization
Authors: Jin Fang , Huang Feng , Zhang Guobiao , Li Bing , Lv Jianguo
Published: June 2023

The sediments in the South China sea are mainly composed of clayey silt, characterized by weak cementation, low strength, and poor permeability. These characteristics lead to slow gas and water transport and low gas production efficiency in the production process, which is not conducive to reservoir stability. Therefore, this paper studied the influence of different factors on the displacement and permeability of hydrate-bearing sediments by using remolded cores from the South China Sea. It was found that, when the depressurization method was used for hydrate decomposition, the displacement change in sediments could be divided into three stages: depressurization stage, decomposition stage, and creep stage. During the decompression stage, sediment deformation was rapid and displacement was small. During the decomposition process of hydrates, sediment deformation was slow and displacement was maximum. The creep stage had the slowest deformation and the smallest displacement. The displacement increased with the increase in initial porosity, hydrate saturation, effective pressure, and depressurization amplitude. The permeability of the sediments was lower than that of the original sediments after hydrate decomposition. This permeability damage increased with the increase in the sediment porosity, hydrate saturation, depressurization range and effective pressure. Furthermore, the displacement of sediments was positively correlated with the permeability damage.
Compositional Analysis of Biodiesel Particulate Matter (BPM) from a Non-Road Diesel Generator
Authors: Lu Mingming , Tzillah Aisha , Chai Ming , Aloraimi Omer
Published: June 2023

There have been multiple studies of biodiesel particulate matter (BPM) emissions over the years, but few are on non-road diesel engines despite their higher emissions and less regulation. The goal of this paper is to further investigate the impacts of biodiesel fuel on particulate matter emissions. Compositional analysis of BPM was performed on a non-road diesel generator under various loads using different diesel and biodiesel blends. In order to account for organic compositions from both petroleum diesel and biodiesel, two types of analytical columns were used, one for polar compounds such as fatty acid methyl esters (FAME) and another non-polar column for hydrocarbons and PAHs (polycyclic aromatic hydrocarbons). In the BPM emitted, FAME constituted 6% to 11% of the total mass at different loads, which is the highest among the soluble organic fractions. This is an indication that biodiesel fuel might not be completely combusted in this diesel engine. The PAH fraction of the B50 (50% biodiesel) is much less than that found in petroleum diesel PM (B0). The elemental carbon fraction of the B50 particulate matter is less than that from B0. The lower PAH and soot from biodiesel blends may correspond to lower toxicity.
A Review on Buried Interface of Perovskite Solar Cells
Authors: Pu Yu , Su Haijun , Liu Congcong , Guo Min , Liu Lin , Fu Hengzhi
Published: June 2023

Perovskite solar cells (PSCs) have been developed rapidly in recent years because of their excellent photoelectric performance. However, interfacial non-radiative recombination hinders the improvement of device performance. The buried interface modification strategy can minimize the non-radiation recombination in the interface and can obtain the high efficiency and stability of PSCs. In this review, we introduce the device structure and the charge carrier dynamics (charge transfer, extraction, and collection) at the interface. We further summarize the main sources of non-radiative recombination at the interface, such as energy alignment mismatch and interface defects, and methods to characterize them. In contrast to the previous review of perovskite solar cells, the important roles of buried interfaces in regulating energy level alignment, passivating surface defects, modulating morphology, and so on are reviewed in detail based on the latest research, and strategies for reducing interfacial nonradiative recombination are provided. In the end, the potential development and challenges of buried interfaces for high-performance and stable PSCs are presented.
Comparison of Hydrogen Production Efficiency by Rhodopseudomonas palustris MP3 and Rhodopseudomonas harwoodiae SP6 Using an Iron Complex as an Enhancement Factor
Authors: Kanwal Fariha , Tahir Arifa , Tsuzuki Takuya , Nisbet David , Chen Junhong , Torriero Angel A. J.
Published: June 2023

In the present study, an iron(II)-nanoscale organic complex (Fe-NO) was used as an enhancement factor by two different Rhodopseudomonas species of purple non-sulphur bacteria (PNSB) to produce hydrogen (H2). The Fe-NO complex was synthesised using FeSO4·7H2O and Eucalyptus viminalis—a native Australian plant leaf extract—in a 1:2 and 2:1 concentration ratio. Besides, FeSO4·7H2O was also used as a source of iron(II) for comparison with the Fe-NO complex. The photo-fermentative bacterial cultures were isolated from a fishpond, and only two strains, MP3 and SP6, were found viable after several attempts of quadrate streaking. After phylogenetic analysis, these strains were designated as R. palustris MP3 and R. harwoodiae SP6. After comparison with the control, the results showed that the PNSBs manifested an approximately 50% higher H2 yield when the 1:2 Fe-NO complex was used in the fermentation broth at 10 mg/L concentration, where 10.7 ± 0.54 and 10.0 ± 0.49 mL H2/L were obtained by R. palustris MP3 and R. harwoodiae SP6, respectively. The study revealed that the 1:2 Fe-NO complex could be an important material for efficient H2 production.
Hydrocarbon Fuel Flow and Heat Transfer Investigation in Rotating Channels
Authors: Dong Mengqiang , Huang Hongyan
Published: June 2023

Ram air turbines are used in the power generation systems of hypersonic vehicles, which can address the problem of the high power consumption of weapon systems. However, high incoming air temperatures can cause the turbine blades of power generation to ablate. At this point, the incoming air can no longer be used as a cooling source to cool the turbine blades. To prevent the ablation of the turbine blades of the hypersonic vehicle power generation, hydrocarbon fuel carried by the hypersonic vehicle itself is used to cool the turbine blades. Hence, hydrocarbon fuels under rotating conditions are investigated. The results show that the rotation leads to a strong pressure gradient that causes the density and dynamic viscosity of hydrocarbon fuel to increase dramatically. Compared to the static condition, the density and dynamic viscosity of the hydrocarbon fuel increase by a maximum of 65.1% and 405%, respectively, under the rotating condition. This leads to an obvious reduction in velocity. The comprehensive influence of the physical properties of the fuel, centrifugal force, and Coriolis force causes the convective heat transfer coefficient and Nusselt number of the channel to first increase and then decrease with the increase in the rotational speed. Compared to the static condition, the convective heat transfer coefficient and Nusselt number increase by a maximum of 69.7% and 45.6%, respectively, under the rotating condition. The critical rotational speed of the Nusselt number from rise to fall is 20,000 rpm for different inlet temperature conditions.
Investment Timing Analysis of Hydrogen-Refueling Stations and the Case of China: Independent or Co-Operative Investment?
Authors: Zhao Tian , Liu Zhixin
Published: June 2023

The investment in hydrogen-refueling stations (HRS) is key to the development of a hydrogen economy. This paper focuses on the decision-making for potential investors faced with the thought-provoking question of when the optimal timing to invest in HRS is. To fill the gap that exists due to the fact that few studies explain why HRS investment timing is critical, we expound that earlier investment in HRS could induce the first mover advantages of the technology diffusion theory. Additionally, differently from the previous research that only considered that HRS investment is just made by one individual firm, we innovatively examine the HRS co-investment made by two different firms. Accordingly, we compare these two optional investment modes and determine which is better considering either independent investment or co-operative investment. We then explore how the optimal HRS investment timing could be figured out under conditions of uncertainty with the real options approach. Given the Chinese HRS case under the condition of demand uncertainty, the hydrogen demand required for triggering investment is viewed as the proxy for investment timing. Based on analytical and numerical results, we conclude that one-firm independent investment is better than two-firm cooperative investment to develop HRS, not only in terms of the earlier investment timing but also in terms of the attribute for dealing with the uncertainty. Finally, we offer recommendations including stabilizing the hydrogen demand for decreasing uncertainty, and accelerating firms’ innovation from both technological and strategic perspectives in order to ensure firms can make HRS investments on their own.
Dynamics of Fine Root Decomposition in Different Vegetation Types: Investigating the Impact of Soil Fungal Communities and Enzyme Activities
Authors: Cheng Xuefei , Jiang Siyuan , El-Naggar Ali , Tang Yingzhou , Liu Xin , Zhang Jinchi
Published: June 2023

Fine root decomposition plays a vital role in driving the carbon cycle in terrestrial ecosystems, as it constitutes a substantial part of annual net primary production and, as transient tissues, returns to the soil within relatively short timescales. Soil fungal communities and enzyme activities strongly influence this process. In this study, we used an in situ soil core decomposition method to compare the fine root decomposition rates of Liriodendron chinense (Hemsl.) Sargent, Cunninghamia lanceolata (Lamb.) Hook, and Phyllostachys edulis (Carrière) J.Houz forests over a 1-year period (March 2021–March 2022). We quantified the chemical attributes of fine roots and soil enzymatic activities across different forests, detected fungal communities via ITS rRNA gene sequencing, and forecasted fungal functional groups using the FUNGuild database. The results showed that fine root decomposition was fastest in the Liriodendron chinense (Hemsl.) Sargent forest (77.2%) and the slowest for Cunninghamia lanceolata (Lamb.) Hook (59.2%). Structural equation modeling (SEM) results indicated that the carbon content of fine roots and the functional groups of soil fungi are crucial to fine root decomposition. They not only directly influence fine root decomposition but also promote it through soil enzymatic activities, clearly suggesting that changes in soil enzymatic activities can be employed to explain the ecological effects of the root decomposition process. This study illuminates significant differences in the chemical characteristics of fine roots, soil enzymatic activities, and soil fungal communities among different forest types, all of which significantly affect fine root decomposition.
Multiple Defect Classification Method for Green Plum Surfaces Based on Vision Transformer
Authors: Su Weihao , Yang Yutu , Zhou Chenxin , Zhuang Zilong , Liu Ying
Published: June 2023

Green plums have produced significant economic benefits because of their nutritional and medicinal value. However, green plums are affected by factors such as plant diseases and insect pests during their growth, picking, transportation, and storage, which seriously affect the quality of green plums and their products, reducing their economic and nutritional value. At present, in the detection of green plum defects, some researchers have applied deep learning to identify their surface defects. However, the recognition rate is not high, the types of defects identified are singular, and the classification of green plum defects is not detailed enough. In the actual production process, green plums often have more than one defect, and the existing detection methods ignore minor defects. Therefore, this study used the vision transformer network model to identify all defects on the surfaces of green plums. The dataset was classified into multiple defects based on the four types of defects in green plums (scars, flaws, rain spots, and rot) and one type of feature (stem). After the permutation and combination of these defects, a total of 18 categories were obtained after the screening, combined with the actual situation. Based on the VIT model, a fine-grained defect detection link was added to the network for the analysis layer of the major defect hazard level and the detection of secondary defects. The improved network model has an average recognition accuracy rate of 96.21% for multiple defect detection of green plums, which is better than that of the VGG16 network, the Desnet121 network, the Resnet18 network, and the WideResNet50 network.
Interspecific Growth Reductions Caused by Wild Ungulates on Tree Seedlings and Their Implications for Temperate Quercus-Fagus Forests
Authors: Candaele Romain , Ligot Gauthier , Licoppe Alain , Lievens Julien , Fichefet Violaine , Jonard Mathieu , André Frédéric , Lejeune Philippe
Published: June 2023

Ungulate impacts on forest understory alter tree species composition, with cascading effects on forest functions and resilience against future climate conditions. Indeed, the ungulate browsing pressure on tree seedlings is species-specific and causes contrasted growth reductions that alter tree recruitment rates. Untangling the effects of browsing from the effects of the other factors driving regeneration success is required to guide the forest and ungulate management. In particular, Fagus sylvatica L. strongly dominates temperate Quercus-Fagus forests close to their climax, and it remains unclear if controlling ungulate populations can maintain tree species diversity in naturally regenerated forests. We addressed this question by monitoring 734 pairs of fenced and unfenced 6-m2 plots across a broad gradient of Cervus elaphus L. abundance in Belgian Quercus-Fagus forests managed by continuous cover forestry. Seedling height, density, and vegetation cover were monitored from 2016 to 2021. Species diversity and ecological affinity for light, temperature, and atmospheric humidity conditions were computed from these measures. With ungulates, the mean growth of Betula pendula Roth. and Sorbus aucuparia L. was negligible, whereas, without ungulates, their growth was higher than the growth of other species. With ungulates, the growth of Fagus sylvatica L. and Picea abies (L.) H. Karst was higher than other species. Quercus (Quercus petreae (Matt.) Liebl and Quercus robur L.) growth was the lowest in all conditions. Finally, Carpinus betulus L. was heavily browsed but still grew higher than its competitors with ungulates. Ungulate browsing can then severely affect seedling growth and likely reduce the diversity of future recruited trees. In the study area, browsing unfavored the regeneration of the species that are less shade tolerant, more-drought tolerant, and more-heat tolerant. It thus accelerates the natural succession and reduces forest resilience to heat and drought events. Such an observation was found valid over a wide study area encompassing contrasting levels of Cervus elaphus L. abundance. Combining further reductions of ungulate populations with foodscape improvement is likely required to maintain species diversity in these forests.
Decision-Tree Application to Predict and Spatialize the Wood Productivity Probabilities of Eucalyptus Plantations
Authors: Alvares Clayton Alcarde , Cegatta Ítalo Ramos , Scolforo Henrique Ferraço , Mafia Reginaldo Gonçalves
Published: June 2023

Brazil is one of the world’s wood short-fiber producers, cultivating 7.5 million hectares of eucalypt trees. Foresters and resource managers often face difficulties in surveying reliable Eucalyptus productivity levels for the purpose of purchasing and prospecting lands. Spatial data science (DS) and machine learning (ML) provide powerful approaches to make the best use of the large datasets available today. Agriculture has made great use of these approaches, and in this paper, we explore how forestry can benefit as well. We hypothesized that both DS and ML techniques can be used to improve Eucalyptus productivity zoning based on multiple operational datasets of tree growth and environment. Based on more than 12,000 permanent forest inventory plots of commercial Eucalyptus plantations and the climate, soil, and altitude variables associated with them, a supervised ML approach was adjusted to model the forest plantation productivity. A multi-tuning of the decision-tree (DT) algorithm hyperparameters was prepared to yield 450 DT models, with a better one delivering an RMSE of 53.5 m3 ha−1, split in 35 terminal nodes, here interpreted as Eucalyptus productivity zones. The DT model showed an optimum performance index of 0.83, a coefficient of determination of 0.91, a root mean squared error of 12.3 m3 ha−1, and a mean absolute percentage error only of 3.1% in predicting the testing dataset throughout the study area. The DT rule set was interpreted in a user-friendly table and was prepared to classify any location within the study area in each one of the 35 productivity zones based on the required environment variables of the DT algorithm. The high quality of the model obtained made it possible to spatialize the DT rules, providing a reliable cartographic visualization of the probability levels of true Eucalyptus productivity for a huge region of forest-based industries in Brazil. These data-science techniques also provided a yield gap analysis using a very down-to-earth approach. We estimated a yield gap by an amount of 4.2 × 107 m3, representing a few more than 113,000 ha, or 15% of the current forest base. This is the amount of avoided area expansion to accumulate the same wood stock in case the productivity is raised to the attainable level in each zone. This present study provided deeper analysis and reproducible tools to manage forest assets sustainably.
Electrode Material Optimization of Nitrous Oxide Recovery from Incineration Leachate in a ΔnosZ Pseudomonas aeruginosa/MEC System
Authors: Liu Yong , Yan Bing , Xia Song , Gui Shuanglin , Jiang Haiwei , Nie Hanbing , Sun Dezhi
Published: June 2023

Nitrous oxide (N2O) is not only recognized as a potent greenhouse gas, but it is also used in industry as a clean energy source. In this study, different electrode materials of carbon felt and graphite were equipped in the ΔnosZ P. aeruginosa/microbial electrolysis cell (MEC) systems to explore the optimization mechanism for long-term N2O recovery during incineration leachate treatment. The carbon felt group showed a better performance in N2O recovery across 45 days of operation. The N2O conversion efficiency was above 80% and the proportion of N2O in biogas accounted for 80.6% in the carbon felt group. qRT-PCR analysis was conducted to evaluate the expression of genes involved in denitrification (norB) and electroactivity (phzG, phzM, and phzH) of ΔnosZ P. aeruginosa. The results showed a significant upregulation in the suspended biomass (day 21) and the electron-attached biomass (day 45) from the carbon felt-equipped reactor, which was highly related to the opportunity of biomass exposed to the phenazine derivatives. By the carbon felt optimization in the system, 82.6% of the Pseudomonas genus survived after 45 days of operation. These results indicate that the carbon felt electrode has a more sustainable performance for N2O recovery in the ΔnosZ P. aeruginosa/MEC system.
Fermentative L-Lactic Acid Production Using Bacillus coagulans from Corn Stalk Deconstructed by an Anaerobic Microbial Community
Authors: Yang Xu , Shi Zhiyuan , Wang Tongyu , Meng Xiangyu , Song Lili , Zhang Zhiping , Zhang Jingnan , Wei Tao
Published: June 2023

This study investigated the feasibility of producing L-lactic acid (LA) from dry corn stalk (DCS) that was pretreated by ensiling by an anaerobic microbial community consisting of Bacillus coagulans, Lactobacillus fermentum, and Enterococcus durans. After 28 days of ensiling, the LA and acetic acid content in the microsilage was 2.04 ± 0.08% and 0.38 ± 0.01%, respectively, and the pH was 4.47 ± 0.13. Enterococcus and Lactobacillus became the dominant microbiota during the ensiling process. Twenty-eight-day-old microsilage was then subjected to fermentation by B. coagulans to produce LA in a simultaneous saccharification and co-fermentation process. The enzymatic hydrolysis yield reached >96%. The maximal concentration of LA reached 18.54 ± 0.52 g/L with a substrate concentration of 5%, where the yield of LA was 0.31 ± 0.01 g/g DCS and the optical purity of the product LA was >97%. Anaerobic ensiling is viable for the pretreatment of biomass for the production of value-added chemicals.
Isolation of γ-Aminobutyric Acid (GABA)-Producing Lactic Acid Bacteria with Anti-Inflammatory Effects from Fermented Foods in Korea
Authors: Kim Ye-Lim , Nguyen Trung Hau , Kim Jin-Seong , Park Jeong-Yong , Kang Chang-Ho
Published: June 2023

Lactic acid bacteria have become popular because of their γ-aminobutyric acid (GABA)-producing ability. In the present study, we selected four Levilactobacillus brevis strains (MG5552, MG5405, MG5261, and MG5522) with GABA-producing ability from the 33 strains isolated from various fermented foods in South Korea. We evaluated their GABA-producing ability using thin-layer chromatography and determined the GABA levels produced by each strain using an amino acid analyzer. Moreover, we investigated the anti-inflammatory activity of the selected strains, and the results revealed that the cell-free supernatant of the strains decreased nitric oxide (NO), inducible nitric oxide synthase (iNOS) expression, and nuclear factor kappa B (NF-κB) activity in RAW264.7 macrophages. Therefore, these GABA-producing LAB strains can regulate nerve excitement and act as probiotics with anti-inflammatory activity.
Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
Authors: Rudwan Mohammed Suleiman Mohammed , Fonou-Dombeu Jean Vincent
Published: June 2023

Ontology alignment has become an important process for identifying similarities and differences between ontologies, to facilitate their integration and reuse. To this end, fuzzy string-matching algorithms have been developed for strings similarity detection and have been used in ontology alignment. However, a significant limitation of existing fuzzy string-matching algorithms is their reliance on lexical/syntactic contents of ontology only, which do not capture semantic features of ontologies. To address this limitation, this paper proposed a novel method that hybridizes fuzzy string-matching algorithms and the Deep Bidirectional Transformer (BERT) deep learning model with three machine learning regression classifiers, namely, K-Nearest Neighbor Regression (kNN), Decision Tree Regression (DTR), and Support Vector Regression (SVR), to perform the alignment of ontologies. The use of the kNN, SVR, and DTR classifiers in the proposed method resulted in the building of three similarity models (SM), encoded SM-kNN, SM-SVR, and SM-DTR, respectively. The experiments were conducted on a dataset obtained from the anatomy track in the Ontology Alignment and Evaluation Initiative 2022 (OAEI 2022). The performances of the SM-kNN, SM-SVR, and SM-DTR models were evaluated using various metrics including precision, recall, F1-score, and accuracy at thresholds 0.70, 0.80, and 0.90, as well as error rates and running times. The experimental results revealed that the SM-SVR model achieved the best recall of 1.0, while the SM-DTR model exhibited the best precision, accuracy, and F1-score of 0.98, 0.97, and 0.98, respectively. Furthermore, the results showed that the SM-kNN, SM-SVR, and SM-DTR models outperformed state-of-the-art alignment systems that participated in the OAEI 2022 challenge, indicating the superior capability of the proposed method.
Practical Application of Deep Reinforcement Learning to Optimal Trade Execution
Authors: Byun Woo Jae , Choi Bumkyu , Kim Seongmin , Jo Joohyun
Published: June 2023

Although deep reinforcement learning (DRL) has recently emerged as a promising technique for optimal trade execution, two problems still remain unsolved: (1) the lack of a generalized model for a large collection of stocks and execution time horizons; and (2) the inability to accurately train algorithms due to the discrepancy between the simulation environment and real market. In this article, we address the two issues by utilizing a widely used reinforcement learning (RL) algorithm called proximal policy optimization (PPO) with a long short-term memory (LSTM) network and by building our proprietary order execution simulation environment based on historical level 3 market data of the Korea Stock Exchange (KRX). This paper, to the best of our knowledge, is the first to achieve generalization across 50 stocks and across an execution time horizon ranging from 165 to 380 min along with dynamic target volume. The experimental results demonstrate that the proposed algorithm outperforms the popular benchmark, the volume-weighted average price (VWAP), highlighting the potential use of DRL for optimal trade execution in real-world financial markets. Furthermore, our algorithm is the first commercialized DRL-based optimal trade execution algorithm in the South Korea stock market.
Tropical Red Fruit Blend Foam Mat Drying: Effect of Combination of Additives and Drying Temperatures
Authors: Paiva Yaroslávia Ferreira , Figueirêdo Rossana Maria Feitosa de , Queiroz Alexandre José de Melo , Amadeu Lumara Tatiely Santos , Reis Carolaine Gomes dos , Santos Francislaine Suelia dos , Lima Antônio Gilson Barbosa de , Silva Wilton Pereira da , Gomes Josivanda Palmeira , Leite Daniela Dantas de Farias , Lima Thalis Leandro Bezerra de
Published: June 2023

Foam mat drying is a widely used technique for liquid products because it has a number of advantages; however, for an efficient process, the choice of additives and temperatures is extremely important. The objective of this study was to evaluate the effect of additives and drying temperatures on the powders obtained from the blend of tropical red fruits, such as acerola, guava, and pitanga. The foam formulations were prepared by mixing the pulps of the three fruits in equal proportions (1:1:1), all added with 6% albumin and 1% stabilizing agent: E1, gum Arabic; E2, guar gum; E3, gelatin. The combinations were subjected to beating, and subsequently, they were dried in an oven with forced air circulation at four temperatures (50 to 80 °C), with a mat thickness of 0.5 cm. The obtained powders showed low levels of water and water activity and high levels of bioactive compounds, colors with a predominance of yellow, intermediate cohesiveness, poor fluidity, and solubility above 50%. The best temperature for obtaining the powders was 60 °C. The formulation that produced the best results for the production of the tropical red fruit blend powder was the combination of albumin and gelatin.
Enhancing Nutrient Recovery and Bioactive Compound Extraction from Spirulina through Supercritical Fluid Extraction: Implications for SH-SY5Y Cell Viability
Authors: Martí-Quijal Francisco J. , Pallarés Noelia , Dawidowicz Katarzyna , Ruiz María-José , Barba Francisco J.
Published: June 2023

This work explores the efficiency of supercritical fluid extraction (SFE) to recover minerals, pigments, and antioxidant compounds from the spirulina microalgae. Moreover, the fatty acids and phenolic profiles of the extracts obtained were also investigated, and the effect of the extracts on SH-SY5Y cell viability was tested. The extraction of phycocyanin was improved by SFE compared to conventional extraction, from 2.838 ± 0.081 mg/g dry matter (DM) (control) to 6.438 ± 0.411 mg/g DM (SFE). SFE treatment also improved chlorophyll a and carotenoid recoveries increasing from 5.612 ± 0.547 to 8.645 ± 0.857 mg/g DM and from 0.447 ± 0.096 to 0.651 ± 0.120 mg/g DM, respectively. Regarding minerals, the SFE improved Mg recovery with 77% more than the control extraction. Moreover, palmitoleic, stearic, γ-linolenic, eicosadienoic and eicosatrienoic acids recovery was improved by SFE. Phenolic profiles were identified via triple-TOF-LC-MS-MS. Considering heavy metals, a higher rate of Pb extraction was observed for the SFE extract, while no significant differences were observed for Hg between both extractions. Finally, SFE extract improved cell viability compared to the control extract. Thus, SFE constitutes an interesting tool to sustainably extract high-added-value compounds; however, potential contaminants such as Pb need to be controlled in the resulting extracts.
New Hydrolyzable Tannin with Potent Antioxidant and α-Glucosidase Inhibitory Activity from Black Tea Produced from Camellia taliensis
Authors: Chen Min , Li Na , Zhu Hong-Tao , Zhang Man , Duan Zhao-Hong , Wang Dong , Yang Chong-Ren , Zhang Ying-Jun
Published: June 2023

Camellia taliensis (W. W. Smith) Melchior, belonging to the genus Camellia sect. Thea., is mainly distributed from northern Myanmar to western and southwestern Yunnan province of China, and its leaves have been used to make various teas by the locals of its growing regions. The chemical constituents of C. taliensis are significantly related to those of cultivated tea plants, C. sinensis and C. sinensis var. assamica. The HPLC-ESI-MS analysis of black tea prepared from the leaves of C. taliensis showed a rich existence of polyphenols. Further comprehensive chemical study led to the separation and recognition of 32 compounds (1–32), including one new hydrolyzable tannin, 1-O-galloyl-4,6-tetrahydroxydibenzofurandicarboxyl-β-D-glucopyranose (1), and one new natural product (24). The known compounds referred to seven hydrolyzable tannins (2–8), 10 flavonols and glycosides (9–18), and 14 simple phenolics (19–32). Their structures were elucidated by comprehensive spectroscopic analyses. Among them, 20 compounds (2, 3, 6, 7, 8, 15, 17, 18, 20–22, 24–32) were isolated from black tea for the first time. Most isolates displayed obvious antioxidant activities on DPPH and ABTS+ assays, and the hydrolyzable tannins 1, 3–5, 7, and 8 exhibited stronger inhibitory activities on α-glycosidase than quercetin and acarbose (IC50 = 5.75 and 223.30 μM, respectively), with IC50 values ranging from 0.67 to 2.01 μM.
Optimization of Coffee Oil Extraction from Defective Beans Using a Supercritical Carbon Dioxide Technique: Its Effect on Volatile Aroma Components
Authors: Pattaraprachyakul Wasin , Sawangkeaw Ruengwit , Ngamprasertsith Somkiat , Suppavorasatit Inthawoot
Published: June 2023

Defective green coffee beans are typically discarded due to their negative impacts on coffee qualities compared to normal beans. However, there are some types of defective beans that can cause volatile aroma compounds after roasting similar to those produced by normal beans. This study aimed to optimize conditions for coffee oil extraction by supercritical carbon dioxide using the response surface methodology (RSM). Furthermore, the investigation assessed the aroma-active compounds and sensory quality in extracted coffee oil. Thus, operational temperatures (33.2–66.8 °C), pressure (10–30 MPa) and ethanol (g) to roasted coffee (g) ratio (0.25:1–1.5:1) were optimized for coffee oil extraction. As a result, different oil yields with different key volatile aroma compounds concentrations were obtained and it was found that the optimum conditions for extraction were a temperature of 50 °C, pressure of 30 MPa, and ethanol (g) to roasted coffee (g) ratio of 1:1 to obtain 6.50% (w/w) coffee oil yield. Key volatile aroma compounds, including furfuryl alcohol, 5-methyl furfural, 2,5-dimethylpyrazine, 4-vinylguaiacol, furfuryl acetate, 3-ethyl-2,5-dimethylpyrazine, thiazole, 1-furfurylpyrrole, pyridine, 2,3-butanediol, and 3-methyl-1,2-cyclopentanedione which contributed to the most preferable burnt, sweet, bready, chocolate-like, and roasted flavors, were quantified. Overall, the results suggested that coffee oil extracted from defective beans could be potentially used as a flavoring agent.
Impact of Bioinformatics Search Parameters for Peptides’ Identification and Their Post-Translational Modifications: A Case Study of Proteolysed Gelatines from Beef, Pork, and Fish
Authors: Ambli Mouna , Deracinois Barbara , Jenequin Anne-Sophie , Ravallec Rozenn , Cudennec Benoit , Flahaut Christophe
Published: June 2023

Bioinformatics software, allowing the identification of peptides by the comparison of peptide fragmentation spectra obtained by mass spectrometry versus targeted databases or directly by de novo sequencing, is now mandatory in peptidomics/proteomics approaches. Programming the identification software requires specifying, among other things, the mass measurement accuracy of the instrument and the digestion enzyme used with the number of missed cleavages allowed. Moreover, these software algorithms are able to identify a large number of post-translational modifications (PTMs). However, peptide and PTM identifications are challenging in the agrofood field due to non-specific cleavage sites of physiological- or food-grade enzymes and the number and location of PTMs. In this study, we show the importance of customized software programming to obtain a better peptide and PTM identification rate in the agrofood field. A gelatine product and one industrial gelatine hydrolysate from three different sources (beef, pork, and fish), each digested by simulated gastrointestinal digestion, MS-grade trypsin, or both, were used to perform the comparisons. Two main points are illustrated: (i) the impact of the set-up of specific enzyme versus no specific enzyme use and (ii) the impact of a maximum of six PTMs allowed per peptide versus the standard of three. Prior knowledge of the composition of the raw proteins is an important asset for better identification of peptide sequences.