bluuebunny/crossref_metadata_2025 huggingface dataset:
Trace Element Concentrations of Arsenic and Selenium in Toenails and Risk of Prostate Cancer among Pesticide Applicators
Authors: Dennis Leslie K. , Langston Marvin E. , Beane Freeman Laura , Canales Robert A. , Lynch Charles F.
Published: September 2024

Prostate cancer is a common cancer among males in the US, but little is known about its risk factors, including trace elements. The primary aim of this study was to examine prostate cancer and its association with arsenic and selenium in toenails. We conducted a small, nested case-control study of men residing in Iowa within the Agricultural Health Study cohort, where we also collected toenail samples to test for arsenic and other trace elements. Toenail samples were sent for neutron activation analysis aimed at long-lived trace elements, including arsenic. Logistic regression was used to estimate odds ratios (ORs) for trace element exposures and prostate cancer. A total of 66 prostate cancer cases and 173 healthy controls returned questionnaires, over 99% of which included toenail samples. An increased risk was seen for the highest levels of arsenic (OR = 3.4 confidence interval (CI) of 1.3–8.6 and OR = 2.2, 95% CI of 0.9–5.6) and the highest level of selenium (2.0, 95% CI of 1.0–4.0). These data also show detectable levels of over 50% for 14 of 22 elements detected in the toenails. The association seen here with arsenic and prostate cancer further supports ecological studies finding an association with community levels of arsenic and prostate cancer incidence and mortality.
Doxycycline Plus Bortezomib-Containing Regimens for the Treatment of Light-Chain Amyloidosis in the Frontline Setting: Experience from the Amyloidosis Program of Calgary
Authors: Lewis Ellen , Fine Nowell , McCulloch Sylvia , Tay Jason , Duggan Peter , Neri Paola , Bahlis Nizar , Jimenez-Zepeda Victor H.
Published: September 2024

Background: Pre-clinical and retrospective data suggest that doxycycline added to treatment regimens has benefit in AL amyloidosis. However, a recent multicenter, open-label, randomized controlled trial in AL amyloidosis patients treated with CyBorD did not demonstrate a progression-free survival (PFS) or cardiac PFS benefit with added doxycycline. Objective: The main objective of this study was to explore the role of doxycycline combined with bortezomib-containing regimens (BCRs) for newly diagnosed AL amyloidosis patients with cardiac involvement and to compare them with a cohort of concurrent patients treated with BCR only. Material and Methods: AL amyloidosis patients, newly diagnosed between January 2012 and March 2022, who were treated with BCR at the Amyloidosis Program of Calgary (APC) were evaluated. Results: Sixty-four concurrent patients were identified. Thirty-nine patients received doxycycline in addition to BCR (BCR-D) for a median of 8 months. The overall response rate was similar among the groups. No significant differences in VGPR/CR, dFLC at 1 month, time to first response, time to best response, or organ responses were noted between the BCR alone and BCR-D groups. Summary and Conclusions: Our retrospective study demonstrated that doxycycline combined with BCR failed to prolong OS, PFS, or cardiac responses compared with BCR alone in patients with cardiac AL amyloidosis.
Dataset on the Validation and Standardization of the Questionnaire for the Self-Assessment of Service-Learning Experiences in Higher Education (QaSLu)
Authors: Sánchez-Cabrero Roberto , López-de-Arana Prado Elena , Aramburuzabala Pilar , Cerrillo Rosario
Published: September 2024

This dataset shows the original validation and standardization of the Questionnaire for the Self-Assessment of Service-Learning Experiences in Higher Education (QaSLu). The QaSLu is the first instrument to measure university service-learning (USL), validated following a strict qualitative and quantitative process by a sample of experts in USL and generating rating scales for different profiles of professors. The Delphi method was used for the qualitative validation by 16 academic experts, who evaluated the relevance and clarity of the items. After two consultation rounds, 45 items were qualitatively validated, generating the QaSLu-45. Then, 118 instructors from 43 universities took part as the sample in the quantitative validation procedure. Quantitative validation was carried out through goodness-of-fit measures using confirmatory factor analysis and the final configuration optimized using one-factor robust exploratory factor analysis, determining the most optimal version of the questionnaire under the law of parsimony, the QaSLu-27, with only 27 items and better psychometric properties. Finally, rating scales were calculated to compare different profiles of USL professors. These findings offer a valid, strong, and trustworthy instrument. The QaSLu-27 may be helpful for the design of USL experiences, in addition to facilitating the assessment of such programs to enhance teaching and learning processes.
Influence of Direct Coronal Restoration Materials on the Fracture Resistance of Endodontically Treated Premolars: An In Vitro Study
Authors: Gheorghe Georgiana Florentina , Țâncu Ana Maria Cristina , Amza Oana Elena , Suciu Ioana , Iosif Laura , Cuc Stanca , Petean Ioan , Moldovan Marioara , Dimitriu Bogdan
Published: September 2024

(1) Background: The long-term survival of an endodontically treated tooth depends on a successful root canal treatment as well as an adequate definitive coronal restoration. This study aimed to evaluate the strength of endodontically treated premolars with mesial–occlusal–distal (MOD) cavity preparation restored with different direct coronal restoration materials but from the same manufacturer against fracture. (2) Methods: sixty intact premolars were selected and placed into five groups (n = 12): G1—intact teeth, G2—endodontic treatment and unrestored MOD cavities, G3—endodontic treatment and MOD cavities restored with Tetric PowerFlow and Tetric EvoCeram, G4—endodontic treatment and MOD cavities restored with Multicore Flow and Tetric EvoCeram, and G5—endodontic treatment and MOD cavities restored with Multicore Flow. The specimens were subjected to an axial compression load at a speed of 1.6 mm/min and optically inspected before and after with a stereomicroscope. For each premolars group, the following data were recorded: the compression resistance, the compressive strength, and the maximum force supported. The microstructure of the samples after the compression test was analyzed using scanning electron microscopy (SEM). (3) Results: statistical analysis (ANOVA and Tukey test) showed that there was a statistically significant difference between G1 and the other groups. Even though there was no statistically significant difference between the restored groups, a better mechanical behavior was registered within the G3. (4) Conclusions: this in vitro study indicated that none of the materials used can lead to a higher or at least similar fracture resistance as the intact teeth. The coronal restoration only with nano-hybrid composites may lead to a higher therapeutic benefit for the fracture-susceptible premolars.
The Early Prediction of Kimchi Cabbage Heights Using Drone Imagery and the Long Short-Term Memory (LSTM) Model
Authors: Go Seung-hwan , Park Jong-hwa
Published: September 2024

Accurate and timely crop growth prediction is crucial for efficient farm management and food security, particularly given challenges like labor shortages and climate change. This study presents a novel method for the early prediction of Kimchi cabbage heights using drone imagery and a long short-term memory (LSTM) model. High-resolution drone images were used to generate a canopy height model (CHM) for estimating plant heights at various growth stages. Missing height data were interpolated using a logistic growth curve, and an LSTM model was trained on this time series data to predict the final height at harvest well before the actual harvest date. The model trained on data from 44 days after planting (DAPs) demonstrated the highest accuracy (R2 = 0.83, MAE = 2.48 cm, and RMSE = 3.26 cm). Color-coded maps visualizing the predicted Kimchi cabbage heights revealed distinct growth patterns between different soil types, highlighting the model’s potential for site-specific management. Considering the trade-off between accuracy and prediction timing, the model trained on DAP 36 data (MAE = 2.77 cm) was deemed most suitable for practical applications, enabling timely interventions in cultivation management. This research demonstrates the feasibility and effectiveness of integrating drone imagery, logistic growth curves, and LSTM models for the early and accurate prediction of Kimchi cabbage heights, facilitating data-driven decision-making in precision agriculture for improved crop management and yield optimization.
Nonparametric Expectile Shortfall Regression for Complex Functional Structure
Authors: Alamari Mohammed B. , Almulhim Fatimah A. , Kaid Zoulikha , Laksaci Ali
Published: September 2024

This paper treats the problem of risk management through a new conditional expected shortfall function. The new risk metric is defined by the expectile as the shortfall threshold. A nonparametric estimator based on the Nadaraya–Watson approach is constructed. The asymptotic property of the constructed estimator is established using a functional time-series structure. We adopt some concentration inequalities to fit this complex structure and to precisely determine the convergence rate of the estimator. The easy implantation of the new risk metric is shown through real and simulated data. Specifically, we show the feasibility of the new model as a risk tool by examining its sensitivity to the fluctuation in financial time-series data. Finally, a comparative study between the new shortfall and the standard one is conducted using real data.
A Case Study of the Possible Meteorological Causes of Unexpected Fire Behavior in the Pantanal Wetland, Brazil
Authors: Couto Flavio T. , Santos Filippe L. M. , Campos Cátia , Purificação Carolina , Andrade Nuno , López-Vega Juan M. , Lacroix Matthieu
Published: September 2024

This study provides insights into large fires in the Pantanal by analyzing the atmospheric conditions that influenced the rapid fire evolution between 13 and 14 November 2023, when fire fronts spread rapidly, leading to critical situations for firefighters. The observation-based analysis helped us to identify some characteristics of the fire’s evolution and the meteorological conditions in the region. Furthermore, two simulations were run with the Meso-NH model, which was configured with horizontal resolutions of 2.5 km and 5 km. The fire behavior, characterized by satellite observations, revealed periods with a sudden increase in active fire numbers. High temperatures and low relative humidity in the region characterized the fire weather conditions. The simulations confirmed the critical fire condition, showing the benefits of increasing the resolution of numerical models for the Pantanal region. The convection-resolving simulation at 2.5 km showed the repeated development of gust fronts in the late afternoon and early evening. This study highlights this dynamic that, coupled with intense surface wind gusts, was crucial for the intensification of the fire spread and unexpected behavior. This study is a first step toward better understanding fire dynamics in the Pantanal through atmospheric modeling, and it can support strategies for firefighting in the region.
Navigating the Digital Transformation of Education: Insights from Collaborative Learning in an Erasmus+ Project
Authors: Farrell Rachel , Rice Marelle , Qualter Declan
Published: September 2024

Collaborative Opportunities for Professional Inquiry Leading to Organisational Transformation (COPILOT) represents an Erasmus+ initiative involving four transnational partners from three EU countries including IES Cristobal de Monroy secondary school [Spain], Laois Offaly Education Training Board (LOETB) [Ireland], University College Dublin (UCD) School of Education [Ireland], and Akadimos, a teacher continuing professional development provider [Greece]. The project encompassed three primary objectives, aligned with the Erasmus+ programmes’s emphasis on supporting teachers and school leaders to address digital transformation. These included the following: the identification of participatory approaches and digital methodologies employed by schools to facilitate digital transformation among educators; exploring and establishing dynamic professional environments in schools by identifying synergies with organisations operating in diverse fields or socio-economic contexts and enhancing the capacity and professionalism of partners to engage in EU/international collaboration. To investigate the outcomes of the project objectives, a small-scale qualitative study was conducted, which involved analysing participants’ reports on the project’s collaborative activities. Additionally, a focus group was held at the conclusion of the project to gain insights into the digital readiness, capacity, and resilience of teachers and school leaders within their respective organisations. Thematic analysis was employed to analyse the collected data. The findings of the study offer encouraging indications regarding the crucial symbiotic role played by school leaders and “early adopter” teachers in promoting digital transformation. Moreover, participating teachers displayed a general willingness to adapt their teaching methods to incorporate more technology, provided they receive both formal and informal support. Furthermore, there exists a willingness among transnational partners to digitise and share resources both at present and in the future.
Robust Remote Sensing Scene Interpretation Based on Unsupervised Domain Adaptation
Authors: Li Linjuan , Zhang Haoxue , Xie Gang , Zhang Zhaoxiang
Published: September 2024

Deep learning models excel in interpreting the exponentially growing amounts of remote sensing data; however, they are susceptible to deception and spoofing by adversarial samples, posing catastrophic threats. The existing methods to combat adversarial samples have limited performance in robustness and efficiency, particularly in complex remote sensing scenarios. To tackle these challenges, an unsupervised domain adaptation algorithm is proposed for the accurate identification of clean images and adversarial samples by exploring a robust generative adversarial classification network that can harmonize the features between clean images and adversarial samples to minimize distribution discrepancies. Furthermore, linear polynomial loss as a replacement for cross-entropy loss is integrated to guide robust representation learning. Additionally, we leverage the fast gradient sign method (FGSM) and projected gradient descent (PGD) algorithms to generate adversarial samples with varying perturbation amplitudes to assess model robustness. A series of experiments was performed on the RSSCN7 dataset and SIRI-WHU dataset. Our experimental results illustrate that the proposed algorithm performs exceptionally well in classifying clean images while demonstrating robustness against adversarial perturbations.
Regional Load Forecasting Scheme for Security Outsourcing Computation
Authors: Chen Qizhan , Zhao Ruifeng , Li Bin , Liu Zewei , Zhuang Huijun , Hu Chunqiang
Published: September 2024

Smart grids generate an immense volume of load data. When analyzed using intelligent technologies, these data can significantly improve power load management, optimize energy distribution, and support green energy conservation and emissions reduction goals. However, in the process of data utilization, a pertinent issue arises regarding potential privacy leakage concerning both regional and individual user power load data. This paper addresses the scenario of outsourcing computational tasks for regional power load forecasting in smart grids, proposing a regional-level load forecasting solution based on secure outsourcing computation. Initially, the scheme designs a secure outsourcing training protocol to carry out model training tasks while ensuring data security. This protocol guarantees that sensitive information, including but not limited to individual power consumption data, remains comprehensively safeguarded throughout the entirety of the training process, effectively mitigating any potential risks of privacy infringements. Subsequently, a secure outsourcing online prediction protocol is devised, enabling efficient execution of prediction tasks while safeguarding data privacy. This protocol ensures that predictions can be made without compromising the privacy of individual or regional power load data. Ultimately, experimental analysis demonstrates that the proposed scheme meets the requirements of privacy, accuracy, and timeliness for outsourcing computational tasks of load forecasting in smart grids.
An Integrated Double-Sided LCC Compensation Based Dual-Frequency Compatible WPT System with Constant-Current Output and ZVS Operation
Authors: Chen Yafei , Liu Yijia , Yang Zhiliang , Gao Pengfei , Wu Jie
Published: September 2024

This article presents an integrated double-sided inductance and double capacitances (DS-LCC) compensation based dual-frequency compatible wireless power transfer (WPT) system. A cascaded single-phase multi-frequency inverter (CSMI) is constructed to generate the independent dual-frequency power transfer signals. In order to achieve the load-independent constant-current output (CCO) at two frequencies, an integrated DS-LCC compensated topology is reconstructed. By configuring the frequency-selective resonating compensation (FSRC) network in the primary side, the power transfer signals at two frequencies can be superimposed into a single transmitting coil, reducing the cost and volume of the system. Furthermore, to implement zero-voltage switching (ZVS) of the CSMI throughout the entire power range, a general parameter design method of the proposed system is also introduced. A 1.5-kW experimental prototype is built to validate the practicability of the presented dual-frequency compatible WPT System. The system can supply power to different loads at two frequencies simultaneously with CCO and ZVS properties. The peak efficiency reaches 91.75% at a 1.2-kW output power.
Challenges in Ground-Penetrating Radar Application in Structural Elements: Determination of the Dielectric Constant of Glued Laminated Timber Case Study
Authors: Varevac Damir , Guljaš Ivica , Ištoka Otković Irena , Radočaj Dorijan
Published: September 2024

In this paper, some of the basic information on Ground-Penetrating Radar (GPR), its applications (especially in the field of civil engineering) and limitations are presented. As a non-destructive technique, GPR is a powerful tool for the investigation of structures and structural members, roads, geological layers, archaeological sites and many more. The technology is based on electromagnetic radiation in the UHF/VHF range (10 MHz to 3 GHz). The choice of the frequency depends on the intended use, depth and size of the target and medium where the target is located. Joined with other testing methods (ultrasound method, dynamic methods with forced or ambient vibrations, electrical conductivity testing, etc.), GPR can provide a deep insight into the investigated object. However, like many other non-destructive methods, the choice of input parameters may affect the results. In this regard, a case study presented in this paper demonstrates not only different applications of GPR in civil engineering but also the determination (calibration) of one of those input parameters: the dielectric constant of glued laminated timber. The challenge here was not only to investigate the influence of the direction of measurements with regards to the direction of the fibers but also to acknowledge the contribution of the test antenna used during testing and dielectric constant calibration.
A Multi-Model Architecture Based on Deep Learning for Longitudinal Available Overload Prediction of Commercial Subsonic Aircraft with Actuator Faults
Authors: Shan Shengqiang , Cheng Yuehua , Jiang Bin , Xu Cheng , Guo Kun , Lin Xingyu
Published: September 2024

Assessing the real-time longitudinal available overload onboard under fault conditions offers vital insights for the fault-tolerant reconfiguration and trajectory planning of commercial subsonic aircraft. After actuator failures in a commercial subsonic aircraft, its aerodynamic model undergoes changes. Traditional methods based on analytical models rely on precise aerodynamic models. However, due to the complexities of the flight environment and uncertainties in disturbances, establishing an accurate aerodynamic model after actuator failures is often challenging. Consequently, traditional methods can yield significant errors when evaluating the available overload under actuator faults. To address this, we introduce a multi-model architecture based on deep learning for the longitudinal available overload prediction of a commercial subsonic aircraft with actuator faults. For flight state data under different working conditions and different faults, Spearman correlation coefficient analysis and the gradient boosting decision tree (GBDT) algorithm are used to remove redundant feature parameters, thereby enhancing the training and prediction speed of the model while reducing the risk of overfitting. To meet prediction accuracy and speed demands, we employ the multi-layer perceptron (MLP) deep learning network to fully explore the environmental features, including uncertainties and disturbances, within the flight state, and the mapping relationships between the flight state and the available overload variations. We incorporate the light gradient boosting machine (LightGBM) and the categorical boosting (CatBoost) algorithms to enhance the model’s prediction speed and fuse it with a longitudinal available overload analytical model to elevate the model’s prediction accuracy, thereby achieving the real-time estimation of the commercial subsonic aircraft’s longitudinal available overload with actuator faults. The results demonstrate that the proposed method achieves a higher accuracy than traditional methods, with a relative error of less than 5%.
A Dynamic Analysis of Sustainable Economic Growth and FDI Inflow in Saudi Arabia Using ARDL Approach and VECM Technique
Authors: Al Shammre Abdullah Sultan , Alshahrani Mariam Nasser
Published: September 2024

This study investigates the relationship between sustainable economic growth and foreign direct investment (FDI) in Saudi Arabia from 1980 to 2023. The ARDL approach and VECM technique are employed to analyze the short-run and long-run dynamics. The short-run results show mixed effects. Sustainable economic growth has a positive impact on current and one-period lagged FDI but a negative impact on the two periods lagged. Trade openness and infrastructure negatively affect FDI in the short run. Interestingly, oil rents and real economic growth also have negative short-run impacts on FDI, but these effects become positive with a longer lag. Long-run analysis reveals a negative relationship between trade openness, infrastructure, and oil rents with FDI, suggesting a potential crowding-out effect. Trade openness has a positive long-run impact on most variables, including sustainable growth, FDI, real growth, and CO2 emissions. Oil rents also have a positive long-run impact on these variables. This study finds six bidirectional causal relationships in the short run, primarily between trade openness, infrastructure, oil rents, and FDI. Unidirectional causality runs from oil rents, trade openness, exchange rate, sustainable growth, and real growth to FDI and infrastructure. Additionally, CO2 emissions cause FDI, and trade openness causes sustainable growth. While sustainable economic growth benefits FDI in the long run, short-term policies regarding trade openness and infrastructure require reevaluation. Oil revenue and real economic growth may initially deter FDI, but this reverses in the long term. To attract sustainable FDI, policymakers should focus on long-term economic growth strategies and consider reforms in trade and infrastructure policies. A comprehensive FDI strategy that moves beyond oil dependence and leverages trade openness is crucial to long-term economic diversification.
Recent Advancements in Petroleum and Gas Engineering
Authors: Wang Xiaochuan , Feng Gan , Hu Yaoqing , Huang Liuke , Xie Hongqiang , Zhao Yu , Jin Peihua , Liang Chao
Published: September 2024

Oil and natural gas resources are crucial energy sources formed during the geological and biological evolution of the Earth [...]
A Photovoltaic and Wind-Powered Electric Vehicle with a Charge Equalizer
Authors: Angamarca-Avendaño Darwin-Alexander , Flores-Vázquez Carlos , Cobos-Torres Juan-Carlos
Published: September 2024

This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV’s five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles.
Enhancing Multi-Junction Solar Cell Performance: Advanced Predictive Modeling and Cutting-Edge CIGS Integration Techniques
Authors: Ziani Zakarya , Mahdad Moustafa Yassine , Bessenouci Mohammed Zakaria , Sekkal Mohammed Chakib , Ghellai Nacera
Published: September 2024

Historically, multi-junction solar cells have evolved to capture a broader spectrum of sunlight, significantly enhancing efficiency beyond conventional solar technologies. In this study, we utilized Silvaco TCAD tools to optimize a five-junction solar cell composed of AlInP, AlGaInP, AlGaInAs, GaInP, GaAs, InGaAs, and Ge, drawing on advancements documented in the literature. Our research focused on optimizing these cells through sophisticated statistical modeling and material innovation, particularly examining the relationship between layer thickness and electrical yield under one sun illumination. Employing III-V tandem solar cells, renowned for their superior efficiency in converting sunlight to electricity, we applied advanced statistical models to a reference solar cell configured with predefined layer thicknesses. Our analysis revealed significant positive correlations between layer thickness and electrical performance, with correlation coefficients (R2 values) impressively ranging from 0.86 to 0.96 across different regions. This detailed statistical insight led to an improvement in overall cell efficiency to 44.2. A key innovation in our approach was replacing the traditional germanium (Ge) substrate with Copper Indium Gallium Selenide (CIGS), known for its adjustable bandgap and superior absorption of long-wavelength photons. This strategic modification not only broadened the absorption spectrum but also elevated the overall cell efficiency to 47%. Additionally, the optimization process involved simulations using predictive profilers and Silvaco Atlas tools, which systematically assessed various configurations for their spectral absorption and current–voltage characteristics, further enhancing the cell’s performance. These findings underscore the critical role of precise material engineering and sophisticated statistical analyses in advancing solar cell technology, setting new efficiency benchmarks, and driving further developments in the field.
Numerical Analysis of Natural Convection in an Annular Cavity Filled with Hybrid Nanofluids under Magnetic Field
Authors: Benkherbache Souad , Amroune Salah , Belaadi Ahmed , Zergane Said , Farsi Chouki
Published: September 2024

This paper presents a numerical study of natural convection in an annular cavity filled with a hybrid nanofluid under the influence of a magnetic field. This study is significant for applications requiring enhanced thermal management, such as in heat exchangers, electronics cooling, and energy systems. The inner cylinder, equipped with fins and subjected to uniform volumetric heat generation, contrasts with the adiabatic outer cylinder. This study aims to investigate how different nanoparticle combinations (Fe3O4 with Cu, Ag, and Al2O3) and varying Hartmann and Rayleigh numbers impact heat transfer efficiency. The finite volume method is employed to solve the governing equations, with simulations conducted using Fluent 6.3.26. Parameters such as volume fraction (ϕ2 = 0.001, 0.004, 0.006), Hartmann number (0 ≤ Ha ≤ 100), Rayleigh number (3 × 103 ≤ Ra ≤ 2.4 × 104), and fin number (N = 0, 2, 4, 6, 8) are analyzed. Streamlines, isotherms, and induced magnetic field contours are utilized to assess flow structure and heat transfer. The results reveal that increasing the Rayleigh number and magnetic field enhances heat transfer, while the presence of fins, especially at N = 2, may inhibit convection currents and reduce heat transfer efficiency. These findings provide valuable insights into optimizing nanofluid-based cooling systems and highlight the trade-offs in incorporating fins in thermal management designs.
Total Polyphenol Content and Antioxidant Activity of Leaves and Fine Roots as Indicators of Drought Resistance in the Native Quercus robur and Alien Quercus rubra
Authors: Masante Tsvetana , Cafà Sofia , Di Iorio Antonino
Published: September 2024

Research Highlights: Environmental abiotic stressors generate secondary stresses in plants, such as osmotic and oxidative stresses, which negatively influence their normal growth, development, and metabolism. Research about other non-enzymatic components with antioxidant capacity has recently focused on polyphenols. However, their role as indicators of drought and shade tolerance in woody species leaves and roots has been poorly explored or was limited to leaves only. Background and Objectives: Under a scenario of increasing drought, understanding the seedling responses in terms of total polyphenols and their antioxidant activity, in particular at the fine root system level, may help to elucidate the native–alien species interaction. Materials and Methods: At the beginning of July, 5-month-old native Quercus robur and alien Quercus rubra seedlings were transferred indoors to the growth chamber and subjected to progressive soil drying for 21 days. Results: The decrease in soil water content was more pronounced for Q. robur (9%) than for Q. rubra (34% of field capacity). Leaf water potential significantly decreased over time in Q. robur but did not differ from the control in Q. rubra. The total polyphenol concentration in Q. robur was markedly lower in the leaves and significantly higher in the fine roots than in Q. rubra. For the leaves, both species showed markedly higher values if well-watered, and the values significantly decreased in response to drought only in Q. rubra. In contrast, the fine root values for both species were markedly higher if droughted and decreased significantly in time only in Q. robur. Differently from the polyphenol concentration, the antioxidant capacity of Q. rubra was always higher in both the leaves and fine roots. Conclusions: The higher antioxidant activity of the alien species Q. rubra revealed by this work, combined with its isohydric behaviour, could further shed some light on our understanding of its competitive performance at the seedling stage against the native Q. robur.
Dynamic Impact of Digital Inclusive Finance and Financial Market Development on Forests and Timber in China: Economic and Social Perspective
Authors: Yasmeen Rizwana , Fu Guo Hong
Published: September 2024

This study investigates how digital inclusive finance, financial development, and technology influenced forest and timber outputs across 31 provinces in China from 2011 to 2021. The findings, derived from panel quantile regression analysis, indicate that digital inclusive finance significantly enhances forest economic output, particularly in regions with lower economic activity, by improving access to critical financial resources such as credit and investment. However, the positive effects diminish at higher levels of economic activity, suggesting potential diminishing returns. Through the marketization of credit distribution and diverse financial instruments, financial development is essential for promoting sustainable forestry practices and adopting new technologies. Based on the findings, the study suggests expanding digital financial services in areas with low forest activity to help people access credit and investments, boosting forest productivity. It also recommends improving financial markets and investing in new forestry technologies to support better forest management and timber production.
Particle Size Effect on Anaerobic Digestion of Fruit and Vegetable Waste
Authors: Vian José , Velasco-Pérez Alejandra , Solar-González Rocío , García-Herrera Tania , Puebla Hector , Vivar-Vera Guadalupe
Published: September 2024

During the anaerobic digestion (AD) of fruit and vegetable waste (FVW), excessive particle size reduction can lead to the overproduction and inhibition of methanogenic microorganisms. This paper presents an in-depth analysis through experimental assays, modeling, and response surface analysis of the effect of particle size on methane production. A simple model was proposed considering the inhibition of the growth of methanogenic microorganisms and surface-based hydrolysis kinetics. The model parameters were estimated using experimental data from batch systems fed with FVW of varying particle sizes (ranging from 1.8 to 1000 μm). Response surface methodology establishes a statistical model for estimating methane production based on particle size and concentration. Numerical and statistical analyses were conducted using Matlab R2024a and Minitab 24 software. A model with an R2 of 0.89 was obtained, which determined an optimal concentration of 8.2 kg·m−3 and a particle size of 742.3 μm, yielding a methane production of 303.3 m3·kg−1 VS, similar to the experimentally obtained range of 300.95 to 316.7 m3·kg−1 VS.
Growth of Lactiplantibacillus plantarum BG112 in Batch and Continuous Culture with Camellia sinensis as Prebiotic
Authors: Rojas-Rejón Óscar Ariel , Gonzalez-Figueredo Carlos , Quintero-Covarrubias Ana Ruth , Saldaña-Jáuregui Alejandro
Published: September 2024

This work aimed to study the effect of Camellia sinensis extract (CSExt) as a particular growth promoter of Lactiplantibacillus plantarum (LP) in batch and continuous production processes. Growth conditions were 1% (v/v) inoculum, pHC = 6.5, 1% of dissolved oxygen (D.O.), 37 °C, and 150 rpm in a 0.2 L bioreactor using a commercial MRS broth (de Man, Rogosa, and Sharpe) and 1% (v/v) or 10% (v/v) CSExt according to the experimental design. In batch experiments, the maximum specific growth rate and the affinity constant increased with the increase in CSExt. In continuous culture, biomass production increased significantly with the addition of 1% (w/v) CSExt at 0.15 (1/h). Kinetic parameters adjusted were similar to those reported in the literature. Substrate affinity and the specific growth rate increased significantly in the presence of CSExt in batch and continuous cultures. Based on the results, prebiotics from plant extracts may function as growth promoters in specific physiological stages. This is the first report showing the change in kinetic parameters of a probiotic strain growing in crude plant extracts.
A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning and System Engineering Integration: A Case Study on TSMC Stock Data
Authors: Chiang Johannes K. , Chi Renhe
Published: September 2024

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes.
Metal Bioaccumulation in the Muscle of the Northern Pike (Esox lucius) from Historically Contaminated River and the Estimation of the Human Health Risk
Authors: Dragun Zrinka , Ivanković Dušica , Tepić Nataša , Filipović Marijić Vlatka , Šariri Sara , Pavin Tatjana Mijošek , Drk Sara , Gjurčević Emil , Matanović Krešimir , Kužir Snježana , Barac Fran , Kiralj Zoran , Kralj Tomislav , Valić Damir
Published: September 2024

The impact of the long-term historical contamination of the Mrežnica River with textile industry wastewaters on metal/metalloid bioaccumulation in the muscles of the young northern pike (Esox lucius) (0+ to 3+) was evaluated, as well as the association of bioaccumulated metal/metalloid concentrations in the muscle with fish physiology. Increased levels of several elements (As, Bi, Cs, Co, Cu, Zn), bioaccumulated in fish muscle, were occasionally found in front of the former factory, but the obtained metal/metalloid concentrations in muscle were in general either comparable or even lower than in the fish from moderately contaminated freshwaters. Calculated target hazard quotients indicated that the current risk for humans, arising from consumption of the northern pike meat originating from a historically contaminated section of the Mrežnica River, was negligible. The influence of biological factors, especially seasonal physiological changes, on metal/metalloid bioaccumulation was confirmed, indicating the importance of the involvement of these parameters in the freshwater pollution assessment, but also in the estimation of the human health hazard. The increased bioaccumulation of several metals/metalloids in fish muscle at a historically contaminated site pointed to the need for continuous supervision of fish health and biodiversity in freshwaters impacted by currently suspended long-term contamination sources.
Enhancing Gluten-Free Crispy Waffles with Soybean Residue (Okara) Flour: Rheological, Nutritional, and Sensory Impacts
Authors: Aussanasuwannakul Aunchalee , Puntaburt Kassamaporn , Pantoa Thidarat
Published: September 2024

The incorporation of okara, a by-product of soybean milk production, into gluten-free products such as crispy waffles poses challenges due to the absence of gluten’s viscoelastic properties and the high fiber content of okara. This study aimed to evaluate the effects of okara flour on the rheological properties, physical attributes, and sensory qualities of gluten-free waffles. Waffle batters with varying levels of okara flour (10%, 20%, 30%, and 40%) were prepared, and their rheological properties were analyzed using oscillatory shear and creep-recovery tests. Physical properties, proximate composition, cholesterol and glucose adsorption capacities, storage stability, and sensory attributes were also assessed. The results demonstrated that increasing okara flour content improved batter elasticity and viscosity (with complex viscosity reaching up to 10,923 Pa·s for 40% okara flour) but decreased spread ratio by up to 45% and increased moisture content by approximately 2.7%. Higher okara content also led to a 16% decrease in brightness (L*) and increased hardness, reaching 325.26 g/s at 40% substitution. Sensory evaluation revealed that waffles with 30% okara flour were preferred for their texture and overall liking, with a score of 7.43 compared to higher substitution levels. Cholesterol and glucose adsorption capacities were high in okara flour, contributing to potential health benefits. Storage stability tests showed acceptable moisture content, water activity, and microbiological safety over 60 days, though hardness decreased by about 42%. In conclusion, okara flour enhances the nutritional profile of gluten-free waffles, but its impact on texture and flavor requires careful formulation adjustments to optimize consumer acceptance.
The Influence of Processing Using Conventional and Hybrid Methods on the Composition, Polysaccharide Profiles and Selected Properties of Wheat Flour Enriched with Baking Enzymes
Authors: Lewko Piotr , Wójtowicz Agnieszka , Kamiński Daniel M.
Published: September 2024

In this study, a developed wheat flour blend (F), consisting of a high content of non-starch polysaccharides, was fortified with cellulase (C) and a cellulase–xylanase complex (CX) and then processed via conventional and hybrid treatment methods. Dry heating (T), hydrothermal treatment (H) and extrusion processing (E) were applied without or with enzyme addition as hybrid treatments. Proximate composition and polysaccharide profiles selected techno-functional and structural properties of modified wheat flours, were analyzed. Conventional and hybrid treatments induced changes in polysaccharide fraction compositions (especially the arabinoxylans) and the rheology of modified flour. Dry heating caused an inconsiderable effect on flour composition but reduced its baking value, mainly by reducing the elasticity of the dough and worsening the strain hardening index, from 49.27% (F) to 44.83% (TF) and from 1.66 (F) to 1.48 (TF), respectively. The enzymes added improved the rheological properties and baking strength, enhancing the quality of gluten proteins. Hydrothermal enzyme-assisted treatment increased flour viscosity by 14–26% and improved the dough stability by 12–21%; however, the use of steam negatively affected the protein structure, weakening dough stretchiness and elasticity. Extrusion, especially enzyme-assisted, significantly increased the hydration properties by 55–67% but lowered dough stability, fat content and initial gelatinization temperature due to the changes in the starch, mostly induced by the hybrid enzymatic–extrusion treatment. The structure of extruded flours was different from that obtained for other treatments where the peak intensity at 20° was the highest, suggesting the presence of amorphous phases of amylose and lipids. The results can be helpful in the selection of processing conditions so as to obtain flour products with specific techno-functional properties.
Effects of High-Pressure Homogenization on the Structure and Functional Properties of Solenaia oleivora Proteins
Authors: Chen Wanwen , Ma Xueyan , Jin Wu , Wen Haibo , Xu Gangchun , Xu Pao , Cheng Hao
Published: September 2024

Solenaia oleivora, a rare freshwater shellfish with high protein quality, is unique to China. However, the poor hydrosolubility and functional properties of Solenaia oleivora proteins hinder their utilization in food products. Herein, the alkaline dissolution-isoelectric precipitation method was used for the extraction of Solenaia oleivora proteins. Furthermore, the impact of high-pressure homogenization (HPH) treatment varying from 0 to 100 MPa on the structure and functional properties of Solenaia oleivora proteins was investigated. The obtained results indicated that HPH treatment decreased the α-helix content and enhanced the β-sheet and random coil content. Furthermore, the HPH caused the unfolding of protein structure, exposing aromatic amino acids, increasing the free thiol group content, and enhancing surface hydrophobicity. As the homogenization pressure increased from 0 to 100 MPa, the particle size of Solenaia oleivora proteins decreased from 899 to 197 nm with the polymer dispersity index (PDI) value decreased from 0.418 to 0.151, the ζ-potential increased from −22.82 to −43.26 mV, and the solubility increased from 9.54% to 89.96%. Owing to the significant changes in protein structure and solubility, the emulsifying, foaming, and digestive properties of Solenaia oleivora proteins have been significantly improved after treatment with HPH.
Implementation of Cellulose-Based Filtration Aids in Industrial Sunflower Oil Dewaxing (Winterization): Process Monitoring, Prediction, and Optimization
Authors: Lužaić Tanja , Nedić Grujin Katarina , Pezo Lato , Nikolovski Branislava , Maksimović Zoran , Romanić Ranko
Published: September 2024

In the production of refined sunflower oil, waxes are removed during the winterization stage, and wax crystals are separated through filtration assisted by filtration aids. Commonly used filtration aids in oil refining include perlite and diatomaceous earth. After winterization, a significant amount of filter cake remains as a by-product and is treated as waste. Today, natural cellulose fibers are being promoted as filtration aids. Their advantages are numerous, both in the production process and from an environmental perspective. However, their only disadvantage is their higher cost. Therefore, in this study, 57 filtration cycles during the industrial sunflower oil winterization step using cellulose-based filtration aids were monitored. Different process parameters, including the pressure differential on the filter, the flow rate of filtered oil, constant pressure period, the quantity of filtered oil, filtration time, the quantity of pre-coating and dosing filtration aids, the volume of filtered oil, the concentration of dosing filtration aid, as well as the mass of separated waxes, were observed. Additionally, artificial neural networks were applied to predict process parameters, optimize the process, and, above all, determine the dosage of filtration aids, which will make the process more economical. The optimal filtration process is performed at a pressure differential of 3.3 bar, lasting a total of 39 h, with 32 h at constant pressure, resulting in 322,503 kg of filtered oil and 90.41 kg of waxes. The optimal quantity of cellulose-based filtration aids employed for pre-coat was 80 kg, and for dosing, 375 kg, with an optimal concentration of 0.12% w/w.
Correction: Khan et al. The Extraction and Impact of Essential Oils on Bioactive Films and Food Preservation, with Emphasis on Antioxidant and Antibacterial Activities—A Review. Foods 2023, 12, 4169
Authors: Khan Sohail , Abdo Abdullah , Shu Ying , Zhang Zhisheng , Liang Tieqiang
Published: September 2024

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