Processing Methods Research Articles

The Power of Thermosonication on Quality Preservation and Listeria Control of Blueberry Juice

Due to the increasing consumer demand for healthy, beneficial foods, natural fruit juices have gained popularity for their rich nutritional value and appealing flavor. However, traditional thermal processing can compromise these quality attributes. This study investigates using pulsed thermosonication, a novel mild thermal processing method, on Listeria innocua inactivation in blueberry juice, chosen for its high phenolic and anthocyanin content. Ultrasonication was applied at 60% and 100% amplitudes combined with heat treatments at 45 °C and 55 °C and compared to control heat treatments. The Weibull model effectively described the inactivation kinetics, showing that the thermosonicated samples required significantly shorter times (1 and 25 min) for a 5-log reduction compared to the heated samples (10 and 60 min). While pH, total soluble solids, and water activity remained unaffected, color parameters improved, and the best retention of phenolics and anthocyanins was observed at 100% amplitude and 45 °C. Rheological properties were unchanged. The findings demonstrate that thermosonication at milder temperatures is more effective than conventional heat treatment for microbial inactivation and quality retention in blueberry juice, suggesting it is a superior processing method for preserving fruit juices’ nutritional and sensory attributes.

Comparative analysis of specimen processing methods for spatial imaging of mulberry bud ultrastructure using the Scanning Electron Microscope

Comparative analysis of specimen processing methods for spatial imaging of mulberry bud ultrastructure using the Scanning Electron Microscope

Influence of (ultra-)processing methods on aquatic proteins and product quality.

Aquatic products are a high-quality source of protein for humans, and the changes in protein during aquatic product processing are crucial for nutritional value, product performance, and consumer health. With the advancement of science and technology, aquatic product processing methods have become increasingly diverse. In addition to traditional methods such as thermal processing (steaming, roasting, and frying) and pickling, emerging non-thermal processing technologies, such as high pressure, ultrasound, and irradiation, are also being applied. During (ultra-)processing, aquatic products undergo complex biochemical reactions, among which protein oxidation significantly affects the quality of aquatic products. Protein oxidation can alter the molecular structure of proteins, thereby changing their functional properties and ultimately impacting product quality. This paper primarily explored the effects of protein changes under different processing methods on aquatic product quality and human health, as well as techniques for controlling protein oxidation. It aims to provide a theoretical basis for selecting appropriate processing methods, improving aquatic product quality, and controlling protein oxidation in aquatic products, and to offer scientific guidance for practical production.

Identifying the quality markers and optimizing the processing of Gastrodiae rhizoma to treat brain diseases

BackgroundGastrodiae rhizoma (GR) refers to the dried tuber of Gastrodia elata Bl. and has been used for many centuries to treat brain diseases, such as Alzheimer’s disease, major depressive disorder, and cerebral ischemia. However, the processing of GR is complex and varied, resulting in unstable clinical treatment effects. The processing protocols significantly affect the active ingredients and curative effects of GR. We can optimize the processing of GR by identifying quality markers to treat brain diseases.MethodsFresh tubers of G. elata Bl. were processed under eight different protocols, and their resulting contents of potentially bioactive compounds were compared using liquid chromatography mass spectrometry to screen the potential quality markers of GR through stoichiometric analysis. The potential quality markers of GR targeting Alzheimer’s disease, major depressive disorder, and cerebral ischemia were identified by network pharmacology, and the potentially neuroprotective effects of these components were validated through simulated docking to likely protein targets. Finally, a fit degree analysis was carried out using different composition ratios and proportions of the disease component degree value, and the therapeutic effects of different processing methods on Alzheimer’s disease, major depressive disorder, and cerebral ischemia were outlined clearly.ResultsWe identified 32 potential therapeutic components and screened 13 quality markers in GR, of which five quality markers (galactinol, glucosyringic acid, parishins C and E, and S-(4-hydroxybenzyl)-glutathione) showed efficacy against all three brain diseases. Furthermore, steaming and microwave-drying during processing can optimize the components of these quality markers for treating the three diseases.ConclusionProcessing protocols significantly affect the therapeutic components of GR and may also impact its effectiveness in treating brain diseases. Accordingly, optimizing the processing methods of GR to correspond to different therapeutic purposes may improve its efficacy against brain diseases.

Watershed Grouping Based on Suspended Sediment Yield Using Morphological Criteria: A Comparison of Hierarchical and Nonhierarchical Techniques

ABSTRACTRegional sediment analysis methods have traditionally been used to reliably estimate sediment yield in ungauged watersheds with adequate hydrometric stations. Watershed‐grouping techniques are a suitable approach for estimating sediment load, which can be used to assess erosion processes, and surface water quality, and plan soil and water conservation measures. Additionally, the relationship between sediment yield and watershed characteristics can be used to identify different stages of erosion evolution and prioritize watersheds for conservation efforts. To this end, this study aimed to assess the applicability of various analytical hierarchy process (AHP) methods, including Ward's, Single linkage, and β‐flexible methods, as well as the nonhierarchical fuzzy C‐means (FCM) method in the Gorganroud and Qareh‐Sou watersheds of northern Iran based on hydrological, geological, climatic, physiographical, and land‐use characteristics. Furthermore, the study evaluated clustering methods to determine the optimal number of subwatershed clusters. The AHP methods identified two, three, or four clusters, while the FCM method identified three clusters as the best number of clusters. The results indicated that the FCM method had a well‐regionalized estimation in the study area with the maximum amount of validation indices (The highest Dunn coefficient 0.31–0.65, the lowest TESS 16.62–36.8, and acceptable Pseudo‐F coefficient 8.1–10.4). Notably, the fuzzy clustering methods associated each input sample with two or more clusters, which reduced the uncertainty of sediment yield estimation. The recommended clustering method can be used for regional sediment analysis in ungauged watersheds and evaluated against other hydrological variables in the study area.

Approach to Weighting the Success Criteria of Toll Road Project of Government Cooperation with Business Entities Procurement Model

In providing funding for toll road development projects, many developing countries are im-plementing PPP pro-curement schemes including Indonesia. This funding scheme is believed to be an innovative procurement process model and has good prospects for the future of the construc-tion industry in the world. However, in its implementation, the Indonesian government is currently faced with various problems that have the potential to have a negative impact on the management and development of toll roads in Indo-nesia. Therefore, the main objective of this research is to develop a method of evaluating the success of PPP-themed toll road projects and determining the current and future strategy of PPP-themed toll road development projects. Methodology This research, using an exploratory sequential mixed methods approach, involves four stages of research. Through a Systematic Literature Review (SLR) of reputable scientific journals from various countries, a questionnaire survey was developed. The survey results were analyzed using multivariate statistical methods with the help of Par-tial Least square analysis software (PLS-SEM), analytical hierarchy process (AHP) and Relative Importance Index (RII) methods. The results of this research found 20 critical success indicators and four dimensions of project success. This study produces a new way to assess the success of PPP-themed toll road projects that is very important and can inspire the government in making policies, legislation and regulations as well as innovative strategies in develop-ing PPP-themed toll roads.

A Review of the Nutritional Composition, Storage Challenges, Processing Technology and Widespread Use of Bamboo Shoots

Bamboo shoots, as the young bamboo stems, are rich in protein, fiber, vitamins, and minerals, as well as many bioactive substances beneficial to health, and are gaining in importance worldwide as a healthy food and dietary supplement. However, fresh bamboo shoots lignify rapidly after harvesting and contain cyanogenic glycosides, limiting the safe and healthy consumption of bamboo shoots. To this end, based on the changes in nutritional composition and the physiological properties of fresh and post-harvest bamboo shoots, factors affecting the preservation of post-harvest bamboo shoots are emphasized, including a series of physical and chemical regimes and various processing methods for post-harvest preservation. Furthermore, a systematic biorefinery approach for using bamboo shoot processing residue to prepare value-added products is also discussed. Finally, the article also discusses issues related to sustainable development, safeguarding food security, and addressing potential health impacts in order to provide a scientific basis for researchers to further develop and increase the added value of bamboo shoots.

Gearbox Fault Diagnosis Based on Adaptive Variational Mode Decomposition–Stationary Wavelet Transform and Ensemble Refined Composite Multiscale Fluctuation Dispersion Entropy

Existing gearbox fault diagnosis methods are prone to noise interference and cannot extract comprehensive fault signals, leading to misdiagnosis or missed diagnosis. This paper proposes a method for gearbox fault diagnosis based on adaptive variational mode decomposition–stationary wavelet transform (AVMD-SWT) and ensemble refined composite multiscale fluctuation dispersion entropy (ERCMFDE). Initially, the kurtosis coefficient and autocorrelation coefficient are presented, and the Intrinsic Mode Functions are denoised through the application of AVMD-SWT. Secondly, the coarse-grained processing method of composite multiscale fluctuation dispersion entropy is extended to encompass three additional approaches: first-order central moment, second-order central moment, and third-order central moment. This enables the comprehensive extraction of feature information from the time series, thereby facilitating the formation of an initial hybrid feature set. Subsequently, recursive feature elimination (RFE) is employed for feature selection. Ultimately, the outcomes of the faults diagnoses are derived through the utilization of a Support Vector Machine with a Sparrow Search Algorithm (SSA-SVM), with the actual faults data collection and analysis conducted on an experimental platform for gearbox fault diagnosis. The experiments demonstrate that the method can accurately identify gearbox faults and achieve a high diagnostic accuracy of 98.78%.

Edge computing-based ensemble learning model for health care decision systems.

A growing number of humans have suffered severe chronic illnesses, which has caused a boost in the requirement for diagnostic and medical treatment procedures that are both accurate and fast. Improved patient conditions and enhanced Decision-Making Systems (DMS) for healthcare professionals are the primary objectives of the Clinical Decision Support System (CDSS) recommended in this research article. The main drawback of traditional Machine Learning (ML) techniques is their failure to predict reliably. To solve this problem, the proposed model creates an Ensemble Extreme Learning Machine (EN-ELM) algorithm that combines predictors trained on several different data sets. This lowers the chance of overfitting. The suggested CDSS uses many different data processing methods, including Adaptive Synthetic (ADASYN) and isolation Forest (iForest), which fix problems like outliers and class imbalance. This approach significantly enhances the framework's classification performance. Also, the CDSS is compatible with an EC model, which enables real-time computation while minimizing the requirement for integrated systems. The recommended CDSS applies iForest and ADASYN to execute large-scale trials validating high standards of accuracy across numerous datasets. Researchers concluded that a suitable ELM classification threshold of 85% is the most effective, which substantially boosts the accuracy of the predictive model. When applied to various medical datasets, such as Hepatocellular Carcinoma (HCC), Cervical Cancer, Chronic Kidney Disease (CKD), Heart Disease, and Arrhythmia, the EN-ELM achieved accuracy rates of 99.36%, 98.15%, 97.85%, 97.06%, and 96.72%, respectively. By measuring this progress, the CDSS could dramatically improve the accuracy of chronic illness diagnosis and treatment, which similarly affects clinicians.

Elliptic Quaternion Matrices: A MATLAB Toolbox and Applications for Image Processing

In this study, we developed a MATLAB 2024a toolbox that performs advanced algebraic calculations in the algebra of elliptic numbers and elliptic quaternions. Additionally, we introduce color image processing methods, such as principal component analysis, image compression, image restoration, and watermarking, based on singular-value decomposition theory for elliptic quaternion matrices; we added these to the newly developed toolbox. The experimental results demonstrate that elliptic quaternionic methods yield better image analysis and processing performance compared to other hypercomplex number-based methods.

Effect of Processing Methods on Amino Acid and Fatty Acid Composition of Parkia biglobosa Seeds

Parkia biglobosa (PB) is among the various underexploited nutritious plants. A comparative study was conducted on the effect processing PB has on the amino and fatty acid composition. Fermented (FPB), defatted (DPB), and protein isolates (PI) samples were prepared from Parkia biglobosa seeds using various processing methods, and evaluated using standard analytical protocols. The PI showed the highest significant total non-essential amino acids, essential amino acids, and conditionally essential amino acids when compared with FPB and DPB. In addition, the PI showed the highest significant (p < 0.05) total neutral amino acids, basic amino acids, total aromatic amino acids, and the lowest percentage cystine ratio of total sulphur amino acids and total amino acids in comparison with FPB and DPB. The PI also showed a better-predicted protein efficient ratio, essential amino acid index, biological value, protein content, and nutritional index than FPB and DPB. Furthermore, PI amino acid composition was compared favourably with the reference scores for a whole hen’s egg, preschool child, and provisional scoring pattern. Arginine and histidine values of PI were higher than the recommended values. The FPB (42.29%) showed the highest polyunsaturated fatty acids compared with the DPB (41.94%) and PI (26.7%). The dominant saturated, monounsaturated, and polyunsaturated fatty acids were stearic acid (15.1 DPB), linoleic acid (42.29 DPB), and oleic acid (14.05 PI). The DPB (1.84) showed a better polyunsaturated to saturated (P:S) ratio than FPB (1.74) and PI (0.91). The results revealed that the processing methods improved the relative amino acid composition, whereas the fatty acid composition improved in DPB. Therefore, PI could serve as an alternative in the formulation of complementary foods.

High-Speed and Low-Noise Gear Finishing by Gear Grinding and Honing: A Review

AbstractGears are important mechanical parts for transmitting power and play an increasingly important role in the machinery industry. The electric vehicle industry developed rapidly and became a vital development field of the automotive industry over the past few years. The conversion of energy increases the requirements for electric vehicle transmissions, which promotes the development of high-speed and low-noise transmission gear. This paper elaborates on the research progress of high-speed and low-noise gear finishing methods. Firstly, this review analyses the machining requirements, finishing, and specific machining technologies of high-speed and low-noise gears. The importance of gear grinding and honing in high-speed and low-noise gear machining is highlighted. Secondly, the applications of gear grinding and honing in gear modification, error compensation, and texture control are analyzed. Furthermore, the role of precision machining technology in improving gear performance is clarified. Finally, the design and processing methods of the modified tooth flank for gear modification are summarized. In addition, the influence of tooth surface texture on noise is described, and the texture change methods are explored. The well-known open problems of gear finishing are finally identified, and some new research interests are also pointed out. This review will provide valuable references for further research on gear finishing.

Numerical simulation of electrically pumped active vertical‐cavity surface‐emitting lasers diodes based on metal halide perovskite

AbstractMetal halide perovskites (MHP)‐based electrically pumped vertical‐cavity surface‐emitting lasers (EPVCSEL) are promising candidates in optoelectronics due to low‐carbon footprint solution processing method. However, significant challenges impede MHP‐EPVCSEL manufacturing: (1) Distributed Bragg Reflectors (DBRs) composed of typical electron transport layers (ETLs) and hole transport layers (HTLs) are not conductive enough. (2) Due to large mobility difference of typical ETLs and HTLs, carriers‐unbalanced injection leads to severe performance degradation. Herein, we propose a potential strategy to address such challenges using MAPbCl3 and CsSnCl3 as carrier transport layers with mobility 3 orders larger than typical ETLs and HTLs. Via transfer matrix method calculations, we find that the reflectance of DBRs composed of MAPbCl3 (130.5 nm)/CsSnCl3 (108 nm) is larger than 91% with 10 pairs of DBRs. Furthermore, the proposed EPVCSEL device simulation shows that MHP‐EPVCSEL has the potential to achieve room temperature continuous wave lasing with a threshold current density of ∼69 A cm−2 and output optical power ∼10−4 W. This work can provide a deep insight into the practical realization of MHP‐EPVCSEL.

Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition

Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under extremely low visibility conditions exacerbated by inhomogeneous illumination, which degrades deep learning performance due to inconsistent signal intensities. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (SDIF) to enhance only the vital signal information. The grayscale banding is eliminated by incorporating a visual optimization strategy based on image gradients. Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. We evaluated our algorithm using data collected from both a fog chamber and outdoor environments and performed comparative analyses with existing methods. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications.

The Influence of Labor Expenses on Current Year Profit of PT Bank Rakyat Indonesia Tbk 2003-2022

This study aims to analyze the impact of Labor expenses on the annual net income of PT BRI (Bank Rakyat Indonesia, Tbk) from 2003 to 2022. Quantitative methods are the core of the scientific rigor of this study. Library and document-based methods are used to collect data. Financial statements serve as secondary sources of information. Data processing methods based on linear regression analysis are used to test the financial statements of PT BRI Tbk for 2003-2022 with a focus on labor cost and profit data. According to the findings of the study, PT BRI Tbk's current year profit is positively and significantly affected by Labor expenses. Based on the data provided, we can conclude that this is the case because t-count = 6.6072> t-table = 1.734 and 0.00 0.05 or sig 0.05 is statistically significant.

Time-encoded photonic quantum states: Generation, processing, and applications

Encoding and processing quantum information in the time-of-arrival of photons offer significant advantages for quantum information science and technology. These advantages include ease of experimental realization, robustness over photon state transmission, and compatibility with existing telecommunication infrastructure. Additionally, time-of-arrival encoding has the potential for high-rate quantum communication and holds promise for the future development of quantum internet. This review explores the generation, processing, and applications of time-encoded quantum states, focusing on both single-photon states, energy–time entanglement, and time-bin entanglement. We summarize the nonlinear optics platforms and advanced laser and modulation techniques utilized for photon sources that enable quantum information encoding onto the photons' time-of-arrival. We also highlight advanced quantum state processing methods in the time domain, including the Franson interferometry, optical switch-based schemes, and state-of-the-art measurement and detection schemes that allow for high-speed and multi-dimensional quantum operations. Finally, we review the mainstream implementations mainly including the quantum communication demonstrations and outline future directions for developing practical quantum networks leveraging time-encoded photon states.

The Use of Interactive Teaching Methods in Educational Institutions

The article is dedicated to the study of the use of interactive teaching methods in educational institutions and their impact on the effectiveness of the learning process. In the context of dynamic changes in society and the rapid development of information technologies, the education system requires new approaches to teaching that foster active student engagement and the development of critical thinking, creative abilities, and communication skills. Interactive teaching methods, which involve dialogic interaction between participants in the educational process, open new opportunities for improving the quality of knowledge acquisition and the development of practical skills among young people. The article analyzes theoretical and practical approaches to the implementation of interactive methods in the educational process. In particular, various forms of interactive work are considered, such as group and paired activities, discussions, role-playing games, problem-based learning, and the use of interactive technologies (multimedia tools, online platforms), among others. Special attention is given to methods that enhance learning by promoting student collaboration, teamwork skills, decision-making under uncertainty, and problem-solving abilities. The article also emphasizes the advantages of interactive methods compared to traditional teaching approaches. It has been found that interactive learning leads to deeper comprehension of the material, increases student motivation, reduces passivity in the learning process, and develops the ability to apply knowledge in practice. However, it also highlights the need to adapt interactive methods to specific educational conditions and student populations, as well as the importance of adequate teacher preparation. The article provides recommendations for the effective use of interactive methods in the educational process, including the integration of interactive technologies in school and higher education, teacher professional development, and the incorporation of modern information technologies.

Phenolics in soymilk manufactured from black and Proto soybeans by two continuous-ultrahigh-temperature-processing technologies inhibit DU145-prostate cancer cell proliferation through apoptosis.

Plant genotypes and processing technologies affect health properties of foods. How thermal processes with different sterilization values influence polyphenols in soymilk manufactured from different genotypes, particularly black soybean has not been well characterized. This study's aims were to investigate how one- and two-phase ultrahigh temperature (UHT) processing technologies, with wide differences of lethality (F0 158.5 and 6.35, respectively), affected anti-prostate cancer DU145-cell properties of black soymilk compared to light-yellow-Proto soymilk. Phenolics were extracted from soymilk and used for chemical, cell cycle and apoptosis analyses. Total isoflavones and genistein in black soymilk were significantly higher than Proto soymilk by either processing methods. Compared to one-phase processing, two-phase produced higher gallic acid in both soybeans, and higher oxygen radical absorbance capacity (ORAC) in black soymilk. Soymilk processed from both genotypes by both UHT methods inhibited DU145 cells. Two-phase-UHT processed black soymilk was more effective than one-phase UHT-processed soymilk. IC50 values (mg/mL) of black and yellow soy extracts against prostate cancer cells differed only by 11%-25%, which were lower than the differences of total isoflavone (29%-33%) or genistein (>50% between two beans). The mechanism by which soymilk inhibited DU145 cell proliferation was through apoptosis as evidenced by cell cycle analyses and expressions of caspase-3, Bcl-2, and PARP-1 proteins. Antioxidant properties, isoflavones, and phenolic acids were negatively correlated with prostate-cancer-cell inhibition IC50 (p<0.05) with ORAC having the highest coefficient (r=-0.98). Overall, two-phase-UHT processing of soybean would produce soymilk products with a higher health benefit than a one-phase UHT method. PRACTICAL APPLICATION: This study characterized the potential prostate cancer prevention effect of soymilk's phenolic extract in black soybean and compared with yellow soybean. The crude extract can be prepared much less costly than purified isoflavones and has potential to be developed into a dietary supplement. This study shows differences of soymilks made by continuous high-temperature processing of two soybean types and can serve as a scientific foundation for future clinical research and commercialization.

Designing the next generation of polymers with machine learning and physics-based models

Abstract The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset ( &lt; 200 unique polymers), we developed quantitative structure-property relationships (QSPRs) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R 2 greater than 0.77 across all properties. We then explored new polymers by creating a library of over ∼10 000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ∼10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation.

Exploring local lignocellulosic substrates for the production of edible mushrooms in Northwestern Argentina

La Rioja province annually produces approximately 75,000 tons of agricultural residues and derived materials from agro-industrial activities, which could potentially be incorporated into oyster mushroom cultivation. This study aimed to evaluate the viability of this lignocellulosic biomass as a substrate for the cultivation of edible mushrooms belonging to the genus Pleurotus. Initially, the mycelial growth of two species (P. ostreatus and P. djamor) was assessed by formulating combinations of local substrates. Experimental crops were grown employing the most promising substrates, which were subsequently selected for chemical characterization. It was found that both strains exhibited maximum mycelial growth in the substrate formulated with jojoba leaf litter. A comparison of the two strains revealed no direct correlation between mycelium growth and productive performance. The highest biological efficiency (BE) values were obtained when P. ostreatus was cultivated in treatments combining jojoba leaf litter and grape pomace with olive pomace. Furthermore, these treatments showed suitable chemical properties and were formulated from problematic waste generated in large quantities in the region without proper processing and disposal methods. In this context, there is potential to ensure a continuous supply of this lignocellulosic biomass for cultivating these mushroom species over an extended period of time, thus providing a sustainable alternative for these regional by-products.