Falls are extremely damaging to the elderly. The number of elderly who have experienced falls has increased over the years, several of the elderly stay alone or in in badly maintained elderly homes. This makes a low-cost fall detection system a necessity. There has been huge improvements in terms of IoT systems, ML algorithms. Varied data sets have been collected across the world for fall detection. These data sets have a very little in common among them, in terms of user demographics, sensors used, the ADL and Fall activities Hence in this paper we present a data set that has wide user demographics, we used various sensors – such as accelerometer, gyroscope, magnetometer and hear rate. We used wrist worn sensors to collect data. In this paper we present a detailed analysis of the data set we collected using common ML algorithms such as – Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM). We analyzed the performance of these algorithms for variations in accuracy with respect to age, gender, height, weight and health issues and we have identified outliers by analyzing each incorrect prediction. This paper provides the complete details of the data collection methodology, The methods used for analysis and presents the results of analysis in complete detail.

The goal of this article was to evaluate the antioxidant and anti-inflammatory like activities of three species from genus Primula L. growing in Georgia: Primula macrocalyx, Primula woronowii and Primula saguramica . First, fractions containing shoot and root parts were air-dried, grinded and extracted by 80% ethanol followed by condensation and freeze-drying. Second, further chromatographic separations were done on Diaion HP-20 with the following solvents: H2O, methanol (50% and 100 %) and ethylacetate 100% to obtain the expected fractions.

Machinery degradation assessment can offer meaningful prognosis and health management in-formation. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain dis-criminator to extract invariant features from different machine operating conditions. The case studies with the FEMTO-ST bearing dataset and the Monte Carlo method for RUL prediction during the degradation process are conducted. The experiment results fully exhibited the signif-icant advantages of the proposed method compared to other state-of-the-art techniques.

Human Action Recognition is widely used in multiple fields to recognize activities and extract spatial and temporal information. This paper focuses on analyzing various methods as well as provides extensive knowledge of the foundational concepts of the HAR. A dataset is also very crucial for any research study so we discussed the popular dataset and its features in this paper.

Background: Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Because of their narrative structure, echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. Objectives: To develop an NLP-based approach for extracting and categorizing data from echocardiography reports by accurately converting continuous (e.g. LVOT VTI, AV VTI, and TR Vmax) and discrete (e.g. Regurgitation severity) outcomes in semi-structured narrative format into structured and categorized format, allowing for future research or clinical use. Methods: 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline Echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. Results: Continuous outcomes including LVOT VTI, AV VTI, and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC=1.00, P< 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. Good level (ICC =0.75-0.9, P<0.05) of inter-rater reliability were observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva, and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. Conclusions: The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

U-Net, based on a deep convolutional neural network (CNN), has been clinically used to au-to-segment normal organs and potentially target volumes. However, CNNs with local geometric dependencies may limit the accuracy of segmentation. Additionally, the performance of CNNs can vary depending on the selection of network hyper-parameters, which was mitigated by the proposition of nnU-Net. We chose a vision transformer architecture called VT U-Net, which features a self-attention excluding the convolution layer, to overcome the limitations of CNNs by utilizing global geometric information of images. The VT U-Net v.2 became more powerful thanks to the adaptive hyper-parameter optimizer embedded in nnU-Net. However, despite leveraging the benefits of nnU-Net, VT U-Net v.2 still had additional network hyper-parameters that needed to be optimally chosen. Accordingly, among various hyper-parameters, this study attempted to find the optimal combination of the patch size and the embedded dimension regarding the transformer. From the 4-fold cross-validation, the modified VT U-Net v.2 showed the highest average performance for planning target volume (PTV) segmentation among the investigated networks. Though nnU-Net was based on convolution layers, the adaptive hyper-parameter optimizers turned out to enhance the performance. It was also confirmed that network hyper-parameters affected the segmentation accuracy of vision transformers.

The extraction method for simultaneously analyzing fatty acids (Myristic acid, cis-Palmitvaccenic acid, Palmitic acid, Palmitic-9,9-d2 acid, Oleic acid, Stearic acid and Stearic-2,2-d2 acid) related to biodiesel fuel in large algae was developed. The introduction of trimethylaminoethyl (TMAE) derivatives into the hydroxyl group (-OH) functional group of the analyte was able to improve ionization and obtain high sensitivity. Liquid Chromatography (LC) was used to investigate the optimal mobile phase composition and flow rate for the separation of five derived fatty acids. In this study, a solid phase extraction (SPE) column was introduced for efficient extraction of fatty acids, and the optimal conditions for SPE were set by comparing the recovery rate according to the solvent for each step (loading, washing, and elution) of SPE from macroalgae.

To address the deficiency of existing relation extraction models in effectively extracting relational triplets pertaining to railway traffic knowledge in Tibet, this paper constructs a Tibet Railway Traffic text dataset and provides an enhanced relation extraction model. The proposed model incorporates subject feature enhancement and relational attention mechanisms. It leverages a pre-trained model as the embedding layer to obtain vector representations of text. Subsequently, the subject is extracted and its semantic information is augmented using an LSTM neural network. Furthermore, during object extraction, the multi-head attention mechanism enables the model to prioritize relations associated with the aforementioned features. Finally, objects are extracted based on the subjects and relations. The proposed method has been comprehensively evaluated on multiple datasets, including the Tibet Railway Traffic text dataset and two public datasets. The results on the Tibet dataset achieves an F1-score of 93.3\%, surpassing the baseline model CasRel by 0.8\%, indicating a superior applicability of the proposed model. On the other hand, the model achieves F1-scores of 91.1\% and 92.6\% on two public datasets, NYT and WebNLG, respectively, outperforming the baseline CasRel by 1.5\% and 0.8\%, which highlights the good generalization ability of the proposed model.

Using straightforward and cost-effective methods, persimmon leaves were converted into a high-quality powder. This powder was applied as an adsorbent for the removal of Cu(II) and Cd(II) from aqueous solutions. Scanning electron microscopy (SEM) indicated the presence of particles with non-homogeneous sizes and rough texture. Elemental analysis and the EDX technique confirmed the presence of elements such as sulfur, phosphorus, nitrogen, and oxygen. The results of ¹³C nuclear magnetic resonance (¹³C-NMR), obtained through the cross-polarization technique, suggest the presence of groups containing sulfur and oxygen. Infrared spectroscopy (FTIR) indicated the existence of amine and hydroxyl groups. The material was used in solid-phase extraction of Cu(II) and Cd(II) in batch experiments, and its adsorption capacity was evaluated as a function of time, pH, and analyte concentration. Kinetic equilibrium was reached within 5 minutes, and the experimental data were fitted to the pseudo-second-order kinetic model. The optimum pH for adsorption of both metal species was observed to be around 5.0. The adsorption isotherms were adjusted using the modified Langmuir equation, and the maximum amount of metal species extracted from the solution was determined to be 0.213 mmol g^-1 for Cu(II) and 0.215 mmol g^-1 for Cd(II), with high linear correlation coefficients for both metals. The persimmon leaves are typically abundant during the growing season, and since they are seasonal, the Diospyros kaki L.f. tree undergoes the natural process of leaf abscission, ensuring the availability of leaves for application.

A topic of growing interest in urban remote sensing is the automated extraction of geometrical building information for 3D city modeling. Roof geometry information is useful for applications such as urban planning, solar potential estimation and telecommunication installation planning, and wind flow simulations for pollutant diffusion analysis. Recent research has proven that the advance in remote sensing technologies and deep learning methods offer the prospects of deriving the roof structure information accurately and efficiently. In this study, we propose a Vectorized Roof Extractor- method based on Fully Convolutional Networks (FCNs) and advanced polygonization method to extract roof structure from aerial imagery and a normalized Digital Surface Models (nDSM) in a regularized vector format. The roof structure consists of building outlines, external edges of the building roof, inner rooflines, internal intersections of the main roof planes. The methodology is comprised of segmentation, vectorization and post-processing for outer rooflines, external edges of the building roof, and inner rooflines, and internal intersections of the main roof planes. For the comparison, we adapt the Frame field Learning (FFL) method originally designed to extract building polygons [1]. Our experiments are conducted on a custom data set derived for the city of Enschede, The Netherlands, using aerial imagery, nDSM and manually digitized training polygons. The results show that the proposed Vectorized Roof Extractor outperformed adapted FFL on PoLiS distance with values of 3.5 m and 1.2 m for outlines and inner rooflines, respectively. Furthermore, the model surpassed the adapted FFL on PoLiS-thresholded F-score for outlines and inner rooflines, with 0.31 and 0.57, respectively. The Vectorized Roof Extractor produced adequate visual results, with straighter walls and fewer missed inner roofline detections. It can predict buildings with common walls thanks to skeleton graph computation. To summarize, the proposed method is suitable for urban applications and has the potential to be improved further.

The National Institute of Justice reported that current methods for processing sexual assault samples have a high failure rate, with 60 to 80 percent of tested kits unable to produce usable DNA profiles. Even when samples test positive for male DNA, 34 percent of sexual assault kits (SAK) do not yield recovered male DNA after differential extraction. Less than 30% recovery of available sperm DNA contributes to this low success rate. The SpermX™ method (SX) has been shown to recover 80 percent or more of sperm DNA from sexual assault samples. An interlaboratory evaluation compared SX to standard differential extraction (DE) protocols. Mock samples with known ratios of female epithelial cells and sperm cells were processed using both methods. Results revealed that SX consistently provided CODIS up-loadable DNA profiles, even with as few as 25 sperm cells, whereas DE failed to produce usable results. On average, SX yielded a seven-fold increase in the recovery of unshared male alleles compared to DE. In conclusion, SX outperformed DE in recovering higher quantities of male DNA with minimal female carryover in sexual assault-type samples. This improved success rate in obtaining usable DNA profiles can significantly aid in solving sexual assault cases. Keywords: Differential extraction, Sexual Assault Kit (SAK), Sperm DNA, STRmix™, Combined DNA Index System (CODIS), Forensic DNA analysis, Low Sperm Samples, DNA Mixtures, Autosomal STR Analysis, SpermX.

Keywords: Arachis quality; pod separation; plant extraction, peanut

In South Korea, the residential real estate market is influenced not just by traditional supply and demand dynamics, but also by external factors such as housing policies and macroeconomic conditions. Given the significant role of housing assets in individual wealth, market fluctuations can have profound implications. While prior research has utilized variables like GDP growth rate, patent issuance, and birth rates, and employed models like LSTM and ARIMA for housing price predictions, many overlook key localized factors. Notably, the impact of subway stations and living SOC facilities on housing prices, especially in metropolitan areas, remains underexplored. This study addresses these gaps by analyzing usage trends across subway stations, assessing the influence of living SOC facilities on housing values, and identifying the optimal machine learning model for price predictions near transport hubs. Through a comparative analysis of machine learning techniques, we aim to provide insights for more informed housing price determinations, promoting a more stable real estate market.

Supercritical fluid extraction (SFE) is an innovative green technology for the extraction of phytochemicals from plants. Therefore, this study aimed to evaluate the application of SFE and to optimize the extraction condition of the Thai herbal formula, Kleeb Bua Daeng (KBD). A Box-Behnken design (BBD) with response surface methodology was used to determine the effect of extraction time, temperature, and pressure on response variables including extraction yield, total phenolic content (TPC), total flavonoid content (TFC), total carotenoid content (TCC) and total anthocyanin content (TAC) of KBD formula. The highest percentage extraction yield, TPC, TFC, and TCC of the extracts were 3.81%, 464.56 mg gallic acid equivalents/g extract, 217.19 mg quercetin equivalents/g extract, and 22.26 mg β-carotene equivalents/g extract, respectively. The results indicated that SFE is a suitable method of extraction for green recovery of some phytochemicals from the KBD formula.

Background: Cataract and Age-related Macular Degeneration are two characteristic diseases of the so-called elderly age (75-90 years and older). Both determine an important visual impairment, so it is often difficult to understand how much of the visual loss is given by the cataract opacities and how much by the degeneration of the macula and this assumes a relevant medico-legal query in clinical practice. Methods: From that pool we randomized in group 735 eyes with wAMD (males 328, females 407) Average age: 76.23±13.87 years, and in Group 819 eyes without clear signs of wAMD (males 361, females 451) Average age: was 75.88±18.21 years to randomize them we used the online randomization program (http://www.graphpad.com/quickcalcs/index.cfm) selecting random numbers and then randomly assign subjects to groups to undergo our observational study. All patients were examined with full ophtalmological visit including Best Corrected Visual Acuity (with Bailey-Lawson Chart), Anterior Segment examination, Fundus Examination, in all cases with wAMD was also performed OCT. ANOVA multifactorial statistical analysis was drawn. Results: number of patients with unsatisfactory surgery is significantly higher in the group of AMD patients, and this is easily understandable from what has been said above, but I would like to underline that in most patients there are very positive results, therefore cataract surgery is also indicated in AMD patients. Conclusions: Cataract surgery has a positive impact on the patient's life in most cases, you shouldn't be afraid to operate. However, it is necessary to find surgical measures capable of minimizing the consequences of the intervention on the retina.

It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.

The root extracts of Symphytum officinale (comfrey) are commonly used in traditional medicine to treat muscle, joint, skin and bone disorders. However, little information is available on the biomedical functions of comfrey leaves. In this study, sequencing results of Internal Transcribed Spacer and trnL-trnF genes showed that plants purchased from the local market were comfrey and named as S. officinale WL (WL). The optimal extraction conditions of WL leave with the highest extract yield and contents of total phenols and flavonoids by microwave-assisted extraction were identified. The antioxidative and anti-inflammatory activities and possible molecular mechanism(s) of the WL leaf extract (WLE) were evaluated. Furthermore, the major component of WLE was identified as rosmarinic acid by HPLC. Results showed that the optimal extract condition was obtained with 750W microwave power, 50℃, 75% methanol, the solid-to-solvent ratio of 1:10, and 15 minutes. Results of all DPPH, ABTS and superoxide radical scavenging activities, reducing power, ferrous ion chelating activity, and ferric reducing antioxidant power showed high antioxidative capacities of WLE. Furthermore, WLE showed obvious DNA protecting activity. WLE attenuated lipopolysaccharides-stimulated inflammation by suppressing iNOS, COX-2, IL-1β, IL-6, and TNF-α expressions in RAW264.7 macrophages. These attenuations involved in inactivations of lipopolysaccharides-induced NF-κB and MAPK signaling pathways.

The capability of extracting information and analyze it into a common format is essential for performing predictions, comparing projects through cost benchmarking, and for having a deeper understanding of the project costs. However, the lack of standardization and the manual inclusion of the data makes this process very time-consuming, unreliable, and inefficient. To tackle this problem, a novel approach with a big impact is presented combining the benefits of data mining, statistics, and machine learning to extract and analyze the information related to railway costs infrastructure data. To validate the suggested approach, data from 23 real historical projects from the client network rail was extracted, allowing their costs to be comparable. Finally, some machine learning and data analytics methods were implemented to identify the most relevant factors allowing for costs benchmarking. The presented method proves the benefits of data extraction being able to gather, analyze and benchmark each project in an efficient manner, and deeply understand the relationships and the relevant factors that matter in infrastructure costs.

Context: Despite the effort put into developing standards for structuring construction cost, and the strong interest into the field. Most construction companies still perform the process of data gathering and processing manually. That provokes inconsistencies, different criteria when classifying, misclassifications, and the process becomes very time-consuming, particularly on big projects. Additionally, the lack of standardization makes very difficult the cost estimation and comparison tasks. Objective: To create a method to extract and organize construction cost and quantity data into a consistent format and structure, to enable rapid and reliable digital comparison of the content. Method: The approach consists of a two-step method: Firstly, the system implements data mining to review the input document and determine how it is structured based on the position, format, sequence, and content of descriptive and quantitative data. Secondly, the extracted data is processed and classified with a combination of data science and experts’ knowledge to fit a common format. Results: A big variety of information coming from real historical projects has been successfully extracted and processed into a common format with 97.5% of accuracy, using a subset of 5770 assets located on 18 different files, building a solid base for analysis and comparison. Conclusion: A robust and accurate method was developed for extracting hierarchical project cost data to a common machine-readable format to enable rapid and reliable comparison and benchmarking.

Geothermal energy is a weather-independent, permanently available renewable energy source. In addition to producing heat and electricity, geothermal energy can be used to extract critical raw materials dissolved in the heat-carrying fluid. Researchers working on the frontiers of geothermal energy utilisation may be able to transform ultra-deep metallic mineral formations into “orebody-Enhanced Geothermal Systems”. In the planned technology, the metal-containing geological formation would be manipulated in such a way that cogeneration of energy and metals is possible and can be optimised in the future according to market needs. The potential of geothermal energy for sustainable mineral extraction and energy production aligns with Sustainable Development Goal (SDG) 7, which aims to ensure access to affordable, reliable, sustainable, and modern energy for all. This new technology could substantially decrease Europe’s dependency on importing critical metallic minerals (such as lithium and tungsten) and energy; over and above, it has a small environmental footprint, including very low carbon emissions

The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual’s walking habit and thus increase the accuracy in diagnosis. It is observed that the TE-value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling to reduce the data acquisition period significantly. The extracted feature set is then inputted into fundamental classification algorithms, with the Support-Vector-Machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step of a large project aiming to identify specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.

The internal wave recognition algorithm in an ocean data buoy system can be used to realize the real-time and flexible observation of internal waves, but there is no accurate automatic recognition method. To meet the need for automatic, real-time, and reliable internal wave recognition, an automatic internal wave recognition algorithm has been proposed for a tightly profiled intelligent buoy system. The sea profile temperature data collected by the Bailong buoy system in the Andaman Sea in 2018 were used to train and test the internal wave recognition neural network model, which consists of two parts: feature extraction and feature classification. The experiment compares the long short-term memory network (LSTM), convolutional neural network (CNN) with different layers, and deep neural network (DNN) without a feature extraction network and adjusts the number of convolutional nuclei and convolutional strides to improve the feature extraction efficiency. Experiments show that the best results can be obtained when a CNN layer is used as the feature extraction network, the convolutional step length is 4, the number of convolutional kernels is 5. The recall reaches 95.31% and the precision is 97.53%. The internal wave identification delay of the algorithm is 5.0862 minutes, the number of parameters is 1593, and the number of calculations is 3024. The algorithm can be directly deployed to the ocean data buoy system to realize the demand for automatic, real-time and reliable internal wave identification at the buoy end.

The reagents and raw materials used, as well as the products obtained (bio-oil, reaction condensates, polyols and sugar phases) were characterized by elemental analysis, infrared spectroscopy, thermogravimetry and high performance liquid chromatography with mass spectrometry. The heating value of the bio-oils is higher than that of the original biomass (higher heating value of Eucalyptus sawdust bio-oil 29 MJ/kg versus 19.5 MJ/kg of the original Eucalyptus sawdust). The analyses of the bio-oils allowed to identify the presence of high added-value compounds, such as levulinic acid and furfural. Finally, a study of the accelerated aging of the liquefied biomass showed that the biofuel density increases with the storage time due to the occurrence of repolymerization reactions.

Nowadays, brain signal processing is performed rapidly in various brain-computer interface (BCI) applications. Most researchers focus on developing new methods for the future or improving the basic implemented models to identify the optimum standalone feature set. Our research focuses on four ideas. One of them introduces future communication models, and the others are for improving old models or methods. These are: 1) new communication imagery model instead of speech imager using the mental task: Due to speech imagery is very difficult, and it is impossible to imagine sound for all of the characters in all of the languages. Our research introduces a new mental task model for all languages that call Lip-sync imagery. This model can use for all characters in all languages. This paper implemented two lip-sync for two sounds, characters or letters. 2) New combination Signals: Selecting an inopportune frequency domain can lead to inefficient feature extraction. Therefore, domain selection is so important for processing. This combination of limited frequency ranges proposes a preliminary for creating Fragmentary Continuous frequency. For the first model, two s intervals of 4 Hz as filter banks were examined and tested. The primary purpose is to identify the combination of filter banks with 4Hz (scale of each filter bank) from the 4Hz to 40Hz frequency domain as new combination signals (8Hz) to obtain well and efficient features using increasing distinctive patterns and decreasing similar patterns of brain activities.3) new supplement bond graph classifier for SVM classifier: When SVM linear uses in very noisy, the performance is decreased. But we introduce a new bond graph linear classifier to supplement SVM linear in noisy data. 4) a deep formula recognition model: it converts the data of the first layer into a formula model (formula extraction model). The main goal is to reduce the noise in the subsequent layers for the coefficients of the formulas. The output of the last layer is the coefficients selected by different functions in different layers. Finally, the classifier extracts the root interval of the formulas, and the diagnosis does based on the root interval. For all of the ideas achieved the results of implementing methods. The results are between 55% to 98%. Less result is 55% for the deep detection formula, and the highest result is 98% for new combination signals.

Development and implementation of safe natural alternatives to synthetic pesticides are urgent needs that will provide ecological solutions for control of plant diseases, bacteria, viruses, nematodes, pests, and weeds, to ensure economic stability of the farmers and food security as well as protection of the environment and human health. Unambiguously, production of botanical pesticides will allow sustainable and efficient use of natural resources and finally decrease the use of chemical inputs and burden. This is underlined by the strict regulations on pesticide residues in agricultural products and harmonized with the Farm to Fork strategy aimed to reduce pesticide use by 55%, by 2030. Recognizing the urgent need for natural pesticides development, this work is an overview of the current research on the valorization of Mediterranean plants as potential source of biopesticides. More specifically, the extraction methods, the chemical composition, the biopesticidal activity, the commonly used assays for evaluating the antimicrobial, the pesticidal, the repellant and the herbicidal activity of plant extracts as well as toxicological and safety aspects of biopesticides formulation are discussed in detail. Finally, the aspects that have not yet been investigated or are under- investigated and future perspectives are highlighted.

Thrombosis is characterized by the pathological formation of fibrin clots within a blood vessel, leading to the obstruction of blood flow. Fibrinolytic enzymes from microorganisms have exhibited promising effects to dissolve clots in a more efficient and safe way. Then, the aim of this study was to evaluate the biomass and fibrinolytic enzyme production of Tetradesmus obliquus under autotrophic and mixotrophic conditions using different concentration of corn steep liquor (CSL). Different extraction and precipitating methods were tested, and the enzyme was purified by ion exchange chromatography. More advantageous culture condition was mixotrophic using 0.25% CSL, showing the highest values of biomass productivity (Px = 169.3 ± 44.36 mg∙L-1day-1) and specific growth rate (µmax = 0.17 ± 0.00 day-1), and significant fibrinolytic production (391.34 ± 40.03 U∙mg-1). Moreover, fibrinolytic activity was higher when extracted by homogenization and precipitated using acetone, which exhibited clear zone of fibrin degradation in the fibrinolytic plate assay. Additionally, the purified enzyme showed specific activity of 1176.90 ± 140.37 U∙mg-1 and molecular weight around 97 kDa. Finally, the enzyme has higher enzymatic activity than various fibrinolytic enzymes, and the obtained enzyme has potential to be developed as a therapeutic agent in thrombosis treatment. Additional studies are need to investigate the biochemical properties and biological profile of this enzyme.

To improve the accuracy of short-term multi-energy load prediction models for integrated energy systems, the historical development law of the multi-energy loads must be considered. Moreover, understanding the complex coupling correlation of the different loads in the multi-energy systems and accounting for other load-influencing factors, such as weather, may further improve the forecasting performance of such models. In this study, a two-stage fuzzy optimization method is proposed for the feature selection and identification of the multi-energy loads. To enrich the information content of the prediction input feature, we introduced a copula correlation feature analysis in the proposed framework, which extracts the complex dynamic coupling correlation of multi-energy loads and applies Akaike information criterion (AIC) to evaluate the adaptability of the different copula models presented. Furthermore, we combined a NARX neural network with Bayesian optimization and an extreme learning machine model optimized using a genetic algorithm to effectively improve the feature fusion performances of the proposed multi-energy load prediction model. The effectiveness of the proposed short-term prediction model was confirmed by the experimental results obtained using the multi-energy load time-series data of an actual integrated energy system.

This study investigates uranium solvent extraction under AMEX process conditions. The use of pure extractants without diluents or phase modifiers allows not only to reduce the use of volatile organic compounds, but also to provide higher extraction yields without third phase formation. Pure extractants are protonated amines or quaternary ammoniums with suitable counter ion, which act at the interface between ion pairs and protic ionic liquids. The mixture of sulfates anion (SO42-) and bis(trifluoromethanesulfonyl)imide anion (NTf2-) revealed unexpected nonlinear extraction behaviors, which appear highly important to rationalize for optimized application. Spectroscopic analysis (NMR, UV-vis, FT-IR, and EXAFS) showed that uranium extraction occurs via protonated amine and three sulphates. Nonlinear extraction could further be interpreted by considering water and acid transfer between the two phases: at lower sulphate ratios, the release of acid from the organic phase into the aqueous phase was shown to influence the number of protonated amines in the organic phase, affecting uranium extraction before its enhancement. Furthermore, the extraction loss at higher sulphate ratios, was assigned to the destabilization of bidentate uranium-sulphate complexes due to a competition between water and sulphates.

A novel approach based on response surface methodology (RSM) was developed to predict and apply the best extraction conditions for the isolation of various-type polyphenolic antioxidants from the dried flower buds of Magnolia x soulangeana Soul.-Bod. var. ‘Lennei’ (Magnoliaceae). As a method of choice, ultrasound-assisted extraction was used and the optimized extraction parameters (temperature, time of extraction, the composition of aqueous-ethanolic extractants and solvent to solid ratio) have been determined using a central composite design factorial analysis, followed by appropriate spectrophotometric methods (with Folin–Ciocalteu and 2,2-diphenyl-1-picrylhydrazyl reagents) to determine the antioxidant and antiradical potential of extracts, respectively. The use of 66.8% (V/V) ethanol, as the extraction solvent during the 55.2 min extraction protocol, and the ratio of extractant volume to herbal substance of 46.8 mL/g gave the highest total yield of bioactive antioxidant phenolics in the extract obtained. For this herbal preparation, qualitative and quantitative analysis was performed using combined chromatographic (LC), spectroscopic (PDA) and tandem mass spectrometric (ESI-QTOF–MS/MS) techniques. A detailed phytochemical profiling, conducted for the first time, documented substantial amounts of polyphenolic antioxidants, especially phenylethanoids and flavonoids in the flower buds of M. soulangeana ‘Lennei’. Their average total content exceeded 30.3 and 36.5 mg/g dry weight, respectively.

Pomegranate (Punica granatum L.) is well known for its high content in bioactives including polyphenols, flavonoids and tannins, which have been shown to exhibit a wide range of biological activity such as antioxidant, antimicrobial and anticancer effects. It is worth noting that the majority of these molecules are found in the peels (PP), which are usually disposed of after processing, causing a significant amount of waste, amounting to more than 3.6 million t/y. This work investigates MW-assisted extraction (MAE) in water for the recovery of antioxidants from PP, including the optimization of temperature and extraction times. The total phenolics, anthocyanin, flavonoid and tannin contents of the recovered extracts have been determined, as have their antioxidant activities, which were found to be 356.35 mgGAE/gextr., 303.97 µgCy3G/gextr., 37.28 mgQE/gextr., 56.48 mgGAE/gextr. and 1.43 µmolTE/gextr., respectively (according to the adopted reference). All results have been compared with those obtained using a conventional protocol. In addition, the potential for water recycling by means of nanofiltration downstream in optimized MAE has been investigated, leading to overall water reuse of approx. 75%. Energy consumption (20.92W/mgGAE) and the common green metrics, RME, E-Factor and PMI (PME), have been considered in evaluating the proposed PP valorization strategy. Finally, the biological activity of the main products has been assessed. The antimicrobial properties of the PP extracts against three Gram-positive and three Gram-negative bacteria, and their antiproliferative activity towards human cancer cells have been tested. The S. aureus bacteria was the most susceptible to the PP extracts. All tested products displayed antiproliferative activity in HeLa cells when higher concentrations were tested, with D-PP/NF being the most effective. This result was also confirmed in a clonogenic analysis, which generally indicated the possible anti-cancer activity of pomegranate peel extracts obtained using this green approach.

Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions. The article begins by introducing the sources and processing methods of RS data, which serve as the foundation for subsequent analysis. Moreover, it highlights the techniques and methodologies employed for lithological classification in vegetated areas. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact accurate lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to enhance classification accuracy and the exploration of novel RS technologies and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

Cascalote pods are an important source of phenolic compounds, mainly recovered using solvent extraction methods. As a sustainable alternative, this study aimed to evaluate solid-state fermentation (SSF) to enhance the extractability of total phenolic compounds (TPC) with antioxidant activity (AA) from cascalote pods. Aspergillus niger GH1 was selected based on the amount of TPC released and AA measured (ABTS, DPPH, FRAP) in a shorter time. Moreover, moisture, temperature, inoculum size, and mineral composition were evaluated. The largest amount of TPC released was 124.17 mg/gdm (g of dry material) after 12 h of culture, strongly and highly significantly correlated with the AA. Moisture and KH2PO4 concentration were the main influencing factors on TPC release. Treatment 6 [1x 107 spores/gdm, 30 °C, 60% moisture, mineral composition (g/L): KH2PO4, 1.52; NaNO3, 7.65; and MgSO4, 1.52] was selected due to the highest values of both TPC and AA. SSF-assisted extraction allowed increase 118 % and 93 % in TPC and AA values, respectively. Corilagin, lagerstannin, geraniin, and ellagic acid were the main phenolic compounds identified by RP-HPLC-ESI-MS in cascalote extracts. The results obtained demonstrate the feasibility of SSF-assisted extraction as a biotechnological alternative for the recovery of important bioactive molecules from this underutilized material.

The production of polyurethane films from bark-derived polyols requires the complete solubility of all components in the polyurethane synthesis media. In this study, a comprehensive investigation was conducted to achieve the copolymerization of black alder bark water extract-derived polyol with isocyanate for polyurethane film synthesis in THF solution. A fractionation approach using tetrahydrofuran (THF) as a solvent was employed to dissolve the extract, followed by filtration and removal of the solvent. The resulting THF-soluble fraction comprised 62±1% of the dry weight of the alder bark extract, mainly consisting of the xyloside form of the diarylheptanoid compound oregonin, along with oligomeric flavonoids and carbohydrates. The THF-insoluble fraction was the most enriched with carbohydrate compounds, followed by the crude extract and THF-soluble fraction. Another approach was aimed at obtaining bark extractives based liquid polyols suitable for producing rigid PU foams. For this purpose, oven-dried crude black alder bark water extract was liquefied with polyethylene glycol (PEG 400). The liquefaction of black alder bark and pine bark extractives was studied under different conditions, including varying temperature ranges (130-170⁰C), catalyst concentrations (0-1,5%), and bark extract content in the mixture (15-30%). The results showed that the use of sulphuric acid as a catalyst (1-1.5%) significantly improved the solubility of both extractives, enabling the attainment of extract concentrations up to 25% at 170⁰C. However, increasing the extract content beyond a certain threshold led to incomplete solubility. It was shown that the PEG-insoluble fractions consist mainly of carbohydrate components. To increase the content of biomass in liquid polyols, the effect of glycerol additions into the liquefaction agent is under study. The findings provide insights into tailoring bark-sourced polyols for polyurethane foam production through appropriate liquefaction conditions.

This publication investigates the potential application of bark extractives as a biopolyol and extracted bark residues as a filler for producing polyurethane materials. The aim is to enhance the properties of polymeric composites while reducing the environmental impact associated with the complex utilization of bark.The comprehensive study of compositions of extractives isolated from black alder (Alnus glutinosa) and Baltic pine (Pinus silvestris) barks was performed using microwave-assisted water extraction. The yield of the extracts obtained under different extraction conditions, as well as the total phenolic and monomeric carbohydrate content in the extractives, were studied. Furthermore, UHPLC-HRMS analysis was conducted to study the detailed composition of the bark extracts.The total content of hydroxyl groups and the relative portion of aliphatic and phenolic groups in the isolated extracts were determined depending on the extraction regimes and bark origination. The dominant compounds in black alder bark extracts isolated at 70-90⁰C were identified as diarylheptanoids and their derivatives bearing sugar units, with oregonin being the main compound. Pine bark extracts isolated at the same temperature were found to be rich in oligomeric proanthocyanidins. Extracts from both black alder and pine barks obtained at different times of isothermal heating between 130-150°C were rich in carbohydrates and showed an increased proportion of aliphatic OH groups.The study also focused on the composition of residual biomass. It was indicated by Py-GC/MS/FID and wet chemistry methods that residues of both black alder and pine bark are enriched with lignin in comparison with initial barks. Therefore, it can be proposed that extracted bark residues introduced into polyurethane compositions will act as a charcoal formation promoter, thus increasing the flame resistance of materials.Overall, the study provides valuable insights into the chemical composition of bark extractives and extracted bark residues for their potential application as biopolyol and fillers of polyurethane materials, contributing to a more sustainable approach in polymer composite production.

Onion is not just an edible food but its seeds can also be an important source of valuable oil with various sensitive applications mainly in pharmaceutical, cosmetic and food fields. This has encouraged the use of a supercritical fluid extraction in the present study to obtain oil from local onion seeds, hence avoiding even traces of organic solvents. For several advantages carbon dioxide at supercritical conditions was used as the fluid. The effects of four key operating parameters like pressure, temperature, particle size and CO2 mass flowrate were investigated in ranges of [150, 250 bars], [35, 65°C], [0.25, 0.85mm] and [50, 100g/min], respectively, using a Box-Behnken experimental design, for a constant extraction time of 3 hours. This led to optimal operating conditions of 250 bar, 57°C, 0.25mm and 50g CO2/min for the highest oil extraction yield value of 24.36%. Furthermore, gas chromatography-mass spectrometry (GC-MS) was used to identify qualitatively and quantitatively the chemical composition of the onion seed oils. About ten components were identified in the extracts, but with four major ones, namely 9,12-Octadecadienoic acid ,methyl ester, 9,12-Octadecadienoic acid (Z,Z)-,methyl ester, 9-Octadecadienoic acid (Z)-,methyl ester and Hexadecanoic acid, methyl ester, present with significant percentages, depending on the operating parameters values. These fatty acids are well known for their benefits for human health. Finally the study shows the importance and the merits of fluids when used under particular conditions of pressure and temperature, in very sensitive industrial fields.

Drugs-drugs interactions(DDI) are entities composed of different chemical substructures (functional groups). In existing methods that predict drug–drug interactions based on the usage of substructures, each node is considered the center of a substructure, and adjacent nodes eventually become centers of similar substructures, resulting in redundancy. Furthermore, the significant differ- ences in structure and properties among compounds can lead to unrelated pairings, making it difficult to integrate information. This heterogeneity negatively affects the prediction results. To address these issues, we propose a drug–drug interaction prediction method based on substructure signature learning (DDI-SSL). This method extracts useful information from local subgraphs surrounding drugs and effectively utilizes substructures to assist in predicting drug side effects. Additionally, a deep clustering algorithm is used to aggregate similar substructures, allowing any individual subgraph to be reconstructed using this set of global signatures. Furthermore, we developed a layer-independent collaborative attention mechanism to model the mutual influence between drugs, generating signal strength scores for each class of drugs to mitigate noise caused by heterogeneity. Finally, we evaluated DDI-SSL on a comprehensive dataset and demonstrated improved performance in DDI prediction compared to state-of-the-art methods.

Reductive leaching in the Bayer cycle using of iron (2+) allows Al extraction to be significantly increased by magnetization of Al-goethite and Al-hematite. However, the use of expensive iron (2+) salts or iron powder as a source of iron (2+) leads to a significant increase in production costs. In this work, the feasibility of a new method, the reductive leaching of bauxite using an electrolysis process, was investigated. Reduction of iron minerals of boehmitic bauxite in both Bayer solution and purely alkaline solutions were carried out. Experiments were performed using a plate cathode and a bauxite suspension in alkaline solution, as well as using a bulk cathode with a stainless-steel mesh at the bottom of the cell as the current supply. During the electrolysis process, the potential of the cathode relative to the reference electrode was measured as a function of current at different concentrations of solid (100-300 g L–1) and suspension temperatures (95-120 °C). It is shown that the current efficiency using suspension and plate cathode with the predominant deposition of Fe doesn’t exceed 50% even with the addition of magnetite to increase the contact of solid phase with the current supply. With the use of a bulk cathode, the reduction of iron minerals leads predominantly to the formation of magnetite with the efficiency of using electric current more than 80%. As a result of preliminary desilication and electroreduction it is possible to extract more than 97% of Al from bauxite, and to increase the iron content in the bauxite residue to 57-58%.

Citrus peels are considered as a rich source of valuable biomolecules like pectin as polymer of polysaccharide acid. In this study, response surface methodology was used to optimize pectin extraction from Citrus clementina peels using citric acid as extraction solvent. The effect of parameters conditioning the extraction process and pectin yield (pH, temperature, extraction time, solid/liquid ratio, and raw material particle size) was investigated using a Box-Behnken design. The quality of the extracted pectin was assessed both chemically (moisture, ash, protein, and sugar content) and functionally (gelling power and emulsifying activity). According to the screening experiment, the factors pH, temperature, and particle size were the main factors influencing the pectin yield. The adjusted mathematical model enabled us to plot response surfaces in order to determine optimal extraction conditions. The highest production yield of pectin (26.6%) was obtained at the optimal conditions of: pH = 1.5; temperature 100 °C and particle size = 0.1 mm for an extraction time of 30min. Compared to the predicted value of 26.6%, the experimental extraction yield was about 21.4% of pectin. Concerning the functional properties, the extracted pectin has a high gelling power of 164°SAG and an emulsifying activity of 38.5%.

This comprehensive review paper provides a detailed analysis of the extraction methods employed in the dimension stone industry. Natural rock materials used in construction, have been historically extracted using primitive techniques such as thermal shock, hammer and chisel, and plug and feather. However, significant technological advancements have revolutionized the industry, leading to the widespread adoption of mechanized methods. One highly efficient technique is diamond wire cutting, which involves the utilization of diamond-engraved bits affixed to a tensioned steel wire. This method has demonstrated remarkable capabilities in minimizing waste generation and ensuring exceptional machining quality. Nonetheless, challenges such as high operating costs and wire breakage persist and require further investigation. In addition to diamond wire cutting, this review explores the utilization of circular diamond saws and frame sawing as alternative extraction methods. The geotechnical characteristics of the stone play a critical role in the selection of appropriate machinery, and researchers worldwide have conducted numerous studies to investigate the impact of each parameter on the extraction efficiency of these mechanized techniques. Moreover, controlled blasting with explosives emerges as a promising alternative for dimension stone extraction, warranting further exploration and optimization.

Extracting railway tracks is crucial for creating electronic railway maps. Traditional methods require significant manual labor and resources, while existing neural networks have limitations in efficiency and precision. To address these challenges, a railway track extraction method using an improved DeepLabV3+ model is proposed, which incorporates several key enhancements. Firstly, the encoder part of the method utilizes the lightweight network MobileNetV3 as the backbone extraction network for DeepLabV3+. Secondly, the decoder part adopts the lightweight universal upsampling operator CARAFE for upsampling. Lastly, to address any potential extraction errors, morphological algorithms are applied to optimize the extraction results. Additionally, a dedicated railway track segmentation dataset is created to train and evaluate the proposed method. The experimental results demonstrate that the model achieves impressive performance on the railway track segmentation dataset and DeepGlobe dataset. The MIoU scores are 88.93% and 84.72%, with Recall values of 89.02% and 86.96%. Moreover, the overall accuracy stands at 97.69% and 94.84%. The algorithm's operation time is about 5% lower in comparison to the original network. Furthermore, the morphological algorithm effectively eliminates errors like holes and spots. These findings indicate the model's accuracy, efficiency, and the enhancement brought by the morphological algorithm in error elimination.

The aim of this work was to determine the potential of using a pulsed electric field (PEF) and ultra-sound (US) apparatus to produce mitragynine extracts from Mitragyna speciosa dried leaves. Four modes of the device were tested: PEF, US, US + PEF, and PEF + US. The results were compared with extracts obtained using a conventional technique (maceration, as the control). Changes in the mitragynine content were determined with the liquid chromatography/mass spectrometry (LC-MS/MS) method. The LC-MS/MS analysis showed that the mitragynine contents from kratom extracts using four different modes were different. The highest extraction (106.63 ± 0.85 mg/L) of mitragynine was achieved by the PEF + US procedure, followed by US + PEF (97.27 ± 1.33 mg/L), with increased extraction efficiencies of 45.81 ± 0.59% and 33.00 ± 1.85%, respectively. Moreover, the total energy consumption under the combination technique was 25.0% lower than that with PEF assistance. Furthermore, scanning electron microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) provided confirmatory evidence for the extraction of M. speciosa by the prototype. This study confirmed that PEF and US devices can be considered a green alternative method and may contribute to the application of agricultural products.

Coal permeability data are critical in the prevention and control of coal and gas outbursts in mines and are an important reservoir parameter for the development of coalbed methane. The mechanism by which permeability is affected by gas pressure is complex. We used a self-developed true triaxial seepage experimental device that collects lignite and anthracite coal samples, sets fixed axial pressure and confining pressure, and changes gas pressure by changing the orientation of the coal seam to study the influence of the gas pressure on the permeability of the coal seam under the conditions of different coal types and different bedding orientations. Coal permeability decreased rapidly and then decreased slowly and tended to be stable with the increase in gas pressure. This conforms to the power exponential fitting relationship, and the fitting degree reaches more than 99%. The permeabilities of lignite and anthracite were basically the same under various pressure conditions, indicating that the influence of coal type on coal permeability was not significant. The comparison of the two anthracite coal samples showed that the sample's permeability with a bedding plane vertical to the seepage direction was significantly lower than that of the bedding plane parallel to the seepage direction, indicating that gas seeped more easily along the bedding. The sensitivity coefficient of permeability with the change in gas pressure was calculated. The analysis showed that coal permeability was sensitive to changes in gas pressure during the low-pressure stage. When the gas pressure was greater than 0.8 MPa, the sensitivity coefficient was significantly reduced, which may be related to the slow increase in the amount of gas absorbed by the coal seam in the high-pressure stage. A theoretical calculation model of coal seam permeability considering adsorption/desorption and seepage effects was proposed and then verified with experimental results showing that the theoretical model better reflected the permeability characteristics of coal and predicted its permeability. Using the finite element simulation software COMSOL, the extraction efficiency of the coal seam gas under different gas pressure conditions was simulated. The results showed that with an increase in gas pressure, coal permeability and extraction efficiency decreased. In the low-pressure stage, the reduction in the extraction efficiency was more obvious than that in the high-pressure stage.

The affordable production of valuable antioxidant extracts from plant resources depends on the availability of enabling technologies, such as hydrodynamic cavitation (HC), characterized by unrivalled effectiveness, efficiency, and scalability in the extraction of natural products. As sources of phytochemicals showing biological effects on human health, including anti-cancer properties, pomegranate (Punica granatum L.) fruits were processed using HC. Fractions collected during the process (M1-M5) were lyophilized (L), filtered (A), or used as such (C), for identifying their best conservation practice. Biochemical profile and in vitro antioxidant power were investi-gated by spectrophotometric and chromatographic assays. The antiproliferative effect of L-M3, resulting from just 15 minutes of extraction, was tested on human breast cancer line (AU565-PAR) and peripheral blood mononuclear (PBMCs) cells from healthy donors. M3-M4 fractions from L and C series revealed the highest antiradical activity and phenolic content. Cell growth, death, and redox state were assessed, showing that the pomegranate extract M3 significantly reduced tumor cell proliferation and intracellular oxygen reactive species. No effect on PBMCs was de-tected. Thus, the use of antioxidants isolated using HC as adjuvants in anticancer therapies might be advantageous and affordable; moreover, since oxidative stress contributes to cancer devel-opment, its reduction might be the leading mechanism.

In this study, we had investigated the use of high-pressure homogenization (HPH) as a novel pretreatment method for the extraction of sodium alginate (SA) from Laminaria japonica. Our results demonstrated that under optimal condition of 100 MPa HPH pressure, 4 cycles, pH 6.0, and 0.5% ethylene diaminetetra acetic acid tetrasodium salt (EDTA) concentration (w/v) for 3.0 hours, the extraction yield of HPH reached 34%, which was higher than that achieved by previous methods such as complex enzyme hydrolysis (CE), ultrasonic-assisted extraction (UAE), and complex enzyme-ultrasonic combined method (CE-UC). To further characterize the extracted SA, we conducted a comprehensive analysis using SEM, FTIR, MRS, NMR, XRD, TGA, and T-AOC assay. Our findings revealed the following: 1) HPH treatment significantly disrupted the structure of L. japonica cells and reduced their crystallinity to 76.27%, the lowest among all pretreatment samples; 2) the extracted SA had a greater tendency to form elastic gels; and 3) the antioxidant activity of HPH-extracted SA reached 0.02942 mgVceq∙mg-1, which was the highest among all tested methods. Based on these results, we concluded that HPH extraction not only increased the yield of SA but also enhanced its antioxidant activity.

Botnet Attacks are one of the many types of ways to attack users in their day to day browsing, often of different trends and techniques to take your information without your control. Most firewalls can amply protect you against these trends and techniques, but scarily enough, recent articles and research have shown that they are starting to bypass said firewalls through layers of attacks, confusing your best line of defense. In this Report, we will be looking into exactly what are Botnets and the different subsets, what we can do to combat Botnet Attacks, where do they come from, their origin, the analysis of their kit and what we can put together from information collected. Through this we will be able to reach a conclusion of what we can use to possibly mitigate the effect they have on the internet and stop them from enacting their purpose.

In order to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional network (TCN) is proposed. Firstly, construct a high-dimensional feature set in the multi-domain of vibration signals, and use comprehensive evaluation indicators to preliminarily screen performance degradation indexes with good sensitivity and strong trend. Secondly, the kernel principal component analysis (KPCA) method is adopted to eliminate redundant information between multi-domain features, and construct a health index (HI) based on convolutional auto-encoder (CAE) network. Thirdly, a TCN-based performance degradation trend prediction model is constructed, and direct multi-step prediction is used to predict the performance degradation trend of the monitored object. On this basis, the validity of the proposed method is verified using the bearing public data, and it is successfully applied to performance degradation trend prediction of rolling contact fatigue specimen. The results show that the feature set can be reduced from 14 dimensions to 4 dimensions by using KPCA, while 98.33% of the information of the original feature set is retained. Furthermore, the method of constructing HI based on CAE network is effective. The change process of the HI constructed truly reflects the performance degradation process of the rolling contact fatigue specimen. Compared with the two commonly used HI construction methods, auto-encoding (AE) network and gaussian mixture model (GMM), this method has obvious advantages. At the same time, the prediction model based on TCN can accurately predict the performance degradation of the rolling contact fatigue specimen with the root mean square error 0.0146 and the mean absolute error 0.0105, which has better performance and higher prediction accuracy than the prediction model based on the long short-term memory (LSTM) network and the gated recurrent unit (GRU). This method has general significance and may be extended to the performance degradation prediction of other mechanical equipment/parts.

Ship pilots and maritime safety administration have an urgent need for more accurate and earlier warnings on strong wind gusts. This study firstly investigates the “Oriental Star” cruise ship cap-sizing event in 2015, one of the deadliest shipwreck events in recent years, and explores all related hydro-meteorological components in a global mesoscale model. It is found that, rather than the missing signal in raw surface wind prediction, the cumulus precipitation variable (CP) increases dramatically during the accident occurrence, which significantly corresponds to the sub-grid strong wind gust. The effective lead-time can be extended from 24hr (deterministic model) to 48hr (en-semble model). This finding is then verified in another two recent deadly cruise boat accidents. The introduction of the new variable is bearing the hope of improvement to the current maritime safeguard system, on predicting sub-grid strong wind gusts for small-size cruise boats in offshore and inland rivers. Finally, an automatic-response system is developed to provide economical con-vection prediction via Inmarsat email communication, aiming to explore operational severe con-vective gust early-warning and appropriate numerical mesoscale model application.

Fit-for-purpose land administration (FFPLA) seeks to simplify cadastral mapping via lowering the costs and time associated with conventional surveying methods. The approach can be applied to both initial establishment and on-going maintenance of system. In Ethiopia, cadastral maintenance remains an on-going challenge, especially in rapidly urbanizing peri-urban areas, where farmers' land rights and tenure security are often jeopardized. Automatic Feature Extraction (AFE) is an emerging FFPLA approach, proposed as an alternative for mapping and updating cadastral boundaries. This study explores the role of the AFE approach for updating cadastral boundaries in the vibrant peri-urban areas of Addis Ababa. Open-source software solutions are utilized to assess the (semi-) automatic extraction of cadastral boundaries from orthophotos (segmentation), designation of 'boundary' and 'non-boundary' outlines (classification), and delimitation of cadastral boundaries (interactive delineation). Both qualitative and quantitative assessments of the achieved results (validation) are undertaken. A high-resolution orthophoto of the study area and a reference cadastral boundary shape file are used, respectively, for extracting the parcel boundaries and validating the interactive delineation results. Qualitative (visual) assessment verified the completed extraction of newly constructed cadastral boundaries in the study area, although non-boundary outlines such as footpaths and artefacts are also retrieved. For the buffer overlay analysis, the interactively delineated boundary lines and the reference cadastre were buffered within the spatial accuracy limits for urban and rural cadasters. As a result, the quantitative assessment delivered 52% correctness and 32% completeness for a buffer width of 0.4m and 0.6m, respectively, for the interactively delineated and reference boundaries. The study further demonstrated the potentially significant role AFE could assist in delivering fast, affordable, and reliable cadastral mapping. Further investigation, based on user input and expertise evaluation, could help to improve the approach and apply it to a real-world setting.

Mechanochemical technology is developing rapidly, judging by the scientific information in both basic and applied studies. However, many issues and points of view remain to be discussed. This review presents some new key issues for the optimization of mechanochemical processes in theoretical and practical aspects. Emphasis is placed on powder technology aspects, which are not always discussed compared to functional or microscopic viewpoints. The transfer of chemical species across the interparticle interface between organic and inorganic species during the mechanosynthesis of nanocomposite oxides offers many new possibilities, which are discussed here in detail. Since all material transport is preceded by charge transfer, its driving force has been searched for using terminology beyond the well-established electrochemical ones. The role of organic compounds during the whole process is emphasized, regardless of their survival in the final product. The similarity with the pharmaceutical phenomena is pointed out, although its mentality is very different from that of the synthesis of nanocomposites. Not only for the rational amorphization of the active pharmaceutical ingredient, but also for the stabilization of molecular dispersion states with the participation of excipients are discussed. The effects of liquids, either added or formed by mechanochemical auto-liquefaction, are presented with reference to the comparison between wet and dry grinding. Finally, mechanisms of apparent stabilization of mechanically activated states of products are elucidated to investigate the practical applicability of mechanochemically synthesized products

Visual signals are the upmost important source for robots, vehicles or machines to achieve human-like intelligence. Human beings heavily depend on binocular vision to understand the dynamically changing world. Similarly, intelligent robots or machines must also have the innate capabilities of perceiving knowledge from visual signals. Until today, one of the biggest challenges faced by intelligent robots or machines is the matching in stereovision. In this paper, we present the details of a new principle toward achieving a robust matching solution which leverages on the use and integration of top-down image sampling strategy, hybrid feature extraction, and RCE neural network for incremental learning (i.e., cognition) as well as robust match-maker (i.e., recognition). A preliminary version of the proposed solution has been implemented and tested with data from Maritime RobotX Challenge (www.robotx.org). The contribution of this paper is to attract more research interest and effort toward this new direction which may eventually lead to the development of robust solutions expected by future stereovision systems in intelligent robots, vehicles and machines.

The focus of this study was to evaluate the effectiveness of incorporation of phenolic compounds from blueberry juice into pear slices using a combination of ohmic heating (OH) and vacuum impregnation (VI), followed by air- or freeze-drying. The results showed that OH is a valuable technique to increase the content of bioactive compounds and antioxidant capacity of blueberry juice. The optimal OH treatment is heating the juice at 50 °C for 20 min under an electric field of 13 V/cm. Furthermore, the combination of VI and OH showed the efficiency of enriching pear slices with bioactive compounds from blueberry juice. The best treatment is to immerse the slices for 90 min at 50 °C and an electric field of 7.8 V/cm. The presence of anthocyanin pigments from blueberry juice affects the color parameters of pear slices by increasing the a* parameter and decreasing the b* and L* parameters. However, both freeze-drying and air-drying (at temperatures of 40, 50, and 60 °C) negatively affected the phenolic content and antioxidant capacity. Notably, air-drying at 60 °C highest levels of phenolic compounds and antioxidant potential for both impregnated and non-impregnated pear slices among the drying processes applied.

In recent years, there has been a growing interest in utilizing natural antioxidants as alternatives to synthetic additives in food products. Apples and apple by-products have gained attention as a potential source of natural antioxidants due to their rich phenolic content. This review aims to provide a comprehensive overview of the antioxidant properties of apples and apple by-products, their extraction techniques as well as potential applications in food products because of their an-tioxidant or nutritional properties. The findings here reported highlights the proper utilization of apples and their by-products in food to reduce the detrimental effect on the environment and provide a positive impact on the economy.

A morphological feature estimation method is presented, that can be used to monitor fish from low contrast underwater images and videos. The fish orientation is classified in order to apply a shape alignment technique that is based on the Ensemble of Regression Trees machine learning method. Shape alignment allows the estimation of fish dimensions (length, height, mass) and the location of fish body parts of particular interest such as the eyes and gills. The developed system can be exploited both for fish monitoring in open sea as well as aquacultures where non-invasive, low cost morphological feature extraction is essential. Fish health can be assessed from malformations in body shape, the color of the eyes or the gills as well as the fish behavior. The proposed method can estimate the position of 18 landmarks with an accuracy of about 95% from low resolution and low contrast underwater images where the fish can be hardly distinguished from its background. Hardware acceleration can be applied at various stages of the proposed method for real time video processing. As a case study, the developed system has been trained and tested with several Mediterranean fish species in the category of seabream or similar: diplodous sargus annularis (white seabream), diplodus annularis seabream (spawn), oblada melanura (saddled seabream), pagrus pagrus (common seabream), etc. A large dataset with low resolution underwater videos and images has been created in the context of this work.

Deep convolutional neural networks (CNN) are favored methods widely used in medical image processing due to their assured shown performance. Recently, the emergence of new lung diseases and the possibility of early detection of their symptoms has attracted many researchers to classify diseases by training deep CNNs on lung CT images. The trained networks are expected to distinguish between lung indications in different diseases, especially at the early stages of them. With the hope of achieving this purpose, we proposed an efficient deep CNN called AFEX-Net with adaptive feature extraction layers that successfully extract distinguishing features and classify chest CT images. The efficiency of the proposed network has two aspects: it is a lightweight network with low number of parameters and fast training and it has adaptive pooling layers and adaptive activation functions to increase its level of compatibility to the input data. The proposed network has been evaluated on a dataset with more than 10K chest CT slices, while an efficient pre-processing method is developed to remove any bias from the images. Additionally, we evaluated the performance of the proposed model on the public COVID-CTset dataset to prove the generalisability of our model. The obtained results confirm the competence of the proposed network in confronting medical images, where prompt and accurate learning is required.

Keywords: artificial bone; conical implants; cylindrical implants; dental implants; implant stability quotient; insertion torque; polyurethane; post-extraction sites; removal torque.

This paper proposes an approach for image data extraction and driving behavior analysis using geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was used to derive the relationship between aggressive driving behavior and road facilities. Several traffic improvements were proposed for specific intersections and roads. The results indicate that lane ratios without lane markings and with straight lane markings are important features that affect aggressive driving behaviors.

With the proliferation and development of social media platforms, social media data has become an important source for acquiring spatio-temporal information on various urban events. Providing accurate spatio-temporal information for events contributes to enhancing the capabilities of urban management and emergency response. However, existing research on mining spatio-temporal information of events often focuses solely on textual content, neglecting data from other modalities such as images and videos. Therefore, this study proposes an innovative spatio-temporal information extraction method for multi-modal social media data (MIST-SMMD), which extracts the spatio-temporal information of events from multi-modal data on Weibo at coarse and fine-grained hierarchical levels, serving as a beneficial supplement to existing urban event monitoring methods. This paper takes the "July 20th Zhengzhou Heavy Rainfall" incident as an example, to evaluate and analyze the effectiveness of the proposed method. The results indicate that in the coarse-grained spatial information extraction using only textual data, our method achieves a Spatial Precision of 87.54% within a 60m range, and reaches 100% Spatial Precision for ranges beyond 200m. For fine-grained spatial information extraction, the introduction of other modal data such as images and videos results in a significant improvement in Spatial Error. These results demonstrate the ability of the MIST-SMMD method to extract spatio-temporal information from urban events at both coarse and fine levels, and confirms the significant advantages of multi-modal data in enhancing the precision of spatial information extraction.

The vehicles number continuously growing lead to increasingly intense and congested traffic and it will additionally demand the braking system, and drivers behave more aggressively and as result is required that the braking system to be durable and efficient. For this is necessary the study the braking system behavior in conditions of intense and moderate traffic to increase the safety of traffic participants, respectively to demonstrate the need for more frequent replacement of some braking system elements. Thus, on a vehicle were performed a series of successive tests, through which the degree of wear of the brake pads and discs was monitored periodically and as a result the efficiency evolution of the braking system. The tests were carried out both in laboratory (on dynamometer) and in traffic to establish the efficiency of the braking system according to some parameters considered essential. The experimental tests showed that the recommendations regarding the frequency of replacement of brake pads and disks are inconsistent with their actual wear. Therefore, the aim of this paper is the establishment of the braking system efficiency of an auto-vehicle, subject to testing depending on the auto-vehicle mass, travel speed, distance driven, and braking time, based on experimental on stand and in-traffic tests, according the road safety regulations.

Tea (Camellia sinensis) is one of the most popular beverages worldwide. Many types of tea prod-ucts continuously emerge in an endless stream; so, the classification or verification of tea be-comes more difficult. Aroma is a vital indicator of tea quality. The present study deals with the identification of aroma compounds in 18 different kinds of tea belonging to three typical tea va-rieties, including green tea, oolong tea, and black tea, using GC-IMS and GC×GC-O-MS. More-over, the clustering of all 18 tea samples and the in-depth correlation analysis between sensory evaluation and instrumental data were performed using the PCA and OPLS-DA. The results re-vealed that in all 18 kinds of tea, a total of 85 aroma compounds were detected by GC-IMS, whereas 318 were detected by GC×GC-O-MS. The PCA result revealed that green tea, oolong tea, and black tea could be clearly separated based on their peak areas. The OPLS-DA result showed that a total of 49 aroma compounds with VIP value > 1.0 could be considered as the potential in-dicators to quickly classify or verify tea types. This study not only compared the aroma differ-ences across different types of teas, but also provided ideas for the rapid monitoring of tea qual-ity and variety.

Variations in groundwater levels that are noticed in different parts of the world are typically the primary focus of research carried out in hydrology. The fluctuating levels of groundwater can be ascribed to several different factors, some of which include an increase in the demand for water, inappropriate irrigation practices, improper management of soil, and unregulated extraction from aquifers. It is necessary to effectively manage groundwater resources to have a trustworthy method of measuring and forecasting groundwater levels. The dynamics of groundwater are inherently uneven and difficult to understand. As a result, applying methods driven by data could potentially yield significant gains in hydrology. In this study, two data-driven models were utilized to estimate groundwater levels at a total of 4 monitoring wells located in Colorado state, USA. These models included ARIMA and ANN. The comparative analysis of the algorithms made use of a data set that had one month's worth of information from each of the years 1980 to 2019. The models were put through their paces, and the results of those tests were statistically and visually assessed. To evaluate the relative accuracy and precision of the models, the mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE) were used. When compared to ARIMA, the analysis demonstrates that ANN can produce the most accurate forecasts of groundwater levels in Colorado state USA.

A simple and rapid method for the extraction of D-series resolvins (RvD1, RvD2, RvD3, RvD4, RvD5) released into Leibovitz's L-15 complete medium by head kidney cells from Atlantic salmon and further liquid chromatography triple quadrupole mass spectrometry determination is proposed. A three-level factorial design was proposed to select the optimal concentrations of internal standards that were used in the evaluation of the performance parameters such as linear range (0.1-50 ng/mL), limits of detection and quantification (0.05 and 0.1 ng/mL, respectively) and recovery values (98.99±0.00). The optimized method was used to determine the stimulated production of resolvins by head kidney cells exposed to docosahexaenoic acid, and the results indicated that it is possible that the production was controlled by circadian responses.

Proso millet starch (PMS) as an unconventional and underutilized cereal starch is becoming increasingly popular worldwide due to its health-promoting properties. This review summarizes research progress in the isolation, characterization, modification, and applications of PMS. PMS can be isolated from proso millet grains by acidic, alkaline, or enzymatic extraction. PMS exhibits typical A-type polymorphic diffraction patterns and shows polygonal and spherical granular structures with a granule size of 0.3-17 µm. PMS is modified by chemical, physical, and biological methods. The native and modified PMS are analyzed for swelling power, solubility, pasting properties, thermal properties, retrogradation, freeze-thaw stability, and in vitro digestibility. The improved physicochemical, structural, and functional properties and digestibility of modified PMS are discussed in terms of their suitability for specific applications. The potential applications of native and modified PMS in food and non-food products are presented. Future prospects for research and commercial use of PMS in the food industry are also highlighted.

Exploration geochemistry is an essential method for discover new mineral areas. However, with the increase of exploration degree, relying solely on "strong, broad and multiple" anomalies to guide exploration work has become increasingly challenging. How to effectively identify valuable geochemical information remains a hot and frontier topic in the field. Thus we proposed a geological connotation method (GCM) that provides an objective geological connotation to big geochemical data based on regional geological and geochemical characteristics of known typical deposits. There are mainly two types of geochemical anomalies expression: (1) the metallogenic intensity anomaly map (MIAM) obtained by accumulating specific anomalous elements, which directly reflects the degree of material exchange and superposition modification during mineralization; (2) the metallogenic type anomaly map (MTAM) obtained by accumulating the mineralization concentration coefficients of elements related to a certain genetic type, which can highlight the anomalies caused by a specific genetic type of deposit. These have been tried and applied in the exploration of the Gangdese, North Himalayas, Nyenchen Tanglha and other metallogenic belts in Tibet, and have made a series of breakthroughs in mineral resources exploration represented by Qulong, Zhunuo and Zhaxikang. The advantage of GCM is that it not only solves the problem of missing ore in the verification of "strong, broad and multiple" anomalies, but also can quickly and preferably delineate the strongest mineralized areas, and better overcome the influence of numerous factors on the anomalies, such as the chemical nature of elements, redox environment, geomorphic landscape and weathering and stripping, so that the anomaly evaluation becomes simple, objective and highlights the regularity.

We introduce the Open Research Knowledge Graph Agriculture Named Entity Recognition (the ORKG Agri-NER) corpus and service for contribution-centric scientific entity extraction and classification. The ORKG Agri-NER corpus is a seminal benchmark for the evaluation of contribution-centric scientific entity extraction and classification in the agricultural domain. It comprises titles of scholarly papers that are available as Open Access articles on a major publishing platform. We describe the creation of this corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism focused on capturing scientific entities in agriculture that reflect the direct contribution of a work; 2) a performance benchmark for named entity recognition of scientific entities in the agricultural domain by empirically evaluating various state-of-the-art sequence labeling neural architectures and transformer models; and 3) a delineated 3-step automatic entity resolution procedure for the resolution of the scientific entities to an authoritative ontology, specifically AGROVOC that is released in the Linked Open Vocabularies cloud. With this work we aim to provide a strong foundation for future work on the automatic discovery of scientific entities in the scholarly literature of the agricultural domain.

In pursuit of carbon neutrality, the demand for metals that are necessary for the development of clean energy technologies is rapidly increasing. Metallurgical waste, such as slag, presents a promising secondary source of these key metals. This research aims to develop an eco-friendly hydrometallurgical process to recover Cu, Ni, and Co from discarded copper/nickel slag. The high-pressure acid leaching (HPAL) was used to selectively leach Ni, Cu, and Co from the fayalite slag, yielding high leaching efficiencies of 99.9%, 89.4%, and 99.9%, respectively, with low Fe and Si tenors to the pregnant leach solution (PLS). The solvent extraction (SX) technique utilizing LIX 984N was used to selectively extract and enrich copper from the dilute PLS to about 23 g/L Cu with a very low Fe concentration of 0.05 g/L. Potassium amyl xanthate (PAX) solution was used to form Ni and Co xanthate complexes from the raffinate solution. Nickel was selectively recovered using ammonia solution, while cobalt xanthate complex was thermally decomposed and recovered as cobalt oxide solids of about 25 wt.% Co. A comprehensive process flowsheet is presented, furthermore, to realize the real application of the developed slag cleaning process, a preliminary economic evaluation was performed.

In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We have developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We have also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms could address. Results showed that automatic machine learning platforms could provide a fast track for organizations seeking digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms could provide a viable option to many business cases and henceforth provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.

Structured extraction of emergency event information can effectively enhance the ability to re-spond to emergency events. This article focuses on the extraction of Chinese document-level emergency events, which mainly faces two key issues in this field: first, related datasets; Secondly, existing DEE (document-level event extraction) studies mostly use sequence annotation to extract candidate entities in the subtask of candidate entity extraction without considering the problem of role overlapping between candidate entities. On the one hand, this article constructs a Chinese document-level emergency extraction dataset, CDEEE, which first annotates the issues of argu-ment scattering, multiple events, and role overlapping. On the other hand, this article proposes a model RODEE for the problem of role overlapping in DEE tasks. This model first uses two in-dependent modules to represent the head and tail positions of candidate entities, then uses a multiplication attention mechanism to interact with the two to obtain a scoring matrix. Finally, role-overlapping candidate entities are predicted to assist in completing DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results showed that RODEE can effectively solve the problem of role overlapping in candidate entities and improve the performance of the DEE model.

This article presents a state-of-the-art system to extract and synthesize causal statements from company reports into a directed causal graph. The extracted information is organized by the relevance to different stakeholder groups’ benefits (customers, employees, investors, and the community/environment). The presented method of synthesizing extracted data into a knowledge graph comprises a framework that can be used for similar tasks in other domains, e.g. medical information. The current work addresses the problem of finding, organizing, and synthesizing a view of the cause-and-effect relationships based textual data, in order to inform and even prescribe the best actions that may affect target business outcomes related to different stakeholders’ benefits (customers, employees, investors, and the community/environment).

This study aims to focus on using the Volterra series and machine learning for forecasting random and chaotic wind speed regimes, since the calm weather is mostly noticed at the local site, making the dataset selection difficult. A novel method is proposed to predict Volterra kernels up to the third order, using a forward-back propagation neural network with 12-month measurements at Fujairah site (UAE). Both daily and monthly wind speed data sets are investigated for forecasting. The three dominant hourly and daily kernels are extracted for each day and each month. Predicted future Volterra kernels are estimated from past values using both statistical analysis and individual neuro networks for each of the Volterra kernel coefficients. Due to the random nature of wind speed at the local site, a two-layer with 4 neurons per layer neuro network is used to locate the most variable and intense speed during 8-hours in the day. Forecasted wind speed is determined with errors arising from different sources such as the utilization of only 3rd-order Volterra kernels and the difficulty of machine training of the employed shallow network. Nevertheless, this work depicts a useful algorithm to forecast chaotic and random wind speed regimes. Computational time is a trade of the complexity of Volterra mathematical analysis.

This study compares the performance of existing studies on multimodal emotion recognition, and proposes a model that fuses two modalities with the speaker's text and voice signals as input values and detects depression. Based on the DAIC-WOZ dataset, voice features were extracted using CNN, text features were extracted using Transformers, and two modalities were fused through a tensor fusion network. We also build a model to detect whether the speaker is depressed or not using LSTM in the final layer. This study suggests the possibility of increasing access to mental illness diagnosis by enabling patients to detect depression on their own in daily conversations. If the model proposed in this study is developed and the voice conversation system is connected, it will be easier for patients who cannot visit the hospital periodically or who are reluctant to visit the hospital to check their condition and seek recovery. Furthermore, it can be expanded to multi-label classification for various mental diseases and used as a simple self-mental disease diagnosis tool.

Covid-19 is a lung disease caused by a Coronavirus family virus. Due to its extraordinary prevalence and death rates, it has spread quickly to every country in the world. Thus, achieving peaks and outlines and curing different types of relapses is extremely important. Given the worldwide prevalence of Coronavirus and the participation of physicians in all countries, Information has been gathered regarding the properties of the virus, its diverse types, and the means of analyzing it. Numerous approaches have been used to identify this evolving virus. It is generally considered the most accurate and acceptable method of examining the patient's lungs and chest through a CT scan. As part of the feature extraction process, a method known as fractional Fourier transform (FrFT) has been applied as one of the time-frequency domain transformations. The proposed method was applied to a database consisting of 2481 CT images. Following the transformation of all images into equal sizes and the removal of non-lung areas, multiple combination windows are used to reduce the number of features extracted from the images. In this paper, the results obtained for KNN and SVM classification have been obtained with accuracy values of 99.84% and 99.90%, respectively.

Phytochemicals are gaining popularity due to their antioxidant effects and potential protection against inflammation, cardiovascular disease, and certain types of cancer. The retention of these phytochemicals during extraction must be maximized. This research focused on extracting bioactive compounds from Psidium guajava Linn leaves using solvent extraction (SE), microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE) with distilled water (DW) or 60% (v/v) ethanol/water (ET). Phytochemical screening demonstrated that all of the screening showed positive results in all extraction methods, except glycoside. There were no significant (p&gt;0.05) differences in total phenolic (TPC) and flavonoid content (TFC) during MAE, SE, and UAE in case of ET. However, SE shows better (p&lt;0.05) extraction of TPC and TFC than UAE and MAE in case of DW. ET shows better (p&lt;0.05) extraction of TPC and TFC than DW. Antioxidant analysis shows the same trend though both DPPH and FRAP values were higher in MAE and SE for ET and DW, respectively. MAE/ET showed the highest inhibitory activity and dose-dependent response using SW480 cell via MTT assay. HPLC and TLC analysis reveal the fingerprint of morin, might function as an anticancer agent. In conclusion, MAE/ET is the most efficient among the extraction techniques in terms of anticancer activity.

Abstract: Pomegranate peels are important source of polyphenols with remarkable interest in food, pharmaceutical and cosmetic industry. An improved extraction of total polyphenolic com-pounds (TPC) from pomegranate peels by Solid-State Fermentation (SSF) was achieved. A Box Hunter & Hunter (BHH) followed by Central Composite Design (CCD) were performed to assess the effect of the process variables on TPC release. The statistic designs indicate that the best TPC extraction (234.85 mg GAE/gdm) by means of SSF occurs, at 42°C, 50 % moisture, pH 5.0, mineral solution (g/L): NaNO3 (3.83), KH2PO4 (1.52), MgSO4 (4.66) and KCl (1.52) at 36 h. At the best fer-mentation conditions, TPC (248.78±1.24 mgGAE/gdm) increased 5.96-fold than values previously reported and antioxidant activity [AA] increased 5.81-fold than the value obtained before the SSF optimization. High-valued citric acid, α and β punicalin, α and β punicalagin, punigluconin, gal-loyl-HHDP hexoside and ellagic acid molecules were identified. The increased extraction of TPC by SSF provides a suitable alternative for pomegranate peels valorization through the re-covery of molecules with high added value with potential use in food, pharmacy and cosmetic industries; a diversification in the use of food agroindustry by-products is obtained as approach to the circular economy model through biotechnological processes.

Accurate evaluation of health status of lithium-ion batteries must be deemed as of great significance, insofar as utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration in, or even fatal hazards to, the health of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS), which has distinct advantages such as fast or non-destructive over its competitors, suffices in detecting lithium plating and thus has been attracting increasing attention in the field of battery management, but its ability of assessing quantitatively the degree of lithium plating remains largely unexplored hitherto. On this point, this work seeks to narrow that gap, by proposing an EIS-based method that can quantify the degree of lithium plating. The core conception is to eventually circumvent the reliance on state-of-health measurement, and use instead the impedance spectrum, to acquire an estimate on battery capacity loss. To do so, the effects of solid electrolyte interphase formation and lithium plating on battery capacity must be first decoupled, so that the mass of lithium plating can be quantified. Then, based on an impedance spectrum measurement, the parameters of the fractional equivalent circuit model (ECM) of the battery can be identified. These fractional ECM parameters are received as inputs by an artificial neural network, which is tasked to establish a correspondence between the model parameters and the mass of lithium plating. The empirical part of the work revolves around the data collected from an aging experiment, and the validity of the proposed method is truthfully attested by dismantling the batteries, which is otherwise not needed during the actual uptake of the method.

A rapid and efficient method using an alkyl functionalized magnetic nanoparticles-based extraction technique combined with Ultra-High Performance Liquid Chromatography was developed for the detection of trace amounts of polycystic aromatic hydrocarbons in tea leaves. As a popular coating for chromatographic column packing materials, C18-alkyl has been demonstrated to be effective in separating polycystic aromatic hydrocarbons. And the magnetism of the nanomaterials accelerates the extraction process while their high surface ratio enables desirable dispersity in the sample matrix. Meanwhile, the adsorbents can be washed and reused 30 times without compromise on recovery, which greatly reduced the budget. The effects of various parameters were investigated and optimized, and the recoveries for five analytes were in the range of 84.8–103.7%. The reproducibility rate of intra-day and inter-day were below 7.1% and 11.3% respectively. The limits of detection and limits of quantification ranged from 1.69–9.97 ng g-1 and 5.12–30.21 ng g-1, indicating satisfactory sensitivity. Thus, the proposed methodology is rapid, highly efficient, and economical, and it expands the application of magnetic cleanup approaches in complex food matrices.

In this article, we introduce a new approach to human movement by defining the movement as a static object or a super object in one two-dimensional image. This method can allow researchers to label and describe the total movement as an object isolated from a reference video. This ap-proach allows us to perform various tasks, including finding similar movements in a video, measuring, and comparing movements, generating new similar movements, and defining chore-ography by controlling specific parameters in the human body skeleton. As a result of the pre-sented approach, we can eliminate the need to label images manually, disregard the problem of finding the beginning and the end of a movement, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one il-lustrates how to verify and score a requested movement. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supply-ing sufficient training data for deep learning applications (DL). A Variational Auto Encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate two use cases. These use cases demonstrated the versatility of our innovative concept in measuring, categorizing, inferring hu-man behavior, and generating gestures for other researchers.

In the era of big data, feature engineering has proved its efficiency and importance in dimensionality reduction and useful information extraction from original features. Feature engineering can be expressed as dimensionality reduction and is divided into two types of methods such as feature selection and feature extraction. Each method has its pros and cons. There are a lot of studies to combine these methods. Sparse autoencoder (SAE) is a representative deep feature learning method that combines feature selection with feature extraction. However, existing SAEs do not consider the feature importance during training. It causes extracting irrelevant information. In this paper, we propose a parallel guiding sparse autoencoder (PGSAE) to guide the information by two parallel guiding layers and sparsity constraints. The parallel guiding layers keep the main distribution using Wasserstein distance which is a metric of distribution difference, and it suppresses the leverage of guiding features to prevent overfitting. We perform our experiments using four datasets that have different dimensionality and number of samples. The proposed PGSAE method produces a better classification performance compared to other dimensionality reduction methods.

When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.

In commercial terms, Virgin Olive Oil (VOO) is considered an exceptional food of excellent sensory and nutritional quality, for its taste and genuine odour and, as well as for its bioactive compounds, of a great interest for the health. This quality can be affected by oxidative degradation, chemical as well as enzymatic (activity of oxidative endogenous enzymes from polyphenol oxidase and peroxidase olive fruit type), of essential components during extraction and conservation of VOO. In the bibliography, the reduction of oxygen during the malaxation process and the storage of the oil has been studied in different ways. However, research about the reduction of oxygen in the crushing of olive fruit, malaxation of the paste and/or both at the same time in “real extraction condition” is scarce. The reduction of oxygen has been compared to control conditions (concentration of atmospheric oxygen of 21%). Batches of 200 kg of olive fruit, Picual cultivar, were used and the following treatments were applied: Control (21% O2 Mill - 21% O2 Mixer), “IM-NM”: Inerted milling -Normal malaxation (6.5% O2 Mill-21% O2 Mixer), “NM-IM”: Normal milling-Inerted malaxation (21% O2 Mill-4 % O2 Mixer) and “IM-IM”: Inerted milling-Inerted malaxation (5.5% O2 Mill-10% O2 Mixer). The parameters of commercial quality covered by Regulation: free acidity, peroxide value and absorbency in ultra-violet (k 232 and k270) did not suffer any change in relationship to the control, the oils belong to the comercial category of “Extra virgin olive oil”. The phenolic compounds are involved in the distinctive bitter and purgent taste and, their healthy properties and their oxidative stability, increased with the downsizing amounts of oxygen regarding control and the IM-NM, NM-IM and IM-IM treatments with an average of 4, 10 and 20 %, respectively. By contrast, total volatile compounds decrease between a 10-20 % in all oxygen reduction treatments. The volatile compounds arising from lipoxygenase pathway, which are responsable for the green and fruity notes of VOO, also decreased their concentration with the treatments, between 15-20%. The results show how the reduction of oxygen in the milling and malaxation stages of olive fruit can modulate the content of phenols, volatile compounds, carotenoids and chlorophyll pigments of the VOO avoiding the degradation of compound with sensorial and nutritional interest.

The excessive use of synthetic insecticides in modern agriculture has led to the contamination of the environment and the development of insect resistance. In this study, we evaluated the potential of tangerine (Citrus reticulata L.) peel essential oil (EO) as a natural insecticide against greenhouse whitefly (Trialeurodes vaporariorum W. (Homoptera: Aleyrodidae)), a common pest in greenhouse production. Petroleum ether (PET) and n-hexane (HEX) were used as solvents to extract essential oil (EO) from tangerine peels. The yield of EO was 1.59 % and 2.00 % (m/m) for PET and HEX, respectively. The insecticidal activity of EO was evaluated by measuring the mortality rate (MR) of greenhouse whiteflies at different time intervals. The results showed that PET and HEX extracts of tangerine EO effectively controlled the greenhouse whitefly. With both solvents, it was observed that with doses of 12.5% (v/v), similar results were achieved as with the positive control (corresponding to the commercial insecticide imidacloprid). Additionally, the FTIR analysis found that the EO contained d-limonene, which may be the source of its insecticidal properties. Therefore, tangerine peel essential oil is an excellent natural insecticide candidate for controlling greenhouse whiteflies effectively and sustainably.

Sesquiterpene lactones (STLs) are a large group of terpenoids most commonly found in plants of the Asteraceae family, e. g. in chicory plants, displaying a wide range of interesting biological activities. However, further studies on the biological potential of chicory derived STLs and analogues are challenging as only four of these molecules are commercially available (as analytical standards), and to date there are no published or patented simple extraction-purification processes capable of large scale STLs isolation. In this work we describe a novel three-step large scale extraction and purification method for the simultaneous purification of 11,13-dihydrolactucin (DHLc) and lactucin (Lc) starting from a chicory genotype rich in these STLs and in the corresponding glycosyl and oxalyl conjugated forms. After a small-scale screening on 100 mg of freeze-dried chicory root powder, the best results were achieved with a 17 h water maceration at 30°C. With these conditions we managed to increase the content of DHLc and Lc, at the same time favouring the hydrolysis of their conjugated forms. On a larger scale, the extraction of 750 g of freeze-dried chicory root powder, followed by a liquid-liquid extraction step and a reversed-phase chromatography, allowed the recovery of 642.3 ± 76.3 mg of DHLc and 175.3 ± 32.9 mg of Lc. The two pure STLs were subsequently used in the context of semi-synthesis to generate analogues for biological evaluation as antibacterial agents. In addition, other described chicory STLs that are not commercially available were also synthesized or extracted to serve as analytical standards for the study. In particular, lactucin-oxalate and 11,13-dihydrolactucin-oxalate were synthesized in two steps starting from Lc and DHLc, respectively. On the other hand, 11β,13-dihydrolactucin-glycoside was obtained after a MeOH/H₂O (70/30) extraction, followed by a liquid-liquid extraction step and a reversed-phase chromatography. Together, this work will help facilitate the evaluation of the biological potential of chicory derived STLs and their semisynthetic analogues.

Less than half of e-waste plastics is sorted worldwide and this rate is likely to decline as major processing countries have banned importation of e-waste plastics. This forces the development of decentralized processing facilities, also known as microfactories. The present work investigates the recyclability of different grades of acrylonitrile-butadiene-styrene (ABS) copolymer, polycarbonate, and polypropylene, which were found to be very abundant in a recycling site in the UK. The determination of the matrix relied on the resin identification code imprinted in the e-waste plastic and subsequent FTIR analysis. The melt-blend extrusion technology enabled the valorization of the wasted thermoplastics as 3D filament without significant degradation of the polymers. The recycled materials maintained the tensile strength around 2.5 MPa in agreement with the specifications offered by virgin polymers. Further characterization was done by means of laser microscope, thermogravimetric analysis, and XRF to determine the commercial viability of the recycled filament. A modified solvent-based method was developed with acetone to remove the brominated flame retardants: 25g/100mL, 30 minutes of contact time, and 4 extraction steps. The FTIR results show that the degradation of the rubbery dispersed phase corresponding to the butadiene can be accumulated in the less soluble fraction of the waste ABS.

Due to the health crisis caused by SARS-CoV-2, the creation of a new vaccine platform based on mRNA was implemented. Globally, around 13.32 billion COVID-19 vaccine doses of diverse platforms have been given, and up to this date, 69.7% of the total population received at least one injection of a COVID-19 vaccine. Although these vaccines prevent hospitalization and severe forms of the disease, increasing evidence has shown they do not produce sterilizing immunity, allowing people to suffer frequent re-infections. Recent research has also raised concerns that mRNA vaccines could induce immune tolerance, which, added to that caused by the virus itself, could complicate the clinical course of a COVID-19 infection. Furthermore, recent investigations have found high IgG4 levels in people who were administered two or more injections of mRNA vaccines. It has been suggested that an increase in IgG4 levels could have a protecting role by preventing immune over-activation, similar to that occurring during successful allergen-specific immunotherapy by inhibiting IgE-induced effects. Altogether, evidence suggests that the reported increase in the IgG4 levels detected after repeated vaccination with the mRNA vaccines is not a protective mechanism; rather, it may be a part of the immune tolerance mechanism to the spike protein that could promote unopposed SARS-CoV2 infection and replication by suppressing natural antiviral responses. IgG4-induced suppression of the immune system due to repeated vaccination can also cause autoimmune diseases, promotes cancer growth, and autoimmune myocarditis in susceptible individuals.

Recent studies have been able to detect α-synuclein (αSyn) seeding in formaldehyde-fixed paraffin-embedded (FFPE) tissues from patients with synucleinopathies using seed amplification assays (SAAs), but with relatively low sensitivity due to limited protein extraction efficiency. With the aim of introducing an alternative option to frozen tissues, we developed a streamlined protein extraction protocol for evaluating disease-specific seeding in FFPE human brain. We evaluated the protein extraction efficiency of different tissue preparations, deparaffinizations, and protein extraction buffers using formaldehyde-fixed and FFPE tissue of a single Lewy body disease (LBD) subject. Alternatively, we incorporated heat-induced antigen-retrieval and dissociation using a commercially available kit. Our novel protein extraction protocol has been optimized to work with 10 sections of 4.5-µm-thickness or 2-mm-diameter micro-punch of FFPE tissue that can be used to seed SAAs. We demonstrated that extracted proteins from FFPE still preserve seeding potential and further show disease-specific seeding in LBD and multiple system atrophy. To the best of our knowledge, our study is the first to recapitulate disease-specific αSyn seeding behavior in FFPE human brain, with structural validation. Our findings open new perspectives in re-evaluating archived human brain tissue, extending the disease-specific seeding assays to larger cohorts to facilitate molecular subtyping of synucleinopathies.

In the chemical processing industries, sensors for pumps are among the most commonly used machinery. Condition-based maintenance (CBM) and prognosis health management (PHM) determine the most cost-effective time to overhaul pumps. In order to determine the status of the pump, a signal-emitting accelerometer is employed. Stationarity-based feature extraction from amplitude signals is used to process the signal. Utilizing the time-domain function, multiple statistical results were produced. Eight fault codes were classified using support vector machine method. The enormous amount of data points necessitated the use of feature selection. In terms of accuracy, precision, recall, and F1 score, the Chi-square feature selection method exceeds other approaches.

The present study concerns the application of electrochemical chloride extraction to concrete designed with partial replacements of Ordinary Portland Cement (OPC) from different binders, namely ground granulated blast furnace (GGBS), pulverized fly ash (PFA) and Silica Fume (SF), to attend the need of corrosion repair for modern constructions. Electrochemical Chloride Extraction (ECE) was applied to the specimens at a current density of 2A/m2 for 4 weeks. The chloride types and binding mechanisms appeared to have important implications on the different performance of treatment perceived for all binders. Considering total chlorides, removal was more significant for OPC and GGBS when compared to others. The main difference noted was that for GGBS the removal of bound chlorides was more significant due to a possible release of adsorbed chlorides from the silicate hydrates, present at higher intensities in this case. However, even though GGBS had greater rates of bound chloride removal it had still the same percentage as OPC for total efficiency due to the fact that in OPC the removal of free chlorides is more expressive.

In the East, it has already become a tradition to prepare dry powdered seasonings from the fruits of the sumac plant (Rhus coriaria L.). In this work, we tested their processing by extraction. Sumac was collected in mid-September in a mountain forest near the village of Gezik (Guba, Azerbaijan). Before extraction, it was first dehydrated in a drying cabinet SH-DO-149 FG (Southern Bark) to an air-dry state; then crushed in a Tube Mill control mill (Germany) into pieces of 1-3 mm in size. One- and two-stage maceration extraction was tested together with water or acidified water at temperatures from 20 to 80 °C. Evaporation of primary extracts was carried out in a rotary evaporator Lab Rot 2 L (China). The concentration of coloring substances in the extract (in units of optical density) was used as an output parameter. It was found that the use of two-stage extraction and acidified water with a concentration of citric acid 0.3 g/cm³ contributes to the extraction of the maximum amount of coloring substances and increases the transparency of the resulting extracts, which allows the extracted extracts to be concentrated directly to obtain a new type of natural food dye. The involvement of sumac fruits in industrial processing will increase the need for these medicinal fruits, and this, in turn, will serve as an incentive for the introduction of this medicinal plant into culture.

Deep learning (DL) models have been recently widely used to extract task-oriented patterns from large scale of datasets, and to improve the data image understanding and analysis accuracy in many different decision-making processes for tasks such as image classification, segmentation, detection, and so on. However, in practice, the performances of DL models are easily affected by environmental illumination conditions. Conversely, DL models can also be utilized for extracting the illumination hints from the images, and these hints are critically useful for improving the model robustness, classifying the environmental scenes, estimating scene depth information, and rendering 3D objects. In this study, an extensive and exhaustive review is carried out for DL based color constancy, indoor and outdoor illumination estimation, and image depth estimations with the considerations of strengths and weaknesses of DL models. This study also explores the different network designs and the paradoxes in parameter optimization during the model training. Current technology barriers involved in implementing these models and recommendations to overcome these barriers are also suggested in the review.

Auto-labeling is one of the main challenges in 3D vehicle detection. Auto-labeled datasets can be used to identify objects in LiDAR data, which is a challenging task due to the large size of the dataset. In this work, we propose a novel methodology to generate new 3D based auto-labeling datasets with a different point of view setup than the one used in the most recognized datasets (KITTI, WAYMO, etc.). The performance of the methodology has been further demonstrated with the development of our own dataset with the auto-generated labels and tested under boundary conditions on a bridge in a fixed position. The proposed methodology is based on the YOLO model trained with the KITTI dataset. From a camera-LiDAR sensory fusion, it is intended to auto-label new datasets while maintaining the consistency of the Ground Truth. The main contribution of this work is a novel methodology to auto-label autonomous driving datasets using YOLO as the main labelling system. The performance of this approach is measured retraining the contrast models of the KITTI benchmark.

A unique two-column ²³⁰U/²²⁶Th generator has been developed. The focus was hold on obtaining ²²⁶Th of high purity in a solution amenable to further labeling. The first column of the proposed generator filled with TEVA Resin held ²³⁰U, from which ²²⁶Th was eluted with 7 M HCl solution. UTEVA Resin pretreated with nitric acid solution was used as a sorbent for the second column for thorium retention. ²²⁶Th was extracted with 0.01-0.05 M citric buffer solution. One cycle of generator milking took 5-7 minutes and produced > 90% of ²²⁶Th in 1.5 ml of eluate, pH 4.5-5.0. The proposed two-column ²³⁰U/²²⁶Th generator was tested over two months including a second loading of ²³⁰U additionally accumulated from ²³⁰Pa. The ²³⁰U impurity in ²²⁶Th eluate was less than 0.01% allowing to use it directly in synthesis of radiopharmaceutical compounds.

Acanthopanax senticosus(rupr. rt. Maxim.; AS) is a medicinal plant used in the clinical treatment of cerebrovascular diseases and central nervous system disorders, significantly improving blood lipid levels and endothelial cell function in patients with acute cerebral infarction. Isofraxidin, one of the active ingredients of AS, is the core of the medical effects, and its extraction and purification depend on organic solvents. Deep eutectic solvents (DESs) is a new green solvent, synthesized by intermolecular hydrogen bonding between hydrogen bond donors (HBD) and hydrogen bond acceptors (HBA), which has non-toxic, high separation and purification efficiency, and environmentally friendly compared with traditional organic solvents. In this paper, DES was used for the extraction of isofraxidin from AS. The primary findings demonstrate that the DESs had a viscosity higher than that of ethanol, and even adding a tiny amount of water (about 10%) would trigger solvent redistribution, leading to a considerable reduction in solvent viscosity. In comparison to ethanol, the extraction rate of isofraxidin by DESs was 2-3 times higher. Thus, this work developed a new technique for using green extraction of isofraxidin that has some practical implications.

Symbolic pole/zero analysis is an important step when designing an analog operational amplifier. Generally, a simplified symbolic analysis of analog circuits suffers from NP-hardness, i.e., an exponential growth of the number of symbolic terms of the transfer function with the circuit size. In this study, we present a mathematical model combined with a heuristic-metaheuristic solution method for the symbolic pole/zero simplification in operational transconductance amplifiers (OTA). At first, the circuit is symbolically solved and an improved root splitting method is applied to extract symbolic poles/zeroes from the exact expanded transfer function. Then, a hybrid algorithm based on heuristic information and a metaheuristic technique using simulated annealing is performed for the simplification of the derived symbolic pole/zero expressions. The developed method has been tested on three analog OTAs. The obtained results show the effectiveness of the proposed method to achieve accurate simplified symbolic pole/zero expressions with the least complexity.

Cowpea chlorotic mottle virus (CCMV) is a plant virus explored as a nanotechnological platform. The robust self-assembly mechanism of its capsid protein allows for drug encapsulation and targeted delivery. Additionally, the capsid nanoparticle can be used as a programmable platform to display different molecular moieties. In view of future applications, efficient production and purification of plant viruses is a key step. In established protocols, the need for ultracentrifugation is a significant limitation due to cost, difficult scalability, and safety issues. In addition, the purity of the final virus isolate often remains unclear. Here, an advanced protocol for the purification of CCMV from infected plant tissue was developed, focusing on efficiency, economy, and final purity. The protocol involves precipitation with PEG 8000, followed by an affinity extraction using a novel peptide aptamer. The efficiency of the protocol was validated using size exclusion chromatography, MALDI-TOF mass spectrometry, reversed-phase HPLC, and sandwich immunoassay. It was demonstrated that the final eluate of the affinity column is of exceptional purity (98.4%) determined by HPLC and detection at 220 nm. The scale-up of our proposed method seems to be straightforward, which opens the way to the large-scale production of such nanomaterials. This highly improved protocol may facilitate the use and implementation of plant viruses as nanotechnological platforms for in vitro and in vivo applications.

Almond beverages are gaining ever-increasing consumer preference in the growing market of non-alcoholic vegetable beverages, ranking in first place among oilseed-based drinks, mainly due to the perceived healthy benefits. However, the high cost of the raw material, time and energy consuming pre-treatments such as soaking, blanching and peeling, and post-treatments such as thermal sterilization, leading also to the loss of valuable macronutrients and micronutrients, hinder the sustainability, affordability and spread of almond beverages. Hydrodynamic cavitation processes were applied, as a single-unit operation, to the extraction in water of almond skinless kernels in the form of flour and fine grains, and to whole almond seeds in the form of coarse grains, up to high concentrations. The results showed full compliance with a high-end commercial product and with the expected levels based on the properties of the raw materials. The concentrated extract obtained from whole almond seeds showed a comparatively much higher antiradical activity, likely due to the contribution of the almond kernel skin. In conclusion, hydrodynamic cavitation could represent a convenient alternative processing route to the production of conventional and new integral almond beverages, avoiding multiple and costly technological steps, while affording fast production cycles of potentially healthier beverages.

Variational Auto-Encoders (VAEs) are deep generative models used for unsupervised learning, however their standard version is not topology-aware in practice since the data topology may not be taken into consideration. In this paper, we propose two different approaches with the aim to preserve the topological structure between the input space and the latent representation of a VAE. Firstly, we introduce InvMap-VAE as a way to turn any dimensionality reduction technique, given an embedding it produces, into a generative model within a VAE framework providing an inverse mapping into original space. Secondly, we propose the Witness Simplicial VAE as an extension of the Simplicial Auto-Encoder to the variational setup using a witness complex for computing the simplicial regularization, and we motivate this method theoretically using tools from algebraic topology. The Witness Simplicial VAE is independent of any dimensionality reduction technique and together with its extension, Isolandmarks Witness Simplicial VAE, preserves the persistent Betti numbers of a data set better than a standard VAE.

Canola is the major oilseed crop of Canada. The de-oiled material is an important by-product due to its rich phenolic profile and high protein content. This co-processing stream from canola is primarily utilized as animal feed but represents an invaluable source of nutraceuticals. Microwave-assisted solvent extraction (MAE), as a green extraction method, has received considerable attention in recent times. The ease of use and application of many solvents at the same time makes the MAE one of the best methods for studying multiple solvents at the same time. The formation of canolol, from sinapine and sinapic acid, is primarily dependant on temperature which favors the decarboxylation reaction. Hence, MAE using green extractants can be used to enhance the yield of canolol. This study examined the effects of different pre-treatment temperature-time combinations of 140, 150, 160, and 170℃ for 5, 10, 15, 20 and 30 minutes on the extraction of canolol and other canola endogenous phenolic compounds. Three antioxidant assays assessed the antioxidant activity of the different extracts obtained by MAE confirming the microwave as a novel and versatile instrument for enhancing the yield of canolol. Improvements in the antioxidant activity of the different extracts further established the efficacy of the current method for isolating important natural phenolic derivatives for utilization by the nutraceutical industry.

In this study, the effect of extraction conditions on the total phenolic content of clove buds was investigated. Clove buds were extracted using a deep eutectic solvents like mixtures choline chloride: lactic acid in the 1:2 molar ratio. Additionally, the HPLC-DAD method was used to determine the main components of the clove extracts, including eugenol, eugenol acetate, and β-caryophyllene. The conditions used in the extraction with choline chloride: lactic acid (molar ratio 1:2) included extraction temperature (t = 40 – 80°C), water addition (5.6 – 40%), and extraction time (30 – 90 min). Determination of the phenolic compounds was done by the Folin-Ciocalteu method. The optimum operating conditions for the total phenolic content were identified at 77°C, 30 min, and a water addition of 40%. Based on the results, the highest amount of eugenol (307.26 ± 8,44 mg/g dry raw material) was determined in clove extracts which were extracted under condition of 60°C, 22.8%, and 30 min.

A model for the simulation of ion beam extraction from an electron cyclotron resonance ion source is proposed. It is based on the simple fact that charged particles follow magnetic field lines. Therefore magnetic field lines are used to generate initial conditions for ray-tracing. This model reproduces in simulation experimentally obtained results. The importance of correlations in phase-space caused by the magnetic field is shown in simulation. This model also describes the physics of space-charge and its compensation in the extracted (fast) ion beam by low energy electrons. Simulation provides the possibility to test theoretical assumptions, as well as to optimize technical designs.

Blind deconvolution (BD) is one of the effective methods that help pre-process vibration signals and assist in bearing fault diagnosis. Currently, most BD methods design an optimization criterion and use frequency or time domain information independently to optimize a deconvolution filter. It recovers weak periodic impulses related to incipient faults. However, the random noise interference may cause the optimizer to overfit. The time-domain-based BD methods tend to extract fault-unrelated single peak impulse, and the frequency-domain-based BD methods tend to retain the maximum energy frequency component, which will lose the fault-related harmonics frequency components. To solve the above issue, we propose a hybrid criterion that combines the kurtosis for time domain optimization and the $G-l_1/l_2$ norm for the frequency domain. These two criteria are monotonically increasing and decreasing, so they mutually constrain to avoid overfitting. After that, we design a multi-task one-dimensional convolutional neural network with time and frequency branches to achieve an optimal solution for this hybrid criterion. The multi-task neural network realizes the simultaneous optimization of two domains. Experimental results show that our proposed method outperforms other state-of-the-art methods.

Surface defect detection is one of the most widely used research areas in the field of image processing and machine vision. Detection of surface defects is used in visual inspection systems and medical image analysis. In this manuscript, an innovative method for detecting surface defects based on energy changes in co-occurrence matrices is presented in several directions. The method presented in this manuscript includes two stages of learning and testing. In the learning phase, to extract texture features, the gray level co-occurrence matrix operator is applied on the healthy image of the desired level. Then the energy value of the output matrix is ​​calculated. In the following, changes in the amount of energy are considered as statistical characteristics that are a good representative of the image of a healthy surface. Finally, with its help, a suitable threshold for the health of the images is obtained. Then, in the test phase, with the help of the calculated quorum, the defective windows that have suffered from non-normality are distinguished from the healthy surface sections. In the results section, the efficiency of the mentioned method has been measured on medical images and stone and ceramic images, and its detection accuracy has been compared with some previous effective methods. The advantages of the presented method include high accuracy, low calculations and compatibility with all types of levels due to the use of the learning stage. The proposed approach can be used in medical applications to diagnose abnormalities such as diseases. All extracted features are statistical, so its detection speed is higher than deep neural networks.