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.

Tooth bud removal called Ibyinyo is the practice of removing the developing tooth buds, usually done on an infant, typically performed by traditional healers who believe that this practice will reduce fever and diarrhea in children. This practice is most prevalent in East-African countries, including Rwanda. These procedures are mostly performed in non-sterile conditions using basic sharp instruments. We will discuss the case of a 10-year-old female patient who was presented at the dental clinic, with two malformed permanent canine teeth. Clinical examination revealed malformed enamel and elongated permanent right maxillary canine tooth left mandibular canine tooth with crown malformation. She was also presented with retained (primary) left maxillary lateral incisor tooth and missing left maxillary canine tooth. In addition, the ectopic eruption of left maxillary central incisor tooth and missing permanent right mandibular canine tooth were noted. All these complications resulted from tooth bud removal that the patient had experienced in her early childhood. Her malformed right maxillary and left lower mandibular canine teeth were reshaped, using composite filling materials, to improve her appearance aesthetically. Ibyinyo is preventable damage, done out of ignorance and superstitious practices that can be stopped by growing social awareness. Therefore, educating parents through community-based campaigns on the detrimental consequences of early childhood tooth bud removal through Ibyinyo practice might be helpful to eradicate this harmful and unnecessary practice.

Deep convolutional neural network (CNN) has shown their great advantages in the single image deraining task. However, most existing CNN-based single image deraining methods still suffer from residual rain streaks and details lost. In this paper, we propose a deep neural network including the Multi-scale feature extraction module and the channel attention module, which are embed in the feature extraction sub-network and the rain removal sub-network respectively. In the feature extraction sub-network, the Multi-scale feature extraction module is constructed by a Multi-layer Laplacian pyramid, and is then integrated multi-scale feature maps by a feature fusion module. In the rain removal sub-network, the channel attention module, which assigns different weights to the different channels, is introduced for preserving image details. Experimental results on visually and quantitatively comparison demonstrate that the proposed method performs favorably against other state-of-the-art approaches

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.

The wheat aleurone layer is, according to millers, the main bran fraction. It is a source of nutritionally valuable compounds, such as dietary fibres, proteins, minerals and vitamins, that may exhibit health benefits. Despite these advantages, the aleurone layer is scarce on the market, probably due to issues related to its extraction. Many processes exist with some patents, but a choice must be made between the quality and quantity of the resulting product. Nonetheless, its potential has been studied mainly in bread and pasta. While the nutritional benefits of aleurone-rich flour addition to bread agree, opposite results have been obtained concerning its effects on end-product characteristics (namely loaf volume and sensory characteristics), thus ensuing different acceptability responses from consumers. However, the observed negative effects of aleurone-rich flour on bread dough could be reduced by subjecting it to pre- or post-extracting treatments meant to either reduce the particle size of the aleurone’s fibres or to change the conformation of its components.

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e. feature engineering, model selection, etc), principles (i.e. bias-variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, output interpretability, etc), models (i.e. neural networks, regression models, etc), methods (i.e. Cross-Validation, data augmentation, etc), metrics (i.e. Mean Squared Error, F1-score, etc) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community over complete and rigorous machine learning pipelines and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers due to brevity, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are not AI experts.

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.

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.

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.

Safe sample transport is of great importance for infectious diseases diagnostics. Various treatments and buffers are used to inactivate pathogens in diagnostic samples. At the same time, adequate sample preservation, particularly of nucleic acids, is essential to allow an accurate laboratory diagnosis. For viruses with single-stranded RNA genomes of positive polarity, such as foot-and-mouth disease virus (FMDV), however, naked full-length viral RNA can itself be infectious. In order to assess the risk of infection from inactivated FMDV samples, two animal experiments were performed. In the first trial, six cattle were injected with FMDV RNA (isolate A22/IRQ/24/64) into the tongue epithelium. All animals developed clinical disease within two days and FMDV was reisolated from serum and saliva samples. In the second trial, another group of six cattle was exposed to FMDV RNA by instilling it on the tongue and spraying it into the nose. The animals were observed for 10 days after exposure. All animals remained clinically unremarkable and virus isolation as well as FMDV genome detection in serum and saliva were negative. No transfection reagent was used for any of the animal inoculations. In conclusion, cattle can be infected by injection with naked FMDV RNA, but not by non-invasive exposure to the RNA. Inactivated FMDV samples that contain full-length viral RNA carry only a negligible risk of infecting animals.

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.

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.

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.

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.

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.

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.

Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is that how to integrate highly sparse genetic genomics data with a mass of minor effects into prediction model for improving prediction power. We find that deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower dimensional continuous data in a non-linear way. This idea may provide benefits in risk prediction based on genome-wide data associated e.g. integrating most of the information in the genotype data. Hence, we developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for risk prediction. Specifically, we first reduced the number of biomarkers via a univariable regression model to a moderate size. Then a trainable auto-encoder was used to extract compact representations from the reduced data. Next, we performed a LASSO problem process over a grid of tuning parameter values to select the optimal combination of extracted features. Finally, we applied such feature combination to two prognostic models, and evaluated predictive effect of the models. The results of simulation studies and real data applying indicated that these highly compressed transformation features could better improve predictive performance and did not easily lead to over-fitting.

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, in particular to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel Smell Tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with emotional charge of situations. The experimental results show that we can detect smells and emotions with F1 score of 92.7 and 79.2, respectively.

The pharmaceutical industry is one of the largest pollutants and generator of toxic solvent resi-dues for the environment and human beings. In the last decade, there has been a huge evolution in the design and development of new environmentally friendly products and processes, in re-sponse to the global environmental concern and the growing demand for products with quality ingredients and manufacturing processes aimed at achieving sustainability. Consumers are looking for products with the benefits of natural products, with sustainable and environmentally friendly formulations with few synthetic molecules as possible. Thus, based on the origins of the pharmaceutical industry, it is important to return to Nature and enjoy the benefits that natural products bring to us. Deep eutectic solvents appear as an alternative to toxic solvents commonly used to extract bioactive compounds. These solvents are considered green solvents because they are made up of natural compounds such as sugars, amino acids, and carboxylic acids. In addition, they are simple to prepare, with an atomic economy of 100%, with attractive physicochemical properties. This paper is a comprehensive review in which is described the main properties, characteristics, and production methods of deep eutectic solvents as well as its application to extract from natural sources bioactive compounds with pharmaceutical interest.

Mapping building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep learning models have been the main focus of most recent research endeavours aiming to extract pixel-based building roof plane areas from remote sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pix-el-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: Stadsveld – 't Zwering neighbourhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld – 't Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings.

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.

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an es-sential biomarker for directly assessing tissue viability and function in neurologic disorders. For quantitative mapping of OEF, an integrative model of quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) was recently pro-posed. However, QQ assumes Gaussian noise in both susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low sig-nal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic noise in mGRE complex signals. The proposed mcQQ was implemented using a deep learning framework (mcQQ-NET) and compared with the existing deep learning-based QQ (QQ-NET) in simulations, ischemic stroke patients, and healthy subjects. Both mcQQ-NET and QQ-NET used identical training and testing datasets and schemes for a fair comparison. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the sub-acute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaf-fected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR. Therefore, mcQQ-NET improves OEF accuracy by better reflecting realistic data noise characteristics.

Nuts are a staple food consumed throughout the world, and their purity is essential to maintaining human health. However, fungal mycotoxins pose one of the real challenges in keeping nuts clean for human consumption. The high cost of equipment, reagents used, standard samples, as well as increased requirements for personnel qualifications limit the use of chromatographic methods in testing laboratories, both manufacturing enterprises and organizations monitoring food safety. The present study aimed to test a new method for extracting aflatoxin B1 using sodium chloride and citric acid instead of methanol present in imported nuts obtained from local markets in Astana (Kazakhstan). The proposed method for extracting aflatoxin B1 from nuts is chemically safe, since the substances used belong to hazard class 3 of the international classification and do not require separate premises in food safety laboratories of the Astana markets and special training of specialists. The technical objective of the method is to replace the reagent (methanol) during the sample preparation process when conducting cocuret enzyme-linked immunosorbent assay. Aflatoxin B1 levels varied depending on the type of nut. Aflatoxin contamination was observed in 35.8% of samples analyzed. These levels of mycotoxin contamination are alarming and raise serious questions about the purity of imported nuts to ensure food safety.

Agriculture is pivotal in national economies, with pest detection significantly influencing food quality and quantity. Pest classification remains challenging in automated agriculture monitoring systems, exacerbated by the non-uniform pest scales and the scarcity of high-quality datasets. In this study, we constructed a pest dataset by acquiring domain-agnostic images from the Internet and resizing them to a standardized 299x299 pixel format. Additionally, we employed diffusion models to generate supplementary data. While Convolutional Neural Networks (CNNs) are prevalent for prediction and classification, they often lack effective global information integration and discriminative feature representation. To address these limitations, we propose the RS Transformer, an innovative model that combines elements like the Region Proposal Network, Swin Transformer, and ROI Align. Additionally, we introduce the Randomly Generated Stable Diffusion Dataset (RGSDD) to augment the availability of high-quality pest datasets. Extensive experimental evaluations demonstrate the superiority of our approach compared to both two-stage models (SSD and Faster R-CNN) and one-stage models (YOLOv3, YOLOv4, YOLOv5m, YOLOv8, and DETR). We rigorously assess performance using metrics such as mean Average Precision (mAP), F1Score, Recall, and mean Detection Time (mDT). Our research contributes to advancing pest detection methodologies in automated agriculture systems, promising improved food production and quality.

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 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.

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.

(200 words) Imidazolium ionic liquids containing acetylacetone, thenoyltrifluoroacetone, or 8-hydroxyquinoline, respectively, were used as the extracting agents for the separation of traces of iron (III) from its aqueous solutions with or without citric and oxalic acids. The results show that 8-hydroxyquinoline in imidazolium ionic liquids extract iron quantitatively from all the tested solutions including complexing ones, regardless indications of unexpected iron behavior/speciation.

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.

The perovskite solar cells (PSCs) have been significantly improved by utilizing inorganic hole transporting layer (HTL), such as nickel oxide. Despite the promising properties, there are still limitations due to defects. Recently, research on self-assembled monolayers (SAM) is being actively conducted, which show promise in reducing defects and enhancing device performance. In this study, we successfully engineered a p-i-n perovskite solar cell structure utilizing HC-A1 and HC-A4 molecules. These SAM molecules were found to enhance the grain morphology and uniformity of the perovskite film, which are critical factors in determining optical properties and device performance. Notably, HC-A4 demonstrated superior performance due to its distinct hydrophilic properties with a contact angle of 50.3°, attributable to its unique functional groups. Overall, the HC-A4 applied film exhibited efficient carrier extraction properties, attaining a carrier lifetime of 117.33 ns. Furthermore, HC-A4 contributed the superior device performance with its favorable band alignment, achieving the highest device efficiency of 20%.

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.

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.

Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, we propose novel hybrid machine learning approaches that exploit auxiliary numerical data. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reduction and the dependency degree is used as a fitness function. We investigate the effect of the attribute reduction process with thirty different classification and prediction models in addition to the proposed hybrid model. Then, different machine learning models are constructed based on classification and regression techniques to predict solar radiation. Moreover, other hybrid prediction models are formulated to use the output of the numerical model of Weather Research and Forecasting (WRF) as learning elements in order to improve the prediction accuracy. The proposed methodologies are evaluated using a data set that is collected from different regions in Saudi Arabia.

This paper presents a novel method to extract local features, which instead of calculating local extrema computes global maxima in a discretized scale-space representation. To avoid obtaining precise scales by interpolation and to achieve perfect rotation invariance, two essential techniques, increasing the width of kernels in pixel and utilizing disk-shaped convolution template are adopted in this method. Since the size of a convolution template is finite and finite templates can introduce computational error into convolution, we sufficiently discuss this problem and work out an upper bound of the computational error. The upper bound is utilized in the method to ensure that all features obtained are computed under a given tolerance. Besides, the technique of relative threshold to determine features is adopted to reinforce the robustness for the scene of changing illumination. Simulations show that this new method attains high performance of repeatability in various situations including scale change, rotation, blur, JPEG compression, illumination change and even viewpoint change.

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.

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.

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.

Electrical Resistance Tomography (ERT) has been used in the literature to monitor the gas-liquid separation. However, the image reconstruction algorithms used in the studies take a considerable amount of time to generate the tomograms, which is far above the time scales of the flow inside the inline separator and, as a consequence, the technique is not fast enough to capture all the rele-vant dynamics of the process, vital for control applications. This article proposes a new strategy based on the physics behind the measurement and simple logics to monitor the separation with a high temporal resolution by minimizing both the amount of data and the calculations required to reconstruct one frame of the flow. To demonstrate its potential, the electronics of an ERT system are used together with a high-speed camera to measure the flow inside an inline swirl separator. For the 16-electrode system used in this study, only 12 measurements are required to reconstruct the whole flow distribution with the proposed algorithm, 10x less than the minimum number of measurements of ERT (120). In terms of computational effort, the technique was shown to be 1000x faster than solving the inverse problem non-iteratively via the Gauss-Newton approach, one of the computationally cheapest techniques available. Therefore, this novel algorithm has the potential to achieve measurement speeds in the order of 104 times the ERT speed in the context of inline swirl separation, pointing to flow measurements at around 10kHz while keeping the aver-age estimation error below 6 mm in the worst case scenario.

Deep learning is based on matrix computing with a large amount of hidden parameters that is not visible outside the computing module. Deep learning is a non-linear system and a linear approach is not possible. It is natural for people to visualize the algorithm and to follow some hidden parameters. In this paper, we propose a simple graphical programming of a nonlinear system based on drawing the simplest unit, such as a single neuron model. A more complex scheme of a classical neural network is obtained by commands copy-move-place of one neuron. The number of neurons and layers can be chosen arbitrarily. Once the scheme is complete, the implementation code is evaluated with symbolic parameters and nonlinear activation functions. This cannot be done manually. With the symbolic expression of outputs in terms of inputs and symbolic parameters, including symbolic activation pure functions, some other properties can be derived in closed form. This unique original approach can help scientists and developers and design numerical algorithms for machine learning and to understand how deep learning algorithms work.

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.

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.

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

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.

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.

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.

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.

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.

The present study evaluated the antioxidant, antidiabetic properties, and biocompatibility of Medicago sativa and Solidago virgaurea extracts enriched in flavonoid and isoflavonoid compounds. The extracts were obtained by accelerated solvent extraction and laser irradiation. Then, nanofiltration was used for the concentration of flavonoid and isoflavonoid compounds from extracts. The extracts were analyzed for antioxidant capacity using DPPH radical scavenging and reducing power methods, while the antidiabetic property was tested by α-amylase and α-glucosidase inhibition and in vitro on a murine insulinoma cell line (β-TC-6). M. sativa obtained by laser irradiation and concentrated by nanofiltration had the highest DPPH• scavenging (IC50 = 105.2±1.1 and reducing power activities (IC50 = 40.98±0.2 µg/mL). M. sativa extracts had higher inhibition on α-amylase (IC50 = 23.9±1.2, respectively 26.8±1.1), while S. virgaurea had the highest α-glucosidase inhibition (9.3±0.9 ?g/mL respectively 8.6±0.7 ?g/mL). The results obtained after evaluating the antidiabetic in vitro activity showed that the treatment with M. sativa and S. virgaurea flavonoid- and isoflavonoid-rich extracts stimulated the insulin secretion of β-TC-6 cells, both under normal conditions as well as in hyperglycemic conditions. This paper argued that M. sativa and S. virgaurea flavonoid-rich and isoflavonoid-rich extracts could be an excellent natural source with promising antidiabetic potential.

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.

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and L1 loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.

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.

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.

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.

With the increasing availability of large digitized fine art collections, automated analysis and classification of paintings is becoming an interesting area of research. However, due to domain specificity, implicit subjectivity, and pervasive nuances that vaguely separate art movements, analyzing art using machine learning techniques poses significant challenges. Residual networks, or variants thereof, are one the most popular tools for image classification tasks, which can extract relevant features for well-defined classes. In this case study, we focus on the classification of a selected painting 'Portrait of the Painter Charles Bruni' by Johann Kupetzky and the analysis of the performance of the proposed classifier. We show that the features extracted during residual network training can be useful for image retrieval within search systems in online art collections.

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.

This paper first introduces the main classification of brain diseases and the main causes of these diseases. It is followed by wavelet analysis, which is a mathematical function or wave-like pattern used to transform data, which can decompose the signal into different wavelet functions, each of which is related to a specific scale or frequency. Wavelet analysis has been applied in many fields such as image processing, data analysis and engineering. In addition, wavelet is also used in the analysis of biological signals. Wavelet analysis breaks down brain signals, such as EEG or fMRI data, into various frequency components at different scales. Wavelet analysis provides time-frequency localization. Pattern recognition can be enhanced by isolating salient features in the data. Wavelet denoising can effectively separate noise from underlying brain signals. Wavelet denoising usually adopts threshold method. Wavelet analysis can enhance pattern recognition by isolating salient features in the data. Researchers can train machine learning models based on these wavelet-derived features to recognize specific patterns associated with different neurological disorders. Wavelet analysis can track these changes by continuously evaluating the frequency content of the neuroimage data over time. Through continuous efforts, wavelet theory and technology have become valuable tools in the field of neuroscience and brain disease research.

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.

Accurate weight measurement is pivotal for monitoring the growth and well-being of cattle. However, the conventional weighing process, which involves physically placing cattle on scales, is labor-intensive and distressing for the animals. Hence, the development of automated cattle weight prediction techniques assumes critical significance. This study proposes a weight prediction approach for Korean cattle using 3D segmentation-based feature extraction and regression machine learning techniques from incomplete 3D shapes acquired from real farm environments. In the initial phase, we generated mesh data of 3D Korean cattle shapes using a multiple-camera system. Subsequently, deep learning-based 3D segmentation with the PointNet network model was employed to segment two dominant parts of the cattle. From these segmented parts, three crucial dimensions of Korean cattle were extracted. Finally, we implemented five regression machine learning models (CatBoost regression, LightGBM, Polynomial regression, Random Forest regression, and XGBoost regression) for weight prediction. To validate our approach, we captured 270 Korean cattle in various poses, totaling 1190 poses of 270 cattle. The best result was achieved with mean absolute error (MAE) of 25.2 kg and mean absolute percent error (MAPE) of 5.81% using the random forest regression model.

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.

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 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.

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.

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.

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.

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.

In this study, the extraction of rare earth elements (REEs) from chloride solutions containing mixtures of these metals with solutions of the mixtures of P507 and Cyanex 272 (1:1) of various concentrations was experimentally studied. It was shown that the distribution coefficients of all REEs decrease with increasing concentration of these metals in the initial solution, which is associated with the loading of the organic phase. The most significant improvement in the extraction is observed for heavy group of rare earth elements. The extractability of REEs enhances with increasing atomic number of the element, as is typical for the extraction of these metals with acidic organophosphorus extractants. The data obtained show that separation factors of adjacent rare earth elements mainly decrease with increasing concentration of metals in the initial aqueous solution. Increasing the concentration of the extractant mixture does not have a significant effect on the values of the adjacent REE separation factors. Analysis of the experimental results shows that two commercial products can be obtained from a concentrate of the medium-heavy group. In the first cascade, yttrium can be separated, in the second, a concentrate of dysprosium and terbium can be obtained.

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.

The groundbreaking Advanced Forest-Based Tagging Framework (AFTF) represents a paradigm shift in the domain of information extraction from medical texts. AFTF introduces a novel dual-binary tree structure that redefines how entity-relation triples are extracted. This innovative approach directly tackles the limitations of traditional linear and graph-based methods, effectively addressing challenges related to overlapping triples and computational efficiency. The AFTF model stands as a beacon of excellence, surpassing established baselines by significant margins in comprehensive evaluations on two pivotal medical datasets. Notably, AFTF achieves remarkable improvements in F1 scores, showcasing its prowess in accurate information extraction from complex medical narratives. Beyond its exceptional performance in the medical domain, AFTF exhibits remarkable versatility. This adaptability is vividly demonstrated through its robust performance across three diverse public datasets, further affirming its position as a versatile and reliable solution for information extraction tasks. This paper provides a comprehensive exposition of the AFTF architecture, shedding light on its innovative design principles and its efficient handling of intricate medical texts. AFTF represents a groundbreaking step forward in the realm of information extraction, promising enhanced accuracy, efficiency, and adaptability for a wide range of applications.

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.

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.

Speciation analysis is a relevant topic since the (eco)toxicity, bioavailability, bio (geo)chemical cycles, and mobility of a given element depend on its chemical forms (oxidation state, organic ligands, etc.). Reliability of analytical results for chemical species of elements depends mostly on the maintaining of their stability during sample pretreatment step and on the selectivity of further separation step. Solid phase extraction (SPE) is a matter of choice as most suitable and widely used procedure for both enrichment of chemical species of elements and their separation. Features of sorbent material are of great importance to ensure extraction efficiency from one side and selectivity from other side of SPE procedure. This review presents an update on the application of polymeric materials in solid-phase extraction used in non-chromatographic methods for speciation analysis.

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.

(1) Background: Information overload challenges decision-making in the Industry 4.0 era. While Natural Language Processing (NLP), especially Automatic Text Summarization (ATS), offers solutions, issues with factual accuracy persist. This research bridges cognitive neuroscience and NLP, aiming to improve model interpretability. (2) Methods: This research examined four fact extraction techniques: dependency relation, named entity recognition, part-of-speech tagging, and TF-IDF, in order to explore their correlation with human EEG signals. Representational Similarity Analysis (RSA) was applied to gauge the relationship between language models and brain activity. (3) Results: Named entity recognition showed the highest sensitivity to EEG signals, marking the most significant differentiation between factual and non-factual words with a score of -0.99. The dependency relation followed with -0.90, while part-of-speech tagging and TF-IDF resulted in 0.07 and -0.52, respectively. Deep language models such as GloVe, BERT, and GPT-2 exhibited noticeable influences on RSA scores, highlighting the nuanced interplay between brain activity and these models. (4) Conclusions: Our findings emphasize the crucial role of named entity recognition and dependency relations in fact extraction and demonstrate the independent effects of different models and TOIs on RSA scores. These insights aim to refine algorithms to reflect human text processing better, thereby enhancing ATS models’ factual integrity.

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.

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.

Tuning tensor program generation involves navigating a vast search space to find optimal program transformations and measurements for a program on target hardware. The complexity of this process is further amplified by the exponential combinations of transformations, especially in heterogeneous environments. This research addresses these challenges by introducing a novel approach that learns the joint neural network and hardware features space, facilitating knowledge transfer to new, unseen target hardware. A comprehensive analysis is conducted on the existing state-of-the-art dataset, TenSet, including a thorough examination of test split strategies and the proposal of methodologies for dataset pruning. Leveraging an attention-inspired technique, we tailor the tuning of tensor programs to embed both neural network and hardware-specific features. Notably, our approach substantially reduces the dataset size by up to 53% compared to the baseline without compromising Pairwise Comparison Accuracy (PCA). Furthermore, our proposed methodology demonstrates competitive or improved mean inference times with only 25%40% of the baseline tuning time across various networks and target hardware. The attention-based tuner can effectively utilize schedules learned from previous hardware program measurements to optimize tensor program tuning on previously unseen hardware, achieving a top-5 accuracy exceeding 90%. This research introduces a significant advancement in auto-tuning tensor program generation, addressing the complexities associated with heterogeneous environments and showcasing promising results regarding efficiency and accuracy.

: Peach processing generates significant amounts of by-products including peels, pomace, and seeds that are often discarded as waste, despite their rich content of bioactive components. Various extraction methods, such as solvent extraction, ultrasound-assisted extraction, and alkaline and acid hydrolysis, have been employed to recover valuable components from peach by-products and repurpose them in various products. These compounds have shown potential applications in the food, pharmaceutical, and cosmetic industries due to their antioxidant, antimicrobial, and anti-inflammatory properties. Furthermore, these wastes can also be used to produce functional ingredients, natural colorants, dietary supplements Alternative uses consist the use as animal feed, as composting materials and biofuels. This comprehensive review provides an overview of the valorization of peach by-products, focusing on their valorization and extraction techniques and potential applications.

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.

Fire outbreaks continue to cause damage despite the improvements in fire-detection tools and algorithms. It is still challenging to implement a well performing and optimized approach, which is sufficiently accurate, and has tractable complexity and low false alarming rate. Small amount of fire and identification of fire from a long distance is also a challenge in previously proposed techniques. In this study, we propose a novel hybrid model based on Convolutional Neural Networks (CNN) to detect and analyze fire intensity. 21 convolutional layers, 24 Rectified Linear Unit (ReLU) layers, 6 pooling layers, 3 fully connected layers, 2 dropout layers, and a softmax layer are included in the proposed 57-layer CNN model. Our proposed model performs instance segmentation in order to distinguish between fire and non-fire events. To reduce the intricacy of the proposed model, we also propose a key-frame extraction algorithm. The proposed model uses Internet of Things (IoT) devices to alert the relevant person by calculating the severity of fire. Our proposed model is tested on a publicly available dataset having fire and normal videos. The achievement of 95.25 % classification accuracy, 0.09% False Positive Rate (FPR), 0.65 percent False Negative Rate (FNR), and a prediction time of 0.08 seconds validates the proposed system.

This study focuses on the reasoning of symptoms of TCM internal diseases. Taking cough as an example, the BERT-BiLSTM-CRF model is used for entity recognition. Experiments show that the model has the best entity recognition effect on three types of texts, including teaching cases, clinical cases and literature data, and the F1 value is up to 0.967. It can effectively identify symptoms, course of disease, tongue condition and pulse condition in TCM diagnosis and treatment texts, which lays a solid foundation for intelligent assisted syndrome differentiation and treatment of TCM. By constructing four types of entity corpora and using three mainstream entity recognition models for experiments, it is found that the BERT-BiLSTM-CRF model has a good entity recognition effect in three types of data: teaching cases, clinical cases and literature data. Experimental results show that the F1 values of the BERT-bilSTM-CRF model in teaching cases, clinical cases and literature data are 0.967, 0.82 and 0.91, respectively, which provides an effective method for information extraction in the field of TCM syndrome differentiation diagnosis and treatment, and lays a foundation for further research on knowledge reasoning.

With the development of artificial intelligence, the ability of capturing the background characteristics of hyperspectral imagery (HSI) is improved and promising performance in hyperspectral anomaly detection (HAD) tasks is yielded. However, existing methods proposed in recent years still suffer from certain limitations: 1) constraints are lacking in the deep feature learning process in terms of the issue for absence of prior background and anomaly information. 2) hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. 3) architectures of fully connected deep network in hyperspectral anomaly detectors lead to low utilization of spatial information and destruction for original spatial relationship of hyperspectral imagery, and disregard spectral correlation between adjacent pixels. 4) hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. With the consideration of above problems, in this paper, we propose a novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency (FCAE-DCAC) for HAD which is carried out in a self-supervised learning-based processing. Firstly, the density-based spatial clustering of applications with noise algorithm and connected component analysis are utilized for successive spectral and spatial clustering to obtain more precise prior background and anomaly information, which facilitates the separation between background and anomaly samples during the training of our method. Subsequently, a novel fully convolutional auto-encoder (FCAE) integrated with spatial-spectral joint attention (SSJA) is proposed to enhance the utilization of spatial information and augment feature expression. In addition, a latent feature adversarial consistency network is proposed to achieve pure background reconstruction with the ability of learning actual background distribution in hyperspectral imagery. Finally, a triplet loss is introduced to enhance the separability between background and anomaly, and the reconstruction residual serves as the anomaly detection result. We evaluate the proposed method on seven groups of real-world hyperspectral datasets, and the experimental results confirmed the effectiveness and superior performance of the proposed method versus nine state-of-the-art methods.

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 residue of chestnut processing generates a large amount of waste material, a resource not adequately exploited. The phenolic composition of water extracts from discarded pericarp of four (MURG, LOCG, ILDP and COEV) chestnut accessions and one marron variety belonging to the Sardinian biodiversity was studied. The antioxidant capacity of cold and hot water extracts was determined by DPPH, ABTS and cyclic voltammetry tests. The antiproliferative effect on normal cells (fibroblasts), and colon (RKO and SW48), breast (MCF7) and melanoma (B16F10) cancer cells, was evaluated by biological assays. MTT test demonstrated that temperature and different extraction times significantly influenced the growth of cells, both normal and tumor. The fibroblast viability was significantly reduced by moderate doses of cold extracts, but only by doses greater than 250 µg/ml of hot extracts, regardless of the accession or cultivar. An even more marked effect was observed when RKO and SW48 were treated with cold extracts, while treatments on B16F10 melanoma cells resulted less effective. Differently, the cold extracts of all accessions induced a significant increase in MCF7 cell viability starting from moderate doses (50 µg/ml), whereas hot extracts had a similar, but less evident, effect compared to reference fibroblasts.

German chamomile galenic preparations are used to treat mild skin diseases, inflammation and spasms, and they have also reported to have anxiolytic and sedative effects. The medicinal use of chamomile is well-known in ethnomedicine. Since chamomile has shown to have some adverse effects, there is a need to develop new formulations, such as dry extracts, for the present herb. The aims of this study were (1) to develop a novel method for preparing essential oils and dry extracts from German chamomile flowers, (2) to reveal the phytochemical composition of such extracts, and (3) to verify the analgesic and soporific activity of extracts in vivo with a rodent animal model. Furthermore, our study aims to gain knowledge of the ethnomedical status of chamomile in the past and presence. Essential oils and dry extracts were successfully prepared from the tincture and aqueous extracts obtained after hydrodistillation and from a tincture powder. Total 22 phenolic compounds (7 hydroxycinnamic acids, 13 flavonoids and 2 phenolic acids), were found in the dry extracts by using UPLC-MS/MS. Total 9 main terpenoids were identified in the chamomile oil, and these terpenoids represent the chemotype rich in bisabolol oxides A and B. In the highest yield, the ratio of phenolic compounds and extract was in the range of 1:14-1:16 and triple extraction was performed. In in-vivo studies with mice and rats, the extracts showed analgesic activity and improvements in sleep. The highest sedative effect in rodents was found with the extract prepared by using a 70% aqueous ethanol solution for extraction. The administration of such extract at the dose of 50 mg/kg significantly prolonged the sleeping time in rats.

Pineapple weed (Matricaria discoidea DC.) is a widely spread plant in Europe and North America. In ethnomedicine, it is well known for its anti-inflammatory and spasmolytic activity. The aim of the research was to develop novel methods of M. discoidea herb complex processing for creating the essential oil and the dry extracts and to investigate their phytochemical composition. Moreo-ver, the molecular docking of main substances and in vivo their soporific and analgesic activity were studied. The essential oil and two dry extracts from M. discoidea were prepared. Total 16 phenolic compounds (7 flavonoids, 7 hydroxycinnamic, 2 phenolic acids) in the dry extracts were identified by means of UPLC-MS/MS. In the essential oil 9 main terpenoids were identified by gas chromatography (GC). We showed that the phenolics extraction from the present herb was successful by using 70% ethanol in a triple extraction and in the range of 1:14-1:16. The in-vivo studies with rodents showed analgesic activity of M. discoidea extracts and the improvements in the sleep of animals. The dry extracts of M. discoidea herb did not show any toxicity. The molecular docking showed the high probability of COX-1,2 inhibition and NMDA receptor antagonism induced by the extracts.

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.

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%.

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.

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.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of high-repetition-rate CO2 pulsed Laser on the target extraction and tracking performance of the infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between target and dazzling images is performed to quantitatively describe laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that laser power and target position are crucial factors affecting the detection performance of infrared imaging-guided weapon systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of infrared imaging-guided weapons at different distances. And laser dazzling will produce a lot of false edge information, which seriously affects the performance of pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for functional damage to the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to evaluating damage results. Finally, the proposed evaluation formula aligns with experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging-guided weapons.

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.

In industrial settings, gears play a crucial role by assisting various machinery functions such as speed control, torque manipulation, and altering motion direction.The malfunction or failure of these gear components can have serious repercussions, resulting in production halts and financial losses. As a result, there is an increasing requirement to monitor the state of these components in order to avoid such issues from occurring. To address this need, research efforts have focused on early defect detection in gears in order to reduce the impact of possible failures. This study focused on analyzing vibration and thermal datasets from two extruder machine gearboxes using an autoencoder Long Short-Term Memory (LSTM) model. The major goal is to implement an outlier detection approach to detect and classify defects. The results of this study highlighted the extraordinary performance of the Autoencoder LSTM model, which achieved an impressive accuracy rate of 94.42% in recognizing malfunctioning gearboxes within the extruder machine system. Furthermore, the study used a thorough global metrics evaluation methodology to further test the model’s dependability and efficacy, consequently substantiating the proposed approach’s validity.

Wild strawberry (Fragaria vesca L.) leaves possess diverse antioxidant properties, mainly due to the different classes of phenolics. In this study, microwave-assisted extraction (MAE) and accelerated solvent extraction (ASE) of wild strawberry phenolics were carried out to understand the impact of extraction temperature, extraction time and solvent-to-sample ratio (SSR) on the quantitative and qualitative properties of the obtained extracts. The highest total phenolic content (8027 mg GA/100 g DW), as well the highest DPPH• antiradical activity (903 mol TE/g DW) was obtained with ASE at 150 °C, static time 5 min and SSR of 40:1; while the highest ABTS•+ antiradical activity (681 mol TE/g DW,) and FRAP (2389 mol TE/g DW) was obtained with MAE after 5 min at 80 °C and a SSR of 40:1. The correlation analysis showed that phenolics largely contributed to the antioxidant properties of the studied MAE extracts, while correlation was not found in ASE extracts. A total of 54 different phenolics were identified by UPLC/MS-MS in MAE and ASE extracts obtained at optimal extraction conditions. MAE extract was shown to have a higher content of phenolic acids (40%), but lower content of proanthocyanidins (88%), flavonols (29%), flavan-3-ols (50%) and flavones (39%) than ASE extract.

The study aimed to isolate and characterize bacteriocin-producing Enterococcus species from Ethiopian traditional fermented foods and beverages. The bacteriocin bioassay was tested against food-borne pathogens at various temperatures, pH, solvents, additives, and enzymes. A solvent extraction method was established to concentrate bacteriocins from Enterococcus species. The study found that crude extracts with proteinase K, pepsin, and trypsin deactivated antimicrobial activity, confirming their proteinaceous nature. Organic solvents were more effective at extracting bacteriocins from CFS, with chloroform alone and in combination providing rapid and efficient recovery. The optimal bacteriocin production and strong inhibition activity were achieved at 37°C with a pH range of 5.5 to 6.5 incubated for 16-18 hours. The six Enterococcus faecium species were the best bacteriocin-producing isolates. Optimizing bacteriocin production and changing environmental growing circumstances can enhance the application of bacteriocins as food preservation agents. Traditional fermented products are potential sources of bacteriocin-producing LAB.

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.

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.

With the rapid advancements in remote sensing technology, the spectral information from hyperspectral remote sensing images has become increasingly rich, facilitating detailed spectral analysis of the Earth's surface objects. However, this abundance of spectral data poses significant challenges in data processing, such as the curse of dimensionality leading to the “Hughes” phenomenon, "strong correlation" due to high resolution, and "non-linear characteristics" caused by varied surface reflectance rates. Therefore, dimensionality reduction of hyperspectral data has become a crucial task. This paper, grounded in manifold theory and manifold learning techniques, and considering the non-linear structures and features in hyperspectral remote sensing data, elucidates the principles and processes of dimensionality reduction in hyperspectral remote sensing images using manifold learning, with a formalized expression of the process. This article introduces spectral information divergence (SID) into the nearest neighbor graph computation of manifold learning algorithms. The principles and computational processes of nearest neighbor graph algorithms based on Euclidean distance (ED), spectral angle mapping (SAM), and SID are studied, and a comparative analysis of the dimensionality reduction effects under these three metrics in hyperspectral data is conducted. Experiments on feature extraction under different metrics were performed using the publicly available Indian Pines hyperspectral dataset. The intrinsic features obtained post-dimensionality reduction were used as inputs for classification algorithms in ground objects classification experiments, with algorithm runtime, overall accuracy, and Kappa coefficient as evaluation metrics for dimensionality reduction quality. The results demonstrate that nearest neighbor graph computation based on SAM and SID outperforms traditional ED methods; SAM-based computation has the lowest time complexity, while SID-based manifold learning yields the highest accuracy in ground objects classification. Thus, manifold learning based on SAM and SID metrics proves to be an effective method for feature extraction in hyperspectral remote sensing data, underscoring the potential of manifold learning techniques in the dimensionality reduction of hyperspectral remote sensing images.

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.

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

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.