Numerous protocols have been published for extracting DNA from phlebotomines. Nevertheless, their small size is generally an issue in terms of yield, efficiency, and purity, for large-scale individual sand fly DNA extractions when using traditional methods. Even though this can be circumvented with commercial kits, these are generally cost-prohibitive for developing countries. We encountered these limitations when analysing parasite infection in Lutzomyia spp. by PCR [1] and, for this reason, we evaluated various modifications on a previously published protocol ([2] and Acardi personal communication). The most significant variation was the use of a different lysis buffer [3] to which added Ca2+ (buffer TESCa), because this ion protects proteinase K against autolysis, increases its thermal stability, and could have a regulatory function for its substrate-binding site [4]. Individual sand fly DNA extraction success was confirmed by amplification reactions using internal control primers that amplify a fragment of the cacophony gene [5,6]. To the best of our knowledge, this is the first time a lysis buffer containing Ca2+ has been reported for the extraction of DNA from sand flies.

The aim of this paper is to develop an intelligent event-driven Electrocardiogram (ECG) processing module in order to achieve an efficient solution for diagnosis of the cardiac diseases. The suggested method acquires the signal with an event-driven A/D converter (EDADC). The output of EDADC is passed through the activity selection and interpolation blocks. It allows focusing only on the important signal parts and resampling it uniformly. Later on, the signal is de-noised. The autoregressive (AR) method is used to extract the classifiable features of the de-noised signal. Afterwards, the output is classified by employing different robust classification techniques such as support vector machines (SVMs), K- Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The event-driven feature enables to adapt the system processing load according to the signal temporal variations. This interesting feature of the devised system aptitudes a drastic reduction in its processing activity and therefore in the power consumption as compared to the traditional ones. A comparison of the performance of different classifiers is also made in terms of accuracy. Results show that the proposed system is a potential candidate for an automatic diagnosis of the cardiac diseases.

Sports video analysis is an interesting and emerging field in the domain of computer vision. It has become a very popular subject among scholars all over the world in the last decade due to the need of automated classification of events such as offsides, in/out situations, goals and so on. The most important problem in sports video analysis is detection and tracking of ball and players. Extraction of court lines is also another important problem since it is necessary in the classification of aforementioned events. In this study, considering some of the previous work on the subject, we develop a method for tracking the ball and players in tennis videos where the camera position and orientation is supposed fixed.

Saliency detection in synthetic aperture radar (SAR) image is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR image. Firstly, we extract four features of SAR image as the input of the MSD model, which include the intensity, orientation, uniqueness and global contrast. Then, the saliency map is generated by the multitask sparsity pursuit (MTSP) which integrates the multiple features collaboratively. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps of the source images, an image fusion method is proposed for the SAR and color optical image fusion. The experimental results of real data show the proposed image fusion method is superior to the presenting methods in terms of several universal quality evaluation indexes, as well as in the visual quality. The salient areas in the SAR image can be highlighted and the spatial and spectral details of color optical image can also be preserved in the fusion result.

Lavander distillation leads to huge quantities of solid waste produced yearly. This waste is usually discarded resulting in serious environmental issues. However, it still contains residual essential oil and other bioactive compounds. This research reports on the development and comparison of optimized solid-liquid separation methods, i.e. microwave (MAE) and ultrasound assisted extraction (UAE) of phenolic compounds from lavender distillation waste. Optimal pretreatment conditions, such as waste moisture content and particle size were also determined. The extracts were spray or freeze-dried and the resulting powders were characterized for their physicochemical properties. The majority of the original phenolic compounds in lavender were found in the leachate fraction after distillation (61%), while 43% in the solid waste. Drying of the solid waste before extraction affected the process efficiency. UAE led to higher phenolic content and antioxidant properties compared to MAE. Drying (spray or freeze) the extracts did not significantly affect their phenolic content, while the use of maltodextrin as a drying agent improved the drying process yield especially in the freeze-drying method. It is concluded that valorization of lavender distillation wastes can be achieved via an integrated process consisting of a green extraction method and a consequent drying process that result in a stable bioactive powder.

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.

Biomass as a whole offers a more diverse potential for valorisation than any other renew-able energy source. As one of the stages in the separation of bio-oil involves the liquid mixture of acetol and acetic acid, and as both components are particularly well suited for valorisation, a hybrid method has been developed for their separation with a high purity level through an approach combining liquid-liquid extraction and rectification. In order to design and simulate the flowsheet, the ChemCAD 7.0 simulation software has been used. Sensitive analyses have been carried out to investigate the influence of the different pa-rameters in the rectification columns such as the reflux ratio, the feed stage location, and the vapour/bottom molar flow ratio. The effect of different extractants and of their excess on the separation process as well as the possibility of regenerating the extractant has also been studied. Tri-n-octylamine has accordingly been selected as a separating agent which has been fully recycled. The end result for separating an initial 48/52 wt% ace-tol/acetic-acid liquid mixture has been acetol with a purity of 99.4 wt% and acetic acid with a purity of 100 wt%.

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.

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

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

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

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.

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.

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.

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

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

In 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.

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.

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

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.

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.

The ionic liquid A327H+Cl- is generated by reaction of the tertiary amine A327 (industrial mixture of tri-octil and tri-decyl amines) and hydrochloric acid solutions. Further, the extraction of Au(III) by A327H+Cl- ionic liquid under various variables, including metal and ionic liquid concentrations, has been investigated. Results indicate that A327H+AuCl4- is formed, by an exothermic (ΔHº= -3 kJ/mol) reaction, in the organic solution. Aqueous ionic strength influences the formation constants values and the specific interaction theory (SIT) is used to estimate the interaction coefficient between AuCl4- and H+. Gold (III) is stripped using thiocyanate media, and from the strip solutions, gold is precipitated as gold nanoparticles.

A renewable cyclic monoterpene obtained from citrus peel, limonene is widely used as a fra-grance, nutraceutical ingredient, antibacterial, biopesticide, and green extraction solvent. Indus-trial demand largely exceeds supply. After reviewing recent advances in the recovery of limonene from citrus peel and residues with a particular attention to benign-by-design extractive processes, we focus on the latest results in its dehydrogenation to p-cymene via heterogeneous catalysis.

Asphalt binder requires more investigation to be accurately and precisely extracted since it is a significant procedure for quality control quality assurance (QC/QA) and subsequent binder characterization. In this research, the authors provided a hands-on experience with binder extraction to deliver recommendations concerning the sensitive steps that may affect the outcomes (extracted binder content, P_be%). Based on the extraction by the centrifuge method, two mineral matter determination methods (ashing and centrifuge) were addressed. Field cores were investigated with comparing the P_be% to the actual binder content, P_ba%. Analysis of variance (ANOVA) and Tukey Post-Hoc statistical analyses, in addition to linear least square regression analysis, were used to show the significance of difference according to 38 variant cores randomly obtained from the field segments (in-service roads) via the first two weeks from the construction date. Such cores involved reclaimed asphalt pavement (RAP), reclaimed asphalt shingles (RAS), and a wide range of additives. The two extraction methods were compared with concluding that the centrifuge method was highly recommended based on a quantitative evaluation, which delivered the same average P_ba% based on the 38 cores. Furthermore, the centrifuge method provided much saving in the experimental time (almost half the time required for the ashing method). It was found that the ashing outcomes were equal to the centrifuge outcomes with disregarding the ammonium carbonate addition. Thus, it could be recommended to reassess the ammonium carbonate addition as it might excessively compensate for fake minerals that have not been lost by the ignition oven.

Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition-state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady-state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain’s power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, 9 visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on Short Time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady-state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation (SD), maximum (max), and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal, and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady-state, respectively.

The procedures for the extraction and separation of lipids and nutraceutics from microalgae using classic solvents have been used many times. However, these production methods usually require expensive and toxic solvents. Based on our studies involving the use of eco-sustainable methodologies and alternative solvents, we select ethanol (EtOH) and cyclopentyl methyl ether (CPME) for extracting bio-oil and lipids from algae. Different percentage of EtOH in CPME favors the production of an oil rich of SFA useful to production biofuel or rich of compounds bioactive. The proposed method for obtain a rich extract of saturated or unsaturated fatty acids from dry algal biomass is disclosed is eco-friendly and allows a good extraction yield. The method is compared both in extracted oil percentage yield and in extracted fatty acids selectivity to extraction by supercritical carbon dioxide.

The aim of this study was to examine brightness effect, which is the perceptual property of visual stimuli, on brain responses obtained during visual processing of these stimuli. For this purpose, brain responses of the brain to changes in brightness were explored comparatively using different emotional images (pleasant, unpleasant and neutral) with different luminance levels. Moreover, electroencephalography recordings from 12 different electrode sites of 31 healthy participants were used. The power spectra obtained from the analysis of the recordings using short time Fourier transform were analyzed, and a statistical analysis was performed on features extracted from these power spectra. Statistical findings obtained from electrophysiological data were compared with those obtained from behavioral data. The results showed that the brightness of visual stimuli affected the power of brain responses depending on frequency, time and location. According to the statistically verified findings, the distinctive effect of brightness occurred in the parietal and occipital regions for all the three types of stimuli. Accordingly, the increase in the brightness of pleasant and neutral images increased the average power of responses in the parietal and occipital regions whereas the increase in the brightness of unpleasant images decreased the average power of responses in these regions. However, the increase in brightness for all the three types of stimuli reduced the average power of frontal and central region responses (except for 100-300 ms time window for unpleasant stimuli). The statistical results obtained for unpleasant images were found to be in accordance with the behavioral data. The results also revealed that the brightness of visual stimuli could be represented by changing the activity power of the brain cortex. The main contribution of this research was to comprehensively examine brightness effect on brain activity for images with different emotional content and different frequency bands at different time windows of visual processing for different brain regions. The findings emphasized that the brightness of visual stimuli should be viewed as an important parameter in studies using emotional image techniques such as image classification, emotion evaluation and neuro-marketing.

In this paper we propose a novel self-supervised approach of keywords and keyphrases retrieval and extraction by an end-to-end deep learning approach, which is trained by contextually self-labelled corpus. Our proposed approach is novel to use contextual and semantic features to extract the keywords and has outperformed the state of the art. Through the experiment the proposed approach has been proved to be better in both semantic meaning and quality than the existing popular algorithms of keyword extraction. In addition, we propose to use contextual features from bidirectional transformers to automatically label short-sentence corpus with keywords and keyphrases to build the ground truth. This process avoids the human time to label the keywords and do not need any prior knowledge. To the best of our knowledge, our published dataset in this paper is a fine domain-independent corpus of short sentences with labelled keywords and keyphrases in the NLP community.

Weed seedbank is an indication of future weed infestation potential of the species and is essential for making strategic planning for its sustainable management. Parthenium weed (Parthenium hysterophorus L.) is an invasive alien species threatening the biodiversity and the environment in Malaysia. A study was, therefore, conducted to estimate the soil seedbank of the weed at four soil depths of four villages of Kuala Muda, Kedah. The aim was to indicate the critical s of parthenium weed seedbank in Malaysia. Soil samples were collected from the sites using a soil core. The seeds were extracted from the soil samples with sieve shaker at the Universiti Malaysia Kelantan laboratory, Jeli Campus. The study indicates that the weed seedbank is in critical level at the area. The highest number of weed seeds (6915/m2) was found in Kg. Kongsi 6, followed by Kg. Sungai Tok Rawang (4481 seeds/m2). The top layer of soil, 0-5 cm, contained the maximum number of weed seeds (4878 seeds/m2) and a significant number of seeds (316 seeds/m2) were noticed at 10-15 cm soil depth. The study suggests the Malaysian government to take immediate action to control parthenium weed seedbank of the sites.

Enhancing the Efficiency of Natural Gas Liquid (NGL) Extraction and Fractionation Trains: An Integrated Approach of Simulation Analysis, Advanced Modifications, and Technological Advancements. Through comprehensive research, innovative solutions such as the heat pump aided distillation (HPAD) system and self-heat recuperation technology (SHRT) have been developed to significantly reduce the energy consumption associated with conventional distillation systems. To identify a practical system for NGL fractionation trains, this study extensively examined and compared various HPAD and SHRT options to retrofit a single column. The objective was to find the most suitable and efficient solution for the fractionation process of natural gas liquids (NGLs). The retrofit options analyzed in this study encompassed a range of techniques, including vapor compression (VC), mechanical vapor re-compression (MVR), thermal vapor re-compression (TVR), bottom flashing (BF), side heat exchanger (SHE), intermediate heating and cooling (IHC), self-heat recuperative (SHR), and modified self-heat recuperative (MSHR) distillation. These methods were carefully examined to determine their suitability and effectiveness in improving the performance of NGL fractionation trains.In this study, a depropanizer column, typically employed in conventional NGL plants, was selected as a case study. The simulation software Aspen HYSYS V7.3 was employed to model and analyze eight retrofit designs based on predefined criteria. The simulation data was carefully evaluated to identify the most efficient design for minimizing energy consumption. Among the retrofit options, the mechanical vapor re-compression (MVR) technique demonstrated the most significant energy cost reduction, with a remarkable 68.11 percent improvement compared to the base case conventional column. These findings highlight the potential of MVR as an effective solution for lowering energy costs in NGL fractionation trains.Following the MVR retrofit option, the study found that vapor compression (VC) achieved a considerable energy cost reduction of 66.65 percent, closely followed by modified self-heat recuperative (MSHR) at 64.02 percent, bottom flashing (BF) at 62.88 percent, self-heat recuperative (SHR) at 55.85 percent, side heat exchanger (SHE) at 54.23 percent, intermediate heating and cooling (IHC) at 39.54 percent, and thermal vapor re-compression (TVR) also at 39.54 percent. These findings highlight the significant potential for energy savings offered by these retrofit options, with VC being the most popular choice, closely followed by MSHR, BF, SHR, SHE, IHC, and TVR.

In order to reduce the waste of Akebia trifoliata peel and maximize its utilization. In this study, on the basis of single factor experiment and response surface method, the optimum technological conditions for the extraction of soluble dietary fiber from Akebia trifoliata peel by compound en-zyme method were obtained. The chemical composition, physical and chemical properties, structural characterization and biological activity of the purified soluble dietary fiber (AP-SDF) from the Akebia trifoliata peel were analyzed. We discovered that that the optimum yield was 20.87 % under the conditions of cellulase addition 600 U/g, enzymolysis time 100 min, solid-liquid ratio 1:24 g/mL and enzymolysis temperature 51 °C. At the same time, AP-SDF was a po-rous network structure cellulose type I acidic polysaccharose mainly composed of arabinoxylan (36.03%), galacturonic acid (27.40%) and glucose (19.00%), which possessed the structural charac-teristic peaks of the infrared spectra of polysaccharides, and the weight average molecular weight (Mw) was 95.52 kDa with good uniformity. In addition, AP-SDF exhibited high oil hold-ing capacity (15.11 g/g), good water holding capacity, swelling capacity, certain antioxidant ca-pacity in vitro, hypoglycemic activity in vitro of α-glucosidase inhibition and hypolipidemic ac-tivity in vitro of the binding ability of bile acids and cholesterol. These results will provide a theoretical basis for the development of functional products with antioxidant, hypoglycemic and hypolipidemic effects, it has certain application value in related industries.

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.

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.

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.

Tooth extraction is the most common procedure in dental practice. However, in the long term, it may cause alveolar ridge atrophy. This systematic review aimed to evaluate the role of platelet-rich fibrin (PRF) in post-exodontic alveolar ridge preservation in terms of its effectiveness in the regeneration of bone tissue as assessed by imaging and its efficacy compared to physiological bone healing. The study is presented in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. This systematic review was conducted using electronic databases such as PubMed, Scopus, Web of Science, and Science Direct. The grey literature search was conducted in the New York Academy of Medicine Grey Literature Report. All the studies in this systematic review were randomized controlled trials (RCTs). The risk of bias was performed according to the Cochrane Handbook for Systematic Reviews of Interventions 6.2 (RevMan 6.2). Considering the inclusion and exclusion criteria, we included 17 randomized clinical trials, published up to 2022 investigating the efficacy of PRF in post-exodontic bone regeneration. Based on the results of clinical studies, it can be stated that despite not being statistically significant, PRF promotes neoformation and prevents bone loss between three and four months post-extraction.

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

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.

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.

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

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

Nowadays, an analysis of virtual media to predict society’s reaction to any events or processes is a task of great relevance. Especially it concerns meaningful information on healthcare problems. Internet sources contain a large amount of pharmacologically meaningful information useful for pharmacovigilance purposes and repurposing drug use. An analysis of such a scale of information demands developing the methods that require the creation of a corpus with labeled relations among entities. Before, there have been no such Russian language datasets. This paper considers the first Russian language dataset where labeled entity pairs are divided into multiple contexts within a single text (by used drugs, by different users, by the cases of use, etc.), and a method based on the XLM-RoBERTa language model, previously trained on medical texts to evaluate the state-of-the-art accuracy for the task of indication of the four types of relationships among entities: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown based on the presented dataset from the Russian Drug Review Corpus, the developed method achieves the F1-score of 81.2% (obtained using cross-validation and averaged for the four types of relationships), which is 7.8% higher than the basic classifiers.

Background: the devastating outbreak of COVID-19 poses serious challenges for the diagnostics laboratories, which are often facing global shortage of reagents and equipment. With the aim of increasing the diagnostic throughput for SARS-CoV-2 molecular test, the purpose of this study was to validate an additional RNA extraction method respect to those already recommended by WHO and the US Centers for Disease Control and Prevention (CDC). Methods: a new protocol for RNA extraction from nasopharyngeal swab was set up, adapting the Qiagen RNeasy 96 plate and validated on a set of 100 clinical samples analyzed in parallel by Roche-Magnapure method (already recommended by CDC guidelines). Results: the internal control and target genes analysis showed a good agreement between the two extraction methods indicating that the two methods can be considered equivalent and that the RNeasy-adapted method can be applied for the SARS-CoV-2 diagnostics. The addition of this new extraction method resulted in a throughput increase for SARS-CoV-2 molecular test of about 2000 samples/month during the initial months of the pandemic emergency in which the lack of reagents for the extraction led to an insufficient sample processing throughput of the analysis of the swabs.

Objectives: This meta-analysis was designed to assess the effect of addition of a bead-beating step during DNA extraction to effectively isolate Trichuris trichura DNA for quantitative Polymerase Chain Reaction (qPCR)-based diagnosis. Abstract was reported according to PRISMA-DTA abstract checklist. Methods: Eligibility criteria: qPCR-based molecular studies comparing the inclusion of bead-beating step during the DNA extraction from stool samples with extraction without the step were included in the analysis. Information sources: Studies using real patient samples in community settings were included. PubMed and Google search engine were searched in December 2019. Risk of bias and applicability: Risk of bias and applicability were assessed using QUADAS-2 checklist. Synthesis of results: Odds ratio for individual studies were combined to estimate Random Effects Model odds ratio. Additional literature were searched to discuss biochemical nature of helminth eggs. Results:Included studies: A total of six independent sub-studies were gathered from two published original articles. Division of the two major studies into six sub-studies was indispensable due to natures of the study carried. 128 of total 192 samples (in all studies) were positive for Trichiuris trichiura when bead-beating was used during DNA extraction compared to 108/192 when bead-beating was excluded. Combined odds ratio was 1.66 (95% CI: 1.059 to 2.602). Biochemical nature of helminth eggs was discussed. Discussions: Strengths and limitations: Though only two article were included in the study, six exclusive individual sub-studies were analyzed. Inherent differences in the background prevalence of helminth in study population could impact sensitivity of qPCR. Interpretation: It was found that the inclusion of the bead-beating step during DNA extraction significantly increased the sensitivity of the test.

This study was focused on the development of oil-producing microorganisms to generate lipids. The yeast Rhodotorula mucilaginosa (R. mucilaginosa) was selected for liquid-state cultivation, and the conditions for growth of the yeast cells were assessed. Additionally, the relationships between different nutrient elements and the growth of R. mucilaginosa were explored. The lipid accumulation of R. mucilaginosa is increased under nitrogen-restricted conditions. As the concentration of the carbon source increases, the accumulation of lipids is increased. However, if the carbon source concentration is further increased, the growth of yeast is inhibited. From a large-scale liquid fermentation culture with a fixed inoculation amount of 5%, and from a batch of culture experiments, it was determined that a suitable oil-producing culture was obtained on the 6th day, and the optimum conditions involved a carbon source concentration of 60 g/L, a nitrogen source concentration of 0.5 g/L, and a KH2PO4 concentration of 7.0 g/L. After utilizing different carbon sources in this study, it was found that glucose was the carbon source most conducive to the accumulation of R. mucilaginosa lipids. In addition, the extraction method and solvent for the extraction of R. mucilaginosa lipids were chosen. The acid-heat method using the green organic solvent ethyl acetate exhibited the best performance for extraction of yeast lipids under environmentally friendly and safe conditions. The analysis of lipids showed that the fatty acids obtained primarily contained C16:0, C18:1 and C18:2, and especially C18:1 (41%) lipids, indicating that R. mucilaginosa lipids are a good bio-oil source for the production of biodiesel.

The production of instant green tea requires hot-water extraction, which is time consuming and contributes to losses in aromatic compounds. In this study, an ultrasonic-assisted technology was used to improve the extraction efficiency of green tea, thereby shortening extraction time from 45 to 15 min. In pure water, the dissolution of caffeine and theanine did not change significantly, but total catechin dissolution increased by 0.23 mg/mL and total tea polyphenol dissolution decreased by 3.2 mg/mL. In 76.2% ethanol, the dissolution of caffeine and theanine did not change significantly, but total catechin dissolution increased by 1.57mg/mL and total tea polyphenol dissolution decreased by 1.5 mg/mL. Additionally, we used microwave-assisted technology to further improve the extraction efficiency of green tea, which shortened the extraction time to 2 min. However, the extraction rate remained unchanged. In pure water, the dissolution of caffeine and theanine did not change significantly, but the dissolution of total catechins increased by 0.41 mg/mL and the dissolution of tea polyphenols decreased by 2.9 mg/mL. Ultrasonic treatment increased the proportion of 3-hydroxybutan-2-one, (5S)-5-(hydroxymethyl)oxolan-2-one and 2-phenylethanol, which were the main aroma compounds in tea. Microwave treatment changed the aroma compounds in tea, causing losses in aroma compounds with low boiling point and maintaining (5S)-5-(hydroxymethyl)oxolan-2-one. The taste and aroma of instant green tea improved based on sensory evaluation results.

Diesel engines, as power equipment, are widely used in the fields of automobile industry, ship and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in time domain and angular domain, on which the current diagnosis methods based, are easily affected by working conditions or hard to extract accurate enough, as the diesel engine keeps running in transient and non-stationary process. This work arms at diagnosing this fault mainly based on frequency band features which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively，a decomposition technique based on improved variational mode decomposition is investigated in this work. As the connection between the features and the fault is fuzzy, the random forest algorithm is used to analyze the correspondence between features and faults. In addition, the feature dimension is reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition show that the method based on variational mode decomposition and random forest is capable to detect valve clearance fault effectively.

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.

The present work describes the optimization and validation of a highly selective and sensitive analytical method using solid phase extraction and liquid chromatography tandem mass spectrometry (SPE LC-MS/MS) for the determination of some frequently prescribed pharmaceuticals in urban wastewater received and treated by Sharjah sewage treatment plant (STP). The extraction efficiency of different SPE cartridges was tested and the simultaneous extraction of pharmaceuticals was successfully accomplished using hydrophilic-lipophilic-balanced reversed phase Waters^® Oasis HLB cartridge (200 mg/ 6 mL) at pH 3. The analytes were separated on an Aquity BEH C₁₈ column (1.7 µm, 2.1 mm x 150 mm) using gradient elution and the mass spectrometric analysis were performed in multiple reactions monitoring (MRM) selecting two precursor ions to produce ion transition for each pharmaceutical using positive electrospray ionization (+ESI) mode. The correlation coefficient values in the linear calibration plot for each target compound exceeded 0.99 and the recovery percentages of the investigated pharmaceuticals were more than 84%. Limit of detection (LOD) varied between 0.1-1.5 ng/L and limit of quantification (LOQ) was 0.3-5 ng/L for all analytes. The precision of the method was calculated as the relative standard deviation (RSD%) of replicate measurements and was found to be in the ranges of 2.2% to 7.7% and 2.2% to 8.6% for inter and intra-day analysis, respectively. All of the obtained validation parameters satisfied the requirements and guidelines of analytical method validation.

Pharmaceuticals contain biologically active components that can pollute water courses as a result of the excretions from individuals and/or uncontrolled release of residues from chemical plants, and they can pose a hazard to health. Pharmaceutical residues can persist at low concentrations in the environment, and thus may be potentially harmful to aquatic animals and to humans. Controlling and monitoring such residues are therefore a prime interest, for example, a solid-phase extraction uses solid sorbents to purify and preconcentrate the residues prior to their chemical analysis. In the present study, poly (acrylonitrile-co-divinylbenzene-80) sorbents are synthesised by varying the comonomer feed ratios under precipitation polymerisation conditions to deliver a family of porous polymer microspheres. Acrylonitrile confers polar characters onto the sorbents, and the acrylonitrile-derived nitrile groups can be chemically transformed via polymer-analogous reactions into thioamide and sulfonated residues which make the sorbents even more suitable for the capture of polar analytes, including selected pharmaceuticals. The utility of the porous thioamide-sulfonated containing sorbents is demonstrated via the dispersion-solid phase extraction of mefenamic acid from aqueous media; mefenamic acid is an anthranilic acid derivative which is a potent, non-steroidal anti-inflammatory drug which is found in environmental waters at low concentrations.

Automated Electrocardiogram (ECG) processing is an important technique which helps in identifying abnormalities in the heart before any formal diagnosis. This research presents a real-time and lightweight R-assisted feature extraction algorithm and a heartbeat classification scheme which achieves highly accurate abnormality detection. In the proposed algorithm, we extract fifteen features from each heartbeat taken from raw Lead-II ECG signals. The features carry medically valuable information such as locations, amplitude and energy of ECG waves (P, Q, R, S, T waves) which are then used for detection of any abnormality that might be present in the heartbeat using various classification algorithms. We have used four popular databases from Physionet and extracted ten thousand ECG signals from each for training the models and benchmarking results. Four classification models i.e. Naïve Bays, k-Nearest Neighbor, Neural Network, Decision Tree were used for abnormality detection validating the efficiency of the system.

The vibration signal of the engine contains strong background noise and many kinds of modulating components, which is difficult to diagnose. Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). VMD can effectively avoid endpoint effect and modal aliasing. However, VMD cannot effectively eliminate the random noise in the signal, so the random decrement technique is introduced to solve the problem. Based on the crankshaft bearing fault simulation experiment, the four kinds of wear state vibration signals are decomposed by VMD, and the modal components with smaller permutation entropy are selected as fault components. Then the fault component is processed by the random decrement technique, and the Hilbert envelope spectrum of the fault component is obtained. Compared with the fault feature extraction method based on EMD and EEMD, the feature extraction results of the proposed method are better than those of the above two methods. The simulation analysis and the simulation test of the crankshaft bearing fault verify the effectiveness of the proposed method.

At present, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed and treatment results. EMR has been recognized as a valuable resource for large scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation and data reduction. For semi-structured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (Named Entity Recognition) and RE (Relation Extraction). In this paper, we introduce the process of EMR processing, including data collection, data preprocessing, data mining, evaluation and knowledge application, analyze the current status of the key technologies, such as data preprocessing and data mining, and provide an overview of the application domains and prospects of EMR mining technologies. Finally, we summarize the existing problems in the research of EMR mining, and review the development trends.

More and more works are using machine learning techniques while adopting supervisory control and data acquisition (SCADA) system for wind turbine anomaly or failure detection. While parameter selection is important for modelling a wind turbine’s health condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. Moreover, after proving that Copula, a multivariate probability distribution for which the marginal probability distribution of each variable is uniform is capable of simplifying the estimation of mutual information, an empirical copula based mutual information estimation method (ECMI) is introduced for an application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

The removal of chromium(VI) from acidic (HCl) medium through non-dispersive extraction with strip dispersion (NDXSD) was investigated using a microporous PVDF membrane support in a permeation cell. The ionic liquid Cyphos IL102 (phosphonium salt) in Solvesso 100 was used as organic phase. In NDXSD the stripping phase (NaOH) is dispersed in the organic phase in the cell side with an impeller stirrer adequate to form a strip dispersion. This pseudo-emulsion phase (organic + strip solutions) provides a constant supply of the Cyphos IL102/Solvesso 100 to the membrane phase. Various hydrodynamic and chemical parameters, such as variation in the feed and pseudo-emulsion stirring speeds, HCl and Cr(VI) concentrations in the feed phase, and carrier concentration, were investigated. From the experimental data, several mass transfer coefficients were estimated, as well as it was investigated the performance of the present system against other ionic liquids and the presence of base metals in the feed phase.

Though nowadays germanium do not reach the range of popularity than other metals, i.e. rare earth elements, its utility in target industries makes of it also a strategic metal. Though germanium can be find in a series of raw materials, the principal source for its recovery is from secondary wastes of the zinc industry, and also, the recycle of germanium-bearing waste materials is becoming of interest. In this recovery, Hydrometallurgy is performing a key role to the successful achievement of this goal. The present work reviews the most recent applications (2023 and 2024 years) of hydrometallurgical operations on the recovery of germanium from different solid and liquid sources.

Inadequately managed agricultural waste significantly impacts the environment, health, and economy. This form of pollution arises from the underutilization, poor awareness, and insufficient treatment of agricultural waste. Fruit and vegetable wastes are valuable sources of bioactive compounds. This study aims to revalorize discarded waste from red habanero chili peppers (Capsicum chinense) by extracting bioactive compounds through different extraction processes: maceration (ME), maceration assisted by ultrasound (US), Soxhlet extraction (SE), supercritical fluid extraction (SFE), and supercritical fluid extraction with a co-solvent (SFEC). Extraction processes have significant effects on extraction efficiency and phytochemical profile (capsaicinoids and carotenoids recovery). The results indicate that the highest efficiency process is obtained with SFEC, also a high phytochemicals recovery (14.9 mg of total capsaicinoids and total carotenoids 292.09 µg per gram of sample). Concerning to phytochemical profile in the extract, the maceration process gets the highest concentration of compound followed by US and SFEC. These data reveal that the use of SFE and SFEC extraction is recommended for extraction of phytochemicals with biologically activity from red habanero chili pepper waste for diverse industries application.

This narrative review provides a thorough examination of medication-related osteonecrosis of the jaw (MRONJ), encompassing its etiology, the role of drug holidays, and contemporary treatment approaches. By systematically analyzing literature from 2022 to 2023, this paper synthesizes the latest findings and trends in MRONJ research. The study emphasizes the importance of a holistic understanding of MRONJ, beginning with its etiological factors, assessing the therapeutic implications of drug holidays, and evaluating the efficacy of current treatment modalities. This review aims to offer clinicians and researchers a consolidated resource, highlighting the intricacies of MRONJ management and proposing directions for future research. Through this comprehensive approach, the paper seeks to advance the understanding and treatment of MRONJ, ensuring an evidence-based, patient-centric approach in clinical practice.

Eggplant or brinjal is one of the most consumed and important nutrient-dense tropical solanaceous vegetable crops grown worldwide. Little leaf is a disease especially widespread in brinjal in India where it is associated with the presence of phytoplasmas. To clarify the epidemiology of this disease and verify its seed transmission potential, seedlings and their progeny derived from symptomatic mother plants field-collected in Dharwad district of Karnataka State, India, were sowed under insect proof conditions in greenhouse. The resulting first- and second-generation seedlings obtained were tested at different times after germination by DNA extraction and amplification of 16S rRNA gene of phytoplasmas. Tissues from field collected symptomatic brinjal plants were also tested following the same procedure. The amplicons obtained were subjected to restriction fragment length polymorphism (RFLP) analysis and sequencing for phytoplasma identification. The detected phytoplasmas belonged to 16SrI, 16SrII, 16SrIII, 16SrV, 16SrVI, and 16SrXII groups. Several fruits produced from the first-generation seedlings showed germination of the seeds inside the fruit and resulted to be phytoplasma positive. This demonstrates the seed transmission of phytoplasmas in eggplants for two subsequent generations. This finding highlights the risk of additional source of infection represented by asymptomatic and infected seedlings under field conditions and contribute to explain the epidemic spread of phytoplasmas in brinjal cultivations in India and in the other eggplant growing areas of the world.

In the realm of artificial intelligence, knowledge graphs have become a fascinating area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1,500 generic medicines. It offers a groundbreaking approach to storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, as well as queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as diverse range of healthcare professionals as well as AI applications.

In this paper, we demonstrate that fixed-weight layers, generated from random distribution or logistic functions, can effectively extract significant features from input data, resulting in high accuracy on a variety of tasks, including Fisher’s iris, Wisconsin Breast Cancer, and MNIST datasets. We have observed that logistic functions yield high accuracy with less dispersion in results. We have also assessed the precision of our approach under conditions of minimizing spikes number generated in the network, it is a practically useful for reducing energy consumption in spiking neural networks. Our findings reveal that the proposed method demonstrates a highest accuracy on Fisher’s iris and MNIST datasets with logistic regression. Furthermore, they surpass the accuracy of the conventional (non-spiking) approach using logistic regression in the case of Wisconsin Breast Cancer. We have also investigated the impact of non-stochastic spike generation on accuracy.

Underwater image processing faces significant challenges due to the absorption and scattering of light as it travels through water. A self-supervised learning network based on the self-attention mechanism is proposed for underwater visual applications in this paper. The goal is to improve the effectiveness and stability of feature extraction from underwater images. By incorporating self-attention mechanism, the sensitivity of the original network architecture to degraded features of blurred underwater images is enhanced. The network proposed in this paper is trained using transfer learning and evaluated on various underwater image datasets. With distributive, quantitative, and qualitative advantages compared with other methods, experimental results have demonstrated that the algorithm presented shows a slighter decline in feature extraction ability for blurred images as the turbidity of water increases.

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.

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.

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

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

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

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.

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.

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.

Sesamum indicum is considered an underutilized oil-bearing seed in the semi-arid regions of Ghana. Nonetheless, it is a promising source of food with both nutritional and therapeutic benefits. The aim of the present study was to evaluate the antioxidant properties S. indicum seeds using different extraction solvents. The seeds were obtained from the local farmers and prepared for analysis. The bioactive compounds present in the seeds were extracted using hexane, ethyl acetate, ethanol and water and their yields quantified. Total phenolic content (TPC), Condensed tannin content (TTC) and Total antioxidant capacity (TAC) , and DPPH radical scaveging assay were analyzed using standard methods. Antinutrients such as saponins, alkaloids, phytates and oxalates were also analyzed from the powdered seeds. Two chemometric methods; hierarchical cluster analysis (HCA) and Pearson correlation were employed to evaluate the interdependence of the various parameters to result in their antioxidant properties. The re-sults revealed that the solvents utilized had a significant impact on the extraction yield, phyto-chemical component concentration, and antioxidant activities. Hexane extracts of S. indicum seeds significantly exhibited the highest antioxidant activity (p < 0.05). It was marked with the highest TAC value of 232.6 ± 6.267 mg/g AAE and a strong DPPH scavenging activity with an IC50 of 52.81 ± 2.30 µg/mL. Correlations (p < 0.05) was established between TPC,CTC, TAC and DPPH radical scavenging activity) of the extracts. Antinutrients such as; phytate, oxalate, saponins and alkaloids were found to be 7.691 ± 0.8576, 1.501 ± 0.1375, 21.33 ± 4.619 and 317.33 ± 30.29 mg/g respectively. Data obtained suggest that S. indicum possess rich bioactive compounds that can be used in neutraceuticals and food products.

The main intention of the present work was to investigate the ability of cellulose-degrading enzymes (C-DE) to release fatty acids (FAs) from complex matrices of cereal by-products during enzymatic hydrolysis (EH). For this purpose, three types of cereal bran (CB), i.e., wheat, rye, and oat were used as a lignocellulose substrate for three commercially available hydrolytic enzymes, i.e., Viscozyme L, Viscoferm, and Celluclast 1.5 L. The yield and composition of FAs after EH was assessed and confronted with the yield obtained after either conventional Soxhlet extraction or alkaline-assisted hydrolysis (A-AH) with 10% KOH in 80% MeOH and subsequent liquid-liquid extraction. The experimental results demonstrated that up to 6.3% and 43.7% higher total FAs yield can be achieved within EH of rye bran using Celluclast 1.5 L than by A-AH and Soxhlet extraction, respectively. However, the application of Viscoferm for EH of wheat bran ensured up to 7.7% and 13.4% higher total FAs yield than A-AH and Soxhlet extraction, respectively. The concentration of essential linolenic acid (C18:3) in lipids extracted after EH of rye bran with Celluclast 1.5 L was up to 24.4% and 57.0% higher than in lipids recovered by A-AH and Soxhlet extraction, respectively. In turn, the highest content of linolenic in wheat bran lipids was observed after EH with Viscoferm and Viscozyme L, ensuring 17.0 and 13.6% higher yield than after A-AH, respectively. SEM analysis confirmed substantial degradation of CB matrix promoted by the ability of C-DE to act specifically on 1,4-β-D-glycosidic bonds in cellulose and on 1,2-α-,1,3-α-, and 1,5-α-L- arabinofuranoside and 1,4-β-D-xylosidic bonds in arabinoxylans, arabinans, and other arabinose-containing hemicelluloses. Structural alteration in cells integrity greatly contributed to the release of bound FAs and their better transfer into the extraction solvent. It has been shown that the proposed process of EH can be used for the efficient release of FAs from the CB matrix more sustainably and with a safer profile, thereby representing the further sustainable production of FAs for certain purposes.

Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

Simultaneous extraction of trypsin inhibitors and soy isoflavones from soybean meal was investigated using the non-destructive phytochemical extraction process, namely aqueous micellar system. The ethoxylated aliphatic alcohols Genapol X-080, Tergitol 15-S-7, and Tergitol 15-S-9, all non-toxic and biodegradable surfactants, were assessed as potential extractants. A Box-Behnken multifactorial design with the application of the Derringer desirability was used to determine the conditions that maximized the trypsin inhibitors and isoflavone extraction while minimizing the protein extraction. The optimum condition of 5% m/m of surfactant in 50 mM aqueous sodium citrate solution pH 4.5, at 45 °C for 45 min, was established for the three surfactants. The novel methodology would allow the extraction of the main soybean antinutritional factors, trypsin inhibitors, and the valuable isoflavones, preserving the nutritional quality of the treated material. This represents a sustainable alternative methodology for industrial purposes due to its low cost, biodegradability, non-toxicity, and easy scaling up.

Vanadium containing slurry is a by-product of vanadium pentoxide by hydrometallurgical methods from vanadium slag. It is promising technogenic raw material for vanadium production. The phase analysis of vanadium-containing slurry by X-ray diffraction method has shown that it contains vanadium in spinel form (FeO∙V2O3). The various oxidation roasting methods for slurry treatment have been studied for increasing vanadium extraction into the solution. It has shown that the most effective additive is 1% CaCO3 at a roasting temperature of 1000 °C. The oxidation roasting of vanadium-containing slurry with the additive led to increase acid-soluble form of V2O5 from 1.5 to 3.7% and decrease the content of FeO∙V2O3 from 3 to 0.4%. These results have confirmed the efficiency of the application of oxidation roasting to convert vanadium compounds into acid-soluble forms. The conversion mechanism of spinel to acid-soluble phases during oxidation roasting with additives was investigated by thermogravimetric analysis and thermodynamic simulation. It has shown that the formation of acid-soluble calcium vanadates during oxidation roasting without additives occurs at temperatures above 800 °C, but СаСО3 addition allows to reduce this temperature to 600 °C.

A novel polyphenol‑coated CoFe₂O₄ system was synthesized as a magnetic adsorbent by chemical oxidative polymerization process for magnetic solid-phase extraction of lawsone. The synthesized nanoadsorbent showed a spherical morphology with diameters under 50 nm by scanning electron microscopy images. The extraction efficiency of this adsorbent was studied towards the extraction of lawsone from saline aqueous solution in dispersion mode. Major parameters including the type and volume of desorption solvent, amount of sorbent, desorption time, extraction time, extraction temperature, ionic strength and pH were optimized. Under the optimum conditions the relative standard deviation in 0.005 µg mL^-1 (inter-day n = 6; intra-day: n = 6; and adsorbent to adsorbent n = 4) were obtained as 5.2, 8.07 and 11.7%, respectively. A linear calibration curve in the range of 0.003–0.5 µg mL^-1 with R² = 0.993 was obtained. The limit of detection and limit of quantification of the method were 0.001 µg mL^-1 and 0.003 µg mL^-1, respectively. The relative recovery percentages were in range of 90-96.4% for henna leaves, henna shampoo, and henna dermal lotion real samples.

Molecular analysis is an easier means to identify and isolate a specific gene which has imperative function for growth, body composition , fat deposition, metabolic and skeletal traits as well as the molecular genetics selection on individual genes is a very efficient method to genetically improve economically important traits in chickens. Insulin- like growth factor 1 ( IGF-1)is a member of a heterogeneous group of peptides with important growth.Transforming growth factorβ ( TGF-β) belongs to a large family of growth and differentiation factors that play a pivotal role in a great variety of biological activities including morphogenesis, development and differentiation. DNA was extracted from 48 chickens sampled from three strains Lohman (17) , Sinai (24) and Gimmizah (7) IGF-1 gene and TGFβ2 gene were amplified using PCR protocol. Electrophoresis was carried out on the products of PCR , bands viewed on transilluminator. The size of IGF-1 gene was 675 bp while the size of TGFβ2 gene was 188bp. Sharp bands were purified and sequenced and used the dendrogram to show the relationships between other vertebrate species

In this research, low-fat dried Chinese sausage was formulated with mango peel pectin (MPP) extracted by microwave assisted extraction (MAE) (0%, 5%, 10% and 15% (w/w). The extractable yield of pectin attained from peel of Nam Dok Mai variety was achieved at 13.85% using 700-watt power. The extracted MPP were of high equivalent weight (1,485.78 mg/mol), degree esterification (77.19%) and methoxyl content (19.33%) with the structure of more porosity as compared to that of the conventional method. Spectrum scans by Fourier transform infrared spectrophotometer (FT-IR) advised that the extracted MPP gave the similar wave number profiles as the commercial pectin. Quality attributes of the Chinese sausages were accessed and compared with the control formula (CTRL). At higher concentrations of MPP, the product had positively increased colour intensity. The texture profile of the sausage illustrated that only the hardness value was comparable with the CTRL, while springiness, cohesiveness, gumminess and chewiness were statistically lower (p < 0.05). Furthermore, the sensory evaluation by experienced panellists (n=12) indicated that 5% MPP similarly represented overall acceptability with the CTRL. Consequently, MPP can be effectively applied at low level as fat replacement in Chinese sausage allowing colour improvement and product of healthier option.

A valorization process of spent coffee grounds (SCG) was studied. Thus, a two-stage process, a stage of extraction of the polyphenols and a stage of obtaining activated carbon (AC) by a carbonization process, was performed. The extraction was carried out with a hydro-alcoholic solution in a pressure reactor, modifying time and temperature. To optimize the extraction of polyphenols, a two-level factorial design with three replications at the central values was used. The best results were obtained by performing the extraction at 80 °C during 30 min, using a mixture of EtOH:H2O 1:1 (v/v) as extraction solution. Caffeine and chlorogenic acid were the most abundant compounds in the analyzed extracts, ranging from 0.09 to 4.8 mg∙g^-1 and 0.06 to 9.7 mg∙g^-1, respectively. The precursor obtained in the extraction stage were transformed into AC. An experimental design was realized in order to analyze the influence of different variables in the AC obtained process (reaction time and amount of potassium hydroxide used). Actived carbons with BET specific surface (SBET) comprised between 1600 m^2∙g^-1 and 2330 m²∙g^-1 had a microporous surface. Under the optimum conditions, the obtained AC presented a maximum adsorption capacity of methylene blue (qm) between 411 mg∙g^-1 and 813 mg∙g^-1.

Fabric phase sorptive extraction, an innovative integration of solid phase extraction and solid phase microextraction principles, has been combined with gas chromatography-mass spectrometry for the rapid extraction and determination of nineteen organochlorine pesticides in various fruit juices and water samples. FPSE consolidates the advanced features of sol-gel derived extraction sorbents with the rich surface chemistry of cellulose fabric substrate, which could directly extract target analytes from complex sample matrices and substantially simplifies the sample preparation operation. Important FPSE parameters including sorbent chemistry, extraction time, stirring speed, type and volume of back-extraction solvent and back-extraction time have been optimized. Calibration curves were obtained in the concentration range 0.1-500 ng/mL. Under the optimum conditions, the limits of detection were obtained in the range 0.007-0.032 ng/mL with satisfactory precision (RSD<6%). The relative recoveries obtained by spiking organochlorine pesticides in water and selected juice samples were in the range of 91.56–99.83%. The sorbent sol-gel poly(ethylene glycol)-poly(propylene glycol)-poly(ethylene glycol) was applied for the extraction and preconcentration of organochlorine pesticides in aqueous and fruit juice samples prior to analysis with gas chromatography-mass spectrometry. The results demonstrated that the present method is simple, rapid, and precise for the determination of organochlorine pesticides in aqueous samples.

In this paper, we propose a novel approach of video skimming by exploiting the fusion of video temporal information and keyword information representation extracted from multi-model video information including audio, text and visual indices. In addition, we introduce the brand-safe filtering and sentiment analysis in order to only reserve the user-friendly content in the video skim. In the experiment by using the videos from YouTube-8M dataset, we have proved that the semantic conservation in the video skim from the proposed approach highly outperforms the approaches by only partial information of the video in conserving the semantic content of the video.

High Brassicaceae consumption reduces the risk of developing several cancer types, probably due to their glucosinolate amount. Extracts from Sinapis nigra L. and Sinapis alba L. have been obtained from leaves and seeds under different conditions using ethanol/water mixtures because well accepted by food industry. The EtOH/H2O 8:2 mixture gives better yields in glucosinolate amounts from grinded seeds, mainly sinalbin in S. alba and sinigrin in S. nigra. The highest antiproliferative activity in both non-tumour and tumour cell lines was induced by S. alba seeds extract. To evaluate whether Sinapis spp effect was only due to glucosinolate content or it was influenced by the extracts’ complexity, cells were treated with extracts or glucosinolates, in the presence of myrosinase. Pure sinigrin did not modify cell proliferation, while pure sinalbin was less effective than the extract. The addition of myrosinase increased the antiproliferative effects of the S. nigra extract and sinigrin. Antiproliferative activity was correlated to MAPKs modulation, which was cell and extract-dependent. Cell-cycle analysis evidenced a proapoptotic effect of S. alba on both tumour cell lines and of S. nigra only on HCT 116. Both extracts showed good antimicrobial activity in disc diffusion tests and on ready-to-eat fresh salad. These results underline the potential effects of Sinapis spp in chemoprevention and food preservation.

In addition to image filtering in the spatial and frequency domains, fractal characteristics induced algorithms offers considerable flexibility in the design and implementations of image processing solutions in areas such as image enhancement, image restoration, image data compression and spectrum of applications of practical interests. Facing up to a real-world problem of identifying workpiece surface defects, a generic adaptive fractal filtering algorithm is proposed, which shows advantages on the problems of target recognition, feature extraction and image denoising at multiple scales. First, we reveal the physical principles underlying between signal SNR and its representative fractal dimension indicative parameters, validating that the fractal dimension can be used to adaptively obtain the image features. Second, an adaptive fractal filtering algorithm (Abbreviated as AFFA) is proposed according to the identified correlation between the image fractal dimensions and the scales of objects, and it is verified by a benchmarking image processing case study. Third, by using the proposed fractal filtering algorithm, surface defects on a flange workpiece are identified. Compared to conventional image processing algorithms, the proposed algorithm shows superior computing simplicity and better performance Numerical analysis and engineering case studies show that the fractal dimension is eligible for deriving an adaptive filtering algorithm for diverse-scale object identification, and the proposed AFFA is feasible for general application in workpiece surface defect detection.

This paper describes the use of the Chinese Gaofen-1 (GF-1) satellite imagery to automatically extract tertiary Linear Archaeological Traces of Tuntian Irrigation Canals (LATTICs) located in the Miran site. The site is adjacent to the ancient Loulan Kingdom at the eastern margin of the Taklimakan Desert in western China. GF-1 data was processed following atmospheric and geometric correction, and spectral analyses were carried out for multispectral data. The low values produced by SSI indicate that it is difficult to distinguish buried tertiary LATTICs from similar backgrounds using spectral signatures. Thus, based on the textual characteristics of high-resolutionGF-1 panchromatic data, this paper proposes an automatic approach that combines joint morphological bottom and hat transformation with a Canny edge operator. The operator was improved by adding stages of geometric filtering and gradient vector direction analysis. Finally, the detected edges of tertiary LATTICs were extracted using the GIS-based draw tool and converted into shapefiles for archaeological mapping within a GIS environment. The proposed automatic approach was verified with an average accuracy of 95.76% for 754 tertiary LATTICs in the entire Miran site and compared with previous manual interpretation results. The results indicate that GF-1 VHR PAN imagery can successfully uncover the ancient tuntian agricultural landscape. Moreover, the proposed method can be generalized and applied to extract linear archaeological traces such as soil and crop marks in other geographic locations.

A possible way to valorize citrus peels, which are byproducts of the juice extraction industry, is to use them as natural biopreservatives. In this paper we present early results from a compared Solvent Free Microwave Extraction (SFME) with Hydro-Distillation (HD) and Cold Pressing (CP) of essential oils (EOs) using fresh orange peel (Citrus sinensis L. var. Valencia late), a by-product in the production of orange juice in Algeria. The EOs were analyzed by gas chromatography coupled to mass spectrometry (GC-MS). All extracted C. sinensis EOs were chemotype limonene (94.64 to 95.48%). SFME is performed without added any solvent or water. SFME increases EO yield and eliminate wastewater treatment, resulting in a great progress in terms of time and cost efficiency. In its second part, the present study was conducted to evaluate “in vitro”, the antioxidant activities of Solvent Free Microwave (SFM) extracted orange EO by using the DPPH• (2,2-di-phenyl-1-picrilhydrazyl) free radical scavenging assay. The ability of orange EO to scavenge the free radical DPPH• was high, exceeding 80%. The result of the DPPH assay gives an IC₅₀ range value of 89.25 μg/mL (0.09 mg/mL) for the studied sample. Accordingly to the scientific literature, C. sinensis EO tested in the present study presented strong antioxidant activity, when looking to its values of AAI = 1.12 μg/mL. The feasibility of biopreservation used EOs as an alternative to synthetic techniques for liquid whole egg (LWE) stored under commercial retail conditions was investigated. The orange EO extracted by SFM was screened for its antibacterial and antioxidant activities in LWE at concentrations of 0.1, 0.3 and 0.5%. The TBA-RS results showed that the EO treatments significantly (p < 0.05) reduced the lipid oxidation in LWE. The long term oxidative, microbial and organoleptical stability of the LWE during display was positively influenced by orange EO treatments. Therefore, the results obtained here confirm that EO treatment as a promising technology to extend the commercial shelf-life of liquid egg products during retail/display.

A multi-tool analytical practice was used for characterization of 16^th century carpet manufactured in Cairo. Mild extraction method with hydrofluoric acid enabled isolation of intact flavonoids and their glycosides, anthraquinones, tannins and indigoids from fibre samples. High-performance liquid chromatography coupled to spectroscopic and mass spectrometric detectors was used for identification of natural dyes present in the historical samples. Weld, young fustic and brazilwood were identified as the dye sources in yellow thread samples. Red fibres have been colored with lac dye, whereas green fibre shades were obtained with indigo and weld. Tannin-containing plant material in combination with indigo and weld were used to obtain brown hue of thread. Four uncommon and thus-far unknown dye components were also found in the historical samples. These compounds probably represent unique fingerprint of dyed threads from this region. Scanning electron microscopy with energy-dispersive X-ray detector (SEM-EDS) and Fourier transformation infrared spectroscopy (FT-IR) were used for identification and characterization of substrates and mordants present in the historical carpet. Carbon and oxygen were detected in large quantities as a part of the wool protein. The presence of aluminum, iron and calcium indicated their usage as mordants. FT-IR analysis showed bands characteristic to woolen fibres and SEM micrographs definite structure of wool.

Background: A new access technique was developed to reduce postoperative adverse events after the extraction of impacted maxillary wisdom teeth. Hence, this study aimed to assess the occurrence of adverse events after extraction of maxillary impacted wisdom teeth using traditional or modified access. Methods: Two different surgical incision designs were used for bilateral wisdom tooth extractions in 30 patients. The traditional incision was performed distal to the second molar in the center of the tuberosity, followed by a buccal releasing incision. After tooth extraction the wound was secured by sutures. The new technique consists of an oblique incision from the distal palatal aspect of the tuberosity towards the buccal aspect of the second molar. After tooth extraction, cyanoacrylate glue was used on the wound. Results: Lower pain was reported by patients at the site treated with the new technique (p&lt;0.01). Edema, postsurgical bleeding, and hematoma were similar in both groups. The surgical time was shorter for the new technique (p&lt;0.01). Conclusions: The new technique applied for the extraction of impacted maxillary wisdom teeth reduced postsurgical pain and the duration of surgery.

Pyrethroid pesticides (PYRs) as the third most widely used pesticides, following organophosphorus and carbamate pesticides, which are of significant importance in the analysis and detection of vegetables. However, the current pretreatment technology for PYRs confronts challenges of difficult separation and low enrichment efficiency, resulting in a cumbersome and time-consuming pretreatment process with poor selectivity. Here, a simple and efficient magnetic solid-phase extraction (MSPE) strategy was developed to simultaneously purify and enrich five PYRs in vegetables, with the magnetic covalent organic framework nanomaterial COF-SiO2@Fe3O4 as an adsorbent. The COF-SiO2@Fe3O4 was prepared by a simple solvothermal conditions method, using Fe3O4 as magnetic core, benzidine and 3,3,5,5-tetraaldehyde biphenyl as two building units. COF-SiO2@Fe3O4 could effectively capture the targeted PYRs by virtue of its abundant π-electron system and hydroxyl groups. The impact of various experimental parameters on extraction efficiency was investigated to optimize the MSPE conditions, including adsorbent amount, extraction time, elution solvent type and elution time. Subsequently, method validation was conducted under the optimal conditions in conjunction with gas chromatography-mass spectrometry (GC-MS). Within the range of 5.00–100 μg·kg-1 (1.00–100 μg·kg-1 for bifenthrin and 2.5–100 μg·kg-1 for fenpropathrin), the five PYRs exhibited a strong linear relationship, with determination coefficients ranging from 0.9990 to 0.9997. The limits of detection (LODs) were 0.3–1.5 μg·kg-1, and the limits of quantification (LOQs) were 0.9–4.5 μg·kg-1. The recoveries were 80.2–116.7% with relative standard deviations (RSDs) below 7.0%. Finally, COF-SiO2@Fe3O4, NH2-SiO2@Fe3O4 and Fe3O4 were compared as MSPE adsorbents for PYRs. The results indicated that COF-SiO2@Fe3O4 was an efficient and rapid selective adsorbent for PYRs. This method holds promising prospects for the determination of PYRs in real samples.

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%.

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.

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.

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.

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

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.

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

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

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

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

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.

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

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