In this paper, a capacitor microphone system is presented to improve the conversion of mechanical energy to electrical energy using a nonlinear auto regressive moving average-L2 (NARMA-L2) and model predictive control (MPC) controllers for the analysis of the open loop and closed loop system. The open loop system response shows that the output voltage signal need to be improved. The comparison of the closed loop system with the proposed controllers have been analyzed and a promising result have been obtained using Matlab/Simulink.

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.

Enormous quantities of softwood and hardwood tree bark are generated yearly in the pulp and woodworking industry. Chemical compounds which can be found in bark extracts have important biological properties and therefore a high potential for using in the production of special formulations. In the present study, extraction of Picea abies bark with organosolv solvents was investigated to achieve a high yield of desirable compounds. For the extraction four different solvent ratios of ethanol and water were applied. The temperature range of extraction was set between 40 and 100 °C and stirring velocity of 400 rpm. Extractions were performed with a solid/liquid ratio of 1:20. Yields of phenolics, flavonoids and tannins were determined using gallic acid for total phenolic content (TPC), quercetin for total flavonoid content (TFC) and tannic acid for total tannins content (TTC) as calibration compounds. Qualitative analysis of phenolic constituents was performed after their derivatization, by gas chromatography coupled with mass spectrometry identification. The total yield of bark extract of 14.75 wt.% was achieved by using an ethanol/water ratio of 50% (v/v) and a temperature of 100 °C. Gas chromatography results show 9 aromatic carboxylic acids as phenolic acids and quercetin and (+)-catechin as flavonoid compounds. Highest concentrations of TPC = 3.21 mg gallic acid equivalent/g, TCF = 0.62 mg quercetin equivalent/g and TTC = 0.84 tannic acid equivalent/g were determined at the same process conditions by photometric method. This study determined the conditions for organosolv extraction of useful compounds and suggests that some of extractives, e.g., taxifolin and rhamnetin presented in the spruce bark extracts, have antiviral (SARS-CoV-2) effects. Therefore, this warrants further evaluation of the antiviral properties of spruce bark constituents.

Nymphaea lotus L. is the medicinal plant that has long been used as food, cosmetic and traditional medicines in Africa and Asia since the ancient time. Its flavonoids and other interesting phytochemical compounds from rhizome, leaf, and the whole flowers have been reported in the previous published researches. However, stamens, which are essential for reproductive functions, may also represent new alternative sources of potential antioxidant flavonoids as investigated in this study. The innovative green chemistry method i.e. ultrasound-assisted extraction (USAE) as well as macroporous resin (MPR) purification procedure were employed in this current research. The optimal ultrasound-assisted extraction condition is 90 % (v/v) aqEtOH with 34.65 khz ultrasonic frequency and 46 minutes of extraction time. Comparing with heat reflux extraction (HRE) conventional method, the significant gain of 1.35 total flavonoids content was obtained using optimized USAE conditions, jumping to 2.80 when this USAE associated with MPR purification. Not only in vitro cell free antioxidant activity of N. lotus stamen extracts, but also in cellulo antioxidant investigation using yeast model showed the same trend to indicate that the best antioxidant flavonoid can be found in USAE coupled with MPR purification. Moreover, the key antioxidant genes expression in yeast model such as SIR2 and SOD2 were also expressed at the highest level in yeast cell treated with the extract from USAE together with MPR purification. Consequently, it can be seen that the USAE combined with MPR purification can help to enhance the flavonoids antioxidant potential of the stamens extract from this medicinal species.

Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the performance of such devices exceeds 90\% in controlled environments, myoelectric devices still face challenges in robustness to variability of daily living conditions. Within this survey, the intrisic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where performance was 13% and 20% lower in realistic environments compared to controlled environments for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

Inclusions in steels and alloys are known as factors that could lower deformation, mechanical, corrosion and other properties. Study of inclusions in nickel based alloys is important since these materials could suffer from corrosion degradation in harsh operational conditions, where inclusions could lead to a pitting initiation. For estimation of a harmful effect of different inclusions on corrosion resistance of Ni-based alloys, inclusion characteristics (such as composition, morphology, size, number and location) on film filter and dissolution of metal matrix around different inclusions on surfaces of metal samples after electrolytic extraction (EE) were investigated in two samples of industrial Ni-based alloys (alloy 718 and EP718). It was found that both Ni-based alloys have various inclusions: carbides (large size NbTi-C and small multicomponent carbides), nitrides TiNb-N and sulfides (TiNb-S in EP718 alloy). The higher harmful effect on corrosion resistance of metal was detected around sulfides and small carbides containing Mo, W, Cr. Dissolution effect was also observed around large carbides and nitrides, especially around inclusions having size more than 10 µm. Moreover, the dissolution of metal matrix around inclusions and clusters located on the grain boundaries is 2.1-2.7 times larger compared to that for those inclusions inside of grains of the given alloy samples.

A statistical analysis of semantic memory should reflect the complex, multifactorial structure of the relations among its items. Still, a dominant paradigm in the study of semantic memory has been the idea that the mental representation of concepts is structured along a simple branching tree spanned by superordinate and subordinate categories. We propose a generative model of item representation with correlations that overcomes the limitations of a tree structure. The items are generated through "factors" that represent semantic features or real-world attributes. The correlation between items has its source in the extent to which items share such factors and the strength of such factors: if many factors are balanced, correlations are overall low; whereas if a few factors dominate, they become strong. Our model allows for correlations that are neither trivial nor hierarchical, but may reproduce the general spectrum of correlations present in a data-set of nouns. We provide an estimate of the number of concepts that can be stored and retrieved by a large-scale cortical network, the Potts network, which is perhaps approximately 10⁷ with human cortical parameters. When this storage capacity is exceeded, however, retrieval fails completely only for balanced factors; above a critical degree of imbalance, a phase transition leads to a regime where the network still extracts considerable information about the cued item, even if not recovering its detailed representation: partial categorization seems to emerge spontaneously as a consequence of the dominance of particular factors, rather than being imposed ad hoc. We argue this to be a relevant model of semantic memory resilience in Tulving’s remember/know paradigms.

Machine Learning has completely transformed health care system, which transmits medical data through IOT sensors. So it is very important to encrypt them to protect patient data. encrypting medical images from a performance perspective consumes time; hence the use of an auto encoder is essential. An auto encoder is used in this work to compress the image as a vector prior to the encryption process. The digital image passes across description function and a decoder to get back the image in the proposed work; various experiments are carried out on hyper parameters to achieve the highest outcome of the classification. The findings demonstrate that the combination of Mean Square Logarithmic Error as the loss function, ADA grad as an optimizer, two layers for the encoder, and another reverse for the decoder, RELU as the activation function generates the best auto encoder results. The combination of Mean square error (lose function), RMS prop (optimizer), three layers for the encoder and another reverse for the decoder, and RELU (activation function) has the best classification result. All the experiments with different hyper parameter has run almost very close to each other even when changing the number of layers. The running time is between 9 and 16 second for each epoch.

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.

A nonrenewable resource extraction game model is analyzed in a differential game theory framework with random duration. If the c.d.f. of the final time is discontinuous, the related subgames are differentiated based on the position of the initial instant with respect to the jump. We investigate properties of optimal trajectories and of imputation distribution procedures if the game is played cooperatively.

Marine invertebrates are model organisms in several areas of biological sciences, being a source of massive biological information. Although, the scientific relevance of marine invertebrates, the research with them can be limited for their tissue characteristics and troubles for the replication of physical and chemical properties of seawater. Thence, the main goal of this laboratory workflow is to provide a useful methodological approach to reduce the experimental limitations during the study of marine invertebrates. The present study describes experimental methodologies for the collection, transport, and maintenance of sessile tunicates. Also, an approach to observe and characterize, a diverse population of blood cells in marine invertebrates, by several cytological stains and electron microscopy. Lastly, suggestions and protocols to extract quality RNA from samples with high concentrations of salts, pigments, secondary metabolites, and polysaccharides. This methodological approach can be easily adapted to other marine invertebrates, moreover uses low-cost reagents and widely available laboratory equipment. Making possible the study of different types of marine animals in diverse locations.

Silybum marianum (L.) Gaertn. (aka milk thistle) constitute the almost exclusive source of silymarin, a mixture of different flavonolignans, and is thus considered as a unique model for their extraction. The present research deals with ultrasound-assisted extraction (UAE) of S. marianum flavonolignans and their quantification using LC system. The optimal conditions for UAE were: aqueous EtOH 54.5% (v/v) as solvent, applying an ultrasound frequency of 36.6 kHz during an extraction time of 60 min at 45°C with a liquid to solid ratio of 25:1 ml/g DW. Following optimization, the extraction method was validated according to international standards of the association of analytical communities (AOAC) in order to ensure its precision and accuracy for the quantitation of the individual silymarin components. The efficiency of UAE was compared with maceration protocol of the same duration. The optimized and validated conditions allowed highest extraction yields of flavonolignans in comparison to maceration. The antioxidant capacity of the extracts was confirmed by the CUPRAC assays and inhibition of advanced glycation end products. The skin anti-aging action was also confirmed toward the strong in vitro inhibition capacity of the obtained extract against collagenase and elastase enzymes. The procedure presented here allows a green efficient extraction and quantification of the main flavonolignans from the fruits of S. marianum with attractive antioxidant and anti-aging activities for future cosmetic applications.

Cold brew coffee is a new trend in coffee industry. This paper presents pilot studies into several aspect of this beverage. Using an online survey, the current practices of cold brew coffee preparation were investigated identifying a rather large variability with a preference for extraction of medium roasted Arabica coffee using 50-100 g/l at 8°C for about 1 day. Sensory testing using ranking and triangle tests showed that cold brew may be preferred over iced coffee (cooled down hot extracted coffee). Extraction experiments at different conditions combined with nuclear magnetic resonance (NMR) analysis showed that the usual extraction time may be longer than necessary as most compounds are extracted within only a few hours, while increasing turbulence (e.g. using ultrasonication) and temperature may additional increase the speed of extraction. NMR analysis also revealed a possible chemical differentiation between cold brew and hot brew using multivariate data analysis. Decreased extraction time and reduced storage times could be beneficial for cold brew product quality as microbiological analysis of commercial samples detected samples with spoilage organisms and contamination with Bacillus cereus.

Deep learning classification is the state-of-the-art of machine learning approach. Earlier work proves that the deep convolutional neural network has successfully and brilliantly in different applications such as images or video data. Recognizing and clarifying the remote sensing aspect of the earth's surface and exploit land cover and land use (LCLU). First, this article summarized the remote sensing emerging application and challenges for deep learning methods. Second, we propose four approaches to learn efficient and effective CNNs to transfer image representation on the ImageNet dataset to recognize LCLU datasets. We use VGG16, Inception-ResNet-V2, Inception-V3, and DenseNet201 models to extract features from the EACC dataset. We use pre-trained CNNs on ImageNet to extract features. For feature selection we proposed principal component analysis (PCA) to improve accuracy and speed up the model. We train our model by multi-layer perceptron (MLP) as a classifier. Lastly, we apply the multi-granularity encoding ensemble model. We achieve an overall accuracy of 92.3% for the nine-class classification problem. This work will help remote sensing scientists understand deep learning tools and apply them in large-scale remote sensing challenges

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

Mushrooms have long rich history in folk medicine, traditional and functional foods due to high content of dietary myco-nutrients. Currently, there is increased interest in finding appropriate food-grade green ex-traction systems capable of extracting these bioactive compounds from dietary mushrooms for applica-tions in various food, pharmacological or nutraceutical formulations. Herein, we evaluated a modified Swiss water process (SWP) method using alkaline and acidic pH at low and high temperature under pressurized conditions as a suitable green food grade solvent to obtained extracts enriched with my-co-nutrients (dietary phenolics, total antioxidants (TAA), vitamins, and minerals) from Chaga. Ultra-high performance liquid chromatography coupled to high resolution accurate mass tandem mass spectrometry (UHPLC-HRAMS-MS/MS) was used to assess the phenolic compounds and vitamin levels in the extracts, while inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the mineral con-tents. Over twenty phenolic compounds were quantitatively evaluated in the extracts and the highest total phenolic content and antioxidant activity was observed at pH11.5 at 100°C. The most abundant phenolic compounds present in Chaga extracts included phenolic acids such as protocatechuic acid 4-glucoside (0.7-1.08µg/mL), syringic acid (0.62-1.18µg/mL), and myricetin (0.68-1.3µg/mL). Vitamins are being reported for the first time in Chaga. pH 2.5 at 100°C treatment shows superior effects in extracting the B vitamins whereas pH 2.5 at 60 and 100°C treatments were outstanding for extraction of total fat-soluble vitamins. Vitamin E content was the highest for the fat-soluble vitamins in the Chaga extract under acidic pH (2.5) and high temp. (100°C) and ranges between 50 to 175 µg/100g Chaga. Antioxidant minerals ranged from 85.94 µg/g (pH7 at 100°C) to 113.86 µg/g DW (pH2.5 at 100°C). High temperature 100°C and a pH of 2.5 or 9.5. The treatment of pH11.5 at 100°C was the most useful for recovering phenolics and antioxidants from Chaga including several phenolic compounds reported for the first time in Chaga. SWP is being proposed herein for the first time as a novel, green food-grade solvent system for the extraction of myco-nutrients from Chaga and have potential applications as a suitable approach to extract nutrients from other matrices. Chaga extracts enriched with bioactive myconutrients and antioxidants may be suitable for further use or applications in the food and nutraceutical industries.

Introduction: Novel robotic surgery systems (da Vinci Xi) are superior to classical open and laparoscopic techniques with its clear and three-dimensional view. We aimed to present the first case low anterior resection of rectal cancer and vaginal specimen extraction with Da Vinci Xi.Case: A 75-year-old female patient with rectum adenocarcinoma was undergone robotic-assisted low anterior resection (LAR) of the rectum, vaginal removal of the specimen, colorectal anastomosis and loop ileostomy. The operation time was 190 minutes. There were no postoperative complications. Pathological tumor stage was stage pT1N0 with negative proximal, distal and radial resection margins. The patient was discharged on the third postoperative day.Conclusion: Robot-assisted LAR, total mesorectal excision, vaginal removal of the specimen, colorectal anastomosis, and loop ileostomy can be performed easily and safely with Da Vinci Xi at early stage rectal cancer. And the vaginal extraction of the specimen avoids us from a traditional abdominal incision.

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, the localization system based on received signal strength (RSS) is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm, named joint denoising auto-encoder (JDAE), which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on UJIIndoorLoc dataset and Tampere dataset, experimental results show that the proposed model increases the positioning accuracy dramatically comparing with other existing methods.

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.

The purpose of this study is to propose a consecutive manufacturing process system to secure the productivity of excellent STKN540B steel tube in the respect of economy and safety as the supporting material for mega structures such as building, bridge and ship. The components of consecutive manufacturing are press-bending, orbital auto welding and steel tube correction. By using STKN540B a high-strength steel material with low yield point that requires a special manufacturing process unlike other steel materials, an actual tube manufacturing is carried out at each stage in this experimental study. With this, the quality of steel tube and the efficiency of the manufacturing process are analyzed to draw out some points to improve in the future.

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.

Although rRT-PCR is the gold standard method for SARS-CoV-2 detection, some factors, such as amplification inhibitors presence, lead to false-negative results. Here we describe differences between rRT-PCR results for SARS-CoV-2 infection in normal and diluted samples, simulating the need for dilution due to amplification inhibitors presence. Viral RNA extraction of nasopharyngeal swabs samples from 20 patients previously detected as 'Negative' and 21 patients detected as 'Positive' for SARS-CoV-2 was realized with the EasyExtract DNA-RNA (Interprise®). rRT-PCR was realized with OneStep/COVID-19 (IBMP) kit with normal and diluted (80µl of H₂O RNAse free) samples, totaling 82 tests. The results indicate that there is an average variation (ɑ < 0.05) delaying Cq between the amplification results of internal control (IC), N Gene (NG), and ORF-1ab (OF) of 1.811 Cq, 3.840 Cq, and 3.842 Cq, respectively. The extraction kit does not completely purify the inhibitor compounds, therefore non-amplification by inhibitors may occur. In this study, we obtained a 19.04% false-negative diagnosis after sample dilution, and this process reduces the efficiency of rRT-PCR to 29.80% for detecting SARS-CoV-2. Knowing the rRT-PCR standards of diluted samples can help in the identification of false-negative cases, and consequently avoid a wrong diagnosis.

The presented research faces the problem of early detection of sepsis for patients in the Intensive Care Unit. The PhysioNet/Computing in Cardiology Challenge 2019 facilitated the development of automated, open-source algorithms for the early detection of sepsis from clinical data. A labeled clinical records dataset for training and verification of the algorithms was provided by the challenge organizers. However, a relatively small number of records with sepsis, supported by Sepsis-3 clinical criteria, led to highly unbalanced dataset (only 2% records with sepsis label). A high number of unbalanced data records is a great challenge for machine learning model training and is not suitable for training classical classifiers. To address these issues, a number of various models were investigated. A solution including feature selection and data balancing techniques was proposed in this paper. In addition, several performance metrics were investigated. Results show, that for successful prediction, a particular model having few or more predictors based on the length of stay in the Intensive Care Unit should be applied.

Onions are an excellent source of a variety of essential antioxidants, polyphenols, and other phytonutrients. These onions were used to enhance the meal's flavour and aroma. Additionally, onions have long been recognised for their ability to change food texture, making it more pleasant for eating. Among the most significant phenolic compounds identified in onions was Quercetin, which has been proven to be very helpful for maintaining good health by reducing the risk of heart disease, cancer, and a number of other severe conditions. Polyphenol extraction is essential for the effective usage and research of these phytochemicals. Previously, the extraction process was complicated by a variety of traditional methods, but with the aid of contemporary technology, the procedure was simplified. Additionally, it was found that red onions had greater health benefits than yellow onions, which were the most commonly used kind at the time. Apart from their health benefits, polyphenols were well recognised for their industrial use in dyeing, polymer manufacturing, and resin synthesis.

Atlantic salmon (Salmo Salar) is one of the fish species that rich in omega-3 fatty acids. Consumption of omega-3 fatty acids can lower down the risk of cardiovascular disease and coronary heart disease. The extraction of omega-3 fish oil from Atlantic salmon by using hexane and isopropanol was conducted in this research. Response Surface Methodology (RSM) was used to study the impact of parameters which were temperature (50-90°C), centrifuge speed (1000-3000rpm) and solvent ratio of hexane to isopropanol (0.5-1.5) to the percentage of oil yield. The results obtained shows that 15.23%± 0.61 of oil yield from Atlantic salmon under an optimum condition of 70°C, 3000 rpm and solvent ratio of hexane to isopropanol of 1.5. The acid value, peroxide value and p-anisidine value of the fish oil obtained were 7.48 mg KOH/g, 1.94 meq/kg and 5.56 respectively which were all within the acceptable limit which stipulated by Global Organization for EPA and DHA (GOED) and Food and Agricultural Organization of the United Nations (FAO). The FTIR analysis proved the presence of alkane and carboxylic acid in the fish oil. Furthermore, GC-MS analysis showed that the fish oil contains higher monounsaturated fatty acids (MUFA) contents followed by polyunsaturated fatty acids (PUFA) and 4.5525% of total omega-3 fatty acids.

This paper proposes a novel approach, based on the adaptive rate processing and analysis, for the isolated speech recognition. The idea is to smartly combine the event-driven signal acquisition and windowing along with adaptive rate processing, analysis and classification for realizing an effective isolated speech recognition. The incoming speech signal is digitized with an event-driven A/D converter (EDADC). The output of EDADC is windowed with an activity selection process. These windows are later on resampled uniformly with an adaptive rate interpolator. The resampled windows are de-noised with an adaptive rate filter and their spectrum are computed with an adaptive resolution short time Fourier transform (ARSTFT). Later on, the magnitude, Delta and Delta-Delta spectral coefficients are extracted. The Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The system functionality is tested for a case study and results are presented. An 8.2 times reduction in acquired number of samples is achieved by the devised approach as compared to the classical one. It aptitudes a significant computational gain and power consumption reduction of the proposed system over the counter classical ones. An average subject dependent isolated speech recognition accuracy of 96.8% is achieved. It shows that the proposed approach is a potential candidate for the automatic speech recognition applications like rehabilitation centers, smart call centers, smart homes, etc.

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.

Speech emotion recognition remains a heavy lifting in natural language processing. It has strict requirements to the effectiveness of feature extraction and that of acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address these challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recall on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

With the fast development of the mobile Internet, the online platforms of social networks have rapidly been developing for the purpose of making friends, sharing information, etc. In these online platforms, users being related to each other forms social networks. Literature reviews have shown that social networks have community structure. Through the studies of community structure, the characteristics and functions of networks structure and the dynamical evolution mechanism of networks can be used for predicting user behaviours and controlling information dissemination. Therefore, this study proposes a deep community detection method which includes (1) matrix reconstruction method, (2) spatial feature extraction method and (3) community detection method. The original adjacency matrix in social network is reconstructed based on the opinion leader and nearer neighbors for obtaining spatial proximity matrix. The spatial eigenvector of reconstructed adjacency matrix can be extracted by an auto-encoder based on convolution neural network for the improvement of modularity. In experiments, four open datasets of practical social networks were selected to evaluate the proposed method, and the experimental results show that the proposed deep community detection method obtained higher modularity than other methods. Therefore, the proposed deep community detection method can effectively detect high quality communities in social networks.

Manual interpretation of these huge amounts of image volumes are susceptible to inter-reader variability and human error. Thus, accurate automated CAD scheme is highly desirable in clinical pathological diagnosis. In this research, plethora of machine learning paradigms (e.g. feature extraction, dimensionality reduction and supervised classification methods) were explored, evaluated, compared and analyzed to identify the optimal pathway for brain MR images (normal vs neoplastic) binary classification task. External validation dataset was used to test the generalizability of the optimal predictive models implemented. Relevant and informative features were selected to construct cross-validated decision tree and eventually simple rule set was built based on the decision tree. The experimental results show that almost all pattern recognition paradigms achieve high accuracy with careful selection of number of attributes. LDA+ELM with 55 features are the optimal pipelines which achieve perfect classification when training and test data are of same source; and achieving (accuracy=97.5%, AUC=0.989, sensitivity=95% and specificity=100%) under balanced test dataset; (accuracy=99.5%, AUC=0.988, sensitivity=95% and specificity=100%). Cross-validated decision tree model also shows comparable performance: accuracy=98.8%, AUC=99.1%, sensitivity=99.6% and specificity=98.2%. Three highly relevant and robust attributes are visualized and selected for construction of decision tree models and finally a rule sets are read directly off the decision tree. This rule sets can potentially serve as fast and accurate classification algorithm.

This paper provides a review of an emerging field in the food processing sector, referring to efficient and safe food supply chains, ’from farm to fork’, as enabled by Artificial Intelligence (AI). Recent advances in machine and deep learning are used for effective food production, energy management and food labeling. Appropriate deep neural architectures are adopted and used for this purpose, including Fully Convolutional Networks, Long Short-Term Memories and Recurrent Neural Networks, Auto-Encoders and Attention mechanisms, Latent Variable extraction and clustering, as well as Domain Adaptation. Three experimental studies are presented, illustrating the ability of these AI methodologies to produce state-of-the-art performance in the whole food supply chain. In particular, these concern: (i) predicting plant growth and tomato yield in greenhouses, thus matching food production to market needs and reducing food waste or food unavailability; (ii) optimizing energy consumption across large networks of food retail refrigeration systems, through optimal selection of systems that can get shut-down and through prediction of the respective food de-freezing times, during peaks of power demand load; (iii) optical recognition and verification of food consumption expiry date in automatic inspection of retail packaged food, thus ensuring safety of food and people’s health.

Almond (Prunus dulcis (Mill.) D.A.Webb) is one of the most important nut crops both in terms of area and production. Over the last decades, an important part of the beneficial actions for health associated with their consumption was attributed to the phenolic compounds, mainly accumulated in almond skin. Interestingly, after cold-pressed oil extraction, most of these antioxidant phenolic compounds are retained in a skin-enriched by-product, so-called almond cold-pressed oil residue. In Morocco, ranked fifth producer in the world, this production generates an important part of this valuable byproduct. In the present study, using a multivariate Box-Behnken design, an ultrasound-assisted extraction (USAE) method of phenolic compounds from Moroccan almond cold-pressed oil residue was developed and validated. Response surface methodology resulted in the optimal extraction conditions: the use of aqueous EtOH 53.0% (v/v) as green solvent, applying an US frequency of 27.0 kHz for an extraction duration of 29.4 min. The present USAE allowed substantial gains in terms of extraction efficiency compared to conventional heat reflux extraction. Applied to 3 different local Beldi genotypes growing at 3 different experimental sites, the optimal USAE conditions led to a total phenolic content of 13.86 mg/g dry weight (DW). HPLC analysis revealed that the main phenolic compounds from this valuable byproduct were: chlorogenic acid followed by protocatechuic acid, p-hydrobenzoic acid and p-coumaric acid. The accumulation of these phenolic compounds appeared to be more dependent on the genetic background than on the environmental impact here represented by the 3 experimental culture sites. Both in vitro cell free and cellular antioxidant assays were performed, and revealed the great potential of these extracts. In particular, correlation analysis evidenced the prominent roles of chlorogenic acid, protocatechuic acid and p-hydrobenzoic acid. To summarize, the USAE method presented here is a quick, green, simple and efficient validated USAE for the possible valorization of antioxidant phenolic compounds from Moroccan almond cold-Pressed oil residues, making it possible to generate extracts with attractive antioxidant activities for future nutraceutical and/or cosmetic applications.

Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.

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).

: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle this challenge. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced to XAI. However, many deficiencies, particularly lack of explanation assessment methods and uncertainty quantification, plague this young field. In this paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as marker for anomalies expanding the prognostic explanation scope to include model’s confidence. Also, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of PHM-XAI publications. The quality of the explanation is finally examined employing local accuracy and consistency properties. The method is tested on real-world gas turbine anomalies and synthetic turbofan data failure prediction. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

Transportation of goods is as old as human civilizations : past networks and their evolution shed light on long term trends. Transportation impact on climate change is measured as major, as well as the impact on spreading a pandemic. These two reasons motivate the importance of providing relevant and reliable historical geographic datasets of these networks. This paper focuses on reconstructing the railway network in France at its maximal extent, a century ago. The active stations and lines are well documented by the French SNCF, in open public data. However, that information ignores past stations (ante 1980), which represent probably more than what is recorded in public data. Additional open data, individual or collaborative (eg. Wikipedia) are particularly valuable, but they are not always geo-coded, and two more sources are necessary to completing that geo-coding: ancient maps and aerial photography. Therefore, remote sensing and volunteer geographic information are the two pillars of past railway reconstruction. The methods developed are adapted to the extraction of information from these sources: automated parsing of Wikipedia Infoboxes, data extraction from simple tables, even from simple text. That series of sparse procedures can be merged into a comprehensive computer-assisted process. Beyond this, a huge effort in quality control is necessary when merging these data: automated wherever possible, or finally visually controlled by observation of remote sensing information. The main output is a reliable dataset, under ODbl, of more than 9100 stations, which can be combined with the information about the 35000 communes of France, for a large variety of studies. This work demonstrates two thesis: (a) it is possible to reconstruct transport network data from the past, and generic computer assisted methods can be developed; (b) the value of remote sensing and volunteered geo info is considerable (what archeologists already know).

The objective of this study was to develop an ultrasonic-assisted procedure for the extraction of total phenolics from Citrus aurantium L. blossoms (CAB) and evaluate the free radical scavenging activity, anti-HMG-CoA reductase activity of total phenolics. In this work, a Box-Behnken design based on the single-factor experiments was used to explore the optimum extraction process. Under the optimum conditions (extraction solvent 70.31% ethanol, extraction temperature 61.94 °C, extraction time 51.73 min and liquid-to-solid ratio 35.63 mL/g), the extraction yield of total phenolics was 95.84 mg gallic acid equivalents (GAE)/g dry matter (DM), which was highly consistent with the theoretical value (96.12 mg GAE/g DM). The total phenolic extract showed excellent free radical scavenging properties against DPPH·, ABTS+·, ·OH and ·O2-, with the IC50 values of 197.007, 83.878, 218.643 and 158.885 μg/mL, respectively, and the extracts also showed good inhibition of HMG-CoA reductase activity, with the IC50 value of 117.165 μg/mL. Total phenolics from CAB could be a potential source of natural free radical scavenger and HMG-CoA reductase inhibitor.

The spatial distribution of elements can be regarded as a numerical field of concentration values with a continuous spatial coverage. An active area of research is to discover geologically meaningful relationships among elements from their spatial distribution. To solve this problem, we propose an association rule mining method based on clustered events of spatial auto-correlation and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China. The elemental data for stream sediments were first clustered into HH (high-high), LL (low-low), HL (high-low), and LH (low-high) groups by using local Moran’s I clustering map (LMIC). Then the Apriori algorithm was used to mine the association rules among different elements in these clusters. More than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences, and occur in the upper reaches of the stream and catchment areas. In addition, we found that the Indosinian granodiorite is enriched in sulfophile elements, e.g., Zn, Ag and Cd, and the Variscan granite quartz diorite (P1γδο) coexists with Cu and associated elements. Therefore, the proposed algorithm is an effective method for mining co-existence patterns of elements and provides an insight into their enrichment mechanisms.

Background: The gut microbiome plays a major role in chronic diseases, of which several are characterized by an altered composition and diversity of bacterial communities. Large-scale sequencing projects allowed characterizing the perturbations of these communities. However, translating these discoveries into clinical applications remains a challenges. To facilitate routine implementation of microbiome profiling in clinical settings, portable, real-time, and low-cost sequencing technologies are needed. Results: Here, we propose a computational and experimental protocol for whole genome quantitative metagenomics studies of human gut microbiome with Oxford Nanopore sequencing technology (ONT) that could be applied to other microbial ecosystems. We developed a bioinformatic protocol to analyse ONT sequences taxonomically and functionally and optimized pre-analytic protocols including stool collection and DNA extraction methods to maximize read length. This is a critical parameter for the sequence alignment and classification. Our protocol was evaluated using simulations of metagenomic communities which reflect naturally occuring compositional variations. Next, we validated both protocols using stool samples from a bariatric surgery cohort, sequenced with ONT, Illumina and SOLiD technologies. Results revealed similar diversity and microbial composition profiles. Conclusion: This protocol can be implemented in the clinical or research setting, bringing rapid personalized whole genome profiling of target microbiome species.

Despite UAV multi-target detection exhibits considerable developmental potential worldwide, it suffers distinct challenges compared with traditional tasks in this field. These challenges include insufficient feature extraction capabilities for small targets, limited capabilities for multi-dimensional feature fusion, as well as constraints on hardware computation parameters. Especially in mission scenarios such as disaster detection, these challenges will be further amplified. Consequently, this paper introduces SSE-YOLO, an innovative YOLO framework algorithm specially designed to address these challenges. To enhance the model's feature extraction capability, we employ the SPDConv module to replace the original Conv in the backbone section, utilizing depth-separable convolution instead of traditional convolution pooling. Concurrently, we eliminate the SPPF module at the bottom and address a new Separate Kernel Attention Pyramid Pooling (SKAPP) module, substantially enhancing the feature fusion capability at the model's core. Moreover, to address the challenge of multi-dimensional feature fusion, we replace the Concat module of the neck and head with E-BiFPN, transmitting feature information from the backbone to the lower network through four CBS blocks, which effectively resolves the issue of lost contextual information in the model. Meanwhile, SSE-YOLO undergoes ablation experiments on the VisDrone2019 dataset to evaluate its effectiveness against alternative methods, and experimental results illustrate the model's exceptional precision in detecting UAV targets. In comparison to models with comparable experimental accuracy, SSE-YOLO requires remarkably fewer parameters. On the VisDrone2019-DET-test-dev dataset, SSE-YOLO enhances mAP by 17.3%, with a 42.5% reduction in the parameter amounts. Therefore, the proposed method effectively tackles the challenge of reconciling low parameters and high accuracy, presenting a novel pathway for deep learning-based UAV multi-target detection.

This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These data sets include BI (bodily injury), PD (property damage) and COLL (collision) coverage, each documenting policy characteristics and claims across a four year period. The datasets are explored, including summaries of all variables, then the methods for modeling are set forth. Models are tuned and the tuning results are displayed, after which we train the final models and seek to explain select predictions. All of the code will be made available on GitHub. Data was provided by a private insurance carrier to the CAS after anonymizing the data set. This data is available to actuarial researchers for well-defined research projects that have universal benefit to the insurance industry and the public. Our hope is that the methods demonstrated here can be a good foundation for future ratemaking models to be developed and tested more efficiently.

Followed by the aspect of respect for rights 20, the social and environmental issue has a score of 19.9, the dialogue issue is 19. In short, the proximity plan indicates that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín environmental dialogue table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are closer together today. The actor from the Archbishopric of Huancayo referred to communication, is within the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are currently closer together. The actor of the Archbishopric of Huancayo referred to communication, is within the driving variables of power.

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.

Continuous research into the availability of phosphorus (P) in forest soil is critical for sustainable management of forest ecosystems. In this study, we used sequential chemical extraction and 31P-nuclear magnetic resonance spectroscopy (31P-NMR) to evaluate the form and distribution of inorganic P (Pi) and organic P (Po) in Casuarina forest soils of a subtropical coastal sand dune at Houlong in Taiwan. The soil samples were collected from humic (+2-0 cm) and mineral layers (mineral-I: 0-10, mineral-II: 10-20 cm) at two topographic locations (upland and lowland) by elevation. Sequential chemical extraction revealed that the NaOH-Po fraction, as moderately recalcitrant P, was the dominant form in humic and mineral-I layers in both upland and lowland soils, whereas the cHCl-Pi fraction was the dominant form in the mineral-II layer. Resistant P content, including NaOH-Pi, HCl-Pi, cHCl-Pi, and cHCl-Po fractions, was higher in the upland than lowland in the corresponding layers; however, labile P content, NaHCO3-Po, showed the opposite pattern. Content of resistant Pi (NaOH-Pi, HCl-Pi, and cHCl-Pi) increased significantly with depth, but that of labile Pi (resin-Pi and NaHCO3-Pi) and recalcitrant Po (NaHCO3-Po, NaOH-Po, and cHCl-Po) decreased significantly with depth at both locations. 31P-NMR spectroscopy revealed inorganic orthophosphate and monoesters-P as the major forms in this area. The proportions of Pi and Po evaluated by sequential chemical extraction and 31P-NMR spectroscopy were basically consistent. The results indicated that the soils were in weathered conditions. Furthermore, the P distribution and forms significantly differed between the upland and lowland by variation in elevation and eolian aggradation effects in this coastal sand dune landscape.

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.

The effectiveness of forest resources depends on the comprehensiveness and rationality of their consumption and processing into finished products. This article discusses a problem of utilising solid fir wood greenery residues generated during the industrial production of essential oils. Bioconversion is considered to be the most promising utilization method. The objective of this research was to study the chemical composition of bioconversion products of fir wood greenery-based substrates. The РР-3.2 strain of Pleurotus pulmonarius (Fr.) Quél was used as a biodestructor. In the process of bioconversion, the contents of polysaccharides and lignin substances is reduced to 38 and 28 % respectively. Up to 20 % of protein accumulates in bioconversion products of fir wood greenery. The amount of nucleic acids is not more than 1.5 g per 1 kg, the contents of heavy metals, such as mercury, cadmium, arsenic, lead, do not exceed maximum permissible concentration standards. The substrate weight loss reaches 15 %. When fallen leaves and post-extraction poplar bud residues are added to the substrate, the substrate-destroying activity of fungi rises, as well as the protein content increases by 3 %. The digestibility of products as a result of bioconversion increases 1.6–2.8 times depending on the substrate composition. The obtained data enable to recommend post-fermented substrates based on fir wood greenery and balsam poplar biomass for use as a protein feed additive.

Semen Allii Fistulosi is the seed of Allium fistulosum L. of the Liliaceae family. The purpose of this study was to extract, characterize, and evaluate the antioxidant activity in vitro of proteins from Semen Allii Fistulosi (PSAF). Using single factor and orthogonal design, the optimum conditions of extraction were determined to be as follows: extraction time 150 min, pH 8.5, temperature 60℃, and ratio (v/w, mL/g) of extraction solvent to raw material 35. The isoelectric point of the pH was determined to be about 4.4 and 10.2, by measuring the protein content of PSAF solutions at different pH. The amino acid composition of PSAF was determined by high performance liquid chromatography (HPLC), and the results suggested that the species of amino acids contained in the PSAF was complete. Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS–PAGE) analysis showed the molecular weight was mainly between 40 and 55 kDa, and Fourier-transform infrared spectroscopy (FTIR) characterized prevalent protein absorption peaks. PSAF exhibited potent scavenging activities against DPPH assays, via targeting of hydroxyl and superoxide radicals, while chelating Fe2+ activity, and demonstrating weak reducing power. This work revealed that PSAF possessed potential antioxidant activity in vitro, suggesting potential for use of PSAF as a natural antioxidant.

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

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.

One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. At a time when the speed and reliability of results, especially for COVID-19 positive patients, is important, the development of applications that would facilitate the work of untrained staff involved in the evaluation is also crucial. Our model takes the form of a simple and intuitive application, into which you only need to upload X-rays: tens or hundreds at once. In just a few seconds, the physician will determine the patient's diagnosis, including the percentage accuracy of the estimate. While the original idea was a mere binary classifier that could tell if a patient was suffering from pneumonia or not, in this paper we present a model that distinguishes between a bacterial disease, a viral infection, or a finding caused by COVID-19. The aim of this research is to demonstrate whether pneumonia can be detected or even spatially localized using a uniform, supervised classification.

Quebec is the third-largest wine grape producing province in Canada, and the industry is constantly expanding. Traditionally, 90% of the grapevine cultivars grown in Quebec were rustic or semi-rustic and largely dominated by winter hardy interspecific hybrid Vitis sp. cultivars. Over the years, the winter protection techniques adopted by growers and climate changes have offered an opportunity to establish V. vinifera L. cultivars (e.g. Pinot Noir). We characterized the virome of leafroll-infected interspecific hybrid cultivar and compared it to the virome of V. vinifera cultivar to support and facilitate the transition of the industry. A dsRNA sequencing method was used to sequence symptomatic and asymptomatic grapevine leaves of different cultivars. The results suggested a complex virome in terms of composition, abundance, richness, and phylogenetic diversity. Three viruses, grapevine Rupestris stem pitting-associated virus, grapevine leafroll-associated virus (GLR) 3 and 2 and hop stunt viroid (HSVd) largely dominated the virome. However, their presence and abundance varied among grapevine cultivars. The symptomless grapevine cultivar Vidal was frequently infected by multiple virus and viroid species and different strains of the same virus, including GLR virus 3 and 2. Our data shows that viruses and viroids associated with the highest number of grapevines expressing symptoms included HSVd, GLR3 and GLR2, in gradient order. However, co-occurrence analysis revealed that the presence of GLR species was randomly associated with the development of virus-like symptoms. These findings and their implication for grapevine leafroll disease management were discussed.

Sweet cherry pomace is a by-product that can be a source of bioactive phenolic compounds. Usually, polyphenols have been extracted using conventional extraction methodologies. However, a significant fraction, called non-extractable polyphenols (NEPs), remains retained in the conventional extraction residues. Therefore, this work is aimed, for the first time, to investigate the release of NEPs from cherry pomace combining pressurized liquid extraction (PLE) and enzyme-assisted extraction (EAE) using Promod enzyme. A response surface methodology was employed to study the influence of temperature, time, and pH on the NEPs extraction. The response variables were the total phenolic content (TPC) measured by Folin-Ciocalteu method, total proanthocyanidin (PA) content evaluated by vanillin, DMAC, and butanol/HCl assays, and total antioxidant capacity determined by Trolox equivalent antioxidant capacity and inhibition of hydroxyl radical assays. The results indicated that PLE-EAE was more suitable and selective to obtain NEPs from sweet cherry pomace than PLE alone. In fact, the extracts obtained by PLE-EAE displayed higher TPC, PA content, and bioactivity than the extracts obtained by PLE under the same extraction conditions, and those obtained by conventional methods. Moreover, size-exclusion chromatography profiles showed that the combination of PLE and EAE enabled the recovery of NEPs with higher molecular weight than PLE without EAE treatment.

Endometriosis is an oestrogen-dependant reproductive disease, with genetic, vascular, neural, inflammatory and auto-immune characteristics. There are many theories about the etiology of endometriosis, however, all of these theories have limitations and do not explain all the locations that endometriosis is found or types of patients with endometriosis. The objective of this paper is to postulate the hypothesis that endometriosis is caused by Maternal Microchimerism, the presence of maternal cells in the fetus. A literature review was conducted, analysing the characteristics, current etiological theories of endometriosis, theory limitations and relationship of maternal microchimerism and endometriosis. At time of writing, there was no literature on maternal microchimerism and endometriosis. These results suggest that Maternal Microchimerism could be a cause of endometriosis. This could account for the genetic and auto-immune characteristics seen in people with endometriosis, inducing a micro-environment for vascular, neural and epigenetic changes. This could also account for account for endometriosis seen in non-menstruating patients, such as men, fetuses and post-menopausal women and endometriosis found in non-peritoneal locations. If the hypothesis of Maternal Microchimerism is correct, endometriosis could be considered a pregnancy-related disease that could affect all humans, changing the accepted demographics of patients and potentially new diagnostic techniques and treatment options for patients with endometriosis. Further studies are needed to test this hypothesis.

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

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.

Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

Is the production of the “AnchoisFert” organic fertilizer via the circular economy process based on omega-3 lipid extraction of milled anchovy fillet leftovers with citrus limonene followed by mild drying economically and technically viable? This study answers this question and identifies the main obstacles, chiefly of economic and organizational nature, to be overcome prior to commercialization of this new organic fertilizer. Along with non-variable and proven efficacy, successful competition with conventional organic and inorganic fertilizers requires affordable cost and regular supply. This, inter alia, requires to establish new and mutually beneficial relationship between bioeconomy firms and fish processing companies.

A novel machine learning approach is presented in this paper, based on extracting latent information and using it to assist decision making on ambulance attendance and conveyance to a hospital. The approach includes two steps: in the first, a forward model analyzes the clinical and, possibly, non-clinical factors (explanatory variables), predicting whether positive decisions (response variables) should be given to the ambulance call, or not; in the second, a backward model analyzes the latent variables extracted from the forward model to infer the decision making procedure. The forward model is implemented through a machine, or deep learning technique, whilst the backward model is implemented through unsupervised learning. An experimental study is presented, which illustrates the obtained results, by investigating emergency ambulance calls to people in nursing and residential care homes, over a one-year period, using an anonymized data set provided by East Midlands Ambulance Service in United Kingdom.

Demand for nutmeg essential oil in Indonesia and the world is increasing. Nutmeg essential oil is obtained from young nutmeg seeds extracted by water steam distillation (WSD). The weakness of the extraction process with WSD is the long distillation time (24 hours), wasteful of fuel, wasteful of manpower, produces polluting gases which damage the environment and requires a lot of water as a coolant. The purpose of this study was to compare the WSD and SCFE methods for extracting nutmeg essential oil. The research was conducted by extracting nutmeg essential oil using two methods are WSD and SCFE. The results showed that the chemical composition of the nutmeg essential oil produced by the two methods was almost similar. Nutmeg essential oil produced by both methods shows the same characterization. The yield between the two methods is almost the same, in the range of 10-12% using extraction time 15-60 minutes. The SCFE method has advantages over WSD are a faster extraction time, does not require heating materials and CO2 gas can be reused. In conclusion, SCFE is an alternative method to increase the production of nutmeg oil.

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

Heavy metals have been implicated as Lagoon pollutants from sources such as agriculture, mining and manufacturing industries and waste water treatment works. A study was carried out in the University of Lagos lagoon to investigate the seasonal bioavailability of the heavy metal contaminants Cd, Cr, Cu, Pb and Zn. The physical parameters pH, redox potential, temperature, TDS and conductivity were measured on site. Dried sediment samples were extracted using the Community Bureau of Reference (BCR) sequential procedure and analysed by ICP-OES. A certified reference material (CRM), BCR 701 (lake sediment) was used for quality assurance with recoveries ranging between 80-120%. Statistical analysis (ANOVA) showed that there was a significant difference between metal distribution in the dry and wet seasons. Cu, Pb and Zn were in the available fractions (carbonate, Fe/Mn oxide and organic). Cu was highest in the Fe/Mn oxide and organic fractions. This indicated that an increase in organic matter and reducing agents will avail the Cu. Zn was distributed in all fractions while Pb was found in the Fe/Mn oxide fraction (3.93- 21.3 %). Results showed that the bioavailability of Cu, Pb and Zn was high. Metal bioavailability by BCR indicates a potential risk of pollution in lagoon sediments as the available metals exceeded the permissible Sediment Quality Assessment Guidelines (SQAG’s) from Environmental Protection Agency (EPA).

The extracts from the aerial parts of Helianthus salicifolius A. Dietr and Helianthus tuberosus L. collected in June were obtained using carbon dioxide supercritical fluid extraction with water as co-solvent. The antimicrobial activity in vitro of these extracts were determined against the reference species of Gram-positive and Gram-negative bacteria as well as fungi, representing by the yeast species of Candida spp. The following parameters were estimated: minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) or minimum fungicidal concentration (MFC). Both extracts were found to possess antimicrobial activity with MIC = 0.62-5 mg mL-1 for bacteria and MIC = 5-10 mg mL-1 for yeasts, showing bactericidal (MBC/MIC = 2-4) or fungicidal effect (MFC/MIC = 1-2 ). The highest activity was observed against S. aureus ATCC 29213 (MIC = 0.62 mg mL-1 for H. salicifolius extract; MIC = 2.5 mg mL-1 for H. tuberosus extract). Bactericidal effect of both extracts against S. aureus ATCC 29213 was confirmed by time-kill assay. Higher antioxidant activity was found for H. tuberosus extract (EC50 = 0.332 mg mL-1) as compared to that of H. salicifolius (EC50 = 0.609 mg mL-1). The total polyphenol content (TPC) expressed as gallic acid equivalents (GAE) was 13.75 ± 0.50 mg GAE (g of H. salicifolius extract)-1 and 33.06 ± 0.80 mg GAE (g of H. tuberosus extract)-1. There was a correlation between the antioxidant potential of both extracts and TPC but not between antistaphylococcal activity and TPC. The obtained data suggest potential application of these extracts as the natural preparations with the biocidal activity, including those with antistaphylococcal activity. Besides, both extracts may be regarded as potential natural conservants in cosmetics as well as natural preservatives in food.

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

The aim of this work was the study and evaluation of winery by-products in the framework of circular bioeconomy. Grape seeds and grape skins from Greek traditional Ionian Islands varieties were analyzed in an attempt to provide the appropriate basis for model development of their sustainable exploitation at a local or regional level. The wastes collected directly from the wineries immediately after the vinification process and analyzed by chromatographic and spectroscopic techniques. Also, annual production and yields were estimated. Grape seed oil quality was evaluated based on fatty acid methyl esters (FAMEs) composition. Grape skins phenolic fraction was extracted by an eco-friendly, non-toxic water-glycerol solvent system and were detected qualitatively. Also, total phenolic content (TPC) and antioxidant activity were measured. Based on estimated yields, our results demonstrate that winery by-products have the potential to promote the cyclical bioeconomy in a modern economic growth model that will reduce waste, and environmental costs as they can be reused as whole material in foods, dietary supplements, cosmetic ingredients, food colorants and preservatives.

Scintillators play a key role in the detection chain of several applications which rely on the use of ionizing radiation, and it is often mandatory to extract and detect the generated scintillation light as efficiently as possible. In positron emission tomography (PET), for example, both energy resolution and coincidence resolving time, two of the key detection parameters, depend strongly on the total amount of light which reaches the photodetector surface as well as its spatial and temporal distribution. Typical inorganic scintillators do however feature a high index of refraction, which impacts light extraction efficiency in a negative way. Furthermore, several applications such as preclinical PET rely on pixelated scintillators with small pitch. In this case, applying reflectors on the crystal pixel surface, as done conventionally, can have a dramatic impact of the packing fraction and thus the overall system sensitivity. This paper presents a study on light extraction techniques, as well as combinations thereof, for two of the most used inorganic scintillators (LYSO and BGO). Novel approaches, employing distributed Bragg reflectors (DBRs), metal coatings, and a modified Photonic Crystal (PhC) structure, are described in detail and compared with commonly used techniques. The nanostructure of the PhC is surrounded by a hybrid organic/inorganic silica sol-gel buffer layer which ensures robustness while maintaining its performance unchanged. We observed in particular a maximum light gain of about 41% on light extraction and 21% on energy resolution for BGO, a scintillator which has gained interest in the recent past due to its prompt Cherenkov component and lower cost.

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

The aquaculture industry has become a sustainable provider of food to humans. Remaining challenges include disease issues, as well as ethical concerns for discomfort and stress among farmed fish. There is a need for reliable biomarkers to monitor welfare in fish, and the stress hormone cortisol has been suggested as a good candidate. This study presents a novel method for measurement of cortisol in fish feces based on enzymatic hydrolysis, liquid-liquid extraction, derivatization, and finally instrumental analysis by liquid chromatography coupled with tandem mass spectrometry. Hydrolysis and extraction conditions were optimized. Cortisol appeared to be mostly conjugated to sulfate and less conjugated to glucuronic acid in the studied samples of feces from farmed Atlantic salmon. The method was suitable for quantification of cortisol after enzymatic deconjugation by either combined glucuronidase and sulfatase activity, or by glucuronidase activity alone. The limit of detection was 0.15 ng/g and the limit of quantification was 0.34 ng/g, and the method was linear (R2>0.997) up to 380 ng/g, for measurement of cortisol in wet feces. Method repeatability and intermediate precision were acceptable, both with a CV of 11%. Stress level was high in fish released into seawater, and significantly reduced after eight days.

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.

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

Pedestrian multi-target tracking technology plays an important role in artificial intelligence, driverless, virtual reality and other fields. The pedestrian multi-target tracking algorithm DeepSORT based on detection is widely used in industry. It mainly tracks multiple pedestrian targets continuously and keeps their ID unchanged. In order to improve the applicability and tracking accuracy of DeepSORT algorithm, this paper improved the IOU distance measurement in the matching process. At the same time, ResNet50 is used as the feature extraction backbone network, and combined with FPN (Feature Pyramid Network), the appearance features of multi-layer pedestrians are fused to improve the tracking accuracy of DeepSORT algorithm. The proposed algorithm is verified on the public data set MOT-16 and it’s tracking accuracy is enhanced to 4.1%.

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.

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

Introduction. Bacterial resistance to antibiotics is estimated to cause the major number of deaths by 2050 if we do not find strategies to slow down the rise of drug resistance [1]. Reviews on Mediterranean Wild Edible Plants (MWEPs) with antimicrobial properties are scarce in the mean databases (Pubmed, Scopus and WoS). Hence, we proceeded to a new review of the studies on MWEPs. Methods. We used Wild Edible Plant and Antimicrobial as keywords. We only included the Mediterranean plants, and studies in non-Mediterranean countries, but for plants growing in Mediterranean basin. Exclusion criteria were the document type, studies not concerning plant, plants not edible, not antimicrobial properties, or totally out of topic. Results. Finally, the number of studies reviewed, starting from one hundred and ninety-two, was thirty-eight (19,8%), concerning the antimicrobial properties of seventy-four MWEPs species, be-longing to twenty-five Families. Fifty-seven (77%) out of seventy-four species, proved to be antimicrobial with a stringent threshold selection. Conclusions. The studies are still very heterogeneous. We still know too little about MWEPs properties, but what we already know seriously recommends continuing.

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

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

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.

A polymer inclusion membrane (PIM) composed of 50 wt% poly(vinylidenefluoride-co-hexafluoropropylene) (PVDF-HFP) as its base polymer, 40 wt% Aliquat® 336 as its extractant and 10 wt% dibutyl phthalate (DBP) as plasticizer provided efficient extraction of vanadium(V) from its sulfate solutions adjusted to pH 2.5. It was suggested that V(V) was extracted as VO2SO4− via an anion exchange mechanism. Quantitative back-extraction was achieved in a sulfuric acid solution (6 mol L-1) containing 1 v/v% of hydrogen peroxide. It was assumed that the back-extraction process involved the oxidation of VO2+ to VO(O2)+ by hydrogen peroxide. The newly developed PIM with the optimized composition mentioned above exhibited excellent selectivity for V(V) in the presence of metallic species present in digests of spent alumina hydrodesulfurization catalysts (i.e., Al(III), Co(II), Cu(II), Fe(III), Mn(II), and Ni(II)). The co-extraction of Mo(VI) with V(V) was eliminated by its selective extraction at pH 1.1. The optimized PIM was characterized by contact angle measurements, atomic-force microscopy (AFM), energy dispersive X-ray spectroscopy (EDS), thermogravimetric analysis (TGA)/derivatives thermogravimetric analysis (DTGA), and the stress-strain measurements.

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

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixe assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

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.

A partial extraction procedure was used to study the distribution of uranium in the mineral phases of rocks of an aquifer of sandy-clay deposits of the Vendian in the northwest of Russia. This work is a part of a research project to develop a method for combined radiocarbon and uranium-isotope dating of groundwater. Representative aliquots of each core sample were subjected to five "partial" extractions by treatment with: distilled water, low mineralized fresh natural groundwater, minopolycarboxylic acid chelating agent (0.05M EDTA), 0.5M HCl, 15M HNO₃, and a total digestion, with U isotopes reported in this study for each procedure. The following mineral phases of core samples: adsorbed material, carbonate minerals, amorphous iron oxides, aluminosilicates partial digestion and a crystalline iron oxides, aluminosilicates total digestion and a clay/quartz resistate were characterized. Red-colored siltstones depleted in uranium in relatively readily soluble mineral phases. The concentration of adsorbed uranium was established in the amount of 15.8±2.1 - 30.5±3.9 μg/kg. Carbonate minerals contain even less of this element. In iron hydroxides and the most readily soluble aluminosilicates, its concentrations are in the range 168±24 - 212±28 μg/kg. The most insoluble fraction contains 1.65±0.21 - 4.32±0.45 mg/kg of uranium. In green-colored siltstones, the concentration of adsorbed uranium is much higher: 106±14 - 364±43 μg/kg. Carbonate minerals and amorphous iron oxides contain 1.91±0.21 - 2.34±0.26 mg/kg of uranium. In aluminosilicates and a clay/quartz resistate, uranium concentrations are 5.6±0.5 - 16.8±1.4 mg/kg. Elevated values of ²³⁴U:²³⁸U activity ratio prevail in the adsorbed material and iron hydroxides. In aluminosilicates and clay/quartz resistate, the values decrease. This indicates the replacement of primary sedimentogenic uranium by secondary hydrogenic uranium adsorbed on the surface of minerals and coprecipitated with iron hydroxides. The results obtained made it possible to carry out preliminary quantitative estimates of the retardation factor and recoil loss factor of uranium in the groundwater of siltstones of the studied Vendian aquifer.

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

Biorefining by enzymatic hydrolysis (EH) of lignocellulosic waste material due to low costs and affordability has received enormous interest amongst scientists as a potential strategy suitable for the production of bioactive ingredients and chemicals. In the present study, a sustainable and eco-friendly approach to the extraction of bound ferulic acid (FA) has been demonstrated using a single-step EH by a mixture of lignocellulose-degrading enzymes. For comparative purposes of the efficiency of EH, an online SFE-SFC-MS extraction and analysis approach was applied. The experimental results demonstrated up to 369.3 mg 100 g−1 FA released from rye bran after 48 h EH with Viscozyme L. The EH of wheat and oat bran with Viscoferm for 48 h resulted in 255.1 and 33.5 mg 100 g−1 of FA, respectively. The extraction of FA from bran matrix using the SFE-CO2-EtOH delivered up to 464.3 mg 100 g−1 of FA, though the extractability varied depending on the parameters used. The 10-fold and 30-fold scale-up experiments confirmed the applicability of EH as a bioprocessing method valid for industrial-scale. The highest yield of FA in both scale-up experiments was obtained from rye bran after 48 h of EH with Viscozyme L. In purified extracts, the absence of xylose, arabinose, and glucose as final degradation products of lignocellulose was proven by a HPLC-RID system. Up to 94.0% purity of FA was achieved by SPE using the polymeric reversed-phase Strata X column and 50% EtOH as eluent.

Recent advances in DNA sequencing technology have shown that the human milk microbiota of healthy women varies substantially. The gDNA extraction method may influence the observed variation, biasing the microbiological reconstruction after all. In this study, a genomic DNA extraction method for DNA isolation from human milk samples was standardized and compared with commercial and standard hose make methods. Spectrophotometric measurements, gel electrophoresis, and PCR amplifications were used as criteria for evaluating the quantity, quality, and functionality of the extracted DNA. Furthermore, the standardized method of extracting gDNA from human milk was evaluated for its ability to isolate functional DNA from gram-positive, and gram-negative bacteria and fungi, to improve the reconstruction of microbiological profiles. The novel DNA extraction method increased the quantity and quality of the gDNA extracted compared with commercial and standard house-make protocols. This method even allowed PCR amplification of the V3-V4 regions of the 16S ribosomal gene in all samples, and the ITS-1 region of the fungal 18S ribosomal gene in 95 % of the samples as well. It is concluded that the proposed method provides better performance for the extraction of gDNA from complex samples such as human milk.

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.

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.

The combination of organic matter, iron oxide, and clay minerals is of great significance for the adsorption of copper ions (Cu). The purpose of this study is to explore the characteristics of Cu adsorption and laws governing Cu complexation to organic–inorganic, organic–clay mineral, and iron-oxide–clay mineral complexes in the sediments in the estuary of plateau fault and sinking lake——Dianchi Lake. In this study, Cu adsorption tests were performed on the three complexes, in order to study the kinetic behavior of adsorption, Langmuir and Freundlich isotherm models were used. The samples before and after adsorption were characterized via scanning electron microscope (SEM), Fourier infrared spectroscopy (FTIR), and X-ray diffraction (XRD). Our results show that, the Freundlich isotherm models model was able to describe adsorbent behavior in comparison to the Langmuir models. During the Cu adsorption process, the iron-oxide–clay mineral complex is able to adsorb Cu, via coordination exchange, through the –OH contained therein. Organic-matter–clay mineral complexes bonded to the surfaces of clay minerals by replacing the hydroxyl groups with functional groups (carboxyl groups or phenolic hydroxyl groups) contained in the organic matter. Organic–inorganic composites then adsorbed Cu through the coordination exchange of –OH in the polar functional groups of alcohols, phenols, and carboxylic acids. The adsorption capacity of Cu in these three sediment complexes was observed to have the following order: organic–inorganic complex &gt; organic-matter–clay mineral complex &gt; iron-oxide–clay mineral complex. The semi-quantitative analysis results of Fourier Infrared Spectroscopy show that the organic matter (changes in the peak area of functional groups such as carboxyl groups) in the organic-inorganic composite material has an important effect on the amount of copper ions adsorbed by clay minerals.

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

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

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

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

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%.

For any molecule, network, or process of interest, to keep up with new publications on these, is becoming increasingly difficult. For many cellular processes, molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large scale molecular interaction maps and database curation. Text mining and Natural Language Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and machine learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention based models, a special type of neural network (NN)-based architectures that have recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at a sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conduct a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.

The crystals, C11H4BrF5N2S, (I), 1-((4-bromothiophen-2-yl)methylene)-2-(perfluorophenyl)hydrazine and C12H6BrF5N2S, (II), 1-((4-bromo-5-methylthiophen-2-yl)methylene)-2-(perfluorophenyl)hydrazine are molecules with two rings and hydrazone part like a centre of the molecule. The compounds have been synthesized and characterized by elemental, spectroscopic (¹H-NMR) analysis. The crystal structures of the solid phase were determined by single crystal X-ray diffraction method. They crystallize in the monoclinic space group with Z = 4 and Z = 2 molecules per unit-cell. The compound (I) crystallizes as a racemate in the centrosymmetric space group and the compound (II) crystallizes as a non-racemate in the non-centrosymmetric space group. The “absolute configuration and conformation for bond values” were derived from the anomalous dispersion (ad) for (II). The crystal structures are revealed diverse non-covalent interactions such as intra- and interhydrogen bonding, π-ring···π-ring, C-H···π-ring and they were investigated. The expected stereochemistry of hydrazones atoms C7, N2 and N1 were confirmed for (I) and (II). The hole molecule of the (I), and (II) possesses “a boat conformation” like a 6-membered ring. The results of the single crystal studies are reproduced with the help of Hirshfeld surface study and Gaussian software.

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.

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.

This study compares the efficacy of the two most commonly used surgical methods for pterygium in the Polish population, conjunctival autograft versus amniotic membrane transplantation, and to evaluate the postopera-tive recurrence rate. We retrospectively analysed the medical records of 65 patients who underwent surgery for primary or recurrent pterygium at an ophthalmology clinic in Bialystok, Poland between 2016 and 2020. Surgical success (no regrowth) occurred in almost half of the amniotic membrane patients (44%) and in most of the conjunctival autograft patients (79%); this was a significant relationship. The odds of successful surgery were 79% lower for subjects with amniotic membranes than for those with conjunc-tival autografts (OR with 95% CI = 0.21 (0.05; 0.94]; p = 0.045). Our study confirms that in Polish Caucasian population the success rate of the pro-cedure using conjunctival autograft versus the use of amniotic membrane, is in favoured for the procedure with conjunctival graft.

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.

DNA extraction for downstream molecular diagnostic applications can be an expensive, time-consuming process. We devised a method to quickly extract genomic DNA from environmental samples based on sodium hydroxide lysis of cells with or without capture by magnetic beads, for subsequent PCR or quantitative PCR. The final DNA extraction method using NaOH is extremely low-cost and can be completed in 10 minutes at room temperature. NaOH extraction was effective for Gram-negative and Gram-positive bacteria in samples from air, soil, sewage, food, laboratory surfaces, and chicken cloacal swabs. The NaOH extraction method was comparable to commercial kits for extraction of DNA from pig fecal samples for 16S amplicon sequencing analyses. We demonstrate that an impinger and portable pump can efficiently capture bacteria in poultry facilities for rapid DNA extraction for quantification of total bacteria and for detection of specific species using qPCR. The air sampling and NaOH extraction procedures are well-suited for routine, high throughput screening, and for metagenomic analyses for specific pathogens, even in resource-limited situations.

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.