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This paper presents a qualitative study based on 12 in-depths interviews conducted with specialists who possess extensive experience in both governmental and non-governmental institutions and have a profound understanding of the women's empowerment process in Egypt. The objectives of this study were to explore the perspectives of institutional experts regarding the challenges faced by the women's empowerment process in Egypt, as well as the role institutions play in substantiating this process. There is a large and a growing interest in investigating the empowerment of women in emerging countries such as Egypt. As the literature suggests, women empowerment is not only linked to economic growth, but also to the establishment of a healthier and more sustainable society. The study concludes that despite Egypt's numerous achievements in promoting women's progress in both economic and social realms, and the existence of a flourishing and sustainable institutional activism, there is still room for improvement in women's empowerment. Institutional experts acknowledge that, in order to enhance the implementation of policies and programs, institutions need to work harder towards better law enforcement, improved education, a shift in societal mindset, more targeted funding policies to reach women in the greatest need, and the cultivation of the potential for community development. Additionally, institutions are expected to take the lead in transforming society into one that is more inclusive and supportive of women. [ABSTRACT FROM AUTHOR]

• This study focused on applicability of optimized algorithms in alteration mapping. • The ML algorithms considered for optimization included QDA, CKNN, and BDT. • The optimization process employed various techniques, such as GS, RS, BO, and PCA. • Using the optimized algorithms holds promise for enhancing the precision of models. • The study highlights the implications of ML methods for mineral explorations. World-class large to sub-economic small porphyry copper deposits (PCDs) are primarily found in the Kerman Cenozoic Magmatic Arc (KCMA) which is a fascinating area for geological remote sensing investigations because of its well-exposed rocks and roughly vegetated surfaces. Remote hydrothermal alteration mapping is a critical component of mineral exploration and resource assessment, vital for identifying PCDs. This study explored the application of ML techniques, such as quadratic discriminant analysis (QDA), cosine K-nearest neighbor (CKNN), and bagging decision tree (BDT), in remote hydrothermal alteration mapping. Moreover, the study highlights the transformative impact of optimization methods such as grid search (GS), random search (RS), Bayesian optimization (BO), and principal component analysis (PCA) methods in fine-tuning these algorithms to achieve superior results. These algorithms were found accurate and helpful in identifying PCD-related argillic, phyllic, propylitic, and iron oxide/hydroxide alteration-type zones based on field observations, petrographic studies, and XRD analysis. This research revealed evidence for widespread phyllic and silicic alteration zones, as well as confined argillic and iron oxide/hydroxide zones surrounded by wider regions of propylitic alteration. As the field of ML continues to advance, the future holds promise for even more refined and innovative approaches to hydrothermal alteration mapping. This study underscores the pivotal role that optimized ML algorithms play in revolutionizing mineral exploration practices and paving the way for a more sustainable and responsible resource assessment industry. [ABSTRACT FROM AUTHOR]

Food pairing was the process of combining food or food and beverages to produce sensory characteristics. The consumption of snacks with commercial chili sauce as a supplement was widespread among consumers. Research on food pairing of commercial chili sauce with French fries was limited. The research aimed to describe and understand the sensory profile of chili sauce with its accompanying foods and identify changes in sensory attribute intensity when chili sauce was paired based on consumer perception. The methods used were Quantitative Descriptive Analysis and Rate All That Apply. The main ingredients were four commercial chili sauces and three commercial French fries. The sensory characteristics of chili sauce and French fries pairs were evaluated using QDA with trained panelists and RATA with consumer panelists. The results from the QDA method showed dominant characteristics such as potato flavor, crunchy texture, oily mouthfeel, sweet taste, and sour taste. According to PCA and spider web in QDA, it can be observed that the sample pairs with the highest intensity were sample pairs 47 and 87. Based on the RATA test results, According to PCA and spider web in RATA, it can be observed that the sample pair with the highest intensity was sample pair 65. [ABSTRACT FROM AUTHOR]

Epilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications. [ABSTRACT FROM AUTHOR]

Schizophrenia is a severe mental disorder that impairs a person's mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement. [ABSTRACT FROM AUTHOR]

Purpose – This study investigates the barriers to the adoption of Virtual Reality (VR) in the tourism industry. Although VR has great potential to enhance the tourist experience, the adoption of this technology is still limited in the tourism sector. Building on the fundamental principles of the Technology-Organization-Environment (TOE) theory and its contribution to perceptions of technology adoption, this study aims to fill the knowledge gap regarding the specific barriers to VR adoption by tourism enterprises. Methodology – To achieve this objective, interviews were conducted with managers of tourism companies, and the data was analysed using qualitative methodology through MAXQDA 20 software. Conclusions – The results reveal that the main barriers identified by managers mainly include lack of knowledge about VR, particularly in the tourism sector. The perceived lack of usefulness, limited experience with the technology, and reluctance to invest in technological equipment also emerge as barriers to VR adoption. Originality of research – This study can help companies in the tourism sector to develop more effective strategies to overcome these barriers, thereby improving the tourist experience and increasing their competitiveness in the market using VR equipment.

Purpose -- This study investigates the barriers to the adoption of Virtual Reality (VR) in the tourism industry. Although VR has great potential to enhance the tourist experience, the adoption of this technology is still limited in the tourism sector. Building on the fundamental principles of the Technology-Organization-Environment (TOE) theory and its contribution to perceptions of technology adoption, this study aims to fill the knowledge gap regarding the specific barriers to VR adoption by tourism enterprises. Methodology -- To achieve this objective, interviews were conducted with managers of tourism companies, and the data was analysed using qualitative methodology through MAXQDA 20 software. Conclusions -- The results reveal that the main barriers identified by managers mainly include lack of knowledge about VR, particularly in the tourism sector. The perceived lack of usefulness, limited experience with the technology, and reluctance to invest in technological equipment also emerge as barriers to VR adoption. Originality of research -- This study can help companies in the tourism sector to develop more effective strategies to overcome these barriers, thereby improving the tourist experience and increasing their competitiveness in the market using VR equipment. [ABSTRACT FROM AUTHOR]

The main goal of governments is to guarantee that every individual worldwide, regardless of disability, have access to education. However, learners with disabilities exhibit higher rates of school and college dropouts than regular students. The main goal of contemporary research is to anticipate such drop-outs slightly earlier and offer recommendations and alternative pathways for their career enhancement by utilizing a novel approach LSOWL–CNN method. Initially, the dataset is pre-processed to increase the classification performance. Following that, the features are extracted and by employing ENT-QDA, the features are reduced. Rules based on fuzzy data will be created based on the features. A classification method called Linear Scaling Owl Optimal control Method with CNN (LSOWL–CNN) is employed to train the system using the created rules and testing the data the final results are classified. Results of the experiments showed that the suggested model attained an Accuracy of 96.817% which outperforms the existing models which is implementing in a python tool. [ABSTRACT FROM AUTHOR]

Fine flavor cocoa (FFC) is known for its unique flavor and aroma characteristics, which vary by region. However, a comprehensive overview of the common sensory attributes used to describe FFC beans and chocolate is lacking. Therefore, a systematic review was conducted to analyze existing literature and identify the most commonly used sensory attributes to describe FFC beans and chocolate. A systematic search of the Web of Science and Scopus databases was conducted in May 2023, and Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines were followed to ensure transparency and reproducibility. This review summarizes the origins of cocoa and explores their unique flavor profiles, encompassing caramel, fruity, floral, malty, nutty, and spicy notes. Although some origins may exhibit similar unique flavors, they are often described using more specific terms. Another main finding is that although differences in sensory attributes are anticipated at each production stage, discrepancies also arise between liquor and chocolate. Interestingly, fine chocolate as the final product does not consistently retain the distinctive flavors found in the liquor. These findings emphasize the need for precise descriptors in sensory evaluation to capture flavor profiles of each origin. As such, the exploration of attributes from bean to bar holds the potential to empower FFC farmers and chocolate producers to effectively maintain quality control. [ABSTRACT FROM AUTHOR]

Schizophrenia is a severe mental disorder that impairs a person’s mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.

Abstract Epilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications.

In many real classification problems where a limited number of training samples is available, the linear classifiers based on discriminant analysis are unable to deliver accurate results. Moreover, the testing and/or the training data can be erroneous due to noise contamination which further degrades their performance. Regularization techniques become imperative to deal with these problems. However, the existing regularization techniques, to some extent, mainly focus on data scarcity issues but completely ignore the noisy nature of the testing and/or the training data. We propose a novel regularized quadratic discriminant analysis (R-QDA) classifier which addresses both issues simultaneously. The procedure involves a reformulation of the discriminant function of the conventional QDA classifier into least square problems and then solving them by using regularized least squares (Reg-LS) based on $\ell _{2}$ -norm. In contrast to existing R-QDA techniques, the proposed R-QDA classifier employs two regularization parameters pertaining to each class, which can be independently selected by various robust techniques. Numerical results demonstrate the effectiveness of the proposed method over classical R-QDA methods, especially in high noise regimes.

Silver needle is a common type of white tea in Indonesia. The silver needle is produced from unbloomed pekoe, is pointed, slightly hairy, and silver in color. Quantitative descriptive analysis is a sensory evaluation method that relies on trained panelists' abilities in the experiment. The purpose of this study is to create a sensory profile of white tea from Indonesia using the QDA method. Five tea samples registered to the Indonesian National Agency for Drug and Food Control (Brand Gamboeng, Ennie 1, and Cap Pucuk from the West Java, Teh Dandang from Central Java, and Brand Heizl from East Java) were evaluated by trained panelists with extensive experience who had passed selection and training processes. Water was used as the neutralizer and crackers as the carrier. Three grams of each sample were brewed at 98°C with 150 mL of water for 7 min. Each panelist described the attribute, determined the reference, and evaluated all samples on a 15-cm scale. The results of the focus group discussion has obtained 12 lexicon sensory attributes, namely: yellow-brown, burned aroma, floral aroma, dry aroma, woody aroma, green aroma, burned flavor, green flavor, fermented flavor, floral flavor, bitter taste, astringent aftertaste, and bitter aftertaste. The results also showed that the "Heizl" white tea had the following sensory characteristics: yellow-brown, burned aroma, burned flavor, fermented flavor, bitter taste, bitter aftertaste, and astringent aftertaste. Moreover, the "Dandang" white tea had the following attributes: dry aroma, floral aroma, woody aroma, green aroma, green flavor, and floral flavor.

A Napa Valley Sauvignon blanc wine was bottled with 200 each of a natural cork, a screw cap, and a synthetic cork. As browning is an index for wine oxidation, we assessed the brown color of each bottle with a spectrophotometer over 30 months. A random-effects regression model for longitudinal data on all bottles and closure groups found a browning growth trajectory for each closure group. Changes in the wine's browning behavior at 18 months and 30 months showed that the browning of the wine bottles appeared to slow down later in the storage period, especially for natural corks. The between-bottle variation was the highest for the natural cork. At 30 months, we separated the bottles by the extent of browning and samples were pulled from the high, mid, and low levels of browning levels for each closure. The degree of browning is inversely correlated with free SO2 levels ranging from 5 to 12 mg/L. However, a Quantitative Descriptive Analysis (QDA™) sensory panel could not detect any difference in their aroma and flavor profile between closure types regardless of browning level. Even low levels of free SO2 retain protection against strong oxidation aromas, and visual browning detected by spectrophotometer seemed to precede oxidative aroma and flavor changes of the aging Sauvignon blanc.

Silver needle is a common type of white tea in Indonesia. The silver needle is produced from unbloomed pekoe, is pointed, slightly hairy, and silver in color. Quantitative descriptive analysis is a sensory evaluation method that relies on trained panelists' abilities in the experiment. The purpose of this study is to create a sensory profile of white tea from Indonesia using the QDA method. Five tea samples registered to the Indonesian National Agency for Drug and Food Control (Brand Gamboeng, Ennie 1, and Cap Pucuk from the West Java, Teh Dandang from Central Java, and Brand Heizl from East Java) were evaluated by trained panelists with extensive experience who had passed selection and training processes. Water was used as the neutralizer and crackers as the carrier. Three grams of each sample were brewed at 98°C with 150 mL of water for 7 min. Each panelist described the attribute, determined the reference, and evaluated all samples on a 15-cm scale. The results of the focus group discussion has obtained 12 lexicon sensory attributes, namely: yellow-brown, burned aroma, floral aroma, dry aroma, woody aroma, green aroma, burned flavor, green flavor, fermented flavor, floral flavor, bitter taste, astringent aftertaste, and bitter aftertaste. The results also showed that the "Heizl" white tea had the following sensory characteristics: yellow-brown, burned aroma, burned flavor, fermented flavor, bitter taste, bitter aftertaste, and astringent aftertaste. Moreover, the "Dandang" white tea had the following attributes: dry aroma, floral aroma, woody aroma, green aroma, green flavor, and floral flavor. [ABSTRACT FROM AUTHOR]

Organic food is preferred than conventionally grown foods, for their nutritional benefits with better sensory values. The impact of storage of organic and conventional Cucumis sativus L. in different domestic packaging at temperatures and durations on the shelf life and sensory qualities was studied. Results showed that packaging had significant effect on the shelf life of both organic and conventional Cucumis during storage. The triangle difference test showed significant difference between organic and conventional Cucumis stored in different packaging and storage temperatures. The Quantitative Descriptive Analysis by Principal Component Analysis showed that the strongly correlated attributes were total smell, sweet taste, and size, and fresh smell and bitter taste. The organic and conventional Cucumis stored in cling film showed highest preference in the affective study. Therefore, storage in domestic packaging can be considered as a strategy for increasing shelf life and sensory quality organic and conventional Cucumis.

Indian gooseberry (Phyllanthus emblica) is a seasonal and highly nutritious fruit with shorter shelf-life and astringent taste limiting its utilization. The enzymatic browning and flavor loss are major concerns which makes pre-processing a crucial step for further processing. In this context, dry microwave blanching (MWB) was explored as an alternative to hot-water (HW) blanching to reduce its nutrition losses particularly ascorbic acid (AA) and makes it easier for removal of seed kernel. This study focused on the effect of MW power level (200–500 W) and blanching time (50–100 s) on AA content, color attributes (L*, a*, b*), and enzymatic inhibition in Indian gooseberry. The optimized MWB conditions were 294 W MW power with 70 s blanching time providing satisfactory enzymatic inhibition (75.47%), and retention of AA (417.70 mg/100 g pulp) and color attributes (L*: 58.42, a*: 4.57, b*: 26.23). As compared to HWB (80 °C, 5 min), MWB showed least AA degradation, higher total phenolic content and softening percent with less total color difference owing to its shorter processing time and dry blanching. Quantitative descriptive analysis (QDA) demonstrated that raw and MW blanched Indian gooseberries are the most accepted, followed by HW blanched samples. MWB showed better nutrient retention than HWB with easier seed removal showed the application of MW radiation for blanching of other fruits. [ABSTRACT FROM AUTHOR]

To investigate the characteristic aromatic compounds, present in the traditional fermented koumiss of the Kazakh ethnic group in different regions of Xinjiang, GC-IMS, and GC-MS were used to analyze the volatile compounds in koumiss from four regions. A total of 87 volatile substances were detected, and esters, acids, and alcohols were found to be the main aroma compounds in koumiss. While the types of aroma compounds in koumiss were similar across different regions, the differences in their concentrations were significant and displayed clear regional characteristics. The fingerprint spectrum of GC-IMS, combined with PLS-DA analysis, indicates that eight distinctive volatile compounds, including ethyl butyrate, can be utilized to distinguish between different origins. Additionally, we analyzed the OVA value and sensory quantification of koumiss in different regions. We found that aroma components such as ethyl caprylate and ethyl caprate, which exhibit buttery and milky characteristics, were prominent in the YL and TC regions. In contrast, aroma components such as phenylethanol, which feature a floral fragrance, were more prominent in the ALTe region. The aroma profiles of koumiss from the four regions were defined. These studies provide theoretical guidance for the industrial production of Kazakh koumiss products. [ABSTRACT FROM AUTHOR]

As one of the most promising wine regions in China, the eastern foothills of the Helan Mountain (EFHM) in the Ningxia Hui Autonomous Region has attracted great attention recently. Geographically, EFHM is divided into six sub-regions, namely Shizuishan, Xixia, Helan, Qingtongxia, Yongning and Hongsipu. However, there have been few reports on the character and differences between wines in the six sub-regions. In this experiment, a total of 71 commercial Cabernet Sauvignon wines from six sub-regions were collected, and their phenolic compounds, visual properties and mouthfeel were investigated. The results showed that wines from the six sub-regions of EFHM showed distinctive phenolic profiles and could be distinguished through the OPLS-DA mode using 32 potential markers. In terms of color, Shizuishan wines showed higher a* values and lower b* values. The sensory evaluation showed that Hongsipu wines had higher astringency strength and lower tannin texture. The overall results implied that the phenolic compounds of wines in different sub-regions were affected by terroir conditions. To the best of our knowledge, this is the first time that a wide coverage of phenolic compounds has been analysed for wines from the sub-regions of EFHM, which could provide valuable information in deciphering the terroir of EFHM. [ABSTRACT FROM AUTHOR]

So-called meat hybrids are a new class of products where a fraction of the meat product (e.g., 20%) is replaced with alternative protein sources, such as plant-based ones. Research suggests that these products could serve as a low-threshold offer for a specific target group that wants to cut down on meat, thereby facilitating the transition toward a more healthy and sustainable diet. Nonetheless, data demonstrate that meat hybrids with a high substantial meat substitution level often fail in the market. This study summarises findings on the physicochemical properties, sensory, and acceptance of six different meat hybrids (70% meat and 30% plant proteins) that were collected in the framework of a case study in the project AiF 196 EN. For this purpose, sensory characteristics were collected via two QDA sessions and a hedonic consumer test. Furthermore, the hybrid recipes were analysed in their proximate composition. The respective recipes varied in protein source (soybean, pumpkin, and pea) and mode of incorporation [textured vegetable protein (TVP), high moisture extrudate (HME)]. It was shown that a meat hybrid with a relatively high share of 30% plant-based proteins with peas as a protein source and TVP as a processing method can still attract consumers.

To elucidate the effects of the different terroir on wine aroma in six sub-regions of Eastern Foothills of Helan Mountain in Ningxia, a premium wine-producing region in China, 71 Cabernet Sauvignon wines were investigated by gas chromatography-mass spectrometry (GC-MS), check-all-that-apply (CATA), and quantitative descriptive analysis (QDA). The bidirectional orthogonal partial least squares-discriminant analysis (O2PLS-DA) results showed that the Cabernet Sauvignon dry red wines from Xixia (XX) and Yongning (YN) had similar volatile profiles due to their geographical proximity and were characterized by higher concentrations of esters, higher alcohols, and volatile phenols because the similar aromatic profiles were detected in their dry red wines. Shizuishan (SZS) and Hongsipu (HSP) wines showed clear differences compared to the wines of the other four sub-regions, being mainly characterized by relatively higher phenolic aldehydes and volatile phenols. The concentrations of methoxypyrazines and norisoprenoids varied mainly depending on the climate diversity of the sub-regions. The highest 3-isobutyl-2-methoxypyrazine (IBMP) concentration was presented in the Helan (HL) wines. The Qingtongxia (QTX) wines have the highest β-damascenone, which might be influenced by the fact that QTX has the lowest effective accumulated temperature and the highest sunshine duration among the five sub-regions. Esters including ethyl octanoate, ethyl decanoate, ethyl butanoate, ethyl hexanoate, and isoamyl acetate were the highest in HL. Additionally, the herbaceous, black berry, and red berry notes in HL and QTX were the most outstanding. [ABSTRACT FROM AUTHOR]

A method based on liquid–liquid microextraction assisted by ultrasound energy (UALLME) and UPLC-ESI/QDa detection was developed for SeMet analysis in cashew nut samples. The UALLME conditions were optimized through an experimental design of three factors in two levels with central point, which was observed the highest yield of SeMet extraction from selenized yeast CRM when working with extracting solvent in extreme values of pH; also, occurrence of strong interaction between the variables "sample mass and extracting solvent pH" in the analyte response was detected. For the instrumental analysis, genistein showed as a suitable SeMet internal standard when was compared its efficiency in correcting the analyte signal with the SeMet similar compound (Se-methylselenocystin—SeMeSeC). As coextractives from the sample matrix influenced the analysis of the target compound by an overestimation of the analytical signal, the matrix-matched calibration strategy must be used for quantification. The method was validated and presented appropriate sensitivity, accuracy, instrumental, intra-assay and intra-day precisions, linearity, and linear range. The selectivity was guaranteed performing quantifications in the selected ion recording (SIR) acquisition mode. Analyses in commercial samples from three different brands demonstrated a probable absence of the analyte; however, the authors highlight the need to expand the research about the presence of SeMet in cashew nuts due to the importance of this nut to the economy of the state of Ceará and the benefits of SeMet to human health. [ABSTRACT FROM AUTHOR]

Groundwater quality is assessed by conducting water sampling and laboratory analysis. Field-based measurements are costly and time-consuming. This study introduces a machine learning (ML)-based framework and innovative application of stacking ensemble learning model, for predicting groundwater quality in an unconfined aquifer located in northern Iran. The groundwater quality index (GWQI) from 250 wells was evaluated and classified. We considered various influential factors such as proximity to residential areas, evaporation, aquifer transmissivity, precipitation values, population density, distance to industrial centers, distance to water resources, and topography. Three different ML classifiers were employed to establish relationships between GWQI and the aforementioned factors: the AdaBoost classifier (ADA), quadratic discriminant analysis (QDA), and stacking ensemble learning (SEL). A novel model was introduced dubbed quadratic-ada-stacking ensemble learning (QA-SEL) to predict GWQI. The performance of these algorithms was evaluated through the receiver-operating characteristic (ROC) and multiple statistical efficiency indicators, including overall accuracy, precision, recall, and the F-1 score. All three ML algorithms displayed a high degree of accuracy in their GWQI predictions. Nonetheless, the QA-SEL method was identified as the most effective model due to its superior accuracy (overall accuracy, precision, recall = 0.95, 0.95, 0.96, ROC = 0.96, respectively). Following model optimization and testing, the QA-SEL model and a GIS were employed to map GWQI classes across the entire area. The produced GWQI map was validated by comparing the measured and predicted GWQI on the map. This study offers an economically efficient model for groundwater quality prediction, which can be replicated in other plains., Competing Interests: Declarations. Ethical approval: Not applicable. Consent to participate: All authors consent to participate in this manuscript. Consent for publication: All authors consent to publish this manuscript. Conflict of interest: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Heart disease is a danger to people's health because of its prevalence and high mortality risk. Predicting cardiac disease early using a few simple physical indications collected from a routine physical examination has become difficult. Clinically, it is critical and sensitive for the signs of heart disease for accurate forecasts and concrete steps for future diagnosis. The manual analysis and prediction of a massive volume of data are challenging and time-consuming. In this paper, a unique heart disease prediction model is proposed to predict heart disease correctly and rapidly using a variety of bodily signs. A heart disease prediction algorithm based on the analysis of the predictive models' classification performance on combined datasets and the train-test split technique is presented. Finally, the proposed technique's training results are compared with the previous works. For the Cleveland, Switzerland, Hungarian, and Long Beach VA heart disease datasets, accuracy, precision, recall, F1-score, and ROC-AUC curves are used as the performance indicators. The analytical outcomes for Random Forest Classifiers (RFC) of the combined heart disease datasets are F1-score 100%, accuracy 100%, precision 100%, recall 100%, and the ROC-AUC 100%. The Decision Tree Classifiers for pooled heart disease datasets are F1-score 100%, accuracy 98.80%, precision 98%, recall 99%, ROC-AUC 99%, and for RFC and Gradient Boosting Classifiers (GBC), the ROC-AUC gives 100% performance. The performances of the machine learning algorithms are improved by using five-fold cross validation. Again, the Stacking CV Classifier is also used to improve the performances of the individual machine learning algorithms by combining two and three techniques together. In this paper, several reduction methods are incorporated. It is found that the accuracy of the RFC classification algorithm is high. Moreover, the developed method is efficient and reliable for predicting heart disease. [ABSTRACT FROM AUTHOR]

Background and Aims: The sensory experience of wine aroma is challenging to study. Given the presence of numerous and trace level volatiles, the subtle aroma nuances involved, as well as the complexity of human odour processing, the contribution of individual compounds and mixtures can be difficult to determine. In white wines, the volatile compounds eliciting stone fruit aromas are not well understood. Methods and Results: Factorial designs were used with odorants added to model wine and assessed using sensory quantitative descriptive analysis. In model Viognier‐like wines, several monoterpenes were confirmed to convey stone fruit attributes Apricot and Peach, which were strongly suppressed by aldehydes which imparted Cardboard‐like odours. Importantly, lactones increased Apricot aroma when combined with the monoterpenes. For model unoaked Australian Chardonnay wine, sensory‐directed screening followed by factorial studies showed that aliphatic ethyl esters, in particular ethyl octanoate, directed Peach aroma. Fatty acids were strong suppressors of the Peach attribute and gave Cheesy odours. Conclusions: Apricot and peach aromas in Viognier and Chardonnay, although perceptually similar, were caused by different chemical compound families: grape‐derived monoterpenes with lactones and yeast‐derived fatty acid ethyl esters, respectively. Significance of the Study: Having confirmed the compounds responsible for apricot and peach white wine aromas, there is potential to modify their concentration through established viticultural and winemaking practices. [ABSTRACT FROM AUTHOR]

The alcoholic beverage industry has grown exponentially in terms of the production of alcohol-containing products in an artisanal manner. Thus, it is proposed to implement an agroindustrial production alternative by making fruit wine from curuba (Passiflora mollissima) using a Saccharomyces Cerevisiae (Var. Bayanus) yeast strain. Three phases were developed; the first consisted of obtaining and fermenting the curuba pulp; the second phase included physicochemical and microbiological characterization, evaluating parameters such as pH, acid-base titration, soluble solids, color, viscosity, yeast count by deep seeding on YGC agar and LAB count on MRS agar to verify malolactic fermentation; finally, sensory analysis was performed through a quantitative descriptive analysis (QDA) in solitary and paired. The result obtained was a beverage with a pH of 3,14±0,02, a viscosity of 2,31±0,02 cP, soluble solids content of 7,65±0,07 °Brix and an alcohol content of 12,91±0,19 v/v. Thus, the study demonstrates that curuba has a high biotechnological potential in the elaboration of fermented beverages, offering an additional alterna- tive for the elaboration and commercialization of new products in the department of Huila (Colombia). [ABSTRACT FROM AUTHOR]

Epilepsy is a severe neurological disorder that causes seizures. It is detected by analyzing the electrical impulses of the human brain. Monitoring the brain is commonly done using an electroencephalogram (EEG). Seizure detection from the large recorded EEG dataset is a demanding task. However, numerous machine learning classifiers and the appropriate features can detect seizures. The Hjorth and statistical parameters are used in this study to provide an effective method for field programmable gate array (FPGA) realization of epileptic seizure detectors from EEG signals. Mobility, interquartile range (IQR), median absolute deviation (MAD), energy, non-linear energy, and simple square integral (SSI) are the various features analyzed in this work. The hardware architecture of the seizure detection system is captured in Verilog hardware descriptive language and realized on the Artix-7 FPGA. This paper presents three separate seizure detection models designed by pairing three different features with the Quadratic discriminant analysis (QDA) classifier. Among these three models, the energy and non-linear energy-based seizure detection system offers better performance than existing seizure detection systems because it possesses the lowest dynamic power consumption (0.116 µW), the highest design accuracy (99.4 %), and the highest sensitivity (100 %), which makes it the best seizure detection system for real-time applications. [ABSTRACT FROM AUTHOR]

Single malt whiskies are popular around the world. The 37 esters, 10 alcohols, 10 phenols, 8 aldehydes, 8 acids, 6 alkanes, 4 ketones and 3 heterocycles were detected in the single malt whiskies extracted from different origins in Scotland by HS-SPME (headspace solid-phase microextraction) and LLE (liquid-liquid extraction) and identified by GC-MS (gas chromatography-mass spectrometry). QDA (quantitative descriptive analysis) combined with multivariate statistics were used to investigate the effects of the differential aroma-active compounds on the sensory aroma characteristics. O-cresol, ethyl 2-methylbutyrate, 5-methylfurfural and propionic acid might influence the flavor of single malt whiskies of different origins in Scotland. The 54 key whisky aroma compounds were analyzed, with isoamyl acetate having the highest OAV (odour activity value) of all whisky samples. In addition, the aromas of volatile compounds were analyzed using OSME (odor-specific magnitude estimation) and OAV. Based on these facts, the researchers developed a flavour wheel with 10 aroma profiles. This paper evaluates Scotch single malt whisky from a Chinese perspective, which helps the development of the Chinese whisky market. [Display omitted] ● Analyzing different regions of single malt whiskies in Scotland. ● The main volatile aromatic compounds were identified in single malt whiskies. ● The OAV value of isoamyl acetate was the highest among the single malt whiskies. ● The researchers developed a flavour wheel with 10 aroma profiles. [ABSTRACT FROM AUTHOR]

The fast differentiation and classification of herb samples are complicated processes due to the presence of many various chemical compounds. Traditionally, separation techniques have been employed for the identification and quantification of compounds present in different plant matrices, but they are tedious, time-consuming and destructive. Thus, a non-targeted approach would be specifically advantageous for this purpose. In the present study, spectroscopy in the visible and near-infrared range and pattern recognition techniques, including the principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), super k-nearest neighbor (SKNN) and support vector machine (SVM) techniques, were applied to develop classification models that enabled the discrimination of various commercial dried herbs, including mint, linden, nettle, sage and chamomile. The classification error rates in the validation data were below 10% for all the classification methods, except for SKNN. The results obtained confirm that spectroscopy and pattern recognition methods constitute a good non-destructive tool for the rapid identification of herb species that can be used in routine quality control by the pharmaceutical industry, as well as herbal suppliers, to avoid mislabeling. [ABSTRACT FROM AUTHOR]

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The food industry generates a great amount of food waste and by-products, which in many cases are not fully valorized. Press cakes, deriving from oilseeds extraction, represent interesting co-products due to their nutritional value, high biopolymers content, and the presence of bioactive phytochemicals. Gluten-free breads (GFBs) are products that have disadvantages such as unsatisfactory texture, low nutritional value, and short shelf life, so natural additives containing proteins and hydrocolloids are in demand to increase GFBs value. In this study, extract from flaxseed by-product (FOCE—Flaxseed Oil Cake Extract) was used to replace water (25–100%) in GFBs formulations and their nutritional value, antioxidant properties, and sensory features were investigated. The results showed that GFBs with FOCE had an elevated nutritional and nutraceutical profile (up to 60% more proteins, significantly increased K, Mg, and P levels). Moreover, the addition of FOCE improved the technological parameters (increased specific volume, number of cells and height/width ratio, reduced density, average size, and perimeter of cells), antioxidant potential, and overall sensory quality of GFBs. This study showed an encouraging way of using a by-product that, due to its high content of proteins, polysaccharides, minerals, and antioxidants, can add value to GFBs. [ABSTRACT FROM AUTHOR]

The effectiveness of using essential oils against fish parasites and pathogenic bacteria as environmentally friendly phytotherapeutic agents in the aquaculture industry is demonstrated. A commercial additive composed of garlic essential oil, thymol and carvacrol (AROTEC‐G®) was added to a diet for gilthead seabream (Sparus aurata) as a protection strategy against Sparicotyle chrysophrii. The intense aromatic properties of these essential oils might lead to the need of a suppression time to reverse possible changes in the organoleptic quality of the fish. Two experimental groups were set up: an experimental group fed a commercial feed supplemented with AROTEC‐G® (diet A) during the first 4 weeks of the nutritional assay, and after which the treatment was suspended for 14, 21 and 28 days after a first sampling performed (day 0); and a control group, fed the same basal diet without AROTEC‐G® supplementation (diet C). A physicochemical evaluation measured the pH, colour and texture, and a descriptive quantitative sensory analysis (QDA) was carried out using a trained panel for gilthead seabream fillets. Overall, the findings showed that the use of dietary essential oils did not result in significant sensory differences after day 0 of the study, although slight differences were observed in some of the physicochemical parameters analysed. [ABSTRACT FROM AUTHOR]

Background & aimsThree-dimensional high-definition anorectal manometry (3D-HDAM) is used to assess anal sphincter function; it determines profiles of regional pressure distribution along the length and circumference of the anal canal. There is no consensus, however, on the best way to analyze data from 3D-HDAM to distinguish healthy individuals from persons with sphincter dysfunction. We developed a computer analysis system to analyze 3D-HDAM data and to aid in the diagnosis and assessment of patients with fecal incontinence (FI).MethodsIn a prospective study, we performed 3D-HDAM analysis of 24 asymptomatic healthy subjects (control subjects; all women; mean age, 39 ± 10 years) and 24 patients with symptoms of FI (all women; mean age, 58 ± 13 years). Patients completed a standardized questionnaire (FI severity index) to score the severity of FI symptoms. We developed and evaluated a robust prediction model to distinguish patients with FI from control subjects using linear discriminant, quadratic discriminant, and logistic regression analyses. In addition to collecting pressure information from the HDAM data, we assessed regional features based on shape characteristics and the anal sphincter pressure symmetry index.ResultsThe combination of pressure values, anal sphincter area, and reflective symmetry values was identified in patients with FI versus control subjects with an area under the curve value of 1.0. In logistic regression analyses using different predictors, the model identified patients with FI with an area under the curve value of 0.96 (interquartile range, 0.22). In discriminant analysis, results were classified with a minimum error of 0.02, calculated using 10-fold cross-validation; different combinations of predictors produced median classification errors of 0.16 in linear discriminant analysis (interquartile range, 0.25) and 0.08 in quadratic discriminant analysis (interquartile range, 0.25).ConclusionsWe developed and validated a novel prediction model to analyze 3D-HDAM data. This system can accurately distinguish patients with FI from control subjects.

Sweetener as a food additive which gives sweet taste has a different profile from sucrose. Intensity profile and attribute sensation become very useful in developing mixed sweeteners from single sweeteners to create commercial sweeteners having a sucrose-like profile. However, there is only few data of attribute intensity and descriptive profiles of single sweeteners. The aim of this research was to analyze the des-criptive profile of single and mixed sweeteners using quantitative descriptive analysis (QDA), time intensity (TI), and temporal dominance of sensations (TDS) methods. The results of QDA descriptive profile showed that samples of a single sweetener T3 as well as mixed sweetener containing C2 and C5 had the same characteristics as sucrose. The results of TI descriptive profiles showed that the time intensity curves of sweet attribute of a sample of two mixed sweeteners C1 and C2, and a single sweetener T3 were similar to that of sucrose. The results of the descriptive profile using TDS methods generally showed that the do-minant attributes of all samples were sweet, sweet aftertaste, and licorice. Bitter and bitter aftertaste attri-butes were found in single sweetener T7, while mixed sweetener C1 had best sensory characteristics than the others.

The progressing digitalization is increasingly permeating all spheres of life: also science. It has reached not only natural sciences, but also the humanities, such as qualitative educational research. For example, for the analysis of qualitative data in educational science researchers are increasingly using computer-assisted tools. This so-called QDA (qualitative-data-analyses) software was developed in the early 1980s in the course of the spreading digitalization to support the category based analyzes of qualitative research. Providing an overview about the current literature situation regarding QDA software and the issues addressed a systematical bibliographic research in pertinent databases were conducted. As a result, five different types of literature could be identified. Even though all categories are demonstrated only one is presented in detail. The paper focus on the category in which methodological issues are negotiated and illustrates the therein addressed arguments. Possibilities and limitations of QDA software for educational research are illustrated and past and present discussions regarding this software are outlined. In particular, it is emphasized that so far limitations of QDA software are rarely addressed. In order to deal with this void in the future more comprehensively a set of questions is proposed, which allows to reflect the use of QDA software in a specific research project in an extensive manner. Concluding further questions are raised and it is discussed if also sequential analyzes can be carried out with current QDA software or if they need new digital tools.