Automated food item recognition and recipe recommendation systems have gained increasing importance in dietary management and culinary applications. Recent advancements in Computer Vision, particularly in object detection, classification, and image segmentation, have facilitated progress in these areas. However, many existing systems remain inefficient and lack seamless integration, resulting in limited solutions capable of both identifying food items and providing relevant recipe recommendations. Furthermore, modern neural network architectures have yet to be extensively applied to food recognition and recipe recommendation tasks. This study aims to address these limitations by developing a system based on the MobileNetV2 architecture for accurate food item recognition, paired with a recipe recommendation module. The system was trained on a diverse dataset of fruits and vegetables, achieving high classification accuracy (97.2%) and demonstrating robustness under various conditions. Our findings indicate that the modified model, the MobileNetV2 model, can effectively recognize different food items, making it suitable for real-time applications. The significance of this research lies in its potential to improve dietary tracking, offer valuable culinary insights, and serve as a practical tool for both personal and professional use. Ultimately, this work advances food recognition technology, contributing to enhanced health management and fostering culinary creativity. Some potential applications of this work include personalized dietary management, automated meal logging for fitness apps, smart kitchen assistants, restaurant ordering systems, and enhanced food analysis for nutrition tracking.

The pollution of the planet has exceeded all limits. One aspect of concern is the environmental burden caused by heavy metals. The issue with these metals is that they tend to accumulate in the environment, leading to adverse effects. In the present work, we deal with the detection and removal of specific heavy metals (Lead, Cadmium, Mercury, Zinc, Chromium, Cobalt, Copper, Iron, Nickel, Selenium, Silver and Arsenic European wines with activated carbon from potato peels (AC-Pot ) and banana peels (AC-Ban). In addition to heavy metals, we use the same activated carbon to remove (N-phosphonomethyl)glycine. Activated carbon derived from potato peels (AC-Pot) and from banana peels (AC-Ban) was prepared and characterized. Then, we selected ten wine samples of various European countries such as Greece, Italy, Austria, France, Portugal, etc., and measured their content of the above heavy metals as well as the chemical substance (N-phosphonomethyl)glycine. We repeated the measurements a few days after the addition of the activated carbons in the wine samples; the same procedure was carried out three more times over 2 years on the same wine samples. The results obtained were quite satisfactory, and the conclusions drawn were very useful for further study. Some metals were more present in the samples, while others had higher rates of removalafter the activated carbon treatment.

Face verification, a critical task in computer vision with significant implications for security, surveillance, and biometric applications, involves determining whether two facial images represent the same individual, even when captured under varying conditions such as lighting changes, pose, or facial expression variations. Despite recent advances in the field, achieving a high accuracy in face verification remains challenging, especially in scenarios involving occlusions or poor image quality. Improving the methods used to compare facial embeddings has become a key area of research for developing more robust and reliable face verification systems. Traditionally, metrics such as L1, L2, and cosine similarity have been employed to compare facial embeddings in computer vision. However, when used in isolation, each metric has inherent limitations, particularly in its ability to generalize across complex and diverse datasets. This study explores the effectiveness of combining various metrics to enhance the comparison of facial embeddings. We aim to improve accuracy by leveraging state-of-the-art CNN-based face verification methods, including AdaFace and ArcFace, as well as advanced vision transformer-based approaches such as Swin transformers. To achieve this, we developed a range of combinations of metrics using machine learning techniques, including Logistic Regression, k-Nearest Neighbors, Support Vector Machines, LightGBM, and XGBoost. The CASIA-WebFace dataset was used for training metric-combining models, and the BUPT-BalancedFace dataset was used for evaluation, ensuring balanced comparisons across demographic groups. The experimental results showed that while cosine similarity outperformed L1 and L2 metrics, the combination of multiple metrics was more effective than models relying on a single metric in both CNN-based facial verification and vision transformer-based methods. CNN-based models were more effective than transformer-based ones. The combined strategies resulted in models that achieved a better balance among recall, precision, and F1-score. In particular, the accuracy of these models increased by 1.1% compared to the best models that used a single metric.

The use of activated carbon to remove colorants and odors from wine is a common practice in the wine industry. However, its use for the removal of heavy metals is at a research stage. The use of any additive must not negatively affect the original quality of the wine and it must not alter its organoleptic characteristics, as well as its appearance. In this research, we focused on the physicochemical characteristics of wine after the use of two activated carbons from potato peel (AC-Pot) and from banana peel (AC-Ban) that we prepared and characterized in our laboratories. In order to reach safe conclusions, we chose to focus on the measurement of specific measurements and indicators in ten wine samples before and after the application of the two active carbons. After selecting ten wine samples from all over Europe, before the addition of active carbons, we measured the following parameters: total acidity, active acidity, volatile acidity, free sulfites, total sulfites, total polyphenols, color intensity and color shade. We repeated the measurements a few days after adding the activated carbons to the wine samples, and the same procedure was performed three more times over a period of 2 years on the same wine samples. The results obtained were quite satisfactory and the conclusions drawn were very useful for further study.

The purpose of this study was to use a near-infrared (NIR) spectroscopic sensing system to monitor each cow's milk quality accuracy every 20 seconds and every time the cow was milked. We used the milk fat, lactose, milk urea nitrogen (MUN), and somatic cell count (SCC) as four indices of milk quality. Raw milk samples were obtained from four Holstein cows belonging to Hokkaido University. Using an NIR sensing system, raw milk's NIR spectra were recorded every 20 seconds while the cows were being milked. The wavelengths of the spectra ranged from 700 to 1050 nm. Using the MilkoScan instrument, milk fat, lactose, and MUN were measured while a Fossomatic instrument was used to measure SCC. Partial least squares regression analysis was used to generate calibration models in order to verify the precision and accuracy of the models. The obtained results demonstrated that the accuracy of each cow's milk quality measurement every 20 seconds and at one milking time during the milking process was outstanding for milk fat and comparable for milk lactose. For every cow, the MUN and SCC findings showed a considerable difference. These findings showed that the measurement of each cow's milk quality could have an effect on the calibration models' precision and accuracy.

Body posture dynamics have garnered significant attention in recent years due to their critical role in understanding the emotional states conveyed through human movements during social interactions. Emotions are typically expressed through facial expressions, voice, gait, posture, and overall body dynamics. Among these, body posture provides subtle yet essential cues about emotional states. However, predicting an individual's gait and posture dynamics poses challenges, given the complexity of human body movement, which involves numerous degrees of freedom compared to facial expressions. Moreover, unlike static facial expressions, body dynamics are inherently fluid and continuously evolving. This paper presents an effective method for recognizing 17 micro-emotions by analyzing kinematic features from the GEMEP dataset using video-based motion capture. We specifically focus on upper body posture dynamics (skeleton points and angle), capturing movement patterns and their dynamic range over time. Our approach addresses the complexity of recognizing emotions from posture and gait by focusing on key elements of kinematic gesture analysis. The experimental results demonstrate the effectiveness of the proposed model, achieving a high accuracy rate of 96.34% on the GEMEP dataset using a deep neural network (DNN). These findings highlight the potential for our model to advance posture-based emotion recognition, particularly in applications where human body dynamics are key indicators of emotional states.

With the increased consumption and production of fresh vegetables, the safety risks of vegetables, especially the risk of foodborne disease outbreaks, are also increasing. The food safety risks caused by pathogenic microorganisms in contaminated fresh vegetables are becoming leading threats to human health. Quantitative Microbial Risk Assessment (QMRA) is the core of preventing and controlling microbial hazard risks. This study aims to identify the main foodborne pathogens in fresh vegetables and review the applications of QMRA for assessing these major foodborne pathogens in fresh vegetables.

A comprehensive review was conducted by searching the databases including Web of Science, PubMed, Scopus and CNKI for the articles with terms related to “applications of QMRA” and “foodborne pathogens in vegetables” or “foodborne diseases caused by fresh vegetables” between 2000 and 2024.The main pathogens in fresh vegetables that cause outbreaks of foodborne diseases have been identified through analyzing the reports of foodborne disease events that have occurred internationally in recent years. The studies of Quantitative Microbial Risk Assessment applications for these main pathogens have been reviewed. The current applications and future studies of QMRA in assessing main pathogenic microorganisms in fresh vegetables and ready to eat vegetables have been summarized.

The review identified the top four pathogens associated with fresh vegetables that cause foodborne disease outbreaks are Salmonella, E. coli, Listeria monocytogenes, and Norovirus. Monte Carlo simulation approach is the most common and widely used technique for QMRA models applied to fresh and ready-to-eat vegetables, as it is easy-to-implement. The study on the applications of QMRA methods highlighted the key contamination variables and processes in the fresh produce chain as the cross-contaminations from farm to fork including soil, irrigation water, manure, human handling, storage, temperature, packaging, retail conditions, and evaluated the effectiveness of the preventive measures that have been implemented.

The basis of the negative reaction of colorectal cancer (CRC) to the application of certain treatment strategies is the acquisition of aggressive features during the process of epithelial–mesenchymal transition (EMT). It is known that the Wnt/β-catenin signal pathway is deregulated in CRC, and some specific markers are observed to be overexpressed in this disease, such as β-catenin transcription factor. Under the regulation of Wnt/β-catenin signaling appears the expression of the membrane proteins E-cadherin and N-cadherin, which are constitutive elements of intercellular junctions based on which the migration of cancer cells is controlled. Royal jelly (RJ) has already been recognized as a natural treatment with certain anti-cancer activities and antimetastatic potential, yet the exact molecular mechanism of these activities is still unknown.

This study aimed to assess the migratory potential of the colorectal cancer cell line SW-480 by applying the Transwell test, as well as to evaluate the expression of β-catenin, E-cadherin and N-cadherin at the gene level by using the qPCR method. Additionally, the E-cadherin and N-cadherin protein expression rates were determined by using immunofluorescence. All assays were carried out 24 h after treatment with RJ at two selected concentrations (10 and 100 μg/mL).

The results showed significant inhibition of SW-480 cells' migratory potential and downregulation of β-catenin gene expression after treatment with RJ. Concurrently, an increase in E-cadherin and the inhibition of N-cadherin at the protein level were induced by RJ at both applied concentrations. The difference in the SW-480 cells' responses to the two applied RJ concentrations was obvious, and the higher concentration (100 μg/mL) was more effective.

This study presents RJ as a promising therapeutic candidate for inhibiting the migratory potential of colorectal carcinoma cells by targeting regulatory and effector markers of EMT, thus offering potential avenues for modulating the aggressiveness of CRC.

The phenomenal advancements and vehement use of Software Defined Networks (SDNs) require robust cyberattack defense strategies. This arises from traditional intrusion detection systems (IDS), which often fail to solve the intricate security problems posed by SDNs. Defenses are challenged by substantial data volumes and the intricacies of changing network setups, resulting in inadequate attack detection and mitigation. Moreover, while advantageous for network administration, SDN's centralised architecture presents exploitable vulnerabilities. Therefore, to tackle these challenges, this paper introduces an innovative defence mechanism against cyberattacks in SDN. This leverages advanced deep learning techniques to enhance the precision and accuracy of detection and mitigation. This suggested model incorporates an Enhanced CenterNet architecture specifically designed for network traffic, supplemented with knowledge graphs to overcome traditional feature extraction restrictions. In this architecture, to enhance attack classification robustness, a hybrid DenseNet-201 model incorporating network topology, user behavior, and historical attack patterns is implemented. This is further augmented by adversarial training to counter sophisticated attacks. Especially, this dynamic defense mechanism, orchestrated by a remote SDN controller, reconfigures network resources in real-time for a prompt response to distributed denial-of-service (DDoS) attacks. To verify the effectiveness of the proposed model, an experimental analysis has been conducted on InSDN and DDoS-SDN datasets. This analysis is implemented in the Python/Mininet environment. From the results, it is observed that the proposed model achieved significant improvements in precision (4.5%), accuracy (5.9%), recall (4.5%), AUC (2.9%), and specificity (3.9%) of attack detection, reducing response delay by 10.4% when compared to conventional deep reinforcement learning (DRL) and hybrid quantum-classical convolution neural network (HQCNN). Additionally, it improves attack prevention precision (1.9%), accuracy (1.5%), recall (2.5%), AUC (3.5%), and specificity (2.9%), with a 3.5% delay reduction. Thus, this work significantly advances SDN cyberattack defense mechanisms and provides a robust solution to evolving security challenges.

Introduction: Kombucha tea is a probiotic fermented acidic tea obtained from a symbiotic culture of bacteria and yeasts (SCOBY) mainly acetic acid bacteria (AAB), lactic acid bacteria (LAB) and yeasts attached to a floating biofilm of bacterial cellulose, in a medium containing sugars and tea and its consumption is linked to beneficial effects. The aim of this study was to prepare kombucha tea by using alternative plant raw materials used in the Mediterranean basin in order to increase the bioactivity of the final product. Methods: Two kombucha systems were fermented for 12 days, one system by using SCOBY (10g/L), sugar (10 % w/v) and a mixture (1% w/v) of equal quantities of green tea and mountain tea (Sideritis spp) and in the second system, sugar was replaced by carob syrup from Ceratonia siliqua L., a xerophytic endemic species typical of the Mediterranean climate. Physicochemical and microbiological analyses were performed and total phenolic content and antioxidant activity were measured at 0, 6 and 12 days of fermentation. The SCOBY was observed by Scanning Electron Microscopy (SEM). Results: Both systems fermented the available sugars and produced a slightly carbonated, aromatic and acidic (pH 3.12-3.39) probiotic beverage with a low alcohol content (0.5-0.7% ABV). Yeasts and AAB remained at high probiotic levels (>7 logCFU/mL) and LAB at 4-5 logCFU/mL. The kombucha produced with carob syrup had at the end of fermentation an increased polyphenol content, more than three times than the sugar-based kombucha (773 mgGAE/L and 233 mgGAE/L respectively) and the antioxidant activity was increased by 2.4 times. SEM revealed an extended net of bacterial cellulose with bacteria and yeasts attached. Conclusion: Carob syrup can be used as an alternative and sustainable fermentable substrate for the preparation of Kombucha and increases significantly its bioactivity.