Accurate forecasting of infectious disease incidence is essential for timely intervention and effective government planning. This paper presents a comprehensive study comparing various forecasting models for daily COVID-19 mortality rates in Italy. The models evaluated include the autoregressive integrated moving average (ARIMA) model and three neural network-based models: backpropagation neural networks (BPNNs), radial basis function neural networks (RBFNNs), and Elman recurrent neural networks (ERNNs). RBFNN demonstrated superior performance with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE), outperforming ARIMA and other neural networks by better capturing non-linear patterns in mortality data. The models’ performance ranking from best to worst was RBFNN, ERNN, BPNN, and ARIMA. These results underscore the effectiveness of neural network models, particularly RBFNN, in accurately forecasting COVID-19 mortality rates. The implications of these findings are significant for public health policy. The improved accuracy of RBFNN in short-term mortality prediction provides valuable insights for pandemic response planning, enabling health authorities to make informed decisions on resource allocation, public health advisories, and emergency preparedness. This study contributes to the literature on infectious disease modeling by demonstrating the advantages of neural networks over traditional statistical methods and offering practical guidance for selecting forecasting models in epidemic scenarios. Our evaluation of forecasting methods thus provides a critical foundation for enhancing predictive accuracy in disease incidence and supporting more responsive public health management.

Introduction

Active food packaging technology encompasses systems that incorporate active substances into the polymeric matrix.

The embedded components exhibit antimicrobial, antifungal, antioxidant properties and are able to absorb or reduce oxygen, carbon dioxide or ethylene, thereby enhancing the quality and safety of food products.[1]

The utilization of two-dimensional materials, such as graphene, has facilitated the advent of novel avenues for the advancement of active packaging (AP). The integration of these materials with polymers has the potential to enhance the barrier, thermal, and mechanical properties of packaging.[2,3]

The objective of this paper is to provide a comprehensive overview of patented two-dimensional materials in the field of active packaging.

Methods

Patent searches were carried out using Espacenet, a database provided by the European Patent Office using a combination of keywords in the title/abstract/claims search fields with Boolean and proximity operators and classification codes.

Results

China is the country with the highest number of patent applications filed for 2D-materials in AP technology, followed by the United States, and Japan. A significant number of applicants opted to utilize the PCT procedure, which permits the postponement of entry into the national phases.

The number of patent applications filed has increased significantly from 2016 to 2022.

Graphene and graphene oxide are the most frequently claimed compounds in patents, followed by layered double hydroxides and hexagonal boron nitride.

Other two-dimensional materials, such as transition metal dichalcogenides, reduced graphene oxide, MXenes and graphitic carbon nitrides (g-C₃N₄), have been the subject of fewer patent applications.

Conclusions

China, the USA and Japan are the countries with the highest number of filings. In terms of the materials most frequently claimed in patent applications, carbon allotropes are the most prevalent.

Cereals, water, hops and adjuncts have been described as the major contributors to the mineral content in beer, in contrast yeast, industrial processes and containers will have a minor contribution. Beer mineral content contributes to the quality and flavour of the commercially produced beer. We have studied the content of Fe, Cu, Cr, Mg, Mn, V and Zn in nine different bottles of a popular brand of beer (5 alcoholic, 4 non-alcoholic) from the Madrid Region in Spain. Elements were monitored by ICP-MS following appropriate methods. Fe was detected only in the samples of non-alcoholic beer, meanwhile traces of V were detected in the samples of the alcoholic version. Except for Fe, levels were higher in the alcoholic beer, which could be attributed to differences in the brewing and manufacturing processes, such as reverse osmosis and filtration. Levels of Cu and Zn were below the permissible limit in wine (1 and 5 mg/L; respectively) set by the international organisation for grapes and wine. The daily dietary intakes for each element were (in µg/person, respectively) as follows, for alcoholic (0, 3.225, 3.036, 4244.81, 4.495, 28.255, 7.016) and non-alcoholic (72.937, 2.137, 2.094, 2434.63, 3.036, 20.064, 0) beer. The percentages that they would cover of the established RDAs for Fe, Cu, Cr, Mg, Mn, Zn for males (0.01; 900; 35; 420,000; 2,300 and 11,000 µg day^-1 person^-1) for consumers of alcoholic beer would be low: 0, 0.358, 8.674, 1.011, 0.195, 0.257%. Moreover, the intake of V from alcoholic beer would not represent a significant risk as a daily intake of 10-100 mg/day is considered safe from food sources. Our results would suggest that beer would not constitute an important source of these essential minerals.

Fruit pomace, which is the pulp and peels leftover from processing or juicing fruit, is a nutrient-rich and adaptable waste that can be made into useful goods like natural colours, biofuels, and dietary supplements, or it can be utilized as compost and animal feed. The goal of the current study was to create and evaluate fruit pomace jam using various juice waste leftovers in terms of its rheological and biochemical characteristics. Four types of pomace jams were made: mixed-fruit pomace jam (MFPJ), guava pomace jam (GPJ), pineapple pomace jam (PPJ), and apple pomace jam (APJ). In prepared pomace jams, surface morphology showed heterogeneous ultrastructure with pocket formation, with KPJ exhibiting the best 3D gel network. PPJ additionally exhibited the maximum values of the colour coordinates (L*, a*, and b*). When dietary fibre amounts were compared to other pomace jams and commercial fruit jams, GPJ had the highest, at 7.21 g/100 g. The highest amount of phenolic content (343.22 mg GAE/100 g) was found in MFPJ. There were noticeable levels of carotenoids in GPJ (6.86 mg/100 g) and MFPJ (4.21 mg/100 g). The antioxidant capacity of pomace jams shows the following trend for radical scavenging activity (%) and reducing power potential (PPJ) according to the ABTS and FRAP assays: GPJ> APJ> MFPJ> PPJ. The research effectively produced and identified fruit pomace jams with favourable rheological, biochemical, and antioxidant characteristics, emphasizing their potential as wholesome and environmentally friendly substitutes for conventional fruit jams.

Introduction

This study investigates the development and evaluation of an advanced automated system for glaucoma detection using deep learning techniques. Traditional diagnostic methods for glaucoma are often time-consuming and reliant on ophthalmologist expertise, leading to inconsistencies and delays in treatment. By utilizing state-of-the-art transformer-based models, this research aims to improve the accuracy and efficiency of glaucoma detection.

Methods

Five publicly available retinal fundus image datasets—ODIR-5K, ACRIMA, RIM-ONE, ORIGA, and REFUGE—were merged into one large dataset to ensure comprehensive model training and evaluation. The SegFormer model was employed for optic cup and disc segmentation, addressing the limitations of traditional CNNs in feature discrimination. This model captures both local and global contexts in fundus images, which is critical for accurate glaucoma detection. Segmented images were then classified using the Swin Transformer, known for its hierarchical architecture and ability to efficiently process high-resolution images through shifted window self-attention mechanisms. Data manipulation and preprocessing were conducted using Pandas and NumPy to optimize model performance.

Results

The combination of SegFormer for segmentation and Swin Transformer for classification resulted in superior performance compared to standalone models and other CNN-based approaches. The proposed model achieved an accuracy of 97.8%, precision of 97.5%, recall of 98.29%, and an F1-score of 98.33%. This significantly outperformed other state-of-the-art CNN models, demonstrating the effectiveness of transformer-based architectures in glaucoma detection.

Conclusions

This research showcases the potential of integrating SegFormer and Swin Transformer models for automated glaucoma detection. The high accuracy and scalability of this system suggest broader applications in medical diagnostics, offering a reliable and efficient solution for clinical settings.

The rapid adoption of Online Social Networks (OSNs) has redefined how people interact, share information, and collaborate. Yet, these platforms also introduce notable privacy and security concerns. This study investigates the inherent vulnerabilities of OSNs and evaluates the role of Two-Step Verification (2SV) as a countermeasure to strengthen user security.

Our research utilizes a multi-faceted approach, including a comprehensive literature review and an analysis of data breaches, privacy policy infractions, and user experiences within OSNs. The surveys capture user insights, and the case studies of security incidents highlight specific technical vulnerabilities. To assess 2SV, we examine its effectiveness in blocking unauthorized access, its impact on user experience, adoption trends, and its performance in comparison to alternative security measures such as strong passwords and biometrics.

The findings show that common OSN vulnerabilities include data breaches, phishing attempts, and weak privacy safeguards. User insights reveal varying levels of privacy risk awareness, with many being unclear about the available security options, including 2SV. Our evaluation finds that 2SV is effective in reducing unauthorized access and has achieved moderate user adoption due to its balance between security and usability. However, comparisons with options like biometrics point to 2SV’s limitations in both convenience and security resilience.

This study offers valuable perspectives on OSN security challenges and the potential of 2SV as a mitigation strategy. These insights contribute to ongoing efforts to enhance privacy and security across online social platforms.

The oral route is the most preferred method of pharmaceutical delivery across all age groups due to its safety, convenience, and cost-effectiveness. This preference has driven innovation in fast-dissolving tablets (FDTs), or mouth-dissolving tablets (MDTs), which dissolve rapidly in the mouth without water, addressing issues faced by patients with swallowing difficulties, including pediatric and geriatric populations.

FDTs offer significant advantages over traditional dosage forms, enabling rapid breakdown in the buccal cavity for direct absorption through the buccal mucosa, ensuring quick therapeutic effects. This rapid disintegration and absorption enhances bioavailability and hastens drug effects by bypassing the gastrointestinal tract and first-pass metabolism.

This review examines the formulation, mechanism, advantages, and challenges associated with FDTs, including their properties and various preparation methods like freeze-drying, tablet molding, and spray drying. Special emphasis is placed on natural polymers, such as starch, chitin, chitosan, alginates, and xanthan gum, and various mucilages, derived from plants, animals, and microorganisms. These polymers are valued for their biocompatibility, biodegradability, and non-toxic properties, serving as binders, disintegrants, taste-masking agents, and stabilizers.

The review also explores regulatory considerations, emphasizing safety, efficacy, and quality control when using natural polymers. This review highlights recent advances in developing new natural polymers, sustainable sourcing practices, and improvements in extraction and purification technologies.

In conclusion, natural polymers significantly enhance FDT formulations, improving patient compliance and therapeutic outcomes. This review underscores their critical role in the evolving landscape of fast-dissolving drug delivery systems.

Rich and underutilized, pomegranate peel is a powerful source of nutrients with significant concentrations of dietary fibre, antioxidants, vitamins C and E, potassium, and punicalagin, among other bioactive substances. This study investigated the process of extracting bioactive substances from pomegranate peel, an agricultural food waste. Pomegranate peel extractions were carried out under liquid and supercritical conditions using carbon dioxide (CO2), an environmentally acceptable solvent. We also investigated whether ethanol might be used as a cosolvent in tiny amounts of up to 30%. The extracts' antiradical activity, volatile organic chemicals, total polyphenolic levels, and individual polyphenolic profiles were assessed. When 30% ethanol was utilized as a cosolvent in both liquid (at 25 MPa and 30 °C) and supercritical (at 40 MPa and 50 °C) CO2 extraction, the best yields were obtained. Furthermore, the extracts made with liquid CO2 plus 30% ethanol had the highest concentrations of terpenes, specifically limonene, and naringin (36.41) among pomegranate peel extracts. According to ABTS+ and DPPH measurements, this extract type exhibited the highest antiradical activity (32.33–6-3.78 µmolTE g-1). These results indicate that the extraction using a liquid CO2 and ethanol mixture may be a good substitute for conventional solvent extraction, which uses 70% less organic solvent, and yields extracts rich in volatile organic chemicals and with strong antiradical activity.

Tomato (Solanum Lycopersicum L.) is the most popular fruit crop worldwide. However, the deterioration of the flavor quality of commercial tomatoes is one of the main causes of consumer complaints. One of the most important factors influencing the synthesis of aromatic compounds in tomato is the growing medium, though studies on the effect of the growing substrate on its volatile profile are limited. Therefore, the main objective of this work was to improve the aromatic and flavor properties of tomato through the use of novel growing media obtained from the remains of Posidonia oceanica (PO), favoring the revalorization of residues. A greenhouse experiment was carried out in pots with tomato seedling cv. sweet cherry with three treatment groups—control (50% peat–50% perlite), PO (50% PO washed/sieved–50% perlite), and IP (50% PO unaltered–50% perlite)—for 9 weeks under controlled temperature conditions (18°C/27°C (night/day)), 60% relative humidity, and two daily irrigations of 100 mL with tap water. To evaluate the effectiveness of the treatments, fresh weight, total soluble solids, and the concentrations of organic and volatile compounds were measured by SPME-GC/MS. The average weights of the fruits obtained were 4.97, 5.77, and 4.65 g, and they had sugar contents of 8.7, 7.4, and 10.0 ºBx for the control, PO, and IP groups, respectively. This variation was due to the high salinity of the IP sample, which resulted from not washing away the salts from the Posidonia oceanica debris, favoring the production of sweeter, although smaller, tomatoes. Among the volatile compounds identified by SPME-GC/MS, it stands out that the tomatoes from the IP substrate, followed by PO and the control, presented the highest concentrations of octanal and hexyl acetate, conferring citrusy, fresh, and sweet aromas. Additionally, the tomatoes from PO also had a notable concentration of nonanal and alpha-terpineol, contributing to their herbal and fresh notes.

Lactose, or milk sugar, is the most essential and essential carbohydrate found in mammalian milk. Aside from fat and protein, it is the most important component of milk solids in cow's milk. Due to low levels of intestinal lactase, also known as lactase-phlorizin hydrolase (LPH), a β-D-galactosidase present in the apical surface of the intestine microvilli, around 70% of the global adult population is lactose intolerant. This might be brought on by the adult loss of intestinal lactase, a disorder caused by a recessive autosomal gene that varies in racial populations in humans. These days, a lot of goods are sold to people who are lactose intolerant as dairy substitutes. A lactose-free diet is a crucial part of treatment for people with lactose intolerance thus those who are impacted must avoid certain dairy foods and non-dairy items that have lactose. To mitigate lactose sensitivity and enhance human health and well-being, lactose-free dairy products are suggested as substitutes. Probiotics and fermented dairy products have been shown in recent research to alter the metabolic processes of the intestinal microbiota and perhaps reduce lactose intolerance symptoms. According to studies, sweet kefir has a microbial diversity that is comparable to that of typical milk kefir, suggesting that it could be a viable probiotic substitute. Nonetheless, it has been demonstrated that sweet kefir contains probiotic qualities, including the ability to adhere to mucosa by penetrating its lumen and allow microorganisms to colonize the mucosa. Thus, clinical investigations have indicated possible health effects such immunomodulation, anticancer, anti-obesity, inhibition of inflammatory agents, decrease of oxidative stress, and antibacterial activity. Plant based milk has recently been developed with sorghum seeds, sesame seeds, cantaloupe seeds. The highest fat contents were found in coconut milk, sesame milk, and cantaloupe seed milk respectively.