Hemp (Cannabis sativa L.) seeds contain a high concentration of proteins and biologically active compounds. The protein content is even higher in case of lipid part removal in oil production. The remaining part is considered a leftover, usually being used in animal feed. The aim of this study was to investigate the physicochemical composition of hemp seed cake flour, its nutritional quality and its impact on bread quality parameters. The properties of hemp seed cake flour were assessed in terms of protein quality, mineral composition, polyphenols and antioxidant activity. Hemp seed cake proved to be an important source of high-quality protein (31.62% d.m.) with the presence of eight essential amino acids. The biologically active potential of hemp seed cake has been demonstrated by the high content of polyphenols, especially those from the Cannabisin group. Hemp seed cake flour was incorporated in wheat flour at levels from 5 to 40% (w/w) to investigate its influence on bread quality parameters. The addition of hemp seed cake flour increased the total phenol content of bread, thus greatly enhancing the antioxidant activity. The protein content of bread was found to be enhanced from 11.11% d.m (control sample) to 18.18% d.m (for sample with 40% hemp seed cake flour). On the other hand, the addition of hemp seed cake flour led to decreased bread porosity, increased hardness and decreased resilience in the seed cake. Although, all bread samples recorded sensorial attributes ranging between “slightly like” and “like it very much”.

Food security is an important lever for the implementation of rights-based legislation, policies, and programs, while being a public health and socio-economic priority. Foodborne illnesses have a major impact on public health, and nutritional interventions are essential therapeutic strategies to combat them. Gluten ingestion has been linked to several clinical disorders, collectively called gluten-related disorders. The most serious of these is celiac disease. The only way to treat celiac disease is to stick to a gluten-free diet for life. Following a strict diet is also the only way to prevent the long-term consequences of the disease. Public policies are essential to ensure the food security of people with gluten-related disorders. The aim of the research is to assess the level of care for people with celiac disease in the Republic of Moldova, in terms of public policies, to ensure a sustainable sector that effectively satisfies the food security of people with disorders associated with gluten consumption. To assess the level of care for people with gluten-related disorders, the working algorithm was taken, with reference to global public policies in support of people with celiac disease, developed and validated by Falcomer et al., Focused on 6 items. The results of the study showed that the Republic of Moldova does not have adequate policy support to ensure food security for people with gluten-related disorders, which poses major challenges and, as a result, may increase the complications of these problems.

Effective blood glucose management is essential for individuals with type 1 diabetes, particularly when dietary adjustments involve staple foods like rice. As a primary carbohydrate worldwide, rice significantly influences the glycemic index (GI) based on its type and cooking method. This study investigated the impact of rice type and boiling duration on the GI in healthy adults using an in vivo approach aligned with ISO 2010 standards. The glycemic response to four rice types (white round-grain, parboiled medium-grain, white long-grain, and whole-grain long-grain) was measured through postprandial blood glucose levels under both standard and extended boiling conditions to assess their implications for dietary glycemic control. Ten healthy participants (mean age 25 years, body mass index (BMI) 23.0 ± 1.6 kg/m²) consumed rice samples containing 50 g of available carbohydrates, prepared under controlled boiling conditions. Postprandial glycemic response was measured at regular intervals over 2 h following ingestion, with glucose solution as a reference food. The GI was calculated based on the incremental area under the glycemic response curve for each rice sample. Extended boiling significantly increased the GI across all rice types. White round-grain rice exhibited the highest relative increase (+15.8%) in the GI, while whole-grain long-grain rice, despite showing a greater percentage increase (+25.4%), maintained the lowest overall GI due to its high amylose and fiber content. Rice types with higher amylopectin content demonstrated faster glycemic responses and higher GI compared to high-amylose types. This study highlights rice type and cooking time as critical factors influencing postprandial glycemic response. Shorter boiling durations may benefit individuals requiring strict glycemic control, particularly those with diabetes, underscoring the importance of personalized dietary guidance for managing glycemic outcomes effectively.

This study presents a global analysis of research priorities for technologies relevant to Sustainable Development Goals (SDGs). We examine 18 technological domains across countries, introducing a novel within-country rank metric to normalize differences in research output. Using a combination of linear regression and K-means cluster analysis, we identify factors influencing overall productivity and reveal distinct patterns in research priorities among nations. Our analysis of Web of Science total publication data yields five country clusters with specific technological focus areas: Eco-Tech Innovators, Cyber-Digital Architects, Bio-Industrial Pioneers, Geo-Data Security Analysts, and Cyber-Sustainable Integrators. We find that while economic indicators strongly predict overall research productivity, countries with similar economic profiles often exhibit divergent research priorities. Artificial Intelligence emerges as a top priority across all clusters, while areas such as blockchain and digital twins show lower prioritization despite their theoretical importance. Our findings reveal unexpected similarities in research focus among geopolitically diverse countries and highlight regional patterns in technological emphasis. This study offers valuable information for policymakers and researchers, enhancing our understanding of the global landscape of SDG-relevant technological research and potential avenues for international collaboration.

This study investigates the landscape of cyber-financial risks across Europe by identifying distinct risk profiles based on financial literacy, digital comfort, and economic vulnerability. Utilizing data from the Flash Eurobarometer 525 survey, we employ K-means cluster analysis to categorize European citizens into five distinct cyber-financial risk profiles: Cyber-Savvy Pragmatists, Digital-Savvy Vulnerable individuals, Cautious Traditionalists, Comfortably Naive individuals, and Digitally Hesitant At-Risk individuals. Our findings show significant variations in risk profiles across demographic groups and European regions, with gender disparities and educational differences. We examine how self-perceived financial knowledge and financial decision-making patterns differ across these profiles, uncovering potential overconfidence in some groups that may exacerbate cyber-financial vulnerabilities. This study also explores regional distributions of risk profiles, highlighting areas of heightened cybersecurity concern across Europe. Our results indicate that cybersecurity strategies should be tailored to address the specific vulnerabilities of each risk profile, with particular attention to gender-based disparities and regional variations. This research contributes to a better understanding of cyber-financial risks in Europe and offers actionable information for policymakers, financial institutions, and cybersecurity professionals in developing targeted interventions to enhance digital financial security across diverse populations.

Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy.