City of Knowledge

The global rise in honey demand has been accompanied by increasing concerns over adulteration and synthetic substitutes, complicating quality control for regulators and importers. The quality and freshness of Apis mellifera honey are widely assessed using indicators such as 5-hydroxymethylfurfural (HMF) content and diastase activity. HMF increases with excessive heating and storage, while diastase activity declines under the same conditions. This study analyzed 140 honey samples from Panama (2022-2024), including artisanal and commercially available products. HMF quantification was performed using a validated solid-phase extraction (SPE) method coupled with ultra-high-performance liquid chromatography with diode array detection (UHPLC-DAD), following ICH guidelines. Diastase activity was measured with the Phadebas® enzymatic method. Validation confirmed excellent specificity, linearity (r2=0.9995), precision (RSD<2.5%), accuracy (98-102% recovery), and sensitivity (LOD = 0.15µg/mL; LOQ = 0.45µg/mL). Results showed that 50% of commercial honeys did not meet regulatory limits (HMF≤80mg/kg; diastase≥8 DN), suggesting inadequate storage, thermal abuse, or adulteration, while most artisanal honey samples complied with national and international standards. These findings provide essential evidence for honey quality monitoring in Panama and highlight the importance of enforcing regulations to safeguard consumers and support authentic local products.

Human activities in Panama, such as agriculture, industry, and transport, have led to the release of pollutants that affect the health of marine and coastal ecosystems. However, there is a lack of bibliographic compilation studies to understand the current state of research on marine pollution in Panama. In recent years, bibliometric studies have attracted attention due to the development of new analytical and integrative online tools. This study conducts a bibliometric analysis of marine pollution and its environmental effects on Panama’s coastal areas. The results show consistent growth in scientific production, with increased collaboration among researchers. However, the involvement of national institutions is limited, highlighting the need to strengthen local research. Most publications focus on environmental sciences, with a recent shift towards studying a broader range of pollutants.

ABSTRACT: Phytosomes are lipid based vesicular nanocarries that encapsulate phytoconstituents within phospholipid layers, designed to overcome the limitations of herbal extracts such as inadequate lipid solubility, restricted permeability, low bioavailability, and chemical instability, while improving the absorption, stability, and therapeutic efficacy of phytoconstituents or herbal extracts in pharmaceutical, nutraceutical, and cosmetic formulations. The review aims to provide an overview of phytosomes, including their complexation mechanism structural features, formulation methodologies, and characterization techniques for evaluation of this phyto-phospholipid complex. The review briefly summarizes phytosomes technology, including structural, molecular interaction between active agent and lipids, formulation techniques, storage conditions, stability considerations, and therapeutic applications, commercially available phytosome-based formulations, Challenges faced by technology emphasizing the role and importance of phytosomes as a promising platform for turning herbal active agents into effective and standardized dosage forms.

The contamination of non-opioid drugs with fentanyl presents a risk at US music festivals, where environmental factors exacerbate overdose-related risks. Naloxone is an opioid antagonist medication that can be used by festival attendees to reverse opioid overdose. We conducted a field survey at a 4-day Colorado music festival (N=227) to characterize attendees' prior experience with naloxone and to investigate whether attendees carry naloxone with them in both festival and non-festival settings. We also assessed attendees' level of confidence in responding to an overdose with naloxone and asked attendees to report any barriers they experienced to carrying naloxone at festivals. Prevalence of carrying naloxone in any context was 55.5% (N=126). Festival carriage was more common than carriage in non-festival settings, with 55.1% reporting at least "sometimes" carrying at festivals compared with 32.7% outside festival settings. In addition, 8.4% had used naloxone to respond to an overdose, and 4.4% had personally been administered naloxone. Among participants who reported carrying naloxone, 65.3% had never received training. Regarding confidence in overdose response, 29.9% of naloxone carriers reported being only "slightly confident" or "not at all confident." Barriers included access, festival restrictions and legal concerns, lack of education, training, and awareness, convenience factors, and perceived responsibility of carrying naloxone. Findings highlight the need for increased naloxone access, training, and awareness among festival attendees. Actionable recommendations include free or low-cost naloxone distribution, on-site training, and transparent entry policies to reduce barriers and promote overdose response as a community responsibility.

Centralized access control architectures demonstrate serious scalability problems and vulnerability to single points of failure along with privacy risks during IoT network expansion. The recently developed Blockchain and Federated Learning based Access Control Technology (BFL-ACT) serves as a distributed access control solution for IoT networks that uses blockchain technology combined with federated learning and cryptographic methods to enhance participant privacy protection and manage vast network sizes effectively. The protection of sensitive information and model updates during both training and inference phases utilizes integrated homomorphic encryption alongside lightweight cryptographic primitives. This paper demonstrates the system design and protocol flows for BFL-ACT and develops its threat model before constructing a prototype IoT testbed for performance benchmarking. The BFL-ACT approach demonstrates superior intrusion detection capabilities and increased robustness against attacks by malicious actors compared to standard centralized and non-blockchain federated learning systems while maintaining low communication costs and near real-time inference speeds ideal for practical IoT systems.

Through the provision of individualized material recommendations, recommendation systems are an essential component of online education. In this paper, hybrid optimizing techniques are proposed with the goal of improving the accuracy of recommendations. These techniques involve mixing the Imperialist Competitive Algorithm (ICA) with Simulated Annealing (SA) and Particle Swarm Optimization (PSO). In the ICA-SA approach, exploring is improved through the use of ICA, while SA is utilized for the purpose of local refining. Within the framework of ICA-PSO, autonomous nations that employ PSO-based movement techniques in order to improve their flexibility are presented. The application of these strategies improves the performance of deep neural networks while performing recommendations jobs. In a number of different test instances, the results show that ICA-PSO-DNN works better than other approaches that are currently in use. It achieves greater accuracy, recall, and F-measure. The ICA-PSO-DNN algorithm displays improved recommendations accuracy in comparison to both the logistic regression and solo deep learning systems currently available. A robust selecting features and better efficiency are both guaranteed by the hybrid technique, which efficiently strikes a balance between studying and exploiting respectively. The research sheds light on the influence that bio-inspired optimized methods have on the improvement of e-learning recommendation systems and offers beneficial insights into how these methods might be applied in contexts with vast amounts of data. Through the demonstration of the efficacy of hybrid optimization methodologies in enhancing recommended accuracy, this study makes a contribution to the continuous advancement of sophisticated system suggestions in the field of digital learning. Compared to existing models, the suggested technique yields a remarkable 98% accuracy.

Additive manufacturing (AM) with clay and ceramic-based materials is gaining momentum as a sustainable alternative in construction, yet its advancement depends on bridging experimental practice with predictive modeling. This review synthesizes advances in mathematical formulations and numerical tools applied to clay, geopolymers, alumina, and related extrusion-based pastes. Classical rheological models, including the Bingham and Herschel–Bulkley formulations, remain central for characterizing yield stress, structuration, and flow stability. Meanwhile, finite element (FEM) and computational fluid dynamics (CFD) approaches are increasingly supporting predictions of deformation, shrinkage, drying, and sintering. Despite these advances, their application to natural clay systems remains limited due to heterogeneity, moisture sensitivity, and the lack of standardized constitutive parameters. Recent studies emphasize that validation is essential: rheometry, layer stability tests, in situ monitoring, and prototyping provide necessary calibration for reliable simulation. In parallel, parametric and generative design workflows, particularly through Rhino and Grasshopper ecosystems, illustrate how digital methods can link geometric logic, fabrication constraints, and performance criteria. Overall, the literature demonstrates a transition from isolated modeling efforts toward integrated, iterative frameworks where rheology, numerical simulation, and experimental validation converge to improve predictability, reduce trial-and-error, and advance scalable and sustainable clay- and ceramic-based AM.