Centro Escolar University (CEU) (Tagalog: Pamantasang Centro Escolar) is a private university in Manila, Philippines. It was founded in 1907 by Librada Avelino and Carmen de Luna as the Centro Escolar de Señoritas. CEU has six campuses: the main campus, Mendiola; the Malolos campus; the Las Piñas campus; the Cebu City campus; and the Makati campuses (Gil Puyat and Legaspi Village campuses). More than 50 academic programs are offered.CEU is accredited by the Federation of Accrediting Agencies of the Philippines and the Philippine Association of Colleges and Universities' Commission on Accreditation (as a Level IV school), and has full autonomous status from the Commission on Higher Education (CHED). It is certified by the Institute of Corporate Directors, Department of Trade and Industry, SGS S.A., International Organization for Standardization, and the ASEAN University Network In 2022, CEU was awarded with the Safety Seal from the Department of the Interior and Local Government for meeting minimum public health standards following the COVID-19 pandemic shutdowns.
Fully decentralised federated learning enables collaborative model training among edge devices without relying on a central coordinator, thereby avoiding single points of failure and supporting spontaneous collaboration in pervasive environments. However, the absence of coordination introduces challenges that go beyond data heterogeneity alone. In realistic decentralised settings, devices often start from different model initializations, possess limited and non-IID local data, and interact over unstructured communication graphs, making naive parameter averaging ineffective and potentially destructive. In this paper, we address decentralised learning under combined data and initial model heterogeneity by proposing DecDiff+VT, a coordination-free decentralised learning algorithm specifically designed for such environments. DecDiff+VT integrates two complementary mechanisms: DecDiff, a disruption-aware aggregation strategy that updates local models towards their neighborhood average with a magnitude inversely proportional to model disagreement, and a lightweight virtual teacher (VT) mechanism based on soft-label regularization to improve local generalization in the absence of strong or centralized teacher models. Extensive experiments on image classification and activity recognition benchmarks (MNIST, Fashion-MNIST, EMNIST, CIFAR-10, and UCI-HAR) show that DecDiff+VT consistently outperforms or matches state-of-the-art decentralised baselines, achieving faster convergence, improved generalization, and greater robustness to overfitting, without incurring additional communication or memory overhead compared to standard decentralised averaging.
Abstract Although numerous studies have isolated anticancer compounds from Ficus spp., there are still key gaps that hinder clinical translation. This study aimed to provide a quantitative overview of compounds derived from Ficus spp., identify leading contributors, evaluate collaboration networks, and highlight research gaps and future directions. A bibliometric analysis was conducted using the Scopus® database (from inception to mid-2025). Data on publication and citation trends, as well as leading countries, institutions, authors, and journals, were analyzed. Socioeconomic indicators were obtained from the IMF and World Bank databases. Statistical analysis was performed using Microsoft® Excel, and bibliometric networks were visualized using VOSviewer (v1.6.20). A total of 316 original and review articles were included, showing a slow increase in publications from the 2010s onward. Most studies originated from India, China, and Saudi Arabia, while leading authors were affiliated with institutions in Indonesia, Italy, and Belgium. Frequently used keywords included “human”, “article”, and “plant extract”. Highly cited articles indicate a gradual shift toward applied and commercial research interests. Overall, the findings demonstrate growing global interest in the anticancer potential of Ficus spp., alongside persistent translational limitations. Research remains largely focused on crude extract screening and in vitro assays, with limited progression to in vivo and clinical studies. Future work should emphasize standardized extraction protocols, isolation and characterization of bioactive compounds, mechanistic and toxicity studies, and translational pharmacological validation to support the development of clinically relevant Ficus-derived anticancer agents.
Population aging increases the importance of routine laboratory assessment for identifying subclinical hematological and metabolic abnormalities among older adults. This study evaluated the hematological, biochemical, renal, and lipid profiles of community-dwelling older adults in Manila and examined selected correlations among clinically relevant laboratory parameters. A cross-sectional descriptive-correlational design was employed among 206 participants aged 60 years and above who were recruited from different districts of Manila. Venous blood samples were analyzed for hemoglobin, hematocrit, platelet count, red blood cell indices, fasting blood glucose, glycated hemoglobin, lipid parameters, blood urea nitrogen, creatinine, and uric acid. Descriptive statistics and Pearson product-moment correlation analysis were used. Most participants had normal hemoglobin, platelet, red blood cell index, fasting blood glucose, glycated hemoglobin, blood urea nitrogen, and creatinine levels. However, low hemoglobin and hematocrit values were observed in 19.9% and 26.2% of participants, respectively. Lipid abnormalities were prominent, with elevated total cholesterol in 54.9%, elevated triglycerides and very-low-density lipoprotein in 31.6%, low high-density lipoprotein in 32.0%, and elevated low-density lipoprotein in 17.0%. Elevated uric acid was found in 21.4% of participants. Hemoglobin was strongly correlated with hematocrit, while fasting blood glucose showed a weak but significant positive correlation with glycated hemoglobin. The findings indicate that generally preserved hematological and renal profiles may coexist with substantial lipid, glycemic, and uric acid abnormalities. Routine community-based laboratory screening may support early detection and targeted preventive interventions among older adults.
