Search Results (1,629)

The windowpane oyster (Placuna placenta) is common in coastal areas of the Philippines, thriving in brackish waters. Its shells underpin the local craft industries. While its meat is edible, only small amounts are consumed locally, most going to waste. Utilization of this potential nutrient source is hindered by the lack of information concerning its organic and mineral content, the possible presence of heavy metal ions, and the risk of microbial pathogens. We report extensive analysis of the meat from Placuna placenta, harvested during three different seasons to account for potential variations. This comprises proximate analysis, mineral, antioxidant, and microbial analyses. While considerable seasonal variation was observed, the windowpane oyster was found to be a rich source of protein, fats, minerals, and carbohydrates, comparing well with the meats of other shellfish and land animals. Following pre-cooking (~90 °C, 25–30 min), the standard local method for food preparation, no viable E. coli or Salmonella sp. were detected. Mineral content was broadly similar to that reported in fish, although iron, zinc, and copper were more highly represented, nevertheless, heavy metals were below internationally acceptable levels, with the exception of one of three samples, which was slightly above the only current standard, FSANZ. Whether the arsenic was in the safer organic form, which is commonly the case for shellfish, or the more toxic inorganic form remains to be established. This and the variation of arsenic over time will need to be considered when developing food products. Overall, the meat of the windowpane oyster is a valuable food resource and its current (albeit low-level) use should lower any barriers to its acceptance, making it suitable for commercialization. The present data support its development for high-value food products in urban markets. Full article

Recently, the usage of mobile phone-based games has increased due to the growing accessibility and convenience they provide. Using a descriptive-quantitative design, a disaster-based mobile application was developed in this study to enhance disaster literacy among the private senior high schools in science, technology, engineering, and mathematics (STEM) education in Davao City, the Philippines. The developed application was provided together with survey questionnaires to 364 students randomly selected from different schools in Davao City usingF a simple random sampling method. The technology acceptance (TAM) model was used to explain how users accepted the new technology. The mobile application was designed with features in four disaster scenarios—fire, flood, volcano, and earthquake. The results revealed a high acceptance, with an average score of the perceived usefulness (PE) of 4.52, perceived ease of use (PEOU) of 4.44, and a behavioral intention (BI) of 4.12. The students accepted the application to enhance disaster risk reduction and management. Aligned with SDG 4 and SDG 11, the application can be used to equip users with the knowledge to respond to disasters and ensure community resilience. Full article

Legumes are among the most important crops globally, serving as a major food source for protein and oil. In tropical regions, the cultivation of legumes has expanded significantly due to the increasing demand for food, plant-based products, and sustainable agriculture practices. However, tropical environments pose unique challenges, including high temperatures, erratic rainfall, soil infertility, and a high incidence of pests and diseases. Indeed, legumes are vulnerable to infections caused by bacteria, fungi, oomycetes, viruses, and nematodes. This review highlights the importance of legumes in tropical farming and discusses major diseases affecting productivity and their impact on the economy, environment, and lives of smallholder legume farmers. We emphasize the use of legume genetic resources and breeding, and biotechnology innovations to foster resistance and address the challenges posed by pathogens in legumes. However, an integrated approach that includes other cultivation techniques (e.g., crop rotation, rational fertilization, deep plowing) remains important for the prevention and control of diseases in legume crops. Finally, we highlight the contributions of plant genetic resources to smallholder resilience and food security. Full article

Turbidity currents pose significant threats to offshore seabed infrastructures, including subsea hydrocarbon production facilities and submarine communication cables. These powerful underwater flows can damage pipelines, potentially causing hydrocarbon spills that endanger local communities, the environment, and negatively impact energy production infrastructures. Therefore, a comprehensive understanding of the spatio-temporal development and destructive force of turbidity currents is essential. While numerical computation of 3D flow, sediment transport, and substrate exchange is possible, field-scale simulations are computationally intensive. In this study, we develop a simplified morphodynamic approach to model the flow properties of channelized turbidity currents and the associated trends of sediment accretion and erosion. This model is applied to the Baco–Malaylay submarine system to investigate the dynamics of a significant turbidity current event that impacted a submarine pipeline offshore the Philippines. The modeling results align with available seabed assessments and observed erosion trends of the protective rock berm. Our simplified modeling approach shows good agreement with simulations from a fully 3D numerical model, demonstrating its effectiveness in providing valuable insights while reducing computational demands. Full article

Typhoon Yagi (2024) was a rapidly moving storm that lasted for eight days and made landfall in three locations, producing heavy rainfall over Hainan and Vietnam. This study aims to investigate the dynamical processes contributing to the heavy rainfall, concentrating on enthalpy flux (EF) and moisture flux (MF). The results indicate that both EF and MF increased significantly during the typhoon’s intensification stage and were high at the time of landfall. Before landfalling at Hainan, latent heat flux (LHF) reached 600 W/m², while sensible heat flux (SHF) was recorded as 80 W/m². Landfall at Hainan resulted in a decrease in LHF and SHF. LHF and SHF subsequently increased to 700 W/m² and 100 W/m², respectively, as noted prior to the landfall in Vietnam. The increased LHF led to higher evaporation, which subsequently elevated moisture flux (MF) following the landfall in Vietnam, while the region’s topography further intensified the rainfall. The mean daily rainfall observed over Philippines is 75 mm on 2 September (landfall and passing through), 100 mm over Hainan (landfall and passing through) on 6 September, and 95 mm at over Vietnam on 7 September (landfall and after), respectively. Heavy rainfall was observed over the land while the typhoon was passing and during the landfall. This research reveals that Typhoon Yagi’s intensity was maintained by a well-organized and extensive circulation system, supported by favorable weather conditions, including high sea surface temperatures (SST) exceeding 30.5 °C, substantial low-level moisture convergence, and elevated EF during the landfall in Vietnam. Full article

This study analyzes artificial intelligence (AI)-based technologies for food waste reduction in restaurant management, particularly in the case of the Philippines. Using the multiple-stakeholder target-oriented robust-optimization (MS-TORO) approach, AI solutions are ranked based on cost, feasibility, infrastructure requirements, and effectiveness. The key findings highlight that Too Good To Go is the most practical AI solution due to its affordability and focus on surplus food redistribution, making it ideal for resource-limited settings. The study emphasizes the need for government support, financial incentives, and public–private partnerships to facilitate AI adoption. Additionally, integrating AI-driven waste reduction with food security initiatives and sustainability projects can enhance their impact. Addressing economic and infrastructural challenges is crucial for maximizing AI’s potential in food waste management in developing economies. Full article

This paper contains three newly recorded genera (Wittonola László, Ronkay & Ronkay, 2015; Aeneanola László, Ronkay & Ronkay, 2013; and Evonima Walker, 1865) and each new species (W. bicyanasp. nov., A. crassasp. nov., and E. palawanensissp. nov.) from Palawan, the Philippines. We provide illustrations of three new species and their genitalia, photographs of collecting sites, and checklists of the three genera. Full article

Based on the new high-resolution multibeam bathymetry data collected by the “Dongfanghong 3” vessel in 2023 in the Parece Vela Basin (PVB) and previous magnetic anomaly data, we systematically analyze the seafloor topographical changes of abyssal hills and oceanic core complexes (OCCs) in the “Chaotic Terrain” region, and the revised seafloor spreading model is constructed in the PVB. Using detailed analysis of the seafloor topography, we identify typical geomorphological features associated with seafloor spreading, such as regularly aligned abyssal hills and OCCs in the PVB. The direction variations of seafloor spreading in the PVB are closely related to mid-ocean ridge rotation and propagation. The formation of OCCs in the “Chaotic Terrain” can be explained by links to the continuous and persistent activity of detachment faults and dynamic adjustments controlled by variations of deep magma supply in the different segments in the PVB. We use 2D discrete Fourier image analysis of the seafloor topography to calculate the aspect ratio (AR) values of abyssal hills in the western part of the PVB. The AR value variations reveal a distinct imbalance in magma supply across various regions during the basin spreading process. Compared to the “Chaotic Terrain” area, the region with abyssal hills indicates a higher magma supply and greater linearity on seafloor topography. AR values fluctuated between 2.1 and 1.7 of abyssal hills in the western segment, while in the “Chaotic Terrain”, they dropped to 1.3 due to the lower magma supply. After the formation of the OCC-1, AR values increased to 1.9 in the eastern segment, and this shows the increase in magma supply. Based on changes in seafloor topography and variations in magma supply across different segments of the PVB, we propose that the seafloor spreading process in the magnetic anomaly linear strip 9-6A of the PVB mainly underwent four formation stages: ridge rotation, rift propagation, magma-poor supply, and the maturation period of OCCs. Full article

Background/Objectives: Maternal exposures are known to influence the risk of isolated cleft lip with or without cleft palate (CL/P)—a common and highly heritable birth defect with a multifactorial etiology. Methods: To identify new risk loci, we conducted a genome-wide gene–environment interaction (GEI) analysis of CL/P with maternal smoking and vitamin use in Filipinos (N_cases = 540, N_controls = 260). Since GEI analyses are typically low in power and the results can be difficult to interpret, we applied multiple testing frameworks to evaluate potential GEI effects: a one degree-of-freedom (1df) GxE test, the 3df joint test, and the two-step EDGE approach. Results: While no genome-wide significant interactions were detected, we identified 11 suggestive GEIs with smoking and 24 with vitamin use. Several implicated loci contain biologically plausible genes. Notable interactions with smoking include loci near FEZF1, TWIST2, and NET1. While FEZF1 is involved in early neuronal development, TWIST2 and NET1 regulate epithelial–mesenchymal transition, which is required for proper lip and palate fusion. Interactions with vitamins encompass CECR2—a chromatin remodeling protein required for neural tube closure—and FURIN, a critical protease during early embryogenesis that activates various growth factors and extracellular matrix proteins. The activity of both proteins is influenced by folic acid. Conclusions: Our findings highlight the critical role of maternal exposures in identifying genes associated with structural birth defects such as CL/P and provide new paths to explore for CL/P genetics. Full article

Waste-to-energy (WtE) are clean technologies that support a circular economy by providing solutions to managing non-recyclable waste while generating alternative energy sources. Despite the promising benefits, technology adoption is challenged by financing constraints, technical maturity, environmental impacts, supporting policies, and public acceptance. A growing number of studies analyzed the acceptability of WtE and identified the factors affecting the adoption of WtE technologies. This study aims to analyze these research hotspots, technologies, and acceptability factors by combining bibliometric and systematic analyses. An initial search from the Web of Science and Scopus databases identified 817 unique documents, and the refinement resulted in 109 for data analysis. The results present a comprehensive overview of the state-of-the-art, providing researchers a basis for future research directions. Among the WtE technologies in the reviewed literature are incineration, anaerobic digestion, gasification, and pyrolysis, with limited studies about refuse-derived fuel and landfilling with gas recovery. The identified common factors include perceived risks, trust, attitudes, perceived benefits, “Not-In-My-BackYard” (NIMBY), awareness, and knowledge. Moreover, the findings present valuable insights for policymakers, practitioners, and WtE project planners to support WtE adoption while achieving sustainable, circular, and low-carbon economies. Full article

The intensifying global competition for cobalt resources and the increasing likelihood of trade decoupling and disruption are profoundly impacting the global energy transition. In a globalized trade environment, a decline in cobalt supply from exporting countries can spread through the trade network, negatively affecting demand countries. Quantitative analysis of the negative impacts of export supply declines in various countries can help identify early risks in the global supply chain, providing a scientific basis for energy security, industrial development, and policy responses. This study constructs a trade network using trade data on metal cobalt, cobalt powder, cobalt concentrate, and ore sand from the upstream (mining, selection, and smelting) stages of the cobalt industry chain across 155 countries and regions from 2000 to 2023. Based on this, an impact diffusion model is established, incorporating the trade volumes and production levels of cobalt resources in each country to measure their resilience to shocks and determine their direct or indirect dependencies. The study then simulates the impact on countries (regions) when each country’s supply is completely interrupted or reduced by 50%. The results show that: (1) The global cobalt trade network exhibits a ‘one superpower, multiple strong players’ characteristic. Congo (DRC) has a far greater destructive power than other countries, while South Africa, Zambia, Australia, Russia, and other countries have higher destructive power due to their strong storage and production capabilities, strong smelting capabilities, or as important trade transit countries. (2) The global cobalt trade network primarily consists of three major risk areas. The African continent, the Philippines and Indonesia in Southeast Asia, Australia in Oceania, and Russia, the United States, China, and the United Kingdom in Eurasia and North America form the primary risk zones for global cobalt trade. (3) When there is a complete disruption or a 50% reduction in export supply, China will suffer the greatest average demand loss, far exceeding the second-tier countries such as the United States, South Africa, and Zambia. In contrast, European countries and other regions worldwide will experience the smallest average demand loss. Full article

Background: We previously reported an interim safety and immunogenicity analysis of a Phase 3 trial in the Philippines of the EuCorVac-19 (ECV-19) COVID-19 vaccine with the COVISHIELD^TM (CS) comparator (ClinicalTrials.gov identifier NCT05572879). Here, we present full-year humoral immunogenicity analysis. Methods: Healthy adults over 18 years of age received two injections of ECV-19 or CS vaccines, with 4 weeks between prime and boost. Analysis was carried out in individuals with immunogenicity measurements available at all 4 timepoints (weeks 0, 6, 30, and 56; n = 535 for ECV-19 and n = 260 for CS). Results: 2 weeks after boosting (week 6), ECV-19 elicited higher median anti-RBD IgG (1512 vs. 340 BAU/mL, p < 0.001) and neutralizing antibodies (1280 vs. 453 median microneutralization (MN) titer, p < 0.001) compared to CS. Anti-RBD IgG remained higher for ECV-19 compared to CS through week 30 (412 vs. 238 BAU/mL, p < 0.001) and 56 (425 vs. 260 BAU/mL, p < 0.001). MN titers remained higher for ECV-19 compared to CS through week 30 (640 vs. 453, p < 0.001) and 56 (453 vs. 320, p < 0.001). Correlation between anti-RBD IgG and neutralization titers persisted throughout the study. Women generally exhibited greater antibody responses than men. In the first six months following immunization, the ECV-19 group had a median antibody half-life of 80 days for anti-RBD IgG and 112 days for MN titer. In the subsequent six months, antibody half-life increased to 237 days for anti-RBD IgG and 168 days for MN titer. Conclusions: Following initial prime-boost vaccination, ECV-19 maintained higher anti-RBD IgG and neutralizing antibody titers relative to the CS comparator over a full-year period. Full article

Background/Objectives: Tuberculosis (TB) remains a major public health challenge in Davao City, Philippines, with persistent issues in both disease burden and treatment outcomes. Understanding the risk factors for TB and its unsuccessful treatment is essential for guiding effective interventions. This study aimed to evaluate the association of sociodemographic and clinical factors with TB occurrence and to identify predictors of unsuccessful TB treatment outcomes among patients in Davao City. Methods: A retrospective cohort study was conducted using data from 521 patients diagnosed with drug-susceptible TB at Davao Chest Center between January 2021 and May 2024. The sociodemographic and clinical profiles of the patients were described using descriptive statistics. Chi-square tests were used to assess the associations between sociodemographic and clinical variables with TB risk and treatment outcomes. Results: The patient cohort was predominantly aged 31–50 years (n = 201, 38.58%), male (n = 284, 54.51%), and married (n = 285, 54.70%), with most residing in Districts I and II (n = 98, 38% each), and had no previous TB treatment (n = 344, 66.03%). Among the 456 patients assessed for comorbidities, 56.14% (n = 256) had at least one comorbidity. Evaluation of the risk factors for TB occurrence among the study population revealed that comorbidity status was not significantly associated with an increased risk of TB diagnosis (p = 0.682). However, among patients diagnosed with TB, the presence of comorbidities was significantly associated with unsuccessful treatment outcomes (p = 0.003). Conclusions: Although sociodemographic factors did not significantly influence TB risk or treatment outcomes, the presence of comorbidities was a significant predictor of unsuccessful TB treatment. These findings highlight the importance of integrating comorbidity management with TB care to improve treatment success in high-burden urban settings. Full article

With the rapid proliferation of artificial intelligence (AI) applications, an increasing number of edge devices—such as smartphones, cameras, and embedded controllers—are being tasked with performing AI-based inference. Due to constraints in storage capacity, computational power, and network connectivity, these devices are often categorized as operating in resource-constrained environments. In such scenarios, deploying powerful Transformer-based models like ChatGPT and Vision Transformers is highly impractical because of their large parameter sizes and intensive computational requirements. While lightweight Transformer models, such as MobileViT, offer a promising solution to meet storage and computational limitations, their robustness remains insufficient. This poses a significant security risk for AI applications, particularly in critical edge environments. To address this challenge, our research focuses on enhancing the robustness of lightweight Transformer models under resource-constrained conditions. First, we propose a comprehensive robustness evaluation framework tailored for lightweight Transformer inference. This framework assesses model robustness across three key dimensions: noise robustness, distributional robustness, and adversarial robustness. It further investigates how model size and hardware limitations affect robustness, thereby providing valuable insights for robustness-aware model design. Second, we introduce a novel adversarial robustness enhancement strategy that integrates lightweight modeling techniques. This approach leverages methods such as gradient clipping and layer-wise unfreezing, as well as decision boundary optimization techniques like TRADES and SMART. Together, these strategies effectively address challenges related to training instability and decision boundary smoothness, significantly improving model robustness. Finally, we deploy the robust lightweight Transformer models in real-world resource-constrained environments and empirically validate their inference robustness. The results confirm the effectiveness of our proposed methods in enhancing the robustness and reliability of lightweight Transformers for edge AI applications. Full article

This study investigates the critical determinants of customer retention in casual dining restaurants within the context of the post-pandemic “new normal.” Anchored in service quality and consumer behavior theories, the research examines the influences of food quality, health measures, perceived price, brand image, ambiance, and location on customer decision making. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 336 respondents in the National Capital Region, Philippines were analyzed to assess the relationships among these variables and their effects on restaurant selection and customer retention. The results reveal that food quality (β = 0.698, p < 0.05) exerts the strongest influence on restaurant selection, followed by health measures (β = 0.477, p = 0.001), perceived price (β = 0.378, p < 0.02), and brand image (β = 0.341, p < 0.035). Furthermore, health measures (β = 0.436, p = 0.002) and restaurant selection (β = 0.475, p < 0.05) significantly enhance customer retention, while ambiance and location were not found to be significant predictors. These findings offer theoretical contributions to the service quality and consumer trust literature and provide practical and policy-relevant insights for food establishments adapting to health-driven consumer expectations. The study highlights the need for the strategic integration of safety protocols, pricing value, and brand positioning to foster long-term loyalty and resilience in the evolving food service market. Full article