The flavonoid-rich fraction from the ethanolic extract of the leaves of Cosmos caudatus Kunth. (Kenikir) was analyzed by ultra-performance liquid chromatography-mass spectrometry with a quadrupole and time-of-flight analyzer featuring an electrospray ionization source (QToF-ESI-MS/MS). The deprotonated molecules [M−H]⁻ were analyzed, and the flavonoids were tentatively annotated based on accurate mass and product-ion patterns. Chemical analysis of the fraction identified quercetin and three C-3 hydroxy group-substituted quercetin glycosides, namely quercetin 3-O-rutinoside (rutin), quercetin 3-O-β-D-glucopyranoside (isoquercitrin), and quercetin 3-O-arabinofuranoside. The results show that the fragmentation behavior of flavonol 3-O-glycosides can be analyzed based on the breakage of glycosidic link and interglycosidic linkages. Removal of the sugar moiety allows the distinctive product ions to be identified. Investigation of negative ion ESI-MS/MS spectra of flavonol O-glycosides allows their rapid characterization as flavonol 3-O-glycoside and enables their direct analysis in crude plant extracts. This study provides a detailed analysis of the MS/MS fragmentation pathways of 3-O-glycosylated flavonoids in a flavonoid-rich fraction of C. caudatus.
Corn cob (Zea mays L.), a common agricultural waste, may act as a natural inhibitor of the α/β-glucosidase enzymes, which are crucial for blood glucose regulation and carbohydrate metabolism. This potential arises from secondary metabolites in corn cob, including phenolics, flavonoids, polysaccharides, and steroids, which have been reported to exhibit α/β-glucosidase inhibitory activity. Despite its abundance, corn cob has been largely underexplored as a source of dual α-/β-glucosidase inhibitors, particularly through fraction-based bioactivity profiling. This study aims to assess the potential of the methanol extract and fractions (n-hexane, ethyl acetate, and butanol) of corn cob as α- and β-glucosidase inhibitors. The methodology includes drying and grinding corn cob, followed by maceration and fractionation. The inhibitory activities of the extract and fractions against α- and β-glucosidase were evaluated in vitro using spectrophotometry with a microplate reader and thin-layer chromatography (TLC) bioautography. Furthermore, the secondary metabolite content of the bioactive fraction was determined via liquid chromatography-tandem mass spectrometry (LC-MS/MS). The corn cob extract and fractions exhibited inhibitory activity against α- and β-glucosidase, with the n-hexane fraction showing the lowest observed IC50 values (IC50=22.11±1.07 µg/mL for α-glucosidase and IC50=77.28±0.29 µg/mL for β-glucosidase). LC-MS/MS profiling of the n-hexane fraction led to the detection of 21 compounds; of these, 10 were putatively identified as fatty acids, steroids, terpenoids, and flavonoids, which act as active inhibitors of α- and β-glucosidase. These findings highlight the novel potential of corn cob waste as a dual enzyme inhibitor and a promising natural therapeutic candidate for managing diabetes and hyperglycemia.
Toddler highchairs are essential for parents, restaurants, and daycares to support children’s eating activities. However, products on the market have several limitations, including insufficient stability, limited height adjustment, and uncomfortable materials. This study aims to develop a safer, more comfortable, and flexible ergonomic toddler feeding chair using a structured product development approach supported by computer-aided design (CAD) in SolidWorks, digital structural analysis, and considerations of environmental responsibility. The research follows a phased product development methodology: needs identification, concept development, system design, structural analysis, digital validation, and production cost estimation. The chair incorporates an aluminum frame to enhance stability. The seat combines polypropylene and foam for user comfort, while folding and height-adjustment mechanisms ensure adaptability across different conditions. Static simulation in SolidWorks confirmed that the design withstands loads up to 30 kg, with stress, deformation, and safety factor values within acceptable limits. Environmental responsibility was considered at the material level through the selection of recyclable materials (such as aluminum and polypropylene) with anticipated long product life and efficient resource use; no formal life-cycle assessment was conducted. Although digital validation produced promising results, further work is required to develop prototypes and conduct physical user testing to evaluate ergonomics and usability under real conditions. With these improvements, the proposed design shows potential to outperform conventional products and achieve broad applicability, while further environmental validation remains necessary to substantiate sustainability outcomes.
Accurate and consistent rainfall data are essential for climatological analysis and disaster risk management, particularly in regions with complex climatic dynamics such as Indonesia. This study evaluates the performance of Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 07B (V07B) against its predecessor, version 06B (V06B), in estimating seasonal rainfall magnitude and peak timing across Indonesia over a complete 20-year period (2001–2020). Results show that IMERG V07B demonstrates significant improvements in accuracy, with lower relative bias (RB) (V06B = +19.04%; V07B = +9.68%) and root mean square error (RMSE) (V06B = 109.92 mm/month; V07B = 102.65 mm/month), along with slightly higher correlation coefficient (CC) (V06B = 0.77; V07B = 0.78). These improvements are consistent across most stations and are attributed to updates in the Goddard profiling algorithm to account more effectively for surface type and orographic influence. Despite substantial differences in monthly rainfall magnitudes between the versions, particularly over oceanic, mountainous, and coastal regions, the annual cycle phase remains relatively consistent. Harmonic fitting further enhances peak timing accuracy, increasing the proportion of correct estimates from 62.69% (V06B) to 71.64% (V07B), underscoring the suitability of V07B for seasonal rainfall analysis. Spatial assessments reveal that V07B generally produces higher rainfall estimates than V06B over land and ocean but lower values in coastal zones. Based on these findings, IMERG V07B is recommended for seasonal zoning and rainfall pattern studies in Indonesia. Further research using sub-daily and near-real-time data is needed to support high-impact applications such as flood early warning systems.
Chicken meat, among the most consumed animal proteins globally, is highly susceptible to spoilage due to its high moisture and nutrient content, promoting rapid microbial growth under inadequate storage. This study developed and evaluated an anthocyanin-based indicator label using hibiscus flower extract (Hibiscus rosa-sinensis L.) and red galangal (Alpinia purpurata) for freshness monitoring of raw chicken meat in intelligent packaging. Indicator films were formulated at three hibiscus extract concentrations (14%, 16%, 18%) combined with ganyong starch at 6% and 8%, and evaluated through color change (mean RGB via ImageJ, NIH, USA), pH, weight loss, and organoleptic testing at 25°C over 12 hours, with observations every 3 hours. Results showed visually distinguishable color transitions from red to dark green corresponding to meat deterioration, with the strongest response in the A1S formulation (14% hibiscus extract, 6% ganyong starch). Meat pH increased progressively from 5.64 at hour 0 to 6.67 at hour 12, consistent with volatile amine accumulation during decomposition. Organoleptic acceptability declined during storage, reaching the non-acceptable range by the later observation period. Pearson correlation analysis revealed a statistically significant but weak negative relationship between pH and mean RGB (r = -0.218; p = 0.039; r² = 0.047), indicating pH explained only 4.7% of color response variance. These results should be interpreted as preliminary rather than strong predictive validation. Given the absence of microbiological confirmation, TVB-N measurement, and instrumental colorimetry, the findings nonetheless suggest the potential of hibiscus-based films as freshness indicator labels for intelligent packaging.
A Brushless DC (BLDC) motor is a synchronous motor with a trapezoidal back electromotive force (EMF) waveform, which is typically operated with nonlinear control systems. This device depends on its speed controller to operate robustly. In most control applications, BLDC motor drives employ a proportional-integral-derivative (PID) controller to regulate speed; however, adjusting the controller's parameters is very challenging. Also, the PID controller may not be robust enough to maintain optimal motor performance when a disturbance occurs. Thus, a sliding mode controller (SMC) is proposed in this study, as it has been proven to achieve robust performance in the presence of external interference. This study presents a tuned SMC using an elite-based particle swarm optimization (EPSO) for the BLDC motor's control system. The aim is to determine the SMC’s parameters optimally, obtain a fast speed response without overshooting during its operation, and minimize the torque ripple. In this study, three performance indicators – integral time absolute error (ITAE), integral time squared error (ITSE), and integral squared error (ISE) are used to measure the effectiveness of optimizing the SMC. The results show that the lowest torque ripple and the best speed response curve are obtained when ITSE is used as the performance indicator. Finally, the proposed control system demonstrates superior performance considering external disturbances.
This study addresses a critical research gap in assessing indoor air quality (IAQ) in adaptively reused heritage structures. IAQ assessment measured chemical and biological air pollution parameters in the century-old Cagayan Waterworks Tank, repurposed as the Cagayan de Oro City Museum. Using standardized air sampling methodologies, the proponent measured the Air Quality Index (AQI), the Index of Microbial Air Contamination (IMA), and bacterial pollution levels. Results revealed a dichotomy in pollution parameters: chemical pollutants showed favorable conditions, with an average AQI of 12 (categorized as "GOOD"), while biological parameters suggested concerning levels, with an average IMA of 51 (classified as “POOR”, since it falls at the lower boundary of the 51–75 range) and bacterial contamination at 728 CFU/m³ (indicating a "HIGH" degree of bacterial pollution). Statistical analysis, including Spearman's correlation tests, observed an exploratory positive monotonic trend between microbial contamination indices and bacterial pollution levels, suggesting an association with ventilation and HVAC system deficiencies in the adaptively reused heritage building. The findings provide preliminary evidence supporting targeted intervention strategies to reduce biological pollutants, including the implementation of indoor air quality (IAQ) monitoring systems, enhancements to sanitation protocols, HVAC system rehabilitation, and spatial reorganization. This research contributes valuable insights into sustainable heritage preservation by identifying the specific indoor air quality challenges in adaptively reused heritage structures.
The research was carried out to investigate the physicochemical characteristics and concentration of Vitamin C of the microencapsulated tomato powder (MTP) as affected by these two factors and their level: inlet temperature (1) and maltodextrin concentration (2). Results showed that as the concentration of maltodextrin increases, Vitamin C content and encapsulation efficiency also increase. However, as the inlet temperature increases, encapsulation efficiency also decreases. Both factors lowered the moisture level and water activity of the powders, and their interaction influenced flowability, cohesion, and wettability time. Treatment 4 had the highest percentage yield (13.5 %) and encapsulation efficiency (71.12 %). The powder was also found to retain have a Vitamin C content of 124.16 mg/100 g along with excellent flowability and lower cohesion capacity. The said powder also had low water activity and moisture level, suggesting good storage stability. The predicted best powder treatment (Treatment 4) possessed considerable Vitamin C stability over time. These findings suggest that microencapsulated tomato powder could be a Vitamin C-rich ingredient with tailored functional properties for several food products.
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with persistent hyperglycemia. Inhibitors of α-glucosidase play a crucial role in controlling postprandial blood glucose, but the use of synthetic agents such as acarbose is often limited by gastrointestinal side effects. This study aimed to investigate the secondary metabolite profile of jeruk kunci (Citrus swinglei Burkill ex Harms) essential oil and to evaluate its inhibitory activity against α-glucosidase. C. swinglei essential oil was extracted by steam distillation, yielding 0.135% (w/w) of a colorless oil with a characteristic aroma. The chemical composition was analyzed by gas chromatography-mass spectrometry (GC-MS), while thin-layer chromatography (TLC) combined with bioautography was used for qualitative enzyme inhibition screening. The inhibitory activity of the essential oil was quantitatively determined using a microplate reader assay. GC-MS analysis identified 12 major volatile compounds dominated by monoterpenes, particularly bicyclo[3.1.0]hex-2-ene, 4-methyl-1-(1-methylethyl), -Pinene, β-Pinene, -myrcene, 1,3-cyclohexadiene, 1-methyl-4-(1-methylethyl), -cymene, D-limonene, -terpinene, cyclohexene, 1-methyl-4-(1-methylethylidene), linalool, terpinen-4-ol, and -terpineol. TLC-bioautography confirmed the presence of active metabolites, indicated by white inhibition zones. The essential oil exhibited significant α-glucosidase inhibition with an IC50 value of 230 0.28 µg/mL. These results support the use of Southeast Asian citrus species as candidates for the development of plant-based antidiabetic agents.
Deep learning inference can be accelerated by using field-programmable gate arrays (FPGAs) and reduced-precision floating-point types, but existing half-precision arithmetic unit designs often do not fully comply with the IEEE 754-2008 standard, particularly in rounding and remainder operations. This paper describes a model of a synchronous, variable-latency, half-precision, floating-point arithmetic unit written in VHDL and synthesizable on an FPGA. The model, based on previous work, performs addition, multiplication, division, and remainder, and rounds the result using four of the five IEEE 754-2008 rounding modes. Its rounding algorithm is discussed in detail. The model was tested using 246 test vectors representing four operations, five floating-point data, five exceptions, and four rounding modes, simulated using Lattice Diamond 3.10 and Active-HDL 10.3. The model yielded correctly-rounded results to 244 of 246 test vectors. The two vectors that yielded inexact results involved a very large number multiplied by a very small number, attributable to insufficient intermediate storage width. Completion times with rounding ranged from 1 to 39 clock cycles for addition, 1 to 62 for multiplication, 1 to 102 for division, and 1 to 60 for remainder. The model has since been extended to single- and double-precision variants and verified on an FPGA, confirming its scalability across floating-point formats and its potential for use in deep learning inference applications.
Energy plays a vital role in enabling social and economic development through its generation, conversion, distribution, and use. In the Philippines, ongoing efforts to boost economic growth and global competitiveness are driving a continuous rise in energy demand. However, the country’s continued dependence on fossil fuel-based power generation poses serious environmental risks, particularly in terms of greenhouse gas emissions and climate change. This study investigates the impact of varying discount rates on energy transition plans under the following scenarios using the TIMES model: RE_PH with 35% renewable energy (RE) penetration by 2030 and 50% by 2040; RE_PH1 with 40% by 2030 and 55% by 2040; and RE_PH2 with 45% by 2030 and 60% by 2040. Results show that lower discount rates promote earlier investment in renewable energy technologies to meet the 2030 and 2040 policy targets across all modeled scenarios. Specifically, at a 3% discount rate, it indicates that deployment of capital-intensive renewable energy technologies, such as pumped hydro storage and onshore wind becomes a more economically viable option and is deployed in period 2024 for PP-PUMP-HYD and 2030 and onwards for PP-ONS, respectively. These early investments can lead to significant operation cost savings compared to baseline 10% discount rate. Furthermore, the 3% discount rate enhances the economic attractiveness of early RE deployment across all scenarios, resulting in lower cumulative investment, lower operating costs, and lower electricity prices over time. These findings highlight the critical role of discount rates in promoting sustainable energy transitions and informing long-term climate planning.
This study assesses the stability of a three-story glue-laminated timber (glulam) office building in Jakarta (Site Class SE, SDC D). Four alternative beam–column cross-sections (P1: 20×40; 20×20 cm, P2: 25×45; 25×25 cm, P3: 30×50; 30×30 cm, and P4: 40×60; 40×40 cm) were evaluated according to SNI 7973:2013, while the seismic parameters were determined according to SNI 1726:2019. Seismic response was assessed using the equivalent lateral force (ELF) procedure and linear time-history analysis (LTHA) with seven recorded ground motions scaled to the Jakarta MCEr target spectrum, assuming a semi-rigid diaphragm. Both procedures used a linear-elastic structural model, and the calculated displacements were amplified using the code-prescribed deflection amplification factor. The peak interstory drift ratio remained below 2% in both principal directions, with drift profiles consistent with first mode-dominated response. Models P2 and P3 met the strength and stiffness requirements while maintaining moderate drift, therefore providing the best balance under the assumptions adopted in this study. Section sizes within the P2–P3 range are recommended for this prototype, subject to adequate connection design. Further studies should examine taller or irregular buildings using explicit torsional checks and nonlinear connection models where inelastic behavior is of interest.
Artificial Neural Networks (ANNs), particularly Bayesian Regularization Neural Networks (BRNNs), have demonstrated strong capabilities for modeling complex nonlinear patterns in time-series data. This study explores the hybridization of Self-Exciting Threshold Autoregressive (SETAR) models with BRNN and Multilayer Perceptron Neural Networks (MLPNNs) to improve forecasting accuracy of nonlinear cyclical data, using the Canadian Lynx dataset. The hybrid models aim to capture regime shifts and nonlinear dynamics more effectively. Results show that the SETAR-BRNN hybrid outperforms individual models and other hybrids, achieving a Mean Absolute Percentage Error (MAPE) of 2.261%, representing a 45.8% reduction compared to the standalone SETAR model (MAPE = 4.17%) and a 14.7% reduction compared to BRNN alone (MAPE = 2.65%). Additionally, the SETAR-BRNN model reduces the Root Mean Square Error (RMSE) by 65.3% relative to the MLPNN-SETAR hybrid and suggest a forecasting performance across multiple horizons. The findings indicate that integrating BRNN into the SETAR framework significantly enhances predictive accuracy and model robustness for nonlinear, regime-dependent time series. This highlights the effectiveness of the SETAR-BRNN hybrid approach in accurately modeling complex cyclical behaviors and regime shifts in real-world data.
The subgrade is an essential component of road pavement systems. It must provide adequate strength to support the complete pavement structure. A weak, clayey subgrade poses challenges because it can lead to the failure of the overlying pavement. A deficient subgrade necessitates treatment and stabilization. This study primarily aimed to do a laboratory assessment of Adtuyon clay subgrade soil reinforced with geotextiles. The soil samples were subjected to the Atterberg limit tests, compaction tests, and the California Bearing Ratio (CBR) test. Three geotextile-reinforced soil samples, with geotextile placed at 1/4, 1/2, and 3/4 of the mold height measured from the top, were compacted and tested for CBR. A plate load test was performed in the field. The resilient modulus (Mr) of the soil samples was estimated utilizing a prediction model. The laboratory results indicated that the reinforced soil samples exhibited higher CBR values than the unreinforced samples. A rising CBR value was noted as the geotextile was positioned from the top to the bottom of the CBR mold. The reinforced sample positioned at 3/4 of the geotextile had the greatest CBR value. Furthermore, the incorporation of geotextiles enhanced the settlement behavior of the reinforcement and elevated the anticipated Mr values. Consequently, it was determined that geotextile reinforcement enhanced the engineering properties of the Adtuyon clay subgrade. It is advisable to conduct studies employing multi-layer geotextiles as reinforcement and to perform quality assessments of geotextiles to further investigate the interaction between geotextiles and the Adtuyon subgrade soil.
Boron Neutron Capture Therapy (BNCT) offers a promising avenue for treating aggressive cancers by selectively targeting malignant cells while sparing healthy tissue. A key determinant of BNCT efficacy is the precise and sufficient accumulation of boron-10 (B-10) atoms at the tumor site, a challenge that remains a major obstacle in BNCT. To address this, this study employs the Particle and Heavy Ion Transport Code System (PHITS) to investigate the feasibility of using a carborane-monoclonal antibody conjugate (CMAC) as a boron-delivery agent. This study integrates a carborane-based monoclonal antibody construct with Monte Carlo particle transport modeling to characterize secondary particle production and penetration behavior relevant to BNCT micro-dosimetry. In the simulation, a soft tissue phantom containing 25 ppm of B-10 was irradiated with an epithermal neutron beam at 1.0 & times; 10(-2) MeV, yielding a neutron flux of 1.2 & times; 10(9 )cm(-2) s(-1). Monte Carlo analysis of the secondary particle fluence showed the production of alpha particles and Li-7 ions with high linear energy transfer and subcellular penetration ranges, along with lower-energy photons with broader dispersion. These results suggest that CMAC may induce sufficient B-10(n,alpha)Li-7 reactions to achieve localized cytotoxic effects, indicating its potential viability as an effective boron-delivery agent forBNCT.
Efforts to maximize the ability of Ultra-Wideband Impulse Radar (UWB-IR) technology to detect human presence through walls have progressed for years. This study optimized the human-detection capability of UWB-IR for people counting and localization behind walls through signal-processing techniques. These techniques include Clutter Suppression, Noise Reduction, Filtering, Digital Downconversion (DDC), Frame Differencing, Peak Detection, and Clustering. The system was evaluated across 15 unique test scenarios involving human subjects positioned behind walls made of plywood and concrete. Accuracy was assessed by comparing the estimated number of people and their positions with ground-truth data. The average count accuracy for both plywood panel and concrete wall is relatively high, with an overall percentage accuracy of maximum percentage error of 16%. In contrast, distance estimation is better for plywood panel, with a maximum percentage error of 10.85%. Overall, this study achieved Multi-Human Detection, People Counting, and Human Localization with an average accuracy of at least 92.11% across all delimitations considered.
This paper developed a one-point testing method for determining the plastic limit of fine-grained soils using a fall cone penetration apparatus with a heavier, 240-gram cone, as proposed by C.P. Wroth and M.D. Wood (1978). The original method required multiple penetration trials at varying moisture contents to develop a regression line from points plotted on a semilogarithmic moisture content -penetration graph. The researcher simplified the testing procedure by deriving an equation that estimates the moisture content corresponding to a 20-millimeter penetration of the modified fall cone from a single instance of penetration. The value obtained, when combined with the liquid limit, can then be used to calculate the soil's plastic limit. Statistical analysis of multipoint modified fall cone plastic limit tests on was used. The one-point method developed yielded plastic limit values that are in close agreement with those obtained by the multipoint penetration method (R = 0.8162, R-2 = 0.6662, T-stat < T-crit). The values obtained via the one-point penetration method also had very little impact on the evaluation of the following geotechnical parameters: bearing capacity, consolidation, liquefaction, and AASHTO and USCS classifications. The method was also verified to be applicable to soils outside of the model equation's development set, yielding plastic limit values statistically equal to their multipoint counterparts (R = 0.8157, R-2 = 0.6654, T-stat < T-crit). Lastly, statistically significant time and sample savings of 65.27% and 47.60% respectively, were observed when using the one point penetration method instead of the multipoint penetration method.
Platostoma palustre (Blume) A.J. Paton, a natural biopolymer, has shown promise for use in enhanced oil recovery due to its thickening properties. However, limited research has examined how environmental factors such as sand grain size and salinity affect its adsorption behavior and resulting in viscosity changes, which are key parameters in crude oil production from reservoirs. This study investigates how different sand grain sizes (0.149 mm and 0.420 mm) and salinity levels (10,000 and 20,000 ppm) influence the adsorption of P. palustre (Blume) A.J.Paton and how this adsorption affects the solution’s viscosity. The biopolymer was tested in batch systems with concentrations ranging from 2,000 to 6,000 ppm. Four adsorption isotherm models: Henry, Langmuir, Freundlich, and Harkins-Jura were used to analyze adsorption behavior. Results showed that adsorption increased with smaller grain sizes and lower salinity, leading to a significant reduction in solution viscosity. Among the models, Harkins-Jura provided the best overall fit, particularly under high salinity and coarse sand conditions. These findings suggest that optimizing both the environmental conditions and polymer concentration is crucial for maximizing the performance of P. palustre (Blume) A.J. Paton in applications such as polymer flooding.
The geometry of a shear web significantly affects the performance of wind turbines. This study investigates the mechanical behavior of novel shear web configurations inspired by banana leaf midrib topology, compared to conventional designs. Finite Element Method (FEM) and Fluid-Structure Interaction (FSI) simulations were employed to evaluate stress distribution, deflection, and modal frequencies under varying wind speeds (3-7 m/s). The aerodynamic forces acting on the blade were derived from airfoil profiles inspired by banana leaf midribs. Results show that the blade with a short banana leaf midrib shear web achieved the lowest deflection, while the long midrib configuration minimized stress. Additionally, the dual-web configuration exhibited the lowest natural frequencies. These findings demonstrate that biomimetic shear web designs can enhance structural efficiency and offer promising alternatives to conventional blades.
This study evaluated three object detection models for estimating traffic density on Elias Angeles St., Naga City, using mean average precision (mAP). The object detection model classified vehicles into five classes: private cars, jeepneys, trucks, motorcycles, and tricycles. Closed-circuit television (CCTV) footage was subjected to adaptive background subtraction and morphological opening to produce 320px & times; 320px images for use as a dataset for object detection models. Using Common Objects in Context means Average Precision at Intersection over Union (COCO mAP at IoU=50 as the metric for mAP. Faster Region-Convolutional Neural Network (Faster R-CNN) achieved the highest mAP of 92.54%, compared with You Only Look Once version 3 (YOLOv3) and Single Shot MultiBox Detector (SSD). In the traffic density estimation, vehicle size was accounted for; consequently, a private car was used as the standard vehicle type.
