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Simultaneous inference for empirical best predictors in generalized linear mixed models: A poverty study in West Java
Full text linkAccurate poverty mapping at the district and municipal levels remains challenging due to small sample sizes in household surveys, which often result in unstable direct estimates. To address this issue, this study employs microdata from the 2023 National Socioeconomic Survey (SUSENAS) to estimate household-level poverty proportions across 27 districts and municipalities in West Java Province using a binomial Generalized Linear Mixed Model (GLMM) combined with the Empirical Best Predictor (EBP) and Simultaneous Confidence Intervals (SCI). The GLMM framework captures household characteristics and random area effects to account for spatial heterogeneity. Three SCI approachesBonferroni correction, Bootstrap-t, and the Simes procedurewere implemented to evaluate EBP uncertainty while controlling the family-wise error rate. Results reveal substantial disparities, with Tasikmalaya (21.7%), Bandung Barat (15.5%), and Cianjur (12.8%) consistently above the provincial average of (6.8%), while urban areas such as Cimahi, Bekasi, and Depok report poverty rates below 2%. All methods achieved full empirical coverage (ECP = 100%), although interval widths differed: Bonferroni produced the widest intervals (AIW = 44.99), Bootstrap-t yielded the narrowest and most efficient (AIW = 29.16), and Simes provided intermediate but highly consistent results (AIW = 33.24). These findings underscore the methodological importance of integrating GLMM, EBP, and SCI for small area estimation while offering practical insights for evidence-based policy development and poverty reduction strategies in Indonesia
Species and index diversity of macrofungi from two different locations in East Kalimantan
Full text linkEast Kalimantan represents the lowland rainforest ecosystem with a vast number of macrofungi species in Indonesia. This study aimed to evaluate the diversity of macrofungi within the two different locations in East Kalimantan. The study was conducted in several phases, including planning, survey, preparation, sampling, and data analysis. Sampling was carried out systematically in 20 20 m plots. Collected samples were processed, photographed, and identified. Ecological indices, including the Shannon-Wiener diversity index, species evenness, and the Simpson dominance index, were calculated to provide a detailed analysis of the macrofungal community structure. This study found that location 1 recorded 24 species from 21 genera, while location 2 had 22 species from 20 genera. The Shannon-Wiener diversity index indicated moderate diversity at both sites, with values of 2.82 for location 1 and 2.65 for location 2. Daedaleopsis confragosa was the most frequent and dominant species in location 1, contributing 12.03% relative density, while Marasmiellus candidus dominated location 2 with 18.51% relative density. Species evenness was medium in both locations, and the dominance index was low, highlighting the ecological significance of these fungal species in their respective habitats. The results emphasize the importance of studying fungal ecology to support conservation and sustainable environmental management. The study revealed a wide variety of macrofungi that play vital roles in the tropical ecosystems of these two locations in East Kalimantan
Synthesis and characterization of alumina-chitosan modified monolithic activated carbon biosorbent from oil palm empty fruit bunches for acid mine drainage remediation
Full text linkThis study reports the synthesis and characterization of a monolithic activated carbon adsorbent modified with alumina and chitosan (Al-Chit/OAC), derived from oil palm empty fruit bunches (OPEFB). The adsorbent was fabricated through pyrolysis, followed by alumina incorporation and chitosan impregnation. FTIR analysis confirmed the presence of functional groups including OH stretching (3640cm), CH stretching (2920cm), CN/CO stretching (10551031cm), and AlO vibrations (693, 522, 495cm), indicating successful surface modification. TGA revealed two major stages of thermal degradation, with a total mass loss of 17.4% and a final residue of 17.55%, reflecting the presence of thermally stable inorganic components. SEM imaging showed a heterogeneous and porous surface with agglomerated particles and interparticle voids, suggesting enhanced surface accessibility. Even though we didn't test how well it absorbs substances, the physical and chemical properties of the composite show it could be very useful for cleaning up acid mine drainage (AMD) in the future. Further studies are recommended to validate its adsorption performance
In silico characterization of adh1 gene encoding alcohol dehydrogenase 1 (ADH1) from non-conventional yeast, Wickerhamomyces and Pichia spp
Full text linkWickerhamomyces anomalus and Pichia kudriavzevii have high potential to produce bioethanol under high stress condition, due to their stress-tolerant properties. To elucidate and develop an efficient and sustainable bioethanol production, characterization of ethanol fermentation reactions is highly substantial. Ethanol fermentation employs key enzyme ADH1 encoded by ADH1 gene, important for conversion of acetaldehyde to ethanol. However, structural studies about alcohol dehydrogenase1 from these genera of yeasts are limited. This study aimed to detect the alcohol dehydrogenase 1 gene from Pichia spp. Using computational-bioinformatics approaches. The adh1 gene was amplified by PCR, visualized by electrophoresis, and analysed for sequence homology by BlastN and BlastP. The enzyme structure was constructed by SWISS-MODEL and I-TASSER with validation by Ramachandran plot, QMEAN4, and Local Quality Estimate. The Similarity and homology analysis of ADH1 genes and their corresponding protein sequence of yeast isolates showed that the ADH1gene was successfully detected. Multiple sequence alignment (MSA) and phylogenetic tree revealed that W. anomalus BT1-BT6 has close evolutionary relationship with ADH1 from Saccharomyces cerevisiae sequence while P. kudriavzevii IP4 showed different pattern. The ADH 1 enzyme model, generated using the SWISS-MODEL web server, demonstrated the best stereochemical quality, with a Ramachandran plot value of 100% for W. anomalus BT1 and 99.3% for P. kudriavzevii IP4. Superimposition of 3D-predicted model of ADH1 from W. anomalus BT1 and P. kudriavzevii 1P4 showed an exact match with amino acid in Zn2+ binding sites, confirming the ADH1 metaloenzyme properties. These findings provide structural insights about ADH1 genes and protein properties which can be used further for the development of efficient and high productivity of bioethanol productions through genetic and protein engineering
The validation of urea determination in saliva using optical urea biosensor with p-dimethylaminobenzaldehyde (DMAB) reagent
Full text linkThe validation of urea determination in saliva using the biosensor method and the p-dimethylaminobenzaldehyde (DMAB) method has been successfully conducted. Anthocyanin compounds from Chatarantus roseus flowers were extracted using the maceration method with methanol as the solvent, yielding a total of 22.60% with a total concentration of 6.01 mg/L. The obtained extract was subjected to qualitative anthocyanin testing, showing a positive result was indicated by the formation of a reddish color and quantitative testing with the formation of a faded yellow color. The maximum wavelength ( max) of anthocyanin was 664 nm with an absorbance of 0.674. The saliva samples were obtained from three different ages i.e: children, teenagers, and adults. During the saliva collection procedure, participants were instructed to abstain from consuming any food or beverages, with the exception of water, for a minimum of 1 hour prior to sample collection. All samples were stored in a freezer at 4-8C until needed for analysis. Linearity test results were assessed using a calibration curve, yielding a coefficient of determination (R) of 0.978 with a sensitivity of 0.033 for the biosensor and an R of 0.975 with a sensitivity of 0.685 for DMAB. The limit of detection (LOD) values were determined as 7.203 10 M for the biosensor and 6.984 10 M for DMAB, while the limit of quantification (LOQ) values were 2.182 10 M and 2.116 10 M, respectively. Statistical analysis using a t-test showed tcalculated = 1.314, compared to ttable = 4.302, indicating that tcalculated ttable. This result suggests no statistically significant difference between the biosensor and DMAB methods, confirming that the biosensor method is comparable in performance to the DMAB method. Additionally, urea concentration measurements showed that adult samples exhibited the highest urea levels among the tested samples, which may indicate an association with potential health risks, including dental caries, kidney failure, and liver damage
Analysis of VAE-LSTM Performance in Detecting Anomalies in Average Daily Temperature Data in Jakarta 2000-2023
Full text linkClimate change is happening worldwide, so global climate conditions are a major concern. In densely populated urban areas such as Jakarta, it is impossible to avoid the impacts of climate change, particularly the daily changes in air temperature. Therefore, a sophisticated and efficient approach is needed to find inconsistencies in daily air temperature data to provide critical information for sustainable urban planning and efforts to reduce risks. This research will combine two innovative approaches for hybrid anomaly detection. The method combines generative methods and can extract complex features, such as variational autoencoder (VAE), along with the temporal coding capabilities of long-short-term memory (LSTM), a type of Recurrent Neural Network (RNN). The data used in this study is the average daily air temperature data in Jakarta, obtained from the Kemayoran Meteorological Station and provide by the Meteorology, Climatology, and Geophysics Agency (BMKG). The data used is daily from April 2000 to December 2023. The threshold used to detect anomalies was 229.5, which resulted in excellent performance, namely F1-Score 0.985, Recall 1.000, and Precision 0.971. The VAE-LSTM model identified all dates with significant temperature anomalies, including January 21, 2014, February 22, 2014, November 12, 2014, and February 9, 2015. These dates are significant as they represent extreme weather events that can have severe implications for urban planning and climate change adaptation. The anomalies fall into the categories of point and contextual anomalies. This study contributes to climate research by providing evidence of the effectiveness of deep learning-based hybrid models in detecting complex and context-sensitive temperature anomalies
The effectiveness of naphthaleneacetic acid and kinetin on the development of plantlet book cuttings of Chrysanthemum ornamental plants
Full text linkThe high demand for chrysanthemum flowers, coupled with the challenge of acquiring quality seeds in large quantities within a short timeframe, presents significant problems in this study. One potential solution is to propagate plants and utilize plant growth regulators to accelerate their development. Tissue culture techniques, which are essential in agriculture and biotechnology, are employed to produce new plants by isolating, developing, and duplicating specific plant cells or tissues in the laboratory. This study aims to assess the effects of two plant growth regulators Naphthalene Acetic Acid (NAA) and Kinetin on the growth and development of stem cuttings or nodes from ornamental chrysanthemum plants (chrysanthemum) in vitro using MS media. The research utilized a completely randomized design (CRD) factorial approach featuring two treatment factors: NAA and Kinetin. A total of nine treatment combinations were tested, with NAA concentrations of 0.5, 1.0, and 1.5 ml L-1, and Kinetin concentrations of 1, 2, and 3 ml L-1. Each treatment was repeated three times. Observations included measurements of plant height, leaf count, root count, and wet weight. The best results for each parameter were observed in treatment K3N2, which yielded a plant height of 30.73 mm and an average of 18.33 leaves. The optimal results for root count and wet weight were found in treatment K3N3, which produced an average of 13.83 roots and 3.57 grams, respectively. Overall, increasing the concentrations of NAA and Kinetin positively influenced all parameters evaluated
Electrofacies classification of a mixed carbonate-siliciclastic reservoir using machine learning techniques
Full text linkMany scientific fields, including the geosciences, have successfully employed machine learning to address numerous significant issues. Current studies show that the application of machine learning within the geosciences is still in its early stages, and there is a huge potential for this technique that need to be explored. This research focuses on the Late Permian Beekeeper Formation from the Perth Basin, Australia. It aims to improve our understanding of the application of machine learning to characterise subsurface rock formations. The objectives of this study are threefold: (1) to conduct cutting, crossplot, and modern machine learning analyses on a mixed carbonate-siliciclastic reservoir; (2) to compare the results from the aforementioned analyses and to interpret the electrofacies and lithofacies; and (3) to understand the degree of accuracy of the application of machine learning in the characterisation of the subsurface rock formations. Cutting, crossplotting, and modern machine learning analyses have been conducted to achieve the aim and objectives of this study. Seven electrofacies, associated with nine lithofacies, were identified within the studied data, and these were classified into carbonate-dominated facies group, siliciclastic-dominated facies group, and mixed carbonate-siliciclastic facies group. Results also show the presence of stratal and compositional mixing within the Beekeeper Formation. A combination of cutting, crossplot, and machine learning analyses can provide a better, more accurate, and more reliable interpretation of the facies of the Beekeeper Formation. This study is expected to advance our understanding of the application of machine learning in geosciences
