Lampung University

An integrated two-step co-pyrolysis (ITSC) process was developed to valorize heterogeneous municipal solid waste (MC-MSW) co-processed with Indonesian brown coal (BC), employing unmodified natural mineral catalysts (kaolin, dolomite, zeolite) for vapor-phase upgrading to improve bio-oil properties. The MC-MSW: BC blend (4:1, w/w) underwent pyrolysis at 550 °C; resulting vapors (paraffins, olefins, aromatics) were catalytically upgraded and analyzed using GC–MS and standardized ASTM protocols (ASTM D445 for viscosity, ASTM D4052 for density, ASTM D92 for flash point, ASTM D5865 for calorific value). Kaolin produced the highest liquid yield (44.0 wt

Stunting attributable to malnutrition remains a global public health problem impacting the long-term physical and cognitive growth of children. In recent years, artificial intelligence (AI) has been applied in public health research to help diagnose and predict stunting. This study seeks to review trends in AI research on stunting prediction and intervention, and to identify existing challenges and opportunities. The articles were screened using the Systematic Literature Review (SLR) method with the PRISMA protocol through databases like PubMed, ScienceDirect, Scopus, and Google Scholar. The analysis of the data was performed using VOSviewer and Microsoft Excel. The results showed that the most used models in predicting stunting were Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (XGBoost, LGBM), and Artificial Neural Network(ANN). Model evaluation is usually done through metrics such as AUC-ROC, accuracy, sensitivity, and specificity. Although AI has shown promise in identifying and predicting stunting, a few challenges remain: One is of data access and quality; others are model interpretability and integration within healthcare networks. Towards increasingly promising application outcomes: future directions for home-based health data prediction of the Internet of Things (IoT), Explainable AI (XAI), Multimodal AI, and natural language processing (NLP) models.

Interest in online learning has grown over the past decades, with the heutagogical approach gaining traction, especially in doctoral programs requiring learner autonomy. This study aims to explore doctoral students' lived experiences with the heutagogical approach in online learning, including their perceptions of its effectiveness and their interpretation of learning outcomes and quality assurance processes. Using a phenomenological design, data were collected from graduate students at Universitas Negeri Yogyakarta, Indonesia, through indepth interviews and classroom observations over one semester. Observations revealed three key phases: (1) design (students co-developed learning objectives and project scopes aligned with their dissertations); (2) development (students conducted self-directed research with weekly discussions and iterative feedback); and (3) implementation (peer evaluations and final project submission for formal review). Students perceived the approach as effective, recognizing its flexibility and autonomy, while also acknowledging challenges such as demands for self-regulation, technical constraints, and limited face-to-face interaction. The approach proved effective in balancing independent learning with academic achievement, reflected in scientific publications and intellectual property rights. This study highlights the heutagogical approach as a viable pedagogical model for doctoral online learning, emphasizing the critical balance between learner autonomy, structured guidance, and institutional support to ensure sustained academic excellence.

Particleboard (PB) is mainly produced using formaldehyde-based adhesives, which release hazardous formaldehyde emissions. Thus, replacing those adhesives is necessary to support the Sustainable Development Goals No. 12 (responsible consumption and production) and 13 (climate action). In this study, low-quality gum rosin (GR) was upcycled into PB adhesive, and its adhesion performance and hydrolytic stability were enhanced with the addition of maleic anhydride (MA) and polymeric 4,4-methylene diphenyl diisocyanate (pMDI). Sawdust was used as a raw material for the sustainable PB production. The GR was dissolved in an organic solvent at 70% w/v. MA and pMDI were incorporated into the GR solution at different levels (2-15%) to produce a cross-linked adhesive for PB. GR-bonded PB panels were fabricated at 150 degrees C for 10 min at 20% and 30% adhesive content. Adhesive properties, adhesion performance, and hydrolytic stability of GR-bonded PB were investigated according to the standard. Adding MA and pMDI increased the solids content, specific gravity, and viscosity of the adhesive, which influenced the adhesion performance and hydrolytic stability of PB. Statistical analysis revealed that adding 15% pMDI could produce a high-performance PB panel without formaldehyde emission. This study suggests that GR-pMDI adhesive has potential as an alternative PB adhesive.

This study investigates the anti-inflammatory potential of bioactive metabolites derived from sungkai leaves (Peronema canescens Jack) using an in vitro approach. The metabolites were obtained through maceration followed by solvent partitioning and chromatographic separation, including vacuum liquid chromatography (VLC), column chromatography (CC), and thin-layer chromatography (TLC). Structural characterization of the resulting fractions was performed using Liquid Chromatography-Mass Spectrometry (LC-MS/MS). The anti-inflammatory activity was evaluated in vitro using a modified Bovine Serum Albumin (BSA) protein denaturation assay. Among the tested extracts, the ethyl acetate fraction exhibited the strongest inhibitory activity with an IC50 value of 52.12 mu g/mL, compared to the n-hexane, dichloromethane, and methanol residue extracts. LC-MS/MS analysis tentatively suggested the presence of a compound with molecular formula C16H12O5, corresponding to a flavonoid structure identified as acacetin. This findings indicate that sungkai leaf extract contains putative flavonoid metabolites that may contribute to its observed anti-inflammatory activity. However, further isolation and structural confirmation are required to establish a definitive relationship between the identified compounds and the biological activity.