IPB University (Indonesian: Institut Pertanian Bogor, abbreviated as IPB) is a state-run agricultural university based in the city of Bogor, Indonesia. The institute began as an agricultural school formed by the Dutch colonial regime in the early 20th century. After independence it was part of the University of Indonesia before becoming an independent institute on September 1, 1963. Dr. Arif Satria, S.P., M.Si. serves as its director.
Soil temperature (ST) plays a critical role in regulating biogeochemical processes in soils, yet its spatiotemporal mapping remains a challenge. Existing ST maps are largely derived from land-surface models and are typically available only at coarse spatial resolutions, limiting their usefulness for agricultural applications. This study aims to predict daily ST at 10-cm depth intervals from the surface to 120 cm and to generate maps at 80 m spatial resolution. We developed a differentiable model (DM) that integrates neural networks (NN) estimated surface soil temperature (SST) with a physics-based heat-transfer model. The model was trained and validated using three years of spatiotemporal ST observations from 33 monitoring sites across Tasmania, Australia (25,191 records). Our results show that the DM improved prediction accuracy relative to the purely physics-based model (PBM) and enhanced the stability and generalisability of the fully data-driven NN. Compared with fully data-driven NN models, the DM reduced prediction error by up to 25%, exhibited low variability, and performed well in soils with high soil organic carbon. By explicitly estimating SST, the DM provided a mathematically optimised upper boundary condition, resulting in smoother, more physically consistent ST profiles across depth and more reliable seasonal spatial patterns than the NN models. The SST estimates also showed coherent temporal behaviour, with lower day-to-day variability and warmer summer conditions than air temperature. Furthermore, our findings demonstrate that coupling machine learning with process-based heat transfer can improve the accuracy, physical realism, and scalability of soil temperature prediction.
Cellulose-based hydrocolloids are emerging as renewable oleogelators for structuring edible oils into solid-fat alternatives, yet their performance remains difficult to compare because reported systems differ in cellulose chemistry, processing route, and testing conditions. This review synthesizes recent progress in ethyl cellulose, methylcellulose, hydroxypropyl methylcellulose, carboxymethyl cellulose, microcrystalline cellulose, cellulose nanocrystals, and cellulose nanofibrils as oleogel structuring agents for food applications. Particular emphasis is placed on how degree of substitution, hydrophobicity, crystallinity, aspect ratio, interfacial behavior, and fibrillar entanglement regulate gelation mechanism, network strength, oil binding, oxidative stability, rheology, and food performance. Preparation routes, including heating–cooling, emulsion or foam templating, solvent exchange, high-shear processing, and ultrasonication, are compared in terms of structural control, scalability, thermal load, solvent use, food-grade compatibility, and suitability for application. Cellulose-based oleogels show strong potential for reducing saturated and trans fats in bakery, confectionery, meat, dairy, spreadable, and emerging printed food systems. However, their translation into food applications remains constrained by processing intensity, moisture sensitivity, solvent-related safety concerns, regulatory uncertainty for certain nanocellulose systems, and limited evidence of digestion. Future progress should integrate standardized rheological benchmarking, food-specific safety assessment, scalable low-energy processing, and hybrid cellulose-network design. Overall, cellulose-based oleogels offer a versatile platform for clean-label lipid structuring, provided that mechanistic understanding, safety validation, and industrial feasibility are developed together.
Indonesia generates vast quantities of tapioca, sago starch, and oil palm empty fruit bunch (OPEFB), creating significant opportunities for biomass valorization. Hydrogels made of tapioca or sago carboxymethyl starch (T-CMS, S-CMS) and oil palm empty fruit bunch (OPEFB) carboxymethyl cellulose (CMC; at 15
This review explores the emerging role of electro-fermentation (EF) and its engineering as an advanced bioelectrochemical process for heavy metals removal and recovery from mining wastewater, including acid mine drainage (AMD), tailings effluents, and metal-rich industrial discharges. It specifically communicates recent advancements in electro-fermentation mechanisms, reactor configurations, microbial electron transfer pathways, and integration methods for sustainable mining wastewater remediation. Recent studies indicate that EF engineering improves heavy metal removal via electro-assisted microbial reduction, cathodic metal deposition, sulfide-mediated precipitation, biosorption, and bioaccumulation. These processes are facilitated by extracellular electron transfer (EET), which governs electron exchange between microorganisms and electrodes or other electron acceptors. EET can occur through both direct electron transfer (DET) and indirect/mediated electron transfer (IET/MET) pathways, or may also involve direct interspecies electron transfer (DIET) in microbial consortia. The removal efficiency can be up to 80–99
Conventional methods using deoxyribonucleic acid (DNA) and proteins as molecular biomarkers for halal meat authentication are highly sensitive and specific. However, their analytical performance in thermally processed meat is affected by DNA fragmentation and heat-induced protein denaturation. Therefore, volatile organic compound (VOC)–based approaches have emerged as promising alternatives. Nevertheless, the reported VOC profiles remain inconsistent across studies, even for the same species analyzed using similar methods, highlighting the need for a systematic evaluation. This study examined recent trends in VOC-based pork and beef differentiation through a systematic literature review and bibliometric analysis of publications from 2014 to 2025. After applying several selection criteria, 73 journal articles were retrieved from the Scopus database. Bibliometric analysis revealed an upward annual growth rate of 31.4
