bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wettability improvement of carbon nanotube for supercapacitor electrode
Authors: Devianto H , Luthfiana N , Widiatmoko P , Nurdin I , Prakoso T 
Published: April 2021

Abstract
Carbon Nanotube (CNT) has outstanding properties such as electrical conductivity, specific surface area, charge transport capability, mesoporosity, and very high electrolyte accessibility. Based on these properties, CNTs are very suitable to be used as high-performance supercapacitor electrodes which can be seen from the capacitance. One of the factors that can lead to increase capacitance is the high interface interaction between CNT and the electrolyte. The interface interaction of CNT and electrolyte can be improved by increasing the CNT wettability using hydrophilization. CNT is synthesized by pyrolysis with Palm Oil Mill Effluent (POME) as the main raw material. In this study, hydrophilization was carried out using chemical activation HNO3 with various concentrations and activation times (1M for 1, 3, 6 hours, and 13M for 1 hour). The results of CNT structure were characterized using Scanning Electron Microscopy (SEM), Transmission Electron Microscope (TEM), and X-Ray Diffraction (XRD), as well as electrochemically characterized using cyclic voltammetry (CV), Electrochemical Impedance Spectroscopy (EIS) dan galvanostatic charge-discharge (GCD), as supported data to evaluate which concentration and activation time with the highest efficiency to improve CNT performance as a supercapacitor. Based on CV characterization, the capacitance of CNT before hydrophilization, and after hydrophilization using HNO3 (1M for 1 hour, 3 hours, 6 hours, and 13M for 1 hour) were 10 F/g, 13.30 F/g, 14.28 F/g, 26.95 F/g and 24.5 F/g respectively. When CNT hydrophilized at 13M for 1 hour, capacitance decreased. Due to surface damage of CNT, therefore the performance of the supercapacitor decreased. This was supported by the results of the GCD characterization, where some charges could not completely release.
Cultivation of ‘Botryococcus Braunii’ Microalgae for Hydrocarbons Production and CO2 Bio-fixation
Authors: Dewi R G , Srikaton D , Sitorus S , Sevie G N , Bunga P , Permata S 
Published: April 2021

Abstract
CO2 fixation for climate change mitigation actions through photosynthesis can be carried out by growing intensively managed microalgae. The capability of microalgae in the CO2 fixation is favourable compare to trees or crops, yielding three to five times more biomass per land area than typical plants. Another interesting feature of microalgae is microalgal farms can use arid, semi-arid land or highly saline water, which is practically not suitable for agriculture and other biomass production activities. In addition, algae biomass contains fibre, fatty acid, lipid, protein, polysaccharide, and hydrocarbon. These materials could be extracted for biofuels or biochemicals. Biofuels derived from microalgae are environmentally friendly and carbon neutral. However, microalgal farming seen only for producing biomass as sources of fuels and chemicals probably uneconomical. The yields of microalgal farm to produce fuels/chemicals are also low. Therefore, microalgal farm with the objective of solely producing fuels/chemicals is economically unfeasible. If it is designed for CO2 mitigation measures and all at once for fuels/chemicals production, such endeavour could be economically justifiable. This paper presents results of a research on microalgae cultivation for producing biomass and hydrocarbons for biofuels that simultaneously fixing the CO2 emissions from coal power plant effluent through photosynthesis process, which also for the implementation of circular economy concepts. A strain of ‘Botryococcus Braunii’ microalgae, which known can produce high hydrocarbons and lipids throughout its growth period has been used. The research identified and evaluated the effect of cultivation medium and CO2 concentration to the growth rate, biomass production, lipid and hydrocarbon content and type. The microalgae were cultivated in bioreactor supported with airlift system for supplying CO2/air and for mixing process. Results of the research show that modified Chu13 (MChu13) is appropriate cultivation medium for the growth, hydrocarbon/lipid production, and CO2 fixation from B. braunii. The magnitude of biomass production in MChu13 is 0.145 g/L.9days, while in modified BG11 is 0,118 g/L.9days, Bold Bassal Medium (BBM) is 0.09 g/L.9days, and Bristol is 0.023 g/L.9days. This biomass production rate is in lined with the CO2 fixation rate. In MChu13 the dry weight of B.brauni is 0.268 g/L.7days while in Bristol only 0.229 g/L.7days.
Study on Indonesian plastic marine debris based on river survey
Authors: Abidin A Z , Steven S 
Published: April 2021

Abstract
In response to Jambeck’s claim on Indonesian plastic marine debris, some academicians, scientists, as well as associations such as The Indonesian Olefin and Plastic Industry Association (INAPLAS), Indonesian Plastic Recycle Association (ADUPI), and Indonesian Plastic Recycle Industry Association (APDUPI) conducted a deeper plastic balance examination to clarify and enlight the Indonesian plastic marine debris amount. This study continued the previous work, which employed the river survey method to conduct a more accurate calculation in the actual condition. From the river survey results, Indonesian plastic marine debris amount is about 0.038 MT/y and it will reduce to 0.004 MT/y if the trash trap is strictly-applied. This result is not much different compare to the seashore approach but again also clarifies that Indonesia is not the second-largest contributor country for plastic marine debris. Moreover, asides from conserving the marine and river environments, installing a trash trap on the river could increase local people’s income at once.
Energy and Exergy Analysis on the Rotary Kiln Unit of RKC-2 PT. Semen Gresik – Tuban Plant
Authors: Hayati Nasution Mala , Rasyid Syahputra Irwan , Rahman Darul 
Published: April 2021

Abstract
Cement industry is an industry that intensively uses energy in the process. One of the main units in a cement plant that consumes the most energy is the rotary kiln. Rotary kiln is used for calcination process of cement raw material into clinker. It operates at 900-1450°C. It obtains energy from coal combustion process. Energy conservation in rotary kiln can be used to optimize production costs. Energy conservation can be carried out through energy identification. One method of energy identification is through energy and exergy analysis. In this study, energy and exergy analysis was carried out at the rotary kiln unit of RKC-2 PT. Semen Gresik - Tuban Plant. The analysis was carried out based on the calculation of the mass balance, energy balance, enthalpy balance, entropy balance and exergy balance. These calculations are used to obtain energy efficiency, exergy efficiency and irreversibility. Based on the research results, the energy efficiency, exergy efficiency, and irreversibility were 69,2%; 50,48% and 49,52% respectively.
Separation of Potassium from The Model Solution
Authors: Puspita Ratna , Susanto Herri 
Published: April 2021

Abstract
Potassium content in biomass ashes, especially empty fruit bunches was quite high and had not been utilized maximally. This research dealt with the development of a technique for recovering potassium salt from a solution of ash extraction. Experiments were carried out usung a solution model containing KCl, NaCl, MgCl2, and CaCl2 with concentrations of 164.19, 8.39, 7.44, dan 39.60 mg/L respectively. Results of a preliminary experiment indicated that the solubilities of KCl and NaCl were reversed: 280.4 and 280.4 (g/L in water) to 6.9 and 14.2 (g/L in absolute ethanol), while the solubilities of MgCl2 and CaCl2 were still high (68.7 and 383.5 g/L in ethanol). Using these values of solubility, a combined evaporation and adding ethanol was trialed firstly to increase the precipitation of KCl. Evaporation of KCl solution 164.19 g/L until an evaporation leves of 15% did not give any precipitation yet, while adding ethanol to the remaining solution (at solution ratios of 4/1 and 3/2 ethanol) yielded precipitations with the potassium recoveries of 60% and 48% respectively. At an evaporation level of 30%, the evaporation yielded a 5% potassium recovery, while the combined technique gave 60% and 50% recoveries for adding ethanol with ratios of 4/1 and 3/2 respectively. The application of the combined technique on the mixed salts solution was modified slightly. The precipitate from the evaporation (containing KCl and NaCl) was redissolved into water and followed by adding ethanol to precipitate only KCl. Thus, two precipitates were finally obtained: (i) precipitate from adding ethanol to filtrate of evaporation, and (ii) precipitate from dissolving precipitate from evaporation. At an evaporation level of 30%, the evaporation only gave a precipitate recovery of about 10%, while the application of combined technique gave bout 80% and 60% recoveries by adding ethanol with ratios of 4/1 and 3/2 respectively. Hopefully this technique could separate KCl from its mixture with NaCl, MgCl2, and CaCl2.
Natural Gas Network Design using Superstructure Method in East Java Indonesia
Authors: Panca Anugraha Rendra , Renanto  , Juwari  
Published: April 2021

Abstract
East Java is one of the provinces in Indonesia which has total natural gas reserves of 4.66 trillion standard cubic feet (TSCF) spread across several locations. Natural gas in this province is used by a variety of consumers, such as petrochemical industries, power plants, industrial fuel, transportation and household needs. However, there are obstacles in the utilization of natural gas due to differences in operating time and differences capacity between suppliers and consumers. Therefore, to optimize the utilization of natural gas in East Java, a natural gas network design is required which is considering the operating time and the capacity of suppliers (source point) and consumers (sinks point). In this study, a natural gas network design of East Java area was developed by modelling superstructure methods which consider the operating time and capacity. The superstructure natural gas network model developed in this study was optimized using GAMS software. From 5 source points and 6 sink points, an optimum natural gas network design has been obtained with a total gas distribution of 4832.8 billon standard cubic feet (BSCF) in a period of 30 years. Due to mismatch of operating time, it is also known that the amount of excess gas supply from this area (export gas) is 1364.1 BSCF and the demand for gas supply from other areas (import gas) is 1105.7 BSCF. With this superstructure method, it is possible to know the optimum network configuration and the natural gas balance in an area which has different operating time, flowrate and capacity between sources and sinks.
Synthesis of Maleic-Modified Rosin Ester from Pine Rosin
Authors: Prakoso Tirto , Kumalasari Ihda , Jiwandaru Bramantha , Hernas Soerawidjaja Tatang , Mufti Azis Muhammad , Indarto Antonius 
Published: April 2021

Abstract
Rosin is the major component of Pine sap that has a composition of up to 90%. Rosin is an organic compound that consists of diterpene tricyclic compounds with carboxyl functional group. The presence of this functional group makes rosin becoming acidic and corrosive. Furthermore, rosin also has double bonds that are prone to oxidation when exposed to air. To improve the weaknesses, modifications shall be done. Esterification reaction can be done through transforming into a more stable functional group, ie. ester, while Diels-Alder reaction will saturate the double bonds into more stable cyclic compounds. The results indicate that synthesizing maleic modified rosin ester could improve the properties of pine rosin. Esterification was done using a ratio of glycerol to rosin of 1:3 mol. Esterification was done for 6 hours at 250°C at room and vacuum pressure. The Diels-Alder reaction was done using maleic anhydride at 1:1 stoichiometric ratio to rosin, reacted at 150°C for 1 hour at vacuum condition.
Finding the optimum concentration of silicon carbide and graphite particles on the tensile strength, hardness and wear properties of aluminium matrix composites by RSM approach
Authors: M Bakkiyaraj 
Published: April 2021

Abstract
The present investigation aims to determine the optimum concentration of SiC and graphite reinforcements in AA7075 matrix based hybrid composites to obtain better tensile strength, hardness, coefficient of friction and wear rate using RSM. Reinforcement particles of SiC varied from 5% to 15% whereas graphite particles varied from 3% to 7% in the AA7075 matrix. A central composite design of RSM was adopted to develop the numerical model, and the statistical data were derived using analysis variances to evaluate adequacy of the model. Meanwhile, the conformity test has been conducted to validate the developed numerical model. Following this, the output responses of the optimized conditions were taken into account for the characterization of microstructure, fractured surface, and worn surface characters using optical microscope, and scanning electron microscope. The effect of SiC and graphite particles on the tensile strength, hardness, and wear properties and its result were reported in detail along with comparison of previous researchers.
Spatially multiplexed interferometric microscopy: principles and applications to biomedical imaging
Authors: Picazo-Bueno José Ángel , Trusiak Maciej , García Javier , Micó Vicente 
Published: April 2021

Abstract
Digital holographic microscopy (DHM) applied to quantitative phase imaging (QPI) has been successfully demonstrated as a powerful label-free method to analyse the optical properties of cells. Spatially multiplexed interferometric microscopy (SMIM) is a DHM technique that implements a common-path interferometric layout in the embodiment of a standard microscope to achieve QPI. More concretely, SMIM introduces three minimal modifications: (a) replaces the broadband illumination of the microscope by a coherent or partially coherent light source, (b) divides the input plane into two or three regions for transmission in parallel of both imaging and reference beams, and (c) includes a one-dimensional (1D) diffraction grating or a beam splitter cube for holographic recording. Hence, SMIM is a cost-effective, extremely simple, and highly stable manner of converting a standard bright field microscope into a holographic one. The goal of this contribution is to present the SMIM approaches implemented using a 1D diffraction grating, and highlight vast range of capabilities for biomedical imaging including super-resolved, reflective, transflective, noise-reduced and single-shot slightly off-axis amplitude and phase imaging.
Hardware realization of the multiply and accumulate operation on radio-frequency signals with magnetic tunnel junctions
Authors: Leroux Nathan , Mizrahi Alice , Marković Danijela , Sanz-Hernández Dédalo , Trastoy Juan , Bortolotti Paolo , Martins Leandro , Jenkins Alex , Ferreira Ricardo , Grollier Julie 
Published: April 2021

Abstract
Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but the digitization of analog signals and the use of general purpose hardware non-optimized for training make the process slow and energetically costly. Recent theoretical work has proposed to use nano-devices called magnetic tunnel junctions, which exhibit intrinsic RF dynamics, to implement in hardware the multiply and accumulate (MAC) operation—a key building block of neural networks—directly using analog RF signals. In this article, we experimentally demonstrate that a magnetic tunnel junction can perform a multiplication of RF powers, with tunable positive and negative synaptic weights. Using two magnetic tunnel junctions connected in series, we demonstrate the MAC operation and use it for classification of RF signals. These results open a path to embedded systems capable of analyzing RF signals with neural networks directly after the antenna, at low power cost and high speed.
Learning to Imagine
Authors: Kind Amy 
Published: April 2021

Abstract
Underlying much current work in philosophy of imagination is the assumption that imagination is a skill. This assumption seems to entail not only that facility with imagining will vary from one person to another, but also that people can improve their own imaginative capacities and learn to be better imaginers. This paper takes up this issue. After showing why this is properly understood as a philosophical question, I discuss what it means to say that one imagining is better than another and then discuss the kinds of imagination training and techniques that might be employed in an effort to get better at imagining. The discussion of these techniques draws insight from consideration of other skills-based activities, as well as from consideration of the creation of art and our engagement with literature and poetry. Over the course of this discussion, we also gain further insight into the nature of imagination.
In situradiation explains the frequency of dioecious palms on islands
Authors: Cássia-Silva Cibele , Freitas Cíntia G , Jardim Lucas , Bacon Christine D , Collevatti Rosane G 
Published: April 2021

AbstractBackground and AimsDioecy has evolved up to 5000 times in angiosperms, despite the potentially high intrinsic costs to unisexuality. Dioecy prevents inbreeding, which is especially relevant on isolated islands when gene pools are small. Dioecy is also associated with certain dispersal traits, such as fruit size and type. However, the influence of dioecy on other life history traits and island distribution remains poorly understood. Here, we test the effect of dioecy on palm (Arecaceae) speciation rates, fruit size and frequency on islands.MethodsWe used phylogenetic comparative methods to estimate the ancestral state of the sexual system and its impact on speciation rates and fruit size. Frequency of sexual systems, effect of insularity on the probability of being dioecious, and phylogenetic clustering of island dioecious vs. mainland species were inferred. Lastly, we determined the interplay of insularity and sexual system on speciation rates.Key ResultsPalms repeatedly evolved different sexual systems (dioecy, monoecy and polygamy) from a hermaphrodite origin. Differences in speciation rates and fruit size among the different sexual systems were not identified. An effect of islands on the probability of the palms being dioecious was also not found. However, we found a high frequency and phylogenetic clustering of dioecious palms on islands, which were not correlated with higher speciation rates.ConclusionsThe high frequency and phylogenetic clustering may be the result of in situ radiation and suggest an ‘island effect’ for dioecious palms, which was not explained by differential speciation rates. This island effect also cannot be attributed to long-distance dispersal due to the lack of fruit size difference among sexual systems, and particularly because palm dispersal to islands is highly constrained by the interaction between the sizes of fruit and frugivores. Taken together, we suggest that trait flexibility in sexual system evolution and the in situ radiation of dioecious lineages are the underlying causes of the outstanding distribution of palms on islands.
Evaluating disease similarity based on gene network reconstruction and representation
Authors: Li Yang , Keqi Wang , Wang Guohua 
Published: April 2021

AbstractMotivationQuantifying the associations between diseases is of great significance in increasing our understanding of disease biology, improving disease diagnosis, re-positioning and developing drugs. Therefore, in recent years, the research of disease similarity has received a lot of attention in the field of bioinformatics. Previous work has shown that the combination of the ontology (such as disease ontology and gene ontology) and disease–gene interactions are worthy to be regarded to elucidate diseases and disease associations. However, most of them are either based on the overlap between disease-related gene sets or distance within the ontology’s hierarchy. The diseases in these methods are represented by discrete or sparse feature vectors, which cannot grasp the deep semantic information of diseases. Recently, deep representation learning has been widely studied and gradually applied to various fields of bioinformatics. Based on the hypothesis that disease representation depends on its related gene representations, we propose a disease representation model using two most representative gene resources HumanNet and Gene Ontology to construct a new gene network and learn gene (disease) representations. The similarity between two diseases is computed by the cosine similarity of their corresponding representations.ResultsWe propose a novel approach to compute disease similarity, which integrates two important factors disease-related genes and gene ontology hierarchy to learn disease representation based on deep representation learning. Under the same experimental settings, the AUC value of our method is 0.8074, which improves the most competitive baseline method by 10.1%. The quantitative and qualitative experimental results show that our model can learn effective disease representations and improve the accuracy of disease similarity computation significantly.Availability and implementationThe research shows that this method has certain applicability in the prediction of gene-related diseases, the migration of disease treatment methods, drug development and so on.Supplementary informationSupplementary data are available at Bioinformatics online.
Multimodal regularized linear models with flux balance analysis for mechanistic integration of omics data
Authors: Magazzù Giuseppe , Zampieri Guido , Angione Claudio 
Published: April 2021

AbstractMotivationHigh-throughput biological data, thanks to technological advances, have become cheaper to collect, leading to the availability of vast amounts of omic data of different types. In parallel, the in silico reconstruction and modeling of metabolic systems is now acknowledged as a key tool to complement experimental data on a large scale. The integration of these model- and data-driven information is therefore emerging as a new challenge in systems biology, with no clear guidance on how to better take advantage of the inherent multisource and multiomic nature of these data types while preserving mechanistic interpretation.ResultsHere, we investigate different regularization techniques for high-dimensional data derived from the integration of gene expression profiles with metabolic flux data, extracted from strain-specific metabolic models, to improve cellular growth rate predictions. To this end, we propose ad-hoc extensions of previous regularization frameworks including group, view-specific and principal component regularization and experimentally compare them using data from 1143 Saccharomyces cerevisiae strains. We observe a divergence between methods in terms of regression accuracy and integration effectiveness based on the type of regularization employed. In multiomic regression tasks, when learning from experimental and model-generated omic data, our results demonstrate the competitiveness and ease of interpretation of multimodal regularized linear models compared to data-hungry methods based on neural networks.Availability and implementationAll data, models and code produced in this work are available on GitHub at https://github.com/Angione-Lab/HybridGroupIPFLasso_pc2Lasso.Supplementary informationSupplementary data are available at Bioinformatics online.