Effect of acid-catalyzed methanolysis on the bioactive components of rice bran oil

The change in bioactive components in oil derived from rice bran oil after acid-catalyzed methanolysis was investigated in this study. The effects of catalyst amount, molar ratio of methanol to oil, reaction time, and nitrogen purging on acid-catalyzed methanolysis were investigated to find the optimum condition in converting all free fatty acids and acylglycerides into biodiesel with minimum loss of bioactive components. Acid-catalyzed esterification at 60 8C using 5 wt% of sulphuric acid as the catalyst can convert all free fatty acids (initial content = 59.19%) and acylglycerides (initial content = 19.31%) into fatty acid methyl esters in 5 h with a molar ratio of methanol to oil = 40. After the reaction, the losses of squalene, atocopherol, g-tocotrienol, campesterol, stigmasterol, b-sitosterol, and g-oryzanol are 50.07%, 18.06%, 63.09%, 21.68%, 28.74%, 25.42%, and 35.43%, respectively. When nitrogen purging was applied during the reaction, the losses of the aforementioned bioactive components became 42.54%, 0.00%, 43.47%, 23.47%, 26.66%, 24.07%, and 29.76%, respectively. In addition, oxidation products were not detected by GC–MS during acid-catalyzed methanolysis. From the present investigation, loss of bioactive components can be mitigated by carried out the reaction under nitrogen atmosphere

PRODUKSI BIODIESEL DARI LUMPUR AKTIF BASAH DALAM KONDISI SUBKRITIS

Sebuah metode baru dalam mengkonversi Lumpur aktif basah menjadi biodiesel diusulkan dalam penelitian ini. Air digunakan sebagai reagen hidrolisis untuk meningkatkan ekstraksi lipid dalam Lumpur aktif dan sebagai k-atalis untuk- konversi lipid murni menjadi biodiesel dalam kondisi subkritis. Metode ini mampu mencapai konversi 90% dari FAME dalam waktu yang wajar tanpa memerlukan katalis asam/basa. Karena air digunakan sebagai katalis, proses penghilangan air tidak- lagi diperlukan. Oleh karena itu, metode ini mengurangi biaya pengolahan secara signifikan dalam produksi biodiesel dari Iumpur akti

A non-catalytic in situ process to produce biodiesel from a rice milling by-product using a subcritical water-methanol mixture

A non-catalytic method to produce biodiesel in situ from a rice milling by-product, i.e. rice bran, using subcritical water-methanol mixture has been investigated. The method was found to be unaffected by initial moisture and free fatty acids (FFA) contents in rice bran so that no pretreatment was required. The yield and purity of biodiesel were higher under CO2 atmosphere than those under N atmosphere due the ability of the gas to acidify water-methanol mixture. Oil extraction from the bran was identified as the limiting step and complete oil extraction could be achieved in 3 h at 200oC, 4 MPa (under CO2 atmosphere) and 43.8 wt% methanol concentration. Consequently, the highest biodiesel yield was also achieved at those operating conditions. The experimental data suggested that hydrolysis of rice bran oil into FFA followed by methyl-esterification of FFA into biodiesel could be the preferred reaction path to direct transesterification of oil. Subcritical water-methanol mixture was also able to break down complex carbohydrates in rice bran into simple sugars soluble in aqueous phase so that it could be separated easily from biodiesel

Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences

Speaking rate refers to the average number of phonemes within some unit time, while the rhythmic patterns refer to duration distributions for realizations of different phonemes within different phonetic structures. Both are key components of prosody in speech, which is different for different speakers. Models like cycle-consistent adversarial network (Cycle-GAN) and variational auto-encoder (VAE) have been successfully applied to voice conversion tasks without parallel data. However, due to the neural network architectures and feature vectors chosen for these approaches, the length of the predicted utterance has to be fixed to that of the input utterance, which limits the flexibility in mimicking the speaking rates and rhythmic patterns for the target speaker. On the other hand, sequence-to-sequence learning model was used to remove the above length constraint, but parallel training data are needed. In this paper, we propose an approach utilizing sequence-to-sequence model trained with unsupervised Cycle-GAN to perform the transformation between the phoneme posteriorgram sequences for different speakers. In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data. Preliminary evaluation on two datasets showed very encouraging results.Comment: 8 pages, 6 figures, Submitted to SLT 201

The Non-linear Relationship between Muscle Voluntary Activation Level and Voluntary Force Measured by the Interpolated Twitch Technique

Interpolated twitch technique (ITT) is a non-invasive method for assessing the completeness of muscle activation in clinical settings. Voluntary activation level (VA), measured by ITT and estimated by a conventional linear model, was reported to have a non-linear relationship with true voluntary contraction force at higher activation levels. The relationship needs to be further clarified for the correct use by clinicians and researchers. This study was to established a modified voluntary activation (modified VA) and define a valid range by fitting a non-linear logistic growth model. Eight healthy male adults participated in this study. Each subject performed three sets of voluntary isometric ankle plantar flexions at 20, 40, 60, 80 and 100% maximal voluntary contraction (MVC) with real-time feedback on a computer screen. A supramaximal electrical stimulation was applied on tibia nerve at rest and during contractions. The estimated VA was calculated for each contraction. The relationship between the estimated VA and the actual voluntary contraction force was fitted by a logistic growth model. The result showed that according to the upper and lower limit points of the logistic curve, the valid range was between the 95.16% and 10.55% MVC. The modified VA estimated by this logistic growth model demonstrated less error than the conventional model. This study provided a transfer function for the voluntary activation level and defined the valid range which would provide useful information in clinical applications

Green Separation of Bioactive Natural Products Using Liquefied Mixture of Solids

Bioactive natural products are secondary metabolites of plants and animals generated through various biological pathways. They are the main sources of new drugs, functional food and food additives. Since their contents in plant and animal tissues are extremely small compared to those of primary metabolites, the separations of bioactive principles from complex matrixes are often the inherent bottleneck in the utilization of bioactive natural products. A novel separation technique based on a liquefied mixture of solids at its eutectic compositions is presented in this chapter. The mixture can be prepared from natural primary metabolites and therefore can be considered as a green solvent. The separation of bioactive compounds (γ-oryzanol) from rice bran oil-based biodiesel using green methods with minimum energy requirement is discussed. Other applications for separations of alkaloid and phenolic compounds from their plant matrices are also presented. Different raw materials require different separation techniques due to the presence of different impurities, and the current trend is to use green methods with minimum energy requirement. This overview of recent technological advances, discussion of pertinent problems and prospect of current methodologies in the separation of bioactive natural products may provide a driving force for the development of novel separation techniques

On Optimizing Signaling Efficiency of Retransmissions for Voice LTE

The emergence of voice over LTE enables voice traffic transmissions over 4G packet-switched networks. Since voice traffic is characterized by its small payload and frequent transmissions, the corresponding control channel overhead would be high. Semi-persistent scheduling (SPS) is hence proposed in LTE-A to reduce such overhead. However, as wireless channels typically fluctuate, tremendous retransmissions due to poor channel conditions, which are still scheduled dynamically, would lead to a large overhead. To reduce the control message overhead caused by SPS retransmissions, we propose a new SPS retransmission protocol. Different from traditional SPS, which removes the downlink control indicators (DCI) directly, we compress some key fields of all retransmissions' DCIs in the same subframe as a fixed-length hint. Thus, the base station does not need to send this information to different users individually but just announces the hint as a broadcast message. In this way, we reduce the signaling overhead and at the same time, preserve the flexibility of dynamic scheduling. Our simulation results show that, by enabling DCI compression, our design improves signaling efficiency by 2.16\times, and the spectral utilization can be increased by up to 60%

Hey! I Have Something for You: Paging Cycle Based Random Access for LTE-A

The surge of M2M devices imposes new challenges for the current cellular network architecture, especially in radio access networks. One of the key issues is that the M2M traffic, characterized by small data and massive connection requests, makes significant collisions and congestion during network access via the random access (RA) procedure. To resolve this problem, in this paper, we propose a paging cycle-based protocol to facilitate the random access procedure in LTE-A. The high-level idea of our design is to leverage a UE's paging cycle as a hint to preassign RA preambles so that UEs can avoid preamble collisions at the first place. Our rpHint has two modes: (1) collision-free paging, which completely prevents cross-collision between paged user equipment (UEs) and random access UEs, and (2) collision-avoidance paging, which alleviates cross-collision. Moreover, we formulate a mathematical model to derive the optimal paging ratio that maximizes the expected number of successful UEs. This analysis also allows us to adapt dynamically to the better one between the two modes. We show via extensive simulations that our design increases the number of successful UEs in an RA procedure by more than 3× as compared to the legacy RA scheme of the LTE

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%