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The effect of Perilla frutescens L. leaves extract (PFLE) on melanogenesis regulation in B16F10 cells was investigated. The extract had a significant inhibitory effect on melanogenesis in a dose-dependent manner without cytotoxicity at concentrations between 2.5 and 10 μg/mL. The PFLE was formulated as an underarm serum (PL serum) and evaluated for skin irritation and its skin whitening efficacy on 30 Thai women twice daily for 4 weeks. Changes in skin melanin and erythema index, L* a* values were studied. A gradual decrease in melanin index from 37.94±0.66 to 35.90±0.64 (5.38%), and a significant decrease in erythema index from 11.32±0.79 to 10.21±0.11 (9.80%) could be observed during a 4-week period. Therefore, the efficacy of PL serum in improving skin was demonstrated by a significant decrease in melanin and erythema index compared with initial values and skin tone was improved without skin irritation. Extract from Perilla frutescens L. leaves can be used as a novel whitening cosmeceutical ingredient.

This paper discusses a visual recognition system, for identifying the Pa-phiopedilum orchid, often called the Venus slipper. The dataset consists of 100 sample images for each of 15 species of orchid, for a total of 1500 images. All the images of this dataset were taken at the Paphiopedilum orchid gardens and manually classified by experts. This work also implemented a recognition system based on a deep learning approach using a combination of convolutional neural network (CNN) and the Inception-v3 feature extractor of the TensorFlow platform. The implemented recognition system can deliver recognition rates of up to 98.6%, demonstrating excellent recognition performance by the CNN model. Finally, we demonstrate a prototype orchid recognition system, implemented as an Android mobile application.

This paper aims to investigate the identification of sectors of stock exchange that were positively or negatively driven by fundamental monetary tools. A Bayesian approach for dynamic model averaging (DMA) algorithm is analyzed and implemented to deliver the results for identifying what actually drives specific sector of stock exchange, where the posterior inclusion probability is crucial quantity for this investigation. Stock Exchange Thailand (SET) indexes was used as a case study for this work. The predictors used in this paper are: borrowing rate: BR, policy rate: PR, treasury bill rate: TBR, government bond yield: GBYLT, minimum overdraft rate: MOR, minimum loan rate: MLR, minimum retail rate: MRR, discount rate: DISR, savings rate: SAVER, deposit rate: DEPR and lending rate: LENDR. These factors are considered as the most important monetary policy tools from Bank of Thailand. The empirical results demonstrated that each SET index sector responses to those economic variables differently. Some of them are not actually related for specific point of time; some other times, however, they affect SET indexes. Filtered time-varying parameters allow us to indicate the relationship between stock price return and interest rates. According to statistical evidence, we found that the relationship is as of the time-varying characteristic and is unable to absolute identify whether it is positively or negatively related to stock price return for each sector. The impact of a sector on SET indexes depends on a period of time and can also be considered by using seasoning parameters.

The goal of this work is to accurately estimate the modal frequencies and dispersion curves from a measured ocean acoustics signal. A particle filtering approach, a class of sequential Monte Carlo methods, is developed for modal frequency identification and dispersion curves estimation from a time-frequency representation of ocean acoustics signal. The adaptive resampling algorithm for enhancing the quality of a set of particles after likelihood calculation is implemented to improve the accuracy of the modal estimates as well as the dispersion curves of the signal. Results demonstrate the advantages in implementing the adaptive resampling into the conventional sequential importance sampling particle filter (SIS-PF) instead of using the sequential importance resampling (SIR) scheme. The noise robustness of the proposed method is demonstrated through examples where the realizations of different Signal-to-Noise Ratio (SNR) levels were used to test the performance of the adaptive resampling method. The results display the evidences that the adaptive resampling particle filter (AR-PF) is superior to the SIR-PF. Via root mean square error (RMSE), the AR-PF delivers smaller errors than those obtained by the SIR-PF for all SNR levels, emphasizing the benefit in incorporating the adaptive resampling into the PF for modal frequency identification and dispersion curves estimation of ocean acoustics signal.

Simulated impact damage testing was investigated by fractal image analysis using response surface methodology (RSM) with a central composite design (CCF) on quality of ‘Glom Sali’ guava for drop heights (0.2, 0.4, and 0.6 m), number of drops (1, 3, and 5) and storage temperature conditions (10, 20, and 30 °C). After 48 h, impacted fruit were determined and analyzed for bruise area (BA), bruise volume (BV), browning index (BI), total color difference (∆E), image analysis for bruise area (BAI), and fractal dimension (FD) at the bruising region on peeled guava. Results showed that the correlation coefficient (r = −0.6055) between ∆E and FD value was higher than ∆E and either BA (r = 0.3132) or BV (r = 0.2095). The FD variable was determined as a better indicator than conventional measurement (BA or BV) for pulp browning and impact bruising susceptibility. The FD variable also exhibited highest R2adj value (81.69%) among the other five variables, as the highest precision model with high determination coefficient value (R2adj) (&gt, 0.8) for impact bruising prediction. Recommended condition of the FD variable to minimize impact bruising was drop height of 0.53 m for five drops under storage at 30 °C. FD variable assessed by image analysis was shown to be a highly capable measurement to determine impact bruising susceptibility in guava fruit.

The efficiency of different thermal insulation covers in minimizing temperature fluctuations in cool chain management was investigated to reduce postharvest loss and maintain okra quality during storage and transportation. The four thermal insulation covering materials: (1) heat reflective sheet with thin nonwoven (HRS + TNNW), (2) heat reflective sheet with thick nonwoven (HRS + TKNW), (3) metalized Tyvek® (MTyvek) and (4) metalized foam sheet (MFS) were studied and compared with perforated linear low-density polyethylene (P-LLDPE) as the typical handing package for okra distribution alongside no covering as the control. The material properties, transpiration rate, vital heat, temperature profiles (air and pulp temperatures), relative humidity, mass loss and incidence of decay were determined throughout a simulated supply chain. Results exhibited that HRS + TNNW and HRS + TKNW covers had the lowest thermal heat energy (Qx) and moderate R-value. These two covers maintained low temperature fluctuation with the lowest rate of air and pulp temperature changes, reflecting in lowest mass loss and decay in okra. The HRS + TNNW cover yielded less decay (1%) in okra, compared to commercial covers, MTyvek (16%) and MFS (9%). Results showed that HRS + TNNW exhibited great potential as a thermal insulation cover to reduce postharvest loss in okra (5%) compared to typical handling (11–18%) and could be considered as alternative material to reduce the use of foam sheets in cool chain management distribution packaging of okra under ambient environment conditions.

Nowadays, agricultural and food technology require the integration of advanced computer technology and sophisticated computational approach for enhancing the characterization and quality of produces and their products. Huge amount of data was gathered and it needs to be processed and analyzed with confidence that the useful information is being extracted accurately. Therefore, sophisticated computing methods are the most important parts of the overall system. Particle filtering has been recognized as an efficient tool to deliver the accurate state model prediction especially in highly nonlinear and non-Gaussian dynamical systems. In this work, a particle filter (PF) was designed for parameter estimation of respiratory of spinach storage under modified atmosphere. The Michaelis-Menten model was examined in this work for spinach respiratory mechanism under different atmospheric storage conditions to illustrate the performance of the method. The estimating results from the PF were compared to the conventional estimation techniques widely used in literature. From the experimental and computational results, we found that the particle filtering method delivers higher accuracy, outperforming the existing conventional regression method and the extended Kalman filter.

Advanced time series models have been intensively developed and used to predict in financial data such as foreign exchange data (forex). In this paper, we implement the random compression method to reduce a large dimensional forex data into much smaller matrix form. Then, Bayesian inferences on vector autoregression are used to obtain all interesting parameters. Subsequently, the models are able to perform out-of-sample prediction up to 14 days ahead of forecast. For empirical works, 30 forex pairs are used in this paper. The results show that Bayesian compressed vector autoregression (BCVAR) and time-varying BCVAR (TVP-BCVAR) deliver excellent forecasting on AUD-JPY, CAD-CHF, CAD-JPY, EUR-DKK, EUR-MXN, and EUR-TRY forex datasets according to mean square forecasting error, outperforming the traditional benchmark Bayesian Autoregression.

Problem-Based Learning (PBL) is a student oriented learning method that using real-life practical problems to start the learning process in order to encourage problem solving skills. In User Interface Design course, the teaching and learning are not only focus on the theoretical knowledge but also focus on motivating students in soft skills. Therefore, this research purposes a Class User Interface Design of Problem-Based Learning Modeling (UIDPBL) in order to promote the 21st century of 4Cs skills - communication, collaboration, creativity, and critical thinking skills. The proposed model applies the PBL concept to the class User Interface Design. The study is evaluated by both summative assessment and formative assessment. The student learning out come as the final grading and student feedback from a questionnaire at the end of the UIDPBL cycle are taken into account. The results presented that our proposed model, UIDPBL, is able to encourage students to gain the 21st century of 4Cs skills.

Arrhythmia, a serious heart disease, is one of the main causes of sudden cardiac death around the globe. Cardiac arrhythmias can occur to anyone and at any age. The electrocardiogram (ECG) arrhythmia is a series of the abnormal electrical pulses recorded from heart by using electrocardiograph. In this study, we present a method for arrhythmia detection from the ECG signal based on the average energy and zero-crossing quantities that are extracted from the ECG records. The proposed system is composed of ECG signal preprocessing, feature extraction, and arrhythmia detection. The ECG signals used in this study were obtained from MIT-BIH arrythmia, MIT-BIH normal sinus rhythm and QT databases. To distinguish between the normal and abnormal (arrhythmia) ECG signal, we implement a support vector machine (SVM) as a classifier. K-fold cross validation technique is employed to evaluate and validate the performance of the proposed system. We measure the performance of the system via accuracy, sensitivity, specificity, and precision. The best detection results based on the average energy feature with 10-fold cross validation presents are 96.67 % average accuracy, 93.33% sensitivity, 100% specificity and 100% precision.

In previous studies, Perilla frutescens pomace was shown to contain bioactive phenolic compounds and good anti-oxidative activity. However, reports about collagenase activity and melanogenesis inhibitory effects of P. frutescens pomace are limited. This study aimed to evaluate the bioactivity of P. frutescens pomace extract and incorporate the extract into a cosmetic formulation for evaluating its effects on collagenase and melanogenesis inhibition on human skin. The P. frutescens seeds after an oil pressing process were extracted with ethanol (70% v/v) in order to examine the remaining phytochemical compounds, the bioactivity in pomace perilla, and its efficacy as a skincare product. In this study, total phenolic and total flavonoid contents of P. frutescens seed extract (PFSE) were determined using spectrophotometry. The free radical scavenging activity was determined with 2, 2&prime, diphenyl-1-picrylhydrazyl (DPPH) radicals and 2,2&prime, azino-bis (3-ethylbenzthiazoline-6-sulphonic acid) ABTS radicals. Additionally, the effects on collagenase activity, melanin content, and alpha-Melanocyte stimulating hormone (&alpha, MSH) on the viability of cultured B16F10 melanoma cells were investigated. Skin irritation and efficacy of PFSE cream for skin elasticity and skin color were also clinically evaluated. The total phenolic content with gallic acid equivalents (GAE) value and total flavonoids content with catechin equivalents (CE) value were, respectively, 92.79 &plusmn, 1.19 and 56.02 &plusmn, 2.83 mg/g. Furthermore, PFSE significantly inhibited the collagenase activity (p &lt, 0.001) at the concentration of 400 &micro, g/mL (82 &plusmn, 3.23%). These results clearly demonstrated the anti-melanogenic effects on B16F10 cells without causing any cytotoxicity or death. Although there was a slight improvement in skin elasticity in the 4th week compared to the previous week, the 4th week melanin content of the skin significantly decreased from the beginning (p &lt, 0.05) without any irritations. In conclusion, PFSE could be cosmetically considered as a key ingredient that effectively lessens the effects of skin aging and skin hyperpigmentation disorders.

It has been previously shown using synthetic data that dispersion tracking with particle filtering can be used for sediment sound speed inversion. Here, dispersion tracking is performed with data collected in the Gulf of Mexico for sediment sound speed and thickness and water column depth estimation. In this experiment, sound that propagates a long distance from the source allows the identification of dispersion curves reflecting the different group velocities of modal frequencies within and across modes. Although the data are noisy, dispersion curves are tracked with sequential filtering and used for inversion. Probability density functions of the three unknown parameters are obtained. Water column depth is estimated with little uncertainty. The estimated sound speed is representative of sandy sediment and the sediment thickness matches to a large extent prior knowledge.

Vibration damage on guava peel is an important criterion for fruit grading classification and consumer perception. Response surface methodology (RSM) using central composite design (CCD) with three independent variables as frequency (7, 13.5 and 20 Hz), vibration duration (15, 30 and 45 min), and acceleration (2.942, 5.884 and 8.826 m s−2) were employed for vibration testing of ‘Glom Sali’ guava. Twenty simulated vibration treatments were conducted at 20 °C for 48 h to assess and analyze browning index (BI), total color difference (TCD), digital image analysis of bruise area (BA) and normalized fractal dimension difference (ΔFD/FD0) for vibration bruising damage on guava peel. Results showed that acceleration was an important factor in vibration bruising, particularly combined at 8.826 m s−2. For a fixed target value of ΔFD/FD0 at 1.088 and BA at 10 % of the total area, the optimized treatment condition was frequency level 13.5 Hz, vibration duration 30 min and acceleration level 6.570 m s−2. The correlation coefficient (r) between TCD and either BA (0.7724) or ΔFD/FD0 (0.7484) showed a higher relationship than BI (0.4605), while ΔFD/FD0 value had greater reliability and repeatability than BA to predict vibration bruising in guava. Results demonstrated that fractal dimension difference assessed by image analysis was a potential methodology to determine the severity of vibration bruising in guava fruit.

This paper presents a stochastic description and evaluation of the ocean acoustics signal for the interpretation of the signal in the frequency domain in order to estimate the modal frequency and dispersion curves of the ocean acoustics signal. Thus, this investigation considered the analysis and simulation of an ocean acoustics time-series in the frequency-domain where the signal in the time-domain was corrupted by white Gaussian noise. With a stochastic property of the measured acoustics signal, we derived the accurate posterior probability distribution of the ocean acoustics modal frequencies and then incorporated a mathematical model of the signal propagating in a dispersive media to obtain a likelihood function for a sequential Bayesian filtering framework that is used for modal frequency and dispersion estimation. Sequential Bayesian filtering, a particle filter in particular, is implemented for frequency and dispersion estimation to test the accuracy of the derived model. Demonstrated via simulation results, the proposed method performs excellent estimation as compared to the conventional MAP estimator especially at high noise level. Finally, evaluated by the Monte Carlo root mean squared errors (MCRMSE), the proposed stochastic model offers excellent tracking results with smaller errors than those provided by the existing methods even in low signal-to-noise ratios (SNRs), illustrating the noise robustness and superiority of the proposed model over the other approaches.

This paper presents a new algorithm for adaptive resampling, called percentile-based resampling (PBR) in a sequential Bayesian filtering, i.e., particle filter (PF) in particular, to improve tracking quality of the frequency trajectories under noisy environments. Since the conventional resampling scheme used in the PF suffers from computational burden, resulting in less efficiency in terms of computation time and complexity as well as the real time applications of the PF. The strategy to remedy this issue is proposed in this work. After state updating, important high particle weights are used to formulate the pre-set percentile in each sequential iteration to create a new set of high quality particles for the next filtering stage. The number of particles after PBR remains the same as the original. To verify the effectiveness of the proposed method, we first evaluated the performance of the method via numerical examples to a complex and highly nonlinear benchmark system. Then, the proposed method was implemented for frequency estimation for two time-varying signals. From the experimental results, via three measurement metrics, our approach delivered better performance than the others. Frequency estimates obtained by our method were excellent as compared to the conventional resampling method when number of particles were identical. In addition, the computation time of the proposed work was faster than those recent adaptive resampling schemes in literature, emphasizing the superior performance to the existing ones., Frequency tracking; Frequency estimation; Bayesian filtering; Adaptive resampling; Particle filter; Markov chain Monte Carlo; Signal processing; Electrical engineering; Computer engineering

End-to-end system reliability and availability of different IMS-based network communication scenarios have been simulated and analyzed by using the combination of effective stochastic processes and models. Besides, both system reliability and availability characteristics of Intra and Inter IP multimedia subsystem (IMS) domain communication networks have been simulated and compared with different redundancy conditions. Moreover, parallel redundancies of the core IMS units were optimized at different availability or reliability standard conditions. The results demonstrate interesting system reliability characteristics of different communication scenarios and the results reveal possibility to create high reliability and availability system for the Next Generation Networks.