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The influence of various drying characteristics in the experiment was explored in this study. The drying time and moisture content were used to evaluate the experimental outcomes. The drying of bitter gourd slices using a halogen dryer was done at varied thicknesses (3, 5, and 7 mm) and temperatures (60 °C, 65 °C and 70 °C). The results revealed that the drying time and equilibrium moisture content are considerably affected by the material drying thickness and drying temperature. Furthermore, the Generalized Regression Neural Network (GRNN) model is employed in this study to train and predict the moisture content of bitter gourd as an output parameter. The temperature, bitter gourd thickness, and drying time were considered as input parameters for the GRNN model. Three statistic measures as the R-square, the Root mean square error (RMSE) and the Mean relative percent error (P) were used to validate the accuracy of the trained GRNN model. In training with nine experimental condition datasets, the average score values of R-square, RMSE and P were obtained at 0.995197, 1.498966 and 0.091617, respectively. The test of trained GRNN has been conducted with good agreement between experimental data points and predicted points. The result revealed that GRNN was effective in predicting the moisture content of bitter gourd in a halogen dryer.

In order to investigate hydrodynamic phenomena in two-phase flow conditions in nuclear safety analysis, a series of two-phase flow experiments were conducted using a single flow channel in which air and water were simultaneously injected into the test section. The experiments under atmospheric pressure conditions were carried out with the water velocity and the air velocity covering the ranges from 0.2 to 1.5 m/s and 0.05 to 0.2 m/s, respectively. The technique of two-sensor conductivity probe was used for the measurement of bubble parameters. The experimental results presented and analyzed in this study are the local time-averaged void fraction and bubble velocities at three axial positions L/D = 14.4, 51.2 and 71.3.

Background Infections are among the leading causes of maternal mortality and morbidity. The Global Maternal Sepsis and Neonatal Initiative, launched in 2016 by WHO and partners, sought to reduce the burden of maternal infections and sepsis and was the basis upon which the Global Maternal Sepsis Study (GLOSS) was implemented in 2017. In this Article, we aimed to describe the availability of facility resources and services and to analyse their association with maternal outcomes. Methods GLOSS was a facility-based, prospective, 1-week inception cohort study implemented in 713 health-care facilities in 52 countries and included 2850 hospitalised pregnant or recently pregnant women with suspected or confirmed infections. All women admitted for or in hospital with suspected or confirmed infections during pregnancy, childbirth, post partum, or post abortion at any of the participating facilities between Nov 28 and Dec 4 were eligible for inclusion. In this study, we included all GLOSS participating facilities that collected facility-level data (446 of 713 facilities). We used data obtained from individual forms completed for each enrolled woman and their newborn babies by trained researchers who checked the medical records and from facility forms completed by hospital administrators for each participating facility. We described facilities according to country income level, compliance with providing core clinical interventions and services according to women's needs and reported availability, and severity of infection-related maternal outcomes. We used a logistic multilevel mixed model for assessing the association between facility characteristics and infection-related maternal outcomes. Findings We included 446 facilities from 46 countries that enrolled 2560 women. We found a high availability of most services and resources needed for obstetric care and infection prevention. We found increased odds for severe maternal outcomes among women enrolled during the post-partum or post-abortion period from facilities located in low-income countries (adjusted odds ratio 1middot84 [95% CI 1middot05-3middot22]) and among women enrolled during pregnancy or childbirth from non-urban facilities (adjusted odds ratio 2middot44 [1middot02-5middot85]). Despite compliance being high overall, it was low with regards to measuring respiratory rate (85 [24%] of 355 facilities) and measuring pulse oximetry (184 [57%] of 325 facilities). Interpretation While health-care facilities caring for pregnant and recently pregnant women with suspected or confirmed infections have access to a wide range of resources and interventions, worse maternal outcomes are seen among recently pregnant women located in low-income countries than among those in higher-income countries; this trend is similar for pregnant women. Compliance with cost-effective clinical practices and timely care of women with particular individual characteristics can potentially improve infection-related maternal outcomes. Funding UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, WHO, Merck for Mothers, and US Agency for International Development. Copyright (c) 2021 World Health Organization; licensee Elsevier. This is an Open Access article under the CC BY 3.0 IGO license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In any use of this article, there should be no suggestion that WHO endorses any specific organisation, products, or services. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.

A successful fault detection and diagnosis (FDD) strategy for chillers is highly dependent on three core aspects, an accurate reference model, a sensitive fault detection technique and an effective fault identification method. An accurate model plays an important role in the performance of chiller FDD strategy. In this study, the accuracies of three models, MLR (multiple linear regression), KRG (Kriging) and RBF (radial basis function), are compared under various modeling criteria. The EWMA (exponentially-weighted moving average) residual control chart, which is a more powerful tool in detecting small shifts than t-statistics based method, is adopted as a sensitive fault detection technique. The most sensitive feature parameter method is improved to effectively identify faults. Seven common faults in typical centrifugal chillers are considered. Three strategies (MLR-EWMA, KRG-EWMA and RBF-EWMA), which are the combinations of three reference models with EWMA and the most sensitive feature parameter method, are validated and compared by using two sample datasets with different sizes from ASHRAE RP-1043. Through the comparisons of diagnosis performance, the most accurate chiller reference model is found and suggested to detect and diagnose faults in chiller systems. The comparison results show that the RBF-based strategy has the best diagnosis performances among the three strategies.

Hidden Markov model (HMM)-based synthesized speech is intelligible but not natural especially under limited data condition because of over-smoothing of the speech spectra and F0 envelope. One solution is using voice conversion methods to convert over-smoothed speech parameters to natural ones. Although conventional conversion methods transform speech spectra and F0 envelope to natural ones to improve naturalness, they cause unexpected distortions in acceptable intelligibility of synthesized speech e.g. destroying tonal information. The aim of this study is to develop a method for improving naturalness without violating acceptable intelligibility by employing our novel asymmetric bilinear model (ABM) involving non-negative matrix factorization (NMF) to separate the naturalness and intelligibility of synthesized speech. Subjective evaluations carried out on Vietnamese data confirm that the achieved synthesis quality is higher than other methods under limited data condition. Moreover, proposed method is capable of modifying over-smoothed F0 envelope without destroying tonal information., International Conference on Advances in Information and Communication Technology,ICTA 2016, Proceedings of the International Conference, ICTA 2016. Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 538)

HMM-based synthesized voices are intelligible but not natural especially in limited data condition because of over smoothing speech spectra in time-frequency domain. Improving naturalness is a critical problem of HMM-based speech synthesis. One solution for the problem is using voice conversion techniques to convert over-smoothed spectra to natural spectra. Although conventional conversion techniques transform speech spectra to natural ones to improve naturalness, they cause unexpected distortions on acceptable intelligibility of synthesized speech. The aim of the paper is to improve naturalness without violating intelligibility of synthesized speech employing an asymmetric bilinear model (ABM) to separate intelligibility and naturalness. In the paper, an ABM was implemented on modulation spectrum domain of Mel-cepstral coefficient (MCC) sequence to enhance fine structure of spectral parameter trajectory generated from HMMs. Subjective evaluations carried out on English data confirm that the achieved naturalness of proposed method is competitive with other methods in large data condition and outperform other methods in limited data condition. Moreover, modified rhyme test (MRT) shows that acceptable intelligibility of synthesized speech is well-preserved with proposed method.

The fault detection and diagnosis (FDD) of centrifugal chillers is always a complex difficulty in HVAC systems. This paper develops an online fault detection and diagnosis strategy based on non-linear radial basis function (RBF) to online detect and diagnose the fault of centrifugal chillers. The RBF is adopted to develop the reference feature parameter (FP) models. Exponentially-weighted moving average (EWMA) residual control charts of FP is used to detect the faults. A rule-based diagnostor is developed to online identify the fault. Seven common faults are taken in account for typical centrifugal chillers. The FDD strategy proposed was validated by using the experimental data from the ASHRAE RP-1043 project and the operating data of a centrifugal chiller in an office building of Hong Kong. The test results show that the RBF-EWMA method has achieved significant improvements in accuracy and reliability by comparing with the previous method with SVR-EWMA. The proposed RBF-EWMA method is robust for fault detection and diagnosis in centrifugal chiller systems.

In this paper, we present data mining techniques (context-aware technique) into the search engine. As an option for search engine, this paper presents the integration of identifying and synthesizing Vietnamese languge into search engine to query and returns the result through voice interaction between users and Search Engine.

Wood is a complex, heterogeneous and anisotropic material. Its mechanical properties are highly variable according to the species and scale considered. In order to better understand the behaviour of wood in relation to its structure, this PhD work proposes an experimental study at the tissue scale. This work is performed on several types of wood: poplar, including tension wood and spruce. Considering the small dimensions of the tested samples, a "non-contact" method is used to limit perturbations, therefore ensuring the measurement precision.The first series of measurements were realised in the three zones (tension, normal, opposite) and for the three material directions (longitudinal, radial and tangential) of an inclined poplar tree. The results obtained allowed us to compare the mechanical properties of poplar wood in all zones in relation to their microscopic features.The second series of tests were performed in the chamber of an ESEM (Environmental Scanning Electron Microscope) on hardwood (poplar) and softwood (spruce). With the images obtained during the test, from the elastic zone to the densification, the response of each type of wood to the same solicitation is presented and commented.Finally, some numerical modelling by the MPM (Material Point Method) is proposed as prospects of the present work. It will allow the mechanical behaviour of wood in large deformation to be predicted., Le bois est un matériau complexe, hétérogène et anisotrope. Ses propriétés mécaniques varient fortement en fonction de l'essence et de l'échelle considérées. Pour mieux comprendre le comportement du bois en fonction de sa structure, nous proposons dans le cadre de cette thèse une étude expérimentale à l'échelle des tissus qui est réalisée sur plusieurs types de bois : bois de peuplier, y compris bois de tension et bois d'épicéa. Compte tenu des faibles dimensions des échantillons testés, les mesures de déformation sont effectuées " sans contact " afin de s'assurer de ne pas perturber l'échantillon en cours de l'essai.Les premières séries de mesures sont réalisées sur les trois zones (tendue, normale, opposée) et selon les 3 directions matérielles (longitudinale, radiale et tangentielle) d'une tige de peuplier inclinée. Les résultats obtenus permettent de comparer des comportements mécaniques du bois de peuplier dans toutes les zones considérées en fonction de ses propriétés microscopiques.Les deuxièmes séries de mesures sont réalisées en compression, grandes déformations, dans la chambre d'un ESEM (Microscope électronique à balayage environnemental) sur du bois feuillu (peuplier) et sur du bois résineux (épicéa). Grâce aux images obtenues en période d'essai, du domaine élastique jusqu'à la densification, la réponse de chaque type du bois à la même sollicitation est illustrée.Enfin, une partie de la modélisation numérique par la MPM (Materiel Point Method) est proposée dans notre projet envisagé comme prolongement de ce travail. Il permettra de simuler le comportement mécanique du bois en grande déformation.