Nonparametric estimation of the fragmentation kernel based on a PDE stationary distribution approximation

We consider a stochastic individual-based model in continuous time to describe a size-structured population for cell divisions. This model is motivated by the detection of cellular aging in biology. We address here the problem of nonparametric estimation of the kernel ruling the divisions based on the eigenvalue problem related to the asymptotic behavior in large population. This inverse problem involves a multiplicative deconvolution operator. Using Fourier technics we derive a nonparametric estimator whose consistency is studied. The main difficulty comes from the non-standard equations connecting the Fourier transforms of the kernel and the parameters of the model. A numerical study is carried out and we pay special attention to the derivation of bandwidths by using resampling

Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net

Anomaly detection in the sound from machines is an important task in machine monitoring. An autoencoder architecture based on the reconstruction error using a log-Mel spectrogram feature is a conventional approach for this domain. However, because of the non-stationary nature of some sounds from the target machine, such a conventional approach does not perform well in those circumstances. In this paper, we propose a novel approach regarding the choice of used features and a new auto-encoder architecture. We created the Mixed Feature, which is a mixture of different sound representations, and a new deep learning method called Fully-Connected U-Net, a form of autoencoder architecture. With experiments on the same dataset as the baseline system, using the same architecture for all types of machines, the experimental results showed that our methods outperformed the baseline system in terms of the AUC and pAUC evaluation metrics. The optimized model achieved 83.38% AUC and 64.51% pAUC on average overall machine types on the developed dataset and outperformed the published baseline by 13.43% AUC and 8.13% pAUC

SYNTHESIS OF STARCH MODIFIED MONTMORILLONITE AS AN EFFECTIVE ADSORBENT FOR Pb (II) REMOVAL FROM WATER

The adsorbent is prepared by the montmorillonite co-modification with starch for the removal of Pb (II) ions from aqueous solution. The Fourier-transformed infrared (FTIR), X-ray diffraction (XRD) spectroscopies were used to determine the structure and characteristics of the adsorbent. The main factors affecting the removal of Pb (II) ions were investigated, including the effect of pH, contact time, adsorbent dosage and the initial concentration of Pb (II). Batch process can be used for adsorption and equilibrium studies. The experimental data were fitted using Freundlich and Langmuir adsorption models. The Langmuir isotherm best fitted the experimental data with R2 0.99 and maximum Pb (II) adsorption capacity of 21.5 mg/g indicated monolayer adsorption. Kinetic studies using pseudo-first-order and pseudo-second-order rate models showed that the process complied well with the pseudo second-order rate model

A multi-microcontroller-based hardware for deploying Tiny machine learning model

The tiny machine learning (TinyML) has been considered to applied on the edge devices where the resource-constrained micro-controller units (MCUs) were used. Finding a good platform to deploy the TinyML effectively is very crucial. The paper aims to propose a multiple micro-controller hardware platform for productively running the TinyML model. The proposed hardware consists of two dual-core MCUs. The first MCU is utilized for acquiring and processing input data, while the second is responsible for executing the trained TinyML network. Two MCUs communicate to each other using the universal asynchronous receiver-transmitter (UART) protocol. The multi-tasking programming technique is mainly applied on the first MCU to optimize the pre-processing new data. A three-phase motors faults classification TinyML model was deployed on the proposed system to evaluate the effectiveness. The experimental results prove that our proposed hardware platform was improved 34.8% the total inference time including pre-processing data of the proposed TinyML model in comparing with single micro-controller hardware platform

Factors affecting the decision to choose a university of high school students: A study in An Giang Province, Vietnam

It is important to provide high school students with the necessary information for them to consult and make a decision to choose a university. The study aims to identify and evaluate the influence of factors in the decision to choose a university for high school students. The questionnaire survey method was used to collect data from 393 students from eight high schools in An Giang Province, Vietnam. Exploratory factor analysis and linear regression were used to analyze the data. The research results show that students are quite satisfied and quite certain with their decision to choose a university, while there are six important factors affecting the decision to choose a university. Influential factors with decreasing order of magnitude are: i) Factors consulted by teachers, family, friends, and relatives; ii) Factors of future job opportunities; iii) Factors of media activities; iv) Factors of learning conditions; v) Factors of university reputation; vi) Factors belong to the students themselves. The findings of the study show that there is no statistically significant difference between the group of males and females, between grades 10, 11, and 12. Besides, there is a statistically significant difference between students in high schools. The findings of this study have theoretical and practical implications for university admissions in Vietnam. Proposals made to university administrators were discussed. From the research results, we want to help students find the right university, and support universities to improve the efficiency of admissions

MULTI-PIXEL PHOTON COUNTER FOR OPERATING A TABLETOP COSMIC RAY DETECTOR UNDER LOOSELY CONTROLLED CONDITIONS

The multi-pixel photon counter (MPPC) has recently emerged as a great type of silicon photomultiplier to replace or compensate for conventional vacuum-based photomultiplier tubes. An MPPC provides many advantageous features, such as high electrical gain, outstanding photon detection efficiency, fast timing response, immunity to magnetic fields, low-voltage operation, compactness, portability, and cost-effectiveness. This article examines the electrical and optical characteristics of an MPPC under loosely controlled environmental conditions. We also report a measurement of the light yield captured by the MPPC when a cosmic ray passes through the plastic scintillator, demonstrating that such a setup is suitable as a simple, cost-effective tabletop cosmic ray detector for educational and research purposes

A Survey on Some Parameters of Beef and Buffalo Meat Quality

A survey was carried out on 13 Vietnamese Yellow cattle, 14 LaiSind cattle and 18 buffalos in Hanoi to estimate the quality of longissimus dorsi in terms of pH, color, drip loss, cooking loss and tenderness at 6 different postmortem intervals. It was found that the pH value of longissimus dorsi was not significantly different among the 3 breeds (P>0.05), being reduced rapidly during the first 36 hours postmortem, and then stayed stable. The value was in the range that was considered to be normal. Conversely, the color values L*, a* and b* tended to increase and also stable at 36 hours postmortem, except that for LaiSind cattle at 48 hours. According to L* scale, the meat of Yellow and LaiSind cattle met the normal quality but the buffalo meat was considered to be dark cutters. The tenderness of longissimus dorsi was significantly different among the breeds (P<0.05). The value was highest at 48 hours and then decreased for LaiSind and buffalo, but for Yellow cattle the value decreased continuously after slaughtering In terms of tenderness buffalo meat and Yellow cattle meat were classified as “intermediate”, while LaiSind meat was out of this interval and classified as “tough”. Drip loss ratio was increased with the time of preservation (P<0.05). The cooking loss ratio was lowest at 12 hours and higher at the next period, but there was no significant difference among the periods after 36 hours postmotem.Peer reviewe

Investigating the diversity of arbuscular mycorrhizal fungi from Gymnema sylvestre and Curcuma longa in Vietnam

Arbuscular mycorrhizal (AM) fungi are soil eukaryotes that belong to phylum Glomeromycota and have symbiosis with the vast majority of higher plants’ roots. AM fungi are believed to be coevolved with terrestrial plants, the abundance and diversity of AM fungal communities as a result are host plant dependent. A survey of AM fungi from the rhizospheres of medicinal plants in Northern Vietnam including gurma Gymnema sylvestre and turmeric Curcuma longa was carried out. From the extracted total DNAs of the medicinal plants’ rhizosphere soil samples, 35 mycorrhizal fungal species were identified by analyzing small subunit rRNA gene sequences. Result revealed that genus Glomus is the most abundant in the AM communities of G. sylvestre and C. longa, followed by Gigaspora and Acaulospora. Besides, AM species belonging to genera Scutellospora, Diversispora and Rhizophagus were observed in almost all rhizosphere soil samples. The spore counting by wet sieving and decanting method uncovered a variation in AM spore density of gurma and turmeric rhizosphere. In general, AM species were found more abundantly and more diverse in collected rhizome soil samples of C. longa (27 species belonging to 10 genera) than of G. sylvestre (17 species found belonging to 7 genera). The observed difference in AM communities of G. sylvestre and C. longa supports evidence for the dependence of AM fungal species on host plants, and indicates that AM fungi may have relation to the host plants’ secondary metabolite production