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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.

Microplastic (MP) pollution has become a global concern and a hot issue in Vietnam, especially along the coastal hydrosphere. The investigation was carried out by collecting the sediment samples from three typical urbanised beaches in Da Nang: My Khe, T20, and Son Thuy. The qualitative identification and quantitative analysis of MP samples were conducted using micro-Fourier-transform-infrared spectroscopy (μFTIR). A tailored analysis based on adaptation procedure of well-known ones was applied including 5 steps: (1) Drying and homogenising sediment sample; (2) Digestion and cleaning sediment sample; (3) MPs separation by the flotation; (4) MPs sample filtration; (5) MPs quantification and identification. The obtained data showed that the abundances of MPs at three beaches of Son Thuy, T20, and My Khe were 1,460±758, 1,799±370, and 29,232±2,577 items/kg dry sediment, respectively. MPs were classified by different sizes, in which the one with sizes being smaller than 150 μm was accounted for the highest proportion of 77.83% at Son Thuy, 87.96% at T20, and 65.91% at My Khe beach. The chemical composition of MPs with various polymers was precisely identified, in which three dominant polymers were determined as PTFE [Polytetrafluoroethylene (Teflon)], EVOH (Ethylene vinyl alcohol), and PA [Polyamide (Nylon)]. Preliminary results of MPs analysis in sediment samples of the three selected beaches in Da Nang can be interpreted as a solid basis for further investigation of MP debris in the shoreline, offshore, and other related samples towards conclusions about the sources of MP pollution in the marine environment in Vietnam’s coastal hydrosphere in future.

A new picrotoxane terpenoid glycoside, austrobuxusin N (1), together with four known compounds, austrobuxusin A-D (2-5), were isolated from the leaves of the Australian endemic plant Austrobuxus swainii (Beuzev. & C.T. White) Airy Shaw. The chemical structure of 1 was elucidated by 1D- and 2D-NMR spectroscopy, along with MS data. The sugar moiety in 1 was determined to be β-D-glucose by acid hydrolysis and subsequent comparison of its specific rotation with that of standard. The relative configuration of the aglycone was assigned by ROESY NMR experiment and density functional theory (DFT) calculation of NMR data coupled with DP4 analysis. Cytotoxicity test revealed that compound 1 exhibited 71% inhibition against Caco-2 cell line at the concentration of 166 µM.

Air quality is getting worse worldwide, especially in cities with high population density and many industrial parks. Raising community awareness and applying science and technology are effective ways to mitigate the negative impacts of industrialization and pollution on the natural environment as well as public health. This work presents the design and deployment of an IoT-based air quality monitoring system, named the Environmental Monitoring System (EnMoS). LoRa (Long-Range) wireless communication technology and innovation sensors being used aim to facilitate the development of data communication network over a large area, improving sensing reliability, extending battery life as well as reducing total system costs. The air quality factors such as particulate matter (PM2.5 and PM10), carbon dioxide (CO2), air temperature and humidity, after being read from the sensors were uploaded to a real-time database server for Air Quality Index (AQI) calculation. In addition, for indicating conveniently obtained AQI values a web page is also developed to provide an interactive map along with corresponding charts. A case study on an actual LoRa network consisting of three sensing nodes and a gateway were conducted for validating the feasibility of the system.

This article investigates the moderating role of board independence in the relationship between debt financing and performance of emerging market firms. We have used an empirical model in which the firm’s accounting profitability is a dependent variable and the independent variables are debt financing, board independence, the interaction variable made of debt financing and board independence as well as various control variables. Our analysis is based on a panel data set of 300 listed firms in Vietnam between 2013 and 2017. Our study finds that debt financing has a significantly negative effect and that board independence reduces the adverse impact of debt financing on accounting profitability. Our results are consistent across different estimation models and methods.

This study focuses on determination of quantity, shape, size and polymer types of microplastics in Pacific oysters (Crassostrea gigas) cultured in Danang Bay, Vietnam. Visual verification and chemical identification of microplastics was carried out by μFT-IR spectra using Nicolet iN10 MX Infrared Imaging Microscope. The average abundance of microplastic in oysters was 1.88 ± 1.58 particles/g (wet weight) and 18.54 ± 10.08 particles/individual. The prevalent shape of microplastics included three main forms as fragment, fiber and bead corresponding to 73.71; 25.84 and 0.45%, respectively. Additionally, the size of microplastics was a range of 22.4-1318.8 μm, and the most common size was less than 100 μm, accounted for 77.30%. Fifteen polymer types were detected while Nylon was the most abundant polymer type with 50.56%. In this study, microplastics accumulation with their abundance and polymeric structures could be considered as reliable evidence for further studies on assessment of their potential risk to human health.

Given the increasing importance of green bond as the main funding source for the Sustainable Development Goals, the green bond is an emerging concept in the region of Southeast Asia. In addition, the concurrent Covid-19 pandemic has caused disruption to the development of green bond around the world. This research explores the current development status of the green bond in Southeast Asian countries. A total of thirty-two semi-structured interviews were held with capital market participants in Southeast Asian countries. The results highlight barriers, opportunities, and regulation difficulties, and expected growth for the development of the green bond market. This research is concluded by indicating several propositions that can be tested in the future to generalize the findings from this work. We thus extend the knowledge of green bond in the financial markets of Southeast Asian countries, which also delivers implications for practitioners and policy-makers regarding the development of green bond in Southeast Asian countries.Compte tenu de l'importance croissante de l’obligation verte en tant que principal attribut financier des objectifs de développement durable (ODD), l'obligation verte est un concept émergent dans la région de l'Asie du Sud-Est. En outre, la pandémie concomitante de Covid-19 a perturbé le développement des obligations vertes dans le monde. Cette étude explore l'état actuel du développement de l'obligation verte dans les pays d'Asie du Sud-Est. Au total, trente-deux entretiens semi-directifs ont été menés avec des acteurs du marché des capitaux dans les pays d'Asie du Sud-Est. Les résultats mettent en évidence les obstacles, les opportunités et les difficultés de réglementation, ainsi que la croissance attendue pour le développement du marché des obligations vertes. Dans sa conclusion, cette étude émet plusieurs propositions qui peuvent être testées à l’avenir pour généraliser les résultats de ce travail. Nous étoffons ainsi la connaissance concernant les obligations vertes sur les marchés financiers des pays d'Asie du Sud-Est, ce qui comporte également des implications pour les praticien·ne·s et les décisionnaires en ce qui concerne le développement des obligations vertes dans les pays d'Asie du Sud-Est.

The anthrax pathogen Bacillus anthracis poses a significant threat to human health. Identification of B. anthracis is challenging because of the bacterium’s close genetic relationship to other Bacillus cereus group species. Thus, molecular detection is founded on species-specific PCR targeting single-copy genes. Here, we validated a previously recognized multi-copy target, a species-specific single nucleotide polymorphism (SNP) present in 2–5 copies in every B. anthracis genome analyzed. For this, a hydrolysis probe-based real-time PCR assay was developed and rigorously tested. The assay was specific as only B. anthracis DNA yielded positive results, was linear over 9 log10 units, and was sensitive with a limit of detection (LoD) of 2.9 copies/reaction. Though not exhibiting a lower LoD than established single-copy PCR targets (dhp61 or PL3), the higher copy number of the B. anthracis–specific 16S rRNA gene alleles afforded ≤2 unit lower threshold (Ct) values. To push the detection limit even further, the assay was adapted for reverse transcription PCR on 16S rRNA transcripts. This RT-PCR assay was also linear over 9 log10 units and was sensitive with an LoD of 6.3 copies/reaction. In a dilution series of experiments, the 16S RT-PCR assay achieved a thousand-fold higher sensitivity than the DNA-targeting assays. For molecular diagnostics, we recommend a real-time RT-PCR assay variant in which both DNA and RNA serve as templates (thus, no requirement for DNase treatment). This can at least provide results equaling the DNA-based implementation if no RNA is present but is superior even at the lowest residual rRNA concentrations.

The anthrax pathogen Bacillus anthracis poses a significant threat to human health. Identification of B. anthracis is challenging because of the bacterium’s close genetic relationship to other Bacillus cereus group species. Thus, molecular detection is founded on species-specific PCR targeting single-copy genes. Here, we validated a previously recognized multi-copy target, a species-specific SNP present in 2-5 copies in every B. anthracis genome analyzed. For this, a hydrolysis probe-based real time PCR assay was developed and rigorously tested. The assay was specific as only B. anthracis DNA yielded positive results, was linear over 9 log10 units and was sensitive with a limit of detection (LoD) of 2.9 copies/reaction. Though not exhibiting a lower LoD than established single copy PCR targets (dhp61 or PL3), the higher copy number of the B. anthracis–specific 16S rRNA gene allele afforded ≤2 unit lower threshold (Ct) values. To push the detection limit even further, the assay was adapted for reverse transcription PCR on 16S rRNA transcripts. This RT-PCR assay was also linear over 9 log10 units and was sensitive with a LoD of 6.3 copies/reaction.In a dilution-series of experiments, the 16S RT-PCR assay achieved a thousand-fold higher sensitivity than the DNA-targeting assays. For molecular diagnostics, we recommend a real time RT-PCR assay variant in which both DNA and RNA serve as templates (thus, no requirement for DNase treatment). This will at least provide results equaling the DNA-based implementation if no RNA is present but will be superior even at the lowest residual rRNA concentrations.

PurposeThis paper aims to investigate the moderating effects of corporate governance mechanisms on the financial leverage–profitability relation in emerging market firms.Design/methodology/approachThe paper examines the impacts by estimating the empirical model in which a firm’s accounting profitability is a dependent variable, while financial leverage, board size, board independence, CEO duality, CEO ownership, state ownership and the interaction variables are predictors. The paper uses the panel data set of 295 listed firms in Vietnam in the period 2011-2015 and two key econometric methods for panel data, namely, the two-stage least square instrumental variable and general moments method.FindingsThe paper finds the evidence for the significant and positive effect of board size, board independence and state ownership on the financial leverage–profitability relation. The effect of CEO duality on the financial leverage–profitability relation tends to be negative, and the impact CEO ownership inclines to be positive, although both of them are statistically insignificant. The results are consistent across different estimation methods.Originality/valueThis paper is the first investigating the moderating effect of various corporate governance mechanisms on the financial leverage–profitability relationship in emerging market firms.

There is an increasing need to implement the Convolutional Neural network (CNN) with an FPGA thanks to its design flexibility over an ASIC and low power consumption over a GPU. The size of the network and the resource of the target FPGA board should be considered to deploy the CNN Network successfully. However, previous works use the fixed dataflow which is not optimized for each layer. As a result, high on-chip buffer utilization and frequent memory access are required. The row-based weight reuse scheme is efficient in reducing input/output buffer size. However, it causes resource underutilization for layers with small feature maps size. This paper proposes an adaptive row reuse scheme by applying each level of row-reuse for each layer depending on its characteristic. Finally, the proposed design is implemented with a Xilinx KCU1500 board, and the accelerator achieves 994.74 GOPS of the throughput for U-Net. For general CNN implementation, the proposed scheme achieves 1080.9 GOPS when running VGG16 with 1.7 times less buffer size compared to previous works.

Convolutional neural networks (CNNs) require both intensive computation and frequent memory access, which lead to a low processing speed and large power dissipation. Although the characteristics of the different layers in a CNN are frequently quite different, previous hardware designs have employed common optimization schemes for them. This paper proposes a layer-specific design that employs different organizations that are optimized for the different layers. The proposed design employs two layer-specific optimizations: layer-specific mixed data flow and layer-specific mixed precision. The mixed data flow aims to minimize the off-chip access while demanding a minimal on-chip memory (BRAM) resource of an FPGA device. The mixed precision quantization is to achieve both a lossless accuracy and an aggressive model compression, thereby further reducing the off-chip access. A Bayesian optimization approach is used to select the best sparsity for each layer, achieving the best trade-off between the accuracy and compression. This mixing scheme allows the entire network model to be stored in BRAMs of the FPGA to aggressively reduce the off-chip access, and thereby achieves a significant performance enhancement. The model size is reduced by 22.66-28.93 times compared to that in a full-precision network with a negligible degradation of accuracy on VOC, COCO, and ImageNet datasets. Furthermore, the combination of mixed dataflow and mixed precision significantly outperforms the previous works in terms of both throughput, off-chip access, and on-chip memory requirement., Accepted for publication in IEEE Transaction on Circuit and System for Video Technology

Cytological profiling (CP) assay against a human olfactory neuroshpere-derived (hONS) cell line using a library of traditional Chinese medicinal plant extracts gave indications that the ethanolic extract of Macleaya cordata (Willd) R. Br. elicited strong perturbations to various cellular components. Further chemical investigation of this extract resulted in the isolation of two new benzo[c]phenanthridine alkaloids, (6R)-10-methoxybocconoline (1) and 6-(1-hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2). Their planar structures were elucidated by extensive 1D and 2D NMR studies, together with MS data. The absolute configuration for position C-6 of 1 and relative configurations for position C-6 and C-1' of 2 were assigned by density functional theory (DFT) calculations of ECD and NMR data, respectively. Also isolated were fourteen known metabolites, including ten alkaloids (3-12) and four coumaroyl-containing compounds (13-16). Cytological profiling of the isolates against Parkinson's Disease (PD) patient-derived olfactory cells revealed bocconoline (3) and 6-(1-hydroxyethyl)-5,6-dihydrochelerythrine (4) significantly perturbated the features of cellular organelles including early endosomes, mitochondria and autophagosomes. Given that hONS cells from PD patients model some functional aspects of the disease, the results suggested that these phenotypic profiles may have a role in the mechanisms underlying PD and signified the efficacy of CP in finding potential chemical tools to study the biological pathways in PD.

To avoid the accuracy drop caused by slowing down the refresh rate of a DRAM, the proposed approximate DRAM flexibly controls the refresh operation for different bits of data. Data are reorganized and mapped to different DRAM devices according to their bit significance. More critical bits are stored in more frequently refreshed devices while non-critical bits are stored in less frequently refreshed devices. Compared to the conventional DRAM, the proposed approximate DRAM requires only a separation of the chip select signal for each device in a DRAM rank and a minor change in the memory controller. Simulation results show that the refresh power consumption is reduced by 66.5 % with a negligible accuracy drop in computation for state-of-the-art deep networks.

There is a trend for demand response (DR) market as a dedicated competitive environment for trading DR. In this market, aggregators participate as DR providers, while system operator, retailers and distributors are the DR buyers. Scheduling DR through the DR markets leads to a fair allocation of the benefits and payments across all participants. However, the integration of the DR markets into the existing power markets leads to technical and economic challenges. Those challenges associated with the integration of the DR markets into the energy/reserve markets are addressed in this study. To clear the DR markets jointly with the energy/reserve market, a bilevel approach is proposed in which the upper level belongs to energy/reserve market problem and the lower level includes DR market clearance. The proposed bilevel programming problem is then recast as a mixed-integer linear programming problem which can be solved using commercially available software. Finally, numerical results are provided to illustrate the performance of the proposed approach, demonstrating it brings about lower reserve price and higher social welfare compared with the existing markets.

This paper proposes a novel approach to energy exchange between electric vehicle (EV) load and wind generation utilities participating in the day-ahead energy, balancing, and regulation markets. An optimal bidding/offering strategy model is developed to mitigate wind energy and EV imbalance threats, and optimize EV charging profiles. A new strategy model is based on optimizing decision making of a wind generating company (WGenCO) in selecting the best option among the use of the balancing or regulation services, the use of the energy storage system (ESS) and the use of all of them to compensate wind power deviation. Energy imbalance is discussed using conventional systems, ESS, and EV-Wind coordination; results are compared and analyzed. Stochastic intra-hour optimization is solved by mixed-integer linear programming (MILP). Uncertainties associated with wind forecasting, energy price, and behavior of EV owners based on their driving patterns, are considered in the proposed stochastic method and validated through several case studies.

Computer architecture simulators play a crucial role in the verification of a new system&rsquo, s design. However, a single simulator may not be sufficient in covering detailed modeling of the entire system, thereby lacking in the simulation of a specific functionality under investigation. In this case, combining two simulators is necessary to compensate for the drawbacks of a single simulator. This paper proposes the integration of DRAMSim2, a simulator that thoroughly models DDR-SDRAM main memory architecture, into the application-level+ simulator McSimA+. The challenges of achieving an efficient integration, especially the integration of a cycle-accurate simulator into an event-driven environment, are addressed. The combined simulator achieves high accuracy due to cycle-accurate simulation while maintaining high speed and flexibility of the event-driven application-level+ simulator. The new simulator&rsquo, s overall system performance and the accuracy of the newly-integrated power model are verified against the gem5 simulator.

In this paper we describe an analytical method for estimating uncertainty in distribution loads, with applications to network monitoring and optimal meter placement. Our model works by assessing the impact of consumption aggregation from customers of different types, in order to determine the mean and variance of loading profile in each distribution transformer in the network. Such parameters are then adjusted by correlating with real-time information taken from the supervisory control and data acquisition system. Studies have been conducted on actual data to verify the proposed model and show its application for the optimal meter placement in distribution networks.