•Validation of eight Satellite-derived Precipitation Estimate (SPE) over Vietnam.•SPE datasets included uncorrected and gauge-corrected products.•GPM IMERGF-V6 achieved the best overall performance ...among SPE datasets.•There is a confidence for using SPE in determining monthly streamflow in large river basins.
This study evaluates eight Satellite-derived Precipitation Estimate (SPE) datasets, which include uncorrected SPE and gauge-corrected SPE products from Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA), Global Precipitation Measurement (GPM), Climate Hazards group Infrared Precipitation (CHIRP), and Precipitation Estimation form Remotely Sensed Information using Artificial Neural Networks (PERSIANN). These datasets are utilized with six representative river basins, corresponding to six sub-climate zones in Vietnam, during the period 2002–2017. The evaluations were carried out in two parts: 1) inter-comparison of the SPE products with rain gauges, for the six basins; 2) comparison of streamflow simulations, using the Soil and Water Assessment Tool (SWAT) forced by precipitation from rain gauge and SPE products. The results indicated that the gauge-corrected SPE datasets exhibited slightly better over the uncorrected datasets in comparison with rain gauges, but showed much higher performances as inputs in hydrological simulations. The GPM Integrated Multi-satellitE Retrievals for GPM (IMERG) Final run version 06B (GPM IMERGF-V6) exhibited the best overall performances among SPE products, in comparison with the rain gauges for the simulation of streamflow. This study is the first of its kind to validate GPM IMERG products in Vietnam, indicating the strong capability of the new IMERG retrieval algorithms. The CHIRP with stations (CHIRPS) dataset demonstrates a relatively low bias, could benefit long-term water resources planning for droughts. In monthly streamflow simulations, the SPE-driven simulations outperformed rain gauge-driven simulations in a larger basin (North West Region), which has low rain-gauge density. The results of this study could be a guide to determine the suitability of different SPE products for hydrological simulations.
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•The networked SnO2 nanowire sensors can detect NO2 gas under UV-radiation at room temperature.•Pulsed UV-radiation significantly enhanced NO2 gas response of networked SnO2 nanowire ...sensors.•Enhanced response under UV-radiation attributed to photo-adsorption and -desorption gas molecules.
A unique combination of high response and fast response-recovery is still a challenge in the development of room-temperature gas sensors. Herein, we demonstrated the on-chip growth of nanojunction-networked SnO2 NW sensors to work under UV-radiation at room temperature. The morphological, compositional, and structural properties of synthesized SnO2 nanowires were examined using field emission electron microscopy, energy dispersive spectroscopy, X-ray diffraction, and high-resolution transmission electron microscopy, respectively. The results presented the SnO2 NWs with smooth surfaces were entangled between the Pt electrode. Besides, the internal properties showed the SnO2 NWs were crystallized as the tetragonal rutile structure of SnO2. The use of UV-radiation with the optimum intensity of 50 μW/cm2 increased the gas response to 5 ppm NO2 up to 7-fold, while response and recovery times decreased about 8- and 4-fold, respectively. Moreover, alternative use of pulsed UV-radiation (provided only during the air recovery phase) can enhance significant gas response as compared with continuous UV-radiation. The enhancement of gas response could be attributed to the photo-adsorption and -desorption of NO2 molecule due to the photogeneration of electron-hole pairs. The combination of NW-NW nanojunctions and pulsed UV-radiation is expected to be a novel strategy for high-performance room temperature gas sensors.
Three new sesquiterpenoids, 13-hydroxyl-atractylenolide II (1), 4-ketone-atractylenolide III (2), and eudesm-4(15)-ene-7β,11-diol (3), along with eleven known compounds (4–14), were isolated from the ...rhizomes of Atractylodes macrocephala. The structures and relative configurations of 1–3 were determined by analysis of the spectroscopic data, and the absolute configurations of 1 and 2 were assigned by circular dichroism technique. The anti-inflammatory activities of these isolates were evaluated against lipopolysaccharide-induced nitric oxide production in macrophage RAW264.7 cells; compounds 4, 7, and 8 exhibited moderate efficacy with IC50 values of 48.6±0.5, 46.4±3.2, and 32.3±2.9 µM, respectively.
•Porous network Fe2O3 effectively prepared from Fe3O4/rGO.•The porous network Fe2O3 exhibited good performance to ethanol gas.•The developed strategy can employed for preparation of other porous ...network metal oxide.
Nanoporous network metal oxides are potential candidates for various applications such as filtration, biomaterials devices, and sensing materials. The present work focused on the simple and scalable fabrication of the α-Fe2O3 nanoporous network for ethanol gas sensor using Fe3O4/reduced graphene oxide (rGO) as a precursor. The analyzed morphology and crystal structure indicated that the α-Fe2O3 nanoporous network was formed due to some factors during thermal procedures such as the phase transformation from magnetite to hematite, nanoparticle agglomeration, and combustion of rGO. The ethanol gas-sensing properties of the α-Fe2O3 nanoporous network were investigated. The response to 100ppm ethanol gas was as high as 9.5, while the cross-gas responses to 100ppm NH3, H2, and CO gases were all lower than 2.0. These values indicated a good selectivity of the sensors. Furthermore, the 90% response times to ethanol gas were less than 5s at 400°–450°C. The proposed strategy has potential in the preparation of other porous network metal oxides to achieve high-performance gas sensors.
In this study, we use a time-varying parameter vector autoregression (TVP-VAR) in conjunction with the extended joint connectedness approach to examine the influences of the economic globalization ...measured by foreign direct investment (FDI) as well as trade openness (TO), on renewable and non-renewable energy consumption, by characterizing the connectedness of these variables, from 1987 to 2020 in Vietnam. Our results demonstrate that abolishing the state monopoly in foreign trade influences the system-wide dynamic connectedness of trade openness, which peaked in 1989. Net total directional connectedness of FDI and energy consumption suggests that both the consumption of renewable and non-renewable energy consistently act as net contagion shock receivers, and FDI is a critical net transmitter the whole time. Trade openness behaves consistently as a critical net shock transmitter in 1989 but turned into an essential net receiver from 1990 to 2020. In a system with trade openness, the consumption of non-renewable energy consistently acts as a net contagion shock receiver, and renewable energy consumption is a critical net transmitter in the whole sample. Pairwise connectedness reveals that FDI consistently appears as a shock transmitter to renewable and non-renewable energy consumption. Trade openness could be either a transmitter or a receiver of shock from non-renewable energy, depending on the period, and is a net receiver of shocks from renewable energy consumption during our sample. The findings of this paper are critical for Vietnam’s government to make a greater contribution to the expansion of global commerce and a sustainable environment.
•This study tackles the challenge of unbalanced stream gauge distributions by leveraging publicly available datasets and deep learning to enhance hydrological modeling in under-gauged ...regions.•Thisstudy introduces a novel framework combining LSTM-based deep learning with GLDAS and GSIM data for effective runoff prediction across continents.•This study assesses the sensitivity of LSTM models using data from diverse regions, emphasizing the importance of hydrological similarities for accurate runoff predictions in ungauged basins.•LSTM models demonstrate superior runoff prediction skills over traditional GLDAS datasets, influenced by the hydrological similarities between training and test regions.
This study introduces a framework that strategically applies a Long Short-Term Memory (LSTM)-based approach for monthly runoff prediction in South Africa and Central Asia. The framework is distinct in its utilization of newly developed Global Land Data Assimilation System (GLDAS)-derived climate dynamics variables and Global Streamflow Indices and Metadata Archives (GSIM)-derived static descriptors, ensuring a consistent evaluation of Deep Learning (DL) performance across multiple continents, including North and South America, and Western Europe. Seven LSTM models were trained using seven different datasets, each representing a combination of these data-rich regions. We assessed the sensitivity of these seven training data sets to LSTM models by testing these trained models in predicting monthly basin-scale runoff across 214 test catchments located in South Africa and Central Asia. Our results show that runoff predictions generated by LSTM within the test domain could exhibit better prediction skills compared to those derived from GLDAS datasets. The performance of the trained LSTMs appears to be linked to hydrological similarities between the data-rich regions and the test basins. Also, our results indicate the importance of selecting the appropriate input sources for the LSTM models to achieve accurate runoff predictions at the test region. We emphasize the possibility of utilizing LSTM models that are leveraging on either North American catchments or a combination of South American and Western European catchments to predict basin-scale runoff in the test regions. To this end, this study harnesses the burgeoning availability of publicly stream-gauge datasets and DL to enhance water information prediction in ungauged regions, responding to the challenge of geographically unbalanced stream gauge instruments.
Fabrication of a high-performance room-temperature (RT) gas sensor is important for the future integration of sensors into smart, portable and Internet-of-Things (IoT)-based devices. Herein, we ...developed a NO2 gas sensor based on ultrathin MoS2 nanoflowers with high sensitivity at RT. The MoS2 flower-like nanostructures were synthesised via a simple hydrothermal method with different growth times of 24, 36, 48, and 60 h. The synthesised MoS2 nanoflowers were subsequently characterised by scanning electron microscopy, X-ray diffraction, Raman spectroscopy, energy-dispersive X-ray spectroscopy and transmission electron microscopy. The petal-like nanosheets in pure MoS2 agglomerated to form a flower-like structure with Raman vibrational modes at 378 and 403 cm−1 and crystallisation in the hexagonal phase. The specific surface areas of the MoS2 grown at different times were measured by using the Brunauer–Emmett–Teller method. The largest specific surface area of 56.57 m2 g−1 was obtained for the MoS2 nanoflowers grown for 48 h. This sample also possessed the smallest activation energy of 0.08 eV. The gas-sensing characteristics of sensors based on the synthesised MoS2 nanostructures were investigated using oxidising and reducing gases, such as NO2, SO2, H2, CH4, CO and NH3, at different concentrations and at working temperatures ranging from RT to 150 °C. The sensor based on the MoS2 nanoflowers grown for 48 h showed a high gas response of 67.4% and high selectivity to 10 ppm NO2 at RT. This finding can be ascribed to the synergistic effects of largest specific surface area, smallest crystallite size and lowest activation energy of the MoS2-48 h sample among the samples. The sensors also exhibited a relative humidity-independent sensing characteristic at RT and a low detection limit of 84 ppb, thereby allowing their practical application to portable IoT-based devices.
PurposeGiven the importance of leadership practices and knowledge resources in fostering innovation capabilities of firms, the purpose of this study is to explore the influence of transformational ...leadership on exploitative and exploratory innovation via mediating role of knowledge management capability. This study also attempts to increase understanding of the appropriate mechanisms for firms to pursue innovation capability by examining the moderating mechanism of competitive intensity.Design/methodology/approachThis study utilized the structural equation modeling and cross-sectional design to test hypotheses in the proposed research model using survey data collected from 351 participants in 120 manufacturing and service firms.FindingsThe findings indicate that transformational leadership induces greater effect on exploratory innovation compared to its effect on exploitative innovation. The mediating role of knowledge management capability between transformational leadership and aspects of innovation capability is also supported. Especially, the influences of knowledge management capability on exploratory innovation capability are enhanced and depended on the degree of competitive intensity.Research limitations/implicationsFuture research should examine the mediating mechanisms of knowledge acquisition, knowledge sharing and knowledge application to provide deeper insight on the role of specific components of knowledge management capability in linking transformational leadership and innovation capability.Practical implicationsThe paper highlights the important role of transformational leadership practices for fostering knowledge management capability and specific aspects of innovation capability under high level of competitive pressure.Originality/valueThe paper is unique in the attempts to provide a prospective solution for firms to pursue and improve innovation based on the meaningful insights on the mediating role of knowledge management capability and moderating effect of competitive intensity in the relationship between transformational leadership and specific dimensions of innovation capability.
•The UFGs in the microstructure of the alloy Ti5Al3V1.5Mo were obtained by the MDF process.•The superplastic properties of Ti5Al3V1.5Mo titanium alloys were demonstrated through tensile tests.•The ...effect of deformation process parameters on superplasticity properties of the two-phase alloy is analyzed.•The study results show a significant improvement in the superplasticity properties of two-phase titanium alloys with MDF.
In this study, ultrafine grains in microstructures of Ti5Al3Mo1.5 V titan alloy was achieved by the multidirectional forging (MDF) process. The essence of the MDF is pressing steps combined with rotations of the workpiece in all directions within a forging cycle. The MDF process was performed for one to three cycles at temperatures from 850 °C to 950 °C. The achieved average grain size in the microstructure is about 1 µm at 900 °C after the third cycle. Subsequently, the superplastic properties after the MDF process were studied. Tensile tests that evaluate the superplastic deformation ability of alloy are performed at high temperatures from 800 °C to 900 °C and the strain rates in the range 10-3 s−1 –9.10-3 s−1. The result obtained the maximum relative elongation of 1120%, showing an exceptional enhancement of superplasticity of the studied alloy.
Middle East.
Droughts are a major natural disaster in almost every region of the world, causing negative impacts on natural resources and water basin management. However, it is challenging to study ...drought mechanisms in transboundary rivers where hydrometeorological observations are often not available or limited due to administrative issues. This study aims to assess drought conditions at three Iraqi transboundary river basins – (a) Mosul River Basin (between Iraq and Turkey), (b) Qadisiyah River Basin (between Iraq, Syria and Turkey), and (c) Dukan River Basin (between Iraq and Iran). The Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) and satellite datasets have been used to calculate various drought indices and reservoir areas for the study period between 1987 and 2019.
We exhibited the usefulness of FLDAS and satellite datasets in analyzing the variations and trends in hydro-meteorological variables and reservoirs surface areas over three transboundary river basins. Results exposed a significant drought exacerbation over the study regions during the periods of 1989–1991, 2000–2003, 2007–2012, and 2015–2018. Based on our analysis on drought duration and severity for inside- and outside- Iraq, we suggest the long-term meteorological drought indices (12-,24-month timescales) in monitoring drought conditions. Our results could be beneficial for water and natural resources managers in understanding spatial variability and impact of droughts.
•Evaluation of ten different drought indices•Understanding of drought conditions based on administrative boundary•Study on three transboundary river basins across different climactic conditions
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