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Large-size woody biomass is a valuable renewable resource to replace fossil fuels in biorefinery processes. The preprocessing of wood chips and briquettes is challenging to manage, especially in an industrial setting, as it generates a significant amount of dust and noise and occasionally causes unexpected accidents. As a result, a substantial amount of resources, energy, labor, and space are needed. The thermochemical conversion behavior of large-size woody biomass was studied to reduce energy consumption for chipping. Large-size wood was 1.5 m in length, 0.1 m in breadth, and stacked 90 cm in height. This strategy has many benefits, including increased effectiveness and reduced CO2 emissions. The target of this paper presents the thermochemical process, and large-size wood was chosen because it provides high-quality product gas while reducing the preprocessing fuel cost. This review examines the benefits of thermochemical conversion technologies for assessing the likelihood of carbon neutrality.

The new GHG calculator named SECTOR (Source-selective and Emission-adjusted GHG CalculaTOR for Cropland) is based on the IPCC Tier 2 approach for rice as well as other crops. The new features of SECTOR facilitate high flexibility in terms of entering newly obtained emission factors, easy data transfer from crop statistics for entering activity data and detailed specifications of GHG scenarios. A new procedure of entering frequency-based data on current water management practices was also developed. Moreover, the tool allows deviating from the 2006 IPCC Guidelines by considering field records with high background levels of N₂O emissions in the overall assessment of GHG emissions. This article assesses different applications of the tool, namely as add-ons to field measurements, for GHG calculation at national/sectorial scale and within measurement, reporting and verification of development projects. SECTOR is downloadable in the form of templates that can be used to develop custom versions with varying levels of disaggregated data entries at different scales. A case study for rice production in one Vietnamese province demonstrates the potential to display GHG results in combination with GIS. SECTOR can easily be adjusted to incorporate new emission factors and calculation procedures expected in forthcoming revisions of the IPCC Guidelines.

River deltas and estuaries are disproportionally-significant coastal landforms that are inhabited by nearly 600 M people globally. In recent history, rapid socio-economic development has dramatically changed many of the World's mega deltas, which have typically undergone agricultural intensification and expansion, land-use change, urbanization, water resources engineering and exploitation of natural resources. As a result, mega deltas have evolved into complex and potentially vulnerable socio-ecological systems with unique threats and coping capabilities. The goal of this research was to establish a holistic understanding of threats, resilience, and adaptation for four mega deltas of variable geography and levels of socio-economic development, namely the Mekong, Yellow River, Yangtze, and Rhine deltas. Compiling this kind of information is critical for managing and developing these complex coastal areas sustainably but is typically hindered by a lack of consistent quantitative data across the ecological, social and economic sectors. To overcome this limitation, we adopted a qualitative approach, where delta characteristics across all sectors were assessed through systematic expert surveys. This approach enabled us to generate a comparative assessment of threats, resilience, and resilience-strengthening adaptation across the four deltas. Our assessment provides novel insights into the various components that dominate the overall risk situation in each delta and, for the first time, illustrates how each of these components differ across the four mega deltas. As such, our findings can guide a more detailed, sector specific, risk assessment or assist in better targeting the implementation of risk mitigation and adaptation strategies.

Mixed convection flow and heat transfer within various cavities including lid-driven walls has many engineering applications. Investigation of such a problem is important in enhancing the performance of the cooling of electric, electronic and nuclear devices and controlling the fluid flow and heat exchange of the solar thermal operations and thermal storage.The main aim of this fundamental investigation is to examine the influence of a two-phase hybrid nanofluid approach on mixed convection characteristics including the consequences of varying Richardson number, number of oscillations, nanoparticle volume fraction, and dimensionless length and dimensionless position of the solid obstacle.The migration of composite hybrid nanoparticles due to the nano-scale forces of the Brownian motion and thermophoresis was taken into account. There is an inner block near the middle of the enclosure, which contributes toward the flow, heat, and mass transfer. The top lid cover wall of the enclosure is allowed to move which induces a mixed convection flow. The impact of the migration of hybrid nanoparticles with regard to heat transfer is also conveyed in the conservation of energy. The governing equations are molded into the non-dimensional pattern and then explained using the finite element technique. The effect of various non-dimensional parameters such as the volume fraction of nanoparticles, the wave number of walls, and the Richardson number on the heat transfer and the concentration distribution of nanoparticles are examined. Various case studies for Al2O3-Cu/water hybrid nanofluids are performed.The results reveal that the temperature gradient could induce a notable concentration variation in the enclosure.The location of the solid block and undulation of surfaces are valuable in the control of the heat transfer and the concentration distribution of the composite nanoparticles.

Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and Zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and Zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.

Sensorless induction drive systems are more popular due to their reliability and low cost. Therefore, it is very beneficial to use sensorless drive systems where the rotor speed can be estimated by means of an intelligent control algorithm instead of the use of directly measuring methods. This paper presents a method of the online speed estimation for a three-phase induction motor in Indirect Field Oriented Control (IFOC) scheme accompanying an Artificial Neural Network (ANN). The error-back propagation algorithm is used for training the neural network. The error between rotor flux linkages in the adaptive model and the reference model is back propagated to adjust weights of the neural network model to estimate the motor speed. The simulation results obtained using MATLAB/Simulink show that the estimated motor speed always tracks the actual motor speed with very small error as long as the sampling time is small enough and the learning rate can be chosen appropriately.

Livestock manure and its management are significant sources of greenhouse gas (GHG). In most Southeast Asian countries, the current GHG emissions are estimated by the Intergovernmental Panel on Climate Change (IPCC) Tier 1 approach using default emission factors. Sun-drying is the dominant manure treatment in Vietnam, and in this study, we measured GHG emissions during manure drying using a chamber-based approach. Results show the emission factors for CH4and N2O were 0.295 ± 0.078 g kg−1volatile solids (VS) and 0.132 ± 0.136 g N2O-N kg−1Ninitial, respectively. We monitored the total bacterial/archaeal community using 16S rRNA gene amplicon sequencing and measured the abundance of functional genes required for methanogenesis (mcrA), nitrification (amoA) and denitrification (nirK,nirSandnosZ) processes. Methane emission occurred only at the beginning of the drying process (days 1 to 3). The results of amplicon sequencing indicated that the relative abundance of methanogens also decreased during this period. Although some nitrification activity was detected, there was no significant N2O emission. These findings well describe the manure management system in south Vietnam and the GHG emission from this manure category, paving the way for higher Tier estimations using country-specific values.