Littérature scientifique sur le sujet « FULLCOMP »

Abstract The FullCAM model was developed for full carbon accounting in agriculture and forests at project and national scales. For forest systems, FullCAM links the empirical CAMFor model to models of tree growth (3PG), litter decomposition (GENDEC), and soil carbon turnover (RothC). Our objective was to calibrate RothC within the FullCAM framework using 2 long-term forestry experiments where productivity had been manipulated and archived and new soil samples were available for analysis of carbon within the various pools described by RothC. Inputs of carbon to soil at these trials were estimated by calibrating FullCAM to temporal data on above-ground growth, litterfall, and accumulation of litter. Two alternative submodels are available in FullCAM (CAMFor and GENDEC) for predicting decomposition of litter, and thus the input of carbon into the soil. Calibration of RothC was most sensitive to the partitioning of carbon during decomposition of debris between that lost as CO2 and that transferred to soil. Turnover of soil carbon was best simulated when the proportion of carbon lost to CO2 from relatively labile pools of debris was 77% (when simulated by CAMFor) and 95% (when simulated by GENDEC), whereas resistant pools of debris lost about 40% to CO2 during decomposition. Although rates of decomposition of pools of soil carbon were originally developed in RothC for agricultural soils, these constants were found to be also suitable for soils under plantation systems.

Harnessing sustainably sourced forest biomass for renewable energy is well-established in some parts of the developed world. Forest-based bioenergy has the potential to offset carbon dioxide emissions from fossil fuels, thereby playing a role in climate change mitigation. Despite having an established commercial forestry industry, with large quantities of residue generated each year, there is limited use for forest biomass for renewable energy in Queensland, and Australia more broadly. The objective of this study was to identify the carbon dioxide mitigation potential of replacing fossil fuels with bioenergy generated from forest harvest residues harnessed from commercial plantations of Pinus species in southeast Queensland. An empirical-based full carbon accounting model (FullCAM) was used to simulate the accumulation of carbon in harvest residues. The results from the FullCAM modelling were further analysed to identify the energy substitution and greenhouse gas (GHG) emissions offsets of three bioenergy scenarios. The results of the analysis suggest that the greatest opportunity to avoid or offset emissions is achieved when combined heat and power using residue feedstocks replaces coal-fired electricity. The results of this study suggest that forest residue bioenergy is a viable alternative to traditional energy sources, offering substantive emission reductions, with the potential to contribute towards renewable energy and emission reduction targets in Queensland. The approach used in this case study will be valuable to other regions exploring bioenergy generation from forest or other biomass residues.

<p><span lang="EN-GB">Deteksi Covid-19 merupakan tahapan penting untuk mengenali secara dini pasien terduga Covid-19 sehingga dapat dilakukan langkah lanjutan. Salah satu cara pendeteksian adalah melalui citra sinar-x paru. Namun demikian, selain dibutuhkan suatu model algoritma yang dapat menghasilkan akurasi tinggi, komputasi yang ringan merupakan hal yang dibutuhkan sehingga dapat diaplikasikan dalam alat pendeteksi. Model deep CNN dapat melakukan deteksi dengan akurat namun cenderung memerlukan penggunaan memori yang besar. CNN dengan parameter yang lebih sedikit dapat menghemat <em>storage </em></span><span lang="EN-GB">maupun penggunaan memori sehingga dapat berproses secara real time baik berupa alat pendeteksi maupun sistem pengambilan keputusan via <em>cloud</em>. Selain itu, CNN dengan parameter yang lebih kecil juga dapat untuk diaplikasikan pada FPGA dan perangkat keras lainnya yang mempunyai kapasitas memori terbatas. Untuk menghasilkan deteksi COVID-19 pada citra sinar-x paru yang akurat namun komputasinya juga ringan, kami mengusulkan arsitektur CNN kecil namun handal </span><span lang="EN-GB">dengan menggunakan teknik pertukaran <em>channel</em> yang disebut ShuffleNet. Dalam penelitian ini, kami menguji dan membandingkan kemampuan ShuffleNet, EfficientNet, dan ResNet50 karena mempunyai jumlah parameter yang lebih kecil dibanding CNN pada umumnya seperti VGGNet atau FullConv yang menggunakan lapisan konvolusi secara penuh namun mempunyai kemampuan deteksi yang mumpuni. Kami menggunakan 1125 citra sinar-x dan mencapai akurasi 86.93 % dengan jumlah parameter model yang 18.55 kali lebih sedikit dari EfficientNet dan 22.36 kali lebih sedikit dari ResNet50 untuk mendeteksi 3 kategori yaitu Covid-19, Pneumonia, dan normal melalui uji 5-<em>fold crossvalidation</em>. Memori yang diperlukan oleh masing-masing arsitektur CNN tersebut untuk melakukan sekali deteksi berhubungan secara linier dengan jumlah parameternya dimana ShuffleNet hanya memerlukan memori GPU sebesar 0.646 GB atau 0.43 kali dari ResNet50, 0.2 kali dari EfficientNet, dan 0.53 kali dari FullConv. Lebih lanjut, ShuffleNet melakukan deteksi paling cepat yaitu sebesar 0.0027 detik.</span></p><p><span lang="EN-GB"><br /></span></p><p><em><strong><span lang="EN-GB">Abstract</span></strong></em></p><p><em>Covid-19 detection is an important step in identifying early patients with suspected Covid-19 so that further steps can be taken. One way of detection is through pulmonary x-ray images. However, besides requiring an algorithm model that can produce high accuracy, lightweight computation is needed so that it can be applied in a detector. The deep CNN model can detect accurately but tends to require large memory usage. CNN with fewer parameters can save storage and memory usage so that it can process in real time both in the form of detection devices and decision-making systems via the cloud. In addition, CNN with smaller parameters can also be applied to FPGA and other hardware that have limited memory capacity. To produce accurate COVID-19 detection on x-ray images with lightweight computation, we propose a small but reliable CNN architecture using a channel shuffle technique called ShuffleNet. In this study, we tested and compared the capabilities of ShuffleNet, EfficientNet, and ResNet because they have a smaller number of parameters than usual deep CNN, such as VGGNet or FullConv which uses a full convolution layers with a robust detection capability. We used 1125 x-ray images and achieved an accuracy of 86.93% with a number of model parameters of 18.55 times less than EfficientNet and 22.36 times less than ResNet50 to detect 3 categories namely Covid-19, Pneumonia, and normal through the 5-fold cross validation. The memory required by each CNN architecture to perform one detection is linearly related to the number of parameters where ShuffleNet only requires GPU memory of 0.646 GB or 0.43 times that of ResNet50, 0.2 times of EfficientNet, and 0.53 times of FullConv. Furthermore, ShuffleNet performs the fastest detection at 0.0027 seconds. </em></p><p><em><strong><span lang="EN-GB"><br /></span></strong></em></p>

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Mechanized mining of Longwall top coal caving mining method was used in 1201 work face of thick soft coal seam in Du Jiacun Mine. The face is back to the wind drift along the seam floor excavation, transport drift along the seam roof boring the two crossheading basic fullcoal roadway, due to the relatively soft coal body, joints fractured, coal strength is low, and the coal-rock inclination, roadway section, the weakness of the surrounding rock rock, resulting in the excavation and support difficulties. This chapter based on numerical computation FLAC3D large-scale numerical analysis software, on the inclination special soft coal seam using a different roadway, support method, numerical simulation analysis of the support parameters, the contrast in different roadway support system support parameters, the deformation of surrounding rock displacement, stress distribution, the distribution of plastic zone, the big dip is extremely soft for Du Village coal mine mining Gateway support programs, and through on-site ground pressure observation of program verification.

This study used simulation modelling to investigate fire and carbon dynamics for projected warmer and drier climates in the south-eastern Australian high country. A carbon accounting model FullCAM and the landscape fire regime simulator FIRESCAPE were combined and used to simulate several fire management options under three climate scenarios – the recent climate (1975–2005); a moderate climate projected for 2070 (B1); and a more extreme climate projected for 2070 (A1FI). For warmer and drier climates, model simulations predicted (i) an increase in fire incidence; (ii) larger areas burned; (iii) higher mean fire intensities; (iv) shorter fire cycle lengths; (v) a greater proportion of fires burning earlier in the fire season; (vi) a reduction in carbon stores; (vii) a reduction in carbon sequestration rates; and (viii) an increase in the proportion of stored carbon emitted to the atmosphere. Prescribed burning at historical or twice historical levels had no effect on fire or carbon dynamics. In contrast, increasing the initial attack success (a surrogate for suppression) partially offset the adverse effects of warmer and drier climates on fire activity, but not on carbon dynamics. For the south-eastern Australian high country, simulations indicated that fire and carbon dynamics are sensitive to climate change, with simulated fire management only being able to partially offset the adverse effects of warmer and drier climate.

Spray deposition has previously been studied within a potato canopy In a followup study spray drift was evaluated using three treatments from the previous research plus two treatments encompassing an additional grower standard and a spray drift standard Treatments included (1) an airassisted Gambetti sprayer with fullcone nozzles applying spray at a rate of 400 litres/ha (2) a rotary atomizer spraying system (Proptec) applying spray at a rate of 200 litres/ha (3) a dropleg application spraying above (25) and below canopy (75) at 260 litres/ha (4) a conventional hydraulic nozzle spray boom with Guardian AIRtrade; Twin nozzles applying 200 litres/ha and (5) a conventional hydraulic nozzle spray boom with standard 11003 nozzles at 300 litres/ha Data were normalized per nozzle and application rate (200 litres /ha) With the exception of the Gambetti application results indicated a similar pattern with very low deposition beyond 10 m downwind The Gambetti results showed very low deposition near 0 m and spray cloud reaching 40 m but it is believed that the turbulent airflow from the Gambetti sprayer adversely affected the deposition samplers through a high level of disturbance so more data are necessary for further analysis of this effect

Water spray cooling is an important technology which has been used in a variety of engineering applications for cooling of materials from high-temperature nominally up to 900°C, especially in steelmaking processes and heat treatment in hot metals. The effects of cooling water temperature on spray cooling are significant for hot steel plate cooling applications. The local heat flux measurements are introduced by a novel experimental technique in which test block assemblies with cartridge heaters and thermocouples are used to measure the heat flux distribution on the surface of hot steel plate as a function of heat flux gauge. The spray is produced from a fullcone nozzle and experiments are performed at fixed water impact density of G and fixed nozzle-to-target spacing. The results show that effects of water temperature on forced boiling heat transfer characteristics are presented for five different water temperatures between 5 to 45°C. The local heat flux curves and heat transfer coefficients are also provided to a benchmark data for the actual spray cooling of hot steel plate cooling applications.