With a view to evaluate the effects of hydraulic retention time and volatile solids loading rate on methane production using water hyacinth, experiments were conducted in a 160 dm3 capacity, well-mixed continuous digestion unit at ambient temperature. The optimal design of biogas plant using water hyacinth was elucidated, to provide a unit giving a maximum methane yield of 0.16 nm3 kg?1 volatile solids added, which was 60% greater than the conventional plant using cowdung. The calculations of optimal design factors were based on reactor size, hydraulic retention time and volatile solids loading rate; considerations were made of capital cost, running costs and operational revenue. 相似文献
The presentations made, as well as the discussions, in the panels at the workshop, Thermal Challenges in Next Generation Electronic Systems (THERMES), are summarized in this paper. The panels dealt with diverse topics including thermal management roadmaps, microscale cooling systems, numerical modeling from the component to system levels, hardware for future high performance and Internet computing architectures, and transport issues in the manufacturing of electronic packages. The focus of the panels was to identify barriers to further progress in each area that require the attention of the research community. 相似文献
A water-soluble polysaccharide fraction was prepared from dehulled rapeseed meal (winter rapeseed variety Casino). Further purification yielded two major fractions having a high content of arabinose and galactose residues, with Ara/Gal ratios of 5.4 (G1) and 1.8 (G2). The Ara/Gal ratio of the high molecular weight fraction G1 was stable over the whole gel filtration peak, indicating that the arabinose and galactose residues are part of the same polysaccharide. The high molecular weight fraction G1 was studied further by methylation analysis and several NMR techniques. Structural studies showed G1 to consist mainly of arabian fragments, which have terminal alpha-L-arabinofuranosyl groups with anomeric carbons bound (1-->5) (A) or (1-->2) (B), and 2,5-substituted arabinosyl residues with anomeric carbons bound (1-->5) (D) or (1-->2) (C) to adjacent arabinosyl residues. The A:B:C:D ratios were 2:1:1:1 according to results from NMR and methylation analysis. 相似文献
In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods. 相似文献
ABSTRACTFabrication of electronic materials from nanocomposite of biopolyesters reinforced with carbon nanotubes can be regarded as the effective alternative for conventional nanocomposites consisting of non-biodegradable polymers. Commercial availability of biopolyester-based nanocomposites is limited because of their high cost compared to other polymers, but the factor of their compostable nature is worthless for environmental protection. Such nanocomposites have potential applications in biodegradable sensors, EMI materials, etc. In this review, the current progress of biopolyester/CNTs nanocomposites in the field of biodegradable electronics is reviewed and also the impact of CNTs dispersion on electrical, thermal and mechanical properties of eco composites is stipulated. 相似文献
In this report, a free space frequency‐time‐domain technique is presented for characterizing the electrical properties and thickness of the sample using multiple reflections and fabry‐perot resonance phenomenon. The retrieval of constitutive electromagnetic parameters of the sample has been carried out by comparing the measured reflection coefficient data from the sample at two different incident angles. The relative permittivity as well as relative permeability along with the thickness of different samples viz., beryllia, silicon, and plexiglass have been evaluated with high accuracy in the frequency range 1 to 15 GHz. The method is also experimentally validated by successfully reconstructing the unknown material properties of two different samples. The unique advantage of this method lies in non‐requirement of any prior knowledge of the sample's thickness for measuring the complex relative dielectric constant as well as relative permeability of the sample. To determine the electromagnetic properties of the sample, the sole knowledge of reflection coefficient data are needed. Moreover, the method does not involve any additional measurement for the reference calibration. The simple, cost‐effective proposed scheme is quite useful in many applications like accurate determination of signal strength in indoor wireless communication, through wall imaging, food industry, and so on. 相似文献
Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.