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Background Tremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data. Results In this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein–Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with “inclusion” relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the “inclusion” relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases. Conclusions The gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones.

Cross-modal retrieval aims to find the similarity between different modal data, while the hash retrieval method improves retrieval efficiency. This paper proposes a cross-modal hash retrieval method based on multiple similarity matrices. This paper proposes an unsupervised cross-modal hash retrieval method based on multiple similarity matrices. This paper uses a weighted combination method to construct fusion features through hash features and original features. Based on the three features, the auxiliary similarity matrix of each of the three features is established. Finally, the fusion matrix is constructed by a weighted combination of the similarity matrix of the original features and the hash features. These four different matrices include similarity matrices with varying forms of features and similarity matrices with varying construction methods, which concentrate the similarity information of other modalities. The loss function between different similarity matrices and the loss function between different modalities are calculated through these four different matrices. Considering that most models have a single method for extracting text features, this paper uses text self-attention to strengthen the effect of text feature extraction so that the final performance of this paper is effectively improved. In order to verify the impact of this article, the results are tested on the Wikipedia, MIRFlickr, and NUS-WIDE datasets, and the results prove that the effect of this article has certain advantages compared with the latest methods.

The presence of chloride ions can facilitate the COD removal efficiency due to the involvement of active chlorine species in the electro-oxidation process, but few attentions have been paid to the negative effect of the electro-generated oxychlorides on electro-oxidation performance. In this study, the effects of oxychlorides were investigated as functions of current density and phenol concentration using DSA anodes in terms of the evaluation of the COD removal performance and the biological toxicity. The results show that oxychlorides formed in the electro-oxidation system could result in the over-evaluation of the COD removal performance. Increasing current density (15-50 mA cm

It is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments.

Image hashing is a useful technology of many multimedia systems, such as image retrieval, image copy detection, multimedia forensics and image authentication. Most of the existing hashing algorithms do not reach a good classification between robustness and discrimination and some hashing algorithms based on dimensionality reduction have high computational cost. To solve these problems, we propose a robust and fast image hashing based on two-dimensional (2D) principal component analysis (PCA) and saliency map. The saliency map determined by a visual attention model called LC (luminance contrast) method can ensure good robustness of our hashing. Since 2D PCA is a fast and efficient technique of dimensionality reduction, the use of 2D PCA helps to learn a compact and discriminative code and provide a fast speed of our hashing. Extensive experiments are carried out to validate the performances of our hashing. Classification comparison shows that our hashing is better than some state-of-the-art algorithms. Computational time comparison illustrates that our hashing outperforms some compared algorithms based on dimensionality reduction.

Writing has always been one of the difficulties in teaching Chinese as a foreign language. With the advent of the information society, modern information technology, represented by networking, digitalization, multimedia, and intelligence, has become a creative tool to expand human capabilities, changing people’s traditional way of life, study, and work. Guided by the theory of integration of information technology and curriculum, combined with the characteristics of Chinese writing, this paper puts forward that the goal of integration of information technology and Chinese writing is to change the traditional teaching structure, create an ideal environment for students to learn Chinese and improve their Chinese information literacy. A practical teaching platform for college Chinese writing is constructed, which is designed from the user role and based on user access control. The design includes the roles of graduate students, foreign teachers, administrators, and users. Each user has different operation rights, module rights, and management rights. According to a small amount of online information and the results of a previous course, we can make a good prediction. The results show that this method is effective, and we can predict students’ academic performance through their online behavior.

With the rapid development of deep sequencing technologies, a large amount of high-throughput data has been available for studying the carcinogenic mechanism at the molecular level. It has been widely accepted that the development and progression of cancer are regulated by modules/pathways rather than individual genes. The investigation of identifying cancer-related active modules has received an extensive attention. In this paper, we put forward an identification method ModFinder by integrating both biological networks and gene expression profiles. More concretely, a gene scoring function is devised by using the regression model with [Formula: see text]-step random walk kernel, and the genes are ranked according to both of their active scores and degrees in the PPI network. Then a greedy algorithm NSEA is introduced to find an active module with high score and strong connectivity. Experiments were performed on both simulated data and real biological one, i.e. breast cancer and cervical cancer. Compared with the previous methods SigMod, LEAN and RegMod, ModFinder shows competitive performance. It can successfully identify a well-connected module that contains a large proportion of cancer-related genes, including some well-known oncogenes or tumor suppressors enriched in cancer-related pathways.

The real-time judgment of normal driving in the communication signal section of ZPW-2000R track circuit is studied. The model of normal operation judgment of track circuit based on BP neural network is established. The experimental results show that the accuracy of the judgment of normal driving occupancy of track circuit based on neural network model reaches 100%, which provides an intelligent method for the judgment of the driving occupancy signal of track circuit.

The vehicle-to-infrastructure communication is a kind of communication mode of the vehicular ad-hoc network (VANET). It is paramount to allocate roadside units (RSUs) appropriately to assure the quality of communication service. Sarubbi et al. have proposed a greedy heuristic allocation algorithm Delta-r. It examines the Δ ρ2 ρ1 -deployment constraint, a metric for evaluating the performance of VANET, with relative contact time. Unfortunately, the relative contact time of vehicles at urban cells may present `false high', which would play an opposite effect on making a decision. In order to solve the problem, a new allocation algorithm Delta-uc is presented, which examines the Δ ρ2 ρ1 -deployment constraint with a useful contribution. It gets rid of the negative effect of `extra' contact time by means of retaining merely the contributing relative duration of a vehicle at an urban cell. Furthermore, an effective recombination operator is introduced basing on algorithm Delta-uc, and a parthenogenetic algorithm UCPGA is presented to solve the allocation problem. Experimental comparisons were performed on the real mobility trace of Cologne, Germany. The experimental results demonstrate that in many Δ ρ2 ρ1 -deployments, the number of RSUs produced by the Delta-uc and UCPGA algorithms are, respectively, fewer than those produced by algorithms Delta-r and DeltaGA.

Equivalence ratio is one of the important operating parameters for biomass gasification. The oxygen carrier possesses high producer gas production in a wide range of equivalence ratio (oxygen carrier/biomass ratio), which is exceedingly desirable for biomass chemical looping gasification (CLG). In this study, the effect of different equivalence ratios on the CO selectivity (the evolved gas composition) of Fe2O3, Fe2O3/CaO and CaFe2O4 oxygen carriers in biomass char CLG was investigated in a fixed bed reactor. The results showed that the CO selectivity of CaFe2O4 had little changes and decreased from 70.39% to 59.79% and 73.40% to 63.98% with increasing equivalence ratio from 0.5 to 3.0 at 900 °C and 1000 °C, respectively. In sharp contrast, the CO selectivity of Fe2O3 changed markedly, which declined respectively from 65.86% to 31.13% (900 °C) and 69.68% to 47.27% (1000 °C). Fe2O3/CaO illustrated the similar tendency as Fe2O3 at 900 °C but as CaFe2O4 at 1000 ℃. The three oxygen carries exhibited a high CO production at lower equivalence ratio, and CaFe2O4 displayed the best performance of CO selectivity in the given equivalence ratio range. X-ray diffraction (XRD) spectrums showed that the oxygen carriers were transformed to different iron phases with difference equivalent ratios. The Fe (III) in the three oxygen carries was reduced completely to Fe (0) at the equivalence ratios of 0.5 and 1.0, while FeO and Ca2Fe2O5 were the dominant species in the spent oxygen carriers as equivalence ratio was further increased. The trends of CO selectivity obtained by thermodynamic analysis for three oxygen carries were consistent with the experimental results.

Non-petroleum route to produce transportation fuels is alternative way to address the fast depletion of the crude oil reservoir, especially in China. In this work, series of nanocrystal H[Fe, Al]ZSM-5 zeolite samples with different SiO2/Al2O3 ratio were prepared with hydrothermal method using sodium aluminate and tetraethyl orthosilicate as Al and Si sources, respectively, and the tetrapropylammonium hydroxide was used for structure-directing agent (SDA). The synthesized zeolites were characterized in detail by UV–Vis, X-ray diffraction, scanning electron microscope, N2 sorption, X-ray fluorescence, ammonia temperature-programmed desorption and pyridine-FTIR techniques. Fe species were incorporated into ZSM-5 framework, resulting in the formation of some new active sites. For the Iron-containing nanocrystal ZSM-5 samples with different SiO2/Al2O3 ratio, the B and L acid strength and amounts are determined by the coexistence and synergistic effect of Al- and Fe-based acid sites. The catalytic results illustrated that the H[Fe, Al]ZSM-5 zeolite with SiO2/Al2O3 ratio for 45 exhibited the best catalytic activity and stability due to its maximum B acid amount and higher B/L ratio. The DME conversion remains at >98% within the 135 h investigation. Moreover, the gasoline yield was up to 72.5%, and the selectivity for aromatics and durene could be reduced to 40% and 1%, respectively.

Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.

The deformation of blades under complex loads of multiple working conditions will reduce the energy conversion efficiency. To reduce the deviation of the blade shape in practical working conditions, a combination and optimization method of blade design schemes under multiple working conditions, based on the entropy weight vague sets, is proposed. The sensitivity of each working condition index is analyzed based on the information entropy, and the satisfaction degree of the design scheme based on the design requirements and experiences is described with the vague set. The matching degree of different design schemes for multiple working conditions is quantified according to the scoring function. The combination and optimization of the design scheme are verified by numerical simulation analysis. The results show that the proposed design scheme has a smaller blade shape deviation than the traditional design scheme under multiple working conditions.

A thermodynamic study on a toluene chemical looping reforming process with six metal oxides was conducted to evaluate the product distribution for selecting an appropriate oxygen carrier with thermodynamic favorability towards high syngas yield. The results show that a suitable operation temperature for most oxygen carriers is 900 °C considering syngas selectivity and solid C formation whether the toluene is fed alone or together with fuel gas. The syngas selectivity of all oxygen carriers decreases with the increasing equivalence ratio, but the decrease degrees are quite different due to their different thermodynamic natures. With the increasing amounts of H2 and CO, the syngas selectivity for various oxygen carriers correspondingly decreases. The addition of CO2 and H2O(g) benefits reducing the solid C formation, whereas the addition of CH4 leads to more solid C being produced. Under the simulated gasification gas atmosphere, a synergetic elimination of solid C and water–gas shift reactions are observed. In terms of syngas selectivity, Mn2O3 possesses the best performance, followed by CaFe2O4 and Fe2O3, but NiO and CuO exhibit the lowest performance. BaFe2O4 presents a high H2 selectivity but a very poor CO selectivity due to the formation of BaCO3, which has a high thermodynamic stability below 1200 °C. Nevertheless, Mn2O3 is more likely to form solid C than feeding toluene alone and has a lower melting point. Considering syngas selectivity, carbon deposit and melting point, CaFe2O4 exhibits the highest performance concerning the tar chemical looping.

It is widely accepted that driver pathways offer important information for precision and personalized medicine of cancer treatment, hence the problems of driver pathways identification have become a research hotspot in bioinformatics. In this paper, an improved collaborative mutation driver pathways model ICMDP is proposed by integrating the somatic mutation, copy number variation and gene expression data. The model has two characteristics:(1) each individual pathway has moderate mutual exclusion and high coverage; (2) collaborative driver pathways exhibit significant common mutations in cancer samples, and the genes in collaborative driver pathways are related. Meanwhile, a parthenogenetic algorithm PA-ICMDP is proposed for solving the ICMDP model. Experiments were performed to compare algorithms PA-ICMDP, CoMDP and GAMTOC by using real biological data sets, i.e., the samples of glioblastoma and ovarian. The experimental results indicate that the PA-ICMDP algorithm can not only identify important collaborative driver pathways with higher co-occurrence mutation rates, but also detect more important driver genes such as MET, MDM2, GAB2, TERT, TBX3 and so on. In addition, the EICMDP model and the PA-EICMDP algorithm are put forward by extending the ICMDP model and the PAICMDP algorithm respectively. They can effectively identify other important pathways that collaborate with known driver pathways. The experimental results indicate that the methods presented in this paper may become suitable tools for mining driver genes and driver pathways related to cancer development.

Chemical looping reforming/gasification of toluene, the model compound representing biomass tar, was studied by using two oxygen carriers, 2CuO-2NiO/Al2O3 (molar ratio) and CaFe2O4. Their reaction performances were evaluated in a TG-FTIR and a laboratory-scale fixed bed reactor. The active lattice oxygen of the 2CuO-2NiO/Al2O3 oxygen carrier demonstrated excellent reactivity and completely oxidized toluene to CO2 at 600–700 °C, while the fixed bed reactor data indicated that 2CuO-2NiO/Al2O3 oxygen carrier could catalyze the cracking of toluene to produce H2 after loss of lattice oxygen. The CaFe2O4 oxygen carrier exhibited a good performance of chemical looping partial oxidation of toluene to CO and H2 with a fairly high reactivity. Over CaFe2O4 oxygen carrier, the CO and H2 dominated reaction products in the temperature range of 800–900 °C except at the startup stage. Catalytic cracking of toluene by CaFe2O4 to produce H2 was also observed after partial loss of lattice oxygen. Stable performance of CaFe2O4 with high reactivity to syngas (CO and H2) was demonstrated during a 3-cyle test in the fixed bed reactor. After reduction of action with toluene, scanning electron microscopy (SEM) and X-ray diffraction (XRD) indicated that 2CuO-2NiO/Al2O3 was reduced to metallic copper and nickel with the mixture of NiAl2O4, and CaFe2O4 was reduced to metallic iron and CaO. In summary, two oxygen carriers displayed high toluene conversion at the beginning stage and the conversion of toluene increased to about 100% in the subsequent stage.

Catalytic upgrading of anisole and guaiacol in an atmospheric dielectric barrier discharge (DBD) plasma reactor was studied. Compared to thermal upgrading in a fixed bed, the DBD plasma method can convert the model compound to benzene and toluene with high selectivity under relatively low temperature. The synergistic effect between H2 plasma and Ni-Mo/SiO2 catalyst is crucial to the high deoxygenation rate. The primary reactions occurring during the DBD upgrading process include demethylation, methyl transfer, and hydrogenolysis. XRD spectra indicate that phase change should not be the primary reason for catalyst deactivation; GC–MS, NH3-TPD, and regeneration experiments show that carbon deposition is the cause for deactivation. NH3-TPD analysis reveals that a combination of medium and strong acid sites plays an important role in the hydrogenolysis of phenols to benzenes. The present work shows DBD catalytic upgrading as a promising route for the C O bond hydrogenolysis reaction.

Most of the existing image captioning models mainly use global attention, which represents the whole image features, local attention, representing the object features, or a combination of them; there are few models to integrate the relationship information between various object regions of the image. But this relationship information is also very instructive for caption generation. For example, if a football appears, there is a high probability that the image also contains people near the football. In this article, the relationship feature is embedded into the global-local attention to constructing a new Pyramid Attention mechanism, which can explore the internal visual and semantic relationship between different object regions. Besides, to alleviate the exposure bias problem and make the training process more efficient, we propose a new method to apply the Generative Adversarial Network into sequence generation. The greedy decoding method is used to generate an efficient baseline reward for self-critical training. Finally, experiments on MSCOCO dataset show that the model can generate more accurate and vivid captions and outperforms many recent advanced models in various prevailing evaluation metrics on both local and online test sets.

Mutation profiles together with prior knowledge such as interactions between genes/proteins provide abundant critical information for the identification of driver modules, which is very important for analyzing mutational heterogeneity in human cancers. Due to the negative effects of inevitable false positive interactions in the PPI network, subcellular localization data are exerted to filter out them firstly, and somatic mutation profiles are used to weight the retained interactions. Five novel recombination operators are introduced basing on the vertex degrees and the edge weights in the PPI network, and a parthenogenetic algorithm is devised for solving the presented identification model which takes into account network connectivity, mutual exclusivity, coverage, and hops between genes within a module. Extensive experimental results indicate that compared with two state-of-the-art computational methods Hotnet2 and MEXCOwalk, the proposed method exhibits competitive performance in most cases in terms of recovering known cancer genes, providing modules that have satisfied coverage and mutual exclusivity, and are enriched for mutations in specific cancer types. Many identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in the literature. In addition, the proposed method does identify many cancer related genes missed by methods Hotnet2 and MEXCOwalk, including some recognized genes covering many types of cancers but having low mutation frequency.

Layered double hydroxides (LDHs), also known as hydrotalcite-like anionic clays, are very convenient precursors with a tunable flexibility toward multifunctional nanomaterials, especially in energy storage. Typical methods to improve lithium storage are to introduce additional or self-generating carbonaceous supports to LDH-derived transition metal oxides as anode nanomaterials which can host lithium mainly though a conversion mechanism. Here, we describe a preparation of mesoporous spinel ferrite composite (MgFe 2 O 4 /ZnFe 2 O 4 ) for lithium storage, which is assisted by a combined conversion and alloying mechanism. The composite is derived by a thermal decomposition of a scalablely produced single-resource precursor of ternary Mg 2+ Zn 2+ Fe 3+ -layered double hydroxide (Mg 2+ Zn 2+ Fe 3+ -LDH), and subsequent selective etching. Electrochemical test shows that the electrode delivers an exceptional electrochemical performance, i.e., a reversible capacity of 1190 mA h g −1 after 100 cycles at 100 mA g −1 , and, in particular, a reversible capacity of 981 mA h g −1 at 500 mA g −1 after 330 cycles, as well as a reversible capacity of 541 mA h g −1 at 2000 mA g −1 after 1000 cycles. The high electrochemical performance could be attributed to the following features: the combined alloying and conversion mechanisms of ZnFe 2 O 4 , synergistic MgFe 2 O 4 , and slight-content MgO as a non-active matrix, as well as an appropriate specific area and mesoporous size distribution. Our results show that the cation-tunable LDH precursor-derived synthesis route might be an alternative to prepare multiple-component composites of spinel ferrites and transition metal oxides.

Although pre-column derivatization with n-propylamine and acetic anhydride combined with the gas chromatograph was a useful method for the analysis of monosaccharide composition, failure often occurs because of lack of detail information on the mechanism as well as the operating key point in derivatization process. In this study, the key points in the derivatization (lactonization time, the amount of n-propylamine and acetic anhydride) were investigated and optimized to improve the method. Under the optimal conditions, the derivatives of seven neutral monosaccharides and two uronic acids were simultaneouly obtained, after which they were well separated and detected by GC. It was also found that all derivatives of monosaccharides were stable even stored for 20days. The linearity, sensitivity, precision, reproducibility and recovery rate of the improved method were evaluated. Thereafter, five polysaccharide samples from different sources were analyzed to validate the improved method.

The investigation of commonalities among different cancers is one of the important problems for deciphering cancers and will be helpful for personalized therapy in cancer treatment. Zhang et al. have presented the ComMDP method to solve this problem in 2017. However, when the number of samples among different cancers varies largely, accumulating the absolute weight value of every cancer, performed by the ComMDP method, may lead to missing some driver pathways. In this paper, an improved mathematical model is proposed by replacing the absolute weight values with the relative ratios of them, and introducing variance to minimize the dispersion of each ratio. By introducing a kind of short chromosome code and a greedy based recombination operator, a pathenogenetic algorithm PGA-MDP is put forward for solving this model. Experimental results indicate that the PGA-MDP algorithm is indeed able to detect some biologically meaningful gene sets which are missed by the ComMDP one. Hence it may become a useful complementary tool for identifying cancer pathways.

Cervical cancer is the third most common gynecologic cancer in the world. Exploration of the molecular mechanism underlying cervical cancer pathogenesis will provide new insights into the development of novel therapies. In this study, we were aimed to characterize a novel miRNA in cervical cancer tumorigenesis. First, we measured the expressional change of miR-144-3p in clinical tissues and cancer cells. Second, we employed cell proliferation, cell migration, and invasion assays to understand its functional role in cervical cancer. Then, we confirmed in vitro findings in xenograft cancer model. Last, we mapped out a downstream target of miR-144-3p and validated its functional role in cancer cells. In the results, miR-144-3p was found significantly downregulated in cervical cancer cells and tissues. Over-expressing miR-144-3p suppressed cancer cells growth and metastasis. Consistent with in vitro results, over-expressing miR-144-3p led to tumor growth inhibition in vivo. Further on, MAPK6 was identified as an endogenous target of miR-144-3p in cervical cancer. Knocking down MAPK6 inhibited cervical cancer cells proliferation, migration, and invasion potential. Our investigation was the first time to report miR-144-3p as a tumor suppressive miRNA in cervical cancer. It inhibited tumor growth by targeting MAKP6. The newly identified signalling axis may serve as novel therapeutic targets to manage cervical cancer.

Model counting is the problem of computing the number of models or satisfying assignments for a given propositional formula, and is # P -complete. Owing to its inherent complexity, approximate model counting is an alternative in practice. Model counting using the extension rule is an exact method, and is considered as an alternative to resolution-based methods for model counting. Based on the exact method, we propose two approximate model counting algorithms, and prove the time complexity of the algorithms. Experimental results show that they have good performance in the accuracy and efficiency.

Based on a promising process of biomass to liquid (BTL) fuels, a pilot-scale plant including gasification, direct synthesis of dimethyl ether (DME) and DME to gasoline was developed. The evaluations of the designed facilities and the optimization of the operation parameters for the whole system were carried out. The several running tests demonstrated the feasibility of converting biomass to high octane gasoline in the developed pilot plant. The adopted critical components of the facilities were verified and the anticipated design goal was achieved. The operating results showed that both the pressure and gas hourly space velocity (GHSV) not only influenced the CO conversion and the DME yield, but also had a significant effect on the manipulation of the reaction heat in the adiabatic reactor. High pressure and low GHSV favored the high CO conversion and the DME yield. Considering CO conversion and temperature controlling of the adiabatic reactor, the optimized pressure and GHSV of the synthesis process were 2.2–2.5 MPa and 1200 h−1, respectively. The per-pass conversion of CO and the production capacity of gasoline were about 45% and 4.4 kg/h, respectively under this condition.

With increasing energy demand and environmental problems in China, energy gases, such as natural gas(NG), coalbed methane(CBM) and coke-oven gas(COG) are alternatives of coal with great potential due to abundant reserves and remarkable environmental superiority for carbon reduction. Based on a large number of sampling data derived from comprehensive field investigations, energy gases used in China are researched on their compositions, net calorific values(NCVs), resources distributions and annual productions firstly in this paper, and then the Chinese-specific carbon contents by gas type and consuming sector are calculated to establish basic data for accurate estimation of carbon emissions. Results show that Chinese-specific carbon contents for NG, CBM and COG are 15.19 kg/GJ [15.15–15.37 kg/GJ], 15.13 kg/GJ [14.72–15.39 kg/GJ] and 11.41 kg/GJ [9.95–12.34 kg/GJ], respectively. Compared to IPCC, both carbon contents of NG and CBM are in close proximity to the default value, while COG has an obviously lower carbon content. Little difference exists among carbon contents of NG utilized in four main sectors including industry, power generation, households and vehicles, while a relatively obvious difference exists among those of COG. Carbon emissions would be greatly reduced by increasing the share of gas consumption in each sector. The market circumstances, application technologies, development bottlenecks as well as emission reduction potentials are studied respectively for the four utilization sectors with bright prospects. Finally, effective policy recommendations are given on the development of energy gases for the purpose of achieving a win-win of economy and environment.

The pyrolysis processes of the selected combustible solid wastes (CSWs) including poplar wood, polyethylene (PE), paper, rubber, rice and polyethylene terephthalate (PET) were investigated by thermogravimetric (TG) analysis. The pseudocomponents of the six kinds of selected CSWs were determined based on Weibull mixture model. The DAEM reaction model was used to study the pyrolysis kinetics of the samples. The results showed that the pyrolysis temperature of selected CSWs was ranged from 413.15 to 979.14 K and the Weibull mixture model can reproduce the TG and DTG curves accurately. The TG curve can be understood as a sum of two or three parallel reactions, where each single reaction was represented by one Weibull model component. It was observed that there were three pseudocomponents for the poplar and paper samples. There was one pseudocomponent for the PE sample. However, the rubber, rice and PET samples had two pseudocomponents. The activation energy (E α ) changed from 120 to 225 kJ mol−1 in the range of the conversion rate from 0.15 to 0.85 for the six kinds of selected CSWs.

To investigate the lignin pyrolysis mechanism, two α-O-4 and one completely substituted β-O-4 lignin dimeric model compounds were studied using in situ synchrotron vacuum ultraviolet photoionization time-of-flight mass spectrometry (SVUV PIMS) at 350–500 °C. The collision-reduced vacuum condition, in situ characteristic, and “soft” ionization technique of this reactor system allowed for the direct detection of thermolysis radicals and high-boiling-point compounds. For the α-O-4 compound 4-(benzyloxy)phenol (BOP) pyrolysis, benzyl radical, p-semiquinone radical, toluene, and bibenzyl were confirmed, supplying firm evidence for the free radical dominant mechanism. Experiments of the p-methoxy substituent on the hydroxyl position of BOP show that the p-methoxy substituent can lower the Caromatic–Cα and Oether–Caromatic bond dissociation energies. For β-O-4 compound guaiacylglycerol-β-guaiacyl ether (GGGE), it is inferred that the C–O homolysis mechanism was minor and the concerted reaction dominated in the e...

With the rapid development of high-throughput sequencing technologies, huge volumes of generated cancer genomics data make it into reality to understand the carcinogenic pathogenesis from the molecular level. It is believed that the study of commonalities among different cancers is one of the significant problems for understanding cancers, and will be beneficial for personalized therapy and precision medicine in cancer treatment. The ComMDP method is a useful one for solving this problem. However, when there is a substantially difference among the number of samples, the method of accumulating the absolute weight value of every cancer, employed by the ComMDP method, may give rise to missing some driver pathways. In this paper, two mathematical models CDP-V and CDP-H, replacing the absolute weight values with relative ones, are presented by using variance and harmonic mean, respectively. By devising a sort of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm is proposed for solving these two models. Extensive experiments were performed on both simulated and real cancer data. The experimental results show that given several types of cancer, the gene sets identified based on the presented models and algorithm not only mutate in a large proportion of samples of these cancers, but have close proportion of mutated samples in each cancer. In addition, some biologically meaningful gene sets, which are missed by the ComMDP one, are indeed detected. Hence the identified methods based on the presented models and algorithm may become useful complementary tools for identifying cancer pathways.