Natural hydrogen emanations in Namibia: Field acquisition and vegetation indexes from multispectral satellite image analysis

Natural hydrogen exploration is now active in various places of the world. Onshore, correlation between natural H₂ generation and the presence of iron rich rocks especially from Archean and Neoproterozoic cratons have been observed. Emanations and accumulations of H₂ have already been confirmed in such geological settings in Australia, South Africa and Brazil. The geological similitude and the presence of numerous sub circular depressions that are a good proxy for hydrogen emanations suggest that hydrogen resources may also exist in Namibia. We present here the results of a data acquisition campaign which allowed us to confirm the presence of natural hydrogen in this country in the vicinity of Neoproterozoic Banded Iron Formation. The H₂ content in the soil, as in Brazil, is variable within the depressions in time and space and is particularly time sensitive across the day. Comparison of the H₂ signal versus time within these two regions shows a similar behavior of the soils with an increase of the H₂ flow at the middle of the day. In addition, these new data allow us to better constrain the morphological characteristics of such H₂-emiting depressions. By using satellite images and digital elevation model we propose a new proxy to differentiate potentially H₂-emiting features from other type of depressions such as Salt Pan. The Landsat multispectral images and their processing through NDVI and SAVI indexes, that highlight a ring of healthy vegetation around the sub circular area with scarce vegetation already observed appear able to discriminate between H₂ emitting structures and other soft depressions.     

Recent progress on low-cost ceramic membrane for water and wastewater treatment

Great progress in the development of low-cost ceramic membranes from alternative materials have been achieved recently towards various application especially water and wastewater treatment. However, their significance has not been fully recognized and understood especially in term of their microstructural analysis such as formation of grain growth and microcracks. This review paper summarizes fabrication method, alternative materials, microstructure, wettability, mechanical properties and application of low-cost ceramic membrane. The fabrication method including slip casting, tape casting, extrusion, pressing method and phase inversion technique are described. Alternative materials used in low-cost ceramic membrane fabrication are discussed and categorized into clays, agricultural waste, industrial waste and animal bone waste. The mechanisms of morphology formation, microstructure and wettability properties are analysed. Modification strategies for the surface of low-cost ceramic membrane are discussed, and classified into modification for separation application, modification for photocatalytic application and modification for membrane distillation and membrane contactor system. Modification improves the membrane structure by changing the pore size, porosity and wettability properties of low-cost ceramic membranes. Mechanical properties of low-cost ceramic membranes are also discussed in detail towards several mechanism, like grain growth phenomenon and formation of microcracks which also considered as membrane defects. Grain growth phenomenon can be divided into normal and abnormal grain growth. Meanwhile, formation of microcracks could be occurred in single-phase polycrystalline ceramics that have anisotropic grains or biphasic polycrystalline grains. The application of low-cost ceramic membrane in seawater desalination, oily wastewater treatment, heavy metal adsorption, textile separation and photocatalytic application are reviewed. Finally, some possible opportunities and challenges for further development of low-cost ceramic membrane are pointed out.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

A novel graph-based clustering method using noise cutting

Recently, many methods have appeared in the field of cluster analysis. Most existing clustering algorithms have considerable limitations in dealing with local and nonlinear data patterns. Algorithms based on graphs provide good results for this problem. However, some widely used graph-based clustering methods, such as spectral clustering algorithms, are sensitive to noise and outliers. In this paper, a cut-point clustering algorithm (CutPC) based on a natural neighbor graph is proposed. The CutPC method performs noise cutting when a cut-point value is above the critical value. Normally, the method can automatically identify clusters with arbitrary shapes and detect outliers without any prior knowledge or preparatory parameter settings. The user can also adjust a coefficient to adapt clustering solutions for particular problems better. Experimental results on various synthetic and real-world datasets demonstrate the obvious superiority of CutPC compared with k-means, DBSCAN, DPC, SC, and DCore.     

Sustainable hydrogen roadmap: A holistic review and decision-making methodology for production,utilisation and exportation using Qatar as a case study

Hydrogen is seen as a promising and inevitable energy carrier in the transition towards a carbon-free energy era. This study reviews the potential for carbon-free hydrogen production, utilisation and exportation from the State of Qatar. The study aims to introduce a roadmap for current and future exploration of carbon-free hydrogen production and exportation from Qatar, for which an assessment of several available alternatives for the production of hydrogen in Qatar is performed. These alternatives include the use of natural gas as a feedstock for hydrogen production through steam methane reforming (SMR), solar integrated steam methane reforming with carbon capture, as well as the possibilities for hydrogen production from electrolysis using renewables and ammonia as another intermediate. The potential of each alternative is reviewed based on selected technical, economic and environmental criteria. The findings of this review study indicate that the production and exportation of blue ammonia currently present the best pathway for Qatar, while green hydrogen is expected to become as competitive as blue ammonia in the mid-future. It is widely accepted that as the technologies associated with clean hydrogen production improve, and the cost of renewable energy falls, green hydrogen will become quite competitive in the region.     

基于深度学习的文本自动摘要方案

针对自然语言处理（NLP）生成式自动摘要领域的语义理解不充分、摘要语句不通顺和摘要准确度不够高的问题，提出了一种新的生成式自动摘要解决方案，包括一种改进的词向量生成技术和一个生成式自动摘要模型。改进的词向量生成技术以Skip-Gram方法生成的词向量为基础，结合摘要的特点，引入词性、词频和逆文本频率三个词特征，有效地提高了词语的理解；而提出的Bi-MulRnn+生成式自动摘要模型以序列映射（seq2seq）与自编码器结构为基础，引入注意力机制、门控循环单元（GRU）结构、双向循环神经网络（BiRnn）、多层循环神经网络（MultiRnn）和集束搜索，提高了生成式摘要准确性与语句流畅度。基于大规模中文短文本摘要（LCSTS）数据集的实验结果表明，该方案能够有效地解决短文本生成式摘要问题，并在Rouge标准评价体系中表现良好，提高了摘要准确性与语句流畅度。     

The effects of fuel variability on the electrical performance and durability of a solid oxide fuel cell operating on biohythane

Biohythane is typically composed of 60/30/10 vol% CH₄/CO₂/H₂ and can be produced via two-stage anaerobic digestion of renewable and low carbon biomass with much greater efficiency compared with CH₄/CO₂ biogas. This work investigates the effects of fuel variability on the electrical performance and fuel processing of a commercially available anode supported solid oxide fuel cell (SOFC) operating on biohythane mixtures at 750 °C. Cell electrical performance was characterised using current-voltage curves and electrochemical impedance spectroscopy. Fuel processing was characterised using quadrupole mass spectroscopy. It is shown that when H₂/CO₂ is blended with CH₄ to make biohythane, the SOFC efficiency is significantly increased, high SOFC durability is achieved, and there are considerable savings in CH₄ consumption. Enhanced electrical performance was due to the additional presence of H₂ and promotion of CH₄ dry reforming, the reverse Boudouard and reverse water-gas shift reactions. These processes alleviated carbon deposition and promoted electrochemical oxidation of H₂ as the primary power production pathway. Substituting 50 vol% CH₄ with 25/75 vol% H₂/CO₂ was shown to increase cell power output by 81.6% at 0.8 V compared with pure CH₄. This corresponded to a 3.4-fold increase in the overall energy conversion efficiency and a 72% decrease in CH₄ consumption. A 260 h durability test demonstrated very high cell durability when operating on a typical 60/30/10 vol% CH₄/CO₂/H₂ biohythane mixture under high fuel utilisation due to inhibition of carbon deposition. Overall, this work suggests that decarbonising gas grids by substituting natural gas with renewably produced H₂/CO₂ mixtures (rather than pure H₂ derived from fossil fuels), and utilising in SOFC technology, gives considerable gains in energy conversion efficiency and carbon emissions savings.     

Eugenol,citral, and hexanal,alone or in combination with heat,affect viability,biofilm formation,and swarming on Shiga-toxin-producing Escherichia coli

Shiga-toxin-producing Escherichia coli strains are pathogenic for humans and cause mild to severe illnesses. In this study, the antimicrobial effect of citral, eugenol, and hexanal in combination with heat shock (HS) was evaluated in terms of the growth, biofilm formation, swarming, and expression of virulence genes of STEC serotypes (O157:H7, O103, O111, and O26). Eugenol was the most effective compound against the growth of E. coli strains (MBC = 0.58 to 0.73 mg/mL), followed by citral (MBC = 0.86 to 1.26 mg/mL) and hexanal (MBC = 2.24 to 2.52 mg/mL). Biofilm formation and swarming motility have great variability between STEC strains. Natural compounds—alone or combined with HS—inhibited biofilm formation; however, swarming motility was induced by most treatments. The expression of the studied genes during biofilm formation and swarming under natural antimicrobials was affected but not in a uniform pattern. These treatments could be used to control contamination of STEC and inhibit biofilm formation.     

Research on the hydrogen injection strategy of a direct injection natural gas/hydrogen rotary engine considering apex seal leakage

The purpose of the present paper is to investigate the hydrogen injection strategy on the combustion performance of a natural gas/hydrogen rotary engine. Considering that apex seal leakage (ASL) is an inevitable problem in the actual working process of a rotary engine, the action of ASL cannot be ignored for an in-depth study of its combustion performance. Therefore, in this paper, a 3D dynamic simulation model that put the effect of ASL into consideration was established. Furthermore, based on the established 3D model, the combustion process of a natural gas/hydrogen rotary engine under various hydrogen injection angle (HIA) and hydrogen injection timing (HIT) was investigated. The results indicated that the hydrogen jet flow first impacted on the rotor wall after entering the cylinder, and then diffused under the action of the vortexes in the cylinder. Therefore, the HIA and HIT could change the hydrogen distribution by changing the hydrogen impact location and the intensities of the vortexes in the cylinder. In addition, the ideal hydrogen distribution at the ignition timing which could improve the combustion efficiency was given. That is, under the premise of ensuring minimized hydrogen leakage, the hydrogen should mainly distribute in the middle and the front of the cylinder, and a high hydrogen concentration is maintained near the spark plug.     

Computational personality recognition from Facebook text: psycholinguistic features,words and facets

ABSTRACT

Advances in the Natural Language Processing (NLP) and machine learning fields have led to the development of automated methods for the recognition of personality traits from text available from social media and similar sources. Systems of this kind exploit the close relation between lexical knowledge and personality models – such as the well-known Big Five model – to provide information about the author of an input text in a non-intrusive fashion, and at a low cost. Although now a well-established research topic in the field, the computational recognition of personality traits from text still leaves a number of research questions worth further exploration. In particular, this paper attempts to shed light on three main issues: (i) whether we may develop psycholinguistics-motivated models of personality recognition when such knowledge sources are not available for the target language under consideration; (ii) whether the use of psycholinguistic knowledge may be still superior to contemporary word vector representations; and (iii) whether we may infer certain personality facets from a corpus that does not explicitly convey this information. In this paper these issues are dealt with in a series of individual experiments of personality recognition from Facebook text, whose initial results should aid the future development of more robust systems of this kind.