A pilot study on affective classification of facial images for emerging news topics

The proliferation of news reports published in online websites and news information sharing among social media users necessitates effective techniques for analysing the image, text and video data related to news topics. This paper presents the first study to classify affective facial images on emerging news topics. The proposed system dynamically monitors and selects the current hot (of great interest) news topics with strong affective interestingness using textual keywords in news articles and social media discussions. Images from the selected hot topics are extracted and classified into three categorized emotions, positive, neutral and negative, based on facial expressions of subjects in the images. Performance evaluations on two facial image datasets collected from real-world resources demonstrate the applicability and effectiveness of the proposed system in affective classification of facial images in news reports. Facial expression shows high consistency with the affective textual content in news reports for positive emotion, while only low correlation has been observed for neutral and negative. The system can be directly used for applications, such as assisting editors in choosing photos with a proper affective semantic for a certain topic during news report preparation.

People’s perceptions about the importance of forests on Borneo

We ascertained villagers’ perceptions about the importance of forests for their livelihoods and health through 1,837 reliably answered interviews of mostly male respondents from 185 villages in Indonesian and Malaysian Borneo. Variation in these perceptions related to several environmental and social variables, as shown in classification and regression analyses. Overall patterns indicated that forest use and cultural values are highest among people on Borneo who live close to remaining forest, and especially among older Christian residents. Support for forest clearing depended strongly on the scale at which deforestation occurs. Deforestation for small-scale agriculture was generally considered to be positive because it directly benefits people’s welfare. Large-scale deforestation (e.g., for industrial oil palm or acacia plantations), on the other hand, appeared to be more context-dependent, with most respondents considering it to have overall negative impacts on them, but with people in some areas considering the benefits to outweigh the costs. The interviews indicated high awareness of negative environmental impacts of deforestation, with high levels of concern over higher temperatures, air pollution and loss of clean water sources. Our study is unique in its geographic and trans-national scale. Our findings enable the development of maps of forest use and perceptions that could inform land use planning at a range of scales. Incorporating perspectives such as these could significantly reduce conflict over forest resources and ultimately result in more equitable development processes.

Large scale read classification for next generation sequencing

Next Generation Sequencing (NGS) has revolutionised molecular biology, resulting in an explosion of data sets and an increasing role in clinical practice. Such applications necessarily require rapid identification of the organism as a prelude to annotation and further analysis. NGS data consist of a substantial number of short sequence reads, given context through downstream assembly and annotation, a process requiring reads consistent with the assumed species or species group. Highly accurate results have been obtained for restricted sets using SVM classifiers, but such methods are difficult to parallelise and success depends on careful attention to feature selection. This work examines the problem at very large scale, using a mix of synthetic and real data with a view to determining the overall structure of the problem and the effectiveness of parallel ensembles of simpler classifiers (principally random forests) in addressing the challenges of large scale genomics.

Effective routing with utilisation of cross-layer information for industrial wireless sensor networks

This thesis focuses on providing reliable data transmissions in large-scale industrial wireless sensor networks through improving network layer protocols. It addresses three major problems: scalability, dynamic industrial environments and coexistence of multiple types of data traffic in a network. Theoretical developments are conducted, followed by simulation studies for verification of theoretic results. The approach proposed in this thesis has been shown to be effective for large-scale network implementation and to provide improved data transmission reliability for both periodic and sporadic traffic.

Decomposition analysis of air pollution abatement in China: empirical study for ten industrial sectors from 1998 to 2009

This study analyzes the management of air pollutant substance in Chinese industrial sectors from 1998 to 2009. Decomposition analysis applying the logarithmic mean divisia index is used to analyze changes in emissions of air pollutants with a focus on the following five factors: coal pollution intensity (CPI), end-of-pipe treatment (EOP), the energy mix (EM), productive efficiency change (EFF), and production scale changes (PSC). Three pollutants are the main focus of this study: sulfur dioxide (SO2), dust, and soot. The novelty of this paper is focusing on the impact of the elimination policy on air pollution management in China by type of industry using the scale merit effect for pollution abatement technology change. First, the increase in SO2 emissions from Chinese industrial sectors because of the increase in the production scale is demonstrated. However, the EOP equipment that induced this change and improvements in energy efficiency has prevented an increase in SO2 emissions that is commensurate with the increase in production. Second, soot emissions were successfully reduced and controlled in all industries except the steel industry between 1998 and 2009, even though the production scale expanded for these industries. This reduction was achieved through improvements in EOP technology and in energy efficiency. Dust emissions decreased by nearly 65% between 1998 and 2009 in the Chinese industrial sectors. This successful reduction in emissions was achieved by implementing EOP technology and pollution prevention activities during the production processes, especially in the cement industry. Finally, pollution prevention in the cement industry is shown to result from production technology development rather than scale merit. © 2013 Elsevier Ltd. All rights reserved.

Determinants of eco-efficiency in the Chinese industrial sector

This study measures productive inefficiency within the context of multi-environmental pollution (eco-efficiency) in the Chinese industrial sector. The weighted Russell directional distance model is applied to measure eco-efficiency using production technology. The objective is to clarify how external factors affect eco-efficiency. The major findings are that both foreign direct investment and investment for pollution abatement improve eco-efficiency as measured by air pollutant substances. A levy system for wastewater discharge improves eco-efficiency as measured by wastewater pollutant substances. However, an air pollutant levy does not significantly affect eco-efficiency as measured by air pollutants.

Social networking and digital gaming media convergence : Classification and its consequences for appropriation

Abstract Within the field of Information Systems, a good proportion of research is concerned with the work organisation and this has, to some extent, restricted the kind of application areas given consideration. Yet, it is clear that information and communication technology deployments beyond the work organisation are acquiring increased importance in our lives. With this in mind, we offer a field study of the appropriation of an online play space known as Habbo Hotel. Habbo Hotel, as a site of media convergence, incorporates social networking and digital gaming functionality. Our research highlights the ethical problems such a dual classification of technology may bring. We focus upon a particular set of activities undertaken within and facilitated by the space – scamming. Scammers dupe members with respect to their ‘Furni’, virtual objects that have online and offline economic value. Through our analysis we show that sometimes, online activities are bracketed off from those defined as offline and that this can be related to how the technology is classified by members – as a social networking site and/or a digital game. In turn, this may affect members’ beliefs about rights and wrongs. We conclude that given increasing media convergence, the way forward is to continue the project of educating people regarding the difficulties of determining rights and wrongs, and how rights and wrongs may be acted out with respect to new technologies of play online and offline.

The cytochrome P-450 isoenzyme CYP2E1 in the biological processing of industrial chemicals : consequences for occupational and environmental medicine

The importance of the isoform CYP2E1 of the human cytochrome P-450 superfamily of enzymes for occupational and environmental medicine is derived from its unique substrate spectrum that includes a number of highly important high-production chemicals, such as aliphatic and aromatic hydrocarbons, solvents and industrial monomers (i.a. alkanes, alkenes, aromatic and halogenated hydrocarbons). Many polymorphic genes, such as CYP2E1, show considerable differences in allelic distribution between different human populations. The polymorphic nature of the human CYP2E1 gene is significant for inter-individual differences in toxicity of its substrates. Since the substrate spectrum of CYP2E1 includes many compounds of basic relevance to industrial toxicology, a rationale for metabolic interactions of different CYP2E1 substrates is provided. In-depth research into the inter-individual phenotypic differences of human CYP2E1 enzyme activities was enabled by the recognition that the 6-hydroxylation of the drug chlorzoxazone is mediated by CYP2E1. Studies on CYP2E1 phenotyping have pointed to inter-individual variations in enzyme activities. There are consistent ethnic differences in CYP2E1 enzyme expression, mostly demonstrated between European and Japanese populations, which point to a major impact of genetic factors. The most frequently studied genetic polymorphisms are the restriction fragment length polymorphisms PstI/RsaI (mutant allele: CYP2E1*5B) located in the 5′-flanking region of the gene, as well as the DraI polymorphism (mutant allele: CYP2E1*6) located in intron 6. These polymorphisms are partly related, as they form the common allele designated CYP2E1*5A. Striking inter-ethnic differences between Europeans and Asians appear with respect to the frequencies of the CYP2E1*5A allele (only approximately 5% of Europeans are heterozygous, but 37% of Asians are, whilst 6% of Asians are homozygous). Available studies indicate a wide variation in human CYP2E1 expression, which are very likely based on complex gene-environment interactions. Major inter-ethnic differences are apparent on the genotyping and the phenotyping levels. Selected cases are presented where inter-ethnic variations of CYP2E1 may provide likely explanations for unexplained findings concerning industrial chemicals that are CYP2E1 substrates. Possible consequences of differential inter-individual and inter-ethnic susceptibilities are related to individual expressions of clinical symptoms of chemical toxicity, to results of biological monitoring of exposed workers, and to the interpretation of results of epidemiological or molecular-epidemiological studies.

Nephrotoxicity and nephrocarcinogenicity of dinitrotoluene : new aspects to be considered

Technical dinitrotoluene (DNT) is a mixture of 2,4- and 2,6-DNT. In humans, industrial or environmental exposure can occur orally, by inhalation, or by skin contact. The classification of DNT as an 'animal carcinogen' is based on the formation of malignant tumors in kidneys, liver, and mammary glands of rats and mice. Clear signs of toxic nephropathy were found in rats dosed with DNT, and the concept was derived of an interrelation between renal toxicity and carcinogenicity. Recent data point to the carcinogenicity of DNT on the urinary tract of exposed humans. Between 1984 and 1997, 6 cases of urothelial cancer and 14 cases of renal cell cancer were diagnosed in a group of 500 underground mining workers in the copper mining industry of the former GDR and having high exposures to explosives containing technical DNT. The incidences of both urothelial and renal cell tumors in this group were 4.5 and 14.3 times higher, respectively, than anticipated on the basis of the cancer registers of the GDR. The genotyping of all identified tumor patients for the polymorphic enzymes NAT2, GSTM1, and GSTT1 identified the urothelial tumor cases as exclusively 'slow acetylates'. A group of 161 miners highly exposed to DNT was investigated for signs of subclinical renal damage. The exposures were categorized semi-quantitatively into 'low', 'medium', 'high', and 'very high'. A straight dose-dependence of the excretion of urinary biomarker proteins with the ranking of exposure was seen. Biomarker excretion (alpha1-microglobulin, glutathione S-transferases alpha and pi) indicated that DNT-induced damage was directed toward the tubular system. New data on DNT-exposed humans appear consistent with the concept of cancer initiation by DNT isomers and the subsequent promotion of renal carcinogenesis by selective damage to the proximal tubule. The differential pathways of metabolic activation of DNT appear to apply to the proximal tubule of the kidney and to the urothelium of the renal pelvis and lower urinary tract as target tissues of carcinogenicity.

Locality-sensitive hashing for protein classification

Determination of sequence similarity is a central issue in computational biology, a problem addressed primarily through BLAST, an alignment based heuristic which has underpinned much of the analysis and annotation of the genomic era. Despite their success, alignment-based approaches scale poorly with increasing data set size, and are not robust under structural sequence rearrangements. Successive waves of innovation in sequencing technologies – so-called Next Generation Sequencing (NGS) approaches – have led to an explosion in data availability, challenging existing methods and motivating novel approaches to sequence representation and similarity scoring, including adaptation of existing methods from other domains such as information retrieval. In this work, we investigate locality-sensitive hashing of sequences through binary document signatures, applying the method to a bacterial protein classification task. Here, the goal is to predict the gene family to which a given query protein belongs. Experiments carried out on a pair of small but biologically realistic datasets (the full protein repertoires of families of Chlamydia and Staphylococcus aureus genomes respectively) show that a measure of similarity obtained by locality sensitive hashing gives highly accurate results while offering a number of avenues which will lead to substantial performance improvements over BLAST..