Mississippi State University

Fast pyrolysis utilizes biomass to produce a product that is used both as an energy source and a feedstock for chemical production. Considerable efforts have been made to convert wood biomass to liquid fuels and chemicals since the oil crisis in mid-1970s. This review focuses on the recent developments in the wood pyrolysis and reports the characteristics of the resulting bio-oils, which are the main products of fast wood pyrolysis. Virtually any form of biomass can be considered for fast pyrolysis. Most work has been performed on wood, because of its consistency and comparability between tests. However, nearly 100 types of biomass have been tested, ranging from agricultural wastes such as straw, olive pits, and nut shells to energy crops such as miscanthus and sorghum. Forestry wastes such as bark and thinnings and other solid wastes, including sewage sludge and leather wastes, have also been studied. In this review, the main (although not exclusive) emphasis has been given to wood. The literature on wood/biomass pyrolysis, both fast and slow, is surveyed and both the physical and chemical aspects of the resulting bio-oils are reviewed. The effect of the wood composition and structure, heating rate, and residence time during pyrolysis on the overall reaction rate and the yield of the volatiles are also discussed. Although very fast and very slow pyrolyses of biomass produce markedly different products, the variety of heating rates, temperatures, residence times, and feedstock varieties found in the literature make generalizations difficult to define, in regard to trying to critically analyze the literature.

Self-efficacy theory asserts that personal mastery expectations are the primary determinants of behavioral change. Further, it is suggested that individual differences in past experiences and attribution of success to skill or chance result in different levels of generalized self-efficacy expectations. To measure these generalized expectancies, a Self-efficacy Scale was developed. A factor analysis yielded two subscales: a General Self-efficacy subscale (17 items) and a Social Self-efficacy subscale (6 items). Confirmation of several predicted conceptual relationships between the Self-efficacy subscales and other personality measures (i.e., Locus of Control, Personal Control, Social Desirability, Ego Strength, Interpersonal Competence, and Self-esteem) provided evidence of construct validity. Positive relationships between the Self-efficacy Scale and vocational, educational, and military success established criterion validity. Future research and clinical uses of the scale were discussed.

Sorghum, an African grass related to sugar cane and maize, is grown for food, feed, fibre and fuel. We present an initial analysis of the ∼730-megabase Sorghum bicolor (L.) Moench genome, placing ∼98% of genes in their chromosomal context using whole-genome shotgun sequence validated by genetic, physical and syntenic information. Genetic recombination is largely confined to about one-third of the sorghum genome with gene order and density similar to those of rice. Retrotransposon accumulation in recombinationally recalcitrant heterochromatin explains the ∼75% larger genome size of sorghum compared with rice. Although gene and repetitive DNA distributions have been preserved since palaeopolyploidization ∼70 million years ago, most duplicated gene sets lost one member before the sorghum–rice divergence. Concerted evolution makes one duplicated chromosomal segment appear to be only a few million years old. About 24% of genes are grass-specific and 7% are sorghum-specific. Recent gene and microRNA duplications may contribute to sorghum’s drought tolerance. The Sorghum bicolor genome sequence is published this week. Sorghum is a cereal grown widely as food, animal feed, fibre and fuel. Tolerant to hot, dry conditions, it is a staple for large populations in the West African Sahel region. Comparisons of the genome with those of maize and rice shed light on the evolution of grasses and of C4 photosynthesis, which is particularly efficient at assimilating carbon at high temperatures. In addition, protein coding genes and miRNAs that could contribute to sorghum's drought tolerance may also be found. Sorghum yield improvement has lagged behind that of other crops and the availability of the genome sequence could provide a vital boost to work on its improvement. Sorghum is an African grass that is grown for food, animal feed and fuel. The current paper presents an initial analysis of the ∼730 megabase genome of Sorghum bicolor. Genome analysis and its comparison with maize and rice shed light on grass genome evolution and also provide insights into the evolution of C4 photosynthesis, as well as protein coding genes and miRNAs that might contribute to sorghum's drought tolerance.

A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller–Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube.

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

In the last few decades, pharmaceuticals, credited with saving millions of lives, have emerged as a new class of environmental contaminant. These compounds can have both chronic and acute harmful effects on natural flora and fauna. The presence of pharmaceutical contaminants in ground waters, surface waters (lakes, rivers, and streams), sea water, wastewater treatment plants (influents and effluents), soils, and sludges has been well doccumented. A range of methods including oxidation, photolysis, UV-degradation, nanofiltration, reverse osmosis, and adsorption has been used for their remediation from aqueous systems. Many methods have been commercially limited by toxic sludge generation, incomplete removal, high capital and operating costs, and the need for skilled operating and maintenance personnel. Adsorption technologies are a low-cost alternative, easily used in developing countries where there is a dearth of advanced technologies, skilled personnel, and available capital, and adsorption appears to be the most broadly feasible pharmaceutical removal method. Adsorption remediation methods are easily integrated with wastewater treatment plants (WWTPs). Herein, we have reviewed the literature (1990-2018) illustrating the rising environmental pharmaceutical contamination concerns as well as remediation efforts emphasizing adsorption.

Top management teams make strategic decisions, and the products of their decision making influence organizational performance. However, a subtle paradox is embedded in this relationship. This study focuses on conflict as the crux of this paradox and provides evidence from two different samples of conflict's consistent yet contradictory effects on decision quality, consensus, and affective acceptance.

Continuum robotics has rapidly become a rich and diverse area of research, with many designs and applications demonstrated. Despite this diversity in form and purpose, there exists remarkable similarity in the fundamental simplified kinematic models that have been applied to continuum robots. However, this can easily be obscured, especially to a newcomer to the field, by the different applications, coordinate frame choices, and analytical formalisms employed. In this paper we review several modeling approaches in a common frame and notational convention, illustrating that for piecewise constant curvature, they produce identical results. This discussion elucidates what has been articulated in different ways by a number of researchers in the past several years, namely that constant-curvature kinematics can be considered as consisting of two separate submappings: one that is general and applies to all continuum robots, and another that is robot-specific. These mappings are then developed both for the single-section and for the multi-section case. Similarly, we discuss the decomposition of differential kinematics (the robot’s Jacobian) into robot-specific and robot-independent portions. The paper concludes with a perspective on several of the themes of current research that are shaping the future of continuum robotics.

Document Reviewers: Rudolf A. de Boer (CPG Review Coordinator) (Netherlands), P. Christian Schulze (CPG Review Coordinator) (Germany), Magdy Abdelhamid (Egypt), Victor Aboyans (France), Stamatis Adamopoulos (Greece), Stefan D. Anker (Germany), Elena Arbelo (Spain), Riccardo Asteggiano (Italy), Johann Bauersachs (Germany), Antoni Bayes-Genis (Spain), Michael A. Borger (Germany), Werner Budts (Belgium), Maja Cikes (Croatia), Kevin Damman (Netherlands), Victoria Delgado (Netherlands), Paul Dendale (Belgium), Polychronis Dilaveris (Greece), Heinz Drexel (Austria), Justin Ezekowitz (Canada), Volkmar Falk (Germany), Laurent Fauchier (France), Gerasimos Filippatos (Greece), Alan Fraser (United Kingdom), Norbert Frey (Germany), Chris P. Gale (United Kingdom), Finn Gustafsson (Denmark), Julie Harris (United Kingdom), Bernard Iung (France), Stefan Janssens (Belgium), Mariell Jessup (United States of America), Aleksandra Konradi (Russia), Dipak Kotecha (United Kingdom), Ekaterini Lambrinou (Cyprus), Patrizio Lancellotti (Belgium), Ulf Landmesser (Germany), Christophe Leclercq (France), Basil S. Lewis (Israel), Francisco Leyva (United Kingdom), AleVs Linhart (Czech Republic), Maja-Lisa Løchen (Norway), Lars H. Lund (Sweden), Donna Mancini (United States of America), Josep Masip (Spain), Davor Milicic (Croatia), Christian Mueller (Switzerland), Holger Nef (Germany), Jens-Cosedis Nielsen (Denmark), Lis Neubeck (United Kingdom), Michel Noutsias (Germany), Steffen E. Petersen (United Kingdom), Anna Sonia Petronio (Italy), Piotr Ponikowski (Poland), Eva Prescott (Denmark), Amina Rakisheva (Kazakhstan), Dimitrios J. Richter (Greece), Evgeny Schlyakhto (Russia), Petar Seferovic (Serbia), Michele Senni (Italy), Marta Sitges (Spain), Miguel Sousa-Uva (Portugal), Carlo G. Tocchetti (Italy), Rhian M. Touyz (United Kingdom), Carsten Tschoepe (Germany), Johannes Waltenberger (Germany/Switzerland) All experts involved in the development of these guidelines have submitted declarations of interest. These have been compiled in a report and published in a supplementary document simultaneously to the guidelines. The report is also available on the ESC website www.escardio.org/guidelines For the Supplementary Data which include background information and detailed discussion of the data that have provided the basis for the guidelines see European Heart Journal online.

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

To better determine the history of modern birds, we performed a genome-scale phylogenetic analysis of 48 species representing all orders of Neoaves using phylogenomic methods created to handle genome-scale data. We recovered a highly resolved tree that confirms previously controversial sister or close relationships. We identified the first divergence in Neoaves, two groups we named Passerea and Columbea, representing independent lineages of diverse and convergently evolved land and water bird species. Among Passerea, we infer the common ancestor of core landbirds to have been an apex predator and confirm independent gains of vocal learning. Among Columbea, we identify pigeons and flamingoes as belonging to sister clades. Even with whole genomes, some of the earliest branches in Neoaves proved challenging to resolve, which was best explained by massive protein-coding sequence convergence and high levels of incomplete lineage sorting that occurred during a rapid radiation after the Cretaceous-Paleogene mass extinction event about 66 million years ago.

Two draft sequences of Gossypium hirsutum, the most widely cultivated cotton species, provide insights into genome structure, genome rearrangement, gene evolution and cotton fiber biology. Upland cotton is a model for polyploid crop domestication and transgenic improvement. Here we sequenced the allotetraploid Gossypium hirsutum L. acc. TM-1 genome by integrating whole-genome shotgun reads, bacterial artificial chromosome (BAC)-end sequences and genotype-by-sequencing genetic maps. We assembled and annotated 32,032 A-subgenome genes and 34,402 D-subgenome genes. Structural rearrangements, gene loss, disrupted genes and sequence divergence were more common in the A subgenome than in the D subgenome, suggesting asymmetric evolution. However, no genome-wide expression dominance was found between the subgenomes. Genomic signatures of selection and domestication are associated with positively selected genes (PSGs) for fiber improvement in the A subgenome and for stress tolerance in the D subgenome. This draft genome sequence provides a resource for engineering superior cotton lines.

This revision of the classification of eukaryotes follows that of Adl et al., 2012 [J. Euk. Microbiol. 59(5)] and retains an emphasis on protists. Changes since have improved the resolution of many nodes in phylogenetic analyses. For some clades even families are being clearly resolved. As we had predicted, environmental sampling in the intervening years has massively increased the genetic information at hand. Consequently, we have discovered novel clades, exciting new genera and uncovered a massive species level diversity beyond the morphological species descriptions. Several clades known from environmental samples only have now found their home. Sampling soils, deeper marine waters and the deep sea will continue to fill us with surprises. The main changes in this revision are the confirmation that eukaryotes form at least two domains, the loss of monophyly in the Excavata, robust support for the Haptista and Cryptista. We provide suggested primer sets for DNA sequences from environmental samples that are effective for each clade. We have provided a guide to trophic functional guilds in an appendix, to facilitate the interpretation of environmental samples, and a standardized taxonomic guide for East Asian users.

Liquid phase sintering (LPS) is a process for forming high performance, multiple-phase components from powders. It involves sintering under conditions where solid grains coexist with a wetting liquid. Many variants of LPS are applied to a wide range of engineering materials. Example applications for this technology are found in automobile engine connecting rods and high-speed metal cutting inserts. Scientific advances in understanding LPS began in the 1950s. The resulting quantitative process models are now embedded in computer simulations to enable predictions of the sintered component dimensions, microstructure, and properties. However, there are remaining areas in need of research attention. This LPS review, based on over 2,500 publications, outlines what happens when mixed powders are heated to the LPS temperature, with a focus on the densification and microstructure evolution events.

Abiotic stress is a primary threat to fulfill the demand of agricultural production to feed the world in coming decades. Plants reduce growth and development process during stress conditions, which ultimately affect the yield. In stress conditions, plants develop various stress mechanism to face the magnitude of stress challenges, although that is not enough to protect them. Therefore, many strategies have been used to produce abiotic stress tolerance crop plants, among them, abscisic acid (ABA) phytohormone engineering could be one of the methods of choice. ABA is an isoprenoid phytohormone, which regulates various physiological processes ranging from stomatal opening to protein storage and provides adaptation to many stresses like drought, salt, and cold stresses. ABA is also called an important messenger that acts as the signaling mediator for regulating the adaptive response of plants to different environmental stress conditions. In this review, we will discuss the role of ABA in response to abiotic stress at the molecular level and ABA signaling. The review also deals with the effect of ABA in respect to gene expression.

Hydrogen is considered to be the most viable energy carrier for the future. Producing hydrogen from ethanol steam reforming would not only be environmentally friendly but also would open new opportunities for utilization of renewable resources, which are globally available. This paper reviews the current state of the steam reforming process of ethanol, examines different catalysts, and, finally, makes a comparative analysis. Different catalysts have been used for the steam reforming of ethanol. Depending on the type of catalysts, reaction conditions, and the catalyst preparation method, ethanol conversion and hydrogen production vary greatly. It was observed that Co/ZnO, ZnO, Rh/Al2O3, Rh/CeO2, and Ni/La2O3−Al2O3 performed the best, in regard to the steam reforming of ethanol. Currently, hydrogen production from ethanol steam reforming is still in the research and development stage.

The behavioral agency model suggests that to preserve socioemotional wealth, loss-averse family firms usually invest less in R&D than nonfamily firms. However, behavioral agency model predictions are inconsistent with the well-accepted premise that family firms have a long-term investment orientation. We reconcile these seemingly incompatible predictions by adding insights from the myopic loss aversion framework, which deals with the impact of decision-making time horizons. The combination of these two prospect theory derivatives led us to hypothesize that family firms usually invest less in R&D than nonfamily firms but the variability of their investments will be greater owing to differences in the compatibility of long- and short-term family goals with the economic goals of a firm. However, when performance is below aspiration levels, we theorize that family goals and economic goals tend to converge. In this situation, the R&D investments of family firms are expected to increase and the variability of those investments decrease, relative to nonfamily firms. Analysis of 964 publicly held family and nonfamily firms from the Standard & Poor's 1500 between 1998 and 2007 support our hypotheses, confirming a need to take the heterogeneity of family firms more fully into account.

Classification and identification of the materials lying over or beneath the earth's surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS), and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework. In particular, we also investigate a special case of multi-modality learning (MML)-cross-modality learning (CML) that exists widely in RS image classification applications. By focusing on “what,” “where,” and “how” to fuse, we show different fusion strategies as well as how to train deep networks and build the network architecture. Specifically, five fusion architectures are introduced and developed, further being unified in our MDL framework. More significantly, our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs). To validate the effectiveness and superiority of the MDL framework, extensive experiments related to the settings of MML and CML are conducted on two different multimodal RS data sets. Furthermore, the codes and data sets will be available at https://github.com/danfenghong/IEEE_TGRS_MDL-RS, contributing to the RS community.

Information privacy refers to the desire of individuals to control or have some influence over data about themselves. Advances in information technology have raised concerns about information privacy and its impacts, and have motivated Information Systems researchers to explore information privacy issues, including technical solutions to address these concerns. In this paper, we inform researchers about the current state of information privacy research in IS through a critical analysis of the IS literature that considers information privacy as a key construct. The review of the literature reveals that information privacy is a multilevel concept, but rarely studied as such. We also find that information privacy research has been heavily reliant on student-based and USA-centric samples, which results in findings of limited generalizability. Information privacy research focuses on explaining and predicting theoretical contributions, with few studies in journal articles focusing on design and action contributions. We recommend that future research should consider different levels of analysis as well as multilevel effects of information privacy. We illustrate this with a multilevel framework for information privacy concerns. We call for research on information privacy to use a broader diversity of sampling populations, and for more design and action information privacy research to be published in journal articles that can result in IT artifacts for protection or control of information privacy.

Sequencing of the genome of the butterfly Heliconius melpomene shows that closely related Heliconius species exchange protective colour-pattern genes promiscuously. Heliconius butterflies are an excellent system in which to study ecology, behaviour, mimicry and speciation. The genome of the postman butterfly Heliconius melpomene has now been sequenced. Using genomic resequencing of individuals from distinct lineages, the authors document heterogenous patterns of genomic diversity associated with adaptively divergent wing-colour patterns. As the second lepidopteran genome to be sequenced, Heliconius offers novel opportunities for comparative genomics within this economically significant insect order, which includes many pest species, as well as the only domesticated insect, the silkmoth Bombyx mori. The evolutionary importance of hybridization and introgression has long been debated1. Hybrids are usually rare and unfit, but even infrequent hybridization can aid adaptation by transferring beneficial traits between species. Here we use genomic tools to investigate introgression in Heliconius, a rapidly radiating genus of neotropical butterflies widely used in studies of ecology, behaviour, mimicry and speciation2,3,4,5. We sequenced the genome of Heliconius melpomene and compared it with other taxa to investigate chromosomal evolution in Lepidoptera and gene flow among multiple Heliconius species and races. Among 12,669 predicted genes, biologically important expansions of families of chemosensory and Hox genes are particularly noteworthy. Chromosomal organization has remained broadly conserved since the Cretaceous period, when butterflies split from the Bombyx (silkmoth) lineage. Using genomic resequencing, we show hybrid exchange of genes between three co-mimics, Heliconius melpomene, Heliconius timareta and Heliconius elevatus, especially at two genomic regions that control mimicry pattern. We infer that closely related Heliconius species exchange protective colour-pattern genes promiscuously, implying that hybridization has an important role in adaptive radiation.