Showing papers by "Mississippi State University published in 2020"

TRY plant trait database : Enhanced coverage and open access

Abstract: 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.

A comprehensive quantification of global nitrous oxide sources and sinks

Abstract: Nitrous oxide (N2O), like carbon dioxide, is a long-lived greenhouse gas that accumulates in the atmosphere. Over the past 150 years, increasing atmospheric N2O concentrations have contributed to stratospheric ozone depletion1 and climate change2, with the current rate of increase estimated at 2 per cent per decade. Existing national inventories do not provide a full picture of N2O emissions, owing to their omission of natural sources and limitations in methodology for attributing anthropogenic sources. Here we present a global N2O inventory that incorporates both natural and anthropogenic sources and accounts for the interaction between nitrogen additions and the biochemical processes that control N2O emissions. We use bottom-up (inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and top-down (atmospheric inversion) approaches to provide a comprehensive quantification of global N2O sources and sinks resulting from 21 natural and human sectors between 1980 and 2016. Global N2O emissions were 17.0 (minimum-maximum estimates: 12.2-23.5) teragrams of nitrogen per year (bottom-up) and 16.9 (15.9-17.7) teragrams of nitrogen per year (top-down) between 2007 and 2016. Global human-induced emissions, which are dominated by nitrogen additions to croplands, increased by 30% over the past four decades to 7.3 (4.2-11.4) teragrams of nitrogen per year. This increase was mainly responsible for the growth in the atmospheric burden. Our findings point to growing N2O emissions in emerging economies-particularly Brazil, China and India. Analysis of process-based model estimates reveals an emerging N2O-climate feedback resulting from interactions between nitrogen additions and climate change. The recent growth in N2O emissions exceeds some of the highest projected emission scenarios3,4, underscoring the urgency to mitigate N2O emissions.

More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification

Abstract: 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 datasets. Furthermore, the codes and datasets will be available at this https URL, contributing to the RS community.

The New Tree of Eukaryotes

Abstract: For 15 years, the eukaryote Tree of Life (eToL) has been divided into five to eight major groupings, known as ‘supergroups’. However, the tree has been profoundly rearranged during this time. The new eToL results from the widespread application of phylogenomics and numerous discoveries of major lineages of eukaryotes, mostly free-living heterotrophic protists. The evidence that supports the tree has transitioned from a synthesis of molecular phylogenetics and biological characters to purely molecular phylogenetics. Most current supergroups lack defining morphological or cell-biological characteristics, making the supergroup label even more arbitrary than before. Going forward, the combination of traditional culturing with maturing culture-free approaches and phylogenomics should accelerate the process of completing and resolving the eToL at its deepest levels.

Achieving sustainable aquaculture: Historical and current perspectives and future needs and challenges

Abstract: Important operational changes that have gradually been assimilated and new approaches that are developing as part of the movement toward sustainable intensive aquaculture production systems are presented via historical, current, and future perspectives Improved environmental and economic sustainability based on increased efficiency of production continues to be realized As a result, aquaculture continues to reduce its carbon footprint through reduced greenhouse gas emissions Reduced use of freshwater and land resources per unit of production, improved feed management practices as well as increased knowledge of nutrient requirements, effective feed ingredients and additives, domestication of species, and new farming practices are now being applied or evaluated Successful expansion into culture of marine species, both off and on shore, offers the potential of substantial increases in sustainable intensive aquaculture production combined with integrative efforts to increase efficiency will principally contribute to satisfying the increasing global demand for protein and food security needs

Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN

Abstract: Multisensor fusion is of great importance in Earth observation related applications For instance, hyperspectral images (HSIs) provide wealthy spectral information while light detection and ranging (LiDAR) data provide elevation information, and using HSI and LiDAR data together can achieve better classification performance In this paper, an unsupervised feature extraction framework, named as patch-to-patch convolutional neural network (PToP CNN), is proposed for collaborative classification of hyperspectral and LiDAR data More specific, a three-tower PToP mapping is first developed to seek an accurate representation from HSI to LiDAR data, aiming at merging multiscale features between two different sources Then, by integrating hidden layers of the designed PToP CNN, extracted features are expected to possess deeply fused characteristics Accordingly, features from different hidden layers are concatenated into a stacked vector and fed into three fully connected layers To verify the effectiveness of the proposed classification framework, experiments are executed on two benchmark remote sensing data sets The experimental results demonstrate that the proposed method provides superior performance when compared with some state-of-the-art classifiers, such as two-branch CNN and context CNN

Changes to virus taxonomy and the Statutes ratified by the International Committee on Taxonomy of Viruses (2020).

Abstract: This article reports the changes to virus classification and taxonomy approved and ratified by the International Committee on Taxonomy of Viruses (ICTV) in March 2020 The entire ICTV was invited to vote on 206 taxonomic proposals approved by the ICTV Executive Committee at its meeting in July 2019, as well as on the proposed revision of the ICTV Statutes All proposals and the revision of the Statutes were approved by an absolute majority of the ICTV voting membership Of note, ICTV has approved a proposal that extends the previously established realm Riboviria to encompass nearly all RNA viruses and reverse-transcribing viruses, and approved three separate proposals to establish three realms for viruses with DNA genomes

Genomic diversifications of five Gossypium allopolyploid species and their impact on cotton improvement.

Abstract: Polyploidy is an evolutionary innovation for many animals and all flowering plants, but its impact on selection and domestication remains elusive. Here we analyze genome evolution and diversification for all five allopolyploid cotton species, including economically important Upland and Pima cottons. Although these polyploid genomes are conserved in gene content and synteny, they have diversified by subgenomic transposon exchanges that equilibrate genome size, evolutionary rate heterogeneities and positive selection between homoeologs within and among lineages. These differential evolutionary trajectories are accompanied by gene-family diversification and homoeolog expression divergence among polyploid lineages. Selection and domestication drive parallel gene expression similarities in fibers of two cultivated cottons, involving coexpression networks and N6-methyladenosine RNA modifications. Furthermore, polyploidy induces recombination suppression, which correlates with altered epigenetic landscapes and can be overcome by wild introgression. These genomic insights will empower efforts to manipulate genetic recombination and modify epigenetic landscapes and target genes for crop improvement.

A Global Blue Revolution: Aquaculture Growth Across Regions, Species, and Countries

Abstract: Discussions about global aquaculture production and prospects for future growth largely focus on Asia, where most global production takes place. Countries in Asia accounted for about 89% of global ...

The New Scope of Virus Taxonomy: Partitioning the Virosphere Into 15 Hierarchical Ranks

Abstract: Virus taxonomy emerged as a discipline in the middle of the twentieth century. Traditionally, classification by virus taxonomists has been focussed on the grouping of relatively closely related viruses. However, during the past few years, the International Committee on Taxonomy of Viruses (ICTV) has recognized that the taxonomy it develops can be usefully extended to include the basal evolutionary relationships among distantly related viruses. Consequently, the ICTV has changed its Code to allow a 15-rank classification hierarchy that closely aligns with the Linnaean taxonomic system and may accommodate the entire spectrum of genetic divergence in the virosphere. The current taxonomies of three human pathogens, Ebola virus, severe acute respiratory syndrome coronavirus and herpes simplex virus 1 are used to illustrate the impact of the expanded rank structure. This new rank hierarchy of virus taxonomy will stimulate further research on virus origins and evolution, and vice versa, and could promote crosstalk with the taxonomies of cellular organisms.

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

Abstract: With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.

Development of a Highly Effective African Swine Fever Virus Vaccine by Deletion of the I177L Gene Results in Sterile Immunity against the Current Epidemic Eurasia Strain.

Abstract: African swine fever virus (ASFV) is the etiological agent of a contagious and often lethal disease of domestic pigs that has significant economic consequences for the swine industry. The disease is devastating the swine industry in Central Europe and East Asia, with current outbreaks caused by circulating strains of ASFV derived from the 2007 Georgia isolate (ASFV-G), a genotype II ASFV. In the absence of any available vaccines, African swine fever (ASF) outbreak containment relies on the control and culling of infected animals. Limited cross-protection studies suggest that in order to ensure a vaccine is effective, it must be derived from the current outbreak strain or at the very least from an isolate with the same genotype. Here, we report the discovery that the deletion of a previously uncharacterized gene, I177L, from the highly virulent ASFV-G produces complete virus attenuation in swine. Animals inoculated intramuscularly with the virus lacking the I177L gene, ASFV-G-ΔI177L, at a dose range of 102 to 106 50% hemadsorbing doses (HAD50), remained clinically normal during the 28-day observational period. All ASFV-G-ΔI177L-infected animals had low viremia titers, showed no virus shedding, and developed a strong virus-specific antibody response; importantly, they were protected when challenged with the virulent parental strain ASFV-G. ASFV-G-ΔI177L is one of the few experimental vaccine candidate virus strains reported to be able to induce protection against the ASFV Georgia isolate, and it is the first vaccine capable of inducing sterile immunity against the current ASFV strain responsible for recent outbreaks.IMPORTANCE Currently, there is no commercially available vaccine against African swine fever. Outbreaks of this disease are devastating the swine industry from Central Europe to East Asia, and they are being caused by circulating strains of African swine fever virus derived from the Georgia 2007 isolate. Here, we report the discovery of a previously uncharacterized virus gene, which when deleted completely attenuates the Georgia isolate. Importantly, animals infected with this genetically modified virus were protected from developing ASF after challenge with the virulent parental virus. Interestingly, ASFV-G-ΔI177L confers protection even at low doses (102 HAD50) and remains completely attenuated when inoculated at high doses (106 HAD50), demonstrating its potential as a safe vaccine candidate. At medium or higher doses (104 HAD50), sterile immunity is achieved. Therefore, ASFV-G-ΔI177L is a novel efficacious experimental ASF vaccine protecting pigs from the epidemiologically relevant ASFV Georgia isolate.

Immune Responses Regulated by Cannabidiol.

Abstract: Introduction: Cannabidiol (CBD) as Epidiolex® (GW Pharmaceuticals) was recently approved by the U.S. Food and Drug Administration (FDA) to treat rare forms of epilepsy in patients 2 years of age and older. Together with the increased societal acceptance of recreational cannabis and CBD oil for putative medical use in many states, the exposure to CBD is increasing, even though all of its biological effects are not understood. Once such example is the ability of CBD to be anti-inflammatory and immune suppressive, so the purpose of this review is to summarize effects and mechanisms of CBD in the immune system. It includes a consideration of reports identifying receptors through which CBD acts, since the "CBD receptor," if a single one exists, has not been definitively identified for the myriad immune system effects. The review then provides a summary of in vivo and in vitro effects in the immune system, in autoimmune models, with a focus on experimental autoimmune encephalomyelitis, and ends with identification of knowledge gaps. Conclusion: Overall, the data overwhelmingly support the notion that CBD is immune suppressive and that the mechanisms involve direct suppression of activation of various immune cell types, induction of apoptosis, and promotion of regulatory cells, which, in turn, control other immune cell targets.

Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture

Abstract: Earth observation using multisensor data is drawing increasing attention. Fusing remotely sensed hyperspectral imagery and light detection and ranging (LiDAR) data helps to increase application performance. In this article, joint classification of hyperspectral imagery and LiDAR data is investigated using an effective hierarchical random walk network (HRWN). In the proposed HRWN, a dual-tunnel convolutional neural network (CNN) architecture is first developed to capture spectral and spatial features. A pixelwise affinity branch is proposed to capture the relationships between classes with different elevation information from LiDAR data and confirm the spatial contrast of classification. Then in the designed hierarchical random walk layer, the predicted distribution of dual-tunnel CNN serves as global prior while pixelwise affinity reflects the local similarity of pixel pairs, which enforce spatial consistency in the deeper layers of networks. Finally, a classification map is obtained by calculating the probability distribution. Experimental results validated with three real multisensor remote sensing data demonstrate that the proposed HRWN significantly outperforms other state-of-the-art methods. For example, the two branches CNN classifier achieves an accuracy of 88.91% on the University of Houston campus data set, while the proposed HRWN classifier obtains an accuracy of 93.61%, resulting in an improvement of approximately 5%.

A Multi-Agent Deep Reinforcement Learning Based Voltage Regulation Using Coordinated PV Inverters

Abstract: This paper proposes a multi-agent deep reinforcement learning-based approach for distribution system voltage regulation with high penetration of photovoltaics (PVs). The designed agents can learn the coordinated control strategies from historical data through the counter-training of local policy networks and centric critic networks. The learned strategies allow us to perform online coordinated control. Comparative results with other methods show the enhanced control capability of the proposed method under various conditions.

Dispersion tuning and route reconfiguration of acoustic waves in valley topological phononic crystals.

Abstract: The valley degree of freedom in crystals offers great potential for manipulating classical waves, however, few studies have investigated valley states with complex wavenumbers, valley states in graded systems, or dispersion tuning for valley states. Here, we present tunable valley phononic crystals (PCs) composed of hybrid channel-cavity cells with three tunable parameters. Our PCs support valley states and Dirac cones with complex wavenumbers. They can be configured to form chirped valley PCs in which edge modes are slowed to zero group velocity states, where the energy at different frequencies accumulates at different designated locations. They enable multiple functionalities, including tuning of dispersion relations for valley states, robust routing of surface acoustic waves, and spatial modulation of group velocities. This work may spark future investigations of topological states with complex wavenumbers in other classical systems, further study of topological states in graded materials, and the development of acoustic devices. The valley degree of freedom gives additional flexibility to tunable phononic and photonic crystals. Here, the authors realise a honeycomb phononic structure where both the size of the cavities and of the air channel can be actively tuned, allowing several functionalities in a broad frequency range.

Spatial–Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification

Abstract: In recent years, deep learning has presented a great advance in the hyperspectral image (HSI) classification. Particularly, long short-term memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs. However, the loss of spatial information makes it quite difficult to obtain better performance. In order to address this problem, two novel deep models are proposed to extract more discriminative spatial–spectral features by exploiting the convolutional LSTM (ConvLSTM). By taking the data patch in a local sliding window as the input of each memory cell band by band, the 2-D extended architecture of LSTM is considered for building the spatial–spectral ConvLSTM 2-D neural network (SSCL2DNN) to model long-range dependencies in the spectral domain. To better preserve the intrinsic structure information of the hyperspectral data, the spatial–spectral ConvLSTM 3-D neural network (SSCL3DNN) is proposed by extending LSTM to the 3-D version for further improving the classification performance. The experiments, conducted on three commonly used HSI data sets, demonstrate that the proposed deep models have certain competitive advantages and can provide better classification performance than the other state-of-the-art approaches.

Discriminative Reconstruction Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection

Abstract: The rich and distinguishable spectral information in hyperspectral images (HSIs) makes it possible to capture anomalous samples [i.e., anomaly detection (AD)] that deviate from background samples. However, hyperspectral anomaly detection (HAD) faces various challenges due to high dimensionality, redundant information, and unlabeled and limited samples. To address these problems, this article proposes an unsupervised discriminative reconstruction constrained generative adversarial network for HAD (HADGAN). Our solution is mainly based on the assumption that the number of normal samples is much larger than the number of abnormal ones. The key contribution of this article is to learn a discriminative background reconstruction with anomaly targets being suppressed, which produces the initial detection image (i.e., the residual image between the original image and reconstructed image) with anomaly targets being highlighted and background samples being suppressed. To accomplish this goal, first, by using an autoencoder (AE) network and an adversarial latent discriminator, the latent feature layer learns normal background distribution and AE learns a background reconstruction as much as possible. Second, consistency enhanced representation and shrink constraints are added to the latent feature layer to ensure that anomaly samples are projected to similar positions as normal samples in the latent feature layer. Third, using an adversarial image feature corrector in the input space can guarantee the reliability of the generated samples. Finally, an energy-based spatial and distance-based spectral joint anomaly detector is applied in the residual map to generate the final detection map. Experiments conducted on several data sets over different scenes demonstrate its state-of-the-art performance.

Biodegradable nanofiber-based piezoelectric transducer

Abstract: Piezoelectric materials, a type of "smart" material that generates electricity while deforming and vice versa, have been used extensively for many important implantable medical devices such as sensors, transducers, and actuators. However, commonly utilized piezoelectric materials are either toxic or nondegradable. Thus, implanted devices employing these materials raise a significant concern in terms of safety issues and often require an invasive removal surgery, which can damage directly interfaced tissues/organs. Here, we present a strategy for materials processing, device assembly, and electronic integration to 1) create biodegradable and biocompatible piezoelectric PLLA [poly(l-lactic acid)] nanofibers with a highly controllable, efficient, and stable piezoelectric performance, and 2) demonstrate device applications of this nanomaterial, including a highly sensitive biodegradable pressure sensor for monitoring vital physiological pressures and a biodegradable ultrasonic transducer for blood-brain barrier opening that can be used to facilitate the delivery of drugs into the brain. These significant applications, which have not been achieved so far by conventional piezoelectric materials and bulk piezoelectric PLLA, demonstrate the PLLA nanofibers as a powerful material platform that offers a profound impact on various medical fields including drug delivery, tissue engineering, and implanted medical devices.

Dissecting the economic impact of soybean diseases in the United States over two decades.

Abstract: Soybean (Glycine max L. Merrill) is an economically important commodity for United States agriculture. Nonetheless, the profitability of soybean production has been negatively impacted by soybean diseases. The economic impacts of 23 common soybean diseases were estimated in 28 soybean-producing states in the U.S., from 1996 to 2016 (the entire data set consisted of 13,524 data points). Estimated losses were investigated using a variety of statistical approaches. The main effects of state, year, pre- and post-discovery of soybean rust, region, and zones based on yield, harvest area, and production, were significant on “total economic loss” as a function of diseases. Across states and years, the soybean cyst nematode, charcoal rot, and seedling diseases were the most economically damaging diseases while soybean rust, bacterial blight, and southern blight were the least economically damaging. A significantly greater mean loss (51%) was observed in states/years after the discovery of soybean rust (2004 to 2016) compared to the pre-discovery (1996 to 2003). From 1996 to 2016, the total estimated economic loss due to soybean diseases in the U.S. was $95.48 billion, with $80.89 billion and $14.59 billion accounting for the northern and southern U.S. losses, respectively. Over the entire time period, the average annual economic loss due to soybean diseases in the U.S. reached nearly $4.55 billion, with approximately 85% of the losses occurring in the northern U.S. Low yield/harvest/production zones had significantly lower mean economic losses due to diseases in comparison to high yield/harvest/production zones. This observation was further bolstered by the observed positive linear correlation of mean soybean yield loss (in each state, due to all diseases considered in this study, across 21 years) with the mean state wide soybean production (MT), mean soybean yield (kg ha-1), and mean soybean harvest area (ha). Results of this investigation provide useful insights into how research, policy, and educational efforts should be prioritized in soybean disease management

Hyperspectral Pansharpening With Deep Priors

Abstract: Hyperspectral (HS) image can describe subtle differences in the spectral signatures of materials, but it has low spatial resolution limited by the existing technical and budget constraints. In this paper, we propose a promising HS pansharpening method with deep priors (HPDP) to fuse a low-resolution (LR) HS image with a high-resolution (HR) panchromatic (PAN) image. Different from the existing methods, we redefine the spectral response function (SRF) based on the larger eigenvalue of structure tensor (ST) matrix for the first time that is more in line with the characteristics of HS imaging. Then, we introduce HFNet to capture deep residual mapping of high frequency across the upsampled HS image and the PAN image in a band-by-band manner. Specifically, the learned residual mapping of high frequency is injected into the structural transformed HS images, which are the extracted deep priors served as additional constraint in a Sylvester equation to estimate the final HR HS image. Comparative analyses validate that the proposed HPDP method presents the superior pansharpening performance by ensuring higher quality both in spatial and spectral domains for all types of data sets. In addition, the HFNet is trained in the high-frequency domain based on multispectral (MS) images, which overcomes the sensitivity of deep neural network (DNN) to data sets acquired by different sensors and the difficulty of insufficient training samples for HS pansharpening.

A planet within the debris disk around the pre-main-sequence star AU Microscopii

Abstract: AU Microscopii (AU Mic) is the second closest pre-main-sequence star, at a distance of 979 parsecs and with an age of 22 million years1 AU Mic possesses a relatively rare2 and spatially resolved3 edge-on debris disk extending from about 35 to 210 astronomical units from the star4, and with clumps exhibiting non-Keplerian motion5-7 Detection of newly formed planets around such a star is challenged by the presence of spots, plage, flares and other manifestations of magnetic 'activity' on the star8,9 Here we report observations of a planet transiting AU Mic The transiting planet, AU Mic b, has an orbital period of 846 days, an orbital distance of 007 astronomical units, a radius of 04 Jupiter radii, and a mass of less than 018 Jupiter masses at 3σ confidence Our observations of a planet co-existing with a debris disk offer the opportunity to test the predictions of current models of planet formation and evolution

The CLAS12 Spectrometer at Jefferson Laboratory

Abstract: The CEBAF Large Acceptance Spectrometer for operation at 12 GeV beam energy (CLAS12) in Hall B at Jefferson Laboratory is used to study electro-induced nuclear and hadronic reactions. This spectrometer provides efficient detection of charged and neutral particles over a large fraction of the full solid angle. CLAS12 has been part of the energy-doubling project of Jefferson Lab’s Continuous Electron Beam Accelerator Facility, funded by the United States Department of Energy. An international collaboration of 48 institutions contributed to the design and construction of detector hardware, developed the software packages for the simulation of complex event patterns, and commissioned the detector systems. CLAS12 is based on a dual-magnet system with a superconducting torus magnet that provides a largely azimuthal field distribution that covers the forward polar angle range up to 35 ∘ , and a solenoid magnet and detector covering the polar angles from 35° to 125° with full azimuthal coverage. Trajectory reconstruction in the forward direction using drift chambers and in the central direction using a vertex tracker results in momentum resolutions of < 1% and < 3%, respectively. Cherenkov counters, time-of-flight scintillators, and electromagnetic calorimeters provide good particle identification. Fast triggering and high data-acquisition rates allow operation at a luminosity of 1035 cm −2 s −1 . These capabilities are being used in a broad program to study the structure and interactions of nucleons, nuclei, and mesons, using polarized and unpolarized electron beams and targets for beam energies up to 11 GeV. This paper gives a general description of the design, construction, and performance of CLAS12.

Perceptions of offsite construction in the United States: An investigation of current practices

Abstract: New offsite construction practices are emerging in the construction industry to address a broad set of productivity issues that cut across organizational, technological, and strategic domains. Despite all its benefits from sustainable and economic perspectives, offsite construction still faces significant resistance from both the construction industry and the built-environment market. Evaluating advances in this industry is critical, especially because there is a need to determine the factors that are preventing the industry from more fully and rapidly adopting offsite strategies. This paper aims to investigate the industry perspective regarding the adoption of offsite strategies and provide an understanding of the development of the offsite construction industry over time. In this regard, the “State-of-the-Art of Modular Construction Symposium” was held in May 2017 at the University of Florida with the intent to bring together stakeholders engaged in multi-trade prefabrication to debate the drivers, challenges and future directions of the offsite industry. During this event, data was collected by means of unstructured interviews and a questionnaire for the purpose of determining the characteristics the U.S. offsite construction industry. A SWOT framework was used to assess the strengths and weaknesses of the industry adopting offsite strategies as well as the external opportunities and threats. The questionnaire survey data analysis showed the current drivers, core elements, barriers, and possible solutions to the barriers of further implementation of offsite construction. The results are intended to help construction organizations understand the potential benefits of offsite construction and assist them in creating a roadmap for their future strategic development.