scholarly journals Regularized Regression: A New Tool for Investigating and Predicting Tree Growth

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1283
Author(s):  
Stuart I. Graham ◽  
Ariel Rokem ◽  
Claire Fortunel ◽  
Nathan J. B. Kraft ◽  
Janneke Hille Ris Lambers
Keyword(s):  
Regression Model ◽  
Tree Growth ◽  
High Performance ◽  
Future Research ◽  
Regularized Regression ◽  
Species Identity ◽  
Explanatory Variables ◽  
Sample Tree ◽  
Neighborhood Models ◽  
Out Of Sample

Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due to the many empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, but it is unclear whether such an ecologically unrealistic model can provide accurate insights on tree growth. Rapid computation is becoming increasingly important as ecological datasets grow in size, and may be essential when using neighborhood models to predict tree growth beyond sample plots or into the future. We built a novel regularized regression model of tree growth and investigated whether it reached the same conclusions as a commonly used neighborhood model, regarding hypotheses of how tree growth is influenced by the species identity of neighboring trees. We also evaluated the ability of both models to interpolate the growth of trees not included in the model fitting dataset. Our regularized regression model replicated most of the classical model’s inferences in a fraction of the time without using high-performance computing resources. We found that both methods could interpolate out-of-sample tree growth, but the method making the most accurate predictions varied among focal species. Regularized regression is particularly efficient for comparing hypotheses because it automates the process of model selection and can handle correlated explanatory variables. This feature means that regularized regression could also be used to select among potential explanatory variables (e.g., climate variables) and thereby streamline the development of a classical neighborhood model. Both regularized regression and classical methods can interpolate out-of-sample tree growth, but future research must determine whether predictions can be extrapolated to trees experiencing novel conditions. Overall, we conclude that regularized regression methods can complement classical methods in the investigation of tree growth drivers and represent a valuable tool for advancing this field toward prediction.

scholarly journals Effects of Steel Fiber Percentage and Aspect Ratios on Fresh and Harden Properties of Ultra-High Performance Fiber

2021 ◽  
Vol 2 (3) ◽  
pp. 501-515
Author(s):  
Rajib Kumar Biswas ◽  
Farabi Bin Ahmed ◽  
Md. Ehsanul Haque ◽  
Afra Anam Provasha ◽  
Zahid Hasan ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Aspect Ratio ◽  
High Performance ◽  
Steel Fiber ◽  
Setting Time ◽  
Fiber Reinforced Concrete ◽  
Future Research ◽  
Manufacturing Cost ◽  
Aspect Ratios ◽  
High Performance Fiber

Steel fibers and their aspect ratios are important parameters that have significant influence on the mechanical properties of ultrahigh-performance fiber-reinforced concrete (UHPFRC). Steel fiber dosage also significantly contributes to the initial manufacturing cost of UHPFRC. This study presents a comprehensive literature review of the effects of steel fiber percentages and aspect ratios on the setting time, workability, and mechanical properties of UHPFRC. It was evident that (1) an increase in steel fiber dosage and aspect ratio negatively impacted workability, owing to the interlocking between fibers; (2) compressive strength was positively influenced by the steel fiber dosage and aspect ratio; and (3) a faster loading rate significantly improved the mechanical properties. There were also some shortcomings in the measurement method for setting time. Lastly, this research highlights current issues for future research. The findings of the study are useful for practicing engineers to understand the distinctive characteristics of UHPFRC.

scholarly journals Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Yuhang Yang ◽  
Zhiqiao Dong ◽  
Yuquan Meng ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Sampling Design ◽  
Three Dimensional ◽  
Measurement Data ◽  
Data Driven ◽  
Surface Measurement ◽  
Smart Manufacturing ◽  
Future Research ◽  
3D Surface

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

scholarly journals Materials for Production of High and Ultra-High Performance Concrete: Review and Perspective of Possible Novel Materials

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4304
Author(s):  
Markssuel Teixeira Marvila ◽  
Afonso Rangel Garcez de de Azevedo ◽  
Paulo R. de de Matos ◽  
Sergio Neves Monteiro ◽  
Carlos Maurício Fontes Vieira
Keyword(s):  
High Performance ◽  
High Performance Concrete ◽  
Review Article ◽  
Cement Matrix ◽  
Bibliographic Databases ◽  
Future Research ◽  
Chemical Additives ◽  
Ultra High Performance Concrete ◽  
Conventional Concrete ◽  
Essential Components

This review article proposes the identification and basic concepts of materials that might be used for the production of high-performance concrete (HPC) and ultra-high-performance concrete (UHPC). Although other reviews have addressed this topic, the present work differs by presenting relevant aspects on possible materials applied in the production of HPC and UHPC. The main innovation of this review article is to identify the perspectives for new materials that can be considered in the production of novel special concretes. After consulting different bibliographic databases, some information related to ordinary Portland cement (OPC), mineral additions, aggregates, and chemical additives used for the production of HPC and UHPC were highlighted. Relevant information on the application of synthetic and natural fibers is also highlighted in association with a cement matrix of HPC and UHPC, forming composites with properties superior to conventional concrete used in civil construction. The article also presents some relevant characteristics for the application of HPC and UHPC produced with alkali-activated cement, an alternative binder to OPC produced through the reaction between two essential components: precursors and activators. Some information about the main types of precursors, subdivided into materials rich in aluminosilicates and rich in calcium, were also highlighted. Finally, suggestions for future work related to the application of HPC and UHPC are highlighted, guiding future research on this topic.

scholarly journals Model predictive control for autonomous ground vehicles: a review

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Method ◽  
Theoretical Background ◽  
Future Research ◽  
Centralized Control ◽  
Ground Vehicles ◽  
Uncertain Environments ◽  
Autonomous Ground Vehicles

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

Phase-Shifting Electron Holography for Accurate Measurement of Potential Distributions in Organic and Inorganic Semiconductors

Microscopy ◽  
2020 ◽  
Author(s):  
Kazuo Yamamoto ◽  
Satoshi Anada ◽  
Takeshi Sato ◽  
Noriyuki Yoshimoto ◽  
Tsukasa Hirayama
Keyword(s):  
High Performance ◽  
Focused Ion Beam ◽  
Phase Measurement ◽  
Ion Beam ◽  
Functional Materials ◽  
Phase Shifting ◽  
Future Research ◽  
Electron Holography ◽  
Preparation Technique ◽  
Inorganic Semiconductors

Abstract Phase-shifting electron holography (PS-EH) is an interference transmission electron microscopy technique that accurately visualizes potential distributions in functional materials, such as semiconductors. In this paper, we briefly introduce the features of the PS-EH that overcome some of the issues facing the conventional EH based on Fourier transformation. Then, we present a high-precision PS-EH technique with multiple electron biprisms and a sample preparation technique using a cryo-focused-ion-beam, which are important techniques for the accurate phase measurement of semiconductors. We present several applications of PS-EH to demonstrate the potential in organic and inorganic semiconductors and then discuss the differences by comparing them with previous reports on the conventional EH. We show that in situ biasing PS-EH was able to observe not only electric potential distribution but also electric field and charge density at a GaAs p-n junction and clarify how local band structures, depletion layer widths, and space charges changed depending on the biasing conditions. Moreover, the PS-EH clearly visualized the local potential distributions of two-dimensional electron gas (2DEG) layers formed at AlGaN/GaN interfaces with different Al compositions. We also report the results of our PS-EH application for organic electroluminescence (OEL) multilayers and point out the significant potential changes in the layers. The proposed PS-EH enables more precise phase measurement compared to the conventional EH, and our findings introduced in this paper will contribute to the future research and development of high-performance semiconductor materials and devices.

scholarly journals Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 596
Author(s):  
Marco Buzzelli ◽  
Luca Segantin
Keyword(s):  
Real World ◽  
High Performance ◽  
Ad Hoc ◽  
Future Research ◽  
Levels Of Detail ◽  
Excellent Starting Point ◽  
Starting Point ◽  
Hierarchical Nature ◽  
Multiple Levels ◽  
Entire Dataset

We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.

Deciphering Links Between Sexual Violence and Castes in India

2021 ◽  
pp. 2455328X2110325
Author(s):  
Yogendra Musahar
Keyword(s):  
Regression Model ◽  
Sexual Violence ◽  
Economic Status ◽  
Family Health ◽  
Uttar Pradesh ◽  
Binary Logistic Regression ◽  
Wealth Index ◽  
Bivariate Analysis ◽  
Binary Logistic Regression Model ◽  
Explanatory Variables

The recent incident, the gang rape and murder of a 19-year-old woman in Hathras, a small village in Uttar Pradesh of India, once again sparks a debate on links between sexual violence and castes in India. This article aims to examine the links between sexual violence and castes in India. This study utilizes the national representative National Family Health Survey 4 (NFHS-4, 2015–16) data. A bivariate analysis was carried out to analyse the data. A binary logistic regression model was applied to predict the effect of explanatory variables, viz. type of place of residence, years of schooling complete, economic status in terms of wealth index and finally castes on predicted variable, i.e. sexual violence. The binary regression model indicates that there were links between sexual violence and castes. For secured and dignified life of women, caste-based sexual violence must be annihilated.

scholarly journals Characteristics and Trends of Research on New Energy Vehicle Reliability Based on the Web of Science

2018 ◽  
Vol 10 (10) ◽  
pp. 3560 ◽  
Author(s):  
Xian Zhao ◽  
Siqi Wang ◽  
Xiaoyue Wang
Keyword(s):  
Electric Vehicle ◽  
Energy Demand ◽  
High Performance ◽  
High Energy ◽  
Research Field ◽  
High Energy Density ◽  
Future Research ◽  
Charging System ◽  
New Energy ◽  
New Energy Vehicle

In order to satisfy the increasing energy demand and deal with the environmental problem caused by the conventional energy vehicle; the new energy vehicle (NEV), especially the electric vehicle (EV), has attracted increasing attention and the corresponding research has developed rapidly in recent years. The electric vehicle requires a battery with high energy density and frequent charging. In order to ensure high performance of the electric vehicle; the reliability of its charging system is extremely important. In this paper; an overview of the research on electric vehicle charging system reliability from 1998 to 2017 is presented from a bibliometric perspective. This study provides a comprehensive analysis of the current research climate and the emerging trends from the following four aspects: basic characteristics of publication outputs; including annual publication outputs and document types; collaboration analysis of countries/territories; institutions and authors; co-citation analysis of cited authors and cited references; co-occurrence analysis of subjects and keywords. By using CiteSpace; the collaboration relationship; co-citation and co-occurrence networks are shown clearly. According to the analysis results; studies in this research field will keep developing rapidly in the near future and several future research directions are proposed in the conclusions.

scholarly journals Comparative Profiling and Discovery of Novel Glycosylated Mycosporine-Like Amino Acids in Two Strains of the Cyanobacterium Scytonema cf. crispum

2016 ◽  
Vol 82 (19) ◽  
pp. 5951-5959 ◽  
Author(s):  
Paul M. D'Agostino ◽  
Vivek S. Javalkote ◽  
Rabia Mazmouz ◽  
Russell Pickford ◽  
Pravin R. Puranik ◽  
...  
Keyword(s):  
Amino Acids ◽  
New Zealand ◽  
Uv Radiation ◽  
High Performance ◽  
Parent Compound ◽  
Future Research ◽  
Content Type ◽  
Chromatography Analysis ◽  
Paralytic Shellfish Toxins ◽  
Cell Extracts

ABSTRACTThe mycosporine-like amino acids (MAAs) are a group of small molecules with a diverse ecological distribution among microorganisms. MAAs have a range of physiological functions, including protection against UV radiation, making them important from a biotechnological perspective. In the present study, we identified a putative MAA (mys) gene cluster in two New Zealand isolates ofScytonemacf.crispum(UCFS10 and UCFS15). Homology to “Anabaena-type”mysclusters suggested that this cluster was likely to be involved in shinorine biosynthesis. Surprisingly, high-performance liquid chromatography analysis ofS. cf.crispumcell extracts revealed a complex MAA profile, including shinorine, palythine-serine, and their hexose-bound variants. It was hypothesized that a short-chain dehydrogenase (UCFS15_00405) encoded by a gene adjacent to theS. cf.crispummyscluster was responsible for the conversion of shinorine to palythine-serine. Heterologous expression of MysABCE and UCFS15_00405 inEscherichia coliresulted in the exclusive production of the parent compound shinorine. Taken together, these results suggest that shinorine biosynthesis inS. cf.crispumproceeds via anAnabaena-type mechanism and that the genes responsible for the production of other MAA analogues, including palythine-serine and glycosylated analogues, may be located elsewhere in the genome.IMPORTANCERecently, New Zealand isolates ofS. cf.crispumwere linked to the production of paralytic shellfish toxins for the first time, but no other natural products from this species have been reported. Thus, the species was screened for important natural product biosynthesis. The mycosporine-like amino acids (MAAs) are among the strongest absorbers of UV radiation produced in nature. The identification of novel MAAs is important from a biotechnology perspective, as these molecules are able to be utilized as sunscreens. This study has identified two novel MAAs that have provided several new avenues of future research related to MAA genetics and biosynthesis. Further, we have revealed that the genetic basis of MAA biosynthesis may not be clustered on the genome. The identification of the genes responsible for MAA biosynthesis is vital for future genetic engineering.

Corrosion prevention prospects of polymeric nanocomposites: A review

2018 ◽  
Vol 35 (2) ◽  
pp. 181-202 ◽  
Author(s):  
Ayesha Kausar
Keyword(s):  
Corrosion Protection ◽  
High Performance ◽  
Barrier Properties ◽  
Metal Corrosion ◽  
Future Research ◽  
Polymeric Nanocomposites ◽  
Bipolar Plates ◽  
Corrosion Prevention ◽  
Covalent Interaction ◽  
Structural Parts

Corrosion is a serious problem for implementing metallic components and devices in industrial zones. Considerable effort has been made to develop corrosion prevention strategies. Initially, paints, pigments, and organic coatings have been applied to prevent metal corrosion. Consequently, conjugated polymers, epoxy resin, phenolics, acrylic polymers, and many thermoplastics as well as thermoset resins have been used to inhibit corrosion. Lately, nanofillers such as fullerene, nanodiamond, graphene, graphene oxide, carbon nanotube, carbon black, nanoclay, and inorganic nanoparticle have been introduced in polymeric matrices to harness valuable corrosion protection properties of the nanocomposite. Corrosion protection performance of a nanocomposite depends on nanofiller dispersion, physical and covalent interaction between matrix/nanofiller and nanofiller adhesion to the substrate. Moreover, a high performance anti-corrosion nanocomposite must have good barrier properties, and high scratch, impact, abrasion, and chemical resistance. Thus, polymeric nanocomposites have been found to prevent corrosion in aerospace and aircraft structural parts, electronic components, bipolar plates in fuel cells, and biomedical devices and systems. However, numerous challenges need to be addressed in this field to attain superior corrosion resistant nanocomposites. Future research on polymer nanocomposites has the potential to resolve the current challenges of metal corrosion through entire replacement of metal-based materials with advanced nanomaterials.

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