Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history

Timber production is the purpose for managing plantation forests, and its spatial and quantitative information is critical for advising management strategies. Previous studies have focused on growing stock volume (GSV), which represents the current potential of timber production, yet few studies have investigated historical process-harvested timber. This resulted in a gap in a synthetical ecosystem service assessment of timber production. In this paper, we established a Management Process-based Timber production (MPT) framework to integrate the current GSV and the harvested timber derived from historical logging regimes, trying to synthetically assess timber production for a historical period. In the MPT framework, age-class and current GSV determine the times of historical thinning and the corresponding harvested timber, by using a "space-for-time" substitution. The total timber production can be estimated by the historical harvested timber in each thinning and the current GSV. To test this MPT framework, an empirical study on a larch plantation (LP) with area of 43,946 ha was conducted in North China for a period from 1962 to 2010. Field-based inventory data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) and Landsat-8 OLI (Operational Land Imager) data for estimating the age-class and current GSV of LP. The random forest model with PALSAR backscatter intensity channels and OLI bands as input predictive variables yielded an accuracy of 67.9% with a Kappa coefficient of 0.59 for age-class classification. The regression model using PALSAR data produced a root mean square error (RMSE) of 36.5 m(3) ha(-1). The total timber production of LP was estimated to be 7.27 x 10(6) m(3), with 4.87 x 10(6) m(3) in current GSV and 2.40 x 10(6) m(3) in harvested timber through historical thinning. The historical process-harvested timber accounts to 33.0% of the total timber production, which component has been neglected in the assessments for current status of plantation forests. Synthetically considering the RMSE for predictive GSV and misclassification of age-class, the error in timber production were supposed to range from -55.2 to 56.3 m(3) ha(-1). The MPT framework can be used to assess timber production of other tree species at a larger spatial scale, providing crucial information for a better understanding of forest ecosystem service. (C) 2016 Elsevier B.V. All rights reserved.ArticleINTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION.52:155-165(2016)journal articl

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Purpose: While there are no clear indications of whether central lymph node dissection is necessary in patients with T1-T2, non-invasive, clinically uninvolved central neck lymph nodes papillary thyroid carcinoma (PTC), this study seeks to develop and validate models for predicting the risk of central lymph node metastasis (CLNM) in these patients based on machine learning algorithms. Methods: This is a retrospective study comprising 1,271 patients with T1-T2 stage, non-invasive, and clinically node negative (cN0) PTC who underwent surgery at the Department of Endocrine and Breast Surgery of The First Affiliated Hospital of Chongqing Medical University from February 1, 2016, to December 31, 2018. We applied six machine learning (ML) algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Neural Network (NNET), coupled with preoperative clinical characteristics and intraoperative information to develop prediction models for CLNM. Among all the samples, 70% were randomly selected to train the models while the remaining 30% were used for validation. Indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance. Results: The results showed that ~51.3% (652 out of 1,271) of the patients had pN1 disease. In multivariate logistic regression analyses, gender, tumor size and location, multifocality, age, and Delphian lymph node status were all independent predictors of CLNM. In predicting CLNM, six ML algorithms posted AUROC of 0.70–0.75, with the extreme gradient boosting (XGBoost) model standing out, registering 0.75. Thus, we employed the best-performing ML algorithm model and uploaded the results to a self-made online risk calculator to estimate an individual's probability of CLNM (https://jin63.shinyapps.io/ML_CLNM/). Conclusions: With the incorporation of preoperative and intraoperative risk factors, ML algorithms can achieve acceptable prediction of CLNM with Xgboost model performing the best. Our online risk calculator based on ML algorithm may help determine the optimal extent of initial surgical treatment for patients with T1-T2 stage, non-invasive, and clinically node negative PTC

Nutrient recovery technologies for management of blackwater: A review

Nutrient recovery and recycling are of great importance in sustainable development. Blackwater (BW) refers to wastewater from toilets, which contains feces, urine, water, and toilet paper from flush toilets. The highly concentrated nutrients of blackwater could be collected through source separation and treated adequately to recover nutrients efficiently and economically. The review intends to give an overview of the characteristics of BW and different techniques to recover nutrients and other valuable products. A number of these technologies are currently under development or being tested at laboratory or pilot scale. The perspective for blackwater nutrient recovery technologies is very positive due to their great potential. For application of source-oriented sanitation infrastructure and systems, there is still a long way to go for development of commercial technologies and valuable products

The flavonoids from <i>Polygonum avicular</i>

1319-1320A new flavonoid, 5, 3'-dihydroxy-4'-O-angeloxyflavone-7-O-β--glucopyranoside 1 has been isolated from Polygonum aviculare, together with quercetin -3-O-α--arabifuranoside (2, avicularin), luteolin-7-O- β--glucopyranoside 3, luteolin-5-O-β--glucopyranoside 4, quercetin-3-O-β--glucopyranoside 5 and vitexin 6 and their structures have been established by spectroscopic methods and chemical reactions

Bundle Block Adjustment of Airborne Three-Line Array Imagery Based on Rotation Angles

In the midst of the rapid developments in electronic instruments and remote sensing technologies, airborne three-line array sensors and their applications are being widely promoted and plentiful research related to data processing and high precision geo-referencing technologies is under way. The exterior orientation parameters (EOPs), which are measured by the integrated positioning and orientation system (POS) of airborne three-line sensors, however, have inevitable systematic errors, so the level of precision of direct geo-referencing is not sufficiently accurate for surveying and mapping applications. Consequently, a few ground control points are necessary to refine the exterior orientation parameters, and this paper will discuss bundle block adjustment models based on the systematic error compensation and the orientation image, considering the principle of an image sensor and the characteristics of the integrated POS. Unlike the models available in the literature, which mainly use a quaternion to represent the rotation matrix of exterior orientation, three rotation angles are directly used in order to effectively model and eliminate the systematic errors of the POS observations. Very good experimental results have been achieved with several real datasets that verify the correctness and effectiveness of the proposed adjustment models

Undercooling, Thermal Stability, and Application in Exothermic Catalytic Reaction of SiO₂ Encapsulated SnZnCu Microspheres

SiO2 encapsulated SnZnCu microspheres (several micrometers to about 30 μm in diameter) with very low undercooling, narrow freezing/melting range, and high thermal cycling stability have been produced and used as the temperature stabilizer of the packed bed in highly exothermic Fischer–Tropsch reaction. The core–shell structured SnZnCu@SiO2 microspheres are prepared in a two-step way, namely SnZnCu microspheres are firstly produced via a molten LiCl–KCl–CsCl eutectic-based metal emulsion method, and then a sol–gel approach is employed to coat them with a uniform, anti-leakage SiO2 layer. It is found that raising the amount of Zn to 4.0 at.% is critical for achieving a very low undercooling (0.04Cux@SiO2 vs. about 84 °C for Sn@SiO2) and a narrow freezing/melting peak width, and both undercooling and peak width are almost unchanged as the Cu content (x) increases from 1.5 to 3.0 at.%. However, their thermal cycling stability depends positively on the amount of Cu and can be remarkably improved when 3.0 at.% Cu is added. The results also show that low undercooling and narrow freezing/melting peak width are associated with the formation of Sn–Zn–Cu ternary eutectic and metastable phase Cu5Zn8, and poor thermal cycling stability of SnZn0.04Cux@SiO2 microspheres with low Cu content is related to the decomposition of Cu5Zn8 during thermal cycling. By embedding thermally stable SnZn0.04Cu0.03@SiO2 microspheres into the Co/SiO2 catalyst for Fischer–Tropsch synthesis, the temperature gradient in the catalyst bed can be significantly reduced by suppressing the formation of hot spots or thermal runaway and thus rapid deactivation of Co catalyst that occurs in the SnZn0.04Cux@SiO2-absent Co/SiO2 catalyst can be avoided

Mechanism of <i>Astragalus membranaceus</i> Alleviating Acquired Hyperlipidemia Induced by High-Fat Diet through Regulating Lipid Metabolism

Astragalus membranaceus (AM) is a food and medicinal homologous plant. The current research is aimed to investigate the beneficial effects and mechanisms of AM in treating acquired hyperlipidemia. The network pharmacology and bioinformatics analysis results showed 481 AM-related targets and 474 acquired hyperlipidemia-associated targets, and 101 candidate targets were obtained through the intersection, mainly enriched in endocrine resistance, AGE-RAGE in diabetic complications and p53 signaling pathways. Quercetin, kaempferol, calycosin, formononetin and isorhamnetin were determined as the candidate active components of AM in the treatment of acquired hyperlipidemia. Moreover, key targets of AM, namely, AKT serine/threonine kinase 1 (AKT1), vascular endothelial growth factor A (VEGFA), cyclin D1 (CCND1) and estrogen receptor 1 (ESR1), were screened out, which were closely related to adipogenesis, fatty acid metabolism and bile acid metabolism. The subsequent animal experiments showed that AM extract treatment improved the lipid profiles of the high-fat diet (HFD)-fed mice by reducing lipogenesis and increasing lipolysis and lipid β-oxidation, which were associated with the downregulating of AKT1 and CCND1, and the upregulating of VEGFA and ESR1 in liver and adipose tissue. Overall, AM alleviated acquired hyperlipidemia through regulating lipid metabolism, and AKT1, VEGFA, CCND1 and ESR1 might be the key targets