Article
Engineering, Civil
Zhiyong Liu, Jianfeng Xue, Jianzhong Ye, Jiangu Qian
Summary: This paper proposes a two-stage analysis method considering the interaction between undercrossing twin tunnels to estimate the induced response of existing upper tunnel. The results show that the new excavation has a greater impact on the settlement of upper tunnels compared to the first excavation, and the interaction makes it difficult to predict the settlement of existing tunnels.
TRANSPORTATION GEOTECHNICS
(2021)
Article
Environmental Sciences
Xiaozhuo Sun, Xiankui Zeng, Jichun Wu, Dong Wang
Summary: This study proposes a two-stage data-driven method to calibrate physical parameters and hyperparameters separately in groundwater/hydrology models, avoiding the issue of the independence assumption. Through three case studies, it was found that the two-stage method can improve parameter estimation overfitting and model prediction bias.
WATER RESOURCES RESEARCH
(2021)
Article
Optics
Stefano Biasi, Riccardo Franchi, Filippo Mione, Lorenzo Pavesi
Summary: Non-Hermitian physics in optics has provided a fertile ground for studying intriguing phenomena such as mode coalescence or exceptional points. In this study, we demonstrate a coherent interferometric excitation method that allows us to estimate both the real and imaginary parts of eigenvalues in optical microresonators. We also investigate the conditions for the merging of resonant doublets and the degeneracy splitting in one direction caused by non-Hermitian intermodal coupling.
PHOTONICS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Haomin Wang, Yi Peng, Gang Kou
Summary: Pairwise comparison is a powerful tool in decision making, but exact ratios for decision makers are difficult to provide due to knowledge limitations. A two-stage ranking method is proposed in this study to minimize ordinal violation, which proved effective in experiments involving numerical examples and real-world applications.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Jun Zhang, Xuedong Chen
Summary: This paper proposes a novel two-stage prediction model for accurate stock price prediction by using a decomposition algorithm, a nonlinear ensemble strategy, and three individual machine learning models. The first stage decomposes the stock price time series into sub-series using VMD, and then three individual machine learning models (SVR, ELM, and DNN) are used to predict the decomposed sub-series. The second stage employs an ELM-based nonlinear ensemble strategy to combine the preliminary stock price predictions. Empirical results demonstrate that the proposed model outperforms other competitor models in terms of accuracy evaluation, improvement percentage comparison, and statistical test.
Article
Mathematics, Applied
Chang-Yuan Cheng, Kuang-Hui Lin, Chih-Wen Shih
Summary: This study examines the impact of resource competition and environmental adaptation on the evolution of ecological populations by analyzing a model with two life stages and competition between two species. The findings reveal that competition strength is determined by the maturation times of the species and multiple competition outcomes exist.
JOURNAL OF DYNAMICS AND DIFFERENTIAL EQUATIONS
(2022)
Article
Thermodynamics
Wanli Peng, Julian Gonzalez-Ayala, Juncheng Guo, Jucan Dong, Qi Qin
Summary: The aim of this study was to analyze the energetic performance of a novel coupled system composed of a two-stage sodium thermal electrochemical converter and a two-stage thermoelectric generator. The main methods involved establishing a model of the coupling system, obtaining analytical expressions for power outputs and efficiencies, and discussing the influences of various parameters. The results showed significant improvements in efficiency and power output density compared to standalone systems, demonstrating the potential for efficient heat utilization.
APPLIED THERMAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Min Cao, Zhi Gao, Bharath Ramesh, Tiancan Mei, Jinqiang Cui
Summary: The paper proposes a two-stage density-aware single image deraining method with gated multi-scale feature fusion and conditional Generative Adversarial Network. Experimental results demonstrate the method's superior effectiveness and generalization ability, outperforming the current state-of-the-art techniques.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Information Systems
Yingqi Wang, Hongyu Han, Xin He, Rui Zhai
Summary: In this paper, the SASC (Sentiment Analysis based on Sentiment Clustering) method is proposed to address the issues of low accuracy and poor stability in review sentiment clustering methods. By utilizing two-stage sentiment clustering, hidden sentiment information in review texts is captured to enhance the accuracy and stability of the results. Specifically, the first stage introduces a review representation vector construction method using LDA topic model, while the second stage employs K-means algorithm for further optimization of sentiment clustering results. Experimental results on widely used datasets showcase that the SASC method outperforms other methods in terms of clustering accuracy and stability.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Business, Finance
Wei Chen, Haoyu Zhang, Lifen Jia
Summary: The paper proposes a diversified portfolio selection method based on stock prediction, which reduces the risk of losses for investors and improves returns.
NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE
(2022)
Article
Environmental Studies
C. J. Barrett, K. Bradley, A. Brazier
Summary: The curled octopus and common octopus are resident to the waters around the UK, but the latter is considered rare due to population crashes and range contractions. There is an emerging fishery for octopus in the UK, but it is difficult to understand population dynamics and assess stocks due to the lack of species-specific landings data. The need for species-specific octopus landings data is driven by the government's fisheries management plans and the growing demand for sustainable seafood.
Article
Engineering, Industrial
Mateus Martin, Horacio Hideki Yanasse, Maristela O. Santos, Reinaldo Morabito
Summary: This paper addresses three variants of the two-dimensional cutting stock problem and proposes integer linear programming models for them. Computational experiments show that satisfactory solutions can be obtained by reducing the number of open stacks.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Hexiang Bai, Gregoire Mariethoz
Summary: The paper introduces an efficient multipoint simulation method based on information theory, and proposes an edge-based two-stage strategy to achieve speed-up and reduce computational cost with promising experimental results.
COMPUTERS & GEOSCIENCES
(2021)
Article
Geochemistry & Geophysics
Tao Zhang, Sinong Quan, Zhen Yang, Weiwei Guo, Zenghui Zhang, Hongping Gan
Summary: Ship detection is an active topic in the field of Earth observation. This paper proposes a two-stage ship detection model and a new ship detection method. Experimental results show that this method is more effective in detecting small ships compared to other methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Biodiversity Conservation
Lukasz Mikolajczyk, Ryszard Laskowski, Elzbieta Ziolkowska, Agnieszka J. Bednarska
Summary: The study proposes a methodological approach to describe agricultural landscape properties tailored for specific species, simplifying complex landscape descriptions into a few main shaping factors for further analysis. The results suggest that for pollinators with a home range radius above ca. 100 m, both local habitat counts and large-scale landscape properties are important for biodiversity management.
ECOLOGICAL INDICATORS
(2021)
Article
Biology
Kasper Kansanen, Petteri Packalen, Matti Maltamo, Lauri Mehtatalo
Summary: This study introduces an estimator for population totals in forest surveys based on circular plot sampling, using distance-based detection probabilities derived from stochastic geometry. The estimator is shown to be unbiased for Poisson forests and provides variance estimates and approximate confidence intervals, with simulation results demonstrating lower error values compared to existing methods.
Article
Forestry
Tomi Karjalainen, Lauri Mehtatalo, Petteri Packalen, Terje Gobakken, Erik Naesset, Matti Maltamo
CANADIAN JOURNAL OF FOREST RESEARCH
(2020)
Article
Environmental Sciences
Ranjith Gopalakrishnan, Daniela Ali-Sisto, Mikko Kukkonen, Pekka Savolainen, Petteri Packalen
Article
Forestry
Roope Ruotsalainen, Timo Pukkala, Annika Kangas, Mari Myllymaki, Petteri Packalen
Summary: The study found that increasing carbon prices and reducing error levels led to decreased losses in NPV. Inclusion of carbon payments in maximizing NPV reduced the impact of errors on losses, indicating that the value of collecting more accurate forest inventory data may decrease as carbon prices rise.
CANADIAN JOURNAL OF FOREST RESEARCH
(2021)
Article
Forestry
Diogo N. Cosenza, Lauri Korhonen, Matti Maltamo, Petteri Packalen, Jacob L. Strunk, Erik Naesset, Terje Gobakken, Paula Soares, Margarida Tome
Summary: In this study, the performances of OLS, kNN, and RF in forest yield modeling were compared, revealing that OLS and RF had similar and higher accuracies compared to kNN. Variable selection did not significantly impact RF performance, while heuristic and exhaustive selection methods had similar effects on OLS. Caution is advised when building kNN models for volume prediction, with a preference for OLS with variable selection or RF with all variables included.
Review
Forestry
M. Maltamo, P. Packalen, A. Kangas
Summary: Forest management inventories provide critical information for forest management planning at the stand level. The use of wall-to-wall remote-sensing data has enabled a paradigm shift from subjective visual assessments to model-based inferences. While advances in optical and Lidar-based sensors have improved accuracy in forest attribute estimation, challenges remain in obtaining species-specific stand attribute information and assessing tree quality in mixed stands.
CANADIAN JOURNAL OF FOREST RESEARCH
(2021)
Article
Forestry
Roope Ruotsalainen, Timo Pukkala, Annika Kangas, Petteri Packalen
Summary: Errors in forest stand attributes, particularly in mean diameter, can lead to greater inoptimality losses in forest management objectives. Avoiding large errors in basal area and mean diameter is crucial, especially in stands with high basal area.
ANNALS OF FOREST SCIENCE
(2021)
Article
Environmental Sciences
Syed Adnan, Matti Maltamo, Lauri Mehtatalo, Rhei N. L. Ammaturo, Petteri Packalen, Ruben Valbuena
Summary: This study introduces a new mathematical approach by using the Gini coefficient to replace the traditional FHD method in describing the vertical complexity of LiDAR height profiles, with extensions to higher-dimensional variables. The research found differences in the accuracy of LiDAR models in different forest structural types, with implications for estimating forest aboveground biomass.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Geochemistry & Geophysics
Janne Raty, Petri Varvia, Lauri Korhonen, Pekka Savolainen, Matti Maltamo, Petteri Packalen
Summary: This study compared single-photon and linear-mode airborne LiDAR for predicting species-specific volumes in forests, and found that the linear-mode Riegl VQ-1560i performed the best.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Forestry
Petteri Packalen, Jacob Strunk, Matti Maltamo, Mari Myllymaki
Summary: In airborne laser scanning (ALS)-based forest inventories, there is often a discrepancy between the circular plot shape used for model fitting and the square shape of population elements used for predictions. This study found that for equal area square and circular plots, there was no evidence of systematic prediction error when a model fitted to one shape was used to predict for the other shape. However, using a model fitted to circular plots to predict for square plots slightly underestimated the root mean square error (RMSE) value.
Article
Forestry
Roope Ruotsalainen, Timo Pukkala, Veli-Pekka Ikonen, Petteri Packalen, Heli Peltola
Summary: This study aimed to reduce the risk of wind damage in forested landscapes by utilizing stand neighbourhood information and terrain elevation information. The results showed that minimizing the differences in canopy height between adjacent stands resulted in homogeneous landscapes and continuous cover management was economically beneficial. The best weighting scheme for calculating the mean canopy height difference between adjacent stands was the deviation between the mean elevation of the boundary and the mean elevation of the terrain within 100 m of the boundary.
Article
Remote Sensing
Fangting Chen, Zhengyang Hou, Svetlana Saarela, Ronald E. McRoberts, Goran Stahl, Annika Kangas, Petteri Packalen, Bo Li, Qing Xu
Summary: Remote sensing has improved forest inventory by using model-based inference to estimate parameters of interest from fine to coarse spatial resolution, facilitating decision making for natural resource management.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Forestry
Mari Myllymaki, Sakari Tuominen, Mikko Kuronen, Petteri Packalen, Annika Kangas
Summary: This study investigates the value of metrics computed from tree locations and tree sizes for characterizing the structural naturalness of forests. The study finds that forests evaluated as structurally natural have larger variations in tree size and species composition, as well as a more clustered spatial pattern of trees. However, the link between the inspected metrics and naturalness evaluated in the field is weak.
Article
Environmental Sciences
J. Kostensalo, L. Mehtatalo, S. Tuominen, P. Packalen, M. Myllymaki
Summary: This study developed a framework for building structurally representative tree maps using airborne laser scanning data and ground measurements, which can accurately map the attributes of forests. Compared to other methods, this approach improves the accuracy of mapping forest attributes.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Forestry
Mikko Kukkonen, Eetu Kotivuori, Matti Maltamo, Lauri Korhonen, Petteri Packalen
Summary: The study proposes a method for predicting tree volumes by species using photogrammetric UAS data and Sentinel-2 images, fitted with models based on airborne laser scanning data. The approach shows promise for species-specific small area forest management inventories in Finland and similar forest conditions where suitable field plots are available.
