Month: April 2024
Survey of automatic plankton image recognition: challenges, existing solutions and future perspectives
Abstract
Planktonic organisms including phyto-, zoo-, and mixoplankton are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to follow and understand these changes. Advances in imaging technology have enabled novel possibilities to study plankton populations, but the manual classification of images is time consuming and expert-based, making such an approach unsuitable for large-scale application and urging for automatic solutions for the analysis, especially recognizing the plankton species from images. Despite the extensive research done on automatic plankton recognition, the latest cutting-edge methods have not been widely adopted for operational use. In this paper, a comprehensive survey on existing solutions for automatic plankton recognition is presented. First, we identify the most notable challenges that make the development of plankton recognition systems difficult and restrict the deployment of these systems for operational use. Then, we provide a detailed description of solutions found in plankton recognition literature. Finally, we propose a workflow to identify the specific challenges in new datasets and the recommended approaches to address them. Many important challenges remain unsolved including the following: (1) the domain shift between the datasets hindering the development of an imaging instrument independent plankton recognition system, (2) the difficulty to identify and process the images of previously unseen classes and non-plankton particles, and (3) the uncertainty in expert annotations that affects the training of the machine learning models. To build harmonized instrument and location agnostic methods for operational purposes these challenges should be addressed in future research.
A new approach for hydrograph data interpolation and outlier removal for vector autoregressive modelling: a case study from the Odra/Oder River
Abstract
This study presents a new approach for predicting water levels of the Odra/Oder river using vector autoregressive models (VAR). We use water level time series from 27 gauging stations, on which we interpolate no-data gaps using the LinAR method and detect outliers with two separate methods: the extreme values (EV) approach and the isolation forest (IFO) algorithm. Before removing potential outliers, we propose a hydrological evaluation based on multivariate data analysis. Finally, we consider three separate data scenarios, i.e. LinAR (no outlier rejection), EV, and IFO. VAR models for six prediction gauges were built in a moving window manner on the most recent 720 hourly water levels prior to each prediction. The analysis covered the time range from January 2016 to May 2022 and resulted in \(\varvec{\approx }\) 1,000,000 water level forecasts (3 scenarios x 6 gauges x 55,000 hourly time steps) with lead time of 72 h. The analysis of root mean squared error (RMSE) indicates that the VAR model performs well, especially for 24-hour predictions, with RMSE values ranging from 8 to 28 cm. The model was also found to have skills in predicting a rising limb of a hydrograph. Our numerical experiments showed the susceptibility of the VAR predictions to artefacts. The IFO method was found to detect outliers skilfully, which allowed to produce the most accurate VAR-based predictions.
Thermal and Energy Transport Prediction in Non-Newtonian Biomagnetic Hybrid Nanofluids using Gaussian Process Regression
Abstract
Hybrid nanofluids are a type of nanofluid that is created by combining two different types of nanoparticles with a traditional fluid. These nanofluids have unique physicochemical properties that make them more effective at transferring heat than traditional nanofluids. This research paper focuses on predicting thermal and energy transport in non-Newtonian biomagnetic hybrid nanofluids that contain gold and silver nanoparticles, using Gaussian process regression (GPR). The study uses blood as the traditional fluid and incorporates the effects of thermal radiation, thermophoresis, Brownian motion and activation energy into the model equation. The governing nonlinear partial differential equations are simplified to a set of ordinary differential equations using similarity replacements. The shooting method, along with the Runge–Kutta-Fehlberg fourth–fifth-order scheme, is used to solve the transformed equations using MATLAB. The results of the study are presented through figures and tables, which include the coefficient of skin friction, Nusselt number, Sherwood number and motile microbe’s flux, illustrated with surface plots. The GPR model is developed using four basic function kernels (squared exponential, exponential, rational quadratic and matern32 functions) and evaluated using statistical indicators such as RMSE, MSE, MAE and R. The predicted results and simulated numerical values are in good agreement with the coefficient of determination (R2) of 0.999999 for all parameters. The study also finds that GPR models with exponential kernel functions outperform other kernel functions in both the Oldroyd-B and Casson hybrid nanofluid data sets. However, the findings indicate that nanofluids and hybrid nanofluids have superior thermal qualities and stability, making them promising candidates for various thermal applications including solar thermal systems, automotive cooling systems, heat sinks, engineering, medical areas and thermal energy storage.
The predictive role of carotid artery flow time for anesthesia-induced hypotension in high-risk elderly patients
Abstract
Hypotension induced by general anesthesia is associated with postoperative complications, increased mortality, and morbidity, particularly elderly patients. The aim of this study was to investigate the effectiveness of corrected carotid artery flow time (FTc) for predicting hypotension following anesthesia induction in patients over 65 years old. After faculty ethical committee approval and written informed consent, 138 patients (65 years and older, ASA physical status I–III) who scheduled for elective surgery were included in this study. In the pre-operative anesthesia unit, the carotid artery FTc value was measured by ultrasound and hemodynamic values were recorded. Following anesthesia induction with propofol, hemodynamic data were recorded at 1-minute intervals for 3 min. Measurements were terminated prior to endotracheal intubation, as direct laryngoscopy and endotracheal intubation could cause sympathetic stimulation and hemodynamic changes. Hypotension occurred in 52 patients (37.7%). The preoperative FTc value of the patients who developed hypotension was statistically lower (312.5 ms) than the patients who did not (345.0 ms) (p < 0.001). The area under the ROC curve for carotid artery FTc was 0.93 (95% CI for AUC:0.89–0.97; p < 0.001) with an optimal cut-off of value for predicting post-anesthesia hypotension 333 ms, a sensitivity of 90.4% and a specificity of 84.9%. As a result of the multiple logistic regression model, carotid artery FTc emerged as the sole independent risk factor for hypotension following anesthesia induction. Preoperative carotid artery FTc measurement is a simple, bedside, noninvasive, and reliable method for predicting anesthesia-induced hypotension in elderly patients.
A hippocampus-accumbens code guides goal-directed appetitive behavior
Abstract
The dorsal hippocampus (dHPC) is a key brain region for the expression of spatial memories, such as navigating towards a learned reward location. The nucleus accumbens (NAc) is a prominent projection target of dHPC and implicated in value-based action selection. Yet, the contents of the dHPC→NAc information stream and their acute role in behavior remain largely unknown. Here, we found that optogenetic stimulation of the dHPC→NAc pathway while mice navigated towards a learned reward location was both necessary and sufficient for spatial memory-related appetitive behaviors. To understand the task-relevant coding properties of individual NAc-projecting hippocampal neurons (dHPC→NAc), we used in vivo dual-color two-photon imaging. In contrast to other dHPC neurons, the dHPC→NAc subpopulation contained more place cells, with enriched spatial tuning properties. This subpopulation also showed enhanced coding of non-spatial task-relevant behaviors such as deceleration and appetitive licking. A generalized linear model revealed enhanced conjunctive coding in dHPC→NAc neurons which improved the identification of the reward zone. We propose that dHPC routes specific reward-related spatial and behavioral state information to guide NAc action selection.
Import Penetration and Corporate Misconduct: A Natural Experiment
Abstract
Corporate misconduct receives significant attention in the business ethics literature. This paper studies how corporate misconduct is impacted by import penetration from China, which is largely exogenous to the U.S. product market. Using this natural experiment, we find that heightened Chinese import penetration curbs corporate misconduct of U.S. firms. The effect is more pronounced for firms with weaker corporate governance and firms more vulnerable to product market competition. The findings provide implications for firms facing increased import penetration. Firms may consider improving corporate governance and exploring avenues for differentiation as potential strategies to cope with the competition. In addition, we address the exogeneity concern derived from the influence of Chinese value penetration. Furthermore, we find that competition-related policies such as tariff reduction and U.S. granting China Permanent Normal Trade Relations (PNTR) status also lower corporate misconduct. Our work adds to the debates on competition and corporate misconduct in a cross-country competitive landscape.
Import Penetration and Corporate Misconduct: A Natural Experiment
Abstract
Corporate misconduct receives significant attention in the business ethics literature. This paper studies how corporate misconduct is impacted by import penetration from China, which is largely exogenous to the U.S. product market. Using this natural experiment, we find that heightened Chinese import penetration curbs corporate misconduct of U.S. firms. The effect is more pronounced for firms with weaker corporate governance and firms more vulnerable to product market competition. The findings provide implications for firms facing increased import penetration. Firms may consider improving corporate governance and exploring avenues for differentiation as potential strategies to cope with the competition. In addition, we address the exogeneity concern derived from the influence of Chinese value penetration. Furthermore, we find that competition-related policies such as tariff reduction and U.S. granting China Permanent Normal Trade Relations (PNTR) status also lower corporate misconduct. Our work adds to the debates on competition and corporate misconduct in a cross-country competitive landscape.