Single-cell screening of indigenous hydrocarbon-degrading bacteria for efficient controlling the groundwater hydrocarbon pollution of operating industrial - Jiawen Yang; Weitao Zhao; Zhihui Chen; Lei Shen; Qingyan Lin; Zehao Xu; Qianbin Pan; Jiawei Yang; Chaofeng Shen (Journal of Hazardous Materials, 2026)

Research on Calibration Methods and Experiments for Six-Component Force Sensors - Hongyang Zhao; Bowen Zhao; Xu Liang; Qianbin Lin (Journal of Marine Science and Engineering, 2025)
The measurement accuracy of six-component force sensors is crucial for reliable hydrodynamic model test results. To enhance data precision, this study presents an efficient calibration device based on a dual-axis rotational mechanism, enabling multi-degree-of-freedom attitude adjustment of the sensor. By applying known forces and moments through various loading conditions and employing the least squares method to obtain a 6 × 6 calibration coefficient matrix, we effectively reduce system errors and external disturbances. The effectiveness of the proposed calibration method is validated using rotational arm tests with a KCS standard ship model. The results indicate that most calibration point errors are below 1%, with the maximum error not exceeding 7%, and the measured data show good agreement with international standards. This method offers high calibration efficiency and accuracy, making it well-suited for the calibration of multi-component force sensors and for use in hydrodynamic, wind tunnel, and other multi-disciplinary experimental applications, promising potential for wider use.

Design of an Experimental Teaching Platform for Flow-Around Structures and AI-Driven Modeling in Marine Engineering - Hongyang Zhao; Bowen Zhao; Xu Liang; Qianbin Lin (Journal of Marine Science and Engineering, 2025)
Flow past bluff bodies (e.g., circular cylinders) forms a canonical context for teaching external flow separation, vortex shedding, and the coupling between surface pressure and hydrodynamic forces in offshore engineering. Conventional laboratory implementations, however, often fragment local and global measurements, delay data feedback, and omit intelligent modeling components, thereby limiting the development of higher-order cognitive skills and data literacy. We present a low-cost, modular, data-enabled instructional hydrodynamics platform that integrates a transparent recirculating water channel, multi-point synchronous circumferential pressure measurements, global force acquisition, and an artificial neural network (ANN) surrogate. Using feature vectors composed of Reynolds number, angle of attack, and submergence depth, we train a lightweight AI model for rapid prediction of drag and lift coefficients, closing a loop of measurement, prediction, deviation diagnosis, and feature refinement. In the subcritical Reynolds regime, the measured circumferential pressure distribution for a circular cylinder and the drag and lift coefficients for a rectangular cylinder agree with empirical correlations and published benchmarks. The ANN surrogate attains a mean absolute percentage error of approximately 4% for both drag and lift coefficients, indicating stable, physically interpretable performance under limited feature inputs. This platform will facilitate students’ cross-domain transfer spanning flow physics mechanisms, signal processing, feature engineering, and model evaluation, thereby enhancing inquiry-driven and critical analytical competencies. Key contributions include the following: (i) a synchronized local pressure and global force dataset architecture; (ii) embedding a physics-interpretable lightweight ANN surrogate in a foundational hydrodynamics experiment; and (iii) an error-tracking, iteration-oriented instructional workflow. The platform provides a replicable pathway for transitioning offshore hydrodynamics laboratories toward an integrated intelligence-plus-data literacy paradigm and establishes a foundation for future extensions to higher Reynolds numbers, multiple body geometries, and physics-constrained neural networks.