This article, which is an extension of the previous work of the authors (Tanveer and Ahmad (2022)) on yaw dynamics, investigates the modeling and control of heave degree-of-freedom of a compact custom designed ROV. Wherein, nonlinear data-driven modeling strategy is adopted to develop a high-fidelity heave dynamic model. The proposed modeling approach uses open-loop real-time experimental data to derive a high-fidelity NARX model of the vehicle. The resulting model accommodates the dynamics of the system in addition to the tether dynamics. The advantage of this approach is its ability to eliminate the need for intricate controller tuning. The identified model consistently demonstrated fitness scores ranging from 82% to 92% in both self-validation and cross-validation tests conducted on distinct datasets. This relative advantage is exemplified in real-time through the testing of a Genetic Algorithm Proportional-Integral (GAPI) controller. The performance of GAPI is subsequently compared with the relatively recent Marine Predators Algorithm (MPA) and the more conventional root-locus tuned PI controllers. The experimental results demonstrate that GAPI provides the most favorable response, achieving a 35%, 76%, and 44% improvement in rise time, percent overshoot and peak time, respectively. Furthermore, the controller effort required by GAPI running on an ATmega328 chipset is 22% less than its counterparts.
Generally, facilitating a slick switch to cleaner cooking fuels for households in the countryside has been a challenge. Based on survey data from the China Family Panel Survey for 2022 and provincial statistics, this study examined the effects of relevant factors on household cooking fuel utilization in rural China at the province, household, and neighborhood levels using a multilevel spatial logit model. The findings clearly indicate that new quality productive forces, household cultural consumption, and neighborhood effects significantly support households to adopt cleaner cooking fuels in rural areas. Further studies show that policies on green financial reforms and innovation, straw-burning ban guidelines, and atmospheric priority control areas activate new productive forces to support rural households’ switch to cleaner cooking fuels. These findings contribute to knowledge and action programs for the green transformation of rural energy consumption.
The poor endurance of hafnium oxide (HfO2)-based ferroelectric field-effect transistors (FeFETs) limits their applications. From a novel perspective of ferroelectric domain engineering, we propose and fabricate a high endurance HfO2-based FeFET with monolayer graphene (GR) inserted in the gate oxide for the first time. The introduction of GR between the ferroelectric (FE) layer and the interfacial layer (IL) increases the number of domains in the ferroelectric (FE) layer and reduces the electric field of the IL. Meanwhile, the low density of states (DOS) of monolayer GR suppresses the charge injection to further optimize the endurance. Experimental results show that the endurance of the GR-intercalated FeFET (GR-FeFET) exceeds 108 cycles, which is more than 2 orders of magnitude higher than that of the conventional FeFET. The gate leakage is also effectively suppressed by the GR layer. This work opens a new avenue for improvement of the endurance of FeFETs and demonstrates GR-FeFETs as potential candidates for next-generation embedded memory applications.
WeproposeanODEapproachtosolvingmultiplechoicepolynomialprogram- ming (MCPP) after assuming that the optimum point can be approximated by the ex- pected value of so-called thermal equilibrium as usually did in simulated annealing. The explicit form of the feasible region and the affine property of the objective function are both fully exploited in transforming an MCPP problem into an ODE system. We also show theoretically that a local optimum of the former can be obtained from an equilib- rium point of the latter. Numerical experiments on two typical combinatorial problems, MAX-k-CUT and the calculation of star discrepancy, demonstrate the validity of the ODE approach, and the resulting approximate solutions are of comparable quality to those obtained by the state-of-the-art heuristic algorithms but with much less cost. When compared with the numerical results obtained by using Gurobi to solve MCPP directly, our ODE approach is able to produce approximate solutions of better quality in most instances. This paper also serves as the first attempt to use a continuous algorithm for approximating the star discrepancy.
We investigate how exposure to the One-Child Policy (OCP) during early adulthood affects marriage and fertility in China. Exploring fertility penalties across provinces over time and the different implementations by ethnicity, we show that the OCP significantly increases the unmarried rate among the Han ethnicity but not among the minorities. The OCP increases Han-minority marriages in regions where Han-minority couples are allowed for an additional child, but the impact is smaller in other regions. Finally, the deadweight loss caused by lower fertility accounts for 10 percent of annual household incomes, and policy-induced fewer marriages contribute to 30 percent of the fertility decline.
Recent advances on time series forecasting mainly focus on improving the forecasting models themselves. However, when the time series data suffer from potential structural breaks or concept drifts, the forecasting performance might be significantly reduced. In this paper, we introduce a novel approach called Optimal Starting Point Time Series Forecast (OSP-TSP) for optimal forecasting, which can be combined with existing time series forecasting models. By adjusting the sequence length via leveraging the XGBoost and LightGBM models, the proposed approach can determine the optimal starting point (OSP) of the time series and then enhance the prediction performances of the base forecasting models. To illustrate the effectiveness of the proposed approach, comprehensive empirical analysis have been conducted on the M4 dataset and other real world datasets. Empirical results indicate that predictions based on the OSP-TSP approach consistently outperform those using the complete time series dataset. Moreover, comparison results reveals that combining our approach with existing forecasting models can achieve better prediction accuracy, which also reflect the advantages of the proposed approach.
