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Multiple studies have indicated that long non-coding RNAs are aberrantly expressed in cancers and are pivotal in developing various tumors. No studies have investigated the expression and function of long non-coding antisense RNA PCNA-AS1 in esophageal squamous cell carcinoma (ESCC). In this study, the expression of PCNA-AS1 was identified by qRT–PCR. Cell function assays were used to explore the potential effect of PCNA-AS1 on ESCC progression. A prediction website was utilized to discover the relationships among PCNA-AS1, miR-2467-3p and proliferating cell nuclear antigen (PCNA). Dual luciferase reporter gene and RNA immunoprecipitation (RIP) assays were executed to verify the binding activity between PCNA-AS1, miR-2467-3p and PCNA. As a result, PCNA-AS1 was highly expressed in ESCC and was associated with patient prognosis. PCNA-AS1 overexpression strongly contributed to ESCC cell proliferation, invasion and migration. PCNA-AS1 and PCNA were positively correlated in ESCC. Bioinformatics analysis, RIP and luciferase reporter gene assays revealed that PCNA-AS1 could act as a competitive endogenous RNA to sponge miR-2467-3p, thus upregulating PCNA. In conclusion, the current outcome demonstrates that PCNA-AS1 may be a star molecule in the treatment of ESCC.

Bridge crane control systems are complex, multivariable, and nonlinear. However, traditional fuzzy PID control methods rely heavily on expert experience for initial parameter tuning and lack adaptive adjustment for the fuzzy universe. To address these issues, we propose a variable universe fuzzy PID controller based on the improved sparrow search algorithm (ISSA-VUFPID). First, tent chaotic mapping is introduced to initialize the sparrow population, enhancing the algorithm's global search capability. Second, the positioning strategy of the northern goshawk exploration phase is integrated to improve the search thoroughness of sparrow discoverers within the solution space and to accelerate the optimization process. Last, an adaptive t-distribution perturbation strategy is employed to adjust the positions of sparrow followers, enhancing the algorithm's optimization ability in the early search phase and focusing on local exploitation in the later phase to improve solution accuracy. The improved algorithm is applied to tune the initial parameters of the PID controller. Additionally, system error and its rate of change are introduced as dynamic parameters into the scaling factor, which is used to achieve adaptive adjustment of the fuzzy universe, thereby enhancing the safety and reliability of the control system. Simulation results demonstrate that the proposed ISSA-VUFPID control method outperforms ISSA-FPID and ISSA-PID control methods. It reduces the trolley's positioning time and minimizes the load's maximum swing angle, demonstrating strong adaptability and robustness. This approach greatly enhances the robustness and safety of bridge crane operations. [ABSTRACT FROM AUTHOR]

Short-term power load forecasting plays a key role in daily scheduling and ensuring stable power system operation. The problem of the volatility of the power load sequence and poor prediction accuracy is addressed. In this study, a learning model integrating intelligent optimization algorithms is proposed, which combines an ensemble-learning model based on long short-term memory (LSTM), variational modal decomposition (VMD) and the multi-strategy optimization dung beetle algorithm (MODBO). The aim is to address the shortcomings of the dung beetle optimizer algorithm (DBO) in power load forecasting, such as its time-consuming nature, low accuracy, and ease of falling into local optimum. In this paper, firstly, the dung beetle algorithm is initialized using a lens-imaging reverse-learning strategy to avoid premature convergence of the algorithm. Second, a spiral search strategy is used to update the dynamic positions of the breeding dung beetles to balance the local and global search capabilities. Then, the positions of the foraging dung beetles are updated using an optimal value bootstrapping strategy to avoid falling into a local optimum. Finally, the dynamic-weighting coefficients are used to update the position of the stealing dung beetle to improve the global search ability and convergence of the algorithm. The proposed new algorithm is named MVMO-LSTM. Compared to traditional intelligent algorithms, the four-quarter averages of the RMSE, MAE and R2 of MVMO-LSTM are improved by 0.1147–0.7989 KW, 0.09799–0.6937 KW, and 1.00–13.05%, respectively. The experimental results show that the MVMO-LSTM proposed in this paper not only solves the shortcomings of the DBO but also enhances the stability, global optimization capability and information utilization of the model. [ABSTRACT FROM AUTHOR]

Path planning and obstacle avoidance are fundamental problems in unmanned ground vehicle path planning. Aiming at the limitations of Deep Reinforcement Learning (DRL) algorithms in unmanned ground vehicle path planning, such as low sampling rate, insufficient exploration, and unstable training, this paper proposes an improved algorithm called Dual Priority Experience and Ornstein–Uhlenbeck Soft Actor-Critic (DPEOU-SAC) based on Ornstein–Uhlenbeck (OU noise) and double-factor prioritized sampling experience replay (DPE) with the introduction of expert experience, which is used to help the agent achieve faster and better path planning and obstacle avoidance. Firstly, OU noise enhances the agent's action selection quality through temporal correlation, thereby improving the agent's detection performance in complex unknown environments. Meanwhile, the experience replay is based on double-factor preferential sampling, which has better sample continuity and sample utilization. Then, the introduced expert experience can help the agent to find the optimal path with faster training speed and avoid falling into a local optimum, thus achieving stable training. Finally, the proposed DPEOU-SAC algorithm is tested against other deep reinforcement learning algorithms in four different simulation environments. The experimental results show that the convergence speed of DPEOU-SAC is 88.99% higher than the traditional SAC algorithm, and the shortest path length of DPEOU-SAC is 27.24, which is shorter than that of SAC. [ABSTRACT FROM AUTHOR]

To address the anti-swing issue of the payload in bridge cranes, Proportional–Integral–Derivative (PID) control is a commonly used method. However, parameter tuning of the PID controller relies on empirical knowledge and often leads to system overshoot. This paper proposes an Improved Sparrow Search Algorithm (ISSA) to optimize the gains of PID controllers, alleviating adverse effects on payload oscillation and trolley positioning during the operation of overhead cranes. First, tent map chaos mapping is introduced to initialize the sparrow population, enhancing the algorithm's global search capability. Then, by integrating sine and cosine concepts along with nonlinear learning factors, the updating mechanism of discoverer positions is dynamically adjusted, expediting the solving process. Finally, the Lévy flight strategy is employed to update follower positions, thereby enhancing the algorithm's local escape capability. Additionally, a fitness function containing overshoot penalties is proposed to address overshoot issues. Simulation results indicate that the overshoot rates of all algorithms remain less than 3%. Moreover, compared with the Sparrow Search Algorithm (SSA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Whale optimization Algorithm (WOA), the optimized PID control system with the ISSA algorithm exhibits superior control performance and possesses certain robustness and adaptability. [ABSTRACT FROM AUTHOR]

This paper sheds light on the effectiveness of a concrete target-based performance evaluation system (TPES) for localities in enhancing local environmental enforcement in China. We conduct a difference-in-differences (DID) specification and exploit unique firm-level data to investigate the effects of the TPES on SO2 emission reduction through the Two control Zones (TCZ) policy. The results suggest that the TPES significantly reduces SO2 emissions of firms located at the TCZ cities, both in extensive and intensive margins. We also find that the treatment effects vary largely across enterprise sizes, ownership types, and local economic development. Our findings further verify how the long-standing distortions in the decentralized implementation of nationwide environmental regulations in China are remedied with a proper performance evaluation system. [ABSTRACT FROM AUTHOR]

Food waste is an excellent organic matter for anaerobic fermentation. This study provided a cost-effective and highly efficient volatile fatty acid (VFA) production strategy by the addition of zero-valent iron (ZVI). Results showed that VFA concentration of 44.6 g/L was obtained with the optimized conditions of 200-mesh iron powder at a dosage of 20.0 g, fermentation time of 11 d, total solids (TS) of 10 wt.%, and fermentation temperature of 37 ℃. Further, the iron of different particle sizes (iron scraps, 200-mesh iron powder, and 800-mesh iron powder) had a differential influence on total organic carbon (TOC), total nitrogen (TN), and VFA concentrations. For the reactor containing 200-mesh iron powder, the conversion rate of organic compound into VFA increased with the increase of dosage, which reached 58.4% at the 40.0 g dosage. The mechanism revealed that the VFA production was enhanced by micro-electrolysis, which can rapidly inactivate bacteria and increase the conversion of macromolecular organics into micromolecular organics. [ABSTRACT FROM AUTHOR]

The trade-off between environmental regulation and job creation has been a dilemma for policymakers in the past decades. Exploiting the enterprise-level sample data, we conduct a difference-in-difference-in-differences (DDD) specification to estimate the overall effect of China's Two Control Zones (TCZ) policy on labor demand, measured as the number of enterprise-level employees. We find that the industrial enterprises in TCZ cities, where the TCZ policy has been implemented after 1998, employed fewer workers in more polluting industries. Furthermore, these employment effects are very heterogeneous among different enterprise ownerships and control zones. The TCZ policy significantly decreased the labor demand in private enterprises or those located in the Acid Rain Control Zones (ARCZ) but had little impact on their state-owned and foreign-invested counterparts or those located in the Sulfur Dioxide Pollution Control Zones (SPCZ). [ABSTRACT FROM AUTHOR]

Correctly anticipating PV electricity production may lessen stochastic fluctuations and incentivize energy consumption. To address the intermittent and unpredictable nature of photovoltaic power generation, this article presents an ensemble learning model (MVMD-CLES) based on the whale optimization algorithm (WOA), variational mode decomposition (VMD), convolutional neural network (CNN), long and short-term memory (LSTM), and extreme learning machine (ELM) stacking. Given the variances in the spatiotemporal distribution of photovoltaic data and meteorological features, a multi-branch character extraction iterative mixture learning model is proposed: we apply the MWOA algorithm to find the optimal decomposition times and VMD penalty factor, and then divide the PV power sequences into sub-modes with different frequencies using a two-layer algorithmic structure to reconstruct the obtained power components. The primary learner is CNN–BiLSTM, which is utilized to understand the temporal and spatial correlation of PV power from information about the weather and the output of photovoltaic cells, and the LSTM learns the periodicity and proximity correlation of the power data and obtains the corresponding component predictions. The second level is the secondary learner—the output of the first layer is learned again using the ELM to attenuate noise and achieve short-term prediction. In different case studies, regardless of weather changes, the proposed method is provided with the best group of consistency and constancy, with an average RMSE improvement of 12.08–39.14% over a single-step forecast compared to other models, the average forecast RMSE increased by 5.71–9.47% for the first two steps. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the traditional sparrow search algorithm (SSA) in path planning, such as its high time-consumption, long path length, it being easy to collide with static obstacles and its inability to avoid dynamic obstacles, this paper proposes a new improved SSA based on multi-strategies. Firstly, Cauchy reverse learning was used to initialize the sparrow population to avoid a premature convergence of the algorithm. Secondly, the sine–cosine algorithm was used to update the producers' position of the sparrow population and balance the global search and local exploration capabilities of the algorithm. Then, a Lévy flight strategy was used to update the scroungers' position to avoid the algorithm falling into the local optimum. Finally, the improved SSA and dynamic window approach (DWA) were combined to enhance the local obstacle avoidance ability of the algorithm. The proposed novel algorithm is named ISSA-DWA. Compared with the traditional SSA, the path length, path turning times and execution time planned by the ISSA-DWA are reduced by 13.42%, 63.02% and 51.35%, respectively, and the path smoothness is improved by 62.29%. The experimental results show that the ISSA-DWA proposed in this paper can not only solve the shortcomings of the SSA but can also plan a highly smooth path safely and efficiently in the complex dynamic obstacle environment. [ABSTRACT FROM AUTHOR]

The incomplete and unstable property rights of farmland in rural China hinder the development of the nation's farmland rental market. The Chinese government implemented a land titling program in 2008 and conducted several pilot programs in various provinces to remove the barriers resulting from ambiguous property rights. Using data from the China Health and Retirement Longitudinal Study, this paper investigates the effects of rural land titling on market participation in rural China. First, we provide novel evidence regarding the impact of land titling on land market participation and investigate the heterogeneous wealth effects and geographic locations of land rental decisions. Second, we examine the endogeneity of land titling program implementation through empirical analysis. Our results indicate that clear and well-defined farmland property rights increase land rental activities and raise rental prices. We also find a significantly negative wealth effect in land transfer decisions, indicating that land titling encourages poorer farmers to rent out farmland. Land titling is found to promote land transfer, with the strongest effect in central China, and raise rental rates, with the largest increase in western China. [ABSTRACT FROM AUTHOR]