Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

Accelerating Oxygen Electrocatalysis Kinetics on Metal-Organic Frameworks via Bond Length Optimization

Metal-organic frameworks(MOFs)have been developed as an ideal platform for exploration of the relationship between intrinsic structure and catalytic activity,but the limited catalytic activity and stability has hampered their practical use in water splitting.Herein,we develop a bond length adjustment strategy for optimizing naphthalene-based MOFs that synthesized by acid etching Co-naphthalenedicarboxylic acid-based MOFs(donated as AE-CoNDA)to serve as efficient catalyst for water splitting.AE-CoNDA exhibits a low overpotential of 260 mV to reach 10 mA cm^(−2)and a small Tafel slope of 62 mV dec^(−1)with excellent stability over 100 h.After integrated AE-CoNDA onto BiVO_(4),photocurrent density of 4.3 mA cm^(−2)is achieved at 1.23 V.Experimental investigations demonstrate that the stretched Co-O bond length was found to optimize the orbitals hybridization of Co 3d and O 2p,which accounts for the fast kinetics and high activity.Theoretical calculations reveal that the stretched Co-O bond length strengthens the adsorption of oxygen-contained intermediates at the Co active sites for highly efficient water splitting.

An extended state observer with adjustable bandwidth for measurement noise

In this paper,a bandwidth-adjustable extended state observer(ABESO)is proposed for the systems with measurement noise.It is known that increasing the bandwidth of the observer improves the tracking speed but tolerates noise,which conflicts with observation accuracy.Therefore,we introduce a bandwidth scaling factor such that ABESO is formulated to a 2-degree-of-freedom system.The observer gain is determined and the bandwidth scaling factor adjusts the bandwidth according to the tracking error.When the tracking error decreases,the bandwidth decreases to suppress the noise,otherwise the bandwidth does not change.It is proven that the error dynamics are bounded and converge in finite time.The relationship between the upper bound of the estimation error and the scaling factor is given.When the scaling factor is less than 1,the ABESO has higher estimation accuracy than the linear extended state observer(LESO).Simulations of an uncertain nonlinear system with compound disturbances show that the proposed ABESO can successfully estimate the total disturbance in noisy environments.The mean error of total disturbance of ABESO is 15.28% lower than that of LESO.

Impact of an official accountability audit on industrial structure adjustment:a case study of environmental regulation in China

As a unique environmental regulation in China,the official accountability audit was piloted in 2014.With a focus on prioritizing the ecological environment,officials in pilot districts have implemented economic construction,adjusted industrial structures,and promoted coordinated development between the economy and environment.The effects of implementation have garnered widespread attention from society.However,there is limited research on the impact of an accountability audit on industrial structure adjustments.Using the“Accountability Audit of Officials for Natural Resource Assets(Trial)”released in 2015 as a quasi-natural experiment,this study collected panel data from 279 cities between 2013 and 2017.It then empirically analyzed the impact mechanism and effects of the accountability audit on industrial structure adjustment using the Propensity Score Matching and Difference-in-Differences model.The research findings indicate that the accountability audit directly impacted industrial structure adjustment,promoting the upgrading of the primary industry to the secondary industry and restricting the development of the tertiary industry.In addition,the audit is beneficial for enterprise entry,but not conducive to technological innovation,and has no significant impact on foreign direct investment.This conclusion fills a gap in the existing research and provides valuable insights for policymakers.

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

Differential pressure difference based altitude control of a stratospheric satellite

An autonomous altitude adjustment system for a stratospheric satellite(StratoSat)platform is proposed.This platform consists of a helium balloon,a ballonet,and a two-way blower.The helium balloon generates lift to balance the platform gravity.The two-way blower inflates and deflates the ballonet to regulate the buoyancy.Altitude adjustment is achieved by tracking the differential pressure difference(DPD),and a threshold switching strategy is used to achieve blower flow control.The vertical acceleration regulation ability is decided not only by the blower flow rate,but also by the designed margin of pressure difference(MPD).Pressure difference is a slow-varying variable compared with altitude,and it is adopted as the control variable.The response speed of the actuator to disturbance can be delayed,and the overshoot caused by the large inertia of the platform is inhibited.This method can maintain a high tracking accuracy and reduce the complexity of model calculation,thus improving the robustness of controller design.

Research and Practice on the Construction of Professional Cluster in Application-Oriented Local Undergraduate Universities:Taking Environ-mental Chemical Engineering Professional Cluster in Pingdingshan University as an Example

Based on the analysis of the importance of professional cluster construction by ecological theory,with the change of social demand for talents,this paper explores the practice of environmental chemical professional cluster construction in Pingdingshan University,including gradually perfecting teaching conditions and reforming teaching mode,breaking through the limitations of resources,integrating the boundaries of colleges and departments,integrating multiple resources,innovating systems and mechanisms,reconstructing professional clusters,decon-structing professional connotations,reorganizing curriculum systems,etc.,in order to better build the ecological chain network of education in application-oriented colleges and universities,realize the deep integration of industry and education,train future-oriented interdisciplinary applied talents of new engineering,and realize the construction of characteristic professional cluster in application-oriented colleges.

Adjustment of Administrative Divisions and Upgrading of Industrial Structure in Hefei Metropolitan Area:Taking"Partitions of Chaohu"as a Quasi-natural Experiment

Administrative regions are an important environment for the operation of China's market economy.The relevant economic subjects cannot predict the policy of adjustment of administrative divisions and carry out conscious migration behavior,adjustment of administrative divisions can be regarded as a quasi-natural experiment.The three cities of Hefei,Wuhu,and Ma'anshan,which are directly related to the adjustment of the administrative division of Chaohu,are taken as the treatment group,and the seven adjacent cities of Lu'an,Huainan,Chuzhou,Bengbu,Anqing,Chizhou,and Tongling are taken as the control group.Differences-in-Differences method and relevant control variables affecting the upgrading of industrial structure are used to test.The test results show that"Partitions of Chaohu"has a significant industrial structure upgrading effect by promoting the optimization of spatial layout,the cross-regional flow of production factors and the effective management of Chaohu Lake Basin.At the same time,the increase of total retail sales of consumer goods,urban fixed assets investment,public utility expenses in science,education,culture and health,and population plays a significant positive role in promoting the upgrading of industrial structure,while foreign direct investment plays a certain inhibition role in the upgrading of industrial structure.In order to meet the ever-developing space demands and enhance the impact on surrounding areas,the Hefei metropolitan area should be driven by technological innovation,strengthen the integration of industrial chains,improve the business environment and transportation network,and continuously promote the upgrading of industrial structure and the formation and development of new productive forces.

Rigidloop Adjustable可调袢钢板治疗踝关节骨折并下胫腓联合损伤

目的比较Rigidloop Adjustable(RLA)可调袢钢板与螺钉治疗踝关节骨折合并下胫腓联合损伤的临床效果。方法回顾性分析2019年9月至2022年5月武汉市第三医院收治的44例踝关节骨折合并下胫腓联合损伤的患者。踝关节骨折行切开复位内固定后,下胫腓联合采用RLA可调袢钢板固定22例,2枚皮质骨螺钉三皮质骨固定22例。应用AOFAS踝与后足功能评分、术侧与健侧踝关节活动度平均差值及术前、术后随访时正位X线片下胫腓间隙(tibiofibular bottom clear space,TBCS)、下胫腓重叠距离(tibiofibular bottom overlap,TBOL)比较两组临床效果。结果患者均获得随访,随访时间12~18个月,平均(16.41±1.66)个月。患者踝关节功能满意,无伤口感染、皮肤激惹等并发症。术后3、6、12个月时,RLA可调袢钢板组双侧踝关节活动度差值均显著小于螺钉组,差异有统计学意义(P<0.05);术后3、6、12个月时,两组的AOFAS评分比较,差异无统计学意义(P>0.05);TBCS、TBOL术前、术后即刻、术后3个月及末次随访时,差异无统计学意义(P>0.05)。结论RLA可调袢钢板和螺钉均可有效固定下胫腓联合,与螺钉固定相比,RLA可调袢钢板组可早期功能锻炼,获得更好的踝关节活动度。

Cross-Cultural Adjustment and Coping:U.S.Students in Taiwan Residents Higher Education

The aim of this study was to investigate the adjustment problems of students from the United States enrolled in universities in the East,specifically in Taiwan,their problems related to cultural adaptation,and the process of adjustment to student life in Taiwan.Under investigation were cultural adjustment and coping skills as these students transitioned from West to East.Qualitative data were collected from interviews with participants and faculty members as well as participant observations.Results indicated that U.S.students found their own ways to acclimate to their new academic setting as well as to social relations,cross-cultural issues,and the linguistic environment in Taiwan to achieve effective adaptation.They made changes in themselves to cope with all situations they encountered.This study provides suggestions for international students abroad in Taiwan,for the Taiwan Residents government,and for universities or colleges in terms of what they should offer to current and future international students.

"通关利窍"针刺法治疗卒中后吞咽困难立法辨证及机制探讨

“Tongguan Liqiao”acupuncture method established by Xuemin Shi,a master of traditional Chinese medicine in China and academician of the Chinese Academy of Engineering,derives from the famous“Xing Nao Kai Qiao”therapy(activating the brain and opening the orifices)acupuncture method.It fundamentally takes“adjusting the spirit”as the root of selecting acupoints based on syndrome differentiation.Neiguan(PC 6),Renzhong(DU 26),and Sanyinjiao(SP 6)are selected as the main acupoints to adjust the spirit and revive the brain,nourish the liver and kidney,which treats the root cause.Acupoints such as Lianquan(RN 23),Yifeng(SJ 17),Wangu(GB 12),and Fengchi(GB 20)in the neck region are selected for“unblocking orifices,”which relieves the symptoms.This acupuncture method features deep needling,which aligns with modern swallowing function anatomy.The method can improve the function of poststroke dysphagia by enhancing cerebral blood supply and metabolism and promoting neural functional remodeling.

Cameroon Climate Predictions Using the SARIMA-LSTM Machine Learning Model: Adjustment of a Climate Model for the Sudano-Sahelian Zone of Cameroon

It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and adaptation actions to adapt societies to Climate Change, to limit losses and damages [1] [2]. As far as adaptation actions are concerned, numerical simulation, due to its results, its costs which require less investment than tests carried out on complex mechanical structures, and its implementation facilities, appears to be a major step in the design and prediction of complex mechanical systems. However, despite the quality of the results obtained, biases and inaccuracies related to the structure of the models do exist. Therefore, there is a need to validate the results of this SARIMA-LSTM-digital learning model adjusted by a matching approach, “calculating-test”, in order to assess the quality of the results and the performance of the model. The methodology consists of exploiting two climatic databases (temperature and precipitation), one of which is in-situ and the other spatial, all derived from grid points. Data from the dot grids are processed and stored in specific formats and, through machine learning approaches, complex mathematical equations are worked out and interconnections within the climate system established. Through this mathematical approach, it is possible to predict the future climate of the Sudano-Sahelian zone of Cameroon and to propose adaptation strategies.

Optimized CUDA Implementation to Improve the Performance of Bundle Adjustment Algorithm on GPUs

The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared to 654 seconds for the sequential implementation, resulting in a speedup of 30×. Our optimized CUDA implementation presented in this paper has achieved a 3× speedup for the largest dataset compared to the previous naïve CUDA implementation.

Research of Future Directions and Considerations for Implementation of the Forest City Strategy

A “Forest City” (FC) is an urban area that has a significant amount of forest cover. It is now a green urban development strategy that is supported by numerous nations. This essay compares the many FC implementation strategies used in developed and developing countries and explores potential future paths for this tactic. The variations between FC in terms of measurement targets, air purification, street trees, and forestry development are thoroughly compared in this research. This essay goes on to explore FC’s potential in the future regarding policy changes and the environment based on this comparison. Therefore, this essay focuses on the necessity of considering industrial innovation, encouraging biodiversity, lowering greenhouse gas emissions, paying attention to forest restructuring, and being more responsive to the issues provided by urbanization in the future global implementation of FC.

Temperature-Based Fan Speed Optimization Strategy

As energy efficiency and indoor comfort increasingly become key standards in modern residential and office environments,research on intelligent fan speed control systems has become particularly important.This study aims to develop a temperature-feedback-based fan speed optimization strategy to achieve higher energy efficiency and user comfort.Firstly,by analyzing existing fan speed control technologies,their main limitations are identified,such as the inability to achieve smooth speed transitions.To address this issue,a BP-PID speed control algorithm is designed,which dynamically adjusts fan speed based on indoor temperature changes.Experimental validation demonstrates that the designed system can achieve smooth speed transitions compared to traditional fan systems while maintaining stable indoor temperatures.Furthermore,the real-time responsiveness of the system is crucial for enhancing user comfort.Our research not only demonstrates the feasibility of temperature-based fan speed optimization strategies in both theory and practice but also provides valuable insights for energy management in future smart home environments.Ultimately,this research outcome will facilitate the development of smart home systems and have a positive impact on environmental sustainability.

Research on Biometric Identification Method of Nuclear Cold Source Disaster Based on Deep Learning

In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the disaster-causing creatures hairy shrimp and jellyfish were established. Then, in order to solve the problems of low recognition accuracy and unrecognizable small entities in disaster biometrics, Gamma correction algorithm was used to optimize the image of the data set, improve the image quality and reduce the noise interference. Transposed convolution is introduced into the convolution layer to increase the recognition accuracy of small targets. The experimental results show that the recognition rate of this algorithm is 6.75%, 7.5%, 9.8% and 9.03% higher than that of ResNet-50, MobileNetv1, GoogleNet and VGG16, respectively. The actual test results show that the accuracy of this algorithm is obviously better than other algorithms, and the recognition efficiency is higher, which basically meets the preset requirements of this paper.

Pollinating fig wasp Ceratosolen solmsi adjusts the offspring sex ratio to other foundresses

Local mate competition theory predicts that offspring sex ratio in pollinating fig wasps is female-biased when there is only one foundress, and increased foundress density results in increased offspring sex ratio. Information of other foundresses and clutch size have been suggested to be the main proximate explanations for sex ratio adjustment under local mate competition. Our focus was to show the mechanism of sex ratio adjustment in a pollinating fig wasp, Ceratosolen solmsi Mayr, an obligate pollinator of the functionally dioecious fig, Ficus hispida Linn., with controlled experiments in the field. First, we obtained offspring from one pollinator and offspring at different oviposition sequences, and found that offspring sex ratio decreased with clutch size, and pollinators produced most of their male offspring at the start of bouts, followed by mostly females. Second, we found that offspring sex ratio increased with foundress density, and pollinators did adjust their offspring sex ratio to other females in the oviposition patches. We suggest that when oviposition sites are not limited, pollinators will mainly adjust their offspring sex ratio to other foundresses independent of clutch size changes, whereas adjusting clutch size may be used to adjust sex ratio when oviposition sites are limited.

A Self-calibration Bundle Adjustment Algorithm Based on Block Matrix Cholesky Decomposition Technology

In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innovation points are reflected in the following aspects:①The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear;②The complexities of time and space tend to O(n)in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly;③The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes;④Some measures are employed to improve the optimization effects.Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth.This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.