Continual Reinforcement Learning for Intelligent Agricultural Management under Climate Changes

Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(RL)methods often falter under such extreme conditions.To address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management strategies.Leveraging the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions.By incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather scenarios.The CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe droughts.Our results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased temperatures.This pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.

Temperature field evolution and heat transfer during continual local induction cladding

The evolution of temperature field of the continual motion induction cladding and the depth of heat affected zone are studied in this study.A three-dimensional finite element model for the point type continual induction cladding is established to investigate temperature distributions of fixed and motion induction cladding modes.The novel inductor is designed for cladding of curved surfaces.The modeling reliability is verified by the temperature measurements.The influence of process parameters on the maximum temperature and the generation and transfer of heat are studied.Quantitative calculation is performed to its melting rate to verify the temperature distribution and microstructures.The results show that a good metallurgical bond can be formed between the cladding layer and substrate.The melting rate gradually falls from the top of the cladding layer to the substrate,and the grain size in the substrate gradually rises.The heat affected zone is relatively small compared to integral heating.

On the realization of seismic microzonation of Almaty (Kazakhstan) in ground accelerations based on the “continual” approach

Seismic microzonation for Almaty city for the first time use probabilistic approach and hazard is expressed in terms of not only macroseismic intensity,but also Peak Ground Acceleration(PGA).To account for the effects of local soil conditions,the continual approach proposed by A.S.Aleshin[1,2]was used,in which soil coefficients are a function of the continuously changing seismic rigidity.Soil coefficients were calculated using the new data of geological and geophysical surveys and findings of previous geotechnical studies.The used approach made it possible to avoid using soil categories and a jump change in characteristics of soil conditions and seismic impact.The developed seismic microzonation maps are prepared for further introduction into the normative documents of the Republic of Kazakhstan.

Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter sharing.In this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new classes.Moreover,prior knowledge is unavailable during training and evaluation.Existing works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory information.To better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge.Specifically,besides the previous raw samples,we store the corresponding logits and features in the memory.Furthermore,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the memory.With the same number of samples for replay,our method can use more past knowledge to prevent interference.We conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory.We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

Numerical analysis of 3D spot continual induction hardening on curved surface of AISI 1045 steel

In order to investigate and predict the material properties of curved surface AISI 1045 steel component during spot continual induction hardening(SCIH),a 3D model for curved surface workpieces which coupled electromagnetic,temperature and phase transformation fields was built by finite element software ANSYS.A small size inductor and magnetizer were used in this model,which can move along the top surface of workpiece flexibly.The effect of inductor moving velocity and workpiece radius on temperature field was analyzed and the heating delay phenomenon was found through comparing the simulated results.The temperature field results indicate that the heating delay phenomenon is more obvious under high inductor moving velocity condition.This trend becomes more obvious if the workpiece radius becomes larger.The predictions of microstructure and micro-hardness distribution were also carried out via this model.The predicted results show that the inductor moving velocity is the dominated factor for the distribution of 100% martensite region and phase transformation region.The influencing factor of workpiece radius on 100% martensite region and phase transformation region distribution is obvious under relatively high inductor moving velocity but inconspicuous under relatively low inductor moving velocity.

CONTINUAL CONDITIONS BETWEEN LAYERS FOR LAMINATED PLATES

In this paper, various forms of functional on blending energy principles of composite laminated plates are gir en, which guarantee satisfied continual conditions of displacements and stress between layers, and then the reliability of the functional are proved by the computing example.

THE CONTINUAL DIFFERENTIABLE PEAK-UNIMODAL SOLUTIONS OF FEIGENBAUM’S FUNCTIONAL EQUATIONS

For the famous Feigenbaum's equations, in this paper, we established its constructive theorem of the peak-unimodal, then we found out other paths to explore the peak-unimodal solutions. For example, we proceed on the direction to try the non-symmetrical continuous peak-unimodal solutions and C1 solutions.

是continual还是continuous?——一组英语同源形容词辨析

英语中一些与动词有关的形容词变化比较复杂,除了一些可以用作形容词的现在分词与过去分词有些规律以外,其它与动词有关的一些形容词的构成就没什么规律。有时与一个动词相联的形容词往往有二个或二个以上,而且其意义也有不同,本文将列举一些常用的这一类形容词以及它们的辨析。

The Paradigm of the Seismic Zonation Continuality

Basic concepts of seismic zonation in Russia are the degree of intensity and soil categories that correspond to discrete structure in the ratio “seismic impact-ground reaction”. Meanwhile, the parameters of seismic effects, and the parameters of soil properties are continuous in the space. The report expounds the basic theory, adequately representing the above mentioned continuality. Thus, many the concepts of seismic zonation, used now, become either more correct, or unnecessary.

Enforcement of Regulations of the People's Republic of China on Disclosure of Government Information and Continual Development of a Group of Basic Human Rights

Introduction 2013 saw the sixth anniversary of the formulation of Regulations of the People＇s Republic of China on Disclosure of Government Information （hereinafter referred to as the Regulations） and the fifth anniversary of their being put into effect. As the Regulations were stipulated and put into force,

Parametric Selection and Continual Combination of Building Surface

A method to reparametrize G retional curve to obtain a C^1 curve is given. A practical G^1 continual connective between adjacent NURUS patches along common guadratic boundary curve is presented in this paper, and a specific algorithm for control points and weights of NURBS patches is discussed.

Objective Assessment in Continual Medical Education (CME) Medical Objective Assessment in System Control

Existing examinations in postgraduate education and continuing medical education (CME) are not perfect. Modern assessment does not reflect disadvantages of older responders, for whom more time for reply is needed. Specialists with wide clinical experience may choose more than one correct answer in alternative questions. Reduced ability to remember in older people restricts examination without additional sources of information. We offer an individualised system for testing doctors. It provides personalised choice of examination questions using multiple choice questions with weight characteristics and absence of distractors, interactive cooperation in case of negative answers and the final decision of an expert in relation to the person tested. A special algorithm is proposed for typical questions that combines the advantages of known approaches to testing. The questioning system is complex for the creators of tests, but is more convenient and objective than existing ones for medical doctors.

Continual few-shot patch-based learning for anime-style colorization

The automatic colorization of anime line drawings is a challenging problem in production pipelines.Recent advances in deep neural networks have addressed this problem;however,collectingmany images of colorization targets in novel anime work before the colorization process starts leads to chicken-and-egg problems and has become an obstacle to using them in production pipelines.To overcome this obstacle,we propose a new patch-based learning method for few-shot anime-style colorization.The learning method adopts an efficient patch sampling technique with position embedding according to the characteristics of anime line drawings.We also present a continuous learning strategy that continuously updates our colorization model using new samples colorized by human artists.The advantage of our method is that it can learn our colorization model from scratch or pre-trained weights using only a few pre-and post-colorized line drawings that are created by artists in their usual colorization work.Therefore,our method can be easily incorporated within existing production pipelines.We quantitatively demonstrate that our colorizationmethod outperforms state-of-the-art methods.

Continual Education is the Motivation of Sustainable Development of a Telecom Enterprise

The continuous investment into manpower resource, is the radical power toassure the sustainable development of enterprises. The enterprises both at home and abroad attachhigh importance to the continuous education of their employees and consolidate training to inspiretheir employees. In order to face increasingly drastic global competition, the telecom enterprisesin our country should consolidate continuous education, make training plans to adapt to thelong-term development of the enterprises and establish the effective mechanism of encouragement ofcontinuous education.

Leveraging ROTI map derived from Indonesian GNSS receiver network for advancing study of Equatorial Plasma Bubble in Southeast/East Asia

This paper highlights the crucial role of Indonesia’s GNSS receiver network in advancing Equatorial Plasma Bubble(EPB)studies in Southeast and East Asia,as ionospheric irregularities within EPB can disrupt GNSS signals and degrade positioning accuracy.Managed by the Indonesian Geospatial Information Agency(BIG),the Indonesia Continuously Operating Reference Station(Ina-CORS)network comprises over 300 GNSS receivers spanning equatorial to southern low-latitude regions.Ina-CORS is uniquely situated to monitor EPB generation,zonal drift,and dissipation across Southeast Asia.We provide a practical tool for EPB research,by sharing two-dimensional rate of Total Electron Content(TEC)change index(ROTI)derived from this network.We generate ROTI maps with a 10-minute resolution,and samples from May 2024 are publicly available for further scientific research.Two preliminary findings from the ROTI maps of Ina-CORS are noteworthy.First,the Ina-CORS ROTI maps reveal that the irregularities within a broader EPB structure persist longer,increasing the potential for these irregularities to migrate farther eastward.Second,we demonstrate that combined ROTI maps from Ina-CORS and GNSS receivers in East Asia and Australia can be used to monitor the development of ionospheric irregularities in Southeast and East Asia.We have demonstrated the combined ROTI maps to capture the development of ionospheric irregularities in the Southeast/East Asian sector during the G5 Geomagnetic Storm on May 11,2024.We observed simultaneous ionospheric irregularities in Japan and Australia,respectively propagating northwestward and southwestward,before midnight,whereas Southeast Asia’s equatorial and low-latitude regions exhibited irregularities post-midnight.By sharing ROTI maps from Indonesia and integrating them with regional GNSS networks,researchers can conduct comprehensive EPB studies,enhancing the understanding of EPB behavior across Southeast and East Asia and contributing significantly to ionospheric research.

Continual learning fault diagnosis:A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments

As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments. Therefore, adequately collecting fault data in advance is difficult. Limited by the characteristics of DL, training existing models directly with new fault data of new submachines leads to catastrophic forgetting of old tasks, while the cost of collecting all known data to retrain the models is excessively high. DL-based fault diagnosis methods cannot learn continually and adaptively in dynamic environments. A new Continual Learning Fault Diagnosis method(CLFD) is proposed in this paper to solve a series of fault diagnosis tasks with machine increments. The stability–plasticity dilemma is an intrinsic issue in continual learning. The core of CLFD is the proposed Dual-branch Adaptive Aggregation Residual Network(DAARN).Two types of residual blocks are created in each block layer of DAARN: steady and dynamic blocks. The stability–plasticity dilemma is solved by assigning them with adaptive aggregation weights to balance stability and plasticity, and a bi-level optimization program is used to optimize adaptive aggregation weights and model parameters. In addition, a feature-level knowledge distillation loss function is proposed to further overcome catastrophic forgetting. CLFD is then applied to the fault diagnosis case with machine increments. Results demonstrate that CLFD outperforms other continual learning methods and has satisfactory robustness.

Threshold public key encryption scheme resilient against continual leakage without random oracles

Threshold public key encryption allows a set of servers to decrypt a ciphertext if a given threshold of authorized servers cooperate. In the setting of threshold public key encryption, we consider the question of how to correctly decrypt a ciphertext where all servers continually leak information about their secret keys to an external attacker. Dodis et al. and Akavia et al. show two concrete schemes on how to store secrets on continually leaky servers. However, their construc- tions are only interactive between two servers. To achieve continual leakage security among more than two servers, we give the first threshold public key encryption scheme against adaptively chosen ciphertext attack in the continual leak- age model under three static assumptions. In our model, the servers update their keys individually and asynchronously, without any communication between two servers. Moreover, the update procedure is re-randomized and the randomness can leak as well.

Estimation of the minimum effective dose of tramadol for postoperative analgesia in infants using the continual reassessment method

Tramadol is a potent analgesic.However,the analgesia efficacy of tramadol,particularly its minimum effective dose(MED),is not clear.The aim of this study is to find MED of tramadol for postoperative analgesia in infants.The continual reassessment method(CRM)was performed to find MED.Infants undergoing surgeries were included in the 3 phases of this series.In each phase,24 participants were allocated a different tramadol dose.Pain intensity was measured by face,legs,activity,cry,consolability(FLACC)measurement at 3-hour intervals.Tramadol was considered ineffective if the FLACC score was higher than 4 in 10 at anytime.In phase 1,seven dose levels were used within the range 0.1-0.4 mg·kg^(-1)·h^(-1).Phase 1 was insufficient to identify the MED,and we increased the dose to 0.4-0.8 mg·kg^(-1)·h^(-1) in phase 2.Phase 2 was insufficient to identify the MED.In phase 3,local anesthetic wound infiltration was introduced,and the tramadol dose levels tested were the same as in phase 1.The successful analgesia probability of tramadol 0.4 mg·kg^(-1)·h^(-1) was 82.1%(95%CI,0.742-0.925)in phase 1.In phase 2,it was 84.7%(95%CI,0.789-0.991)with the dose 0.8 mg·kg^(-1)·h^(-1).Phase 1 and phase 2 were insufficient to identify the MED.In phase 3,the successful analgesia probability for dose 0.35 mg·kg^(-1)·h^(-1) was 96.7%(95%CI,0.853-0.997).We have demonstrated that tramadol provides insufficient analgesia for surgeries considered to cause moderate-to-severe postoperative pain in infants if used as the sole analgesic,and that local anesthetic wound infiltration enhances the efficacy of tramadol.

Silencing the Guns Internal and external challenges to Africa’s security persist,but long-term outlook remains positive

The world in 2024 was far from peaceful.The conflict between Russia and Ukraine has persisted for over 1,000 days.Meanwhile,a new wave of violence in the Palestinian-Israeli conflict,ongoing for more than a year,spilled over into Lebanon,Iran,and even Syria.Africa also faced new challenges to peace and security over the past year.Armed conflicts continued in various regions,particularly in the Horn of Africa,while terrorism and external interference exacerbated regional instability.These challenges made peace and security a crucial focus for African nations and an increasingly important aspect of China-Africa cooperation.

Instructions for Authors

GENERAL INFORMATION International Journal of Ophthalmology—IJO,published continuously since 2008,a global ophthalmological scientific publication and a peer-reviewed,open access periodical published in print and online monthly(ISSN:2222-3959 print,ISSN:2227-4898 online).IJO is sponsored by the Chinese Medical Association Xi’an Branch,China and obtains guidance,and support from World Health Organization(WHO)and International Council of Ophthalmology(ICO).