Neurogenesis dynamics in the olfactory bulb:deciphering circuitry organization, function, and adaptive plasticity

Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inhibitory interneurons. The generation of these new neurons in the olfactory bulb supports both structural and functional plasticity, aiding in circuit remodeling triggered by memory and learning processes. However, the presence of these neurons, coupled with the cellular diversity within the olfactory bulb, presents an ongoing challenge in understanding its network organization and function. Moreover,the continuous integration of new neurons in the olfactory bulb plays a pivotal role in regulating olfactory information processing. This adaptive process responds to changes in epithelial composition and contributes to the formation of olfactory memories by modulating cellular connectivity within the olfactory bulb and interacting intricately with higher-order brain regions. The role of adult neurogenesis in olfactory bulb functions remains a topic of debate. Nevertheless, the functionality of the olfactory bulb is intricately linked to the organization of granule cells around mitral and tufted cells. This organizational pattern significantly impacts output, network behavior, and synaptic plasticity, which are crucial for olfactory perception and memory. Additionally, this organization is further shaped by axon terminals originating from cortical and subcortical regions. Despite the crucial role of olfactory bulb in brain functions and behaviors related to olfaction, these complex and highly interconnected processes have not been comprehensively studied as a whole. Therefore, this manuscript aims to discuss our current understanding and explore how neural plasticity and olfactory neurogenesis contribute to enhancing the adaptability of the olfactory system. These mechanisms are thought to support olfactory learning and memory, potentially through increased complexity and restructuring of neural network structures, as well as the addition of new granule granule cells that aid in olfactory adaptation. Additionally, the manuscript underscores the importance of employing precise methodologies to elucidate the specific roles of adult neurogenesis amidst conflicting data and varying experimental paradigms. Understanding these processes is essential for gaining insights into the complexities of olfactory function and behavior.

On News Language from the Perspective of Pragmatics——By Applying Adaption Theory

News is an important medium to transmit social information and reflect social reality. Its effects are irreplaceable and the news language which is a carrier to spread information is in fact influenced by many factors such as society, economy and culture. In order to exert its function to gain different social ends, the presentation of news language owns distinguishing features. This article starts from Verschueren's Adaption Theory to have the final conclusion that the adaption to the social, mental and physical world is the key element for the determination of the originality in news language.

Marine green alga Cladophora aokii Yamada and its epiphytes from Yinggehai, Hainan, China： morphological taxonomy, formation and analysis of its environmental adaption

Based on specimens collected in Yinggehai, Hainan, China from 2013 to 2016, a stable epiphytic taxon is found on the surface of the individual of marine green alga Cladophora aokii Yamada. According to the morphological characteristics, the taxonomy of Cl. aokii and its epiphytes is carried out. There are some epiphytes attached on Cl. aokii Yamada including Cl. fascicularis （Mertens ex C. Agardh） Kfitzing, Chaetomorpha pachynerna （Montagne） Kiitzing, Cerarniurn carnouii Dawson, Licmophora abbreviata Agardh, Lyngbya sp. and Chattonella sp.. The formation of the individual of Cl. aokii is dissected and explained, which can help to analyze the adaption in details among this species, its epiphytes and native marine environment. The results reveal the marine macroepiphytic taxonomy in Ha/nan, China, and preliminarily explain the adaptive relationship between macroalgae and environment.

Bandwidth adaption for kernel particle filter

A novel particle filter bandwidth adaption for kernel particle filter （BAKPF） is proposed. Selection of the kernel bandwidth is a critical issue in kernel density estimation （KDE）. The plug-in method is adopted to get the global fixed bandwidth by optimizing the asymptotic mean integrated squared error （AMISE） firstly. Then, particle-driven bandwidth selection is invoked in the KDE. To get a more effective allocation of the particles, the KDE with adap- tive bandwidth in the BAKPF is used to approximate the posterior probability density function （PDF） by moving particles toward the posterior. A closed-form expression of the true distribution is given. The simulation results show that the proposed BAKPF performs better than the standard particle filter （PF）, unscented particle filter （UPF） and the kernel particle filter （KPF） both in efficiency and estimation precision.

From adaption to death: endoplasmic reticulum stress as a novel target of selective neurodegeneration?

Neurodegenerative disease is a condition in which subpopulations of neuronal cells of the brain and spinal cord are selectively lost. A common event in many neurodegenerative diseases, such as Parkinson＇s disease （PD）, Alzheimer＇s disease （AD）, amyotrophic lateral sclerosis （ALS）, multiple sclerosis and prion diseases, is the increased level of endoplasmic reticulum （ER） stress caused by accumulation and deposits of inclusion bodies that contain abnormal aggregated proteins. However, the exact contributions to and causal effects of ER stress in neuron degeneration are not clear （Lindholm et al., 2006）.

Research on Rapid Initial Adaption to the Environment of a Plateau

We designed two types of pre-adaption plans for this study. One was a pre-adaption training with progressive intermittent hypoxia, with a constant lower pressure oxygen tank used in the plain before arriving at the plateau （PG）. The other was by progressively increasing the time of exposure to hypoxia with oxygen supplied in stages after radical plateau （RG）. By testing the blood oxygen saturation （SpO2）, heart rate （HR）, and quality of sleep after arriving at the 3800 m high plateau, results showed that the pre-acclimatization and radical groups performed better than the control group （CG）. Both strategies were equivalent in terms of effects and principles in providing more flexible choices for acclimatization.

A MODEL OF NEURAL CIRCUIT WITH ADAPTION AND NONLINEAR CONNECTION

Based on physiological properties of synapse, soma and axon, this paper presents and analyses a model of neural circuit which can approximately simulate input-output relation, strength-duration curve, adaption and nonlinear connection of real neuron. The obtained results show that the model approximates to realistic principles of neural computation better than the available neural networks. The impulse-coded WTA(winner takes all) networks constructed with the above model find the winner more effectively than the analog WTA. Finally, the two important concepts: time competition and strength competition are introduced, which illustrate that the model has abilities to perform series and parallel information processing.

Technology Demand Assessment of Adaption of Chinese Cities to Climate Change

Climate change adaptation is the process of preparing and actively adjusting to meet the climate change (negative effects and potential opportunities). Urban adaptation is aimed at the sensitivity level of risks and specific impacts of cities under the impact of climate change, and to develop policies and investment programs to reduce the vulnerability of cities to climate change risk. Urban adaptive action provides the basis and direction for the construction of urban resilience and sustainable development. Identifying the demand of adaption technologies, promoting the practical implementation of international technology transfer and reducing domestic emissions have important significance for the global response to climate change and improvement of the ability of urban adaptation. In this paper, through in-depth analysis on the concept and connotation of climate change, climate disasters and urban adaptation to climate change, the evaluation framework and steps of urban adaptation to climate change technology are determined, and six priority application technologies which can maximize the overall efficiency of sustainable development, improve the ability to adapt to climate change and at the same time reduce the cost at the greatest extent are identified.

Markov Based Rate Adaption Approach for Live Streaming over HTTP/2

Dynamic adaptive streaming over HTTP （DASH） has been widely deployed. However, large latency in HTTP/1.1 cannot meet the requirements of live streaming. Data- pushing in HTFP/2 is emerging as a promising technology. For video live over HTTP/2, new challenges arise due to both low-delay and small buffer constraints. In this paper, we study the rate adaption problem over HTFP/2 with the aim to improve the quality of experience （QoE） of live streaming. To track the dynamic characteristics of the streaming system, a Markov-theoretical approach is employed. System variables are taken into account to describe the system state, by which the system transi- tion probability is derived. Moreover, we design a dynamic reward function considering both the quality of user experience and dynamic system variables. Therefore, the rate adaption problem is formulated into a Markov decision based optimization problem and the best streaming policy is obtained. At last, the effectiveness of our proposed rate adaption scheme is demonstrated by numerous experiment results.

A Novel Clutter Suppression Algorithm for Low-Slow-Small Targets Detecting Based on Sparse Adaptive Filtering

Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.

Diabetic retinopathy identification based on multi-sourcefree domain adaptation

AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled data.METHODS:A multi-SFDA method was proposed for DR identification.This method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target domain.Besides,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances.Validation is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks.It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains.CONCLUSION:The multi-SFDA method provides an effective approach to overcome the challenges in DR identification.The method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Adaptive Robust Servo Control for Vertical Electric Stabilization System of Tank and Experimental Validation

A tracking stability control problem for the vertical electric stabilization system of moving tank based on adaptive robust servo control is addressed.This paper mainly focuses on two types of possibly fast timevarying but bounded uncertainty within the vertical electric stabilization system:model parameter uncertainty and uncertain nonlinearity.First,the vertical electric stabilization system is constructed as an uncertain nonlinear dynamic system that can reflect the practical mechanics transfer process of the system.Second,the dynamical equation in the form of state space is established by designing the angular tracking error.Third,the comprehensive parameter of system uncertainty is designed to estimate the most conservative effects of uncertainty.Finally,an adaptive robust servo control which can effectively handle the combined effects of complex nonlinearity and uncertainty is proposed.The feasibility of the proposed control strategy under the practical physical condition is validated through the tests on the experimental platform.This paper pioneers the introduction of the internal nonlinearity and uncertainty of the vertical electric stabilization system into the settlement of the tracking stability control problem,and validates the advanced servo control strategy through experiment for the first time.

Robust adaptive leaderless consensus of unknown non-minimum phase linear multi-agent systems subject to disturbances and/or unmodeled dynamics

This article investigates the problem of robust adaptive leaderless consensus for heterogeneous uncertain nonminimumphase linear multi-agent systems over directed communication graphs. Each agent is assumed tobe of unknown nominal dynamics and also subject to external disturbances and/or unmodeled dynamics. Anovel distributed robust adaptive control strategy is proposed. It is shown that the robust adaptive leaderlessconsensus problem is solved with the proposed control strategy under some sufficient conditions. Two examplesare provided to demonstrate the efficacy of the proposed control strategy.

Translation, Cultural Adaption and Validation of Hospital Survey on Patient Safety Culture in Kosovo

Background: An essential condition to improve patient safety is considered to ensure a supportive patient safety culture. Measuring the culture of patient safety in all health care institutions may be a first step to target improvements. Creating a culture of safety requires eliminating the culture of blame. In order to formulate actions for improvement, it is important for hospitals to assess their baseline scores for the existing safety culture and to determine the areas of priority. Aim: The aim of this study was first to measure the use, translation in Albanian and adaptation of the Hospital Survey on Patient Safety Culture (HSOPSC) assessment as a tool for improving patient safety in Kosovo Hospitals. The second aim was to measure the level of patient safety culture in Kosovo, in seven hospitals and one University Clinical Center (hospitals with over 50 beds, including psychiatric hospitals). Method: The questionnaire (HSOPSC) was translated into Albanian for use in the Kosovo. It was used forward-backward translation: the questions were translated into Albanian by one translator and then translated back into English by an independent translator who was blinded to the original questionnaire. Results: In the eight-factor model, the internal consistency of the factors and the construct validity of the HSOPSC questionnaire were mostly satisfactory. The construct validity was sufficient for all subscales, except for the 4 other subscale regarding intention to report incidents which correlated poorly with other subscales. In total, HSOPSC has 12 dimensions. Cronbach’s α showed that in Kosovarian society, we could use only 8 dimensions model. Conclusion: The hypothesis that HSOPSC would be a suitable instrument to provide important indicators for the improvement of patient safety culture was tested and it was confirmed, that HSOPSC could be used as 8 dimension model. HSOPSC is suitable to improve patient safety culture and provide each hospital with a basic profile on patient safety culture and recommendations for an oriented intervention plan.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Cavitation Evolution Around a Twist Hydrofoil by Large Eddy Simulation(LES)with Mesh Adaption

The cavitating flow around a Delft Twist-11 hydrofoil is simulated using the large eddy simulation approach.The volume-of-fluid method incorporated with the Schnerr-Sauer cavitation model is utilized to track the water-vapor interface.Adaptive mesh refinement(AMR)is also applied to improve the simulation accuracy automatically.Two refinement levels are conducted to verify the dominance of AMR in predicting cavitating flows.Results show that cavitation features,including the U-type structure of shedding clouds,are consistent with experimental observations.Even a coarse mesh can precisely capture the phase field without increasing the total cell number significantly using mesh adaption.The predicted shedding frequency agrees fairly well with the experimental data under refinement level 2.This study illustrates that AMR is a promising approach to achieve accurate simulations for multiscale cavitating flows within limited computational costs.Finally,the force element method is currently adopted to investigate the lift and drag fluctuations during the evolution of cavitation structure.The mechanisms of lift and drag fluctuations due to cavitation and the interaction between vorticity forces and cavitation are explicitly revealed.

Study on Paths of Affect Factors on Adaption to University Life of Chinese International Students in Korea —Choosing Korean Training Chinese Students as Main Investigation Objects

The purpose of this paper is to study effect factors of adaptation to university life of Chinese International Students in Korea. 374 Korean language trainees are chosen as investigation objects by means of random cluster sampling. Questionnaire data are collected by using the survey form and then paths of effect factors on adaption to university life of Korean training Chinese students in Korea are analyzed. The results indicate that Korean language proficiency, physical symptoms, loneliness, self-efficacy, acculturation stress are direct effect factors on Chinese students adapting to university life in Korea. Besides, Korean language proficiency, physical symptoms, loneliness are not only direct effect factors on self-efficacy and acculturation stress but also indirect effect factors on Chinese students adapting to university life in Korea. In order to improve ability of Korean training Chinese students adapting to university life in Korea, Korean language proficiency and self-efficacy should be improved. And, some effective interventions should be given to reducing physical symptoms, loneliness and acculturation stress.