ACORDE Project:Adaptable Residential Buildings for a New Social Framework

ACORDE project seeks to maximize buildings´flexibility,conceiving a residential building made of legally independent functional units that can be connected to or disconnected from each other with easy,plug-in facilities,in a way that permits to purchase exactly what the user need,as well as allows future changes for buildings to be adapted to the needs of their occupants,the user decides how to best utilize the space by acquiring or separating functional units along the years.For this to be possible,in addition to the legally independent modules concept,a diaphanous and very well organized structure is projected as well as a very solid and efficient envelope to ensure durability and minimal maintenance.The efficient occupancy of space leads to an optimal use of energy.

Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

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.

ADAPTABLE DESIGN OF MACHINE TOOLS STRUCTURES

Adaptable design aims to extend the utilities of design and product. The specific methods are developed for practical applications of adaptable design in the design of mechanical structures, including adaptable platform, interface and module designs. Adaptable redesign problems are formulated as adaptable platform design under adaptability bound constraints. Analysis tools are then suggested for the implementation of the redesign of machine tool structures, including variation techniques based sensitivity analysis, similarity-based commonality analysis, performance improvement, and adaptability measures. The specific measure of adaptability for machine tool structures is measured through the quantification of the structural similarity and performance improvement gained from adaptable design. The method provides designers with an approach that brings adaptability into the design process, with considering the cost and benefits of such adaptability. The redesign of CNC spiral bevel gear cutting machine structures has been included to illustrate these concepts and methods.

A Binary Images Watermarking Algorithm Based on Adaptable Matrix

A watermarking algorithm of binary images using adaptable matrix is presented. An adaptable matrix is designed to evaluate the smoothness and the connectivity of binary images. The watermark is embedded according to the adaptable matrix in this algorithm. In the proposed watermarking algorithm, each image block implements a XOR operation with the binary adaptable matrix, which has the same size with the image block, and in order to embed the watermark data, a multiplication operation are also implemented with the weight matrix. The experimental results show that proposed scheme has a good performance.

ADAPTABLE DESIGN IN PRODUCT DEVELOPMENT

A newly emerging design pattern, named as adaptable design （AD）, which aims at developing products that are adaptable from design to post-life cycle, is discussed. AD consists of four main phases： product modeling, design platform, specific design and product redesign. A new process-based design data model （PDDM） is presented which is organized according to the principles of convenient knowledge extraction, data representation, layout, sharing and reuse. Based on the PDDM, a universal design platform for product family development is established, which has characters of modularity, parameter-driven, variant design, etc. The framework of the platform is also proposed as a conceptual structure and overall logical organization for generating a family of products. AD methodology is successfully applied to develop a family of tunnel boring machine （TBM） for different engineering projects, with the efficiency of our developing team being greatly increased.

Towards Wide-Open, Adaptable DCF Implementation for Dynamic Networks

Abstract： This work proposes a Field Programmable Gate Array （FPGA）-oriented architecture for the IEEE 802.11 Distributed Coordination Function （DCF） transceiver. We describe the functional blocks carrying out the Carrier Sense Multiple Accesses with Collision Avoidance （CSMA/CA）, develop the interfaces to the application layer and the physical layer, and implement it on FPGA devices by utilizing Very-high-speed-integrated-circuit Hardware Description Language （VHDL）.

Reshapeable,rehealable and recyclable sensor fabricated by direct ink writing of conductive composites based on covalent adaptable network polymers

Covalent adaptable network(CAN)polymers doped with conductive nanoparticles are an ideal candidate to create reshapeable,rehealable,and fully recyclable electronics.On the other hand,3D printing as a deterministic manufacturing method has a significant potential to fabricate electronics with low cost and high design freedom.In this paper,we incorporate a conductive composite consisting of polyimine CAN and multi-wall carbon nanotubes into direct-ink-writing 3D printing to create polymeric sensors with outstanding reshaping,repairing,and recycling capabilities.The developed printable ink exhibits good printability,conductivity,and recyclability.The conductivity of printed polyimine composites is investigated at different temperatures and deformation strain levels.Their shape-reforming and Joule heating-induced interfacial welding effects are demonstrated and characterized.Finally,a temperature sensor is 3D printed with defined patterns of conductive pathways,which can be easily mounted onto 3D surfaces,repaired after damage,and recycled using solvents.The sensing capability of printed sensors is maintained after the repairing and recycling.Overall,the 3D printed reshapeable,rehealable,and recyclable sensors possess complex geometry and extend service life,which assist in the development of polymer-based electronics toward broad and sustainable applications.

A SPEAKER ADAPTABLE VERY LOW BIT RATE SPEECH CODER BASED ON HMM

This paper presented a speaker adaptable very low bit rate speech coder based on HMM (Hidden Markov Model) which includes the dynamic features, i.e., delta and delta delta parameters of speech. The performance of this speech coder has been improved by using the dynamic features generated by an algorithm for speech parameter generation from HMM because the generated speech parameter vectors reflect not only the means of static and dynamic feature vectors but also the covariance of those. The encoder part is equivalent to an HMM based phoneme recognizer and transmits phoneme indexes, state durations, pitch information and speaker characteristics adaptation vectors to the decoder. The decoder receives those messages and concatenates phoneme HMM sequence according to the phoneme indexes. Then the decoder generates a sequence of mel cepstral coefficient vectors using HMM based speech parameter generation technique. Finally the decoder synthesizes speech by directly exciting the MLSA(Mel Log Spectrum Approximation) filter with the generated mel cepstral coefficient vectors, according to the pitch information.

Adaptable Processing： Stage Development

The development and evolution of Microsoft Office and Microsoft Windows shells are based in general on the special methodology of software creation and implementation such as macros, subroutine, custom commands, and specialized features. Microsoft Office for Mac has for long been criticized for its lack of support of Unicode and BiDi languages, notably Arabic and Hebrew. This has not changed in the Office 2008 version. Microsoft Office 2010 （also called Office 2010 and Office 14） is the current version of the Microsoft Office productivity suite for Microsoft Windows, and the successor to Microsoft Office 2007. With Office 2010, users are in control, getting things done and producing amazing results however and wherever they work best. Ms Office 2010 is the last version of Microsoft Office with support for Windows XP, Windows Server 2003, Windows Vista, and Windows Server 2008 due to Office 2013 requiring Windows 7, Windows Server 2008 R2, Windows 8, Windows Server 2012, or Windows RT. Adaptive hardware （Ugurdag, 2006） reflects the capability of a system to maintain or improve its performance in the context of internal or external changes. Adaptation at hardware levels increases the system capabilities beyond what is possible with software-only solutions. Algorithms, techniques, and their implementation in hardware are developed over a diverse variety of applications. The methodology of the On-Off-Line adaptable processors （Todoroi ＆ Micusha, 2012） support development of adaptable software and hardware. Automatic creation of the Off-Line adaptable processors are proved. Development of the On-Line and On-Off-Line adaptable processors based on Off-Line processing creation method is proposed （Todoroi, Micu^a, ＆ Todoroi, 2009; Todoroi, 2008a, 2008b）

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.

Taking Diplomatic Strategy Adaptable to Environment

Looking from China’s neighboring environment, the America factor is undoubtedly the most important factor in analyzing China’s neighboring security. Speaking of U. S. influence on China’s neighboring security, some scholars tend to believe that the unfavorable factors play a dominant role. This conclusion comes from the "new moonshape encirclement" already existing since the Cold War in America,

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.

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.

Quantitatively probing interactions between membrane with adaptable wettability and oil phase in oil/water separation

The membrane method based on adaptive wettability shows great advantages in oil-water separation.At present,researches focus on the excellent application performance of the membrane material,while the quantitative analysis of interactions in oil-water separation is rarely recognized.Herein,we constructed an adaptable wettability membrane with multiple polymer networks by polydopamine(PDA)and mussel-inspired amphiphilic polymer.Based on the Owens three-probe liquid method,the surface energy of the modified membrane was verified to meet the adaptive wettability conditions,with surface energies(γ-8)of 147.6 mJ m^(−2)(superhydrophilic/underwater superoleophobic)and 49.87 mJ m^(−2)(superhydrophobic/superoleophobic),respectively.The adhesion or repulsion of the membrane to the oil phase under different conditions during the separation process was quantified by the chemical probe AFM technique.In addition,the oil-water selective separation mechanism was further analyzed in a simplified membrane microchannel model.The results show that the different wetting produces capillary additional pressure in opposite directions,resulting in different energies to be overcome when the oil or water passes through the microchannels,thus achieving selective separation.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

An Adaptive Program Recommendation System for Multi-User Sharing Environment

More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme.