Adaptive

Abstract:- Theory of learning styles is steadily evolving in several decades and reveals importance of such styles for construction and adoption of adaptive e-learning platforms. Courseware delivery adaptive to learning styles needs precise definition of types of learning objects most appropriate for individual given learning character with its specific learning style. The present article provides results of a case study of courseware delivery of various types of learning objects such tasks, projects, essays, games and intermediate tests with adaption based on learning styles. It presents evaluation of dependability of learning object types on the styles of learning defined by Honey and Mumford and shows how practical results may differ from expected ones for some of the cases.

In this paper we develop an adaptive MIMO channel estimation algorithm for space-time block coded OFDM systems. The presented algorithm is based on Expectation Maximization (EM) technique by decomposing the superimposed received signals into their signal components, and estimating the channel parameters of each signal component separately. We also study and compare our proposed EM-based algorithm with a previously introduced recursive-least-squares based algorithm for MIMO OFDM systems. At each iteration the EM algorithm decomposes the problem of multi-channel estimation into channel estimation for each transmit-receive link. In this paper we also study the Doppler spread tolerance of our proposed algorithm in a fast fading environment, and investigate how it affects the system BER performance.

During the last two decades, optical stellar interferometry has become an important tool in astronomical investigations requiring spatial resolution well beyond that of traditional telescopes. This book, first published in 2006, was the first to be written on the subject. The authors provide an extended introduction discussing basic physical and atmospheric optics, which establishes the framework necessary to present the ideas and practice of interferometry as applied to the astronomical scene. They follow with an overview of historical, operational and planned interferometric observatories, and a selection of important astrophysical discoveries made with them. Finally, they present some as-yet untested ideas for instruments both on the ground and in space which may allow us to image details of planetary systems beyond our own.

Conservationists and government authorities acknowledge that adaptive reuse of historic buildings contributes to urban sustainability. Traditional Chinese shophouses are a major historic building typology found in the old districts of Asian cities. In Hong Kong, the few remaining shophouses are generally deteriorating and are increasingly under threat of demolition for urban renewal. However, adaptive reuse of these buildings has created many social concerns. In light of these concerns, evaluating adaptive reuse potential needs to incorporate a much broader sustainability framework than simply physical building conditions. This study examines the extent to which obsolescence, heritage value and redevelopment pressures have affected the adaptive reuse of shophouse intervention in government-led urban renewal projects. Interestingly, the results based on adaptive reuse potential find no consistent relationships among the adaptive reuse actions of eight cases in urban renewal districts of Hong Kong. Instead, the question of revitalizing shophouses is determined by whether they are within an urban renewal redevelopment plan. This study further explores the broader urban sustainability framework reflected in eight case studies of shophouse revitalization in three renewal districts. It includes a qualitative synthesis of the social, environmental, economic, and political considerations contributing to the adaptive reuse of shophouses in urban renewal.

This paper deals with an experimental comparison between a proportional derivative (PD) controller and an adaptive nonlinear state feedback one, both applied on a tethered autonomous underwater vehicle. The aim is to show the behavior of the closed loop system in the nominal case for each of these two controllers, and then to test their robustness towards some parameters changes. The PD on one hand has a good performance for systems with an unknown model. The adaptive control law on the other hand is known to adjust the unknown parameters of the plant in order to converge to the desired trajectory. This study shows experimental results performed using each of the above mentioned control laws.

This paper deals with the study of various types of Beamforming algorithms. A beam former is a processor used in conjunction with an array of sensors to provide a versatile form of spatial filtering. The sensor array collects spatial samples of propagating wave fields, which are processed by the beam former. The objective is to estimate the signal arriving from a desired direction in the presence of noise and interfering signals. A beam former performs spatial filtering to separate signals that have over lapping frequency content but originate from different spatial locations.

The relationship between intelligence and creativity may be linked to the difficulties in defining and measuring methodology. Threshold theory is one of the theories which is used to explain the relationship between them. The aim of this study is to investigate the structures which the creative thinking ability of the gifted students and their intellectual structure is grouped and the structure which their creative thinking ability are alone. Data was gathered using Wechsler Intelligence Scale for Children-R and Torrance Thinking Creativity Test (TTCT). Confirmatory factor analyses were conducted with data from 278 gifted primary school students which contained the grade range of 1 to 3. The results indicate that the TTCT subscores consist of 2 factors called adaptive and innovative rather than a single factor. Besides, the results of the analyses provide support that creativity and intelligence are independent from each other.

The purpose of this paper is to present the way some widely used knowledge management models are structured. We will describe the most important characteristics of each model, with our comments about its usefulness in the economical environment. The models we chose to analyze are as follow: von Krogh and Roos, Nonaka/Takeuchi, Wiig, Boisot and Bennet. We will discuss about main factors involved, about types of knowledge and elements which forms it and of course about advantages and disadvantages of these models. The paper will end with a general conclusion, which will largely synthesize our conclusions about each knowledge model

Studies describing subthalamic (STN) local field potentials (LFPs) recorded during deep brain stimulation (DBS) in patients with Parkinson's disease (PD), within the first month after DBS electrode implant, show that DBS modulates specific STN oscillations: whereas low-frequency (LF) oscillations (2-7 Hz) increase, beta oscillations (8-30 Hz) variably decrease. No data show whether LFPs remain stable for longer than one month after DBS surgery. Having long-term information is essential especially for use as a long-term feedback control signal for adaptive DBS systems. To evaluate how STN activity behaves years after prolonged chronic stimulation in PD we studied STN LFPs at rest without DBS and during ongoing DBS, in 11 parkinsonian patients 7 years (7.54±1.04) after STN electrode implantation for DBS (hyperchronic group) and in 16 patients 3 days after STN electrode implantation (acute group). STN LF and beta-band LFPs recorded at rest at 7 years contained almost the same information as those recorded at 3 days. STN recordings showed similar LFP responses to DBS in the acute and hyperchronic stages: whereas during ongoing DBS the LF power band increased for the whole population, beta activity decreased only in nuclei with significant beta activity at baseline. The LF/beta power ratio in all nuclei changed in both study groups, suggesting that this variable might be an even more informative marker of PD than the single LF and beta bands. Because STN LFP activity patterns and STN LFP responses to DBS stay almost unchanged for years after DBS electrode implantation they should provide a consistent feedback control signal for adaptive DBS.

Figure 1: The overview (left) of an interior scene illuminated by traditional shadow mapping and close look (right) to certain areas. Both constant depth bias (first column) and slope scale depth bias (second column) suffer from shadow acne and shadow detachment to different extent. Our method (third column) has no visible acne and preserves more shadow details. Dual depth layers depth bias (fourth column) is used as reference to compare our method against.

This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system. To achieve that, an innovative structure of the adaptive PID controller is defined, an additional PI is designed to make sure that the reference model produces stable output signals and three adaptive gains are included to guarantee stability and robustness of the closed-loop system. Then, the performance of the model reference adaptive PID controller on the behaviour of the closed-loop system is compared to a PI controller designed on MATLAB when both closed-loop systems are under various conditions. The results demonstrate that the MRAPIDC performs significantly better than the conventional PI controller. Keywords: Adaptive Linearization MIT MRAPIDC Nonlinear Parameters Stability This is an open access article under the CC BY-SA license. 1. INTRODUCTION Adaptive control of uncertain processes has become more and more important in industry. Adaptive controllers differ from ordinary ones, because their parameters are variable, and there is a mechanism for adjusting these parameters online based on signals in the system [1]. The design of an adaptive PI controller to stabilize a mass damper-spring system under parameters' uncertainties was proposed in [2]. The designed adaptive PI controller adjusts to parameters' variations, and the output of the process follows the set points, regardless of the values of the parameters. But it does not guarantee stability when external disturbances and large variations of parameters occur. In [3], the design of a PID controller on MATLAB to maintain the level of liquid constant in a coupled-tank system (CTS) was proposed. The control parameters were found using the trial and error methodology and the results were analysed in MATLAB/Simulink environments. Proportional (P), proportional integral (PI), proportional derivative (PD) and proportional integral derivative (PID) controllers were applied on the process and their performances were compared to select the most suitable control solution. The PID controller showed superior results, but it did not guarantee stability to disturbances and variations of plant parameters. Adaptive controllers, as opposed to conventional constant gain controllers (PID controllers), are very effective in handling situations where the variations of parameters and environmental changes are very frequent with the application of model reference adaptive control scheme in a first order system [4].

This paper proposes an adaptive reconfiguration scheme to reduce the effect of shadows on solar panels. A switching matrix connects a solar adaptive bank to a fixed part of a solar photovoltaic (PV) array, according to a model-based control algorithm that increases the power output of the solar PV array. Control algorithms are implemented in real time. An experimental reconfiguration PV system with a resistive load is presented and is shown to verify the proposed reconfigurations.

The turning of thin-walled, hollow, cylindrical parts is often not possible when using the common hydraulically operated 3-or 4-jaw chucks. This is due to the deformation of the part by the clamping force of the chuck. Even low clamping forces cause elastic deformations of the clamped workpiece, which results in surface errors due to machining after the unclamping and elastic recovery of the part. For this reason, several approaches have been adopted in the past in order to quantify and minimise the clamping-induced deformations. In this paper a model-based error compensation for thin-walled, cylindrical parts in turning is presented as a universal solution. In this work, a compensation by different models is designed and realised by an intelligent, adaptive and interchangeable turning tool holder with integrated sensors and actuators.

An adaptive estimation method is developed to generate real-time estimates of the position and orientation of the end-effector of an industrial robot and estimates of the root mean squared errors in these estimates, using real-time measurements of the position of a point on the end-effector, in addition to the usual measurements of the joint positions. To compensate for the lack of real-time measurements of the orientation of the end-effector, a Kalman filter is used to update a lookup table model of the kinematics of the robot that most affect the orientation of the end-effector. Measurements of the positions of a point on the end-effector collected during a short sequence of motions of the last axis of the robot are used by the Kalman filter to update this lookup table model. The updated lookup table model, together with real-time measurements of the position of a point on the end-effector and real-time measurements of the joint positions, is used by the estimator to compensate for the effects of geometric errors in the robot's structure and temperature variations on the position and orientation of the end-effector. In an application to a six-axis industrial robot, the adaptive estimation method is shown to substantially outperform the direct forward kinematics method whereby the position and orientation of the end-effector are estimated based upon joint position measurements alone.

Received Mar 31, 2020 Revised Jun 20, 2020 Accepted Jul 6, 2020 This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system. To achieve that, an innovative structure of the adaptive PID controller is defined, an additional PI is designed to make sure that the reference model produces stable output signals and three adaptive gains are included to guarantee stability and robustness of the closed-loop system. Then, the performance of the model reference adaptive PID controller on the behaviour of the closed-loop system is compared to a PI controller designed on MATLAB when both closed-loop systems are under various conditions. The results demonstrate that the MRAPIDC performs significantly better than the conventional PI controller.

This paper examines the creative collaborative practices and processes of the historic 1966 "9 Evenings: Theatre & Engineering" event that spawned the Experiments in Art & Technology (E.A.T.) group in New York City. It presents the cultural origins and collaborative goals of this group of avant-garde artists and Bell Labs engineers who came together to produce the landmark event based on first-hand accounts, and examines the elements and intentions of their creative and collaborative processes. It concludes with speculation as to the significance of this historical event as a source for further exploration into the nature of creative collaboration, dialogue, and collective wisdom.

This paper presents a new approach to total variation (TV) based image deconvolution/deblurring, which is adaptive in the sense that it doesn't require the user to specify the value of the regularization parameter. We follow the Bayesian approach of integrating out this parameter, which is achieved by using an approximation of the partition function of the probabilistic prior interpretation of the TV regularizer. The resulting optimization problem is then attacked using a majorization-minimization algorithm. Although the the resulting algorithm is of the iteratively reweighted least squares (IRLS) type, thus suffering of the infamous "singularity issue", we show that this issue is in fact not problematic, as long as adequate initialization is used. Finally, we report experimental results which show that the proposed methodology achieves state-of-the-art performance, on par with TVbased methods with hand tuned regularization parameter, as well as with the top wavelet-based methods.

Advanced structural ceramics are widely used in engineering applications. They are useful due to high chemical inertness and sublimation temperature. Optimization of ceramic grinding process is essential as ceramics have high hardness and low surface toughness which can lead to large number of defects. The ceramic grinding optimization problem is formulated as non-linear constrained optimization problem. This paper proposes to solve the optimization problem by using a recently proposed real coded Adaptive Quantum inspired Evolutionary Algorithm. It is free from user selectable parameters in evolutionary operators as the same is determined adaptively. The proposed algorithm does not require mutation for maintaining diversity. The results also show that the proposed algorithm is fast and robust in comparison to the known state of art methods available for solving such problems.

Here we present a hybrid system that uses a reinforcement learning agent to improve the performance of a genetic algorithm on the travelling salesman problem (TSP). The agent uses Q(λ) learning to estimate state-action utility values, which it uses to implement high-level adaptive control over the genetic algorithm. In this way the agent influences selection of both crossover and mutation operators as well as the selection of individuals for breeding, at every generation.

In nonparametric regression problems involving multiple predictors, there is typically interest in estimating the multivariate regression surface in the important predictors while discarding the unimportant ones. Our focus is on defining a Bayesian procedure that leads to the minimax optimal rate of posterior contraction (up to a log factor) adapting to the unknown dimension and anisotropic smoothness of the true surface. We propose such an approach based on a Gaussian process prior with dimension-specific scalings, which are assigned carefully-chosen hyperpriors. We additionally show that using a homogenous Gaussian process with a single bandwidth leads to a sub-optimal rate in anisotropic cases.

Abstract: Most e-learning courses are still offered linearly as online book supplemented by interactive media and exercises. Therefore representing the whole content of an e-learning course by some kind of hierarchical table of content is sufficient. When using an adaptive e-learning system, individual learning paths may be used and suggested to the users, e.g. in dependence of a given answer of an exercise. This paper presents our extended concept how to represent such e-learning content of an adaptive system using the metaphor of a map. The work incorporates different perspectives from cartography and geomatics, pedagogy, didactics, information visualization and computer science into a practicable method for representing e-learning content as learning map using cities for learning units and buildings for knowledge units. Therefore this work might also be considered in the discussion of spatial turn.

An adaptive pole placement design for not necessarily minimum phase systems is analyzed with respect to stability. Conditions are given for boundedness of closed-loop signals when the process is subject to bounded disturbances. The most restrictive one can be avoided in a local stability result.

Energy efficiency is a central challenge in sensor networks, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low-power radio mode for putting the radio to sleep. Fixed low-power modes involve an inherent trade-off: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low-power sleep modes based on current traffic conditions in the network. It first introduces a comprehensive node energy model, which includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded microcontroller energy component for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFID impulse protocol enhanced with adaptive low-power modes, and comparing it against BMAC and IEEE 802.15.4, for both MicaZ and TelosB platforms under varying data rates. The comparative analysis confirms that RFID impulse with adaptive low-power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low-power modes on the basis of data rates for each node platform, and provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low-power mode, and node platform for a given set of traffic requirements of a sensor network application.

We demonstrate that native grass species from coastal and geothermal habitats require symbiotic fungal endophytes for salt and heat tolerance, respectively. Symbiotically conferred stress tolerance is a habitat-specific phenomenon with geothermal endophytes conferring heat but not salt tolerance, and coastal endophytes conferring salt but not heat tolerance. The same fungal species isolated from plants in habitats devoid of salt or heat stress did not confer these stress tolerances. Moreover, fungal endophytes from agricultural crops conferred disease resistance and not salt or heat tolerance. We define habitat-specific, symbiotically-conferred stress tolerance as habitatadapted symbiosis and hypothesize that it is responsible for the establishment of plants in highstress habitats. The agricultural, coastal and geothermal plant endophytes also colonized tomato (a model eudicot) and conferred disease, salt and heat tolerance, respectively. In addition, the coastal plant endophyte colonized rice (a model monocot) and conferred salt tolerance. These endophytes have a broad host range encompassing both monocots and eudicots. Interestingly, the endophytes also conferred drought tolerance to plants regardless of the habitat of origin. Abiotic stress tolerance correlated either with a decrease in water consumption or reactive oxygen sensitivity/generation but not to increased osmolyte production. The ability of fungal endophytes to confer stress tolerance to plants may provide a novel strategy for mitigating the impacts of global climate change on agricultural and native plant communities.

The purpose of this paper is to present Sixas® an auxiliary new-age model decoded for measuring the performance effectiveness of both an organization and an employee. The model was developed upon critically reviewing the present models and human resources practices of most successful organizations. The model’s six impactful elements i.e., Actualize, Adaptive, Agile, Align, Analytical, and Automate are the basis for calling the model so. The Sixas® model acts as a plugin to any existing performance evaluation systems that organizations follow and this can be integrated into any core engines of PMSs of any organization to maximize efficiency and productivity. Sixas® amalgamates with the dimensions of any organization like its system, mechanism, operation, evaluation, method, and ways and means. The Sixas® criteria can be set through the EXEMPLARY framework -- Explicit, Exciting, Manageable, Periodical, Levelheaded, Adjustable, Resilient, and Yielding. The scope, objectives, and establishment process of the said model are detailed in the manuscript. In the context of converting adversaries into opportunities, this model serves the purpose of adopting the new normal in this era of globalization and pandemic situations like the one we live in during the Covid-19 lockdown this model represents a holistic perspective. It is a cost-effective yet trouble-free integration. It is driven by business, performance achievement and introspection with feedforward methodology. These are the salient features of the model. However, the commitment of management ethics, continuous monitoring in performance management through Sixas® are needed to successfully implement the model.

The paper purposes the removal of noise in digital gray scale images that often observed in scanned documents. Generally, Data i.e. picture, text can be contaminated by an additive noise during the process of scanning. This methodology prevents this type of noise known as Salt and Pepper noise (SP Noise) which causes white and black spots on the original image. We are designing a new algorithm for removal of these white and black spots after the knowledge of Median Filter, Adaptive Filter and the new proposed algorithm will definitely protect the image from noise and distortion. Firstly, Adaptive Histogram Equalization is done on the original image. Secondly apply Adaptive contrast Enhancement Technique on the resultant image. After Contrast Enhancement we apply filters Such as Homomorphic filtering. These filters are applied sequentially on distorted images for removing the image.

Adaptive nonlinear models (ANMs) are currently being proposed for use in actuarial and financial modeling. The techniques of these models included such things as neural networks and genetic algorithms. While there is a general awareness of the nature of these ANM techniques, there is often only vague familiarity with the details of how these techniques are implemented. This article is intended to help alleviate this situation. Its purpose is to present an overview of ANM techniques, which includes an explanation of what they are, how they work, and a description of their key features.

The workshop will explore in depth the nature of freedom and constraint in the creative process in digital fine art from the perspective of embodied mind. The problem is crucial to our understanding of the creative process in fine art. The aims and objectives of the workshop are to bring into visibility critical insights into the creative process, thereby potentially empowering digital artists.

In Vietnam, the principal of a high school is the title prescribed in education law. The principal of the school is appointed by the chairman of the provincial/district People's Committee. In fact, the work of fostering and appointing educational managers is often carried out under the "post-supplementary" regime, i.e. appointing managerial positions before sending them to fostering educational management. The principal is an adult who goes to school. They have work experience and this experience affect their academic performance. Principals only learn when they find learning beneficial to their work, and they study actively when solving practical problems. Principals learn through their own activities. Their knowledge is not equal in both theory and practice. When we consider human learners from an e-learning system perspective, we consider the competency (such as knowledge, skill, and abilities) of the human learner. Within this model, we should also consider competency of the self-improvable resources. In most current e-learning systems, activities' content and order are presented in a static manner without taking into consideration the learners competencies. In this paper, we adopted a competency based approach to offer each learner (the principal of a high school) an individualized learning path for the acquisition of the competences required in the educational management field.

This paper attempts to establish the fact that Multi State Network Classification is essential for performance enhancement of Transport protocols over Satellite based Networks. A model to classify Multi State network condition taking into consideration both congestion and channel error is evolved. In order to arrive at such a model an analysis of the impact of congestion and channel error on RTT values has been carried out using ns2. The analysis results are also reported in the paper. The inference drawn from this analysis is used to develop a novel statistical RTT based model for multi state network classification. An Adaptive Multi State Proactive Transport Protocol consisting of Proactive Slow Start, State based Error Recovery, Timeout Action and Proactive Reduction is proposed which uses the multi state network state classification model. This paper also confirms through detail simulation and analysis that a prior knowledge about the overall characteristics of the network helps in enhancing the performance of the protocol over satellite channel which is significantly affected due to channel noise and congestion. The necessary augmentation of ns2 simulator is done for simulating the multi state network classification logic. This simulation has been used in detail evaluation of the protocol under varied levels of congestion and channel noise. The performance enhancement of this protocol with reference to established protocols namely TCP SACK and Vegas has been discussed. The results as discussed in this paper clearly reveal that the proposed protocol always outperforms its peers and show a significant improvement in very high error conditions as envisaged in the design of the protocol. The protocol is also evaluated to have very good fairness property under different error conditions.

This paper describes the eBag infrastructure, which is a generic infrastructure inspired from work with school children who could bene t from a electronic schoolbag for collaborative handling of their digital material. The eBag infrastructure is written on top of the Context-aware HyCon framework and collaborative web services based on Web-DAV. A ubiquitous login mechanism has been built based on BlueTooth sensor networks. The eBag infrastructure has been tried out in eld tests with school kids. In this paper we discuss experiences and design issues for ubiquitous Web integration in interactive school environments with multiple interactive whiteboards and workstations. This includes proposals for specialized and adaptive XLink structures for organizing school materials as well as issues in login/logout based on proximity of di erent display surfaces.

Recently, there has been great interest in the integration of dispersed generation units at the distribution level. This requires new analysis tools for understanding system performance. This paper presents an adaptive distributed power flow solution method based on the compensation-based method. The comprehensive distributed system model includes three-phase nonlinear loads, lines, capacitors, transformers, and dispersed generation units. The numerical properties of the compensation-based power flow method are compared and analyzed under different situations, such as load unbalance, sudden increase of one-phase loads, degree of meshed loops, number of generator nodes and so on. Based on these analyses, an adaptive compensation-based power flow method is proposed that is fast and reliable while maintaining necessary accuracy. It is illustrated that this adaptive method is especially appropriate for simulation of slow dynamics.

There is growing interest in how best to adapt and readapt treatments to individuals to maximize clinical benefit. In response, adaptive treatment strategies (ATS), which operationalize adaptive, sequential clinical decision making, have been developed. From a patient's perspective an ATS is a sequence of treatments, each individualized to the patient's evolving health status. From a clinician's perspective, an ATS is a sequence of decision rules that input the patient's current health status and output the next recommended treatment. Sequential multiple assignment randomized trials (SMART) have been developed to address the sequencing questions that arise in the development of ATSs, but SMARTs are relatively new in clinical research. This article provides an introduction to ATSs and SMART designs. This article also discusses the design of SMART pilot studies to address feasibility concerns, and to prepare investigators for a full-scale SMART. We consider an example SMART for the development of an ATS in the treatment of pediatric generalized anxiety disorders. Using the example SMART, we identify and discuss design issues unique to SMARTs that are best addressed in an external pilot study prior to the full-scale SMART. We also address the question of how many participants are needed in a SMART pilot study. A properly executed pilot study can be used to effectively address concerns about acceptability and feasibility in preparation for (that is, prior to) executing a full-scale SMART. Copyright

Ever since ancient times, emotions have been studied and intrigued the scientists. In this article, we set out to see how perspectives upon the concept of emotions have changed over time and according to each approach. We briefly discuss about two major Greek philosophers, Plato and Aristotles, move on to more recent times with Descartes and Darwin, change the pace again with Sartre as the representative of phenomenology and end up with the cognitivists' approach presented by Johson and Laird, Lazarus, Frijda and Ellis, with the main focus on Ellis' ABC model based on the Rational Emotive Behavior Theory.

Phenotypic variation in morphological characters like body shape or meristic count has long been used to delineate stocks of ®sh. It is now well known that there may be both environmental and genetic components to this variation. Genetic studies often fail to support the population differentiation suggested by morphological variation. This may re¯ect environmental induction of the morphological differences, or rapid genetic change in the morphological characters due to selection. Patterns of morphological variation that conform to those expected in response to selection are sometimes taken as evidence of genetic divergence. However, controlled rearing of suspected stocks in a common environment is needed to con®rm genetic differentiation. Even then, phenotypic differences do not guarantee genetic differences because morphological differences in offspring can re¯ect environmental differences experienced by parents before fertilization. In many cases, controlled rearing has con®rmed the morphological differences that point to differentiation into separate stocks adapted to different environments. In some cases, controlled rearing has revealed that phenotypic similarity among groups of ®sh hides genetic differentiation that compensates for differences between stocks in environmental in¯uences. In other cases, widespread patterns that were assumed to re¯ect genetic responses to selection have been revealed by controlled rearing experiments to be environmentally-induced. This raises the possibility that phenotypic plasticity may itself be adaptive, allowing stocks, like chameleons, to shift their appearance to match their ecological circumstances. #

As modern applications and systems are growing fast and continuously changing, back-end services in general and database services in particular are being challenged with dynamic loads and differential query behaviour. The traditional best practice of designing database-creating fixed relational schemas prior to deployment-becomes irrelevant. While newer database technologies such as document based and columnar are more flexible, they perform better only under certain conditions that are hard to predict. Frequent manual modifications of database structures and technologies under production require expert skills, increase management costs and often ends up with sub-optimal performance. In this paper we propose AdaptaBase-a solution for performance optimization of database technologies in accordance with application query demands by using machine learning to model application query behavioural patterns and learning the optimal database technology per each behavioural pattern. Experiments present a reduction in query execution time of over 25% for the relational-columnar model selection, and over 30% for the relation-document based model selection.