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The McLaren Artura Spider is a newly launched version of the Artura, which has undergone significant improvements. Despite initial issues with the Artura, 2000 coupes have been delivered since 2022. The Spider variant features a retractable roof and has a comprehensive makeover that enhances its driving experience. The engine has received a 20bhp boost, resulting in improved power delivery and performance. The Artura Spider maintains McLaren's high standards in terms of dynamics and steering, with updates to the chassis and suspension. Existing Artura owners will not be able to upgrade their chassis, but they can benefit from the engine upgrade. Overall, the Artura Spider offers an exciting and rewarding supercar experience. [Extracted from the article]

The advent of Isogeometric analysis enabled advances towards the straightforward connection between geometric design and mechanical modelling phases. 3D approaches of the Isogeometric Boundary Element Method (IGABEM) stand out in this context, because it requires information only from the boundary as well as the Computer-Aided Design (CAD) models. Thus, the 3D IGABEM best fulfils the isogeometric paradigm, since the geometric representation provided by CAD packages can be interpreted directly by BEM as mesh. However, there is a lack of information regarding convergence and accurate mesh refinement for the proper mechanical fields representation in the 3D IGABEM. Although the IGA models from CAD are geometrically exact in various problems, they usually are not refined enough for the accurate mechanical fields representation. This study proposes mesh adaptivity strategies for the 3D IGABEM formulation in elastostatics, which provide accurate geometric representation and mechanical fields description and contribute towards the full coupling of BEM and CAD schemes. The proposed strategy utilises the error based upon the hypersingular residuals, which provides point-wise error estimates at the boundary. Then, errors based on displacements/tractions or strains can be assessed. The adaptive scheme utilises mesh optimality criteria for both local and global conditions. The refinement strategy applies the knot insertion process, which makes the adaptivity procedure robust and accessible since it does not require iterative communication with the CAD system. The proposed adaptive strategy based on strains error provides good convergence rates in comparison to globally uniform refinement for homogeneous and nonhomogeneous bodies. Additionally, the mesh adaptivity strategy can be applied for fibre-reinforced IGABEM formulations, for which a different error estimator is proposed accounting for the coupling 1DBEM/BEM and FEM/BEM. The strain-based error estimator identifies the required mesh refinement for minimising the oscillating adherence forces surrounding the fibre discontinuity regions. Five applications demonstrate the accuracy of the proposed adaptive schemes, in which the globally homogeneous refinement is a reference. • Mesh adaptive strategies for 3D IGABEM formulations based on hypersingular residuals • Mesh adaptive strategies for analyses based on IGA models provided directly from CAD • Proposition of a-posteriori error estimator at the boundary based on strains • Mesh adaptive strategies for fibre-reinforced 3D IGABEM formulations [ABSTRACT FROM AUTHOR]

A recent study conducted by the Medical Physics Department focused on evaluating a deep learning-based auto-segmentation software for prostate cone beam computed tomography (CBCT) images in clinical adaptive radiation therapy. The study involved ten patients who received radiation therapy for the prostate gland and seminal vesicles. The software's accuracy was compared to contours created by expert radiation oncologists, and metrics such as Dice similarity coefficient (DSC), centre of mass (COM) shift, and volume relative variation (VRV) were used for comparison. The results showed that the software's performance was comparable to that of human operators, particularly for femoral heads, while soft tissue organs had lower accuracy. The researchers concluded that the software's use in clinical practice is justified based on its accuracy and comparison with inter-operator variability. [Extracted from the article]

The projective dictionary pair learning (DPL) model jointly seeks a synthesis dictionary and an analysis dictionary by extracting the block-diagonal coefficients with an incoherence-constrained analysis dictionary. However, DPL fails to discover the underlying subspaces and salient features at the same time, and it cannot encode the neighborhood information of the embedded coding coefficients, especially adaptively. In addition, although the data can be well reconstructed via the minimization of the reconstruction error, useful distinguishing salient feature information may be lost and incorporated into the noise term. In this article, we propose a novel self-expressive adaptive locality-preserving framework: twin-incoherent self-expressive latent DPL (SLatDPL). To capture the salient features from the samples, SLatDPL minimizes a latent reconstruction error by integrating the coefficient learning and salient feature extraction into a unified model, which can also be used to simultaneously discover the underlying subspaces and salient features. To make the coefficients block diagonal and ensure that the salient features are discriminative, our SLatDPL regularizes them by imposing a twin-incoherence constraint. Moreover, SLatDPL utilizes a self-expressive adaptive weighting strategy that uses normalized block-diagonal coefficients to preserve the locality of the codes and salient features. SLatDPL can use the class-specific reconstruction residual to handle new data directly. Extensive simulations on several public databases demonstrate the satisfactory performance of our SLatDPL compared with related methods. [ABSTRACT FROM AUTHOR]

Purpose: W. Ross Ashby's elementary non-trivial machine, known in the cybernetic literature as the "Ashby Box," has been described as the prototypical example of a black box system. As far as it can be ascertained from Ashby's journal, the intended purpose of this device may have been to exemplify the environment where an "artificial brain" may operate. This paper describes the construction of an elementary observer/controller for the class of systems exemplified by the Ashby Box – variable structure black box systems with parallel input. Design/methodology/approach: Starting from a formalization of the second-order assumptions implicit in the design of the Ashby Box, the observer/controller system is synthesized from the ground up, in a strictly system-theoretic setting, without recourse to disciplinary metaphors or current theories of learning and cognition, based mainly on guidance from Heinz von Foerster's theory of self-organizing systems and W. Ross Ashby's own insights into adaptive systems. Findings: Achieving and maintaining control of the Ashby Box requires a non-trivial observer system able to use the results of its interactions with the non-trivial machine to autonomously construct, deconstruct and reconstruct its own function. The algorithm and the dynamical model of the Ashby Box observer developed in this paper define the basic specifications of a general purpose, unsupervised learning architecture able to accomplish this task. Originality/value: The problem exemplified by the Ashby Box is fundamental and goes to the roots of cybernetic theory; second-order cybernetics offers an adequate foundation for the mathematical modeling of this problem. [ABSTRACT FROM AUTHOR]

This paper proposes a new variable forgetting factor QRD-based recursive least squares algorithm with bias compensation (VFF-QRRLS-BC) for system identification under input noise. A new variable forgetting factor scheme is proposed to improve its convergence speed and steady-state mean squares error. A new method for recursive estimation of the additive noise variance is also proposed for reliable bias compensation. The mean and mean-square asymptotic behaviors of the algorithm are analyzed and a self-calibration scheme is further proposed to improve the steady-state mean squares error (MSE) due to finite sample effect. Simulations show that the proposed VFF approach offers improved tracking and steady-state MSE performance over the conventional recursive least squares method and its fixed FF counterpart. A linear array architecture is proposed for the realization of this algorithm and several hardware efficient techniques are introduced to avoid the expensive cubic root and division operations required. The proposed algorithm is validated on Xilinx Zynq®-7000 AP SoC ZC702 Field Programmable Gate Array (FPGA). For a 10-tap finite impulse response (FIR) system, the implementation requires only about 11.5k slice look-up table (LUT)s, 4.5k slice registers and 50 DSP48s and it can work up to about 0.58 MHz sample rate with a 200 MHz system clock. The hardware resources are considerably lower than traditional techniques using divider and cubic root realization. The linear array architecture also serves as an attractive alternative to the systolic array in medium to low rate applications due to its reduced hardware usages. [ABSTRACT FROM AUTHOR]

The article discusses runtime verification requirements for self-adaptive critical software, noting the trend towards autonomic computing systems which continually adjust to their operating environments. An approach which combines quantitative verification and mathematical model checking is described. Other topics include the formal application specifications for a piece of software, monitor-analyze-plan-execute (MAPE) closed control loops, and the use of Markovian decision models. The verification process can identify, predict, and reconfigure software requirement violations.

This article examines automatic recovery from computer software failure. With complex concurrent critical systems like autonomous robots, unmanned air vehicles, and space systems, every component is a potential point of failure. This applies not only to embedded systems but also to purely software systems such as distributed and cyber applications. Typical attempts to secure such systems are both brittle and incomplete due to reliance on manual identification of and solutions to potential failures such as by using exception mechanisms.

A study conducted by researchers at Nanjing Forestry University in China focuses on major depressive disorder (MDD) and the identification of imaging genetic patterns. The study highlights the challenges in identifying these patterns and proposes a novel association analysis model based on a self-expressive network. The researchers build a multi-modality phenotype network using brain imaging data and genetic variations, and apply intra-class similarity information to construct self-expressive networks. The results of the study enhance the association between MDD risk genetic variations and the multi-modality phenotype network, and identify potential new risk variations associated with MDD. [Extracted from the article]

Highlights • Smart cross-domain data sharing. Patient's medical records are encrypted using a cross-domain access policy, which can be access by the authorized users in the entire system. • Smart self-adaptive access control. The access control is self-adaptive to normal and emergency situations. A break-glass access method is designed for the emergency situation. • Smart deduplication. This system supports smart deduplication and provides a new access policies combination method, where no plaintext message is leaked. Abstract In this paper, a privacy-preserving smart IoT-based healthcare big data storage system with self-adaptive access control is proposed. The aim is to ensure the security of patients' healthcare data, realize access control for normal and emergency scenarios, and support smart deduplication to save the storage space in big data storage system. The medical files generated by the healthcare IoT network are encrypted and transferred to the storage system, which can be securely shared among the healthcare staff from different medical domains leveraging a cross-domain access control policy. The traditional access control technology allows the authorized data users to decrypt patient's sensitive medical data, but also hampers the first-aid treatment when the patient's life is threatened because the on-site first-aid personnel are not permitted to get patient's historical medical data. To deal with this dilemma, we propose a secure system to devise a novel two-fold access control mechanism, which is self-adaptive for both normal and emergency situations. In normal application, the healthcare staff with proper attribute secret keys can have the data access privilege; in emergency application, patient's historical medical data can be recovered using a password-based break-glass access mechanism. To save the storage overhead in the big data storage system, a secure deduplication method is designed to eliminate the duplicate medical files with identical data, which may be encrypted with different access policies. A highlight of this smart secure deduplication method is that the remaining medical file after the deduplication can be accessed by all the data users authorized by the different original access policies. This smart healthcare big data storage system is formally proved secure, and extensive comparison and simulations demonstrate its efficiency. [ABSTRACT FROM AUTHOR]

The maximum balanced biclique problem (MBBP) is an important extension of the maximum clique problem (MCP), which has wide industrial applications. In this paper, we propose a new local search framework for MBBP where four heuristics are incorporated to improve its performance. Our framework alternates between an extension phase via adding vertex pairs and a restarting phase via removing vertex pairs. Three heuristics are proposed for selecting the pairs for addition and removal. The first heuristic is a prediction score function to greedily select the vertex pairs for addition, which makes use of the structural information of the problem. The second heuristic is a self-adaptive restarting heuristic that removes a dynamic number of vertex pairs from the candidate solution to allow the search to continue from a new search area. The third heuristic is proposed for solving massive graphs and is called the two-mode perturbation heuristic. It is used for selecting pairs of vertices for addition and lowers the average complexity for this task. We also introduce a k-bipartite core reduction rule to decrease the scale of all massive instances, which helps our algorithm find optimal solutions for many massive instances. These techniques lead to two efficient local search algorithms for MBBP. Experimental results demonstrate that the proposed algorithms can scale up to massive instances with billions of edges and that the proposed algorithms outperform state-of-the-art MBBP algorithms on standard benchmarks. [ABSTRACT FROM AUTHOR]

Scaled Boundary Finite Element Method (SBFEM) and a temporally adaptive algorithm are combined to solve viscoelastic problems. By expanding variables at a discretized time interval, a spatially and temporally coupled viscoelastic problem is decoupled into a series of recursive spatial problems, which are solved by SBFEM, the computing accuracy in the time domain is controlled via a self-adaptive process. For the cyclic symmetric structures, the cyclic symmetry is exploited to reduce the computational expense of SBFEM, both the eigenvalue and system equations of SBFEM are partitioned into a number of smaller independent problems, which are solved by a partitioning algorithm. Two numerical examples are given to verify and illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

Large-scale multi-objective optimization problems (LS-MOP) are complex problems with a large number of decision variables. Due to its high-dimensional decision space, LS-MOP poses a significant challenge to multi-objective optimization methods including multi-objective evolutionary algorithms (MOEAs). Following the algorithmic framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), an enhanced algorithm with adaptive neighborhood size and genetic operator selection, named self-adaptive MOEA/D (SaMOEA/D), is developed for solving LS-MOP in this work. Learning from the search history, each scalar optimization subproblem in SaMOEA/D varies its neighborhood size and selects a genetic operator adaptively. The former determines the size of the search scope, while the latter determines the search behavior and as a result the newly generated solution. Experimental results on 20 LS-MOP benchmarks have demonstrated that SaMOEA/D outperforms or performs similarly to the other four state-of-the-art MOEAs. The effectiveness of the self-adaptive strategies has also been experimentally verified. Furthermore, SaMOEA/D and the comparing algorithms are then applied to solve a challenging real-world problem, the multi-objective reservoir flood control operation problem. Optimization results illustrate the superiority of SaMOEA/D. [ABSTRACT FROM AUTHOR]

This study proposes a new self-adaptive binary variant of a differential evolution algorithm, based on measure of dissimilarity and named SabDE. It uses an adaptive mechanism for selecting how new trial solutions are generated, and a chaotic process for adapting parameter values. SabDE is compared against a number of existing state of the art algorithms, on a set of benchmark problems including high dimensional knapsack problems with up to 10,000 dimensions as well as on the 15 learning based problems of the Congress on Evolutionary Computation (CEC 2015). Experimental results reveal that the proposed algorithm performs competitively and in some cases is superior to the existing algorithms. [ABSTRACT FROM AUTHOR]

In order to further improve the adaptability of attribute reduction and enhance its application performance in large-scale attribute reduction, a more efficient attribute self-adaptive co-evolutionary reduction algorithm by combining quantum elitist frogs and cloud model operators (QECMASCR) is proposed in this paper. Firstly, quantum chromosome is used to encode the evolutionary population, and a multilevel elitist pool of quantum frogs is constructed in which quantum elitist frogs can fast guide the evolutionary population into the optimal area. Secondly, a reversible cloud mode based on attribute entropy weight is designed to adjust the quantum cloud revolving angle, so that the scope of search space can be adaptively controlled under the guidance of qualitative knowledge. In addition, both the quantum cloud mutation operator and quantum cloud entanglement operator are used to make quantum frogs be adaptive to get the optimal set of attribute reduction fast. Thirdly, an improved decomposition framework of attribute self-adaptive co-evolution is adopted to capture interdependencies of decision variables. It can decompose the large-scale attribute set into reasonable-scale subsets according to two kinds of the best performance fitness and assignment credit. Thus, some optimal elitists in different memeplexes of multilevel elitist pool are selected out to evolve their representing attribute subsets, which can increase the cooperation and efficiency of attribute reduction. So the global minimum attribute reduction can be achieved steadily and efficiently. Experimental results indicate the proposed QECMASCR algorithm achieves the better superior performance than existing representative algorithms. Moreover it is applied into MRI segmentation, and the effective and robust segmentation results further demonstrate its stronger applicability. [ABSTRACT FROM AUTHOR]

Cooperative jamming weapon-target assignment (CJWTA) problem is a key issue in electronic countermeasures (ECM). Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Finally, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution (SLDDE) algorithm is proposed by introducing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computational simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outperforms two algorithms which are proposed recently for the weapon-target assignment problems. [ABSTRACT FROM AUTHOR]

Purpose – The paper presents the principal elements of automatic adaptivity built in our 2D software for monolithic solution of multiphysics problems based on a fully adaptive finite element method of higher order of accuracy. The adaptive techniques are illustrated by appropriate examples. Design/methodology/approach – Presented are algorithms for realization of the h-adaptivity, p-adaptivity, hp-adaptivity, creation of curvilinear elements for modelling general boundaries and interfaces. Indicated also is the possibility of combining triangular and quadrilateral elements (both classical and curved). Findings – The presented higher-order adaptive processes are reliable, robust and lead to a substantial reduction of the degrees of freedom in comparison with the techniques used in low-order finite element methods. They allow solving examples that are by classical approaches either unsolvable or solvable at a cost of high memory and time of computation. Research limitations/implications – The adaptive processes described in the paper are still limited to 2D computations. Their computer implementation is highly nontrivial (every physical field in a multiphysics task is generally solved on a different mesh satisfying its specific features) and in 3D the number of possible adaptive steps is many times higher. Practical implications – The described adaptive techniques may represent a powerful tool for the monolithic solution of complex multiphysics problems. Originality/value – The presented higher-order adaptive approach of solution is shown to provide better results than the schemes implemented in professional codes based on low-order finite element methods. Obtaining the results, moreover, requires less time and computer memory. [ABSTRACT FROM AUTHOR]

The article focuses on the adaptive software algorithms that provide active lens-to-sensor alignment to improve camera performance. It mentions that focus quality of a camera is characterized by using modulation transfer function (MTF) at a given lens position. It states that assembly speed is also one consideration of camera manufacturers aside from high performance. It adds that machine-vision data, active alignment, and post-attach data are parts of the intelligent alignment process.

Abstract: Anomaly detection is indispensable for satisfying security services in mobile ad hoc network (MANET) applications. Often, however, a highly secure mechanism consumes a large amount of network resources, resulting in network performance degradation. To shift intrusion detection from existing security-centric design approaches to network performance centric design schemes, this paper presents a framework for designing an energy-aware and self-adaptive anomaly detection scheme for resource constrained MANETs. The scheme uses network tomography, a new technique for studying internal link performance based solely on end-to-end measurements. With the support of a module comprising a novel spatial-time model to identify the MANET topology, an energy-aware algorithm to sponsor system service, a method based on the expectation maximum to infer delay distribution, and a Self-organizing Map (SOM) neural network solution to profile link activity, the proposed system is capable of detecting link anomalies and localizing malicious nodes. Consequently, the proposed scheme offers a trade-off between overall network security and network performance, without causing any heavy network overload. Moreover, it provides an additional approach to monitor the spatial-time behavior of MANETs, including network topology, link performance and network security. The effectiveness of the proposed schemes is verified through extensive experiments. [Copyright &y& Elsevier]

Abstract: The security of digital image attracts much attention recently. A hash-based digital image encryption algorithm has been proposed in Ref. . But both the theoretical analysis and computer simulation show the characteristic of diffusion is too weak to resist Chosen Plaintext Attack and Known Plaintext Attack. Besides, one bit difference of the plain pixel will lead to only one corresponding bit change of the cipher pixel. In our improved algorithm, coupled with self-adaptive algorithm, only one pixel difference of the plain-image will cause changes of almost all the pixels in the cipher-image (NPCR>98.77%), and the unified average changing intensity is high (UACI>30.96%). Both theoretical analysis and computer simulation indicate that the improved algorithm can overcome these flaws and maintain all the merits of the original one. [Copyright &y& Elsevier]

The article offers the author's insights on the preference setup of various groups and individuals to adaptive sound streaming for iOS devices. Multimedia specialist Matthew Gunkel suggests the setup of all media streams at 29.97 frames per second (fps) while Deutsche Welle complies with the instruction provided by the Apple Inc. According to media technology architect Larry Bouthillier, he utilizes three stream setups distributed through HTTP Live Streaming and Flash Media Server (FMS).

Viking, believing private investment opportunities are "robust", is increasing its wagers in the space and opening Global Opportunities to new cash, Halvorsen said in the letter. Viking has been one of the hedge fund world's biggest successes since its founding in 1999, registering only four down years, including the most recent one. Viking Hedge Fund Blames 2021 Losses on "Underestimating" Covid. [Extracted from the article]

Self-adaptive software is one of the key discoveries in the field of evolutionary computation, originally invented in the framework of so-called Evolution Strategies in Germany. Self-adaptivity enables the algorithm to dynamically adapt to the problem characteristics and even to cope with changing environmental conditions – as they occur in unforeseeable ways in many real-world business applications.In evolution strategies, self-adaptability is generated by means of an evolutionary search process that operates on the solutions generated by the method as well as on the evolution strategy’s parameters, i.e., the algorithm itself. By focusing on a basic algorithmic variant of evolution strategies, the fundamental idea of self-adaptation is outlined in this paper.Applications of evolution strategies for NuTech’s clients include the whole range of business tasks, including R&D, technical design, control, production, quality control, logistics, and management decision support. While such examples can of course not be disclosed, we illustrate the capabilities of evolution strategies by giving some simpler application examples to problems occurring in traffic control and engineering. [Copyright &y& Elsevier]

Long range LiDAR systems are essential for safe level 3-plus autonomy, but must be adaptive and software configurable. [ABSTRACT FROM AUTHOR]

A novel hybrid learning process based on the "decompose-ensemble" principle is proposed in this paper, integrating the NSRX learning structure with extreme learning machine (ELM) as an efficient predictor. While training the proposed model, the self-adaptive decomposition method of empirical mode decomposition (EMD) is first used to divide a training set of tourist arrival series into several relatively regular sub-series. Then, these decomposed sub-series are reconstructed into three components of high, moderate, and low frequency based on the balance of reconstructed components' relative stationarity and the fluctuation patterns between components and the original data series. Next, extracted features and forecasting results for the three components, obtained via ELM, are combined with d -lags historical data from the undecomposed training set; this set serves as the training sample input to train the hybrid model for enhanced tourist arrival prediction. For illustration and verification purposes, the proposed learning paradigm is applied to predict Hong Kong's monthly inbound tourist arrivals from 14 source markets from January 2007 to December 2018. Empirical results demonstrate that the proposed novel ensemble-learning paradigm outperforms all benchmark models, including five popular single models and five ensemble models, in terms of prediction accuracy. These findings suggest that the proposed model shows promise in forecasting complicated time series demonstrating high volatility and irregularity. [ABSTRACT FROM AUTHOR]

End effects of Local Mean Decomposition (LMD) are regarded as a typical problem leading to a distorted decomposed waveform and interfere with the extraction of characteristics. This paper proposes a novel self-adaptive point extension approach based on a Bidirectional Long Short-Term Memory (Bi-LSTM) regression network to eliminate this problem. This approach divides the existing samples into two parts and conducts two training processes, in which the first-training obtains the optimal network initialization parameters and the second-training gets the final extension network to identify the correct extremum. A simulated signal is used to demonstrate the advantages of the proposed approach over BPNN, LSTM, and characteristic segment approaches. The standard LMD method is combined with the proposed extension to form an improved LMD algorithm (ILMD). Finally, ILMD is applied to three experimental vibration signals which are collected from different loading conditions. The results demonstrate that ILMD can accurately extract failure and rotational characteristic frequencies of rolling element bearings with higher amplitude, and accordingly, the error caused by end effects does not influence the extracted information. • A novel point extended approach based on Bi-LSTM regression network. • A self-adaptive trained approach is developed to save insufficient samples. • Three vibrational signals under different loading conditions used to method validate. [ABSTRACT FROM AUTHOR]

At the front, new lower-friction ball joints are claimed to deliver sharper steering feel, while the front brakes move to new two-piece floating discs bit by better pads. Oh, and there's an app-based data-loging system so track enthusiasts can see where they're quick ... and where they're not. The control software for the adaptive dampers now evaluates road conditions 10 times faster for improved damper reactions, while the rear bushings for the lower B-arm have been stiffened for better toe-in characteristics under load. [Extracted from the article]

The article informs about the launch of software, ADDPLAN 6.0 PE, by Aptiv Solutions developed for adaptive design and simulation for population enrichment trials. It mentions that the software provides the statistical methodology, simulation and analysis tools for adaptive clinical trials including a population enrichment design. It mentions that it is the first fully validated adaptive design software with such functionality to be made available to the market.

The article offers tips for improving access to library services for people with disabilities. It suggests the installation of the adaptive software that provides optical character recognition such as the Kurzweil 3000 scanned documents. It also emphasizes the importance of a wireless access that would benefit individuals who have adaptive software installed on their laptops. The Web site accessibility is one major factor in the use of adaptable technology for Web browsing.

The article describes a self-adaptive computer software medical-assistance service-based system application. A diagram is presented of the runtime quantitative verification of this system, including system specifications, requirements, and the autonomic computing monitor-analyse-plan-execute (MAPE) control loop.

New approaches to elementary mathematics curriculum, instruction, technology and assessment are providing opportunities to personalize learning for each student, creating highly effective, student-centered learning environments. In this web seminar, the director of curriculum at McGraw-Hill discussed ideas, strategies and resources for delivering a positive, measurable impact on student outcomes through personalized learning in K6 math instruction. [ABSTRACT FROM AUTHOR]

Since no organization can possibly protect against 100% of cybersecurity threats 100% of the time, an adaptive and layered security approach can help create a feedback loop of threat visibility, detection and prevention that consistently becomes more effective. B are a popular and well-recognized type of endpoint security b , which protect enterprises against signature-based attacks and scan files for malicious threats by consulting against threat intelligence databases. Taking a proactive approach to security enables enterprises to more readily adapt to the changing threat landscape and initiate rapid incident response measures to halt breaches before they can expose sensitive data - or better, before they gain access at all. [Extracted from the article]

The American Medical Informatics Association wants the Food and Drug Administration to improve its conceptual approach to regulating medical devices that leverage self-updating artificial intelligence algorithms. [ABSTRACT FROM AUTHOR]

(Bloomberg Opinion) -- Don't Study for That Final, Let Software Take It: Stephen Carter What if students could sleep in on the day of the final examination and let a computer program take the test for them? But I wonder whether the harm suffered by the student who outperforms on final exams might be balanced by the harm avoided by the student who underperforms. [Extracted from the article]

"This Is the Uber Market": Tech IPOs Soar Past Their FAANG Peers While the titans of technology are feeling the pain of a possible sector rotation, a more youthful class of stocks in the sector are beating the market at a 16-to-1 pace this year. Other top performers include Internet security firm Zscaler Inc. (up 153 percent), digital subscription billing platform Zuora Inc. (up 109 percent), electronic signature firm DocuSign Inc. (up 100 percent), and Chinese video streamers iQiyi Inc. (up 82 percent) and Bilibili Inc. (up 65 percent). [Extracted from the article]

The article offers information on the factors that need consideration in choosing adaptive technologies for blind and low-vision students in the U.S. It suggests the importance of choosing a widely-used program because there is a higher chance that a user can find a colleague to answer questions they may have. For screen readers, it points out that a university can decide to provide their students with their preferred program or tell them that they need to use the one preferred by the office.

Focuses on Adaptive Software Architecture from Veritas Software Corp., unveiled at the Vision 2002 conference in May 2002. Description of the software architecture.

The article focuses on benefits and concerns regarding virtual education programs at schools in Chicago, Illinois. A pilot program is discussed in which the school day is lengthened and students spend 90 minutes using online courses to catch up or to retake classes they have failed. Other topics include advanced software that adapts to student progress and ability, the social aspects of physical classes, and state-assessment scores of students who used online educational software.

The article features new educational software and electronic media products that relate to the K-12 curriculum. A computer-based adaptive assessment for students in K-2 is provided by the new MAP for Primary Grades, released by Northwest Evaluation Association. A Macintosh edition is now available for the software Clicker 5, the K-6 writing and creativity tool. It supports early readers and writers, including struggling learners and those requiring adaptive technology.

Provides information on TeamTrack, a Web based tool that lets people see how the company is doing overall, developed by Serena. Features of the software; Benefits offered to clients; Price of TeamTrack.