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The measurement matrix which plays an important role in compressed sensing has got a lot of attention. However, the existing measurement matrices ignore the energy concentration characteristic of the natural images in the sparse domain, which can help to improve the sensing efficiency and the construction efficiency. Here, the authors propose a simple but efficient measurement matrix based on the Hadamard matrix, named Hadamard-diagonal matrix (HDM). In HDM, the energy conservation in the sparse domain is maximised. In addition, considering the reconstruction performance can be further improved by decreasing the mutual coherence of the measurement matrix, an effective optimisation strategy is adopted in order to reducing the mutual coherence for better reconstruction quality. The authors conduct several experiments to evaluate the performance of HDM and the effectiveness of optimisation algorithm. The experimental results show that HDM performs better than other popular measurement matrices, and the optimisation algorithm can improve the performance of not only the HDM but also the other popular measurement matrices.

This chapter contains sections titled: Diffusion of Innovations The Lifecycle Curve Lifecycle Phases

Renewable resource based microgrids provide reliable and cost-effective electricity with low carbon emissions. The flexibility of operating in grid-connected or islanded modes enables a microgrid to serve loads reliably. In the case of unexpected events happening to the main grid, the microgrid will isolate itself and operate in islanded mode to prevent any adversary impacts. The availability of renewable generation in the microgrid has significant impacts on the islanding strategy and different scenarios need to be considered. This study proposes a comprehensive microgrid self-healing strategy under different circumstances. The proposed strategy encompasses generation re-dispatch, network reconfiguration, and load shedding. The microgrid self-healing problem is formulated as a mixed-integer quadratic programming problem, which provides a globally optimal solution to facilitate smooth islanding of the microgrid. A modified Consortium for Electric Reliability Technology Solutions microgrid is used to conduct simulation under various scenarios. The simulation results demonstrate the efficiency of the proposed self-healing strategy in minimising costs of load shedding and generation with optimal switching actions.

The growing demand for the cloud community market towards attracting and sustaining the incoming and the available cloud users is addressed actively to meet the competitive environment. There is a good scope for improving the provider capabilities in the cloud in order to satisfy the users with attractive benefits. The study introduces an effective virtual machine (VM) migration strategy using an optimisation algorithm in such a way to facilitate the user selection of the providers based on their budgetary requirements in running their own platforms. The constraints associated with the selection of the provider include cost, revenue, and resource, which are altogether confined as an elective factor. The optimisation algorithm employed for the VM migration is referred to as Taylor series-based salp swarm algorithm (Taylor-SSA) that is the integration of the Taylor series with SSA. The evaluation of the method is progressed using three setups by varying the number of providers and users. The cost, the revenue, and the resource of the proposed method are analysed and concluded that the proposed method acquired a minimal cost, maximal resource gain and revenue.

In previous articles, we formalized the problem of optimal allocation strategies for a (perfect) vaccine in an infinite-dimensional metapopulation model. The aim of the current paper is to illustrate this theoretical framework with multiple examples where one can derive the analytic expression of the optimal strategies. We discuss in particular the following points: whether or not it is possible to vaccinate optimally when the vaccine doses are given one at a time (greedy vaccination strategies); the effect of assortativity (that is, the tendency to have more contacts with similar individuals) on the shape of optimal vaccination strategies; the particular case where everybody has the same number of neighbors.

We introduce Self-Competitive Distillation (SCD), a parameter-neutral training strategy aimed at influencing optimization dynamics without increasing model size or relying on external teachers. Two identical instances of the same architecture, initialized with different random seeds, are trained jointly and dynamically exchange asymmetric teacher–student roles based on instantaneous performance, enabling knowledge transfer between diverging optimization trajectories. Under fixed parameter and training budgets, SCD is observed to improve the realized effective capacity of lightweight architectures, yielding a higher test accuracy at matched epochs. Across multiple lightweight vision models and datasets, SCD demonstrates gains in both in-domain performance and cross-domain generalization, as measured by xScore. These results suggest that, within the evaluated experimental conditions, SCD can help mobile models make more effective use of training dynamics, while the underlying architecture remains the primary determinant of effective capacity in resource-constrained settings.

Optimal Reactive Power Dispatch (ORPD) is one of the main challenges in power system operations. ORPD is a non-linear optimization task that aims to reduce the active power losses in the transmission grid, minimize voltage variations, and improve the system voltage stability. This paper proposes an intelligent augmented social network search (ASNS) algorithm for meeting the previous aims compared with the social network search (SNS) algorithm. The social network users’ dialogue, imitation, creativity, and disputation moods drive the core of the SNS algorithm. The proposed ASNS enhances SNS performance by boosting the search capability surrounding the best possible solution, with the goal of improving its globally searched possibilities while attempting to avoid getting locked in a locally optimal one. The performance of ASNS is evaluated compared with SNS on three IEEE standard grids, IEEE 30-, 57-, and 118-bus test systems, for enhanced results. Diverse comparisons and statistical analyses are applied to validate the performance. Results indicated that ASNS supports the diversity of populations in addition to achieving superiority in reducing power losses up to 22% and improving voltage profiles up to 90.3% for the tested power grids.

ABSTRACT Effective maintenance strategies that balance system performance and robustness are crucial for both public and private enterprises. In response to increasing competition and the high cost of infrastructure and equipment, decision-makers are increasingly relying on mathematical modeling to support optimization and resilience. This paper introduces an adaptation criterion for selecting the optimal maintenance policy by simultaneously evaluating performance and robustness, replacing the conventional cost-based model with a Robust Quantification Model (RQM). Our proposed economic criterion is a linear combination of three key elements: the asymptotic average maintenance cost per unit of time (C∞), the standard deviation (σ) of the maintenance cost per replacement cycle (MCPRC), which measures the variability of total maintenance costs over each renewal cycle, and the weight parameter (λ) reflecting the trade-off between cost performance and robustness. This combined criterion allows for a joint assessment of stability and economic efficiency. We evaluate the three techniques (Block Replacement strategy (BR), Periodic Inspection Replacement strategy (PIR) and Quantile-based Inspection Replacement strategy (QIR)) using the proposed robust economic criterion, and compare their effectiveness under uncertain system degradation modeled using a homogeneous Gamma process. Monte Carlo simulations are employed to estimate the expected cost and variability across multiple degradation scenarios, ensuring empirical accuracy of the proposed criterion. The simulation results demonstrate that the proposed criterion effectively captures both the average cost behavior and its variability, providing a comprehensive tool for selecting resilient and cost-effective maintenance strategies in degrading systems.

Ultra-reliable low-latency communication (URLLC) presents significant challenges in simultaneously guaranteeing stringent latency bounds, ultra-high reliability, and efficient resource utilization under dynamic channel conditions. To address these joint constraints, a novel framework that integrates a reconfigurable intelligent surface (RIS) with cooperative non-orthogonal multiple access (NOMA) is proposed for short-packet communications. Two distinct phase configuration designs for the RIS are considered, i.e., a near-user priority strategy (NUPS) and a far-user priority strategy (FUPS). The NUPS configures the RIS to enhance the received signal power for the near user, while the FUPS optimizes the phase shifts to maximize the received power for the far user. Closed-form expressions that characterize the average block error rate (BLER) of the near and far users under the two proposed strategies in the presence of hardware impairments are derived. Specifically, the analysis for the far user considers both selection combining (SC) and maximum ratio combining (MRC) reception schemes. Based on the average BLER, we then derive a closed-form expression for the effective throughput. Simulation findings reveal the following: (1) The far user in the proposed cooperative NOMA achieves a lower average BLER than in the non-cooperative NOMA. (2) When the RIS is deployed in close proximity to the base station (BS), the NUPS can effectively leverage the RIS to enhance the far user’s signal quality through cooperation, without sacrificing the near user’s priority; and (3) SC serves as a low-complexity alternative that achieves near-optimal performance when inter-user channel conditions are favorable.

The expansion of forest plantations to supply the wood market requires selection of genotypes that can provide high gains, which may compromise long-term tree breeding programs. With the final purpose of producing quality sawn wood, the aims of this study were to: (a) estimate genetic parameters for a progeny/provenance test of Plathymenia foliolosa Benth; (b) carry out optimized selection based on an inbreeding rate (F) from 0 to 7% in progeny/provenance tests evaluated at 35 months for P. foliolosa and Cordia trichotoma (Vell.) Arrab. ex Steud, and at 42 months for Zeyheria tuberculosa (Vell.) Bureau ex Verl.; (c) develop a practical index for balancing gains and F; d) and indicate the most appropriate genetic base structure based on effective population size (Ne) to subsidize conservation strategies, breeding programs, and clonal orchard with those species native from Brazil. Diameters at breast height (DBH) were obtained in progeny/provenance tests conducted in the south of Bahia–Brazil. For P. foliolosa, the moderated heritability (h2a) value of 0.55 demonstrated favorable conditions for selection based on DBH. For this trait, there is better possibility of achieving progressive gains by exploiting genetic variability into the families (CVgi). Simulation of scenarios without optimization for P. foliolosa, C. trichotoma and Z. tuberculosa showed a high possibility of gains for breeding programs, ranging between 95.6 and 311.05%. However, in those scenarios, high rates of F were also observed. For the three species, selection based on the selection optimization index (SOI) was proposed in order to balance ideal increments in genetic gains and maintenance of genetic diversity. With the use of SOI, it was possible to simulate selection of scenarios with good prospects of gain balanced with Ne in a practical and direct manner for germplasm conservation strategies, production of improved seeds in clonal orchards, and tracking recombination cycles within breeding populations.