Search Results (1,184)

This study integrates a Systematic Literature Review (PRISMA 2020) with a bibliometric analysis to examine how global research on microalgae biotechnology has incorporated innovation management, technology transfer, and dynamic capabilities. A total of 418 records were retrieved from Scopus and Web of Science for the period 2015–2025, of which 133 studies met the inclusion criteria after deduplication and screening based on an adapted PICO framework. Bibliometric indicators were generated using Bibliometrix (R) and VOSviewer (version 1.6.20) to identify publication trends, leading countries, collaboration networks, and thematic structures. The results suggest a progressive shift from predominantly techno-biological research toward approaches that emphasize technology maturity, innovation processes, and organizational capabilities. Three main analytical outcomes were identified: (i) studies addressing dynamic capabilities related to organizational learning and strategic reconfiguration (14.1%); (ii) research focused on technology readiness levels (TRL) and technology adoption, reflecting the transition from laboratory-scale research to pilot and industrial implementation (22.9%); and (iii) analyses of innovation ecosystems highlighting university–industry collaboration, governance mechanisms, and bioeconomy-oriented policies (17.7%). Nevertheless, approximately 22% of the literature remains exclusively technical, indicating a persistent disciplinary bias. By integrating innovation management, technology transfer, and dynamic capabilities as complementary analytical lenses, this review develops a comprehensive framework for understanding how microalgae biotechnology contributes to the consolidation of sustainable bioeconomy-oriented innovation ecosystems. The findings underscore the potential of technology governance and TRL-based management to bridge the gap between scientific research and industrial deployment. Full article

The increasing electrification of urban transportation has formulated a tightly coupled energy-mobility nexus. Under extreme disaster events or grid faults, rapidly restoring power supply capacity and re-dispatching shared electric vehicle (EV) fleets are critical for enhancing system resilience. Existing co-optimization methods face the curse of dimensionality when dealing with high-dimensional discrete grid reconfigurations and continuous spatio-temporal EV queuing dynamics. While multi-agent deep reinforcement learning (MADRL) offers real-time responsiveness, it inherently struggles to satisfy strict physical constraints, frequently generating infeasible and unsafe actions. To bridge this gap, this paper proposes a heuristic-guided safe multi-agent reinforcement learning (Safe-MADRL) framework for the resilient dispatch of the energy-mobility nexus. Instead of relying solely on black-box neural networks, the framework structurally embeds physical models and heuristic solvers into the learning loop. A quantum particle swarm optimization (QPSO) algorithm acts as a heuristic action refiner to ensure that grid topology actions strictly comply with non-linear power flow and voltage constraints. Simultaneously, a mixed-integer linear programming (MILP) model coupled with a single-queue multi-server (SQMS) model serves as a safety projection layer. This layer mathematically guarantees EV battery energy continuity and accurately quantifies spatio-temporal queuing delays at charging stations. Case studies on a coupled IEEE 33-node distribution system and a regional transportation network demonstrate that the proposed Safe-MADRL framework achieves zero physical violations during training and significantly outperforms traditional mathematical optimization and pure learning-based methods in computational efficiency, system power loss reduction, and overall operational economy. Full article

The integration of distributed energy resources (DERs), such as solar photovoltaics, wind turbines, and energy storage systems, into distribution networks necessitates accurate estimation of hosting capacity (HC). This paper presents an optimization-based approach for HC assessment and enhancement, which considers both overvoltage and line overload constraints and incorporates the reactive power (VAR) capabilities of DER inverters. Furthermore, the methodology is extended to include network reconfiguration, leveraging switchable branches to alleviate network congestion and further enhance DER integration. The proposed method utilizes a linearized power flow model to ensure computational efficiency and formulates the problem as a convex optimization task when considering only inverter VAR capabilities. The framework jointly addresses overvoltage, line overload, and inverter VAR capability constraints through linear and second-order cone constraints. In the extended formulation that includes network reconfiguration, binary decision variables are introduced to model switch statuses, resulting in a mixed-integer optimization problem. Simulation results based on the IEEE 33-bus system demonstrate that reactive power optimization can effectively redistribute HC across nodes, improving power quality in congested networks. Additionally, the incorporation of network reconfiguration provides further HC enhancement, particularly in scenarios where fixed network topology severely limits DER integration. Simulation studies are further extended to the UKGDS 95-bus system, which is derived from a real UK distribution network and incorporates a 33/11 kV on-load tap changer (OLTC) transformer, thereby providing a more practically representative validation platform. The results demonstrate that the proposed framework is effective across networks of different scales and complexities. The proposed approach offers a flexible and efficient tool for modern distribution network planning, supporting high-penetration DER integration while maintaining grid stability and operational reliability. Full article

Extreme disasters often induce multiple simultaneous faults in distribution networks, posing significant risks to power supply reliability. Although network reconfiguration and intentional islanding are critical strategies for enhancing system resilience, existing studies typically address them separately and fail to adequately account for the uncertainties associated with renewable energy generation and load demand. To address these limitations, this paper presents a collaborative optimization model for resilient distribution network restoration. A multi-time-step dynamic restoration framework is developed to coordinate network reconfiguration, emergency repair scheduling, distributed generation dispatch, and load shedding. This framework enables unified decision-making for island formation and topology reconfiguration, and incorporates an island integration mechanism to broaden the feasible solution space. To manage source–load uncertainties, chance-constrained programming is introduced, transforming probabilistic security constraints into deterministic equivalents using risk indicator variables, thereby striking a balance between operational security and economic efficiency. In addition, the model optimizes repair sequences under multi-fault conditions to enhance resource utilization. Simulations on a modified IEEE 33-node system validate the effectiveness of the proposed approach in reducing load curtailment, accelerating restoration, and achieving a favorable trade-off between operational risk and economic performance. Full article

Wireless sensor networks increasingly support time-critical monitoring applications, where coverage optimization must often be performed under limited computational resources. This work addresses a previously underexplored WSN coverage problem involving range-free, angular-limited sensors with transmitter-induced sensing degradation and discrete sector orientation. We formulate a mixed combinatorial problem that jointly optimizes K-out-of-N sensor activation and sector assignment under strict feasibility constraints. A constraint-aware genetic algorithm with repair-based feasibility enforcement is proposed and validated against the global optimum obtained via exhaustive enumeration, enabling direct quantification of optimality. The repair mechanism corrects infeasible offspring after each genetic operation to guarantee that exactly K sensors remain active, eliminating the need for penalty-based constraint handling. A brute-force search is used to establish the global optimum of our small-scale scenario, serving as a ground-truth optimality benchmark for evaluating the proposed method. The purpose of this comparison is not to assess competitiveness against other metaheuristic algorithms, but to quantify how closely the proposed approach approximates the true optimal solution under strict problem constraints. The constraint-aware genetic algorithm is developed using an integer chromosome encoding, two initialization strategies, two crossover pairing schemes, elitism, and per-gene mutation, combined with alternative constraint-handling strategies. Two experimental series evaluate the impact of population size, crossover method, mutation probability, and constraint handling using problem-specific metrics, alongside convergence and fitness statistics. The proposed algorithm reliably reaches near-optimal solutions with significantly reduced computational cost when compared to exhaustive search. By integrating problem-specific constraints directly into the process, the proposed evolutionary optimization method effectively balances solution quality and execution time, making it well suited for scenarios requiring rapid sensor reconfiguration. Full article

The Controller Area Network (CAN) remains the backbone of in-vehicle communication, but its lack of built-in security exposes safety-critical systems to cyberattacks. This paper presents a real-time, reconfigurable, specification-driven intrusion detection system (IDS) implemented on a custom test bench that emulates an EV powertrain. The CAN traffic captured from the four-ECU setup formed the dataset used in this study. The IDS enforces a compact, reconfigurable ruleset covering timing bounds, jitter envelopes, identifier whitelists, frame format, data length code (DLC) compliance, bus-load thresholds, application-level CRC, and alive-counter verification. The IDS achieves detection times below 2 ms with false positive rates under 1% for injection, denial of service (DoS), and fuzzy attacks, even at CAN bus loads up to 70%, while microcontroller resource usage remains within the constraints of automotive-grade devices, supporting deployment in embedded environments. The main contributions of this study are as follows: (i) a validated and reproducible EV powertrain test bench with millisecond-level timing, (ii) a deployable and easily reconfigurable ruleset with deterministic runtime, and (iii) a latency-oriented evaluation framework that is portable across automotive microcontroller platforms. The EV powertrain dataset v1.0 was released in a public GitHub repository to facilitate reproducible research and enable future benchmarking studies. Full article

In today’s densely populated and technology-driven smart cities, natural and human-made disasters increasingly threaten the resilience of communication infrastructures, creating critical challenges for maintaining reliable connectivity. The failure of conventional networks during crises significantly hampers emergency response, coordination, and information dissemination. To address these challenges, this paper presents Weighted Average Algorithm-based Clustering and Routing (WAA-CR), a novel, secure, and adaptive UAV-based framework for disaster response and recovery. WAA-CR integrates three key components: shelters or Ground Control Stations (GCSs) as communication anchors and support hubs, survivable clustering and routing using a WAA-based metaheuristic optimizer, and secure and trustworthy drone communication enabled by a lightweight trust evaluation mechanism, and authentication model. The framework formulates a multi-objective optimization model that simultaneously minimizes the number of active UAVs and routing cost, while maximizing trust, communication reliability, and coverage. Cluster head (CH) election and routing decisions are guided by a composite fitness function that considers residual energy, link stability, mobility, and dynamic trust scores. Additionally, an adaptive maintenance mechanism enables dynamic reconfiguration to handle CH failures, trust degradation, or mobility-driven topology changes. Extensive simulations conducted in MATLAB R2020ademonstrate that WAA-CR significantly outperforms existing baseline FANET protocols in terms of energy efficiency, cluster stability, trust accuracy, and end-to-end delivery performance. These results validate the proposed framework’s effectiveness in building resilient, scalable, and secure UAV-based communication networks for post-disaster environments. Full article

Reconfigurable intelligent surfaces (RISs) are regarded as a transformative technique for future wireless networks. Currently, the majority of research efforts have focused on channel estimation scenarios in communication systems assisted by a single passive RIS. However, single-RIS-assisted systems suffer from limited coverage performance, with significant performance degradation observed in dense obstacle environments. To mitigate the adverse impacts imposed by environmental factors, a dual-RIS-assisted communication system exhibits superior adaptability to practical scenarios. This work focuses on investigating such a system. It is worth noting that fully passive RISs lack the capability to process signals independently. Furthermore, when employing pilot-aided algorithms to acquire channel state information (CSI), wireless systems often encounter challenges arising from large channel matrix dimensions, thereby leading to substantial pilot overhead. To address the aforementioned issues, this paper proposes a novel semi-blind channel estimation method for multiple-input multiple-output (MIMO) systems aided by double reconfigurable intelligent surfaces (D-RISs). Specifically, we construct two tensor models, namely the Parallel Factor (PARAFAC) model and the Parallel Tucker2 model, for the received signal in two separate stages. By means of tensor decomposition, the joint channel estimation and symbol detection problem is reformulated as a least squares problem and solved using a two-stage algorithm. In the first stage, the ALS algorithm is adopted to estimate the transmitted symbols and provide initialization for the second stage. Then, in the second stage, the TALS algorithm is employed to obtain the final estimation results of the three sub-channels. Simulation results verify the effectiveness of the proposed receiver. Full article

The rapid development of cyber–physical technologies has led to enhanced observability and controllability of shipboard power systems. However, the reliance of shipboard power systems on information networks undermines the traditional security provided by physical isolation; under malicious attacks, faults in the information domain can propagate rapidly, causing physical power outages and reducing the resilience of shipboard power systems. To address this issue, this paper investigates the cascading failure reconstruction and resilience enhancement in shipboard cyber–physical systems (SCPSs) under uncertain network attacks. First, a cascading failure propagation model is established to capture the interaction between attack paths and system vulnerabilities, revealing how cyberattacks spread through communication links and infiltrate the power topology. Then, a reinforcement learning-based load recovery strategy is developed, in which a masked proximal policy optimization (masked-PPO) algorithm is employed to optimize reconfiguration decisions under operational constraints. The proposed approach enables adaptive and efficient recovery actions in complex cross-domain environments. Case studies based on representative SCPS scenarios demonstrate that the proposed method improves cascading-failure reconfiguration capability by 13.21% and reduces the average decision time by 18.6%, validating its effectiveness, real-time performance, and scalability. Full article

Shrinking and aging societies undermine the economic viability of network-based infrastructure once supported by economies of scale and network externalities. This paper develops a conceptual framing of “Denetworking” as a possible reconfiguration strategy in the contraction phase: reducing dependence on highly asset-specific dedicated networks (e.g., pipes and rail tracks) and shifting service functions to distributed systems or generic shared networks (e.g., roads) while maintaining minimum service standards. Rather than presenting a calibrated optimization model or full life-cycle cost (LCC) estimation, the paper proposes a heuristic decision condition for comparing a “keep” scenario (renew and maintain the dedicated network) with a “shift” scenario (Denetworking) and uses quantitative anchors from public sources to illustrate the associated fiscal and institutional trade-offs. Two Japanese cases are used as contrasting illustrations: physical Denetworking, referring to the reduction in or substitution of dedicated physical network assets, in wastewater services (centralized sewerage to decentralized treatment); and functional Denetworking, referring to the transfer of service functions from dedicated networks to more generic shared networks, in regional mobility (local rail to bus/BRT on the road network). The cross-case discussion suggests that Denetworking may become a rational policy option under certain conditions, particularly when demand density declines near renewal-investment peaks and asset specificity increases lock-in. The paper contributes a conceptual vocabulary and comparative policy framing for discussing infrastructure reconfiguration in shrinking societies and highlights practical issues of timing, cost sharing, phased implementation, and stakeholder engagement. Full article

This study introduces a switchable and tunable multimodal, multi-peak, perfect terahertz absorber, utilizing a composite structure of graphene and double concentric metal rings. From bottom to top, the absorber consists of a gold substrate, a SiO₂ dielectric layer, a patterned graphene layer, another SiO₂ dielectric layer, and double concentric metal rings on the top. The structure achieves three high-absorption resonance peaks in the far-infrared band: a relatively broad peak with 99.05% absorptance at 38.128 THz, and two extremely narrow peaks with 99.56% and 97.23% absorptance at 47.909 THz and 49.873 THz, respectively. Analysis of the absorption spectra and electric field distributions reveals that the generation mechanism of Peak I is Fabry–Pérot cavity resonance, while Peaks II and III result from the coupling between the high-order localized surface plasmons in the outer ring and the graphene surface plasmon polaritons. Benefiting from graphene’s excellent electrical tunability, the absorption peaks’ positions and intensities can be dynamically tuned by varying the Fermi level. The core innovation of this work lies in the high-level integration of multiple functionalities. By leveraging the sensitive response of Peak III to variations in the Fermi level, a four-input AND logic gate is embedded within the metamaterial absorber in this frequency band. The Fermi levels of four independent graphene regions serve as the binary inputs, while the absorption state of Peak III is defined as the logical output. Additionally, the two narrow peaks display high sensitivity to the surrounding refractive index, with sensitivities of 30.1 THz/RIU and 62.5 THz/RIU, demonstrating significant potential for sensing. This multifunctional integrated device combines tunable absorption, a logic gate, and sensing capabilities, making it promising for terahertz communication systems, intelligent sensing networks, and reconfigurable platforms. Full article

Accurate estimation of lithium-ion battery State of Health (SoH) is critical for emerging applications such as reconfigurable battery systems. Although data-driven machine learning methods are promising, they often rely on costly, time-intensive aging experiments and extensive feature engineering. This work proposes a lightweight SoH-prediction framework validated on both physics-informed synthetic aging data and the NASA battery aging dataset. We evaluated Random Forest (RF) and Feedforward Neural Network (FNN) models that use only a limited number of samples from an early segment of the raw discharge voltage curve as input. Results show that RF consistently outperforms FNN across input sizes in deterministic or noise-free environments, achieving an RMSE of 0.07% SoH using just 5 voltage samples. In inherently stochastic experimental data, however, FNN can achieve an RMSE 50% lower than RF (1.28 vs. 2.87), but requires 37× more mathematical operations per inference. These findings emphasize the predictive value of the early-discharge-voltage region and demonstrate that compact, low-feature-complexity models can deliver accurate SoH estimates. Overall, the approach supports a goal of combining informed synthetic data with limited real measurements to build robust, scalable SoH predictors, reducing dependence on labor-intensive degradation testing and feature-heavy pipelines. Full article

The proliferation of Internet of Things (IoT) devices has created unprecedented challenges in cybersecurity, as billions of interconnected devices generate, process, and transmit sensitive data across diverse networks. This study addresses critical security vulnerabilities in IoT ecosystems, focusing on the development of a comprehensive security framework that encompasses device authentication, an attribute access control mechanism, and privacy preservation. This work introduces HADA, a proposed hybrid authentication method that combines the validation of unique credentials and trust value. For the authentication of the data owner and user, the following credentials are validated: identity, certificate, reconfigurable physical unclonable function (PUF), and trust. Differential privacy is used to secure the credentials during information exchange. Then, the newly developed dynamic attribute access control method selects the number of attributes and matches the attributes; these two processes are performed using the Bi-Fuzzy logic and graph neural network (GNN) algorithms, respectively. After matching the data, the user is allowed to access them from the cloud server. For data encryption, the lightweight SKINNY algorithm is implemented in Hyperledger Fabric blockchain. The proposed system performs better than existing methods in terms of throughput, latency, and resource utilization. Full article

The sixth-generation (6G) mobile communication systems are anticipated to be operational by 2030, prompting extensive research efforts by governments and private entities. Designed to meet societal, economic, and technological demands unaddressed by fifth-generation (5G) networks, 6G integrates scalability, security, and reliability with ubiquity and resource-intensive artificial intelligence. Envisaged as multi-band, decentralized, autonomous, flexible, and user-centric, 6G networks incorporate innovative technologies, including cell-free (CF), three-dimensional heterogeneous networks (3D HetNet), reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), as well as artificial intelligence/machine learning (ML). In 6G 3D HetNets, the densification of access points (APs) continues, accommodating increased connections and traffic volumes, alongside the use of higher frequency bands. Although 6G networks are not fully standardized, they target demanding Quality of Service (QoS) standards, such as a peak data rate of 1.0 Tbps and latency of 0.1 ms. This paper conducts a comprehensive literature review on radio resource management (RRM) in 6G cell-free and 3D HetNet systems, emphasizing challenges such as interference mitigation. It presents a taxonomy of RRM approaches, systematically studying, categorizing, and qualitatively analyzing recent techniques, outlining the current state, and indicating future trends, technologies, and challenges shaping 6G systems. Full article

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 10⁴ points/cm² while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging. Full article