Search Results (454)

Climate change is driving urgent demand for resilient crop varieties capable of withstanding extreme and changing conditions. Identifying resilient varieties requires systematic plant phenotyping research under controlled conditions, where dynamic environmental impacts can be studied. Current growth cabinets (GC) provide this capability but remain limited by high costs, static environments, and scalability. These limitations pose a challenge for climate change-based phenotyping research which requires large-scale trials under a variety of dynamic climate conditions. Presented is a microclimate-controlled smart growth cabinet (MCSGC) platform, addressing these limitations through four innovations. The first is dynamic microclimate simulation through programmable environmental ‘recipes’ reproducing real climactic variability. The second is interconnected scalable multi-cabinet for parallel experiments. The third is modular hardware able to reconfigure for different plant species, remaining cost-effective at <$10,000 AUD. The fourth is automated data collection and synchronisation of environmental and phenotypic measurements for Artificial Intelligence (AI) applications. Experimental validation confirmed precise climate control, broad crop compatibility, and high-throughput data generation. Environmental control stayed within ±2 °C for 97.42% while dynamically simulating Hobart, Australia, weather. The MCSGC provides an environment suitable for diverse crops (temperature 14.6–31.04 °C, and Photosynthetically Active Radiation (PAR) 0–1241 µmol·m⁻²·s⁻¹). Multi-species cultivation validated the adaptability of the MCSGC across Cannabis sativa (544.1 mm growth over 34 days), Beta vulgaris (123.6 mm growth over 36 days), and Lactuca sativa (19-day cultivation). Without manual intervention the system generated 456 images and 164,160 sensor readings, creating datasets optimised for AI and digital twin applications. The MCSGC addresses critical limitations of existing systems, supporting advancements in plant phenotyping, crop improvement, and climate resilience research. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article

Unmanned Aerial Vehicles (UAVs) are integral components of future 6G networks, offering rapid deployment, enhanced line-of-sight communication, and flexible coverage extension. However, UAV communications in low-altitude environments face significant challenges, including rapid link variations due to attitude instability, severe signal blockage by urban obstacles, and critical sensitivity to transmitter–receiver alignment. While traditional planar reconfigurable intelligent surfaces (RIS) show promise for mitigating these issues, they exhibit inherent limitations such as angular sensitivity and beam squint in wideband scenarios, compromising reliability in dynamic UAV scenarios. To address these shortcomings, this paper proposes and evaluates a spherical-cap reflective intelligent surface (ScRIS) specifically designed for dynamic low-altitude communications. The intrinsic curvature of the ScRIS enables omnidirectional reflection capabilities, significantly reducing sensitivity to UAV attitude variations. A rigorous analytical model founded on Generalized Sheet Transition Conditions (GSTCs) is developed to characterize the electromagnetic scattering of the curved metasurface. Three distinct 1-bit RIS unit cell coding arrangements, namely alternate, chessboard, and random, are investigated via numerical simulations utilizing CST Microwave Studio and experimental validation within a mechanically stirred reverberation chamber. Our results demonstrate that all tested ScRIS coding patterns markedly enhance electromagnetic field uniformity within the chamber and reduce the lowest usable frequency (LUF) by approximately 20% compared to a conventional metallic spherical reflector. Notably, the random coding pattern maximizes phase entropy, achieves the most uniform scattering characteristics and substantially reduces spatial field autocorrelation. Furthermore, the combined curvature and coding functionality of the ScRIS facilitates simultaneous directional focusing and diffuse scattering, thereby improving multipath diversity and spatial coverage uniformity. This effectively mitigates communication blind spots commonly encountered in UAV applications, providing a resilient link environment despite UAV orientation changes. To validate these findings in a practical context, we conduct link-level simulations based on a reproducible system model at 3.5 GHz, utilizing electromagnetic scale invariance to bridge the fundamental scattering properties observed in the RC to the application band. The results confirm that the ScRIS architecture can enhance link throughput by nearly five-fold at a 10 km range compared to a baseline scenario without RIS. We also propose a practical deployment strategy for urban blind-spot compensation, discuss hybrid planar-curved architectures, and conduct an in-depth analysis of a DRL-based adaptive control framework with explicit convergence and complexity analysis. Our findings validate the significant potential of ScRIS as a passive, energy-efficient solution for enhancing communication stability and coverage in multi-band 6G networks. Full article

Modern power systems face growing challenges in stability assessment due to large-scale renewable energy integration and rapidly changing operating conditions. Data-driven approaches have emerged as promising solutions for real-time stability assessment, yet their performance often degrades under network topology reconfigurations. To address this limitation, the Spatiotemporal Contrastive Graph Convolutional Network (STCGCN) is proposed for the joint task prediction of voltage and transient stability across known and unknown topologies. The framework integrates a graph convolutional network (GCN) encoder to capture spatial dependencies and a temporal convolutional network to model electromechanical dynamics. It also employs supervised contrastive learning to extract discriminative features due to the grid topology variation, enhance stability class separability, and mitigate class imbalance under varying operating conditions, such as fluctuating loads and renewable integration. Case studies on the IEEE 39-bus system demonstrate that STCGCN achieves 89.66% accuracy on in-sample datasets from known topologies and 87.73% on out-of-sample datasets from unknown topologies, outperforming single-task learning approaches. These results highlight the method’s robustness to topology variations and its strong generalization across configurations, providing a topology-aware and resilient solution for real-time joint voltage and transient stability assessment in power systems. Full article

This paper investigates a UAV-assisted wireless communication system that integrates optical wireless communication (LiFi) with conventional RF links to enhance network capacity in crowd-gathering scenarios. While the unmanned aerial vehicle (UAV) serves as a flying base station providing downlink transmission to mobile ground users, the study places particular emphasis on the role of LiFi as a complementary physical layer technology within heterogeneous networks—an aspect closely connected to optical and photonics advancements. The proposed system is designed for environments such as theme parks and public events, where user groups move collectively toward points of interest (PoIs). To maintain quality of service (QoS) under dynamic mobility, we develop a joint optimization framework that simultaneously designs the UAV’s flight path and resource allocation over time. Given the problem’s non-convexity, a block coordinate descent (BCD) based approach is introduced, which decomposes the problem into power allocation and path planning subproblems. The power allocation step is solved using convex optimization techniques, while the path planning subproblem is handled via successive convex approximation (SCA). Simulation results demonstrate that the proposed algorithm achieves rapid convergence within 3–5 iterations while guaranteeing 100% heterogeneous QoS satisfaction, ultimately yielding nearly 15.00 bps/Hz system capacity enhancement over baseline approaches. These findings motivate the integration of coordinated three-dimensional trajectory planning for multi-UAV cooperation as a promising direction for further enhancement. Although LiFi is implemented in free-space optics rather than fiber-based sensing, this work highlights a relevant optical technology that may inspire future cross-domain applications, including those in optical sensing, where UAVs and reconfigurable optical links play a role. Full article

Edge inference systems must sustain real-time performance under dynamic environments such as sensor noise, illumination change, and new object classes. Conventional edge devices deploy static offline-trained models, causing accuracy degradation when the input distribution drifts. This study proposes a runtime-robust edge inference framework that enables continuous adaptation without interrupting execution. The edge device partitions its memory into active and adaptive regions, applying task-specific masked updates generated by a server-side FPGA. The FPGA performs layer-wise importance analysis, partial retraining, and adaptive mask generation using dynamic partial reconfiguration (DPR) to minimize reconfiguration delay. Experiments on MNIST, CIFAR-10, and Tiny ImageNet show that the proposed method reduces adaptation latency by up to 1.3× compared with GPU full retraining while cutting the communication cost to 28% of full model transmission. These results demonstrate that combining masking-based selective updates with FPGA DPR acceleration achieves real-time adaptability, low latency, and communication-efficient learning in cloud–edge collaborative environments. Full article

The agility and scalability of modern industrial control systems critically depend on seamlessly integrating of heterogeneous field devices. However, this integration is fundamentally hindered at the communication level by the diversity of proprietary industrial protocols, which creates data silos and impedes the implementation of advanced control strategies. To overcome this communication barrier, this paper presents GMGbox, a graphical modeling-based protocol adaptation engine. GMGbox encapsulates protocol parsing and data conversion logic into reusable graphical components, effectively bridging the communication gap between diverse industrial devices and control applications. These components are orchestrated by a graphical modeling program engine that enables codeless protocol configuration and supports dynamic loading of protocol dictionary templates to integrate protocol variants, thereby ensuring high extensibility. Experimental results demonstrate that GMGbox can concurrently and reliably parse multiple heterogeneous industrial communication protocols, such as Mitsubishi MELSEC-QNA, Siemens S7-TCP, and Modbus-TCP. Furthermore, it allows engineers to visually adjust protocol algorithms and parameters online, significantly reducing development complexity and iteration time. The proposed engine provides a flexible and efficient data communication backbone for building reconfigurable industrial control systems. Full article

Cross-border e-commerce (CBEC) involves online transactions between sellers and consumers across national borders. Despite increasing volatility in international trade, the CBEC market continues to grow, making it critical to understand how firms manage logistics uncertainty. This study investigates how internal and external uncertainties differently influence logistics service flexibility (LSF) and logistics information system (LIS) utilization. Using survey data from 214 CBEC professionals primarily located in Korea, structural equation modeling (SEM) reveals divergent patterns: (1) external uncertainties enhance logistics flexibility, whereas internal uncertainties show no significant direct effect; (2) internal uncertainties negatively affect LIS utilization, while external uncertainties show a marginally positive relationship; and (3) LIS utilization mediates the negative pathway from internal uncertainty to flexibility. These findings indicate that firms respond asymmetrically to uncertainty sources, challenging the view that uncertainty universally promotes digital adaptation. Framing LSF (reconfiguration) and LIS use (sensing/seizing) as distinct dynamic capabilities, our results show source-contingent activation: external turbulence catalyzes reconfiguration, whereas internal frictions dampen sensing/seizing, indirectly suppressing flexibility. By identifying an indirect-only (negative) pathway from internal uncertainty via LIS, we refine dynamic capability theory in CBEC logistics and delineate boundary conditions under which uncertainty does not automatically induce digital adaptation. Full article

Artificial intelligence (AI) is increasingly positioned as an enabler of strategic renewal and competitiveness for small and medium-sized enterprises (SMEs) in emerging economies. However, its adoption remains limited and uneven, constrained by shortages of skilled talent, weak data infrastructures, and financial barriers. This study examines Ecuadorian SMEs as a representative case within this broader context, analyzing survey data from 385 firms to diagnose AI adoption patterns and validate a structural model linking AI adoption, dynamic capabilities, and strategic innovation performance. Results from Partial Least Squares Structural Equation Modeling (PLS-SEM) confirm that AI adoption enhances innovation and competitiveness both directly and indirectly through dynamic capabilities, specifically firms’ abilities to sense opportunities, seize them through innovation, and reconfigure resources. The model explains 41% of the variance in strategic innovation performance, providing robust empirical support for the proposed AI-Driven Dynamic Capabilities Framework for Strategic Innovation and Competitiveness. The study clarifies how perceptual and contextual enablers of adoption (TAM/TOE) interact with capability-building mechanisms (RBV/DCT), offering a more integrated understanding of how SMEs assimilate AI under resource constraints. These findings demonstrate how SMEs translate early adoption into strategic advantage under conditions of uncertainty. The study also offers actionable guidance by showing that the most effective interventions for SMEs focus on strengthening foundational data and organizational capabilities rather than promoting complex AI systems beyond current readiness levels. Full article

Tunable metalenses are planar optical elements that hold immense potential in the field of integrated optics, enabling reconfigurable focusing without the bulkiness associated with traditional lenses. This study proposes an electrically tunable metalens which integrates poly(3,4-ethylenedioxythiophene)–polystyrenesulfonate (PEDOT:PSS) with a metasurface. The focal length is electrically controlled through electrochemical modulation of the PEDOT:PSS film thickness and deintercalation in an electrolyte. The Fresnel zone plate (FZP) design is employed to simplify the phase profile and reduce optimization complexity. More importantly, the modulated PSO algorithm is implemented to inverse-design the units and suppress inter-unit phase crosstalk. Simulation results demonstrate that the metalens achieves diffraction-limited focusing, with a zoom ratio reaching 10:1. This work provides a feasible strategy for developing high-performance dynamically tunable metalens, with promising applications in miniaturized imaging, microscopy, and integrated photonic systems. Full article

This review traces the historical evolution, conceptual foundations, and contemporary applications of the term homeodynamics across biological, ecological, cognitive, and social systems. Initially coined in the 19th century but largely forgotten, the term re-emerged in the second half of the 20th century as scholars sought to describe dynamic stability in open, self-organizing systems. From Yates’s theoretical formalization in biology to Rattan’s work in biogerontology and recent applications in psychology and organizational theory, homeodynamics has progressively evolved from a synonym of homeostasis to a distinct systems concept. It now denotes the capacity of complex systems to sustain coherence through transitions between multiple temporary equilibria, integrating feedbacks, bifurcations, and adaptive reconfigurations. By revisiting the term’s lineage, this review clarifies its epistemological scope and proposes its use as a heuristic and modeling framework for understanding dynamic stability and regime shifts in living and social systems. Full article

Existing studies on commutation failure during fault recovery (CFFR) in line-commutated converter high-voltage direct current (LCC-HVDC) systems often neglect the critical influence of phase-locked loop phase tracking error (PLL-PTE) and fail to provide effective control strategies to address this issue. This paper investigates the influence of PLL-PTE on CFFR through electromagnetic transient simulations based on a modified CIGRE benchmark model. The study reveals that phase angle jump (PAJ) caused by DC power fluctuations (DPF) and AC network reconfigurations (ANR) is the fundamental source of PLL-PTE, which in turn leads to the occurrence of CFFR. To mitigate this, a novel control strategy is proposed that dynamically adjusts the extinction angle based on historical and predicted PAJ data. Simulation results demonstrate that the proposed method effectively suppresses CFFR under various fault conditions, including different fault types, locations, resistances, and initiation times. Compared with existing control schemes, the proposed approach avoids adverse side effects while exhibiting strong robustness and adaptability. The proposed control strategy significantly enhances the stability and reliability of LCC-HVDC systems, offering great potential for practical application in increasingly complex power grid environments. Full article

This paper presents a fault-tolerant control strategy that dynamically reconfigures the proposed system, and the inverter leg with a fault is isolated through a MOSFET-based clamping branch. With the use of a modified Vector Control (VC) and Pulse-Width Modulation (PWM) technique, the remaining two phases can continue operating. MATLAB/Simulink is used to create a thorough simulation model that examines various fault scenarios and evaluates how well the control process adjusts to each one. The obtained findings demonstrate that, in the event of a fault, the system can maintain accurate speed regulation, maintain a tolerable current balance, and deliver steady torque. The obtained findings demonstrate that, in the event of a fault, the system can maintain accurate speed regulation, maintain a reasonable current balance, and deliver steady torque. In contrast to traditional methods that rely on hardware redundancy, this software-driven technique maintains the electric vehicle’s functionality even when a malfunction arises. In just a few milliseconds, normal operation is restored without the need for more sensors or additional expenses. Because of these characteristics, the suggested approach is a sensible option for actual EV applications. Full article

Generative artificial intelligence (AI) is reshaping higher education (HE) by reconfiguring how knowledge becomes visible, how judgment is exercised, and how recognition is distributed. These systems intervene in the pedagogical and democratic conditions under which plurality, critique, and participation can be sustained. This study examines how students and lecturers interpret and navigate these transformations and what they reveal about the possibilities of democratic education under algorithmic mediation. Drawing on n = 151 written articulations (122 students, 29 lecturers) to open-ended questions collected via LimeSurvey, analyzed through Grounded Theory in combination with biographical interpretation and oriented by education theory (Bildung) and democracy pedagogy, the research reconstructs five orientations that range from pragmatic coping to struggles over recognition. These orientations illuminate how systemic dynamics of acceleration, opacity, and infrastructural authority are refracted into everyday academic practice. They are further synthesized into three broader axes of temporal sovereignty, epistemic opacity and accountability, and recognition ecologies. The findings highlight how fragile orientations emerge as both risks and resources. The study contributes to HE didactics by outlining strategies to transform fragility into pedagogical occasions, emphasizing reflective delay, dialogical engagement with opacity, and diversification of recognition practices. It concludes that democratic education depends on cultivating spaces where algorithmic pressures become educable and fragile orientations can develop into dispositions of reflexivity, critique, and participation. Full article

Artificial intelligence (AI) is driving the evolution of autonomous agriculture towards multi-agent collaborative control, breaking through the limitations of traditional isolated automation. Although existing research has focused on hierarchical control and perception-decision-making technologies for agricultural machinery, the overall integration of these elements in building a resilient physical perception collaborative system is still insufficient. This paper systematically reviews the progress of AI-driven tractor-implement cooperative control from 2018 to 2025, focusing on four major technical pillars: (1) perception-decision-execution hierarchical architecture, (2) distributed multi-agent collaborative framework, (3) physical perception modeling and adaptive control, and (4) staged operation applications (such as collaborative harvesting). The research reveals core challenges such as real-time collaborative planning, perception robustness under environmental disturbances, and collaborative control and safety assurance under operational disturbances. To this end, three solutions are proposed: an AI framework for formalizing agronomic constraints and mechanical dynamics; a disturbance-resistant adaptive tractor-implement cooperative control strategy; and a real-time collaborative ecosystem integrating neuromorphic computing and FarmOS. Finally, a research roadmap is summarized with agronomic constraint reinforcement learning, self-reconfigurable collaboration, and biomechanical mechatronic systems as the core. By integrating the scattered progress in AI, robotics and agronomy, we provide theoretical foundation and practical guidance for scalable and sustainable autonomous farm systems. Full article