Search Results (20,070)

Artificial intelligence has greatly accelerated the design and screening of metal–organic materials, particularly for crystalline systems with well-defined topologies and increasingly standardized structural databases. However, this success has also created a structure-centric design paradigm that is less suitable for metal–organic systems whose functions are governed by process history, interfacial assembly, and dynamic coordination rather than by a single idealized lattice. This Perspective proposes that artificial intelligence (AI)-guided design of metal–organic materials should expand beyond crystalline metal–organic frameworks (MOFs) to encompass a broader structural continuum, ranging from long-range ordered frameworks to dynamic, non-periodic coordination networks. Metal–polyphenol networks (MPNs) are used here as an experimentally tractable example within a broader family of structurally dynamic metal–organic materials, as they arise from coordination interactions between metal ions and polyphenolic ligands, generally lack long-range crystallographic periodicity, and exhibit functions that are governed by interfacial assembly, environmental responsiveness, and pathway-dependent structural evolution. These features challenge conventional descriptor design and database-driven prediction, but also create opportunities for AI approaches that are process-aware, interface-sensitive, and function-oriented. By placing MOFs and MPNs within a unified framework of structural order, this Perspective outlines how machine learning, multimodal characterization, active learning, and closed-loop experimentation could expand metal–organic materials design from topology prediction toward dynamic network optimization. Full article

Automated AI-assisted colony counting may improve standardization in digital microbiology, but performance can be affected by colony density, culture medium, colony morphology, adhesion, and plate artifacts. We evaluated the Starry-300 AI colony counting system using 382 standardized pure culture bacterial and yeast plates across four agar media. AI-assisted counts were compared with a three-reader median ImageJ-assisted manual comparator derived from independent counts by experienced technologists. The AI workflow showed close agreement with the manual consensus comparator across a broad colony density range. Overall, 360/382 plates (94.24%) were within ±10 CFU and 377/382 plates (98.69%) were within ±30 CFU of the manual median count. Error-based and agreement analyses showed a mean absolute error of 3.19 CFU/plate; both the intraclass correlation coefficient and Lin’s concordance correlation coefficient were0.99. AI software analysis required approximately 5–15 s/plate, although this did not include plate handling, correction, or reporting. These findings support the analytical feasibility of reviewable AI-assisted colony enumeration under controlled pure culture conditions. Further validation using primary clinical specimens, mixed cultures, near-threshold samples, and external sites is required before broad clinical implementation. Full article

Accurate thermal management is crucial for ensuring the safety, longevity, and performance of lithium-ion batteries, especially in compact embedded systems like USB chargers, power banks, and IoT nodes. Despite extensive research on predictive thermal models and intelligent control frameworks, their implementation in resource-constrained microcontroller-class devices has been limited. Existing strategies in the literature, such as threshold-based or PID logic, cloud-enabled analytics, machine learning models, and observer-based estimators, are often reactive, computationally intensive, or dependent on external infrastructure, making them unsuitable for low-power, standalone applications. This study introduces a novel Scalable Embedded Thermal Intelligence architecture designed for real-time battery thermal regulation in locally executable, without cloud dependency, low-cost platforms. Unlike conventional methods, the proposed system operates entirely on-device using closed-form models implemented on an ESP32 microcontroller. It combines two synergistic algorithms: a static preemptive model that calculates a safe C-rate at startup based solely on ambient and initial battery temperature, and a dynamic disturbance-aware model that monitors temperature rise per SOC step and adjusts airflow or current adaptively without requiring high memory, floating-point units, or supervisory control. The architecture achieves sub-second response times, <7% RAM, and <25% Flash usage, and does not need cloud connectivity, simulation backend, or complex thermal-management infrastructures such as liquid cooling circuits, phase-change systems, or cloud-supervised architectures. The significant contribution of this work is not the introduction of a new electrochemical–thermal formulation, but the effective integration and application of previously validated closed-form thermal predictors on low-cost microcontroller-class hardware, designed for anticipatory battery thermal regulation while adhering to strict computational limitations. Compared to traditional battery thermal management systems using PCM, liquid-cooling circuits, or cloud-based predictive estimators, the proposed approach eliminates the need for complex thermal hardware, fluidic systems, external computing infrastructure and resource-efficient edge operation. This makes the system suitable for deployment in real-world embedded applications like USB-C smart charging cables, compact IoT power banks, and portable medical devices, where form factors, energy efficiency, and cost are critical. The proposed SETI framework offers a firmware-integrated architecture and a firmware-integrated solution that provides a lightweight embedded alternative for predictive thermal regulation for distributed energy systems and miniaturized electronics. Full article

Voltage and reactive power control in active distribution networks is a safety-critical and highly dynamic problem, where traditional optimization methods often struggle to balance efficiency and robustness under complex operating conditions. Recently, large language models (LLMs) have shown promise in sequential decision-making tasks, but their direct application to power system control remains limited by the lack of physical grounding and safety guarantees. In this paper, we propose SafeVolt, a closed-loop LLM-based framework that integrates multi-candidate action generation, simulator-in-the-loop evaluation, and a fine-tuned expert judge for safety-aware decision making. In addition, a high-level rule distillation mechanism that converts successful control experiences into reusable operational axioms is introduced to enable iterative self-improvement. Experiments on a standard distribution network scenario demonstrate that the proposed method outperforms representative baselines, achieving substantial improvements in average reward, voltage violation rate, reactive power loss, and system stability. In particular, voltage violations and extreme events are substantially reduced, indicating enhanced operational safety. These results suggest that combining LLM reasoning with physical simulation and structured feedback provides a promising direction for reliable and adaptive power system control. Full article

Stick–slip vibration, reversal, axial impact, and dynamic instability are major challenges in deep drilling operations and are closely associated with nonlinear bit–rock interaction. To investigate these phenomena, this study develops a nonlinear axial–torsional coupled dynamic model of a drill-string system by integrating rock surface morphology evolution with a Stribeck dry friction model. The drill string is discretized into a distributed lumped-parameter model with coupled axial and torsional degrees of freedom. A surface morphology matrix is introduced to simulate the rock-cutting process, while the Stribeck friction model is employed to characterise the nonlinear frictional behaviour at the bit–rock interface. Time-domain simulations, bifurcation analysis, and frequency spectrum analysis are performed to investigate the dynamic responses of the system. The results indicate that rock surface morphology evolution significantly influences the contact conditions and frictional behaviour at the bit–rock interface, and together with dry friction induces transitions among steady-state, multi-periodic, and chaotic motions. Stick–slip vibration is accompanied by axial impact, bit bounce, and a reduction in the dominant torsional vibration frequency. In addition, variations in both driving and frictional parameters can trigger dynamic instability and state transitions. The proposed model provides an effective framework for analysing nonlinear drilling dynamics and offers theoretical guidance for drill-string vibration suppression, drilling parameter optimisation, and efficient drilling in complex formations. Full article

The engineering, resource, and financial constraints in space and spacecraft so far have not allowed the incorporation of biological components into a closed-loop bioregenerative life support system (BLSS), despite decades of research. The expected increase in deep-space exploration and planetary bases with limited access to Earth-based resources necessitates the development of self-sustaining hybrid BLSS technology. The created physicochemical systems, together with photosynthetic organisms and bacteria, aim to revitalize the air, produce food, and recycle nutrients and water in mutually beneficial mini-ecosystems. While plants are best in the function of food production and bacteria in waste recycling, the incorporation of microalgae would add immense benefits in optimizing the life support system (LSS) and increasing the degree of closure. Microalgal photobioreactors (PBRs) could perform wastewater treatment (WWT), removing the nitrogen (N) and phosphorus (P) in the human-derived wastewater (WW), and couple it with converting carbon dioxide (CO₂) from the cabin to oxygen (O₂) and food production. As microalgal WWT on Earth is an emerging field with engineering hurdles, power, mass, volume, microgravity fluid dynamics, and other constraints have also prevented their operations in space. However, in space vehicles, there is no need for large upscaling of a laboratory prototype system, and the WW effluent is easier to predict, facilitating microalgal extraplanetary use in comparison to Earth treatment plants. These factors, combined with the qualities of microalgae such as surface-to-volume efficiency, fast growth rate, high yield, and tolerability to WW, etc., have led to many preliminary testbeds, prototypes, and ground demonstrations from space agencies, space centers, and academia, which show promising results. Microalgal participation in space WWT is beyond current operational practice; however, PBRs are on the space agenda, and the scientific community is elaborating the technologies that would allow their successful implementation. Full article

Properties of defects in crystals composed of soft particles, such as colloids, differ markedly from those in metals. In this work, dislocation reactions in a bundle of carbon nanotubes (CNTs) are investigated using relaxational molecular dynamics. The problem is reduced to a two-dimensional model, where the strain state of the CNT bundle is fully determined by the cross-sectional shapes of the nanotubes arranged in a close-packed triangular lattice. A pair of edge dislocations with opposite topological charges is introduced into an uniaxially compressed bundle, and their relaxational dynamics are analyzed as a function of the distance d between the parallel planes along which the dislocations glide. When the dislocations move in the same plane (d = 0), they annihilate, restoring a defect-free structure. For negative distances (d < 0), their interaction results in the formation of a vacancy (d = −1), a bivacancy (d = −2), extended voidions (d = −3, −4), or dislocation dipoles (d < −4). In contrast to metals, vacancy clusters containing more than two missing particles in CNT bundles relax into extended voidions. For positive distances (d > 0), the dislocation reaction generates interstitial-type defects in the form of crowdions, which at sufficiently large separations (d > 4) can also be interpreted as dislocation dipoles. In most cases, except for d = 0 and d = 1, dislocation glide enables complete relaxation of the initial shear strain, even in the presence of defects. However, for d = 0 and d = 1, dislocation annihilation or immobilization limits plastic deformation, resulting in only partial stress relaxation. The observed effects are due to the elliptization of the cross-sections of soft carbon nanotubes in the cores of defects. These findings highlight significant differences in defect behavior between crystals of deformable particles and conventional metallic systems. Full article

The modernization of traditional Chinese medicine (TCM) is significantly impeded by the elusive material basis of its meridian system and by a lack of objective, quantitative diagnostic standards. Recent breakthroughs in photonic technologies and optoelectronic chips offer transformative paradigms to address these systemic bottlenecks. This review systematically evaluates the complete academic and engineering chain of “Photonic TCM,” spanning fundamental mechanisms, optical diagnostics, advanced therapeutics, and core chip-level technologies. Specifically, we analyze how ultra-weak photon emission (UPE), two-photon microscopy, and infrared thermography can objectify meridian dynamics and acupuncture pathways. For clinical translation, laser acupuncture has emerged as a robust, non-invasive modality for managing disorders such as chronic pain and insomnia, supported by cumulative evidence-based data. At the device level, vertical-cavity surface-emitting laser (VCSEL)-based photonic computing chips enable ultrafast herbal medicine recognition, while flexible optoelectronics and lab-on-a-chip systems lay the technical groundwork for wearable neuromodulation. Crucially, this review concludes that the Photonic TCM paradigm is transitioning from isolated clinical validation to integrated engineering implementation. We identify biological tissue scattering and parameter heterogeneities as the primary bottlenecks. To navigate these challenges, we propose that the field’s future should converge toward edge-computing-driven wearable closed-loop systems and multi-dimensional optical big data ecosystems. Ultimately, these technological trajectories will steer TCM from an empirical discipline toward a data-driven, precise, and standardized medical science. Full article

High penetrations of distributed energy resources make active distribution networks strongly time-varying, nonlinear, and spatially coupled, which limits the online applicability of centralized voltage/reactive-power optimization. This paper proposes a data–physics fusion dynamic partitioning method for fast power coordination. A physics-based rolling partition baseline is first developed by integrating node operating behavior, voltage/reactive sensitivity, electrical distance, and feeder topology, providing an interpretable and efficient partitioning scheme for normal operating conditions. For high-volatility and strongly coupled scenarios, a heterogeneous dynamic graph and a heterogeneous spatio-temporal graph attention network are introduced to learn control-oriented latent node embeddings. Physical regularization, boundary-coupling penalties, and temporal smoothing constraints are further embedded into soft clustering to reduce cross-partition coupling and partition fluctuation. Tests on the IEEE 33-bus, IEEE 123-bus, and practical Feeder Z systems show that the dynamic partition closely approximates global OPF results, achieving normalized costs of 1.00017 and 1.00099 on the two IEEE systems with 74.3% and 83.2% time reductions. It further reduces the Feeder Z fixed-partition cost gap by 88.0%, while HST-GAT lowers boundary P/Q exchanges by 1.55%/6.57% under volatile conditions. Full article

Recent global disruptions have exposed the fragility of tightly coordinated supply chains, particularly under transportation and information delays, motivating the need for analytical tools to assess their stability limits. This study analyzes a two-echelon supply chain system to determine how delays affect stability and performance, with an emphasis on the role of feedback coordination. A continuous-time delay-differential modeling framework was developed to examine both uncoupled and coupled configurations. Stability is analyzed through characteristic equations, and explicit closed-form expressions for the critical delay threshold are derived as functions of the coupling gain and shipment rate. The uncoupled system is shown to exhibit delay-independent marginal stability but lacks the ability to regulate downstream inventory. In contrast, the coupled system achieves inventory regulation but introduces delay-dependent stability with a critical delay, beyond which oscillations grow unbounded. A key result revealed an inverse relationship between coupling strength and delay tolerance, highlighting a trade-off between responsiveness and robustness. An optimal control formulation further demonstrates that the stability constraints limit the achievable performance. These findings provide a theoretical explanation for the vulnerability of just-in-time systems and offer practical guidelines for resilient logistics design, enabling supply chain practitioners to quantify stability margins and balance coordination efficiency with robustness to transportation delays. Full article

The rapid growth of populations and industrial activities has intensified the need to optimize resource management and reduce environmental impacts. A promising pathway toward sustainable development is the gradual replacement of fossil fuel vehicles with electric vehicles (EVs). However, managing EV operations, particularly regarding depot siting and vehicle routing, is a complex challenge that requires balancing economic, environmental, and social objectives. This research proposes a model for designing an intelligent and sustainable transportation system for waste collection using EV fleets. The model simultaneously determines optimal depot locations from a set of candidates and identifies efficient vehicle routes. Its dual objectives are to minimize total costs, including depot set-up, operation, and travel costs, and to minimize maximum travel time, ensuring equitable workload distribution among drivers. Beyond reducing costs and emissions, the model incorporates social equity considerations in balancing driver travel times. EV limitations, such as restricted range, are explicitly addressed. To solve small-scale instances, the $ϵ$-constraint method was applied, while medium- and large-scale instances were tackled with two multi-objective metaheuristics: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The results demonstrate the model’s sensitivity to system parameters such as vehicle capacity and demand rates. Statistical comparative analysis revealed that both algorithms successfully optimized the primary objective functions without significant differences. However, they exhibited distinct performance metric strengths; NSGA-II demonstrated statistically significant advantages in computational efficiency, solution quantity, and uniform distribution, while MOPSO excelled in convergence quality and closeness to the true Pareto front. Furthermore, the practical applicability of the proposed model is validated through a real-world case study of a municipal solid waste management network in Southern Tehran. This research contributes a comprehensive framework for optimizing EV-based waste collection systems, offering a meaningful step toward sustainable and intelligent urban transportation. The findings provide a theoretical framework and strategic insights for transportation managers and policymakers seeking effective strategies for environmentally responsible and socially equitable waste collection. Full article

To address the energy efficiency optimization problem in power wireless private networks caused by fixed node positions, strong coupling between relay selection and power allocation, and strict quality of service (QoS) constraints, an intelligent reflecting surface (IRS)-assisted hybrid multi-hop relay network model is proposed. An IRS is deployed on the surface of an obstacle located between the source node and the first-hop relay to specifically enhance the first-hop link. By integrating path planning and cooperative power control, a joint optimization problem is formulated to maximize the system energy efficiency. To tackle the coupling issues in resource allocation, a joint optimization algorithm based on the block coordinate descent framework is developed, where the original problem is decomposed into three subproblems: relay selection, power allocation, and IRS phase shift configuration. These subproblems are solved using a greedy strategy, the Dinkelbach method, and a closed-form phase alignment solution, respectively. Simulation results demonstrate that the proposed algorithm outperforms conventional schemes in terms of system energy efficiency, reliability, and latency, making it suitable for power communication scenarios with extremely stringent QoS requirements. Full article

The paper presents an automated, remotely controlled installation for the continuous-flow disinfection of treated wastewater. The proposed solution ensures the inactivation of microorganisms without heating the fluid and without the use of chemical disinfectants, thus reducing the environmental impact and resource consumption associated with conventional disinfection methods. The destruction of microorganisms is achieved by applying high-intensity electrical pulses, which cause irreversible permeabilization of cell membranes through the phenomenon of electroporation. The installation is fully automated and based on a closed-loop control system, in which a programmable logic controller (PLC) acquires data from specialized sensors and automatically regulates the process variables according to the measured operating conditions. The system implements a closed-loop control strategy, optimizing the amplitude, duration and frequency of the electrical pulses depending on the characteristics of the treated fluid and the working flow rate. By eliminating chemical reagents and limiting thermal effects, the proposed technology contributes to reducing energy consumption and increasing the sustainability of the disinfection process. The integration of electroporation with modern automation and monitoring solutions supports the implementation of circular economy principles and the development of sustainable strategies for the management and reuse of treated wastewater. The proposed PLC-SCADA architecture enables adaptive real-time control of the disinfection process by continuously adjusting pulse amplitude, duration, and repetition frequency according to wastewater characteristics and flow conditions. Compared with conventional chemical disinfection methods, the system eliminates the need for chemical reagents and minimizes the formation of secondary pollutants. In addition, the continuous-flow configuration facilitates integration into existing wastewater treatment infrastructures while supporting sustainable and energy-efficient operation. Full article

This paper investigates the fault-tolerant constrained control problem of a nonlinear quarter-car active suspension system subject to road disturbances, body-state constraints, and mixed actuator faults. When mixed actuator faults, state constraints, unknown nonlinear suspension dynamics, and convergence-time requirements coexist, it remains challenging to simultaneously guarantee fault-tolerant compensation, constraint preservation, and implementable control laws. To address these challenges, a neural-network control method based on an adaptive prescribed-time filter (APF) is proposed. A logarithmic state transformation is introduced to convert the body-displacement and velocity constraints into boundedness problems of transformed variables, and the sprung-mass subsystem is represented in a strict-feedback form. The unknown nonlinearities induced by suspension dynamics, road disturbances, and additive actuator faults are approximated online by radial basis function neural networks. Meanwhile, the APF is employed to avoid repeated differentiation of virtual control laws in backstepping and to achieve practical prescribed-time stability. Lyapunov analysis proves that all closed-loop signals are bounded, the body-state constraints are preserved, and sufficient conditions are obtained for the boundedness of the unsprung-mass dynamics, as well as the safety of suspension travel and tire dynamic load. Simulation results under sinusoidal road excitation and smooth-transition actuator faults show that, compared with PID control, passive suspension, and sliding mode control, the proposed method reduces the body-displacement RMSE by 77.39%, 91.83%, and 73.12%, respectively, and the RMS body acceleration by 70.34%, 87.73%, and 50.22%, respectively, while maintaining suspension travel and tire dynamic load within their safety bounds. Full article

A catastrophic Ms7.8 earthquake occurred in Tangshan in 1976 at a focal depth of approximately 12 km, resulting in severe casualties and substantial economic losses. Given its unique tectonic setting, the seismogenic structure and dynamic genesis of the Tangshan earthquake have long remained a key research topic in seismotectonic studies. To better characterize the tectonic framework, seismogenic mechanisms, and deep–shallow dynamical coupling within the Tangshan seismic zone, we employ multi-scale wavelet decomposition on high-resolution residual gravity anomalies to isolate crustal structure signals across different depth ranges. Integrating these structural signatures with the spatial distribution of seismicity yields a comprehensive framework for interpreting the regional tectonic evolution. The Tangshan seismic zone is positioned within the intricate structural architecture of the Tangshan rhombic fault block, a system embedded within the broader context of the North China Craton (NCC) destruction. Seismicity displays a distinct preferred orientation, with events concentrated along block-bounding faults and gravity anomaly gradient zones. With increasing wavelet decomposition levels, the gravity anomalies exhibit a systematic transition from spatially dispersed patterns associated with shallow structures to more concentrated features reflecting deeper geological domains. Shallow anomalies from the first to third decomposition orders, which are primarily controlled by Quaternary sedimentary layers, show a fragmented distribution that corresponds well with the development of local flower structures and the occurrence of diffuse shallow seismicity. The fourth- to seventh-order anomalies clearly delineate the rhombic block and its bounding peripheral faults, highlighting the structural intersections that hosted the Tangshan mainshock and its associated aftershock sequence. In contrast, the eighth- to tenth-order deep-seated anomalies corresponding to deeper structural levels exhibit pronounced coalescence, effectively imaging mantle upwelling and large-scale density heterogeneities within the lithospheric mantle. These concentrated gravity highs are closely coupled with mantle thermal activity, whose upward ascent induces thermal weakening of the lower crust and facilitates progressive stress transfer toward shallower crustal levels. Concurrently, frictional locking of shallow high-angle faults promotes intense stress accumulation within the rigid basement. The interplay between deep-seated dynamic concentration and shallow structural confinement ultimately triggers the catastrophic coseismic rupture responsible for the Tangshan earthquake. By delineating the structural transition from deep-seated aggregation centers to shallow dispersed fracture zones, this study establishes a robust framework for assessing seismogenic environments and regional seismic hazard potential across the progressively destroyed NCC. Full article