Search Results (2,112)

Soft robotic hands are well suited for handling fragile and geometrically diverse objects, yet many existing designs still rely on fixed finger layouts, which limits grasping adaptability when object size varies substantially. To address this issue, this study proposes a four-finger pneumatic soft robotic hand with a synchronous variable-stroke base mechanism. The design combines a rigid reconfigurable base with compliant soft fingers, allowing the radial positions of the fingers to be adjusted before grasping. A system-level kinematic model is established to describe the relationship between base stroke, finger bending, and the reachable workspace of the hand. A prototype is fabricated, and comparative grasping experiments are conducted under fixed-stroke and variable-stroke configurations using objects with different grasping cross-sections. The results show that the proposed mechanism achieves stable geometric reconfiguration and improves grasping performance when the initial finger spacing is matched to the object size. In particular, the variable-stroke configuration provides better grasp stability and a wider usable grasping range than the fixed-stroke configuration. These findings indicate that geometric reconfiguration at the hand level is an effective way to enhance the adaptability of multi-finger soft robotic hands. Full article

In this work, we analyze the arithmetic building blocks of DNA basecalling to motivate runtime approximate computing in bio systems-on-chip (BioSoCs). We propose and characterize a reconfigurable compressor-tree multiplier whose operating mode can be selected at runtime to trade energy for controlled arithmetic error. Using a 45 nm CMOS evaluation flow, the proposed design demonstrates a clear power–accuracy trade-off across 64 operating modes, achieving about a 58–61% reduction in multiplier power (per multiply under fixed V/f) relative to an accurate Wallace baseline, with mean relative error distance (MRED) in the 1.05–2.88% range. At the application level, we outline a first-order noise-propagation model and, consistent with prior approximate-inference studies, note that task-level quality loss is often within a few percent (up to 5%), motivating end-to-end basecalling evaluation. Application-level evaluation on a TinyX3 DNA basecaller—a compact Bonito-based model—shows that the proposed multiplier with measured REV = 0.012 and MRED = 1.98% preserves near-baseline performance, with negligible degradation in sequence identity and relative length at low perturbation levels and only gradual accuracy decline (confirming ≤ 5% accuracy drop) emerging as perturbations increase into the moderate regime. Finally, a processor-level case study using convolution microbenchmarks (kernel footprints 9–49 weights per output) shows an 11% improvement in energy per instruction and a 12% reduction in energy per MAC when integrating the proposed multiplier into an embedded RISC-V execution engine. Full article

Maritime cross-domain unmanned system-of-systems (MCUSoS), featuring multi-domain collaboration, wide-area coverage, and flexible deployment, plays a vital role in missions such as maritime search and rescue, marine environmental monitoring, and terrain reconnaissance. MCUSoS enables collaborative detection by coordinating heterogeneous unmanned clusters across the aerial, surface, and underwater domains. However, this capability is vulnerable to degradation under cross-domain heterogeneity, communication constraints, and external disturbances such as node failures, link disruptions and malicious interference. To address these challenges, this paper proposes an integrated framework for collaborative detection capability evaluation and resilience enhancement of MCUSoS in multi-disturbance environments. Firstly, a system-of-systems architecture is established by incorporating formation detection modes and multi-level collaborative relationships to characterize its collaborative detection capabilities. Second, a capability evaluation model is developed from the capabilities of collaboration and detection. Based on this, a multi-stage resilience evaluation mechanism is proposed to quantify MCUSoS resilience under three disturbance modes. Additionally, a resilience enhancement strategy combining internal reconfiguration with the external deployment of supplementary detection nodes is designed to recover MCUSoS performance in multi-disturbance environments. Finally, a case study involving 12 clusters of MCUSoS is conducted to validate the effectiveness of the proposed methods. The results demonstrate that the proposed resilience enhancement strategy achieves a recovery rate of up to 74% in the disintegration circle attack scenario and consistently improves the resilience of the MCUSoS under targeted attacks, with the resilience value under low-frequency attacks being 148% higher than that under high-frequency attacks. These findings provide a quantitative basis for resilience evaluation and enhancement in dynamic scenarios. Full article

Electro-optic frequency combs (EOFCs), generated through the microwave-driven modulation of continuous-wave lasers, have emerged as a highly reconfigurable and system-compatible class of optical frequency combs with growing importance in microwave photonics, coherent communications, spectroscopy, and precision metrology. In contrast to mode-locked lasers and Kerr microresonator combs, EOFCs offer electrically programmable repetition rates, deterministic phase coherence, and intrinsic compatibility with radiofrequency electronic systems, making them particularly attractive for integrated and application-oriented implementations. As EOFCs evolve toward broader bandwidths, lower power consumption, and full on-chip integration, their achievable performance is increasingly constrained by the interplay between electro-optic physical mechanisms, modulator architectures, and material platform properties. This review establishes a unified analytical framework that systematically connects EOFC generation mechanisms, device configurations, key performance metrics, and platform-level limitations. We first summarize the fundamental electro-optic effects underpinning EOFC generation and analytically examine representative modulator architectures, including phase modulators, Mach–Zehnder modulators, and microresonator-based schemes, to clarify their respective comb-generation characteristics. Key performance determinants, such as modulation depth, bandwidth, electro-optic efficiency, and optical loss, are then discussed to elucidate their coupled influence on comb-line count, spectral flatness, output power, and phase noise. Subsequently, the performance of EOFCs implemented on major integrated platforms, including Silicon on Insulator (SOI), Indium Phosphide on Insulator (InPOI), Lithium Niobate on Insulator (LNOI), and Lithium Tantalate on Insulator (LTOI), is comparatively reviewed to highlight the material-dependent advantages and constraints. Finally, emerging directions based on heterogeneous integration and ferroelectric materials with ultrahigh electro-optic coefficients are discussed as promising pathways to overcome the current performance bottlenecks. This review provides clear physical insights and engineering guidance for the future development of high-performance, integrated EOFC systems. Full article

Background: Efficient carrier selection and routing in multimodal logistics networks is increasingly complex due to operational uncertainty, fluctuating service reliability, and the need for disturbance-aware decision-support tools. This study develops an integrated optimization framework for simultaneous carrier selection and routing under operational disturbances. Methods: A disturbance-aware multi-criteria network optimization model is proposed that incorporates transportation cost, delivery time, reliability, and economic risk within a unified mathematical formulation. Operational disturbances are represented as parametric perturbations of network costs, enabling recalculation of routing decisions. Computational experiments were conducted on an illustrative multimodal network and additional synthetic networks. Results: The results show that routing decisions remain stable under disturbance levels up to 5% and reconfigure under higher perturbations. Comparative analysis with a classical cost-minimization routing model illustrates a potential reduction in expected economic risk exposure of approximately 25–30% within the illustrative experimental setting while maintaining comparable transportation time. Conclusions: The proposed framework integrates carrier selection and routing decisions in multimodal logistics systems and supports risk-aware decision-making under operational uncertainty. Full article

Precise 3D positioning in GPS-denied environments is a critical enabler of autonomous robotics, industrial automation, and smart logistics within the emerging cyber-physical landscape. This paper presents a distributed and reconfigurable architecture designed to benchmark and provide unified multimodal indoor localization for mobile edge nodes. Unlike rigid commercial solutions, our architecture employs a distributed, reconfigurable framework that allows the rapid interchange of Absolute Localization Methods (UWB, External RGB-D Vision) and Relative Localization Methods (Inertial Odometry, Visual Odometry). We evaluate these modalities individually and in hybrid configurations using a custom low-cost mobile edge node. Experimental results in a controlled environment demonstrate that while all-optical systems offer high precision, a cost-effective fusion of Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) data provides a robust balance of accuracy and reliability. Conversely, we identify significant limitations in monocular visual odometry within feature-poor indoor spaces. The developed platform serves as a reproducible foundation for researchers to prototype hybrid localization algorithms and assess the trade-offs between hardware cost and operational accuracy within complex cyber-physical ecosystems. Full article

Machine learning techniques are increasingly used for computer vision applications in manufacturing. Synthetic data, generated through realistic simulations, are utilized to accelerate the data collection process while optimizing accuracy and precision of ML models. However, in manufacturing there is usually the need for the development of several CV applications that support different production steps. This obstacle requires a systematic approach for generating synthetic datasets that can be used for developing effective CV systems. Hence, this work presents a pipeline for generating photorealistic synthetic datasets, using a set of digital tools such as 3D modeling, photorealistic rendering, automated labeling, and ML training tools. The proposed framework is tested and validated in a robot-assisted packaging case in the dairy industry. The industrial use case provides a pilot-level demonstration that the synthetic dataset generation framework can support the development of CV modules across several production steps and thus it can aid in accelerating commissioning and reconfiguration of industrial automation setups. Moreover, the pilot validation indicates that object detection and recognition models trained on synthetic data can provide sufficient performance for the specific requirements of the examined packaging scenario. Full article

Reconfigurable intelligent surfaces (RISs) have recently attracted attention as a potential solution for improving the reliability of optical wireless communication links, especially when direct transmission (DT) becomes severely degraded due to dynamic channel conditions. In this study, an RIS-assisted architecture based on a modulating retroreflector is proposed for underwater optical wireless communications (MRR-UOWC). In the considered system, both the DT path and the RIS-assisted path transmit the same information simultaneously at the same data rate. The propagation channels are modeled by taking into account propagation loss, Gamma–Gamma turbulence, and pointing error effects. At the receiver, the signals arriving through the direct path and the RIS-reflected path are coherently combined. To evaluate the effectiveness of this configuration, two diversity combining techniques, namely selection combining (SC) and maximum ratio combining (MRC), are investigated. Closed-form analytical expressions for the outage probability (${\mathrm{P}}_{\mathrm{out}}$), average bit-error rate (BER), and ergodic capacity ($\overline{C}$) are derived using the probability density function (PDF), cumulative distribution function (CDF), and moment-generating function (MGF) of the end-to-end signal-to-noise ratio (SNR). The analysis indicates that jointly exploiting the DT and RIS-assisted links can provide noticeable performance gains by leveraging the complementary characteristics of the two propagation paths. Full article

As digital innovations rapidly penetrate aging populations, live-streaming e-commerce acts as a profound external disruption to the informal healthcare subsystem, fundamentally reshaping Health Shared Decision-Making in Elderly Households (HSDM-EH). This study investigates how the nested interplay of affective strategies and algorithmic mechanisms reconfigures this traditional socio-technical balance. Employing a directed content analysis, we conducted methodological triangulation with two complementary data sources: in-depth interviews and behavioral observations from a maximum-variation sample of 40 Chinese families. Our findings reveal a three-stage structural evolution: the de-bounding of the informal healthcare subsystem through the decentering of institutional and familial authority; the synergistic control of affect and algorithms that scales deprofessionalized trust; and the subsequent escalation of systemic friction, marking a failure of organizational resilience. Ultimately, we propose a Socio-Technical Nested Agency Model, demonstrating that algorithmic interventions effect a soft transfer of health authority away from familial oversight to commercial platforms. This socio-technical reconfiguration generates unintended policy feedback that undermines grassroots health initiatives, highlighting the urgent need for cross-sectoral regulatory frameworks to mitigate algorithmic risks and enhance the digital health inclusivity of aging populations. Full article

Artificial intelligence is typically formulated as an information-processing system composed of artificial neurons, where computation is understood as recursive operations connecting inputs and outputs. However, real neural systems are materially embodied and continuously reconfigured by metabolic and physical processes, suggesting that computation cannot be reduced to fixed causal structures. In this paper, we propose a theoretical framework that captures the interplay between informational and material processes as the interaction between two computational schemes: a vertical scheme, representing fixed cause–effect relations, and a horizontal scheme, representing transformations between such relations. We show that the vertical scheme corresponds to Bayesian inference, which updates probability distributions over a fixed hypothesis space, and is consistent with the free-energy minimization principle. In contrast, the horizontal scheme is formalized as inverse Bayesian inference, which modifies the hypothesis space itself by updating likelihood structures based on experienced data. We further demonstrate that the interplay between these schemes can be expressed algebraically as a process of continuously gluing Boolean algebras. This construction yields a non-distributive orthomodular lattice, i.e., quantum logic, without invoking Hilbert space formalism. In this view, quantum logic emerges not as a static logical system but as a structural consequence of dynamically reconfiguring causal contexts. This framework provides a unified perspective in which inference is understood not only as optimization within a fixed model but also as a process that generates and transforms the model itself. It offers a formal basis for describing open-ended computation and suggests a connection to approaches such as unconventional computing and Natural Born Intelligence, where computational structures evolve through interaction with material processes. Unlike existing approaches, this framework derives quantum-logic-like structure from the continual reconfiguration of causal contexts rather than from Hilbert-space assumptions or optimization within a fixed hypothesis space. 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

Rapid urbanization and population growth in West Africa are intensifying pressure on natural resources and reconfiguring telecoupled supply chains, especially for essential household fuels like charcoal and firewood, here collectively referred to as woodfuel, that link urban consumers to distant production landscapes. However, these cross-regional linkages remain poorly understood. This study, therefore, investigates how urban dynamics structure telecoupled woodfuel flows in Benin, based on quantitative and qualitative surveys of roadside charcoal and firewood traders along the country’s major long-distance roads RNIE#2 and RNIE#3. Collected data included sources, destinations, quantities, pricing, and organizational aspects, combined into a system analysis of fuelwood trading across sending, receiving, and corridor (spillover) areas. Results show consumers growing reliance on charcoal, which in our study amounted to 35,770 t year⁻¹ (97% of the total surveyed flow) to urban areas. Roadside trading depends heavily on connectivity, traffic, and regional trade links, with RNIE#2 emerging as the main corridor, channeling 30,960 t year⁻¹ (84% of the total surveyed flow). Contrary to assumptions that woodfuel sources reflect vegetation density, distances to reported sources were short, with supply shadows averaging 11.3 km (SD = 14.5). Urban demand shapes woodfuel flows by concentrating most trade in major cities—especially the Cotonou–Porto-Novo area, which received 83% (28,770 t year⁻¹) of charcoal and 84% (850 t year⁻¹) of firewood traded along the surveyed flow axes of Benin, with market reach distances varying between 1 and 390 km. Full article

Enhancing the robustness and fault tolerance of finite-state machines (FSMs) is crucial for safety-critical systems, such as transportation control systems and medical equipment. This issue becomes particularly important when developing control units for unmanned aerial vehicles (UAVs), which are exposed to external disturbances from electronic warfare (EW) systems. Under such conditions, traditional methods for creating fault-tolerant finite-state machines (FTFSMs), initially designed to address the effects of ionizing radiation that cause rare single-event upsets (SEUs), are often ineffective. This paper proposes a novel method for developing FTFSMs that can withstand multi-bit upsets (MBUs) affecting the FSM’s wires and memory cells due to external disturbances. The FTFSM architecture additionally includes an output register and a concurrent error detection (CED) circuit. When a fault is detected, the FTFSM switches to standby mode. Once the external disturbance ceases, the FTFSM resumes normal operation from the point of interruption without altering the control algorithm. In cases of critical errors, the FSM circuit can be reconfigured via the system processor. Experimental studies have shown that the proposed approach incurs exceptionally low overhead costs. Additionally, the paper presents a technique for calculating the probability of fault detection for FTFSMs implemented in field-programmable gate arrays (FPGAs). Full article