Journals
2026 EN
Castillo Ugalde Arturo · Carrasco Carina · Chavda Brijesh
+2 more
Artificial muscles are essential for advancing bio‐inspired robots, prosthetics, and wearable devices, as they enable compliant, efficient actuation without complex mechanisms. Existing actuation technologies often face limitations such as high voltage requirements, low efficiency, or tethering constraints. This work introduces a solenoid‐based actuator system utilizing soft‐magnetic and hard‐magnetic materials to achieve portable bistable actuation, reducing energy consumption by only requiring power during state transitions. The actuators demonstrate low‐voltage operation, competitive stress and strain performance, and high power efficiency, making them ideal for untethered robots and wearable devices. Preliminary applications in a prosthetic hand and an amphibious robot highlight their potential in terms of low energy consumption, simplicity, and safety.
Journals
2026 EN
Jalaliankhakshour Alireza · Rizzello Gianluca · Cherubini Antonello
+2 more
Electrostatic energy harvesters (EEHs), especially those employing electroactive polymers, show significant potential to be scaled up into real‐world technologies for ambient energy harvesting. Conventional EEH cycles involve rapidly charging and discharging a variable capacitor at specific instants corresponding to maximum and minimum capacitance values. This approach poses two major challenges in practical operation: 1) the precise detection of these extrema is difficult under stochastic inputs and 2) instantaneous charging demands high peak currents and power, creating significant cost and complexity burdens for large‐scale applications. This work proposes control strategies that enable charging and discharging under stochastic excitations while also evaluating the impact of limiting the peak current on the maximum convertible energy. Traditional peak‐triggered controls are compared with newly proposed smooth current control methods that use continuous AC‐like driving voltages, whose phase is bound to the capacitance. Modeling and results on elastomeric generators show that smooth voltage controls can be implemented in a real‐time prediction‐free fashion, while competing in performance with peak‐triggered controls even under realistic stochastic ambient energy harvesting conditions.
Journals
2026 EN
Kim Sojoong · Kim Yeonwoo · Choi Woo Young
+2 more
The increasing demands of data‐centric applications continue to expose the fundamental bandwidth bottleneck of the von Neumann architecture, where memory and computation are separated. Analog processing‐in‐memory (PIM) offers a promising pathway to overcome this limitation, and charge‐trap flash (CTF) synapses stand out as attractive candidates owing to their scalability, multilevel storage, and complementary metal‐oxide‐semiconductor (CMOS) compatibility. In this work, a hardware‐level analog PIM system is presented that integrates CTF synapse arrays, CMOS‐based wordline drivers, and a novel successive integration‐and‐rescaling (SIR) neuron circuit in a chip‐on‐board configuration. Unlike conventional neuron designs that rely heavily on analog‐to‐digital conversion, the proposed SIR neuron performs bit‐sliced accumulation entirely in the analog domain. This architecture not only minimizes analog‐to‐digital converter overhead but also achieves excellent linearity through input‐node stabilization and functional capacitor separation, thereby enhancing both computational accuracy and area efficiency. The fabricated system is validated through handwritten digit classification on the modified national institute of standards and technology dataset, achieving an accuracy of 72.93%, which is only 3.91 percentage points lower than the software baseline under identical precision. These results underscore the pivotal role of the SIR neuron in bridging device‐level innovations with system‐level integration, positioning CTF‐based analog PIM as a scalable and energy‐efficient platform for neuromorphic computing.
Journals
2026 EN
NegreteGallego Ricardo · Andreu Carlos M. · PozueloCampos Sergio
+3 more
Soft materials represent an interdisciplinary frontier in modern science, combining theoretical and experimental knowledge from diverse fields in both fundamental research and cutting‐edge technological applications. Hydrogels have garnered significant interest due to their unique properties. Nevertheless, practitioners often lack sufficient information on how to synthesize hydrogels, and the final composition is traditionally determined through trial and error. This study introduces a novel methodology to identify the optimal composition prior to experimentation. An ordinal regression model for mixture experiments to simulate the synthesis process, an adaptation of the Particle Swarm Optimization algorithm to tackle the complex optimization problem, and an open‐access interactive app to facilitate the calculations are proposed. This work represents a milestone in the hydrogel synthesis field: first, because of the savings in human, economic, and material resources it entails, the latter being consistent with the commitment to environmental sustainability; second, because it aligns with the principles of open science, providing an accessible and reproducible tool for the entire scientific community; and finally, because it seeks to overcome the barrier of expert knowledge to generalize the use of hydrogels. It represents the first step toward the development of an AI system specialized in the design of novel smart materials.
Journals
2026 EN
Sinawang Prima Dewi · GarciaGradilla Victor · Soto Fernando
+4 more
Intelligent Robotic Ingestible Sampling Pill An intelligent, magnetically guided robotic pill (S‐PIRE) integrates a hydrodynamic screw, wireless control, and magnetic docking to collect viscous fluids. As the first reported pill for wireless mucus collection, S‐PIRE marks a step toward in vivo gastrointestinal sampling for disease detection. More details can be found in the Research Article by Utkan Demirci and co‐workers (DOI: 10.1002/aisy.202500330 ).
Journals
2026 EN
Iwata Takamitsu · Nakamura Hajime · Uemura Takafumi
+13 more
Intravascular Electroencephalography The cover image illustrates a microendovascular electroencephalography approach enabling minimally invasive neural recording via cortical veins in a porcine model. It captures localized cortical activities without craniotomy, highlighting its potential for safe, precise brain–computer interface applications. More details can be found in the Research Article by Takufumi Yanagisawa and co‐workers (DOI: 10.1002/aisy.202500487 ).
Journals
2026 EN
Lee Jin Wook · Kim Changha · Park Geun Tae
+3 more
Nanoelectromechanical Content Addressable Memory The cover illustrates a monolithically integrated 3D 1C–1N ternary content‐addressable memory (TCAM) array in CMOS metal layers, demonstrating charge‐domain in‐memory computing for AI applications exemplified by one(few)‐shot learning. More details can be found in the Research Article by Woo Young Choi and co‐workers (DOI: 10.1002/aisy.202500586 ).
Journals
2026 EN
Martyushev Lev L. · Martyushev Leonid M.
ABSTRACT Objectives To test whether the universal two‐parameter DS model, originally proposed in 2015, can accurately describe how human brain and body mass change from conception to old age and reproduce their complex allometric relationship. Methods We analyzed published autopsy data on brain and body mass from conception to ~90 years, encompassing both sexes. Using nonlinear least‐squares regression in Maple, we fitted the DS model to the data, testing single‐ and two‐stage growth scenarios. Model performance was evaluated via residual analysis. Results A two‐stage model—with a transition at ~1.5 years post‐conception—accurately described both brain and body mass trajectories (typically within 10% error). The model successfully reproduced the non‐monotonic, “hook‐shaped” allometric curve of brain vs. body mass, including age‐related declines after ~45 years. Furthermore, rescaling age using development time (derived from the DS model) largely eliminated sex differences in body mass trajectories and equalized male and female life expectancy in biological time. Conclusions The DS model provides a parsimonious, scientifically well‐grounded framework for human ontogenetic growth. It identifies ~1.5 years post‐conception as a critical developmental transition and offers a physiologically meaningful time metric with potential applications in theoretical biology, evolutionary anthropology, biogerontology, etc.
Journals
2026 EN
Benichou Ludovic · Breton Luan · Garcelon Nicolas
+12 more
ABSTRACT Background Facial analysis tools can assist in diagnosing rare genetic syndromes, but their accuracy is limited in ultra‐rare conditions and underrepresented ethnicities due to small, biased datasets. Synthetic facial images could enrich training data and improve equity in diagnostic performance. Methods We developed a synthetic face generation pipeline using diffusion models (DreamShaper XL Turbo), enhanced with LoRA‐based syndrome‐specific domain adaptation and pose conditioning via ControlNet. A total of 4432 synthetic faces were generated across ten rare syndromes, balanced by age (0–18 years), sex (50/50), and ethnicity (33% Caucasian, Afro‐Caribbean, Asian). Synthetic and real data were used in four machine learning designs to train and test ArcFace R‐100, a phenotyping algorithm for syndromic classification. Results Synthetic faces generated from real patient data achieved high phenotypic realism, with classification performance reaching top‐1 accuracy of 0.823 and AUC of 0.991. Adding synthetic images to real training datasets increased accuracy on real test images from 0.766 to 0.869 and improved AUC from 0.988 to 0.993. Performance gains were most significant for ultra‐rare syndromes and Asian individuals (top‐1: 0.971). Training with synthetic images alone yielded lower accuracy (top‐1: 0.606), underscoring their complementary role. Conclusion This study demonstrates that diffusion‐generated synthetic faces can enhance inclusivity and accuracy in AI‐based dysmorphology tools. These synthetic datasets provide a scalable, ethical solution to data scarcity, with applications in clinical training and telemedicine, especially in underserved regions.
Journals
2026 EN
Buono Frank D. · Blaha Ondrej · Lalloo Chitra
+5 more
ABSTRACT Neurofibromatosis Type 1 (NF1) is an autosomal dominant genetic disorder that presents with severe chronic pain (CP) in adults. A limited number of NF1 research studies have evaluated behaviorally based interventions to address CP. The current study evaluated the efficacy of cognitive behavior therapy delivered via mobile application. The three‐arm (treatment as usual [control], iCanCope only [iCC‐NF], iCanCope + contingency management [iCC‐NF + CM]) randomized clinical trial of 108 adults with NF1 and CP was completed during a 2‐month intervention period. Significant improvements in pain interference ( p = 0.005, d = 0.815) occurred in the iCC‐NF + CM group when compared to the control group. Outcomes for pain self‐efficacy ( p = 0.009, d = 0.718), pain inflexibility ( p = 0.026, d = 0.629), and chronic pain acceptance ( p = 0.036, d = 0.653) significantly improved among the iCC‐NF + CM group when compared to the control group. No significant differences were noted between iCC‐NF + CM and iCC‐NF. The current findings offer preliminary evidence of the added benefit of contingency management to mobile pain applications and provide an auxiliary treatment option for individuals with NF1. Trial Registration: ClinicalTrials.gov identifier: 2000029045.