Hypertrophic cardiomyopathy (HCM) is a genetic heart disease affecting approximately 1 in 500 people and is the leading cause of sudden cardiac death (SCD) in young athletes. Current diagnostic methods-cardiovascular magnetic resonance (CMR), echocardiography, and genetic testing-are limited by high costs, operator dependency, or insufficient accuracy, while standard electrocardiogram (ECG) analysis cannot reliably distinguish HCM from acquired left ventricular hypertrophy (LVH). This paper presents a wearable ECG device paired with a classification algorithm that differentiates HCM from acquired LVH using ECG signals alone. The portable device integrates a 3-lead electrode system, an AD8232 signal conditioning module, an Arduino Nano 33 BLE microcontroller, and a lithium polymer battery. The algorithm extracts two quantitative indices-HCM index 1 and HCM index 2-from each heartbeat and classifies patients via dual statistical thresholds. Validation on 483 LVH patients (PhysioNet) and 29 HCM patients (digitized clinical records) yields 75.86% sensitivity, 99.17% specificity, and an F1-score of 80.00%. Leave-one-out cross-validation (LOOCV) confirms generalizability, with cross-validated sensitivity of 72.41%, specificity of 98.96%, and F1-score of 76.36% (95% confidence intervals (CIs) reported). A digitization confound analysis demonstrates that the classification is driven by physiological cardiac features rather than data source artifacts. A simulated device acquisition chain analysis confirms that the wearable hardware's signal characteristics are compatible with the classification algorithm. The system offers a promising tool for affordable HCM screening in resource-limited settings.

The pursuit of wideband tunable near-infrared (NIR) light sources has reached fever pitch due to advances in photonic chips, optical communications, and molecular trace detection. Lanthanide ions (Ln3+), renowned for their characteristic NIR luminescence, have nevertheless been stymied by the challenges of an extremely low molar extinction coefficient and linear absorption. Herein, a senitization methodology is presented that capitalizes on the quantum cutting-engineered heterostructure nanoparticles (CsPbX3: Yb3+-NaYF4: Yb3+, Ln3+), to achieve multi-wavelength NIR luminescence spanning the 900-2200 nm spectrum. The designed heterostructure exhibits an unprecedented absorption bandwidth (200-690 nm) across the UV-visible spectrum, with an exceptionally low stimulation threshold at the sub-microwatt level (50 µW cm-2). The NIR luminescent wavelengths can be precisely modulated by meticulously adjusting the dopant Ln3+ ions leveraging Yb3+-mediated quantum cutting processes. Spectroscopic analysis reveals a directed energy transfer pathway at the perovskite-fluoride heterogeneous interface: perovskite host→Yb3+ (perovskite) →Yb3+ (NaYF4) → Ln3+. This architecture demonstrates multiplexed gas detection capabilities under AI-assisted analysis, facilitating the simultaneous identification and monitoring of multiple marker gases in complex environments. Our work provides a scalable strategy for constructing ultra-broadband NIR light sources, with implications for high-throughput spectroscopic sensing.

Articular cartilage, a unique avascular and low-cell-density connective tissue, relies predominantly on chondrocyte responses to mechanical cues for the maintenance of tissue homeostasis and functional repair. Among the diverse mechanical stimuli encountered in the joint microenvironment, compressive stress stands as the most prominent and physiologically relevant physical signal regulating chondrocyte behavior. This review systematically dissects the multi-layered mechanisms underlying compressive stress-mediated chondrocyte regulation and its translational implications in cartilage tissue engineering and osteoarthritis (OA) intervention. At the molecular level, compressive stress initiates a cascade of mechanosensing, intracellular transduction, and functional output through the synergistic crosstalk of integrin-mediated adhesion complexes, calcium signaling networks, MAPK pathways, and downstream transcriptional regulators (e.g., SOX9, Runx2, Sp1), which collectively orchestrate the balance between anabolic and catabolic metabolism. At the cellular level, articular cartilage's inherent regional heterogeneity, coupled with distinct responses of healthy/pathological chondrocytes and stem cells to compressive parameters (frequency, strain magnitude, loading mode, duration), underscores the need for cell-type-specific mechanical intervention strategies. At the translational level, moderate dynamic compression promotes cartilage repair by preserving extracellular matrix integrity, suppressing inflammatory cascades, and modulating epigenetic landscapes, while aberrant loading exacerbates OA progression via chondrocyte apoptosis, matrix degradation, and pain sensitization. The optimization of scaffold materials (natural polymers, synthetic composites, intelligent responsive matrices) and culture systems (3D bioprinting, microfluidic bioreactors, shear-compression synergistic loading) has emerged as a critical enabler to enhance mechanical regulation efficacy. Despite significant advances, current research is constrained by insufficiently physiological in vitro/in vivo models, lack of standardized loading parameters, unclear pathway crosstalk mechanisms, and limited clinical translation of mechanical-based therapies. Future endeavors should prioritize the elucidation of multi-pathway synergistic networks using multi-omics approaches, establishment of personalized mechanical parameter databases integrating patient-specific factors (age, gender, disease severity), construction of bionic models recapitulating the joint's dynamic microenvironment, and development of combined mechanical-biological therapeutic strategies. These efforts will provide more precise molecular targets and clinically feasible schemes for cartilage repair and OA management.

Dissimilar resistance spot welding of Al-Si-coated B1500HS hot-stamped steel to HC340/590DP dual-phase steel suffers from a narrow process window and HAZ temper softening. A sequential orthogonal-central composite design strategy screened factors and constructed local second-order models for nugget diameter and tensile-shear force. Because the complete tensile-shear CCD dataset is unavailable for independent verification, the tensile-shear model is used strictly as an auxiliary local calibration and is not assigned the same validation level as the nugget-diameter model. Within-batch ANOVA showed that electrode force dominated diameter variation and first-pulse current dominated force variation. A model-assisted variance-aware compromise (7.8/8.5 kA, 2.9 kN, 13/17 cycles) was point-wise validated at 6.5065 ± 0.1366 mm and 15.053 ± 0.1899 kN (n = 20, CV 2.10%/1.26%). The measured performance-optimal orthogonal condition remained Run 11; thus, the compromise is interpreted as a stability-oriented choice rather than a global optimum. A joint-specific HAZ screening envelope (width < 0.7 mm; hardness loss < 50%) is proposed as a descriptive screening criterion only; because HAZ width and microhardness were not measured for the n = 20 validation condition, the envelope was not validated on that condition and remains conditional on the single-factor HAZ data. The framework integrates process optimization with transparent statistical qualification and reports its model calibration limits.

Battery capacity degradation prediction has long been a central topic in battery health analytics, and most studies focus on state of health (SoH) estimation and end of life (EoL) prediction. This study extends the scope to online refinement of the entire capacity fade trajectory (CFT) through EntroLnn, a framework based on entropy-guided transformable liquid neural networks (LNNs). EntroLnn treats CFT refinement as an integrated process rather than two independent tasks for pointwise SoH and EoL. We introduce entropy-based features derived from online temperature fields, applied for the first time in battery analytics, and combine them with customized LNNs that model temporal battery dynamics effectively. The framework enhances both static and dynamic adaptability of LNNs and achieves robust and generalizable CFT refinement across different batteries and operating conditions. The approach provides a high fidelity battery health model with lightweight computation, achieving mean absolute errors of only 0.004577 for CFT and 18 cycles for EoL prediction. This work establishes a foundation for entropy-informed learning in battery analytics and enables self-adaptive, lightweight, and interpretable battery health prediction in practical battery management systems.

Autonomous mobile robot fleets must coordinate task allocation and charging under limited shared resources, yet most battery aware planning methods address only a single robot. This paper extends degradation cost aware task planning to a multi robot setting by jointly optimizing task assignment, service sequencing, optional charging decisions, charging mode selection, and charger access while balancing degradation across the fleet. The formulation relies on reduced form degradation proxies grounded in the empirical battery aging literature, capturing both charging mode dependent wear and idle state of charge dependent aging; the bilinear idle aging term is linearized through a disaggregated piecewise McCormick formulation. Tight big M values derived from instance data strengthen the LP relaxation. To manage scalability, we propose a hierarchical matheuristic in which a fleet level master problem coordinates assignments, routes, and charger usage, while robot level subproblems whose integer part decomposes into trivially small independent partition selection problems compute route conditioned degradation schedules. Systematic experiments compare the proposed method against three baselines: a rule based nearest available dispatcher, an energy aware formulation that enforces battery feasibility without modeling degradation, and a charger unaware formulation that accounts for degradation but ignores shared charger capacity limits.

An almost periodic piecewise linear system (APPLS) is a type of piecewise linear system where the system cyclically switches between different modes, each with an uncertain but bounded dwell-time. Process regulation, especially disturbance rejection, is critical to the performance of these advanced systems. However, a method to guarantee disturbance rejection has not been developed. The objective of this study is to develop an H-infinity performance analysis method for APPLSs, building on which an algorithm to synthesize practical H-infinity controllers is proposed. As an application, the developed methods are demonstrated with an advanced manufacturing system-roll-to-roll dry transfer of two-dimensional materials and printed flexible electronics. Experimental results show that the proposed method enables a less conservative and much better performing H-infinity controller compared with a baseline H-infinity controller that does not account for the uncertain system switching structure.

Biofabrication and biomedical manufacturing are inherently multidisciplinary, integrating living systems with advanced manufacturing to create functional products for applications spanning regenerative engineering and medicine, in vitro disease modeling, drug discovery, and medical devices. As these technologies develop, they are emerging as core enablers of next-generation healthcare and life-science innovation. In the United States (U.S.), rapid progress across fabrication processes, material systems, physics-based modeling, and translation-oriented strategies is expanding the achievable design space and accelerating movement from laboratory demonstrations toward clinical and commercial deployment. We introduce major U.S. research frontiers and highlight representative advances in this field that support applications including organoids and other microphysiological systems for in vitro testing, engineered tissue constructs for in vivo use, and medical devices and biohybrid platforms. We further provide an outlook on advancing robust, ethical biofabrication and biomedical manufacturing in the U.S. research ecosystem.

Precise tension control in roll-to-roll (R2R) manufacturing is difficult under varying operating conditions and process uncertainty. This paper presents a curriculum-based Soft Actor-Critic (SAC) controller for multi-section R2R tension control. The policy is trained in three phases with progressively wider reference ranges, from 27 to 33 N to the full operating envelope of 20 to 40 N, so it can generalize across nominal and disturbed conditions. On a three-section R2R benchmark, the learned controller achieves accurate tracking in nominal operation and handles large disturbances, including 20 N to 40 N step changes, with a single policy and no scenario-specific retuning. These results indicate that curriculum-trained SAC is a practical alternative to model-based control when system parameters vary and process uncertainty is significant.

ECG digitization could unlock billions of archived clinical records, yet existing methods collapse on real-world images despite strong benchmark numbers. We introduce VLM-in-the-Loop, a plug-in quality assurance module that wraps any digitization backend with closed-loop VLM feedback via a standardized interface, requiring no modification to the underlying digitizer. The core mechanism is tool grounding: anchoring VLM assessment in quantitative evidence from domain-specific signal analysis tools. In a controlled ablation on 200 records with paired ground truth, tool grounding raises verdict consistency from 71% to 89% and doubles fidelity separation (ΔPCC 0.03 → 0.08), with the effect replicating across three VLMs (Claude Opus 4, GPT-4o, Gemini 2.5 Pro), confirming a pattern-level rather than model-specific gain. Deployed across four backends, the module improves every one: 29.4% of borderline leads improved on our pipeline; 41.2% of failed limb leads recovered on ECG-Digitiser; valid leads per image doubled on Open-ECG-Digitizer (2.5 → 5.8). On 428 real clinical HCM images, the integrated system reaches 98.0% Excellent quality. Both the plug-in architecture and tool-grounding mechanism are domain-parametric, suggesting broader applicability wherever quality criteria are objectively measurable.

Poly-ether-ether-ketone (PEEK) is a high strength polymer with strong potential for medical use, especially for bone implants. In this research, a novel method of making biomimetic porous PEEK scaffolds is developed. PEEK and polyethersulfone (PES) are blended at a proper ratio to generate a co-continuous phase structure. With solid-state foaming, PES is efficiently removed by leaching to yield interconnected porous PEEK scaffolds. The pore size and pore gradient of the PEEK scaffolds can be easily controlled by controlling the annealing time and temperature of the PEEK/PES blend. This study presents the conditions of the proposed fabrication process and compares the fabricated porous PEEK scaffolds with those fabricated using other methods. It is shown that the structural morphology and mechanical properties of the fabricated porous PEEK is generally comparable to those of trabecular bones, suggesting these scaffolds could be excellent candidates for bone implant applications. [GRAPHICS] .

Laryngeal elevation during swallowing is critical to airway protection, pharyngeal shortening, and upper esophageal sphincter opening. Currently, videofluoroscopy is the only way to quantify amplitude and timing of laryngeal elevation during swallowing. Although wearable devices exist, none are able to quantify amplitude of laryngeal elevation. This proof-of-concept study introduces a novel wearable fabric sensing device equipped with three knitted strain sensors, designed for real-time monitoring and precise classification of swallowing actions. Simultaneous recordings were made with our sensors and submental surface electromyography (sEMG) on 12 healthy adults who performed swallowing and non-swallowing tasks. K-nearest neighbors models were utilized to classify swallowing and non-swallowing tasks using data from knitted sensors and sEMG signals. Surveys were conducted to assess the comfort of wearing the device. The overall accuracy of KNN classifications to predict swallowing versus non-swallowing tasks was 0.97 for the knitted sensor, 0.82 for sEMG, and 0.98 for the combined dataset (knitted sensor + sEMG), while the accuracy to predict specific tasks was 0.75 for the knitted sensor, 0.32 for sEMG, and 0.77 for the combined dataset. Participants rated the knitted sensor’s discomfort an average rating of 7.33/100, indicating a low level of discomfort. Our findings show that laryngeal movement during swallowing can be detected using knitted strain sensors worn on the outside of the neck, with the ability to distinguish between swallowing and non-swallowing tasks, as well as between types of swallows. The detection accuracy is significantly higher than that using the state-of-the-art sEMG method.

Designing and manufacturing compatible multi-band stealth materials remains a great challenge. In this work, a silver-metalized polyimide photochromic composite foam is successfully fabricated by self-activating electroless silver-plating on the surface of the polyimide skeleton and followed by applying a photochromic coating on the upper surface. The effective loading of silver nanoparticles facilitates the rational construction of a conductive network in foam, improving the efficient dissipation of incident electromagnetic waves. In addition, the interconnected conductive network successfully endows it with an excellent Joule heating capability, which can be employed to effectively remove ice and/or mitigate the impact of water vapor on radar stealth performance in cold and wet weather. Besides, the low emissivity silver plating combined with superior thermal insulation of foam enables the material with excellent infrared stealth performance. Moreover, the modulation of self-adaptive photochromic coating brings a prominent visual stealth performance under different sunlight backgrounds. As a result, such excellent radar and infrared stealth performance combined with the adaptive color-switching capability provides the foam with great potential for preparing compatible multi-band materials.

This paper proposes an optimization framework that addresses both cycling degradation and calendar aging of batteries for autonomous mobile robot (AMR) to minimize battery degradation while ensuring task completion. A rectangle method of piecewise linear approximation is employed to linearize the bilinear optimization problem. We conduct a case study to validate the efficiency of the proposed framework in achieving an optimal path planning for AMRs while reducing battery aging.

Stroke stands as a leading cause of disability, often resulting in sensory and motor impairments, particularly in upper limbs. Sensory rehabilitation is vital for functional recovery but is hindered by its reliance on healthcare professionals and the prioritization of motor recovery over sensory restoration. This study introduces a Triboelectric Sensor and Pressure Feedback Ring (TSPF-Ring) as a solution to bridge this gap by amalgamating triboelectric sensing and pneumatic feedback into a wearable device. The sensitive tactile sensor is capable of recognizing multi-dimensional changes such as pressure, proximity and texture, converting these into visual stimuli with a classification accuracy exceeding 99%. Meanwhile, the pneumatic actuator provides adjustable tactile feedback within a 0-12N range. A visual-tactile synchronized rehabilitation training utilizing the TSPF-Ring system was implemented to effectively enhance activation in the patient's sensorimotor cortex, as validated by electroencephalogram experiments. The results indicate that the TSPF-Ring holds promise in improving hand sensory function in stroke patients by promoting neural remodeling through synchronized visual-tactile stimulation, offering a novel wearable device solution for sensory rehabilitation.

To enhance the interfacial adhesion between ultra-high molecular weight polyethylene (UHMWPE) fibers and rubber, a novel environmentally friendly low-temperature dipping system was developed. This system involves reacting polyethylene glycol, butanone oxime-capped hexamethylene diisocyanate, and a cross-linking agent to form a network structure, which is then blended with latex to create a polypolyol-isocyanate-latex (PIL) dipping solution. Initially, the UHMWPE fiber surface was activated using continuous plasma treatment, serving as an alternative to traditional activation solution. Subsequently, the fibers were dipped by the PIL solution. The H pull-out force between the modified UHMWPE fibers and rubber increased by 114 % to 137.6 N, outperforming the conventional resorcinol-formaldehyde-latex (RFL) system. In addition, the PIL system avoids the use of toxic resorcinol and formaldehyde and can cure at lower temperatures, making it more suitable for UHMWPE fibers with low melting points. The modified UHMWPE fiber/rubber composites also exhibited good aging and fatigue stability. This method is characterized by its simplicity, minimal damage to the fibers, and advantages in continuity and scalability, offering new guidance for the interface design of UHMWPE fiber/rubber composites.

Material response (MR) modeling is critical to understanding and predicting the behavior of thermal protection systems (TPS) materials. It is also critical to the design of novel TPS materials. Developing an accurate MR model can be a complex, expensive, and time-consuming process. This paper details the development of a MR model of an Oxy-Acetylene Test Bed (OTB) system. 1dFIAT (One-Dimensional Fully Implicit Ablation and Thermal response program) was used to develop the MR model. Surface thermochemistry was generated using two different programs to create B-prime tables for the MR model. A model ablative material, Phenolic Impregnated Carbon Ablator (PICA), was evaluated at 5 different heat fluxes on the OTB. A sensitivity analysis of 1dFIAT was then performed to investigate the parameters that were contributing most to the error between the predicted and experimental values. A combination of material properties and environmental properties were measured and calculated to fulfill the required inputs to the MR model. The accuracy of the MR model was validated against the OTB experimental results for the PICA material. Learnings and challenges associated with the creation of the numerical model using this method and future applications of this approach are also discussed.

Roll-to-roll (R2R) manufacturing requires precise tension and velocity control under operational constraints. Model predictive control demands gradient computation, while sampling-based methods like MPPI struggle with hard constraint satisfaction. This paper presents an adaptive trajectory bundle method that achieves rigorous constraint handling through derivative-free sequential convex programming. The approach approximates nonlinear dynamics and costs via interpolated sample bundles, replacing Taylor-series linearization with function-value interpolation. Adaptive trust region and penalty mechanisms automatically adjust based on constraint violation metrics, eliminating manual tuning. We establish convergence guarantees proving finite-time feasibility and convergence to stationary points of the constrained problem. Simulations on a six-zone R2R system demonstrate that the adaptive method achieves 4.3% lower tension RMSE than gradient-based MPC and 11.1% improvement over baseline TBM in velocity transients, with superior constraint satisfaction compared to MPPI variants. Experimental validation on an R2R dry transfer system confirms faster settling and reduced overshoot relative to LQR and non-adaptive TBM.