Search Results (290)

Machine-learning surrogates can accelerate transistor design, but optimized predictions require physical consistency and explicit treatment of surrogate uncertainty. Here, we develop an uncertainty-aware machine-learning framework for compact-model-based design of WSe₂ p-channel field-effect transistors using an experimentally calibrated S2DS model. A 5000-device dataset spanning channel length, equivalent oxide thickness, hole mobility, contact resistance, impurity density, trap density, and gate-voltage offset was used to train a censor-aware neural-network surrogate for full p-branch transfer-curve prediction at two drain biases. On an independent 750-device test set, the surrogate achieved a mean pointwise $R^{2 }$ of 0.9891 and an exact-coordinate RMSE of 0.0799 decade. The framework further combined inverse-identifiability analysis, Sobol sensitivity analysis, and multiobjective optimization of saturation on-state current, saturation-bias maximum transconductance, normalized drain-bias threshold shift, and threshold-voltage placement. Screening of 262,144 candidate designs yielded 48 Pareto-optimal solutions, of which nine satisfied the uncertainty-screening criteria and six retained all four performance claims after direct S2DS reevaluation. These results demonstrate a physically grounded and uncertainty-aware approach for efficient WSe₂ transistor design within an experimentally calibrated compact-model domain. Full article

Silk fibroin (SF)- and silk sericin (SS)-based antibacterial hydrogels are increasingly engineered as local antimicrobial platforms, yet cross-study interpretation is limited by inconsistent material reporting and by conflation of bacterial inhibition with tissue repair. We performed a structured evidence-mapping and critical synthesis of a frozen 2020–July 2026 corpus of 94 references. The original 46-record core map was re-audited at the original-article level: 43 full-text-verified, non-retracted primary studies were retained for detailed evidence grading, 2 records available only at abstract/database level were retained descriptively but not graded, and 1 subsequently retracted study was excluded from quantitative synthesis. Among the 43 graded studies, metal-ion/nanozyme/catalytic systems were most common (12/43, 27.9%), followed by release-mediated (11/43, 25.6%), multimodal (9/43, 20.9%), contact-active/anti-adhesive (6/43, 14.0%), and light-responsive systems (5/43, 11.6%). Sixteen studies (37.2%) used deliberately infected animal models, whereas only 4 (9.3%) reached a biofilm or adherent-bacteria-level endpoint in the graded map. Biological claim ceilings (C0–C5) are assessed independently from translation gates spanning material identity, reproducibility, mechanism, host safety, sterilization/storage, resistance, long-term fate, and deployment. Across mechanisms, SF and SS most often function as structural, interfacial, or transport-regulating matrices; direct silk-dependent bactericidal causality remains uncommon. The central translational deficit is failure to quantitatively link silk molecular identity and network architecture to antimicrobial exposure, bacterial killing, host selectivity, and long-term material fate. Full article

Routine model-based diagnosis of sulfuric acid converters can attribute performance loss to catalyst deactivation, fouling, bypass, or maldistribution before establishing whether routine measurements distinguish these causes. This study develops the Converter Condition Inference Framework (CCIF) for four-bed double-contact SO₂ converters, placing a practical identifiability gate before mechanism-specific interpretation. A reported-parameter reaction and energy balance kernel using published feed, kinetics, and physical reaction enthalpy reproduces the industrial benchmark only approximately. An energy balance check indicates that the reported first-bed conversion and temperature require an effective enthalpy of about 1.6 times the physical value under the adopted thermochemical basis. At the reference condition, the uncertainty-scaled local sensitivity matrix has rank 1 for a six-state vector; this result persists across the tested inlet temperature range and rate and heat release perturbations. In the equilibrium-limited reduced model, catalyst activity loss and fouling down to 40% of fresh activity produce no resolvable change in conversion, outlet SO₂ slip, or bed temperatures, whereas bypass alters the temperature signature. Even idealized bed-resolved catalyst condition observations raise the rank only to 5. CCIF therefore reports the evidence-supported state class and required measurement upgrades rather than a validated diagnosis. Unit-specific calibration and catalyst-side evidence remain necessary before mechanism-specific maintenance decisions. Full article

The automation of biodigesters using programmable logic controllers (PLCs) is well established, yet mixing schedules are configured empirically, regardless of substrate characteristics. This study proposes a Substrate-Driven PLC Control (SD-PLC) methodology for fixed-dome biodigesters, in which every timing setpoint is derived from a measured substrate property: the homogenization pulse from the rheological mixing time (Metzner–Otto regime), the maintenance interval from a stratification and a substrate–biomass contact criterion, the duty-cycle ceiling from a net energy balance, and a safety gate from acid–base behavior (pH/EC). The methodology is instantiated on a 13.86 m³ fixed-dome biodigester in the Chillón Valley (Lima, Peru), co-digesting organic waste and guinea pig manure (30:70; theoretical methane potential 371 mL CH₄ g⁻¹ VS; field conversion ≈ 21%). The impeller operates in the transitional regime ($Re\approx 0.9$–$2.6\times {10}^3$), the homogenization pulse is ≈22 min, and the reconciled duty cycle ($\delta \approx 0.13$) remains a factor of four below the energy ceiling (${\delta }^{*}\approx 0.56$). An influence × feasibility matrix identifies a low-cost sensor set (pH, electrical conductivity, temperature, level) that gates mixing. A reduced COD-based dynamic model shows that, under acid shock, continuous mixing suppresses methanogenesis through shear while the absence of mixing fails through contact deficit, so that only the stability-first strategy preserves the process. The principal contribution is the methodology itself—a reproducible mapping from substrate characterization to PLC timing design—rather than the hardware. Full article

Deposit return systems (DRS) can increase the capture of beverage packaging, but material circularity is not determined by return rate alone. A returned container contributes to high-value circularity only if it passes recognition, sorting, pre-processing, and material-specific quality gates. This article evaluates the material-quality performance of three returned beverage-packaging materials—polyethylene terephthalate (PET), aluminium and reusable glass—using a quality-gated high-value recovery framework. The model defines a high-quality recovery index, HQR = R × Q × Y, where R is the return rate, Q is the quality factor of the returned stream, and Y is the reprocessing or reuse yield. The HQR indicator describes the quality of the entire DRS process. The core purpose of the HQR model is to distinguish nominal packaging return from high-quality material recovery and to show whether returned PET, aluminium and reusable glass streams remain suitable for high-value circular pathways. A survey-supported early-stage return scenario (R = 0.50) is compared with the 77% and 90% separate-collection targets used in European policy. To strengthen the PET branch of the model, a pilot PET stream-quality and processing-yield dataset was incorporated, including PET purity, colour composition, non-PET impurities, residual moisture, organic residues, intrinsic viscosity, washed PET flake or pellet yield, and sorting/washing rejection. The pilot data indicate that Lithuania had higher PET quality (98.2% PET purity, 80% clear PET, 1.8% non-PET impurities, IV = 0.74 dL/g, and 84.5% washed PET yield) than the Polish regional average (94.3% PET purity, 72.7% clear PET, 5.7% non-PET impurities, IV = 0.721 dL/g, and 80.3% washed PET yield). At R = 0.50, the pilot-derived PET HQR is approximately 37.8% for Lithuania and 31.6% for the Polish regional average. The results indicate that the same nominal return rate can lead to substantially different high-quality recovery outcomes because PET is constrained by stream purity, colour, contamination, and processing yield; aluminium by alloy and remelting control; and reusable glass by inspection, breakage, and refill compatibility. The proposed framework can support structured DRS operator reporting by identifying the material-quality and yield variables that should be measured alongside mass collection; however, operator-level validation is required before the model can be used as a predictive performance tool. Full article

A collision-free path is not sufficient for visibility-dependent mobile robot tasks: a moving obstacle can block the camera–target line of sight and cause inspection or visual-servoing failure even when the robot remains physically safe. Maintaining visual contact with targets is therefore important in Industry 4.0 environments, yet visibility-preserving maneuvers can conflict with navigation progress and collision avoidance. This work presents the Visibility-Informed Safety and Target Awareness framework with control barrier function filtering and occlusion-evasive local replanning (VISTA-CBF+ELR). The architecture combines visibility-risk planning, target-bearing control, an ELR supervisor, and a CBF quadratic program that keeps collision constraints hard while relaxing field-of-view and occlusion requirements through slack. Counterproductive interventions are limited through persistence, benefit–cost and feasibility gates, progress protection, bounded dwell, recovery, and cooldown. In locked factory simulations, redesigned VISTA achieved 67% and 73% strict-goal success under clean and nominal sensing, whereas Visibility-CEM-2D achieved 87% and 86% but with lower clearance. In matched Gazebo trials, strict success was 19/30 for redesigned VISTA, 26/30 without ELR, and 16/30 for Nav2 Smac+MPPI; zero-clearance collisions were 8/30, 3/30, and 14/30, with no difference surviving multiplicity correction. A separate CEM stress test sustained 6.875 Hz optimization, missed 26.31% of 100 ms deadlines, and held commands on 31.35% of ticks. The results demonstrate repair of the ELR pathology and conditional visibility-risk reduction while exposing safety–visibility trade-offs, transfer limitations, and real-time constraints. Full article

γ-photon tomography provides a non-contact approach for reconstructing and visualizing flow-field parameters. However, the resulting grayscale images often exhibit blurred boundaries and weak texture features, causing conventional colorization methods such as DeOldify to produce cross-region color diffusion and boundary color overflow. To address this, this paper proposes a γ-photon flow-field image colorization algorithm based on the Hybrid Swin Colorization Network (HSCN). A hybrid dual-stream encoder composed of a Swin Transformer semantic stream and a central difference convolution (CDC) gradient branch is combined with cross-stage gradient injection and a spatially gated adaptive fusion mechanism to enhance the perception of high-frequency structures at flow-field boundaries and suppress color overflow. The effectiveness of the algorithm is evaluated in terms of colorization quality and flow-field temperature-parameter inversion using γ-photon flow-field images of two CFD-simulated flow patterns, a large-scale vortical wake and a horizontal wake. The proposed method achieves PSNR, SSIM, FID, and MAE values of 38.7422, 0.9372, 10.7344, and 0.0085, respectively. Compared with DeOldify, PSNR and SSIM are improved by 24.30% and 11.89%, while FID and MAE are reduced by 42.98% and 60.47%, respectively. In addition, HSCN achieved a MAPE of 12.65% across 15 boundary and temperature-transition locations in three representative samples, compared with 31.24% for DeOldify and 28.70% for DDColor. Full article

The semiconductor industry faces converging pressures from energy-constrained edge electronics and energy-bottlenecked high-performance computing, motivating heterogeneous monolithic three-dimensional (M3D) integration as a system-level response. M3D imposes a strict back-end-of-line (BEOL) thermal budget on upper-tier devices, restricting the channel materials and contact processes available and degrading conventional thin-film transistor performance. The source-gated transistor (SGT), in which drain saturation is set by gate-modulated injection across an engineered source barrier rather than by drain-side channel pinch-off, provides a device-level response: low saturation voltage, high output impedance, large intrinsic gain, and tolerance to channel-length variation, all achieved with moderate-mobility and nonideal-contact channel materials. This review organizes reported SGTs by source-barrier architecture and channel-material platform, develops a spatial characterization framework that complements electrical measurements for unambiguous identification of source-controlled operation, and surveys applications across standalone edge electronics and BEOL-compatible upper tiers in M3D stacks. Integrating non-volatile memory mechanisms into the source barrier further extends SGTs into a compute-in-memory and neuromorphic upper-tier role in which the voltage-invariant saturation current itself functions as a programmable, read-bias-robust state variable. Together, these considerations position SGTs as a flexible architectural primitive for heterogeneous M3D platforms that address the energy demands of both edge and high-performance computing. Full article

Background/Objectives: Adeno-associated virus (AAV) capsids undergo pH-dependent conformational gating at the 5-fold symmetry pore, but how these structural dynamics shape serotype-specific behavior and affinity-ligand recognition remains unclear, particularly for the clinically important serotypes AAV8 and AAV9. This study aimed to establish a pH-resolved structural framework linking 5-fold pore dynamics to peptide-ligand recognition and to translate this framework into sequence-based design principles for affinity capture of gene therapy vectors. Methods: AAV8 and AAV9 5-fold capsid assemblies were subjected to 500 ns molecular dynamics simulations under acidic (pH 5), neutral (pH 7), and basic (pH 9) conditions, with analysis of pore volume, inter-residue contact networks, electrostatic potential, and solvent-accessible surface area. In parallel, affinity chromatography using three mixed-mode peptide ligands (RVVAVYRI, TTFRAHHI, and TYHHHHII) was performed on clarified HEK293 lysates containing AAV8 or AAV9, with capsid yield, host-cell-protein clearance, and transduction activity assessed by ELISA, SEC-HPLC, and flow-cytometry-based transduction assays. Results: AAV8 displayed a heterogeneous, bimodal pore conformational landscape at pH 7, whereas AAV9 exhibited a discrete gate-like transition with maximal pore constriction at physiological pH; both serotypes showed pore-proximal contact remodeling with distinct network topologies. Experimentally, TYHHHHII achieved the highest selectivity for genome-containing capsids at pH 7, with transduction activity enrichment factors of 2.82 (AAV8) and 5.61 (AAV9), while TTFRAHHI provided the broadest operational pH range for bulk capsid recovery. Conclusions: These findings establish a structural framework linking pH-dependent pore dynamics to affinity ligand recognition and suggest practical sequence-design rules for ligand engineering: clustered histidines for neutral-pH selectivity, Arg-containing motifs for broad-pH robustness, and aromatic or hydrophobic residues for reinforcement of capsid binding. Full article

Full three-dimensional gear-contact analysis resolves localized tensor fields that simplified models cannot recover, but repeated nonlinear finite element (FE) solutions are costly. This study develops a multi-fidelity physics-informed graph surrogate combining a coarse learning graph, peak-sensitive KDTree projection, gated message passing, and a regularized least-squares finite-difference equilibrium residual. The stress-prior-conditioned benchmark uses a coarse prior derived from the same high-fidelity FE field and therefore is not label-free. Across five random seeds on the 750-case test split, it yields NMSE = (9.1 ± 0.4) × 10⁻⁵, R² = 0.985 ± 0.001, and peak-stress error = 2.5 ± 0.2%. A geometry-only gate provides a preliminary label-free result, with 4.1% peak-stress error for seed 42; its complete multi-seed metrics were not retained. One conditioned forward pass requires 42 ms, excluding preprocessing and prior construction, and peak training memory is 47.6 GB on the reported hardware. Maximum projection outperforms distance-weighted averaging at one fixed graph resolution. All targets are simulated, so the method is presented as a numerical FE surrogate rather than an experimentally validated digital-twin replacement. Full article

Accurate protein function prediction (PFP) is essential for understanding biological systems. However, structure-based graph neural networks often rely on fixed-distance contact maps, which may inadequately capture continuous, multi-scale spatial topologies, while the long-tail distribution of Gene Ontology (GO) labels may bias prediction toward frequent functions. We propose DHST, a deep hybrid structure–topology framework that integrates sequence semantics from a pretrained protein language model with local structural information learned by a residual graph convolutional network. DHST further introduces site-specific persistent homology to encode multi-scale topological invariants and a topology-guided residue-wise gated fusion module to modulate structure–semantics representations using local topological embeddings. The fused residue features are aggregated through dual-path pooling, and a weighted binary cross-entropy loss is used to mitigate the adverse effects of label imbalance. On the PDB dataset, DHST achieved area under the precision–recall curve (AUPR) scores of 0.779, 0.481, and 0.557 for molecular function (MF), biological process (BP), and cellular component (CC), respectively; on the AF2 dataset, the corresponding scores were 0.729, 0.390, and 0.459. The model also demonstrated robust generalization to low-homology proteins and maintained strong predictive performance across GO terms with different levels of functional specificity. Ablation results supported the contributions of the main components. Full article

Zn–Sn–O (ZTO) thin-film transistors (TFTs) are promising indium-free oxide semiconductor devices, but their electrical stability is limited by oxygen-related defect states. In this study, Ga and Si incorporated ZTO TFTs were systematically compared using an identical bottom-gate top-contact device architecture to investigate dopant-dependent defect modulation and bias-temperature stability. Both Ga and Si incorporation induced a positive threshold-voltage shift and reduced the relative contribution of oxygen-deficient bonding components, suggesting modification of oxygen-related defect environments in the ZTO channel. Optical analysis further showed reduced Urbach energies after dopant incorporation, suggesting a decrease in localized band tail states and reduced structural disorder. Under negative bias temperature stress (NBTS), SZTO exhibited the smallest threshold-voltage shift, demonstrating the most effective stability enhancement. These results indicate that Ga incorporation preserves high field-effect mobility while improving stability, whereas Si incorporation more effectively reduces oxygen-related defect features and provides enhanced NBTS stability. This study provides insight into the dopant-dependent defect engineering for the improved reliability of indium free oxide TFTS. Full article

Sprint performance improves throughout adolescence as a result of both structural and neuromuscular development. In particular, increases in body height, lower limb length, and muscle volume have been consistently linked to improvements in sprint performance. However, allometric scaling of force, speed, and power remain lower in adolescents compared to adults even after structural differences are accounted for, implicating neural factors as independent contributors to performance development. The purpose of this study was to investigate differences in neuromuscular activation patterns of the lower limb muscles during maximal sprinting between youth male athletes across two age groups (U19: 17–19 years; U16: 13–16 years). Eighteen athletes performed a 50 m maximal sprint. Spatiotemporal variables (running speed, step frequency, step length) were measured over 30–50 m using a high-speed camera (240 Hz) and timing gates. Electromyographic (EMG) signals were recorded simultaneously from ten lower limb muscles using wireless EMG sensors (2000 Hz): rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gluteus maximus (Gmax), gluteus medius (Gmed), vastus lateralis (VL), vastus medialis (VM), tibialis anterior (TA), gastrocnemius (GAS), and soleus (SOL). Root mean square (RMS) amplitude was calculated across four gait phases (contact, early-swing, mid-swing, late-swing) and normalised to maximal voluntary Isometric contraction (%MVIC). The U19 group demonstrated significantly greater running speed (U19: 9.49 ± 0.39 vs. U16: 8.67 ± 0.25 m·s⁻¹, p < 0.001), step frequency (U19: 4.49 ± 0.12 vs. U16: 4.35 ± 0.16 Hz, p = 0.004), and step length (U19: 2.12 ± 0.12 vs. U16: 1.99 ± 0.06 m, p = 0.010) than U16. The overall pattern of lower limb muscle activation across the gait cycle was broadly similar between groups; however, a significant group × phase interaction was observed for RF (p = 0.003, F = 5.257, η² = 0.247), with post hoc analysis revealing greater RF activation during early swing in U19 (p = 0.033). These findings may indicate that sprint-specific training in youth athletes is associated with not only structural but also neuromuscular differences, specifically reflecting enhanced RF recruitment during the phase-critical moment of early swing—a window in which high-threshold motor unit activation is most mechanically decisive. EMG-based assessment of hip flexor activation during maximal sprinting may provide a complementary tool, pending further validation, for talent identification and training prescription in youth track and field. Full article

Accurate gait event detection using inertial measurement units (IMUs) is essential for temporal gait analysis, but frame-level detection is challenged by sparse initial contact (IC) and foot-off (FO) events. This study evaluated recurrent neural network architectures and training strategies for simultaneous IC and FO detection using a single shank-mounted smartphone IMU. The internal dataset included 28 healthy older adults and 18 individuals with non-specific low back pain (NSLBP). Temporal label expansion substantially improved validation performance for gated recurrent unit (GRU) and long short-term memory models, whereas point-label and class-weighted training performed poorly. The selected label-expanded GRU (LE-GRU) achieved $F_1$ scores above 0.95 for both events and mean absolute temporal errors below 12 ms on held-out internal test folds, with high performance in both cohorts. On an external dataset with different sensor and acquisition characteristics, high performance required full-network fine-tuning, indicating the need for adaptation across datasets. Stance phase and stride time calculated from LE-GRU-predicted events showed high agreement with reference-derived values, with Lin’s concordance correlation coefficients from $0.980$ to $0.994$. These findings demonstrate that temporal label expansion enables accurate GRU-based gait event detection and temporal gait analysis from data collected with a single smartphone IMU. Full article

Robust three-finger grasping under physical-domain variation remains challenging because contact stability can change substantially with object mass, effective friction, and observation noise. This work develops U-GRA, a conservative offline-to-online residual adaptation framework for simulated three-finger grasping. U-GRA introduces a unified prior-preserving and critic-disagreement-regulated architecture that couples a frozen behavioral prior with a spectrally normalized and bounded residual stream, scalar Twin-Q reliability assessment, and critic-conditioned residual fusion. The framework first learns a nominal behavioral prior from successful demonstrations and then freezes it as a stable action anchor during online adaptation. Before execution, the twin critics evaluate a candidate action formed from the prior action and the bounded residual proposal, and their absolute scalar Q-value disagreement conditions a state-dependent gate that regulates residual-injection strength. Experiments are conducted in CoppeliaSim using an offline dataset of 40,000 successful demonstrations and online randomization of object mass, effective friction, and observation noise. Across three independent seeds, U-GRA achieves a mean success rate of $84.8\pm 2.3\%$, a normalized return of $82.7\pm 4.1$, and a jitter value of $0.12\pm 0.03$. Relative to AWAC-Res, the strongest evaluated baseline, U-GRA improves mean success by 9.2 percentage points and reduces jitter by 57.1%. It also retains the highest mean success rate and normalized return over the unseen simulated high-mass–low-friction OOD region. These results provide simulation evidence that preserving a nominal behavioral prior while regulating bounded residual correction through critic disagreement improves three-finger grasping robustness under physical-domain variation. Full article