This study presents a full-system simulation methodology for MEMS, addressing the critical need for reliable performance prediction in microsystem design. While existing digital tools have been widely adopted in related fields, current approaches often remain fragmented and focused on specific aspects of device behavior. In contrast, our proposed framework conducts comprehensive device physics-level analysis by integrating mechanical, thermal and electrical modeling with process simulation. The methodology features a streamlined workflow that enables direct implementation of simulation results into fabrication processes. We model a MEMS gyroscope as an example to verify our simulation approach. Multiphysics coupling is considered to capture real-world device behavior, followed by quantitative assessment of manufacturing variations through virtual prototyping and experimental validation demonstrating the method's accuracy and practicality. The proposed approach not only improves design efficiency but also provides a robust framework for MEMS gyroscope development. With its ability to predict device performance, this methodology is expected to become an essential tool in microsensor research and development.
Growing pressure on UK land, driven by demands for food production, renewable energy, biodiversity recovery and climate mitigation, is intensifying competition for limited land. Vertical farming (VF) offers a land-sparing strategy: by relocating crop production indoors and achieving extremely high yields per unit area, VF can free agricultural land for other uses. However, VF has high greenhouse gas (GHG) emissions compared to traditional field farming due to intensive electricity demand, so its environmental value depends on whether land-sparing outcomes can offset production-stage impacts. This study provides the first UK-wide, full-system assessment of lettuce production, comparing business-as-usual field farming with national-scale VF and alternative uses of spared land. A primary-data life cycle assessment (LCA) quantifies the impacts of VF; a separate primary-data LCA of field farming incorporates DNDC-modelled national soil emissions; and the Land Use Net-Zero Advisor (LUNA) evaluates the GHG, land-carbon and energy implications of repurposing spared land for solar, wind, afforestation, agroforestry and bioenergy. VF reduces land demand by 93% but has higher GHG emissions per-kg than field systems. System-level outcomes depend on how spared land is used. Solar energy provides the strongest mitigation, fully offsetting VF's operational emissions and reducing total system impacts below the field baseline. Forestry and agroforestry generate positive land-carbon outcomes but more modest GHG reductions. Across all scenarios, peat soils dominate land-carbon losses, underscoring the need to avoid land-use change on peat and prioritise restoration. Overall, VF's environmental value arises from the land-use transitions it enables. When incorporated into multifunctional land-use planning, VF can support domestic food production while facilitating renewable-energy deployment and ecosystem restoration within a highly constrained UK land system.
Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for characterizing the structure and electronic properties of paramagnetic coordination compounds. However, accurate computation of paramagnetic NMR (pNMR) chemical shifts for large systems remains a major challenge due to the prohibitive cost of full first-principles calculations. Herein, we extend the eXtended ONIOM (XO) method to paramagnetic systems and develop an XO-pNMR approach for the efficient and precise calculation of 13C pNMR chemical shifts in large paramagnetic molecules. We validate the XO-pNMR method by investigating a series of cobalt(II) porphyrins, including cobalt(II) tetraphenylporphyrin (CoTPP) and its substituted derivatives, all of which possess a single unpaired electron. Benchmark calculations against experimental 13C chemical shifts at 308 K reveal that the CAM-B3LYP functional yields the closest agreement with experimental data in full-system calculations, whereas the PBE0-1/3 functional exhibits optimal performance in XO-pNMR calculations─achieving a mean absolute deviation of only 0.3 ppm relative to full calculations and demonstrating robust wave function stability that outperforms alternative functionals. Application of XO-pNMR to substituted Co(II) porphyrins further demonstrates that the method reliably captures substituent-induced variations in 13C chemical shifts, matching the predictive accuracy of full calculations. Collectively, our results establish XO-pNMR with the PBE0-1/3 functional as a cost-effective and reliable approach for calculating pNMR chemical shifts in large paramagnetic molecules featuring a single paramagnetic center with one unpaired electron.
The long-term safety assessment for geological disposal of high-level radioactive waste (HLW) relies on accurately predicting coupled thermal-hydrological-mechanical-chemical (THMC) processes. This review systematically summarizes advances in multiphysics numerical modeling in the field. It synthesizes THMC coupling mechanisms, mathematical models, and numerical strategies, while critically analyzing limitations in simulating millennial-scale evolution, multi-media coupled responses, and radionuclide migration uncertainties. Current challenges include integrating realistic geological structures, representing multiscale fractured media, and achieving computational efficiency for full-system long-term simulations. In response, key future pathways are outlined: advancing stress-field simulation via corner-point grid and finite element integration; developing composite media multiphysics models; adopting domain-decomposition hybrid discrete methods; and implementing high-performance parallel computing frameworks. Progress in these areas will strengthen predictive confidence and engineering applicability, thereby providing a firmer numerical basis for the safety design and risk management of geological disposal systems.
Magnetic particle imaging (MPI) is an emerging imaging modality that exploits the magnetization response of magnetic nanoparticle tracers. While MPI offers substantially higher resolution compared to magnetic resonance imaging, its translation to human-scale applications remains limited. These challenges stem from the requirement of high-intensity electric currents to generate strong magnetic fields, as well as reduced field uniformity with increasing coil spacing. To overcome these barriers, comprehensive simulation studies are essential for guiding MPI prototype design and performance optimization. In this work, we present a finite element method (FEM)-based design of a three-dimensional (3D) MPI prototype. The system integrates electromagnetic coils for the selection, drive, and focus fields, along with a gradiometer configuration for signal reception. Each coil's geometry and magnetic field were first simulated independently to validate its ability to generate the desired magnetic field and subsequently combined into a full-system design with time-domain input excitation signals. This framework achieved 3D field-free point (FFP) scanning within a 20 mm3 field of view. The selection field provided a gradient of 4, 2, and 2 T/m in z axis, y axis, and x axis, respectively, the drive field produced 20 mT, and the focus fields generated 40 mT (z-axis) and 20 mT (y-axis), enabling controlled spatial movement of the FFP. Overall, this study establishes a complete 3D FEM simulation framework for MPI system design and lays the foundation for future optimization toward clinical-scale applications.
Silicone adhesives in polyimide (Kapton) tape are revealed as hidden initiators of electrolyte decomposition in commercial-scale supercapacitors employing acetonitrile-based electrolytes. This study uncovers a previously unrecognized, moisture-assisted silylation mechanism in which silicone-derived trimethylsilyl species react with acetamide, a hydrolysis product of acetonitrile, in the presence of triethylamine (TETA), forming trimethylsilyl acetamide (TMSA) via nucleophilic substitution. This degradation pathway, activated under elevated voltage (≥4.1 V) and trace moisture, is distinct from known electrode-induced processes and accelerates electrolyte breakdown. A suite of analytical techniques, including gas chromatography-mass spectrometry (GC-MS), X-ray fluorescence (XRF), X-ray photoelectron spectroscopy (XPS), and electrochemical testing, unambiguously identifies the silicone adhesive as the primary source of reactive silicon. Control experiments confirm that TMSA formation requires both silicone adhesives and water, validating the proposed mechanism. These findings challenge the conventional assumption that non-electroactive components are chemically inert and demonstrate that auxiliary materials can drive parasitic side reactions under realistic abuse conditions. This work highlights the critical importance of full-system material compatibility screening in supercapacitor design and provides mechanistic insight for enhancing device longevity and safety.
Monte Carlo (MC) simulations constitute the most accurate tool for dosimetric analysis in small radiation fields, such as those generated by the Leksell Gamma Knife Perfexion (LGK-PFX) system. However, implementing a fully detailed model of the system is computationally demanding, both in terms of processing time and geometric complexity, due to the explicit inclusion of the 576 collimators distributed throughout the device. To address this limitation, we present an efficient and accurate model based on a single phase-space file (PSF) generated from a source-collimator simulation for each collimator size (4, 8, and 16 mm), computed using PenEasy Monte. The resulting PSF is subsequently rotated to the required orientations to reproduce the system response. This approach drastically reduces the effective computational burden, as each reference PSF is generated only once and can then be reused to simulate any desired LGK-PFX configuration. Compared with full-system models that require hundreds of hours of computation and storage on the order of terabytes, the proposed rotating-PSF strategy enables Monte Carlo simulations with moderate computational resources, making MC-based verification feasible in a clinical context. The model was validated by comparing MC-generated dose distributions with measurements performed using EBT4 radiochromic films at Ruber International Hospital (Madrid), as well as with data provided by Elekta and other MC-based studies. A 7%-0.5 mm gamma analysis of the dose profiles showed pass rates above 95%. Output factors (OFs) for the 4 mm and 8 mm collimators were 0.848 ± 0.011 and 0.889 ± 0.011, respectively, in a cubic water phantom. Overall, the rotating PSF approach preserves dosimetric accuracy while substantially improving computational efficiency, providing a practical and clinically relevant solution for dosimetry with the LGK-PFX system.
Predicting how chemical modifications affect drug binding is central to rational drug design. Free energy perturbation (FEP) calculations provide accurate estimates of these binding affinity changes, but existing methods often require substantial computational resources and expert knowledge. Here, we present QligFEP v2.1.0, a flexible open-source workflow based on a graphical and command-line interface for calculating relative binding free energies using spherical boundary conditions, which dramatically reduces simulation system size by confining simulations to a focused region around the binding site. QligFEP features a configurable restraint algorithm that automatically handles diverse chemical transformations, streamlined setup procedures, and enhanced analysis tools. We validated the method using industry benchmarks comprising 16 protein targets and 639 ligand transformations. Statistical analysis demonstrates that QligFEP achieves comparable accuracy to established commercial and open-source alternatives while requiring only a fraction of the computational resources. The perturbation protocol simulates ∼6250 atoms per perturbation leg and completes transformation replicates in under 2 h on standard computational clusters. Unlike full-system simulations, QligFEP's modest computational requirements make FEP accessible for less than $1 on current AWS spot instances. The combination of accuracy, flexibility, and computational efficiency positions QligFEP as a practical solution for accelerating compound optimization in drug discovery, making rigorous binding affinity predictions accessible for large scale applications and to research groups with limited computational infrastructure.
The segmentation of knee cartilage and bone in magnetic resonance images with supervised learning methods needs manual annotations from experts, although it demands high amounts of labeled data. KneeSeg-U presents itself as a deep learning unsupervised framework that extracts cartilage segments and bone structures from knee MR imaging automatically with unmarked scan data. KneeSeg-U provides automated full-system segmentation with precise results through unaided clinical efficacy and by negating the requirement for expert annotations. The creation of our extensive knee MRI dataset involved obtaining various sequences from both open-access repositories and clinical points of origin. During training, we applied domain-specific enhancement approaches along with contrastive learning algorithms to build better generalization abilities. The U-Net architecture applied in KneeSeg-U remains trainable through adversarial methods that incorporate self-supervised learning features with consistency restrictions for achieving precise segmentations. Experimental tests demonstrate KneeSeg-U produces segmentations that match supervised methods through a cartilage Dice similarity measure of 0.87, even as bone Dice similarity reaches 0.91 above typical unsupervised segmentation methodology. The system shows generalization functionality for different MRI protocols and multiple anatomical patterns. The KneeSeg-U framework represents an effective method to automate MRI knee segmentation without needing annotated references for building adaptable methods in clinical research about musculoskeletal disease diagnosis.
The increasing urgency to mitigate plastic pollution has accelerated the shift from linear manufacturing toward circular systems. This review synthesizes current advances in mechanical, chemical, biological, and upcycling pathways, emphasizing how artificial intelligence (AI) is reshaping decision-making, performance prediction, and system-level optimization. Intelligent sensing technologies-such as FTIR, Raman spectroscopy, hyperspectral imaging, and LIBS-combined with Machine Learning (ML) classifiers have improved material identification, reduced reject rates, and enhanced sorting precision. AI-assisted kinetic modeling, catalyst performance prediction, and enzyme design tools have improved process intensification for pyrolysis, solvolysis, depolymerization, and biocatalysis. Life Cycle Assessment (LCA)-integrated datasets reveal that environmental benefits depend strongly on functional-unit selection, energy decarbonization, and substitution factors rather than mass-based comparisons alone. Case studies across Europe, Latin America, and Asia show that digital traceability, Extended Producer Responsibility (EPR), and full-system costing are pivotal to robust circular outcomes. Upcycling strategies increasingly generate high-value materials and composites, supported by digital twins and surrogate models. Collectively, evidence indicates that AI moves from supportive instrumentation to a structural enabler of transparency, performance assurance, and predictive environmental planning. The convergence of AI-based design, standardized LCA frameworks, and inclusive governance emerges as a necessary foundation for scaling circular plastic systems sustainably.
Despite advances in miniaturization of CMOS sensors, current submillimeter diameter microendoscopic systems rely on coherent fiber bundles to transfer the image from its collection point to the detector. Determining the resolution of an endoscopic optical system that employs a fiber bundle is nontrivial, and there is limited published information about how to calculate the theoretical modulation transfer function (MTF) of a system that may include a distal lens, fiber bundle, relay optics, and a camera sensor. While the fiber bundle is frequently considered the resolution-limiting element, greater information may be gained from computing the full system MTF. In this paper, we describe a resolution cascade method that calculates theoretical MTFs for common components, as well as for the entire microendoscopic system. These theoretical calculations compare favorably to experimentally gathered data for four different optical configurations. The software developed for these calculations is freely available to predict the MTF of other coherent fiber bundle-based systems. Brief instructions and necessary assumptions/conditions are described.
Bidirectional interfaces combined with neural decoding algorithms are essential for closed-loop (CL) neuromodulation, enabling simultaneous neural monitoring and responsive optogenetic stimulation. However, implementing these capabilities in compact wireless headstages for freely moving animals remains challenging, as most existing platforms rely on tethered setups and external processors to execute computationally intensive decoders. This work presents the design and optimization of a neural decoder integrated into a bidirectional wireless system for CL optogenetic experiments in rodents. The proposed platform combines 32-channel electrophysiological recording with neuromorphic feature extraction, dimensionality reduction, and a nonlinear support vector machine (NL-SVM) decoder implemented on a resource-constrained Spartan-6 FPGA. Temporal dynamics are captured using spike-count features and leaky integrators, while principal component analysis (PCA) reduces the feature space to six components, enabling sub-millisecond inference with minimal memory and power requirements. Model size is further reduced using k-means clustering during training to limit the number of support vectors. Decoder performance was validated using datasets from non-human primate and rat motor cortex recordings. The proposed decoder achieved accuracy comparable to convolutional neural networks (R 2 = 0.85 vs. 0.87) and outperformed Wiener filters (R 2 = 0.81) while requiring significantly fewer computational resources. The full system was further demonstrated in vivo through wireless closed-loop optogenetic stimulation in rats, achieving a variance accounted for (VAF) of 0.9148. Overall, this work introduces a versatile, fully self-contained, and resource-efficient platform for real-time untethered closed-loop neuroscience experiments.
Microtubules are dynamic biopolymers whose lengths are continuously regulated by the concerted actions of polymerization, depolymerization, and motor-protein activity. While numerous mathematical models have explored the regulation of filament length, most have been formulated in the context of growth and shrinking at a single tip of a microtubule, effectively ignoring the mechanistic description of complex phenomena such as treadmilling. Here, we develop a multiscale model for microtubule length regulation that explicitly couples the kinetics of two classes of kinesin molecular motors to filament dynamics at both microtubule tips. Motor densities along the filament are modeled using one-dimensional parabolic partial differential equations. The microtubule length evolves dynamically through a shrinkage term that depends on motor density and which closes the system. In the adiabatic regime, where motor kinetics are fast relative to length dynamics, we derive a reduced model amenable to analytic study and identify simple parameter relationships distinguishing growth, disassembly, and treadmilling behavior. Numerical simulations of the full system reveal qualitatively distinct dynamical regimes and demonstrate how bidirectional motor transport modulates filament length distributions. We parametrize our model with bothin vivoandin vitrodata and thus lay the foundation for developing mathematical models yielding a better understanding of cytoskeleton dynamics in living cells.
No system is immune to failure. The compromise between reducing failures and improving adaptability is a recurring problem in robotics. Modular robots exemplify this tradeoff, because the number of modules dictates both the possible functions and the odds of failure. We reverse this trend, improving reliability with an increased number of modules by exploiting redundant resources and sharing them locally. We present a unified methodology for local resource sharing; local power sharing balances energy distribution, hybrid communication spreads messages, and local sensor fusion propagates full system state estimate information among the robot collective. We present the experimental results of our methodology applied to a modular robot, Mori3. Despite one module being deprived of its own resources in terms of power, sensing, and communication, the robot collective can successfully perform a locomotion mission in a challenging environment, thanks to neighboring modules supporting each other via our proposed resource-sharing methodology.
At-home rehabilitation for post-stroke patients presents significant challenges, as continuous, personalized care is often limited outside clinical settings. Moreover, the lack of integrated solutions capable of simultaneously monitoring motor recovery and providing intelligent assistance in home environments hampers rehabilitation outcomes. Here, we present a multimodal smart home platform designed for continuous, at-home rehabilitation of post-stroke patients, integrating wearable sensing, ambient monitoring, and adaptive automation. A plantar pressure insole equipped with a machine learning pipeline classifies users into motor recovery stages with up to 94% accuracy, enabling quantitative tracking of walking patterns during daily activities. An optional head-mounted eye-tracking module, together with ambient sensors such as cameras and microphones, supports seamless hands-free control of household devices with an average latency under 1 s with consistent operation. These data streams are fused locally via a hierarchical Internet of Things (IoT) architecture, ensuring low latency and data privacy. An embedded large language model (LLM) agent, Auto-Care, continuously interprets multimodal data to provide real-time interventions-issuing personalized reminders, adjusting environmental conditions, and notifying caregivers. Implemented in a post-stroke context, this integrated smart home platform increased mean user satisfaction from $3.9~\pm ~0.8$ in conventional home environments to $8.4~\pm ~0.6$ with the full system (n = 20). Beyond stroke, the system offers a scalable, patient-centered framework with potential for long-term use in broader neurorehabilitation and aging-in-place applications.
Sustaining pollination while limiting parasite spillover from managed bees requires a quantitative understanding of how supplementation of managed colonies, floral resource competition between managed and wild bees, and cross-host transmission jointly shape community dynamics. We develop and analyse a three-dimensional ODE model for wild bees, managed bees and a brood-targeting parasite, incorporating managed-colony supplementation, asymmetric floral competition acting on wild bees, and brood-mediated transmission with host-specific efficiencies. Using two subsystems, we derive explicit ecological thresholds: an input-loss ratio governing managed-bee persistence and a competition-modified wild-bee persistence threshold (parasite-free subsystem), and closed-form supplementation thresholds for the existence and local stability of managed-parasite coexistence (wild-bee-free subsystem). For the full system, the interior coexistence condition reduces to a single polynomial in managed-bee density, implying a finite number of coexistence equilibria and a simple parity rule for their stability. Bifurcation sweeps in the supplementation rate and in the effective transmission efficiency to managed bees reveal multistability between boundary states and coexistence, as well as interior folds that generate hysteresis. We further find both super- and subcritical Hopf bifurcations on the managed-parasite manifold and on interior branches, producing stable limit cycles whose amplitudes vary continuously between Hopf points. Ecologically, both axes act non-monotonically: intermediate supplementation often stabilizes equilibria or confines community-level oscillations in bee and parasite densities, whereas excessive supplementation risks wild-bee loss. Likewise, increasing effective parasite transmission to managed bees can promote parasite persistence but may also, by depressing managed-bee biomass and easing competition, facilitate wild-bee persistence. From an applied perspective, our analysis delineates supplementation regimes and transmission scenarios that avoid wild-bee exclusion, identifies parameter bands where community-level oscillations in bee and parasite densities are expected, and highlights the risk of abrupt regime shifts near folds.
We investigate the effect of interatomic Coulomb repulsionVand particular states disregarded previously on the Kitaev-transmon system proposed by Pinoet al(2024Phys. Rev. B109075101) which consists of a Josephson junction connecting two double quantum dots (DQDs) modeled by the spinless Kitaev Hamiltonian. For an isolated DQD, we demonstrate that a 'sweet spot' hosting 'poor man's Majorana' states persist in the presence ofV, provided that system parameters are appropriately tuned. For the full system, we demonstrate that, at the sweet spots of both DQDs, all eigenstates are doubly degenerate. This degeneracy arises from the existence of an operator that maps between two decoupled Hilbert subspaces. Away from the sweet spots, the microwave spectrum becomes sensitive to the choice of initial state of the system. In our study, we consider transitions from the ground state (which depending on the flux alternates between the above mentioned subspaces) to all possible excited states. This scenario corresponds to a system initially in thermal equilibrium at low temperature.
Machine-learning interatomic potentials (MLIPs) are increasingly used to replace computationally expensive quantum-mechanical (QM) calculations to obtain the energies and forces in ab initio or multiscale molecular dynamics (MD) simulations. While the computational cost of MLIPs lies between that of QM methods and classical force fields (molecular mechanics, MM), their accuracy is close to that of the chosen reference method (e.g. density functional theory, DFT) with sufficient training data. However, for large biological systems in solution, MLIPs are still too costly to perform long MD simulations, where the full system (i.e. including the solvent) is described by the MLIP. Instead, multiscale approaches analogous to QM/MM (i.e. ML/MM) offer a viable compromise between computational effort and accessible system size and time scales. In this review, we provide a brief overview of recent advances and current developments in this field.
Mn-based Li-rich cation-disordered rocksalt (DRX) materials have emerged as a promising alternative to Ni-rich layered transition-metal oxides thanks to their high energy density and their potentially lower cost. However, their operation at high voltages, up to 4.8 V vs Li/Li+, is required to activate oxygen redox and access high capacities, accelerating the electrolyte and the full system degradation. Here we address this voltage-driven instability by screening electrolyte additives in conventional carbonate-based electrolytes using the model DRX compound Li2MnO2F (LMOF). Among the tested additives, tris(trimethylsilyl) phosphite (TMSPI) stands out, improving capacity retention by 60% relative to the baseline after 80 cycles and nearly doubling the discharge rate capability at 5C (170 vs 90 mAh·g-1). The beneficial effect of TMSPI is further validated in LMOF//graphite full cells, showing a satisfactory capacity (220 mAh·g-1) and cycling stability. The mode of action of TMSPI was elucidated through a multitechnique investigation combining operando online electrochemical mass spectrometry (OEMS), electrochemical characterization, and post-mortem analyses. TMSPI enhances the electrochemical performance by mitigating manganese cation dissolution, preserving mechanical integrity of the electrode through the suppression of aluminum current collector corrosion and limiting impedance growth at the positive electrode interface. OEMS experiments further reveal that extensive gas evolution occurs concomitantly with oxygen redox activity, linking electrolyte degradation to oxygen release from the material. Although TMSPI does not suppress outgassing, it effectively mitigates the formation of acidic species through scavenging of protons, water, and fluoride ions, leading to the formation of silane derivatives such as (CH3)3SiF, (CH3)2SiF2, and SiF4. This work demonstrates that electrolyte engineering through rational additive design offers a simple yet scalable route to significantly improve the performance of high-voltage DRX positive electrodes.
Intercompartmental water exchange in brain and other biological tissue can be probed in vivo with diffusion MRI (dMRI). We assess the accuracy of a recently proposed method for estimating a mean exchange rate by performing Monte Carlo simulations of random walkers through a packing of permeable, randomly placed, parallel cylinders to model water exchange within axonal fiber bundles. The diffusivity and kurtosis of the full system are calculated for a broad range of diffusion times and model parameters. The mean exchange rate is estimated from the logarithmic derivative of the kurtosis with respect to the diffusion time and compared with the exchange rate predicted by the Kärger model (KM), which is exact in certain limits. The mean exchange rate is also compared with the reciprocal exchange time obtained by conventional fitting of the kurtosis time dependence to a two-compartment KM, with a high correlation being found between the two quantities. The estimates from the logarithmic derivative are in good agreement with the KM predictions when the exchange time is long in comparison to the compartment traversal times, which corresponds to barrier-limited exchange. Compared to the standard procedure of fitting the kurtosis to the KM over a broad range of diffusion times, using the logarithmic derivative reduces the data acquisition burden by only requiring a narrow range of times and increases generality in that number of compartments need not be specified. This method may be useful for estimating the mean exchange rate from the kurtosis time dependence measured with dMRI.