Critical Features Research Articles

First-principles insights into the structural, mechanical, electronic, optical, and thermophysical properties of XSrBr3 (X = Na, Ga, and Tl) perovskites: Implications for optoelectronic applications

Non-toxic halide perovskite compounds possess critical inherent and improved features for use in optoelectronic devices. The present work summarizes the physical properties of cubic halide perovskites XSrBr3 (X = Na, Ga, and Tl) based on first-principles theory. The structural, electrical, optical, mechanical, and thermophysical properties of these compounds are studied to assess their potential utility in optoelectronics field. GGA-PBE and GGA-PBEsol functionals were utilized to determine the lattice properties, cell volumes, and formation energies of the compounds. The negative formation energies support the compounds' structural stability. The compounds' bandgaps were computed using the GGA-PBE and Hybrid-HSE06 functionals. Each of the three compounds has a wide indirect bandgap. Optoelectronic devices such as UV detectors, solar panel antireflection coatings, OLED, QLED, and waveguides benefit from a greater static dielectric constant, broad absorption spectra, and low visible reflectance. The elastic stiffness constants Cij use the necessary conditions to demonstrate that the NaSrBr3, GaSrBr3, and TlSrBr3 perovskite structures are mechanically stable. The elastic modulus and other trustworthy properties imply that perovskites are mechanically ductile and soft. In conclusion, the investigation indicates the potential applications of these perovskite materials in photovoltaic and optoelectronic technologies.

Innovative Multistage ML-QSAR Models for Malaria: From Data to Discovery.

Malaria presents a significant challenge to global public health, with around 247 million cases estimated to occur annually worldwide. The growing resistance of Plasmodium parasites to existing therapies underscores the urgent need for new and innovative antimalarial drugs. This study leveraged artificial intelligence (AI) to tackle this complex challenge. We developed multistage Machine Learning Quantitative Structure-Activity Relationship (ML-QSAR) models to effectively analyze large datasets and predict the efficacy of chemical compounds against multiple life cycle stages of Plasmodium parasites. We then selected 16 compounds for experimental evaluation, six of which showed at least dual-stage inhibitory activity and one inhibited all life cycle stages tested. Moreover, explainable AI (XAI) analysis provided insights into critical molecular features influencing model predictions, thereby enhancing our understanding of compound interactions. This study not only empowers the development of advanced predictive AI models but also accelerates the identification and optimization of potential antiplasmodial compounds.

Laser biospeckle method for a fast and reliable microbioassay

Recently, the direct toxicity assessment (DTA) approach or bioassay has attracted much attention, as it can comprehensively evaluate water toxicity without examining a specific/individual chemical component. Bioassays are used to investigate how organisms respond to pollutants compared to controls. In this technique, a microscopic observation is required to know the critical features such as alive/dead, and swimming ability of microorganisms. As the size of the microorganism becomes smaller, observation becomes more difficult due to the narrower focal depth of the imaging system. In our study, to overcome the difficulties, we propose, to our knowledge, a novel technique for the microbioassay utilizing a biospeckle in the diffraction field generated from its movement. We employed Paramecia caudatum sized around 200–300 µm. The sample cell containing Paramecia was illuminated with a laser light with a diameter 15 mm and wavelength 635 nm. The specular component of the transmitted light was filtered out using a spatial filter placed at the Fourier plane and the biospeckle patterns in the diffraction field were acquired using a CCD camera as a movie. It is obvious that the dynamic properties of the biospeckle precisely reflects the movement of the scattering particles or Paramecia. Paramecia were exposed to HCl solutions with pH7.1, control, to 3.7 where all were dead. The swimming abilities of the Paramecia were successfully evaluated based on the crosscorrelation function of the frames of biospeckle patterns. The advantage of the method is that, owing to the non-imaging system, an enormous number of Paramecia can be processed as a whole, and a statistically significant estimation of the swimming ability of Paramecia under the influence of environmental pollution can be derived immediately.

Rational design of MXene-based vacancy-confined single-atom catalyst for efficient oxygen evolution reaction

Two-dimensional transition metal carbides (MXenes) have been demonstrated to be promising supports for single-atom catalysts (SACs) to enable efficient oxygen evolution reaction (OER). However, the rational design of MXene-based SACs depends on an experimental trial-and-error approach. A theoretical guidance principle is highly expected for the efficient evaluation of MXene-based SACs. Herein, high-throughput screening was performed through first-principles calculations and machine learning techniques. Ti3C2(OH)x, V3C2(OH)x, Zr3C2(OH)x, Nb3C2(OH)x, Hf3C2(OH)x, Ta3C2(OH)x, and W3C2(OH)x were screened out based on their excellent stability. Zn, Pd, Ag, Cd, Au, and Hg were proposed to be promising single atoms anchored in MXenes based on cohesive energy analysis. Hf3C2(OH)x with a Pd single atom delivers a theoretical overpotential of 81 mV. Both moderate electron-deficient state and high covalency of metal–carbon bonds were critical features for the high OER reactivity. This principle is expected to be a promising approach to the rational design of OER catalysts for metal-air batteries, fuel cells, and other OER-based energy storage devices.

JointNet: Multitask Learning Framework for Denoising and Detecting Anomalies in Hyperspectral Remote Sensing

One of the significant challenges with traditional single-task learning-based anomaly detection using noisy hyperspectral images (HSIs) is the loss of anomaly targets during denoising, especially when the noise and anomaly targets are similar. This issue significantly affects the detection accuracy. To address this problem, this paper proposes a multitask learning (MTL)-based method for detecting anomalies in noisy HSIs. Firstly, a preliminary detection approach based on the JointNet model, which decomposes the noisy HSI into a pure background and a noise–anomaly target mixing component, is introduced. This approach integrates the minimum noise fraction rotation (MNF) algorithm into an autoencoder (AE), effectively isolating the noise while retaining critical features for anomaly detection. Building upon this, the JointNet model is further optimized to ensure that the noise information is shared between the denoising and anomaly detection subtasks, preserving the integrity of the training data during the anomaly detection process and resolving the issue of losing anomaly targets during denoising. A novel loss function is designed to enable the joint learning of both subtasks under the multitask learning model. In addition, a noise score evaluation metric is introduced to calculate the probability of a pixel being an anomaly target, allowing for a clear distinction between noise and anomaly targets, thus providing the final anomaly detection results. The effectiveness of the proposed model and method is validated via testing on the HYDICE and San Diego datasets. The denoising metric results of the PSNR, SSIM, and SAM are 41.79, 0.91, and 4.350 and 42.83, 0.93, and 3.558 on the HYDICE and San Diego datasets, respectively. The anomaly detection ACU is 0.943 and 0.959, respectively. The proposed method outperforms the other algorithms, demonstrating that the reconstructed images using this method exhibited lower noise levels and more complete image information, and the JointNet model outperforms the mainstream HSI anomaly detection algorithms in both the quantitative evaluation and visual effect, showcasing its improved detection capabilities.

Lightweight bearing fault diagnosis method based on cross-scale learning transformer under imbalanced data*

Abstract Due to the limited amount of failure data in rolling bearing faults, traditional fault diagnosis models encounter challenges such as low diagnostic accuracy and efficiency when dealing with imbalanced data. Additionally, many fault diagnosis models are overly complex and demand high computational resources. To address these issues, a lightweight bearing fault diagnosis method based on cross-scale learnable transformer (CSLT) is proposed for imbalanced data. For difficult-to-classify samples, a learnable generalized focal loss function is defined. The learnable parameters are employed to increase its flexibility, it better addresses the issue of bearing fault diagnosis under imbalanced data conditions. Then, a multi-head broadcasted self-attention mechanism is designed by capturing critical local features of the signal through one-dimensional convolution operations, which not only improves feature extraction capability but also reduces computational complexity. Finally, a dynamic label prediction pruning module is developed to trim redundant labels, which helps in lightening the model and enhancing both feature extraction and diagnostic efficiency. The experimental results demonstrate that the proposed diagnosis method exhibits superior diagnostic precision and efficiency by comparing with other methods.

Forensic Characterisation of Complex Suicides: A Literature Review

Complex suicides refer to using two or more suicide modes. In the literature, complex suicides are distinguished into planned and unplanned. Using multiple methods is related to ensuring a fatal result or because the first method appears too painful, time-consuming, or ineffective. The review aims to provide an overview of the critical features of complex suicides in the forensic context. The review was conducted by searching online databases (PubMed Central and Scopus) up to March 2023. We identified 52 articles that met the inclusion criteria, describing 261 cases. Suicides were classified as planned in 136 cases (52.1%); 105 cases (40.2%) were defined as unplanned, while in the remaining 20 cases (7.7%), the authors did not propose any classification. In 2/261 (0.8%) cases, four modes were indicated, three modes were observed in 34/261 cases (13%), and in 224/261 (85.8%) cases the suicides were carried out using a combination of two distinct methods. The method most frequently employed in the first instance in unplanned suicides were sharp-force injuries and stabbing, followed by mechanical asphyxia (hanging, self-strangulation, plastic bag suffocation) and a fall from height. Regarding planned suicide, the most frequent suicide methods were asphyxia, intoxication (drugs, substances of abuse, or alcohol), and poisoning via toxic substances. Complex suicides represent a relatively rare entity in forensic pathology; nevertheless, they can be challenging for the forensic pathologist in terms of differential diagnosis compared to homicides, due to the sometimes multifaceted presentation.

Mining software insights: uncovering the frequently occurring issues in low-rating software applications.

In today's digital world, app stores have become an essential part of software distribution, providing customers with a wide range of applications and opportunities for software developers to showcase their work. This study elaborates on the importance of end-user feedback for software evolution. However, in the literature, more emphasis has been given to high-rating & popular software apps while ignoring comparatively low-rating apps. Therefore, the proposed approach focuses on end-user reviews collected from 64 low-rated apps representing 14 categories in the Amazon App Store. We critically analyze feedback from low-rating apps and developed a grounded theory to identify various concepts important for software evolution and improving its quality including user interface (UI) and user experience (UX), functionality and features, compatibility and device-specific, performance and stability, customer support and responsiveness and security and privacy issues. Then, using a grounded theory and content analysis approach, a novel research dataset is curated to evaluate the performance of baseline machine learning (ML), and state-of-the-art deep learning (DL) algorithms in automatically classifying end-user feedback into frequently occurring issues. Various natural language processing and feature engineering techniques are utilized for improving and optimizing the performance of ML and DL classifiers. Also, an experimental study comparing various ML and DL algorithms, including multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. Whereas, MLP, RF, BiGRU, GRU, CNN, LSTM, and Classifiers achieved average accuracies of 94%, 94%, 92%, 91%, 90%, 89%, and 89%, respectively. We employed the SHAP approach to identify the critical features associated with each issue type to enhance the explainability of the classifiers. This research sheds light on areas needing improvement in low-rated apps and opens up new avenues for developers to improve software quality based on user feedback.

Firing pattern manipulation of neuronal networks by deep unfolding‐based model predictive control

AbstractThe complexity of neuronal networks, characterized by interconnected neurons, presents significant challenges in control due to their nonlinear and intricate behaviour. This paper introduces a novel method designed to generate control inputs for neuronal networks to regulate the firing patterns of modules within the network. This methodology is built upon temporal deep unfolding‐based model predictive control, a technique rooted in the deep unfolding method commonly used in wireless signal processing. To address the unique dynamics of neurons, such as zero gradients in firing times, the method employs approximations of input currents using a sigmoid function during its development. The effectiveness of this approach is validated through extensive numerical simulations. Furthermore, control experiments were conducted by reducing the number of input neurons to identify critical features for control. Various selection techniques were utilized to pinpoint key input neurons. These experiments shed light on the importance of specific input neurons in controlling module firing within neuronal networks. Thus, this study presents a tailored methodology for managing networked neurons, extends temporal deep unfolding‐based model predictive control to nonlinear systems with reset dynamics, and demonstrates its ability to achieve desired firing patterns in neuronal networks.

Research on improved gangue target detection algorithm based on Yolov8s.

An improved algorithm based on Yolov8s is proposed to address the slower speed, higher number of parameters, and larger computational cost of deep learning in coal gangue target detection. A lightweight network, Fasternet, is used as the backbone to increase the speed of object detection and reduce the model complexity. By replacing Slimneck with the C2F part in the HEAD module, the aim is to reduce model complexity and improve detection accuracy. The detection accuracy is effectively improved by replacing the Detect layer with Detect-DyHead. The introduction of DIoU loss function instead of CIoU loss function and the combination of BAM block attention mechanism makes the model pay more attention to critical features, which further improves the detection performance. The results show that the improved model compresses the storage size of the model by 28%, reduces the number of parameters by 28.8%, reduces the computational effort by 34.8%, and improves the detection accuracy by 2.5% compared to the original model. The Yolov8s-change model provides a fast, real-time and efficient detection solution for gangue sorting. This provides a strong support for the intelligent sorting of coal gangue.

Formation of Linear Plasmonic Heterotrimers Using Nanoparticle Docking to DNA Origami Cages.

The fabrication of complex assemblies with interesting collective properties from plasmonic nanoparticles (NPs) is often challenging. While DNA-directed self-assembly has emerged as one of the most promising approaches to forming such complex assemblies, the resulting structures tend to have large variability in gap sizes and shapes, as the DNA strands used to organize these particles are flexible, and the polydispersity of the NPs leads to variability in these critical structural features. Here, we use a new strategy termed docking to DNA origami cages (D-DOC) to organize spherical NPs into a linear heterotrimer with a precisely defined geometrical arrangement. Instead of binding NPs to the exterior of the DNA templates, D-DOC binds the NPs to either the interior or the opening of a 3D cage, which significantly reduces the variability of critical structural features by incorporating multiple diametrically arranged capture strands to tether NPs. Additionally, such a spatial arrangement of the capture strand can work synergistically with shape complementarity to achieve tighter confinement. To assemble NPs via D-DOC, we developed a multistep assembly process that first encapsulates an NP inside a cage and then binds two other NPs to the openings. Microscopic characterization shows low variability in the bond angles and gap sizes. Both UV-vis absorption and surface-enhanced Raman scattering (SERS) measurements showed strong plasmonic coupling that aligned with predictions by electrodynamic simulations, further confirming the precision of the assembly. These results suggest D-DOC could open new opportunities in biomolecular sensing, SERS and fluorescence spectroscopies, and energy harvesting through the self-assembly of NPs into more complex 3D assemblies.

DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation

Collaborative perception enhances onboard perceptual capability by integrating features from other platforms, effectively mitigating the compromised accuracy caused by a restricted observational range and vulnerability to interference. However, current implementations of collaborative perception overlook the prevalent issues of both limited and low-reliability communication, as well as misaligned observations in remote sensing. To address this problem, this article presents an innovative distributed collaborative perception network (DCP-Net) specifically designed for remote sensing applications. Firstly, a self-mutual information match module is proposed to identify collaboration opportunities and select suitable partners. This module prioritizes critical collaborative features and reduces redundant transmission for better adaptation to weak communication in remote sensing. Secondly, a related feature fusion module is devised to tackle the misalignment between local and collaborative features due to the multiangle observations, improving the quality of fused features for the downstream task. We conduct extensive experiments and visualization analyses using three semantic segmentation datasets, namely Potsdam, iSAID, and DFC23. The results demonstrate that DCP-Net outperforms the existing collaborative perception methods comprehensively, improving mIoU by 2.61% to 16.89% at the highest collaboration efficiency and achieving state-of-the-art performance.

Integrating Shapley Values into Machine Learning Techniques for Enhanced Predictions of Hospital Admissions

(1) Background: Predictive modeling is becoming increasingly relevant in healthcare, aiding in clinical decision making and improving patient outcomes. However, many of the most potent predictive models, such as deep learning algorithms, are inherently opaque, and their decisions are challenging to interpret. This study addresses this challenge by employing Shapley Additive Explanations (SHAP) to facilitate model interpretability while maintaining prediction accuracy. (2) Methods: We utilized Gradient Boosting Machines (GBMs) to predict patient outcomes in an emergency department setting, with a focus on model transparency to ensure actionable insights. (3) Results: Our analysis identifies “Acuity”, “Hours”, and “Age” as critical predictive features. We provide a detailed exploration of their intricate interactions and effects on the model’s predictions. The SHAP summary plots highlight that “Acuity” has the highest impact on predictions, followed by “Hours” and “Age”. Dependence plots further reveal that higher acuity levels and longer hours are associated with poorer patient outcomes, while age shows a non-linear relationship with outcomes. Additionally, SHAP interaction values uncover that the interaction between “Acuity” and “Hours” significantly influences predictions. (4) Conclusions: We employed force plots for individual-level interpretation, aligning with the current shift toward personalized medicine. This research highlights the potential of combining machine learning’s predictive power with interpretability, providing a promising route concerning a data-driven, evidence-based healthcare future.

Deep learning with local spatiotemporal structure preserving for soft sensor development of complex industrial processes

Data-driven soft sensors have emerged as indispensable tools for predicting quality variables in complex industrial processes because of their cost-effectiveness and ease of maintenance. In particular, soft sensors based on deep learning have been utilized in extensive research and successful applications in recent years. However, traditional deep learning methods capture hierarchical data features by minimizing global fitting errors, neglecting the local structural characteristics implied in the original data. In this paper, we propose a new deep learning method for soft sensor development. Utilizing autoencoders as the foundational architecture of our network, a new semisupervised strategy is adopted for layerwise pretraining optimization. On the one hand, more representative data features are extracted by maintaining the local spatiotemporal structure of the data; on the other hand, layer-by-layer supervised learning is employed to identify the critical features that are aligned with the ultimate task, which aids in obtaining the optimal network parameters and improving the resulting prediction accuracy. Subsequently, a local spatiotemporal structure-preserving stacked semisupervised autoencoder (LSP-SuAE) is established. To evaluate the feasibility and effectiveness of the proposed approach, experiments are carried out in a real industrial process. A soft sensor based on the LSP-SuAE is developed to predict the rate of ethylbenzene conversion during the dehydrogenation of styrene. The experimental results demonstrate that, compared to five other common or similar data-driven modeling methods, LSP-SuAE exhibits higher prediction accuracy and better stability.

Study of the results of Hilbert transformation for some fractional derivatives

ABSTRACT Caputo Fractional Derivative is a base for the Riemann-Liouville Derivative, Katugampola fractional derivative, and other fractional derivatives. In this paper, we have illustrated several critical features of the Caputo Derivative via Hilbert Transformation that have not yet been discussed. We have also investigated the behavior of the Riemann-Liouville integral and studied the results of the k-Riemann-Liouville fractional integral with the applications of Hilbert transformation.

Phase and Amplitude Modes in the Anisotropic Dicke Model with Matter Interactions.

Phase and amplitude modes, also called polariton modes, are emergent phenomena that manifest across diverse physical systems, from condensed matter and particle physics to quantum optics. We study their behavior in an anisotropic Dicke model that includes collective matter interactions. We study the low-lying spectrum in the thermodynamic limit via the Holstein-Primakoff transformation and contrast the results with the semi-classical energy surface obtained via coherent states. We also explore the geometric phase for both boson and spin contours in the parameter space as a function of the phases in the system. We unveil novel phenomena due to the unique critical features provided by the interplay between the anisotropy and matter interactions. We expect our results to serve the observation of phase and amplitude modes in current quantum information platforms.

Intelligent condition prediction model for bridge infrastructure based on evaluating machine learning algorithms

PurposePrediction models are essential tools for transportation agencies to forecast the condition of bridge decks based on available data, and artificial intelligence is paramount for this purpose. This study aims at proposing a bridge deck condition prediction model by assessing various classification and regression algorithms.Design/methodology/approachThe 2019 National Bridge Inventory database is considered for model development. Eight different feature selection techniques, along with their mean and frequency, are used to identify the critical features influencing deck condition ratings. Thereafter, four regression and four classification algorithms are applied to predict condition ratings based on the selected features, and their performances are evaluated and compared with respect to the mean absolute error (MAE).FindingsClassification algorithms outperform regression algorithms in predicting deck condition ratings. Due to its minimal MAE (0.369), the random forest classifier with eleven features is recommended as the preferred condition prediction model. The identified dominant features are superstructure condition, age, structural evaluation, substructure condition, inventory rating, maximum span length, deck area, average daily traffic, operating rating, deck width, and the number of spans.Practical implicationsThe proposed bridge deck condition prediction model offers a valuable tool for transportation agencies to plan maintenance and resource allocation efficiently, ultimately improving bridge safety and serviceability.Originality/valueThis study provides a detailed framework for applying machine learning in bridge condition prediction that applies to any bridge inventory database. Moreover, it uses a comprehensive dataset encompassing an entire region, broadening the model’s applicability and representation.

Unveiling the molecular landscape of cognitive aging: insights from polygenic risk scores, DNA methylation, and gene expression

BackgroundAging represents a significant risk factor for the occurrence of cerebral small vessel disease, associated with white matter (WM) lesions, and to age-related cognitive alterations, though the precise mechanisms remain largely unknown. This study aimed to investigate the impact of polygenic risk scores (PRS) for WM integrity, together with age-related DNA methylation, and gene expression alterations, on cognitive aging in a cross-sectional healthy aging cohort. The PRSs were calculated using genome-wide association study (GWAS) summary statistics for magnetic resonance imaging (MRI) markers of WM integrity, including WM hyperintensities, fractional anisotropy (FA), and mean diffusivity (MD). These scores were utilized to predict age-related cognitive changes and evaluate their correlation with structural brain changes, which distinguish individuals with higher and lower cognitive scores. To reduce the dimensionality of the data and identify age-related DNA methylation and transcriptomic alterations, Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was used. Subsequently, a canonical correlation algorithm was used to integrate the three types of omics data (PRS, DNA methylation, and gene expression data) and identify an individual “omics” signature that distinguishes subjects with varying cognitive profiles.ResultsWe found a positive association between MD-PRS and long-term memory, as well as a correlation between MD-PRS and structural brain changes, effectively discriminating between individuals with lower and higher memory scores. Furthermore, we observed an enrichment of polygenic signals in genes related to both vascular and non-vascular factors. Age-related alterations in DNA methylation and gene expression indicated dysregulation of critical molecular features and signaling pathways involved in aging and lifespan regulation. The integration of multi-omics data underscored the involvement of synaptic dysfunction, axonal degeneration, microtubule organization, and glycosylation in the process of cognitive aging.ConclusionsThese findings provide valuable insights into the biological mechanisms underlying the association between WM coherence and cognitive aging. Additionally, they highlight how age-associated DNA methylation and gene expression changes contribute to cognitive aging.

Repositioning Work and Leisure: Digital Nomads Versus Tourists

ABSTRACTA growing yet fragmented debate addressing digital nomadism has recently emerged across different disciplines, including management, organizational behavior, and mobility studies. However, the intersection between digital nomadism and leisure and tourism activities remains blurred. Integrating existing theoretical assessments, this conceptual paper scrutinize digital nomadism from a tourism and leisure perspective, and attempts to provide a broader understanding of this phenomenon. The paper repositions digital nomads in the center of tourism and travel discourse by describing their key characteristics, mobility motives, destination selection process, and critical features, singling them out from conventional business/leisure tourists. This paper significantly contributes to an emerging stream of tourism research addressing digital nomadism as an emerging lifestyle market and provides timely practical implications for policymakers and industry practitioners.

Improved tactile speech perception and noise robustness using audio-to-tactile sensory substitution with amplitude envelope expansion

Recent advances in haptic technology could allow haptic hearing aids, which convert audio to tactile stimulation, to become viable for supporting people with hearing loss. A tactile vocoder strategy for audio-to-tactile conversion, which exploits these advances, has recently shown significant promise. In this strategy, the amplitude envelope is extracted from several audio frequency bands and used to modulate the amplitude of a set of vibro-tactile tones. The vocoder strategy allows good consonant discrimination, but vowel discrimination is poor and the strategy is susceptible to background noise. In the current study, we assessed whether multi-band amplitude envelope expansion can effectively enhance critical vowel features, such as formants, and improve speech extraction from noise. In 32 participants with normal touch perception, tactile-only phoneme discrimination with and without envelope expansion was assessed both in quiet and in background noise. Envelope expansion improved performance in quiet by 10.3% for vowels and by 5.9% for consonants. In noise, envelope expansion improved overall phoneme discrimination by 9.6%, with no difference in benefit between consonants and vowels. The tactile vocoder with envelope expansion can be deployed in real-time on a compact device and could substantially improve clinical outcomes for a new generation of haptic hearing aids.