Limited Capabilities Research Articles

Technological and market sensing capabilities as drivers of SME participation in public procurement: an empirical test of the moderating role of financial capability

PurposePublic procurement presents substantial market opportunities for small- and medium-sized enterprises (SMEs), which can contribute to their economic growth. However, limited dynamic capabilities often pose challenges for SMEs to participate effectively in public procurement markets. Drawing on dynamic capability (DC) theory, this study explores whether financial capability (FNCP) influences SMEs’ ability to leverage their technological capability (TECC) and marketing sensing capability (MKSC) and actively engage in public procurement.Design/methodology/approachData for this study were collected from 248 SME managers in the Ilala District, Tanzania, using a cross-sectional questionnaire survey and stratified random sampling technique. The proposed hypotheses were tested empirically through confirmatory factor analysis (CFA) and the Hayes PROCESS macro.FindingsTECC and MKSC demonstrated significant positive associations with SME participation in public procurement (SMPP). Moreover, the interaction between TECC and FNCP as well as the interaction between MKSC and FNCP demonstrate a significant positive effect, suggesting that FNCP strengthens the impact of TECC and MKSC on SMPP.Research limitations/implicationsThe scope of this study was limited to SMEs in the Ilala District of Tanzania, hence affecting the generalizability of the findings to other contexts. More importantly, the study findings enrich the understanding of DC theory, signifying that the integration and reconfiguration of MKSC, TECC and FNCP add significant value to SMPP.Practical implicationsThe findings suggest that policymakers, support institutions and SME managers should focus on enhancing SMEs' MKSC and TECC to improve their participation in public procurement. In addition, improving SMEs' access to financial resources can further strengthen these effects, enabling more inclusive participation in public procurement.Originality/valueThe study contributes to the literature on SMPP by highlighting the critical roles of MKSC and TECC. It also underscores the importance of FNCP as a moderator in these relationships, which has not been addressed in the existing literature. By integrating these factors, the study offers a comprehensive framework for understanding the dynamics that influence SMPP from financial, technological and marketing perspectives, particularly in developing economies like Tanzania.

Injectable Alginate-Based Hydrogels Encapsulating Engineered Endothelial Extracellular Vesicles for the Treatment of Critical Limb Ischemia.

Critical limb ischemia (CLI) is a peripheral arterial disease resulting from chronic inflammation of vascular systems. Recent studies have shown that inhibiting macrophage inflammation has the potential to treat CLI, and extracellular vesicles (EVs) from endothelial cells can inhibit macrophage activation. However, the limited cell-targeting capabilities and rapid clearance of EVs from the injection site limit the in vivo application of the EVs. Here, we modified endothelial EVs with platelet membranes (pM/EVs) to boost the inhibition effects on macrophage inflammation and developed an injectable alginate-based collagen composite (ACC) hydrogel for localized delivery of pM/EVs (pM/EVs@ACC) for CLI treatment. We found that pM/EVs can effectively inhibit macrophage inflammation in vitro. Furthermore, pM/EVs@ACC treatment significantly promotes the recovery of limb functions, restoring the feet' blood supply and relieving inflammation. Our findings provide compelling evidence that the pM/EVs@ACC injectable system mediating delivery of pM/EVs is a promising strategy for CLI treatment.

Enhancing surgical navigation: a robust hand-eye calibration method for the Microsoft HoloLens 2.

Optical-see-through head-mounted displays have the ability to seamlessly integrate virtual content with the real world through a transparent lens and an optical combiner. Although their potential for use in surgical settings has been explored, their clinical translation is sparse in the current literature, largely due to their limited tracking capabilities and the need for manual alignment of virtual representations of objects with their real-world counterparts. We propose a simple and robust hand-eye calibration process for the depth camera of the Microsoft HoloLens2, utilizing a tracked surgical stylus fitted with infrared reflective spheres as the calibration tool. Using a Monte Carlo simulation and a paired-fiducial registration algorithm, we show that a calibration accuracy of 1.65mm can be achieved with as little as 6 fiducial points. We also present heuristics for optimizing the accuracy of the calibration. The ability to use our calibration method in a clinical setting is validated through a user study, with users achieving a mean calibration accuracy of 1.67mm in an average time of 42s. This work enables real-time hand-eye calibration for the Microsoft HoloLens2, without any need for a manual alignment process. Using this framework, existing surgical navigation systems employing optical or electromagnetic tracking can easily be incorporated into an augmented reality environment with a high degree of accuracy.

Sim-to-real transfer of adaptive control parameters for AUV stabilisation under current disturbance

Learning-based adaptive control methods hold the potential to empower autonomous agents in mitigating the impact of process variations with minimal human intervention. However, their application to autonomous underwater vehicles (AUVs) has been constrained by two main challenges: (1) the presence of unknown dynamics in the form of sea current disturbances, which cannot be modelled or measured due to limited sensor capability, particularly on smaller low-cost AUVs, and (2) the nonlinearity of AUV tasks, where the controller response at certain operating points must be excessively conservative to meet specifications at other points. Deep Reinforcement Learning (DRL) offers a solution to these challenges by training versatile neural network policies. Nevertheless, the application of DRL algorithms to AUVs has been predominantly limited to simulated environments due to their inherent high sample complexity and the distribution shift problem. This paper introduces a novel approach by combining the Maximum Entropy Deep Reinforcement Learning framework with a classic model-based control architecture to formulate an adaptive controller. In this framework, we propose a Sim-to-Real transfer strategy, incorporating a bio-inspired experience replay mechanism, an enhanced domain randomisation technique, and an evaluation protocol executed on a physical platform. Our experimental assessments demonstrate the effectiveness of this method in learning proficient policies from suboptimal simulated models of the AUV. When transferred to a real-world vehicle, the approach exhibits a control performance three times higher compared to its model-based nonadaptive but optimal counterpart.

Solving linear systems on quantum hardware with hybrid HHL++

The limited capabilities of current quantum hardware significantly constrain the scale of experimental demonstrations of most quantum algorithmic primitives. This makes it challenging to perform benchmarking of the current hardware using useful quantum algorithms, i.e., application-oriented benchmarking. In particular, the Harrow-Hassidim-Lloyd (HHL) algorithm is a critical quantum linear algebra primitive, but the majority of the components of HHL are far out of the reach of noisy intermediate-scale quantum devices, which has led to the proposal of hybrid classical-quantum variants. The goal of this work is to further bridge the gap between proposed near-term friendly implementations of HHL and the kinds of quantum circuits that can be executed on noisy hardware. Our proposal adds to the existing literature of hybrid quantum algorithms for linear algebra that are more compatible with the current scale of quantum devices. Specifically, we propose two modifications to the Hybrid HHL algorithm proposed by Lee et al., leading to our algorithm Hybrid HHL++\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {HHL}^{++}$$\\end{document}: (1) propose a novel algorithm for determining a scaling factor for the linear system matrix that maximizes the utility of the amount of ancillary qubits allocated to the phase estimation component of HHL, and (2) introduce a heuristic for compressing the HHL circuit. We demonstrate the efficacy of our work by running our modified Hybrid HHL on Quantinuum System Model H-series trapped-ion quantum computers to solve different problem instances of small-scale portfolio optimization problems, leading to the largest experimental demonstrations of HHL for an application to date.

Research on Space Operation Control of Air Float Satellite Simulator Based on Constraints Aware Particle Filtering-Nonlinear Model Predictive Control

This paper addresses the challenges of close proximity operations, such as rendezvous, docking, and fly-around maneuvers for micro/nano satellites, which require high control precision under the low power and limited computational capabilities of spacecraft. Firstly, a three-degree-of-freedom air float simulator platform is designed for ground-based experiments. Subsequently, model predictive controllers based on constraints aware of particle filtering (CAPF-NMPC) are developed for executing operations such as approach, fly-around, and docking maneuvers. The results validate the effectiveness of the experimental system, demonstrating position control accuracy less than 0.03 m and attitude control accuracy less than 3°, maintaining lower computational resource consumption. This study offers a practical solution for the onboard deployment of optimized control algorithms, highlighting significant value for further engineering applications.

ASAP: A lightweight authenticated secure association protocol for IEEE 802.15.6 based medical BAN

Medical Body Area Networks (MBANs), a specialized subset of Wireless Body Area Networks (WBANs), are crucial for enabling medical data collection, processing, and transmission. The IEEE 802.15.6 standard governs these networks but falls short in practical MBAN scenarios. This paper introduces ASAP, a Lightweight Authenticated Secure Association Protocol integrated with IEEE 802.15.6. ASAP prioritizes patient privacy with randomized node ID generation and temporary shared keys, preventing node tracking and privacy violations. It optimizes network performance by consolidating Master Keys (MK), Pairwise Temporal Keys (PTK), and Group Temporal Keys (GTK) creation into a unified process, ensuring the efficiency of the standard four-message association protocol. ASAP enhances security by eliminating the need for pre-shared keys, reducing the attack surface, and improving forward secrecy. The protocol achieves mutual authentication without pre-shared keys or passwords and supports advanced cryptographic algorithms on nodes with limited processing capabilities. Additionally, it imposes connection initiation restrictions, requiring valid certificates for nodes, thereby addressing gaps in IEEE 802.15.6. Formal verification using Verifpal confirms ASAP's resilience against various attacks. Implementation results show ASAP's superiority over standard IEEE 802.15.6 protocols, establishing it as a robust solution for securing MBAN communications in medical environments.

Spectrum Sensing Method Based on STFT-RADN in Cognitive Radio Networks.

To address the common issues in traditional convolutional neural network (CNN)-based spectrum sensing algorithms in cognitive radio networks (CRNs), including inadequate signal feature representation, inefficient utilization of feature map information, and limited feature extraction capabilities due to shallow network structures, this paper proposes a spectrum sensing algorithm based on a short-time Fourier transform (STFT) and residual attention dense network (RADN). Specifically, the RADN model improves the basic residual block and introduces the convolutional block attention module (CBAM), combining residual connections and dense connections to form a powerful deep feature extraction structure known as residual in dense (RID). This significantly enhances the network's feature extraction capabilities. By performing STFT on the received signals and normalizing them, the signals are converted into time-frequency spectrograms as network inputs, better capturing signal features. The RADN is trained to extract abstract features from the time-frequency images, and the trained RADN serves as the final classifier for spectrum sensing. Experimental results demonstrate that the STFT-RADN spectrum sensing method significantly improves performance under low signal-to-noise ratio (SNR) conditions compared to traditional deep-learning-based methods. This method not only adapts to various modulation schemes but also exhibits high detection probability and strong robustness.

Merdeka Belajar in Islamic Religious Education Learning (Case Study of Implementing the Merdeka Belajar Curriculum at SMP Negeri 1 Ngantru Tulungagung)

This research analyzes “Merdeka Belajar” in Islamic religious education learning in implementing “Kurikulum Merdeka Belajar” at SMP Negeri 1 Ngantru Tulungagung. The research method used is a qualitative research approach. Data collection techniques are used to explore data by conducting interviews, observations, interviews, and documentation. The results of his research were that planning the “Kurikulum Merdeka” for Islamic Education Subjects at SMP Negeri 1 Ngantru Tulungangung was carried out using assessment planning in stages, including cognitive and non-cognitive diagnostic assessments, process assessments in the form of trigger questions and summative assessments. Implementation of the “Kurikulum Merdeka” for Islamic Education Subjects at SMP Negeri 1 Ngantru Tulungangung is done by implementing assessments before and after learning. In practice, if educators want to know students' cognitive abilities, educators present questions that are related to the learning material. Implementing the Merdeka Belajar curriculum starts with preliminary, core, and closing activities. Obstacles to implementing the “Kurikulum Merdeka” include limited material in student books, limited facilities and infrastructure, material in the Merdeka curriculum not yet fully mastered by teachers, implementation of an inclusive school system, limited technology capabilities that support the implementation of the “Kurikulum Merdeka.”

Managerial Blurring of Employee Goals

Many researchers and practitioners believe that the lack of specificity of employee goals is detrimental to the effectiveness of performance management in firms. Using an inductive grounded theory approach, this study identifies two managerial factors that could impact negatively goal specificity in real-life corporate environments. First, ‘goal blurring’ is a counterintuitive purposeful managerial behavior that results in blurred employee goals being set during annual reviews. Second, ‘specifying capability’ incorporates factors that can result in a lower level of specificity of employee goals with no purposeful intent. The findings reveal two main reasons managers set less specific goals: (1) limited capability and (2) choice to purposefully blur employee goals to maintain flexibility in evaluating their subordinates. While non-purposeful blurring occurs because of circumstances that make setting specific goals difficult and limit the capabilities of managers to do so, purposeful blurring of goals occurs with the main aim of maintaining flexibility in evaluating employees. As a result, blurred goals are set for employees despite managers’ awareness of the benefits of setting specific goals, which is supported by extensive research and management practice. Our work reveals the purposeful managerial blurring of employee goals, a phenomenon that has a significant impact on firm performance, though it has not been previously identified as such, or explained.

Robust speech command recognition in challenging industrial environments

Speech is among the main forms of communication between humans and robots in industrial settings, being the most natural way for a human worker to issue commands. However, the presence of pervasive and loud environmental noise poses significant challenges to the adoption of Speech-Command Recognition systems onboard manufacturing robots; indeed, they are expected to perform in real time on hardware with limited computational capabilities and also to be robust and accurate in such complex environments. In this paper, we propose an innovative system based on an End-to-End architecture with a Conformer backbone. Our system is specifically designed to achieve high accuracy in noisy industrial environments and to guarantee a minimal computational burden to meet stringent real-time requirements while running on computing devices that are embedded in robots. In order to increase the generalization capability of the system, the training procedure is driven by a Curriculum Learning strategy combined with dynamic data augmentation techniques, that progressively increase the complexity of input samples by increasing the noise during the training phase. We have conducted extensive experimentation to assess the effectiveness of our system, using a dataset composed of more than 50,000 samples, of which about 2,000 have been acquired during the daily operations of a Stellantis Italian factory. The results confirm the suitability of the proposed approach to be adopted in a real industrial environment; indeed, it is able to achieve, on both English and Italian commands, an accuracy higher than 90%, maintaining a compact model size (the network is 1.81 MB) and running in real-time on an industrial embedded device (namely 41ms over an NVIDIA Xavier NX).

Present and future distribution of the deep-sea habitat-forming sponge - Pheronema carpenteri ( ) in a changing ocean

Sponges play vital roles in the ecosystem function of the deep sea. Some species, such as the birds' nest sponge Pheronema carpenteri, can form highly structured and dense habitats (i.e., aggregations), which contribute to the increase of nearby biodiversity. Climate change is expected to have a pronounced impact on the deep sea, particularly on Vulnerable Marine Ecosystems such as those formed by the glass sponge Pheronema carpenteri. These ecosystems are especially vulnerable to climate change and other anthropogenic activities since they are formed by sensitive species with slow growth rates and limited dispersal capability, which can hinder their adaptive capability and recovery after disturbance. The impact that climate change will have on Pheronema carpenteri remains unclear, although it is expected to influence the species' available suitable habitat and distribution range. The aim of this study was to predict the distribution of the glass sponge Pheronema carpenteri both for present day and under several future climate scenarios in the North Atlantic. An ensemble modelling approach was employed, combining Maximum Entropy, Generalized Additive Models and Random Forest techniques. Changes in available suitable habitat were projected to present day and to three future climatic scenarios (RCP 2.6, RCP 4.5 and RCP 8.5). Depth, temperature and dissolved oxygen were identified as the key predictor variables of habitat suitability, which patterns suggest a strong influence of the Mediterranean Outflow Water in shaping the present day distribution of the species, particularly in the eastern North Atlantic. Our results indicate a potential expansion of available suitable habitat in the northernmost region of the study area, with a contraction at lower latitudes, more prominent in the Portuguese archipelago of the Azores. Under the worst-case scenario (RCP 8.5), the area of suitable habitat will likely double compared to present, occupying approximately 6% of the total study area. The management and conservation of areas where Pheronema aggregations can occur should be articulated between different countries, particularly in the Northeast Atlantic since, cumulatively, most of Pheronema's climate refugia occurs within their EEZs. Nonetheless, a significant proportion of the species' climate refugia is located in areas within the High Seas (i.e., Rockall plateau).

AI-Assisted Self-Powered Vehicle-Road Integrated Electronics for Intelligent Transportation Collaborative Perception.

Collaborative perception between a vehicle and the road has the potential to enhance the limited perception capability of autonomous driving technologies. With this background, self-powered vehicle-road integrated electronics (SVRIE) with a multilevel fractal structure is designed to play a dual role, including a SVRIE device integrated into vehicle tires and a SVRIE array embedded into a road surface. The pressure sensing capability and anti-crosstalk performance of the SVRIE array are characterized separately to validate the feasibility of applying the SVRIE in a cooperative vehicle-infrastructure system. It is demonstrated that the SVRIE based on the multi-layered fractal structure exhibits maximum performance in collaborative sensing and interaction between vehicles and road information, such as vehicle motion, road surface condition, and tire life cycle health monitoring. Traditional data analysis methods are often of questionable accuracy. Therefore, a convolutional neural network is used to classify the vehicle and road conditions with accuracy of at least 88.3%. The transfer learning model is constructed to enhance the road surface identification capabilities with 100% accuracy. The accuracies of the vehicle tire motion recognition and tire health monitoring are 97% and 99%, respectively. This work provides new ideas for collaborative perception between vehicles and roadsides.

Quantitative Analysis of Tooth-Whitening Efficiency Using Natural Bleaching Agents: An In Vitro Spectrophotometric Evaluation

Abstract Aim: While conventional whitening agents provide effective color change, concerns linger regarding enamel health. This study explored the potential of natural alternatives for coffee stain removal on extracted teeth, aiming to identify safe and efficacious options for patients seeking noninvasive bleaching solutions. Materials and Methods: This study employed 50 extracted permanent anterior teeth divided into five groups (negative control, positive control, strawberry, pineapple, and honey). Standardized coffee staining was applied to all teeth followed by 3 weeks of twice-daily treatment with their respective solutions. Shade changes were measured objectively using a Konica Minolta Spectrophotometer CM-5 at baseline and after 1, 7, 14, and 21 days post-treatment. Statistical analysis was conducted with Kruskal–Wallis and Tukey post hoc tests (IBM® SPSS®). Results: Strawberry juice exhibited equivalent ΔE (color change) compared with 35% hydrogen peroxide (P > 0.99), indicating similar stain removal efficacy. Both pineapple juice and honey solution displayed significantly lower ΔE values compared with the control and strawberry juice groups (P = 0.002 and <0.001, respectively), suggesting limited bleaching capability. Conclusion: This study demonstrates the potential of strawberry juice as a viable alternative to conventional bleaching agents, offering comparable stain removal efficacy on coffee-stained teeth and further highlighting the need for further research on their effectiveness in clinical settings. Meanwhile, pineapple juice and honey solution exhibited limited bleaching potential. These findings contribute to the expansion of safe and potentially noninvasive teeth-whitening options.

MRCNN: Multi-input residual convolution neural network for three-dimensional reconstruction of bubble flows from light field images

Accurate measurement of bubbles in air-water two-phase flows holds immense significance in the realm of thermal hydraulics assessments within nuclear reactors. Nevertheless, conventional bubble measurement techniques grapple with challenges encompassing system intricacy, limited real-time capabilities, and inaccuracies stemming from their inherent two-dimensional (2-D) nature. In response, we pioneered an innovative three-dimensional (3-D) analysis approach that leverages light field (LF) imaging diagnosis and deep learning algorithms. Unlike traditional 2-D reconstruction methods, our approach enables direct computation of bubble depth from LF images using digital refocusing technology. Following calibration, a seamless transformation is established between the camera coordinate system and the real-world coordinate system using a sharpness evaluation algorithm. This calibration process ensures precise alignment of refocused images with real-world positions. Subsequently, fully automated and highly accurate computations of bubble depth are realized from input images via the incorporation of a multi-input residual convolution neural network (MRCNN). The limitations of traditional two-dimensional imaging techniques are effectively addressed by this methodology, resulting in a reduction in measurement errors. The study confirms the feasibility of employing LF imaging diagnosis and deep learning algorithms for bubble measurements in an air-water two-phase flow, offering a significant improvement over traditional methods.

Fall-related gait pattern recognition based on surface electromyography using a hybrid neural network with transfer learning

Gait analysis across different individuals presents significant challenges due to individual variability. In this study, we developed a gait analysis system to enable continuous decoding of gait patterns based on surface electromyography (EMG). Our objective was to address the long-term challenges associated with neural networks, particularly their limited generalization capability and the heavy training burden on new subjects in gait pattern recognition. Fifteen healthy subjects were recruited to imitate two fall-related gait patterns and collect myoelectric signals from eight lower limb muscles. We proposed a transfer learning-based framework and investigated eight adaptation schemes using varying quantities of target training samples. In the cross-subject evaluation, our framework demonstrated robust generalization capability and reduced training burden. Even with a limited number of target training samples, the framework outperformed state-of-the-art machine learning models and discriminative neural networks that have previously shown excellent recognition performance in subject-specific evaluation. This study highlights the promising potential of using transfer learning in conjunction with neural networks for gait pattern recognition across different individuals. It also provides valuable insights into the diagnosis, monitoring, and treatment of various medical conditions using surface EMG-based gait analysis systems.

A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks

In industrial sectors such as shipping, chemical processing, and energy production, centrifugal pumps often experience failures due to harsh operational environments, making it challenging to accurately identify fault types. Traditional fault diagnosis methods, which heavily rely on existing fault datasets, suffer from limited generalization capabilities, especially when substantial labeled and specific fault sample data are lacking. This paper proposes a novel fault diagnosis approach for centrifugal pumps, utilizing a digital twin (DT) framework powered by a graph transfer learning model to address this issue. Firstly, a high-fidelity DT model is constructed to simulate the flow-induced vibration response of the impeller under different health states to enrich the type and scale of the dataset. Secondly, a graph convolutional neural networks (GCN) model is constructed to learn the knowledge of simulation data, and the Wasserstein distance between simulation data and measured data is optimized for adversarial domain adaptation, thereby achieving efficient cross-domain fault diagnosis. Experimental results demonstrate that the proposed algorithm delivers effective fault diagnosis with minimal prior knowledge and outperforms comparable models. Furthermore, the DT system developed using the proposed model enhances the operational reliability of centrifugal pumps, reduces maintenance costs, and presents an innovative application of DT technology in industrial fault diagnosis.

Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol. A database of 429 patients subjected to lumbar MRI, radiographs and dual-energy x-ray absorptiometry within 6 months was created from an institutional database. Several machine learning models were trained and tested (373 patients for training, 86 for testing) with the following objectives: (1) direct estimation of the vertebral bone mineral density; (2) classification of T-score lower than - 1 or (3) lower than - 2.5. The models took as inputs either the images or radiomics features derived from them, alone or in combination with metadata (age, sex, body size, vertebral level, parameters of the imaging protocol). The best-performing models achieved mean absolute errors of 0.15-0.16g/cm2 for the direct estimation of bone mineral density, and areas under the receiver operating characteristic curve of 0.82 (MRIs) - 0.80 (radiographs) for the classification of T-scores lower than - 1, and 0.80 (MRIs) - 0.65 (radiographs) for T-scores lower than - 2.5. The models showed good discriminative performances in detecting cases of low bone mineral density, and more limited capabilities for the direct estimation of its value. Being based on routine imaging and readily available data, such models are promising tools to retrospectively analyse existing datasets as well as for the opportunistic investigation of bone disorders.

Robust Path Planning via Learning from Demonstrations for Robotic Catheters in Deformable Environments.

Navigation through tortuous and deformable vessels using catheters with limited steering capability underscores the need for reliable path planning. State-of-the-art path planners do not fully account for the deformable nature of the environment. This work proposes a robust path planner via a learning from demonstrations method, named Curriculum Generative Adversarial Imitation Learning (C-GAIL). This path planning framework takes into account the interaction between steerable catheters and vessel walls and the deformable property of vessels. In-silico comparative experiments show that the proposed network achieves a 38% higher success rate in static environments and 17% higher in dynamic environments compared to a state-of-the-art approach based on GAIL. In-vitro validation experiments indicate that the path generated by the proposed C-GAIL path planner achieves a targeting error of 1.26 ±0.55mm and a tracking error of 5.18 ±3.48mm. These results represent improvements of 41% and 40% over the conventional centerline-following technique for targeting error and tracking error, respectively. The proposed C-GAIL path planner outperforms the state-of-the-art GAIL approach. The in-vitro validation experiments demonstrate that the path generated by the proposed C-GAIL path planner aligns better with the actual steering capability of the pneumatic artificial muscle-driven catheter utilized in this study. Therefore, the proposed approach can provide enhanced support to the user in navigating the catheter towards the target with greater accuracy, effectively meeting clinical accuracy requirements. The proposed path planning framework exhibits superior performance in managing uncertainty associated with vessel deformation, thereby resulting in lower tracking errors.

Utilizing multiple inputs autoregressive models for bearing remaining useful life prediction

Accurate prediction of the remaining useful life (RUL) of rolling bearings is crucial in industrial production, yet existing models often struggle with limited generalization capabilities due to their inability to fully process all vibration signal patterns. We introduce a novel multi-input autoregressive model to address this challenge in RUL prediction for bearings. Our approach uniquely integrates vibration signals with previously predicted RUL values, employing feature fusion to output current window RUL values. Through autoregressive iterations, the model attains a global receptive field, effectively overcoming the limitations in generalization. Furthermore, we innovatively incorporate a segmentation method and multiple training iterations to mitigate error accumulation in autoregressive models. Empirical evaluation on the PMH2012 dataset demonstrates that our model, compared to other backbone networks using similar autoregressive approaches, achieves significantly lower root mean square error (RMSE) and Score. Notably, it outperforms traditional autoregressive models that use label values as inputs and non-autoregressive networks, showing superior generalization abilities with a marked lead in RMSE and Score metrics.