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Low-frequency normal-mode analysis (NMA) is widely used to predict collective protein motions, but direct Cartesian scaling progressively distorts the alpha-carbon (Cα) backbone as the amplitude increases. We present backbone-angle-compatible as-rigid-as-possible normal-mode analysis (BA-ARAP-NMA), a nonlinear Cα structure-generation method that constrains first-order changes in adjacent and next-nearest Cα distances during anisotropic network model (ANM) calculations and applies a co-rotational as-rigid-as-possible (ARAP) correction during structure generation. Tests on 35 experimentally characterized two-state protein pairs showed that the geometric constraints retained most of the input-to-second-state displacement information. In 34 of 35 pairs, recalculating the constrained modes increased the concentration of the retained transition information in the leading low-frequency modes. Across matched displacements, BA-ARAP-NMA substantially reduced local Cα distance and angle distortions in every pair. At the protein-pair level, BA-ARAP-NMA generally yielded lower root mean square deviation (RMSD) to the paired state and higher transition coverage than linear Cα-ANM while maintaining improved virtual-angle accuracy. BA-ARAP-NMA therefore extends Cα normal-mode structure generation beyond direct linear scaling, retaining transition-related collective information while providing amplitude-ordered structures with frame-level geometric measurements for subsequent rebuilding and refinement. Full article

(This article belongs to the Section Biophysics)

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32 pages, 1024 KB

Open AccessArticle

How AI Empowers the Double-Helix Approach: Examining a Decision Support Framework for Personalised Learning Trajectories in Gifted Education

by Mostafa Aboulnour Salem

Educ. Sci. 2026, 16(9), 1441; https://doi.org/10.3390/educsci16091441 (registering DOI) - 3 Sep 2026

Abstract

Artificial intelligence (AI) is expanding opportunities for adaptive and personalised learning that responds to learners’ abilities, interests, and educational needs. However, formal gifted education programmes may still offer limited opportunities for individualised learning pathways. In this study, gifted education programmes refer to formal provision for identified gifted students that supports differentiated depth, complexity, pace, challenge, enrichment, and talent development. This study proposes an AI-Supported Double-Helix Framework for Personalised Learning in Gifted Education and examines preliminary empirical support for its hypothesised relationships. The framework integrates the Unified Theory of Acceptance and Use of Technology (UTAUT), AI-Supported Adaptive Learning (ASAL), Personalised Learning Experience (PLE), and curriculum integration. Using a quantitative cross-sectional design, PLS-SEM was applied to data from 437 gifted secondary school students. The measurement model demonstrated satisfactory reliability, convergent validity, discriminant validity, and measurement invariance. Performance Expectancy showed the strongest positive association with ASAL, which was positively associated with PLE. PLE was positively associated with perceived Double-Helix Framework Implementation (DHFI), which was subsequently associated with perceived Learning Effectiveness, Talent Development, and Future Readiness. A secondary multi-group analysis indicated that most structural relationships were comparable across gender groups. The study extends UTAUT by linking technology adoption factors with AI-supported adaptation, personalised learning, curriculum integration, and perceived developmental outcomes. It also situates these processes within the context of Education 4.0 through adaptive, data-informed, and future-oriented learning. However, the cross-sectional self-report design provides preliminary empirical support only for statistical relationships among the measured constructs. It does not establish the framework’s educational effectiveness or causal effects on learning. Longitudinal, experimental, behavioural, and performance-based studies are needed to evaluate its educational effectiveness and practical implementation. Full article

(This article belongs to the Special Issue Artificial Intelligence and Personalised Learning)

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25 pages, 2793 KB

Open AccessArticle

Heritage Education and Citizenship in Early Childhood and Primary Education Based on Controversy: Conceptions of Future Teachers

by Sergio Sampedro-Martín, Elisa Arroyo-Mora, Myriam José Martín-Cáceres and José María Cuenca-López

Soc. Sci. 2026, 15(9), 597; https://doi.org/10.3390/socsci15090597 (registering DOI) - 3 Sep 2026

Abstract

This study is based on heritage education as the ideal framework for laying the foundations of a democratic, participatory citizenship that is committed to its community and to social and ecological justice from an early age. Thus, with the aim of improving teacher training, this research seeks to identify the conceptions held by trainee Early Childhood and Primary Education teachers regarding controversial heritage, heritage education and its link to citizenship education. To this end, a quantitative questionnaire was administered to 328 students on the Early Childhood and Primary Education Degree programmes at the University of Huelva (Spain). The data collected were analysed using Excel and JASP, with a system of categories serving as a framework for interpretation. The results indicate that trainee teachers consider it appropriate to address controversial heritage from the earliest educational stages and conceptualise citizenship through a combination of civic responsibility, sustainable development, and ecosocial transformation. Differences between degree programmes were limited to a small number of items and showed small effect sizes; these differences should be interpreted cautiously because prior training in heritage education was unevenly distributed across the two programmes. Full article

(This article belongs to the Special Issue Civic Education in the Digital Age)

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47 pages, 2401 KB

Open AccessArticle

A System for Designing and Comprehensive Structural–Parametric Optimization of Traffic Flow Implemented Within a Machine-Building Enterprise

by Oxana Nurzhanova, Irina Khrustaleva, Viacheslav Shkodyrev, Mikhail Khrustalev, Olga Zharkevich, Adilkhan Bakenov, Karshiga Zakirov and Artur Safaryan

Appl. Sci. 2026, 16(17), 8790; https://doi.org/10.3390/app16178790 - 3 Sep 2026

Abstract

The article is devoted to the current problem of optimizing transport flows implemented within a mechanical engineering enterprise, where the efficiency of logistics operations directly affects the productivity and competitiveness of production. Optimization of the parameters of transport flows implemented within a mechanical engineering enterprise is a crucial component of technical production planning. The paper presents the results of the cargo transportation process analysis, defining many of its structural elements. Using these structural components, a multi-level model for designing and comprehensive optimization of transport flows has been developed. A two-level model of comprehensive structural and parametric optimization of individual transport operations has been proposed. A multi-level system of indicators for assessing the effectiveness of individual transport operations and transport routes has been developed. The article presents the results of testing the proposed model. During the study, a structural analysis of transport flows at a mechanical engineering enterprise was conducted, and a system for their comprehensive optimization was developed. The key element of the system was a modular algorithm for designing transport operations, ensuring flexible planning taking into account actual production constraints. The use of a robust approach and vector optimization criteria made it possible to develop solutions that are robust to uncertainties: a reduction in the target indicator of uniformity of labor intensity distribution by 22.5% and in the indicator of uniformity of cargo weight distribution by 17.33% was achieved. Moreover, the efficiency coefficient of vehicle utilization in terms of carrying capacity increased by 21.67%, and the actual values of the target indicators exceeded the optimization ones by an average of 6.4% and 2.72%, respectively, which confirmed the operability and stability margin of the solutions. The practical significance of the approach lies in the possibility of autonomous use of the system or its integration into the general production management system. The implementation of the subsystem will improve the rhythm of transport operations, reduce downtime, and minimize the risk of disruptions to production cycles. Full article

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37 pages, 1716 KB

Open AccessReview

State of the Art and Recent Advancements in the GIS-Based Approaches for Landfill Site Selection

by Firomsa Bidira, Mateusz Jakubiak and Kamil Maciuk

Sustainability 2026, 18(17), 9080; https://doi.org/10.3390/su18179080 - 3 Sep 2026

Abstract

Proper municipal solid waste (MSW) management is vital for mitigating environmental degradation and protecting public health. Landfill site selection remains a complex spatial decision-making challenge, balancing ecological, social, and economic parameters. This study presents a comprehensive systematic review of 175 peer-reviewed articles published between 2016 and 2026, evaluating the evolution of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) frameworks. The findings indicate that road accessibility (93.7%), surface and groundwater protection (91.4%), slope gradients (84.6%), and settlement buffer zones (78.9%) represent the most critical and universally applied siting criteria. Digital Elevation Models (81.9%) and geological maps (57.3%) serve as foundational geospatial datasets. While the Analytic Hierarchy Process (AHP) remains the dominant weighting technique (63.41%), recent trends show an increasing adoption of hybrid multi-criteria models and optimisation algorithms. Geographically, research output is led by India, Iran, and Turkey, peaking significantly in 2025. Crucially, this review exposes prominent methodological shortcomings, notably a heavy reliance on subjective expert validation (73.8%), whereas quantitative validation, sensitivity analysis, and uncertainty assessment remain critically underutilised. In contrast to earlier reviews, this review offers a thorough and critical synthesis of GIS- and MCDA-based approaches to landfill site selection by carefully evaluating methodological advancements, examining the advantages, disadvantages, and limitations of current approaches, and incorporating statistical trends with a structured methodological framework. This approach highlights important research gaps and offers evidence-based suggestions for creating more transparent, reliable, and sustainable techniques for landfill site selection by selecting, screening, and including relevant articles. To foster sustainable urban planning, future research must prioritise standardised evaluation frameworks, rigorous uncertainty quantification, and the integration of artificial intelligence and machine learning with spatial modelling. Full article

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22 pages, 39802 KB

Open AccessArticle

High-Resolution 3D GPR Imaging of Concealed Surface Masonry in Pompeian Walls: Performance Analysis of Contact and Non-Contact Surveys

by Sara Donzelli, Lorenza Petrini and Maurizio Lualdi

Remote Sens. 2026, 18(17), 3002; https://doi.org/10.3390/rs18173002 - 3 Sep 2026

Abstract

Antenna–surface coupling is a key factor controlling the quality of Ground Penetrating Radar (GPR) data, governing the efficiency of electromagnetic energy transmission into the investigated medium. In cultural heritage applications, however, direct antenna contact is often not feasible due to the fragility of decorated surfaces, requiring non-contact configurations whose impact on high-resolution imaging remains insufficiently quantified. This study investigates the effect of antenna coupling on high-resolution 3D GPR imaging of concealed masonry at Pompeii through electromagnetic simulations and a controlled in situ comparison of contact and non-contact acquisitions on a plastered wall. The experimental campaign was conducted at the House of the Red Walls (VIII, 5, 37) on an opus mixtum masonry, selected as the most geometrically and electromagnetically challenging test case among regular Pompeian construction techniques. A 3 GHz antenna was employed, and three acquisition configurations were analysed: direct contact, and non-contact setups with antenna elevations of 3 cm and 5 cm. The results show that even moderate antenna elevation significantly reduces coupling efficiency at the air–plaster interface, leading to a progressive degradation of imaging performance. While the overall masonry arrangement remains recoverable in all configurations, increasing stand-off distance reduces the detectability of individual units and degrades geometric accuracy, with vertical mortar joints being the most affected elements. These findings demonstrate that, given the combined electromagnetic and geometric characteristics of the construction materials used at Pompeii, near-contact GPR acquisition is required for reliable imaging of masonry arrangement under the investigated conditions, highlighting the critical role of antenna coupling in high-frequency GPR surveys of fragile architectural surfaces. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Earth, Moon and Planetary Exploration (Second Edition))

14 pages, 2531 KB

Open AccessArticle

Frequency-Offset-Estimation-Assisted Transformer Neural Equalization for a 4.6 km Optical-Heterodyne RoF–Wireless OFDM Link

by Zhihang Ou, Wen Zhou, Ye Zhou, Jiali Chen, Xin Lu, Hansong Ma, Sicong Xu, Jie Zhang, Hanyu Zhang, Yubin Zhang and Jianjun Yu

Sensors 2026, 26(17), 5615; https://doi.org/10.3390/s26175615 - 3 Sep 2026

Abstract

To address the issues of subcarrier orthogonality loss and inter-carrier interference (ICI) caused by carrier frequency offset (CFO), this paper proposes and experimentally validates a frequency offset estimation (FOE)-assisted dual-domain Transformer equalizer within an advanced, high-capacity optical-heterodyne radio-over-fiber (RoF)–wireless orthogonal frequency division multiplexing (OFDM) transmission system. To rigorously test the algorithm’s robustness under extreme physical conditions, the experimental platform integrates offline 16-GBaud signal generation, optical I/Q modulation, dual-optical-tone transport over a single-mode-fiber RoF feeder, remote photonic heterodyne frequency conversion based on a uni-traveling-carrier photodiode (UTC-PD), 4.6 km free-space wireless transmission, and 160-GSa/s ultra-high-speed real-time sampling. In this system, the receiver front-end employs an FOE module to pre-compensate for the dominant global CFO-induced phase rotation; subsequently, a low-complexity, compact local-window Transformer is utilized to perform adaptive residual compensation for local data-dependent impairments—such as residual waveform distortion and residual ICI—in both the time and frequency domains (before and after the Fast Fourier Transform, or FFT). This synergistic architecture, combining a physical model-driven approach with a self-attention mechanism, effectively mitigates the adverse impact of global frequency offset on neural network convergence. Experimental results demonstrate that, under conditions of strictly aligned multiply accumulate (MAC) operation complexity, the dual-domain architecture achieves significantly superior performance—in terms of bit error rate (BER), error vector magnitude (EVM), and constellation quality—compared to traditional linear DSP methods and baseline networks such as DNNs, CNNs, and LSTMs. Operating in 16 GBaud QPSK mode with an input optical power of 0 dBm, the system achieves a BER of 1.89 × 10⁻⁴, representing performance improvements of approximately 5.98-fold and 1.92-fold over the standalone Transformer and FOE-assisted DNN schemes, respectively. Full article

(This article belongs to the Special Issue Advances in Optical Fiber Sensors and Fiber Lasers)

41 pages, 18343 KB

Open AccessArticle

Patent Entropy and Spatial Governance: Innovation Dynamics and Institutional Legacy of Clean Energy in China’s Northeastern Old Industrial Base

by Jinguo Meng, Yangyang Wang, Lu Li, Chunyu Liu, Jing Lv and Haiyao Wang

Energies 2026, 19(17), 4178; https://doi.org/10.3390/en19174178 - 3 Sep 2026

Abstract

The transition to clean energy in China’s traditional industrial regions requires technological substitution and fundamentally restructures the relationship among the state, the market, and enterprises. This study examines the influence of industrial legacy and governance structures on clean energy innovation in Northeast China, with a primary focus on Jilin and comparative analyses of Liaoning and Heilongjiang. Using patent data from 2000 to 2025, we employ Shannon entropy, location quotients and logistic modeling to analyze innovation dynamics across temporal, structural, spatial and institutional dimensions. Our analysis reveals three governance regimes reflecting different industrial legacies and identifies a significant mismatch between resources and innovation: resource-rich cities generate a low number of patents relative to their resources. A high invalidation rate suggests maintenance deficits in publicly funded innovation. These patterns imply that governance arrangements may contribute to the structural weaknesses observed in regional innovation systems. We propose a differentiated strategic framework adapted to the industrial legacy of each province, suggesting that old industrial bases require reforms addressing both resource endowment and institutional capacity. Our findings have implications for regional innovation governance in resource-dependent economies undergoing energy transition. Full article

(This article belongs to the Special Issue Economic and Political Determinants of Energy: 3rd Edition)

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21 pages, 4828 KB

Open AccessArticle

“Queer Joy in Sex Ed Is an Act of Repair”: Queering Sex Education in New Brunswick, Canada, Through Cellphilm and Zine Production

by Casey Burkholder, Megan Hill and Melissa Keehn

Youth 2026, 6(3), 124; https://doi.org/10.3390/youth6030124 - 3 Sep 2026

Abstract

Sex Education Queer Youth Need (SEQuYN) is a collaborative, arts-based action research project addressing gaps in sexuality education for 2SLGBTQ+ youth in New Brunswick, Canada. The project responds to the exclusion of diverse sexual identities and experiences from school curricula, a pressing issue across Canada. Launched in Spring 2020 amid COVID-19, SEQuYN involved gathering anonymous responses from 25 youth aged 18–29 about their sexual educational needs and experiences. Youth participants answered questions such as, “What do you wish you had learned in school-based sex education?” and “How can we reflect trans experiences in sex ed?” The research team transformed these responses into cellphilms (cellphone + film production). Later, we co-produced zines during a 2024 workshop, exploring the inclusion of queer joy in sex education. Our analysis, framed by queer joy, explores the process of making and sharing cellphilms and zines to mobilise sexual health information and queer youth perspectives, to foster the sex education queer youth need. However, we also question how likely New Brunswick teachers are to integrate these creative methods into their practice, as the existing curricula and policy context erases the sense of urgency or need. Full article

(This article belongs to the Special Issue Youth Participation, Leadership and Agency for Decolonial Health Equity)

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21 pages, 7162 KB

Open AccessArticle

Mechanical and Thermal Properties of Cu-Alloyed AZ91 Magnesium Alloy Subjected to T4 and T6 Heat Treatments

by Song-Jeng Huang, Cheng-Yen Yang and Sathiyalingam Kannaiyan

J. Compos. Sci. 2026, 10(9), 475; https://doi.org/10.3390/jcs10090475 - 3 Sep 2026

Abstract

In this study, AZ91 magnesium alloy was used as the matrix material, and Cu powder was added at contents of 1 and 2 wt.% as an alloying addition. The AZ91–Cu alloys were fabricated by gravity casting combined with mechanical stirring. Subsequently, T4 solution treatment and T6 artificial aging treatment were conducted to investigate the effects of Cu content and heat treatment conditions on the microstructure, mechanical properties, and thermal conductivity. The results showed that Cu addition promoted the formation of Al₄Cu₉ intermetallic compounds. After T4 treatment, part of the β-Mg₁₇Al₁₂ phase dissolved into the α-Mg matrix, resulting in a more homogeneous microstructure. After T6 treatment, fine second phases re-precipitated, leading to a precipitation strengthening effect. In terms of mechanical properties, the T6-treated AZ91-2Cu alloy exhibited the highest hardness, yield strength, and ultimate tensile strength, reaching 83.73 HV, 117.94 MPa, and 173.82 MPa, respectively. In contrast, the highest thermal conductivity of 64.74 W/(m·K) was obtained for the as-cast AZ91-2Cu alloy. Therefore, although T6 aging maximized the mechanical strength, it did not simultaneously maximize thermal conductivity, demonstrating a trade-off between mechanical strengthening and thermal transport performance. Full article

(This article belongs to the Section Composites Manufacturing and Processing)

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27 pages, 1027 KB

Open AccessArticle

Privacy-Preserving Power System Anomaly Detection via Physics-Guided Sparse Graph Temporal Prediction and Homomorphic Inference

by Yuxuan Li, Jie Hua, Weidong Huang and Ali Anaissi

Technologies 2026, 14(9), 550; https://doi.org/10.3390/technologies14090550 - 3 Sep 2026

Abstract

Energy systems are crucial to residential life and industrial production. During practical operation, these systems may experience various anomalies that disrupt the stability of system operation. Recent years have witnessed remarkable progress in power system anomaly detection. However, existing methods still suffer from two limitations. First, detection algorithms neglect privacy protection, although privacy security is also a critical issue in energy systems. Second, existing studies have difficulty characterizing latent dependencies and topology changes, which limits detection performance. To bridge these gaps, we present a power system anomaly detection method that integrates physics-informed sparse graph temporal modeling with homomorphic encryption, enabling anomalous-event identification and anomalous-bus localization under privacy-preserving conditions. Specifically, we construct a sparse graph using the power-grid topology and normal measurement residuals. We then obtain system-state predictions through polynomial graph temporal prediction and physics-guided affine correction and use anomaly scores to diagnose anomalous conditions. Furthermore, we employ homomorphic encryption to perform ciphertext computation for the affine prediction model without exposing historical measurement data, thereby enabling privacy-preserving remote anomaly detection. We conduct experiments on IEEE bus benchmarks to verify the effectiveness of the proposed method under multiple anomaly scenarios. Full article

(This article belongs to the Section Electrical Technologies)

26 pages, 3220 KB

Open AccessArticle

Development of a Micromobility Riding Evaluation Platform for an Indoor Riding Lane Based on Multi-View Overhead Video Integration

by Kimihiko Iwata, Makoto Shinnishi, Takashi Hikasa, Mutsumi Suganuma and Satoshi Takahashi

Sensors 2026, 26(17), 5614; https://doi.org/10.3390/s26175614 - 3 Sep 2026

Abstract

This study developed a simple and scalable e-scooter riding evaluation platform that reduces on-site implementation effort by relying primarily on image analysis. The platform supports repeated riding trials under consistent conditions in an indoor environment. By combining the areas covered by two drones, the system recorded an entire long and narrow indoor riding lane. A pretrained YOLO model was fine-tuned to construct a rider detection model adapted to the experimental environment. ORB-based image registration and trajectory integration then transformed the riding trajectories obtained from the two cameras into a common coordinate system. Riding speed, riding duration, and the radius of curvature of the two curves were calculated. The results revealed differences among subjects in speed variation, stability across riding trials, and turning characteristics, including stable low-speed riding, sustained high-speed riding, and deceleration before turning. For most subjects, the inter-camera junction discrepancy was within 10 cm, indicating general internal consistency of trajectory integration under the experimental conditions. These results suggest that the proposed system can serve as a video-based platform for quantitatively evaluating observable riding behavior, including riding trajectory and speed characteristics, in an indoor riding lane. Full article

(This article belongs to the Section Sensing and Imaging)

26 pages, 1752 KB

Open AccessArticle

Super-Resolution-Assisted Farmland Boundary Extraction from Medium-Resolution Satellite Image: A Real-ESRGAN and YOLO Segmentation Framework

by Junyao Yu, Hui Yin, Xiaofan Huang, Jiaying Liu, Shangguo Yang, Baisheng Zeng, Jiayu Zhang, Xuanyan Wang and Bo Xiong

Sensors 2026, 26(17), 5613; https://doi.org/10.3390/s26175613 - 3 Sep 2026

Abstract

This study addresses the issue of insufficient spatial resolution in remote sensing images for farmland boundary identification in precision agriculture. It proposes a framework that combines Real-ESRGAN, a GAN-based blind super-resolution algorithm, with YOLO, a real-time instance segmentation framework, to improve farmland boundary extraction accuracy from medium-resolution satellite imagery. Using GF-2 imagery of the agricultural area of Nanxiong City, Guangdong Province, a manually annotated farmland boundary dataset was constructed. The experiments were conducted in this single study area (Nanxiong City); the generalization of the proposed framework to other regions, crops, and sensor platforms requires further validation. The super-resolution preprocessing restored a 1 m resolution from 4 m input while enhancing boundary-related high-frequency details and mitigating aliasing-induced field merging. In farmland boundary recognition, the super-resolved 1 m images achieved mAP@0.5 of 0.755 and mAP@0.5:0.95 of 0.628, approaching the resampled 1 m reference (0.823 and 0.733) and clearly outperforming the resampled 4 m baseline (zero accuracy). The reported mAP values are validation-set best-checkpoint figures and therefore represent an optimistic upper bound under the current spatially autocorrelated split. The framework provides a cost-effective solution for large-scale farmland boundary extraction and precision agricultural management. Full article

(This article belongs to the Special Issue Artificial Intelligence and Intelligent Sensing Applications in Precision Agriculture)

17 pages, 28532 KB

Open AccessArticle

VentrEX: An Anatomically Guided Deep Learning Pipeline for Ventricular Segmentation in Cine Cardiac MRI

by Abla Bedoui, Julieta Anahí Rancati, Ignacio Lugones and Mohammed Cherkaoui

J. Imaging 2026, 12(9), 416; https://doi.org/10.3390/jimaging12090416 - 3 Sep 2026

Abstract

Automated segmentation of the left and right ventricles (LVs and RVs) in cine cardiac MRI (CMR) underpins reliable volumetry and mass estimation. However, papillary muscles and trabeculae (PM/T) introduce clinically meaningful variability and exacerbate cross-dataset domain shift. We present VentrEX, an anatomically guided pipeline. The core segmenter, VentrEX-Seg, is a 3D encoder-decoder with parallel channel-spatial attention and a Transformer bottleneck. Training is performed exclusively on ACDC. A lightweight PM/T module automatically extracts papillary and trabecular burdens and standardizes cavity volumes. External evaluation is zero-shot (no fine-tuning) on Sunnybrook (LV) and MM-WHS MRI (RV). We report Dice, HD95 (mm); for volumetry, we use Bland-Altman analyses (LV and RV volumes). Attention/Grad-CAM visualizations support interpretability. On ACDC, VentrEX achieved higher Dice and lower boundary error than U-Net, nnU-Net, CBAM, and VentrEX-Seg. Zero-shot performance was preserved externally (e.g., Sunnybrook LV Dice 0.9053, HD95 4.95 mm; MM-WHS RV Dice 0.9236, HD95 6.61 mm). Patient-level Bland–Altman analyses characterized LV and RV volumetric agreement. Qualitative overlays and 3D reconstructions showed fewer PM/T “leaks” and anatomically plausible borders across ED/ES. Single-source training with dual zero-shot external tests demonstrates robustness under domain shift. The combination of parallel attention and a Transformer bottleneck enables accurate, transparent cine-CMR segmentation across datasets. Full article

(This article belongs to the Special Issue Advancing Magnetic Resonance Imaging: Emerging Technologies, Computation, and Clinical Applications)

26 pages, 6563 KB

Open AccessArticle

INDI: A Low-Cost LLM-Enabled Multimodal Campus Guide Robot

by José Varela-Aldás, Christian P. Carvajal, Josue Cadena and Carolina Del-Valle-Soto

Computers 2026, 15(9), 582; https://doi.org/10.3390/computers15090582 - 3 Sep 2026

Abstract

University technology campuses contain specialized laboratories, academic programs, and services that can be difficult for first-time visitors to identify. This paper presents INDI, a custom mobile campus guide robot that combines spoken interaction, synthesized speech, touchscreen feedback, animated facial states, head motion, and predefined mobile guidance behaviors. The platform retains the modular mechanical concept of an earlier prototype while replacing its Raspberry Pi and open-loop remote-control architecture with an NVIDIA Jetson Nano, an Arduino Uno motor-control bridge, ROS 1 nodes, encoder feedback, and dual PID speed loops. The robot weighs 2.37 kg, measures

39 \times 27 \times 69.5

cm, reaches a software-limited maximum speed of 0.4 m/s, and provides 27 min of continuous operation in the reported tests. Ten repetitions of each motion test produced mean displacements of 1.058 m and 2.182 m for 1 m and 2 m commands, respectively, and mean rotations of 89.2° and 180.3° for 90° and 180° commands. Voice trials achieved 90% correct interaction in a quiet environment and 70% under nearby conversational noise. In an exploratory user study with 13 participants, 16 of 20 assigned tasks were completed and the mean overall rating was 4.31/5. The results demonstrate the feasibility of an integrated, modular, physically embodied information service, while also revealing accumulated linear-motion error, sensitivity to ambient speech, limited battery duration, and the need for grounded institutional knowledge and autonomous localization. These findings are presented as preliminary evidence of technical and interaction feasibility rather than as confirmatory evidence of usability or campus-scale autonomous navigation. Full article

(This article belongs to the Special Issue Advanced Human–Robot Interaction 2026)

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