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This study presents an integrated design methodology for earthquake demolition rescue robots by combining UXMs, Kano, and QFD to improve design rationality and performance in extreme rescue scenarios. It addresses key gaps in existing approaches, particularly the lack of systematic experiential data acquisition, quantitative requirement analysis, and effective design translation. UXMs are applied to reconstruct critical task scenarios and identify high-load nodes and user experience variations. The Kano model is used to prioritise and classify user requirements, which are then translated into engineering characteristics through QFD. Based on this framework, a conceptual robot design is developed using the FBS model and evaluated through process-level simulation and usability assessment. The results demonstrate that the proposed method enables structured requirement transformation and supports traceable design decisions. Simulation indicates the consistency of task workflows and coordination among functional modules at the process level. A System Usability Scale score of 80.22 indicates a relatively high level of perceived usability at the conceptual evaluation stage. The proposed methodology provides a structured and traceable conceptual design framework for earthquake rescue robots. While the current validation is based on conceptual-level evaluation, the methodology offers a traceable design pathway that may be extended to other high-risk emergency equipment with further empirical testing. Full article

(This article belongs to the Section Mechanical Engineering)

23 pages, 685 KB

Open AccessReview

Hydrogen Production from Biomass Through Conversion Pathways and Energy Efficiency Analysis—A Review

by Nevena M. Mileva, Penka Zlateva, Angel Terziev and Krastin Yordanov

Sustainability 2026, 18(9), 4470; https://doi.org/10.3390/su18094470 - 1 May 2026

Abstract

Hydrogen is increasingly seen as a viable energy carrier in the transition to low-carbon energy systems, mainly because of its high gravimetric energy density and the absence of carbon emissions at the point of use. In this context, producing hydrogen from biomass represents a practical and sustainable option, as it allows the use of renewable and waste resources while supporting circular economy principles. This work examines the main pathways for hydrogen production from biomass, considering both thermochemical and biochemical routes, with a focus on their energy performance and practical limitations. The analysis shows that thermochemical processes, particularly gasification, remain the most developed and scalable solutions for converting solid biomass into hydrogen-rich gas, although their performance depends strongly on feedstock properties, reactor design, and operating conditions. By comparison, biochemical processes such as dark fermentation and photofermentation are more suitable for wet biomass but are limited by lower hydrogen yields and issues related to process stability. From a thermal engineering standpoint, system performance is influenced by heat transfer constraints, the energy demand of endothermic reactions, and the efficiency of gas cleaning, while parameters such as temperature, steam-to-biomass ratio, and equivalence ratio play a key role in optimization. Advanced approaches, including catalytic and sorption-enhanced gasification, show potential for improving performance. Overall, efficient hydrogen production requires a system-level approach, as no single technology can be considered universally optimal. Full article

(This article belongs to the Special Issue Sustainable Development of the Renewable Energy and Hydrogen Technologies)

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15 pages, 856 KB

Open AccessArticle

Task-Aware Preprocessing Selection for Underwater Sparse 3D Reconstruction via Lightweight Machine Learning Under Grouped Evaluation Protocol

by Ning Hu and Senhao Cao

Electronics 2026, 15(9), 1923; https://doi.org/10.3390/electronics15091923 - 1 May 2026

Abstract

Underwater image enhancement has been widely studied to improve visual quality; however, its impact on downstream geometric tasks such as sparse 3D reconstruction remains insufficiently understood. In particular, visually enhanced images do not necessarily lead to improved feature matching or reconstruction performance. This work addresses the problem of selecting appropriate preprocessing strategies for underwater Structure-from-Motion (SfM) pipelines from a task-oriented perspective. We propose a lightweight machine-learning-based preprocessing selector that predicts reconstruction performance from image statistics and recommends suitable enhancement strategies for each input sequence. To ensure reliable evaluation, we introduce a grouped leave-one-parent-sequence-out protocol that avoids overlap-induced bias common in clip-wise splitting. Experiments are conducted on challenging underwater datasets derived from the Real-world Underwater Image Enhancement (RUIE) benchmark, with the primary comparison variable defined as the number of reconstructed sparse 3D points. Supporting geometric variables, including the number of registered images, mean track length, and mean reprojection error, are recorded for interpretation. Results show that preprocessing choices significantly affect reconstruction outcomes and that the optimal strategy is scene-dependent. The proposed selector consistently improved over raw input on the evaluated grouped subset and remained competitive with a strong fixed preprocessing baseline. The grouped leave-one-parent-sequence-out protocol is intended to reduce overlap-induced bias common in clip-wise splitting and to provide a more conservative estimate of generalization. This work highlights the importance of task-aware preprocessing and reliable evaluation in underwater vision systems, offering practical insights for deploying enhancement strategies in real-world 3D reconstruction pipelines. Full article

(This article belongs to the Special Issue Advances of Artificial Intelligence and Vision Applications: Third Edition)

21 pages, 799 KB

Open AccessArticle

Optimizing EMG-Based Transtibial Movement Classification for Real-Time Prosthetic Control: A Feature Engineering and Multi-Window Voting Study

by Carlos Gabriel Mireles-Preciado, Diana Carolina Toledo-Pérez, Roberto Augusto Gómez-Loenzo, Marcos Aviles and Juvenal Rodríguez-Reséndiz

Algorithms 2026, 19(5), 351; https://doi.org/10.3390/a19050351 - 1 May 2026

Abstract

Objective: This study investigates the optimization of surface EMG (sEMG) classification for seven transtibial movements using short analysis windows (64 ms) suitable for real-time control of below-knee prostheses. Methods: We systematically evaluated feature engineering strategies, dimensionality reduction techniques, and classification approaches using linear Support Vector Machines on four-channel sEMG data from the transtibial region. We compared amplitude-based versus derivative-based time-domain features, integrated frequency-domain features, and implemented multi-window majority voting with 50% overlap. Results: Evaluated across nine subjects (four male, five female), the optimized system achieves a population-level accuracy of

70.16 % \pm 7.09 %

with multi-window majority voting (per-subject range: 60.71–78.57%), with voting consistently improving accuracy over single-window classification by

+ 7.06

% on average. We demonstrate that PCA provides zero benefit for linear classifiers when all features are retained. Documented failed approaches include adaptive windowing and spectral entropy features. Conclusion: Careful feature engineering combining time-domain (MAV2, RMS, VAR, MAX, LOG, IEMG) and frequency-domain features (MPF, MF, band powers) with multi-window voting substantially recovers accuracy losses from aggressive window reduction while maintaining sub-100 ms latency suitable for prosthetic control. This work provides a validated methodology across multiple subjects for optimizing EMG classification latency–accuracy trade-offs, demonstrates that PCA is unnecessary for linear classifiers with well-engineered features, and documents negative results to guide future prosthetic control research. Full article

(This article belongs to the Special Issue Machine Learning Techniques for Brain Data Analysis Using EEG, EMG or Image Data)

18 pages, 638 KB

Open AccessArticle

A Comprehensive Evaluation Method for the Medium- and Low-Speed Maglev Trains Suspension System Based on Gaussian Mixture Model

by Mengcheng Li, Xingyu Zhou and Xiaolong Li

Actuators 2026, 15(5), 255; https://doi.org/10.3390/act15050255 - 1 May 2026

Abstract

Maglev trains, as an emerging transportation modality, have attracted significant attention with respect to their safety and ride comfort. In this study, the improved R index and τ-distance index are incorporated into the evaluation framework, and a data-driven comprehensive evaluation method for the suspension system of medium- and low-speed maglev trains is developed based on a Gaussian mixture model, enabling a comprehensive assessment of suspension gap stability and operational smoothness. Experimental results demonstrate that the proposed method can accurately identify various motion modes of the suspension system and provide effective early warnings of abnormal operational states. Compared with conventional error integral performance indices, this method exhibits superior anomaly detection sensitivity and enhanced interpretability of the results. Computational efficiency analysis indicates that the proposed method meets the requirements for online real-time monitoring. Under different operating conditions, the GMM trained on normal operational data maintains stable evaluation performance, demonstrating favorable robustness. Full article

(This article belongs to the Section Control Systems)

22 pages, 883 KB

Open AccessReview

Valorization of By-Products for Functional Ingredients in Meat and Meat Replacers: A Circular Bioeconomy Approach

by Ana Leite, Lia Vasconcelos, Alfredo Teixeira and Sandra S. Q. Rodrigues

Foods 2026, 15(9), 1567; https://doi.org/10.3390/foods15091567 - 1 May 2026

Abstract

To address the pressing dual challenge of meeting global protein demand while mitigating environmental impacts, the food sector must transition to a circular bioeconomy. In this context, this review comprehensively examines the valorization of plant and animal byproducts, emphasizing how the recovery and application of their inherent bioactive and functional compounds can transform waste into high-value resources. Plant processing residues, such as fruit peels and pomace, and animal residues, such as blood and bones, are increasingly recognized as untapped sources of functional ingredients. These by-products yield bioactive compounds with health benefits. Simultaneously, the same or different compounds serve as structural building blocks, offering valuable technological properties. They improve water-holding capacity, texture, and emulsion stability in both traditional meats and plant-based analogs. While upcycling these materials reduces disposal costs and formulation expenses, challenges remain regarding compositional variability, regulatory barriers, and consumer perception of “waste-derived” ingredients. Ultimately, integrating advanced processing technologies such as enzymatic hydrolysis and fermentation is essential to building a resilient, sustainable, and circular global food system. Full article

(This article belongs to the Special Issue Meat and Its Replacers: Green Processing and Quality Innovation)

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21 pages, 6514 KB

Open AccessArticle

BIM-Based Attention Class Indicators for Network-Scale Road Safety Barrier Asset Management

by Gaetano Bosurgi, Giuseppe Cantisani, Orazio Pellegrino and Giuseppe Sollazzo

Appl. Sci. 2026, 16(9), 4454; https://doi.org/10.3390/app16094454 - 1 May 2026

Abstract

Road safety barriers represent a core component of the road with relevant consequences on effective safety for users. Maintaining these components in adequate conditions, within the quality admissibility thresholds, in compliance with all economic and management constraints, is a primary need for road administrators. In this paper, the authors propose an original procedure to classify the state of efficiency of road safety barriers, at the network scale and relying on conventional administrative data, in an optimized BIM environment, to simplify evaluations and management procedures. Through purpose-built algorithms based on selected geometric and functional parameters of the different road barriers, the algorithm provides a preliminary classification of the various segments, evidencing attention class indicators, useful as preliminary alert signals and for anticipating detailed investigations that can ensure significant economic efficiencies. The method was tested on a 10 km long motorway segment in Italy, evidencing the potential advantages of such an innovative approach to support, as a final goal, a comprehensive infrastructure digital model for virtual inspections, evaluating road component “health” state and properly implementing maintenance strategies. This approach improves network-scale monitoring and maintenance-related activity prioritization phases for road safety barriers, leveraging administrative data. This methodology functions as a BIM-based asset screening tool, as it offers a digital decision support system that identifies critical segments, to optimize the allocation of physical resources and prioritize on-site inspections where they are most needed. Full article

(This article belongs to the Special Issue Building Information Modeling (BIM) and Digital Twins for Sustainable and Resilient Construction)

14 pages, 4593 KB

Open AccessArticle

Particle Emissions Characterization from Non-Asbestos Organic Brake Pads During On-Road Harsh Braking

by Tawfiq Al Wasif-Ruiz, José A. Sánchez-Martín, Carmen C. Barrios-Sánchez and Ricardo Suárez-Bertoa

Sustainability 2026, 18(9), 4463; https://doi.org/10.3390/su18094463 - 1 May 2026

Abstract

With the progressive decline of tailpipe emissions, non-exhaust sources such as brake wear are becoming an increasingly important contributor to traffic-related particulate matter in urban environments. In this context, improving real-world characterization of brake wear particles is essential for air-pollution assessment, source apportionment, and the development of cleaner and more sustainable road transport systems. Here, we investigated the emissions levels, particle size distribution and elemental composition of particles released during harsh real-world braking events by a single light-duty vehicle braking system equipped with an original manufacturer (OEM) non-asbestos organic (NAO) pad formulation. Using a direct on-vehicle sampling system combined with real-time particle sizing and high-resolution microscopy, we observed that particle emissions remained close to background levels at speeds up to 100 km/h, but rose sharply at 120 km/h, reaching 3.7 × 10⁷ #/cm³ in the 8–10 nm size range. This increase suggests that higher speeds are associated with elevated particle emissions, likely due to the higher braking temperatures reached at increased vehicle speeds. The emitted particles were mainly spherical agglomerates rich in iron, titanium, barium, zirconium, and sulphur, consistent with NAO pad formulations. Our results show that the investigated NAO pad system can deteriorate under thermal stress, potentially leading to higher levels of nanoparticle emissions compared to low-metallic or semi-metallic pads investigated under similar conditions. These findings provide real-world evidence relevant to urban air quality research, support the refinement of non-exhaust emissions inventories, and highlight the importance of thermally resilient friction-material formulations for mitigating residual particulate emissions in increasingly cleaner transport systems. Full article

(This article belongs to the Section Sustainable Transportation)

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12 pages, 266 KB

Open AccessCommentary

Primary Care or Primary Problem? Aligning Access Pathways with Patient Needs Across the Care Continuum

by Gregory J. Privitera, James J. Gillespie and Alexa Walton

J. Mark. Access Health Policy 2026, 14(2), 27; https://doi.org/10.3390/jmahp14020027 - 1 May 2026

Abstract

In the United States, access to healthcare is shaped not only by patient need but also by payer policies that determine which providers are reimbursable, how care is sequenced, and what constitutes a legitimate entry point into the system. These gatekeeping functions, while valuable for supporting clinical prioritization, risk stratification, and continuity of care, can also unintentionally reinforce structural inequities and credential hierarchies that delay or limit timely and equitable care, particularly for historically marginalized populations. While reform efforts often focus on expanding benefits or provider networks, fewer address the underlying design of access itself or the rules that govern how patients enter care. It is argued in this paper that a more equitable and efficient healthcare system requires multi-entry care models, in which nurses, behavioral health clinicians, pharmacists, and community health workers may serve as condition-appropriate, reimbursable first points of contact within coordinated care teams. Drawing on evidence from Medicare, Medicaid, the Veterans Health Administration, and commercial payers, these models may support cost containment, improve care coordination, facilitate appropriate utilization, and promote earlier patient engagement. While findings from these models are not uniform across all settings, evidence suggests that outcomes are highly dependent on implementation context, system design, and supporting infrastructure. When implemented with appropriate safeguards (such as interoperable health records, team-based care requirements, and coordinated referral tracking), multi-entry systems can preserve continuity while expanding access. Payers are uniquely positioned to lead this transformation by aligning reimbursement policy with patient needs, supporting team-based care infrastructure, and embedding accountability into access pathways, thereby creating a system that can be more responsive, inclusive, and sustainable. Full article

18 pages, 647 KB

Open AccessArticle

Uncovering Latent Structure in Gliomas Using Multi-Omics Factor Analysis

by Catarina Gameiro Carvalho, Alexandra M. Carvalho and Susana Vinga

Genes 2026, 17(5), 540; https://doi.org/10.3390/genes17050540 - 1 May 2026

Abstract

Background: Gliomas are the most common malignant brain tumors in adults, characterized by a poor prognosis. Although the current World Health Organization (WHO) classification provides clear guidelines for classifying oligodendroglioma, astrocytoma, and glioblastoma patients, significant heterogeneity persists within each class, limiting the effectiveness of current treatment strategies. With the increasing availability of large-scale multi-omics datasets resulting from advancements in sequencing technologies and online repositories that provide them, such as The Cancer Genome Atlas (TCGA), it is now possible to investigate these tumors at multiple molecular levels. Methods: In this work, we apply integrative multi-omics analysis to explore the interplay between genomic (mutations), epigenomic (DNA methylation), and transcriptomic (mRNA and miRNA) layers. Our approach relies on Multi-Omics Factor Analysis (MOFA), a Bayesian latent factor analysis model designed to capture sources of variation across different omics types. Results: Our results highlight distinct molecular profiles across the three glioma types and identify potential relationships between methylation and genetic expression. In particular, we uncover novel candidate biomarkers associated with survival as well as a transcriptional profile associated with neural system development. Conclusions: These findings may contribute to more personalized therapeutic strategies, potentially improving treatment effectiveness and survival outcomes in this disease. Full article

(This article belongs to the Section Bioinformatics)

47 pages, 2688 KB

Open AccessArticle

Integrating Veterinary Public Health Data into EPCIS-Based Digital Traceability for Dairy Supply Chains

by Stavroula Chatzinikolaou, Giannis Vassiliou, Mary Gianniou, Michalis Vassalos and Nikolaos Papadakis

Foods 2026, 15(9), 1566; https://doi.org/10.3390/foods15091566 - 1 May 2026

Abstract

Dairy foods—particularly cheeses produced from raw or minimally processed milk—remain vulnerable to hazards such as Listeria monocytogenes, where delayed laboratory confirmation can expand recalls, increase food waste, and delay outbreak containment. This study proposes a veterinary-aware digital traceability framework that embeds herd health data, milk-quality testing, and inspection outcomes directly into batch-level EPCIS event records. By representing veterinary public health controls as structured, machine-actionable traceability elements, the framework enables automatic logging of mandatory control points, systematic compliance verification, and rule-based risk state transitions within standard EPCIS infrastructures. Using regulation-consistent dairy simulations modeling delayed Listeria detection during maturation, we evaluate the operational impact of event-level causal traceability within the proposed architecture. Compared with conventional time-window recall strategies, provenance-based trace-forward queries reduced recall scope under the evaluated synthetic scenarios. Integrating structured veterinary controls into EPCIS-based traceability systems supports automated regulatory evidence generation and more targeted recall decisions, contributing to improved auditability and reduced food waste in dairy supply chains. Full article

(This article belongs to the Section Food Security and Sustainability)

20 pages, 2715 KB

Open AccessArticle

An Efficient Multi-Channel Electrotactile Parameter Configuration Method for Personalized Teleoperation

by Kaicheng Zhang, Kairu Li, Peiyao Wang and Yixuan Sheng

Biomimetics 2026, 11(5), 310; https://doi.org/10.3390/biomimetics11050310 - 1 May 2026

Abstract

Electrotactile feedback is a compact approach for providing tactile cues in robotic teleoperation, but personalized calibration remains time-consuming because tactile perception varies across users. To address this problem, this study develops a subject-informed multi-layer finite element model of fingertip electric-field distribution coupled with a neural-response model and proposes a simulation-derived configuration-ranking method termed the Perceived Correctness Score (PCS). A gradient boosting regression model is then used to recommend among 36 candidate electrode diameter–spacing combinations. Validation was conducted using a custom-developed

3 \times 2

multi-channel fingertip electrotactile stimulation system in a shape/area recognition task involving six healthy subjects. The predicted PCS showed a moderate positive correlation with the measured mean recognition accuracy across configurations (Pearson

r = 0.48

p < 0.05

). The model achieved Top-1 exact matching for three of six subjects and Top-5 coverage for five of six subjects. Compared with conventional exhaustive psychophysical calibration, the proposed method reduced the average configuration time from 122.7 min to 16.0 min, corresponding to an efficiency improvement of 87.0%. These results show that model-guided ranking can substantially reduce the burden of individualized electrotactile configuration. Full article

(This article belongs to the Section Locomotion and Bioinspired Robotics)

12 pages, 3232 KB

Open AccessArticle

Ni-MOFs/CNTs Nanohybrid Catalysts for Thermoelectric Hydrogen Peroxide

by Linhao Zhang, Hong Liu, Jianming Zhang and Fagen Wang

Catalysts 2026, 16(5), 409; https://doi.org/10.3390/catal16050409 - 1 May 2026

Abstract

Harnessing low-grade thermal energy from industrial processes and the environment represents an attractive route toward sustainable chemical production. In this work, we report a thermoelectrocatalytic (TE-Catal) system capable of converting small temperature gradients into chemical energy for hydrogen peroxide (H₂O₂) generation. A hybrid catalyst composed of nickel-based metal–organic frameworks (Ni-MOFs) nanoparticles integrated with carbon nanotubes (CNTs), Ni-MOFs/CNTs, was synthesized through a facile one-pot strategy. Under a temperature gradient, the thermoelectric response of the Ni-MOFs induces charge carrier generation through the Seebeck effect, enabling interfacial redox reactions that produce H₂O₂. However, rapid recombination of thermally generated carriers typically limits catalytic efficiency. By coupling Ni-MOFs with conductive CNTs networks, charge separation and transport are significantly enhanced due to the strong interfacial interaction and the high electrical conductivity of CNTs. As a result, the Ni-MOFs/CNTs nanohybrids exhibit greatly improved H₂O₂ generation rate of ~111.7 µmol g⁻¹ h⁻¹ compared with pristine Ni-MOFs (31.8 µmol g⁻¹ h⁻¹). Thermoelectric electrochemical measurements confirm that the CNT incorporation effectively promotes carrier migration and suppresses recombination. This study demonstrates the potential of MOF-based thermoelectric nanostructures for transforming waste heat into valuable chemical products. Full article

(This article belongs to the Special Issue Feature Papers in "Industrial Catalysis" Section, 3rd Edition)

27 pages, 2035 KB

Open AccessArticle

Matching Innovation System Models to Context: An Explanatory Potential Framework

by Homero Malagón, Alfonso Ávila Robinson and Aida Huerta Barrientos

Systems 2026, 14(5), 502; https://doi.org/10.3390/systems14050502 - 1 May 2026

Abstract

Innovation system decision-making is a core component in promoting incentives and conditions necessary for the emergence of innovation. It also plays a critical role in guiding policy and modeling strategies that aim to promote science, technology, and entrepreneurship at national, regional, and local levels. Decision-makers often select innovation system models that do not align with contextual scope, data accessibility, or institutional conditions, undermining their implementation. The lack of alignment between innovation system model assumptions and contextual realities undermines analysis and policy design, particularly when trying to implement a regional model on a national scale without any sort of adaptation. This study presents a framework that aligns innovation system models to specific contexts by providing a decision-making system based on structural analysis. Using a comprehensive collection of relevant previous studies related to the theoretical evolution of innovation system models, this research provides insights regarding the most used types and techniques to compare innovation systems comprising national and regional ISs, helix models, and innovation and entrepreneurship ecosystems. For each model, explanatory potential via structural analysis is operationalized through five indicators derived from multilevel graphs: geopolitical scope, number of actors, vertical and horizontal density, and Shannon’s entropy. These indicators are then systematized into dimensions comprising two feasibility filters and three mechanism-related dimensions, forming the basis for a minimum viable innovation system model selection heuristic. This structural analysis shows that ecosystem lenses capture distributive and adaptive interaction structures; helix models emphasize coordination and governance; and national or regional innovation systems underscore policy reach and institutional boundaries. The results provide a numerical analysis of three different contexts—a national mission, a city entrepreneurship program, and a regional coordination upgrading effort—highlighting areas for improvement in planning, project implementation, and public policy design. Full article

(This article belongs to the Special Issue The Dynamics and Resilience of Innovation Networks in a Disruptive World)

29 pages, 1779 KB

Open AccessArticle

BWT-Enhanced Compression for GIS Raster Data: A Hybrid AV1-Inspired Approach with Burrows–Wheeler Transform

by Yair Wiseman

Big Data Cogn. Comput. 2026, 10(5), 140; https://doi.org/10.3390/bdcc10050140 - 1 May 2026

Abstract

The AVIF (AV1 Image File Format) is a modern, royalty-free image format that leverages the AV1 video codec for superior compression efficiency, supporting both lossy and lossless modes. Its entropy encoding relies on a multi-symbol context-adaptive arithmetic coder (range coding with adaptive cumulative distribution functions (CDFs)), which is effective for general imagery but may not optimally exploit the repetitive structures common in Geographic Information System (GIS) maps/data. This paper proposes replacing AVIF’s entropy encoder with the Burrows–Wheeler Transform (BWT), a reversible preprocessing algorithm that rearranges data to create runs of similar symbols, enhancing subsequent compression. We detail the technical steps for modification, drawing from AV1’s open-source implementation, and explain why BWT is advantageous for GIS raster maps/data, which often feature large uniform areas, limited color palettes, and spatial redundancies. Empirical evidence from related studies on BWT-based image compression shows improvements in lossless scenarios, potentially considerably reducing file sizes over standard methods while preserving data integrity critical for geospatial analysis. This swap could improve storage, transmission, and processing efficiency in GIS applications, such as remote sensing and cartography. The discussion includes challenges like computational overhead and compatibility, with recommendations for implementations. The resulting BWT-AVIF hybrid produces a non-standard AV1 bit-stream that is not compliant with the AV1 or AVIF specifications and therefore requires custom decoders. It is presented here as a research prototype for GIS-specific compression rather than a compliant AVIF extension. Full article

(This article belongs to the Special Issue Intelligent Communication and Sensor Networks for Advanced Signal Processing)

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