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This study investigates the interaction between a manufacturer’s blockchain adoption strategy and a retailer’s demand information sharing strategy in a green supply chain. For four strategy combinations, we establish a multi-stage game-theoretical model of a green supply chain consisting of a single manufacturer and a single retailer. We first derive the optimal pricing, greenness, service level, and profits, followed by sensitivity and comparative analyses. Next, by examining how consumer price sensitivity and the unit adoption cost of blockchain technology interact, we identify equilibrium strategy combinations. Finally, we validate the relevant findings through numerical analysis. The results demonstrate that adopting blockchain can mitigate the double marginalization effect when consumer price sensitivity is moderate, and can enhance product greenness and service level when the adoption cost remains low. Interestingly, the manufacturer is inclined to adopt blockchain irrespective of the degree of consumer skepticism. Meanwhile, the implementation of blockchain may motivate the retailer to share information when price sensitivity falls within a moderate range. These findings present actionable guidance for green supply chains regarding blockchain and information-sharing strategies. Full article

(This article belongs to the Section Sustainable Management)

22 pages, 911 KB

Open AccessArticle

STORM: Hardware-Aware Tiny Transformer Co-Design for Low-Power Inertial Human Activity Recognition

by Alessandro Varaldi, Claudio Genta, Alberto Manzone and Marco Vacca

Electronics 2026, 15(9), 1924; https://doi.org/10.3390/electronics15091924 - 1 May 2026

Abstract

Human Activity Recognition (HAR) from inertial sensors must run continuously on battery-powered wearables under tight latency, memory, and energy budgets. While tiny Transformers can be effective on inertial time series, end-to-end co-design across quantized inference and heterogeneous low-power platforms remains underexplored. We present STORM (Small Transformer for On-node Recognition of Motion), a deployment-oriented [round-mode=places, round-precision=1]19.7k-parameter 1D Transformer co-designed with X-HEEP, an open-source low-power single-core RISC-V SoC, and a tightly coupled streaming CGRA for nonlinear primitives (e.g., softmax). We build a cross-source 8-class benchmark by harmonizing 3 public datasets under a stringent, deployment-aligned protocol that exposes both cross-subject and cross-source shift. Using 1.280 s windows with 0.640 s stride, the protocol models continuous on-node HAR under cross-dataset generalization. After quantization-aware training and INT8 C inference export, STORM achieves [round-mode=places, round-precision=3]0.799/[round-mode=places, round-precision=3]0.801 accuracy/macro-F1 on this benchmark. Deployed on an FPGA prototype of X-HEEP with the streaming CGRA backend, STORM requires round(6739790/ (100* 1000000)* 1000, 1) ms per inference at 100 MHz, while activity-based power analysis estimates a total inference energy of 632.4

μ

J, satisfying the stride-driven real-time constraint. These results support the practical viability of compact attention-based HAR on low-power wearable-class embedded platforms. Full article

(This article belongs to the Special Issue From Circuits to Systems: Embedded and FPGA-Based Applications)

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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50 pages, 9542 KB

Open AccessReview

Nanomaterial-Modified Screen-Printed Electrodes: Advances, Interfacial Engineering Evaluation, and Real-World Applications in Electrochemical Sensing

by Tudor-Alexandru Filip, Vlad-Andrei Scarlatache, Alin Dragomir, Georgiana Prodan-Chiriac and Marius-Andrei Olariu

Chemosensors 2026, 14(5), 107; https://doi.org/10.3390/chemosensors14050107 - 1 May 2026

Abstract

Innovations in nanomaterial science, engineering and printing technologies have increasingly driven advances in electrochemical sensing. Screen-printed electrodes (SPEs) have become a versatile, low-cost, and scalable solution for developing portable electrochemical detection platforms. However, their analytical performance remains intrinsically limited by surface area, electron transfer efficiency, and the immobilization of biomolecules. Recent developments in nanostructured materials, ranging from two-dimensional (2D) materials such as graphene, MXenes, and transition metal dichalcogenides, to one-dimensional nanostructures and hybrid nanocomposites, have transformed the signal transduction landscape of SPE-based electrochemical sensors. Integration of nanomaterials into SPEs has successfully transformed their analytical capabilities, but the diversity of materials and modification strategies has made it difficult to consolidate current knowledge in the field. Strategies that integrate nanomaterials via ink formulation, surface modification, or in situ growth have yielded sensors with unprecedented sensitivity, reproducibility, and selectivity across various chemical and biological targets. This review offers a cross-material synthesis of how nanomaterial engineering transforms the electrochemical performance of SPEs. By integrating insights across morphology, interfacial chemistry, and device-level behavior, it establishes a unified perspective that has been missing from the current literature and clarifies the design principles driving next-generation SPE-based sensing platforms. Full article

(This article belongs to the Special Issue Electrochemical Sensors and Biosensors: Recent Progress, Challenges, and Future Perspectives)

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19 pages, 1536 KB

Open AccessArticle

Economic Journals of the BRICS Countries: Assessment of Academic Influence

by Irina D. Turgel and Olga A. Chernova

Publications 2026, 14(2), 28; https://doi.org/10.3390/publications14020028 - 1 May 2026

Abstract

The BRICS countries are playing an increasingly significant role in shaping a multipolar model of global science. This study aims to assess the academic influence of economic journals published in BRICS countries from the following key perspectives: academic standing, relevance, influence sustainability, internationalization, and external institutional recognition (lack of isolation). The methods of bibliometric, comparative, and cluster analysis were used. The study revealed that the BRICS countries have significantly increased their presence in the Scopus database. However, their scientific publishing landscape is highly heterogeneous. Russia and India exhibit the highest publication volumes among the BRICS countries, albeit with relatively low citation rates and a low level of internationalization. Meanwhile, Chinese, South African, and Indonesian journals have the highest citation rates and strongest integration into the global discourse. Cluster analysis identified five groups of journals with a range of academic influence levels, from peripheral contributors to international leaders. Additionally, country-specific features of their distribution were determined. The present research provides insights into the pivotal role of national journals in overcoming peripherality and strengthening the academic influence of nationwide science. The research methodology can be used to develop strategies that promote nations to become part of the global research community. Full article

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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)

17 pages, 4942 KB

Open AccessArticle

Phase Stability and Competing Crystal Structures in the Formation of the Intermetallic Compounds Cu₅As₂ and Cu₅(As,Sb)₂

by Marianne Mödlinger, Alessia Provino, Pavlo Solokha, Serena De Negri, Antonio Bianco, Cristina Bernini and Pietro Manfrinetti

Solids 2026, 7(3), 24; https://doi.org/10.3390/solids7030024 - 1 May 2026

Abstract

An experimental investigation of the Cu-As-Sb ternary system in the Cu-rich region led to the identification of a new intermetallic phase, Cu₅(As,Sb)₂. The compound crystallizes in the orthorhombic Mg₅Ga₂-type structure (oI28, Ibam), analogous to the binary parent phase Cu₅As₂, with lattice parameters a = 5.968–5.977(1) Å, b = 11.550–11.565(3) Å, c = 5.530–5.573(3) Å. Similar to the parent Cu₅As₂ phase, the ternary compound forms with slight Cu under stoichiometry and exhibits a limited compositional range, with no continuous solid solubility between the binary and ternary phases. The phase formation, compositional stability, and decomposition behavior were systematically studied using a combination of powder and single-crystal X-ray diffraction (XRD, including Rietveld refinement), metallographic analysis with optical and scanning electron microscopy with energy-dispersive X-ray spectroscopy (LOM, SEM-EDXS), electron backscatter diffraction (EBSD) and thermal analysis (DTA, DSC). The results reveal that Cu₅(As,Sb)₂ is a high-temperature phase forming peritectically at 650–635 °C and stable only within a limited temperature interval. No continuous solid solubility exists between the ternary compound and the parent binary phase Cu₅As₂. Its formation occurs in strong competition with that of two other close neighboring solid-solution compounds, [Cu_3−x(As_1−ySb_y) (Cu₃P-type; hP24, P6₃cm) and Cu_3−x(As,Sb) (Cu₉TeSb₂-type; cP32, Pm−3n)], reflecting a complex interplay between composition, solubility ranges and thermal history. No evidence for the existence of high-temperature (HT) and low-temperature (LT) polymorphic phases was found for either the binary compound Cu₅As₂ or the ternary compound Cu₅(As,Sb)₂. Electrical resistivity measurements on a quenched sample indicate metallic behavior. These findings provide new insight into phase stability and structure–property relationships in Cu-As-Sb alloys and contribute to the understanding of competing intermetallic phases in this system. Full article

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18 pages, 13013 KB

Open AccessArticle

Dynamic Transformer Based on Wavelet and Diffusion Prior Guidance for Cardiac Cine MRI Reconstruction

by Bolun Zhao and Jun Lyu

Sensors 2026, 26(9), 2842; https://doi.org/10.3390/s26092842 - 1 May 2026

Abstract

Cardiac magnetic resonance imaging (CMR) is widely used for the diagnosis and functional assessment of cardiovascular diseases because of its noninvasive nature and excellent soft-tissue contrast. However, accelerated cine magnetic resonance imaging (cine MRI) acquisition usually relies on undersampling, which may lead to noise, aliasing artifacts, and detail loss in reconstructed images. To address this issue, we propose a wavelet-guided dynamic Transformer with diffusion priors for cardiac cine MRI reconstruction. Specifically, a diffusion model is introduced into a reduced latent feature space to generate high-frequency prior features with only 8 reverse sampling steps, thereby enhancing detail recovery while maintaining moderate computational cost. In addition, a wavelet-guided dynamic Transformer is designed to capture low-frequency structural information and temporal dependencies across adjacent frames. By combining wavelet-domain decomposition, diffusion priors, and dynamic spatiotemporal modeling, the proposed framework improves reconstruction quality while preserving temporal consistency. Experimental results on multiple cardiac cine MRI datasets show that the proposed method achieves superior reconstruction accuracy and temporal consistency over several competing approaches, while maintaining a favorable balance between computational efficiency and reconstruction performance. These findings indicate that the proposed framework is an effective and robust solution for accelerated cardiac cine MRI reconstruction. Full article

(This article belongs to the Special Issue Advances of Deep Learning in Medical Image Interpretation—2nd Edition)

25 pages, 8679 KB

Open AccessArticle

Real-Time Cardiac Arrhythmia Classification Using TinyML on Ultra-Low-Cost Microcontrollers: A Feasibility Study for Resource-Constrained Environments

by Misael Zambrano-de la Torre, Sebastian Guzman-Alfaro, Andrea Acuña-Correa, Manuel A. Soto-Murillo, Maximiliano Guzmán-Fernández, Ricardo Robles-Ortiz, Karen E. Villagrana-Bañuelos, Jose G. Arceo-Olague, Carlos H. Espino-Salinas, Ana G. Sánchez-Reyna and Erik O. Cuevas-Rodriguez

Bioengineering 2026, 13(5), 532; https://doi.org/10.3390/bioengineering13050532 - 1 May 2026

Abstract

Recent advances in edge computing and Tiny Machine Learning (TinyML) have enabled the deployment of artificial intelligence models directly on microcontrollers with extremely limited computational and memory resources. In this context, this work presents the design, implementation, and validation of a real-time cardiac arrhythmia classification system based on a quantized one-dimensional convolutional neural network (1D-CNN), deployed on an 8-bit Arduino UNO microcontroller. The proposed system integrates end-to-end processing, including ECG signal acquisition using a low-cost AD8232 analog front-end, signal preprocessing, heartbeat segmentation, classification, and real-time visualization on an OLED display. The model was trained and evaluated using the MIT-BIH Arrhythmia Database, considering a reduced three-class problem (Normal, Ventricular, and Supraventricular) to meet the constraints of ultra-low-cost hardware deployment. Under benchmark conditions, the quantized model achieved an accuracy of 97.6%, with a memory footprint below 24 KB and an average inference time of 200 ms per heartbeat, enabling real-time operation on a resource-constrained microcontroller. Real-time experiments were conducted using signals acquired from healthy volunteers to validate system functionality, although no annotated ground truth was available for these recordings, and therefore no diagnostic performance was derived from them. The results demonstrate the feasibility of deploying lightweight deep learning models on ultra-constrained embedded systems using the TinyML paradigm, implemented using TensorFlow 2.15 and TensorFlow Lite. This work should be interpreted as a proof-of-concept platform that highlights the trade-off between classification performance and hardware limitations, providing a foundation for future development of low-cost cardiac monitoring technologies in resource-limited environments. Full article

(This article belongs to the Special Issue Machine Learning in Biomedical Research: Application, Innovation and Exploration)

19 pages, 2109 KB

Open AccessArticle

Translation and Psychometric Validation of the Teachers’ Beliefs and Intentions Questionnaire (TBIQ) in Chilean Early Childhood Education

by Pamela Soto-Ramirez, Marigen Narea, Maria Francisca Morales and Alejandra Caqueo-Urízar

Educ. Sci. 2026, 16(5), 711; https://doi.org/10.3390/educsci16050711 - 1 May 2026

Abstract

The Teachers’ Beliefs and Intentions Questionnaire (TBIQ) assesses educators’ beliefs and intentions regarding the importance of sensitive interactions with young children. Understanding these beliefs is particularly relevant in contemporary educational contexts where teacher–child interactions are viewed as central to children’s learning and development. Despite its use in several countries, there is no validated Spanish version available. This study aimed to translate, culturally adapt, and psychometrically validate a Spanish version of the TBIQ for early childhood education settings in Chile. Following international guidelines for cross-cultural adaptation, the questionnaire was translated into Spanish and administered to early childhood teachers and assistant teachers working in public early childhood education centers. The original two-factor structure (Beliefs and Intentions) was tested using confirmatory factor analyses with robust estimators for ordinal data. Results supported the two-factor model after removing six items with low factor loadings and indicated excellent model fit. Both scales demonstrated high internal consistency. However, measurement invariance across educator roles could not be established, and cross-group comparisons should be interpreted with caution. Despite this limitation, the Spanish version of the TBIQ demonstrates adequate validity and reliability and offers a brief and accessible instrument for research and for the assessment of educators’ beliefs and intentions regarding interaction quality in early childhood education. Full article

(This article belongs to the Special Issue Pedagogy in Early Years Education)

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27 pages, 1264 KB

Open AccessArticle

Synthetic Minority Oversampling for Imbalanced Time Series Classification Based on Path Signature

by Mohnad Abunada, Samir Brahim Belhaouari and Halima Bensmail

Appl. Sci. 2026, 16(9), 4451; https://doi.org/10.3390/app16094451 - 1 May 2026

Abstract

Imbalanced class distributions hinder time series classifiers by underrepresenting rare yet important events. We introduce Path Signature Synthetic Time-series Oversampling (PSSTO), a structure-preserving oversampling method that operates in path signature space to synthesize informative minority samples while pruning low-quality ones. Across 12 public datasets, PSSTO with a random forest improves classification over conventional resampling approaches on average. Pairwise Wilcoxon signed-rank tests against these approaches indicate statistically significant gains. Compared with time series-specific oversamplers, PSSTO with random forest attains the best averages on F1, G-mean, and AUC compared to the strongest alternative. These results show that structure-preserving oversampling in signature space is an effective and broadly applicable remedy for imbalanced time-series classification. Full article

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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, 4381 KB

Open AccessArticle

High-Field Measurements of CoP and Elemental Combinatorics in the MnP-Type Family

by Daniel J. Campbell, John Collini, Kefeng Wang, Limin Wang, Brandon Wilfong, David Graf, Efrain E. Rodriguez and Johnpierre Paglione

Crystals 2026, 16(5), 299; https://doi.org/10.3390/cryst16050299 - 1 May 2026

Abstract

The MnP family of binary compounds presents an intriguingly simple platform to mix-and-match elemental components. Replacement on the transition metal or pnictogen site can alter magnetism, electronic correlations, and electrical properties. Here we report low-temperature properties of CoP, including measurements at magnetic fields exceeding 30 T, revealing de Haas–van Alphen oscillations and a nearly two orders of magnitude increase in resistance. When viewed together with prior work, it is possible to put together a global picture of the role of different atoms in variations in magnetic ordering, lattice coherence, and topological band structure features in this material family. Full article

(This article belongs to the Section Crystalline Metals and Alloys)

16 pages, 3093 KB

Open AccessArticle

Integrating Risk Factors and Symptoms for Urinary Tract Infection Diagnosis Using an Explainable AI Approach in Low-Resource Regions

by Kingsley Attai, Daniel Asuquo, Kingsley Akputu, Okure Obot, Cornelia Thomas, Faith-Valentine Uzoka, Ekerette Attai, Christie Akwaowo and Faith-Michael Uzoka

Information 2026, 17(5), 435; https://doi.org/10.3390/info17050435 - 1 May 2026

Abstract

Urinary Tract Infections (UTIs) represent one of the most prevalent bacterial infections globally, posing significant health burdens, especially in low- and middle-income countries (LMICs), due to delayed diagnoses, limited access to laboratory services, and rising antimicrobial resistance. This study presents a machine learning (ML)-based diagnostic support framework for early UTI detection, leveraging structured clinical data and explainable artificial intelligence (XAI) techniques to enhance interpretability and trust among healthcare providers. A patient dataset containing 4865 records was used in the study to train and test Extreme Gradient Boosting (XGBoost), Decision Tree (DT) and Random Forest (RF) classifiers, while class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The performance of the models was evaluated through accuracy, precision, recall, F1-score, Log Loss, and AUC-ROC, and random forest showed the best results (accuracy: 86.43%, F1-score: 86.71%, AUC-ROC: 0.8695). To ensure that such models can be adopted by stakeholders in the health sector, Local Interpret-able Model-agnostic Explanations (LIME) were integrated, which identified painful urination, urinary frequency, and suprapubic pain as primary predictors in the model. This study shows that interpretable ML models can be helpful in resource-limited regions in predicting UTIs, thereby rendering a solution to improve the management of infections in these regions. Full article

(This article belongs to the Section Artificial Intelligence)

32 pages, 26014 KB

Open AccessArticle

Implementation of an Integrated System for Preventive Maintenance Management and Alerts in Light Vehicles

by Joseph Barreiro-Zambrano, Juan Martinez-Parrales and Roberto López-Chila

Vehicles 2026, 8(5), 100; https://doi.org/10.3390/vehicles8050100 - 1 May 2026

Abstract

Inadequate vehicle maintenance management is one of the main causes of road accidents and elevated operating costs in light vehicles. This paper addresses this problem through the development and implementation of a low-cost integrated system for preventive maintenance management and alerts. The device, based on an open-hardware architecture (Arduino Mega 2560), integrates Global Positioning System (GPS) and mobile communication (GSM/LTE) modules to monitor distance traveled in real time and notify the user via SMS about the proximity of critical services such as oil changes, brake inspections, and timing-belt replacements. Its technical contribution lies in the integration of non-intrusive virtual ignition, filtered GPS-based odometry, configurable MicroSD-based persistence, and progressive SMS alert logic into a low-cost aftermarket system for conventional vehicles without OBD-II dependence. Experimental validation was conducted in the city of Guayaquil using a 2012 Hyundai Accent. Field tests were carried out in three scenarios: a dense urban route, a peripheral road, and interurban routes. Results showed satisfactory accuracy with a global average percentage error of 3.98% compared to the vehicle’s odometer and 100% effectiveness in sending alerts under the tested conditions (20/20 events; exact 95% binomial confidence interval: 83.2–100.0%). These results provide strong evidence of technical feasibility for the proposed architecture under the tested conditions in a representative single-vehicle proof-of-concept, while broader cross-vehicle validation remains necessary before generalizing the system to the wider diversity of aging fleets. Full article

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