Technologies

33 pages, 1423 KB

Open AccessReview

Non-Prosthetic Assistive Technologies for Persons with Hearing Losses: A Survey

by Reemas Alsubaiei, Farah AlHayek, Mariam Alsahhaf, Ghadah Alajmi, Aliah Almutairi, Karim Youssef, Ghina El Mir, Sherif Said, Taha Beyrouthy and Samer Al Kork

Technologies 2026, 14(5), 302; https://doi.org/10.3390/technologies14050302 - 13 May 2026

Abstract

Millions of persons worldwide experience varying degrees of hearing loss, traditionally addressed through prosthetic solutions such as hearing aids and cochlear implants. However, a significant proportion of individuals cannot benefit from these technologies, cannot access them, or choose not to use them. In [...] Read more.

Millions of persons worldwide experience varying degrees of hearing loss, traditionally addressed through prosthetic solutions such as hearing aids and cochlear implants. However, a significant proportion of individuals cannot benefit from these technologies, cannot access them, or choose not to use them. In this context, non-prosthetic assistive technologies have emerged as a complementary paradigm, leveraging advances in sensing, artificial intelligence, and wearable computing to transform acoustic information into alternative perceptual representations rather than restoring auditory function. This survey provides a review of such systems, focusing on technologies that enhance environmental awareness, communication, and social interaction. Existing approaches are categorized along two main dimensions: the tasks they perform and the platforms on which they operate. Task-oriented analysis includes sound recognition (speech and non-speech), sound source localization, emotion recognition, sign language recognition, and related emerging functionalities. Platform-based analysis emphasizes wearable devices and mobile solutions enabling real-time and context-aware assistance. The survey further highlights key research trends, including real-time auditory scene analysis, portable processing, and artificial intelligence. It shows that recent studies increasingly demonstrate that combining auditory, visual, and haptic modalities improves robustness and usability in real-world conditions, particularly in noisy and dynamic environments. Finally, open challenges such as energy efficiency, latency, evaluation methodologies, and user acceptance are discussed. By synthesizing existing work and identifying open research directions, this survey aims to provide a structured foundation for future developments in intelligent, non-prosthetic assistive systems that redefine how auditory information is accessed and interpreted. Full article

(This article belongs to the Section Assistive Technologies)

► Show Figures

Figure 1

19 pages, 479 KB

Open AccessReview

From Acquisition to Validation: Methodological Dependencies and Reproducibility in EEG-Based Alzheimer’s Disease Detection

by Ruimin Wang, Takenao Sugi and Takao Yamasaki

Technologies 2026, 14(5), 301; https://doi.org/10.3390/technologies14050301 - 13 May 2026

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early detection and reliable monitoring remain major clinical challenges. Electroencephalography (EEG) combined with machine learning has attracted growing interest as a scalable and non-invasive approach to AD detection, yet reported classification accuracies vary [...] Read more.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early detection and reliable monitoring remain major clinical challenges. Electroencephalography (EEG) combined with machine learning has attracted growing interest as a scalable and non-invasive approach to AD detection, yet reported classification accuracies vary widely across studies and are rarely comparable or clinically translatable. One important reason is that the analytical pipeline—from data acquisition to model validation—involves numerous methodological choices whose inter-stage dependencies and reproducibility implications are rarely made explicit. In this narrative review, we adopt a methodological chain framework to make these dependencies explicit, organizing EEG-based AD research into five sequential stages: data acquisition, preprocessing, feature representation, modeling, and validation. Choices at each stage can shape downstream analyses, inflate reported performance, and reduce cross-study comparability in ways that are difficult to detect when stages are assessed independently. These effects are particularly consequential in EEG-based AD research, where cohorts are typically small and biomarkers are subtle. We make three primary contributions: (1) we describe inter-stage methodological dependencies that may contribute to reproducibility problems and performance inflation; (2) we synthesize major sources of methodological variability across representative EEG–AD studies and evaluate their differential impact on spectral, connectivity, and complexity features; and (3) we provide practical, stage-aligned recommendations culminating in a minimum reporting checklist. Full article

(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives—Second Edition)

► Show Figures

Figure 1

43 pages, 2338 KB

Open AccessArticle

Micro-Attention CNN Hybrid Architecture for Real-Time Stress Detection Using Minimalistic Bio-Signals

by Chaymae Yahyati, Ismail Lamaakal, Yassine Maleh, Khalid El Makkaoui and Ibrahim Ouahbi

Technologies 2026, 14(5), 300; https://doi.org/10.3390/technologies14050300 - 13 May 2026

Abstract

Real-time psychological stress detection on wearable and edge devices requires models that are accurate, computationally efficient, and small enough for on-device deployment. This paper proposes a Micro-Attention CNN Hybrid Architecture for stress recognition using wearable bio-signals. The model uses six sensor channels, namely [...] Read more.

Real-time psychological stress detection on wearable and edge devices requires models that are accurate, computationally efficient, and small enough for on-device deployment. This paper proposes a Micro-Attention CNN Hybrid Architecture for stress recognition using wearable bio-signals. The model uses six sensor channels, namely tri-axial acceleration, electrodermal activity, heart rate, and skin temperature, and classifies three stress levels: no stress, low stress, and high stress. This study is conducted on a public wearable sensor dataset collected from 15 nurses during hospital work, providing a realistic benchmark for continuous stress monitoring under practical conditions. The proposed architecture combines one-dimensional and depthwise separable convolutions with a lightweight attention module to emphasize the most informative temporal patterns in short multivariate signal segments. To support deployment on resource-constrained devices, we further apply structured pruning, selective quantization-aware training, and post-training quantization. The full-precision model achieves a Macro-F1 score of 99.63%, while the final compressed model retains 98.03% Macro-F1 with a model size of 1.76 kilobytes and a CPU inference latency of 0.40 ms. Additional analyses show that most residual errors occur near the boundary between low stress and neighboring classes, while simple post-compression calibration improves reliability. These results demonstrate that accurate and low-latency stress detection using wearable bio-signals is feasible on compact edge hardware without transmitting raw sensor streams off-device. Full article

(This article belongs to the Special Issue AI-Enabled Smart Healthcare Systems)

► Show Figures

Figure 1

14 pages, 3714 KB

Open AccessArticle

Moving from the Paschen Law to More Accurate Electrical Discharge Models for the Design of Insulation Systems Under Variable Pressure

by Gian Carlo Montanari and Sukesh Babu Myneni

Technologies 2026, 14(5), 299; https://doi.org/10.3390/technologies14050299 - 13 May 2026

Abstract

The Paschen law, especially in its linear approximation, is said to be useful for predicting the partial discharge inception voltage (PDIV) in insulation systems when considering different defect sizes and pressure values. Hence, it is often used for designing electrical insulation systems in [...] Read more.

The Paschen law, especially in its linear approximation, is said to be useful for predicting the partial discharge inception voltage (PDIV) in insulation systems when considering different defect sizes and pressure values. Hence, it is often used for designing electrical insulation systems in aerospace applications. This paper presents a comparison between PDIV estimates provided by the Paschen law and a new model applicable to internal and surface discharges in electrical insulation systems under varying pressure and defect size or creepage distance. It is shown that the Paschen law estimates can often be very far from the measured PDIV values for both surface and internal defects and at pressures above and below standard atmospheric pressure (SAP), which can negatively affect the design and reliability of insulation systems. On the contrary, the proposed model provides accurate and consistent PDIV estimates, which are very close to those measured, for both internal and surface discharges. The lower limit of the model application/validation is 50 mbar from SAP. Full article

► Show Figures

Figure 1

15 pages, 5298 KB

Open AccessArticle

Low-Cost Active Cell Balancing Battery Management System for Electric Vehicles with Cell Charger as Cell Balancer

by Amin Amin, Feri Yusivar, Faiz Husnayain and Aam Muharam

Technologies 2026, 14(5), 298; https://doi.org/10.3390/technologies14050298 - 12 May 2026

Abstract

Cell imbalance in battery packs can cause premature termination during battery discharge and recharge processes. This condition can decrease the usable energy of the battery. The cost of batteries can reach 30–40% of the price of an electric vehicle, so battery cell balancing [...] Read more.

Cell imbalance in battery packs can cause premature termination during battery discharge and recharge processes. This condition can decrease the usable energy of the battery. The cost of batteries can reach 30–40% of the price of an electric vehicle, so battery cell balancing in a battery management system (BMS) and a battery thermal management system (BTMS) is very important to maximize battery capacity, safety, and life. In conventional active balancing studies, the cell-balancing process draws energy from the cells or battery pack, resulting in a reduction in battery pack energy due to power losses during the balancing process. This condition can reduce the range of electric vehicles. In this paper, a battery balancing system with a reduced number of switches and low cost, as well as the use of a cell charger, is proposed. The cell charger will draw energy from the electrical grid so that it can maximize the energy in the battery pack. A balancing current of 3 A from the cell charger is used in the balancing process. A 23S1P 100 Ah LiFePO4 battery pack, consisting of 23 cells, is used for validation. Test results show that the proposed battery balancing system can balance the voltage of 23 battery cells for 40 minutes from the highest and lowest voltage difference of 116.7 mV to 11.8 mV. Full article

► Show Figures

Figure 1

36 pages, 14900 KB

Open AccessSystematic Review

Robot Performance Evaluation for Engineering Applications: A Systematic Review of Metrics, Methods and Practices

by Xiang Wei, Songjie Peng and Baosheng Zhao

Technologies 2026, 14(5), 297; https://doi.org/10.3390/technologies14050297 - 12 May 2026

Abstract

Robotics integration across manufacturing, healthcare, and hazardous environments demands robust performance evaluation. This study proposes a comprehensive Task–Environment–System–Metric (TESM) framework to link operational tasks and environmental constraints with quantifiable metrics. Based on TESM, a multi-level evaluation system is established, covering kinematic/dynamic performance, perception, [...] Read more.

Robotics integration across manufacturing, healthcare, and hazardous environments demands robust performance evaluation. This study proposes a comprehensive Task–Environment–System–Metric (TESM) framework to link operational tasks and environmental constraints with quantifiable metrics. Based on TESM, a multi-level evaluation system is established, covering kinematic/dynamic performance, perception, human–robot interaction (HRI), reliability, and lifecycle economics. We systematically review key evaluation methodologies, including mechanistic modeling, digital twin simulation, physical testing, and multi-criteria decision-making (MCDM). Furthermore, typical engineering applications—ranging from industrial manipulators and mobile robots to collaborative and field systems are analyzed to demonstrate practical implementation. Despite significant progress, challenges persist regarding unified standards, testing fidelity, and the “black box” nature of data-driven assessments in safety-critical scenarios. This review concludes by identifying future research directions, such as establishing benchmark testing platforms, improving lifecycle assessment schemes, and developing modular evaluation tools. These advancements aim to ensure the scalable and reliable deployment of robotic systems in complex engineering environments. Full article

23 pages, 3027 KB

Open AccessArticle

AIoT Ecosystem for Intelligent Water Quality Monitoring Through Edge Processing and Generative Artificial Intelligence

by Giovanni Rafael Caicedo Escorcia, Liliana Vera-Londoño and Jaime Andres Perez-Taborda

Technologies 2026, 14(5), 296; https://doi.org/10.3390/technologies14050296 - 12 May 2026

Abstract

Water quality monitoring remains a critical challenge for achieving Sustainable Development Goal 6, particularly in rural and resource-constrained environments where conventional laboratory-based methods are costly and slow. This study presents the development and field validation of an Artificial Intelligence of Things (AIoT) ecosystem [...] Read more.

Water quality monitoring remains a critical challenge for achieving Sustainable Development Goal 6, particularly in rural and resource-constrained environments where conventional laboratory-based methods are costly and slow. This study presents the development and field validation of an Artificial Intelligence of Things (AIoT) ecosystem for intelligent, low-cost, and real-time water quality assessment using edge computing and generative artificial intelligence. The system integrates a laboratory-developed multiparameter probe measuring temperature, pH, dissolved oxygen, and electrical conductivity with a mobile application and a cloud-based backend. Field validation was conducted in riverine environments in the municipality of Pueblo Bello (Cesar, Colombia), where the system was deployed for in situ data acquisition and real-time inference. A supervised Artificial Neural Network (ANN) was trained to classify water quality based on a Water Quality Index (WQI) ground truth derived from a public dataset, employing KNN-based missing data imputation, interquartile range outlier filtering, stratified balancing, and grid search hyperparameter optimization. The best-performing model achieved 85.1% accuracy and an AUC of 0.87 using only four physical parameters and was successfully deployed in TensorFlow Lite format on both the embedded probe and the mobile application with sub-millisecond inference time. Integration with a generative AI backend provides contextual natural-language interpretations of measurements. These results demonstrate that reduced-parameter edge AI systems can provide reliable environmental diagnostics while enhancing accessibility and citizen engagement for participatory water monitoring. Full article

(This article belongs to the Special Issue Sustainable Water and Environmental Technologies of Global Relevance)

► Show Figures

Graphical abstract

17 pages, 5409 KB

Open AccessArticle

Robot-Assisted Omnidirectional Gait Training: Control System Design and Fall Prediction

by Shuoyu Wang and Taiki Miyaji

Technologies 2026, 14(5), 295; https://doi.org/10.3390/technologies14050295 - 12 May 2026

Abstract

The number of patients with lower-limb dysfunction is increasing each year due to aging, illness, accidents, and other factors. Without timely rehabilitation and rapid recovery of walking function, further physical and mental deterioration may be accelerated, potentially leading to long-term bedriddenness. This study [...] Read more.

The number of patients with lower-limb dysfunction is increasing each year due to aging, illness, accidents, and other factors. Without timely rehabilitation and rapid recovery of walking function, further physical and mental deterioration may be accelerated, potentially leading to long-term bedriddenness. This study discusses gait training in rehabilitation therapy from the perspectives of kinesiology, cognitive science, walking function, and safety, and an omnidirectional gait training robot was developed. This study proposed a control system construction method for an omnidirectional gait training robot based on both prescription-based training and autonomous training. In the prescription-based training system, the target values are derived from the training prescription, and the control objective is to guide the patient to walk along the robot’s prescribed path and speed. In the autonomous training system, the target values are automatically generated based on the patient’s walking intentions, and the control objective is for the robot to safely follow the patient’s movement. A necessary condition for robot-assisted autonomous gait training is effective fall prevention. A fall prediction strategy based on foot position information and handrail pressure data was developed. Using this strategy, the robot can predict falls immediately before they occur, similar to a physical therapist, thereby reducing the risk of falls during gait training. Experimental results demonstrate the feasibility of implementing this strategy. Full article

(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives—Second Edition)

► Show Figures

Graphical abstract

24 pages, 5277 KB

Open AccessArticle

Modeling and Implementation of a Practical Methodology to Size LCL Filter in a Photovoltaic Park

by Judith Gálvez-García, Vicente Torres-García, Juan Ramón Rodríguez, José Ángel Barrios and Alberto Cavazos

Technologies 2026, 14(5), 294; https://doi.org/10.3390/technologies14050294 - 12 May 2026

Abstract

This paper presents a sizing and optimization methodology for LCL filters tailored to high-capacity modular power systems. The approach prioritizes the strategic selection of the resonance frequency, an asymmetric inductance design, and strict harmonic current limits. The methodology is validated through a case [...] Read more.

This paper presents a sizing and optimization methodology for LCL filters tailored to high-capacity modular power systems. The approach prioritizes the strategic selection of the resonance frequency, an asymmetric inductance design, and strict harmonic current limits. The methodology is validated through a case study simulation of a 126 MW photovoltaic plant in a region of Mexico, analyzing its 2.34 MW inverter architecture. The simulations show that precise capacitor sizing for reactive power management, combined with a passive resistive damping strategy, ensures compliance with grid interconnection standards (IEEE 1547) and power quality standards (IEC 61000). This approach simplifies practical implementation by eliminating the need for complex active damping control algorithms. Additionally, dynamic decoupling is validated through time-domain step responses, and frequency-domain sensitivity analysis confirms robust stability margins even under ±20% variations in passive parameters. Ultimately, the system achieves voltage total harmonic distortion (THD) levels below 0.18%, demonstrating a scalable solution for maintaining grid stability. Full article

► Show Figures

Figure 1

24 pages, 2529 KB

Open AccessArticle

Adaptive L-Wigner Initialization for Sparse Time–Frequency Distribution Reconstruction

by Vedran Jurdana

Technologies 2026, 14(5), 293; https://doi.org/10.3390/technologies14050293 - 11 May 2026

Abstract

Compressed sensing (CS) applied in the ambiguity domain offers an effective approach for recovering time–frequency distributions (TFDs) of non-stationary signals from sparse representations. Existing methods predominantly rely on the Wigner–Ville distribution (WVD) as the initial representation due to its simplicity and high auto-term [...] Read more.

Compressed sensing (CS) applied in the ambiguity domain offers an effective approach for recovering time–frequency distributions (TFDs) of non-stationary signals from sparse representations. Existing methods predominantly rely on the Wigner–Ville distribution (WVD) as the initial representation due to its simplicity and high auto-term concentration. However, the WVD performs poorly for signals with higher-order frequency-modulated (FM) components and is highly sensitive to interference and noise, which then propagate into the reconstruction. This paper introduces the systematic use of the L-Wigner distribution (LWD) as the initial representation for CS-based reconstruction, providing front-end adaptability to signal characteristics. By generating a controllable family of TFDs ranging from the spectrogram to cross-term-free polynomial WVDs, the LWD enables effective suppression of interference and noise while simultaneously enhancing auto-term localization for nonlinear FM components. The proposed LWD-based reconstruction framework is evaluated against the conventional WVD-based method using several state-of-the-art reconstruction algorithms, whose parameters are jointly optimized through a multi-objective meta-heuristic framework to ensure a fair comparison. Experiments on noisy synthetic signals and real-world gravitational-wave data demonstrate consistent performance gains. On synthetic signals, the proposed approach reduces the average reconstruction error index by up to 36.6%, improves the

ℓ_{1}

-reconstruction error by up to 75.8%, and achieves complete elimination of cross-term energy. In addition, robustness analysis under additive white Gaussian noise shows up to a 75% improvement in

ℓ_{1}

performance. For real gravitational-wave data, the method reduces the mean reconstruction index by up to 5.8% while maintaining auto-term preservation and eliminating cross-term artifacts. These results establish the LWD-based initialization as an effective and general strategy for TFD reconstruction in complex signal environments. Full article

25 pages, 1240 KB

Open AccessArticle

Research on Key Evaluation Indicators and A Measurability Framework for the Development Level of Chinese Manufacturing Industry 6.0

by Bin Li and Wai Yie Leong

Technologies 2026, 14(5), 292; https://doi.org/10.3390/technologies14050292 - 11 May 2026

Abstract

The evolution from Industry 4.0 to Industry 6.0 represents a paradigm shift—moving from automation toward an integrated model that incorporates intelligentization, sustainability, and human-centric resilience. While numerous conceptual frameworks have been put forward, empirical research remains scarce, primarily because of the absence of [...] Read more.

The evolution from Industry 4.0 to Industry 6.0 represents a paradigm shift—moving from automation toward an integrated model that incorporates intelligentization, sustainability, and human-centric resilience. While numerous conceptual frameworks have been put forward, empirical research remains scarce, primarily because of the absence of standardized indicators derived from verifiable corporate disclosures. To fill this research gap, the present study develops three quantifiable indices—Intelligence (INT), Sustainability (SUS), and Resilience & Human-centric (RES)—by extracting data from the annual reports and ESG disclosures of 100 Chinese A-share manufacturing enterprises (covering 2022–2024). Fixed-effects panel regression models are employed to assess the impact of these indices on financial performance (ROA, ROE, EPS), market valuation (Tobin’s Q), and sustainability outcomes (ESG ratings). Our findings reveal that INT is the most significant predictor of profitability, with statistically significant positive effects on ROA and ROE—effects that are particularly pronounced among high-tech enterprises. This supports the view that digital capabilities serve as strategic assets. SUS also demonstrates a positive influence on performance, especially in non-high-tech enterprises, where eco-efficiency, regulatory compliance, and ESG-linked financing help offset technological disadvantages. RES contributes to operational and financial stability by enhancing human capital, safety protocols, and organizational practices that reduce performance volatility. Collectively, these results indicate that different types of enterprises follow distinct yet converging pathways toward Industry 6.0: high-tech enterprises capitalize on intelligence to generate innovation rents, while non-high-tech enterprises increasingly rely on sustainability and resilience as strategies to build legitimacy. This study makes significant contributions in three aspects: Methodologically, it differs from previous research that relies on questionnaires and interviews. Instead, it quantifies Industry 6.0 through auditable large-sample key indicators, enhancing the objectivity and operability of the indicators. Empirically, it provides the first empirical evidence on the development path of Industry 6.0 based on data from Chinese manufacturing enterprises. In practical terms, it offers clear references for enterprises and policymakers on the core indicators and their construction framework that should be prioritized during the transformation to Industry 6.0. By linking the index derived from enterprise disclosures with quantifiable performance results, this study effectively bridges the gap between theoretical conceptions and practical applications. It further emphasizes that Industry 6.0 is not merely a technological upgrade but a systematic transformation driven by digitalization, sustainability, and resilience aimed at enhancing enterprise performance and achieving sustainable industrial development. Full article

(This article belongs to the Topic Industrial Big Data and Artificial Intelligence)

25 pages, 9481 KB

Open AccessArticle

Study on the Effect of Microbial/Enzyme-Induced Calcium Carbonate Precipitation Combined with Fiber Reinforcement on the Mechanical Properties and Permeability Resistance of Sand

by Shuquan Peng, Yilin Qi, Ling Fan, Wanqi Huang and Yan Zhou

Technologies 2026, 14(5), 291; https://doi.org/10.3390/technologies14050291 - 11 May 2026

Abstract

Against the backdrop of growing demand for environmentally friendly reinforcement in geotechnical engineering, natural fiber reinforcement combined with microbial-induced calcium carbonate (MICP) and enzyme-induced calcium carbonate (EICP) technologies has garnered significant attention due to their eco-friendly and efficient advantages. However, few studies have [...] Read more.

Against the backdrop of growing demand for environmentally friendly reinforcement in geotechnical engineering, natural fiber reinforcement combined with microbial-induced calcium carbonate (MICP) and enzyme-induced calcium carbonate (EICP) technologies has garnered significant attention due to their eco-friendly and efficient advantages. However, few studies have reported the combined application of these three techniques for sand consolidation. This study employs a combined MICP-EICP approach with natural fiber reinforcement to enhance the overall strength of sandy soils and investigate related rock fracture permeability phenomena. Tests conducted include calcium carbonate content, unconfined compressive strength, permeability coefficient, and permeability flow rate. Results indicate that when brown fiber length is 6 mm and dosage is 0.8%, the unconfined compressive strength of MICP-EICP composite specimens reaches a maximum of 0.61 MPa, calcium carbonate content peaks at 7.07%, and permeability coefficient drops to a minimum of 0.0044 cm/s. This composite method offers a highly promising and sustainable improvement solution for geotechnical engineering applications such as sand consolidation, crack sealing, and cultural relic restoration. It not only optimizes mechanical properties but also enhances the utilization rate of waste materials. Full article

(This article belongs to the Section Innovations in Materials Science and Materials Processing)

► Show Figures

Figure 1

18 pages, 1827 KB

Open AccessArticle

Design Insights for Exploring Identity Bubbles with Alternate Reality Gameplay

by Guilherme Almeida, Mariana Seiça and Licínio Roque

Technologies 2026, 14(5), 290; https://doi.org/10.3390/technologies14050290 - 10 May 2026

Abstract

To activate conscious reflection regarding personal identity and identity-building processes in our daily lives is an increasing social concern. With this aim, we designed an Alternate Reality Game that invites participants to collectively explore these themes. Participants played with a prototype, evoking themes [...] Read more.

To activate conscious reflection regarding personal identity and identity-building processes in our daily lives is an increasing social concern. With this aim, we designed an Alternate Reality Game that invites participants to collectively explore these themes. Participants played with a prototype, evoking themes of identity through emergent dynamics from gameplay and interpersonal interactions. We analyzed participants’ appropriation of the prototype through logged activity, direct observation and interviews. The identified dynamics enabled iterative redesign and further exploration of the players’ interaction and behaviors. From this process, we synthesized four design insights as our main findings that may guide further research in the field: (1) how to explore design–play–reflect as a co-design process supported on individual appropriation, (2) how ARGs generate reflective social phenomena, such as varied social identity and power narratives, (3) how ARG design can open doors to balance power dynamics, and (4) how ARG designs can become generative social theories. Our main contributions, alongside the designed prototype, are these four insights, and their potential scalability to other ARG designs that seek to provoke social phenomena and collaborative interventions. Full article

(This article belongs to the Special Issue Disruptive Technologies: Big Data, AI, IoT, Games, and Mixed Reality)

► Show Figures

Figure 1

14 pages, 1434 KB

Open AccessArticle

Automated Information Extraction from Safety and Material Data Sheets—A Domain-Specific NLP Pipeline for Structured Material Data Management in Battery Cell Production

by Simon Otte, Felix Bayer, Sebastian Schabel and Jürgen Fleischer

Technologies 2026, 14(5), 289; https://doi.org/10.3390/technologies14050289 - 9 May 2026

Abstract

The performance of lithium-ion batteries is strongly determined by material properties, which are provided in technical data sheets but often in inconsistent formats and terminology. Automated extraction of these parameters could enable downstream applications such as process optimization, traceability, and hazard assessment. However, [...] Read more.

The performance of lithium-ion batteries is strongly determined by material properties, which are provided in technical data sheets but often in inconsistent formats and terminology. Automated extraction of these parameters could enable downstream applications such as process optimization, traceability, and hazard assessment. However, current approaches are unsuitable for industrial use. This work presents a prototype NLP-based extraction pipeline for material and safety data sheets. Using fine-tuned SpaCy models, F1-scores above 0.7 are achieved for key parameters such as CAS number, molecular mass, and density. The resulting structured material database provides a foundation for data-driven applications in battery cell production. The feasibility of domain-specific NLP for automated material information extraction is demonstrated and potential pathways for integration with process control and optimization workflows are discussed. Full article

(This article belongs to the Section Information and Communication Technologies)

► Show Figures

Figure 1

22 pages, 946 KB

Open AccessArticle

Assessment of Geometric Scaling Factors and Anisotropic Phase Formation in GMAW-Additively Manufactured Duplex Stainless Steel (ER2209) Components

by Uhamir Patrick, Stefanija Klaric and Sara Havrlisan

Technologies 2026, 14(5), 288; https://doi.org/10.3390/technologies14050288 - 8 May 2026

Abstract

Duplex stainless steel (DSS) blends impressive mechanical and chemical characteristics to withstand aggressive environments. Its fabrication by Gas Metal Arc Welding-Additive Manufacturing is an emerging research topic. However, its sensitive grain structure and alloy composition are prone to deterioration by repeated thermal shocks. [...] Read more.

Duplex stainless steel (DSS) blends impressive mechanical and chemical characteristics to withstand aggressive environments. Its fabrication by Gas Metal Arc Welding-Additive Manufacturing is an emerging research topic. However, its sensitive grain structure and alloy composition are prone to deterioration by repeated thermal shocks. Whether optimal weld parameters can resolve these challenges without additional costs from special fillers, gases, or mechanisms is a valid question. In this study, how different wire feed speeds, travel speeds, and weld voltages, chosen from a set of preliminary beads, translate into wall dimensions, phase formation and distribution, morphological transformation, and elemental segregation is investigated. The unique DSS microstructures were characterised using scanning electron microscopy and energy-dispersive spectroscopy to reveal differences in microstructural evolution and ferrite-austenite (α-γ) structure. The deposited walls exhibited satisfactory geometric quality with negligible distortions. However, the height suppression was noticeable at the deposition energy (DE) of 755 J/mm. Metallographic analysis revealed low γ phase formation (<30%) at low DE (227 J/mm) and excessive γ formation (>70%) in the high DE wall (755 J/mm). The parameters WFS:TS = 15, TS = 35 cm/min, WFS = 525 cm/min, and V = 20.804 volts suppressed the elemental segregation while maintaining a suitable phase balance without post-processing. Full article

(This article belongs to the Section Innovations in Materials Science and Materials Processing)

35 pages, 6044 KB

Open AccessArticle

Artificial Intelligence in Web Accessibility: Towards a Theory of LLM-Assisted Remediation for Visual Disabilities

by Guillermo Vera-Amaro, Rodolfo Vera-Amaro, Miguel Felix Mata-Rivera and José Rafael Rojano-Cáceres

Technologies 2026, 14(5), 287; https://doi.org/10.3390/technologies14050287 - 8 May 2026

Abstract

Recent research on web accessibility has explored the use of artificial intelligence (AI), particularly large language models (LLMs), to support accessibility remediation. However, the field lacks a theoretical perspective explaining how LLMs can be integrated to systematically support this process. This study proposes [...] Read more.

Recent research on web accessibility has explored the use of artificial intelligence (AI), particularly large language models (LLMs), to support accessibility remediation. However, the field lacks a theoretical perspective explaining how LLMs can be integrated to systematically support this process. This study proposes a theory of LLM-assisted web accessibility remediation. It is built through the integration of qualitative evidence, prior literature, accessibility standards, and empirical studies on LLM-based remediation. The resulting theory provides an explanatory framework describing how LLMs can assist web accessibility remediation through iterative cycles of analysis, transformation, and validation, and identifies key factors including prompting strategies, input representations, and validation mechanisms. This work provides a conceptual foundation for understanding and systematically studying LLM-assisted accessibility remediation, and supports both research and practice by guiding future studies and informing the design of models, methods, tools, and accessibility engineering practices. Full article

(This article belongs to the Section Information and Communication Technologies)

► Show Figures

Figure 1

2 pages, 147 KB

Open AccessCorrection

Correction: Geiger et al. Monitoring of Hip Joint Forces and Physical Activity After Total Hip Replacement by an Integrated Piezoelectric Element. Technologies 2024, 12, 51

by Franziska Geiger, Henning Bathel, Sascha Spors, Rainer Bader and Daniel Kluess

Technologies 2026, 14(5), 286; https://doi.org/10.3390/technologies14050286 - 8 May 2026

Abstract

The authors wish to make the following corrections to their paper [...] Full article

52 pages, 1693 KB

Open AccessReview

A Comprehensive Review of Liquid-Injector Technologies for Space Propulsion

by Raluca Andreea Roșu, Daniel-Eugeniu Crunțeanu, Emilia Georgiana Prisăcariu and Oana Dumitrescu

Technologies 2026, 14(5), 285; https://doi.org/10.3390/technologies14050285 - 6 May 2026

Abstract

Liquid rocket engine injectors play a fundamental role in determining combustion efficiency, stability, and overall propulsion performance. This review paper provides a comprehensive analysis of liquid-injector technologies used in space propulsion systems, with emphasis on their historical evolution, atomization mechanisms, and cross-domain insights [...] Read more.

Liquid rocket engine injectors play a fundamental role in determining combustion efficiency, stability, and overall propulsion performance. This review paper provides a comprehensive analysis of liquid-injector technologies used in space propulsion systems, with emphasis on their historical evolution, atomization mechanisms, and cross-domain insights from aviation fuel injection systems. The study begins by examining the fundamental processes governing liquid jet breakup, including primary and secondary atomization, ligament formation, and droplet generation, together with the non-dimensional parameters that control these phenomena. The historical development of injector architectures -from early orifice-based and impinging designs to modern coaxial and pintle configurations—is then discussed in relation to increasing performance requirements and combustion stability challenges. A comparative perspective with aviation gas turbine injectors is introduced to highlight similarities in atomization physics and differences in operating conditions and design constraints. The paper also reviews experimental and numerical approaches used to characterize spray formation and injector performance. The results indicate that injector geometry and flow conditions strongly influence mixing efficiency, droplet size distribution, and combustion–acoustic coupling mechanisms. The study concludes that integrating cross-domain knowledge and modern design techniques is essential for advancing injector performance in next-generation propulsion systems. Full article

30 pages, 1309 KB

Open AccessArticle

Efficient QKD in Non-Ideal Scenarios with User-Defined Output Length Requirements

by Andres Martin-Megino, Blanca Lopez, Ivan Vidal and Francisco Valera

Technologies 2026, 14(5), 284; https://doi.org/10.3390/technologies14050284 - 6 May 2026

Abstract

Quantum Key Distribution (QKD) enables two parties to securely share encryption keys by leveraging the principles of quantum mechanics, offering protection against eavesdropping. In practical implementations, QKD systems often rely on a layered architecture where a key manager stores secret key material in [...] Read more.

Quantum Key Distribution (QKD) enables two parties to securely share encryption keys by leveraging the principles of quantum mechanics, offering protection against eavesdropping. In practical implementations, QKD systems often rely on a layered architecture where a key manager stores secret key material in a buffer and delivers it to higher communication layers as needed. However, this buffer can be depleted under high demand, requiring efficient replenishment strategies that minimize resource waste. Given the importance of optimizing time and resources in quantum cryptography protocols, we introduce a variable-length adaptation of the BB84 protocol designed to meet user-defined output key length constraints in non-ideal scenarios. We present a method for dynamically configuring the protocol’s initial parameters to generate secret keys of a desired length. To validate our approach, we developed simulation tools to model general QKD networks and discrete-variable protocols. These tools were used to implement and evaluate our strategies, which were developed within the BB84 framework but can be extended to other QKD protocols under reasonable assumptions. The results highlight their usefulness in optimizing quantum resource usage and supporting key management, contributing to the long-term goal of scaling and strengthening secure quantum networks. Full article

(This article belongs to the Section Quantum Technologies)

► Show Figures

Figure 1

Journal Description

Technologies

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI