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Developing a BIM–GIS-Based Digital Twin for the Operation and Maintenance of an Urban Ring Road: The M-30 Case Study
From Casting to Printing: Rheological Modification of General-Purpose RTV-2 Silicones for Material Extrusion
Early Detection of Flying Obstacles Using Optical Flow to Assist the Pilot in Avoiding Mid-Air Collisions
NGS Data of Local Soil Bacterial Communities Reflecting the Ditch Profile of a Neolithic Rampart from Hachum (Germany)
Stiffness Analysis at Failure State of Reinforced Concrete and Prestressed Concrete Tubular Members Under Internal Blast Loading

Journal Description

Applied Sciences

Applied Sciences is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, Inspec, Embase, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering )
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our authors say about Applied Sciences.
Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal, JETA and AI in Medicine.

Impact Factor: 2.5 (2024); 5-Year Impact Factor: 2.7 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2076-3417

Latest Articles

29 pages, 1798 KB

Open AccessArticle

C&RT-Based Optimization to Improve Damage Detection in the Water Industry and Support Smart Industry Practices

by Izabela Rojek and Dariusz Mikołajewski

Appl. Sci. 2026, 16(8), 3681; https://doi.org/10.3390/app16083681 (registering DOI) - 9 Apr 2026

A water company’s water supply network is responsible for distributing good-quality water in quantities that meet customer needs, ensuring proper operation of the water supply network to ensure adequate pressure at the receiving points, efficiently repairing faults, and planning and executing maintenance, modernization, [...] Read more.

A water company’s water supply network is responsible for distributing good-quality water in quantities that meet customer needs, ensuring proper operation of the water supply network to ensure adequate pressure at the receiving points, efficiently repairing faults, and planning and executing maintenance, modernization, and expansion work. Managing a water supply network is a complex and complex process. A crucial challenge in water company management is detecting and locating hidden water leaks in the water supply network. Leak location in water distribution networks is a key challenge for utilities, as undetected leaks lead to water losses, increased energy consumption, and reduced service reliability. With the development of cyber-physical systems (CPSs), the integration of physical infrastructure with real-time digital monitoring has enabled more adaptive and responsive water operations. Data-driven decision-making in CPS in the water industry leverages classification and regression trees (C&RTs) to analyze real-time sensor data—such as pressure, flow, and consumption—to classify system states and predict potential faults. By transforming operational data into interpretable decision rules, C&RTs enable automated and timely maintenance actions that improve reliability, reduce water loss, and support intelligent infrastructure management. The aim of this study is to develop and evaluate AI-based optimization methods to enhance sustainability, efficiency, and resilience in the water industry by enabling autonomous, data-driven decision-making within CPSs, supporting smart industry practices, and addressing practical challenges associated with the actual implementation of smart water management solutions using simple solutions such as C&RTs. The accuracy of the best classifier was 86.15%. Further research will focus on using other types of decision trees that will improve classification accuracy. Full article

(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges, 3rd Edition)

32 pages, 1293 KB

Open AccessArticle

Early Detection of Re-Identification Risk in Multi-Turn Dialogues via Entity-Aware Evidence Accumulation

by Yeongseop Lee, Seungun Park and Yunsik Son

Appl. Sci. 2026, 16(8), 3680; https://doi.org/10.3390/app16083680 (registering DOI) - 9 Apr 2026

In multi-turn conversational AI, individually innocuous personally identifiable information (PII) fragments disclosed across successive turns can accumulate into a re-identification risk that no single utterance reveals on its own. Existing PII detectors operate on isolated utterances and therefore cannot track this cross-turn evidence [...] Read more.

In multi-turn conversational AI, individually innocuous personally identifiable information (PII) fragments disclosed across successive turns can accumulate into a re-identification risk that no single utterance reveals on its own. Existing PII detectors operate on isolated utterances and therefore cannot track this cross-turn evidence build-up. We propose a stateful middleware guardrail whose core design principle is speaker-attributed entity isolation: every extracted PII fragment is attributed to its originating conversational participant, and evidence is accumulated in entity-isolated subgraphs that prevent cross-entity contamination. The system signals re-identification onset

t_{pred}

at the earliest turn where combination-based rules grounded in the uniqueness literature are satisfied. On a 184-record template-synthetic evaluation corpus, the gated NER configuration leads on primary timeliness (OW@5

= 73.4 %

, MAE

= 1.357

turns); the full system achieves OW@5

= 70.7 %

with MAE

= 2.442

turns as an alternative operating mode for ambiguity-sensitive disclosure patterns. We further evaluate behavior on a 300-record mutation stress set, test RULE_B on the ABCD external corpus, and supplement RULE_A evaluation with both a proxy-labeled transfer analysis on PersonaChat and a manual annotation study on 151 Switchboard dialogues. The reported results should be interpreted as an initial empirical reference point rather than a sufficient endpoint for autonomous runtime enforcement. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems—2nd edition)

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27 pages, 1991 KB

Open AccessReview

Oxygen-Releasing Calcium Peroxide Nanoparticles for Biomedical Applications: From Synthesis to Clinical Relevance

by Beatriz Pereira and Catarina Santos

Appl. Sci. 2026, 16(8), 3678; https://doi.org/10.3390/app16083678 (registering DOI) - 9 Apr 2026

Calcium peroxide nanoparticles (CaO₂ NPs) have recently attracted increasing attention as oxygen-generating nanomaterials with potential biomedical applications. Their ability to release molecular oxygen and reactive oxygen species (ROS) in aqueous environments enables modulation of hypoxic and oxidative microenvironments, which play critical roles [...] Read more.

Calcium peroxide nanoparticles (CaO₂ NPs) have recently attracted increasing attention as oxygen-generating nanomaterials with potential biomedical applications. Their ability to release molecular oxygen and reactive oxygen species (ROS) in aqueous environments enables modulation of hypoxic and oxidative microenvironments, which play critical roles in infection control, tumor progression, and tissue regeneration. Despite growing interest in oxygen-releasing biomaterials, the literature specifically addressing CaO₂ nanomaterials remains comparatively limited and fragmented, particularly when compared with the extensive body of work on calcium oxide-based systems. This review provides a comprehensive overview of CaO₂ nanoparticles, focusing on synthesis strategies, physicochemical properties, and emerging biomedical applications. Conventional bottom-up synthesis routes based on calcium salts, calcium hydroxide, and calcium oxide are critically compared, highlighting the influence of reaction parameters and stabilizing agents on particle size, morphology, crystallinity, and colloidal stability. Surface modification strategies, including polyethylene glycol, polyvinylpyrrolidone, and hyaluronic acid, are also discussed for their role in improving nanoparticle stability, regulating decomposition kinetics, and enhancing biocompatibility. The mechanisms governing oxygen and ROS generation are analysed in relation to antibacterial activity, hypoxia alleviation in tumor microenvironments, and oxygen-supplying biomaterials for tissue engineering and wound healing. In addition, key challenges associated with oxidative stress responses are discussed. Finally, the review outlines current limitations and perspectives regarding the clinical translation of CaO₂-based nanotherapeutic systems. Overall, this work aims to consolidate the currently dispersed knowledge on CaO₂ nanoparticles and provide a critical framework to guide future research in oxygen-releasing nanomedicine. Full article

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19 pages, 2420 KB

Open AccessArticle

Epoxy Resin Mastery: Crafting Educational Models with Precision

by Martins Bode, Gertrude Strance, Dzintra Kazoka and Mara Pilmane

Appl. Sci. 2026, 16(8), 3673; https://doi.org/10.3390/app16083673 (registering DOI) - 9 Apr 2026

Organ preservation is a necessary and diverse process in morphological studies and is traditionally achieved through formalin fixation and plastination. A comparatively innovative method is organ embedding in epoxy resin, which provides durable and non-toxic models during manipulation. This study aimed to create [...] Read more.

Organ preservation is a necessary and diverse process in morphological studies and is traditionally achieved through formalin fixation and plastination. A comparatively innovative method is organ embedding in epoxy resin, which provides durable and non-toxic models during manipulation. This study aimed to create 30 models: 12 from human specimens and 18 from animal specimens. Samples were incubated in 96.2% ethanol for 24 h to disinfect and remove formalin and excess fat, followed by 100% glycerol incubation for 2 h under vacuum to create a protective interface between the tissue and the activated epoxy resin. Afterward, the tissues were fixed in scaffolds and embedded in epoxy resin. Once hardened, the models were post-processed to enhance clarity and longevity. Each model was mounted on a wooden platform featuring a QR code linking to a presentation describing the visible anatomical structures. Some modifications were made to previously described protocols to optimize the method, improving quality and reducing preparation time. Among the 30 models, two anatomical and two clinical cases of organ preservation were especially interesting. Despite the numerous challenges and limitations, this method yields promising potential for morphological studies, allowing safe organ manipulation without protective equipment and anatomical documentation via QR code-linked presentations. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

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30 pages, 7859 KB

Open AccessArticle

Electromagnetic Forces in U-Shaped Rectangular Busbars Used in Power Systems Under Two-Phase Short-Circuit Faults: Experiments and Coupled FEM Validation

by Cristian-Eugeniu Sălceanu, Dumitru Cazacu, Daniela Iovan, Daniel Ocoleanu and Marius Boncea

Appl. Sci. 2026, 16(8), 3672; https://doi.org/10.3390/app16083672 (registering DOI) - 9 Apr 2026

This study examines a U-shaped rectangular copper busbar under a two-phase short circuit, combining high-power laboratory measurements with a coupled transient finite-element electromagnetic model. Short-circuit currents and forces were recorded using a high-speed acquisition system, while the model coupled an RL circuit with [...] Read more.

This study examines a U-shaped rectangular copper busbar under a two-phase short circuit, combining high-power laboratory measurements with a coupled transient finite-element electromagnetic model. Short-circuit currents and forces were recorded using a high-speed acquisition system, while the model coupled an RL circuit with electric currents and magnetic fields to compute flux density and Lorentz forces. Eight test cases (93 V to 125.75 V) produced peak currents up to 35.76 kA and forces exceeding 1 kN. The model accurately reproduces peak currents, while computed forces agree well in magnitude and temporal evolution with measurements. Results show maximum field and force concentrations at inner corners and segment junctions, identifying critical mechanical regions. The study provides validated insight into this busbar configuration and a workflow applicable to other non-standard geometries. Full article

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

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27 pages, 4848 KB

Open AccessArticle

Exploring Domestic Lighting Practices in Adulthood and Early Ageing

by Turid Borgestrand Øien, Nanet Mathiasen, Anne Kathrine Frandsen and Senja Ruohonen

Appl. Sci. 2026, 16(8), 3671; https://doi.org/10.3390/app16083671 (registering DOI) - 9 Apr 2026

Understanding lighting practices is crucial for ensuring social robustness and sensitivity to context when implementing technical solutions in real-life settings. As lighting can create functional, sustainable, and aesthetically pleasing environments, it is important to bridge the gap between scientific knowledge and social and [...] Read more.

Understanding lighting practices is crucial for ensuring social robustness and sensitivity to context when implementing technical solutions in real-life settings. As lighting can create functional, sustainable, and aesthetically pleasing environments, it is important to bridge the gap between scientific knowledge and social and physical situations. Domestic lighting is no exception; however, the sociocultural, perceptual, and sensory qualities of light have been neglected in engineering-oriented practices, while ethnographic approaches to domestic lighting seldom cover the material and technical aspects of the phenomenon. The role of light evolves according to people’s changing needs and abilities, as seen in age-related changes and incipient vision loss, so a broader understanding of domestic lighting practices can help in preparing for senior life. Combining methods from ethnography, architecture, and engineering, this article provides new knowledge on the dynamics of the socio-technical elements of domestic lighting. Interviews, lighting measurements, and field observations conducted in 37 Danish homes revealed that the mundane, everyday practices of the home environment embody patterns as well as diverging conventions and norms. People navigate their domestic lighting in accordance with specific activities and orchestrate micro-atmospheres between light and darkness, resulting in a composite palette of task light, isles of light, and lightscapes. Full article

(This article belongs to the Special Issue High-Quality Lights and Lighting Systems: Parametrization, Design and Optimization)

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19 pages, 5624 KB

Open AccessArticle

Non-Contact Bearing Fault Diagnostics: Experimental Investigation of Microphones Position and Distance

by Emanuele Voltolini, Andrea Toscani, Enrico Armelloni, Marco Cocconcelli, Lorenzo Fendillo and Elisabetta Manconi

Appl. Sci. 2026, 16(8), 3670; https://doi.org/10.3390/app16083670 (registering DOI) - 9 Apr 2026

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and [...] Read more.

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

(This article belongs to the Collection Bearing Fault Detection and Diagnosis)

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28 pages, 1509 KB

Open AccessArticle

Quantifying Structural Divergence Between Human and Diffusion-Based Generative Visual Compositions

by Necati Vardar and Çağrı Gümüş

Appl. Sci. 2026, 16(8), 3669; https://doi.org/10.3390/app16083669 (registering DOI) - 9 Apr 2026

The rapid proliferation of text-to-image generative systems has transformed visual content production, yet the structural characteristics embedded in their compositional outputs remain insufficiently understood. Rather than approaching human–AI differentiation as a purely classification problem, this study investigates whether a controlled set of AI-generated [...] Read more.

The rapid proliferation of text-to-image generative systems has transformed visual content production, yet the structural characteristics embedded in their compositional outputs remain insufficiently understood. Rather than approaching human–AI differentiation as a purely classification problem, this study investigates whether a controlled set of AI-generated and human-designed posters exhibits measurable structural divergence under thematically matched conditions. A dataset of jazz festival posters was analyzed using interpretable geometric and information-theoretic descriptors, including spatial density (padding ratio), edge density, chromatic dispersion, and entropy-based measures. Instead of relying on deep neural detection architectures, we employed a transparent machine-learning framework to examine intrinsic structural separability within feature space. Results demonstrated highly stable group separation (ROC-AUC = 0.99; 95% CI: 0.978–0.999) under cross-validated evaluation. Distributional analysis further revealed a pronounced divergence in spatial density allocation (Kolmogorov–Smirnov statistic = 0.76, p < 10⁻²⁸), accompanied by a very large effect size (Cohen’s d = 1.365). While padding ratio emerged as the dominant discriminative factor, additional entropy- and chromatic-based descriptors contributed to group separation even when spatial density was excluded (AUC = 0.903). These findings indicate that AI-generated and human-designed posters can diverge in negative space allocation and chromatic organization under controlled thematic and platform-specific conditions. The study contributes to the explainable analysis of generative visual systems by reframing human–AI differentiation as a structural divergence problem grounded in interpretable image statistics rather than as a model-specific artifact detection task. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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11 pages, 324 KB

Open AccessArticle

Influence of Ankle Joint Mobility on Stretch-Shortening Cycle Contribution in Vertical Jump Performance

by Giuseppe Di Lascio, Giuseppe Giardullo, Fiore Mazza, Giovanni Esposito, Vincenzo Manzi and Gaetano Raiola

Appl. Sci. 2026, 16(8), 3668; https://doi.org/10.3390/app16083668 (registering DOI) - 9 Apr 2026

The Stretch-Shortening Cycle (SSC) is a fundamental neuromuscular mechanism that enhances explosive performance by storing and releasing elastic energy, particularly through the Achilles tendon. While tendon stiffness and neuromuscular coordination are known to influence SSC efficiency, the role of ankle joint mobility in [...] Read more.

The Stretch-Shortening Cycle (SSC) is a fundamental neuromuscular mechanism that enhances explosive performance by storing and releasing elastic energy, particularly through the Achilles tendon. While tendon stiffness and neuromuscular coordination are known to influence SSC efficiency, the role of ankle joint mobility in this context remains unclear. This study aimed to investigate the relationship between ankle dorsiflexion range of motion and SSC contribution during vertical jump performance in trained adults. Twenty-seven physically active participants (19 males, 8 females) were assessed for ankle dorsiflexion using the Leg Motion system and performed both Squat Jump (SJ) and Countermovement Jump (CMJ) on a force platform. SSC contribution was calculated as the difference between CMJ and SJ heights, expressed in both absolute (centimeters) and relative (percentage) terms. Participants were categorized into high and low mobility groups based on the median dorsiflexion value (13 cm). Statistical analyses, including Pearson correlation (r = −0.262, p = 0.186), linear regression (R² = 0.069), and independent t-tests, showed no significant association between ankle mobility and SSC contribution. No meaningful performance differences were observed between the two groups in CMJ, SJ, or SSC metrics. These findings suggest that ankle dorsiflexion does not independently predict SSC utilization in vertical jumping among trained individuals. Other factors such as tendon stiffness, explosive strength, and neuromuscular coordination may play a more decisive role. Future research should include dynamic mobility assessments and more diverse populations to better understand the interplay of these variables. Full article

(This article belongs to the Special Issue Advances in Foot Biomechanics and Gait Analysis, 2nd Edition)

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26 pages, 2496 KB

Open AccessArticle

Integrated Airline Recovery Under Uncertain Disruptions: A Fuzzy Programming Approach

by Shuai Wu, Yanfeng Jia, Xiufeng Chen and Dayi Qu

Appl. Sci. 2026, 16(8), 3667; https://doi.org/10.3390/app16083667 (registering DOI) - 9 Apr 2026

Disruption management is critical for airline operations, yet existing recovery models often assume deterministic disruption durations, limiting their effectiveness in real-world, uncertain environments. This paper addresses the integrated airline recovery problem under uncertain disruptions. To capture this uncertainty, delay times are modeled as [...] Read more.

Disruption management is critical for airline operations, yet existing recovery models often assume deterministic disruption durations, limiting their effectiveness in real-world, uncertain environments. This paper addresses the integrated airline recovery problem under uncertain disruptions. To capture this uncertainty, delay times are modeled as fuzzy variables and a fuzzy chance-constrained programming model is developed, aimed at minimizing total recovery costs. The model is transformed into a deterministic equivalent using trapezoidal fuzzy numbers. An improved Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is designed to efficiently solve the problem, balancing solution quality and computational efficiency through insert, exchange, and cancel. The local search process is enhanced by incorporating the acceptance criteria of the simulated annealing algorithm. The proposed method is validated using real-world airline data. Results show that, compared to the traditional GRASP algorithm, the improved GRASP algorithm can obtain better solutions in a shorter time; the solutions in deterministic scenarios tends to be more conservative, leading to resource waste; the proposed method can achieve airline recovery at the minimum recovery cost. Sensitivity analysis reveals that selecting an appropriate confidence level significantly influences recovery costs. This paper provides a robust framework for enhancing operational resilience and passenger satisfaction under uncertain conditions, offering practical insights for real-world application. Full article

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25 pages, 5394 KB

Open AccessArticle

Towards the Development of Multiscale Digital Twins for Fiber-Reinforced Composite Materials Using Machine Learning

by Brandon L. Hearley, Evan J. Pineda, Brett A. Bednarcyk, Joseph R. Baker and Laura G. Wilson

Appl. Sci. 2026, 16(8), 3666; https://doi.org/10.3390/app16083666 - 9 Apr 2026

Material considerations are often neglected when developing digital twins, particularly at the relevant length scales that drive material and structural performance. For reinforced composite materials, the microscale has the largest impact on nonlinear material behavior and progressive damage, and thus accurately representing the [...] Read more.

Material considerations are often neglected when developing digital twins, particularly at the relevant length scales that drive material and structural performance. For reinforced composite materials, the microscale has the largest impact on nonlinear material behavior and progressive damage, and thus accurately representing the disordered microstructure of a composite due to processing and manufacturing is critical to developing the material digital twin in the multiscale hierarchy. Automating microstructure characterization is typically done by either training convolutional neural network models using a pretrained encoder or using prompt-based segmentation tools. In this work, a toolset for developing segmentation models is presented, combining these two methods to enable rapid annotation, training, and deployment of microscopy segmentation models for automated material digital twin development without user knowledge of machine learning. Additionally, a Bayesian optimization framework is developed for generating statistically equivalent representative volume elements (SRVE) to a segmented microstructure using a random microstructure generator that implements soft body dynamics. Progressive failure analysis of random, statistically equivalent, and ordered microstructures is compared to the segmented microstructure subject to transverse loading to demonstrate the importance of accurately representing the driving material length scale of a composite digital twin. Ordered microstructures over-predicted crack initiation and ultimate strength and strain. Random and optimized RVE microstructures better agreed with the segmented simulation results, with no significant difference observed between the two methodologies. The improvement in predicted macroscale behavior for models that capture disordered microstructures due to manufacturing processes demonstrates the importance of capturing microstructure features in composites modeling and indicates that SRVEs that capture microstructural features of the physical material can be used in material digital twin development. Further, the toolsets provided in this work allow for rapid development of composite material digital twins without user expertise in machine learning. This has enabled the development of an integrated workflow to automatically characterize and idealize composite microstructures and generate representative geometric models for efficient micromechanics analysis. Full article

(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence, 2nd Edition)

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19 pages, 6501 KB

Open AccessArticle

Study on Near-Field Spectral Characteristics and Vibration Control of Multi-Hole Blasting Based on VMD

by Dasong Zhang, Hongyan Xu, Hui Chen, Jinggang Zhang, Sifan Wei, Yuanxiang Mu and Fei Gao

Appl. Sci. 2026, 16(8), 3665; https://doi.org/10.3390/app16083665 - 9 Apr 2026

To explore the spectral characteristics of near-field vibration signals from multi-hole millisecond-delay blasting in open-pit mines and the modulation effect of delay time on blasting energy distribution, field blasting vibration tests with multi-gradient delays were conducted taking an open-pit coal mine in Xinjiang [...] Read more.

To explore the spectral characteristics of near-field vibration signals from multi-hole millisecond-delay blasting in open-pit mines and the modulation effect of delay time on blasting energy distribution, field blasting vibration tests with multi-gradient delays were conducted taking an open-pit coal mine in Xinjiang as the engineering background. Particle Swarm Optimization (PSO) optimized Variational Mode Decomposition (VMD) and Hilbert-Huang Transform (HHT) were introduced for the refined processing and frequency band energy ratio analysis of the measured signals, and field vibration control tests were subsequently carried out. The results show that compared with the traditional Empirical Mode Decomposition (EMD), the PSO-optimized VMD can effectively overcome the mode aliasing phenomenon. By extracting the high-frequency Intrinsic Mode Function (IMF7) that characterizes the instantaneous detonation impulse, the actual delay time was successfully inverted to be 10.47 ms. The inter-hole delay time significantly affects the time-frequency distribution of vibration energy. Under the 25 ms delay condition, the energy ratio of the high-frequency band is the highest, and the low-frequency energy accumulation degree is the lowest, which is most conducive to shortening the vibration duration and accelerating energy attenuation. Control tests further confirmed that adopting a 17 ms delay in the near-slope area can effectively control the peak particle velocity (PPV) in the near field, while adopting a 23 ms delay in the middle and far areas can further reduce the low-frequency energy concentration. The research results demonstrate a dynamic matching strategy for millisecond delays based on spatial distance differences, which has important guiding significance for realizing safe and efficient blasting vibration control in open-pit mines. Full article

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16 pages, 5067 KB

Open AccessArticle

Modeling of Water Quality in Deep Tunnels Coupling Temperature–Depth Effects

by Xiaomei Zhang, Qingmin Zhang, Yuanjing Yang, Yuntao Guan and Rui Chen

Appl. Sci. 2026, 16(8), 3664; https://doi.org/10.3390/app16083664 - 9 Apr 2026

As large-scale underground storage infrastructure, deep tunnels exhibit distinct water quality dynamics driven by ground temperature gradients. Currently, there is limited investigation into water quality modeling for deep tunnel systems. Unraveling the correlation between temperature–depth gradients and water quality evolution is crucial for [...] Read more.

As large-scale underground storage infrastructure, deep tunnels exhibit distinct water quality dynamics driven by ground temperature gradients. Currently, there is limited investigation into water quality modeling for deep tunnel systems. Unraveling the correlation between temperature–depth gradients and water quality evolution is crucial for the operation and management of such systems. In this study, field experiments were carried out in the Qianhai–Nanshan Deep Tunnel to investigate complex water quality behavior, leading to the development of chemical oxygen demand (COD) and ammonia nitrogen (NH₃–N) models that incorporate temporal variation, temperature, and burial depth. Results indicate that temperature is the dominant factor influencing water quality in deep tunnel storage. Increased ground temperature promotes the degradation and mass transport of pollutants within the tunnel system. Owing to temperature–depth effects, the deeply buried Qianhai tunnel significantly reduces river discharge pollution after water storage, with COD and NH₃–N removal rates reaching 74.9% and 26.8%, respectively. Temperature-controlled experiments showed that COD and NH₃–N reduction rates varied between 60–94% and 10–30% across a temperature range of 20–34 °C. The proposed model was validated against experimental data, achieving Nash–Sutcliffe efficiency coefficients of 0.7–0.8. This study provides a methodological foundation for simulating complex aquatic environments and offers a decision-support tool for optimizing the operational strategies of deep tunnel systems. However, the model’s current generalization capability is constrained by the limited experimental conditions (20–34 °C, 12 days) and the lack of experimental replicates, which should be systematically addressed in future studies. Full article

(This article belongs to the Special Issue Environmental Issues in Geotechnical Engineering)

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25 pages, 1844 KB

Open AccessArticle

Retrieval-Augmented Large Language Model-Based Framework for Hierarchical Classification of Public Feedback on Transportation Infrastructure

by Milan Knezevic, Trevor Neece, Marko Vukojevic, Lev Khazanovich and Aleksandar Stevanovic

Appl. Sci. 2026, 16(8), 3663; https://doi.org/10.3390/app16083663 - 9 Apr 2026

Transportation agencies receive large volumes of free-form public comments describing infrastructure conditions, safety concerns, and service issues. These comments are often processed manually for downstream operational actions, which is time-consuming, inconsistent across reviewers, and difficult to scale, thereby limiting their value for operational [...] Read more.

Transportation agencies receive large volumes of free-form public comments describing infrastructure conditions, safety concerns, and service issues. These comments are often processed manually for downstream operational actions, which is time-consuming, inconsistent across reviewers, and difficult to scale, thereby limiting their value for operational decision-making. This study presents a machine learning and Large Language Model (LLM) framework for automated triage of free-form public comments, assigning each report to a three-level hierarchical taxonomy consisting of Category, Subcategory, and Final Decision. The proposed framework uses agency historical data together with retrieval-based evidence, where semantically similar past comments are provided to the LLM as contextual support to better align predictions with agency-specific labeling practices. The framework was evaluated using TF-IDF with Logistic Regression, TF-IDF with Linear SVM, embedding-based kNN with cosine similarity, few-shot LLM prompting, and retrieval-based LLM prompting. Results show that retrieval-based prompting achieved the best overall performance, with the highest accuracy at both the Category and Subcategory levels. At the Final Decision level, retrieval-based prompting slightly outperformed kNN, while few-shot prompting performed worse. Error analysis showed that many misclassifications were semantically plausible alternatives, reflecting the overlap across infrastructure-related complaint categories. When a second candidate label was allowed, further improving performance. Latency analysis also indicated that the framework can process more than 2000 comments in under 30 min, supporting faster and more consistent agency workflows. Full article

(This article belongs to the Special Issue Intelligent Transportation and Mobility Analytics)

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17 pages, 1790 KB

Open AccessReview

Advancements, Challenges, and Innovations in Mechanical and Animal Testing of Lumbar Spine Implants

by Zachary Comella, Raydeep Kainth, Yosuf Arab, Elizabeth Beaulieu, Maohua Lin, Rudy Paul, Richard Sharp, Talha S. Cheema and Frank D. Vrionis

Appl. Sci. 2026, 16(8), 3662; https://doi.org/10.3390/app16083662 - 9 Apr 2026

Lumbar spine disorders often require surgical intervention using medical implants to stabilize or replace damaged structures. As the prevalence of these surgeries increases due to an aging population, rigorous preclinical evaluation is critical. This narrative review aims to summarize current testing methods, identify [...] Read more.

Lumbar spine disorders often require surgical intervention using medical implants to stabilize or replace damaged structures. As the prevalence of these surgeries increases due to an aging population, rigorous preclinical evaluation is critical. This narrative review aims to summarize current testing methods, identify gaps in clinical translatability, and explore the role of emerging computational technologies. Mechanical testing protocols established by the American Society for Testing and Materials (ASTM) and the International Organization for Standardization (ISO) provide essential standardized data on structural integrity but fail to replicate the complex biological interactions of the human spine. Similarly, animal models offer insights into biological responses like osseointegration but are limited by quadrupedal biomechanics and anatomical differences. Recent advancements in Artificial Intelligence (AI) and Finite Element Analysis (FEA) enable rapid, patient-specific modeling and high-throughput screening, significantly reducing the time and cost of physical testing. Future innovations include 3D-printed personalized implants, bio-responsive materials, and genetically modified animal models to bridge existing translatability gaps. In conclusion, improving the clinical success of lumbar spine implants requires an integrated framework that combines mechanical, biological, and computational approaches. This interdisciplinary collaboration is vital for developing safer and more effective treatments for patients. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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19 pages, 9983 KB

Open AccessArticle

Broadband Dielectric Properties of Glycerol–Water Mixtures with Salt Additives

by Moaz M. Altarawneh

Appl. Sci. 2026, 16(8), 3661; https://doi.org/10.3390/app16083661 - 9 Apr 2026

In the current study, the dielectric behavior of ternary mixtures composed of glycerol and water with various salt additives is investigated over a frequency range that extends from 0.5 to 20 GHz and at temperatures between 5 and 55 °C. The investigated mixtures [...] Read more.

In the current study, the dielectric behavior of ternary mixtures composed of glycerol and water with various salt additives is investigated over a frequency range that extends from 0.5 to 20 GHz and at temperatures between 5 and 55 °C. The investigated mixtures consisted of glycerol and water with glycerol volume ratios of 20%, 40%, and 60%. To explore the salt addition’s effect on the dielectric properties, different moderate ionic strengths of glycerol–water mixtures were prepared with NaCl concentrations of 0.10, 0.20, and 0.30 M for the same glycerol volume ratios. The ion-specific effects on the dielectric properties were investigated for prepared mixtures with a 0.10 M concentration of Na₂SO₃, NaNO₃, and KCl for the 20% glycerol ratio to explore ions with different charge density and hydration tendencies. Using dielectric spectroscopy, the frequency dependence of the real (

ε^{'}

) and imaginary (

ε^{″}

) dielectric constants was measured, and the associated dielectric parameters were extracted using the Cole–Cole model. This study shows that increasing the salt concentration results in a slight decrease in

ε^{'}

while

ε^{″}

increases dramatically, especially at lower frequencies, due to enhanced DC conductivity. An isopermittivity behavior is observed in

ε^{'}

as the temperature changes across all mixtures, and it is found to be insensitive to the addition of salt, indicating that it is mainly dictated by the glycerol–water dipolar relaxation network. Among the tested mixtures is the 20% glycerol mixture with 0.10 M KCl, which exhibits the highest

ε^{″}

value in the low-frequency range, attributed to its relatively high DC conductivity. Additionally, the dielectric properties of mixtures with higher glycerol ratios are found to be less sensitive to the addition of salt due to their high viscosity and the higher structured solvent network, which collectively limit ionic mobility and suppress changes in dielectric response. Full article

(This article belongs to the Special Issue Advanced Dielectric Materials for Multifunctional Energy Storage Applications)

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26 pages, 6432 KB

Open AccessArticle

MFF-Net: Multi-Feature Fusion Network for Speech Deception Detection

by Zhihao Zhou, Mingxing Li and Shucheng Huang

Appl. Sci. 2026, 16(8), 3660; https://doi.org/10.3390/app16083660 - 9 Apr 2026

Speech deception detection often suffers from overly narrow feature representations and limited robustness under changing recording conditions. To address these limitations, we propose MFF-Net, a multi-feature fusion network with three modules. The first module uses a Gated-BiLSTM with attention to model sequences of [...] Read more.

Speech deception detection often suffers from overly narrow feature representations and limited robustness under changing recording conditions. To address these limitations, we propose MFF-Net, a multi-feature fusion network with three modules. The first module uses a Gated-BiLSTM with attention to model sequences of acoustic-prosodic (ACO) and nonlinear dynamic (NLD) features. The second module uses a hybrid Transformer–CNN branch on Mel spectrograms, with a Levenberg–Marquardt (LM) refinement stage for the Mel-branch classifier. The third module fuses the two branches via cross-attention before a softmax decision. On the English Real-Life Trial Deception Database (RTDD), MFF-Net achieves 79.22% accuracy and 77.69% F1. On our Chinese Lie Dataset (CLD), it reaches 78.29% accuracy and 79.13% F1, outperforming strong baselines. Together with speaker-aware evaluation and clarified training objectives, the results support MFF-Net as a reproducible approach for deception detection across complementary corpora. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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19 pages, 7293 KB

Open AccessArticle

Structural and Geotechnical Assessment of Onshore Wind Turbine Foundation for Service Life Extension: A Case Study

by Evandro Medeiros Braz and Rui Carneiro de Barros

Appl. Sci. 2026, 16(8), 3659; https://doi.org/10.3390/app16083659 - 9 Apr 2026

This study presents a structural and geotechnical assessment of an onshore wind turbine foundation that has been in service for approximately 15 years. It aimed to evaluate its suitability for service life extension under the current operational conditions, within the broader context of [...] Read more.

This study presents a structural and geotechnical assessment of an onshore wind turbine foundation that has been in service for approximately 15 years. It aimed to evaluate its suitability for service life extension under the current operational conditions, within the broader context of decision-making in aging wind farms. The investigation integrated original design documentation, detailed field inspections, in situ and laboratory geotechnical testing, and advanced 3D numerical modeling incorporating soil–structure interaction effects. Verification procedures followed international standards and current guidelines for the design and reassessment of wind turbine foundations. Critical structural and geotechnical aspects, including internal forces and reinforcement demand, stiffness, bearing resistance, settlement, and global stability, are examined to verify performance under the current operational loading conditions. The results provide a sound technical basis for strategic decision-making regarding service life extension or decommissioning of wind turbines in established wind farms, and constitute an essential baseline for any future structural upgrading associated with repowering strategies. Full article

(This article belongs to the Section Civil Engineering)

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11 pages, 715 KB

Open AccessArticle

Network-Level Time-of-Day Boundary Optimization for Urban Signal Control Based on Traffic Detector Data

by Ji-yeong Seo and Seon-ha Lee

Appl. Sci. 2026, 16(8), 3658; https://doi.org/10.3390/app16083658 - 9 Apr 2026

Although time-of-day (TOD) signal operation is widely adopted in urban signal control systems, its boundary settings are often determined empirically without systematic validation. This study presents a network-level, data-driven framework for optimizing TOD boundaries using citywide traffic detector data. One-year traffic volume data [...] Read more.

Although time-of-day (TOD) signal operation is widely adopted in urban signal control systems, its boundary settings are often determined empirically without systematic validation. This study presents a network-level, data-driven framework for optimizing TOD boundaries using citywide traffic detector data. One-year traffic volume data collected at 15-min intervals from vehicle detection systems in Daejeon, South Korea, were aggregated to construct a representative daily demand profile. K-means clustering was employed to identify homogeneous temporal traffic states, and candidate TOD boundaries were derived based on cluster transitions. To ensure operational feasibility, a minimum segment length constraint was incorporated. The optimal number of clusters was determined using the silhouette score, resulting in a three-period TOD structure. Compared with a conventional fixed TOD configuration, the proposed approach reduced intra-segment variability by 34.87% in terms of sum of squared errors (SSE) and significantly lowered root mean squared error (RMSE). The results demonstrate that clustering-based TOD boundary optimization enhances temporal homogeneity while maintaining practical applicability for network-level urban signal control. Full article

(This article belongs to the Section Transportation and Future Mobility)

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25 pages, 1970 KB

Open AccessArticle

Optimisation of Photovoltaic Generation and Energy Storage Systems in Portuguese Semi-Detached Households in Social-Housing Neighbourhoods to Mitigate Energy Poverty

by João M. P. Q. Delgado and Bárbara P. Costa

Appl. Sci. 2026, 16(8), 3657; https://doi.org/10.3390/app16083657 - 8 Apr 2026

The building sector is responsible for 40% of CO₂ emissions in Portugal, making the integration of renewable energy systems increasingly relevant. Photovoltaic (PV) technologies have become more accessible due to declining levelized costs of energy, and when coupled with battery energy storage [...] Read more.

The building sector is responsible for 40% of CO₂ emissions in Portugal, making the integration of renewable energy systems increasingly relevant. Photovoltaic (PV) technologies have become more accessible due to declining levelized costs of energy, and when coupled with battery energy storage systems (BESSs), they can enhance grid independence, reduce household energy expenses, and mitigate peak load stress. However, high upfront costs still limit adoption, particularly among vulnerable communities. This study evaluates the technical, economic, and environmental performance of PV systems, with and without BESSs, compared with an existing solar thermal configuration in a social-housing neighbourhood in Porto, Portugal. Numerical simulations were conducted for three scenarios, optimising system sizing and ensuring hourly energy flow balance between generation, storage, and grid supply. Results indicate that all configurations are technically feasible within Porto’s climate conditions, though with distinct investment needs, payback periods, and CO₂ reduction outcomes. The findings offer practical guidance for designing renewable energy solutions tailored to social housing, supporting both decarbonization goals and long-term mitigation of energy poverty. Full article

(This article belongs to the Special Issue Energy Transition in Sustainable Buildings)

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