Applied Sciences

37 pages, 1489 KB

Open AccessArticle

Data-Driven Optimisation of Endoscopy Department Resources Through Statistical Analysis and Mixed-Integer Linear Programming

by Laia Llunas-Mestres, Francesca L. Aguilar Paredes, Luis Barranco-Priego, Miguel Pantaleón Sánchez, Pere Marti-Puig and Jordi Cusido

Appl. Sci. 2026, 16(4), 1864; https://doi.org/10.3390/app16041864 (registering DOI) - 13 Feb 2026

Abstract

The efficient use of resources represents a critical challenge for public healthcare systems facing increasing demand. In this study, an operational analysis was conducted at Hospital del Mar (Barcelona) to demonstrate that persistent bottlenecks and capacity deficits are primarily organizational and not only [...] Read more.

The efficient use of resources represents a critical challenge for public healthcare systems facing increasing demand. In this study, an operational analysis was conducted at Hospital del Mar (Barcelona) to demonstrate that persistent bottlenecks and capacity deficits are primarily organizational and not only quantitative. Through a prospective observational study and exploratory data analysis (EDA), it was identified that high apparent workloads often coexist with structural inefficiencies, particularly regarding the unpredictable demand of urgent and inpatient procedures. To address these gaps, a Mixed-Integer Linear Programming (MILP) model was implemented to optimize spatial and temporal resource allocation. Unlike reactive scheduling, this data-driven approach explicitly incorporates capacity reserves for non-programmable activities and ensures realistic time slots without increasing physical or human resources. It is shown that MILP-optimized scheduling significantly balances workload, eliminates artificial overlaps, and improves room utilization—reaching rates of 99.5%. The findings highlight that temporal agenda design constitutes a critical, yet underutilized, lever for hospital management. A scalable tool for evidence-based decision-making is provided by this framework, allowing for a clear distinction between apparent productivity and real efficiency. The proposed model is considered highly transferable to other clinical settings facing similar operational constraints. Full article

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

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22 pages, 4283 KB

Open AccessArticle

Sodium Hydroxide-Activated Persulfate Remediation of Sites Contaminated with Moderate Concentrations of Aniline and Analysis of the Microbial Community Characteristics

by Chaoqun Si, Yuling Zhang and Xiao Yang

Appl. Sci. 2026, 16(4), 1863; https://doi.org/10.3390/app16041863 (registering DOI) - 13 Feb 2026

Abstract

With the widespread application of aniline, its improper discharge in industrial production and accidental leaks during production and transportation have led to contamination of soil and groundwater environments. Persulfate advanced oxidation technology shows great potential for remediating organic compound pollution. This study focused [...] Read more.

With the widespread application of aniline, its improper discharge in industrial production and accidental leaks during production and transportation have led to contamination of soil and groundwater environments. Persulfate advanced oxidation technology shows great potential for remediating organic compound pollution. This study focused on a specific aniline-contaminated site to identify oxidants and activators, optimize their ratios, and investigate the effects of relevant factors on oxidative remediation. It also examined changes in the microbial communities at the site before and after contamination, as well as before and after oxidative remediation. We found that the removal efficiency of aniline was most significant when the molar ratio of the optimal oxidant (sodium peroxydisulfate (Na₂S₂O₈)) to the optimal activator (sodium hydroxide (NaOH)) was 1:1. When the initial aniline concentration in the soil reached 1000 mg/kg, it was necessary to increase the amount of the oxidant appropriately to maintain efficient removal. Environmental temperature and the content of organic matter in the soil had a relatively minor impact on the oxidation reaction. Following oxidation, the soil ammonia nitrogen content decreased, whereas nitrate and nitrite concentrations increased, with a significant rise in nitrate levels and substantial production of sulfate ion. Moreover, after aniline contamination, the microbial diversity in the soil of the site decreased, and the abundance of the genus Hydrogenophilus significantly increased. It is noteworthy that following oxidation treatment, the soil microbial community demonstrated a rapid trajectory of functional and structural recovery. Within five days of oxidant application, the community composition shifted from being predominantly composed of aniline-degrading taxa to closely resembling the original site in both community structure and inferred functional potential. Full article

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32 pages, 4183 KB

Open AccessArticle

A Child–Robot Interaction Framework for Designing Inclusive Educational Robotics in Resourced Classrooms: (Robo-ICARE)

by Sandra Cano, Rocio Hidalgo, Carlos Alberto Peláez, Andrés Solano, Francisca Guzmán, Ignacio Reyes and Klinge Orlando Villalba

Appl. Sci. 2026, 16(4), 1862; https://doi.org/10.3390/app16041862 (registering DOI) - 13 Feb 2026

Abstract

This study presents Robo-ICARE (Robotics-Inclusive Child-Centered Approach for Robotics in Education), a Child–Robot Interaction (cHRI) framework for designing inclusive educational robotics in under-resourced and multigrade classroom contexts. The framework addresses gaps in existing educational robotics approaches by integrating user-centered design, affective expressiveness, pedagogical [...] Read more.

This study presents Robo-ICARE (Robotics-Inclusive Child-Centered Approach for Robotics in Education), a Child–Robot Interaction (cHRI) framework for designing inclusive educational robotics in under-resourced and multigrade classroom contexts. The framework addresses gaps in existing educational robotics approaches by integrating user-centered design, affective expressiveness, pedagogical alignment and inclusivity, with particular attention to learner variability and contextual constraints. Robo-ICARE comprises six interrelated design dimensions—embodiment, interactivity, intelligence, pedagogical alignment, inclusivity, and user-centered process—intended to guide the design implementation, and evaluation of educational robots in authentic classroom environments. The framework is operationalized and empirically examined through a case study involving COODI, a low-cost and emotionally expressive educational robot co-designed with students and teachers in rural Chile. The study follows a primarily qualitative, exploratory design-based research approach, supported by descriptive quantitative measures. Empirical evaluation with 49 participants focuses on usability, user experience, engagement, and emotional acceptance, rather than on causal measurement of learning gains. The findings indicate that the proposed framework supports the design of emotionally engaging and context-sensitive educational robotics experiences that are feasible in low-resource settings. Overall, this work contributes a design-guiding, empirically grounded framework that bridges child–robot interaction theory and educational practice, offering a replicable approach for inclusive educational robotics in under-resourced classroom contexts. Full article

(This article belongs to the Special Issue The Application of Digital Technology in Education)

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26 pages, 6448 KB

Open AccessArticle

Integrated Numerical Modeling of Dam Breach: Breach Formation, Reservoir Drawdown, and Impact on Downstream Small Dams

by Larissa Balakay, Oxana Kuznetsova, Tatyana Dedova, Nataliya Tusseyeva and Madiyar Sarybayev

Appl. Sci. 2026, 16(4), 1861; https://doi.org/10.3390/app16041861 (registering DOI) - 13 Feb 2026

Abstract

This study presents a comprehensive numerical simulation of reservoir dam failure based on the two-dimensional hydrodynamic model MIKE 21. To reproduce the real accident process, a detailed digital elevation model derived from LiDAR survey data was constructed, incorporating valley microtopography, river channel geometry, [...] Read more.

This study presents a comprehensive numerical simulation of reservoir dam failure based on the two-dimensional hydrodynamic model MIKE 21. To reproduce the real accident process, a detailed digital elevation model derived from LiDAR survey data was constructed, incorporating valley microtopography, river channel geometry, and hydraulic structure elements. The modeling was performed in a stepwise manner and included the simulation of breach formation using a time-varying digital elevation model, the drawdown of the reservoir, and the propagation of the dam-break flood wave in the downstream reach, as well as an assessment of the hydrodynamic impact of the flow on small dams located further downstream. The simulations produced spatiotemporal distributions of flow depths and velocities, quantified the temporal evolution of reservoir water volume, and determined overflow parameters at the small dams. Based on the analysis of bed shear stress distribution, zones of increased hydrodynamic loading were identified and compared with observed damage areas. The results confirm the applicability of the adopted modeling framework for detailed reconstruction of dam-break events. The proposed approach can be applied both to the analysis of past dam failures and for predictive purposes when assessing the potential consequences of possible accidents at other reservoirs. The methodology enables preliminary evaluation of inundation zones, erosion intensity, and impacts on downstream hydraulic structures, making it a valuable tool for safety assessment and the planning of protective measures in areas with complex terrain conditions. Full article

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59 pages, 2897 KB

Open AccessReview

Medicinal Plants in the Space Exploration Era: Opportunities and Challenges for Mitigating Spaceflight-Induced Health Hazards

by Francesca Pettinau and Alessandro Orrù

Appl. Sci. 2026, 16(4), 1860; https://doi.org/10.3390/app16041860 - 12 Feb 2026

Abstract

Since the dawn of civilization, humanity has looked to the sky, seeking to expand knowledge beyond Earth’s boundaries. The last eight decades have witnessed remarkable progress in space exploration, paving the way for increasingly longer space journeys and the establishment of human settlements [...] Read more.

Since the dawn of civilization, humanity has looked to the sky, seeking to expand knowledge beyond Earth’s boundaries. The last eight decades have witnessed remarkable progress in space exploration, paving the way for increasingly longer space journeys and the establishment of human settlements on the Moon and Mars. These achievements have been made possible by advances in multiple scientific disciplines, including the rise of space medicine, astropharmacy, astrobiology, and astrobotany, each addressing how biological and technological systems adapt to extraterrestrial environments. Nevertheless, the space environment remains profoundly inhospitable to human life, making the protection of health and the assurance of long-term sustainability a key strategic goal in space exploration programs. Within this multidisciplinary framework, the potential role of medicinal plants remains underexplored. Historically central to healthcare, medicinal plants provide a vast repertoire of bioactive compounds and molecular scaffolds, many of which have inspired modern drugs. This review explores how medicinal plants could contribute to human well-being beyond Earth—not only as sources of therapeutic agents to mitigate spaceflight-induced ailments but also as biomanufacturing platforms for on-demand production of pharmaceuticals. Ultimately, medicinal plants could continue to play a pivotal role in supporting human health, also in space, but it poses new challenges and requires further scientific and technological advances. Full article

(This article belongs to the Special Issue Biological Activities of Medicinal Plants and Their Potential Applications)

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21 pages, 9135 KB

Open AccessBrief Report

Application of Opposing-Coils Transient Electromagnetic Method in Urban Potential-Fault Detection

by Sixin Zhu, Shuo Cai, Xu Zhao, Fuyao Cui and Haolin Wang

Appl. Sci. 2026, 16(4), 1859; https://doi.org/10.3390/app16041859 - 12 Feb 2026

Abstract

Urban environments face heightened seismic risks due to dense infrastructure and population concentration. Traditional seismic methods often face significant practical limitations in cities due to space constraints, traffic disruption, and acoustic noise, necessitating reliable alternative geophysical approaches for fault screening. This study evaluates [...] Read more.

Urban environments face heightened seismic risks due to dense infrastructure and population concentration. Traditional seismic methods often face significant practical limitations in cities due to space constraints, traffic disruption, and acoustic noise, necessitating reliable alternative geophysical approaches for fault screening. This study evaluates the efficacy and practical utility of the opposing-coils transient electromagnetic method (OCTEM) as an effective alternative to conventional seismic techniques for detecting shallow-fault-like resistivity signatures under complex urban electromagnetic noise. By employing dual coaxial coils with opposing currents, the OCTEM suppresses primary-field interference, enabling high-resolution imaging of subsurface structures at depths of 0–200 m. A case study in Tiancheng Chengyuan, Cangzhou City, China, demonstrates the OCTEM’s capability to reliably delineate stratigraphic interfaces and resistivity anomalies under challenging electromagnetic background conditions. Field data exhibited a mean square relative error of 4.01%, validating its data quality and measurement stability. The survey successfully identified stratigraphic continuity and localized heterogeneity features within the investigation zone. These results establish the OCTEM as a robust and efficient tool for urban fault screening, particularly in environments where traditional high-resolution seismic methods are impractical or economically unfeasible. Full article

24 pages, 3143 KB

Open AccessArticle

Modulating Peroxidase-like Activity of Fe₃O₄@Pt@poly-LDOPA and Its Application as Multifunctional Magnetic Probes Towards SARS-CoV-2 Detection

by Lorico Delos Santos Lapitan, Jr., Jan Olgierd Górniaszek, Maciej Trzaskowski and Mariusz Pietrzak

Appl. Sci. 2026, 16(4), 1858; https://doi.org/10.3390/app16041858 - 12 Feb 2026

Abstract

We report a Fe₃O₄@Pt@poly-LDOPA nanozyme that displays enhanced peroxidase (POD)-like activity. Polymerisation of levodopa onto the surface of Fe₃O₄@Pt yields a carboxyl-rich poly-LDOPA shell that is available for bioconjugation with antibodies and other types of [...] Read more.

We report a Fe₃O₄@Pt@poly-LDOPA nanozyme that displays enhanced peroxidase (POD)-like activity. Polymerisation of levodopa onto the surface of Fe₃O₄@Pt yields a carboxyl-rich poly-LDOPA shell that is available for bioconjugation with antibodies and other types of receptors. Physicochemical characterisation confirmed the integrity of the Fe₃O₄ core, successful Pt modification, and formation of the polymer coating under acidic and basic conditions. Steady-state kinetic analysis using the Michaelis–Menten model revealed robust catalytic performance toward both substrates: for H₂O₂, V_max = 4.0 × 10⁻⁸ M·s⁻¹ and K_m = 25.13 mM; for TMB, V_max = 6.07 × 10⁻⁸ M·s⁻¹ and K_m = 0.229 mM, indicative of high turnover and strong apparent affinity for the chromogenic substrate. A nanozyme-linked immunosorbent assay for the SARS-CoV-2 nucleocapsid was developed. The anti-nucleocapsid antibodies were immobilised onto Fe₃O₄@Pt@poly-LDOPA via EDC/NHS. In buffer, the calibration range (1.0–100 ng·mL⁻¹) afforded an LOD of 6.95 ng·mL⁻¹. In 10% human serum, reduced background and improved nanozyme dispersion yielded a linear low-concentration response (0.1–10 ng·mL⁻¹), with an LOD of 0.0036 ng·mL⁻¹. These results establish Fe₃O₄@Pt@poly-LDOPA as a promising inorganic–organic nanozyme platform that combines catalytic effectiveness, magnetic manipulability, and facile bioconjugation for immunosensing of various disease-related biomarkers. Full article

(This article belongs to the Special Issue Application of Magnetic Nanoparticles)

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20 pages, 2326 KB

Open AccessArticle

A LLaMA-Based Efficient Fine-Tuning Method for Image Captioning Using Multi-Feature Dynamic Prompts

by Yongyang Yin, Hengyu Cao, Chunsheng Zhang, Faxun Jin, Xin Liu and Jun Lin

Appl. Sci. 2026, 16(4), 1857; https://doi.org/10.3390/app16041857 - 12 Feb 2026

Abstract

To address the trade-off between parameter scale and generation quality in Vision-Language Models (VLMs), this study proposes a Multi-Feature Dynamic Instruction Tuning (MFDIT) image captioning model based on LLaMA. By integrating CLIP-based global features with SAM-derived local features, the model constructs a multi-level [...] Read more.

To address the trade-off between parameter scale and generation quality in Vision-Language Models (VLMs), this study proposes a Multi-Feature Dynamic Instruction Tuning (MFDIT) image captioning model based on LLaMA. By integrating CLIP-based global features with SAM-derived local features, the model constructs a multi-level visual representation. Additionally, a Dynamic Prompt Adapter is designed to enable cross-modal semantic alignment with adaptive flexibility. Combined with a Low-Rank Adaptation (LoRA) fine-tuning strategy, the proposed method enhances the model’s capability in describing diverse images while training only 20 million parameters, accounting for merely 0.05% of the total parameter volume. Experimental results demonstrate that the model achieves a CIDEr score of 126.7 on the MSCOCO dataset, surpassing traditional adapter-based approaches by 3.0 points. Moreover, in the MME Benchmark evaluation, the proposed model outperforms the mainstream LLaMA-Adapter V2 by 7.3% and 3.8% in OCR and object counting tasks, respectively. Ablation studies further validate the synergistic effects of multi-feature fusion and dynamic instruction optimization. This research provides an efficient solution for parameter-efficient multimodal model training and potential deployment in resource-constrained environments. Full article

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

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22 pages, 3519 KB

Open AccessArticle

Oxidation Stability of SiO₂ and TiO₂ Nanofluids for High Voltage Insulation

by Samson Okikiola Oparanti, Youssouf Brahami, Issouf Fofana and Reza Jafari

Appl. Sci. 2026, 16(4), 1856; https://doi.org/10.3390/app16041856 - 12 Feb 2026

Abstract

Mineral oils are increasingly being replaced by plant-based insulating liquids, known as natural esters, because of their biodegradability and high fire safety characteristic. However, their wider use in high-voltage and unsealed transformer applications is still limited due to concerns about thermo-oxidative stability and [...] Read more.

Mineral oils are increasingly being replaced by plant-based insulating liquids, known as natural esters, because of their biodegradability and high fire safety characteristic. However, their wider use in high-voltage and unsealed transformer applications is still limited due to concerns about thermo-oxidative stability and the relatively limited long-term performance data available compared to mineral oils. This study investigates improving the oxidation stability of natural esters through nanotechnology. A canola-based insulating liquid was used as the base fluid and modified with TiO₂ and SiO₂ nanoparticles of different sizes. Nanoparticle concentrations ranged from 0.05 to 0.25 wt.%, while Span 80 (sorbitan monooleate, non-ionic surfactant) served as a surfactant to ensure uniform dispersion and long-term colloidal stability. The nanofluids were subjected to accelerated aging to evaluate oxidation resistance, and key properties such as acidity, viscosity, and dissipation factor were monitored throughout the process. Dielectric performance was assessed using AC breakdown voltage testing, with results interpreted through two-parameter Weibull statistics. The TiO₂-based nanofluids demonstrated superior thermo-oxidative stability compared to both the base oil and the SiO₂-modified samples. Formulations containing smaller TiO₂ nanoparticles (5 nm) exhibited the lowest increases in viscosity, acid value, and dissipation factor, indicating strong resistance to degradation under thermal stress. In dielectric performance, SiO₂ nanofluids reached 65.8 kV, while TiO₂ nanofluids achieved a higher value of 72.4 kV, confirming their greater effectiveness. Although the nanoparticles are not biodegradable, their use at low concentrations significantly enhances the oxidative and dielectric stability of natural esters, helping extend fluid life and reduce dependence on petroleum-based insulating liquids. Full article

(This article belongs to the Special Issue Recent Advances and Innovations in Microfluidics)

14 pages, 1239 KB

Open AccessArticle

Reliable Belt-Style Depositor Design in a Food Processing Plant

by Tyler F Baker II, Wolday Desta Abrha and Erkan Kaplanoglu

Appl. Sci. 2026, 16(4), 1855; https://doi.org/10.3390/app16041855 - 12 Feb 2026

Abstract

Considering consumer health, consistency in processes, and developing trust among the public, food manufacturing facilities are expected to adhere to strict regulatory policies. Along with these expectations, machinery capabilities, especially considering reliability, maintainability, and hygienic designs, would play a significant role in delivering [...] Read more.

Considering consumer health, consistency in processes, and developing trust among the public, food manufacturing facilities are expected to adhere to strict regulatory policies. Along with these expectations, machinery capabilities, especially considering reliability, maintainability, and hygienic designs, would play a significant role in delivering quality products and developing efficient processes. This paper focuses on a belt-style depositor machine, whose primary purpose is to deposit product pieces onto product passing below it. First, the key issues with the current machine are pinpointed. Next, alternative designs are provided aimed at testing, evaluating, and building belt-driven depositing machines. The original design experienced persistent belt tracking issues, frequent maintenance interruptions, and sanitation concerns due to its complex, heavy components. The project applied the Define, Measure, Analyze, Design, and Verify (DMADV) framework to test alternative belt configurations and implement improvements that significantly reduced maintenance time, improved tracking reliability, and enhanced hygienic design. Lab and real-world tests compared three prototypes, namely the V-Rib, Crowned Roller, and Pin Drive. The prototypes were compared against defined performance targets. The final system, built around a self-tracking V-Rib belt with modular components and reduced tool disassembly, demonstrated a 75% reduction in belt change time, and improved product consistency and compliance with sanitation standards. This redesign offers a replicable model for upgrading depositor systems across production lines. Full article

(This article belongs to the Special Issue Industrial System Reliability Modeling and Optimization)

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24 pages, 5493 KB

Open AccessArticle

Bond–Slip Behavior Between Shotcrete and I-Shaped Steel with Studs in Tunnels: Experimental and Numerical Study via Push-Out Tests

by Minhao Li, Muyu Li and Junfu Lu

Appl. Sci. 2026, 16(4), 1854; https://doi.org/10.3390/app16041854 - 12 Feb 2026

Abstract

The bond–slip behavior between I-shaped steel and shotcrete crucially influences the cooperative deformation and overall bearing capacity of tunnel initial support. To clarify the bond–slip mechanism between the steel section and shotcrete, this study conducted push-out tests on five groups of stud-reinforced specimens, [...] Read more.

The bond–slip behavior between I-shaped steel and shotcrete crucially influences the cooperative deformation and overall bearing capacity of tunnel initial support. To clarify the bond–slip mechanism between the steel section and shotcrete, this study conducted push-out tests on five groups of stud-reinforced specimens, investigating the effects of stud geometry and arrangement. The tests revealed two primary failure modes—tensile and expansion failure—governed by stud quantity and strength. The typical bond–slip curve comprises four stages: non-slip, ascending, descending, and residual. The quantitative results highlight that stud arrangement and diameter significantly impact bearing capacity. The quincuncial layout achieved an ultimate load of 372.4 kN, which is 16.8% and 54.9% higher than that of the double-row (318.9 kN) and single-row (240.5 kN) parallel arrangements, respectively. Increasing the stud diameter from 13 mm to 16 mm boosted the ultimate load by 16.7% (from 240.5 kN to 280.7 kN). The residual load was approximately 62–87% of the ultimate load. Based on these findings, a bond–slip constitutive model was developed, showing high agreement with experimental data. This study provides theoretical support for optimizing steel–shotcrete support systems in tunnels, though generalizability is limited by sample size and the exclusion of long-term load effects. Full article

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15 pages, 1711 KB

Open AccessArticle

A Portable D-Shaped POF-SPR Sensor Integrated with NanoMIPs for High-Affinity Detection of the SARS-CoV-2 RBD Protein

by Alice Marinangeli, Jessica Brandi, Devid Maniglio and Alessandra Maria Bossi

Appl. Sci. 2026, 16(4), 1853; https://doi.org/10.3390/app16041853 - 12 Feb 2026

Abstract

The rapid and accurate detection of SARS-CoV-2 biomarkers remains a critical requirement for effective outbreak control and decentralized diagnostics. Although RT-PCR is the current gold standard, its reliance on centralized laboratories and long processing times limits its applicability in point-of-care settings. In this [...] Read more.

The rapid and accurate detection of SARS-CoV-2 biomarkers remains a critical requirement for effective outbreak control and decentralized diagnostics. Although RT-PCR is the current gold standard, its reliance on centralized laboratories and long processing times limits its applicability in point-of-care settings. In this context, optical biosensing platforms based on surface plasmon resonance (SPR) offer attractive features, including label-free, real-time, and quantitative detection. This study explores the use of synthetic receptors for the highly sensitive detection of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Specifically, soft molecularly imprinted polymer nanoparticles (nanoMIPs) were employed as synthetic receptors and integrated into a high-sensitivity, portable plasmonic platform based on a D-shaped plastic optical fiber (POF) SPR sensor. The nanoMIPs were selectively imprinted against the RBD, characterized by Dynamic Light Scattering (DLS), Isothermal Titration Calorimetry (ITC), and Scanning Electron Microscopy (SEM) to confirm nanoMIPs size, binding properties, and surface morphology. Next, the nanoMIPs were immobilized onto a gold-coated sensing surface, enabling enhanced specificity, affinity, and signal amplification compared to conventional biological recognition elements. The resulting RBD-SPR-nanoMIPs sensor demonstrated promising analytical performance, exhibiting high selectivity against potentially interfering proteins and an anticipated sensitivity suitable for RBD detection at femtomolar concentrations. The inherent stability of nanoMIPs suggests the potential for reusable SPR sensing platforms, paving the way for next-generation synthetic receptor-based plasmonic biosensors. Full article

27 pages, 7226 KB

Open AccessArticle

Interpretable Deep Learning for Landslide Forecasting in Post-Seismic Areas: Integrating SBAS-InSAR and Environmental Factors

by H. Y. Guo and A. M. Martínez-Graña

Appl. Sci. 2026, 16(4), 1852; https://doi.org/10.3390/app16041852 - 12 Feb 2026

Abstract

Forecasting post-seismic landslide displacement is challenged by the difficulty in distinguishing short-term acceleration from creep and the risk of spatiotemporal leakage. To address this, an interpretable deep-learning framework is developed, integrating SBAS-InSAR time series with an Attention-enhanced Gated Recurrent Unit (Attention-GRU). Prior to [...] Read more.

Forecasting post-seismic landslide displacement is challenged by the difficulty in distinguishing short-term acceleration from creep and the risk of spatiotemporal leakage. To address this, an interpretable deep-learning framework is developed, integrating SBAS-InSAR time series with an Attention-enhanced Gated Recurrent Unit (Attention-GRU). Prior to modeling, a multi-stage preprocessing strategy, including empirical mode decomposition, is applied to mitigate noise and delineate active deformation zones. Unlike standard architectures, the model’s temporal attention mechanism adaptively amplifies critical precursory acceleration phases. Furthermore, a strict landslide-object-based partitioning strategy is employed to rigorously mitigate spatiotemporal leakage. The framework was evaluated in the Le’an Town landslide cluster using multi-source data. Targeting identified hazardous regions, the method achieved an R² of 0.93 and reduced MAPE by 42.7% relative to the SVR baseline. This reflects a location-specific predictive capability, within active zones rather than regional generalization. SHapley Additive exPlanations (SHAP) further confirmed the model captures physical relationships, such as sensitivity to 25–35° slopes and vegetation degradation. Ultimately, the proposed framework offers a transparent, physically interpretable tool for operational hazard mitigation. Full article

(This article belongs to the Special Issue Remote Sensing Image Processing and Application, 2nd Edition)

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17 pages, 1083 KB

Open AccessArticle

Assessing VOC Dispersion from Hydrocarbon Storage Tanks: A Case Study on Emission Temporal Resolution

by Francesca Tagliaferri, Selena Sironi and Marzio Invernizzi

Appl. Sci. 2026, 16(4), 1851; https://doi.org/10.3390/app16041851 - 12 Feb 2026

Abstract

Volatile Organic Compound emissions from hydrocarbon storage tanks are a key source of industrial air pollution and odour nuisance. This study evaluates odour impact predictions using annual average emissions versus time-resolved emissions from fixed roof tanks and external floating roof tanks that store [...] Read more.

Volatile Organic Compound emissions from hydrocarbon storage tanks are a key source of industrial air pollution and odour nuisance. This study evaluates odour impact predictions using annual average emissions versus time-resolved emissions from fixed roof tanks and external floating roof tanks that store different hydrocarbon products, including crude oil and refined products such as gasoline, diesel, kerosene, fuel oil, and bitumen. Odour emission rates are derived from field measurements and laboratory data and implemented in CALPUFF dispersion modelling. The results show that for fixed roof tanks, time-resolved emissions predict odour separation distances up to four times greater than annual averages, with central domain differences reaching 300% and boundary variations between 50–100%. In contrast, floating roof tanks show minimal variation (<10%) between the two methods. These findings highlight that odour perception is driven by short-term peaks rather than long-term averages and that using annualized emissions may significantly underpredict odour impacts, especially for fixed roof tanks. Accurate temporal characterization is thus essential for realistic odour assessments and effective regulatory compliance. Full article

(This article belongs to the Section Environmental Sciences)

16 pages, 3475 KB

Open AccessArticle

Hydrogen/Oxygen Transfer Mechanisms and Endogenous Methyl Features in Dealkaline Lignin Pyrolysis Revealed by Isotope Tracing

by Shaoxuan Hu, Yichen Zhang, Gang Li, Xiang Han, Anning Zhou, Bin Su, Qiuhong Wang, Zhenmin Luo and Fuxin Chen

Appl. Sci. 2026, 16(4), 1850; https://doi.org/10.3390/app16041850 - 12 Feb 2026

Abstract

Lignin pyrolysis is a pivotal route for biomass valorization, yet the intricate radical reaction network involved results in ambiguous hydrogen/oxygen transfer pathways and product formation mechanisms, severely impeding precise control over directed conversion processes. This study employed a combination of multi-isotope tracing techniques [...] Read more.

Lignin pyrolysis is a pivotal route for biomass valorization, yet the intricate radical reaction network involved results in ambiguous hydrogen/oxygen transfer pathways and product formation mechanisms, severely impeding precise control over directed conversion processes. This study employed a combination of multi-isotope tracing techniques and GC-MS analysis to elucidate the formation mechanisms of four phenolic products during the 500 °C hydrothermal pyrolysis of dealkaline lignin. Experiments using D₂O and H₂¹⁸O revealed that the M + 2 signal was predominantly derived from double deuterium substitution, with an abundance difference spanning 13–81 folds. Phenol exhibited the highest M + 1 abundance (3.947) due to the full exposure of its exchangeable hydrogen sites, while its M + 2 abundance ranked second only to that of 2-methylphenol. For 2-methylphenol, the hyperconjugation effect of the ortho-methyl group activated the phenolic structure, leading to the highest M + 2 abundance among all products (M + 2/M + 1 = 2.3). In contrast, 3-methylphenol showed relatively low abundances (M + 2/M + 1 = 1.67) because the meta-methyl group lacked activating effects and introduced steric hindrance. For guaiacol, the steric hindrance of the methoxy group completely overshadowed its electronic activation effect, resulting in the lowest M + 2 abundance (1.545). CD₃OD tracing experiments and the absence of detectable M + 3 peaks confirmed that the methyl groups in 2-methylphenol and 3-methylphenol were entirely endogenous to the structural units of lignin itself. By precisely tracking the migration pathways of hydrogen and oxygen, this study revealed that hydrogen transfer dominated the pyrolysis process, while oxygen transfer was hindered and methyl groups exhibited endogenous characteristics. These findings establish a mechanistic foundation for designing efficient catalysts tailored to lignin pyrolysis and for rationally steering product selectivity. Full article

(This article belongs to the Section Energy Science and Technology)

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32 pages, 5510 KB

Open AccessArticle

Optimizing Foam Lightweight Soil Embankments: Enhancing Stability and Mitigating Settlement in Soft Soil Foundations

by Junjie Gong, Xin Liu, Yuan Gao, Zhiwei Shao, Tao Cheng and Baoning Hong

Appl. Sci. 2026, 16(4), 1849; https://doi.org/10.3390/app16041849 - 12 Feb 2026

Abstract

Foam lightweight soil (FLS) has emerged as a promising material in geotechnical engineering due to its low density, high load-bearing capacity, and ability to incorporate industrial by-products such as fly ash. It offers significant advantages in mitigating settlement and improving stability for embankments [...] Read more.

Foam lightweight soil (FLS) has emerged as a promising material in geotechnical engineering due to its low density, high load-bearing capacity, and ability to incorporate industrial by-products such as fly ash. It offers significant advantages in mitigating settlement and improving stability for embankments constructed on soft soil foundations. However, the combined influence of key parameters—including layered filling thickness, bulk density, and geogrid reinforcement—on the long-term performance of FLS embankments remains insufficiently understood. This study investigates the settlement behavior and stability of FLS embankments through a combination of field experiments and finite element simulations over a 15-year period. The results indicate that layered filling thicknesses of 500–600 mm achieve the best balance between settlement control and construction feasibility. When the thickness exceeds 800 mm, not only does the uniformity deteriorate, but the settlement also increases. Experimental results showed that a medium bulk density of 6 to 8 kN/m³ is optimal as a balance between strength and settlement behavior. Furthermore, geogrid reinforcement significantly improved stability, with safety factors increasing by up to 1.87 compared to unreinforced sections. The findings provide practical guidelines for the design and construction of FLS embankments, particularly for bridge approaches and soft soil foundations. In addition to improving structural performance, the incorporation of industrial by-products highlights the potential of FLS as a sustainable and cost-effective material for future infrastructure development. Full article

28 pages, 1672 KB

Open AccessArticle

Human-in-the-Loop Digital Twin Modeling for Smart Civil Infrastructure Operation and Maintenance

by Zhe Sun, Yibing Wang, Weicheng Guo and Qinglei Meng

Appl. Sci. 2026, 16(4), 1848; https://doi.org/10.3390/app16041848 - 12 Feb 2026

Abstract

Traditional inspection and diagnosis methods for civil infrastructure operation and maintenance (CI O&M) rely heavily on human efforts. Such efforts are always affected by subjective judgment and human errors due to engineering knowledge and prior experiences of field engineers. On the other hand, [...] Read more.

Traditional inspection and diagnosis methods for civil infrastructure operation and maintenance (CI O&M) rely heavily on human efforts. Such efforts are always affected by subjective judgment and human errors due to engineering knowledge and prior experiences of field engineers. On the other hand, recent development of AI-driven tools could achieve effective information acquisition but lacks interpretability and engineering credibility. How to integrate human knowledge with AI capacity for safe and effective CI O&M is thus necessary in this new era. This paper presents a human-in-the-loop digital twin (HITL-DT) framework that enables safety risk sensing, prediction and control for smart CI O&M. The proposed framework fuses human cognition (i.e., individual perception and team situation awareness), AI and engineering knowledge for 1) risk sensing and diagnosis based on spatiotemporal changes and 2) risk prediction and control for smart CI O&M. Qualitative analysis indicates that the HITL-DT approach produces more explainable, trustworthy, and actionable diagnostic outputs, which enhance the reliability and proactivity of CI O&M. Full article

(This article belongs to the Special Issue Advances in Performance Monitoring and Anomaly Detection Across Structures and Industrial Processes)

20 pages, 2251 KB

Open AccessArticle

A Polynomial Model for Estimation of Ex-Vivo HIFU Thermal Lesion Dynamics Based on Pressure Amplitude and Sonication Time

by Francesca Parrotta, Selene Tognarelli and Arianna Menciassi

Appl. Sci. 2026, 16(4), 1847; https://doi.org/10.3390/app16041847 - 12 Feb 2026

Abstract

High-intensity focused ultrasound (HIFU) thermal therapy exploits concentrated acoustic energy to ablate pathological tissues with millimetric precision deep in the body. Accurate prediction of thermal effects is essential for tuning the treatment shooting parameters—such as source pressure amplitude and sonication time—as well as [...] Read more.

High-intensity focused ultrasound (HIFU) thermal therapy exploits concentrated acoustic energy to ablate pathological tissues with millimetric precision deep in the body. Accurate prediction of thermal effects is essential for tuning the treatment shooting parameters—such as source pressure amplitude and sonication time—as well as for maximizing efficacy and preserving surrounding healthy tissue. This study presents a computational model developed in COMSOL Multiphysics to simulate the physics of HIFU thermal phenomena, accounting for acoustic propagation and heat diffusion in biological tissues. The model was validated through experimental tests on ex vivo chicken breast tissue within a robotic ultrasound-guided HIFU (USgHIFU) platform, with lesion dimensions serving as the primary metric for validation. Building upon the validated simulation, we define a polynomial-based model that analytically predicts lesion dimensions based on the input shooting parameters. This approach significantly reduces the COMSOL computational cost and execution time, making it well-suited for integration into a real-time treatment planning workflow for clinical use. A desktop application implementing the inverse formulation of the polynomial model was developed, allowing shooting parameters to be computed from target lesion dimensions through a simple and intuitive interface. By enabling a rapid estimation of lesion size, this solution supports a more standardized strategy for non-invasive oncological therapies. Full article

(This article belongs to the Special Issue Therapeutic Ultrasound Across Scales: From Cellular Models to Clinical Applications)

25 pages, 6150 KB

Open AccessArticle

Multi-Domain Representation Learning for Bearing Fault Diagnosis with Phase and Transient Preservation

by Bingbing Hu, Jing Zhu, Shilei Liang and Liao Ting

Appl. Sci. 2026, 16(4), 1846; https://doi.org/10.3390/app16041846 - 12 Feb 2026

Abstract

Reliable bearing fault diagnosis under complex operating conditions is often hindered by the loss of critical information during feature extraction, particularly for weak fault signatures embedded in vibration signals. To address this challenge, this work proposes a parallel multi-domain deep learning framework that [...] Read more.

Reliable bearing fault diagnosis under complex operating conditions is often hindered by the loss of critical information during feature extraction, particularly for weak fault signatures embedded in vibration signals. To address this challenge, this work proposes a parallel multi-domain deep learning framework that emphasizes the preservation and complementary exploitation of time-domain, frequency-domain, and time–frequency representations. The proposed framework integrates temporal modeling to capture long-range signal evolution, phase-aware frequency-domain analysis to preserve amplitude–phase coherence, and transient-enhanced time–frequency representations to highlight weak impulsive features in noisy environments. To effectively integrate heterogeneous representations, a dynamic self-attention-based fusion strategy is introduced, enabling adaptive interaction and importance reweighting among multi-domain features. Experimental studies conducted on bearing datasets from Huazhong University of Science and Technology and the University of Cincinnati demonstrate that the proposed method achieves diagnostic accuracies of 99.63% and 99.82%, respectively, significantly outperforming state-of-the-art deep learning and multi-domain diagnostic methods, with accuracy improvements exceeding 20% compared to representative baseline models. Furthermore, ablation and robustness analyses confirm that the coordinated preservation and fusion of multi-domain information significantly enhance diagnostic reliability and generalization performance under complex operating conditions. Full article

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