Multidisciplinary Digital Publishing Institute (Switzerland)
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Modeling the Dynamics of Electric Field-Assisted Local Functionalization in Two-Dimensional Materials
No full textElectric field-assisted local functionalization of materials is a resist-free technique generally applied at the nanoscale, which has been understood within the paradigm of the water meniscus. Using a home-made prototype the authors applied this technique at scales compatible with the biosensor industry (tens of microns). However, interpreting these results requires a different paradigm. The expansion of the oxidized region over time in two-dimensional materials under a localized electric field is modeled from first physical principles. Boltzmann statistics is applied to the oxyanion incorporation at the perimeter of the oxidized zone, and a new general relation between oxide radius and time is formulated. It includes the reduction in the energy barrier due to the field effect and its dependence on the oxide radius. To gain insight into this dependence whatever the layers structure, 2D material involved, or electrical operating conditions, simple structures based on multilayer stacks representing the main constituents are proposed, where the Poisson equation is solved using finite element calculations. This enables to derive energy barriers for oxyanion incorporation at varying spot radii which are consistent with those resulting from fitting experimental data. The reasonable agreement obtained provides researchers with a new tool to predict the evolution of local functionalization of 2D layers as a function of the following fabrication parameters: time, applied voltage, and relative humidity, solely based on materials properties
From Thermal Springs to Saline Solutions: A Scoping Review of Salt-Based Oral Healthcare Interventions
No full textBackground: Therapeutic applications of saline solutions in oral healthcare range from mineral waters to standardized sodium chloride preparations. Despite widespread traditional use, their scientific foundation remains inadequately characterized. This scoping review aimed to systematically map the available evidence for salt-based oral health interventions, characterize study populations and outcomes, and identify research gaps to guide future investigations. Methods: Following JBI methodology and PRISMA-ScR guidelines, four electronic databases (PubMed, Scopus, Web of Science, and Cochrane Library) were systematically searched for publications from 2000 to 2025. Studies were classified along a spectrum from geological mineral waters to artificial preparations. Narrative synthesis was employed with systematic gap identification. Results: Seventeen studies met inclusion criteria, with a median sample size of 41 participants and a median follow-up of 4 weeks. Evidence distribution revealed concentration on hypersaline Dead Sea derivatives (n = 7, 41%) and European thermal waters (n = 5, 29%), with limited representation of marine-derived (n = 1, 6%) and simple saline solutions (n = 3, 18%). Reported outcomes included periodontal parameters, xerostomia symptoms, viral load, mucositis severity, and dentin hypersensitivity, with variable methodological quality across studies. Heterogeneity in interventions, comparators, and outcome measures precluded direct comparisons. Conclusions: The current evidence base for salt-based oral interventions remains limited and methodologically heterogeneous. While preliminary findings suggest potential applications across multiple clinical domains, small sample sizes, short follow-up periods, and inconsistent outcome measures preclude definitive recommendations. Standardized protocols and adequately powered trials are needed before evidence-based clinical integration
Construction of a CFD Simulation and Prediction Model for Pesticide Droplet Drift in Agricultural UAV Spraying
No full textThis study employed a combined approach of computational fluid dynamics (CFD), numerical simulations, and wind tunnel tests to investigate droplet drift characteristics and develop prediction models in order to address the issues of low pesticide utilization rates and high drift risk, associated with droplet drift during agricultural unmanned aerial vehicle (UAV) spraying, as well as the unreliable results of field experiments. Firstly, a numerical model of the rotor wind field was established using the multiple reference frame (MRF) method, while the realizable k-ε turbulence model was employed to analyze the flow field. The model’s reliability was verified through wind field tests. Next, the Euler–Lagrange method was used to couple the wind field with droplet movement. The drift characteristics of two flat-fan nozzles (FP90-02 and F80-02) were then compared and analyzed. The results showed that the relative error between the simulated and wind tunnel test values was within 20%. Centrifugal nozzle experiments were carried out using single-factor and orthogonal designs to analyze the effects of flight height, rotor wind speed, flight speed, and droplet size on drift. The priority order of influence was found to be “rotor wind speed > flight height > flight speed”, while droplet size (DV50 = 100–300 µm) was found to have no significant effect. Based on the simulation data, a multiple linear regression drift prediction model was constructed with a goodness of fit R2 value of 0.9704. Under the verification condition, the relative error between the predicted and simulated values was approximately 10%. These results can provide a theoretical basis and practical guidance for assessing drift risk and optimizing operational parameters for agricultural UAVs
An Automated Framework for Estimating Building Height Changes Using Multi-Temporal Street View Imagery
No full textBuilding height is an important indicator for describing the three-dimensional structure of cities. However, monitoring its changes is still difficult due to high labor costs, low efficiency, and the limited resolution and viewing angles of remote sensing images. This study proposes an automatic framework for estimating building height changes using multi-temporal street view images. First, buildings are detected by the YOLO-v5 model, and their contours are extracted through edge detection and hole filling. To reduce false detections, greenness and depth information are combined to filter out pseudo changes. Then, a neighboring region resampling strategy is used to select visually similar images for better alignment, which helps to reduce the influence of sampling errors. In addition, the framework applies cylindrical projection correction and introduces a triangulation-based method (HCAOT) for building height estimation. Experimental results show that the proposed framework achieves an accuracy of 85.11% in detecting real changes and 91.23% in identifying unchanged areas. For height estimation, the HCAOT method reaches an RMSE of 0.65 m and an NRMSE of 0.04, which performs better than several comparison methods. Overall, the proposed framework provides an efficient and reliable approach for dynamically updating 3D urban information and supporting spatial monitoring in smart cities
Predicting Physical Appearance from Low Template: State of the Art and Future Perspectives
No full textBackground/Objectives: Forensic DNA phenotyping (FDP) enables the prediction of externally visible characteristics (EVCs) such as eye, hair, and skin color, ancestry, and age from biological traces. However, low template DNA (LT-DNA), often derived from degraded or trace samples, poses significant challenges due to allelic dropout, contamination, and incomplete profiles. This review evaluates recent advances in FDP from LT-DNA, focusing on the integration of machine learning (ML) models to improve predictive accuracy and operational readiness, while addressing ethical and population-related considerations. Methods: A comprehensive literature review was conducted on FDP and ML applications in forensic genomics. Key areas examined include SNP-based trait modeling, genotype imputation, epigenetic age estimation, and probabilistic inference. Comparative performance of ML algorithms (Random Forests, Support Vector Machines, Gradient Boosting, and deep learning) was assessed using datasets such as the 1000 Genomes Project, UK Biobank, and forensic casework samples. Ethical frameworks and validation standards were also analyzed. Results: ML approaches significantly enhance phenotype prediction from LT-DNA, achieving AUC > 0.9 for eye color and improving SNP recovery by up to 15% through imputation. Tools like HIrisPlex-S and VISAGE panels remain robust for eye and hair color, with moderate accuracy for skin tone and emerging capabilities for age and facial morphology. Limitations persist in admixed populations and traits with polygenic complexity. Interpretability and bias mitigation remain critical for forensic admissibility. Conclusions: L integration strengthens FDP from LT-DNA, offering valuable investigative leads in challenging scenarios. Future directions include multi-omics integration, portable sequencing platforms, inclusive reference datasets, and explainable AI to ensure accuracy, transparency, and ethical compliance in forensic applications
Decentralized Q-Learning for Multi-UAV Post-Disaster Communication: A Robotarium-Based Evaluation Across Urban Environments
No full textLarge-scale disasters such as earthquakes and floods often cause the collapse of terrestrial communication networks, isolating affected communities and disrupting rescue coordination. Unmanned aerial vehicles (UAVs) can serve as rapid-deployment aerial relays to restore connectivity in such emergencies. This work presents a decentralized Q-learning framework in which each UAV operates as an independent agent that learns to maintain reliable two-hop links between mobile ground users. The framework integrates user mobility, UAV–user assignment, multi-UAV coordination, and failure tracking to enhance adaptability under dynamic conditions. The system is implemented and evaluated on the Robotarium platform, with propagation modeled using the Al-Hourani air-to-ground path loss formulation. Experiments conducted across Suburban, Dense Urban, and Highrise Urban environments show throughput gains of up to 20% compared with random placement baselines while maintaining failure rates below 5%. These results demonstrate that decentralized learning offers a scalable and resilient foundation for UAV-assisted emergency communication in environments where conventional infrastructure is unavailable
High-Fidelity Finite Element Modelling (FEM) and Dynamic Analysis of a Hybrid Aluminium–Honeycomb Railway Vehicle Carbody
No full textThis study presents the development and high-fidelity finite element modelling of an innovative hybrid railway carbody structure, designed to achieve a substantial reduction in mass while maintaining the required mechanical performance under service conditions. The proposed concept integrates a traditional aluminium frame with an advanced honeycomb sandwich panel, joined through adhesive bonding to ensure structural continuity, compensate for thermal effects, and minimize over constraining stresses. Detailed numerical simulations were conducted to evaluate both the static and dynamic behaviour of the structure under the most demanding load cases prescribed by standards. Modal analysis showed excellent agreement with the original carbody, with variations in the first natural frequency about 3%, while a change in the nature of the corresponding eigenvector was observed. Static simulations under maximum vertical loading confirmed comparable stiffness and stress distributions. Localised stress peaks increased by approximately 19%; the corresponding material utilization factor remained below unity, demonstrating that the structure operates safely within its allowable limits. The introduction of the sandwich panel enabled a mass saving of approximately 60% in the replaced components, corresponding to 3.9% if referred to the whole structure. The results validate the structural feasibility and mechanical reliability of the proposed hybrid concept, laying the foundations for the subsequent experimental phase and for refining its predictive accuracy and industrial applicability
Addressing Challenges in Porous Silicon Fabrication for Manufacturing Multi-Layered Optical Filters
No full textThe motivation for this work is to study the cause and present mitigation for some challenges faced in preparing porous silicon. This enables benefiting from the appealing benefits of porous silicon that offers a wide range, simple technique for varying the refractive index. Such challenges include the refractive index values, sensitivity to oxidation, some fabrication parameters, and other factors. Additionally, highly doped p-type silicon is preferred to form porous silicon, but it causes high losses, which necessitates its detachment. We investigate some possible causes of refractive index change, especially after detaching the fabricated layers from the silicon substrate. Thereby, we could recommend simple but essential precautions during fabrication to avoid such a change. For example, the native oxide formed in the pores has a role in changing the porosity upon following some fabrication sequence. Oppositely, intrinsic stress doesn’t have a significant role. On another aspect, the effect of differing etching/break times on the filter’s responses has been studied, along with other subtle details that may affect the lateral and depth homogeneity, and thereby the process success. Solving such homogeneity issues allowed reaching thick layers not suffering from the gradient index. It is worth highlighting that several approaches have been reported; unlike these, our method doesn’t require sophisticated equipment that might not be available in every lab. To well characterize the thin films, it has been found essential that freestanding monolayers are used for this purpose. From which, the wavelength-dependent refractive index and absorption coefficient have been determined in the near infrared region (1000–2500 nm) for different fabricated conditions. Excellent fitting with the measured interference pattern has been achieved, indicating the accurate parameter extraction, even without any ellipsometry measurements. This also demonstrates the refractive index homogeneity of the fabricated layer, even with a large thickness of over 16 µm. Subsequently, multilayer structures have been fabricated and tested, showing the successful nano-manufacturing methodology
A Novel Reactive Power Decoupling Strategy for VSG Inverter Systems Using Adaptive Dynamic Virtual Impedance
No full textVirtual synchronous machine (VSG) technology provides a robust framework for integrating electric vehicle energy storage into modern microgrids. Nonetheless, conventional VSG control often suffers from intense interaction between active and reactive power flows, which can trigger persistent steady-state errors, power fluctuations, and potential system collapse. This research addresses these challenges by developing a 5th-order electromagnetic dynamic model tailored for a two-stage cascaded bridge inverter. By synthesizing a 3rd-order power regulation loop with a 2nd-order output stage, the proposed model captures stability boundaries across an extensive parameter spectrum. Unlike traditional 3rd-order “quasi-steady-state” approaches—which overlook essential dynamics under weak-damping or low-inertia conditions—this study utilizes the 5th-order model to derive an adaptive dynamic virtual impedance decoupling technique. This strategy facilitates real-time compensation of the cross-coupling between active and reactive channels, significantly boosting the inverter’s damping ratio. Quantitative analysis confirms that this approach curtails overshoot by 85.6% and accelerates the stabilization process by 42%, markedly enhancing the overall dynamic performance of the grid-connected system
Selenium Biofortification and an Ecklonia maxima-Based Seaweed Extract Jointly Compose Curly Endive Drought Stress Tolerance in a Soilless System
No full textVegetable cultivation is currently facing complex challenges related to climate change, with negative repercussions on plant performance. In this scenario, the employment of eco-friendly agronomic tools capable of boosting plant tolerance to abiotic stresses is fundamental. Among them, the use of non-microbial biostimulants, such as seaweed extracts (SwEs), and microelements, like selenium (Se), is considered an efficient approach to overcome abiotic stresses. In this experiment, the performance of chicory plants cultivated under three different irrigation levels (100%, 75% or 50% of substrate water holding capacity) and treated with SwE, Se or their combination (SwE + Se) was evaluated. The results revealed that drought stress significantly decreased growth, productivity and relative water content but increased soluble solid content, dry matter percentage, and proline and malondialdehyde concentrations. The application of Swe, Se or Swe + Se enhanced growth, productive features and soluble solid content and reduced dry matter percentage, proline and malondialdehyde compared to the control. Based on our results, Se and SwE combined application could be a valuable approach to face moderate drought stress on curly endive plants and improve productive and quality traits