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Selected Papers from the 6th International Electronic Conference on Applied Sciences

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A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 4421

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Prof. Dr. Nunzio Cennamo

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Guest Editor

Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
Interests: optical sensors; biosensors and chemical sensors; optical fiber sensors and optoelectronic devices
Special Issues, Collections and Topics in MDPI journals

Dr. Stefano Toldo

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Guest Editor

Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA 22903, USA
Interests: acute myocardial infarction; heart failure; cardiac function; myocardial infarction; echocardiography; hypertension; cardiomyopathies; chronic heart failure; cardiovascular physiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue has been compiled in collaboration with the 6th International Electronic Conference on Applied Sciences, which has been organized by Applied Sciences and will take place from 9 to 11 December 2025 on the MDPI Sciforum platform (https://sciforum.net/event/ASEC2025). We welcome submissions from conference participants, with the aim being to publish selected extended versions of the presented papers in this Special Issue of Applied Sciences.

The following areas are covered:

Applied biosciences and bioengineering;
Nanosciences, chemistry, and materials science;
Computing and artificial intelligence;
Electrical, electronics, and communications engineering;
Mechanical and aerospace engineering;
Energy, environmental, and earth science;
Food science and technology;
Applied physical science.

Prof. Dr. Nunzio Cennamo
Dr. Stefano Toldo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

applied biosciences and bioengineering nanosciences
chemistry and materials science computing and artificial intelligence electrical
electronics and communications engineering mechanical and aerospace engineering energy
environmental and earth science food science and technology applied physical science

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Published Papers (8 papers)

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Research

19 pages, 744 KB

Open AccessArticle

AI-Driven Threat Detection and Automated Incident Response for Securing Cloud Workloads

by Anton Chagovec, Teodora Bakardjieva, Antonina Ivanova, Fatima Sapundzhi, Veselina Spasova and Andriana Ivanova

Appl. Sci. 2026, 16(13), 6454; https://doi.org/10.3390/app16136454 (registering DOI) - 29 Jun 2026

Abstract

The increasing adoption of cloud computing has expanded organizational attack surfaces and created additional opportunities for identity abuse, ransomware operations, data exposure, and configuration-related security incidents. Conventional monitoring environments based primarily on static rules, fragmented telemetry, and manual triage often struggle to prioritize high-severity incidents in real time. This study evaluates the operational impact of an integrated AI-augmented cloud-native SIEM/XDR/SOAR architecture for cloud threat detection and automated incident response. A sequential mixed-methods comparative case study was conducted across two enterprise-style security environments: an AI-augmented architecture combining cloud-native SIEM, XDR telemetry unification, behavioral analytics, AI-assisted correlation, generative-AI analyst support, and SOAR automation, and a conventional baseline environment based on manual triage and signature-based controls. Three attack scenarios were analyzed: phishing-led account takeover, multi-stage ransomware, and shadow-IT data exfiltration. The AI-augmented architecture reduced mean time to triage from 17.4 h in the conventional baseline to 10.7 min and enabled ransomware containment in under five minutes through pre-configured automated response playbooks. The results also showed improved prioritization of high-severity incidents, reduced analyst review burden, and a high automated closure rate. The findings provide operational evidence for the evaluated security architecture. Limitations include single-vendor dependency, non-equivalent false-positive classification mechanisms, proprietary model internals, calibration requirements, and detection gaps involving legitimate third-party services and password-protected content. Full article

(This article belongs to the Special Issue Selected Papers from the 6th International Electronic Conference on Applied Sciences)

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24 pages, 9851 KB

Open AccessArticle

Comparative Analysis of Three- and Five-Level NPC Converters with Predictive Current Control for Reactive Power Compensation: Simulation Study and Experimental Validation of the Three-Level Topology

by Oscar Paredes, Julio Pacher, Alfredo Renault, Jorge Rodas, Leonardo Comparatore, Carlos Paredes, Paola Maidana, Christian Medina, Hugo Lezcano, Marcos Gómez, Marco Rivera and Patrick Wheeler

Appl. Sci. 2026, 16(13), 6331; https://doi.org/10.3390/app16136331 - 24 Jun 2026

Viewed by 150

Abstract

This paper presents a comparative analysis of three-level (3L-NPC) and five-level (5L-NPC) Neutral-Point-Clamped converters using Finite Control Set Model Predictive Control (FCS-MPC) for reactive power compensation. The research addresses a critical gap by providing a direct performance comparison under identical operating conditions, supported by simulation and experimental validation of a 3L-NPC prototype. The study evaluates harmonic performance, dynamic response, and DC-link balance. Results demonstrate that the 5L-NPC topology significantly outperforms the 3L-NPC, achieving a simulated grid current Total Harmonic Distortion (THD) of 3.36% compared to 7.84% for the 3L-NPC. This 57.1% reduction in THD allows the 5L-NPC to comply with the IEEE Std. 519-2022 limit (<5%), whereas the 3L-NPC experimental results (9.9% THD) highlight the impact of practical non-idealities such as dead time and sensor noise. While the 5L-NPC offers superior power quality, it entails higher hardware complexity, evaluating 125 switching states compared to 27 in the 3L-NPC. These findings provide quantitative guidelines for selecting NPC topologies in high-performance grid compensation systems. Full article

(This article belongs to the Special Issue Selected Papers from the 6th International Electronic Conference on Applied Sciences)

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15 pages, 905 KB

Open AccessArticle

A Fourth–Order Rayleigh–Plesset Approximation for Nonlinear Bubble Dynamics in Viscoelastic Media

by Elena V. Carreras-Casanova and Christian Vanhille

Appl. Sci. 2026, 16(10), 5081; https://doi.org/10.3390/app16105081 - 20 May 2026

Viewed by 423

Abstract

Understanding the dynamics of gas bubbles in viscoelastic media is crucial for applications involving stable cavitation under ultrasound, such as drug delivery, materials processing, and biomedical imaging. The Rayleigh-Plesset equation formulated in terms of bubble volume variation, incorporating viscoelastic effects via the linear Kelvin–Voigt model, is extended here to a fourth-order approximation. This formulation allows a more accurate description of nonlinear bubble dynamics at finite acoustic amplitudes. The resulting equation is solved numerically under various acoustic conditions, with particular emphasis on driving frequencies near the bubble’s resonance and differences between Newtonian and viscoelastic media. To identify the physical conditions under which higher-order nonlinearities become necessary, a decision-tree classification analysis is performed. The results show that the proximity to resonance and the excitation amplitude are the primary determinants of higher-order nonlinear effects, while rheological properties act as modulators, with viscosity exerting a stronger influence than elasticity within the explored ranges. This work provides a physically interpretable criterion for selecting the appropriate model order, improving the prediction and control of nonlinear bubble oscillations under ultrasound excitation in viscoelastic media. Full article

(This article belongs to the Special Issue Selected Papers from the 6th International Electronic Conference on Applied Sciences)

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25 pages, 2129 KB

Open AccessArticle

Forecasting Solar Energy Production Through Modeling of Photovoltaic System Data for Sustainable Energy Planning

by Fatima Sapundzhi, Slavi Georgiev, Ivan Georgiev and Venelin Todorov

Appl. Sci. 2026, 16(10), 5053; https://doi.org/10.3390/app16105053 - 19 May 2026

Viewed by 253

Abstract

This paper investigates solar energy production forecasting at a monthly temporal resolution using a pooled neural network framework applied to the Chikalov photovoltaic systems in southwestern Bulgaria. The study considers several related PV installations with unequal time-series lengths and formulates the forecasting task as one-step-ahead prediction of the next monthly total energy yield, measured in kWh, in a global pooled setting. Two complementary neural architectures are compared: a multilayer perceptron (MLP), which serves as a nonlinear feed-forward benchmark based on lagged observations and seasonal descriptors, and a gated recurrent unit (GRU), which explicitly models sequential temporal dependence. In both cases, seasonality is represented through cyclical calendar encodings, while model selection is performed by chronological hyperparameter search using a separate validation block. Forecast accuracy is assessed by RMSE, MAE, coefficient of determination (R²), MAPE, and sMAPE, and uncertainty is quantified through validation residual prediction intervals. The results show that the MLP achieves stronger validation performance, whereas the GRU provides better final out-of-sample generalization after refitting on the combined training and validation data. For both architectures, the best configurations are obtained with a 12-month input horizon, indicating that one full annual cycle contains the most informative memory for forecasting monthly aggregated photovoltaic energy yield in the considered dataset. After refitting on the combined training and validation data, the GRU achieved the best final out-of-sample performance, with RMSE = 296.38 kWh, MAE = 213.16 kWh, R² = 0.9231, MAPE = 7.52%, and sMAPE = 7.49%. Overall, the findings demonstrate that pooled neural modeling is an effective framework for monthly PV production forecasting and can provide practically useful support for sustainable energy planning, monitoring, and optimization. Full article

(This article belongs to the Special Issue Selected Papers from the 6th International Electronic Conference on Applied Sciences)

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27 pages, 5016 KB

Open AccessArticle

Numerical Simulation of Water Table Dynamics Under Tidal Influence for Construction Planning in a Coastal Urban Area of Mazatlán, México

by David Beltrán-Vargas, Fernando García-Páez, Manuel Martínez-Morales and Cuauhtémoc Franco-Ochoa

Appl. Sci. 2026, 16(10), 4760; https://doi.org/10.3390/app16104760 - 11 May 2026

Viewed by 237

Abstract

Coastal construction projects often require excavation below the water table, where tidal variability and urban infrastructure generate complex groundwater conditions. This study presents a numerical simulation of water table dynamics in a coastal urban area of Mazatlán, México, influenced by tidal forcing, a lake, and an impermeable seawall. Six critical scenarios were modeled using MODFLOW 6 and ModelMuse interface, covering the period from November 2023 to April 2024. The scenarios correspond to astronomical tide events during the new moon phase, when maximum and minimum tide levels occurred within 24 h. These conditions are related to the highest piezometric levels observed in field. Model calibration was based on 18 field observations collected at 09:00, 12:00, and 15:00 across the selected dates. Model outputs closely matched the field observations, with a root mean square error (RMSE) of 0.056 m, and a mean absolute error (MAE) of 0.049 m. Although the differences are minimal, they reflect short-term variability and limited fluctuation during calibration. However, the full monitoring period showed groundwater levels ranging from −0.10 to 0.53 m above mean sea level (masl), emphasizing the importance of understanding short-term dynamics. This modeling approach supports construction planning, helping to anticipate risks and promote better and informed construction practices. Full article

(This article belongs to the Special Issue Selected Papers from the 6th International Electronic Conference on Applied Sciences)

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16 pages, 3160 KB

Open AccessArticle

Soil-Aware Deep Learning for Robust Interpretation of Low-Strain Pile Integrity Tests

by Bora Canbula, Övünç Öztürk, Vehbi Özacar and Tuğba Özacar

Appl. Sci. 2026, 16(9), 4189; https://doi.org/10.3390/app16094189 - 24 Apr 2026

Viewed by 387

Abstract

The Low-Strain Pile Integrity Test (LSPIT), standardized in ASTM D5882, is widely used as a rapid and economical non-destructive technique for assessing pile continuity in deep foundation systems. However, interpretation of LSPIT reflectograms remains strongly dependent on expert judgment and is influenced by soil–pile interaction effects such as damping and radiation losses, which can alter waveform morphology and confound automated defect screening. This study proposes a soil-aware deep learning framework that combines image-based reflectogram features with categorical geotechnical context describing the dominant soil regime at the measurement site. Reflectogram images are processed with a pretrained ConvNeXt-Large backbone, while soil information derived from Unified Soil Classification System (USCS) logs is represented as a categorical auxiliary input and mapped to a learnable embedding. The resulting multimodal design conditions waveform interpretation based on site context rather than relying on signal morphology alone. The framework is examined on an assembled benchmark of 510 expert-labeled reflectograms (404 intact and 106 defective), including a nine-site subset of 182 field records with explicit soil annotations. On the assembled benchmark, the model yields 99.41% accuracy and a weighted F1-score of 0.9941; on the nine-site subset, the observed accuracy is 99.45% with zero missed defective cases. Balanced accuracy, specificity, missed-detection rate, false-alarm rate, and confidence intervals are additionally reported to better align the evaluation with engineering screening practice. The study also states the current limits of the evidence base, including partial soil annotation, dominant-soil simplification, restricted soil coverage, and the absence of leave-site-out and interpretability-focused validation. Overall, the results support soil-aware multimodal learning as a promising proof-of-concept direction for more context-aware automated LSPIT interpretation, while also identifying the validation steps still required for broad field deployment. Full article

(This article belongs to the Special Issue Selected Papers from the 6th International Electronic Conference on Applied Sciences)

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12 pages, 3135 KB

Open AccessArticle

Efficient Nanoparticle Sorting Through an Optofluidic Waveguide Splitter for Early Cancer Diagnosis: A Numerical Study

by Aurora Elicio, Morteza Maleki, Giuseppe Brunetti and Caterina Ciminelli

Appl. Sci. 2026, 16(9), 4162; https://doi.org/10.3390/app16094162 - 23 Apr 2026

Viewed by 459

Abstract

In this work, we present a numerical proof-of-concept study of a device for nanoparticle sorting, targeting size ranges relevant to exosome-like dimensions (typically 40–200 nm), which remains challenging for passive sorting techniques. The system consists of three silicon waveguides embedded in a CYTOP layer and arranged in a two-step directional-coupler configuration, integrated with a microchannel that carries a water-based buffer as the carrier fluid, transporting the suspended nanoparticles. Three-dimensional Finite Element Method (3D-FEM) simulations were performed, incorporating both optical and hydrodynamic forces to track particle dynamics within the microchannel and demonstrate controlled, size-selective particle deflection. First, numerical simulations show that nanospheres with diameters ranging from 500 nm to 700 nm can be effectively separated by the transverse trapping force at a 4:1 power-splitting ratio. Then, to extend the concept toward smaller size ranges, a bifurcated microchannel is introduced, enabling fluid-assisted transport in low-optical-field regions and allowing reliable separation of particles with smaller diameters (between 200 nm and 400 nm), accompanied by an 8:1 power-splitting ratio. These results demonstrate, within a numerical framework, the feasibility of an integrated photonic–microfluidic approach for size-selective nanoparticle sorting. The proposed strategy may support future pre-processing steps in liquid biopsy workflows, particularly for enriching nanoscale components such as exosome-sized vesicles, rather than constituting a direct diagnostic tool. Full article

(This article belongs to the Special Issue Selected Papers from the 6th International Electronic Conference on Applied Sciences)

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14 pages, 1731 KB

Open AccessArticle

Inactivation of Respiratory Syncytial Virus in Aerosols by Means of Selected Radiated Microwaves

by Pietro Bia, Alessandro Filisetti, Margherita Losardo and Antonio Manna

Appl. Sci. 2026, 16(7), 3253; https://doi.org/10.3390/app16073253 - 27 Mar 2026

Viewed by 568

Abstract

Human respiratory syncytial virus (RSV) is the predominant etiological agent responsible for lower respiratory tract infections in young children. Recurrent infections throughout an individual’s lifespan can lead to significant morbidity, particularly in the elderly and in adults, influencing the trends of hospitalization rates. Consequently, it is imperative to develop technologies that can sanitize environments from this pathogen while being compatible with human presence. Structure Resonant Energy Transfer (SRET) is the scientific principle underlying a sanitization technology that has demonstrated efficacy against several enveloped viruses, including SARS-CoV-2 and Influenza A viruses. SRET employs specific frequencies of electromagnetic waves to effectively disrupt the structural integrity of viral envelopes through dipole coupling. This disruption leads to the inactivation of the virus, rendering it non-infectious. The objective of this study is to analyse the effect of a specific SRET sanitization method on RSV. The sanitization test was conducted in aerosol form within a BSL-3 laboratory, exploring the frequency band from 8 to 16 GHz. An optimal sub-band was identified, giving an inactivation efficiency up to 99.5%. In conclusion, it has been demonstrated that the microwave non-thermal sanitization method is effective against RSV. These results confirm its potential as a viable approach for environmental decontamination. Full article

(This article belongs to the Special Issue Selected Papers from the 6th International Electronic Conference on Applied Sciences)

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