Applied Sciences

18 pages, 25539 KB

Open AccessArticle

Astronomically Constrained Third-Order Sequence Stratigraphy of the Eocene Eh₃ Member in the Lacustrine Biyang Depression, Central China

by Dan Ye, Yiping Zhang, Binbin Hao, Xuewei Xiao, Xue Yan, Jian Wang and Ke Xu

Appl. Sci. 2026, 16(10), 4881; https://doi.org/10.3390/app16104881 (registering DOI) - 13 May 2026

Abstract

The middle Eocene represents a shift from the Early Eocene greenhouse climate to a subsequent cooling trend, during which orbital-scale forcing exerted a strong influence on continental depositional systems. The third Member of the Hetaoyuan Formation (Eh₃) in the Biyang Depression [...] Read more.

The middle Eocene represents a shift from the Early Eocene greenhouse climate to a subsequent cooling trend, during which orbital-scale forcing exerted a strong influence on continental depositional systems. The third Member of the Hetaoyuan Formation (Eh₃) in the Biyang Depression of the Nanxiang Basin preserves a thick lacustrine succession, but its third-order sequence subdivision has remained controversial because of insufficient temporal control. In this study, natural gamma-ray (GR) logs from wells A1 and A2 were analyzed using an integrated workflow that combines cyclostratigraphy, INPEFA (Integrated Prediction Error Filter Analysis), wavelet transform, and sedimentary noise modeling. The GR records reveal clear astronomical signals corresponding to ~405 kyr long eccentricity, ~100 kyr short eccentricity, ~40 kyr obliquity, and ~20 kyr precession. Astronomical tuning to the 405 kyr cycle yields a depositional duration of ~10.3 Myr and a basal age of ~49.6 Ma. Integrated stratigraphic analysis identifies nine third-order sequence boundaries and eight third-order sequences. In addition, sedimentary noise modeling detects a prominent ~1.2 Myr long-period obliquity modulation signal, which is interpreted to govern long-term lake-level fluctuations and third-order sequence development. These results provide a time-constrained framework for sequence subdivision and demonstrate the importance of orbital forcing in shaping lacustrine stratigraphic architecture. Full article

(This article belongs to the Section Earth Sciences)

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17 pages, 5725 KB

Open AccessArticle

Determination of the Pressure-Relief Environment of Roadways Beneath the Goaf and Surrounding Rock Control for Gob-Side Entry Retaining by Roof Cutting

by Zhenkai Zheng, Bingyuan Hao, Xuanqi Liu and Heping Hao

Appl. Sci. 2026, 16(10), 4880; https://doi.org/10.3390/app16104880 (registering DOI) - 13 May 2026

Abstract

In view of the complex stress environment of the underlying roadway and the difficulty in ensuring the stability of entry retaining under the combined action of the goaf and the residual coal pillar, this paper takes the auxiliary intake roadway of the 8307 [...] Read more.

In view of the complex stress environment of the underlying roadway and the difficulty in ensuring the stability of entry retaining under the combined action of the goaf and the residual coal pillar, this paper takes the auxiliary intake roadway of the 8307 working face in Xinjing Mine as the engineering background and carries out research focusing on two aspects: the determination of the pressure-relief environment beneath the goaf and residual coal pillar, and surrounding rock control through roof cutting and pressure relief. Two conditions, namely the structural basis and the stress basis, are proposed for determining whether a roadway beneath the goaf is located in a pressure-relief environment. For the identification of the stress basis, a floor stress calculation model is established, and the stress calculation method is corrected, thereby clarifying the necessity of entry retaining by roof cutting under a pressure-relief environment. Meanwhile, a FLAC3D numerical model is established to comparatively analyze the stress and displacement characteristics of the surrounding rock of the roadway under roof-cutting and non-roof-cutting conditions, and the results are verified by an in situ industrial test. The theoretical calculation results indicate that the 8307 auxiliary intake roadway satisfies the two conditions for being located in a stress reduction zone. The numerical simulation results show that, after roof cutting, the vertical stress on the solid coal side decreases by 19.7%, and the maximum vertical displacement decreases by 20.6%. The field statistical results show that the average convergence between the roof and floor after entry retaining is 399 mm, the average convergence of the two ribs is 160 mm, and the average deformation rate of the retained-entry section is 17.44%, which satisfies the reuse requirement. The research results can provide a reference for the design of entry retaining by roof cutting under similar goaf conditions. Full article

(This article belongs to the Section Applied Industrial Technologies)

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22 pages, 1041 KB

Open AccessArticle

Phase-Faithful Compression for Marine Parallel Phase-Shifting Digital Holography via Spatiotemporal Decomposition

by Xinran Liu and Haoran Meng

Appl. Sci. 2026, 16(10), 4879; https://doi.org/10.3390/app16104879 (registering DOI) - 13 May 2026

Abstract

Continuous in situ marine holographic observation generates data volumes that challenge onboard storage and transmission. Parallel phase-shifting digital holography (PPSDH) is especially sensitive to compression because phase retrieval depends on consistent four-channel demodulation. We present a training-free spatiotemporal compression framework for sparse-particle marine [...] Read more.

Continuous in situ marine holographic observation generates data volumes that challenge onboard storage and transmission. Parallel phase-shifting digital holography (PPSDH) is especially sensitive to compression because phase retrieval depends on consistent four-channel demodulation. We present a training-free spatiotemporal compression framework for sparse-particle marine PPSDH sequences based on background–residual decomposition and a shared four-channel processing path. The background is coded once per temporal window by a discrete wavelet transform (DWT) followed by principal component analysis (PCA), and the dynamic residual is decorrelated by temporal principal component analysis before quantization and entropy coding. The framework is evaluated on three primary 64-frame marine PPSDH sequences using a common reconstruction-and-evaluation pipeline with wrapped-phase root-mean-square error (PhaseRMSE) as the primary metric and amplitude peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as secondary references; expanded supplementary checks are also reported for nine additional selected 64-frame groups spanning sparse to transitional occupancy. On the primary sequence and within the high-fidelity achieved-rate overlap with the JPEG Pleno anchor codec INTERFERE, the proposed framework reduces PhaseRMSE by about 3.3-fold to 3.4-fold while increasing amplitude PSNR by about 11 dB and preserving amplitude SSIM above 0.99997. Lower-bitrate sweeps further quantify the rate–fidelity trade-off rather than claiming universal low-rate superiority. These results support BG–Res spatiotemporal coding as a practical phase-fidelity-oriented option for the tested sparse-to-transitional marine PPSDH conditions; extension to dense scenes, broader marine conditions, and downstream biological tasks requires separate validation. Full article

15 pages, 706 KB

Open AccessArticle

Divergent Kinetic Modeling of Wine Aging Across Bulk Storage and Bottle Environments

by Piernicola Masella, Agnese Spadi, Ferdinando Corti, Alessandro Parenti and Giulia Angeloni

Appl. Sci. 2026, 16(10), 4878; https://doi.org/10.3390/app16104878 (registering DOI) - 13 May 2026

Abstract

Physicochemical transformations in wine aging are strongly influenced by storage environment and scale. While kinetic modeling has been extensively applied to bulk aging systems, bottle aging is often treated as a continuation of cellar evolution despite representing a different physicochemical regime. A reaction-kinetics [...] Read more.

Physicochemical transformations in wine aging are strongly influenced by storage environment and scale. While kinetic modeling has been extensively applied to bulk aging systems, bottle aging is often treated as a continuation of cellar evolution despite representing a different physicochemical regime. A reaction-kinetics framework was applied to assess whether wine aging in bulk and bottle environments can be described by a unified model or instead requires divergent quantitative descriptions. A Sangiovese red wine was aged for six months under controlled conditions in inert bulk systems (stainless steel and a non-porous composite material), a porous bulk system (raw earthenware), and glass bottles. Key physicochemical parameters, including dissolved oxygen, oxidation–reduction potential, free sulfur dioxide, anthocyanins, polymerized pigments, and colorimetric indices, were monitored through non-invasive and laboratory analysis. Exploratory multivariate analysis showed that inert systems follow overlapping compositional trajectories, indicating stable chemical evolution, whereas bottle-aged wines exhibited greater variability. Kinetic analysis revealed comparable oxygen-limited behavior and buffered oxidation–reduction evolution in inert bulk systems, whilst bottle aging displayed different oxygen and sulfur dioxide dynamics, consistent with scale effects and altered oxygen partitioning. Overall, bottle aging cannot be reliably predicted by extrapolation of bulk storage kinetics and requires boundary-condition-aware descriptors accounting for scale and environmental constraints. Full article

(This article belongs to the Special Issue Advances in Particle and Powder Technology: Theory, Methods, and Applications)

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16 pages, 349 KB

Open AccessArticle

Reflectance of the Women Skin from the Ultraviolet to the Far-Infrared Spectrum Across Different Body Regions at Incidence Angles of 20° and 60°

by Magdalena Hartman-Petrycka, Joanna Witkoś, Patrycja Zagórna and Sławomir Wilczyński

Appl. Sci. 2026, 16(10), 4877; https://doi.org/10.3390/app16104877 (registering DOI) - 13 May 2026

Abstract

Background: Directional hemispherical reflectance (DHR) is a precise method for evaluating skin reflectance and is widely used in dermatological, photobiological, and cosmetic or pharmaceutical research. Reflectance measurements may support emissivity-related interpretation, particularly in the infrared range, being influenced by chromophore content, epidermal structure, [...] Read more.

Background: Directional hemispherical reflectance (DHR) is a precise method for evaluating skin reflectance and is widely used in dermatological, photobiological, and cosmetic or pharmaceutical research. Reflectance measurements may support emissivity-related interpretation, particularly in the infrared range, being influenced by chromophore content, epidermal structure, and physiological factors such as hydration, pigmentation, and surface heterogeneity. However, most in vivo studies have focused on limited spectral ranges or selected anatomical sites. This study aimed to assess skin directional hemispherical reflectance across a broad spectral range and to provide an integrated dataset supporting emissivity-related interpretation in the infrared region. Methods: The study included 20 women aged 22–50 years (27 ± 9 years) with Fitzpatrick skin phototypes II–III. Reflectance measurements were performed at 14 anatomical sites using an ET 100 emissometer (1.9–21 µm) and an SOC 410 Solar DHR reflectometer (335–2500 nm). Infrared measurements were conducted at incidence angles of 20° and 60° to assess angular effects. Data were statistically analyzed. Results: The lowest reflectance values were observed within 335–380 nm, 1700–2500 nm, and 1.5–21 µm, whereas the highest reflectance was recorded in the 590–720 nm and 700–1100 nm bands. Reflectance symmetry between body sides was observed. In the infrared range, reflectance decreased with increasing wavelength, while mid- and far-infrared values were more uniform across locations. The highest reflectance values were noted for the thigh, calf (crural region), forearm, and palmar surface of the hand, whereas the lowest values were observed in the neck, abdominal region, and dorsal surface of the hand. Measurements at 60° incidence yielded higher reflectance values than those at 20°, while preserving spatial patterns. Conclusions: Directional hemispherical reflectance provides a robust approach for assessing skin reflectance across a broad spectral range. Reflectance depends on wavelength, anatomical location, and physiological factors, including epidermal thickness, pigmentation, and sebum presence. The integrated analysis of spectral, anatomical, and angular variability may support improved interpretation of skin optical properties and contribute to reference data for biomedical and infrared imaging applications. Full article

23 pages, 1757 KB

Open AccessArticle

Gain-Scheduled Control of a Wheeled Inverted-Pendulum Robot with Load-Induced Equilibrium Drift Compensation

by Yuchen Song, Gao Wan and Xiaohua Cao

Appl. Sci. 2026, 16(10), 4876; https://doi.org/10.3390/app16104876 (registering DOI) - 13 May 2026

Abstract

Wheeled inverted-pendulum robots with movable upper structures and variable payloads exhibit configuration-dependent equilibrium drift and payload-dependent dynamic variation, which complicate balancing control. This paper proposes a gain-scheduled controller–observer framework for payload-adaptive balancing of such a robot. First, the multi-body system is reduced to [...] Read more.

Wheeled inverted-pendulum robots with movable upper structures and variable payloads exhibit configuration-dependent equilibrium drift and payload-dependent dynamic variation, which complicate balancing control. This paper proposes a gain-scheduled controller–observer framework for payload-adaptive balancing of such a robot. First, the multi-body system is reduced to a control-oriented equivalent inverted-pendulum model through center-of-mass lumping, from which a parameter-varying linearized model is established. On this basis, an H∞ state-feedback controller with input constraints is synthesized in a linear matrix inequality (LMI) framework, and an augmented-state observer is designed to estimate the residual equilibrium offset induced by payload variation. To improve robustness over the operating range, the frozen-point design is extended to a sampled-model multi-model synthesis framework, and gain scheduling is implemented with respect to the measurable arm angle. Nonlinear Simscape simulations show that the proposed method can recover balance at representative fixed operating points, compensate effectively for load-induced equilibrium drifts, and preserve stable balancing performance under slow arm-angle variation. Quantitative comparisons with an LQR baseline further support the effectiveness of the proposed framework for payload-adaptive balancing control. Full article

(This article belongs to the Section Robotics and Automation)

32 pages, 2375 KB

Open AccessArticle

Mission-Gilded Baroque Terracotta Sculptures by Lorenzo Vaccaro: A Multi-Analytical Investigation of Materials and Degradation

by Andrea Macchia, Laura Palermo, Camilla Zaratti, Irene Angela Colasanti, Federica Valentini and Tilde de Caro

Appl. Sci. 2026, 16(10), 4875; https://doi.org/10.3390/app16104875 (registering DOI) - 13 May 2026

Abstract

This study presents a multi-analytical investigation of two Baroque gilded terracotta sculptures—Hercules and the Nemean Lion (Hercules A) and Hercules and the Lernaean Hydra (Hercules B)—attributed to Lorenzo Vaccaro (1655–1706) and preserved at the Museo Civico Gaetano Filangieri in Naples. This research [...] Read more.

This study presents a multi-analytical investigation of two Baroque gilded terracotta sculptures—Hercules and the Nemean Lion (Hercules A) and Hercules and the Lernaean Hydra (Hercules B)—attributed to Lorenzo Vaccaro (1655–1706) and preserved at the Museo Civico Gaetano Filangieri in Naples. This research aimed to reconstruct the original manufacturing technique, characterize materials introduced by successive restoration interventions, and identify active degradation mechanisms. A systematic diagnostic approach integrating UV fluorescence imaging, digital optical microscopy, portable energy-dispersive X-ray fluorescence spectroscopy (EDXRF), Raman spectroscopy, Fourier-transform infrared spectroscopy in attenuated total reflectance mode (FTIR-ATR), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and spectrocolorimetry was applied. The original gilding system—comprising a ferruginous silico-aluminous terracotta substrate, a calcium sulfate ground, a lead-white imprimitura, an iron-rich bole, and a thin gold leaf—is consistent with documented Baroque mission gilding practices in Southern Italy. Analytical evidence further documented extensive non-original interventions, including copper-based artificial patination, bronze powder (porporina) integration, poly (vinyl acetate) adhesives, and acrylic protective coatings. Raman spectroscopy identified the in situ conversion of intentionally applied tenorite (CuO) to malachite (Cu₂CO₃(OH)₂) as an active degradation pathway. Spectrocolorimetric measurements quantified chromatic alterations of up to ΔE = 52 attributable to accumulated surface deposits. The proposed integrated methodology constitutes a replicable diagnostic framework for investigating gilded terracotta artefacts in museum collections. Full article

(This article belongs to the Special Issue Non-Destructive Techniques for Heritage Conservation)

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31 pages, 8109 KB

Open AccessArticle

An Explainable and Robust Framework for Telkari Jewelry Recognition Using Deep Feature Representations and ANOVA-Based Feature Selection

by Sukru Aykat, Sabahattin Akgul, Fevzi Cakmak and Tarik Demir

Appl. Sci. 2026, 16(10), 4874; https://doi.org/10.3390/app16104874 (registering DOI) - 13 May 2026

Abstract

Telkari is a decorative art based on the handcrafting of fine silver wires. Identifying Telkari jewelry is quite challenging due to its diverse designs and styles. Recognizing these jewelry items, which come in thousands of varieties, requires experts in Telkari. In this study, [...] Read more.

Telkari is a decorative art based on the handcrafting of fine silver wires. Identifying Telkari jewelry is quite challenging due to its diverse designs and styles. Recognizing these jewelry items, which come in thousands of varieties, requires experts in Telkari. In this study, we propose an approach for Telkari recognition using computer vision techniques, aiming to simulate the analysis of Telkari jewelry by Telkari experts. In the first stage, we created a dataset of Telkari by collecting images of Telkari products produced by Telkari masters in the Midyat district of Mardin, Turkey. In the second stage, the performance of the MobileNetV2 and ResNet50 deep learning models was examined using both direct classification and hyperparameter-tuned approaches. Furthermore, the feature vectors extracted from the deep learning models were trained using traditional machine learning algorithms, SVM, KNN, XGB, and RF, after selecting discriminative features with ANOVA. Among the hyperparameter-tuned models, ResNet50 outperformed MobileNetV2. Among the hybrid approaches, the SVM model trained on features obtained from ResNet50 achieved the highest performance with an overall accuracy of 99.56%. Furthermore, the interpretability techniques GradCAM, t-SNE, and LIME were used to examine the model’s decision-making processes, confirming that they focused on relevant visual regions and that predictions were based on the complex interactions among multiple features. In conclusion, this study provides a robust methodology for developing a highly accurate and transparent classification system for fields such as cultural heritage preservation and digitization. Full article

32 pages, 2072 KB

Open AccessReview

Cybernetics of Balance Control

by Pietro Morasso

Appl. Sci. 2026, 16(10), 4873; https://doi.org/10.3390/app16104873 (registering DOI) - 13 May 2026

Abstract

Fighting against gravity is a common challenge for all terrestrial animals, including most mammals. It means, in particular, avoiding falls to the ground while performing daily tasks, such as standing up, locomotion, or foraging for food. This means that balance control in humans [...] Read more.

Fighting against gravity is a common challenge for all terrestrial animals, including most mammals. It means, in particular, avoiding falls to the ground while performing daily tasks, such as standing up, locomotion, or foraging for food. This means that balance control in humans involves a wide variety of contexts and balance paradigms, such as upright standing, hand standing, tightrope walking, ice-skater spinning, bicycling, whole-body gesturing, and fingertip stick balancing, among others. From the cybernetic point of view, the underlying control problem is to keep the CoP (Center of Pressure) and the CoM (Center of Mass) aligned dynamically on the common vertical axis, and this means that the variety of balance strategies can be reduced to two basic paradigms: the CoP strategy (the CoP is the control variable and the CoM is the controlled variable) and the CoM strategy (the CoM is simultaneously the control and the controlled variable). It is suggested that the two balance strategies are implemented by combining four basic control paradigms, as a function of the task and environmental conditions: • Opportunistic control: exploitation of a physical phenomenon as the gyroscopic effect. • Stiffness control: exploitation of the elastic properties of skeletal muscles. • Feedback control: measuring an incipient fall index and closing the loop in real time. In particular, it is shown that a phase-space-based formulation of intermittent feedback control can compensate for the destabilization effect of conventional continuous control due to the large feedback delay. • Feedforward control: exploitation of an internal body model to generate stable whole-body synergies in an anticipatory manner. Such control paradigms are illustrated by summarizing the results of experimental and simulated data. Full article

(This article belongs to the Special Issue Research on Biomechanics, Equipment Development, Motor Control and Learning of Human Movements)

21 pages, 1834 KB

Open AccessArticle

Experimental Study on the Effect of Leading-Edge Curvature Optimization on Pressure Fluctuations in Transonic Compressor Blades

by Ye Yang, Shaozun Hong, Yuan Yi, Xingya Da and Junqiang Wu

Appl. Sci. 2026, 16(10), 4872; https://doi.org/10.3390/app16104872 (registering DOI) - 13 May 2026

Abstract

To investigate the impact of continuous leading-edge curvature on the aerodynamic performance of transonic compressor blade profiles, schlieren observations and surface pulsating pressure measurements were conducted on the baseline profile CM1.2 and its optimized variant CM1.2-Y. The results indicate that the optimized profile [...] Read more.

To investigate the impact of continuous leading-edge curvature on the aerodynamic performance of transonic compressor blade profiles, schlieren observations and surface pulsating pressure measurements were conducted on the baseline profile CM1.2 and its optimized variant CM1.2-Y. The results indicate that the optimized profile can effectively reduce unsteady pressure pulsations at Mach numbers of 0.8 and 1.05, with a maximum reduction of 14.6 dB. At Mach number 0.95, the optimized design eliminates high-pressure pulsation regions on the pressure surface but intensifies local loading on the suction surface. The optimization of leading-edge curvature effectively reduces the extreme pulsations on the suction surface caused by shock wave interference under most operating conditions, and significantly improves the wave structure on the pressure surface, thereby comprehensively reducing the pressure pulsation level of the blade profile. Full article

(This article belongs to the Special Issue Advances in Aircraft Design, Optimization and Flight Control)

24 pages, 604 KB

Open AccessSystematic Review

Periodontal Outcomes of Surgically–Orthodontically Treated Impacted Maxillary Canines: Influence of Surgical Technique and Impaction Site—A Systematic Review

by Adina Simona Cosarca, Maria Domenica Campana, Angelo Aliberti, Francesco Giordano, Alina Ormenisan, Francesco Riccitiello and Roberta Gasparro

Appl. Sci. 2026, 16(10), 4871; https://doi.org/10.3390/app16104871 (registering DOI) - 13 May 2026

Abstract

Maxillary canine impaction is a common clinical condition often requiring a combined surgical–orthodontic approach; however, the effect of different surgical strategies on periodontal outcomes remains unclear. This systematic review aimed to evaluate periodontal outcomes following treatment of impacted maxillary canines, with particular focus [...] Read more.

Maxillary canine impaction is a common clinical condition often requiring a combined surgical–orthodontic approach; however, the effect of different surgical strategies on periodontal outcomes remains unclear. This systematic review aimed to evaluate periodontal outcomes following treatment of impacted maxillary canines, with particular focus on the influence of surgical technique (open vs. closed) and impaction site (palatal vs. buccal). A comprehensive search of PubMed, Scopus, and Web of Science was conducted up to January 2026 in accordance with PRISMA guidelines, and the protocol was registered in PROSPERO. Randomized controlled trials, controlled clinical studies, observational studies, case series, and case reports reporting periodontal parameters were included. Primary outcomes comprised probing pocket depth, keratinized tissue width, gingival thickness, plaque and bleeding indices, and gingival recession. Twenty-seven studies were included. Overall, treated canines were associated with periodontal conditions comparable to normally erupted teeth, with probing depths generally within physiological limits and minimal gingival recession. Buccally impacted canines tended to show slightly reduced keratinized tissue compared with palatal impactions, although without clear clinical relevance. No consistent superiority of open or closed techniques was identified. Surgical–orthodontic treatment is generally associated with favorable periodontal outcomes, and treatment selection should be individualized according to anatomical and orthodontic factors. Full article

(This article belongs to the Special Issue Translational Research in Dentistry: Latest Advancements and Prospects)

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19 pages, 1875 KB

Open AccessArticle

Real-Time Structural Stress Monitoring of Gantry Cranes Utilizing Digital Twin Technology

by Min Liu, Keming Chen, Hanbin Xiao, Ze Zhu, Yushuang Yan, Jiawei Zhang, Yujin Han and Feng Zhu

Appl. Sci. 2026, 16(10), 4870; https://doi.org/10.3390/app16104870 (registering DOI) - 13 May 2026

Abstract

To address the challenge of real-time and global monitoring of the structural stress state of large port gantry cranes in complex working environments, this paper proposes a digital twin system framework covering the physical layer, data layer, model layer, and application layer, utilizing [...] Read more.

To address the challenge of real-time and global monitoring of the structural stress state of large port gantry cranes in complex working environments, this paper proposes a digital twin system framework covering the physical layer, data layer, model layer, and application layer, utilizing a container gantry crane as the case study. A multi-dimensional working condition space covering key working condition parameters such as lifting load and trolley position is designed, and a stress surrogate model based on the Radial Basis Function (RBF) neural network is constructed. This realizes a rapid mapping from low-dimensional operating parameters to high-dimensional full-field stress distributions. The surrogate model is integrated into the visualization platform, achieving real-time dynamic rendering and threshold exceedance warning of the stress of the key structures of the crane. The results show that the constructed surrogate model ensures the prediction accuracy (R² > 0.94) and achieves millisecond-level calculation response, demonstrating good real-time performance and reliability. It provides a reference for the digital monitoring of large-scale equipment. Full article

(This article belongs to the Section Mechanical Engineering)

17 pages, 8949 KB

Open AccessArticle

Hybrid Switched-Capacitor Three-Phase Direct AC-AC Converter with Adjustable Output Voltage

by Guanyu Yan, Ruifeng Li, Chuang Liu, Dongbo Guo, Mulin Han and Fengyue Zhao

Appl. Sci. 2026, 16(10), 4869; https://doi.org/10.3390/app16104869 (registering DOI) - 13 May 2026

Abstract

A switched-capacitor (SC) three-phase direct ac/ac converter is presented in this paper. It includes an additional inductor and two SC cells for each phase. Due to the introduction of the inductor, the output voltage can be adjusted. The proposed converter operates at a [...] Read more.

A switched-capacitor (SC) three-phase direct ac/ac converter is presented in this paper. It includes an additional inductor and two SC cells for each phase. Due to the introduction of the inductor, the output voltage can be adjusted. The proposed converter operates at a fixed switching frequency. One of the features of this topology is that the voltage stresses across the switches and capacitors equal half of the high-side voltage. In addition, the self-balancing capability of capacitor voltages and a simple modulation strategy are other characteristics. The main advantage of the proposed converter is the employment of unidirectional switches (a single MOSFET), which avoids the commutation problems in the bidirectional switches. A detailed description of the operation principle, quantitative analysis, and design considerations for the proposed converter is provided. Eventually, a prototype with 55 V/220 V and 3 kW is designed to demonstrate the feasibility and validity of the proposed converter. Full article

(This article belongs to the Special Issue Application of Power Electronics Converters in Electrical Power Systems)

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33 pages, 1482 KB

Open AccessArticle

Water Quality Identification: Integrating IoT Sensors and Deep Learning for Near-Real-Time Water Quality Assessment

by Christina Tsolaki, George Kokkonis, Stavros Valsamidis and Sotirios Kontogiannis

Appl. Sci. 2026, 16(10), 4868; https://doi.org/10.3390/app16104868 (registering DOI) - 13 May 2026

Abstract

The increasing demand for sustainable, affordable smart city infrastructure has heightened the need for low-cost near-real-time water quality monitoring systems. In this study, we propose Water-QI, a low-cost Internet of Things (IoT)-based environmental monitoring platform that combines budget-friendly sensors with deep learning for [...] Read more.

The increasing demand for sustainable, affordable smart city infrastructure has heightened the need for low-cost near-real-time water quality monitoring systems. In this study, we propose Water-QI, a low-cost Internet of Things (IoT)-based environmental monitoring platform that combines budget-friendly sensors with deep learning for water quality index (WQI) assessment and forecasting. The sensing platform measures five key physicochemical parameters, namely temperature, total dissolved solids (TDS), pH, turbidity, and electrical conductivity, enabling continuous multi-parameter monitoring in urban water environments. To model temporal variations in water quality under both cloud-based and edge-oriented deployment scenarios, we evaluate multiple gated recurrent unit (GRU) architectures with different widths and depths. Experiments are conducted at two temporal resolutions, hourly and minute-level, in order to examine the trade-off between predictive accuracy and edge computational latencies. In the hourly scenario, the single-layer GRU with 64 units achieved the best overall balance, reaching a validation RMSE of 0.0281 and a test

R^{2}

of 0.9820, while deeper stacked GRU models degraded performance substantially. In the minute-resolution scenario, shallow wider GRU models produced the best results, with the single-layer GRU with 512 units attaining the lowest validation RMSE (0.025548) and the 256-unit variant achieving nearly identical accuracy with much lower inference cost. The results show that increasing the GRU model length can yield improvements at high temporal granularity, whereas increasing the GRU layer depth consistently harms convergence and generalization. Overall, the findings indicate that shallow GRU architectures provide the most practical solution for accurate, low-cost, and scalable water quality forecasting. In particular, the 64-unit GRU is the most suitable choice for hourly periodic interval operation, while the 256-unit GRU offers the best edge computational speed and accuracy trade-off for minute-level near-real-time inference on resource-constrained devices. Full article

(This article belongs to the Special Issue Applications of Industrial Internet of Things (IIoT) Platforms: 2nd Edition)

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22 pages, 1321 KB

Open AccessArticle

Neural-Chain-Analysis-Based Exit Point Identification Method for Early-Exit DNNs

by Andrii Pukach, Vasyl Teslyuk, Nataliia Lysa and Liubomyr Sikora

Appl. Sci. 2026, 16(10), 4867; https://doi.org/10.3390/app16104867 (registering DOI) - 13 May 2026

Abstract

This work is devoted to the investigation of an actual scientific and applied problem in the identification of exit points for early-exit DNNs based on the analysis of neural chains, which is one of the complex tasks related to the scientific and applied [...] Read more.

This work is devoted to the investigation of an actual scientific and applied problem in the identification of exit points for early-exit DNNs based on the analysis of neural chains, which is one of the complex tasks related to the scientific and applied problems of DNN optimization, including, in particular, those based on the existing early-exit concept. The obtained computational complexity of the developed method is not limited by the latter itself, but instead, it mainly depends on the chosen algorithm for analyzing the occurrences of particular substrings (i.e., trimmed neural chains) into a defined list of strings (i.e., full neural chains). For example, in the framework of the conducted research, the Python operator “in” has been used (for this purpose), which uses an in-built optimized algorithm based on the combination of the Boyer–Moore and Horspool algorithms with a linear scalability, and computational complexity that approaches the arithmetic product of the total number of strings (i.e., full neural chains) in the array by the average length of the string in the same array. The performed practical approbation of the developed method gave positive results in decreasing the overall time for obtaining the final result of the considered DNN, as well as significantly decreasing the following timing parameters of the considered DNN: the minimal time to obtain the final result (reduced by more than 5 times); the average time to obtain the final result (reduced by ~1.4 times); and the total time spent processing all 22,500 modeling cases in total (reduced by ~1.39 times). In terms of the main positive aspects and advantages of the developed method, we could highlight its maximal versatility (in terms of the studied DNNs, their architectural and/or structural features, application areas, and input data representation, as well as further software implementation of the proposed method), together with its maximal simplicity of representation and understanding, which ensures the possibility of working with this method even for novice and inexperienced researchers and users who have only basic knowledge of DNNs. In addition, the main results and conclusions of the conducted research are given, and the prospects for further research are considered. Full article

(This article belongs to the Special Issue Advanced Research in Artificial Neural Networks)

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18 pages, 1704 KB

Open AccessArticle

Automation of Learning Workflows for 3D Modeling Skills in Engineering Education

by Francisco Salmerón-Medina, María Alcalde, Diego Canales, Fabio Gómez-Estern and Francisco Valderrama-Gual

Appl. Sci. 2026, 16(10), 4866; https://doi.org/10.3390/app16104866 (registering DOI) - 13 May 2026

Abstract

This paper presents a novel platform for automated self-paced learning in Computer-Aided Design (CAD) courses within engineering education. The platform fully automates the entire learning cycle, including exercise generation, submission, scheduling, test design, and grading. The central hypothesis posits that complete automation reduces [...] Read more.

This paper presents a novel platform for automated self-paced learning in Computer-Aided Design (CAD) courses within engineering education. The platform fully automates the entire learning cycle, including exercise generation, submission, scheduling, test design, and grading. The central hypothesis posits that complete automation reduces repetitive tasks for instructors, allowing them to dedicate more time to individualized student support. The system also provides key advantages: it generates unique exercises for each student to prevent plagiarism while maintaining comparable complexity; it delivers instantaneous grading and feedback to enhance motivation; and it enables students to work with almost any CAD software, as the evaluation relies on physical properties rather than commercial tools. After several years of successive testing and refinement, the tool can generate and accurately grade frequent activities in large student cohorts, providing abundant data points. Their statistical analysis, via multiple approaches, confirms that the system reliably produces individualized exercises, reduces grading errors, and offers prompt, consistent feedback, thereby supporting a more efficient and engaging learning process. Full article

17 pages, 6228 KB

Open AccessArticle

Optimizing Laboratory Workflow Through Prediction of Test Demand and Quality Control

by Andrzej Opaliński, Krzysztof Regulski, Jakub Swadźba, Paweł Wąsowicz, Agnieszka Kwietniewska-Śmietana and Piotr Sitkowski

Appl. Sci. 2026, 16(10), 4865; https://doi.org/10.3390/app16104865 (registering DOI) - 13 May 2026

Abstract

Efficient management of medical diagnostic laboratories is critical for ensuring high-quality test results, optimal utilization of analytical resources, and continuity of services, particularly under conditions of increased and variable demand. This study addresses the problem of forecasting laboratory workload and supporting quality control [...] Read more.

Efficient management of medical diagnostic laboratories is critical for ensuring high-quality test results, optimal utilization of analytical resources, and continuity of services, particularly under conditions of increased and variable demand. This study addresses the problem of forecasting laboratory workload and supporting quality control processes using data-driven methods. A large-scale dataset comprising over 140 million laboratory tests performed on approximately 11 million patients was integrated into a unified analytical framework. Machine learning and time-series analysis methods were applied to model four key laboratory functions: prediction of the number of tests performed, estimation of reagent demand, detection of analyzer overloads, and assessment of the stability of measurement methods within quality control procedures. The proposed approach combines patient profiling, sequential pattern analysis, neural networks, and classical forecasting models such as ARIMA and exponential smoothing. The results demonstrate high predictive accuracy for both global test volumes and patient-based quality control indicators, with determination coefficients exceeding 90% for workload forecasting and classification accuracy above 98% for identifying unnecessary recalibrations. These findings indicate that the use of machine learning techniques enables effective optimization of laboratory operations by reducing reagent shortages, preventing analyzer overloads, and improving the efficiency of quality control processes. The presented methods support more reliable, cost-effective, and scalable management of modern diagnostic laboratories. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Data Mining in Bioinformatics)

15 pages, 1585 KB

Open AccessArticle

Structure-Aware Protein Language Model with Multi-Branch Ensemble for Nanobody–Antigen Interaction Prediction

by Fangli Ying, Zilong Li, Junjie Mao, Lihua Sun, Aniwat Phaphuangwittayakul and Riyad Dhuny

Appl. Sci. 2026, 16(10), 4864; https://doi.org/10.3390/app16104864 (registering DOI) - 13 May 2026

Abstract

Nanobodies have emerged as highly valuable biotherapeutic and diagnostic reagents due to their high specificity, low immunogenicity, and superior tissue penetration. However, traditional nanobody discovery methods rely on camelid immunization and phage display techniques, which are time-consuming and labor-intensive. Meanwhile, existing computational prediction [...] Read more.

Nanobodies have emerged as highly valuable biotherapeutic and diagnostic reagents due to their high specificity, low immunogenicity, and superior tissue penetration. However, traditional nanobody discovery methods rely on camelid immunization and phage display techniques, which are time-consuming and labor-intensive. Meanwhile, existing computational prediction methods for Nanobody–Antigen Interaction (NAI) suffer from several limitations: first, general Protein–Protein Interaction (PPI) models cannot adapt to the specific binding patterns of NAI; second, sequence-based models struggle to capture critical binding features, resulting in unsatisfactory prediction accuracy. To address these challenges, we propose an NAI prediction method based on Protein Language Models (PLMs). Specifically, a structure-aware PLM is first fine-tuned on PPI datasets to learn universal protein binding patterns. This model performs joint encoding of amino acid sequences and protein local structure-related sequences. It can implicitly learn spatial structural priors solely from sequence inputs, which alleviates the limitation of conventional sequence-based models in capturing structural binding characteristics. Subsequently, we use mean, max and min pooling to extract complementary global sequence features that a single pooling method cannot fully capture. We then apply voting fusion to reduce prediction bias and improve model robustness under class imbalance and small-sample scenarios. Evaluated on the NAI benchmark dataset constructed from SAbDab-nano, the proposed model outperforms the best baseline methods in key metrics including Accuracy, Recall, F1-score, AUC-ROC, and AUPR. It exhibits robust performance under class imbalance and small-sample scenarios, validating the effectiveness of the framework. Full article

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

20 pages, 2297 KB

Open AccessArticle

Quantification of Hydrogen from Electrolysis by Combining a Resistive Electronic Sensor with the Standard Volumetric Method

by Emanuel Mango, Alessandro Fantoni, Manuela Vieira and Rui F. M. Lobo

Appl. Sci. 2026, 16(10), 4863; https://doi.org/10.3390/app16104863 (registering DOI) - 13 May 2026

Abstract

Currently, hydrogen has become an indispensable topic when discussing the energy transition. Determining the amount of hydrogen produced or lost through leaks is a critical issue. Recently, with the emergence of the low-cost MQ-8 resistive semiconductor sensor, which is sensitive to hydrogen and [...] Read more.

Currently, hydrogen has become an indispensable topic when discussing the energy transition. Determining the amount of hydrogen produced or lost through leaks is a critical issue. Recently, with the emergence of the low-cost MQ-8 resistive semiconductor sensor, which is sensitive to hydrogen and responds with an output voltage

V_{o u t}

, there has been considerable interest in its use in small laboratory experiments. The combination of the volumetric method, the MQ-8 sensor, and the BME280 sensor (for temperature, pressure, and humidity) is of significant interest and has industrial applications. This work presents an in-depth study of the combination of the traditional volumetric method with the MQ-8 and BME sensors. Sensor validation metrics were evaluated to ensure the reliability of the results. The pressure remained approximately constant due to the system configuration. The results indicate that for a current of 1 A, it is possible to determine the approximate volume of hydrogen as a function of the sensor’s output voltage. For low currents ranging from 0.76 to 250 mA, the results indicate that it is possible to determine the approximate hydrogen flow rate as a function of the voltage detected by the sensor. With further investigation, it will be possible to propose the use of MQ-8 and BME280 sensors in environments containing hydrogen. Full article

(This article belongs to the Special Issue Technical Advances In and Applications of Low-Cost/Power Sensors)

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