Shape-Memory Property Acting as a Switch to Change the Surface Property of the Film
Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In Silico
PFAS: The Journey from Wonder Chemicals to Environmental Nightmares and the Search for Solutions
The Use of Electric Vehicles to Support the Needs of the Electricity Grid: A Systematic Literature Review
1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System

Journal Description

Applied Sciences

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

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.8 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the first half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our authors say about Applied Sciences.
Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal and JETA.

Impact Factor: 2.5 (2023); 5-Year Impact Factor: 2.7 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2076-3417

Latest Articles

30 pages, 26115 KiB

Open AccessArticle

Application of Active Soil Gas Screening for the Identification of Groundwater Contamination with Chlorinated Hydrocarbons at an Industrial Area—A Case Study of the Former Refrigerator Manufacturer Calex (City of Zlaté Moravce, Western Slovakia)

by Roman Tóth, Edgar Hiller, Veronika Špirová, Ľubomír Jurkovič, Ľubica Ševčíková, Juraj Macek, Claudia Čičáková, Tibor Kovács and Anton Auxt

Appl. Sci. 2024, 14(23), 10842; https://doi.org/10.3390/app142310842 (registering DOI) - 22 Nov 2024

Abstract

Background: Groundwater contamination with chlorinated hydrocarbons (CLHCs), particularly with tetrachloroethylene (PCE) and trichloroethylene (TCE), which are used in industry for degreasing and cleaning, can be considered a serious problem concerning the entire world. In addition to conventional groundwater monitoring from a network of wells, several screening methods have been proposed to identify and delineate groundwater contamination with volatile organic compounds (VOCs), such as soil gas measurement, bioindicators, direct-push technologies or geophysical techniques. The main objectives of this study were to confirm the feasibility of active soil gas screening for the characterisation of groundwater contamination with CLHCs under the wider area of the former refrigerator manufacturer (city of Zlaté Moravce, western Slovakia) and to evaluate the human health risks through exposure to CLHCs present in groundwater. Methods: a conventional site investigation based on concentration measurements using gas chromatography-mass spectrometry from monitoring wells and soil gas measurements using a portable photo-ionisation detector device were applied. Results: The chemical analyses showed the persistent contamination of groundwater, with PCE, TCE and other CLHCs, such as cis-1,2-dichloroethylene (cis-DCE) or 1,1,2-trichloroethane (TCA), being most severe in the zone of the former factory (up to 2690, 83,900, 6020 and 156 µg/L for PCE, TCE, cis-DCE and TCA, respectively), but also extended into the residential zone located 600 m along the groundwater flow line. Soil gas measurements of VOCs and other chemical parameters (methane (CH₄), total petroleum (TP), carbon dioxide (CO₂) and oxygen (O₂)) from a densely designed network of sampling points (n = 300) helped trace the current state of groundwater contamination. Spatial distribution maps of VOCs concentrations in soil gas clearly marked the areas of the highest CLHCs concentrations in groundwater. Principal component analysis (PCA) confirmed a significant correlation of VOCs and CLHCs with the first principal component, PC1, explaining up to 84% of the total variability of the concentration data, suggesting that VOCs in soil gas were a suitable marker of the extent of groundwater contamination with CLHCs. Despite severe groundwater contamination with CLHCs reaching residential areas, local residents were not exposed to non-carcinogenic risks, but a potential carcinogenic risk was present. Conclusions: based on the results, it could be confirmed that soil gas screening is an efficient and quick tool for identifying the sources of groundwater contamination with CLHCs as well as the level of this contamination. Full article

(This article belongs to the Special Issue Latest Advances in Environmental Engineering: Approaches to the Management and Treatment of Water, Air and Waste)

21 pages, 1991 KiB

Open AccessArticle

Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links

by Emil Șorecău, Mirela Șorecău and Paul Bechet

Appl. Sci. 2024, 14(23), 10841; https://doi.org/10.3390/app142310841 (registering DOI) - 22 Nov 2024

Abstract

In the current context of global communications, HF (High Frequency) NVIS (Near Vertical Incidence Skywave) data networks can be of strategic importance, providing short- and medium-range communication capabilities independent of terrestrial configuration and existing conventional communications infrastructure. They are essential in critical conditions, such as natural disasters or conflicts, when terrestrial networks are unavailable. This paper investigates the development of such systems for HF NVIS data communications by introducing a customized Orthogonal Frequency Division Multiplexing (OFDM) protocol with parameters adapted to HF ionospheric propagation, implemented on Software-Defined Radio (SDR) systems, which provide extensive configurability and high adaptability to varying HF channel conditions. This work presents an innovative approach to the application of OFDM narrow-channel aggregation in the HF spectrum, a technique that significantly enhances system performance. The aggregation enables a more efficient utilization of the available spectrum and an increase in the data transmission rate, which represents a substantial advancement in NVIS communications. The implementation was realized using an SDR system, which allows flexible integration of the new OFDM protocol and dynamic adaptation of resources. The work also includes the development of a messaging application capable of using this enhanced HF communication system, taking advantage of the new features of channel aggregation and SDR flexibility. This application demonstrates the applicability of the protocol in real-world scenarios and provides a robust platform for data transmission under conditions of limited access to other means of communication. Thus, this study contributes to the technological advancement of NVIS communications and opens new research and deployment directions in HF communications. Full article

(This article belongs to the Special Issue Cognitive Radio: Trends, Methods, Applications and Challenges)

14 pages, 839 KiB

Open AccessArticle

Evaluation of the Wachtel Healing Index and Its Correlation with Early Implantation Success or Failured at Two Months

by María José Moya-Villaescusa, Arturo Sánchez-Pérez, Nerea Lara-Hernández, Alfonso Jornet-García and José María Montoya-Carralero

Appl. Sci. 2024, 14(23), 10840; https://doi.org/10.3390/app142310840 (registering DOI) - 22 Nov 2024

Abstract

Implants are increasingly used in dentistry. Nevertheless, several factors can cause treatment failure. To assess initial wound healing, various indices have been developed. The purpose of this study was to assess the relationship between early wound healing and implant success. Fifty single implants (25 in women and 25 in men) were placed in patients meeting the inclusion criteria. The implants (Ticare^®) were placed via the two-stage technique. Patients were assessed at 24 h, one week, one month, and two months post-implantation. At the final evaluation, implantation failure or success was recorded. Postoperative pain in these patients at one week after placement was assessed with a visual analogue scale (VAS), and the Wachtel soft tissue early healing index was used to measure healing. Two months after implant placement, the success rate was 92%. No statistically significant relationship was found between the Wachtel index and short-term implant success or failure. Additionally, there was no relationship between implant success or failure and variables such as smoking, diabetes status, age, sex, or guided bone regeneration (GBR). Neither the Wachtel index nor any of the other variables studied is a predictor of early implantation success. Full article

(This article belongs to the Section Applied Dentistry and Oral Sciences)

14 pages, 4355 KiB

Open AccessArticle

Computational Fluid Dynamics Analysis of Gas Suction in Coaxial Flow Venturi Injector: Impact of Gas–Liquid Interface Structure in Mixing Section

by Yihan Zu, Wenchen Zhong, Enle Xu and Zhenyong Miao

Appl. Sci. 2024, 14(23), 10839; https://doi.org/10.3390/app142310839 (registering DOI) - 22 Nov 2024

Abstract

The gas–liquid Venturi injector has been widely applied in industrial production due to its advantages of high entrainment and low energy consumption. In this study, Computational Fluid Dynamics (CFD) was employed to investigate the effect of the gas–liquid interface structure within the mixing section on entrainment behavior by varying the geometry of the mixing section during gas–liquid coaxial flow. The simulation results indicate that along the jet direction, the gas–liquid interface generally transitions from a smooth cylindrical shape to a lobed structure in the mixing section. Surface waves mainly appear in the lobed region. Furthermore, lobed and surface wave structures reduce pressure loss and enhance entrainment. Additionally, the study found that longer mixing sections enhance entrainment under low flow resistance. This study provides valuable insights for achieving high jet entrainment and offers supplementary research on gas–liquid interface structures in jets constrained by solid boundaries. Full article

16 pages, 806 KiB

Open AccessArticle

NMR Analysis of Pulegone in Food Products

by Yifei Yu, Thomas Kuballa and Dirk W. Lachenmeier

Appl. Sci. 2024, 14(23), 10838; https://doi.org/10.3390/app142310838 (registering DOI) - 22 Nov 2024

Abstract

Pulegone is a monoterpene ketone found in a variety of mint species. It has been classified as possibly carcinogenic to humans (Group 2B) by the International Agency for Research on Cancer (IARC). In previous studies, pulegone in food was analyzed exclusively via GC-MS, while ¹H NMR methods were limited to essential oils. The aim of this study was to develop an NMR method for the detection and quantification of pulegone in essential oils and foods. A mixture of methanol-d₄/chloroform-d₁ in a 1:1 ratio (v/v) was identified as the most effective solvent for separating pulegone signals. The essential oils were subjected to analysis at this solvent-mixture ratio. The extraction of pulegone was required for food analysis, and the steam distillation method proved to be more effective than the ultrasonic-assisted extraction method. The highest pulegone concentrations were identified in pennyroyal oil and muña oil, whereas lower levels were observed in other matrices, including corn mint oil and select food items. A toxicological assessment showed that the amount consumed did not exert any adverse effects on human health. Full article

(This article belongs to the Special Issue Recent Applications of Plant Extracts in the Food Industry)

16 pages, 10466 KiB

Open AccessArticle

Hierarchical Residual Attention Network for Musical Instrument Recognition Using Scaled Multi-Spectrogram

by Rujia Chen, Akbar Ghobakhlou and Ajit Narayanan

Appl. Sci. 2024, 14(23), 10837; https://doi.org/10.3390/app142310837 (registering DOI) - 22 Nov 2024

Abstract

Musical instrument recognition is a relatively unexplored area of machine learning due to the need to analyze complex spatial–temporal audio features. Traditional methods using individual spectrograms, like STFT, Log-Mel, and MFCC, often miss the full range of features. Here, we propose a hierarchical residual attention network using a scaled combination of multiple spectrograms, including STFT, Log-Mel, MFCC, and CST features (Chroma, Spectral contrast, and Tonnetz), to create a comprehensive sound representation. This model enhances the focus on relevant spectrogram parts through attention mechanisms. Experimental results with the OpenMIC-2018 dataset show significant improvement in classification accuracy, especially with the “Magnified 1/4 Size” configuration. Future work will optimize CST feature scaling, explore advanced attention mechanisms, and apply the model to other audio tasks to assess its generalizability. Full article

(This article belongs to the Special Issue AI in Audio Analysis: Spectrogram-Based Recognition)

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19 pages, 1147 KiB

Open AccessArticle

Prediction of Corrosion Rate for Carbon Steel Using Regression Model with Commercial LPR Sensor Data

by Kwang-Hu Jung and Jung-Hyung Lee

Appl. Sci. 2024, 14(23), 10836; https://doi.org/10.3390/app142310836 - 22 Nov 2024

Abstract

In this study, a model was proposed to predict the corrosion rate (Mils per Year, MPY) of carbon steel in a 3.5% NaCl solution, with the objective of comparing the effectiveness of a commercial LPR sensor against traditional electrochemical methods, using potentiostat-based LPR techniques. The primary factors considered in the experiments were temperature, flow velocity, and pH, tested through a full factorial design to identify the most influential variables. Statistical analysis showed that temperature and flow velocity had a significant effect on corrosion rate, with their interaction having the most substantial impact. In contrast, pH had no statistically significant influence within the tested conditions, likely due to the dominant effects of temperature and flow velocity in the high-salinity environment. The MPY data were validated through Tafel plots, immersion coupon tests, and other electrochemical techniques to confirm the reliability of the measurements. A regression model trained on 54 MPY data points demonstrated high accuracy, achieving a coefficient of determination (R²) of 0.9733. The model also provided reliable predictions for factor combinations excluded from the training dataset. Additionally, scenario-based evaluations highlighted the model’s performance under simulated operating conditions, while revealing challenges related to sensor contamination during long-term use. These findings emphasize the potential of commercial LPR sensors as effective tools for real-time corrosion monitoring and demonstrate the utility of the regression model in marine environments. Full article

27 pages, 1340 KiB

Open AccessArticle

Fusing Machine Learning and AI to Create a Framework for Employee Well-Being in the Era of Industry 5.0

by Cosmina-Mihaela Rosca and Adrian Stancu

Appl. Sci. 2024, 14(23), 10835; https://doi.org/10.3390/app142310835 - 22 Nov 2024

Abstract

Employees are the most valuable resources in any company, and their well-being directly influences work productivity. This research investigates integrating health parameters and sentiment analysis expressed in sent messages to enhance employee well-being within organizations in the context of Industry 5.0. Our primary aim is to develop a Well-Being Index (WBI) that quantifies employee health through various physiological and psychological parameters. A new methodology combining data collection from wearable devices from 1 January 2023 to 18 October 2024 and advanced text analytics was employed to achieve the WBI. This study uses the LbfgsMaximumEntropy ML classification algorithm to construct the Well-Being Model (WBM) and Azure Text Analytics for sentiment evaluation to assess negative messages among employees. The findings reveal a correlation between physiological metrics and self-reported well-being, highlighting the utility of the WBI in identifying areas of concern within employee behavior. We propose that the employee global indicator (EGI) is calculated based on the WBI and the dissatisfaction score component (DSC) to measure the overall state of mind of employees. The WBM exhibited a MacroAccuracy of 91.81% and a MicroAccuracy of 95.95% after 384 configurations were analyzed. Azure Text Analytics evaluated 2000 text messages, resulting in a Precision of 99.59% and an Accuracy of 99.7%. In this case, the Recall was 99.89% and F₁-score was 99.73%. In the Industry 5.0 environment, which focuses on the employee, a new protocol, the Employee KPI Algorithm (EKA), is integrated to prevent and identify employee stress. This study underscores the synergy between quantitative health metrics and qualitative sentiment analysis, offering organizations a framework to address employee needs proactively. Full article

(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)

18 pages, 11053 KiB

Open AccessArticle

Enhanced Wide-Area Glacier Velocity Monitoring in Svalbard via Synthetic Aperture Radar Offset Tracking Noise Suppression

by Honglei Yang, Songxue Zhao, Zeping Wang, Ao Yan and Zhenhan Shi

Appl. Sci. 2024, 14(23), 10834; https://doi.org/10.3390/app142310834 - 22 Nov 2024

Abstract

Glacier movement is an important indicator of climate change, reflecting the quality and state changes in glacier migration and mass balance in the context of global warming. Although accurately estimating glacier surface flow velocity is crucial for various applications, achieving this is challenging due to factors such as low temporal correlation and high noise effects. This paper presents the pixel offset tracking (POT) technology based on Synthetic Aperture Radar (SAR) data for glacier velocity monitoring, with enhanced cross-correlation matching window and noise suppression approaches. In particular, a noise suppression optimization method and a matching window optimization index suitable for wide-area glacier velocity monitoring are proposed. The inter-annual wide-area two-dimensional plane flow velocity of glaciers in the Svalbard archipelago was obtained by using a total of seven Sentinel-1 data sets from two orbits covering the entire Svalbard archipelago in 2021. The results indicate that 25 large glaciers in Svalbard destabilized in 2021, with a peak flow velocity of 6.18 m/day. At the same time, the influence of climate, topography, and other factors on glacier surface velocity is discussed. The wide-area glacier velocity monitoring method and its application demonstrated in this paper will serve as a valuable reference for studying glacier migration in the Arctic Svalbard archipelago and for other large-scale wide-area deformation monitoring efforts. Full article

(This article belongs to the Special Issue Latest Advances in Radar Remote Sensing Technologies)

17 pages, 2390 KiB

Open AccessArticle

An Attempt to Establish a Mathematical Model for an Unconventional Worm Gear with Bearings

by Simion Haragâș, Roland Ninacs, Ovidiu Buiga, Lucian Tudose, Alexandru Haragâș, Ioana Monica Sas-Boca and Felicia Aurora Cristea

Appl. Sci. 2024, 14(23), 10833; https://doi.org/10.3390/app142310833 - 22 Nov 2024

Abstract

The aim of this paper is to develop a mathematical model for an unconventional worm gear consisting of a globoid worm and a worm wheel where the teeth are bearings. Using rolling elements such the teeth of the worm wheel (ball bearings) transforms the sliding friction to rolling friction during the process of worm gear meshing, improving power. The geometry of the component elements of the gear is analyzed in correlation with its kinematics. After the creation of the mathematical model, it is validated both analytically (through complex graphic representations) and experimentally (by creating, for a particular case, the 3D model and the concrete physical model (prototype) of the gear through 3D printing). Full article

(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)

15 pages, 4989 KiB

Open AccessArticle

End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms

by Jeongho Kim, Joonho Seon, Soohyun Kim, Seongwoo Lee, Jinwook Kim, Byungsun Hwang, Youngghyu Sun and Jinyoung Kim

Appl. Sci. 2024, 14(23), 10832; https://doi.org/10.3390/app142310832 - 22 Nov 2024

Abstract

An unmanned aerial vehicle (UAV) swarm has emerged as a powerful tool for mission execution in a variety of applications supported by deep neural networks (DNNs). In the context of UAV swarms, conventional methods for efficient data processing involve transmitting data to cloud and edge servers. However, these methods often face limitations in adapting to real-time applications due to the low latency of cloud-based approaches and weak mobility of edge-based approaches. In this paper, a new system called deep reinforcement learning-based resilient layer distribution (DRL-RLD) for distributed inference is designed to minimize end-to-end latency in UAV swarm, considering the resource constraints of UAVs. The proposed system dynamically allocates CNN layers based on UAV-to-UAV and UAV-to-ground communication links to minimize end-to-end latency. It can also enhance resilience to maintain mission continuity by reallocating layers when inoperable UAVs occur. The performance of the proposed system was verified through simulations in terms of latency compared to the comparison baselines, and its robustness was demonstrated in the presence of inoperable UAVs. Full article

(This article belongs to the Special Issue Novel Advances in Internet of Vehicles)

29 pages, 9575 KiB

Open AccessArticle

Design and Multi-Objective Optimization of Auxetic Sandwich Panels for Blastworthy Structures Using Machine Learning Method

by Andika, Sigit Puji Santosa, Djarot Widagdo and Arief Nur Pratomo

Appl. Sci. 2024, 14(23), 10831; https://doi.org/10.3390/app142310831 - 22 Nov 2024

Abstract

The design and multi-objective optimization of auxetic sandwich panels (ASPs) are performed to enhance the blastworthiness of armored fighting vehicles (AFVs). Various metastructures in the form of four auxetic geometries are proposed as the sandwich core: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star honeycomb (SH), and tetra-chiral honeycomb (CH). This paper employs a combination of finite element and machine learning methodologies to evaluate blastworthiness performance. Optimization is carried out using the nondominated sorting genetic algorithm II (NSGA-II) method. The optimization results show significant improvements in blastworthiness performance, with notable reductions in permanent displacement and enhancements in specific energy absorption (SEA). Global sensitivity analysis using SHapley Additive exPlanations (SHAP) reveals that cell thickness is the most critical factor affecting blastworthiness performance, followed by the number of cells and corner angle or radius for CH. The application of optimized ASP on AFVs shows promising results, with no failure occurring in the occupant floor. Furthermore, AFVs equipped with the optimized ASP DAH significantly reduce maximum displacement and acceleration by 39.00% and 43.56%, respectively, and enhance SEA by 48.30% compared to optimized aluminum foam sandwich panels. This study concludes that ASPs have potential applications in broader engineering fields. Full article

(This article belongs to the Special Issue Structural Dynamics and Protective Materials)

23 pages, 1514 KiB

Open AccessArticle

Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG

by Adhe Rahmatullah Sugiharto Suwito P, Ayumi Ohnishi, Yudith Dian Prawitri, Riries Rulaningtyas, Tsutomu Terada and Masahiko Tsukamoto

Appl. Sci. 2024, 14(23), 10830; https://doi.org/10.3390/app142310830 - 22 Nov 2024

Abstract

Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of impairment-level recognition in several studies. This study continues our previous work on the use of EMG to recognize finger movement impairment levels. In contrast to our previous work, this study provided a better and more reliable recognition result with improved experimental settings, such as an increased sampling frequency, EMG channels, and extensive patient data. This study employed two data processing mechanisms, inter-subject cross-validation (ISCV) and data-scaled inter-subject cross-validation (DS-ISCV), resulting in two evaluation methods. The machine learning algorithms employed in this study were SVM, random forest (RF), and multi-layer perceptron (MLP). MLP_ISCV achieved the highest average recall score of 0.73 across impairment levels in the spherical grasp task. Subsequently, the highest average recall score of 0.72 among non-majority classes was achieved by SVM_DS-ISCV in the mass extension task. The cross-validation result shows that the proposed method effectively handled the imbalanced dataset without being biased toward the majority class. The proposed method demonstrated the potential to assist doctors in clarifying the subjective assessment of finger movement impairment levels. Full article

(This article belongs to the Special Issue Human Biomechanics and EMG Signal Processing)

15 pages, 9384 KiB

Open AccessArticle

BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection

by Long Guo, Lubin Wang, Qiang Yu and Xiaolan Xie

Appl. Sci. 2024, 14(23), 10829; https://doi.org/10.3390/app142310829 - 22 Nov 2024

Abstract

In the field of bookbinding, accurately and efficiently detecting signature sequences during the binding process is crucial for enhancing quality, improving production efficiency, and advancing industrial automation. Despite significant advancements in object detection technology, verifying the correctness of signature sequences remains challenging due to the small size, dense distribution, and abundance of low-quality signature marks. To tackle these challenges, we introduce the Book Signature Marks Detection (BSMD-YOLOv8) model, specifically designed for scenarios involving small, closely spaced objects such as signature marks. Our proposed backbone, the Lightweight Multi-scale Residual Network (LMRNet), achieves a lightweight network while enhancing the accuracy of small object detection. To address the issue of insufficient fusion of local and global feature information in PANet, we design the Low-stage gather-and-distribute (Low-GD) module and the High-stage gather-and-distribute (High-GD) module to enhance the model’s multi-scale feature fusion capabilities, thereby refining the integration of local and global features of signature marks. Furthermore, we introduce Wise-IoU (WIoU) as a replacement for CIoU, prioritizing anchor boxes with moderate quality and mitigating harmful gradients from low-quality examples. Experimental results demonstrate that, compared to YOLOv8n, BSMD-YOLOv8 reduces the number of parameters by 65%, increases the frame rate by 7 FPS, and enhances accuracy, recall, and mAP50 by 2.2%, 8.6%, and 3.9% respectively, achieving rapid and accurate detection of signature marks. Full article

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28 pages, 3058 KiB

Open AccessArticle

Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values

by Gabriela Bastos Porsani, Juan Bautista Echeverría Trueba and Carlos Fernández Bandera

Appl. Sci. 2024, 14(23), 10828; https://doi.org/10.3390/app142310828 - 22 Nov 2024

Abstract

Model calibration refines design-stage inputs to align with real-world building performance. Accurate parameter selection, especially for highly sensitive variables like air leakage, is crucial. This study compared two building energy model calibration methods. The “classic” method adjusted indoor air capacitance, internal mass, and air infiltration, while a novel method focused on capacitance and internal mass, using empirical data for infiltration. The infiltration values were calculated using the decay equation and the EnergyPlus equations with site-specific coefficients. A triple validation assessed model performance in terms of temperature (CIBSE TM63), energy consumption (minimization), and indoor air quality (represented by

{CO}_{2}

levels in accordance with the ASTM D5157 Standard). Results demonstrated the novel method’s superiority across all three performance metrics. All calibrated models met the CIBSE TM63 criteria even during the validation period, which was five times longer than the training period. Compared to the classic method, models incorporating dynamic empirical infiltration showed a 29% and 26% improvement in MAE and RMSE, respectively, in temperature prediction. In energy consumption results, the novel method models presented a 31% reduction, and for

{CO}_{2}

level agreement, these models achieved a 130% higher R² value than the classic model. In addition, the classic method’s infiltration values failed to meet ASTM D5157 requirements, suggesting reliance on unrealistic parameter values for accurate temperature representation. The incorporation of calculated air leakage data into the BEM allowed a more realistic estimation of capacitance and internal mass values, emphasizing the importance of accurate air infiltration modeling for overall model reliability. Full article

(This article belongs to the Special Issue Energy Efficiency and Thermal Comfort in Buildings)

21 pages, 9713 KiB

Open AccessArticle

CFD Study of the Impact of an Electrical Power Transformer on a Historical Building: Assessment and Solutions

by Fabio Nardecchia, Luca Gugliermetti, Laura Pompei and Federico Cinquepalmi

Appl. Sci. 2024, 14(23), 10827; https://doi.org/10.3390/app142310827 - 22 Nov 2024

Abstract

Historical building reuse is aimed at preservation, where buildings are recovered for new uses connected to cultural activities. This paper presents the analysis of the impact of thermo-fluid dynamics due to a 500 kW electrical power transformer installed inside a historical building. The analysis is performed using computational fluid dynamics simulations validated through measurement campaigns carried out during the summer period. High temperatures and wide humidity variations can damage building plasters and cause malfunctions in power equipment. To avoid these situations, two different installation layouts were studied. One consists of the power transformer directly installed in the environment and cooled by an inlet fan, and the other consists of the power transformer being insulated from the external environment by an enclosure connected to a forced ventilation system. The second layout showed better results both inside and outside the transformer enclosure. The maximum indoor condition was about 4.3 °C, with a −7.2% RH and an airflow rate of 1100 m³/h, and the maximum outdoor air condition was 3.3 °C, with a −1.39% RH and a flow rate of 2200 m³/h. However, the temperatures and humidity inside the building and outside the transformer enclosure were almost the same. Full article

(This article belongs to the Special Issue Energy Efficiency and Thermal Comfort in Buildings)

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16 pages, 8754 KiB

Open AccessArticle

Evaluating Petrophysical Properties Using Digital Rock Physics Analysis: A CO₂ Storage Feasibility Study of Lithuanian Reservoirs

by Shruti Malik, Pijus Makauskas, Ravi Sharma and Mayur Pal

Appl. Sci. 2024, 14(23), 10826; https://doi.org/10.3390/app142310826 - 22 Nov 2024

Abstract

As the global concern over greenhouse gas emissions grows, CO₂ storage in deep saline aquifers and depleted reservoirs has become crucial for climate change mitigation. This study evaluates the feasibility of Lithuanian deep saline aquifers, specifically, Syderiai and Vaskai, for effective CO₂ storage. Unlike previous theoretical analyses, it provides experimental data on static and dynamic reservoir parameters that impact CO₂ injection and retention. Using micro X-ray computed tomography (MXCT) and multi-resolution scanning at 8 µm and 22 µm, digital rock volumes (DRVs) from core samples were created to determine porosity and permeability. The method, validated against analogous samples, identified a representative element volume (REV) within sub-volumes, showing a homogeneous distribution of petrophysical properties in the Lithuanian samples. The results show that DRVs can accurately reflect pore-scale properties, achieving 90–95% agreement with lab measurements, and offer a rapid, efficient means for analyzing storage potentials. These insights confirm that Lithuanian aquifers are promising for CO₂ sequestration, with recommendations for further long-term monitoring and applications of this technique across the region. Full article

(This article belongs to the Special Issue CCUS: Paving the Way to Net Zero Emissions Technologies)

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24 pages, 6256 KiB

Open AccessArticle

Study on Optimization of Rice-Drying Process Parameters and Directional Regulation of Nutrient Quality

by Jinquan Li, Kezhen Chang, Jun Yin, Yi Jin, Xiaokang Yi, Zhongjie Zhang, Yichuan He, Qiaonan Yang, Zhihui Tang, Xiaoyu Liu and Wenfu Wu

Appl. Sci. 2024, 14(23), 10825; https://doi.org/10.3390/app142310825 - 22 Nov 2024

Abstract

The physicochemical components of rice such as starch, protein, fat and water have significant influence on its nutritional value, and the drying process can easily cause changes in these components. In this paper, the effect of technical parameters on the nutritional quality of rice during hot-air drying was studied, and a control method of the rice-drying process based on effective accumulated temperature was proposed to ensure the drying quality and improve the drying efficiency. Through thin-layer drying experiments, hot-air temperature (T), humidity (RH), initial moisture content (MC), wind speed (V) and tempering ratio (TR) were selected as control factors, and the central composite design was adopted to optimize the experimental scheme. The relationship between each factor and nutrient quality was revealed through response surface analysis, and the regression model and process optimization parameters were established. The results show that the optimization parameters are as follows: hot-air temperature, 48.87 °C; humidity, 30.12%; initial moisture content, 21.31%; wind speed, 0.62 m/s; tempering ratio, 2.87; the optimized total drying time is 4.23 h; the effective accumulated temperature is 214.44 °C·h. The contents of protein, fat, amylose and amylopectin were 8.47 g/100 g, 1.97 g/100 g, 15.33 g/100 g and 60.50 g/100 g, respectively. The relative error of the verification test was 4.17%. The optimized process can effectively maintain the nutritional quality of rice and improve drying efficiency. This paper provides a new way to deeply explore the mechanism of rice quality change, and the established process reference chart provides a scientific basis for actual drying operations and the development of an intelligent control system. Full article

(This article belongs to the Special Issue Chemistry, Quality, and Processing Technology of Different Cereals, Pseudocereals, Legumes and Oilseeds Grain Types)

16 pages, 5987 KiB

Open AccessArticle

From Single Shot to Structure: End-to-End Network-Based Deflectometry for Specular Free-Form Surface Reconstruction

by M.Hadi Sepanj, Saed Moradi, Amir Nazemi, Claire Preston, Anthony M. D. Lee and Paul Fieguth

Appl. Sci. 2024, 14(23), 10824; https://doi.org/10.3390/app142310824 - 22 Nov 2024

Abstract

Deflectometry is a key component in the precise measurement of specular (mirrored) surfaces; however, traditional methods often lack an end-to-end approach that performs 3D reconstruction in a single shot with high accuracy and generalizes across different free-form surfaces. This paper introduces a novel deep neural network (DNN)-based approach for end-to-end 3D reconstruction of free-form specular surfaces using single-shot deflectometry. Our proposed network, VUDNet, innovatively combines discriminative and generative components to accurately interpret orthogonal fringe patterns and generate high-fidelity 3D surface reconstructions. By leveraging a hybrid architecture integrating a Variational Autoencoder (VAE) and a modified U-Net, VUDNet excels in both depth estimation and detail refinement, achieving superior performance in challenging environments. Extensive data simulation using Blender leading to a dataset which we will make available, ensures robust training and enables the network to generalize across diverse scenarios. Experimental results demonstrate the strong performance of VUDNet, setting a new standard for 3D surface reconstruction. Full article

(This article belongs to the Special Issue Technical Advances in 3D Reconstruction)

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Open AccessArticle

Design and Analysis of Additional Tendon Path for Determining Bending Shape in Hyper-Redundant Manipulator with Rolling Joints

by Hansoul Kim

Appl. Sci. 2024, 14(23), 10823; https://doi.org/10.3390/app142310823 - 22 Nov 2024

Abstract

Flexible surgical robots are emerging as advanced tools for minimally invasive surgeries, offering greater versatility compared to traditional rigid robots. Unlike commercial endoscopes, the overtube should remain fixed to maintain stability, ensure a clear field of view, and allow surgical tools to perform tasks efficiently. While constant curvature bending of the overtube is sufficient for some lesions, certain lesions require the overtube to bend into specific shapes to achieve appropriate positioning. Various methods for creating different bending shapes have been proposed in previous research, typically involving connecting multiple segments. However, this approach complicates control and reduces both space and cost efficiency. This study proposed a conceptual method for adding a shaping tendon to control the bending shape and mathematically analyzed the effect of this shaping tendon, inserted along an arbitrary path in addition to the main driving tendons for constant curvature bending, on the bending shape of the hyper-redundant manipulator with rolling joints. The overall system was modeled and analyzed from an energy perspective, and the validity of the proposed mathematical modeling was verified through comparison with results obtained from physical experiments. In addition, it was identified that the design parameter determining the tendon path is a significant element in defining the bending shape of the overtube. Full article

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