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Search Results (821)

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Keywords = nondestructive evaluation techniques

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22 pages, 5407 KB  
Article
Optimization and Application of Electromagnetic Ultrasonic Transducer for Battery Non-Destructive Testing
by Xuhang Zhan, Zhangwan Li, Hongchao Chen, Guanlin Yu and Xiaoyu Li
Sensors 2025, 25(19), 6003; https://doi.org/10.3390/s25196003 - 29 Sep 2025
Abstract
The monitoring of safety and health in lithium-ion batteries (LIBs) presents a significant challenge. Ultrasonic detection techniques fulfil the requirements for high sensitivity and non-destructive evaluation in the safety assessment of these batteries. This study concentrates on the application of electromagnetic acoustic transducer [...] Read more.
The monitoring of safety and health in lithium-ion batteries (LIBs) presents a significant challenge. Ultrasonic detection techniques fulfil the requirements for high sensitivity and non-destructive evaluation in the safety assessment of these batteries. This study concentrates on the application of electromagnetic acoustic transducer (EMAT) technology for non-destructive battery testing, utilizing non-contact electromagnetic coupling to generate and receive ultrasonic waves. This method addresses the limitations associated with conventional piezoelectric ultrasonic coupling media, thereby facilitating highly reliable assessment of the internal condition of batteries. Specifically, this paper independently designs an EMAT featuring a Halbach magnet array and a butterfly coil. Based on this design, optimization is performed, and the amplitude of the received signal is increased fourfold compared to the pre-optimization configuration. The optimized transducer is employed to evaluate a set of retired batteries with a nominal capacity of 270 Ah. Experimental results demonstrate that batteries exhibiting capacities below 240 Ah produced average signal amplitudes more than 40% lower than those of batteries with higher capacities. This technology provides a non-contact, disassembly-free approach for rapid performance evaluation of batteries and demonstrates potential for effective application in sorting retired battery units. Full article
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12 pages, 1923 KB  
Article
Microwave Resonant Probe-Based Defect Detection for Butt Fusion Joints in High-Density Polyethylene Pipes
by Jinping Pan, Chaoming Zhu and Lianjiang Tan
Polymers 2025, 17(19), 2617; https://doi.org/10.3390/polym17192617 - 27 Sep 2025
Abstract
With the widespread use of high-density polyethylene (HDPE) pipes in various industrial and municipal applications, ensuring the structural integrity of their joints is crucial. This paper presents a novel defect detection method based on a microwave resonant probe, designed to perform efficient and [...] Read more.
With the widespread use of high-density polyethylene (HDPE) pipes in various industrial and municipal applications, ensuring the structural integrity of their joints is crucial. This paper presents a novel defect detection method based on a microwave resonant probe, designed to perform efficient and non-destructive evaluation of butt fusion joints in HDPE pipes. The experimental setup integrates a microwave antenna and resonant cavity to record real-time variations in resonance frequency and S21 magnitude while scanning the pipe surface. This method effectively detects common defects, including cracks, holes, and inclusions, within the butt fusion joints. The results show that the microwave resonant probe exhibits high sensitivity in detecting HDPE pipe defects. It can identify different sizes of cracks and holes, and can distinguish between talc powder and sand particles. This technique offers a promising solution for pipeline health monitoring, particularly for evaluating the quality of welded joints in non-metallic materials. Full article
(This article belongs to the Special Issue Advanced Joining Technologies for Polymers and Polymer Composites)
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22 pages, 8501 KB  
Article
Estimation of Chlorophyll and Water Content in Maize Leaves Under Drought Stress Based on VIS/NIR Spectroscopy
by Qi Su, Jingyong Wang, Huarong Ling, Ziting Wang and Jingyao Gai
Processes 2025, 13(10), 3087; https://doi.org/10.3390/pr13103087 - 26 Sep 2025
Abstract
Maize (Zea mays) is a key crop, with its growth impacted by drought stress. Accurate, non-destructive assessment of drought severity is crucial for precision agriculture. VIS/NIR reflectance spectroscopy is widely used for estimating plant parameters and detecting stress. However, the relationship [...] Read more.
Maize (Zea mays) is a key crop, with its growth impacted by drought stress. Accurate, non-destructive assessment of drought severity is crucial for precision agriculture. VIS/NIR reflectance spectroscopy is widely used for estimating plant parameters and detecting stress. However, the relationship between key parameters—such as chlorophyll and water content—and VIS/NIR spectra under drought conditions in maize remains unclear, lacking comprehensive models and validation. This study aims to develop a non-destructive and accurate method for predicting chlorophyll and water content in maize leaves under drought stress using VIS/NIR spectroscopy. Specifically, maize leaf reflectance spectra were collected under varying drought stress conditions, and the effects of different spectral preprocessing methods, dimensionality reduction techniques, and machine learning algorithms were evaluated. An optimal data processing pipeline was systematically established and deployed on an edge computing unit to enable rapid, non-destructive prediction of chlorophyll and water content in maize leaves. The experimental results demonstrated that the combination of stepwise regression (SR) for feature selection and a stacking regression model achieved the best performance for chlorophyll content prediction (Rp2 = 0.8740, RMSEp = 0.2768). For leaf water content prediction, random forest (RF) feature selection combined with a stacking model yielded the highest accuracy (Rp2  = 0.7626, RMSEp = 4.12%). This study confirms the effectiveness and potential of integrating VIS/NIR spectroscopy with machine learning algorithms for monitoring drought stress in maize, offering a valuable theoretical foundation and practical reference for non-destructive crop physiological monitoring in precision agriculture. Full article
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14 pages, 3331 KB  
Article
Innovative Hydroponic Culture of Alkanna tinctoria (L.) Tausch: An Approach Towards Sustainable Extraction Process from Plant Roots
by Elodie Bossard, Annalisa Cartabia, Ismahen Lalaymia, Nikolaos Tsafantakis, Nektarios Aligiannis, Ioanna Chinou, Stéphane Declerck and Nikolas Fokialakis
Plants 2025, 14(19), 2987; https://doi.org/10.3390/plants14192987 - 26 Sep 2025
Abstract
Alkanna tinctoria (L.) Tausch is a valuable medicinal plant known for its root-derived hydroxynaphthoquinone enantiomers, alkannin/shikonin (A/S), which exhibit significant pharmaceutical and cosmeceutical potential. However, its limited natural distribution and overharvesting pose conservation challenges, necessitating sustainable cultivation and extraction strategies. The application of [...] Read more.
Alkanna tinctoria (L.) Tausch is a valuable medicinal plant known for its root-derived hydroxynaphthoquinone enantiomers, alkannin/shikonin (A/S), which exhibit significant pharmaceutical and cosmeceutical potential. However, its limited natural distribution and overharvesting pose conservation challenges, necessitating sustainable cultivation and extraction strategies. The application of Natural Deep Eutectic Solvents (NaDESs) has garnered significant attention as sustainable alternatives to conventional solvents. However, their toxicity in living plant systems remains largely unexplored. This study presents the successful establishment of an ex situ hydroponic cultivation system using the nutrient film technique (NFT) to grow A. tinctoria under greenhouse conditions. The system promoted plant acclimatization, vigorous root development, and initial production of A/S derivatives. In parallel, the toxicity evaluation of a bio-based NaDES, LeG_5_20 (levulinic acid–glucose, 5:1, with 20% water), applied as a circulating medium, was assessed. Physiological stress responses of the plants to NaDES circulation were assessed through non-destructive measurements, including stomatal resistance, photosynthetic and transpiration rates, and sub-stomatal CO2 concentration. Short-term (24 min) exposure to NaDES showed no significant adverse effects, while longer exposures (4–8 h) induced marked stress symptoms and loss of leaf area. These findings demonstrate the feasibility of integrating green hydroponic systems with eco-friendly extraction solvents and provide a framework for further optimization of plant age, solvent exposure time, and system design to enable sustainable metabolite recovery without plant destruction. Full article
(This article belongs to the Section Plant Physiology and Metabolism)
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20 pages, 6308 KB  
Article
Evaluation of Deterioration in Cultural Stone Heritage Using Non-Destructive Testing Techniques: The Case of Emir Ali Tomb (Ahlat, Bitlis, Türkiye)
by Mehmet Can Balci
Appl. Sci. 2025, 15(19), 10404; https://doi.org/10.3390/app151910404 - 25 Sep 2025
Abstract
Stone cultural heritage structures built from pyroclastic rocks are susceptible to deterioration due to their sensitivity to atmospheric processes. Detecting such deterioration and periodically examining it using non-destructive testing (NDT) techniques is one of the most critical measures for ensuring its transmission to [...] Read more.
Stone cultural heritage structures built from pyroclastic rocks are susceptible to deterioration due to their sensitivity to atmospheric processes. Detecting such deterioration and periodically examining it using non-destructive testing (NDT) techniques is one of the most critical measures for ensuring its transmission to future generations. In recent years, assessing the properties of building stones through NDT methods has been widely applied in planning the preservation of stone cultural heritages. In this study, deterioration observed on the interior walls of the Emir Ali Tomb, a structure distinguished from other tombs in the region by its exceptional architecture, was investigated through laboratory tests and NDT techniques, including deep moisture measurement, P-wave velocity, and infrared thermography. It was determined that the monument was constructed from four different types of pyroclastic rock, classified according to their textural and geomechanical characteristics. Using data obtained from in situ tests, NDT distribution maps were generated. The deep moisture, P-wave velocity, and infrared thermography maps revealed that the primary cause of deterioration in the monument was related to capillary water rise. Full article
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30 pages, 1350 KB  
Review
Mango Quality Assessment Using Near-Infrared Spectroscopy and Hyperspectral Imaging: A Systematic Review
by Ramesh Kumar Chaudhary, Arjun Neupane, Zhenglin Wang and Kerry Walsh
Agronomy 2025, 15(10), 2271; https://doi.org/10.3390/agronomy15102271 - 25 Sep 2025
Abstract
Mango is considered a high-value tropical fruit, and its commercial and consumer acceptance depends on internal and external quality attributes such as Total Soluble Solids (TSS), Dry Matter Content (DMC), firmness, ripeness, and surface defects. In recent years, non-destructive sensing technologies such as [...] Read more.
Mango is considered a high-value tropical fruit, and its commercial and consumer acceptance depends on internal and external quality attributes such as Total Soluble Solids (TSS), Dry Matter Content (DMC), firmness, ripeness, and surface defects. In recent years, non-destructive sensing technologies such as Near-Infrared Spectroscopy (NIRS) and Hyperspectral Imaging (HSI) have gained prominence for their ability to quickly and accurately evaluate mango quality. In this study, 101 articles published within the last ten years, were systematically retrieved, and 85 research papers were selected for detailed analysis. The review focuses on statistical analysis, conventional machine learning, deep learning, and transformer-based methods applied to mango quality assessment. The objective is to systematically review and analyse data-driven models for non-destructive mango grading using NIRS and HSI technologies, with particular emphasis on data collection methods, preprocessing techniques, dimensionality reduction, and predictive modelling approaches. This review aims to identify the most effective and widely adopted machine learning and deep learning methods, especially transformer models, for accurate and real-time mango quality assessment. Furthermore, it highlights key quality traits evaluated, current research gaps, and future opportunities to advance intelligent, real-time, and automated mango grading systems for practical use in the fruit industry. Full article
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22 pages, 3646 KB  
Article
Machine Learning in the Classification of RGB Images of Maize (Zea mays L.) Using Texture Attributes and Different Doses of Nitrogen
by Thiago Lima da Silva, Fernanda de Fátima da Silva Devechio, Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Liliane Maria Romualdo Altão, Gabriel Pagin, Adriano Rogério Bruno Tech and Murilo Mesquita Baesso
AgriEngineering 2025, 7(10), 317; https://doi.org/10.3390/agriengineering7100317 - 23 Sep 2025
Viewed by 130
Abstract
Nitrogen fertilization is decisive for maize productivity, fertilizer use efficiency, and sustainability, which calls for fast and nondestructive nutritional diagnosis. This study evaluated the classification of maize plant nutritional status from red, green, and blue (RGB) leaf images using texture attributes. A greenhouse [...] Read more.
Nitrogen fertilization is decisive for maize productivity, fertilizer use efficiency, and sustainability, which calls for fast and nondestructive nutritional diagnosis. This study evaluated the classification of maize plant nutritional status from red, green, and blue (RGB) leaf images using texture attributes. A greenhouse experiment was conducted under a completely randomized factorial design with four nitrogen doses, one maize hybrid Pioneer 30F35, and four replicates, at two sampling times corresponding to distinct phenological stages, totaling thirty-two experimental units. Images were processed with the gray-level cooccurrence matrix computed at three distances 1, 3, and 5 pixels and four orientations 0°, 45°, 90°, and 135°, yielding eight texture descriptors that served as inputs to five supervised classifiers: an artificial neural network, a support vector machine, k nearest neighbors, a decision tree, and Naive Bayes. The results indicated that texture descriptors discriminated nitrogen doses with good performance and moderate computational cost, and that homogeneity, dissimilarity, and contrast were the most informative attributes. The artificial neural network showed the most stable performance at both stages, followed by the support vector machine and k nearest neighbors, whereas the decision tree and Naive Bayes were less suitable. Confusion matrices and receiver operating characteristic curves indicated greater separability for omission and excess classes, with D1 standing out, and the patterns were consistent with the chemical analysis. Future work should include field validation, multiple seasons and genotypes, integration with spectral indices and multisensor data, application of model explainability techniques, and assessment of latency and scalability in operational scenarios. Full article
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18 pages, 1182 KB  
Article
Compositional Analysis and Sustainable Valorization of the Calabrian Hazelnut cv. ‘Tonda Calabrese’ and Its Processing Derivatives
by Federica Turrini, Federica Grasso, Aseel Swaidan, Giosuè Costa, Sonia Bonacci, Antonio Procopio, Carmine Lupia, Raffaella Boggia and Stefano Alcaro
Foods 2025, 14(18), 3269; https://doi.org/10.3390/foods14183269 - 20 Sep 2025
Viewed by 395
Abstract
Hazelnut cultivation is a strategic agricultural sector in Italy, with Calabria contributing through the native “Tonda Calabrese” cultivar, valued for its biodiversity. Despite its importance, data on the nutritional and compositional characteristics of this cultivar remain limited. In this study, hazelnuts from three [...] Read more.
Hazelnut cultivation is a strategic agricultural sector in Italy, with Calabria contributing through the native “Tonda Calabrese” cultivar, valued for its biodiversity. Despite its importance, data on the nutritional and compositional characteristics of this cultivar remain limited. In this study, hazelnuts from three different Calabrian producers were analyzed for morphological traits, proximate composition, and elemental content, using both conventional and non-destructive techniques such as CIELab color profiling and ATR-FTIR spectroscopy. The nuts showed high levels of essential micro-elements (Fe, Cu, Zn), aligning with previous findings on other cultivars, and showed no detectable pesticide residues, confirming their nutritional quality. Moreover, this study also aims to explore sustainable valorization strategies for hazelnut by-products, embracing circular economy principles in a “zero waste” approach, including oils and defatted flours. The extracted oils were evaluated for oxidative stability (peroxide value, p-anisidine, TOTOX index) and acidity, meeting Codex Alimentarius quality standards. The residual defatted flour was upcycled through eco-friendly methods, such as Ultrasound-Assisted Extraction (UAE) and Enzyme-Assisted Extraction (EAE), to isolate the polyphenol and protein fractions, respectively. Both extracts exhibited notable antioxidant activity (34.7–35.3 mmol Fe2+ eq/100 g and 64.3–82.2 mmol Fe2+ eq/100 g, respectively), suggesting their potential use as valuable ingredients for dietetic and nutraceutical applications. Full article
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33 pages, 2085 KB  
Review
Advances in Nondestructive Technologies for External Eggshell Quality Evaluation
by Pengpeng Yu, Chaoping Shen, Junhui Cheng, Xifeng Yin, Chao Liu and Ziting Yu
Sensors 2025, 25(18), 5796; https://doi.org/10.3390/s25185796 - 17 Sep 2025
Viewed by 412
Abstract
The structural integrity of poultry eggs is essential for food safety, economic value, and hatchability. External eggshell quality—measured by thickness, strength, cracks, color, and cleanliness—is a key criterion for grading and sorting. Traditional assessment methods, although simple, suffer from subjectivity, low efficiency, and [...] Read more.
The structural integrity of poultry eggs is essential for food safety, economic value, and hatchability. External eggshell quality—measured by thickness, strength, cracks, color, and cleanliness—is a key criterion for grading and sorting. Traditional assessment methods, although simple, suffer from subjectivity, low efficiency, and destructive nature. In contrast, recent developments in nondestructive testing (NDT) technologies have enabled precise, automated, and real-time evaluation of eggshell characteristics. This review systematically summarizes state-of-the-art NDT techniques including acoustic resonance, ultrasonic imaging, terahertz spectroscopy, machine vision, and electrical property sensing. Deep learning and sensor fusion methods are highlighted for their superior accuracy in microcrack detection (up to 99.4%) and shell strength prediction. We further discuss emerging challenges such as noise interference, signal variability, and scalability for industrial deployment. The integration of explainable AI, multimodal data acquisition, and edge computing is proposed as a future direction to develop intelligent, scalable, and cost-effective eggshell inspection systems. This comprehensive analysis provides a valuable reference for advancing nondestructive quality control in poultry product supply chains. Full article
(This article belongs to the Section Smart Agriculture)
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4 pages, 395 KB  
Abstract
Enhanced Defect Characterisation Using Pulsed Phase Thermography: The Impact of Sample Geometry and Signal-Enhancement Techniques
by Shayaan Saghir, Rachael C. Tighe and Ye Chow Kuang
Proceedings 2025, 129(1), 4; https://doi.org/10.3390/proceedings2025129004 - 12 Sep 2025
Viewed by 159
Abstract
In nondestructive evaluation (NDE), pulsed phase thermography (PPT) is a commonly used technique which relies on phase contrast to detect defects. This study presents a methodology to investigate how changes in signal processing and geometrical parameters affect phase contrast. Analytically simulated thermal signals [...] Read more.
In nondestructive evaluation (NDE), pulsed phase thermography (PPT) is a commonly used technique which relies on phase contrast to detect defects. This study presents a methodology to investigate how changes in signal processing and geometrical parameters affect phase contrast. Analytically simulated thermal signals are used to evaluate the phase contrast for varying sample thicknesses and defect sizes, relative to a fixed defect depth. To address the issue of spectral leakage, phase contrasts are recorded using both rectangular and Hamming windows before transformation into the frequency domain. A Gaussian process regression (GPR) modelling scheme is used to observe the relationship between phase contrast and geometrical parameters. The results suggest that both the choice of windowing function and geometrical factors can influence defect detection, offering insights to improve the reliability of PPT-based inspections. Full article
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5 pages, 1361 KB  
Abstract
A Simple Strategy to Reduce the Standing Wave Heat Pattern in Vibro-Thermography Based on 2D-FFT
by Stefano Laureti, Masashi Ishikawa, Rocco Zito, Marco Ricci and Hideo Nishino
Proceedings 2025, 129(1), 9; https://doi.org/10.3390/proceedings2025129009 - 12 Sep 2025
Viewed by 167
Abstract
Vibro-thermography is an effective nondestructive testing technique for detecting closed defects like cracks and delaminations through frictional heat generated under ultrasonic excitation. However, its accuracy is often reduced by standing wave patterns that create periodic temperature artifacts in non-defective areas, leading to false [...] Read more.
Vibro-thermography is an effective nondestructive testing technique for detecting closed defects like cracks and delaminations through frictional heat generated under ultrasonic excitation. However, its accuracy is often reduced by standing wave patterns that create periodic temperature artifacts in non-defective areas, leading to false positives. To overcome this, we propose an image processing approach using 2D Fourier Transform (2D-FFT) to reduce SW-induced patterns in the frequency domain. This enhances defect visibility by suppressing unwanted heat signatures. The method is evaluated on a cracked PMMA plate and a hollow tube of the same material. Full article
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5 pages, 1445 KB  
Abstract
Observation of Internal Structures Using Active Thermography, Optical Coherence Tomography and THz Time-Domain Imaging in the Field of Cultural Heritage
by Kaori Fukunaga, Takuma Takahashi, Hidetaka Ito, Shinji Masuda, Yuuma Ueno and Azusa Nagura
Proceedings 2025, 129(1), 44; https://doi.org/10.3390/proceedings2025129044 - 12 Sep 2025
Viewed by 227
Abstract
Non-destructive evaluation techniques using infrared and terahertz waves were employed to examine an aged violin and an inlaid dish. The results suggest that active thermography can rapidly reveal the general features of deterioration, while optical coherence tomography and THz imaging visualise cross-sectional images [...] Read more.
Non-destructive evaluation techniques using infrared and terahertz waves were employed to examine an aged violin and an inlaid dish. The results suggest that active thermography can rapidly reveal the general features of deterioration, while optical coherence tomography and THz imaging visualise cross-sectional images by scanning. These techniques are complementary and provide useful information for conservation planning. Full article
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5 pages, 978 KB  
Abstract
Thermographic Evaluation of Thermophysical Properties in Bio-Based Foams for Automotive Interior Components
by Giuseppe Dell’Avvocato, Ester D’Accardi, Damiano Rossi, Irene Anguillesi, Maurizia Seggiani, Umberto Galietti and Davide Palumbo
Proceedings 2025, 129(1), 38; https://doi.org/10.3390/proceedings2025129038 - 12 Sep 2025
Viewed by 220
Abstract
This study investigates the use of bio-based polyurethane foams (PUFs) containing phase change material (PCM) microparticles as a sustainable alternative for the automotive sector. These foams are synthesized using polyols derived from waste cooking oil (WCO), aligning with circular economy principles. To evaluate [...] Read more.
This study investigates the use of bio-based polyurethane foams (PUFs) containing phase change material (PCM) microparticles as a sustainable alternative for the automotive sector. These foams are synthesized using polyols derived from waste cooking oil (WCO), aligning with circular economy principles. To evaluate the thermophysical properties of these materials and, more in general, their thermal behavior, the use of non-destructive thermographic techniques has been proposed. This technique enables a rapid, full-field thermal analysis without physical contact, making it especially suitable for porous and heterogeneous structures like foams. As a reference, both virgin and foams with PCM were characterized in terms of density and thermal conductivity using well-established methods. Then, Lock-in thermography has been used as the first attempt technique to investigate variations in thermal behavior due to different thermophysical material properties based on the thermal response in transmission configuration. The thermographic approach proves to be an effective tool not only for assessing thermal behavior but also for supporting quality control and process optimization of sustainable polymeric materials. Full article
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54 pages, 5072 KB  
Review
Comparative Analysis of Autogenous and Microbial-Based Calcite Precipitation in Concrete: State-of-the-Art Review
by David O. Owolabi, Mehdi Shokouhian, Izhar Ahmad, Marshell Jenkins and Gabrielle Lynn McLemore
Buildings 2025, 15(18), 3289; https://doi.org/10.3390/buildings15183289 - 11 Sep 2025
Viewed by 737
Abstract
Cracks in concrete are a persistent issue that compromises structural durability, increases maintenance costs, and poses environmental challenges. Self-healing concrete has emerged as a promising innovation to address these concerns by autonomously sealing cracks and restoring integrity. This review focuses on two primary [...] Read more.
Cracks in concrete are a persistent issue that compromises structural durability, increases maintenance costs, and poses environmental challenges. Self-healing concrete has emerged as a promising innovation to address these concerns by autonomously sealing cracks and restoring integrity. This review focuses on two primary healing mechanisms: autogenous healing and microbial-induced calcite precipitation (MICP), the latter involving the biomineralization activity of bacteria, such as Bacillus subtilis and Sporosarcina pasteurii (formerly known as B. pasteurii). This review explores the selection, survivability, and activity of these microbes within the alkaline concrete environment. Additionally, the review highlights the role of fiber-reinforced cementitious composites (FRCCs), including high-performance fiber-reinforced cement composites (HPFRCCs) and engineered cement composites (ECCs), in enhancing crack control and enabling more effective microbial healing. The hybridization of natural and synthetic fibers contributes to both improved mechanical properties and crack width regulation, key factors in facilitating bacterial calcite precipitation. This review synthesizes current findings on self-healing efficiency, fiber compatibility, and the scalability of bacterial healing in concrete. It also evaluates critical parameters, such as healing agent integration, long-term performance, and testing methodologies, including both destructive and non-destructive techniques. By identifying existing knowledge gaps and performance barriers, this review offers insights for advancing sustainable, fiber-assisted microbial self-healing concrete for resilient infrastructure applications. Full article
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17 pages, 2525 KB  
Article
A Non-Destructive Deep Learning–Based Method for Shrimp Freshness Assessment in Food Processing
by Dongyu Hao, Cunxi Zhang, Rui Wang, Qian Qiao, Linsong Gao, Jin Liu and Rongsheng Lin
Processes 2025, 13(9), 2895; https://doi.org/10.3390/pr13092895 - 10 Sep 2025
Viewed by 401
Abstract
Maintaining the freshness of shrimp is a critical issue in quality and safety control within the food processing industry. Traditional methods often rely on destructive techniques, which are difficult to apply in online real-time monitoring. To address this challenge, this study aims to [...] Read more.
Maintaining the freshness of shrimp is a critical issue in quality and safety control within the food processing industry. Traditional methods often rely on destructive techniques, which are difficult to apply in online real-time monitoring. To address this challenge, this study aims to propose a non-destructive approach for shrimp freshness assessment based on imaging and deep learning, enabling efficient and reliable freshness classification. The core innovation of the method lies in constructing an improved GoogLeNet architecture. By incorporating the ELU activation function, L2 regularization, and the RMSProp optimizer, combined with a transfer learning strategy, the model effectively enhances generalization capability and stability under limited sample conditions. Evaluated on a shrimp image dataset rigorously annotated based on TVB-N reference values, the proposed model achieved an accuracy of 93% with a test loss of only 0.2. Ablation studies further confirmed the contribution of architectural and training strategy modifications to performance improvement. The results demonstrate that the method enables rapid, non-contact freshness discrimination, making it suitable for real-time sorting and quality monitoring in shrimp processing lines, and providing a feasible pathway for deployment on edge computing devices. This study offers a practical solution for intelligent non-destructive detection in aquatic products, with strong potential for engineering applications. Full article
(This article belongs to the Section Food Process Engineering)
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