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28 pages, 2612 KiB  
Article
Optimizing Economy with Comfort in Climate Control System Scheduling for Indoor Ice Sports Venues’ Spectator Zones Considering Demand Response
by Zhuoqun Du, Yisheng Liu, Yuyan Xue and Boyang Liu
Algorithms 2025, 18(7), 446; https://doi.org/10.3390/a18070446 - 20 Jul 2025
Viewed by 184
Abstract
With the growing popularity of ice sports, indoor ice sports venues are drawing an increasing number of spectators. Maintaining comfort in spectator zones presents a significant challenge for the operational scheduling of climate control systems, which integrate ventilation, heating, and dehumidification functions. To [...] Read more.
With the growing popularity of ice sports, indoor ice sports venues are drawing an increasing number of spectators. Maintaining comfort in spectator zones presents a significant challenge for the operational scheduling of climate control systems, which integrate ventilation, heating, and dehumidification functions. To explore economic cost potential while ensuring user comfort, this study proposes a demand response-integrated optimization model for climate control systems. To enhance the model’s practicality and decision-making efficiency, a two-stage optimization method combining multi-objective optimization algorithms with the technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. In terms of algorithm comparison, the performance of three typical multi-objective optimization algorithms—NSGA-II, standard MOEA/D, and Multi-Objective Brown Bear Optimization (MOBBO)—is systematically evaluated. The results show that NSGA-II demonstrates the best overall performance based on evaluation metrics including runtime, HV, and IGD. Simulations conducted in China’s cold regions show that, under comparable comfort levels, schedules incorporating dynamic tariffs are significantly more economically efficient than those that do not. They reduce operating costs by 25.3%, 24.4%, and 18.7% on typical summer, transitional, and winter days, respectively. Compared to single-objective optimization approaches that focus solely on either comfort enhancement or cost reduction, the proposed multi-objective model achieves a better balance between user comfort and economic performance. This study not only provides an efficient and sustainable solution for climate control scheduling in energy-intensive buildings such as ice sports venues but also offers a valuable methodological reference for energy management and optimization in similar settings. Full article
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9 pages, 3832 KiB  
Case Report
Non-Invasive Diagnostic Imaging in Kaposi Sarcoma Evaluation
by Carmen Cantisani, Antonio Di Guardo, Marco Ardigò, Mariano Suppa, Salvador Gonzalez, Caterina Longo, Alberto Taliano, Emanuele Rovaldi, Elisa Cinotti and Giovanni Pellacani
Diagnostics 2025, 15(13), 1665; https://doi.org/10.3390/diagnostics15131665 - 30 Jun 2025
Viewed by 432
Abstract
Background and Clinical Significance: Kaposi sarcoma (KS) is a rare angio-proliferative mesenchymal tumor that predominantly affects the skin and mucous membranes but may involve lymph nodes and visceral organs. Clinically, it manifests as red-purple-brown papules, nodules, or plaques, either painless or painful, often [...] Read more.
Background and Clinical Significance: Kaposi sarcoma (KS) is a rare angio-proliferative mesenchymal tumor that predominantly affects the skin and mucous membranes but may involve lymph nodes and visceral organs. Clinically, it manifests as red-purple-brown papules, nodules, or plaques, either painless or painful, often with disfiguring potential. The diagnosis is traditionally based on clinical and histopathological evaluation, although non-invasive imaging techniques are increasingly used to support diagnosis and treatment monitoring. We report a case of HHV-8-negative Kaposi sarcoma evaluated with multiple non-invasive imaging modalities to highlight their diagnostic utility. Case Presentation: An 83-year-old man presented with multiple painful, violaceous papulo-nodular lesions, some ulcerated, on the lateral aspect of his left foot. Dermoscopy revealed the characteristic rainbow pattern. Dynamic Optical Coherence Tomography (D-OCT) allowed real-time visualization of microvascular abnormalities, identifying large serpentine and branching vessels with clearly delineated capsules. Line-field Optical Coherence Tomography (LC-OCT) showed irregular dermal collagen, vascular lacunae, and the presence of spindle cells and slit-like vessels. Histological analysis confirmed the diagnosis of Kaposi sarcoma, revealing a proliferation of spindle-shaped endothelial cells forming angulated vascular spaces, with red blood cell extravasation and a mixed inflammatory infiltrate. Conclusions: Non-invasive imaging tools, including dermoscopy, D-OCT, and LC-OCT, have emerged as valuable adjuncts in the diagnosis and monitoring of KS. These techniques enable in vivo assessment of vascular architecture and tissue morphology, enhancing clinical decision-making while reducing the need for immediate biopsy. Dermoscopy reveals polychromatic vascular features, such as the rainbow pattern, while D-OCT and LC-OCT provide high-resolution insights into vascular proliferation, tissue heterogeneity, and cellular morphology. Dermoscopy, dynamic OCT, and LC-OCT represent promising non-invasive diagnostic tools for the assessment of Kaposi sarcoma. These technologies provide detailed morphological and vascular information, enabling earlier diagnosis and more personalized management. While histopathology remains the gold standard, non-invasive imaging offers a valuable complementary approach for diagnosis and follow-up, particularly in complex or atypical presentations. Ongoing research and technological refinement are essential to improve accessibility and clinical applicability. Full article
(This article belongs to the Special Issue Optical Coherence Tomography in Non-Invasive Diagnostic Imaging)
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23 pages, 2220 KiB  
Article
The Impact of ESG Certifications on Class A Office Buildings in Madrid: A Multi-Criteria Decision Analysis
by Alfonso Valero
Standards 2025, 5(2), 14; https://doi.org/10.3390/standards5020014 - 21 May 2025
Viewed by 595
Abstract
This study investigates the impact of Environmental, Social, and Governance (ESG) certifications on the performance of Class A office buildings within Madrid’s Central Business District (CBD). Employing a Multi-Criteria Decision Making (MCDM) methodology, the research evaluates 21 office properties, analyzing the influence of [...] Read more.
This study investigates the impact of Environmental, Social, and Governance (ESG) certifications on the performance of Class A office buildings within Madrid’s Central Business District (CBD). Employing a Multi-Criteria Decision Making (MCDM) methodology, the research evaluates 21 office properties, analyzing the influence of ESG certifications on key performance indicators, including green building certifications, valuation, market perception, and financial outcomes. The findings reveal that ESG-certified buildings demonstrate superior performance, commanding higher valuations, mitigating brown discounts, and achieving increased rental rates, thereby enhancing their investment attractiveness. These results underscore the importance of ESG certifications in the Spanish office market and provide valuable insights for investors, developers, and policymakers navigating the integration of sustainability and commercial real estate. Full article
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24 pages, 2586 KiB  
Article
Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition
by Yingzhe Yu, Huiqiong Jia, Li Zhang, Suling Xu, Xiaoxia Zhu, Jiucun Wang, Fangfang Wang, Lianyi Han, Haoqiang Jiang, Qiongyan Zhou and Chao Xin
Bioengineering 2025, 12(3), 282; https://doi.org/10.3390/bioengineering12030282 - 12 Mar 2025
Cited by 1 | Viewed by 1585
Abstract
The rising prevalence of skin lesions places a heavy burden on global health resources and necessitates an early and precise diagnosis for successful treatment. The diagnostic potential of recent multi-modal skin lesion detection algorithms is limited because they ignore dynamic interactions and information [...] Read more.
The rising prevalence of skin lesions places a heavy burden on global health resources and necessitates an early and precise diagnosis for successful treatment. The diagnostic potential of recent multi-modal skin lesion detection algorithms is limited because they ignore dynamic interactions and information sharing across modalities at various feature scales. To address this, we propose a deep learning framework, Multi-Modal Skin-Imaging-based Information-Switching Network (MDSIS-Net), for end-to-end skin lesion recognition. MDSIS-Net extracts intra-modality features using transfer learning in a multi-scale fully shared convolutional neural network and introduces an innovative information-switching module. A cross-attention mechanism dynamically calibrates and integrates features across modalities to improve inter-modality associations and feature representation in this module. MDSIS-Net is tested on clinical disfiguring dermatosis data and the public Derm7pt melanoma dataset. A Visually Intelligent System for Image Analysis (VISIA) captures five modalities: spots, red marks, ultraviolet (UV) spots, porphyrins, and brown spots for disfiguring dermatosis. The model performs better than existing approaches with an mAP of 0.967, accuracy of 0.960, precision of 0.935, recall of 0.960, and f1-score of 0.947. Using clinical and dermoscopic pictures from the Derm7pt dataset, MDSIS-Net outperforms current benchmarks for melanoma, with an mAP of 0.877, accuracy of 0.907, precision of 0.911, recall of 0.815, and f1-score of 0.851. The model’s interpretability is proven by Grad-CAM heatmaps correlating with clinical diagnostic focus areas. In conclusion, our deep multi-modal information-switching model enhances skin lesion identification by capturing relationship features and fine-grained details across multi-modal images, improving both accuracy and interpretability. This work advances clinical decision making and lays a foundation for future developments in skin lesion diagnosis and treatment. Full article
(This article belongs to the Special Issue Artificial Intelligence for Skin Diseases Classification)
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36 pages, 4076 KiB  
Review
A Comparative Review of Alternative Fucoidan Extraction Techniques from Seaweed
by Matthew Chadwick, Loïc G. Carvalho, Carlos Vanegas and Simone Dimartino
Mar. Drugs 2025, 23(1), 27; https://doi.org/10.3390/md23010027 - 7 Jan 2025
Cited by 3 | Viewed by 5264
Abstract
Fucoidan is a sulfated polysaccharide found in brown seaweed. Due to its reported biological activities, including antiviral, antibacterial and anti-inflammatory activities, it has garnered significant attention for potential biomedical applications. However, the direct relationship between fucoidan extracts’ chemical structures and bioactivities is unclear, [...] Read more.
Fucoidan is a sulfated polysaccharide found in brown seaweed. Due to its reported biological activities, including antiviral, antibacterial and anti-inflammatory activities, it has garnered significant attention for potential biomedical applications. However, the direct relationship between fucoidan extracts’ chemical structures and bioactivities is unclear, making it extremely challenging to predict whether an extract will possess a given bioactivity. This relationship is further complicated by a lack of uniformity in the recent literature in terms of the assessment and reporting of extract properties, yield and chemical composition (e.g., sulfate, fucose, uronic acid and monosaccharide contents). These inconsistencies pose significant challenges when directly comparing extraction techniques across studies. This review collected data on extract contents and properties from a selection of available studies. Where information was unavailable directly, efforts were made to extrapolate data. This approach enabled a comprehensive examination of the correlation between extraction techniques and the characteristics of the resulting extracts. A holistic framework is presented for the selection of fucoidan extraction methods, outlining key heuristics to consider when capturing the broader context of a seaweed bioprocess. Future work should focus on developing knowledge within these heuristic categories, such as the creation of technoeconomic models of each extraction process. This framework should allow for a robust extraction selection process that integrates process scale, cost and constraints into decision making. Key quality attributes for biologically active fucoidan are proposed, and areas for future research are identified, such as studies for specific bioactivities aimed at elucidating fucoidan’s mechanism of action. This review also sets out future work required to standardize the reporting of fucoidan extract data. Standardization could positively enhance the quality and depth of data on fucoidan extracts, enabling the relationships between physical, chemical and bioactive properties to be identified. Recommendations on best practices for the production of high-quality fucoidan with desirable yield, characteristics and bioactivity are highlighted. Full article
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14 pages, 1424 KiB  
Article
Rice Disease Classification Using a Stacked Ensemble of Deep Convolutional Neural Networks
by Zhibin Wang, Yana Wei, Cuixia Mu, Yunhe Zhang and Xiaojun Qiao
Sustainability 2025, 17(1), 124; https://doi.org/10.3390/su17010124 - 27 Dec 2024
Cited by 2 | Viewed by 1268
Abstract
Rice is a staple food for almost half of the world’s population, and the stability and sustainability of rice production plays a decisive role in food security. Diseases are a major cause of loss in rice crops. The timely discovery and control of [...] Read more.
Rice is a staple food for almost half of the world’s population, and the stability and sustainability of rice production plays a decisive role in food security. Diseases are a major cause of loss in rice crops. The timely discovery and control of diseases are important in reducing the use of pesticides, protecting the agricultural eco-environment, and improving the yield and quality of rice crops. Deep convolutional neural networks (DCNNs) have achieved great success in disease image classification. However, most models have complex network structures that frequently cause problems, such as redundant network parameters, low training efficiency, and high computational costs. To address this issue and improve the accuracy of rice disease classification, a lightweight deep convolutional neural network (DCNN) ensemble method for rice disease classification is proposed. First, a new lightweight DCNN model (called CG-EfficientNet), which is based on an attention mechanism and EfficientNet, was designed as the base learner. Second, CG-EfficientNet models with different optimization algorithms and network parameters were trained on rice disease datasets to generate seven different CG-EfficientNets, and a resampling strategy was used to enhance the diversity of the individual models. Then, the sequential least squares programming algorithm was used to calculate the weight of each base model. Finally, logistic regression was used as the meta-classifier for stacking. To verify the effectiveness, classification experiments were performed on five classes of rice tissue images: rice bacterial blight, rice kernel smut, rice false smut, rice brown spot, and healthy leaves. The accuracy of the proposed method was 96.10%, which is higher than the results of the classic CNN models VGG16, InceptionV3, ResNet101, and DenseNet201 and four integration methods. The experimental results show that the proposed method is not only capable of accurately identifying rice diseases but is also computationally efficient. Full article
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13 pages, 1076 KiB  
Article
Fruit Sorting Based on Maturity Reduces Internal Disorders in Vapor Heat-Treated ‘B74’ Mango
by Amit Khanal, Muhammad Asad Ullah, Priya Joyce, Neil White, Andrew Macnish, Eleanor Hoffman, Donald Irving, Richard Webb and Daryl Joyce
Horticulturae 2024, 10(12), 1257; https://doi.org/10.3390/horticulturae10121257 - 27 Nov 2024
Cited by 1 | Viewed by 1374
Abstract
Postharvest internal disorders (IDs) in mango fruit present a significant challenge to the industry, with their underlying causes still unclear. This study investigated the relationship between fruit maturity and the susceptibility of vapor heat-treated (VHT) ‘B74’ mangoes to IDs in three experiments. In [...] Read more.
Postharvest internal disorders (IDs) in mango fruit present a significant challenge to the industry, with their underlying causes still unclear. This study investigated the relationship between fruit maturity and the susceptibility of vapor heat-treated (VHT) ‘B74’ mangoes to IDs in three experiments. In the first experiment, fruit were categorized into three maturity groups based on dry matter content (DMC): <15%, 15–17%, and >17%, using a handheld near-infrared device. Half of the fruit in each group underwent VHT, while the remainder were untreated controls. Flesh cavity with white patches (FCWP) was the only disorder observed exclusively in VHT fruit. The incidence and severity of FCWP was significantly higher (p < 0.05) in fruit with <15% DMC, with 12.4% incidence and a severity score of 0.2 on a 0–3 scale (0: healthy and 3: severely affected), compared to more mature fruit. In the second experiment, the fruits were harvested at early and late maturity stages, with average DMC values of 14.5% and 17.4%, respectively. The fruit was subjected to no VHT, VHT, and VHT following a 12 h pre-conditioning period at 37 ± 1 °C. Consistent with the first experiment, FCWP was observed only in VHT fruit, with early-harvested fruit displaying a significantly higher (p < 0.05) FCWP incidence (26.9%) and severity (0.3) compared to late-harvested fruit (8.3% incidence and 0.1 severity). Pre-conditioning significantly reduced FCWP, particularly in early-harvested fruit. In the third experiment, fruit maturity sorted based on density was assessed, followed by VHT and simulated sea freight under controlled (CA) and ambient atmospheres. Fruit density did not effectively differentiate maturity considering DMC as a maturity indicator. Storage conditions significantly reduced (p < 0.05) flesh browning incidence from 71.1% under ambient conditions to 33.3% under CA. This study highlights fruit maturity as a key factor in the susceptibility of ‘B74’ mangoes to postharvest IDs following VHT. Therefore, sorting fruit based on DMC at harvest or at the packing facility prior to VHT serves as a valuable decision support for reducing IDs in VHT fruit. Further research will explore advanced technologies to enable rapid and efficient fruit sorting based on DMC. Full article
(This article belongs to the Special Issue Postharvest Physiology of Horticultural Crops)
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20 pages, 8468 KiB  
Article
Loss of MER Tyrosine Kinase Attenuates Adipocyte Hypertrophy and Leads to Enhanced Thermogenesis in Mice Exposed to High-Fat Diet
by Krisztina Köröskényi, László Sós, Melinda Rostás, Albert Bálint Papp, Endre Kókai, Éva Garabuczi, Dávid Deák, Lívia Beke, Gábor Méhes and Zsuzsa Szondy
Cells 2024, 13(22), 1902; https://doi.org/10.3390/cells13221902 - 18 Nov 2024
Cited by 1 | Viewed by 1919
Abstract
Obesity is characterized by low-grade inflammation that originates predominantly from the expanding visceral adipose tissue, in which adipocytes respond to lipid overload with hypertrophy, and consequently die by apoptosis. Recruited adipose tissue macrophages (ATMs) take up the excess lipids and remove the dead [...] Read more.
Obesity is characterized by low-grade inflammation that originates predominantly from the expanding visceral adipose tissue, in which adipocytes respond to lipid overload with hypertrophy, and consequently die by apoptosis. Recruited adipose tissue macrophages (ATMs) take up the excess lipids and remove the dead cells; however, long-term exposure to high concentrations of lipids alters their phenotype to M1-like ATMs that produce pro-inflammatory cytokines and resistin leading to insulin resistance and other obesity-related pathologies. Mer tyrosine kinase is expressed by macrophages and by being an efferocytosis receptor, and by suppressing inflammation, we hypothesized that it might play a protective role against obesity. To our surprise, however, the loss of Mer protected mice against high-fat diet (HFD)-induced obesity. We report in this paper that Mer is also expressed by adipocytes of both white and brown adipose tissues, and while its activity facilitates adipocyte lipid storage both in vitro and in vivo in mice exposed to HFD, it simultaneously attenuates thermogenesis in the brown adipose tissue contributing to its ‘whitening’. Our data indicate that Mer is one of the adipocyte tyrosine kinase receptors, the activity of which contributes to the metabolic decision about the fate of excess lipids favoring their storage within the body. Full article
(This article belongs to the Section Tissues and Organs)
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14 pages, 4225 KiB  
Article
Experimental Study of Bifacial Photovoltaic Module Performance on a Sunny Day with Varying Backgrounds Using Exergy and Energy Analysis
by A. F. Almarshoud, M. A. Abdel-halim, Radwan A. Almasri and Ahmed M. Alshwairekh
Energies 2024, 17(21), 5456; https://doi.org/10.3390/en17215456 - 31 Oct 2024
Viewed by 2560
Abstract
In this research, ethe performance of bifacial photovoltaic (PV) modules under varying background conditions is explored, specifically green grass, brown clay, and white gravel, on a sunny day. By leveraging both exergy and energy analysis, this research aims to provide a comprehensive evaluation [...] Read more.
In this research, ethe performance of bifacial photovoltaic (PV) modules under varying background conditions is explored, specifically green grass, brown clay, and white gravel, on a sunny day. By leveraging both exergy and energy analysis, this research aims to provide a comprehensive evaluation of bifacial module efficiency compared to traditional monofacial modules. The experimental setup simulates diverse installation environments, including rooftops and ground-mounted systems, by varying background reflectance. Key performance metrics such as energy yield, exergy yield, and overall efficiency were measured. The findings reveal that bifacial modules installed over white gravel backgrounds achieve the highest exergy profile and efficiency during peak solar radiation periods, attributed to the enhanced reflectivity of white gravel. These insights can inform strategic decisions regarding the selection and placement of bifacial modules to optimize energy and exergy outputs in real-world scenarios. This study contributes valuable knowledge to the advancement of renewable energy technologies, offering guidance for researchers, developers, and policymakers focused on sustainable energy solutions. Full article
(This article belongs to the Section A: Sustainable Energy)
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18 pages, 5243 KiB  
Article
Dam Siltation in the Mediterranean Region Under Climate Change: A Case Study of Ahmed El Hansali Dam, Morocco
by Hassan Mosaid, Ahmed Barakat, El Houssaine Bouras, Maryem Ismaili, Mohamed El Garnaoui, Kamal Abdelrahman and Ali Y. Kahal
Water 2024, 16(21), 3108; https://doi.org/10.3390/w16213108 - 30 Oct 2024
Cited by 3 | Viewed by 1925
Abstract
Dams are vital for irrigation, power generation, and domestic water needs, but siltation poses a significant challenge, especially in areas prone to water erosion, potentially shortening a dam’s lifespan. The Ahmed El Hansali Dam in Morocco faces heightened siltation due to its upstream [...] Read more.
Dams are vital for irrigation, power generation, and domestic water needs, but siltation poses a significant challenge, especially in areas prone to water erosion, potentially shortening a dam’s lifespan. The Ahmed El Hansali Dam in Morocco faces heightened siltation due to its upstream region being susceptible to erosion-prone rocks and high runoff. This study estimates the siltation at the dam from its construction up to 2014 using bathymetric data and the Brown model, which is a widely-used empirical model that calculates reservoir trap efficiency. Additionally, the study evaluates the impact of Land Use and Land Cover (LULC) changes and projected future rainfall until around 2076 based on siltation rates. The results indicate that changes in LULC, particularly temporal variations in precipitation, have a significant impact on the siltation of the Ahmed El Hansali dam. Notably, rainfall is strongly correlated with the siltation rate, with an R2 of 0.92. The efficiency of sediment trapping (TE) is 97.64%, meaning that 97.64% of the sediment in the catchment area is trapped or deposited at the bottom of the dam. The estimated annual specific sediment yield is about 32,345.79 tons/km2/yr, and the sediment accumulation rate is approximately 4.75 Mm3/yr. The dam’s half-life is estimated to be around 2076, but future precipitation projections may extend this timeframe due to the strong correlation between siltation and precipitation. Additionally, soil erosion driven by land management practices plays a crucial role in future siltation dynamics. Hence, this study offers a comprehensive assessment of the siltation dynamics at the Ahmed El Hansali dam, providing essential information on the long-term effects of erosion, land use changes, and climate projections. These findings may assist decision makers in managing dam reservoir sedimentation more effectively, ensuring the durability of the dam and extending the reservoir life. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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19 pages, 9721 KiB  
Article
Unveiling the Molecular Mechanisms of Browning in Camellia hainanica Callus through Transcriptomic and Metabolomic Analysis
by Kunlin Wu, Yanju Liu, Yufen Xu, Zhaoyan Yu, Qiulin Cao, Han Gong, Yaodong Yang, Jianqiu Ye and Xiaocheng Jia
Int. J. Mol. Sci. 2024, 25(20), 11021; https://doi.org/10.3390/ijms252011021 - 14 Oct 2024
Cited by 5 | Viewed by 2120
Abstract
Camellia hainanica is one of the camellia plants distributed in tropical regions, and its regeneration system and genetic transformation are affected by callus browning. However, the underlying mechanism of Camellia hainanica callus browning formation remains largely unknown. To investigate the metabolic basis and [...] Read more.
Camellia hainanica is one of the camellia plants distributed in tropical regions, and its regeneration system and genetic transformation are affected by callus browning. However, the underlying mechanism of Camellia hainanica callus browning formation remains largely unknown. To investigate the metabolic basis and molecular mechanism of the callus browning of Camellia hainanica, histological staining, high-throughput metabolomics, and transcriptomic assays were performed on calli with different browning degrees (T1, T2, and T3). The results of histological staining revealed that the brown callus cells had obvious lignification and accumulation of polyphenols. Widely targeted metabolomics revealed 1190 differentially accumulated metabolites (DAMs), with 53 DAMs annotated as phenylpropanoids and flavonoids. Comparative transcriptomics revealed differentially expressed genes (DEGs) of the T2 vs. T1 associated with the biosynthesis and regulation of flavonoids and transcription factors in Camellia hainanica. Among them, forty-four enzyme genes associated with flavonoid biosynthesis were identified, including phenylalaninase (PAL), 4-coumaroyl CoA ligase (4CL), naringenin via flavanone 3-hydroxylase (F3H), flavonol synthase (FLS), Chalcone synthase (CHS), Chalcone isomerase (CHI), hydroxycinnamoyl-CoA shikimate transferase (HCT), Dihydroflavonol reductase (DFR), anthocyanin reductase (LAR), anthocyanin synthetase (ANS), and anthocyanin reductase (ANR). Related transcription factors R2R3-MYB, basic helix-loop-helix (bHLH), and WRKY genes also presented different expression patterns in T2 vs. T1. These results indicate that the browning of calli in Camellia hainanica is regulated at both the transcriptional and metabolic levels. The oxidation of flavonoids and the regulation of related structural genes and transcription factors are crucial decisive factors. This study preliminarily revealed the molecular mechanism of the browning of the callus of Camellia hainanensis, and the results can provide a reference for the anti-browning culture of Camellia hainanica callus. Full article
(This article belongs to the Section Molecular Plant Sciences)
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14 pages, 1695 KiB  
Article
Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
by Hanchi Liu, Jinrong He, Yanxin Shi and Yingzhou Bi
Appl. Sci. 2024, 14(19), 9136; https://doi.org/10.3390/app14199136 - 9 Oct 2024
Cited by 2 | Viewed by 1393
Abstract
Apple core browning not only affects the nutritional quality of apples, but also poses a health risk to consumers. Therefore, there is an urgent need to develop a fast and reliable non-destructive detection method for apple core browning. To deal with the challenges [...] Read more.
Apple core browning not only affects the nutritional quality of apples, but also poses a health risk to consumers. Therefore, there is an urgent need to develop a fast and reliable non-destructive detection method for apple core browning. To deal with the challenges of the long incubation period, strong infectivity, and difficulty in the prevention and control of apple core browning, a novel non-destructive detection method for apple core browning has been developed through combining hyperspectral imaging and dielectric techniques. To reduce the computational complexity of high-dimensional multi-view data, canonical correlation analysis is employed for feature dimensionality reduction. Then, the two low-dimensional vectors extracted from two different sensors are concatenated into one united feature vector; therefore, the information contained in the hyperspectral and dielectric data is fused to improve the detection accuracy of the non-destructive method. At last, five traditional classifiers, such as k-Nearest Neighbors, a support vector machine with radial basis function kernel and polynomial kernel, Decision Tree, and neural network, are trained on the fused feature vectors to discriminate apple core browning. The experimental results on our own constructed dataset have shown that the sensitivity, specificity, and precision of SVM with RBF kernel based on concatenated 70-dimensional feature vectors extracted via canonical correlation analysis reached 99.98%, 99.70%, and 99.70%, respectively, which achieved better results than other models. This study can provide theoretical assurance and technical support for further development of higher accuracy and lower-cost non-destructive detection devices for apple core browning. Full article
(This article belongs to the Section Agricultural Science and Technology)
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17 pages, 4167 KiB  
Article
Study on Rice Origin and Quality Identification Based on Fluorescence Spectral Features
by Yixin Qiu, Yong Tan, Yingying Zhou, Zhipeng Li, Zhuang Miao, Changming Li, Xitian Mei, Chunyu Liu and Xing Teng
Agriculture 2024, 14(10), 1763; https://doi.org/10.3390/agriculture14101763 - 6 Oct 2024
Cited by 2 | Viewed by 1586
Abstract
The origin of agricultural products significantly influences their quality and safety. Fluorescence spectroscopy was used to analyse Japonica rice 830, grown in different areas of Jilin Province, by examining rice seed, brown rice, and rice flour from 12 origins. Fluorescence spectra were pre-processed [...] Read more.
The origin of agricultural products significantly influences their quality and safety. Fluorescence spectroscopy was used to analyse Japonica rice 830, grown in different areas of Jilin Province, by examining rice seed, brown rice, and rice flour from 12 origins. Fluorescence spectra were pre-processed through normalisation and smoothing to remove noise. These processed spectra were input into decision trees, support vector machines (SVMs), K-nearest neighbour (KNN), and neural network models for classification. The analysis revealed that the combined four models achieved an average classification accuracy of 98.05% with a computation time of 180 s, while the reduced-scale models improved accuracy to 98.36% and reduced computation time to 11.25 s. Additionally, prediction models using standard rice starch content values across different states achieved R² values over 0.8. This method provides a rapid, precise approach for assessing rice quality and origin, demonstrating significant potential for application in rice analysis. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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16 pages, 3284 KiB  
Article
Influence of Tea Polyphenols, Chitosan, and Melatonin as the Eco-Friendly Post-Harvest Treatments on the Vase Life of the Cut Chrysanthemum ‘Pingpong’ Group
by Ziyi Yu, Shuangda Li and Yan Hong
Agriculture 2024, 14(9), 1507; https://doi.org/10.3390/agriculture14091507 - 2 Sep 2024
Cited by 3 | Viewed by 2097
Abstract
Vase life is a decisive measure of the marketability of post-harvest physiology in cut flowers. In the process of petal senescence, the cut chrysanthemum (Chrysanthemum × morifolium) ‘Pingpong’ group develops severe capitulum collapse which manifests as wilting and browning, leading to [...] Read more.
Vase life is a decisive measure of the marketability of post-harvest physiology in cut flowers. In the process of petal senescence, the cut chrysanthemum (Chrysanthemum × morifolium) ‘Pingpong’ group develops severe capitulum collapse which manifests as wilting and browning, leading to shorter vase life. Melatonin (MT), tea polyphenols (TPs), and chitosan (CT) are natural alternatives to chemical compounds with proven preservation effects. In this study, the possibility of mitigating capitulum collapse using the preservation solutions of these three eco-friendly ingredients was investigated on four varieties from the ‘Pingpong’ group, aiming to delay the senescence process. The effects on vase life of 0.02/0.04 mmol·L−1 MT, 200/400 mg·L−1 TPs, and 0.10/0.20 g·L−1 CT were, respectively, assessed with the basis of 20 g·L−1 sucrose and 250 mg·L−1 citric acid. The yellow and white varieties tend to have a longer vase life compared with the green and pink varieties. Compared to the control with only base ingredients, the greatest delay in capitulum collapse was observed with 0.04 mmol·L−1 MT in the yellow variety, maximizing the vase life to 13.4 days. MT maintained the best ornamental quality of the capitulum by decelerating fresh weight and flower diameter loss in terms of all varieties. TPs significantly increased flower diameter to improve vase life up to four more days. However, CT caused significant negative effects on vase life, with severe loss of both flower diameter and fresh weight. Therefore, the application of 0.04 mmol·L−1 MT and 200 mg·L−1 TPs was suggested to enhance the marketability of cut ‘Pingpong’, which highlighted the eco-friendly potential of post-harvest treatments. Full article
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16 pages, 4424 KiB  
Article
In Search of Optimum Fresh-Cut Raw Material: Using Computer Vision Systems as a Sensory Screening Tool for Browning-Resistant Romaine Lettuce Accessions
by Ellen R. Bornhorst, Yaguang Luo, Eunhee Park, Bin Zhou, Ellen R. Turner, Zi Teng, Frances Trouth, Ivan Simko and Jorge M. Fonseca
Horticulturae 2024, 10(7), 731; https://doi.org/10.3390/horticulturae10070731 - 12 Jul 2024
Cited by 3 | Viewed by 1534
Abstract
The popularity of ready-to-eat (RTE) salads has prompted novel technology to prolong the shelf life of their ingredients. Fresh-cut romaine lettuce is widely used in RTE salads; however, its tendency to quickly discolor continues to be a challenge for the industry. Selecting the [...] Read more.
The popularity of ready-to-eat (RTE) salads has prompted novel technology to prolong the shelf life of their ingredients. Fresh-cut romaine lettuce is widely used in RTE salads; however, its tendency to quickly discolor continues to be a challenge for the industry. Selecting the ideal lettuce accessions for use in RTE salads is essential to ensure maximum shelf life, and it is critical to have a practical way to assess and compare the quality of multiple lettuce accessions that are being considered for use in fresh-cut applications. Thus, in this work we aimed to determine whether a computer vision system (CVS) composed of image acquisition, processing, and analysis could be effective to detect visual quality differences among 16 accessions of fresh-cut romaine lettuce during postharvest storage. The CVS involved a post-capturing color correction, effective image segmentation, and calculation of a browning index, which was tested as a predictor of quality and shelf life of fresh-cut romaine lettuce. The results demonstrated that machine vision software can be implemented to replace or supplement the scoring of a trained panel and instrumental quality measurements. Overall visual quality, a key sensory parameter that determines food preferences and consumer behavior, was highly correlated with the browning index, with a Pearson correlation coefficient of −0.85. Other important sensory decision parameters were also strongly or moderately correlated with the browning index, with Pearson correlation coefficients of −0.84 for freshness, 0.79 for off odor, and 0.57 for browning. The ranking of the accessions according to quality acceptability from the sensory evaluation produced a similar pattern to those obtained with the CVS. This study revealed that multiple lettuce accessions can be effectively benchmarked for their performance as fresh-cut sources via a CVS-based method. Future opportunities and challenges in using machine vision image processing to predict consumer preferences for RTE salad greens is also discussed. Full article
(This article belongs to the Section Postharvest Biology, Quality, Safety, and Technology)
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