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Search Results (307)

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21 pages, 5409 KiB  
Article
Sustainable Rubber Solutions: A Study on Bio-Based Oil and Resin Blends
by Frances van Elburg, Fabian Grunert, Claudia Aurisicchio, Micol di Consiglio, Auke Talma, Pilar Bernal-Ortega and Anke Blume
Polymers 2025, 17(15), 2111; https://doi.org/10.3390/polym17152111 - 31 Jul 2025
Viewed by 29
Abstract
One of the most important challenges the tire industry faces is becoming carbon-neutral and using 100% sustainable materials by 2050. Utilizing materials from renewable sources and recycled substances is a key aspect of achieving this goal. Petroleum-based oils, such as Treated Distillate Aromatic [...] Read more.
One of the most important challenges the tire industry faces is becoming carbon-neutral and using 100% sustainable materials by 2050. Utilizing materials from renewable sources and recycled substances is a key aspect of achieving this goal. Petroleum-based oils, such as Treated Distillate Aromatic Extract (TDAE), are frequently used in rubber compounds, and a promising strategy to enhance sustainability is to use bio-based plasticizer alternatives. However, research has shown that the replacement of TDAE oil with bio-based oils or resins can significantly alter the glass transition temperature (Tg) of the final compound, influencing the tire properties. In this study, the theory was proposed that using a plasticizer blend, comprising oil and resin, in a rubber compound would result in similar Tg values as the reference compound containing TDAE. To test this, the cycloaliphatic di-ester oil Hexamoll DINCH, which can be made out of bio-based feedstock by the BioMass Balance approach, was selected and blended with the cycloaliphatic hydrocarbon resin Escorez 5300. Various oil-to-resin ratios were investigated, and a linear increase in the Tg of the vulcanizate was obtained when increasing the resin content and decreasing the oil content. Additionally, a 50/50 blend, consisting of 18.75 phr Hexamoll DINCH and 18.75 phr Escorez 5300, resulted in the same Tg of −19 °C as a compound containing 37.5 phr TDAE. Furthermore, this blend resulted in similar curing characteristics and cured Payne effect as the reference with TDAE. Moreover, a similar rolling resistance indicator (tan δ at 60 °C = 0.115), a slight deterioration in wear resistance (ARI = 83%), but an improvement in the stress–strain behavior (M300 = 9.18 ± 0.20 MPa and Ts = 16.3 ± 0.6 MPa) and wet grip indicator (tan δ at 0 °C = 0.427) were observed. The results in this work show the potential of finding a balance between optimal performance and sustainability by using plasticizer blends. Full article
(This article belongs to the Special Issue Exploration and Innovation in Sustainable Rubber Performance)
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15 pages, 842 KiB  
Article
Eucalyptus globulus Pyroligneous Extract as Dietary Additive for Nile Tilapia Health: In Vitro and In Vivo Assessments
by Marcelo Felisberto dos Reis, Nycolas Levy-Pereira, Nathalia Raissa de Alcântara Rocha, Talita Maria Lazaro, Marisa Matias de França, Sofia Harumi Lopes Nishikawa, Silvia Helena Seraphin de Godoy and Ricardo Luiz Moro de Sousa
Microorganisms 2025, 13(8), 1773; https://doi.org/10.3390/microorganisms13081773 - 30 Jul 2025
Viewed by 162
Abstract
Studies on plant extracts as growth promoters and immunostimulants have shown promising results. However, their effects on fish health and growth remain unclear. This study evaluated the in vitro and in vivo effects of Eucalyptus globulus pyroligneous extract (PE) on Nile tilapia. In [...] Read more.
Studies on plant extracts as growth promoters and immunostimulants have shown promising results. However, their effects on fish health and growth remain unclear. This study evaluated the in vitro and in vivo effects of Eucalyptus globulus pyroligneous extract (PE) on Nile tilapia. In vitro, minimal inhibitory and bactericidal concentration (MIC and MBC) and antibiogram analyses showed that PE could eliminate key bacterial strains affecting fish and human health, but only if its volatile components were preserved. In vivo, Oreochromis niloticus juveniles were fed diets containing 0.5% and 1% PE. We assessed fish hematology, phagocytosis, survival against Streptococcus agalactiae, and growth parameters. Fish fed 1% PE had lower erythrocyte and lymphocyte counts but higher neutrophil levels than controls. Their phagocytic capacity was significantly enhanced compared to both the control and 0.5% groups. However, the 0.5% PE group had a higher phagocytic index than both the control and 1% groups. No protection against S. agalactiae or significant effects on growth were observed. In conclusion, distilled E. globulus PE shows potential as an immunostimulant for fish. However, further studies are needed to preserve its volatile compounds and optimize its use in aquaculture. Full article
(This article belongs to the Special Issue Pathogenesis and Antibiotic Resistance Mechanisms of Fish Pathogens)
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23 pages, 5330 KiB  
Article
Explainable Reinforcement Learning for the Initial Design Optimization of Compressors Inspired by the Black-Winged Kite
by Mingming Zhang, Zhuang Miao, Xi Nan, Ning Ma and Ruoyang Liu
Biomimetics 2025, 10(8), 497; https://doi.org/10.3390/biomimetics10080497 - 29 Jul 2025
Viewed by 279
Abstract
Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach [...] Read more.
Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach that incorporates deep reinforcement learning and decision tree distillation to enhance both the optimization capability and explainability. First, a pre-selection platform for the initial design scheme of the compressors is constructed based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The optimization space is significantly enlarged by expanding the co-design of 25 key variables (e.g., the inlet airflow angle, the reaction, the load coefficient, etc.). Then, the initial design of six-stage axial compressors is successfully completed, with the axial efficiency increasing to 84.65% at the design speed and the surge margin extending to 10.75%. The design scheme is closer to the actual needs of engineering. Secondly, Shapley Additive Explanations (SHAP) analysis is utilized to reveal the influence of the mechanism of the key design parameters on the performance of the compressors in order to enhance the model explainability. Finally, the decision tree inspired by the black-winged kite (BKA) algorithm takes the interpretable design rules and transforms the data-driven intelligent optimization into explicit engineering experience. Through experimental validation, this method significantly improves the transparency of the design process while maintaining the high performance of the DDPG algorithm. The extracted design rules not only have clear physical meanings but also can effectively guide the initial design of the compressors, providing a new idea with both optimization capability and explainability for its intelligent design. Full article
(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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37 pages, 5345 KiB  
Article
Synthesis of Sources of Common Randomness Based on Keystream Generators with Shared Secret Keys
by Dejan Cizelj, Milan Milosavljević, Jelica Radomirović, Nikola Latinović, Tomislav Unkašević and Miljan Vučetić
Mathematics 2025, 13(15), 2443; https://doi.org/10.3390/math13152443 - 29 Jul 2025
Viewed by 122
Abstract
Secure autonomous secret key distillation (SKD) systems traditionally depend on external common randomness (CR) sources, which often suffer from instability and limited reliability over long-term operation. In this work, we propose a novel SKD architecture that synthesizes CR by combining a keystream of [...] Read more.
Secure autonomous secret key distillation (SKD) systems traditionally depend on external common randomness (CR) sources, which often suffer from instability and limited reliability over long-term operation. In this work, we propose a novel SKD architecture that synthesizes CR by combining a keystream of a shared-key keystream generator KSG(KG) with locally generated binary Bernoulli noise. This construction emulates the statistical properties of the classical Maurer satellite scenario while enabling deterministic control over key parameters such as bit error rate, entropy, and leakage rate (LR). We derive a closed-form lower bound on the equivocation of the shared-secret key  KG from the viewpoint of an adversary with access to public reconciliation data. This allows us to define an admissible operational region in which the system guarantees long-term secrecy through periodic key refreshes, without relying on advantage distillation. We integrate the Winnow protocol as the information reconciliation mechanism, optimized for short block lengths (N=8), and analyze its performance in terms of efficiency, LR, and final key disagreement rate (KDR). The proposed system operates in two modes: ideal secrecy, achieving secret key rates up to 22% under stringent constraints (KDR < 10−5, LR < 10−10), and perfect secrecy mode, which approximately halves the key rate. Notably, these security guarantees are achieved autonomously, without reliance on advantage distillation or external CR sources. Theoretical findings are further supported by experimental verification demonstrating the practical viability of the proposed system under realistic conditions. This study introduces, for the first time, an autonomous CR-based SKD system with provable security performance independent of communication channels or external randomness, thus enhancing the practical viability of secure key distribution schemes. Full article
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15 pages, 1638 KiB  
Article
MFEAM: Multi-View Feature Enhanced Attention Model for Image Captioning
by Yang Cui and Juan Zhang
Appl. Sci. 2025, 15(15), 8368; https://doi.org/10.3390/app15158368 - 28 Jul 2025
Viewed by 187
Abstract
Image captioning plays a crucial role in aligning visual content with natural language, serving as a key step toward effective cross-modal understanding. Transformer has become the dominant language model in image captioning. Existing Transformer-based models seldom highlight important features from multiple views in [...] Read more.
Image captioning plays a crucial role in aligning visual content with natural language, serving as a key step toward effective cross-modal understanding. Transformer has become the dominant language model in image captioning. Existing Transformer-based models seldom highlight important features from multiple views in the use of self-attention. In this paper, we propose MFEAM, an innovative network that leverages the multi-view feature enhanced attention. To accurately represent the entangled features of vision and text, the teacher model employs the multi-view feature enhanced attention to guide the student model training through knowledge distillation and model averaging from both visual and textual views. To mitigate the impact of excessive feature enhancement, the student model divides the decoding layer into two groups, which separately process instance features and the relationships between instances. Experimental results demonstrate that MFEAM attains competitive performance on the MSCOCO (Microsoft Common Objects in Context) when trained without leveraging external data. Full article
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12 pages, 2715 KiB  
Article
Room-Temperature Plasma Hydrogenation of Fatty Acid Methyl Esters (FAMEs)
by Benjamin Wang, Trevor Jehl, Hongtao Zhong and Mark Cappelli
Processes 2025, 13(8), 2333; https://doi.org/10.3390/pr13082333 - 23 Jul 2025
Viewed by 242
Abstract
The increasing demand for sustainable energy has spurred the exploration of advanced technologies for biodiesel production. This paper investigates the use of Dielectric Barrier Discharge (DBD)-generated low-temperature plasmas to enhance the conversion of fatty acid methyl esters (FAMEs) into hydrogenated fatty acid methyl [...] Read more.
The increasing demand for sustainable energy has spurred the exploration of advanced technologies for biodiesel production. This paper investigates the use of Dielectric Barrier Discharge (DBD)-generated low-temperature plasmas to enhance the conversion of fatty acid methyl esters (FAMEs) into hydrogenated fatty acid methyl esters (H-FAMEs) and other high-value hydrocarbons. A key mechanistic advance is achieved via in situ distillation: at the reactor temperature, unsaturated C18 and C20 FAMEs remain liquid due to their low melting points, while the corresponding saturated C18:0 and C20:0 FAMEs (with melting points of approximately 37–39 °C and 46–47 °C, respectively) solidify and deposit on a glass substrate. This phase separation continuously exposes fresh unsaturated FAME to the plasma, driving further hydrogenation and thereby delivering high overall conversion efficiency. The non-thermal, energy-efficient nature of DBD plasmas offers a promising alternative to conventional high-pressure, high-temperature methods; here, we evaluate the process efficiency, product selectivity, and scalability of this room-temperature, atmospheric-pressure approach and discuss its potential for sustainable fuel-reforming applications. Full article
(This article belongs to the Special Issue Plasma Science and Plasma-Assisted Applications)
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25 pages, 2029 KiB  
Article
Germination Enhances Phytochemical Profiles of Perilla Seeds and Promotes Hair Growth via 5α-Reductase Inhibition and Growth Factor Pathways
by Anurak Muangsanguan, Warintorn Ruksiriwanich, Pichchapa Linsaenkart, Pipat Tangjaidee, Korawan Sringarm, Chaiwat Arjin, Pornchai Rachtanapun, Sarana Rose Sommano, Korawit Chaisu, Apinya Satsook and Juan Manuel Castagnini
Biology 2025, 14(7), 889; https://doi.org/10.3390/biology14070889 - 20 Jul 2025
Viewed by 446
Abstract
Seed germination is recognized for enhancing the accumulation of bioactive compounds. Perilla frutescens (L.) Britt., commonly known as perilla seed, is rich in fatty acids that may be beneficial for anti-hair loss. This study investigated the hair regeneration potential of perilla seed extracts—non-germinated [...] Read more.
Seed germination is recognized for enhancing the accumulation of bioactive compounds. Perilla frutescens (L.) Britt., commonly known as perilla seed, is rich in fatty acids that may be beneficial for anti-hair loss. This study investigated the hair regeneration potential of perilla seed extracts—non-germinated (NG-PS) and germinated in distilled water (0 ppm selenium; G0-PS), and germinated with 80 ppm selenium (G80-PS)—obtained from supercritical fluid extraction (SFE) and screw compression (SC). SFE extracts exhibited significantly higher levels of polyphenols, tocopherols, and fatty acids compared to SC extracts. Among the germinated groups, G0-PS showed the highest bioactive compound content and antioxidant capacity. Remarkably, treatment with SFE-G0-PS led to a significant increase in the proliferation and migration of hair follicle cells, reaching 147.21 ± 2.11% (p < 0.05), and resulted in complete wound closure. In addition, its antioxidant and anti-inflammatory properties were reflected by a marked scavenging effect on TBARS (59.62 ± 0.66% of control) and suppressed nitrite amounts (0.44 ± 0.01 µM). Moreover, SFE-G0-PS markedly suppressed SRD5A1-3 gene expression—key regulators in androgenetic alopecia—in both DU-145 and HFDPCs, with approximately 2-fold and 1.5-fold greater inhibition compared to finasteride and minoxidil, respectively. Simultaneously, it upregulated the expression of hair growth-related genes, including CTNNB1, SHH, SMO, GLI1, and VEGF, by approximately 1.5-fold, demonstrating stronger activation than minoxidil. These findings suggest the potential of SFE-G0-PS as a natural therapeutic agent for promoting hair growth and preventing hair loss. Full article
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15 pages, 1745 KiB  
Article
A Study on the Performance of Vacuum Membrane Distillation in Treating Acidic, Simulated, Low-Level Radioactive Liquid Waste
by Sifan Chen, Yan Xu, Yuyong Wu, Yizhou Lu, Zhan Weng, Yaoguang Tao, Jianghai Liu and Baihua Jiang
Membranes 2025, 15(7), 213; https://doi.org/10.3390/membranes15070213 - 18 Jul 2025
Viewed by 369
Abstract
This study systematically explored the performance of a vacuum membrane distillation (VMD) system equipped with polytetrafluoroethylene (PTFE) hollow fiber membranes for treating simulated, acidic, low-level radioactive liquid waste. By focusing on key operational parameters, including feed temperature, vacuum pressure, and flow velocity, an [...] Read more.
This study systematically explored the performance of a vacuum membrane distillation (VMD) system equipped with polytetrafluoroethylene (PTFE) hollow fiber membranes for treating simulated, acidic, low-level radioactive liquid waste. By focusing on key operational parameters, including feed temperature, vacuum pressure, and flow velocity, an orthogonal experiment was designed to obtain the optimal parameters. Considering the potential application scenarios, the following two factors were also studied: the initial nuclide concentrations (0.5, 5, and 50 mg·L−1) and tributyl phosphate (TBP) concentrations (0, 20, and 100 mg·L−1) in the feed solution. The results indicated that the optimal operational parameters for VMD were as follows: a feed temperature of 70 °C, a vacuum pressure of 90 kPa, and a flow rate of 500 L·h−1. Under these parameters, the VMD system demonstrated a maximum permeate flux of 0.9 L·m−2·h−1, achieving a nuclide rejection rate exceeding 99.9%, as well as a nitric acid rejection rate of 99.4%. A significant negative correlation was observed between permeate flux and nuclide concentrations at levels above 50 mg·L−1. The presence of TBP in the feed solution produced membrane fouling, leading to flux decline and a reduced separation efficiency, with severity increasing with TBP concentration. The VMD process simultaneously achieved nuclide rejection and nitric acid concentration in acidic radioactive wastewater, demonstrating strong potential for nuclear wastewater treatment. Full article
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30 pages, 2023 KiB  
Review
Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
by Yuval Cohen, Amir Biton and Shraga Shoval
Appl. Sci. 2025, 15(14), 7905; https://doi.org/10.3390/app15147905 - 15 Jul 2025
Viewed by 558
Abstract
The integration of advanced computer vision and artificial intelligence (AI) techniques into collaborative robotic systems holds the potential to revolutionize human–robot interaction, productivity, and safety. Despite substantial research activity, a systematic synthesis of how vision and AI are jointly enabling context-aware, adaptive cobot [...] Read more.
The integration of advanced computer vision and artificial intelligence (AI) techniques into collaborative robotic systems holds the potential to revolutionize human–robot interaction, productivity, and safety. Despite substantial research activity, a systematic synthesis of how vision and AI are jointly enabling context-aware, adaptive cobot capabilities across perception, planning, and decision-making remains lacking (especially in recent years). Addressing this gap, our review unifies the latest advances in visual recognition, deep learning, and semantic mapping within a structured taxonomy tailored to collaborative robotics. We examine foundational technologies such as object detection, human pose estimation, and environmental modeling, as well as emerging trends including multimodal sensor fusion, explainable AI, and ethically guided autonomy. Unlike prior surveys that focus narrowly on either vision or AI, this review uniquely analyzes their integrated use for real-world human–robot collaboration. Highlighting industrial and service applications, we distill the best practices, identify critical challenges, and present key performance metrics to guide future research. We conclude by proposing strategic directions—from scalable training methods to interoperability standards—to foster safe, robust, and proactive human–robot partnerships in the years ahead. Full article
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21 pages, 3187 KiB  
Article
Green Extract from Pre-Harvest Tobacco Waste as a Non-Conventional Source of Anti-Aging Ingredients for Cosmetic Applications
by Mariana Leal, María A. Moreno, María E. Orqueda, Mario Simirgiotis, María I. Isla and Iris C. Zampini
Plants 2025, 14(14), 2189; https://doi.org/10.3390/plants14142189 - 15 Jul 2025
Viewed by 442
Abstract
The cigarette production from Nicotiana tabacum generates significant amounts of waste, with an estimated 68.31 million tons of pre- and post-harvest waste discarded annually. The pre-harvest waste includes the upper parts of the plant, inflorescences, and bracts, which are removed to help the [...] Read more.
The cigarette production from Nicotiana tabacum generates significant amounts of waste, with an estimated 68.31 million tons of pre- and post-harvest waste discarded annually. The pre-harvest waste includes the upper parts of the plant, inflorescences, and bracts, which are removed to help the growth of the lower leaves. This study explores the potential of apical leaves from Nicotiana tabacum var. Virginia, discarded during the budding stage (pre-harvest waste). The leaves were extracted using environmentally friendly solvents (green solvents), including distilled water (DW) and two natural deep eutectic solvents (NaDESs), one consisting of simple sugars, fructose, glucose, and sucrose (FGS) and the other consisting of choline chloride and urea (CU). The anti-inflammatory and anti-aging potential of these green extracts was assessed by the inhibition of key enzymes related to skin aging. The xanthine oxidase and lipoxygenase activities were mostly inhibited by CU extracts with IC50 values of 63.50 and 8.0 μg GAE/mL, respectively. The FGS extract exhibited the greatest hyaluronidase inhibition (49.20%), followed by the CU extract (33.20%) and the DW extract (20.80%). Regarding elastase and collagenase inhibition, the CU extract exhibited the highest elastase inhibition, while all extracts inhibited collagenase activity, with values exceeding 65%. Each extract had a distinct chemical profile determined by LC-ESI-QTOF-MS/MS and spectrophotometric methods, with several shared compounds present in different proportions. CU extract is characterized by high concentrations of rutin, nicotiflorin, and azelaic acid, while FGS and DW extracts share major compounds such as quinic acid, fructosyl pyroglutamate, malic acid, and gluconic acid. Ames test and Caenorhabditis elegans assay demonstrated that at the concentrations at which the green tobacco extracts exhibit biological activities, they did not show toxicity. The results support the potential of N. tabacum extracts obtained with NaDESs as antiaging and suggest their promising applications in the cosmetic and cosmeceutical industries. Full article
(This article belongs to the Section Phytochemistry)
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27 pages, 3121 KiB  
Review
A Critical Review of Membrane Distillation Using Ceramic Membranes: Advances, Opportunities and Challenges
by Francesca Alessandro and Francesca Macedonio
Materials 2025, 18(14), 3296; https://doi.org/10.3390/ma18143296 - 12 Jul 2025
Viewed by 656
Abstract
Membrane distillation (MD) has attracted increasing attention as a thermally driven separation process for water purification, desalination, and wastewater treatment. Its primary advantages include high rejection of non-volatile solutes, compatibility with low-grade or waste heat sources, and operation at ambient pressure. Despite these [...] Read more.
Membrane distillation (MD) has attracted increasing attention as a thermally driven separation process for water purification, desalination, and wastewater treatment. Its primary advantages include high rejection of non-volatile solutes, compatibility with low-grade or waste heat sources, and operation at ambient pressure. Despite these benefits, large-scale implementation remains limited due to the lack of membrane materials capable of withstanding harsh operating conditions and maintaining their hydrophobic character. Polymeric membranes have traditionally been used in MD applications; however, their limited thermal and chemical stability compromises long-term performance and reliability. In contrast, ceramic membranes are emerging as a promising alternative, offering superior mechanical strength, chemical resistance, and thermal stability. Nevertheless, their broader adoption in MD is hindered by several challenges, including high thermal conductivity, surface wettability, high fabrication costs, and limited scalability. This review provides a critical assessment of current developments, key opportunities, and ongoing challenges associated with the use of ceramic membranes in MD. Particular emphasis is placed on advances in surface modification techniques and the emerging applications in advanced MD configurations. Full article
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25 pages, 1669 KiB  
Article
Zero-Shot Infrared Domain Adaptation for Pedestrian Re-Identification via Deep Learning
by Xu Zhang, Yinghui Liu, Liangchen Guo and Huadong Sun
Electronics 2025, 14(14), 2784; https://doi.org/10.3390/electronics14142784 - 10 Jul 2025
Viewed by 251
Abstract
In computer vision, the performance of detectors trained under optimal lighting conditions is significantly impaired when applied to infrared domains due to the scarcity of labeled infrared target domain data and the inherent degradation in infrared image quality. Progress in cross-domain pedestrian re-identification [...] Read more.
In computer vision, the performance of detectors trained under optimal lighting conditions is significantly impaired when applied to infrared domains due to the scarcity of labeled infrared target domain data and the inherent degradation in infrared image quality. Progress in cross-domain pedestrian re-identification is hindered by the lack of labeled infrared image data. To address the degradation of pedestrian recognition in infrared environments, we propose a framework for zero-shot infrared domain adaptation. This integrated approach is designed to mitigate the challenges of pedestrian recognition in infrared domains while enabling zero-shot domain adaptation. Specifically, an advanced reflectance representation learning module and an exchange–re-decomposition–coherence process are employed to learn illumination invariance and to enhance the model’s effectiveness, respectively. Additionally, the CLIP (Contrastive Language–Image Pretraining) image encoder and DINO (Distillation with No Labels) are fused for feature extraction, improving model performance under infrared conditions and enhancing its generalization capability. To further improve model performance, we introduce the Non-Local Attention (NLA) module, the Instance-based Weighted Part Attention (IWPA) module, and the Multi-head Self-Attention module. The NLA module captures global feature dependencies, particularly long-range feature relationships, effectively mitigating issues such as blurred or missing image information in feature degradation scenarios. The IWPA module focuses on localized regions to enhance model accuracy in complex backgrounds and unevenly lit scenes. Meanwhile, the Multi-head Self-Attention module captures long-range dependencies between cross-modal features, further strengthening environmental understanding and scene modeling. The key innovation of this work lies in the skillful combination and application of existing technologies to new domains, overcoming the challenges posed by vision in infrared environments. Experimental results on the SYSU-MM01 dataset show that, under the single-shot setting, Rank-1 Accuracy (Rank-1) andmean Average Precision (mAP) values of 37.97% and 37.25%, respectively, were achieved, while in the multi-shot setting, values of 34.96% and 34.14% were attained. Full article
(This article belongs to the Special Issue Deep Learning in Image Processing and Computer Vision)
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20 pages, 3251 KiB  
Review
Chemical Functionalization of Camelina, Hemp, and Rapeseed Oils for Sustainable Resin Applications: Strategies for Tailoring Structure and Performance
by Elham Nadim, Pavan Paraskar, Emma J. Murphy, Mohammadnabi Hesabi and Ian Major
Compounds 2025, 5(3), 26; https://doi.org/10.3390/compounds5030026 - 10 Jul 2025
Viewed by 287
Abstract
This review examines the chemical functionalization of Camelina, hemp, and rapeseed oils for the development of sustainable bio-based resins. Key strategies, including epoxidation, acrylation, and click chemistry, are discussed in the context of tailoring molecular structure to enhance reactivity, compatibility, and material performance. [...] Read more.
This review examines the chemical functionalization of Camelina, hemp, and rapeseed oils for the development of sustainable bio-based resins. Key strategies, including epoxidation, acrylation, and click chemistry, are discussed in the context of tailoring molecular structure to enhance reactivity, compatibility, and material performance. Particular emphasis is placed on overcoming the inherent limitations of vegetable oil structures to enable their integration into high-performance polymer systems. The agricultural sustainability and environmental advantages of these feedstocks are also highlighted alongside the technical challenges associated with their chemical modification. Functionalized oils derived from Camelina, hemp, and rapeseed have been successfully applied in various resin systems, including protective coatings, pressure-sensitive adhesives, UV-curable oligomers, and polyurethane foams. These advances demonstrate their growing potential as renewable alternatives to petroleum-based polymers and underline the critical role of structure–property relationships in designing next-generation sustainable materials. Ultimately, the objective of this review is to distill the most effective functionalization pathways and design principles, thereby illustrating how Camelina, hemp, and rapeseed oils could serve as viable substitutes for petrochemical resins in future industrial applications. Full article
(This article belongs to the Special Issue Compounds–Derived from Nature)
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43 pages, 5558 KiB  
Review
A Comprehensive Review of Permeate Gap Membrane Distillation: Modelling, Experiments, Applications
by Eliza Rupakheti, Ravi Koirala, Sara Vahaji, Shruti Nirantar and Abhijit Date
Sustainability 2025, 17(14), 6294; https://doi.org/10.3390/su17146294 - 9 Jul 2025
Viewed by 403
Abstract
Permeate Gap Membrane Distillation (PGMD) is an emerging desalination technology that offers a promising alternative for freshwater production, particularly in energy-efficient and sustainable applications. This review provides a comprehensive analysis of PGMD, covering its fundamental principles, heat and mass transfer mechanisms, and key [...] Read more.
Permeate Gap Membrane Distillation (PGMD) is an emerging desalination technology that offers a promising alternative for freshwater production, particularly in energy-efficient and sustainable applications. This review provides a comprehensive analysis of PGMD, covering its fundamental principles, heat and mass transfer mechanisms, and key challenges such as temperature and concentration polarization. Various optimisation strategies, including Response Surface Morphology (RSM), Differential Evolution techniques, and Computational Fluid Dynamics (CFD) modelling, are explored to enhance PGMD performance. The study further discusses the latest advancements in system design, highlighting optimal configurations and the integration of PGMD with renewable energy sources. Factors influencing PGMD performance, such as operational parameters (flow rates, temperature, and feed concentration) and physical parameters (gap width, membrane properties, and cooling plate conductivity), are systematically analysed. Additionally, the techno-economic feasibility of PGMD for large-scale freshwater production is evaluated, with a focus on cost reduction strategies, energy efficiency, and hybrid system innovations. Finally, this review outlines the current limitations and future research directions for PGMD, emphasising novel system modifications, improved heat recovery techniques, and potential industrial applications. By consolidating recent advancements and identifying key challenges, this paper aims to guide future research and facilitate the broader adoption of PGMD in sustainable desalination and water purification processes. Full article
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19 pages, 9844 KiB  
Article
DSMBAD: Dual-Stream Memory Bank Framework for Unified Industrial Anomaly Detection
by Hongmin Hu, Xiaodong Wang, Jiangtao Fan, Zhiqiang Zeng, Junwen Lu, Otis Hong and Jihuang Zhang
Electronics 2025, 14(14), 2748; https://doi.org/10.3390/electronics14142748 - 8 Jul 2025
Viewed by 360
Abstract
Industrial image anomaly detection requires the simultaneous identification of local structural and global logical anomalies. Existing methods specialize in single-type anomalies due to divergent feature requirements: structural anomalies demand fine-grained local features, while logical anomalies need semantic features. Consequently, designing a unified network [...] Read more.
Industrial image anomaly detection requires the simultaneous identification of local structural and global logical anomalies. Existing methods specialize in single-type anomalies due to divergent feature requirements: structural anomalies demand fine-grained local features, while logical anomalies need semantic features. Consequently, designing a unified network architecture that effectively captures both features without task conflicts remains a key challenge. To address this problem, we propose a Dual-Stream Memory Bank Anomaly Detection (DSMBAD) framework, which enables the collaborative detection of both structural and logical anomalies from complementary perspectives. The framework consists of two memory banks: one stores patch features for detecting structural anomalies through local feature discrepancies, while the other uses segmentation maps to model component relationships for logical anomaly identification. Additionally, a feature distillation mechanism aligns features from different backbone networks to enhance global semantic information. We also introduce a shape-based anomaly scoring method that quantifies differences in component relationships using spatial–morphological features. Experimental results on the MVTec LOCO AD dataset show that our method achieves 91.0% I-AUROC (logical) and 90.8% (structural), significantly outperforming single-type models. Ablation studies confirm the dual-stream design and module effectiveness, offering a novel unified solution. Full article
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