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Quantum machine learning (QML) has emerged as a promising approach for enhancing image classification by exploiting quantum computational principles such as superposition and entanglement. However, practical applications on complex datasets like CIFAR-100 remain limited due to the low expressivity of shallow circuits and challenges in circuit optimization. In this study, we propose HQCNN–REGA—a novel hybrid quantum–classical convolutional neural network architecture that integrates data re-uploading and genetic algorithm optimization for improved performance. The data re-uploading mechanism allows classical inputs to be encoded multiple times into quantum states, enhancing the model’s capacity to learn complex visual features. In parallel, a genetic algorithm is employed to evolve the quantum circuit architecture by optimizing gate sequences, entanglement patterns, and layer configurations. This combination enables automatic discovery of efficient parameterized quantum circuits without manual tuning. Experiments on the MNIST and CIFAR-100 datasets demonstrate state-of-the-art performance for quantum models, with HQCNN–REGA outperforming existing quantum neural networks and approaching the accuracy of advanced classical architectures. In particular, we compare our model with classical convolutional baselines such as ResNet-18 to validate its effectiveness in real-world image classification tasks. Our results demonstrate the feasibility of scalable, high-performing quantum–classical systems and offer a viable path toward practical deployment of QML in computer vision applications, especially on noisy intermediate-scale quantum (NISQ) hardware. Full article

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22 pages, 4300 KiB

Open AccessArticle

Optimised DNN-Based Agricultural Land Cover Mapping Using Sentinel-2 and Landsat-8 with Google Earth Engine

by Nisha Sharma, Sartajvir Singh and Kawaljit Kaur

Land 2025, 14(8), 1578; https://doi.org/10.3390/land14081578 (registering DOI) - 1 Aug 2025

Abstract

Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning. Full article

(This article belongs to the Special Issue Advances on Land Cover/Land Use Ontologies for Innovative Production/Utilization of Land Information)

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23 pages, 3817 KiB

Open AccessArticle

Experimental and Numerical Study on the Restitution Coefficient and the Corresponding Elastic Collision Recovery Mechanism of Rapeseed

by Chuandong Liu, Haoping Zhang, Zebao Li, Zhiheng Zeng, Xuefeng Zhang, Lian Gong and Bin Li

Agronomy 2025, 15(8), 1872; https://doi.org/10.3390/agronomy15081872 (registering DOI) - 1 Aug 2025

Abstract

In this study, we aimed to address the lack of systematic research on key collision dynamics parameters (elastic restitution coefficient) in the full mechanization of rapeseed operations, which hinders the development of precision agriculture. In this present work, the restitution coefficient of rapeseed was systematically investigated, and a predictive model (R² = 0.959) was also established by using Box–Behnken design response surface methodology (BBD-RSM). The results show that the collision restitution coefficient varies in the range of 0.539–0.649, with the key influencing factors ranked as follows: moisture content (M_c) > material layer thickness (L) > drop height (H). The EDEM simulation methodology was adopted to validate the experimental results, and the results show that there is a minimal relative error (−1% < δ < 1%) between the measured and simulated rebound heights, indicating that the established model shows a reliable prediction performance. Moreover, by comprehensively analyzing stress, strain, and energy during the collision process between rapeseed and Q235 steel, it can be concluded that the process can be divided into five stages—free fall, collision compression, collision recovery, rebound oscillation, and rebound stabilization. The maximum stress (1.19 × 10⁻² MPa) and strain (6.43 × 10⁻⁶ mm) were observed at the beginning of the collision recovery stage, which can provide some theoretical and practical basis for optimizing and designing rapeseed machines, thus achieving the goals of precise control, harvest loss reduction, and increased yields. Full article

(This article belongs to the Section Precision and Digital Agriculture)

12 pages, 1862 KiB

Open AccessArticle

Effect of Si Addition on Microstructure and Mechanical Properties of SiC Ceramic Fabricated by Direct LPBF with CVI Technology

by Yipu Wang, Pei Wang, Liqun Li, Jian Zhang, Yulei Zhang, Jin Peng, Xingxing Wang, Nan Kang, Mohamed El Mansori and Konda Gokuldoss Prashanth

Appl. Sci. 2025, 15(15), 8585; https://doi.org/10.3390/app15158585 (registering DOI) - 1 Aug 2025

Abstract

In this paper, SiC and Si/SiC ceramics were fabricated using direct laser powder bed fusion with chemical vapor infiltration. Their microstructure, mechanical properties and the impacts of silicon addition were analyzed. The incorporation of silicon led to an increase in the relative density of the silicon carbide ceramics from 76.4% to 78.3% and the compression strength increased from 39 ± 13 MPa to 90 ± 8 MPa after laser powder bed fusion with chemical vapor infiltration. The melting and re-solidification of silicon allows the silicon to encapsulate the silicon carbide grains, changing the microstructure and the failure mechanism of the silicon carbide ceramics, resulting in a small amount of silicon residue. In the LPBF-CVI SiC ceramic specimen, the LPBF-formed SiC exhibits a microhardness of 24.2 ± 1.0 GPa. In LPBF-CVI Si/SiC, the spherical dual-phase structure displays a moderately increased hardness (25.9 ± 4.4 GPa), and the CVI-formed SiC exhibits a hardness of 55.3 ± 9.3 GPa. Full article

(This article belongs to the Special Issue Advances in High-Strength Materials Processing: Machining, Surface Integrity Management, and Operational Properties)

23 pages, 1746 KiB

Open AccessReview

Advanced Modification Strategies of Plant-Sourced Dietary Fibers and Their Applications in Functional Foods

by Yansheng Zhao, Ying Shao, Songtao Fan, Juan Bai, Lin Zhu, Ying Zhu and Xiang Xiao

Foods 2025, 14(15), 2710; https://doi.org/10.3390/foods14152710 (registering DOI) - 1 Aug 2025

Abstract

Plant-sourced Dietary Fibers (PDFs) have garnered significant attention due to their multifaceted health benefits, particularly in glycemic control, lipid metabolism regulation, and gut microbiota modulation. This review systematically investigates advanced modification strategies, including physical, chemical, bioengineering, and hybrid approaches, to improve the physicochemical properties and bioactivity of PDFs from legumes, cereals, and other sources. Key modifications such as steam explosion, enzymatic hydrolysis, and carboxymethylation significantly improve solubility, porosity, and functional group exposure, thereby optimizing the health-promoting effects of legume-sourced dietary fiber. The review further elucidates critical structure–function relationships, highlighting PDF’s prebiotic potential, synergistic interactions with polyphenols and proteins, and responsive designs for targeted nutrient delivery. In functional food applications, cereal-sourced dietary fibers serve as a versatile functional ingredient in engineered foods including 3D-printed gels and low-glycemic energy bars, addressing specific metabolic disorders and personalized dietary requirements. By integrating state-of-the-art modification techniques with innovative applications, this review provides comprehensive insights into PDF’s transformative role in advancing functional foods and personalized nutrition solutions. Full article

(This article belongs to the Special Issue Innovative Cereal and Legume-Based Food: Functional and Nutritional Benefits)

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29 pages, 12910 KiB

Open AccessArticle

Co-Creation, Co-Construction, and Co-Governance in Community Renewal: A Case Study of Civic Participation and Sustainable Mechanisms

by Yitong Shen, Ran Tan and Suhui Zhang

Land 2025, 14(8), 1577; https://doi.org/10.3390/land14081577 (registering DOI) - 1 Aug 2025

Abstract

This study focuses on Shanghai, a pioneer city in China’s community renewal practices. In recent years, community renewal driven by civic participation has become a prominent research topic, leading to the emergence of numerous exemplary cases in Shanghai. However, field investigations revealed that many projects have experienced varying degrees of physical deterioration and a decline in spatial vitality due to insufficient maintenance, reflecting unsustainable outcomes. In response, this study examines a bottom-up community renewal project led by the research team, aiming to explore how broad civic participation can promote sustainable community renewal. A multidisciplinary approach incorporating perspectives from ecology, the humanities, economics, and sociology was used to guide citizen participation, while participatory observation methods recorded emotional shifts and maintenance behavior throughout the process. The results showed that civic participatory actions under the guidance of sustainability principles effectively enhanced citizens’ sense of community identity and responsibility, thereby facilitating the sustainable upkeep and operation of community spaces. However, the study also found that bottom-up efforts alone are insufficient. Sustainable community renewal also requires top-down policy support and institutional safeguards. At the end, the paper concludes by summarizing the practical outcomes and proposing strategies and mechanisms for broader application, aiming to provide a reference for related practices and research. Full article

(This article belongs to the Special Issue Planning for Sustainable Urban and Land Development, Second Edition)

44 pages, 4144 KiB

Open AccessArticle

Amelioration of Olive Tree Indices Related to Salinity Stress via Exogenous Administration of Amino Acid Content: Real Agronomic Effectiveness or Mechanistic Restoration Only?

by Helen Kalorizou, Paschalis Giannoulis, Stefanos Leontopoulos, Georgios Koubouris, Spyridoula Chavalina and Maria Sorovigka

Horticulturae 2025, 11(8), 890; https://doi.org/10.3390/horticulturae11080890 (registering DOI) - 1 Aug 2025

Abstract

Salinization of olive orchards constitutes a front-line agronomic challenge for farmers, consumers, and the scientific community as food security, olive logistics, and land use become more unsustainable and problematic. Plantlets of two olive varieties (var. Kalamon and var. Koroneiki) were tested for their performance under soil saline conditions, in which L-methionine, choline-Cl, and L-proline betaine were applied foliarly to alleviate adverse effects. The ‘Kalamon’ variety ameliorated its photosynthetic rates when L-proline betaine and L-methionine were administered at low saline exposure. The stressed varieties achieved higher leaf transpiration rates in the following treatment order: choline-Cl > L-methionine > L-proline betaine. Choline chloride supported stomatal conductance in stressed var. Kalamon olives without this pattern, which was also followed by var. Koroneiki. Supplementation regimes created a mosaic of responses on varietal water use efficiency under stress. The total phenolic content in leaves increased in both varieties after exogenous application only at the highest levels of saline stress. None of the substances applied to olive trees could stand alone as a tool to mitigate salinity stress in order to be recommended as a solid agronomic practice. The residual exploitation of amino acids by the olive orchard microbiome must also be considered as part of an environmentally friendly, integrated strategy to mitigate salinity stress. Full article

(This article belongs to the Special Issue Olive Stress Alleviation Strategies)

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22 pages, 2988 KiB

Open AccessArticle

Enhanced Cuckoo Search Optimization with Opposition-Based Learning for the Optimal Placement of Sensor Nodes and Enhanced Network Coverage in Wireless Sensor Networks

by Mandli Rami Reddy, M. L. Ravi Chandra and Ravilla Dilli

Appl. Sci. 2025, 15(15), 8575; https://doi.org/10.3390/app15158575 (registering DOI) - 1 Aug 2025

Abstract

Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m²area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works. Full article

25 pages, 2859 KiB

Open AccessArticle

Feature-Based Normality Models for Anomaly Detection

by Hui Yie Teh, Kevin I-Kai Wang and Andreas W. Kempa-Liehr

Sensors 2025, 25(15), 4757; https://doi.org/10.3390/s25154757 (registering DOI) - 1 Aug 2025

Abstract

Detecting previously unseen anomalies in sensor data is a challenging problem for artificial intelligence when sensor-specific and deployment-specific characteristics of the time series need to be learned from a short calibration period. From the application point of view, this challenge becomes increasingly important because many applications are gravitating towards utilising low-cost sensors for Internet of Things deployments. While these sensors offer cost-effectiveness and customisation, their data quality does not match that of their high-end counterparts. To improve sensor data quality while addressing the challenges of anomaly detection in Internet of Things applications, we present an anomaly detection framework that learns a normality model of sensor data. The framework models the typical behaviour of individual sensors, which is crucial for the reliable detection of sensor data anomalies, especially when dealing with sensors observing significantly different signal characteristics. Our framework learns sensor-specific normality models from a small set of anomaly-free training data while employing an unsupervised feature engineering approach to select statistically significant features. The selected features are subsequently used to train a Local Outlier Factor anomaly detection model, which adaptively determines the boundary separating normal data from anomalies. The proposed anomaly detection framework is evaluated on three real-world public environmental monitoring datasets with heterogeneous sensor readings. The sensor-specific normality models are learned from extremely short calibration periods (as short as the first 3 days or 10% of the total recorded data) and outperform four other state-of-the-art anomaly detection approaches with respect to F1-score (between 5.4% and 9.3% better) and Matthews correlation coefficient (between 4.0% and 7.6% better). Full article

(This article belongs to the Special Issue Innovative Approaches to Cybersecurity for IoT and Wireless Networks)

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29 pages, 6541 KiB

Open AccessArticle

Lacticaseibacillus paracasei L21 and Its Postbiotics Ameliorate Ulcerative Colitis Through Gut Microbiota Modulation, Intestinal Barrier Restoration, and HIF1α/AhR-IL-22 Axis Activation: Combined In Vitro and In Vivo Evidence

by Jingru Chen, Linfang Zhang, Yuehua Jiao, Xuan Lu, Ning Zhang, Xinyi Li, Suo Zheng, Bailiang Li, Fei Liu and Peng Zuo

Nutrients 2025, 17(15), 2537; https://doi.org/10.3390/nu17152537 (registering DOI) - 1 Aug 2025

Abstract

Background: Ulcerative colitis (UC), characterized by chronic intestinal inflammation, epithelial barrier dysfunction, and immune imbalance demands novel ameliorative strategies beyond conventional approaches. Methods: In this study, the probiotic properties of Lactobacillus paracasei L21 (L. paracasei L21) and its ability to ameliorate colitis were evaluated using an in vitro lipopolysaccharide (LPS)-induced intestinal crypt epithelial cell (IEC-6) model and an in vivo dextran sulfate sodium (DSS)-induced UC mouse model. Results: In vitro, L. paracasei L21 decreased levels of pro-inflammatory cytokines (TNF-α, IL-1β, IL-8) while increasing anti-inflammatory IL-10 levels (p < 0.05) in LPS-induced IEC-6 cells, significantly enhancing the expression of tight junction proteins (ZO-1, occludin, claudin-1), thereby restoring the intestinal barrier. In vivo, both viable L. paracasei L21 and its heat-inactivated postbiotic (H-L21) mitigated weight loss, colon shortening, and disease activity indices, concurrently reducing serum LPS and proinflammatory mediators. Interventions inhibited NF-κB signaling while activating HIF1α/AhR pathways, increasing IL-22 and mucin MUC2 to restore goblet cell populations. Gut microbiota analysis showed that both interventions increased the abundance of beneficial gut bacteria (Lactobacillus, Dubococcus, and Akkermansia) and improved faecal propanoic acid and butyric acid levels. H-L21 uniquely exerted an anti-inflammatory effect, marked by the regulation of Dubosiella, while L. paracasei L21 marked by the Akkermansia. Conclusions: These results highlight the potential of L. paracasei L21 as a candidate for the development of both probiotic and postbiotic formulations. It is expected to provide a theoretical basis for the management of UC and to drive the development of the next generation of UC therapies. Full article

(This article belongs to the Special Issue Probiotics, Postbiotics, Gut Microbiota and Gastrointestinal Health)

15 pages, 5630 KiB

Open AccessArticle

Toxic Effects of Vanillic Acid and Sinapic Acid on Spodoptera frugiperda

by Ya-Nan Deng, Jin-Yan Lv, Xiao-Rong Liu, Dan Niu, Ling-Xin Xu and Jun-Xin Yan

Biology 2025, 14(8), 979; https://doi.org/10.3390/biology14080979 (registering DOI) - 1 Aug 2025

Abstract

The tolerance of the fall armyworm (Spodoptera frugiperda) to plant-derived secondary compounds gradually increases with instars. Therefore, even if plant-based additives are applied at early stages, such as the second or third instar, they may have a differential impact on the ecofriendly control of S. frugiperda. In this study, S. frugiperda larvae were exposed to vanillic acid or sinapic acid at the second and third instar, and physiological and growth parameters were measured. The results showed that the effects of vanillic acid treatment on S. frugiperda were similar at the different instars. They can significantly affect the larval carboxylesterase, glutathione S-transferase, and mixed-function oxidase activities. By reducing larval food intake, food conversion, and utilization efficiency while increasing the food consumption rate, it inhibits weight accumulation. This leads to a significant extension of the development of both the larval and pupal stages, and the adult longevity was reduced. Treatment with sinapic acid at the second instar extended the negative effects on the pupal duration of S. frugiperda when compared to treatment at the third instar, but did not affect adult longevity. Therefore, vanillic acid treatment at the second or third instar stage, can play an important role in the ecofriendly control process of S. frugiperda. The results of this study are of great significance for integrated pest management. Full article

(This article belongs to the Section Toxicology)

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24 pages, 649 KiB

Open AccessReview

Desmosomal Versus Non-Desmosomal Arrhythmogenic Cardiomyopathies: A State-of-the-Art Review

by Kristian Galanti, Lorena Iezzi, Maria Luana Rizzuto, Daniele Falco, Giada Negri, Hoang Nhat Pham, Davide Mansour, Roberta Giansante, Liborio Stuppia, Lorenzo Mazzocchetti, Sabina Gallina, Cesare Mantini, Mohammed Y. Khanji, C. Anwar A. Chahal and Fabrizio Ricci

Cardiogenetics 2025, 15(3), 22; https://doi.org/10.3390/cardiogenetics15030022 (registering DOI) - 1 Aug 2025

Abstract

Arrhythmogenic cardiomyopathies (ACMs) are a phenotypically and etiologically heterogeneous group of myocardial disorders characterized by fibrotic or fibro-fatty replacement of ventricular myocardium, electrical instability, and an elevated risk of sudden cardiac death. Initially identified as a right ventricular disease, ACMs are now recognized to include biventricular and left-dominant forms. Genetic causes account for a substantial proportion of cases and include desmosomal variants, non-desmosomal variants, and familial gene-elusive forms with no identifiable pathogenic mutation. Nongenetic etiologies, including post-inflammatory, autoimmune, and infiltrative mechanisms, may mimic the phenotype. In many patients, the disease remains idiopathic despite comprehensive evaluation. Cardiac magnetic resonance imaging has emerged as a key tool for identifying non-ischemic scar patterns and for distinguishing arrhythmogenic phenotypes from other cardiomyopathies. Emerging classifications propose the unifying concept of scarring cardiomyopathies based on shared structural substrates, although global consensus is evolving. Risk stratification remains challenging, particularly in patients without overt systolic dysfunction or identifiable genetic markers. Advances in tissue phenotyping, multi-omics, and artificial intelligence hold promise for improved prognostic assessment and individualized therapy. Full article

(This article belongs to the Section Cardiovascular Genetics in Clinical Practice)

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27 pages, 4582 KiB

Open AccessArticle

Palazzo Farnese and Dong’s Fortified Compound: An Art-Anthropological Cross-Cultural Analysis of Architectural Form, Symbolic Ornamentation, and Public Perception

by Liyue Wu, Qinchuan Zhan, Yanjun Li and Chen Chen

Buildings 2025, 15(15), 2720; https://doi.org/10.3390/buildings15152720 (registering DOI) - 1 Aug 2025

Abstract

This study presents a cross-cultural comparison of two fortified residences—Palazzo Farnese in Italy and Dong’s Fortified Compound in China—through a triadic analytical framework encompassing architectural form, symbolic ornamentation, and public perception. By combining field observation, iconographic interpretation, and digital ethnography, the research investigates how heritage meaning is constructed, encoded, and reinterpreted across distinct sociocultural contexts. Empirical materials include architectural documentation, decorative analysis, and a curated dataset of 4947 user-generated images and 1467 textual comments collected from Chinese and international platforms between 2020 and 2024. Methods such as CLIP-based visual clustering and BERTopic-enabled sentiment modelling were applied to extract patterns of perception and symbolic emphasis. The findings reveal contrasting representational logics: Palazzo Farnese encodes dynastic authority and Renaissance cosmology through geometric order and immersive frescoes, while Dong’s Compound conveys Confucian ethics and frontier identity via nested courtyards and traditional ornamentation. Digital responses diverge accordingly: international users highlight formal aesthetics and photogenic elements; Chinese users engage with symbolic motifs, family memory, and ritual significance. This study illustrates how historically fortified residences are reinterpreted through culturally specific digital practices, offering an interdisciplinary approach that bridges architectural history, symbolic analysis, and digital heritage studies. Full article

(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

33 pages, 4366 KiB

Open AccessReview

Progress and Prospects of Biomolecular Materials in Solar Photovoltaic Applications

by Anna Fricano, Filippo Tavormina, Bruno Pignataro, Valeria Vetri and Vittorio Ferrara

Molecules 2025, 30(15), 3236; https://doi.org/10.3390/molecules30153236 (registering DOI) - 1 Aug 2025

Abstract

This Review examines up-to-date advancements in the integration of biomolecules and solar energy technologies, with a particular focus on biohybrid photovoltaic systems. Biomolecules have recently garnered increasing interest as functional components in a wide range of solar cell architectures, since they offer a huge variety of structural, optical, and electronic properties, useful to fulfill multiple roles within photovoltaic devices. These roles span from acting as light-harvesting sensitizers and charge transport mediators to serving as micro- and nanoscale structural scaffolds, rheological modifiers, and interfacial stabilizers. In this Review, a comprehensive overview of the state of the art about the integration of biomolecules across the various generations of photovoltaics is provided. The functional roles of pigments, DNA, proteins, and polysaccharides are critically reported improvements and limits associated with the use of biological molecules in optoelectronics. The molecular mechanisms underlying the interaction between biomolecules and semiconductors are also discussed as essential for a functional integration of biomolecules in solar cells. Finally, this Review shows the current state of the art, and the most significant results achieved in the use of biomolecules in solar cells, with the main scope of outlining some guidelines for future further developments in the field of biohybrid photovoltaics. Full article

(This article belongs to the Special Issue Thermal and Photocatalytic Analysis of Nanomaterials: 2nd Edition)

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11 pages, 1941 KiB

Open AccessArticle

Nomenclature and Typification of the Goat Grass Aegilops tauschii Coss. (Poaceae: Triticeae): A Key Species for the Secondary Gene Pool of Common Wheat Triticum aestivum

by P. Pablo Ferrer-Gallego, Raúl Ferrer-Gallego, Diego Rivera, Concepción Obón, Emilio Laguna and Nikolay P. Goncharov

Plants 2025, 14(15), 2375; https://doi.org/10.3390/plants14152375 (registering DOI) - 1 Aug 2025

Abstract

Background: The typification of the name Aegilops tauschii Coss. (Poaceae: Triticeae) is revisited. Several authors cited a gathering from Iberia as the locality and Buxbaum as the collector of as the type, but no actual specimens from this collection have been located, nor is there evidence that such a gathering existed. In 1994, van Slageren designated as lectotype an illustration from Buxbaum’s Plantarum minus cognitarum centuria I (1728), which, although original material, is not the only element cited in the protologue. The protologue mentions several gatherings, some of which are represented by identifiable herbarium specimens qualifying as syntypes. Methods: This work is based on the analysis of the protologue of Aegilops tauschii and the study of specimens conserved in several herbaria. According to the International Code of Nomenclature for algae, fungi, and plants (ICN, Shenzhen Code 2018), an illustration does not hold the same nomenclatural weight as preserved specimens cited in the protologue. Therefore, van Slageren’s lectotypification does not comply with Art. 9.12 of the ICN and must be superseded. Results: The original material includes multiple elements, and a new lectotype is designated from a specimen at PRC from Azerbaijan. Full article

(This article belongs to the Special Issue Taxonomy and Nomenclature of Euro + Mediterranean Vascular Plants)

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