bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Wheat Ideotype under Multiple Abiotic Stresses and Complex Environmental Interplays by Multivariate Analysis Techniques
Authors: Al-Ashkar Ibrahim , Sallam Mohammed , Ibrahim Abdullah , Ghazy Abdelhalim , Al-Suhaibani Nasser , Ben Romdhane Walid , Al-Doss Abdullah
Published: October 2023

Multiple abiotic stresses negatively impact wheat production all over the world. We need to increase productivity by 60% to provide food security to the world population of 9.6 billion by 2050; it is surely time to develop stress-tolerant genotypes with a thorough comprehension of the genetic basis and the plant’s capacity to tolerate these stresses and complex environmental reactions. To approach these goals, we used multivariate analysis techniques, the additive main effects and multiplicative interaction (AMMI) model for prediction, linear discriminant analysis (LDA) to enhance the reliability of the classification, multi-trait genotype-ideotype distance index (MGIDI) to detect the ideotype, and the weighted average of absolute scores (WAASB) index to recognize genotypes with stability that are highly productive. Six tolerance multi-indices were used to test twenty wheat genotypes grown under multiple abiotic stresses. The AMMI model showed varying differences with performance indices, which disagreed with the trait and genotype differences used. The G01, G12, G16, and G02 were selected as the appropriate and stable genotypes using the MGIDI with the six tolerance multi-indices. The biplot features the genotypes (G01, G03, G11, G16, G17, G18, and G20) that were most stable and had high tolerance across the environments. The pooled analyses (LDA, MGIDI, and WAASB) showed genotype G01 as the most stable candidate. The genotype (G01) is considered a novel genetic resource for improving productivity and stabilizing wheat programs under multiple abiotic stresses. Hence, these techniques, if used in an integrated manner, strongly support the plant breeders in multi-environment trials.
Species Delimitation and Genetic Relationship of Castanopsis hainanensis and Castanopsis wenchangensis (Fagaceae)
Authors: Chen Xing , Feng Yi , Chen Shuang , Yang Kai , Wen Xiangying , Sun Ye
Published: October 2023

Castanopsis is one of the most common genus of trees in subtropical evergreen broad-leaved forests and tropical monsoon rainforests in China. Castanopsis hainanensis and Castanopsis wenchangensis are endemic to Hainan Island, but they were once confused as the same species due to very similar morphologies. In this study, nuclear microsatellite markers and chloroplast genomes were used to delimit C. hainanensis and C. wenchangensis. The allelic variations of nuclear microsatellites revealed that C. hainanensis and C. wenchangensis were highly genetically differentiated with very limited gene admixture. Both showed higher genetic diversity within populations and lower genetic diversity among populations, and neither had further population genetic structure. Furthermore, C. wenchangensis and C. hainanensis had very different chloroplast genomes. The independent genetic units, very limited gene admixture, different distribution ranges, and distinct habitats all suggest that C. wenchangensis and C. hainanensis are independent species, thus they should be treated as distinct conservation units.
Historical Assembly of Andean Tree Communities
Authors: González-Caro Sebastián , Tello J. Sebastián , Myers Jonathan A. , Feeley Kenneth , Blundo Cecilia , Calderón-Loor Marco , Carilla Julieta , Cayola Leslie , Cuesta Francisco , Farfán William , Fuentes Alfredo F. , Garcia-Cabrera Karina , Grau Ricardo , Idarraga Álvaro , Loza M. Isabel , Malhi Yadvinder , Malizia Agustina , Malizia Lucio , Osinaga-Acosta Oriana , Pinto Esteban , Salinas Norma , Silman Miles , Terán-Valdéz Andrea , Duque Álvaro
Published: October 2023

Patterns of species diversity have been associated with changes in climate across latitude and elevation. However, the ecological and evolutionary mechanisms underlying these relationships are still actively debated. Here, we present a complementary view of the well-known tropical niche conservatism (TNC) hypothesis, termed the multiple zones of origin (MZO) hypothesis, to explore mechanisms underlying latitudinal and elevational gradients of phylogenetic diversity in tree communities. The TNC hypothesis posits that most lineages originate in warmer, wetter, and less seasonal environments in the tropics and rarely colonize colder, drier, and more seasonal environments outside of the tropical lowlands, leading to higher phylogenetic diversity at lower latitudes and elevations. In contrast, the MZO hypothesis posits that lineages also originate in temperate environments and readily colonize similar environments in the tropical highlands, leading to lower phylogenetic diversity at lower latitudes and elevations. We tested these phylogenetic predictions using a combination of computer simulations and empirical analyses of tree communities in 245 forest plots located in six countries across the tropical and subtropical Andes. We estimated the phylogenetic diversity for each plot and regressed it against elevation and latitude. Our simulated and empirical results provide strong support for the MZO hypothesis. Phylogenetic diversity among co-occurring tree species increased with both latitude and elevation, suggesting an important influence on the historical dispersal of lineages with temperate origins into the tropical highlands. The mixing of different floras was likely favored by the formation of climatically suitable corridors for plant migration due to the Andean uplift. Accounting for the evolutionary history of plant communities helps to advance our knowledge of the drivers of tree community assembly along complex climatic gradients, and thus their likely responses to modern anthropogenic climate change.
Phytochemical Analysis and Antioxidant Activity of Centella Asiatica Extracts: An Experimental and Theoretical Investigation of Flavonoids
Authors: Kandasamy Anbazhakan , Aruchamy Kanakaraj , Rangasamy Praveena , Varadhaiyan Deepha , Gowri Chandrasekar , Oh Tae Hwan , Ramasundaram Subramaniyan , Athinarayanan Balasankar
Published: October 2023

Centella asiatica (CA) is a medicinal plant widely used in the East, with many of its phytoconstituents remaining unexplored. In this study, compounds were extracted and identified from C. asiatica to determine its medicinal properties. Phytochemical screening was conducted on shoot, callus, and cell suspension extracts, revealing the presence of tannins, flavonoids, terpenoids, saponins, and steroids in all three cultures, with no alkaloids detected. IC50 values were determined to evaluate the antioxidant activity of the extracts, with the highest value observed for cell suspension culture (20 µg/mL), followed by shoot culture (19 µg/mL), and then callus extract (10 µg/mL), with ascorbic acid as the standard at an IC50 value of 26.25 µg/mL. Finally, density functional theory was used to analyze the structure–activity relationships of the identified compounds from C. asiatica extract. The results suggest that ultrasonic-assisted extraction yielded the highest recovery and antioxidant activity, with a scavenging activity of 79%. This study provides valuable insights into the phytochemical composition and antioxidant potential of C. asiatica, which may have implications for its use in traditional medicine and future drug development.
Impacts of Micro(nano)plastics on Terrestrial Plants: Germination, Growth, and Litter
Authors: Li Xiaodong , Wang Rongyu , Dai Wei , Luan Yaning , Li Jing
Published: October 2023

Micro(nano)plastics (MNP) are pervasive in various environmental media and pose a global environmental pollution issue, particularly in terrestrial ecosystems, where they exert a significant impact on plant growth and development. This paper builds upon prior research to analyze and consolidate the effects of MNP on soil properties, seed germination, plant growth, and litter decomposition. The objective is to elucidate the environmental behavior of MNP and their mechanisms of influence on the plant life cycle. The unique physicochemical and electrical properties of MNP enable them to modify soil structure, water retention capacity, and pH. They can potentially act as “electron shuttles” or disrupt natural “electron shuttles” in litter decomposition, thereby interfering with nutrient transport and availability in the soil. Furthermore, MNP can physically obstruct nutrient and water channels within plants, impacting nutrient and water absorption. Once infiltrating plant tissues, MNP can form eco-coronas with plant proteins. Together with MNP adsorbed on the plant’s surface and within its tissues, they disrupt normal physiological processes, leading to changes in photosynthesis, biomass, cellular toxicity, genetics, nutrient uptake, and gene expression. These changes, in turn, influence seed germination and plant growth and development. As a burgeoning research field, future studies should delve deeper into various aspects of these changes, such as elucidating the pathways and mechanisms through which MNP enter plant tissues, assessing their intensity and mechanisms of toxicity on different plant species, and exploring the relationship between micro(nano)plastics and “electron shuttles”. These endeavors will contribute to establishing a more comprehensive theoretical framework for understanding the environmental behavior of MNP and their impact on plants.
Prediction of Tribological Properties of UHMWPE/SiC Polymer Composites Using Machine Learning Techniques
Authors: Mohammed Abdul Jawad , Mohammed Anwaruddin Siddiqui , Mohammed Abdul Samad
Published: October 2023

Polymer composites are a class of material that are gaining a lot of attention in demanding tribological applications due to the ability of manipulating their performance by changing various factors, such as processing parameters, types of fillers, and operational parameters. Hence, a number of samples under different conditions need to be repeatedly produced and tested in order to satisfy the requirements of an application. However, with the advent of a new field of triboinformatics, which is a scientific discipline involving computer technology to collect, store, analyze, and evaluate tribological properties, we presently have access to a variety of high-end tools, such as various machine learning (ML) techniques, which can significantly aid in efficiently gauging the polymer’s characteristics without the need to invest time and money in a physical experimentation. The development of an accurate model specifically for predicting the properties of the composite would not only cheapen the process of product testing, but also bolster the production rates of a very strong polymer combination. Hence, in the current study, the performance of five different machine learning (ML) techniques is evaluated for accurately predicting the tribological properties of ultrahigh molecular-weight polyethylene (UHMWPE) polymer composites reinforced with silicon carbide (SiC) nanoparticles. Three input parameters, namely, the applied pressure, holding time, and the concentration of SiCs, are considered with the specific wear rate (SWR) and coefficient of friction (COF) as the two output parameters. The five techniques used are support vector machines (SVMs), decision trees (DTs), random forests (RFs), k-nearest neighbors (KNNs), and artificial neural networks (ANNs). Three evaluation statistical metrics, namely, the coefficient of determination (R2-value), mean absolute error (MAE), and root mean square error (RMSE), are used to evaluate and compare the performances of the different ML techniques. Based upon the experimental dataset, the SVM technique was observed to yield the lowest error rates—with the RMSE being 2.09 × 10−4 and MAE being 2 × 10−4 for COF and for SWR, an RMSE of 2 × 10−4 and MAE of 1.6 × 10−4 were obtained—and highest R2-values of 0.9999 for COF and 0.9998 for SWR. The observed performance metrics shows the SVM as the most reliable technique in predicting the tribological properties—with an accuracy of 99.99% for COF and 99.98% for SWR—of the polymer composites.
Regulation Mechanism of Special Functional Groups Contained in Polymer Molecular Chains on the Tribological Properties of Modified Ti6Al4V
Authors: Liu Mengmeng , Ni Jing , Zhang Caixia , Wang Lihui , Guo Yue , Liu Zhifeng
Published: October 2023

Polymer coatings can effectively improve the surface tribological properties of human implant materials, thereby increasing their service life. In this study, poly(vinylsulfonic acid, sodium salt) (PVS), poly(acrylic acid) (PAA) and poly(vinylphosphonic acid) (PVPA) were used to modify Ti6Al4V surfaces. Experimental analyses were combined with molecular simulation to explore the regulation mechanism of special functional groups contained in polymer molecular chains on the tribological properties of modified surfaces. In addition, the bearing capacities and velocity dependence of different polymer modified surfaces during friction were also explored. The PVS coating, due to physical adsorption, can have an anti-friction effect under NaCl solution lubrication, but is not durable under long-term or repeated usage. Both PAA and PVPA molecular chains can form chemical bonds with Ti6Al4V. Phosphate acid groups can firmly bind to the substrate, and the adsorption of salt ions and water molecules can form a hydrated layer on the PVPA coating surface, achieving ultra-low friction and wear. The adsorption of salt ions would aggravate the surface wear of the PAA-modified Ti6Al4V due to the unfirm binding of carboxyl groups to the substrate, resulting in a high friction coefficient. This study can provide effective guidance for the design of modified polymer coatings on metals.
Evaluation of the Reactivity of Methanol and Hydrogen Sulfide Residues with the Ziegler–Natta Catalyst during Polypropylene Synthesis and Its Effects on Polymer Properties
Authors: Hernández-Fernández Joaquín , González-Cuello Rafael , Ortega-Toro Rodrigo
Published: October 2023

The study focused on the evaluation of the influence of inhibitory compounds such as hydrogen sulfide (H2S) and methanol (CH3OH) on the catalytic productivity and properties of the polymers in the polymerization process with the Ziegler–Natta catalyst. The investigation involved experimental measurements, computational calculations using DFT, and analysis of various parameters, such as molecular weight, melt flow index, xylene solubility, and reactivity descriptors. The results revealed a clear correlation between the concentration of H2S and methanol and the parameters evaluated. Increasing the H2S concentrations, on average by 0.5 and 1.0 ppm, resulted in a drastic decrease in the polymer’s molecular weight. A directly proportional relationship was observed between the flow rate and the H2S concentration. In the case of methanol, the change occurred from 60 ppm, causing a sharp decrease in the molecular weight of the polymer, which translates into an increase in the fluidity index and a decrease in solubility in xylene. The presence of these inhibitors also affected the catalytic activity, causing a reduction in the productivity of the Ziegler–Natta catalyst. Computational calculations provided a deeper understanding of the molecular behavior and reactivity of the studied compounds. The computational calculations yielded significantly lower results compared to other studies, with values of −69.0 and −43.9 kcal/mol for the Ti-CH3OH and H2S interactions, respectively. These results indicate remarkable stability in the studied interactions and suggest that both adsorptions are highly favorable.
Fiber-Type Transistor-Based Chemical and Physical Sensors Using Conjugated Polymers
Authors: Nguyen Ky Van , Lee Donggeun , Kim Youngnan , Lee Wi Hyoung
Published: October 2023

Fiber-type electronics is a crucial field for realizing wearable electronic devices with a wide range of sensing applications. In this paper, we begin by discussing the fabrication of fibers from conjugated polymers. We then explore the utilization of these fibers in the development of field-effect and electrochemical transistors. Finally, we investigate the diverse applications of these fiber-type transistors, encompassing chemical and physical sensors. Our paper aims to offer a comprehensive understanding of the use of conjugated polymers in fiber-type transistor-based sensors.
A Strong, Tough and Fire-Retardant Biomimetic Multifunctional Wooden Laminate
Authors: Han Xiaoshuai , Wang Xiaoyi , Tian Wei , Wang Yuli , Wang Jiangbo , Lam Frank , Jiang Shaohua
Published: October 2023

Mildly delignified wood showed a well-preserved wood cell wall framework, and its derived compressed materials demonstrate excellent mechanical properties and advanced functional material potential. Here, we proposed a simple yet effective approach for making strong, tough, and fire-retardant wooden laminate by a three-step process of mild delignification, infiltrating potassium nonafluoro-1-butanesulfonate (PFBS), and hot-pressing to densify the material. PFBS can be infiltrated into the micro/nano-structures of the mildly delignified wood to achieve a good flame-resistant protective barrier. Flame retardant tests showed that this strong, tough, and fire-retardant wooden laminate has a superior flame-retardant performance to natural wood. Additionally, the wooden laminate also exhibits a simultaneously enhanced tensile strength (175.6 MPa vs. 89.9 MPa for natural wood) and toughness (22.9 MJ m−3 vs. 10.9 MJ m−3 for natural wood). Given these attributes, the resulting wooden laminates are identified as promising candidates for high-performance structural applications, fulfilling stringent requirements for both mechanical resilience and flame-retardant efficacy.
Preparation and Characterization of Polycarbonate-Based Blend System with Favorable Mechanical Properties and 3D Printing Performance
Authors: Liu Hao , Chen Simin , Li Chengdi , Chen Xiao , Li Jinbo , Chen Ping , Xie Fuzhen , Jian Huihua , Huang Xiaoying , Liu Lei
Published: October 2023

Recently, material extrusion (MEX) 3D printing technology has attracted extensive attention. However, some high-performance thermoplastic polymer resins, such as polycarbonate (PC), cannot be processed by conventional MEX printing equipment due to poor processing performance. In order to develop new PC-based printing materials suitable for MEX, PC/poly(butylene adipate-co-terephthalate) (PBAT) blends were prepared using a simple polymer blending technique. It was found that the addition of PBAT component significantly improved processing performance of the PC, making the blends processable at 250 °C. More importantly, the PC was completely compatible with the PBAT, and the PBAT effectively reduced the Tg of the blends, endowing the blends with essential 3D printing performance. Furthermore, methyl methacrylate-butadiene-styrene terpolymer (MBS) was introduced into the PC/PBAT blends to improve toughness. SEM observations demonstrated that MBS particles, as stress concentration points, triggered shear yielding of polymer matrix and absorbed impact energy substantially. In addition, the MBS had little effect on the 3D printing performance of the blends. Thus, a PC/PBAT/MBS blend system with favorable comprehensive mechanical properties and 3D printing performance was achieved. This work can provide guidance for the development of novel MEX printing materials and is of great significance for expanding the variety of MEX printing materials.
Synthesis, Properties, Applications, and Future Prospective of Cellulose Nanocrystals
Authors: Rashid Adib Bin , Hoque Md Enamul , Kabir Nahiyan , Rifat Fahim Ferdin , Ishrak Hasin , Alqahtani Abdulrahman , Chowdhury Muhammad E. H.
Published: October 2023

The exploration of nanocellulose has been aided by rapid nanotechnology and material science breakthroughs, resulting in their emergence as desired biomaterials. Nanocellulose has been thoroughly studied in various disciplines, including renewable energy, electronics, environment, food production, biomedicine, healthcare, and so on. Cellulose nanocrystal (CNC) is a part of the organic crystallization of macromolecular compounds found in bacteria’s capsular polysaccharides and plant fibers. Owing to numerous reactive chemical groups on its surface, physical adsorption, surface grating, and chemical vapor deposition can all be used to increase its performance, which is the key reason for its wide range of applications. Cellulose nanocrystals (CNCs) have much potential as suitable matrices and advanced materials, and they have been utilized so far, both in terms of modifying and inventing uses for them. This work reviews CNC’s synthesis, properties and various industrial applications. This review has also discussed the widespread applications of CNC as sensor, acoustic insulator, and fire retardant material.
Electrode Impact on the Electrical Breakdown of Dielectric Elastomer Thin Films
Authors: Fasolt Bettina , Albuquerque Fabio Beco , Hubertus Jonas , Schultes Günter , Shea Herbert , Seelecke Stefan
Published: October 2023

Dielectric Elastomer Actuators (DEAs) enable the realization of energy-efficient and compact actuator systems. DEAs operate at the kilovolt range with typically microampere-level currents and hence minimize thermal losses in comparison to low voltage/high current actuators such as shape memory alloys or solenoids. The main limiting factor for reaching high energy density in high voltage applications is dielectric breakdown. In previous investigations on silicone-based thin films, we reported that not only do environmental conditions and film parameters such as pre-stretch play an important role but that electrode composition also has a significant impact on the breakdown behavior. In this paper, we present a comprehensive study of electrical breakdown on thin silicone films coated with electrodes manufactured by five different methods: screen printing, inkjet printing, pad printing, gold sputtering, and nickel sputtering. For each method, breakdown was studied under environmental conditions ranging from 1 °C to 80 °C and 10% to 90% relative humidity. The effect of different manufacturing methods was analyzed as was the influence of parameters such as solvents, silicone content, and the particle processing method. The breakdown field increases with increasing temperature and decreases with increasing humidity for all electrode types. The stiffer metal electrodes have a higher breakdown field than the carbon-based electrodes, for which particle size also plays a large role.
Additive Free Crosslinking of Poly-3-hydroxybutyrate via Electron Beam Irradiation at Elevated Temperatures
Authors: Krieg David , Müller Michael Thomas , Boldt Regine , Rennert Mirko , Stommel Markus
Published: October 2023

When applying electron or gamma irradiation to poly-3-hydroxybutyrate (P3HB), main chain scissions are the dominant material reactions. Though propositions have been made that crosslinking in the amorphous phase of P3HB occurs under irradiation, a conclusive method to achieve controlled additive free irradiation crosslinking has not been shown and no mechanism has been derived to the best of our knowledge. By applying irradiation in a molten state at 195 °C and doses above 200 kGy, we were able to initiate crosslink reactions and achieved gel formation of up to 16%. The gel dose Dgel was determined to be 200 kGy and a range of the G values, the number of scissions and crosslinks for 100 eV energy deposition, is given. Rheology measurements, as well as size exclusion chromatography (SEC), showed indications for branching at doses from 100 to 250 kGy. Thermal analysis showed the development of a bimodal peak with a decrease in the peak melt temperature and an increase in peak width. In combination with an increase in the thermal degradation temperature for a dose of 200 kGy compared to 100 kGy, thermal analysis also showed phenomena attributed to branching and crosslinking.
Onopordum nervosum ssp. platylepis Flowers as a Promising Source of Antioxidant and Clotting Milk Agents: Behavior of Spontaneous and Cultivated Plants under Different Drying Methodologies
Authors: Essaidi Ismahen , Dhen Najla , Lassoued Ghada , Kouki Rania , Haouala Faouzi , Alhudhaibi Abdulrahman M. , Alrudayni Hassan A. , Dridi Almohandes Bouthaina
Published: October 2023

This study is a contribution to the valorization of the Onopordum nervosum ssp. platylepis flowers in the agri-food sector. It aims to evaluate the effect of different drying methodologies (room, microwave, convective, oven and freeze-drying) on the chemical composition, the microbiological quality of aqueous extracts and their biological activities, namely clotting milk and antioxidant activities for both spontaneous and cultivated plants. The results showed that the drying methodology has significantly affected the phenolic composition. The extract obtained from freeze-dried flowers showed the best amounts of total phenols (6.19 ± 0.50 mg GAE/g), flavonoids (2.35 ± 0.07 mg QE/g) and condensed tannins (0.038 ± 0.002 mg CE/g) followed by the microwave-dried flowers. However, microwave-dried flowers presented the lowest protein content with only 3.58 ± 0.62 mg BSAE/g. Finally, the biological activities were significantly dependent on the used drying process. The extract of freeze-dried flowers was the most potent to inhibit free radicals (diammonium 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulphonate) (ABTS), 1,1-diphenyl-2-picrylhydrazyl (DPPH)) and to reduce iron with the efficient concentrations EC50s of 0.48 ± 0.02 mg/mL, 0.16 ± 0.04 mg/mL and 1.52 ± 0.19 mg/mL, respectively. It also showed the highest clotting milk activity followed by room-dried and forced convective-dried flowers. As a consequence, freeze drying was the best method for preserving most chemical and biological properties of Onopordum nervosum ssp. platylepis flowers. However, in consideration of production costs and drying duration, the convective dryer is recommended as a more practical choice. All the obtained results do not reveal a significant difference between cultivated and spontaneous plants, which can suggest that the domestication of Onopordum nervosum ssp. platylepis can be useful for industrial applications.
Spirituality and Narrative Identity: Three Case Studies
Authors: Baldwin Clive , Furlotte Charles , Liu Qilin
Published: October 2023

Since the narrative turn in the social sciences from the 1980s onward, there has been an increasing interest in the concept of narrative identity. The concept of narrative identity, however, is open to multiple interpretations. Here, we take a narrative hermeneutic approach and apply one particular model of narrative identity—to three case studies, exploring how each of these was shaped by the storyworlds of the participants. The case studies have been selected as examples of different forms of identity: received, bricolage, and guided. Following this exploration, we reflect on the potential of this approach in other areas of identity work and the implications for narrative understanding in the promotion of social justice.
Geosites in the Gole della Breggia Geopark, Ticino, Southern Switzerland
Authors: Oppizzi Paolo , Pasquaré Mariotto Federico , Stockar Rudolf , Stella Andrea , Corti Noemi , Pedicini Martina , Andò Sergio , Vezzoli Giovanni , Bonali Fabio Luca
Published: October 2023

This work is aimed at illustrating one of the most engaging geoparks in Europe, the Gole della Breggia Geopark. By following its geotrail, the visitors can observe a great deal of features that encompass a wide variety of sedimentary, stratigraphic, tectonic and paleogeographic processes. Its contents are popularized by a comprehensive geotrail that encompasses 23 geostops, enriched with explanatory panels, which enable viewers to gain an overall picture of the complex processes that led to the present-day configuration of the outcrops encountered in the geopark. We have selected five significative outcrops, which we have regarded as fully-fledged geosites, and which encompass the whole 170-million-year interval, from Early Jurassic to Late Miocene times, during which the rocks of the geopark were deposited and deformed. We have performed an assessment of the five geosites, using the most common criteria that can be applied for geosite evaluation. The results of the assessment show that the most valuable geosites are the Lombardian Maiolica, and the Limestone with Pelagic Bivalves. All five geosites, thanks to a QR code added to each figure along the text, can be viewed as virtual geosites (VGs), integrated by 360° videos.
Detection and Mapping of Chestnut Using Deep Learning from High-Resolution UAV-Based RGB Imagery
Authors: Sun Yifei , Hao Zhenbang , Guo Zhanbao , Liu Zhenhu , Huang Jiaxing
Published: October 2023

The semantic segmentation method based on high-resolution RGB images obtained by unmanned aerial vehicle (UAV) provides a cost-effective way to improve the accuracy of detection and classification in forestry. Few studies have explored the impact of sample distribution patterns on deep learning model detection accuracy. The study was carried out using the data from the 4.78 km2 RGB image of a chestnut (Castanea mollissima Blume) plantation obtained by the DJI Phantom 4-RTK, and the model training was conducted with 18,144 samples of manually delineated chestnut tree clusters. The performance of four semantic segmentation models (U-Net, DeepLab V3, PSPNet, and DeepLab V3+) paired with backbones (ResNet-34, ResNet-50) was evaluated. Then, the influence of chestnut data from different planting patterns on the accuracy and generalization performance of deep learning models was examined. The results showed that the combination of DeepLab V3 with ResNet-34 backbone gives the best performance (F1 score = 86.41%), while the combination of DeepLab V3+ with ResNet-50 backbone performed the worst. The influence of different backbone networks on the detection performance of semantic segmentation models did not show a clear pattern. Additionally, different spatial distribution patterns of chestnut planting affected the classification accuracy. The model MIX, trained on comprehensive training data, achieves higher classification accuracies (F1 score = 86.13%) compared to the model trained on single training data (F1 score (DP) = 82.46%; F1 score (SP) = 83.81%). The model performance in complex scenario data training is superior to that of the model in simple scene data training. In conclusion, comprehensive training databases can improve the generalization performance of chestnut classification with different spatial distribution patterns. This study provides an effective method for detecting chestnut cover area based on semantic segmentation, allowing for better quantitative evaluation of its resource utilization and further development of inventories for other tree species.
Utilizing Ground-Penetrating Radar for Water Leak Detection and Pipe Material Characterization in Environmental Studies: A Case Study
Authors: Gamal Mohamed , Di Qingyun , Zhang Jinhai , Fu Changmin , Ebrahim Shereen , El-Raouf Amr
Published: October 2023

Detecting and mapping subsurface utilities in urban areas is crucial for identifying defects or damages in drinking and sewage pipes that can cause leaks. These leaks make it difficult to accurately characterize the pipes due to changes in their reflective properties. This study focused on detecting leaks originating from underground pipes and distinguishing between these various types of pipes. It also aimed to create a visual fingerprint model that displays the reflection characteristics of these pipes during different leak conditions, enabling efficient maintenance and handling procedures on the pipes. To achieve this, a finite-difference time-domain (FDTD) method was used to simulate two types of pipe materials with and without leak areas to construct different scenarios. Additionally, a ground-penetrating radar (GPR) field survey was conducted using a 600 MHz antenna in a part of the El Hammam area on Egypt’s northwest coast. The simulated images produced with numerical modeling were compared with the radar profiles obtained using GPR at particular locations. The numerical simulations and radar profiles demonstrated the noticeable influence of water leaks from the different pipes, wherein the reflection of saturated soil waves was interrupted due to the presence of saturated soil. Envelope and migration techniques were employed in a new application to accurately distinguish between different pipe types, specifically focusing on leak areas. The strong correlation between the real radar profile and the specific signal of a water pipe leak in the simulated models suggests that GPR is a reliable non-destructive geophysical method for detecting water pipe leaks and distinguishing between the different pipe materials in various field conditions. The simulated models, which serve as image-matching fingerprints to identify and map water pipe leaks, help us to comprehend reality better.
Optical Properties and Vertical Distribution of Aerosols Using Polarization Lidar and Sun Photometer over Lanzhou Suburb in Northwest China
Authors: Li Mengqi , Cao Xianjie , Zhang Zhida , Ji Hongyu , Zhang Min , Guo Yumin , Tian Pengfei , Liang Jiening
Published: October 2023

To better understand aerosol vertical distribution and radiation effects, the seasonal variation and vertical distribution characteristics of aerosol optical properties were analyzed based on the aerosol extinction coefficient, depolarization ratio and backscatter Ångström exponent derived from the dual-wavelength polarization lidar at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) from December 2009 to November 2012. Combining the CE-318 sun photometer, the microphysical, optical and vertical distribution characteristics of aerosol during a dust process were discussed comprehensively. The results revealed that the vertical profiles of the aerosol extinction coefficient and depolarization ratio clearly had seasonal variation characteristics. The aerosol optical depth (AOD) integrating with the aerosol extinction coefficient within 0–2 km in the spring, summer, autumn and winter accounted for 48%, 45%, 56% and 58% of the total AOD, respectively. The non-spherical feature was most distinctive in the spring, followed by the winter, autumn and summer. The particle size of aerosol in the lower layer was larger than that in the upper layer according to the vertical profile of the backscatter Ångström exponent. The cluster analysis of backward trajectory showed SACOL is dominated by dust aerosol in the spring and the mixtures of dust with anthropogenic pollution in the winter. A dust event in April 2010 was selected and the analysis showed that it mainly came from the high-altitude and long-range transportation from the Taklamakan Desert. During this period, the extinction coefficient increased up to 0.9 km−1, the maximum AOD was 2.21 and the SSA ranged from 0.92 to 0.99. The radiation force in the atmosphere reached 126.15 W/m2. It can be found that the influence of aerosol on the atmospheric radiation effect cannot be ignored.
Comparison of Deep Neural Networks in the Classification of Bark Beetle-Induced Spruce Damage Using UAS Images
Authors: Turkulainen Emma , Honkavaara Eija , Näsi Roope , Oliveira Raquel A. , Hakala Teemu , Junttila Samuli , Karila Kirsi , Koivumäki Niko , Pelto-Arvo Mikko , Tuviala Johanna , Östersund Madeleine , Pölönen Ilkka , Lyytikäinen-Saarenmaa Päivi
Published: October 2023

The widespread tree mortality caused by the European spruce bark beetle (Ips typographus L.) is a significant concern for Norway spruce-dominated (Picea abies H. Karst) forests in Europe and there is evidence of increases in the affected areas due to climate warming. Effective forest monitoring methods are urgently needed for providing timely data on tree health status for conducting forest management operations that aim to prepare and mitigate the damage caused by the beetle. Unoccupied aircraft systems (UASs) in combination with machine learning image analysis have emerged as a powerful tool for the fast-response monitoring of forest health. This research aims to assess the effectiveness of deep neural networks (DNNs) in identifying bark beetle infestations at the individual tree level from UAS images. The study compares the efficacy of RGB, multispectral (MS), and hyperspectral (HS) imaging, and evaluates various neural network structures for each image type. The findings reveal that MS and HS images perform better than RGB images. A 2D-3D-CNN model trained on HS images proves to be the best for detecting infested trees, with an F1-score of 0.759, while for dead and healthy trees, the F1-scores are 0.880 and 0.928, respectively. The study also demonstrates that the tested classifier networks outperform the state-of-the-art You Only Look Once (YOLO) classifier module, and that an effective analyzer can be implemented by integrating YOLO and the DNN classifier model. The current research provides a foundation for the further exploration of MS and HS imaging in detecting bark beetle disturbances in time, which can play a crucial role in forest management efforts to combat large-scale outbreaks. The study highlights the potential of remote sensing and machine learning in monitoring forest health and mitigating the impacts of biotic stresses. It also offers valuable insights into the effectiveness of DNNs in detecting bark beetle infestations using UAS-based remote sensing technology.
Spatio-Temporal Alignment and Track-To-Velocity Module for Tropical Cyclone Forecast
Authors: Geng Xiaoyi , Liu Zili , Shi Zhenwei
Published: October 2023

The prediction of a tropical cyclone’s trajectory is crucial for ensuring marine safety and promoting economic growth. Previous approaches to this task have been broadly categorized as either numerical or statistical methods, with the former being computationally expensive. Among the latter, multilayer perceptron (MLP)-based methods have been found to be simple but lacking in time series capabilities, while recurrent neural network (RNN)-based methods excel at processing time series data but do not integrate external information. Recent works have attempted to enhance prediction performance by simultaneously utilizing both time series and meteorological field data through feature fusion. However, these approaches have relatively simplistic methods for data fusion and do not fully explore the correlations between different modalities. To address these limitations, we propose a systematic solution called TC-TrajGRU for predicting tropical cyclone tracks. Our approach improves upon existing methods in two main ways. Firstly, we introduce a Spatial Alignment Feature Fusion (SAFF) module to address feature misalignment issues in different dimensions. Secondly, our Track-to-Velocity (T2V) module leverages time series differences to integrate external information. Our experiments demonstrate that our approach yields highly accurate predictions comparable to the official optimal forecast for a 12 h period.
Differential Evolution Particle Swarm Optimization for Phase-Sensitivity Enhancement of Surface Plasmon Resonance Gas Sensor Based on MXene and Blue Phosphorene/Transition Metal Dichalcogenide Hybrid Structure
Authors: Yue Chong , Ding Yueqing , Tao Lei , Zhou Sen , Guo Yongcai
Published: October 2023

A differential evolution particle swarm optimization (DEPSO) is presented for the design of a high-phase-sensitivity surface plasmon resonance (SPR) gas sensor. The gas sensor is based on a bilayer metal film with a hybrid structure of blue phosphorene (BlueP)/transition metal dichalcogenides (TMDCs) and MXene. Initially, a Ag-BlueP/TMDCs-Ag-MXene heterostructure is designed, and its performance is compared with that of the conventional layer-by-layer method and particle swarm optimization (PSO). The results indicate that optimizing the thickness of the layers in the gas sensor promotes phase sensitivity. Specifically, the phase sensitivity of the DEPSO is significantly higher than that of the PSO and the conventional method, while maintaining a lower reflectivity. The maximum phase sensitivity achieved is 1.866 × 106 deg/RIU with three layers of BlueP/WS2 and a monolayer of MXene. The distribution of the electric field is also illustrated, demonstrating that the optimized configuration allows for better detection of various gases. Due to its highly sensitive characteristics, the proposed design method based on the DEPSO can be applied to SPR gas sensors for environmental monitoring.
A Green Approach to Natural Dyes in Dye-Sensitized Solar Cells
Authors: Shukor Nurul Izzati Abdul , Chan Kah-Yoong , Thien Gregory Soon How , Yeoh Mian-En , Low Pei-Ling , Devaraj Nisha Kumari , Ng Zi-Neng , Yap Boon Kar
Published: October 2023

Solar cells are pivotal in harnessing renewable energy for a greener and more sustainable energy landscape. Nonetheless, eco-friendly materials for solar cells have not been as extensive as conventional counterparts, highlighting a significant area for further investigation in advancing sustainable energy technologies. This study investigated natural dyes from cost-effective and environmentally friendly blueberries and mulberries. These dyes were utilized as alternative sensitizers for dye-sensitized solar cells (DSSCs). Alongside the natural dyes, a green approach was adopted for the DSSC design, encompassing TiO2 photoanodes, eco-friendly electrolytes, and green counter-electrodes created from graphite pencils and candle soot. Consequently, the best-optimized dye sensitizer was mulberry, with an output power of 13.79 µW and 0.122 µW for outdoor and indoor environments, respectively. This study underscored the feasibility of integrating DSSCs with sensitizers derived from readily available food ingredients, potentially expanding their applications in educational kits and technology development initiatives.
A Short Video Classification Framework Based on Cross-Modal Fusion
Authors: Pang Nuo , Guo Songlin , Yan Ming , Chan Chien Aun
Published: October 2023

The explosive growth of online short videos has brought great challenges to the efficient management of video content classification, retrieval, and recommendation. Video features for video management can be extracted from video image frames by various algorithms, and they have been proven to be effective in the video classification of sensor systems. However, frame-by-frame processing of video image frames not only requires huge computing power, but also classification algorithms based on a single modality of video features cannot meet the accuracy requirements in specific scenarios. In response to these concerns, we introduce a short video categorization architecture centered around cross-modal fusion in visual sensor systems which jointly utilizes video features and text features to classify short videos, avoiding processing a large number of image frames during classification. Firstly, the image space is extended to three-dimensional space–time by a self-attention mechanism, and a series of patches are extracted from a single image frame. Each patch is linearly mapped into the embedding layer of the Timesformer network and augmented with positional information to extract video features. Second, the text features of subtitles are extracted through the bidirectional encoder representation from the Transformers (BERT) pre-training model. Finally, cross-modal fusion is performed based on the extracted video and text features, resulting in improved accuracy for short video classification tasks. The outcomes of our experiments showcase a substantial superiority of our introduced classification framework compared to alternative baseline video classification methodologies. This framework can be applied in sensor systems for potential video classification.
Is the Urban Landscape Connected? Construction and Optimization of Urban Ecological Networks Based on Morphological Spatial Pattern Analysis
Authors: Zhou Xudan , Hao Chenyao , Bao Yu , Zhang Qiushi , Wang Qing , Wang Wei , Guo Hongliang
Published: October 2023

Urban green ecological space is an important measure of sustainable urban development. Among them, landscape connectivity is one of the key factors in maintaining landscape function. Ecological networks can effectively improve regional ecological quality and promote urban landscape connectivity. However, previous studies on ecological networks have mainly focused on biodiversity conservation and lack research on landscape connectivity. This study used morphological spatial pattern analysis methods and utilized connectivity indices to identify ecological sources in the Chaoyang and Nanguan districts of China’s Changchun City and selected environmental and anthropogenic factors to construct an integrated resistance surface. The minimum cumulative resistance model and network structure index were used for urban ecological network construction and node optimization. The results show that the potential ecological network comprises 17 ecological sources and 34 potential corridors, primarily located in forests and water bodies in the east and south regions. However, the northwest has poor habitat quality and uneven distribution of ecological corridors, that warrant prioritization in future planning, construction, and protection efforts. By introducing six supplemental sources and 25 additional corridors, the function and overall connectivity of the regional ecological network can be improved. The study confirmed that the selection of appropriate connectivity thresholds can improve the accuracy of ecological sources identification, and that the influence of anthropogenic factors on ecological resistance cannot be ignored. This study will provide a scientific basis for promoting urban construction and ecological balance.
Use of Modal Substructure Method to Improve Wind-Resistance Design of Twin Towers
Authors: Qin Wei-feng , Yao Jianfeng , Tu Zhibin , Shen Guohui , Yu Shice , Xie Jiming
Published: October 2023

To provide guidance for the wind-resistant design of super-tall twin towers, this study proposes a new method for the wind-induced response computation and wind-resistant design optimization of twin towers, named the Modal Substructure (MSS) method. The MSS method is established through theoretical deduction, which is utilized to investigate the effects of links on the structural dynamic characteristics of twin towers via parameter analysis. The results show that, in the in-phase mode, the structural link increases the generalized mass of the “linked tower” system but insignificantly affects its stiffness and damping. However, in the out-of-phase mode, the link increases the overall damping and stiffness of the “linked tower” system, with negligible effects on the inertia characteristics of the entire structure. Finally, the accuracy of the MSS method is validated using 570 m-high twin towers as a case study. It was found that although the MSS method is an approximate approach, its results exhibit good accuracy and convenience in exploring the impact of link properties on the dynamic characteristics of twin towers. Therefore, the MSS method can be effectively used to guide the wind-resistant design of linked twin towers.