bluuebunny/crossref_metadata_2025 huggingface dataset:
Majorana Excitons in a Kitaev Chain of Semiconductor Quantum Dots in a Nanowire
Authors: Mohseni Mahan , Allami Hassan , Miravet Daniel , Gayowsky David J. , Korkusinski Marek , Hawrylak Pawel
Published: August 2023

We present here a theory of Majorana excitons, photo-excited conduction electron-valence band hole pairs, interacting with Majorana Fermions in a Kitaev chain of semiconductor quantum dots embedded in a nanowire. Using analytical tools and exact diagonalization methods, we identify the presence of Majorana zero modes in the nanowire absorption spectra.
Paediatric Medicinal Formulation Development: Utilising Human Taste Panels and Incorporating Their Data into Machine Learning Training
Authors: Yoo Okhee , von Ungern-Sternberg Britta S. , Lim Lee Yong
Published: August 2023

This review paper explores the role of human taste panels and artificial neural networks (ANNs) in taste-masking paediatric drug formulations. Given the ethical, practical, and regulatory challenges of employing children, young adults (18–40) can serve as suitable substitutes due to the similarity in their taste sensitivity. Taste panellists need not be experts in sensory evaluation so long as a reference product is used during evaluation; however, they should be screened for bitterness taste detection thresholds. For a more robust evaluation during the developmental phase, considerations of a scoring system and the calculation of an acceptance value may be beneficial in determining the likelihood of recommending a formulation for further development. On the technological front, artificial neural networks (ANNs) can be exploited in taste-masking optimisation of medicinal formulations as they can model complex relationships between variables and enable predictions not possible previously to optimise product profiles. Machine learning classifiers may therefore tackle the challenge of predicting the bitterness intensity of paediatric formulations. While advancements have been made, further work is needed to identify effective taste-masking techniques for specific drug molecules. Continuous refinement of machine learning algorithms, using human panellist acceptability scores, can aid in enhancing paediatric formulation development and overcoming taste-masking challenges.
Temporal Dynamics of an Asymmetrical Dielectric Nanodimer Wrapped with Graphene
Authors: Jiang Xinchen , Huang Yang , Ma Pujuan , Shalin Alexander S. , Gao Lei
Published: August 2023

We theoretically and numerically investigate the temporal dynamics of a nanodimer system consisting of a pair of graphene-wrapped dielectric nanospheres with tunable radii. Considering that symmetry breaks on resonant frequencies, we derive the temporal kinetic equations in an asymmetric form by utilizing the dispersion relation method in dipole limit. The bifurcation diagrams achieved via the analysis on the linear instability and numerical solutions can quantitatively characterize the complex coexistences of stationary and dynamical behaviors in this dimer system, and the asymmetry apparently can increase the number of regimes with the periodic self-oscillation state or chaos. Furthermore, we find that the indefinite switching not only can be triggered among the stationary steady solutions, but it also universally exists among all the possible solutions in a coexistent regime. The switching can be tuned by applying a hard excitation signal with different durations and saturation values. Our results may provide new paths to realize a nonlinear nanophotonic device with tunable dynamical responses or even multi-functionalities.
Efficiency of Recovery of the Bioactive Principles of Plants by Comparison between Solid–Liquid Extraction in Mixture and Single-Vegetable Matrices via Maceration and RSLDE
Authors: Naviglio Daniele , Trifuoggi Marco , Varchetta Francesca , Nebbioso Viviana , Perrone Angela , Avolio Laura , De Martino Eleonora , Montesano Domenico , Gallo Monica
Published: August 2023

The term “officinal” derives from the Latin and includes all medicinal, aromatic and perfume plant species, which have long been a subject of interest for multiple purposes: health, food, pharmacological, cosmetic and so on. In this work, a study on six different species of medicinal plants, particularly characterized by digestive, choleretic and diuretic properties, was carried out: rosemary (Rosmarinus officinalis), sage (Salvia officinalis), laurel (Laurus nobilis), gentian (Gentiana lutea), dandelion (Taraxacum officinale) and rhubarb (Rheum palmatum). The roots and aerial parts of plants were separately extracted with two different techniques—maceration and rapid solid–liquid dynamic extraction (RSLDE)—and the quali/quantitative analysis of active ingredients have been determined by applying dry residue, Folin–Ciocalteu and DPPH assays. Data obtained have provided useful answers regarding the efficiency of the extraction carried out on a mixture or on single plants, allowing us to evaluate the best choice according to the cases and the final uses.
An Efficient Transformation System for Fast Production of VcCHS Transgenic Blueberry Callus and Its Expressional Analysis
Authors: Qin Xuejing , Hu Jing , Xu Guohui , Song Huifang , Zhang Lingyun , Cao Yibo
Published: August 2023

The Agrobacterium tumefaciens-mediated transformation for blueberries remains less efficient than is desirable. A new leaf callus regeneration and genetic transformation system was investigated in blueberries in this study. The leaf explants of cv. ‘Legacy’ and ‘Northland’ were used to establish the stable callus induction system when placed on the woody plant medium (WPM) supplemented with 1.0 mg·L−1 2, 4-D, 0.4 mg·L−1 6-BA for 30 d; then, the callus was sub-cultured in the proliferation medium supplemented with 1.5 mg·L−1 2, 4-D, 0.4 mg·L−1 6-BA in the darkness at 25 °C every 30 days. The co-cultivation of callus with A. tumefaciens was operated on WPM plus 100 μM acetosyringone for 4 days; then, the transferred callus was grown in WPM supplemented with 1.5 mg·L−1 2,4-D, 0.4 mg·L−1 6-BA, 50 mg·L−1 hygromycin, and 200 mg·L−1 cefotaxime. The VcCHS transgenic blueberry callus with both GFP signal and Hyg resistance was obtained from the transformed callus of cv. ‘Northland’. The rate of GFP signal detected in the transformed callus was as high as 49.02%, which was consistent with the PCR assay. Collectively, this study provides a highly efficient genetic transformation system in blueberry callus and a powerful approach for the molecular breeding of blueberries.
Spatial Distribution of Pinus koraiensis Trees and Community-Level Spatial Associations in Broad-Leaved Korean Pine Mixed Forests in Northeastern China
Authors: Pak Unil , Guo Qingxi , Liu Zhili , Wang Xugao , Liu Yankun , Jin Guangze
Published: August 2023

Investigating the spatial distributions and associations of tree populations provides better insights into the dynamics and processes that shape the forest community. Korean pine (Pinus koraiensis) is one of the most important tree species in broad-leaved Korean pine mixed forests (BKMFs), and little is known about the spatial point patterns of and associations between Korean pine and community-level woody species groups such as coniferous and deciduous trees in different developmental stages. This study investigated the spatial patterns of Korean pine (KP) trees and then analyzed how the spatial associations between KP trees and other tree species at the community level vary in different BKMFs. Extensive data collected from five relatively large sample plots, covering a substantial area within the natural distribution range of KP in northeastern China, were utilized. Uni- and bivariate pair correlation functions and mark correlation functions were applied to analyze spatial distribution patterns and spatial associations. The DBH (diameter at breast height) histogram of KP trees in northeastern China revealed that the regeneration process was very poor in the Changbai Mountain (CBS) plot, while the other four plots exhibited moderate or expanding population structures. KP trees were significantly aggregated at scales up to 10 m under the HPP null model, and the aggregation scales decreased with the increase in size classes. Positive or negative spatial associations were observed among different life stages of KP trees in different plots. The life history stages of the coniferous tree group showed positive spatial associations with KP saplings and juvenile trees at small scales, and spatial independence or negative correlations with larger KP trees at greater scales. All broad-leaved tree groups (canopy, middle, and understory layers) exhibited only slightly positive associations with KP trees at small scales, and dominant negative associations were observed at most scales. Our results demonstrate that mature KP trees have strong importance in the spatial patterns of KP populations, and site heterogeneity, limited seed dispersal, and interspecific competition characterize the spatial patterns of KP trees and community-level spatial associations with respect to KP trees, which can serve as a theoretical basis for the management and restoration of BKMFs in northeastern China.
Adapting to Climate Change with Opuntia
Authors: Jorge Ana O. S. , Costa Anabela S. G. , Oliveira M. Beatriz P. P.
Published: August 2023

Adapting our food production chain and increasing the flora and fauna’s livelihood in climate change-affected areas using Opuntia is not only theoretical but already exists in practice in many places. This cactus grows in unsuitable soil for most species as it is adapted to arid and semi-arid soils and hot weather. In these regions, Opuntia protects from erosion and contributes to soil health. The usage of this plant as fodder is also discussed, with immense potential in substituting a part of livestock’s diet and even increasing the quality of the animal’s by-products and decreasing water consumption. This would result in a feed that is low-cost and has a lower environmental impact. It is to be noted that Opuntia has a high potential as an invasive species, with caution always being recommended when dealing with this specie. The high content of specific compounds, such as proline, indicaxanthin, and betanin, found in Opuntia ficus-indica, influence the plant’s adaptation to unfavourable conditions. This collective evidence depicts Opuntia as a crop that can battle climate change and ensure food security.
Plant Extracts from the Yucatan Peninsula in the In Vitro Control of Curvularia lunata and Antifungal Effect of Mosannona depressa and Piper neesianum Extracts on Postharvest Fruits of Habanero Pepper
Authors: Cruz-Cerino Patricia , Cristóbal-Alejo Jairo , Ruiz-Carrera Violeta , Gamboa-Angulo Marcela
Published: August 2023

Plant extracts are a valuable alternative for the control of phytopathogenic fungi in horticultural crops. In the present work, the in vitro antifungal effect of ethanol and aqueous extracts from different vegetative parts of 40 native plants of the Yucatan Peninsula on Curvularia lunata ITC26, a pathogen of habanero pepper (Capsicum chinense), and effects of the most active extracts on postharvest fruits were investigated. Among these, the ethanol extracts of Mosannona depressa (bark from stems and roots) and Piper neesianum (leaves) inhibited 100% of the mycelial growth of C. lunata. The three extracts were partitioned between acetonitrile and n-hexane. The acetonitrile fraction from M. depressa stem bark showed the lowest mean inhibitory concentration (IC50) of 188 µg/mL against C. lunata. The application of this extract and its active principle α-asarone in the postharvest fruits of C. chinense (500 µg/mL) was shown to inhibit 100% of the severity of the infection caused by C. lunata after 11 days of contact. Both samples caused the distortion and collapse of the conidia of the phytopathogen when observed using electron microscopy at 96 h. The spectrum of M. depressa enriched antifungal action is a potential candidate to be a botanical fungicide in the control of C. lunata in cultivating habanero pepper.
Comparative Pollen Morphology of Selected Species of Blumea DC. and Cyathocline Cass. and Its Taxonomic Significance
Authors: Peng Yulan , Pu Xuemei , Yu Qi , Zhou Hailing , Huang Tianfang , Xu Bo , Gao Xinfen
Published: August 2023

The pollen morphology of 20 species from Blumea and Cyathocline Cass. was investigated using a light microscope (LM) and scanning electron microscopy (SEM) to explore their taxonomic significance. This study showed that pollen grains of these species were usually tricolporate, rarely tetracolporate (B. sinuata). Nine pollen types were distinguishable through the exine sculpture characters and the number of apertures. It was easily distinguished Cyathocline from species of Blumea s. str. by its much smaller size (15.04 μm × 15.07 μm) and sparse and longer spines (24 spines, spine length 4.23 μm) with acute apex, which suggest that C. purpurea might not belong to the genus Blumea s. str. The palynological characteristics indicated that Section Macrophllae and Section Paniculatae of Blumea were not monophyletic groups. The pollen morphology differentiation of B. lacera clade is consistent with the interspecific relationship revealed by the molecular phylogenetic tree. However, the pollen morphology of the Blumea densiflora clade is inconsistent with the interspecific relationship based on molecular phylogenetic analysis. This palynology research can only partly support the previously published molecular phylogeny of Blumea s. str.
Research on the Stability of the Spacer Fluid Interface in Dual-Layer Pipe Dual-Gradient Drilling
Authors: Wang Guorong , Li Xiaolei , Zhong Lin , Lv Zhiyu
Published: August 2023

Dual-layer pipe dual-gradient drilling technology is an emerging technology for solving the problem of the narrow safety density window in deepwater drilling. The unstable spacer fluid interface in this technology directly affects the dual-gradient pressure system in the annulus, causing changes in the drilling mud performance and affecting the control of bottom hole pressure and rock removal with drilling mud. Therefore, the key to the stable operation of dual-layer pipe dual-gradient drilling technology is to maintain the stability of the spacer fluid interface. Based on this, a seawater-spacer fluid-drilling mud annular flow model was established in this study, with a bottom hole pressure control step of 0.2 MPa, and the spacer fluid height after a single control was used as the evaluation index to study the influence of annular flow velocity, the spacer fluid properties, and the drill string rotation speed on the stability of the spacer fluid interface. The results show that in the determined conditions of the seawater and drilling mud system, the annular fluid flow rate and the physical parameters of the spacer fluid are the main factors affecting the stability of the spacer fluid interface. When the annular fluid flow rate increased within the range of 0.04~0.2 m/s, the liquidity index of the spacer fluid increased between 0.5 and 0.9, the consistency coefficient increased in the range of 0.6 to 1.4 Pa⋅sn, and the stability of the spacer fluid interface decreased. However, the stability of the spacer fluid interface increased with the increase in its density in the range of 1100~1500 kg/m3. The results obtained in this study can provide a reference for selecting the operating parameters to ensure the stable operation of dual-gradient pressure systems.
The Fermentation of a Marine Probiotic Bacterium on Low-Cost Media Formulated with Industrial Fish Gelatin Waterstreams and Collagen Hydrolysates
Authors: Vázquez José Antonio , Pedreira Adrián , Salmerón Iván , Wardhani Dyah H. , Valcarcel Jesus
Published: August 2023

Chemical effluents generated by the isolation of fish gelatin and collagen hydrolysates produced from the enzyme proteolysis of skin wastes are protein-rich substrates that could be used as nutrients in bacterial bioprocessing. In this study, the suitability of such nutrients in supporting the growth of a marine probiotic bacterium, Phaeobacter sp. DIFR 27-4, was studied. Both gelatin effluents and collagen hydrolysates were obtained from the skins of shark, tuna, salmon and turbot. The chemical composition of the substrates included the complete presence of all quantified amino acids. Low-cost marine culture media were formulated with these protein materials alongside a very low concentration of yeast extract and marine water. In batch cultures with gelatin effluents, the growth rates of the strain DIFR 27-4 were somewhat lower than those found in the control marine commercial media. In the case of the hydrolysates, the bacterial production of biomass was similar or higher than that observed in the control, and larger than that observed in the effluents. A simple evaluation of production costs in the different substrates studied indicated that around a 73–125-fold reduction can be achieved when alternative media are used, in comparison to the use of commercial marine broth.
An Investigation into the Potential of a Penicillium Commune Strain to Eliminate Aromatic Compounds
Authors: Gerginova Maria , Stoyanova Katya , Peneva Nadejda , Dincheva Ivayla , Alexieva Zlatka
Published: August 2023

The quantity of industrially polluted waters is increasing everywhere, of which a significant part is occupied by a number of mono- and poly-aromatic compounds. Toxins enter the soil, sewage, and clean water by mixing with or seeping into them from industrial wastewater. By using 18S RNA and ITS sequences, the Penicillium commune AL5 strain that was isolated from Antarctic soil was identified. This study is dedicated to exploring its capacity to metabolize hazardous aromatic compounds. The strain showed very good potential in the degradation of hydroxylated monophenols and possessed exceptional abilities in terms of resorcinol degradation. The strain’s ability to metabolize 0.3 g/L of p-cresol at 10 °C is notable. The strain is also capable of metabolizing LMW PAHs (naphthalene, anthracene, and phenanthrene) and eliminating all three tested compounds under 23 °C, respectively, 77.5%, 93.8%, and 75.1%. At 10 °C, the process slowed down, but the degradation of naphthalene continued to be over 50%. The quantity of PAH and a few significant intermediary metabolites were determined using GC–MS analysis. Sequencing of the enzymes phenol hydroxylase and catechol 1,2-dioxygenase revealed a close association with the genes and proteins in some fungal strains that can degrade the aromatic compounds examined thus far.
Four Challenges Faced by Early Chinese Buddhist Translators: A Case Study of Zhi Qian’s Chinese Translation of Dhammapada
Authors: Li Zhouyuan
Published: August 2023

This study focuses on the translation of Buddhist scriptures into Chinese, specifically the Faju jing, a Chinese version of the Dhammapada completed in the third century CE. It reveals that the Faju jing is not a straightforward translation but a combination of different sources. The translator, Zhi Qian, faced challenges in integrating multiple translation practices, dealing with diverse original Indian languages, incorporating pre-existing phrases from other translators’ work, and managing divergent opinions within the translation team regarding the translation style. This multi-layered process of translation, involving the participation of multiple translators, also likely occurred in other early translations. These challenges extended beyond mere comprehension of the Indian text, resulting in potential errors and deviations from straightforward translations. It is possible that some mistranslations were a consequence of integrating multiple traditions within the source text, making it difficult for translators to maintain a consistent linguistic framework and leading to errors. Furthermore, this study highlights the remarkable efforts of Chinese translators who collaborated with foreign monks in translation groups. It emphasizes the important role of Chinese translators in integrating diverse translation processes and refining the language to suit Chinese readers. They incorporated earlier translations and modified the language to align with Chinese forms. Overall, this case study sheds light on the complexity of early Chinese Buddhist translations, influenced by the integration of multiple traditions and the localization of the texts. It underscores the significance of Chinese translators in the translation process and their contributions to the development of Chinese Buddhist literature.
“For the First Time in Japan”: The Main Elements of Hangzhou‑Based Zen That Dōgen Transmitted
Authors: Heine Steven
Published: August 2023

The transplantation of Zen from China to Japan during the Kamakura period (1185–1333) depended on a series of intrepid seekers, who journeyed beyond conventional geographical and societal boundaries to discover and appropriate religious customs and beliefs while staying on the mainland that spread and eventually thrived on the islands. The only way to learn the intricate ways of Zen theory was to experience first-hand the relevant people, practices, places, and ritual performances in the Hangzhou/Ningbo region of the northern Zhejiang province. This article first provides a brief synopsis of travelers to and from Hangzhou, including Japanese pilgrims and Chinese émigré monks in addition to some prominent teachers and learners who did not journey but nevertheless exerted a tremendous impact on the transmission process. Then, it analyzes elements of Chinese Chan that were brought across the waters by Dōgen 道元 (1200–1253), who ventured to gain enlightenment in the 1220s. He later claimed that he implemented “for the first time in Japan” 日本国最始 practical and conceptual religious techniques, including diverse personal, material, ritual, textual, rhetorical, and societal components. Although a major transmitter of Chan, Dōgen made significant innovations based on his vision of the ideal Zen community, recast for the structures of medieval Japanese society.
Phenomenology of Conversion: John H. Newman and Aurel Kolnai
Authors: Balazs Zoltan
Published: August 2023

This essay considers J. H. Newman’s and Aurel Kolnai’s conversions from a phenomenological point of view. Newman’s conversion or conversions are often cited as classic examples, though he was reluctant to use this notion, preferring to see his journey as a long process, an account of which is provided in his Apologia Pro Vita Sua. His Grammar of Assent is less often cited in this context, but it helps more to make sense of conversion as an experience. Kolnai was a phenomenologist himself. He also provided a personal account of his conversion in his autobiography. The two thinkers’ reflections on conversion are highly congruent, highlighting four dimensions: familiarity or homecoming (conversion makes one at peace with oneself); discovery of reality or real meaning of a notion (of truth, of God, of the scriptures, of the Church); the paramount importance of conscience (the indispensability of the approval of moral authority); the intelligibility of conversion which makes it, in the end, the proper subject of philosophical inquiry.
Co-Movement and Performance Comparison of Conventional and Islamic Stock Indices during the Pre- and Post-COVID-19 Pandemic Era
Authors: Alamgir Muhammad , Cheng Ming-Chang
Published: August 2023

This study conducts a comparative analysis of the performance of Islamic and conventional indices in both developed and developing countries and territories, considering the pre- and post-COVID-19 pandemic periods. The research employs performance index tools and time–frequency wavelet-based analysis to assess how the COVID-19 pandemic affected the performance, volatility, and co-movement of Islamic and conventional stock indices. The findings reveal that Islamic stock indices are more resilient and tend to outperform conventional stocks during crisis periods in both developed and developing countries and territories, and this trend holds true in the long and short term across most countries. The analysis of wavelet coherence indicates a strong co-movement and coherence between Islamic and conventional indices. Furthermore, the study reveals that in developing countries and territories, the co-movement is characterized by weak coherence and high volatility compared to developed countries and territories. The study highlights the significance of Islamic indices as safe havens for investors during times of crisis, suggesting that including Islamic equities in investment portfolios can potentially yield higher returns compared to conventional indices. This research holds practical value for individual traders involved in the online trading of global stock indices, aiding them in constructing and designing internationally diversified portfolios. Unlike previous studies that focused on specific countries and territories and indices, this study offers a comprehensive examination of the behavior of Islamic and conventional indices across major global markets during both crisis and noncrisis periods. The results contribute significantly to the existing literature and offer valuable insights for investors.
Abandoned Land Mapping Based on Spatiotemporal Features from PolSAR Data via Deep Learning Methods
Authors: Yang Yingpin , Wu Zhifeng , Xiao Wenju , Zhou Ya’nan , Huang Qiting , Wu Tianjun , Luo Jiancheng , Wang Haiyun
Published: August 2023

Monitoring agricultural abandonment is essential in understanding the effects on the environment and food security. Polarimetric synthetic aperture radar (PolSAR) is an efficient approach for the monitoring of large-scale agricultural land cover in cloudy and rainy areas. However, previous studies have not taken advantage of the valuable phase information and not fully utilized the spatiotemporal features of farmland parcels, which has seriously limited the abandoned land identification accuracy. In this study, we developed a new method for the mapping of abandoned land based on the spatiotemporal features from PolSAR Single Look Complex (SLC) images via deep learning methods. First, backscattering coefficients (σ0VV, σ0VH) were derived, and the polarimetric parameters (entropy, anisotropy and mean alpha angle) were obtained based on Cloude–Pottier polarimetric decomposition. Then, the VGG16 deep convolutional network was innovatively used to extract spatial features from both the backscattering coefficients and polarimetric parameters. Next, the separability index was calculated to select the most effective spatial features. Finally, LSTM classifications were conducted based on the time series of backscattering features, the polarimetric parameters, the extracted spatial features and their combinations. The results showed that the introduction of multitemporal polarimetric parameters and spatial features both led to an improvement in the abandoned land identification accuracy. The combination of backscattering features, polarimetric parameters and spatial features yielded the best performance in identifying abandoned land, with producer’s accuracy of 88.29% and user’s accuracy of 84.03%. This study demonstrated the potential of polarimetric parameters and validated the effectiveness of spatiotemporal features in abandoned land identification. It provided a practical method for the production of a highly reliable abandoned land mapping in cloudy and rainy areas.
Urban Vulnerability Analysis Based on Micro-Geographic Unit with Multi-Source Data—Case Study in Urumqi, Xinjiang, China
Authors: Zheng Jianghua , Yu Danlin , Han Chuqiao , Wang Zhe
Published: August 2023

This study introduces a novel approach to urban public safety analysis inspired by a streetscape analysis commonly applied in urban criminology, leveraging the concept of micro-geographical units to account for urban spatial heterogeneity. Recognizing the intrinsic uniformity within these smaller, distinct environments of a city, the methodology represents a shift from large-scale regional studies to a more localized and precise exploration of urban vulnerability. The research objectives focus on three key aspects: first, establishing a framework for identifying and dividing cities into micro-geographical units; second, determining the type and nature of data that effectively illustrate the potential vulnerability of these units; and third, developing a robust and reliable evaluation index system for urban vulnerability. We apply this innovative method to Urumqi’s Tianshan District in Xinjiang, China, resulting in the formation of 30 distinct micro-geographical units. Using WorldView-2 remote sensing imagery and the object-oriented classification method, we extract and evaluate features related to vehicles, roads, buildings, and vegetation for each unit. This information feeds into the construction of a comprehensive index, used to assess public security vulnerability at a granular level. The findings from our study reveal a wide spectrum of vulnerability levels across the 30 units. Notably, units X1 (Er Dao Bridge) and X7 (Gold Coin Mountain International Plaza) showed high vulnerability due to factors such as a lack of green spaces, poor urban planning, dense building development, and traffic issues. Our research validates the utility and effectiveness of the micro-geographical unit concept in assessing urban vulnerability, thereby introducing a new paradigm in urban safety studies. This micro-geographical approach, combined with a multi-source data strategy involving high-resolution remote sensing and field survey data, offers a robust and comprehensive tool for urban vulnerability assessment. Moreover, the urban vulnerability evaluation index developed through this study provides a promising model for future urban safety research across different cities.
SaTSeaD: Satellite Triangulated Sea Depth Open-Source Bathymetry Module for NASA Ames Stereo Pipeline
Authors: Palaseanu-Lovejoy Monica , Alexandrov Oleg , Danielson Jeff , Storlazzi Curt
Published: August 2023

We developed the first-ever bathymetric module for the NASA Ames Stereo Pipeline (ASP) open-source topographic software called Satellite Triangulated Sea Depth, or SaTSeaD, to derive nearshore bathymetry from stereo imagery. Correct bathymetry measurements depend on water surface elevation, and whereas previous methods considered the water surface horizontal, our bathymetric module accounts for the curvature of the Earth in the imagery. The process is semiautomatic, reliable, and repeatable, independent of any external bathymetry data eliminating user bias in selecting bathymetry calibration points, and it can generate a fully integrated and seamless topo-bathymetry digital elevation model (TBDEM) in the same coordinate system, comparable with the band-ratio method irrespective of the regression method used for the band-ratio algorithm. The ASP output can be improved by applying a camera bundle adjustment to minimize reprojection errors and by alignment to a more accurate topographic (above water) surface without any bathymetric input since the derived TBDEM is a rigid surface. These procedures can decrease bathymetry root mean square errors from 30 to 80 percent, depending on environmental conditions, the quality of satellite imagery, and the spectral band used (e.g., blue, green, or panchromatic).
Classification of Fish Species Using Multispectral Data from a Low-Cost Camera and Machine Learning
Authors: Monteiro Filipe , Bexiga Vasco , Chaves Paulo , Godinho Joaquim , Henriques David , Melo-Pinto Pedro , Nunes Tiago , Piedade Fernando , Pimenta Nelson , Sustelo Luis , Fernandes Armando M.
Published: August 2023

This work creates a fish species identification tool combining a low-cost, custom-made multispectral camera called MultiCam and a trained classification algorithm for application in the fishing industry. The objective is to assess, non-destructively and using reflectance spectroscopy, the possibility of classifying the spectra of small fish neighborhoods instead of the whole fish for situations where fish are not completely visible, and use the classification to estimate the percentage of each fish species captured. To the best of the authors’ knowledge, this is the first work to study this possibility. The multispectral imaging device records images from 10 horse mackerel, 10 Atlantic mackerel, and 30 sardines, the three most abundant fish species in Portugal. This results in 48,741 spectra of 5 × 5 pixel regions for analysis. The recording occurs in twelve wavelength bands from 390 nm to 970 nm. The bands correspond to filters with the peculiarity of being highpass to keep the camera cost low. Using a Teflon tape white reference is also relevant to control the overall cost. The tested machine learning algorithms are k-nearest neighbors, multilayer perceptrons, and support vector machines. In general, the results are better than random guessing. The best classification comes from support vector machines, with a balanced accuracy of 63.8%. The use of Teflon does not seem to be detrimental to this result. It seems possible to obtain an equivalent accuracy with ten cameras instead of twelve.
Blockchain-Based Secure Storage and Access Control Scheme for Supply Chain Ecological Business Data: A Case Study of the Automotive Industry
Authors: Li Songjiang , Zhou Tao , Yang Huamin , Wang Peng
Published: August 2023

The reliable circulation of automotive supply chain data is crucial for automotive manufacturers and related enterprises as it promotes efficient supply chain operations and enhances their competitiveness and sustainability. However, with the increasing prominence of privacy protection and information security issues, traditional data sharing solutions are no longer able to meet the requirements for highly reliable secure storage and flexible access control. In response to this demand, we propose a secure data storage and access control scheme for the supply chain ecosystem based on the enterprise-level blockchain platform Hyperledger Fabric. The design incorporates a dual-layer attribute-based auditable access control model for access control, with four smart contracts aimed at coordinating and implementing access policies. The experimental results demonstrate that the proposed approach exhibits significant advantages under large-scale data and multi-attribute conditions. It enables fine-grained, dynamic access control under ciphertext and maintains high throughput and security in simulated real-world operational scenarios.
Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems
Authors: Hajj Suzan , Azar Joseph , Bou Abdo Jacques , Demerjian Jacques , Guyeux Christophe , Makhoul Abdallah , Ginhac Dominique
Published: August 2023

With the proliferation of IoT devices, ensuring the security and privacy of these devices and their associated data has become a critical challenge. In this paper, we propose a federated sampling and lightweight intrusion-detection system for IoT networks that use K-meansfor sampling network traffic and identifying anomalies in a semi-supervised way. The system is designed to preserve data privacy by performing local clustering on each device and sharing only summary statistics with a central aggregator. The proposed system is particularly suitable for resource-constrained IoT devices such as sensors with limited computational and storage capabilities. We evaluate the system’s performance using the publicly available NSL-KDD dataset. Our experiments and simulations demonstrate the effectiveness and efficiency of the proposed intrusion-detection system, highlighting the trade-offs between precision and recall when sharing statistics between workers and the coordinator. Notably, our experiments show that the proposed federated IDS can increase the true-positive rate up to 10% when the workers and the coordinator collaborate.
CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things
Authors: Gui Zhenwen , He Shuaishuai , Lin Yao , Nan Xin , Yin Xiaoyan , Wu Chase Q.
Published: August 2023

Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution to the accuracy of fault prediction, and the progress of equipment faults is non-stationary. More specifically, capturing the time point at which a fault first appears is more important for improving the accuracy of fault prediction. Moreover, the progress of the different faults of equipment varies significantly. Therefore, taking feature differences and time information into consideration, we propose a Causal-Factors-Aware Attention Network, CaFANet, for equipment fault prediction in the Internet of Things. Experimental results and performance analysis confirm the superiority of the proposed algorithm over traditional machine learning methods with prediction accuracy improved by up to 15.3%.
An Intelligent Sorting Method of Film in Cotton Combining Hyperspectral Imaging and the AlexNet-PCA Algorithm
Authors: Li Quang , Zhao Ling , Yu Xin , Liu Zongbin , Zhang Yiqing
Published: August 2023

Long-staple cotton from Xinjiang is renowned for its exceptional quality. However, it is susceptible to contamination with plastic film during mechanical picking. To address the issue of tricky removal of film in seed cotton, a technique based on hyperspectral images and AlexNet-PCA is proposed to identify the colorless and transparent film of the seed cotton. The method consists of black and white correction of hyperspectral images, dimensionality reduction of hyperspectral data, and training and testing of convolutional neural network (CNN) models. The key technique is to find the optimal way to reduce the dimensionality of the hyperspectral data, thus reducing the computational cost. The biggest innovation of the paper is the combination of CNNs and dimensionality reduction methods to achieve high-precision intelligent recognition of transparent plastic films. Experiments with three dimensionality reduction methods and three CNN architectures are conducted to seek the optimal model for plastic film recognition. The results demonstrate that AlexNet-PCA-12 achieves the highest recognition accuracy and cost performance in dimensionality reduction. In the practical application sorting tests, the method proposed in this paper achieved a 97.02% removal rate of plastic film, which provides a modern theoretical model and effective method for high-precision identification of heteropolymers in seed cotton.
Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
Authors: Wells Lucas A. , Chung Woodam
Published: August 2023

Forests are traditionally characterized by stand-level descriptors, such as basal area, mean diameter, and stem density. In recent years, there has been a growing interest in enhancing the resolution of forest inventory to examine the spatial structure and patterns of trees across landscapes. The spatial arrangement of individual trees is closely linked to various non-monetary forest aspects, including water quality, wildlife habitat, and aesthetics. Additionally, associating individual tree positions with dendrometric variables like diameter, taper, and species can provide data for highly optimized, site-specific silvicultural prescriptions designed to achieve diverse management objectives. Aerial photogrammetry has proven effective for mapping individual trees; however, its utility is limited due to the inability to directly estimate many dendrometric variables. In contrast, terrestrial mapping methods can directly observe essential individual tree characteristics, such as diameter, but their mapping accuracy is governed by the accuracy of the global satellite navigation system (GNSS) receiver and the density of the canopy obstructions between the receiver and the satellite constellation. In this paper, we introduce an integrated approach that combines a camera-based motion and tree detection system with GNSS positioning, yielding a stem map with twice the accuracy of using a consumer-grade GNSS receiver alone. We demonstrate that large-scale stem maps can be generated in real time, achieving a root mean squared position error of 2.16 m. We offer an in-depth explanation of a visual egomotion estimation algorithm designed to enhance the local consistency of GNSS-based positioning. Additionally, we present a least squares minimization technique for concurrently optimizing the pose track and the positions of individual tree stem[s].
Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution
Authors: Ribeiro Matheus Henrique Dal Molin , da Silva Ramon Gomes , Larcher José Henrique Kleinubing , Mendes Andre , Mariani Viviana Cocco , Coelho Leandro dos Santos
Published: August 2023

Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities regarding external stimuli, and its signals compose a nonlinear dynamical system. There are many difficulties associated with EEG analysis. For example, noise can originate from different disorders, such as muscle or physiological activity. There are also artifacts that are related to undesirable signals during EEG recordings, and finally, nonlinearities can occur due to brain activity and its relationship with different brain regions. All these characteristics make data modeling a difficult task. Therefore, using a combined approach can be the best solution to obtain an efficient model for identifying neural data and developing reliable predictions. This paper proposes a new hybrid framework combining stacked generalization (STACK) ensemble learning and a differential-evolution-based algorithm called Adaptive Differential Evolution with an Optional External Archive (JADE) to perform nonlinear system identification. In the proposed framework, five base learners, namely, eXtreme Gradient Boosting, a Gaussian Process, Least Absolute Shrinkage and Selection Operator, a Multilayer Perceptron Neural Network, and Support Vector Regression with a radial basis function kernel, are trained. The predictions from all these base learners compose STACK’s layer-0 and are adopted as inputs of the Cubist model, whose hyperparameters were obtained by JADE. The model was evaluated for decoding the electroencephalography signal response to wrist joint perturbations. The variance accounted for (VAF), root-mean-squared error (RMSE), and Friedman statistical test were used to validate the performance of the proposed model and compare its results with other methods in the literature, including the base learners. The JADE-STACK model outperforms the other models in terms of accuracy, being able to explain around, as an average of all participants, 94.50% and 67.50% (standard deviations of 1.53 and 7.44, respectively) of the data variability for one step ahead and three steps ahead, which makes it a suitable approach to dealing with nonlinear system identification. Also, the improvement over state-of-the-art methods ranges from 0.6% to 161% and 43.34% for one step ahead and three steps ahead, respectively. Therefore, the developed model can be viewed as an alternative and additional approach to well-established techniques for nonlinear system identification once it can achieve satisfactory results regarding the data variability explanation.
Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study
Authors: Xu Yeong-Yuh , Shih Chi-Huang , You Yan-Ting
Published: August 2023

Traditionally, the subjective questionnaire collected from game players is regarded as a primary tool to evaluate a video game. However, the subjective evaluation result may vary due to individual differences, and it is not easy to provide real-time feedback to optimize the user experience. This paper aims to develop an objective game fun prediction system. In this system, the wearables with photoplethysmography (PPG) sensors continuously measure the heartbeat signals of game players, and the frequency domain heart rate variability (HRV) parameters can be derived from the inter-beat interval (IBI) sequence. Frequency domain HRV parameters, such as low frequency(LF), high frequency(HF), and LF/HF ratio, highly correlate with the human’s emotion and mental status. Most existing works on emotion measurement during a game adopt time domain physiological signals such as heart rate and facial electromyography (EMG). Time domain signals can be easily interfered with by noises and environmental effects. The main contributions of this paper include (1) regarding the curve transition and standard deviation of LF/HF ratio as the objective game fun indicators and (2) proposing a linear model using objective indicators for game fun score prediction. The self-built dataset in this study involves ten healthy participants, comprising 36 samples. According to the analytical results, the linear model’s mean absolute error (MAE) was 4.16%, and the root mean square error (RMSE) was 5.07%. While integrating this prediction model with wearable-based HRV measurements, the proposed system can provide a solution to improve the user experience of video games.
Wearable and Noninvasive Device for Integral Congestive Heart Failure Management in the IoMT Paradigm
Authors: Ausín José L. , Ramos Javier , Lorido Antonio , Molina Pedro , Duque-Carrillo J. Francisco
Published: August 2023

Noninvasive remote monitoring of hemodynamic variables is essential in optimizing treatment opportunities and predicting rehospitalization in patients with congestive heart failure. The objective of this study is to develop a wearable bioimpedance-based device, which can provide continuous measurement of cardiac output and stroke volume, as well as other physiological parameters for a greater prognosis and prevention of congestive heart failure. The bioimpedance system, which is based on a robust and cost-effective measuring principle, was implemented in a CMOS application specific integrated circuit, and operates as the analog front-end of the device, which has been provided with a radio-frequency section for wireless communication. The operating parameters of the proposed wearable device are remotely configured through a graphical user interface to measure the magnitude and the phase of complex impedances over a bandwidth of 1 kHz to 1 MHz. As a result of this study, a cardiac activity monitor was implemented, and its accuracy was evaluated in 33 patients with different heart diseases, ages, and genders. The proposed device was compared with a well-established technique such as Doppler echocardiography, and the results showed that the two instruments are clinically equivalent.
Hydraulic Measuring Hoses as Pressure Signal Distortion—Mathematical Model and Results of Experimental Tests
Authors: Klarecki Klaudiusz , Rabsztyn Dominik
Published: August 2023

The article presents the results of a developed model and experimental studies of the Minimess® hydraulic signal hose’s influence on the changes in the indications of the pressure transducer during the high dynamics of hydrostatic drives and controls. The model test results show that measuring hoses can be used as hardware low-pass filters during the digital recording of pressure waveforms. However, the cut-off frequency values of the measuring hoses obtained using the model are dramatically lower than those observed during the experiment. The experiment results show that the measuring hoses can only be used without any limitations to measure the average pressure value. In the case of measuring pressure waveforms, the user should carefully choose the measuring hose length. For this reason, the relationship between the measuring hose length and its cut-off frequency should be known.